CN115014421A - Calibration method and device of sensor data, anti-shake method and camera module - Google Patents

Abstract

The present application relates to a method, apparatus, electronic device, storage medium and computer program product for calibrating sensor data. The method includes: determining a target motion state of an electronic device based on first sensor data collected in real time; determining a target calibration mode according to the target motion state; wherein the target calibration mode includes a first calibration mode or a second calibration mode method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the target calibration parameters of the second calibration method are determined based on the sensor data in a historical static state; based on the target calibration The target calibration parameters of the method are used to calibrate the second sensor data collected in real time by the electronic device. The method can enrich the calibration methods of sensor data.

Description

Sensor data calibration method and device, anti-shake method and camera module

technical field

The present application relates to the field of computer technology, and in particular, to a method and device for calibrating sensor data, an anti-shake method and device, a camera module, an electronic device, a computer-readable storage medium, and a computer program product.

Background technique

Various sensors are installed in electronic equipment, and data is collected by various sensors. There are various types of errors in the sensor communication, which cause the sensor to not accurately collect data. Therefore, the sensor data needs to be calibrated.

The traditional method for calibrating sensor data is usually to obtain pre-calibrated parameters on the factory line to calibrate the sensor data, and there is a problem of a single calibration method for the sensor data.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a sensor data calibration method and device, an anti-shake method and device, a camera module, an electronic device, a computer-readable storage medium, and a computer program product, which can enrich sensor data calibration methods.

In a first aspect, the present application provides a method for calibrating sensor data. The method includes:

Determine the target motion state of the electronic device based on the first sensor data collected in real time;

According to the target motion state, a target calibration method is determined; wherein, the target calibration method includes a first calibration method or a second calibration method, and the target calibration parameter of the first calibration method is determined based on the first sensor data , the target calibration parameters of the second calibration method are determined based on sensor data in a historical static state;

Based on the target calibration parameters of the target calibration method, the second sensor data collected in real time by the electronic device is calibrated.

In a second aspect, the present application also provides an apparatus for calibrating sensor data. The device includes:

a calculation module for determining the target motion state of the electronic device based on the first sensor data collected in real time;

The calculation module is further configured to determine a target calibration method according to the target motion state; wherein, the target calibration method includes a first calibration method or a second calibration method, and the target calibration parameter of the first calibration method is based on the target calibration method. The target calibration parameters of the second calibration method are determined based on the sensor data in a historical static state;

The calibration module is configured to calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

In a third aspect, the present application also provides an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Determine the target motion state of the electronic device based on the first sensor data collected in real time;

According to the target motion state, a target calibration method is determined; wherein, the target calibration method includes a first calibration method or a second calibration method, and the target calibration parameter of the first calibration method is determined based on the first sensor data , the target calibration parameters of the second calibration method are determined based on sensor data in a historical static state;

Based on the target calibration parameters of the target calibration method, the second sensor data collected in real time by the electronic device is calibrated.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by the processor, the following steps are implemented:

Determine the target motion state of the electronic device based on the first sensor data collected in real time;

According to the target motion state, a target calibration method is determined; wherein, the target calibration method includes a first calibration method or a second calibration method, and the target calibration parameter of the first calibration method is determined based on the first sensor data , the target calibration parameters of the second calibration method are determined based on sensor data in a historical static state;

Based on the target calibration parameters of the target calibration method, the second sensor data collected in real time by the electronic device is calibrated.

In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

Determine the target motion state of the electronic device based on the first sensor data collected in real time;

According to the target motion state, a target calibration method is determined; wherein, the target calibration method includes a first calibration method or a second calibration method, and the target calibration parameter of the first calibration method is determined based on the first sensor data , the target calibration parameters of the second calibration method are determined based on sensor data in a historical static state;

Based on the target calibration parameters of the target calibration method, the second sensor data collected in real time by the electronic device is calibrated.

The calibration method, device, electronic device, computer-readable storage medium and computer program product of the above sensor data, based on the first sensor data collected in real time, determine the target motion state of the electronic device, and then determine the target calibration method according to the target motion state ; wherein, the target calibration mode includes a first calibration mode or a second calibration mode, the target calibration parameters of the first calibration mode are determined based on the first sensor data, and the target calibration parameters of the second calibration mode are based on historical static conditions. determined by sensor data. Then, the electronic device can use one of multiple calibration methods to calibrate the second sensor data collected in real time, which enriches the calibration methods of the sensor data.

Further, when the electronic device uses the first calibration method to calibrate the second sensor data collected in real time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time. The two sensor data are calibrated online, which reduces the production line process required for offline calibration and the hardware cost of additional calibration equipment, and reduces the cost of sensor data calibration. At the same time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time. The target calibration parameters of the first calibration method are obtained by following changes in the environment, which can meet the requirements of various complex environments such as temperature and humidity drift. High-precision calibration to improve the accuracy of sensor data calibration.

Further, when the electronic device uses the second calibration method to calibrate the second sensor data collected in real time, the calibration method of the sensor data is enriched, and the validity, compatibility, reliability and disaster tolerance of the sensor data calibration can be improved. sex.

In a sixth aspect, the present application also provides an anti-shake method. The method includes:

In the shooting scene of the electronic device, the calibrated sensor data is obtained; the calibrated sensor data is obtained according to any one of the sensor data calibration methods;

determining an anti-shake compensation amount based on the calibrated sensor data;

Shake compensation is performed on the camera module based on the anti-shake compensation amount.

In a seventh aspect, the present application further provides an anti-shake device. The device includes:

an acquisition module, configured to acquire calibrated sensor data in the shooting scene of the electronic device; the calibrated sensor data is obtained according to any one of the sensor data calibration methods;

a determining module for determining an anti-shake compensation amount based on the calibrated sensor data;

An anti-shake module, configured to perform jitter compensation on the camera module based on the anti-shake compensation amount.

In an eighth aspect, the present application further provides a camera module. The camera module includes a memory and a processor, and a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for calibrating sensor data as described above, or the steps of the anti-shake method.

In a ninth aspect, the present application further provides an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

In the shooting scene of the electronic device, the calibrated sensor data is obtained; the calibrated sensor data is obtained according to any one of the sensor data calibration methods;

determining an anti-shake compensation amount based on the calibrated sensor data;

Shake compensation is performed on the camera module based on the anti-shake compensation amount.

In a tenth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by the processor, the following steps are implemented:

In the shooting scene of the electronic device, the calibrated sensor data is obtained; the calibrated sensor data is obtained according to any one of the sensor data calibration methods;

determining an anti-shake compensation amount based on the calibrated sensor data;

Shake compensation is performed on the camera module based on the anti-shake compensation amount.

In an eleventh aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

In the shooting scene of the electronic device, the calibrated sensor data is obtained; the calibrated sensor data is obtained according to any one of the sensor data calibration methods;

determining an anti-shake compensation amount based on the calibrated sensor data;

Shake compensation is performed on the camera module based on the anti-shake compensation amount.

The above-mentioned anti-shake method, device, camera module, electronic equipment, computer-readable storage medium and computer program product, and the calibration process of the sensor data and the optical anti-shake process are in the same closed-loop system, to avoid banding over time. The deviation in the data, and the deviation caused by the calibration process and the optical image stabilization process in different spaces, can improve the accuracy of the calibration. While improving the calibration accuracy of the sensor data, the calibrated sensor data is also used for anti-shake, which improves the performance of the anti-shake algorithm, improves the image quality and expressiveness of the generated image, and can enable higher product performance. force.

Further, the electronic device can acquire calibrated sensor data in real time in the shooting scene, and perform shake compensation on the camera module in real time based on the sensor data, which can increase the timeliness of the shake compensation; and if the calibrated sensor is based on the first The target calibration parameter of a calibration method is determined, and the target calibration parameter is determined by the first sensor data collected in real time in the shooting scene, then the target calibration parameter also takes the influencing factors of the current shooting scene into consideration, and the sensor data can be measured. Calibration to obtain more accurate sensor data, so as to perform more accurate shake compensation for the camera module.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

1 is an application environment diagram of a calibration method for sensor data and an anti-shake method in one embodiment;

2 is a flowchart of a method for calibrating sensor data in one embodiment;

Fig. 3 is the calibration flow chart of the calibration module in one embodiment;

4 is a flowchart of a method for calibrating sensor data in another embodiment;

Fig. 5 is the storage flow chart of the storage module in one embodiment;

6 is a schematic diagram of a team empty in one embodiment;

Fig. 7 is a schematic diagram of a squadron full in one embodiment;

8 is a flowchart of a method for calibrating sensor data in another embodiment;

9 is a flowchart of a method for calibrating sensor data in another embodiment;

10 is a flowchart of a method for calibrating sensor data in another embodiment;

11 is a framework diagram of a method for calibrating sensor data in one embodiment;

Fig. 12 is the calculation flow chart of the calculation module in one embodiment;

13 is a flowchart of an anti-shake method in one embodiment;

14 is a frame diagram of the operation of the optical image stabilization system in an electronic device in one embodiment;

FIG. 15 is a structural block diagram of an apparatus for calibrating sensor data in one embodiment;

16 is a structural block diagram of an anti-shake device in one embodiment;

FIG. 17 is an internal structure diagram of an electronic device in one embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

The sensor data calibration method and the anti-shake method provided by the embodiments of the present application can be applied to the application environment shown in FIG. 1 . The electronic device 100 includes a camera module 102 and a target sensor 104; the camera module 102 may include a lens, an image sensor, a first motor, a second motor and other devices, the target sensor 104 may be an inertial measurement unit, and the inertial measurement unit may It includes at least one of a gyroscope (gyro, gyroscope), an accelerometer (acc, accelerometer), and a magnetometer (mag, magnetometer).

In the shooting scene of the electronic device 100, the electronic device 100 determines the target motion state of the electronic device 100 based on the first sensor data collected in real time by the target sensor 104; A calibration method or a second calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the target calibration parameters of the second calibration method are determined based on the sensor data in a historical static state; based on the target calibration method The target calibration parameters are calibrated, and the second sensor data collected in real time by the electronic device 100 is calibrated to obtain calibrated second sensor data.

Further, the electronic device 100 obtains the calibrated second sensor data; determines the anti-shake compensation amount based on the calibrated sensor data; performs shake compensation on the camera module 102 based on the anti-shake compensation amount to generate an image.

Wherein, the electronic device 100 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices can be smart speakers, smart TVs, smart air conditioners, and smart vehicle-mounted devices. Wait. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, or the like.

In some exemplary embodiments, as shown in FIG. 2 , a method for calibrating sensor data is provided, and the method is applied to the electronic device in FIG. 1 as an example to illustrate, including the following steps:

Step 202: Determine the target motion state of the electronic device based on the first sensor data collected in real time.

The first sensor data refers to data collected in real time by the target sensor, and is used to characterize the attitude of the electronic device.

Optionally, the target sensor may be a sensor independent of the electronic device, or may be a sensor installed inside the electronic device, which is not limited herein. If the target sensor is a sensor independent of the electronic device and attached to the electronic device, the target sensor communicates with the electronic device through a preset communication channel. The preset communication channel may be a wired communication channel or a wireless communication channel. The wireless communication channel may be a Bluetooth channel, a wireless network channel or a near field communication channel, etc., but is not limited thereto.

The types of target sensors that collect data in real time may be one or more, and the number of each sensor may be one or more.

For example, an inertial measurement unit (IMU, Inertial Measurement Unit) is installed in the electronic device, and the IMU data is collected by the inertial measurement unit. Among them, the inertial measurement unit, also called inertial sensor, is a device for measuring attitude data such as the three-axis attitude angle (or angular rate) and acceleration of an object. Inertial measurement units may include gyroscopes, accelerometers, and magnetometers.

For another example, an electric field sensor, a pressure sensor, a light sensor, a fingerprint sensor, a touch sensor, a rotating shaft sensor, etc. may also be installed in the electronic device, but not limited to this.

The target motion state refers to the motion state of the electronic device when the first sensor data is collected in real time. The target motion state includes a stationary state or a non-stationary state. The stationary state refers to the state in which the motion amplitude of the electronic device is smaller than the preset motion amplitude threshold, and the non-stationary state refers to the state in which the motion amplitude of the electronic device is greater than or equal to the preset motion amplitude threshold. The preset motion amplitude threshold may specifically be at least one of an angular velocity threshold, an acceleration threshold, and a velocity threshold.

Further, the non-stationary state specifically includes a moving state or a shaking state. The moving state is the state in which the electronic device is translated, and the shaking state is the state in which the electronic device is shaken.

Optionally, in a preset usage scenario of the electronic device, the electronic device collects first sensor data in real time through the target sensor, and determines the target motion state of the electronic device based on the first sensor data. Wherein, the preset usage scene may be a shooting scene, a compass scene, a map navigation scene, etc., but is not limited thereto.

Step 204: Determine a target calibration method according to the target motion state; wherein, the target calibration method includes a first calibration method or a second calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the second calibration method The target calibration parameters are determined based on historical stationary sensor data.

The target calibration method refers to a calibration method that matches the motion state of the target. The target calibration parameter of the first calibration mode is determined based on the first sensor data, and the first sensor data is collected in real time, that is, the first calibration mode is an online calibration mode. The target calibration parameter of the second calibration method is determined based on the sensor data in the historical static state, and the sensor data in the historical static state is historical data, that is, the second calibration method is an offline calibration method.

The target calibration parameter is a calibration parameter corresponding to the target calibration method, and is used to calibrate the second sensor data collected in real time, so as to reduce the error of the second sensor data.

The historical quiescent state is the quiescent state in which the electronic device is in a historical time period. The historical static state can be the static state of the electronic device when the sensor data was calibrated last time, the static state of the electronic device when the electronic device is assembling the node, or the idle time of the electronic device in the historical time. The static state of the segment electronic device is not limited here.

Exemplarily, during the process of calibrating sensor data last night, the electronic device was in a static state that was a historical static state. Exemplarily, during the process of calibrating sensor data in a shooting scene at a historical moment, the electronic device is in a static state that is a historical static state. Exemplarily, in the process of calibrating sensor data after the system installation of the electronic device is completed at a historical moment, the static state in which the electronic device is located is the historical static state.

Optionally, the electronic device determines the target calibration mode from a plurality of candidate calibration modes according to the target motion state. The plurality of candidate calibration modes include a first calibration mode and a second calibration mode. The multiple candidate calibration methods may also include other calibration methods, which are set according to user needs, which is not limited here.

Exemplarily, the plurality of candidate calibration modes include a first calibration mode and a second calibration mode, and further include a third calibration mode and a fourth calibration mode. The target calibration parameters of the third calibration mode are preset by the user, and the target calibration parameters of the fourth calibration mode are obtained from the background server of the sensor.

When the target calibration method is the first calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data; when the target calibration method is the second calibration method, the historical calibration parameters are acquired as the second calibration method The target calibration parameters of the method, the historical calibration parameters are determined based on the sensor data in the historical static state.

In one embodiment, the electronic device may directly acquire the determined historical calibration parameters as the target calibration parameters of the second calibration mode. In another embodiment, the electronic device may also acquire sensor data in a historical static state, calculate historical calibration parameters based on the sensor data in the historical state, and use the historical calibration parameters as the target calibration parameters of the second calibration method.

Step 206 , calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

The second sensor data refers to data to be calibrated collected in real time by sensors in the electronic device. It should be noted that, in some embodiments, the second sensor data may be sensor data collected in real time after the target calibration parameters are determined, that is, the collection time of the second sensor data is later than the collection time of the first sensor data; in another In some embodiments, the second sensor data may also include the first sensor data, that is, the second sensor data may refer to all sensor data in the entire real-time process.

It can be understood that the electronic device calibrates the second sensor data, and the sensor that collects the second sensor data is a sensor corresponding to outputting standard sensor data in a static state. Standard sensor data is sensor data with an error less than a preset error threshold. For example, the sensor that collects the second sensor data may be a gyroscope, an accelerometer, or the like.

Optionally, the electronic device determines an error value based on target calibration parameters of the target calibration method, and performs difference processing between the second sensor data collected in real time by the electronic device and the error value to obtain calibrated second sensor data. The error value may be the target calibration parameter itself, or may be a value re-counted based on the target calibration parameter, which is not limited herein.

Exemplarily, if the target calibration parameter is an error value, the second sensor data collected in real time by the electronic device is subtracted from the error value to obtain calibrated second sensor data.

Exemplarily, the electronic device may multiply the target calibration parameter by the gain value to obtain the error value, and then subtract the error value from the second sensor data collected in real time by the electronic device to obtain calibrated second sensor data.

In some exemplary embodiments, as shown in FIG. 3 , the electronic device acquires the target calibration parameters 302 and collects the second sensor data 302 in real time, and executes step 306 to perform error calibration on the second sensor data 304 based on the target calibration parameters 302 , to obtain calibrated second sensor data 308 .

The method for calibrating the above sensor data, based on the first sensor data collected in real time, determines the target motion state of the electronic device, and then determines the target calibration method according to the target motion state; wherein, the target calibration method includes the first calibration method or the second calibration method. In the calibration mode, the target calibration parameters of the first calibration mode are determined based on the first sensor data, and the target calibration parameters of the second calibration mode are determined based on the sensor data in a historical static state. Then, the electronic device can use one of multiple calibration methods to calibrate the second sensor data collected in real time, which enriches the calibration methods of the sensor data.

In some possible implementations, when the electronic device uses the first calibration method to calibrate the second sensor data collected in real time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time, The online calibration of the second sensor data can be performed in real time, which reduces the production line process required for the offline calibration and the hardware cost of additional calibration equipment, and reduces the cost of sensor data calibration. At the same time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time. The target calibration parameters of the first calibration method are obtained by following changes in the environment, which can meet the requirements of various complex environments such as temperature and humidity drift. High-precision calibration to improve the accuracy of sensor data calibration. Among them, the temperature and humidity drift refers to the zero point of the sensor drifting with the environment such as temperature and humidity.

In some possible implementations, when the electronic device uses the second calibration method to calibrate the second sensor data collected in real time, the calibration method of the sensor data is enriched, which can improve the validity, compatibility, and reliability of the sensor data calibration. Reliability and disaster tolerance.

In some possible embodiments, determining the target calibration mode according to the target motion state includes: judging whether the target motion state is a static state, and determining the target calibration mode based on the judgment result.

The judgment result includes the first result and the second result. The first result is that the target motion state is a stationary state, and the second result is that the target motion state is a non-stationary state.

In this embodiment, the electronic device determines whether the target motion state is a stationary state, so that the target calibration method can be accurately determined based on the determination result of whether the target is in a stationary state.

In some possible embodiments, determining the target calibration method according to the target movement state includes: if the target movement state is a stationary state, determining the target calibration method to be the first calibration method; if the target movement state is a non-stationary state, determining The target calibration mode is the second calibration mode.

If the motion state of the target is a static state, the target calibration method is determined as the first calibration method; in the case of the first calibration method, the target calibration parameter is determined based on the first sensor data collected in real time.

If the target motion state is a non-stationary state, the target calibration method is determined to be the second calibration method; in the case of the second calibration method, the target calibration parameter is determined based on the sensor data in the historical stationary state.

In this embodiment, the electronic device may determine the target calibration method from a variety of calibration methods based on the target motion state of the electronic device, thereby enriching the calibration methods for sensor data.

In some exemplary embodiments, determining the target motion state of the electronic device based on the first sensor data collected in real time includes: obtaining, according to a preset still time period threshold, from cached real-time sensor data that satisfies the still time period threshold The first sensor data of the electronic device is determined; based on the first sensor data, the target motion state of the electronic device is determined.

The stationary time period threshold refers to a threshold for determining whether the duration of collecting the first sensor data satisfies the condition, that is, a threshold for determining the duration for which the electronic device is in a stationary state. The static time period threshold can be set as required, and can also be adjusted during the actual test. For example, the quiet time period threshold may be 2s (seconds).

Optionally, the electronic device collects real-time sensor data in real time through the target sensor, and stores the real-time sensor data in the cache; real-time detection of the acquisition duration corresponding to the real-time sensor data in the cache, when the acquisition duration meets the static time period threshold, then Traverse the newly stored real-time sensor data in the cache to obtain the first sensor data of the static time period threshold.

It can be understood that the longer the collection time is, the more real-time sensor data is collected, that is, the collection time is positively correlated with the amount of real-time sensor data. Then, based on the quantity of real-time sensor data in the cache, the electronic device can determine the collection time period corresponding to the real-time sensor data in the cache.

In some possible implementations, the electronic device obtains the frequency of data collected by the target sensor, and divides the number of real-time sensor data in the cache by the frequency to obtain the collection duration corresponding to the real-time sensor data in the cache.

In some other possible implementation manners, the electronic device may also acquire the first collection moment of the first real-time sensor data and the second collection moment of the last real-time sensor data in the cache, then the second collection moment is subtracted from the first collection moment , the acquisition duration corresponding to the real-time sensor data in the cache can be obtained.

In other possible implementation manners, the electronic device may also determine the collection duration corresponding to the real-time sensor data in the cache in other manners, which is not limited herein.

It can be understood that in the use scene of electronic equipment, such as in the shooting scene, the electronic equipment will be relatively stationary to capture a clear picture. Therefore, a threshold for the stationary time period can be set to determine whether the electronic equipment is in a stationary state. Whether the duration satisfies the threshold, the first sensor data that satisfies the threshold of the stationary time period can be acquired to determine the target motion state of the electronic device.

In this embodiment, if the buffered real-time sensor data is obtained to satisfy the static time period threshold, it means that the data volume of the buffered real-time sensor data satisfies the condition for determining the motion state of the electronic device, then the buffered real-time sensor data is obtained from the buffered real-time sensor data that satisfies the The first sensor data of the static time period threshold value can more accurately determine the target motion state of the electronic device.

In some exemplary embodiments, as shown in FIG. 4 , another method for calibrating sensor data is provided. Taking the method applied to the electronic device in FIG. 1 as an example, the following steps are included:

In step 402, the data length in the FIFO queue corresponding to the current moment is obtained; the real-time sensor data is buffered in the FIFO queue in real time.

First-in, first-out queue (FIFO, First Input First Output) is a queue in which the first entered instruction is completed and retired first, followed by the execution of the second instruction. Specifically, the FIFO queue may be a circular queue. The circular queue is to connect the sequential queues end to end, and logically regard the table storing the elements of the queue as a ring, which is called a circular queue. The FIFO queue can also be an acyclic queue.

It can be understood that the data length in the FIFO queue represents the quantity of real-time sensor data in the FIFO queue, and the data length is positively correlated with the quantity of real-time sensor data in the FIFO queue, while the quantity of real-time sensor data is directly related to the quantity of real-time sensor data in the FIFO queue. The collection time is positively correlated, so the data length is also positively correlated with the collection time.

Step 404 , according to the relationship between the data length and the collection time length, determine the collection time length corresponding to the data length, and obtain the first sensor data satisfying the still time period threshold when the collection time length satisfies the static time period threshold.

Optionally, the electronic device can preset the relationship between the data length and the collection time length, and determine the collection time length corresponding to the data length according to the relationship; if the collection time length is less than or equal to the static time period threshold, that is, the static time period threshold Continue to perform the step of obtaining the data length in the FIFO queue corresponding to the current moment; if the collection duration is greater than the static time period threshold, that is, the static time period threshold is satisfied, then the first sensor data that meets the static time period threshold is obtained.

Exemplarily, the static time period threshold is 2s (seconds), and the electronic device determines that the collection duration corresponding to the data length in the FIFO queue is 1.5s, then continues to obtain the data length in the FIFO queue corresponding to the current moment; When the electronic device determines that the collection duration corresponding to the length of the data in the FIFO queue is 2.2s, which meets the static time period threshold, the first sensor data that meets the static time period threshold is obtained, that is, the first sensor data within 2s is obtained. .

Step 406 , based on the first sensor data, determine the target motion state of the electronic device.

Step 408: Determine a target calibration method according to the target motion state; wherein, the target calibration method includes a first calibration method or a second calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the second calibration method The target calibration parameters are determined based on historical stationary sensor data.

Step 410 , calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

In this embodiment, the electronic device acquires the data length in the FIFO queue corresponding to the current moment, then according to the relationship between the data length and the collection time length, the collection time length corresponding to the data length can be accurately determined, so that the collection time length satisfies the In the case of the static time period threshold, acquiring the first sensor data satisfying the static time period threshold can accurately acquire the first sensor data with sufficient data volume and more accurately determine the target motion state of the electronic device.

In some optional embodiments, the FIFO queue may specifically be a RingBuffer queue. As shown in FIG. 5 , the electronic device performs step 502 to initialize the RingBuffer queue to obtain the initialized RingBuffer queue. The inertial measurement unit 504 is a target sensor, and stores the first sensor data collected in real time in the initialized RingBuffer queue, and the electronic device performs step 506 to detect in real time whether the initialized RingBuffer queue is full; if the queue is full, then perform step 508; If the queue is not full, step 512 is executed.

In step 508, the electronic device calls the dequeue function to determine whether the initialized RingBuffer queue is empty. If the queue is not empty, step 510 is executed to dequeue the first sensor data in the initialized RingBuffer queue. After judging that the initialized RingBuffer queue is full, call the dequeue function again to determine whether the initialized RingBuffer queue is empty, so as to ensure the correctness of the dequeuing operation.

Step 512, the first sensor data collected in real time is queued.

Step 514, take the team leader.

Step 516, find the length.

Wherein, step 502 specifically includes: setting a preset data length of the RingBuffer queue to be initialized, allocating memory space based on the preset data length, and then setting the head pointer and tail pointer of the RingBuffer queue to be initialized.

Wherein, the preset data length is greater than the data length collected within the static time period threshold. Exemplarily, it is assumed that the inertial measurement unit 504 collects the first sensor data in real time at a frequency of 1000 Hz (1000 data/1 second), and stores the first sensor data collected in real time in the RingBuffer queue, and the static time period threshold is 2s. , then, the length of the first sensor data required to determine the motion state of the target is the data collected in consecutive 2s, that is, time * frequency 2 * 1000 = 2000 pieces of first sensor data. Therefore, the length of the RingBuffer queue to be initialized The preset data length is greater than 2000. Further, the preset data length may be the maximum data storage length of the RingBuffer queue to be initialized, and more data may be stored.

The memory space is allocated based on the preset data length, and a one-dimensional array of the preset data length can be allocated. This one-dimensional array is contiguous memory and can be extended chained storage. Further, the electronic device calls the memory allocation system function to allocate a storage space memory pool with a preset data length, and subsequent processing is performed in the storage space memory pool to avoid algorithm memory leakage.

Set the head pointer and tail pointer of the RingBuffer queue to be initialized, including: the electronic device sets the head pointer and tail pointer to 0, that is, the head pointer front=0, and the tail pointer rear=0, which means that the queue is empty, that is, the queue is empty, As shown in FIG. 6 , the head pointer front=0 and the tail pointer rear=0, indicating that the queue is empty.

Wherein, step 506 may include: if the tail pointer is moved backward by one position and equal to the head pointer, it means the team is full: (rear+1)% Maxsize==front; if the tail pointer is moved backward by one position and is not equal to the head pointer, the team is not full, such as Figure 7 shows that the team is full.

If the queue is full, a dequeue operation is required; if the queue is not full, the first sensor data collected in real time can be entered into the queue.

Wherein, step 508 may include: if the tail pointer is equal to the head pointer, that is, the tail pointer and the head pointer both point to the same position, it means that the queue is empty: rear=front, then the dequeue operation cannot be performed; if the tail pointer is not equal to the head pointer, it means that there is no queue The queue is empty, and the dequeue operation is performed.

The dequeue operation in step 510 may include: dequeue the first sensor data stored at the head of the team pointed to by the head pointer front of the initialized RingBuffer queue: first record the value of the head of the team for returning: last=RingBuffer[front], and then The head pointer front is moved back one bit: front=(front+1)%Maxsize. The dequeue operation is to discard the first sensor data to be dequeued. After the dequeuing operation, the new first sensor is then subjected to the enqueuing operation.

The queue entry operation in step 512 may include: putting the new first sensor data collected in real time into the queue tail pointed to by the tail pointer rear: RingBuffer[rear]=data, and moving the rear pointer rear by one bit: rear=( rear+1)%Maxsize.

Wherein, step 514 may include: acquiring the first sensor data stored at the head of the queue pointed to by the head pointer front of the initialized RingBuffer queue, to perform assignment: data=RingBuffer[front].

Wherein, step 516 may include: calculating the data length of the first sensor data stored in the initialized RingBuffer queue for circular queue operation: (rear-front+Maxsize)%Maxsize.

In one embodiment, determining the target motion state of the electronic device according to the first sensor data includes: determining motion data corresponding to the first sensor data according to the first sensor data; determining the electronic device based on the motion data and the first state discrimination condition target motion state.

Motion data is data that reflects the motion of electronic equipment calculated by mathematical methods. It can be understood that the larger the motion data, the more vigorous the motion of the electronic device. The motion of the electronic device includes at least one of movement and shaking. For example, the motion data may include at least one of mean, variance, standard deviation, median, and mean difference.

其中，μ是均值，N是满足静止时间段阈值的第一传感器的总数，X_i是第i个第一传感数据。Exemplarily, the electronic device calculates each first sensor data to obtain an average value corresponding to the first sensor data.

Among them, μ is the mean value, N is the total number of first sensors that satisfy the static time period threshold, and X _i is the i-th first sensing data.

其中，σ²是方差，μ是均值，N是满足静止时间段阈值的第一传感器的总数，X_i是第i个第一传感数据。Exemplarily, the electronic device calculates each first sensor data to obtain the variance corresponding to the first sensor data.

Among them, σ ² is the variance, μ is the mean value, N is the total number of first sensors that satisfy the static time period threshold, and X _i is the i-th first sensing data.

Optionally, the motion data is an average value, and the method for determining the average value includes: acquiring each first sensor data that satisfies the static time period threshold in the previous round, and each first sensor data for which the average value is to be calculated in this round; The first sensor data of each round meeting the threshold of the stationary time period, and the first sensor data of the average value to be calculated in this round, determine the new first sensor data and the exited first sensor data of this round relative to the previous round. One sensor data; based on the mean value of the previous round, the newly entered first sensor data, and the exited first sensor data, determine the mean value of each first sensor data that satisfies the static time period threshold in the current round.

It can be understood that the electronic device collects the first sensor data in real time and determines the motion data according to the first sensor data in real time, so that there is overlapping data in the first sensor data acquired in adjacent rounds. For each first sensor data obtained in a round, determine the new first sensor data in this round and the first sensor data that exits; multiply the average value of the previous round by the total number of first sensor data in the previous round , add the newly entered first sensor data and subtract the exited first sensor, and then divide the obtained value by the total number of first sensor data in this round to obtain each first sensor that meets the static time period threshold in this round. The mean value of the sensor data.

Exemplarily, each first sensor data that satisfies the static time period threshold in the previous round is [1, 2, 3], the mean value of the previous round x0=(1+2+3)/3, and the current round is to be The first sensor data for calculating the mean value is [4, 2, 3], then the new first sensor data for this round is [4], and the first sensor data for exit is [1], this round satisfies the static time The mean value of each first sensor of the segment threshold value x1=(x0*3-1+4)/3.

Using the above-mentioned mean value calculation method, efficient optimization can be obtained in large-scale data sets, and the mean value of large-scale data sets can be quickly determined.

The first state determination condition is a condition for determining the motion state of the electronic device for the first time. The first state discrimination condition may include at least one of a motion condition and a stationary period condition. The motion condition may include a motion threshold, and may also include a motion range, etc., but is not limited thereto. The still time period condition may include the still time period threshold, and may also include the time of the still time period, the time range of the still time period, etc., but is not limited thereto. The motion threshold can be set as needed or adjusted during the actual test. The static time period threshold can be set as required, and can also be adjusted during the actual test.

Optionally, the electronic device determines motion data corresponding to the first sensor data according to the first sensor data, compares the motion data with the first state discrimination condition, obtains a comparison result, and determines the target motion state of the electronic device based on the comparison result. .

Exemplarily, the first state discrimination condition is used to determine whether the motion state of the electronic device is a static state; the electronic device determines, according to the first sensor data, that the motion data corresponding to the first sensor data is the average value A, and the average value A is the same as the first value A. Compare the state discriminating conditions to obtain a comparison result; if the comparison result is that the mean value A satisfies the first state discriminating condition, it can be determined that the target state of the electronic device is a static state; if the comparison result is that the mean value A does not satisfy the first state discriminating condition, then It can be determined that the target state of the electronic device is a non-stationary state.

In this embodiment, the electronic device determines motion data corresponding to the first sensor data according to the first sensor data, and based on the motion data and the first state discrimination condition, the target motion state of the electronic device can be accurately determined.

In some possible embodiments, the first state discriminating condition includes a motion threshold; determining the target motion state of the electronic device based on the motion data and the first state discriminating condition, including: if the motion data is less than or equal to the motion threshold, determining the electronic device If the motion data is greater than the motion threshold, adjust the first state judgment condition to the second state judgment condition, and determine the target motion state of the electronic device based on the second state judgment condition.

The size of the motion threshold can be set as required. In this embodiment, the motion data may include at least one of mean, variance, and standard deviation.

The second state determination condition is a condition for finally determining the motion state of the electronic device.

If the motion data is less than or equal to the motion threshold, it means that the first sensor data is relatively small, and the target motion state of the electronic device is determined to be a static state; if the motion data is greater than the motion threshold, the first sensor data is relatively large or the first sensor data There is noise data, so it is necessary to adjust the first state determination condition to the second state determination condition, and based on the second state determination condition, determine the target motion state of the electronic device again.

In this embodiment, if the motion data is less than or equal to the motion threshold, it is determined that the target motion state of the electronic device is a static state; if the motion data is greater than the motion threshold, the first sensor data may be relatively large or there may be noise data, then adjust the The first state judging condition to the second state judging condition, and based on the second state judging condition, the target motion state of the electronic device can be more accurately determined.

In some possible embodiments, the first sensor data is data collected by an inertial measurement unit, the inertial measurement unit includes at least one type of sensor, each sensor includes at least 2-axis data; the motion data includes a plurality of first statistical data, each The data collected by the axis corresponds to at least one first statistical data, and each first statistical data corresponds to one motion threshold; if the motion data is less than or equal to the motion threshold, it is determined that the target motion state of the electronic device is a static state, including: if each If the first statistical data are all less than or equal to the corresponding motion threshold, it is determined that the target motion state of the electronic device is a stationary state.

The inertial measurement unit may include at least one of sensors such as a gyroscope, an accelerometer, and a magnetometer, each of which includes data on at least 2 of the X, Y, and Z axes.

If each of the first statistical data is less than or equal to the corresponding motion threshold, indicating that each of the first sensor data is small, it can be determined that the target motion state of the electronic device is a static state.

Exemplarily, the inertial measurement unit includes a gyroscope, an accelerometer, and a magnetometer, and the gyroscope, accelerometer, and magnetometer all include X, Y, and Z axis data, and the data collected in each axis corresponds to a mean value and a variance, and each mean value is obtained. Corresponding to a mean threshold, each variance corresponds to a variance threshold; it can be seen that the inertial measurement unit can obtain motion data of 18 dimensions, corresponding to the motion thresholds of 18 dimensions.

The electronic device can provide 9 queues for storing raw data in 9 dimensions; among them, each sensor of the gyroscope, accelerometer and magnetometer includes 3 dimensions of X, Y and Z axes, so the 3 sensors have a total of 9 The raw data of the dimension can be stored in 9 queues respectively; when it is necessary to determine the target motion state of the electronic device, for the raw data of each dimension, calculate the mean and variance of the raw data of the dimension, then the 9 dimensions The raw data can calculate the motion data of 18 dimensions, and there are 18 dimensions of motion thresholds for gambling.

If the mean of each axis in the gyroscope, accelerometer, and magnetometer is less than or equal to the corresponding mean threshold, and the variance of each axis in the gyroscope, accelerometer, and magnetometer is less than or equal to the corresponding mean threshold, then determine The target motion state of the electronic device is a stationary state.

It can be understood that there may be errors in judging the motion state of the electronic device with single-dimensional data, and this embodiment uses motion data of multiple dimensions to determine the target motion state of the electronic device more accurately.

In some exemplary embodiments, as shown in FIG. 8 , another method for calibrating sensor data is provided. Taking the method applied to the electronic device in FIG. 1 as an example, the method for calibrating sensor data includes the following steps. :

Step 802, according to the preset static time period threshold, obtain the first sensor data satisfying the static time period threshold from the cached real-time sensor data; the first sensor data is the data collected by the inertial measurement unit, and the inertial measurement unit includes at least one type Sensors, each sensor includes at least 2 axes of data.

Step 804: Determine motion data corresponding to the first sensor data according to the first sensor data; the motion data includes a plurality of first statistical data, and the data collected by each axis corresponds to at least one first statistical data; the first state discrimination conditions include Motion threshold, each first statistic corresponds to one motion threshold.

Step 806: If each of the first statistical data is less than or equal to the corresponding motion threshold, determine that the target motion state of the electronic device is a stationary state.

Step 808: If there is second statistical data greater than the corresponding motion threshold in each of the first statistical data, determine the quantity of the second statistical data.

If there is second statistical data greater than the corresponding motion threshold in each of the first statistical data, the second statistical data greater than the corresponding motion threshold is recorded, and the quantity of the second statistical quantity is determined.

Step 810, if the quantity is less than the preset quantity threshold, then adjust the motion threshold corresponding to the second statistical data, and obtain the second motion threshold based on the adjusted motion threshold and conditions other than the adjusted motion threshold in the first discriminating condition. State discriminant condition.

The size of the quantity threshold can be set as required. For example, the number threshold may be one, or two, or the like.

It can be understood that, if there is second statistical data greater than the corresponding motion threshold in each of the first statistical data, and the number of the second statistical data is less than the preset number threshold, it indicates that the first state discrimination condition is not satisfied. The second statistical data is less, and there may be misjudgments. Therefore, the motion threshold corresponding to the second statistical data is adjusted, and based on the adjusted motion threshold and the conditions other than the adjusted motion threshold in the first judgment condition, the first judgment is obtained. The second state discriminating condition is to judge the motion state of the electronic device based on the second state discriminating condition again, which can avoid misjudgment and obtain the motion state of the electronic device more accurately.

Optionally, adjusting the motion threshold corresponding to the second statistical data includes: increasing the motion threshold corresponding to the second statistical data.

The electronic device may increase the motion threshold corresponding to the second statistical data according to a preset ratio. The preset ratio is 25%, 50% or 100%, etc. The preset ratio does not exceed 100%. For example, the motion threshold corresponding to the second statistical data is the mean threshold, and the mean threshold is 0.1. If the corresponding mean threshold is increased according to the preset ratio of 25%, the increased mean value is 0.125; if the preset ratio is 50% By increasing the corresponding mean threshold value, the increased mean value is 0.15; if the corresponding mean value threshold is increased according to the preset ratio of 100%, the increased mean value is 0.2.

Step 812: Determine the target motion state of the electronic device based on the second state discrimination condition.

Step 814: Determine a target calibration method according to the target motion state; wherein, the target calibration method includes a first calibration method or a second calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the second calibration method The target calibration parameters are determined based on historical stationary sensor data.

Step 816 , calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

In this embodiment, it can be understood that if each of the first statistical data contains second statistical data that is greater than the corresponding motion threshold, and the quantity of the second statistical data is less than the preset quantity threshold, then the second statistical data is greater than the corresponding motion threshold. There may be noise data in the data, which may lead to misjudgment. Therefore, adjust the motion threshold corresponding to the second statistical data to obtain a new motion threshold, and then obtain the second state judgment condition. Based on this state judgment condition, the motion state of the electronic device can be more accurately determined. . Further, increasing the motion threshold corresponding to the second statistical data can improve the effectiveness of the algorithm.

In some exemplary embodiments, if each second statistical data is less than or equal to the corresponding adjusted motion threshold, it is determined that the target motion state of the electronic device is a static state; if each second statistical data is greater than the corresponding adjusted motion threshold If the motion threshold is set, the target motion state of the electronic device is determined to be a non-stationary state.

If each of the second statistical data is less than or equal to the corresponding adjusted motion threshold, it means that there is noise data in each of the first sensor data used to determine the second statistical data, so that the determined second statistical data has a large deviation, resulting in Misjudgment, so it is determined that the target motion state of the electronic device is a static state.

If each second statistical data is greater than the corresponding adjusted motion threshold, it means that there is still second statistical data that cannot meet the adjusted motion threshold, and the first sensor data corresponding to the second statistical data does not belong to noise data, then determine the electronic device The target motion state is a non-stationary state.

In this embodiment, after adjusting the motion threshold corresponding to the second statistical data, if each second statistical data is less than or equal to the corresponding adjusted motion threshold, it means that the first sensor data used to determine the second statistical data is in the first sensor data. There is noise data, so that the determined second statistical data has a large deviation and causes misjudgment, then determine that the target motion state of the electronic device is a static state; if each second statistical data is greater than the corresponding adjusted motion threshold, it means that it is still If the second statistical data cannot satisfy the adjusted motion threshold, it is determined that the target motion state of the electronic device is a non-stationary state. The electronic device compares the second statistical data with the adjusted motion threshold again, which can eliminate the interference of the noise data and more accurately determine the motion state of the electronic device.

In some exemplary embodiments, as shown in FIG. 9 , another method for calibrating sensor data is provided. Taking the method applied to the electronic device in FIG. 1 as an example, the following steps are included:

Step 902, according to a preset static time period threshold, obtain first sensor data that satisfies the static time period threshold from the cached real-time sensor data; the first sensor data is data collected by an inertial measurement unit, and the inertial measurement unit includes at least one type Sensors, each sensor includes at least 2 axes of data.

Step 904: Determine motion data corresponding to the first sensor data according to the first sensor data; the motion data includes a plurality of first statistical data, and the data collected by each axis corresponds to at least one first statistical data; the first state discrimination conditions include The motion threshold and the still time period threshold, each first statistic corresponds to one motion threshold.

Step 906, if each of the first statistical data is less than or equal to the corresponding motion threshold, determine that the target motion state of the electronic device is a stationary state.

Step 908: If there is second statistical data greater than the corresponding motion threshold in each of the first statistical data, determine the number of the second statistical data.

Step 910, if the quantity is less than a preset quantity threshold, adjust the motion threshold corresponding to the second statistical data.

Step 912 , if it is determined based on the adjusted motion threshold that the motion state of the electronic device is a non-stationary state, adjust the stationary time period threshold.

Optionally, the electronic device determines an intermediate motion state of the electronic device based on the second statistical data and the corresponding adjusted motion threshold. The intermediate motion state is a motion state that is re-determined after adjusting the first state discrimination condition.

If each second statistical data is less than or equal to the corresponding adjusted motion threshold, it is determined that the intermediate motion state of the electronic device is a static state; if each second statistical data is greater than the corresponding adjusted motion threshold, then it is determined The intermediate motion state of the electronic device is a non-stationary state.

If the intermediate motion state is a non-stationary state, the electronic device may be stationary for a short duration, resulting in noise data in the first sensor data that meets the stationary time period threshold, and the stationary time period threshold is adjusted.

Optionally, if the intermediate motion state is a non-stationary state, the stationary time period threshold is decreased.

The electronic device may decrease the static time period threshold according to a preset ratio. The preset ratio is 10%, 20% or 50%, etc. The preset ratio does not exceed 50%. For example, if the static time period threshold is 2s (seconds), if the static time period threshold is reduced by 10% according to the preset ratio, the reduced static time period threshold is 1.8s; if the static time period is reduced by 20% according to the preset ratio segment threshold, the reduced static time segment threshold is 1.6s; if the static time segment threshold is reduced by a preset ratio of 50%, the reduced static time segment threshold is 1s.

Step 914: Obtain the second state discrimination condition according to the adjusted motion threshold value, the adjusted stationary time period threshold value, and other conditions in the first state discrimination condition except the adjusted motion threshold value and the stationary time period threshold value.

The second state discriminating condition includes an adjusted motion threshold, an adjusted stationary time period threshold, and other conditions in the first state discriminating condition.

In other embodiments, if the first state determination condition includes a motion threshold and a stationary period threshold, the second state determination condition includes an adjusted motion threshold and an adjusted stationary period threshold.

Step 916: Determine the target motion state of the electronic device based on the second state determination condition.

Step 918: Determine a target calibration method according to the target motion state; wherein, the target calibration method includes a first calibration method or a second calibration method, the target calibration parameters of the first calibration method are determined based on the first sensor data, and the second calibration method The target calibration parameters are determined based on historical stationary sensor data.

Step 920 , calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

In this embodiment, the electronic device determines that the motion state of the electronic device is a non-stationary state based on the adjusted motion threshold, and the electronic device may be in a stationary state for a short duration, resulting in the existence of the first sensor data that satisfies the stationary time period threshold. Noise data, then adjust from the dimension of the static time period threshold, according to the adjusted motion threshold, the adjusted static time period threshold, and the first state discrimination condition except the adjusted motion threshold and static time period threshold. other conditions, the second state discriminant condition is obtained. Then, based on the second state judging condition, the noise data of the first sensor or the actual motion data can be more accurately distinguished, and the motion state of the electronic device can be more accurately determined. Further, reducing the static time period threshold and re-determining the motion state of the electronic device based on the second state discriminating condition can improve the effectiveness of the algorithm.

In some exemplary embodiments, determining the target motion state of the electronic device based on the second state discriminating condition includes: determining, from the buffered real-time sensor data, first sensor data that satisfies the adjusted static time period threshold, and obtaining a new and determine new motion data based on the new first sensor data; if the new motion data is less than or equal to the adjusted motion threshold, then determine that the target motion state of the electronic device is a static state; if the new motion data is less than or equal to the adjusted motion threshold If the data is greater than the adjusted motion threshold, it is determined that the target motion state of the electronic device is a non-stationary state.

It is understandable that if the electronic device adjusts the static time period threshold, the originally acquired first sensor data does not meet the adjusted static time period threshold, and needs to be re-determined from the cached real-time sensor data to satisfy the adjusted static time period threshold. of the first sensor data, get the new first sensor is data.

Optionally, if the adjustment of the static time period threshold is to decrease the static time period threshold, the electronic device determines from the first sensor data first sensor data that satisfies the adjusted static time period threshold.

In some possible implementations, the electronic device may randomly determine, from the first sensor data, first sensor data that satisfies the adjusted static time period threshold.

In other possible implementations, the electronic device may determine, from the first sensor data, the latest first sensor data that satisfies the adjusted static time period threshold.

In other possible implementations, the electronic device may determine, from the first sensor data, the oldest first sensor data that satisfies the adjusted static time period threshold.

In other implementation manners, the electronic device may also determine, from the first sensor data, the first sensor that satisfies the adjusted static time period threshold in other manners, which is not limited herein.

If the new motion data is less than or equal to the adjusted motion threshold, it is determined that the target motion state of the electronic device is a stationary state, and the duration of the stationary state is short.

If the new motion data is greater than the adjusted motion threshold, it means that the motion of the electronic device is still large after adjusting the motion threshold and the stationary time period threshold, and the target motion state of the electronic device is determined to be a non-stationary state.

In this embodiment, after adjusting the stationary time period threshold and the motion threshold corresponding to the second statistical data, if the new motion data is less than or equal to the adjusted motion threshold, it means that the duration of the stationary state of the electronic device is short, but It can still be determined that the target motion state of the electronic device is a stationary state; if the new motion data is greater than the adjusted motion threshold, indicating that the motion of the electronic device is still large, the target motion state of the electronic device is determined to be a non-stationary state. The electronic device adjusts the motion threshold and the static time period threshold to exclude the possibility of misjudgment caused by noise that may exist in the first sensor data from multiple dimensions, so as to more accurately determine the motion state of the electronic device.

In some exemplary embodiments, as shown in FIG. 10 , another method for calibrating sensor data is provided. Taking the method applied to the electronic device in FIG. 1 as an example, the following steps are included:

Step 1002: Acquire first sensor data that satisfies the static time period threshold from the cached real-time sensor data according to the preset static time period threshold.

Step 1004: Determine motion data corresponding to the first sensor data according to the first sensor data; the first state discrimination condition includes a motion threshold and a stationary time period threshold.

Step 1006, if the motion data is less than or equal to the motion threshold, determine that the target motion state of the electronic device is a static state.

Step 1008, if the motion data is greater than the motion threshold, adjust the static time period threshold; according to the adjusted static time period threshold and other conditions other than the static time period threshold in the first state discrimination condition, obtain the second state discrimination condition , and based on the second state discrimination condition, determine the target motion state of the electronic device.

If the motion data is greater than the motion threshold, the electronic device may be in a stationary state for a short duration, resulting in noise data in the first sensor data that satisfies the stationary time period threshold. Therefore, the stationary time period threshold is adjusted.

Optionally, adjusting the static time period threshold includes: reducing the static time period threshold.

Step 1010: Determine a target calibration mode according to the target motion state; wherein, the target calibration mode includes a first calibration mode or a second calibration mode, the target calibration parameters of the first calibration mode are determined based on the first sensor data, and the second calibration mode The target calibration parameters are determined based on historical stationary sensor data.

Step 1012 , calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

In this embodiment, if the motion data is greater than the motion threshold, the electronic device may be in a stationary state for a short duration, resulting in noise data in the first sensor data that satisfies the stationary period threshold. Therefore, from the dimension of the stationary period threshold The adjustment is performed, and the second state determination condition is obtained according to the adjusted motion threshold and other conditions in the first state determination condition except the adjusted static time period threshold. Then, based on the second state judging condition, the motion state of the electronic device can be more accurately and accurately determined from this dimension of the stationary time period. Further, reducing the static time period threshold and re-determining the motion state of the electronic device based on the second state discriminating condition can improve the effectiveness of the algorithm.

In other embodiments, step 1008 includes: if the motion data is greater than the motion threshold, adjusting the static time period threshold; determining the first sensor data that satisfies the adjusted static time period threshold from the cached real-time sensor data, and obtaining a new and determine new motion data based on the new first sensor data; determine that the motion state of the electronic device is a non-stationary state based on the new motion data, then adjust the motion threshold; based on the adjusted motion threshold, after the adjustment and other conditions except the static time period threshold and the adjusted motion threshold in the first state discrimination condition, obtain the second state discrimination condition, and based on the second state discrimination condition, determine the target of the electronic device state of motion.

That is to say, the electronic device can first adjust the static time period threshold. If the motion state of the electronic device is determined to be a non-static state based on the adjusted static time period threshold, and then adjust the motion threshold, the first state discrimination condition can be adjusted from multiple dimensions. , to improve the effectiveness of the algorithm.

In one embodiment, determining the target motion state of the electronic device based on the second state discriminating condition includes: determining, from the buffered real-time sensor data, first sensor data that satisfies the adjusted static time period threshold, and obtaining a new first sensor data. a sensor data, and determine new motion data based on the new first sensor data; if the new motion data is less than or equal to the motion threshold, then determine that the target motion state of the electronic device is a static state; if the new motion data is greater than the motion threshold, Then it is determined that the target motion state of the electronic device is a non-stationary state.

It is understandable that if the electronic device adjusts the static time period threshold, the originally acquired first sensor data does not meet the adjusted static time period threshold, and needs to be re-determined from the cached real-time sensor data to satisfy the adjusted static time period threshold. of the first sensor data, get the new first sensor is data.

Optionally, if the adjustment of the static time period threshold is to decrease the static time period threshold, the electronic device determines from the first sensor data first sensor data that satisfies the adjusted static time period threshold.

In some possible implementations, the electronic device may randomly determine, from the first sensor data, first sensor data that satisfies the adjusted static time period threshold.

In some possible implementations, the electronic device may determine, from the first sensor data, the first sensor data that satisfies the adjusted static time period threshold and is within the range of the latest time period.

In other implementation manners, the electronic device may also determine, from the first sensor data, the first sensor that satisfies the adjusted static time period threshold in other manners, which is not limited herein.

If the new motion data is less than or equal to the motion threshold, it is determined that the target motion state of the electronic device is a stationary state, and the duration of the stationary state is short.

If the new motion data is greater than the motion threshold, it means that after adjusting the static time period threshold, the static time period in which the electronic device is in a static state cannot be found, and the target motion state of the electronic device is determined to be a non-static state.

In this embodiment, after adjusting the stationary time period, if the new motion data is less than or equal to the motion threshold, it means that the duration of the stationary state of the electronic device is shorter, but it can still be determined that the target motion state of the electronic device is stationary ; If the new motion data is still greater than the motion threshold, it means that after adjusting the static time period threshold, the static time period in which the electronic device is in a static state cannot be found, and the target motion state of the electronic device is determined to be a non-static state. Adjusting the static time period threshold by the electronic device can exclude the possibility of misjudgment caused by the noise that may exist in the first sensor data from the dimension of the duration of the electronic device being in a static state, so as to more accurately determine the motion state of the electronic device .

In some possible embodiments, the above method further includes: detecting the degree of stability of the electronic device; based on the degree of stability of the electronic device, adjusting the initial state discriminating condition to obtain the first state discriminating condition.

The degree of stability of the electronic device refers to the degree to which the posture of the electronic device is stable. The higher the stability of the electronic device, the higher the probability of the electronic device being in a stationary state, and the longer the duration of being in the stationary state.

The initial state judging condition is an initially set condition for judging the motion state of the electronic device. The initial state discrimination condition may include at least one of a motion condition and a stationary period condition. The motion condition may include a motion threshold, and may also include a motion range, etc., but is not limited thereto. The still time period condition may include the still time period threshold, and may also include the time of the still time period, the time range of the still time period, etc., but is not limited thereto.

In some possible implementations, the electronic device collects IMU data in real time through an inertial measurement unit, and determines the stability of the electronic device based on the IMU data; the size of the IMU data is positively correlated with the stability of the electronic device. The IMU data includes at least one of angular velocity data of a gyroscope, acceleration data of an accelerometer, and magnetic force data of a magnetometer. Magnetic data may include magnetic field strength and direction.

In other possible implementations, the electronic device detects the motion degree of the preview image in real time, and determines the stability of the electronic device based on the motion degree of the preview image; the motion degree of the preview image is negatively correlated with the stability of the electronic device.

It can be understood that the stability of the electronic device is low. When there is a large shake or movement, the degree of motion in the preview screen is also large, and the preview screen has large shake or movement. Therefore, the preview screen based on real-time detection can be used. the degree of movement, and accurately determine the degree of stability of the electronic device.

In other implementation manners, the electronic device may also determine the degree of stability of the electronic device in other manners, which is not limited herein.

In this embodiment, the electronic device can adjust the initial state judging condition based on its own stability to obtain the first state judging condition. The first state judging condition of the current stability of the electronic device, so as to more accurately determine the motion state of the electronic device.

In some other possible embodiments, the above method further includes: detecting the stability of the electronic device; dynamically adjusting the first state discrimination condition based on the stability of the electronic device to obtain a new first state discrimination condition.

In one embodiment, adjusting the initial state discrimination condition based on the stability of the electronic device to obtain the first state discriminating condition includes: if the initial state discriminating condition includes a motion threshold, adjusting the motion threshold based on the stability of the electronic device to obtain The first state discriminating condition; the stability of the electronic device is negatively correlated with the motion threshold; if the initial state discriminating condition includes the stationary time period threshold, then based on the stability of the electronic device, the stationary time period threshold is adjusted to obtain the first state discriminating condition; The stability of the electronic device is positively correlated with the static time period threshold; if the initial state discrimination condition includes the motion threshold and the static time period threshold, then based on the stability of the electronic device, adjust the motion threshold and the static time period threshold to obtain the first state discrimination condition .

If the initial state judging condition includes a motion threshold, the motion threshold is adjusted based on the stability of the electronic device to obtain the first state judging condition; the stability of the electronic device is negatively correlated with the motion threshold. That is to say, the higher the stability of the electronic device is, the lower the motion threshold is to obtain a smaller motion threshold.

If the initial state judging condition includes the static time period threshold, then based on the stability of the electronic device, the static time period threshold is adjusted to obtain the first state distinguishing condition; the stability of the electronic device is positively correlated with the static time period threshold. That is to say, the higher the stability of the electronic device is, the higher the static time period threshold value is, and the larger the static time period threshold value is obtained.

If the initial state judging condition includes a motion threshold and a stationary time period threshold, then based on the stability of the electronic device, the motion threshold and the stationary period threshold are adjusted to obtain the first state judgment condition. That is to say, the higher the stability of the electronic device is, the lower the motion threshold value and the larger the stationary time period threshold value, to obtain a smaller motion threshold value and a larger stationary time period threshold value.

In this embodiment, based on the stability of the electronic device, adjusting at least one of the motion threshold and the static time period threshold in the initial state determination condition can obtain a first state determination condition more suitable for the stability of the electronic device. When the stability of the electronic device is relatively high, at least one of reducing the motion threshold and increasing the stationary time period threshold can improve the accuracy of the determined target motion state of the electronic device, so that in the case of the first calibration method to improve the accuracy of the target calibration parameters. When the stability of the electronic device is low, at least one of increasing the motion threshold and decreasing the static time period threshold can exclude noise data from the first sensor data collected in real time subsequently and improve the effectiveness of the algorithm.

In some possible embodiments, the above method further includes: detecting the temperature drift of the electronic device; and adjusting the initial state determination condition based on the temperature drift of the electronic device to obtain the first state determination condition.

Adjusting the initial state judging condition includes: if the initial state judging condition includes a motion threshold, adjusting the motion threshold based on the degree of temperature drift; the degree of temperature drift and the motion threshold are positively correlated.

Temperature drift refers to the zero point drift caused by temperature changes. If the electronic device is in a temperature drift environment, there will be new factors affecting the calibration of sensor data, and the motion threshold needs to be increased.

In some possible embodiments, the target calibration parameters of the first calibration method are obtained through fine calibration, and the fine calibration method includes: determining the outlier range based on the first sensor data collected in real time, and determining the outlier range from each first sensor data Outlier data within the range of outliers is determined in the method; the outlier data is processed to obtain a processing result, and the updated first sensor data is obtained according to the processing result and the non-outlier data in the first sensor data; based on the updated After obtaining the first sensor data, target calibration parameters of the first calibration mode are determined.

Outliers, also known as outliers, are values that differ significantly from other values in multiple data sets. An outlier range is a range of values that is significantly different from the majority of the multiple data, and the outlier range does not include the majority of the multiple data. The first sensor data in the outlier range is outlier data. The first sensor data that is not within the outlier range is non-outlier data.

Optionally, if the target motion state of the electronic device is a static state, motion data is determined based on the first sensor data collected in real time, and an outlier range is determined according to the motion data.

Exemplarily, the electronic device determines, based on the first sensor data collected in real time, that the motion data are the mean value and the variance, respectively, and the outlier range may be a value range that differs from the mean by more than three standard deviations. where the arithmetic square root of the variance is the standard deviation.

Assuming that based on the first sensor data collected in real time, it is determined that the mean value of the first sensor data is 0.1 and the variance is 0.09, then the standard deviation is the arithmetic square root of the variance, which is 0.3, and the range of outliers is more than three times the standard deviation from the mean value. The range of values, i.e. (-∞, -0.8) & (1.0, +∞).

The electronic device determines outlier data within the range of outliers from each first sensor data. The outlier data is quite different from each first sensor data, and may be potential noise data. Therefore, the outlier data is analyzed. processing to get the processing result.

In some possible implementations, the electronic device performs elimination processing on outlier data to obtain a processing result.

In other possible implementations, the electronic device replaces the outlier data with other non-outlier data to obtain a processing result.

In other implementation manners, the electronic device may also perform other processing on the outlier data, which is not limited herein.

In an embodiment, the electronic device obtains an average value of the updated first sensor data, and uses the average value as a target calibration parameter of the first calibration method.

In another embodiment, the electronic device obtains the median of the updated first sensor data, and uses the median as the target calibration parameter of the first calibration method.

In other implementation manners, the electronic device may also use other manners to determine target calibration parameters that give the first calibration manner based on the updated first sensor data, which is not limited herein.

In this embodiment, the electronic device determines an outlier range based on the first sensor data collected in real time, determines outlier data within the outlier range from each first sensor data, and performs an analysis on the outlier data. processing to obtain a processing result, thereby obtaining more accurate updated first sensor data with less noise data in the updated first sensor data. Then, based on the updated first sensor data, the target calibration parameters of the first calibration mode can be more accurately determined.

In one embodiment, processing the outlier data to obtain a processing result includes: determining non-outlier data matching the outlier data; and replacing the outlier data with the non-outlier data to obtain the processing result.

Specifically, the electronic device determines the non-outlier data with the smallest difference from the outlier data, replaces the non-outlier data with the outlier data, and obtains a processing result.

It can be understood that the range other than the outlier is the range of non-outliers, the range of non-outliers can represent most of the data in each of the first sensor data, and the outlier data is the potential in each of the first sensor data. Therefore, determining the non-outlier data with the smallest difference from the outlier data can characterize the outlier data to the greatest extent, so that the processing result is the closest to the actually collected data.

In some other possible implementations, the electronic device may further determine the mean value of each first sensor data, the mean value is the non-outlier data, and use the mean value as the non-outlier data matching the outlier data.

In some other possible implementations, the electronic device may further determine the median of each first sensor data, where the median is the non-outlier data, and use the mean as the non-outlier matching the outlier data value data.

In some other possible implementation manners, the electronic device may further regard the 0 as non-outlier data matching the outlier data.

In other implementation manners, the electronic device may also determine non-outlier data matching the outlier data in other manners, which is not limited herein.

In some possible embodiments, the method for determining the target calibration parameters of the second calibration method includes: if the target motion states of the electronic device are all non-stationary states, acquiring historical calibration parameters as the target calibration parameters of the second calibration method, The historical calibration parameters are determined based on sensor data in a historical static state, and the historical static state is determined based on real-time sensor data collected at historical moments.

It can be understood that the electronic device collects the first sensor data in real time, and determines the target motion state of the electronic device based on the first sensor data in real time; In the static state, the target calibration parameters cannot be determined based on the real-time collected first sensor data, so the target calibration method is determined as the second calibration method, and the historical calibration parameters are obtained as the target calibration parameters of the second calibration method.

Optionally, if the target motion state of the electronic device is in a non-stationary state within a preset time period, the target calibration method is determined to be the second calibration method, and historical calibration parameters are acquired as the target calibration parameters of the second calibration method. The preset duration can be set as required. For example, the preset time period may be 10 minutes.

In some possible embodiments, the target calibration parameters of the second calibration method are obtained through background calibration. The specific method of the background calibration includes: in response to the electronic device entering an assembly node or an idle time period, acquiring a fourth sensor collected in real time by the electronic device data; determining the historical motion state of the electronic device according to the fourth sensor data; and determining the target calibration parameter based on the fourth sensor data collected in real time when the historical motion state is a static state.

The fourth sensor data is sensor data collected when the electronic device enters an assembly node or during an idle time period.

An assembly node is a node when an electronic device is assembled, system installed, and other factory operations. It can be understood that, in response to entering the assembly node, the electronic device can trigger the calibration of the sensor data, and the calibration of the sensor data is also the first calibration of the sensor data.

During the calibration process of the sensor data, without any process, the electronic device automatically realizes the first calibration of the sensor data, obtains the target calibration parameters, and stores the target calibration parameters locally.

The idle time period refers to a time period during which the electronic device is in an idle state. For example, the idle time period may be a night time period, a standby time period or a shutdown time period, etc., but is not limited thereto.

The idle state can be set according to user needs. Exemplarily, the electronic device is in an idle state when it has no processing tasks at all; when the electronic device is not used by a user, it is in an idle state; when the electronic device performs less than a specified number of processing tasks, it is in an idle state; and so on. Among them, the specified number can be set as required, such as 1 or 2.

In response to entering the idle time period, the electronic device acquires the fourth sensor data collected by the electronic device in real time, and can determine the historical motion state of the electronic device according to the fourth sensor data. The fourth sensor data determines a target calibration parameter, and according to the target calibration parameter, updates the historical calibration parameter stored in the electronic device; when the historical motion state is a non-stationary state, no calibration is performed.

It should be noted that the specific method of determining the historical motion state of the electronic device according to the fourth sensor data is the same as the specific method of determining the target motion state of the electronic device according to the first sensor data, and will not be repeated here.

In this embodiment, in response to entering an assembly node or an idle time period, the electronic device determines the target calibration parameter based on the fourth sensor data collected in real time. It can be understood that, in response to entering the assembly node, the electronic device can obtain the target calibration parameters for the first time, and when the sensor data is subsequently calibrated, if the second calibration method is adopted, the first calibration can also be determined based on the sensor data in a historical static state. The obtained target calibration parameters enrich the calibration methods of sensor data, and can improve the validity, compatibility, reliability and disaster tolerance of sensor data calibration. In response to entering the idle time period, the electronic device can perform calibration of the sensor data without user participation, with a small amount of computation, and a short duration without requiring more extra power consumption.

Figure 11 is a block diagram of a method for calibrating sensor data in one embodiment. The electronic equipment includes a memory module, a calculation module and a calibration module. Among them, the electronic device collects sensor data in real time through the target sensor and inputs it into the first-in, first-out queue of the storage module, the sensor data collected in real time enters the queue at the tail, and the head of the queue leaves the queue. The FIFO queue may be a circular queue, and the circular queue may specifically be a RingBuffer queue.

If the collection duration corresponding to the data length of the sensor data stored in the FIFO queue satisfies the static time period threshold, the acquired sensor data satisfying the static time period threshold is input into the calculation module.

The calculation module is used to preset a threshold value, and the set threshold value includes a static time period threshold value and a motion threshold value. The calculation module calculates the motion data based on the sensor data satisfying the threshold value of the stationary time period, and compares the motion data with the preset threshold value to determine the state. If it is determined that the electronic device is in a stationary state, the target calibration method is determined as the first calibration mode, and the target calibration parameters of the first calibration mode are obtained by performing fine calibration based on sensor data; if it is determined that the electronic device is in a non-stationary state, the target calibration mode is determined as In the second calibration method, target calibration parameters obtained through background calibration are acquired. Among them, the background calibration is the calibration performed by the electronic device in response to entering an assembly node or an idle time period.

The calibration module is used to obtain the target calibration parameters output by the calculation module, and perform error calibration on the sensor data collected in real time based on the target calibration parameters to obtain calibrated sensor data.

FIG. 12 is a calculation flow chart of the calculation module in one embodiment. The electronic device presets a threshold, the threshold includes a motion threshold and a static time period threshold, and the motion threshold may include at least one of mean, variance, standard deviation, median, and mean difference. The electronic device stores the sensor data collected in real time in the queue 1204. If the collection duration corresponding to the data length of the sensor data stored in the queue 1204 satisfies the static time period threshold, the sensor data that meets the static time period threshold is obtained, and calculates Motion data 1206 for various sensor data.

The electronic device executes step 1208 , based on the motion data 1206 and the set threshold, to determine whether the electronic device is in a stationary state; if the determination is yes, then step 1210 is performed; if the determination is no, step 1212 is performed. Among them, background calibration is a calibration performed by the electronic device in response to entering the assembly node 1214 or the idle time period 1216 .

In some exemplary embodiments, as shown in FIG. 13, an anti-shake method is provided, and the method is applied to the electronic device in FIG. 1 as an example for description, including the following steps:

Step 1302 , in the shooting scene of the electronic device, obtain calibrated sensor data; the calibrated sensor data is obtained according to the above-mentioned sensor data calibration method.

The electronic device includes a camera module, and shooting is performed through the camera module. The camera module may include a lens, an image sensor (CMOS, Complementary Metal Oxide Semiconductor), a motor, and other devices.

The shooting scene may be a shooting scene, a shooting video scene, or a shooting picture preview scene, and the like.

Step 1304: Determine the anti-shake compensation amount based on the calibrated sensor data.

The anti-shake compensation amount is a value for compensating for the jitter of the electronic device. The anti-shake compensation amount may include a first anti-shake compensation amount of the lens, and a second anti-shake compensation amount of the image sensor.

Specifically, the electronic device adopts a preset anti-shake algorithm based on the calibrated sensor data to determine the first anti-shake compensation amount of the lens and the second anti-shake compensation amount of the image sensor, respectively.

Step 1306: Perform shake compensation on the camera module based on the anti-shake compensation amount.

The electronic device controls the first motor to perform shake compensation on the lens in the camera module based on the first anti-shake compensation amount, and controls the second motor based on the second anti-shake compensation amount to perform shake compensation on the image sensor in the camera module. While compensating for jitter, the electronic device can obtain a more accurate light signal for exposure through the lens module, thereby generating a clearer image.

In the above-mentioned anti-shake method, in a shooting scene of an electronic device, more accurate calibrated sensor data can be obtained according to the above-mentioned sensor data calibration method, so that a more accurate anti-shake compensation amount can be determined based on the calibrated sensor data , further and more accurately perform shake compensation on the camera module, thereby generating a clearer image.

In addition, the calibration process of the sensor data and the optical anti-shake process are in the same closed-loop system, which avoids the deviation of the data over time, and the deviation caused by the calibration process and the optical anti-shake process in different spaces, which can improve the performance. Calibration accuracy. While improving the calibration accuracy of the sensor data, the calibrated sensor data is also used for anti-shake, which improves the performance of the anti-shake algorithm, improves the image quality and expressiveness of the generated image, and can enable higher product performance. force.

Further, the electronic device can acquire calibrated sensor data in real time in the shooting scene, and perform shake compensation on the camera module in real time based on the sensor data, which can increase the timeliness of the shake compensation; and if the calibrated sensor is based on the first The target calibration parameter of a calibration method is determined, and the target calibration parameter is determined by the first sensor data collected in real time in the shooting scene, then the target calibration parameter also takes the influencing factors of the current shooting scene into consideration, and the sensor data can be measured. Calibration to obtain more accurate sensor data, so as to perform more accurate shake compensation for the camera module.

FIG. 14 is a frame diagram of the operation of the optical anti-shake system in an electronic device in one embodiment. The electronic device obtains the target calibration parameters through the inertial measurement unit calculation, and calibrates the sensor data (IMU data) acquired in real time based on the target calibration parameters to obtain calibrated sensor data. The anti-shake compensation amount is calculated based on the calibrated sensor data, including the first anti-shake compensation amount of the lens and the second anti-shake compensation amount of the image sensor. The electronic device can control the first motor to perform shake compensation on the lens, and control the second motor to perform shake compensation on the image sensor. During the shake compensation process between the lens and the image sensor, the light signal emitted by the light source is projected through the shake-compensated lens and projected onto the shake-compensated image sensor. The image sensor can convert the light signal into an electrical signal, which is then processed by the image signal. The processor performs image signal processing and can generate clear images.

It should be understood that, although the steps in the flowcharts involved in the above embodiments are sequentially displayed according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but may be performed at different times The execution order of these steps or phases is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or phases in the other steps.

Based on the same inventive concept, an embodiment of the present application also provides a sensor data calibration device for implementing the above-mentioned sensor data calibration method. The solution to the problem provided by the device is similar to the solution described in the above method, so the specific limitations in the embodiment of the calibration device for one or more sensor data provided below can refer to the calibration of sensor data above. The limitation of the method is not repeated here.

In one embodiment, as shown in FIG. 15, a sensor data calibration device is provided, including: a calculation module 1502 and a calibration module 1504, wherein:

The computing module 1502 is configured to determine the target motion state of the electronic device based on the first sensor data collected in real time.

The calculation module 1502 is further configured to determine the target calibration mode according to the target motion state; wherein, the target calibration mode includes the first calibration mode or the second calibration mode, and the target calibration parameters of the first calibration mode are determined based on the first sensor data, and the first calibration mode is determined based on the first sensor data. The target calibration parameters of the second calibration method are determined based on the sensor data in the historical static state.

The calibration module 1504 is configured to calibrate the second sensor data collected in real time by the electronic device based on the target calibration parameters of the target calibration method.

The above sensor data calibration device determines the target motion state of the electronic device based on the first sensor data collected in real time, and then determines a target calibration method according to the target motion state; wherein, the target calibration method includes the first calibration method or the second calibration method. In the calibration mode, the target calibration parameters of the first calibration mode are determined based on the first sensor data, and the target calibration parameters of the second calibration mode are determined based on the sensor data in a historical static state. Then, the electronic device can use one of multiple calibration methods to calibrate the second sensor data collected in real time, which enriches the calibration methods of the sensor data.

Optionally, when the electronic device uses the first calibration method to calibrate the second sensor data collected in real time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time, and can be calibrated in real time. The second sensor data is calibrated online, which reduces the production line process required for the offline calibration and the hardware cost of additional calibration equipment, and reduces the cost of sensor data calibration. At the same time, the target calibration parameters of the first calibration method are determined based on the first sensor data collected in real time. The target calibration parameters of the first calibration method are obtained by following changes in the environment, which can meet the requirements of various complex environments such as temperature and humidity drift. High-precision calibration to improve the accuracy of sensor data calibration.

Optionally, when the electronic device uses the second calibration method to calibrate the second sensor data collected in real time, the calibration method of the sensor data is enriched, and the validity, compatibility, reliability and tolerance of the sensor data calibration can be improved. catastrophic.

In some embodiments, the above-mentioned calculation module 1502 is further configured to obtain first sensor data that satisfies the static time period threshold from the cached real-time sensor data according to a preset static time period threshold; based on the first sensor data, determine the electronic device target motion state.

In some embodiments, the above-mentioned device further includes a storage module; the real-time sensor data is the data buffered in the FIFO queue in real time; the above-mentioned storage module is also used to obtain the data length in the FIFO queue corresponding to the current moment; the calculation module 1502 is further configured to determine the collection time length corresponding to the data length according to the relationship between the data length and the collection time length, and obtain first sensor data satisfying the still time period threshold when the collection time length satisfies the still time period threshold.

In some embodiments, the FIFO queue is a circular queue.

In some embodiments, the above calculation module 1502 is further configured to determine motion data corresponding to the first sensor data according to the first sensor data; and determine the target motion state of the electronic device based on the motion data and the first state discrimination condition.

In some embodiments, the first state discrimination condition includes a motion threshold; the above calculation module 1502 is further configured to determine that the target motion state of the electronic device is a static state if the motion data is less than or equal to the motion threshold; if the motion data is greater than the motion threshold, Then, the first state judging condition is adjusted to the second state judging condition, and the target motion state of the electronic device is determined based on the second state judging condition.

In some embodiments, the first sensor data is data collected by an inertial measurement unit, the inertial measurement unit includes at least one type of sensor, and each sensor includes data of at least two axes; the motion data includes a plurality of first statistical data, collected in each axis The data corresponding to at least one first statistical data, and each first statistical data corresponds to one motion threshold; the above-mentioned calculation module 1502 is further configured to determine if each first statistical data is less than or equal to the corresponding motion threshold, then determine the The target motion state is the stationary state.

In some embodiments, the above-mentioned calculation module 1502 is further configured to determine the quantity of the second statistical data if the second statistical data greater than the corresponding motion threshold exists in each of the first statistical data; if the quantity is less than the preset quantity threshold , the motion threshold corresponding to the second statistical data is adjusted, and the second state judgment condition is obtained based on the adjusted motion threshold and conditions other than the adjusted motion threshold in the first judgment condition.

In some embodiments, the above calculation module 1502 is further configured to increase the motion threshold corresponding to the second statistical data.

In some embodiments, the above calculation module 1502 is further configured to determine that the target motion state of the electronic device is a static state if each second statistical data is less than or equal to the corresponding adjusted motion threshold; if each second statistical data exists If it is greater than the corresponding adjusted motion threshold, it is determined that the target motion state of the electronic device is a non-stationary state.

In some embodiments, the first state discrimination condition further includes a stationary time period threshold; the above-mentioned calculation module 1502 is further configured to determine an intermediate motion state of the electronic device based on the adjusted motion threshold; if the intermediate motion state is a non-stationary state, adjust the The static time period threshold; according to the adjusted motion threshold, the adjusted static time period threshold, and other conditions in the first state judgment condition except the adjusted motion threshold and the static time period threshold, the second state judgment condition is obtained .

In some embodiments, the calculation module 1502 described above is also used to reduce the static time period threshold.

In some embodiments, the above calculation module 1502 is further configured to determine the first sensor data that meets the adjusted static time period threshold from the cached real-time sensor data, obtain new first sensor data, and based on the new first sensor data The sensor data determines new motion data; if the new motion data is less than or equal to the adjusted motion threshold, the target motion state of the electronic device is determined to be a static state; if the new motion data is greater than the adjusted motion threshold, the electronic device is determined The target motion state is a non-stationary state.

In some embodiments, the first state judging condition further includes a static time period threshold; the above calculation module 1502 is further configured to adjust the static time period threshold if the motion data is greater than the motion threshold; according to the adjusted static time period threshold and the first A second state judging condition is obtained from other conditions except the static time period threshold in the first state judging condition.

In some embodiments, the calculation module 1502 described above is also used to reduce the static time period threshold.

In some embodiments, the above calculation module 1502 is further configured to determine the first sensor data that meets the adjusted static time period threshold from the cached real-time sensor data, obtain new first sensor data, and based on the new first sensor data The sensor data determines new motion data; if the new motion data is less than or equal to the motion threshold, the target motion state of the electronic device is determined to be a static state; if the new motion data is greater than the motion threshold, the target motion state of the electronic device is determined to be non-active. Stationary state.

In some embodiments, the above calculation module 1502 is further configured to detect the stability of the electronic device; based on the stability of the electronic device, adjust the initial state determination condition to obtain the first state determination condition.

In some embodiments, the above-mentioned calculation module 1502 is further configured to adjust the motion threshold based on the stability of the electronic device if the initial state determination condition includes a motion threshold, to obtain the first state determination condition; the stability of the electronic device is proportional to the motion threshold Negative correlation; if the initial state discriminating condition includes the static time period threshold, then based on the stability of the electronic equipment, adjust the static time period threshold to obtain the first state discriminating condition; the stability of the electronic equipment is positively correlated with the static time period threshold; if the initial The state discriminating condition includes a motion threshold and a stationary time period threshold, then based on the stability of the electronic device, the motion threshold and the stationary time period threshold are adjusted to obtain the first state discriminating condition.

In some embodiments, the above calculation module 1502 is further configured to determine whether the motion state of the target is a stationary state, and determine the target calibration method based on the judgment result.

In some embodiments, the above calculation module 1502 is further configured to determine the target calibration method as the first calibration method if the target motion state is a stationary state; and determine the target calibration method as the second calibration method if the target motion state is a non-stationary state Way.

In some embodiments, the above calculation module 1502 is further configured to determine an outlier range based on the first sensor data collected in real time, and determine outlier data within the outlier range from each first sensor data; The outlier data is processed to obtain a processing result, and the updated first sensor data is obtained according to the processing result and the non-outlier data in the first sensor data; based on the updated first sensor data, the first calibration method is determined. Target calibration parameters.

In some embodiments, the above calculation module 1502 is further configured to determine non-outlier data matching the outlier data; replace the non-outlier data with the outlier data to obtain a processing result.

In some embodiments, the above-mentioned calculation module 1502 is further configured to obtain historical calibration parameters as the target calibration parameters of the second calibration method if the target motion states of the electronic device are all non-stationary states, and the historical calibration parameters are based on the historical static state. The historical static state is determined based on real-time sensor data collected at historical moments.

In some embodiments, the above calibration module 1504 is further configured to acquire fourth sensor data collected in real time by the electronic device in response to the electronic device entering an assembly node or an idle time period; determine the historical motion state of the electronic device according to the fourth sensor data; When the historical motion state is a static state, the target calibration parameter is determined based on the fourth sensor data collected in real time.

Based on the same inventive concept, an embodiment of the present application further provides an anti-shake device for implementing the above-mentioned anti-shake method. The implementation solution provided by the device for solving the problem is similar to the implementation solution described in the above method, so the specific limitations in one or more embodiments of the anti-shake device provided below can refer to the above limitations on the anti-shake method, It is not repeated here.

In some embodiments, as shown in FIG. 16, a sensor data calibration apparatus is provided, including: an acquisition module 1602, a determination module 1604 and an anti-shake module 1606, wherein:

The acquiring module 1602 is configured to acquire calibrated sensor data in the shooting scene of the electronic device; the calibrated sensor data is obtained according to the above-mentioned sensor data calibration method.

The determining module 1604 is configured to determine the anti-shake compensation amount based on the calibrated sensor data.

The anti-shake module 1606 is configured to perform jitter compensation on the camera module based on the anti-shake compensation amount.

The above-mentioned anti-shake device, and the calibration process of the sensor data and the optical anti-shake process are in the same closed-loop system, to avoid data deviation over time, and the calibration process and the optical anti-shake process are brought in different spaces. The deviation can improve the accuracy of calibration. While improving the calibration accuracy of the sensor data, the calibrated sensor data is also used for anti-shake, which improves the performance of the anti-shake algorithm, improves the image quality and expressiveness of the generated image, and can enable higher product performance. force.

Optionally, the electronic device can acquire calibrated sensor data in real time in the shooting scene, and perform shake compensation on the camera module in real time based on the sensor data, which can increase the timeliness of shake compensation; and if the calibrated sensor is based on The target calibration parameter of the first calibration method is determined, and the target calibration parameter is determined by the first sensor data collected in real time in the shooting scene, then the target calibration parameter also takes the influencing factors of the current shooting scene into consideration, and can affect the sensor data. Perform calibration to obtain more accurate sensor data, so as to more accurately perform shake compensation for the camera module.

Each module in the above-mentioned sensor data calibration device or the anti-shake device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or stored in the memory in the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In some embodiments, a camera module is provided, comprising a memory and a processor, a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is made to perform the steps of the above-mentioned method for calibrating sensor data, Or the steps of the above anti-shake method.

Optionally, a camera module is provided, and the camera module performs anti-shake according to the above-mentioned anti-shake method. Optionally, the camera module includes a lens, an image sensor, a first motor and a second motor, the first motor controls the lens to perform shake compensation according to the first anti-shake compensation amount, and the second motor controls the image sensor according to the second anti-shake compensation amount. Perform shake compensation; in the process of shake compensation, the light passing through the lens is projected into the image sensor, and the light signal of the light can be converted into an electrical signal by the image sensor to generate an image.

In some embodiments, an electronic device is provided, and the electronic device may be a terminal, and the internal structure diagram thereof may be as shown in FIG. 17 . The electronic device includes a processor, a memory, an input/output interface, a communication interface, a display unit and an input device. Wherein, the processor, the memory and the input/output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The input/output interface of the electronic device is used for exchanging information between the processor and external devices. The communication interface of the electronic device is used for wired or wireless communication with an external terminal, and the wireless communication can be realized by WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies. The computer program, when executed by the processor, implements a sensor data calibration method or an anti-shake method. The display unit of the electronic device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the electronic device, or a An external keyboard, trackpad, or mouse, etc.

Those skilled in the art can understand that the structure shown in FIG. 17 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may be Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

Embodiments of the present application also provide a computer-readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform a method of calibrating or preventing sensor data. The steps of the shaking method.

Embodiments of the present application also provide a computer program product containing instructions, which, when run on a computer, cause the computer to execute a sensor data calibration method or an anti-shake method.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all It is the information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to a memory, a database or other media used in the various embodiments provided in this application may include at least one of a non-volatile memory and a volatile memory. Non-volatile memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Memory) Access Memory, MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration and not limitation, the RAM may be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The database involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a blockchain-based distributed database, etc., but is not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., and are not limited to this.

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but should not be construed as a limitation on the scope of the patent of the present application. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the present application should be determined by the appended claims.

Claims (29)

1. A method of calibrating sensor data, the method comprising:

determining a target motion state of the electronic equipment based on the first sensor data acquired in real time;

determining a target calibration mode according to the target motion state; the target calibration mode comprises a first calibration mode or a second calibration mode, target calibration parameters of the first calibration mode are determined based on the first sensor data, and target calibration parameters of the second calibration mode are determined based on sensor data in historical static states;

and calibrating the second sensor data acquired by the electronic equipment in real time based on the target calibration parameters of the target calibration mode.

2. The method of claim 1, wherein determining the target calibration mode based on the determination comprises:

if the judgment result shows that the target motion state is a static state, determining that the target calibration mode is a first calibration mode;

and if the judgment result shows that the target motion state is a non-static state, determining that the target calibration mode is a second calibration mode.

3. The method of claim 2, wherein determining the target motion state of the electronic device based on the first sensor data collected in real-time comprises:

according to a preset static time period threshold value, acquiring first sensor data meeting the static time period threshold value from cached real-time sensor data;

based on the first sensor data, a target motion state of the electronic device is determined.

4. The method of claim 3, wherein the real-time sensor data is data buffered in a first-in-first-out queue in real-time; the acquiring, according to a preset stationary time period threshold, first sensor data meeting the stationary time period threshold from the cached real-time sensor data includes:

acquiring the data length in the first-in first-out queue corresponding to the current moment;

according to the relation between the data length and the acquisition time length, the acquisition time length corresponding to the data length is determined, and under the condition that the acquisition time length meets the static time period threshold, first sensor data meeting the static time period threshold are obtained.

5. The method of claim 3, wherein determining a target motion state of an electronic device from the first sensor data comprises:

determining motion data corresponding to the first sensor data according to the first sensor data;

and determining the target motion state of the electronic equipment based on the motion data and the first state discrimination condition.

6. The method of claim 5, wherein the first state discrimination condition comprises a motion threshold; the determining the target motion state of the electronic device based on the motion data and the first state discrimination condition includes:

if the motion data is smaller than or equal to the motion threshold, determining that the target motion state of the electronic equipment is a static state;

and if the motion data is larger than the motion threshold, adjusting the first state judgment condition to a second state judgment condition, and determining the target motion state of the electronic equipment based on the second state judgment condition.

7. The method of claim 6, wherein the first sensor data is data collected by an inertial measurement unit comprising at least 1 sensor, each sensor comprising at least 2 axes of data; the motion data comprises a plurality of first motion data, the data collected by each axis corresponds to at least 1 first motion data, and each first motion data corresponds to 1 motion threshold;

if the motion data is less than or equal to the motion threshold, determining that the target motion state of the electronic device is a static state, including:

and if the first motion data are less than or equal to the corresponding motion threshold values, determining that the target motion state of the electronic equipment is a static state.

8. The method of claim 7, wherein the adjusting the first state discrimination condition to a second state discrimination condition if the motion data is greater than the motion threshold comprises:

if second motion data larger than the corresponding motion threshold exists in each first motion data, determining the quantity of the second motion data;

if the number is smaller than a preset number threshold, adjusting a motion threshold corresponding to the second motion data, and obtaining a second state judgment condition based on the adjusted motion threshold and a condition except the adjusted motion threshold in the first judgment condition.

9. The method of claim 8, wherein the adjusting the motion threshold corresponding to the second motion data comprises: and increasing the motion threshold corresponding to the second motion data.

10. The method of claim 8, wherein determining the target motion state of the electronic device based on the second state discrimination condition comprises:

if each second motion data is smaller than or equal to the corresponding adjusted motion threshold, determining that the target motion state of the electronic equipment is a static state;

and if the second motion data are larger than the corresponding adjusted motion threshold, determining that the target motion state of the electronic equipment is a non-static state.

11. The method of claim 8, wherein the first state discrimination condition further comprises a quiescent period threshold; the obtaining a second state discrimination condition based on the adjusted motion threshold and a condition other than the adjusted motion threshold in the first discrimination condition includes:

if the motion state of the electronic equipment is determined to be a non-static state based on the adjusted motion threshold, adjusting the static time period threshold;

and obtaining a second state discrimination condition according to the adjusted motion threshold, the adjusted stationary time period threshold and other conditions except the adjusted motion threshold and stationary time period threshold in the first state discrimination condition.

12. The method of claim 11, wherein the adjusting the inactivity time period threshold comprises: decreasing the quiescent period threshold.

13. The method of claim 11, wherein the determining a target motion state of the electronic device based on the second state discrimination condition comprises:

determining first sensor data meeting the adjusted stationary time period threshold value from the cached real-time sensor data to obtain new first sensor data, and determining new motion data based on the new first sensor data;

if the new motion data is smaller than or equal to the adjusted motion threshold, determining that the target motion state of the electronic equipment is a static state;

and if the new motion data is larger than the adjusted motion threshold, determining that the target motion state of the electronic equipment is a non-static state.

14. The method of claim 6, wherein the first state discrimination condition further comprises a quiescent period threshold; if the motion data is greater than the motion threshold, adjusting the first state discrimination condition to a second state discrimination condition, including:

if the motion data is larger than the motion threshold, adjusting the stationary time period threshold; and obtaining a second state discrimination condition according to the adjusted stationary time period threshold value and other conditions except the stationary time period threshold value in the first state discrimination condition.

15. The method of claim 14, wherein the adjusting the inactivity time period threshold comprises: decreasing the quiescent period threshold.

16. The method of claim 14, wherein determining the target motion state of the electronic device based on the second state discrimination condition comprises:

determining first sensor data meeting the adjusted stationary time period threshold value from the cached real-time sensor data to obtain new first sensor data, and determining new motion data based on the new first sensor data;

if the new motion data is smaller than or equal to the motion threshold, determining that the target motion state of the electronic equipment is a static state;

and if the new motion data is larger than the motion threshold, determining that the target motion state of the electronic equipment is a non-static state.

17. The method of claim 5, further comprising:

detecting the stability degree of the electronic equipment;

and adjusting an initial state judgment condition based on the stability degree of the electronic equipment to obtain the first state judgment condition.

18. The method of claim 17, wherein adjusting an initial state discrimination condition based on the degree of stability of the electronic device to obtain the first state discrimination condition comprises:

if the initial state discrimination condition comprises a motion threshold, adjusting the motion threshold based on the stability degree of the electronic equipment to obtain the first state discrimination condition; a degree of stability of the electronic device is inversely related to the motion threshold;

if the initial state discrimination condition comprises a stationary time period threshold, adjusting the stationary time period threshold based on the stability degree of the electronic equipment to obtain the first state discrimination condition; the degree of stability of the electronic device is positively correlated with the stationary time period threshold;

if the initial state discrimination condition includes a motion threshold and a stationary time period threshold, the motion threshold and the stationary time period threshold are adjusted based on the stability of the electronic device to obtain the first state discrimination condition.

19. The method according to claim 1, wherein the determining of the target calibration parameter of the first calibration mode comprises:

determining an outlier range based on the first sensor data collected in real time, and determining outlier data in the outlier range from each first sensor data;

processing the outlier data to obtain a processing result, and obtaining updated first sensor data according to the processing result and non-outlier data in the first sensor data;

and determining a target calibration parameter of the first calibration mode based on the updated first sensor data.

20. The method of claim 19, wherein said processing the outlier data to obtain a processed result comprises:

determining non-outlier data matched with the outlier data;

and replacing the outlier data with the non-outlier value data to obtain a processing result.

21. The method of claim 1, wherein the second calibration mode comprises a target calibration parameter determination mode that includes:

if the target motion states of the electronic equipment are all non-static states, acquiring historical calibration parameters as target calibration parameters of the second calibration mode, wherein the historical calibration parameters are determined based on sensor data in historical static states, and the historical static states are determined based on real-time sensor data acquired at historical moments.

22. The method of any of claims 1 to 21, wherein determining the target calibration parameters for the second calibration mode comprises:

responding to the electronic equipment entering an assembly node or an idle time period, and acquiring the fourth sensor data acquired in real time;

determining a historical motion state of the electronic device from the fourth sensor data;

and determining a target calibration parameter based on the fourth sensor data acquired in real time under the condition that the historical motion state is a static state.

23. An anti-shaking method, characterized in that the method comprises:

acquiring calibrated sensor data in a shooting scene of the electronic equipment; the calibrated sensor data is obtained according to the method of any one of claims 1 to 22;

determining an anti-shake compensation amount based on the calibrated sensor data;

and carrying out shake compensation on the camera module based on the shake-proof compensation quantity.

24. An apparatus for calibrating sensor data, comprising:

the computing module is used for determining the target motion state of the electronic equipment based on the first sensor data collected in real time;

the calculation module is also used for determining a target calibration mode according to the target motion state; the target calibration mode comprises a first calibration mode or a second calibration mode, target calibration parameters of the first calibration mode are determined based on the first sensor data, and target calibration parameters of the second calibration mode are determined based on sensor data in historical static states;

and the calibration module is used for calibrating the second sensor data acquired by the electronic equipment in real time based on the target calibration parameters of the target calibration mode.

25. An anti-shake apparatus, comprising:

the acquisition module is used for acquiring calibrated sensor data in a shooting scene of the electronic equipment; the calibrated sensor data is obtained according to the method of any one of claims 1 to 22;

a determining module, configured to determine an anti-shake compensation amount based on the calibrated sensor data;

and the anti-shake module is used for carrying out shake compensation on the camera module based on the anti-shake compensation quantity.

26. A camera module comprising a memory and a processor, the memory having stored thereon a computer program, wherein the computer program, when executed by the processor, causes the processor to perform the steps of the method of calibrating sensor data according to any one of claims 1 to 22, or the steps of the anti-shake method according to claim 23.

27. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the computer program, when executed by the processor, causes the processor to carry out the steps of the method of calibrating sensor data according to any one of claims 1 to 22, or the steps of the anti-shake method according to claim 23.

28. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for calibration of sensor data according to any one of claims 1 to 22, or the steps of the anti-shake method according to claim 23.

29. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the calibration method of sensor data of any one of claims 1 to 22, or the steps of the anti-shake method of claim 23.

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