CN117169801B - Electromagnetic environment monitoring and calibrating system, method, device and medium - Google Patents

Abstract

The embodiments of this specification provide an electromagnetic environment monitoring and calibration system, method, device and medium. The method is executed based on the electromagnetic environment monitoring and calibration system. The system includes an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module and data processing. module, the method includes: obtaining environmental data; determining test signal parameters based on the environmental data and switched radio frequency channel information; the test signal parameters at least include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal; based on the test The signal parameters send out test signals; collect calibration data corresponding to the test signals; and determine monitoring compensation parameters based on the calibration data. This method is implemented through an electromagnetic environment monitoring and calibration device. The method also uses computer instructions stored in a computer-readable storage medium to be read and executed. The present invention can effectively ensure the accuracy of monitoring data by performing real-time compensation and calibration on electromagnetic signals.

Description

An electromagnetic environment monitoring and calibration system, method, device and medium

Technical field

This specification relates to the technical field of electromagnetic environment monitoring, and in particular to an electromagnetic environment monitoring calibration system, method, device and medium.

Background technique

Because radio telescopes have extremely high sensitivity and are extremely susceptible to radio interference from the outside world and themselves, which affects normal operation and scientific output, it is necessary to monitor the electromagnetic environment in which radio telescopes are located. However, when monitoring the electromagnetic environment, changes in the channel frequency caused by each switch of the radio frequency channel and changes in the electromagnetic environment, resulting in changes in the antenna gain and system loss, may affect the accuracy of the monitoring data. Therefore, it is necessary to provide an electromagnetic environment monitoring and calibration system, method, device and medium.

Contents of the invention

In order to automatically calibrate the monitoring data when switching radio frequency channels to ensure the accuracy of the monitoring data, this specification provides an electromagnetic environment monitoring calibration system, method, device and medium.

One or more embodiments of this specification provide an electromagnetic environment monitoring and calibration system. The system includes: an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module, and a data processing module; the environmental monitoring module is configured as Obtain environmental data; the parameter determination module is configured to determine test signal parameters based on the switched radio frequency channel information, the test signal parameters include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal; the calibration The standard source is configured to send the test signal based on the test signal parameters; the data acquisition module is configured to collect calibration data corresponding to the test signal; the data processing module is configured to determine based on the calibration data Monitor compensation parameters.

One or more embodiments of this specification provide an electromagnetic environment monitoring and calibration method, which is executed based on an electromagnetic environment monitoring and calibration system. The system includes an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module and a data processing module. The method includes: obtaining environmental data; determining test signal parameters based on the environmental data and switched radio frequency channel information; the test signal parameters at least include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal; Send the test signal based on the test signal parameters; collect calibration data corresponding to the test signal; and determine monitoring compensation parameters based on the calibration data.

One or more embodiments of this specification provide an electromagnetic environment monitoring and calibration device. The device includes at least one memory and at least one processor; the at least one memory is used to store computer instructions; the at least one processor is used to execute the Some of the instructions in the computer instructions are used to implement the aforementioned method.

One or more embodiments of this specification provide a computer-readable storage medium that stores computer instructions. After the computer reads the computer instructions in the storage medium, the computer executes the foregoing method.

The beneficial effects brought by some embodiments of this specification include but are not limited to: (1) Determining test signal parameters through environmental data and switched radio frequency channel information, and sending out test signals based on the test signal parameters, and collecting test signals corresponding to Calibration data, and determining monitoring compensation parameters based on the calibration data, can perform real-time compensation and calibration of electromagnetic signals, thereby effectively ensuring the accuracy of monitoring data; (2) Based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data , determine the historical reliability of test signals of different electromagnetic frequencies, and focus on strengthening testing of electromagnetic frequencies with lower reliability, which can effectively improve the reliability of calibration results; (3) Antenna gain data obtained by segmenting test signals Determining monitoring compensation parameters with system loss data can effectively ensure the accuracy of monitoring compensation parameters; (4) Determine gain parameters based on antenna information and environmental information, comprehensively considering the impact of antenna information and environmental information on gain parameters, so that The gain parameters are more accurate, which is helpful to improve the accuracy of antenna gain data; (5) Construct an environmental impact map based on the effective environmental data distribution, and determine the gain parameters through the trained gain analysis model based on the environmental impact map and antenna information, The accuracy of the gain parameters can be effectively guaranteed, thus laying the foundation for quickly and accurately determining antenna gain data. In addition, using joint training to train the gain analysis model can not only reduce the number of samples required for training, but also improve training efficiency.

Description of the drawings

This specification is further explained by way of example embodiments, which are described in detail by means of the accompanying drawings. These embodiments are not limiting. In these embodiments, the same numbers represent the same structures, where:

Figure 1 is a module diagram of an electromagnetic environment monitoring and calibration system according to some embodiments of this specification;

Figure 2 is an exemplary flow chart of an electromagnetic environment monitoring calibration method according to some embodiments of this specification;

Figure 3 is an exemplary flow chart of a method for determining test signal parameters according to some embodiments of this specification;

Figure 4 is an exemplary flow chart of a monitoring compensation parameter determination method according to some embodiments of this specification;

Figure 5 is an exemplary flow chart of a method for determining antenna gain data according to some embodiments of this specification;

Figure 6 is an exemplary schematic diagram of a gain analysis model according to some embodiments of the present specification.

Detailed ways

In order to explain the technical solutions of the embodiments of this specification more clearly, the accompanying drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without exerting any creative efforts, this specification can also be applied to other applications based on these drawings. Other similar scenarios. Unless obvious from the locale or otherwise stated, the same reference numbers in the figures represent the same structure or operation.

It will be understood that the terms "system", "apparatus", "unit" and/or "module" as used herein are a means of distinguishing between different components, elements, parts, portions or assemblies at different levels. However, said words may be replaced by other expressions if they serve the same purpose.

As shown in this specification and claims, words such as "a", "an", "an" and/or "the" do not specifically refer to the singular and may include the plural unless the context clearly indicates an exception. Generally speaking, the terms "comprising" and "comprising" only imply the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or apparatus may also include other steps or elements.

Flowcharts are used in this specification to illustrate operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. At the same time, you can add other operations to these processes, or remove a step or steps from these processes.

Figure 1 is a module diagram of an electromagnetic environment monitoring and calibration system according to some embodiments of this specification.

As shown in FIG. 1 , the electromagnetic environment monitoring and calibration system 100 may include an environment monitoring module 110 , a parameter determination module 120 , a calibration standard source 130 , a data acquisition module 140 and a data processing module 150 .

The environmental monitoring module 110 refers to a module used to monitor the electromagnetic environment in real time to obtain environmental data.

The parameter determination module 120 refers to a module used to determine data related to electromagnetic environment monitoring. In some embodiments, the parameter determination module 120 may be used to determine test signal parameters based on the switched radio frequency channel information. Among them, the test signal parameters include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal.

In some embodiments, the parameter determination module 120 may be further configured to: determine basic signal parameters based on the switched radio frequency channel information; determine enhanced signal parameters based on environmental data; and determine test parameters based on the basic signal parameters and enhanced signal parameters. signal parameters.

The calibration standard source 130 refers to a signal emitting device or equipment used to emit test signals. In some embodiments, calibration standard source 130 may be used to generate test signals based on test signal parameters.

The data collection module 140 refers to a module used to collect data related to electromagnetic environment monitoring. In some embodiments, the data collection module 140 can be used to collect calibration data corresponding to the test signal.

The data processing module 150 refers to a module used to process data related to electromagnetic environment monitoring. In some embodiments, the data processing module 150 may be used to determine monitoring compensation parameters based on the calibration data.

In some embodiments, the data processing module 150 may be further configured to: determine antenna gain data based on calibration data and switched radio frequency channel information; determine system loss data based on antenna gain data, test signal parameters and calibration data; and Based on the antenna gain data and system loss data, the monitoring compensation parameters are determined.

In some embodiments, the data processing module 150 may be further configured to: obtain antenna information; determine gain parameters based on the antenna information and environmental data; and determine antenna gain data through a preset method based on the gain parameters.

For more information about the environmental monitoring module, parameter determination module, calibration standard source, data acquisition module and data processing module, please refer to the relevant descriptions in other parts of this manual.

It should be noted that the above description of the electromagnetic environment monitoring and calibration system 100 and its modules is only for convenience of description and does not limit this description to the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine various modules or form a subsystem to connect with other modules without departing from this principle. In some embodiments, the environmental monitoring module 110, parameter determination module 120, calibration standard source 130, data acquisition module 140 and data processing module 150 disclosed in Figure 1 can be different modules in a system, or can be implemented by one module. The functions of two or more modules mentioned above. For example, each module can share a storage module, or each module can have its own storage module. Such deformations are within the scope of this manual.

Figure 2 is an exemplary flow chart of an electromagnetic environment monitoring calibration method according to some embodiments of this specification. As shown in Figure 2, process 200 includes the following steps. In some embodiments, process 200 may be performed by an electromagnetic environment monitoring calibration system.

Step 210: Obtain environmental data.

Because radio telescopes have extremely high sensitivity and are extremely susceptible to radio interference from the outside world and themselves, which affects normal operation and scientific output, it is necessary to monitor the electromagnetic environment. Environmental data refers to relevant data obtained during electromagnetic environment monitoring. In some embodiments, the environmental data may include environmental noise data, environmental obstacle data, environmental vibration data, environmental atmospheric data, electromagnetic interference source data, etc.

Among them, environmental noise data can reflect the noise situation during electromagnetic environment monitoring, such as noise sources. Environmental obstacle data can reflect the obstacle conditions during electromagnetic environment monitoring. Obstacles refer to objects that affect or interfere with the propagation of electromagnetic radiation, such as buildings, trees, etc. Environmental vibration data can reflect vibration conditions during electromagnetic environment monitoring, such as ground vibration conditions caused by the operation of large machinery. Environmental atmospheric data can reflect the atmospheric conditions during electromagnetic environment monitoring, such as atmospheric temperature, humidity, air pressure, etc. Electromagnetic interference source data can reflect the situation of electromagnetic interference sources during electromagnetic environment monitoring, such as the location of electromagnetic interference sources. Electromagnetic interference sources refer to equipment or devices that can produce electromagnetic radiation. For example, sources of electromagnetic interference may include electrical towers, base stations, broadcast stations, etc.

In some embodiments, environmental data may be obtained through an environmental monitoring module. In some embodiments, the environment monitoring module may include multiple sensors, each sensor being used to acquire one type of environmental data. For example, the environment monitoring module may include a sound sensor for acquiring environmental sound data. For another example, the environment monitoring module may include a visual sensor for acquiring data on environmental obstacles. For another example, the environment monitoring module may include a vibration sensor for acquiring environmental vibration data. For another example, the environmental monitoring module may include temperature and humidity sensors, barometers, etc., respectively used to obtain environmental atmospheric data.

In some embodiments, the environment monitoring module may determine the electromagnetic interference source data through the first preset table. For example, since antennas of different radio frequency channels can receive electromagnetic signals in different directions, a first preset table can be constructed based on the antenna direction and the electromagnetic interference source. There is a corresponding relationship between the antenna direction and the location and number of the electromagnetic interference source. According to The antenna direction of the switched radio frequency channel can determine the electromagnetic interference source data. Among them, the location of the electromagnetic interference source can be understood as the actual location of the electromagnetic interference source in the electromagnetic environment. In some embodiments, the location and number of electromagnetic interference sources can be manually input into the electromagnetic environment monitoring system in advance.

It should be noted that a variety of sensors can be set independently of each other, or can be integrated and set in the environmental monitoring module, as long as they can achieve the function of obtaining environmental data.

Step 220: Determine the test signal parameters based on the environmental data and the switched radio frequency channel information.

Since the electromagnetic environment is monitored through radio frequency channels, and different radio frequency channels monitor electromagnetic signals of different electromagnetic frequencies, it is necessary to switch radio frequency channels when monitoring electromagnetic signals of different electromagnetic frequencies. Switching is a way of converting RF channels from one RF channel to another.

The switched radio frequency channel information refers to information related to the switched radio frequency channel. In some embodiments, the switched radio frequency channel information may include antenna information, modulation mode, power limit, etc. corresponding to the switched radio frequency channel.

Antenna information refers to information related to the antenna corresponding to the switched radio frequency channel. For example, the antenna information may include antenna type, antenna receiving frequency range, antenna direction, antenna shape parameters (radius, depth, curvature, etc.), etc.

In some embodiments, the radio frequency channel information can be preset in advance and has a corresponding relationship with the radio frequency channel, that is, different radio frequency channels correspond to different radio frequency channel information. When the electromagnetic environment monitoring calibration system switches different radio frequency channels to achieve electromagnetic environment monitoring , the parameter determination module can automatically obtain the switched radio frequency channel information.

Test signal parameters refer to parameters related to the test signal. In some embodiments, the test signal parameters may include the frequency range, frequency distribution, and test intensity corresponding to different electromagnetic frequencies of the test signal.

The test signal refers to the signal sent by the calibration standard source and used to calibrate the monitoring data. The monitoring data may be signal data obtained by radio frequency channels during electromagnetic environment monitoring.

The frequency range of the test signal refers to the range of the electromagnetic frequency value of the test signal. For example, the frequency range of the test signal can be 30GHz~40GHz, etc.

The frequency distribution of the test signal can reflect the distribution of the electromagnetic frequency of the test signal. In some embodiments, the frequency distribution of the test signal may be uniformly distributed, for example, the electromagnetic frequency of the test signal is 30 GHz, 30.2 GHz, 30.4 GHz, etc. In some embodiments, the frequency distribution of the test signal may be non-uniform distribution, for example, the electromagnetic frequency of the test signal is 30 GHz, 30.2 GHz, 30.7 GHz, etc.

The test intensity corresponding to different electromagnetic frequencies refers to the number of tests corresponding to test signals of different electromagnetic frequencies. The greater the number of tests, the greater the intensity of the test and the more accurate the calibration data corresponding to the test signal.

In some embodiments, the test signal parameters may also include the transmission power of the test signal. When the test signal of the same electromagnetic frequency is tested multiple times, the transmit power of the test signal of the electromagnetic frequency can be different. In some embodiments, the transmission power of the test signal can be set uniformly according to the antenna information (such as the antenna reception frequency range).

In some embodiments, based on the environmental data and the switched radio frequency channel information, the parameter determination module can determine the test signal parameters in a variety of ways. For example, the parameter determination module may determine the test signal parameters through the second preset table. The second preset table can be constructed based on the environmental data, the switched radio frequency channel information and the test signal parameters corresponding to the calibration data with better calibration effect in the historical data.

For more information on how to determine the test signal parameters, see Figure 3 and its related description.

Step 230: Send a test signal based on the test signal parameters.

In some embodiments, the calibration standard source can automatically emit test signals based on test signal parameters. In some embodiments, based on the test signal parameters, the electromagnetic environment monitoring calibration system can generate control instructions to control the calibration standard source to emit test signals. For example, the electromagnetic environment monitoring and calibration system may include a control module. Based on the test signal parameters, the control module may generate a control instruction (such as a signal sending instruction). When the calibration standard source receives the control instruction, it immediately sends out a corresponding test signal.

Step 240: Collect calibration data corresponding to the test signal.

Calibration data refers to the signal data corresponding to the test signal collected by the data acquisition module. In some embodiments, the calibration data may include electromagnetic frequency, electromagnetic power, energy of the electromagnetic signal, wavelength of the electromagnetic signal, etc. collected by the data acquisition module. Understandably, different test signals correspond to different calibration data. In some embodiments, calibration data may be collected through a data collection module. For example, the data acquisition module can collect calibration data through the antenna of the switched radio frequency channel.

For more information about calibration data, please refer to the relevant instructions in other parts of this manual (Figure 4 and its related descriptions).

Step 250: Determine monitoring compensation parameters based on the calibration data.

Monitoring compensation parameters refer to parameters used to calibrate and compensate the relevant parameters of electromagnetic signals. For example, monitoring compensation parameters may include at least power compensation parameters.

In some embodiments, based on the calibration data, the data processing module may determine the monitoring compensation parameters in a variety of ways. For example, the data processing module can compare the calibration data with its corresponding test signal parameters, obtain the electromagnetic power difference between the test signal sent by the calibration standard source and the calibration data collected by the data acquisition module, and convert the electromagnetic power into The difference is determined as the corresponding power compensation parameter. For example, if the transmit power of an electromagnetic signal with a frequency of 30 GHz is 200W and the electromagnetic power in the calibration data is 180W, then the power compensation parameter of the electromagnetic signal with a frequency of 30 GHz in the radio frequency channel is 20W.

For more information on how to determine the monitoring compensation parameters, see Figure 4 and its related description.

Some embodiments of this specification determine test signal parameters through environmental data and switched radio frequency channel information, and send out test signals based on the test signal parameters, and collect calibration data corresponding to the test signals, and determine monitoring compensation parameters based on the calibration data. Real-time compensation and calibration of electromagnetic signals can be performed, thus effectively ensuring the accuracy of monitoring data.

FIG. 3 is an exemplary flow chart of a method for determining test signal parameters according to some embodiments of this specification. As shown in Figure 3, process 300 includes the following steps. In some embodiments, process 300 may be performed by a parameter determination module.

Step 310: Determine basic signal parameters based on the switched radio frequency channel information.

Basic signal parameters refer to the initial values or initial ranges of test signal parameters. It can be understood that the basic signal parameters may include the basic frequency range of the test signal, the basic frequency distribution, and the basic test intensity corresponding to different electromagnetic frequencies.

In some embodiments, the parameter determination module may determine the antenna reception frequency range in the switched radio frequency channel information as the basic frequency range of the test signal.

In some embodiments, the parameter determination module can divide the basic frequency range of the test signal according to preset rules to determine the basic frequency distribution of the test signal. Among them, the preset rules refer to preset frequency division rules. For example, the preset rules may include equal intervals (such as 0.2 GHz between two adjacent frequencies) or unequal intervals (such as 0.2 GHz, 0.4 GH, etc. between two adjacent frequencies). In some embodiments, the basic testing intensity corresponding to different electromagnetic frequencies can be determined by manual preset, and the basic testing intensity corresponding to different electromagnetic frequencies can be the same.

Step 320: Determine enhanced signal parameters based on environmental data.

Enhanced signal parameters refer to test signal parameters used to enhance the test effect. In some embodiments, enhancing signal parameters can be understood as increasing test intensity for test signals of certain key electromagnetic frequencies based on basic signal parameters. It can be understood that the enhanced signal parameters may include increased test intensity values corresponding to key electromagnetic frequencies.

In some embodiments, based on environmental data, the parameter determination module can determine key electromagnetic frequencies through historical data, and then determine the enhanced signal parameters. For example, the parameter determination module can determine, based on historical data, the electromagnetic frequency corresponding to the electromagnetic signal whose data amount exceeds the preset threshold under the historical environmental data that meets the preset conditions as the key electromagnetic frequency; and then respond accordingly It is enough to increase the testing intensity corresponding to key electromagnetic frequencies so that the final testing intensity corresponding to key electromagnetic frequencies is greater than the basic testing intensity corresponding to key electromagnetic frequencies. Among them, the preset conditions refer to preset similarity judgment conditions. For example, the preset condition may be that the similarity to the current environment data is not less than a similarity threshold, etc. It should be noted that the increased value of test intensity corresponding to key electromagnetic frequencies can be determined based on actual conditions.

In some embodiments, determining the enhanced signal parameters based on environmental data may also include: determining the historical reliability of test signals of different electromagnetic frequencies under the same environmental data based on historical monitoring effects; and determining the enhanced signal parameters based on the historical reliability. .

The historical monitoring effect can reflect the accuracy of historical monitoring data. For example, the higher the accuracy of historical monitoring data, the better the historical monitoring effect.

Historical monitoring data refers to the electromagnetic signals of multiple electromagnetic frequencies detected by each radio frequency channel during electromagnetic environment monitoring. The accuracy of historical monitoring data can reflect the degree of consistency between the historical monitoring data and the real electromagnetic interference data in the environment.

In some embodiments, the accuracy of historical monitoring data may be determined based on historical observation data of radio astronomy telescopes. For example, the parameter determination module can determine the accuracy of historical monitoring data by analyzing the difference between the signal-to-noise ratio in historical observation data of radio astronomical telescopes and the expected signal-to-noise ratio. For example, the greater the difference between the signal-to-noise ratio of historical observation data of radio astronomical telescopes and the expected signal-to-noise ratio, the lower the accuracy of historical monitoring data. Among them, the expected signal-to-noise ratio refers to the preset signal-to-noise ratio value. In some embodiments, the expected signal-to-noise ratio may be an empirical value, an experimental value, a simulated value, or the like.

The historical reliability of test signals refers to the reliability of calibration results obtained from test signals of different electromagnetic frequencies under the same environmental data in historical data. In some embodiments, the historical reliability of the test signal can be determined by the accuracy of the historical monitoring data after compensating the electromagnetic signal with the monitoring compensation parameters obtained from the test signal. Understandably, the higher the accuracy of historical monitoring data, the more accurate the monitoring compensation parameters, and the higher the historical reliability of the test signal.

In some embodiments, based on the historical reliability of the test signal, the parameter determination module may use the electromagnetic frequency corresponding to the test signal whose historical reliability is lower than the reliability threshold as the key electromagnetic frequency. The reliability threshold refers to a preset reliability value. In some embodiments, the reliability threshold may be set manually.

In some embodiments, the increased value (enhanced signal parameter) of the test intensity corresponding to the key electromagnetic frequency is related to the historical reliability of the test signal of the key electromagnetic frequency. The lower the historical reliability, the greater the test intensity corresponding to the key electromagnetic frequency. . For example, the increased value of the test intensity corresponding to the key electromagnetic frequency may be an integer value of the product of the historical reliability of the key electromagnetic frequency and its corresponding basic test intensity.

Some embodiments of this specification determine the historical reliability of test signals of different electromagnetic frequencies based on environmental data and historical monitoring effects, and conduct focused and intensive testing on electromagnetic frequencies with low historical reliability, which is beneficial to improving the reliability of calibration results.

In some embodiments, based on environmental data and historical monitoring effects, determining the historical reliability of test signals of different electromagnetic frequencies may also include: obtaining historical monitoring data of the switched radio frequency channel and the accuracy of the historical monitoring data; based on different electromagnetic frequencies. The proportion of historical monitoring data of frequency determines the historical influence of different electromagnetic frequencies; and based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, the historical reliability of test signals of different electromagnetic frequencies is determined.

In some embodiments, there is a corresponding relationship between the historical monitoring data and the accuracy of the historical monitoring data, and one historical monitoring data corresponds to the accuracy of the historical monitoring data. In some embodiments, the parameter determination module can obtain historical monitoring data of the switched radio frequency channel based on historical data, and the accuracy of the historical monitoring data.

The proportion of historical monitoring data of different electromagnetic frequencies refers to the ratio of the number of monitoring data of each electromagnetic frequency to the total number of monitoring data of the entire radio frequency channel. In some embodiments, the parameter determination module can obtain the proportion of the historical monitoring data volume of each electromagnetic frequency by comparing the quantity of monitoring data of each electromagnetic frequency with the total quantity of monitoring data of the entire radio frequency channel.

The historical influence of different electromagnetic frequencies refers to the degree of influence of monitoring data of different electromagnetic frequencies on the entire electromagnetic environment monitoring. In some embodiments, the historical influence of different electromagnetic frequencies can be represented by the proportion of historical monitoring data amounts of different electromagnetic frequencies. The lower the proportion of historical monitoring data of electromagnetic frequency, the less electromagnetic signals of this electromagnetic frequency are in the electromagnetic environment, and the smaller the impact on the entire electromagnetic environment monitoring. In other words, the smaller the historical impact of this electromagnetic frequency. .

In some embodiments, based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, the parameter determination module can determine the historical reliability of test signals of different electromagnetic frequencies through calculation.

In some embodiments, the historical reliability of the test signal of the electromagnetic frequency may be positively related to the historical influence of the electromagnetic frequency and the average accuracy of the historical monitoring data of the electromagnetic frequency. For example, the historical reliability of the test signal of electromagnetic frequency P = the historical influence of electromagnetic frequency P × the average accuracy of the historical monitoring data of electromagnetic frequency P. Among them, the average accuracy of the historical monitoring data of the electromagnetic frequency P can be determined based on the accuracy of multiple historical monitoring data of the electromagnetic frequency P.

It should be noted that the historical reliability of test signals of different electromagnetic frequencies can also be determined through other calculation methods or third preset tables, which can reflect that the greater the historical influence of electromagnetic frequencies, the greater the accuracy of historical monitoring data. The higher the historical reliability of the corresponding electromagnetic frequency is, the greater the relationship is.

Some embodiments of this specification determine the historical reliability of test signals of different electromagnetic frequencies based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, and further consider the role of monitoring data of different electromagnetic frequencies in the entire electromagnetic environment monitoring. The degree of influence can be ensured to a certain extent to ensure the rationality of the historical reliability of test signals of different electromagnetic frequencies, so as to further improve the reliability of the calibration results.

Step 330: Determine the test signal parameters based on the basic signal parameters and enhanced signal parameters.

In some embodiments, based on the basic signal parameters and the enhanced signal parameters, the parameter determination module may merge the basic signal parameters and the enhanced signal parameters, and use the combined parameters as test signal parameters. For example, if the basic signal parameters are (X ₁ , Y ₁ , Z ₁ ) and the enhanced signal parameters are (X ₁ , Y ₁ , Z ₂ ), then the test signal parameters can be (X ₁ , Y ₁ , Z ₁ + Z ₂ ).

In some embodiments of this specification, based on the historical influence of different electromagnetic frequencies and the accuracy of historical monitoring data, the historical reliability of the test signals of different electromagnetic frequencies is determined, and the electromagnetic frequencies with lower reliability are intensively tested. , which can effectively improve the reliability of calibration results.

Figure 4 is an exemplary flowchart of a monitoring compensation parameter determination method according to some embodiments of this specification. As shown in Figure 4, process 400 includes the following steps. In some embodiments, process 400 may be performed by a data processing module.

Step 410: Determine antenna gain data based on the calibration data and the switched radio frequency channel information.

Antenna gain data refers to data related to the radiation efficiency of an antenna in a specific direction.

In some embodiments, based on the calibration data and the switched radio frequency channel information, the data processing module can determine the antenna gain data in various ways.

In some embodiments, the data processing module can determine the corresponding antenna based on the antenna information (such as antenna type, antenna performance, antenna shape parameters (radius, depth, curvature, antenna length, etc.)) in the switched radio frequency channel information. Gain formula, and determine the antenna gain data based on the corresponding antenna gain formula and antenna information.

The antenna gain formula refers to the calculation formula used to determine the antenna gain data. In some embodiments, the data processing module may determine the antenna gain formula according to the antenna type. For example, for a parabolic antenna, the antenna gain formula can be , where,/> is the antenna gain data; D is the diameter of the paraboloid, determined based on the antenna information (such as antenna shape parameters);/> is the wavelength of the electromagnetic signal, determined based on calibration data; 4.5 is empirical data. As another example, for an upright omnidirectional antenna, the antenna gain formula can be/> , where,/> is the antenna gain data; L is the antenna length, determined based on the antenna information;/> is the wavelength of the electromagnetic signal; 2 is the empirical data.

In some embodiments, according to the corresponding antenna gain formula, the data processing module can obtain the antenna gain data by substituting corresponding data (such as antenna information, calibration data, etc.) in the antenna information into the corresponding antenna gain formula.

For more information on how to determine antenna gain data, see Figures 5-6 and their related descriptions.

Step 420: Determine system loss data based on the antenna gain data, test signal parameters and calibration data.

System loss data refers to the energy loss of the electromagnetic environment monitoring and calibration system. In some embodiments, system loss data may include energy losses in all components or modules from the signal source (such as a calibration standard source) to the receiver (such as a data acquisition module).

In some embodiments, the data processing module can calculate the system loss data based on the antenna gain data, test signal parameters, and calibration data.

In some embodiments, the system loss data may be positively correlated with the transmit power of the test signal in the antenna gain data and test signal parameters, and negatively correlated with the energy of the electromagnetic signal in the calibration data. An exemplary calculation formula includes: system loss data = transmission power of test signal + antenna gain data - energy of electromagnetic signal. Regarding the acquisition method of the transmission power of the test signal and the energy of the electromagnetic signal, please refer to Figure 2 and its related description.

In some embodiments, based on the antenna gain data, test signal parameters and calibration data, determining the system loss data may also include: segmenting the test signal according to the test signal parameters to obtain segmented signals; and based on the segmented signal parameters, segmentation Calibration data and antenna gain data corresponding to the segment signal, and determine system loss data corresponding to the segment signal.

The segmented signal refers to the signal obtained by evenly segmenting the test signal according to the test signal parameters. In some embodiments, the data processing module can uniformly segment the test signal according to the frequency distribution of the test signal in the test signal parameters, so that the frequency range of the test signal in each segment is consistent to obtain segmented signals.

For example, in the test signal parameters, the frequency range of the test signal is 30GHz~40GHz, and the frequency distribution of the test signal is 30GHz, 32GHz, 34GHz, 36GHz, 38GHz, and 40GHz. Then the frequency range of the test signal in each segment is respectively It is 30GHz~32GHz, 32GHz~34GHz, 34GHz~36GHz, 36GHz~38GHz, 38GHz~40GHz.

Segmented signal parameters refer to the test signal parameters corresponding to the segmented signal. In some embodiments, the method of determining the system loss data corresponding to the segmented signal based on the segmented signal parameters, the calibration data corresponding to the segmented signal, and the antenna gain data is the same as the method for determining the system loss data based on the antenna gain data, test signal parameters, and calibration data. The method of losing data is similar and will not be described again here. In some embodiments, the system loss data corresponding to each segmented signal can also be used to characterize the energy loss of the electromagnetic environment monitoring and calibration system.

Understandably, different segmented signals correspond to different system loss data. The narrower the segmentation, the more accurate the system loss data obtained, which can lay the foundation for subsequent determination of more accurate monitoring compensation parameters, but at the same time this will lead to system calculations. The pressure is high and the computing efficiency is low. Therefore, by reasonably segmenting the test signal and determining the system loss data corresponding to the segmented signals, while improving the accuracy of the system loss data, it can also take into account the system's computing pressure and improve the computing efficiency. efficiency.

In some embodiments, the above-mentioned segmentation of the test signal may also include: segmenting the test signal based on the historical reliability of different electromagnetic frequencies. The lower the historical reliability, the narrower the segment to which the test signal of the electromagnetic frequency belongs.

The narrower the segment is, the smaller the frequency range of the segment to which the test signal of the electromagnetic frequency with lower historical reliability belongs. For example, the frequency range of the segment to which a conventional electromagnetic frequency test signal belongs is 1 GHz, while the frequency range of the segment to which the electromagnetic frequency test signal with historically low reliability belongs is 0.5 GHz. For a detailed description of the historical reliability of test signals at different electromagnetic frequencies, please refer to Figure 3 and its related description.

In some embodiments of this specification, segmentation based on the historical reliability of test signals at different electromagnetic frequencies can make the system loss data of the segmented signals more targeted, thereby effectively improving the accuracy of the system loss data.

Step 430: Determine monitoring compensation parameters based on the antenna gain data and system loss data.

In some embodiments, the data processing module may calculate the difference between the system loss data and the antenna gain data, and use the difference between the two as the monitoring compensation parameter. For example, the monitoring compensation parameter corresponding to the segmented signal = the system loss data corresponding to the segmented signal - the antenna gain data corresponding to the segmented signal.

In some embodiments of this specification, the monitoring compensation parameters are determined through the antenna gain data and system loss data obtained by segmenting the test signal, which can effectively ensure the accuracy of the monitoring compensation parameters.

Figure 5 is an exemplary flowchart of a method for determining antenna gain data according to some embodiments of this specification. As shown in Figure 5, process 500 includes the following steps. In some embodiments, process 500 may be performed by a data processing module.

Step 510: Obtain antenna information.

For detailed description of antenna information, please refer to Figure 2 and its related description. In some embodiments, the antenna information may be determined based on the switched radio frequency channel information.

Step 520: Determine gain parameters based on the antenna information and environmental data.

Gain parameters refer to the parameters in the antenna gain formula. For example, for a parabolic antenna, the antenna gain formula is , where parameter 4.5 is the gain parameter.

In some embodiments, different antennas correspond to different gain parameters, and the same antenna also has different gain parameters under different environmental data. In some embodiments, based on the antenna information and environmental data, the data processing module may determine the gain parameter through a fourth preset table. Among them, the fourth preset table can be constructed based on antenna information, environmental data and gain parameters.

In some embodiments, determining the gain parameters based on the antenna information and environmental data may also include: determining the effective environmental data distribution corresponding to the antenna based on the antenna direction; and determining the gain parameters through a gain analysis model based on the effective environmental data distribution and the antenna information. .

Antenna direction refers to the direction in which the antenna radiates or receives electromagnetic signals. In some embodiments, antenna direction may be determined based on antenna information.

The effective environmental data distribution can be used to characterize the effective impact of environmental data in various directions on the signal data received by the antenna. For example, for noise of the same frequency and intensity, the noise on the front of the antenna and the noise on the side of the antenna have different effects on the antenna.

In some embodiments, the data processing module can determine the effective environmental data distribution by using algorithm models, calculations, etc. based on the antenna shape parameters (radius, depth, curvature, etc.) and the relative position of each environmental data and the antenna.

For example, for a certain noise source in the environmental data, you can first determine whether the environmental noise data is in the radiation surface of the antenna based on the antenna shape parameters. If it is in the radiation surface of the antenna, you can combine the environmental noise data with The angle between it and the positive direction of the antenna radiation surface (normal vector of the antenna center) is used as an effective environmental data. Therefore, multiple environmental data (including environmental noise data, environmental vibration data, etc.) and their angles with the positive direction of the antenna radiation surface can form an effective environmental data distribution.

In some embodiments, the data processing module can process the effective environmental data distribution and antenna information through a gain analysis model to determine the gain parameters.

The gain analysis model refers to a model used to determine gain parameters based on effective environmental data distribution and antenna information. In some embodiments, the gain analysis model may be a machine learning model. For example, the gain analysis model may include one or a combination of one or more of a Deep Neural Networks (DNN) model, a Graph Neural Networks (GNN) model, or other custom models.

In some embodiments, the input of the gain analysis model may include effective environmental data distribution and antenna information, and the output of the gain analysis model may include gain parameters.

In some embodiments, the gain analysis model can be trained based on a large number of labeled training samples. The training samples may include sample effective environment data distribution and sample antenna information, and the labels may include gain parameters corresponding to the training samples. The training samples can be determined based on historical data, and the labels can be determined based on statistics of gain parameters corresponding to different environmental data and antenna information in the historical data.

In some embodiments, the data processing module can input training samples into the initial gain analysis model, update the parameters of the initial gain analysis model through training iterations, until the trained model meets the preset training conditions, and obtain the trained gain analysis model. Among them, the preset training condition can be that the loss function is less than the threshold, converges, or the training cycle reaches the threshold. In some embodiments, the method of iteratively updating the parameters of the model may include conventional model training methods such as stochastic gradient descent.

For more information about the gain analysis model, see Figure 6 and its related description.

Some embodiments of this specification determine gain parameters through a gain analysis model based on effective environmental data distribution and antenna information, which can ensure the accuracy of the gain parameters, and can also make the gain parameters more targeted, thereby improving the accuracy of the antenna gain data. Accuracy.

Step 530: Determine antenna gain data through a preset method based on the gain parameters.

The default method refers to the preset calculation method. In some embodiments, the preset method may include an antenna gain formula. In some embodiments, the data processing module can calculate by substituting the gain parameters into the corresponding antenna gain formula to obtain the antenna gain data.

Some embodiments of this specification determine gain parameters based on antenna information and environmental information, and comprehensively consider the impact of antenna information and environmental information on gain parameters, which can make the obtained gain parameters more accurate, thus helping to improve the accuracy of antenna gain data.

It should be noted that the above descriptions of process 200, process 300, process 400, and process 500 are only for examples and illustrations, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200, the process 300, the process 400 and the process 500 under the guidance of this description. However, such modifications and changes remain within the scope of this specification.

Figure 6 is an exemplary schematic diagram of a gain analysis model according to some embodiments of the present specification.

As shown in FIG. 6 , the gain analysis model may include an environment embedding layer 630 and a gain analysis layer 650 . In some embodiments, determining gain parameters through a gain analysis model based on effective environmental data distribution and antenna information may include: constructing an environmental impact map 620 based on the effective environmental data distribution 610; inputting the environmental impact map 620 into the environment embedding layer 630, determining Environmental impact characteristics 640-1; and inputting the environmental impact characteristics 640-1 and the antenna information 640-2 into the gain analysis layer 650 to obtain the gain parameters 660.

The environmental impact map 620 refers to a map that can reflect the effective environmental data distribution 610. Environmental impact graph 620 may include multiple nodes and multiple edges. In the environmental impact map 620, nodes may refer to nodes generated based on environmental data and antennas. Types of nodes may include environment data nodes and antenna nodes. Among them, environmental data nodes can be divided into environmental noise data nodes, environmental obstacle data nodes, environmental vibration data nodes, etc. according to environmental data types.

The environment data node refers to a node generated with the environment data in the effective environment data distribution 610. For example, each noise source monitored by the sound sensor (distinguish different noise sources according to the frequency, intensity, timbre and other parameters of the noise), and each vibration source monitored by the vibration sensor (based on the amplitude, frequency, phase and other parameters of the vibration) Differentiating different vibration sources), each electromagnetic interference source, etc. can be used as environmental data nodes.

For example, multiple environmental noise data (corresponding to multiple noise sources) in the effective environmental data distribution 610, such as environmental noise data A1 (corresponding to noise source A1), environmental noise data A2 (corresponding to noise source A2), can be used as environmental noise respectively. Data node a1, environmental noise data node a2. For another example, multiple environmental vibration data (corresponding to multiple vibration sources) in the effective environmental data distribution 610, such as environmental vibration data A1 (corresponding to vibration source B1), environmental vibration data B2 (corresponding to vibration source B2), can be used as environment data respectively. Vibration data node b1, environment vibration data node b2.

In some embodiments, the data processing module can determine the source of the noise by locating environmental noise data monitored by multiple sound sensors. For example, if different sound sensors detect sound directions of the same frequency, the intersection point of the sound directions is the location of the sound source (i.e., the noise source). It should be noted that the method for determining the vibration source is similar to the method for determining the above-mentioned noise source, and will not be described again here.

In some embodiments, the characteristics of the environmental data node may include environmental data type (such as environmental noise data, environmental vibration data, etc.), environmental data content (such as noise frequency, intensity, etc. data, vibration amplitude, phase, frequency, etc. data) , type of electromagnetic interference source, etc.).

The antenna node refers to the node generated by the antenna corresponding to the switched radio frequency channel. In some embodiments, the characteristics of the antenna node may include antenna information, such as antenna type, antenna performance, antenna shape parameters (radius, depth, curvature, etc.), etc.

Multiple nodes can be connected by edges, and the characteristics of edges can reflect the relationship between nodes. In some embodiments, the edges of environmental impact graph 620 may include edges of the first type. The first type of edge refers to the edge that exists between each environment data node and the antenna node. In some embodiments, the characteristics of the first type of edge can reflect the distribution relationship between the corresponding environmental data and the antenna, such as the angle between the environmental data and the positive direction of the antenna radiation surface, etc.

In some embodiments, the edges of the environmental impact graph 620 may also include edges of the second type. The second type of edge refers to an edge that exists between any two environment data nodes of the same environment data type. For example, there is an edge between the environmental noise data node a1 and the environmental noise data node a2. For another example, there is an edge between the environmental vibration data node b1 and the environmental vibration data node b2.

In some embodiments, the characteristics of the second type of edge may be the difference between two environmental data of the same environmental data type connected at both ends of the second type of edge, and the distribution relationship between the two environmental data and the antenna. difference.

For example, the environmental noise data A1 is expressed as (A ₁₁ , A ₁₂ , A ₁₃ ), where A ₁₁ is the noise frequency of A1, A ₁₂ is the noise intensity of A1, A ₁₃ is the noise timbre of A1, and the environmental noise data A1 The angle with the positive direction of the antenna radiation surface is β ₁ ; the environmental noise data A2 is expressed as (A ₂₁ , A ₂₂ , A ₂₃ ), where A ₂₁ is the noise frequency of A2, A ₂₂ is the noise intensity of A2, and A ₂₃ is The noise timbre of A2, the angle between the ambient noise data A2 and the positive direction of the antenna radiation surface is β ₂ , then the difference between the ambient noise data A1 and the ambient noise data A2 can be (A ₁₁ -A ₂₁ , A ₁₂ -A ₂₂ , A ₁₃ -A ₂₃ ) or (A ₂₁ -A ₁₁ , A ₂₂ -A ₁₂ , A ₂₃ -A ₁₃ ), the difference in the distribution relationship between the environmental noise data A1 and the environmental noise data A2 and the antenna can be β ₁ -β ₂ or β ₂ -β ₁ .

Understandably, two environmental data of the same environmental data type may weaken or enhance each other, so an edge existing between any two environmental data nodes of the same environmental data type is regarded as the second type of edge, and each The edges existing between each environmental data node and the antenna node are used as the first type of edges to construct the environmental impact map. The comprehensive impact of multiple environmental data on the gain parameters can be comprehensively considered to obtain more accurate gain parameters.

The environment embedding layer 630 refers to a model used to process the environmental impact map 620 and determine the environmental impact characteristics 640-1. In some embodiments, environment embedding layer 630 may be a machine learning model. For example, the environment embedding layer 630 may include, but is not limited to, a GNN model and the like.

In some embodiments, the input of the environment embedding layer 630 may include the environmental impact map 620, and the output of the environment embedding layer 630 may include the environmental impact feature 640-1.

The gain analysis layer 650 refers to a model used to process the environmental impact characteristics 640-1 and the antenna information 640-2 to determine the gain parameters 660. In some embodiments, gain analysis layer 650 may be a machine learning model. For example, the gain analysis layer 650 may include, but is not limited to, a DNN model and the like.

In some embodiments, inputs to gain analysis layer 650 may include environmental impact characteristics 640-1 and antenna information 640-2, and outputs of gain analysis layer 650 may include gain parameters 660. For specific content of antenna information, please refer to the relevant descriptions in other parts of this manual (Figure 2 and its related descriptions).

In some embodiments, the gain analysis model may be obtained by jointly training the environment embedding layer 630 and the gain analysis layer 650 .

In some embodiments, the data processing module may train the initial environment embedding layer and the initial gain analysis layer based on a large number of labeled training samples. The training sample may include a sample environmental impact map and sample antenna information determined based on the sample's effective environmental data distribution. The training sample is obtained in the same manner as the aforementioned training sample. The label may include the actual gain parameter corresponding to the sample environmental impact map determined based on the effective environmental data distribution of the sample and the sample antenna information. The label may be determined based on manual annotation or other methods.

The exemplary training process includes: inputting the sample environmental impact map into the initial environment embedding layer to obtain the environmental impact characteristics output by the initial environment embedding layer; inputting the environmental impact characteristics output by the initial environment embedding layer and the sample antenna information into the initial gain analysis layer to obtain The gain parameter output by the initial gain analysis layer; construct a loss function based on the label and the gain parameter output by the initial gain analysis layer, and update the parameters of the initial environment embedding layer and the initial gain analysis layer simultaneously. Through parameter update, the trained environment embedding layer and gain analysis layer are obtained.

Some embodiments of this specification construct an environmental impact map based on effective environmental data distribution, and determine gain parameters through a trained gain analysis model based on the environmental impact map and antenna information, which can effectively ensure the accuracy of the gain parameters, thereby enabling fast and Lays the foundation for accurately determining antenna gain data. In addition, using joint training to train the gain analysis model can not only reduce the number of samples required for training, but also improve training efficiency.

Some embodiments of this specification also provide an electromagnetic environment monitoring and calibration device, which includes at least one memory and at least one processor. Wherein, at least one memory is used to store computer instructions, and at least one processor is used to execute part of the computer instructions to implement the methods described in Figures 2-6.

Some embodiments of this specification also provide a computer-readable storage medium that stores computer instructions. After the computer reads the computer instructions in the storage medium, the computer executes the method described in Figures 2-6.

The basic concepts have been described above. It is obvious to those skilled in the art that the above detailed disclosure is only an example and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification, and therefore such modifications, improvements, and corrections remain within the spirit and scope of the exemplary embodiments of this specification.

At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and/or "some embodiments" means a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that “one embodiment” or “an embodiment” or “an alternative embodiment” mentioned twice or more at different places in this specification does not necessarily refer to the same embodiment. . In addition, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in this specification are not intended to limit the order of the processes and methods in this specification. Although the foregoing disclosure discusses by various examples some embodiments of the invention that are presently considered useful, it is to be understood that such details are for purposes of illustration only and that the appended claims are not limited to the disclosed embodiments. To the contrary, rights The claims are intended to cover all modifications and equivalent combinations consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented through hardware devices, they can also be implemented through software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that, in order to simplify the expression disclosed in this specification and thereby help understand one or more embodiments of the invention, in the previous description of the embodiments of this specification, multiple features are sometimes combined into one embodiment. accompanying drawings or descriptions thereof. However, this method of disclosure does not imply that the subject matter of the description requires more features than are mentioned in the claims. In fact, embodiments may have less than all features of a single disclosed embodiment.

In some embodiments, numbers are used to describe the quantities of components and properties. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Grooming. Unless otherwise stated, "about," "approximately," or "substantially" means that the stated number is allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending on the desired features of the individual embodiment. In some embodiments, numerical parameters should account for the specified number of significant digits and use general digit preservation methods. Although the numerical ranges and parameters used to identify the breadth of ranges in some embodiments of this specification are approximations, in specific embodiments, such numerical values are set as accurately as is feasible.

Each patent, patent application, patent application publication and other material, such as articles, books, instructions, publications, documents, etc. cited in this specification is hereby incorporated by reference into this specification in its entirety. Application history documents that are inconsistent with or conflict with the contents of this specification are excluded, as are documents (currently or later appended to this specification) that limit the broadest scope of the claims in this specification. It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and/or the use of terms in the accompanying materials of this manual and the content described in this manual, the descriptions, definitions, and/or the use of terms in this manual shall prevail. .

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Accordingly, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to those expressly introduced and described in this specification.

Claims (4)

1. An electromagnetic environment monitoring and calibration system, characterized in that the system includes: an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module and a data processing module; The environmental monitoring module is configured to obtain environmental data; The parameter determination module is configured to determine test signal parameters based on the switched radio frequency channel information. The test signal parameters include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal. The test intensity corresponding to the different electromagnetic frequencies The test intensity is the number of tests corresponding to the test signals of different electromagnetic frequencies; The calibration standard source is configured to emit the test signal based on the test signal parameters; The data collection module is configured to collect calibration data corresponding to the test signal; The data processing module is configured to determine monitoring compensation parameters based on the calibration data; The data processing module is further configured to: Determine antenna gain data based on the calibration data and the switched radio frequency channel information; Determine system loss data based on the antenna gain data, the test signal parameters, and the calibration data; Determine the monitoring compensation parameters based on the antenna gain data and the system loss data; The data processing module is further configured to: Get antenna information; Determine gain parameters based on the antenna information and the environmental data; Based on the gain parameter, determine the antenna gain data through a preset method; The parameter determination module is further configured as: Based on the switched radio frequency channel information, determine basic signal parameters; Based on the environmental data, determine enhanced signal parameters, where the enhanced signal parameters are test signal parameters used to enhance the test effect; The test signal parameters are determined based on the basic signal parameters and the enhanced signal parameters. 2. An electromagnetic environment monitoring and calibration method, characterized in that it is executed based on the electromagnetic environment monitoring and calibration system as claimed in claim 1, which system includes an environmental monitoring module, a parameter determination module, a calibration standard source, a data acquisition module and a data Processing module, the method includes: Obtain environmental data based on the environmental monitoring module; Based on the environmental data and the switched radio frequency channel information, the test signal parameters are determined through the parameter determination module; the test signal parameters at least include the frequency range, frequency distribution and test intensity corresponding to different electromagnetic frequencies of the test signal. The test intensity corresponding to different electromagnetic frequencies is the number of tests corresponding to the test signals of different electromagnetic frequencies; Based on the test signal parameters, send the test signal through the calibration standard source; Collect calibration data corresponding to the test signal based on the data acquisition module; Based on the calibration data, determine monitoring compensation parameters through the data processing module; Determining monitoring compensation parameters through the data processing module based on the calibration data includes: Determine antenna gain data based on the calibration data and the switched radio frequency channel information; Determine system loss data based on the antenna gain data, the test signal parameters, and the calibration data; Determine the monitoring compensation parameters based on the antenna gain data and the system loss data; Determining the antenna gain data also includes: Get antenna information; Determine gain parameters based on the antenna information and the environmental data; Based on the gain parameter, determine the antenna gain data through a preset method; Determining the test signal parameters through the parameter determination module based on the environmental data and the switched radio frequency channel information includes: Based on the switched radio frequency channel information, determine basic signal parameters; Based on the environmental data, determine enhanced signal parameters, where the enhanced signal parameters are test signal parameters used to enhance the test effect; The test signal parameters are determined based on the basic signal parameters and the enhanced signal parameters. 3. An electromagnetic environment monitoring and calibration device, characterized in that it includes at least one memory and at least one processor; the at least one memory is configured to store computer instructions; The at least one processor is configured to execute part of the computer instructions to implement the method of claim 2. 4. A computer-readable storage medium, characterized in that the storage medium stores computer instructions. After the computer reads the computer instructions in the storage medium, the computer executes the method of claim 2.