CN118971947A - A UAV communication system based on Beidou messages - Google Patents
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Abstract
The invention relates to the technical field of unmanned aerial vehicle communication, and discloses an unmanned aerial vehicle communication system based on Beidou messages. Unmanned aerial vehicle end has integrated flight control, data acquisition, big dipper short message communication and communication self-adaptation etc. module, can gather flight data in real time and carry out two-way short message communication with ground control station through big dipper satellite system. The communication layer provides stable data transmission, defines a communication protocol and performs encryption and decryption processing on the transmission data. The ground control station is responsible for sending control instructions, processing and analyzing data returned by the unmanned aerial vehicle, and uniformly managing and scheduling a plurality of unmanned aerial vehicle tasks. According to the communication system, the communication protocol parameters can be dynamically adjusted by introducing the communication self-adaptive module so as to adapt to different communication conditions, and the stability and the efficiency of unmanned aerial vehicle communication are obviously improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle communication system based on Beidou messages.
Background
Unmanned aerial vehicles are increasingly used in various fields, such as environmental monitoring, agricultural plant protection, logistics distribution and the like. The unmanned aerial vehicle communication system is used as a core component of unmanned aerial vehicle technology, and the stability and reliability of the unmanned aerial vehicle communication system are critical to safe flight and task execution of the unmanned aerial vehicle.
The existing unmanned aerial vehicle communication system mostly adopts traditional wireless communication modes, such as Wi-Fi, 4G/5G and the like, but the communication modes are easy to be interfered in complex environments (such as mountain areas, oceans and the like), so that the communication quality is reduced, and even the communication is interrupted. Meanwhile, when the existing unmanned aerial vehicle communication system faces different communication conditions, the self-adaptive capability is lacked, and communication protocol parameters cannot be dynamically adjusted to adapt to environment changes, so that the unmanned aerial vehicle communication system further limits application of the unmanned aerial vehicle in complex environments. Therefore, we propose an unmanned aerial vehicle communication system based on big dipper message.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle communication system based on Beidou messages, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the unmanned aerial vehicle communication system based on the Beidou message comprises an unmanned aerial vehicle end, a communication layer and a ground control station;
The unmanned aerial vehicle terminal comprises a flight control module, a data acquisition module, a Beidou short message communication module and a communication self-adaption module; the flight control module is used for controlling the flight state of the unmanned aerial vehicle according to a preset flight plan and real-time instructions; the data acquisition module is used for acquiring flight data of the unmanned aerial vehicle in real time, wherein the flight data comprise the position, the speed, the electric quantity and the communication environment parameters of the unmanned aerial vehicle; the Beidou short message communication module is used for carrying out bidirectional short message communication with the ground control station through a Beidou satellite system, transmitting flight data and receiving control instructions; the communication self-adaptation module analyzes communication environment parameters through a machine learning algorithm and dynamically adjusts communication protocol parameters according to analysis results so as to adapt to different communication conditions;
the communication layer comprises a Beidou satellite system, a communication protocol module and a data encryption and decryption module; the Beidou satellite system is used for providing stable data transmission between the unmanned aerial vehicle end and the ground control station; the communication protocol module is used for defining a communication protocol between the unmanned aerial vehicle end and the ground control station, and comprises a data format, a transmission mode and a verification mechanism; the data encryption and decryption module is used for encrypting and decrypting the transmitted data;
The ground control station comprises an instruction sending module, a data processing module, a task scheduling module and a user interface module; the command sending module is used for sending control commands to the unmanned aerial vehicle according to task demands, wherein the control commands comprise flight paths, shooting parameters and return commands; the data processing module is used for receiving the state information returned by the unmanned aerial vehicle and the acquired data, and carrying out real-time processing and analysis; the task scheduling module is used for uniformly managing and scheduling a plurality of unmanned aerial vehicle tasks, and comprises task allocation, priority ordering and resource allocation; the user interface module is used for displaying the flight state of the unmanned aerial vehicle in real time, processing and analyzing the data in real time by the data processing module, and providing an operation function for realizing flight control of the unmanned aerial vehicle.
Preferably, the communication self-adaptive module collects communication environment parameters of the unmanned aerial vehicle in real time, wherein the communication environment parameters comprise signal strength, noise level, channel occupancy rate, signal to noise ratio, bit error rate, delay time, packet loss rate, frequency offset and phase noise;
Analyzing the acquired communication environment parameters through a pre-trained protocol parameter prediction model, wherein the model can output optimal communication protocol parameter adjustment suggestions according to the input communication environment parameters;
According to the analysis result of the machine learning algorithm model, dynamically adjusting the communication protocol parameters of the unmanned aerial vehicle so as to adapt to different communication conditions; the communication protocol parameters include modulation mode, coding rate, transmission power, data packet size, retransmission mechanism, carrier frequency and bandwidth.
Preferably, a neural network algorithm is adopted to construct a protocol parameter prediction model, and the specific steps include:
step 1: constructing a neural network model, wherein the model comprises an input layer, a hidden layer and an output layer, the input layer is used for receiving communication environment parameters of the unmanned aerial vehicle, the hidden layer is used for carrying out nonlinear transformation and feature extraction, and the output layer is used for outputting optimal communication protocol parameter adjustment suggestions;
Step 2: acquiring a large amount of communication environment parameter data and corresponding communication protocol parameter data from an unmanned aerial vehicle communication system to form a training data set; preprocessing the data, including normalizing, removing abnormal values and filling missing values;
step 3: training the neural network model by using a training data set, and adjusting model parameters by an iterative optimization algorithm so that the output of the model gradually approaches to the actual communication protocol parameter adjustment proposal;
step 4: and verifying and testing the trained neural network model.
Preferably, the training process of step 3 includes:
Step 3.1: using the preprocessed training data set as input, inputting the communication environment parameters to an input layer of the neural network;
Step 3.2: nonlinear transformation and feature extraction are carried out through a hidden layer of the neural network, and an output calculation formula of the hidden layer is as follows: where h represents the output of the hidden layer, σ represents the activation function, Representing the weight matrix of the input layer to the hidden layer, x represents the input of the input layer,Indicating the bias of the hidden layer;
step 3.3: transmitting the output of the hidden layer to an output layer, wherein the output calculation formula of the output layer is as follows: Where y represents the output of the output layer, i.e. the predicted communication protocol parameter adjustment proposal, Representing the weight matrix of the hidden layer to the output layer,Representing the bias of the output layer;
Step 3.4: calculating the loss between the output of the output layer and the real communication protocol parameter adjustment proposal, wherein the loss function calculation formula is as follows: where L represents the loss, n represents the number of samples, Representing the output layer output of the i-th sample,A real communication protocol parameter adjustment recommendation representing an ith sample;
Step 3.5: and (3) using a gradient descent optimization algorithm, and carrying out iterative updating on parameters of the neural network model according to the loss function until a preset stopping condition is met, so as to obtain the trained neural network model.
1. Preferably, in step2, each numerical data is subtracted by its mean value and divided by its standard deviation using a Z-score normalization method, so that the processed data conforms to a standard normal distribution, and the normalization processing formula is: Wherein, the method comprises the steps of, wherein, As the raw data is to be processed,As the mean value of the raw data,Is the standard deviation of the original data,Is normalized data.
Preferably, in step 2, for the missing values, a linear interpolation algorithm is used for filling; and for the abnormal value, adopting a threshold value judging method, taking the data points exceeding the preset threshold value range as the abnormal value, and replacing the abnormal value by the average value of the adjacent points.
Preferably, the data encryption and decryption module adopts an AES symmetric encryption algorithm, the encryption key is 128 bits, and the encryption key is generated by a ground control station and distributed to the unmanned aerial vehicle end through a secure channel; before the unmanned aerial vehicle sends the flight data, the data are encrypted by using an AES algorithm and an encryption key, and after the ground control station receives the encrypted data, the ground control station uses the same encryption key to perform decryption processing so as to recover the original flight data.
Preferably, the data checking mechanism of the communication protocol module adopts a CRC check code, the unmanned aerial vehicle terminal generates the CRC check code before transmitting data, and the ground control station performs CRC check code verification after receiving the data.
Preferably, the specific implementation manner of the task scheduling module is as follows:
Receiving unmanned aerial vehicle task input from a user interface module, wherein the unmanned aerial vehicle task input comprises a task type, a task target, a task priority and resources required by a task;
Distributing the received tasks to the proper unmanned aerial vehicle according to the current state, the position, the electric quantity and the task requirements of the unmanned aerial vehicle;
according to the priority and the emergency degree of the tasks, the tasks allocated to the unmanned aerial vehicle are ordered so as to ensure that the tasks with high priority are executed preferentially;
According to task demands, necessary resources are allocated for the unmanned aerial vehicle, wherein the resources comprise communication resources, computing resources and storage resources;
The method comprises the steps of monitoring task execution states of the unmanned aerial vehicle in real time, wherein the task execution states comprise task progress, unmanned aerial vehicle states and abnormal conditions; and dynamically adjusting or rescheduling the task according to the actual execution condition of the unmanned aerial vehicle and the change of the task demand.
Compared with the prior art, the invention has the beneficial effects that:
The self-adaptive communication module can analyze communication environment parameters of the unmanned aerial vehicle in real time, such as signal strength, interference level and the like, and dynamically adjust communication protocol parameters according to analysis results, so that the unmanned aerial vehicle can keep stable data transmission under different communication conditions, communication interruption and error rate are effectively reduced, and communication reliability and stability are improved. Through continuous learning and optimization of a machine learning algorithm on a communication environment, the self-adaptive communication module can select a transmission mode and a data format which are most suitable for the current communication condition, so that the data transmission efficiency is maximized. This not only shortens the data transmission time, but also reduces the overhead due to repeated transmissions and error checking.
The self-adaptive communication module can optimize communication protocol parameters, so that communication faults and data transmission errors are reduced, and the unmanned aerial vehicle task failure or repeated execution cost caused by communication problems is reduced. Meanwhile, the dependence on manual intervention is reduced, and the operation and maintenance cost is further reduced.
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FIG. 1 is a diagram of the overall structure of the present invention;
FIG. 2 is a diagram of the working steps of the adaptive communication module;
FIG. 3 is a flow chart of protocol parameter prediction model training.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the present invention provides a technical solution: an unmanned aerial vehicle communication system based on Beidou message comprises an unmanned aerial vehicle end, a communication layer and a ground control station.
The unmanned aerial vehicle end comprises a flight control module, a data acquisition module, a Beidou short message communication module and a communication self-adaption module. The flight control module is responsible for accurately controlling the flight state of the unmanned aerial vehicle according to a preset flight plan and a real-time received instruction, so that the unmanned aerial vehicle can be ensured to execute according to a set route and task requirements. The data acquisition module acquires flight data of the unmanned aerial vehicle in real time, including but not limited to the position, speed, residual electric quantity and current communication environment parameters of the unmanned aerial vehicle. The Beidou short message communication module utilizes a Beidou satellite system to realize bidirectional short message communication between the unmanned aerial vehicle and the ground control station, and can ensure reliable transmission of data even in areas which are difficult to cover by traditional wireless communication modes such as mountain areas, oceans and the like. The communication self-adaptive module carries out deep analysis on the acquired communication environment parameters through a built-in machine learning algorithm, and dynamically adjusts communication protocol parameters such as data transmission rate, coding mode and the like according to analysis results so as to adapt to continuously changing communication conditions and obviously improve the communication stability and reliability of the unmanned aerial vehicle in a complex environment.
The communication layer comprises a Beidou satellite system, a communication protocol module and a data encryption and decryption module. The Beidou satellite system not only provides a stable data transmission channel between the unmanned aerial vehicle end and the ground control station, but also ensures wide coverage and high reliability of data transmission. The communication protocol module defines the communication specification between the unmanned aerial vehicle and the ground control station, and comprises unification of data formats, selection of transmission modes and a data verification mechanism, so that the accuracy and the high efficiency of communication are ensured. The data encryption and decryption module is responsible for encrypting the transmitted data, protecting the security of the data and preventing information from being illegally intercepted or tampered in the transmission process.
The ground control station is a command center of the unmanned aerial vehicle communication system and comprises an instruction sending module, a data processing module, a task scheduling module and a user interface module. The command sending module sends control commands, such as planning of an fly path, setting of shooting parameters and issuing of a return command, to the unmanned aerial vehicle according to specific task requirements. The data processing module is responsible for receiving the status information returned by the unmanned aerial vehicle and the acquired data, carrying out real-time processing and analysis, and providing data support for subsequent decisions. The task scheduling module is responsible for unified management and scheduling of a plurality of unmanned aerial vehicle tasks, including reasonable allocation of tasks, sequencing of priorities and effective allocation of resources, so that the efficiency of overall operation is improved.
The user interface module is a window for interaction between the ground control station and the user and is used for displaying information such as flight state and data processing result of the unmanned aerial vehicle in real time, and rich operation functions are provided, so that the user is allowed to directly realize flight control of the unmanned aerial vehicle through the interface.
The invention is further illustrated in the following in connection with examples 1 to 4:
Example 1:
The flight control module adopts a PID control algorithm, and calculates corresponding control output including steering engine deflection angle, motor rotation speed and the like according to real-time states (such as position, speed, gesture and the like) and target instructions of the unmanned aerial vehicle so as to ensure that the unmanned aerial vehicle can execute tasks according to preset flight tracks and gestures. According to the output of the flight control algorithm, the module drives actuators such as a steering engine, a motor and the like to act through PWM (pulse width modulation) signals, so that various flight attitudes and track control of the unmanned aerial vehicle are realized.
The data acquisition module integrates various high-precision sensors, such as GPS (Global positioning System), IMU (inertial measurement Unit), barometer, electricity meter and the like, and is used for measuring key parameters of the unmanned aerial vehicle, such as position, speed, gesture, height, residual electricity and the like, and the sensors are connected with the data acquisition module in a serial port communication or bus mode to ensure real-time transmission and accurate acquisition of data, and the data are further sent to a ground control station after being acquired.
The communication self-adaptive module collects communication environment parameters of the unmanned aerial vehicle in real time, wherein the communication environment parameters comprise signal strength, noise level, channel occupancy rate, signal-to-noise ratio, bit error rate, delay time, packet loss rate, frequency offset, phase noise and the like, and the parameters reflect the current communication environment condition of the unmanned aerial vehicle.
The communication self-adapting module analyzes the acquired communication environment parameters by utilizing a pre-trained protocol parameter prediction model. The model is constructed based on a neural network algorithm, and can output optimal communication protocol parameter adjustment suggestions according to input communication environment parameters.
In order to train the neural network model, a large amount of communication environment parameter data and corresponding communication protocol parameter data need to be acquired from the unmanned aerial vehicle communication system to form a training data set.
The communication self-adaptive module can dynamically adjust communication protocol parameters of the unmanned aerial vehicle according to the current communication environment, including a modulation mode, a coding rate, transmission power, data packet size, a retransmission mechanism, carrier frequency, bandwidth and the like, so as to adapt to different communication conditions and ensure stable communication of the unmanned aerial vehicle in a complex environment.
The protocol parameter prediction model is constructed by adopting a neural network algorithm as follows:
Step 1: and constructing a neural network model. The model is designed to include a structure of an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving communication environment parameters of the unmanned aerial vehicle, wherein the communication environment parameters describe the current communication environment conditions of the unmanned aerial vehicle. The hidden layer performs nonlinear transformation and feature extraction, and performs deep processing on the input data to extract features useful for predicting communication protocol parameter adjustment suggestions. And the output layer outputs the optimal communication protocol parameter adjustment suggestion according to the processing result of the hidden layer.
2. Step 2: a training dataset is prepared. And acquiring a large amount of communication environment parameter data and corresponding communication protocol parameter data from the unmanned aerial vehicle communication system, wherein the model can learn the mapping relation between the communication environment parameter and the communication protocol parameter through the data. After the data is acquired, preprocessing including normalization, abnormal value removal and missing value filling is needed to ensure the accuracy and consistency of the data, so that the training effect of the model is improved. Using a Z-score normalization method, subtracting the mean value from each numerical data and dividing by the standard deviation so that the processed data conforms to a standard normal distribution, wherein the normalization processing formula is: Wherein, the method comprises the steps of, wherein, As the raw data is to be processed,As the mean value of the raw data,Is the standard deviation of the original data,Is normalized data.
For the missing values, filling by adopting a linear interpolation algorithm; and for the abnormal value, adopting a threshold value judging method, taking the data points exceeding the preset threshold value range as the abnormal value, and replacing the abnormal value by the average value of the adjacent points.
Step 3: training a neural network model. And training the neural network model by using the preprocessed training data set. The model parameters are continuously adjusted through an iterative optimization algorithm, so that the output of the model gradually approaches to the actual communication protocol parameter adjustment proposal. In the training process, the performance of the model needs to be monitored, the performance of the model on the training set is ensured to be gradually improved, and the condition of over fitting or under fitting does not occur. The method comprises the following specific steps:
Step 3.1: the communication environment parameters are input to an input layer of the neural network using the preprocessed training data set as input.
Step 3.2: nonlinear transformation and feature extraction are carried out through a hidden layer of the neural network, and an output calculation formula of the hidden layer is as follows: where h represents the output of the hidden layer, σ represents the activation function, Representing the weight matrix of the input layer to the hidden layer, x represents the input of the input layer,Indicating the bias of the hidden layer.
Step 3.3: transmitting the output of the hidden layer to an output layer, wherein the output calculation formula of the output layer is as follows: Where y represents the output of the output layer, i.e. the predicted communication protocol parameter adjustment proposal, Representing the weight matrix of the hidden layer to the output layer,Representing the bias of the output layer.
Step 3.4: calculating the loss between the output of the output layer and the real communication protocol parameter adjustment proposal, wherein the loss function calculation formula is as follows: where L represents the loss, n represents the number of samples, Representing the output layer output of the i-th sample,The actual communication protocol parameter adjustment recommendation representing the ith sample.
Step 3.5: and (3) using a gradient descent optimization algorithm, and carrying out iterative updating on parameters of the neural network model according to the loss function until a preset stopping condition is met, so as to obtain the trained neural network model.
Step 4: and verifying and testing the neural network model. And verifying and testing the trained neural network model to ensure the performance of the neural network model in practical application. The purpose of verification and testing is to evaluate the model's performance on unknown data to check its generalization ability. If the model performs well on the validation and test set and is able to accurately output optimal communication protocol parameter adjustment recommendations based on the communication environment parameters, the model can be considered valid and can be deployed for use in an actual unmanned aerial vehicle communication system.
The following is a simplified code example of neural network model training and construction implemented using the Python programming language:
the main steps and functions of the code are as follows:
Data preparation and preprocessing: the library numpy is used to generate simulated communications environment parameters (X) and communications protocol parameters (y) data. And performing Z-score standardization processing on the data by STANDARDSCALER types to ensure the accuracy and consistency of the data and improve the training effect of the model.
Dividing a training set and a testing set: the data is divided into training and testing sets using the train_test_split function to evaluate the performance of the model during training.
Building a neural network model: a neural network is constructed using a Sequential model that includes an input layer, a hidden layer, and an output layer. In the hidden layer, 64 neurons and a ReLU activation function are used for nonlinear transformation and feature extraction. In the output layer, 7 neurons and linear activation functions are used to predict communication protocol parameters.
Compiling a model: the model is compiled using an Adam optimizer and a mean square error loss function for training.
Training a model: training the model using the fit function specifies the training set, training round (epochs), batch size (batch_size), and the proportions of the validation set.
Evaluation model: the performance of the model was evaluated on the test set using evaluate functions and the test loss was printed out.
Example 2:
The Beidou satellite system is used as a core part of a communication layer, and has the main function of providing a stable data transmission channel between the unmanned aerial vehicle end and the ground control station. By utilizing the satellite communication technology, the unmanned aerial vehicle can still keep reliable communication with the ground control station when being far away from the ground base station or being in complex terrain. Through the Beidou satellite system, the unmanned aerial vehicle can transmit flight data, state information and the like to the ground control station in real time, and simultaneously receive instructions and control information from the ground.
The communication protocol module is responsible for defining a communication protocol between the unmanned aerial vehicle end and the ground control station, the communication protocol covers a plurality of aspects such as a data format, a transmission mode, a checking mechanism and the like, and the two sides can accurately and efficiently exchange information. The data format prescribes the coding mode, structure and meaning of each field of the data, so that the unmanned aerial vehicle and the ground control station can accurately analyze and understand information sent by the other party. The transmission mode defines the transmission and receiving modes of data, including synchronous, asynchronous, serial, parallel and other modes, so as to adapt to different communication requirements and scenes. The checking mechanism is used for ensuring the integrity and accuracy of the data, and detecting and correcting errors possibly occurring in the data transmission process by adding check codes, performing cyclic redundancy check and the like. The data checking mechanism of the communication protocol module adopts CRC check codes, the unmanned aerial vehicle end generates the CRC check codes before transmitting data, and the ground control station performs CRC check code verification after receiving the data.
The data encryption and decryption module adopts an AES symmetric encryption algorithm, the length of an encryption key is set to 128 bits, so that the encryption safety is ensured, and the efficiency of the encryption and decryption process is also ensured.
The generation and management of encryption keys is responsible for the ground control station. The ground control station uses a secure random number generation algorithm to generate a 128-bit encryption key and distributes the key to the drone side via a secure channel, such as a dedicated encrypted communication link. Thus, the security of the secret key in the transmission process is ensured, and interception or tampering by a third party is prevented. Before the unmanned aerial vehicle sends the flight data, the unmanned aerial vehicle pre-processes the data so as to ensure that the format and the size of the data meet the requirements of an AES algorithm. Then, the unmanned aerial vehicle side encrypts the data using the AES algorithm and the encryption key received from the ground control station. The encryption process includes multiple rounds of data substitution and replacement operations, ensuring that the original data is converted into ciphertext that cannot be directly interpreted. The encrypted flight data is transmitted to the ground control station via a wireless channel. After receiving the encrypted data, the ground control station uses the same encryption key as the unmanned aerial vehicle to perform decryption processing. The decryption process is the inverse of the encryption process, and the ciphertext is restored to the original flight data through multiple rounds of data replacement and substitution operations.
Suppose that the drone side needs to send a piece of flight data including altitude, speed, and direction to the ground control station. First, the ground control station generates a 128-bit encryption key and distributes the key to the unmanned aerial vehicle through the secure channel. Before the unmanned aerial vehicle sends the flight data, the data is encrypted by using an AES algorithm and the secret key to generate a section of ciphertext which cannot be directly interpreted. And then, the unmanned aerial vehicle end sends the encrypted ciphertext to the ground control station. After receiving the ciphertext, the ground control station uses the same encryption key to perform decryption processing, and successfully recovers the original flight data, including information such as flight altitude, speed, direction and the like.
Example 3:
The specific implementation mode of the task scheduling module is as follows:
The task scheduling module receives unmanned aerial vehicle task inputs from the user interface module that describe the needs of the task, including in particular the task type (e.g., scout, battle, rescue, etc.), task objective (e.g., image acquisition at a particular location, destruction of an enemy objective, etc.), priority of the task (for determining urgency of task execution), and resources (e.g., particular communication devices, computing power, storage space, etc.) required for task execution.
The task scheduling module intelligently distributes the received tasks to the most suitable unmanned aerial vehicle according to the current state, the position and the electric quantity of the unmanned aerial vehicle and the specific requirements of the tasks. The allocation process comprehensively considers the availability of the unmanned aerial vehicle, the distance from the task target, whether the residual electric quantity is sufficient or not and other factors so as to ensure that the task can be efficiently and reliably executed.
After the task allocation is completed, the task scheduling module also sorts the tasks allocated to the unmanned aerial vehicle according to the priority and the emergency degree of the tasks, so that the tasks with high priority can be executed preferentially, and the urgent requirements of task execution are met.
In order to meet the resource requirements in the task execution process, the task scheduling module also allocates necessary resources for the unmanned aerial vehicle according to the specific requirements of the task. These resources may include communication resources (for ensuring stable communication between the drone and the ground control station), computing resources (for processing and analyzing data collected by the drone), and storage resources (for storing data generated during the execution of tasks).
In the task execution process, the task scheduling module can monitor the task execution state of the unmanned aerial vehicle in real time. This includes the progress of the task (e.g., percentage completed), the current status of the drone (e.g., altitude, speed, power, etc.), and whether any anomalies (e.g., equipment failure, communication disruption, etc.) are present. Through real-time monitoring, the task scheduling module can timely discover and process problems in the task execution process.
And finally, according to the actual execution condition of the unmanned aerial vehicle and the change of task demands, the task scheduling module dynamically adjusts or reschedules the task. For example, if a certain unmanned aerial vehicle encounters an emergency (such as sudden weather changes, equipment faults, etc.) when executing a task, the task scheduling module may redistribute the task of the unmanned aerial vehicle to other available unmanned aerial vehicles, or adjust the execution sequence and priority of the task, so as to ensure smooth execution of the task and efficient completion of the overall task.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
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