Unmanned aerial vehicle intelligent control method and system for environment monitoring task

Abstract

The invention discloses an unmanned aerial vehicle intelligent control method and system for an environment monitoring task, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining at least one task environment state of an environment monitoring task execution region; determining a state transition matrix among a plurality of task environment states of the environment monitoring task execution area; evaluating the execution adaptation degree of the task parameter setting state of the unmanned aerial vehicle and all task environment states; constructing a real-time environment change matrix; predicting the next environment state of the environment monitoring task execution area; whether the unmanned aerial vehicle can meet the next task execution requirement or not is evaluated, if yes, the unmanned aerial vehicle continues to execute the environment monitoring task, and if not, the unmanned aerial vehicle of the current task returns. The method has the advantages that the unmanned aerial vehicle is intelligently regulated and controlled in advance based on the real-time monitoring result in the environment monitoring task process of the unmanned aerial vehicle, the monitoring accuracy of the environment monitoring task is effectively improved, and meanwhile damage caused by improper operation of the unmanned aerial vehicle is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an intelligent control method and system for unmanned aerial vehicles used for environmental monitoring tasks. Background Art

[0002] Environmental monitoring mainly involves collecting environmental data from the area to be tested through various monitoring equipment, and through comprehensive analysis of the collected environmental data, discovering possible environmental problems in the current area, making timely warnings, and taking solutions to reduce or avoid the occurrence and expansion of environmental hazards. With the development of drone technology, drones are combined with environmental monitoring equipment to effectively improve the efficiency and convenience of environmental monitoring, taking advantage of their strong maneuverability and convenient environmental monitoring. It also provides the possibility for environmental monitoring in complex terrain and sparsely populated areas, and realizes large-scale, no-dead-angle environmental monitoring. Therefore, it is of great significance to combine drones with environmental monitoring equipment to conduct large-scale, no-dead-angle monitoring of the environment.

[0003] There is a close relationship between the setting of UAV operating parameters, including proportional parameters (P), complete parameters (I), and derivative parameters (D), and its mission execution capability. The mission execution capability of UAVs in different mission environments, including its stability, response speed, accuracy, etc., is largely affected by the setting of its operating parameters. In the existing technology, due to the lack of environmental prediction analysis for the execution area of ​​environmental monitoring tasks, UAVs performing environmental monitoring tasks can only respond after the environment changes. This control method has a delay in regulation and control, which can easily cause UAVs to perform tasks with inappropriate operating parameters. On the one hand, it is easy to cause damage to the UAV, and on the other hand, it causes inaccurate monitoring of environmental monitoring tasks. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for intelligent control of unmanned aerial vehicles for environmental monitoring tasks are provided. The technical solution solves the problem that in the above-mentioned prior art, due to the lack of environmental prediction and analysis of the environmental monitoring task execution area, the unmanned aerial vehicle performing the environmental monitoring task can only respond after the environment changes. This control method has a control delay, which can easily cause the unmanned aerial vehicle to perform the task with inappropriate operating parameters. On the one hand, it can easily cause damage to the unmanned aerial vehicle, and on the other hand, it can cause inaccurate monitoring of the environmental monitoring task.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An intelligent control method for a UAV for an environmental monitoring task, comprising: Based on the mission execution log of the UAV, at least one mission environment state of the environment monitoring mission execution area is obtained; Determine a state transition matrix between multiple task environment states in the environment monitoring task execution area; Obtain the mission parameter setting status of the UAV and evaluate the execution adaptability of the mission parameter setting status of the UAV with all mission environment states; Based on the environmental data of the environmental monitoring mission execution area collected by UAVs in real time, a real-time environmental change matrix is ​​constructed; Based on the state transition matrix between the real-time environment change matrix and the task environment state, the next environment state of the environment monitoring task execution area is predicted; Based on the next environmental status of the environmental monitoring task execution area and the execution adaptability of the UAV's task parameter setting status and the task parameter setting status of all UAVs, evaluate whether the UAV can meet the next task execution requirements. If so, the UAV continues to perform the environmental monitoring task. If not, the current task UAV returns, and based on the next environmental status of the environmental monitoring task execution area, set the task parameters of the UAV that will perform the next task.

[0006] Preferably, the state transition matrix between multiple task environment states for determining the execution area of ​​the environmental monitoring task specifically includes: Based on the UAV's mission execution log, determine the standard environmental parameter array corresponding to each mission environment state in the environmental monitoring mission execution area , ,in, is the standard environment parameter array of the oth task environment state, is the standard environmental parameter value of the next i-th environmental parameter of the o-th task environmental state, is the total number of environmental parameters; based on the UAV's mission execution log, determine the change trend of the standard environmental parameters when the mission environment status changes in the environmental monitoring mission execution area, and form an array of environmental parameter change trends in the environmental monitoring mission execution area , ,in, is the array of environmental parameter change trends when the o-th task environment state changes to the p-th task environment state, is the standard environmental parameter value of the environmental parameter change trend of the i-th environmental parameter when the o-th task environmental state changes to the p-th task environmental state; Based on the standard environmental parameter array corresponding to each task environment state in the environmental monitoring task execution area and the environmental parameter change trend array in the environmental monitoring task execution area, a state transition matrix between task environment states is constructed. , .

[0007] Preferably, the step of evaluating the execution adaptability of the mission parameter setting state of the drone and all mission environment states specifically includes: Determining the mission performance of the drone based on the mission parameter setting state of the drone, wherein the mission performance of the drone includes at least: response speed, stability, operation accuracy and maneuverability; Based on the UAV's mission execution experience, determine the UAV's mission execution performance requirements in each mission environment state; If the UAV's mission execution performance of all tasks is higher than the UAV's mission execution performance requirements under the mission environment state, the execution adaptability between the UAV and the mission environment state is 1; if the UAV's mission execution performance of all tasks is not higher than the UAV's mission execution performance requirements under the mission environment state, the calculation formula for the execution adaptability between the UAV and the mission environment state is:

[0008] in, is the execution adaptability of the UAV to the oth task environment state when the performance of all tasks of the human-machine is not higher than the task performance requirements of the UAV in the oth task environment state. is the total number of types of task execution performance, is the performance value of the j-th task execution performance of the human-machine, is the j-task execution performance requirement under the o-th task environment state, is the minimum value function.

[0009] Preferably, the construction of a real-time environmental change matrix based on the environmental data of the environmental monitoring task execution area collected in real time by the drone specifically includes: Collect the environmental data of the task execution area in real time and build the environmental parameter array of the task execution area , ,in, is the environmental parameter of the i-th environmental parameter of the task execution area; based on the real-time collected environmental data of the task execution area, calculate the change trend of each environmental parameter in the task execution area, and construct the change trend array of the task execution area , ,in, is the changing trend of the i-th environmental parameter in the task execution area; Based on the environmental parameter array of the task execution area and the change trend array of the task execution area, a real-time environment change matrix is ​​constructed. , Preferably, the predicting of the next environmental state of the environmental monitoring task execution area based on the state transition matrix between the real-time environmental change matrix and the task environmental state specifically includes: An array of environment parameters based on the task execution area An array of standard environment parameters with each task's environment state Calculate the vector distance for comparison and determine the baseline environmental status of the task execution area; Calculate the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to each task environmental state; Based on the transition probability formula, the probability of the next change of the task execution area to each task environment state is calculated; The conversion probability formula is as follows: In the formula, is the probability that the next change of the task execution area is the pth task environment state, is the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to the pth task environmental state, is the maximum value among all vector distances, is the minimum value among all vector distances, is the total number of task environment states.

[0010] Preferably, the evaluation of whether the drone can meet the subsequent mission execution requirements based on the subsequent environmental state of the environmental monitoring mission execution area and the mission parameter setting state of the drone and the execution adaptability of the mission parameter setting state of all drones specifically includes: Based on the probability of each mission environment state for the next change in the mission execution area and the execution adaptability of the UAV's mission parameter setting state to all mission environment states, the prediction fitting formula is used to calculate the UAV's next mission execution capability index; Determine whether the next mission execution capability index of the drone is greater than a preset value. If so, determine that the drone can meet the next mission execution requirements. If not, determine that the drone cannot meet the next mission execution requirements. The prediction fitting formula is specifically:

[0011] The drone's next mission execution capability indicators, is the execution adaptability of the UAV to the pth task environment state. Preferably, the setting of the task parameters of the UAV to perform the next task based on the next environmental state of the environmental monitoring task execution area specifically includes: Determine the mission performance of the UAV under limited mission parameter setting modes; And calculate the next mission execution capability index of the UAV under each mission parameter setting mode, screen out the mission parameter setting mode corresponding to the maximum value of the mission execution capability index, and set the mission parameters of the UAV that will perform the next mission.

[0012] Furthermore, an intelligent control system for a drone for environmental monitoring tasks is proposed, which is used to implement the intelligent control method for a drone for environmental monitoring tasks as described above, including: A task environment module, the task environment module is used to obtain at least one task environment state of the environment monitoring task execution area based on the task execution log of the unmanned aerial vehicle, and determine a state transfer matrix between multiple task environment states of the environment monitoring task execution area; A mode adaptation analysis module, the mode adaptation analysis module is electrically connected to the task environment module, and the mode adaptation analysis module is used to obtain the task parameter setting state of the UAV and evaluate the execution adaptability of the task parameter setting state of the UAV with all task environment states; A change analysis module, the change analysis module is electrically connected to the task environment module, the change analysis module is used to construct a real-time environment change matrix based on the environmental data of the environment monitoring task execution area collected by the drone in real time, and predict the next environmental state of the environment monitoring task execution area based on the state transition matrix between the real-time environment change matrix and the task environment state; An intelligent control module, wherein the intelligent control module is electrically connected to the change analysis module and the mode adaptation analysis module, and is used to evaluate whether the drone can meet the next task execution requirements based on the next environmental state of the environmental monitoring task execution area and the task parameter setting state of the drone and the task parameter setting state of all drones. If so, the drone continues to perform the environmental monitoring task; if not, the current task drone returns, and based on the next environmental state of the environmental monitoring task execution area, the task parameters of the drone that will perform the next task are set.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes an intelligent control scheme for UAVs for environmental monitoring tasks, which analyzes real-time environmental changes during the execution of UAV tasks and combines the parameter setting status of the UAV to perform intelligent prediction and analysis of the UAV's ability to perform subsequent environmental monitoring tasks, and intelligently controls the UAV to continue to perform the task or return based on the analysis results. In this way, the adaptability of the UAV to the execution area environment when performing environmental monitoring tasks is greatly guaranteed, and the pre-intelligent regulation of the UAV is realized, which effectively reduces damage and improves the monitoring accuracy of environmental monitoring tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1Flowchart of the intelligent control method of UAV for environmental monitoring tasks proposed in this scheme; Figure 2 This is a flow chart of the method for determining the state transfer matrix in this scheme; Figure 3 A flow chart of the method for evaluating the execution fitness of a UAV in this scheme; Figure 4 A flow chart of the method for constructing a real-time environment change matrix in this solution; Figure 5 A flow chart of the method for predicting the next environmental state of the execution area of ​​the environmental monitoring task in this scheme; Figure 6 A flowchart of the method for evaluating whether the drone can meet the requirements for the next mission execution in this solution; Figure 7 This is a flow chart of the method for setting the mission parameters of the drone that will perform the next mission in this solution. DETAILED DESCRIPTION

[0015] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0016] Reference Figure 1 As shown, an intelligent control method of a UAV for an environmental monitoring task includes: Based on the mission execution log of the UAV, at least one mission environment state of the environment monitoring mission execution area is obtained; and a state transfer matrix between multiple mission environment states of the environment monitoring mission execution area is determined; Obtain the mission parameter setting status of the UAV and evaluate the execution adaptability of the mission parameter setting status of the UAV with all mission environment states; Based on the environmental data of the environmental monitoring mission execution area collected by UAVs in real time, a real-time environmental change matrix is ​​constructed; Based on the state transition matrix between the real-time environment change matrix and the task environment state, the next environment state of the environment monitoring task execution area is predicted; Based on the next environmental status of the environmental monitoring task execution area and the execution adaptability of the UAV's task parameter setting status and the task parameter setting status of all UAVs, evaluate whether the UAV can meet the next task execution requirements. If so, the UAV continues to perform the environmental monitoring task. If not, the current task UAV returns, and based on the next environmental status of the environmental monitoring task execution area, set the task parameters of the UAV that will perform the next task.

[0017] This solution analyzes the real-time environmental changes during the execution of the drone mission and integrates the parameter setting status of the drone to carry out intelligent prediction, so as to evaluate the drone's execution ability in subsequent environmental monitoring tasks. Based on this analysis result, the drone is intelligently controlled to decide whether to continue the mission or return. In this way, the adaptability of the drone to the environmental area when performing environmental monitoring tasks can be greatly improved, and the drone can be intelligently controlled in advance, which can effectively reduce damage and improve the monitoring accuracy of environmental monitoring tasks.

[0018] Reference Figure 2 As shown, the state transfer matrix between multiple task environment states for determining the execution area of ​​the environmental monitoring task specifically includes: Based on the UAV's mission execution log, determine the standard environmental parameter array corresponding to each mission environment state in the environmental monitoring mission execution area , ,in, is the standard environment parameter array of the oth task environment state, is the standard environmental parameter value of the next i-th environmental parameter of the o-th task environmental state, is the total number of environmental parameters, where the standard environmental parameter value can be a specific standard value, for example, obtained by collecting multiple environmental parameters of the mission environment state and averaging them, and the standard environmental parameter value can be a standard value range interval, which is used to represent the range of change of environmental parameters under the mission environment state; based on the UAV's mission execution log, determine the change trend of the standard environmental parameters when the mission environment state changes in the environmental monitoring mission execution area, and form an array of environmental parameter change trends in the environmental monitoring mission execution area , ,in, is the array of environmental parameter change trends when the o-th task environment state changes to the p-th task environment state, is the standard environmental parameter value of the environmental parameter change trend of the i-th environmental parameter when the o-th task environmental state changes to the p-th task environmental state; Based on the standard environmental parameter array corresponding to each task environment state in the environmental monitoring task execution area and the environmental parameter change trend array in the environmental monitoring task execution area, a state transition matrix between task environment states is constructed. , . Since environmental parameters will change when the environmental state changes, for example, when it changes from sunny to rainy, the environmental parameters will change significantly, including lower air pressure, lower temperature, higher humidity, and weaker light. In this scheme, a state transfer matrix is ​​constructed, and the standard environmental parameter array of the task environment state is used as the basis for judging the current environmental state, and the environmental parameter change trend array is used as the parameter change attribute when the environmental state changes. The state transfer matrix between the task environment states is constructed by combining the two to reflect this environmental change state.

[0019] Reference Figure 3 As shown in the figure, the evaluation of the execution adaptability of the UAV's mission parameter setting state and all mission environment states specifically includes: Determine the mission performance of the UAV based on the mission parameter setting status of the UAV, and the mission performance of the UAV includes at least: response speed, stability, operation accuracy and maneuverability; Based on the UAV's mission execution experience, determine the UAV's mission execution performance requirements in each mission environment state; If the execution performance of all tasks of the UAV is higher than the task execution performance requirements of the UAV in the task environment state, the execution adaptability of the UAV and the task environment state is 1; If the performance of all missions of the UAV is not higher than the performance requirements of the UAV in the mission environment, the calculation formula for the execution compatibility between the UAV and the mission environment is: in, is the execution adaptability of the UAV to the oth task environment state when the performance of all tasks of the human-machine is not higher than the task performance requirements of the UAV in the oth task environment state. is the total number of types of task execution performance, is the performance value of the j-th task execution performance of the human-machine, is the j-task execution performance requirement under the o-th task environment state, is the minimum value function.

[0020] It is understandable that different parameter settings of the drone will affect the mission execution performance of the drone. For example, the adjustment of the proportional parameter (P) can affect the response of the drone to the control input. A P value that is too high may cause the drone to react too violently, while a P value that is too low may cause the drone to respond slowly. The adjustment of the complete parameter (I) can affect the cumulative response of the drone under long-term control input. A value of I that is too high may cause the drone to produce too large an overshoot when approaching the target, while a value of I that is too low may cause the drone to fail to reach the target accurately. The adjustment of the derivative parameter (D) can affect the drone's response speed to changes in control input. A value of D that is too high may cause the drone to oscillate in a rapidly changing environment, while a value of D that is too low may cause the drone to fail to respond to environmental changes in a timely manner. Different environmental conditions require different performances of the drone. For example, in windy and rainy conditions, the drone needs to have higher stability. In this solution, the drone's mission adaptability is comprehensively evaluated through the drone's performance under multiple different parameter setting modes and the task execution performance requirements under different environmental conditions, so as to evaluate the mission execution capabilities of different parameter setting modes of the drone in different environments.

[0021] Reference Figure 4 As shown in the figure, based on the environmental data of the environmental monitoring task execution area collected by the UAV in real time, the real-time environmental change matrix is ​​constructed, which specifically includes: Collect the environmental data of the task execution area in real time and build the environmental parameter array of the task execution area , ,in, is the environmental parameter of the i-th environmental parameter of the task execution area; based on the real-time collected environmental data of the task execution area, calculate the change trend of each environmental parameter in the task execution area, and construct the change trend array of the task execution area , ,in, is the changing trend of the i-th environmental parameter in the task execution area; Based on the environmental parameter array of the task execution area and the change trend array of the task execution area, a real-time environment change matrix is ​​constructed. , .

[0022] Reference Figure 5 As shown in FIG. 1 , based on the state transition matrix between the real-time environment change matrix and the task environment state, the next environment state of the environment monitoring task execution area is predicted to include: An array of environment parameters based on the task execution area An array of standard environment parameters with each task's environment state Calculate the vector distance for comparison and determine the baseline environmental status of the task execution area; Calculate the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to each task environmental state; Based on the transition probability formula, the probability of the next change of the task execution area to each task environment state is calculated; The conversion probability formula is as follows: In the formula, is the probability that the next change of the task execution area is the pth task environment state, is the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to the pth task environmental state, is the maximum value among all vector distances, is the minimum value among all vector distances, is the total number of task environment states.

[0023] The change trend can be represented by the slope of the tangent line on the curve of the parameter changing over time. In a preferred embodiment, the change trend can also be represented by the slope of the linear regression equation of the parameter changing over time. In this scheme, the vector distance is calculated by the parameter change trend of the actual environment and the standard change trend in the standard state conversion process. The smaller the vector distance, the closer the parameter change trend of the actual environment is to the standard change trend in the standard state conversion process, and the greater the probability of changing to this state next. Based on this, the conversion probability formula is used to convert the vector distance between the parameter change trend of the actual environment and the standard change trend in the standard state conversion process into the probability that the task execution area will change to the task environment state next.

[0024] Reference Figure 6 As shown, based on the subsequent environmental state of the environmental monitoring task execution area and the execution adaptability of the UAV's task parameter setting state and the task parameter setting state of all UAVs, the evaluation of whether the UAV can meet the subsequent task execution requirements specifically includes: Based on the probability of the next change of the mission execution area being each mission environment state and the execution adaptability of the mission parameter setting state of the UAV with all mission environment states, a prediction fitting formula is used to calculate the next mission execution capability index of the UAV; judging whether the next mission execution capability index of the UAV is greater than a preset value, if so, judging that the UAV can meet the next mission execution requirement, if not, judging that the UAV cannot meet the next mission execution requirement; Among them, the prediction fitting formula is specifically:

[0025] The drone's next mission execution capability indicators, is the execution adaptability between the UAV and the pth mission environment state. Figure 7 As shown, based on the next environmental state of the environmental monitoring task execution area, the task parameters of the drone that will perform the next task are set to specifically include: Determine the mission performance of the UAV under limited mission parameter setting modes; And calculate the next mission execution capability index of the UAV under each mission parameter setting mode, screen out the mission parameter setting mode corresponding to the maximum value of the mission execution capability index, and set the mission parameters of the UAV that will perform the next mission.

[0026] Since drones are required to have different performances under different environmental conditions, for example, in windy and rainy conditions, drones are required to have higher stability. Therefore, when the current parameter setting state of the drone does not meet the next environmental requirements of the drone, the operating state of the drone needs to be adjusted in time.

[0027] Furthermore, based on the same inventive concept as the above-mentioned UAV intelligent control method for environmental monitoring tasks, the present solution also proposes an UAV intelligent control system for environmental monitoring tasks, including: The task environment module is used to obtain at least one task environment state of the environment monitoring task execution area based on the task execution log of the UAV, and determine the state transfer matrix between multiple task environment states of the environment monitoring task execution area; A mode adaptation analysis module, the mode adaptation analysis module is electrically connected to the mission environment module, and the mode adaptation analysis module is used to obtain the mission parameter setting state of the UAV and evaluate the execution adaptability of the mission parameter setting state of the UAV with all mission environment states; A change analysis module, the change analysis module is electrically connected to the task environment module, and the change analysis module is used to construct a real-time environment change matrix based on the environmental data of the environment monitoring task execution area collected by the UAV in real time, and to predict the next environmental state of the environment monitoring task execution area based on the state transition matrix between the real-time environment change matrix and the task environment state; The intelligent control module is electrically connected to the change analysis module and the mode adaptation analysis module. The intelligent control module is used to evaluate whether the drone can meet the next task execution requirements based on the next environmental state of the environmental monitoring task execution area and the task parameter setting state of the drone and the task parameter setting state of all drones. If so, the drone continues to perform the environmental monitoring task. If not, the current task drone returns and sets the task parameters of the drone that performs the next task based on the next environmental state of the environmental monitoring task execution area.

[0028] The working process of the above system is as follows: Step 1: The task environment module obtains at least one task environment state of the environment monitoring task execution area based on the task execution log of the UAV, and determines the state transfer matrix between multiple task environment states of the environment monitoring task execution area; Step 2: The mode adaptation analysis module is used to obtain the mission parameter setting state of the UAV and evaluate the execution adaptability of the mission parameter setting state of the UAV with all mission environment states; Step 3: The change analysis module constructs a real-time environment change matrix based on the environmental data of the environmental monitoring task execution area collected by the drone in real time, and predicts the next environmental state of the environmental monitoring task execution area based on the state transition matrix between the real-time environment change matrix and the task environment state; Step 4: The intelligent control module evaluates whether the drone can meet the next mission execution requirements based on the next environmental status of the environmental monitoring mission execution area and the mission parameter setting status of the drone and the mission parameter setting status of all drones. If so, the drone continues to perform the environmental monitoring mission. If not, the current mission drone returns and sets the mission parameters of the drone that will perform the next mission based on the next environmental status of the environmental monitoring mission execution area.

[0029] To sum up, the advantages of the present invention are: pre-intelligent control of the UAV is performed based on the real-time monitoring results during the UAV environmental monitoring mission, which effectively improves the monitoring accuracy of the environmental monitoring mission and reduces the damage caused by improper operation of the UAV.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent control method for unmanned aerial vehicles used for environmental monitoring tasks, characterized in that: include: Based on the mission execution log of the UAV, at least one mission environment state of the environment monitoring mission execution area is obtained; Determine a state transition matrix between multiple task environment states in the environment monitoring task execution area; Obtain the mission parameter setting status of the UAV and evaluate the execution adaptability of the mission parameter setting status of the UAV with all mission environment states; Based on the environmental data of the environmental monitoring mission execution area collected by UAVs in real time, a real-time environmental change matrix is ​​constructed; Based on the state transition matrix between the real-time environment change matrix and the task environment state, the next environment state of the environment monitoring task execution area is predicted; Based on the next environmental status of the environmental monitoring task execution area and the execution adaptability of the UAV's task parameter setting status and the task parameter setting status of all UAVs, evaluate whether the UAV can meet the next task execution requirements. If so, the UAV continues to perform the environmental monitoring task. If not, the current task UAV returns, and based on the next environmental status of the environmental monitoring task execution area, set the task parameters of the UAV that will perform the next task.

2. The intelligent control method of a drone for environmental monitoring tasks according to claim 1 is characterized in that: The state transfer matrix between multiple task environment states of the environment monitoring task execution area is specifically determined as follows: Based on the UAV's mission execution log, determine the standard environmental parameter array corresponding to each mission environment state in the environmental monitoring mission execution area , ,in, is the standard environment parameter array of the oth task environment state, is the standard environmental parameter value of the next i-th environmental parameter of the o-th task environmental state, is the total number of environmental parameters; based on the UAV's mission execution log, determine the change trend of the standard environmental parameters when the mission environment status changes in the environmental monitoring mission execution area, and form an array of environmental parameter change trends in the environmental monitoring mission execution area , ,in, is the array of environmental parameter change trends when the o-th task environment state changes to the p-th task environment state, is the standard environmental parameter value of the environmental parameter change trend of the i-th environmental parameter when the o-th task environmental state changes to the p-th task environmental state; Based on the standard environmental parameter array corresponding to each task environment state in the environmental monitoring task execution area and the environmental parameter change trend array in the environmental monitoring task execution area, a state transition matrix between task environment states is constructed. , .

3. The intelligent control method of a drone for environmental monitoring tasks according to claim 2 is characterized in that: The evaluation of the execution adaptability of the mission parameter setting state of the UAV and all mission environment states specifically includes: Determining the mission performance of the drone based on the mission parameter setting state of the drone, wherein the mission performance of the drone includes at least: response speed, stability, operation accuracy and maneuverability; Based on the UAV's mission execution experience, determine the UAV's mission execution performance requirements in each mission environment state; If the execution performance of all tasks of the UAV is higher than the task execution performance requirements of the UAV in the task environment state, the execution adaptability of the UAV and the task environment state is 1; If the performance of all missions of the UAV is not higher than the performance requirements of the UAV in the mission environment, the calculation formula for the execution compatibility between the UAV and the mission environment is: in, is the execution adaptability of the UAV to the oth task environment state when the performance of all tasks of the human-machine is not higher than the task performance requirements of the UAV in the oth task environment state. is the total number of types of task execution performance, is the performance value of the j-th task execution performance of the human-machine, is the j-task execution performance requirement under the o-th task environment state, is the minimum value function.

4. The intelligent control method of a drone for environmental monitoring tasks according to claim 3 is characterized in that: The construction of a real-time environmental change matrix based on the environmental data of the environmental monitoring task execution area collected in real time by the UAV specifically includes: Collect the environmental data of the task execution area in real time and build the environmental parameter array of the task execution area , ,in, is the environmental parameter of the i-th environmental parameter of the task execution area; based on the real-time collected environmental data of the task execution area, calculate the change trend of each environmental parameter in the task execution area, and construct the change trend array of the task execution area , ,in, is the changing trend of the i-th environmental parameter in the task execution area; Based on the environmental parameter array of the task execution area and the change trend array of the task execution area, a real-time environment change matrix is ​​constructed. , .

5. The intelligent control method of a drone for environmental monitoring tasks according to claim 4 is characterized in that: The prediction of the next environmental state of the environmental monitoring task execution area based on the state transition matrix between the real-time environmental change matrix and the task environment state specifically includes: An array of environment parameters based on the task execution area An array of standard environment parameters with each task's environment state Calculate the vector distance for comparison and determine the baseline environmental status of the task execution area; Calculate the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to each task environmental state; Based on the transition probability formula, the probability of the next change of the task execution area to each task environment state is calculated; The conversion probability formula is as follows: In the formula, is the probability that the next change of the task execution area is the pth task environment state, is the vector distance between the environmental parameter change trend array and the task execution area change trend array when the baseline environmental state changes to the pth task environmental state, is the maximum value among all vector distances, is the minimum value among all vector distances, is the total number of task environment status types.

6. The intelligent control method of a drone for environmental monitoring tasks according to claim 5 is characterized in that: The evaluation of whether the drone can meet the next mission execution requirements based on the next environmental state of the environmental monitoring mission execution area and the execution adaptability of the mission parameter setting state of the drone and the mission parameter setting state of all drones specifically includes: based on the probability of the next change of the mission execution area being each mission environment state and the execution adaptability of the mission parameter setting state of the drone and all mission environment states, a prediction fitting formula is used to calculate the next mission execution capability index of the drone; Determine whether the next mission execution capability index of the drone is greater than a preset value. If so, it is determined that the drone can meet the next mission execution requirements. If not, it is determined that the drone cannot meet the next mission execution requirements. The prediction fitting formula is specifically: The drone's next mission execution capability indicators, is the execution fitness of the UAV and the pth mission environment state.

7. The intelligent control method of a drone for environmental monitoring tasks according to claim 6 is characterized in that: The step of setting the task parameters of the UAV to perform the next task based on the next environmental state of the environment monitoring task execution area specifically includes: Determine the mission performance of the UAV under limited mission parameter setting modes; And calculate the next mission execution capability index of the UAV under each mission parameter setting mode, screen out the mission parameter setting mode corresponding to the maximum value of the mission execution capability index, and set the mission parameters of the UAV that will perform the next mission.

8. An intelligent control system for unmanned aerial vehicles used for environmental monitoring tasks, characterized in that: The method for realizing the intelligent control of a drone for an environmental monitoring task as claimed in any one of claims 1 to 7 comprises: A task environment module, the task environment module is used to obtain at least one task environment state of the environment monitoring task execution area based on the task execution log of the unmanned aerial vehicle, and determine a state transfer matrix between multiple task environment states of the environment monitoring task execution area; A mode adaptation analysis module, the mode adaptation analysis module is electrically connected to the task environment module, the mode adaptation analysis module is used to obtain the task parameter setting state of the UAV, and evaluate the execution adaptability of the task parameter setting state of the UAV with all task environment states; a change analysis module, the change analysis module is electrically connected to the task environment module, the change analysis module is used to construct a real-time environment change matrix based on the environmental data of the environmental monitoring task execution area collected by the UAV in real time, and predict the next environmental state of the environmental monitoring task execution area based on the state transition matrix between the real-time environment change matrix and the task environment state; An intelligent control module, wherein the intelligent control module is electrically connected to the change analysis module and the mode adaptation analysis module, and is used to evaluate whether the drone can meet the next task execution requirements based on the next environmental state of the environmental monitoring task execution area and the task parameter setting state of the drone and the task parameter setting state of all drones. If so, the drone continues to perform the environmental monitoring task; if not, the current task drone returns, and based on the next environmental state of the environmental monitoring task execution area, the task parameters of the drone that will perform the next task are set.

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