Unmanned aerial vehicle route intelligent planning management system based on big data

Abstract

The invention belongs to the technical field of unmanned aerial vehicle management, and particularly relates to an unmanned aerial vehicle route intelligent planning management system based on big data, which comprises an unmanned aerial vehicle route acquisition module, an unmanned aerial vehicle route analysis and decision module, an unmanned aerial vehicle flight control module, a route adjustment emergency analysis module and an unmanned aerial vehicle management end. All feasible air routes are analyzed through the unmanned aerial vehicle air route analysis and decision module to determine the optimal air route of the unmanned aerial vehicle executing the current flight task, the unmanned aerial vehicle flight control module performs flight control on the unmanned aerial vehicle based on the optimal air route, and the flight safety is improved while the flight efficiency is ensured. And in the flight process of the unmanned aerial vehicle, flight line adjustment emergency analysis is carried out through the flight line adjustment emergency analysis module, and flight line adjustment is carried out when a flight line adjustment high emergency signal is generated, so that the unmanned aerial vehicle is prevented from being damaged, the automation and intelligence degrees are high, and the working difficulty of background management personnel is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle management, and in particular to an intelligent planning and management system for unmanned aerial vehicle routes based on big data. Background Art

[0002] UAVs, also known as unmanned aerial vehicles, are unmanned aircraft that use advanced remote control technology, autonomous flight control systems and sensor technology to perform various tasks in the air. With the rapid development of UAV technology, UAVs are increasingly used in aerial photography, logistics distribution, agricultural plant protection, emergency rescue and other fields. At present, when managing drones, it is often difficult to determine the optimal route from several groups of feasible routes based on flight missions and control the drone to execute the optimal route. In addition, it is impossible to reasonably analyze the urgency of route adjustment and route execution performance during the flight of the drone, which is not conducive to ensuring the flight safety of the drone, increasing the difficulty of the work of back-end managers, and the level of intelligence and automation is low; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a UAV route intelligent planning and management system based on big data, which solves the problems that the prior art is difficult to determine the optimal route from several groups of feasible routes based on the flight mission and control the UAV to execute the optimal route, and is unable to reasonably analyze the urgency of route adjustment and route execution performance of the UAV during flight, and has low intelligence and automation levels.

[0004] To achieve the above object, the present invention provides the following technical solutions: An intelligent planning and management system for unmanned aerial vehicle routes based on big data, including a unmanned aerial vehicle route acquisition module, a unmanned aerial vehicle route analysis and decision module, a unmanned aerial vehicle flight control module, a route adjustment urgency analysis module and a unmanned aerial vehicle management terminal; The UAV route acquisition module acquires the flight mission of the UAV, obtains several feasible routes based on the flight mission, and sends all feasible routes to the UAV route analysis and decision module and the UAV management terminal; The UAV route analysis and decision module analyzes all feasible routes, determines the optimal route for the UAV to perform the current flight mission through analysis, and sends the optimal route to the UAV flight control module and the UAV management end. The UAV flight control module controls the flight of the UAV based on the optimal route; The route adjustment urgency analysis module is used to perform route adjustment urgency analysis during the flight of the UAV, generate a route adjustment high emergency signal or a route adjustment low emergency signal through analysis, and send the route adjustment high emergency signal or the route adjustment low emergency signal to the UAV management end and the UAV flight control module; when the UAV flight control module receives the route adjustment high emergency signal, it adjusts the route of the UAV, and when the UAV management end receives the route adjustment high emergency signal, it issues an early warning.

[0005] Furthermore, the specific analysis process of the drone route analysis and decision module includes: All feasible routes are obtained, and the corresponding feasible routes are marked as i, where i is a natural number greater than 1; the flight path length of the feasible route i is collected and marked as the flight path value, a number of detection points are set on the feasible route, the height difference between two adjacent groups of detection points is marked as the instantaneous lift coefficient, the instantaneous lift coefficient is numerically compared with a preset instantaneous lift coefficient threshold, and the number of instantaneous lift coefficients in the feasible route i that exceed the preset instantaneous lift coefficient threshold is marked as the lift transient value; The maximum turning angle existing in the route segment between two adjacent groups of detection points is marked as the instantaneous turning angle amplitude, the instantaneous turning angle amplitude is numerically compared with the preset instantaneous turning angle amplitude threshold, and the number of instantaneous turning angle amplitudes exceeding the preset instantaneous turning angle amplitude threshold in the feasible route i is marked as the turning transient value; The route initial inspection value is obtained by numerically calculating the flight path value, the ascending and descending transient value, and the turning transient value. All feasible routes are sorted in descending order according to the values ​​of the route initial inspection value, and the last feasible route is marked as the optimal route for the UAV to perform the current flight mission.

[0006] Furthermore, the specific analysis process of the route adjustment urgency analysis module includes: Based on the image of the UAV's forward direction, determine whether there is an obstacle. If there is an obstacle, collect the distance between the UAV and the obstacle and mark it as the obstacle distance detection value. Compare the obstacle distance detection value with the preset obstacle distance detection threshold. If the obstacle distance detection value does not exceed the preset obstacle distance detection threshold, generate a high emergency signal for route adjustment; If there is no obstacle or the obstacle distance detection value exceeds the preset obstacle distance detection threshold, the flight environment value is obtained through analysis, and the flight environment value is numerically compared with the preset flight environment threshold. If the flight environment value exceeds the preset flight environment threshold, a high emergency signal for route adjustment is generated; if the flight environment value does not exceed the preset flight environment threshold, a low emergency signal for route adjustment is generated.

[0007] Furthermore, the analysis and acquisition method of the flight environment value is as follows: The wind speed data and precipitation data of the environment where the drone is located are collected and marked as the navigation wind detection value and the navigation water detection value respectively, the deviation value of the temperature of the environment where the drone is located compared to the preset suitable temperature standard value is marked as the navigation temperature detection value, and the deviation value of the air pressure of the environment where the drone is located compared to the preset suitable air pressure standard value is marked as the navigation pressure detection value; The electromagnetic interference intensity data of the drone's environment is collected and marked as an air disturbance detection value, and the flight environment value is obtained by numerically calculating the air wind detection value, the air water detection value, the air temperature detection value, the air pressure detection value and the air disturbance detection value.

[0008] Furthermore, the UAV management terminal is connected to the route execution monitoring module and the route execution evaluation module in communication, and the route execution monitoring module monitors the flight process of the UAV and sends the flight monitoring information of the UAV to the route execution evaluation module; The route execution evaluation module is used to analyze the route execution performance of the drone, generate a route execution abnormality signal or a route execution qualified signal through analysis, and send the route execution abnormality signal or the route execution qualified signal to the drone management end. When the drone management end receives the route execution abnormality signal, it will issue a corresponding warning.

[0009] Furthermore, the specific analysis process of the route execution evaluation module includes: A detection period is set. If no high emergency signal for route adjustment is generated during the detection period, the actual flight path of the UAV during the detection period is collected, and the actual flight path is compared with the corresponding part of the optimal route of the UAV. The number of times the UAV deviates from the route is determined by comparison and marked as the UAV off-route detection value. The UAV off-route detection value is numerically compared with the preset UAV off-route detection threshold. If the UAV off-route detection value exceeds the preset UAV off-route detection threshold, a route execution abnormality signal is generated.

[0010] Furthermore, if the drone off-course detection value does not exceed the preset drone off-course detection threshold, the duration of the corresponding route deviation process and the corresponding path length are marked as the off-course duration value and the off-course distance value, respectively, and the off-course duration value and the off-course distance value are numerically compared with the preset off-course duration threshold and the preset off-course distance threshold, respectively. If the off-course duration value or the off-course distance value exceeds the corresponding preset threshold, the corresponding route deviation process is marked as a route deviation process; The number of route deviation processes within the detection period is obtained and marked as the route deviation detection value, and the off-course duration values ​​of all route deviation processes are summed up to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed up to obtain the route off-course distance value; The route execution warning coefficient is obtained by numerically calculating the drone off-course detection value, route deviation detection value, route off-time value and route off-distance value, and the route execution warning coefficient is numerically compared with the preset route execution warning coefficient threshold. If the route execution warning coefficient exceeds the preset route execution warning coefficient threshold, a route execution abnormality signal is generated; if the route execution warning coefficient does not exceed the preset route execution warning coefficient threshold, a route execution qualified signal is generated.

[0011] Furthermore, the route execution evaluation module is communicatively connected to the drone operation and measurement module. The route execution evaluation module sends a route execution qualification signal to the drone operation and measurement module. The drone operation and measurement module analyzes the operating status of the drone during the detection period, generates a drone operation warning signal or a drone operation qualification signal through analysis, and sends the drone operation warning signal or the drone operation qualification signal to the drone management end. When the drone management end receives the drone operation warning signal, it issues a corresponding warning.

[0012] Furthermore, the specific analysis process of the drone operation and measurement module is as follows: The real-time temperature and real-time flight speed inside the drone are collected, the real-time temperature is compared with the median of the preset temperature range, and the absolute value is taken to obtain the drone internal temperature value, and the deviation value of the real-time flight speed compared with the set standard flight speed is marked as the drone speed inspection value; and the vibration amplitude of the drone is collected and marked as the drone vibration inspection value, and the drone actual value is obtained by numerically calculating the drone internal temperature value, the drone speed inspection value and the drone vibration inspection value; The actual value of the drone is numerically compared with the preset actual value threshold of the drone. If the actual value of the drone exceeds the preset actual value threshold of the drone, the corresponding actual value of the drone is marked as the abnormal value of the drone. The number of abnormal values ​​of the drone in the detection period is obtained and the ratio is calculated with the total number of actual values ​​of the drone to obtain the abnormal value of the drone, and the average of all actual values ​​of the drone in the detection period is calculated to obtain the actual value of the drone. The abnormal value of the drone and the actual value of the drone are numerically compared with the preset abnormal threshold of the drone and the preset actual threshold of the drone. If the abnormal value of the drone or the actual value of the drone exceeds the corresponding preset threshold, a drone operation warning signal is generated; if both the abnormal value of the drone and the actual value of the drone do not exceed the corresponding preset threshold, a qualified signal of the drone operation is generated.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, all feasible routes are analyzed by the UAV route analysis and decision module to determine the optimal route for the UAV to perform the current flight mission. The UAV flight control module controls the flight of the UAV based on the optimal route, thereby ensuring flight efficiency while improving flight safety. During the flight of the UAV, the route adjustment urgency analysis module performs route adjustment urgency analysis, and performs route adjustment when a high-urgency route adjustment signal is generated, which is beneficial to avoid damage to the UAV. The system has a high degree of automation and intelligence, and significantly reduces the work difficulty of backstage managers. 2. In the present invention, the route execution performance of the UAV is analyzed through the route execution evaluation module, and when a route execution abnormality signal is generated, the background management personnel are reminded to continue to focus on the operation of the UAV and take reasonable control measures as needed, so as to ensure the accuracy of the route execution and flight safety of the UAV, and when a route execution qualified signal is generated, the UAV operation and measurement module analyzes the operating status of the UAV during the detection period, and when a UAV operation warning signal is generated, the background management personnel are reminded to investigate the cause and take corresponding control measures, so as to further avoid damage to the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1: Figure 1 As shown, the present invention proposes an intelligent planning and management system for unmanned aerial vehicle routes based on big data, including a unmanned aerial vehicle route acquisition module, a unmanned aerial vehicle route analysis and decision module, a unmanned aerial vehicle flight control module, a route adjustment urgency analysis module and a unmanned aerial vehicle management terminal; The UAV route acquisition module acquires the flight mission of the UAV, obtains several feasible routes based on the flight mission, and sends all feasible routes to the UAV route analysis and decision module and the UAV management terminal; The UAV route analysis and decision module analyzes all feasible routes, determines the optimal route for the UAV to perform the current flight mission through analysis, and sends the optimal route to the UAV flight control module and the UAV management terminal. The UAV flight control module controls the flight of the UAV based on the optimal route, and can analyze and automatically output the optimal route for the corresponding flight mission, ensuring flight efficiency while improving flight safety, with a high degree of intelligence; the specific analysis process of the UAV route analysis and decision module is as follows: All feasible routes are obtained, and the corresponding feasible routes are marked as i, where i is a natural number greater than 1; the flight path length of the feasible route i is collected and marked as the flight path value, a number of detection points are set on the feasible route, and the height difference between two adjacent groups of detection points is marked as the instantaneous lift coefficient (when the value of the instantaneous lift coefficient is too large, it indicates a sudden rise or fall), the instantaneous lift coefficient is numerically compared with a preset instantaneous lift coefficient threshold, and the number of instantaneous lift coefficients in the feasible route i that exceed the preset instantaneous lift coefficient threshold is marked as the lift transient value; The maximum turning angle in the route segment between two adjacent groups of detection points is marked as the instantaneous turning angle amplitude (wherein, when the value of the turning angle is too large, it indicates that a sharp turn occurs), the instantaneous turning angle amplitude is numerically compared with the preset instantaneous turning angle amplitude threshold, and the number of instantaneous turning angle amplitudes exceeding the preset instantaneous turning angle amplitude threshold in the feasible route i is marked as the turning transient value; By formula The flight path value HYi, the lift transient value HLi and the turn transient value HPi are numerically calculated to obtain the route initial inspection value HXi; wherein, se1, se2, and se3 are preset proportional coefficients, se3>se2>se1>0.253; and, the larger the value of the route initial inspection value HXi, the less suitable the corresponding feasible route i is in general; all feasible routes are sorted in descending order according to the value of the route initial inspection value, and the last feasible route is marked as the optimal route for the UAV to perform the current flight mission.

[0017] The route adjustment urgency analysis module is used to perform route adjustment urgency analysis during the flight of the drone. By analyzing and generating a high-emergency signal for route adjustment or a low-emergency signal for route adjustment, and sending the high-emergency signal for route adjustment or the low-emergency signal for route adjustment to the drone management terminal and the drone flight control module, it can reasonably analyze and evaluate the degree of hidden dangers of the drone route during flight, so as to make timely route adjustments, which is conducive to avoiding damage to the drone. When the UAV flight control module receives a high-emergency signal for route adjustment, it adjusts the route of the UAV to achieve automatic route adjustment to ensure the flight safety of the UAV. When the UAV management terminal receives a high-emergency signal for route adjustment, it issues an early warning. Backstage management personnel can manually control the UAV as needed (including route adjustment or landing the UAV and suspending flight, etc.) to further ensure its flight safety. The specific analysis process of the route adjustment urgency analysis module is as follows: Based on the image of the UAV's forward direction, it is determined whether there is an obstacle. If there is an obstacle, the distance between the UAV and the obstacle is collected and marked as the obstacle distance detection value. The obstacle distance detection value is compared with the preset obstacle distance detection threshold. If the obstacle distance detection value does not exceed the preset obstacle distance detection threshold, it indicates that the route needs to be adjusted in time to avoid collision with the obstacle, and a high emergency signal for route adjustment is generated; If there is no obstacle or the obstacle distance detection value exceeds the preset obstacle distance detection threshold, the flight environment value is obtained through analysis, specifically: the wind speed data and precipitation data (i.e., the data value of the real-time rainfall amount) of the environment where the drone is located are collected and marked as the navigation wind detection value and the navigation water detection value respectively, the deviation value of the temperature of the environment where the drone is located compared to the preset suitable temperature standard value is marked as the navigation temperature detection value, and the deviation value of the air pressure of the environment where the drone is located compared to the preset suitable air pressure standard value is marked as the navigation pressure detection value; The electromagnetic interference intensity data of the environment in which the drone is located is collected and marked as the disturbance detection value. The wind detection value QF, the water detection value QH, the temperature detection value QL, the pressure detection value QP and the disturbance detection value QX are numerically calculated by the formula RY=(uy1*QF+uy2*QH+uy3*QL+uy4*QP+uy5*QX) / 5 to obtain the flight environment value RY; wherein, uy1, uy2, uy3, uy4 and uy5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the flight environment value RY, the greater the adverse effect of the current environment of the drone on its flight; The flight condition value RY is numerically compared with the preset flight condition threshold. If the flight condition value RY exceeds the preset flight condition threshold, it indicates that the current environment of the UAV has a significant adverse effect on its flight, and the UAV's route needs to be adjusted or landed in time, and a high emergency signal for route adjustment is generated; if the flight condition value RY does not exceed the preset flight condition threshold, it indicates that the current environment of the UAV has a minor adverse effect on its flight, and a low emergency signal for route adjustment is generated.

[0018] Embodiment 2: Figure 2As shown, the difference between this embodiment and the first embodiment is that the UAV management end is connected to the route execution monitoring module and the route execution evaluation module in communication, the route execution monitoring module monitors the flight process of the UAV, and sends the flight monitoring information of the UAV to the route execution evaluation module; The route execution evaluation module is used to analyze the route execution performance of the drone, generate route execution abnormality signals or route execution qualified signals through analysis, and send the route execution abnormality signals or route execution qualified signals to the drone management end. When the drone management end receives the route execution abnormality signal, it issues a corresponding warning to remind the back-end management personnel to continue to focus on the operation of the drone and take reasonable control measures as needed, so as to ensure the accuracy of the drone's route execution and flight safety; the specific analysis process of the route execution evaluation module is as follows: A detection period is set. If no high emergency signal for route adjustment is generated during the detection period, the actual flight path of the UAV during the detection period is collected, and the actual flight path is compared with the corresponding part of the optimal route of the UAV. The number of times the UAV deviates from the route is determined by comparison and marked as the UAV off-route detection value. The UAV off-route detection value is numerically compared with the preset UAV off-route detection threshold. If the UAV off-route detection value exceeds the preset UAV off-route detection threshold, a route execution abnormality signal is generated.

[0019] Furthermore, if the drone off-course detection value does not exceed the preset drone off-course detection threshold, the duration of the corresponding route deviation process and the corresponding path length are marked as the off-course duration value and the off-course distance value, respectively, and the off-course duration value and the off-course distance value are numerically compared with the preset off-course duration threshold and the preset off-course distance threshold, respectively. If the off-course duration value or the off-course distance value exceeds the corresponding preset threshold, the corresponding route deviation process is marked as a route deviation process; The number of route deviation processes within the detection period is obtained and marked as the route deviation detection value, and the off-course duration values ​​of all route deviation processes are summed up to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed up to obtain the route off-course distance value; The route execution warning coefficient PX is obtained by numerically calculating the drone off-course detection value PL, the route deviation detection value PN, the route off-course time value PM and the route off-course distance value PW through the formula PX=0.5*(rq1*PL+rq2*PN)+(rq3*PM+rq4*PW) / (rq1+rq2); wherein rq1, rq2, rq3 and rq4 are preset proportional coefficients with values ​​greater than zero, and rq2>rq1>rq3>rq4>0; and the larger the value of the route execution warning coefficient PX, the worse the overall route execution performance of the drone during the detection period; The route execution warning coefficient PX is numerically compared with the preset route execution warning coefficient threshold. If the route execution warning coefficient PX exceeds the preset route execution warning coefficient threshold, it indicates that the route execution performance of the UAV during the detection period is generally poor, and a route execution abnormality signal is generated; if the route execution warning coefficient PX does not exceed the preset route execution warning coefficient threshold, it indicates that the route execution performance of the UAV during the detection period is generally good, and a route execution qualified signal is generated.

[0020] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the route execution evaluation module is communicatively connected to the drone operation and measurement module, the route execution evaluation module sends the route execution qualified signal to the drone operation and measurement module, and the drone operation and measurement module analyzes the operation status of the drone during the detection period, and generates a drone operation warning signal or a drone operation qualified signal through analysis; And the drone operation warning signal or drone operation qualified signal is sent to the drone management end. When the drone management end receives the drone operation warning signal, it will issue a corresponding warning, which can reasonably analyze and accurately judge the operation risk of the drone, and promptly remind the back-end management personnel to investigate the cause and take corresponding control measures, so as to avoid damage to the drone; the specific analysis process of the drone operation and measurement module is as follows: The real-time temperature and real-time flight speed inside the drone are collected, the real-time temperature is compared with the median of the preset temperature range, and the absolute value is taken to obtain the internal temperature value of the drone, and the deviation value of the real-time flight speed compared with the set standard flight speed is marked as the drone speed inspection value; and the vibration amplitude of the drone is collected and marked as the drone vibration inspection value; The drone internal temperature value B, the drone speed inspection value K and the drone vibration inspection value S are numerically calculated by the formula M=(a1*B+a2*K+a3*S) / 3 to obtain the drone actual value M; wherein a1, a2 and a3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the drone actual value M is, the worse the real-time operation condition of the drone is; The actual value M of the drone is numerically compared with the preset drone actual threshold. If the actual value M of the drone exceeds the preset drone actual threshold, it indicates that the real-time operation status of the drone is poor, and the corresponding drone actual value is marked as a drone abnormal value; the number of drone abnormal values ​​in the detection period is obtained and the ratio is calculated with the total number of drone actual values ​​to obtain the drone abnormal value, and the average of all drone actual values ​​in the detection period is calculated to obtain the drone measured value; The drone abnormal value and the drone measured value are numerically compared with the preset drone abnormal threshold and the preset drone measured threshold respectively. If the drone abnormal value or the drone measured value exceeds the corresponding preset threshold, it indicates that the drone's operating performance during the detection period is relatively abnormal, and a drone operation warning signal is generated; if both the drone abnormal value and the drone measured value do not exceed the corresponding preset threshold, it indicates that the drone's operating performance during the detection period is relatively normal, and a drone operation qualified signal is generated.

[0021] The working principle of the present invention is as follows: when in use, the flight mission of the drone is obtained through the drone route acquisition module, and a number of feasible routes are set based on the flight mission. The drone route analysis and decision module analyzes all feasible routes to determine the optimal route for the drone to perform the current flight mission. The drone flight control module controls the flight of the drone based on the optimal route, and can reasonably analyze and automatically output the optimal route of the corresponding flight mission, thereby ensuring flight efficiency while improving flight safety. During the flight of the drone, the route adjustment urgency analysis module performs route adjustment urgency analysis, so as to accurately evaluate the degree of route hazards in the flight of the drone and conduct reasonable analysis and evaluation. When a high-emergency signal for route adjustment is generated, the route is adjusted, which is beneficial to avoid damage to the drone. The degree of automation and intelligence is high, which significantly reduces the work difficulty of backstage managers.

[0022] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent planning and management system for unmanned aerial vehicle routes based on big data, characterized in that: It includes a drone route acquisition module, a drone route analysis and decision module, a drone flight control module, a route adjustment emergency analysis module and a drone management terminal; The UAV route acquisition module acquires the flight mission of the UAV, obtains several feasible routes based on the flight mission, and sends all feasible routes to the UAV route analysis and decision module and the UAV management terminal; The UAV route analysis and decision module analyzes all feasible routes, determines the optimal route for the UAV to perform the current flight mission through analysis, and sends the optimal route to the UAV flight control module and the UAV management end. The UAV flight control module controls the flight of the UAV based on the optimal route; The route adjustment urgency analysis module is used to perform route adjustment urgency analysis during the flight of the UAV, generate a route adjustment high emergency signal or a route adjustment low emergency signal through analysis, and send the route adjustment high emergency signal or the route adjustment low emergency signal to the UAV management end and the UAV flight control module; when the UAV flight control module receives the route adjustment high emergency signal, it adjusts the route of the UAV, and when the UAV management end receives the route adjustment high emergency signal, it issues an early warning.

2. According to the big data-based intelligent planning and management system for unmanned aerial vehicle routes according to claim 1, it is characterized in that: The specific analysis process of the UAV route analysis and decision module includes: All feasible routes are obtained, and the corresponding feasible routes are marked as i, where i is a natural number greater than 1; the flight path length of the feasible route i is collected and marked as the flight path value, a number of detection points are set on the feasible route, the height difference between two adjacent groups of detection points is marked as the instantaneous lift coefficient, and the number of instantaneous lift coefficients exceeding a preset instantaneous lift coefficient threshold in the feasible route i is marked as the lift transient value; and the maximum turning angle existing in the route segment between two adjacent groups of detection points is marked as the instantaneous turning angle amplitude, and the number of instantaneous turning angle amplitudes exceeding the preset instantaneous turning angle amplitude threshold in the feasible route i is marked as the turning transient value; The route initial inspection value is obtained by numerically calculating the flight path value, the ascending and descending transient value, and the turning transient value. All feasible routes are sorted in descending order according to the values ​​of the route initial inspection value, and the last feasible route is marked as the optimal route for the UAV to perform the current flight mission.

3. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 1 is characterized in that: The specific analysis process of the route adjustment urgency analysis module includes: Based on the image of the drone's forward direction, it is determined whether there is an obstacle. If there is an obstacle, the distance between the drone and the obstacle is collected and marked as the obstacle distance detection value. If the obstacle distance detection value does not exceed the preset obstacle distance detection threshold, a high emergency signal for route adjustment is generated; If there is no obstacle or the obstacle distance detection value exceeds the preset obstacle distance detection threshold, the flight environment value is obtained through analysis. If the flight environment value exceeds the preset flight environment threshold, a high emergency signal for route adjustment is generated; if the flight environment value does not exceed the preset flight environment threshold, a low emergency signal for route adjustment is generated.

4. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 3 is characterized in that: The specific method for analyzing and obtaining the flight environment value is as follows: The wind speed data and precipitation data of the environment where the drone is located are collected and marked as the navigation wind detection value and the navigation water detection value respectively, the deviation value of the temperature of the environment where the drone is located compared to the preset suitable temperature standard value is marked as the navigation temperature detection value, and the deviation value of the air pressure of the environment where the drone is located compared to the preset suitable air pressure standard value is marked as the navigation pressure detection value; The electromagnetic interference intensity data of the drone's environment is collected and marked as an air disturbance detection value, and the flight environment value is obtained by numerically calculating the air wind detection value, the air water detection value, the air temperature detection value, the air pressure detection value and the air disturbance detection value.

5. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 1 is characterized in that: The UAV management terminal is connected to the route execution monitoring module and the route execution evaluation module in communication. The route execution monitoring module monitors the flight process of the UAV and sends the flight monitoring information of the UAV to the route execution evaluation module. The route execution evaluation module is used to analyze the route execution performance of the drone, generate a route execution abnormality signal or a route execution qualified signal through analysis, and send the route execution abnormality signal or the route execution qualified signal to the drone management end. When the drone management end receives the route execution abnormality signal, it will issue a corresponding warning.

6. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 5 is characterized in that: The specific analysis process of the route execution evaluation module includes: A detection period is set. If no high emergency signal for route adjustment is generated during the detection period, the actual flight path of the UAV during the detection period is collected, and the actual flight path is compared with the corresponding part of the optimal route of the UAV. The number of times the UAV deviates from the route is determined by comparison and marked as the UAV off-route detection value. If the UAV off-route detection value exceeds the preset UAV off-route detection threshold, a route execution abnormality signal is generated.

7. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 6 is characterized in that: If the drone off-course detection value does not exceed the preset drone off-course detection threshold, the duration of the corresponding route deviation process and the corresponding path length are marked as the off-course duration value and the off-course distance value respectively; if the off-course duration value or the off-course distance value exceeds the corresponding preset threshold, the corresponding route deviation process is marked as a route deviation process; The number of route deviation processes within the detection period is obtained and marked as the route deviation detection value, and the off-course duration values ​​of all route deviation processes are summed up to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed up to obtain the route off-course distance value; The route execution warning coefficient is obtained by numerically calculating the UAV off-course detection value, the route deviation detection value, the route off-time value and the route off-distance value. If the route execution warning coefficient exceeds the preset route execution warning coefficient threshold, a route execution abnormality signal is generated; If the route execution warning coefficient does not exceed the preset route execution warning coefficient threshold, a route execution qualified signal is generated.

8. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 7 is characterized in that: The route execution evaluation module is communicatively connected to the drone operation and measurement module. The route execution evaluation module sends a route execution qualification signal to the drone operation and measurement module. The drone operation and measurement module analyzes the operating status of the drone during the detection period, generates a drone operation warning signal or a drone operation qualification signal through analysis, and sends the drone operation warning signal or the drone operation qualification signal to the drone management end. When the drone management end receives the drone operation warning signal, it issues a corresponding warning.

9. The intelligent planning and management system for unmanned aerial vehicle routes based on big data according to claim 8, characterized in that: The specific analysis process of the drone operation and measurement module is as follows: The real-time temperature and real-time flight speed inside the drone are collected, the real-time temperature is compared with the median of the preset temperature range, and the difference is calculated and the absolute value is taken to obtain the drone internal temperature value, and the deviation value of the real-time flight speed compared with the set standard flight speed is marked as the drone speed inspection value; The vibration amplitude of the drone is collected and marked as the drone vibration inspection value, and the drone actual value is obtained by numerically calculating the drone internal temperature value, the drone speed inspection value and the drone vibration inspection value; If the drone actual value exceeds the preset drone actual value threshold, the corresponding drone actual value is marked as a drone abnormal value; The number of drone abnormality values ​​within the detection period is obtained and the ratio is calculated with the total number of drone actual values ​​to obtain the drone abnormality value, and the average of all drone actual values ​​within the detection period is calculated to obtain the drone measured value. If the drone abnormality value or the drone measured value exceeds the corresponding preset threshold, a drone operation warning signal is generated; If the drone abnormal table value and the drone actual measured value do not exceed the corresponding preset threshold, a drone operation qualified signal is generated.

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