An intelligent planning and management system for drone routes based on big data

Abstract

The present invention belongs to the technical field of drone management, and specifically is an intelligent drone route planning and management system based on big data, comprising a drone route acquisition module, a drone route analysis and decision module, a drone flight control module, a route adjustment urgency analysis module and a drone management terminal; the present invention uses the drone route analysis and decision module to analyze 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, thereby ensuring flight efficiency while improving flight safety, and during the flight of the drone, the route adjustment urgency analysis module performs route adjustment urgency analysis, and performs route adjustment when a high-urgency signal for route adjustment is generated, which is beneficial to avoiding damage to the drone, has a high degree of automation and intelligence, and significantly reduces the work difficulty of backstage management personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of drone management, and in particular to a drone route intelligent planning and management system based on big data. Background Art

[0002] UAVs, also known as unmanned aerial vehicles, are unmanned aircraft that utilize advanced remote control technology, autonomous flight control systems, and sensor technology to perform various missions in the air. With the rapid development of UAV technology, UAVs are increasingly being used in aerial photography, logistics distribution, agricultural plant protection, emergency rescue, and other fields.

[0003] Currently, when managing drones, it is often difficult to determine the optimal route from several groups of feasible routes based on the flight mission 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. This is not conducive to ensuring the flight safety of the drone, increases the difficulty of the back-end management staff, and has a low level of intelligence and automation.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent planning and management system for drone routes based on big data, which solves the problems that the existing technology is 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, and is unable to reasonably analyze the urgency of route adjustment and route execution performance during the drone flight, and has low intelligence and automation levels.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A big data-based intelligent planning and management system for drone routes, including a drone route acquisition module, a drone route analysis and decision-making module, a drone flight control module, a route adjustment urgency analysis module, and a drone management terminal;

[0008] The UAV route acquisition module obtains the UAV's flight mission, 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;

[0009] The UAV route analysis and decision module analyzes all feasible routes and determines the optimal route for the UAV to perform the current flight mission. The optimal route is then sent 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.

[0010] The route adjustment urgency analysis module is used to perform route adjustment urgency analysis during the flight of the UAV, generate a high-emergency route adjustment signal or a low-emergency route adjustment signal through analysis, and send the high-emergency route adjustment signal or the low-emergency route adjustment signal to the UAV management end and the UAV flight control module; when the UAV flight control module receives the high-emergency route adjustment signal, it adjusts the UAV's route, and when the UAV management end receives the high-emergency route adjustment signal, it issues an early warning.

[0011] Furthermore, the specific analysis process of the drone route analysis and decision module includes:

[0012] All feasible routes are obtained and marked as i, where i is a natural number greater than 1. The flight path length of feasible route i is collected and marked as the flight path value. Several 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. The instantaneous lift coefficient is numerically compared with a preset instantaneous lift coefficient threshold, and the number of instantaneous lift coefficients in feasible route i that exceed the preset instantaneous lift coefficient threshold is marked as the lift transient value.

[0013] The maximum turning angle 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 a 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;

[0014] 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.

[0015] Furthermore, the specific analysis process of the route adjustment urgency analysis module includes:

[0016] Based on the image of the drone's forward direction, it determines 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. 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, a high-emergency signal for route adjustment is generated;

[0017] 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.

[0018] Furthermore, the analysis and acquisition method of the flight environment value is as follows:

[0019] The wind speed data and precipitation data of the drone's environment are collected and marked as the navigation wind detection value and the navigation water detection value respectively. The deviation of the drone's environment temperature from the preset suitable temperature standard value is marked as the navigation temperature detection value, and the deviation of the drone's environment air pressure from the preset suitable air pressure standard value is marked as the navigation pressure detection value.

[0020] The electromagnetic interference intensity data of the drone's environment is collected and marked as the flight disturbance detection value, and the flight environment value is obtained by numerically calculating the flight wind detection value, the flight water detection value, the flight temperature detection value, the flight pressure detection value and the flight disturbance detection value.

[0021] Furthermore, 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 UAV flight monitoring information to the route execution evaluation module.

[0022] 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 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.

[0023] Furthermore, the specific analysis process of the route execution evaluation module includes:

[0024] 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 UAV's optimal route. 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.

[0025] 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. 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.

[0026] 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 to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed to obtain the route off-course distance value;

[0027] 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.

[0028] Furthermore, the route execution evaluation module is communicatively connected to the drone operation and measurement module. The route execution evaluation module sends the route execution qualification signal to the drone operation and measurement module. The drone operation and measurement module analyzes the operation 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.

[0029] Furthermore, the specific analysis process of the drone operation and measurement module is as follows:

[0030] 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's internal temperature value. The deviation value of the real-time flight speed compared to the set standard flight speed is marked as the drone's rapid inspection value; and the vibration amplitude of the drone is collected and marked as the drone's vibration inspection value. The drone's actual value is obtained by numerically calculating the drone's internal temperature value, the drone's rapid inspection value, and the drone's vibration inspection value;

[0031] 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 the 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 drone operation qualified signal is generated.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In the present invention, 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 drone based on the optimal route, ensuring flight efficiency while improving flight safety. During the flight of the drone, 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 helps to avoid damage to the drone. The system has a high degree of automation and intelligence, significantly reducing the workload of back-end managers.

[0034] 2. In the present invention, the route execution performance of the drone is analyzed through the route execution evaluation module. When a route execution abnormality signal is generated, the back-end management personnel are reminded to pay continuous and focused attention to the drone operation and take reasonable control measures as needed, thereby ensuring the drone's route execution accuracy and flight safety. When a route execution qualified signal is generated, the drone operation test module analyzes the drone's operating status during the detection period. When a drone operation warning signal is generated, the back-end management personnel are reminded to investigate the cause and take corresponding control measures, thereby further avoiding damage to the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0036] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0037] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0039] Example 1: Figure 1 As shown, the present invention proposes an intelligent planning and management system for UAV routes based on big data, including a UAV route acquisition module, a UAV route analysis and decision module, a UAV flight control module, a route adjustment urgency analysis module and a UAV management terminal;

[0040] The UAV route acquisition module obtains the UAV's flight mission, 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;

[0041] 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:

[0042] All feasible routes are obtained and marked as i, where i is a natural number greater than 1. The flight path length of feasible route i is collected and marked as the flight path value. Several detection points are set on the feasible route, and the altitude difference between two adjacent 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 compared with a preset instantaneous lift coefficient threshold, and the number of instantaneous lift coefficients in feasible route i that exceed the preset instantaneous lift coefficient threshold is marked as the lift transient value.

[0043] 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 turning angle value is too large, it indicates a sharp turn). The instantaneous turning angle amplitude is compared with a 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;

[0044] 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; among them, 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 feasible route at the last place is marked as the optimal route for the UAV to perform the current flight mission.

[0045] 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 route adjustment signal or a low-emergency route adjustment signal, the module sends the high-emergency route adjustment signal or the low-emergency route adjustment signal to the drone management terminal and the drone flight control module. This module can reasonably analyze and evaluate the degree of hidden dangers on the drone's flight route so that the route can be adjusted in a timely manner, which is beneficial to avoid damage to the drone.

[0046] When the UAV flight control module receives a high-emergency route adjustment signal, it adjusts the UAV's route to achieve automatic route adjustment to ensure the UAV's flight safety. When the UAV management terminal receives a high-emergency route adjustment signal, 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) to further ensure its flight safety. The specific analysis process of the route adjustment urgency analysis module is as follows:

[0047] 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. 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 route adjustment signal is generated;

[0048] 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 of the drone's environment (i.e., the data value of the real-time rainfall amount) are collected and marked as the flight wind detection value and the flight water detection value respectively, the deviation value of the drone's environment temperature compared to the preset suitable temperature standard value is marked as the flight temperature detection value, and the deviation value of the drone's environment air pressure compared to the preset suitable air pressure standard value is marked as the flight pressure detection value;

[0049] The electromagnetic interference intensity data of the drone's environment 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 using the formula RY=(uy1*QF+uy2*QH+uy3*QL+uy4*QP+uy5*QX) / 5 to obtain the flight environment value RY; among them, 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 impact of the drone's current environment on its flight;

[0050] The flight environment value RY is numerically compared with the preset flight environment threshold. If the flight environment value RY exceeds the preset flight environment threshold, it indicates that the current environment of the drone has a significant adverse impact on its flight, and the drone's route needs to be adjusted or landed in time, then a high emergency signal for route adjustment is generated; if the flight environment value RY does not exceed the preset flight environment threshold, it indicates that the current environment of the drone has a minor adverse impact on its flight, then a low emergency signal for route adjustment is generated.

[0051] Example 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the UAV management terminal is communicatively connected to the route execution monitoring module and the route execution evaluation module. The route execution monitoring module monitors the flight process of the UAV and sends the UAV flight monitoring information to the route execution evaluation module.

[0052] The route execution evaluation module is used to analyze the drone's route execution performance. Through analysis, it generates a route execution abnormality signal or a route execution qualified signal, and sends the route execution abnormality signal or route execution qualified signal to the drone management terminal. Upon receiving the route execution abnormality signal, the drone management terminal issues a corresponding warning to remind the back-end management personnel to continue to focus on the drone operation and take reasonable control measures as needed to ensure the drone's route execution accuracy and flight safety. The specific analysis process of the route execution evaluation module is as follows:

[0053] 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 UAV's optimal route. 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.

[0054] 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. 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.

[0055] 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 to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed to obtain the route off-course distance value;

[0056] The route execution warning coefficient PX is calculated by numerically calculating the drone's off-course detection value PL, route deviation detection value PN, route off-time value PM, and route off-distance value PW using the formula PX=0.5*(rq1*PL+rq2*PN)+(rq3*PM+rq4*PW) / (rq1+rq2). Here, rq1, rq2, rq3, and rq4 are preset proportional coefficients with values ​​greater than zero, such that rq2>rq1>rq3>rq4>0. Furthermore, a larger value of the route execution warning coefficient PX indicates a worse overall route execution performance of the drone during the detection period.

[0057] 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 drone 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 drone during the detection period is generally good, and a route execution qualified signal is generated.

[0058] Example 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 a route execution qualified signal to the drone operation and measurement module. 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 the analysis.

[0059] 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 issues a corresponding warning, which can reasonably analyze and accurately judge the drone operation risk, and promptly remind the back-end management personnel to investigate the cause and take corresponding control measures to avoid damage to the drone. The specific analysis process of the drone operation test module is as follows:

[0060] 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. The deviation value of the real-time flight speed compared to the set standard flight speed is marked as the drone speed check value; and the vibration amplitude of the drone is collected and marked as the drone vibration check value;

[0061] The drone's actual value M is calculated by numerically calculating the drone's internal temperature value B, the drone's speed inspection value K, and the drone's vibration inspection value S using the formula M=(a1*B+a2*K+a3*S) / 3. Here, a1, a2, and a3 are preset proportional coefficients with values ​​greater than zero. The larger the value of the drone's actual value M, the worse the drone's real-time operating condition.

[0062] The actual value M of the drone is numerically compared with the preset drone actual value threshold. If the actual value M of the drone exceeds the preset drone actual value threshold, it indicates that the real-time operating condition 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.

[0063] 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.

[0064] 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 for 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 to accurately assess the degree of hidden dangers in the route of the drone during flight and perform reasonable analysis and assessment. When a high-emergency signal for route adjustment is generated, the route is adjusted, which is beneficial to avoid damage to the drone. It has a high degree of automation and intelligence, and significantly reduces the difficulty of the work of backstage managers.

[0065] The above formulas are all dimensionless and calculated by taking their numerical values. The formula is a formula for the latest real situation obtained by collecting a large amount of data and performing software simulation. 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 illustrate 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 based on 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 well understand and use the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent planning and management system for drone routes based on big data, characterized by: It includes UAV route acquisition module, UAV route analysis and decision module, UAV flight control module, route adjustment urgency analysis module and UAV management terminal; The UAV route acquisition module obtains the UAV's flight mission, 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 and determines the optimal route for the UAV to perform the current flight mission. The optimal route is then sent 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. The route adjustment urgency analysis module is used to perform route adjustment urgency analysis during the flight of the UAV, generate a high-urgency route adjustment signal or a low-urgency route adjustment signal through analysis, and send the high-urgency route adjustment signal or the low-urgency route adjustment signal to the UAV management terminal and the UAV flight control module; when the UAV flight control module receives the high-urgency route adjustment signal, it adjusts the UAV's route, and when the UAV management terminal receives the high-urgency route adjustment signal, it issues an early warning; The specific analysis process of the UAV route analysis and decision module includes: All feasible routes are obtained and marked as i, where i is a natural number greater than 1; the flight path length of feasible route i is collected and marked as the flight path value; a number of detection points are set on the feasible route; the altitude difference between two adjacent groups of detection points is marked as the instantaneous lift coefficient; and the number of instantaneous lift coefficients in feasible route i that exceed a 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; and the number of instantaneous turning angle amplitudes in feasible route i that exceed a preset instantaneous turning angle amplitude threshold 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; where se1, se2, and se3 are preset proportional coefficients, se3>se2>se1>0.253; all feasible routes are sorted in descending order according to the values ​​of the route initial inspection values, and the last feasible route is marked as the optimal route for the drone to perform the current flight mission; The specific analysis process of the route adjustment urgency analysis module includes: Based on the image of the drone's forward direction, it determines 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; The analysis and acquisition method of flight environment value is as follows: The wind speed data and precipitation data of the drone's environment are collected and marked as the navigation wind detection value and the navigation water detection value respectively. The deviation of the drone's environment temperature from the preset suitable temperature standard value is marked as the navigation temperature detection value, and the deviation of the drone's environment air pressure from 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 the disturbance detection value. The wind detection value QF, water detection value QH, temperature detection value QL, pressure detection value QP and disturbance detection value QX are numerically calculated using the formula RY=(uy1*QF+uy2*QH+uy3*QL+uy4*QP+uy5*QX) / 5 to obtain the flight environment value RY; among them, uy1, uy2, uy3, uy4 and uy5 are preset proportional coefficients with values ​​greater than zero; The drone management terminal is connected to the route execution monitoring module and the route execution evaluation module. The route execution monitoring module monitors the flight process of the drone and sends the drone's flight monitoring information to the route execution evaluation module. The route execution evaluation module is used to analyze the UAV's route execution performance, generate a route execution abnormality signal or a route execution qualified signal through analysis, and send the route execution abnormality signal or route execution qualified signal to the UAV management terminal. The UAV management terminal will issue a corresponding warning when receiving the route execution abnormality signal; The specific analysis process of the route execution assessment module includes: A detection period is set. If no high-emergency route adjustment signal is generated during the detection period, the actual flight path of the drone during the detection period is collected and compared with the corresponding portion of the drone's optimal route. The number of times the drone deviates from the route is determined by comparison and marked as a drone off-route detection value. If the drone off-route detection value exceeds a preset drone off-route detection threshold, a route execution abnormality signal is generated. 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 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 to obtain the route off-course time value, and the off-course distance values ​​of all route deviation processes are summed to obtain the route off-course distance value; The route execution warning coefficient PX is obtained by numerically calculating the drone's off-course detection value PL, route deviation detection value PN, route off-time value PM, and route off-distance value PW using the formula PX=0.5*(rq1*PL+rq2*PN)+(rq3*PM+rq4*PW) / (rq1+rq2). Here, rq1, rq2, rq3, and rq4 are preset proportional coefficients with values ​​greater than zero, and rq2>rq1>rq3>rq4>0. 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. 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. The drone operation and measurement module analyzes the operation status of the drone during the detection period, generates a drone operation warning signal or a drone operation qualified signal through analysis, and sends the drone operation warning signal or the drone operation qualified signal to the drone management end. When the drone management end receives the drone operation warning signal, it issues a corresponding warning.

2. The intelligent planning and management system for drone routes based on big data according to claim 1 is 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, and the difference between the real-time temperature and the median of the preset temperature range is calculated and the absolute value is taken to obtain the drone's internal temperature value. The deviation value of the real-time flight speed from the set standard flight speed is marked as the drone's rapid inspection value; the vibration amplitude of the drone is collected and marked as the drone's vibration inspection value. The drone's internal temperature value B, the drone's rapid inspection value K, and the drone's vibration inspection value S are numerically calculated using the formula M=(a1*B+a2*K+a3*S) / 3 to obtain the drone's actual value M; where a1, a2, and a3 are preset proportional coefficients with values ​​greater than zero; If the actual value of the drone exceeds the preset drone actual value threshold, the corresponding drone actual value will be 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. If the drone abnormal value or the drone measured value exceeds the corresponding preset threshold, 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, a drone operation qualified signal is generated.

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