Distribution network unmanned aerial vehicle intelligent inspection and acceptance system and method based on edge-end cooperation

Abstract

The invention relates to the technical field of unmanned aerial vehicle intelligent inspection and acceptance systems, in particular to a distribution network unmanned aerial vehicle intelligent inspection and acceptance system and method based on edge-end collaboration, and the system comprises an analysis module, a control module and a communication module. The analysis module is used for analyzing and obtaining related information of a planning index, a risk index, an optimization index, a coverage efficiency index and a complex index of a flight path of the unmanned aerial vehicle, and transmitting the related information to the control module; the control module obtains a comprehensive optimization factor of the flight path according to related information of a planning index, a risk index, an optimization index, a coverage efficiency index and a complex index of the flight path of the unmanned aerial vehicle, and transmits the comprehensive optimization factor to the communication module; and the communication module transmits the comprehensive optimization factor of the flight path to a user side. And the control module automatically analyzes to obtain a comprehensive optimization factor of the flight path, so that manual intervention is reduced, and high automation of unmanned aerial vehicle inspection is realized.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent inspection and acceptance systems for unmanned aerial vehicles (UAVs), specifically to an intelligent inspection and acceptance system and method for power distribution networks based on edge-end collaboration. Background Technology

[0002] The intelligent inspection and acceptance system collects data via drones and intelligently analyzes the inspection results on edge devices, thereby achieving an efficient and intelligent inspection and automated acceptance process. This system is suitable for power distribution network inspection in complex environments, and can improve the real-time performance, accuracy, and automation level of inspection and acceptance.

[0003] Application document CN206921161U discloses an intelligent inspection system that includes inspection management, defect handling, and acceptance. The system management platform includes a server and a database module. The database module consists of a system management module, a basic information management module, an inspection scheduling management module, an inspection operation and maintenance analysis module, and a defect elimination and acceptance management module. The inspection handheld terminal includes an NFC module, a positioning module, and a defect reporting module connected to the positioning module. The maintenance handheld terminal includes an inspection record module and a camera module. The management handheld terminal includes an on-site acceptance module and a photography module. The passive NFC radio frequency tag is an NFC tag installed at the inspection site, and there is at least one of them. The system management platform is connected to the inspection handheld terminal, the maintenance handheld terminal, and the management handheld terminal respectively.

[0004] Existing technologies rely on handheld terminals and NFC modules for manual inspection and data collection. Inspection personnel need to manually record information on-site, resulting in low efficiency and excessive manual intervention. Summary of the Invention

[0005] The purpose of this invention is to improve efficiency. To address the shortcomings mentioned above, this invention proposes an intelligent inspection and acceptance system and method for power distribution networks using unmanned aerial vehicles (UAVs) based on edge-end collaboration.

[0006] The present invention adopts the following technical solution:

[0007] An intelligent inspection and acceptance system for power distribution networks using unmanned aerial vehicles (UAVs) based on edge-end collaboration is disclosed. The system includes an analysis module, a control module, and a communication module. The analysis module analyzes and derives relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the UAV's flight path, and transmits this information to the control module. The control module derives a comprehensive optimization factor for the flight path based on these indicators and transmits it to the communication module. The communication module then transmits the comprehensive optimization factor to the user terminal.

[0008] The system obtains the comprehensive optimization factor for each flight path and selects the flight path with the smallest comprehensive optimization factor to execute the flight mission.

[0009] Optionally, the analysis module includes a flight path selection submodule, a data storage submodule, a meteorological detection submodule, a flight path risk detection submodule, a flight path optimization submodule, and a flight environment detection submodule. The flight path selection submodule is used to select a flight path. The data storage submodule sets and stores the planned values ​​of the flight path and the preset values ​​of the UAV's flight speed based on the selected flight path, and is also used to store the field of view width, and transmits the field of view width, the planned values ​​of the flight path, and the preset values ​​of the UAV's flight speed to the control module. The meteorological detection submodule detects and derives the environmental wind speed rating coefficient, environmental visibility rating coefficient, environmental precipitation rating coefficient, and environmental temperature rating coefficient, and transmits them to the control module. The flight path risk detection submodule detects and derives the obstacle density of the selected flight path, and the control module calculates the obstacle density based on the environmental wind speed rating coefficient, environmental visibility rating coefficient, and environmental temperature rating coefficient. The meteorological condition score index is derived from the precipitation score coefficient and the ambient temperature score coefficient and transmitted to the flight path risk detection submodule. The flight path risk detection submodule derives the flight path risk index based on the meteorological condition score index and the obstacle density of the flight path, and transmits it to the control module. The flight path optimization submodule is used to set the flight target and derive the flight path optimization index, which is then transmitted to the control module. The flight environment detection submodule is used to detect and derive the terrain slope and magnetic field strength based on the selected flight path, derive the flight environment complexity index based on the terrain slope and magnetic field strength, and transmit it to the control module. The control module derives the flight coverage efficiency index based on the preset values ​​of the field of view width and the UAV flight speed, and derives the comprehensive optimization factor of the flight path based on the planned value of the flight path, the flight path risk index, the flight path optimization index, the flight coverage efficiency index, and the flight environment complexity index.

[0010] Optionally, the meteorological detection submodule includes a wind speed detection unit, a visibility detection unit, a precipitation detection unit, and a temperature detection unit; the wind speed detection unit is used to detect the ambient wind speed and obtain an ambient wind speed rating coefficient, and transmit it to the control module; the visibility detection unit is used to detect the ambient visibility and obtain an ambient visibility rating coefficient, and transmit it to the control module; the precipitation detection unit is used to detect the precipitation and obtain an ambient precipitation rating coefficient, and transmit it to the control module; the temperature detection unit is used to detect the ambient temperature and obtain an ambient temperature rating coefficient, and transmit it to the control module.

[0011] Optionally, the flight path risk detection submodule includes an obstacle density analysis unit and a risk detection unit; the obstacle density analysis unit analyzes the selected flight path and obtains the obstacle density of the flight path, and transmits it to the control module and the risk detection unit; the control module transmits the meteorological condition score index to the risk detection unit; the risk detection unit obtains the risk index of the flight path based on the meteorological condition score index and the obstacle density of the flight path, and transmits it to the control module.

[0012] Optionally, the flight environment detection submodule includes a terrain analysis unit, a magnetic field detection unit, and an environment analysis unit; the terrain analysis unit analyzes the selected flight path and obtains the terrain slope, and transmits it to the environment analysis unit; the magnetic field detection unit analyzes the selected flight path and obtains the magnetic field strength, and transmits it to the environment analysis unit; the environment analysis unit obtains the environmental complexity indicators of flight based on the terrain slope and magnetic field strength, and transmits them to the control module.

[0013] Optionally, when calculating the comprehensive optimization factor of the flight path, the control module satisfies the following formula:

[0014] Among them, F opt d is the comprehensive optimization factor for the flight path, w is the planned value of the flight path, and α is the risk index of the flight path. The risk index ranges from [1.2, 2.1]. The larger the risk index, the greater the risk of the current path. α is the optimization index of the flight path. The optimization index ranges from [0.7, 1.5]. The value increases in the order of priority: coverage, energy saving, inspection efficiency, data accuracy, or safety. c is the coverage efficiency index of the flight path, and e is the environmental complexity index of the flight path. The environmental complexity index ranges from [1.1, 2.8]. The more unsuitable the environment, the larger the value.

[0015] This embodiment also provides a method for intelligent inspection and acceptance of distribution network drones based on edge-end collaboration, applied to an intelligent inspection and acceptance system for distribution network drones based on edge-end collaboration, including the following steps: Step S1: The analysis module analyzes and obtains relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the drone flight path, and transmits them to the control module; Step S2: The control module obtains the comprehensive optimization factor of the flight path based on the relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the drone flight path, and transmits it to the communication module; Step S3: The communication module transmits the comprehensive optimization factor of the flight path to the user terminal.

[0016] The beneficial effects achieved by this invention are:

[0017] 1. The control module automatically analyzes and derives the comprehensive optimization factors for the flight path, reducing manual intervention and achieving a high degree of automation in UAV inspection;

[0018] 2. The control module calculates the "comprehensive optimization factor of the flight path". Combined with indicators such as path planning, risk, and coverage efficiency, it can realize intelligent decision-making for UAV flight paths and automatic recording of acceptance data, thereby improving the accuracy and consistency of acceptance work.

[0019] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0021] Figure 2 This is a schematic diagram of the meteorological detection submodule in this invention;

[0022] Figure 3 This is a schematic diagram of the flight path risk detection submodule in this invention;

[0023] Figure 4 This is a schematic diagram of the flight environment detection submodule in this invention;

[0024] Figure 5 This is a diagram illustrating the relationships within the present invention.

[0025] Figure 6 This is a flowchart of the method of the present invention;

[0026] Figure 7 This is a schematic diagram of the overall structure of Embodiment 2 of the present invention;

[0027] Figure 8 This is a schematic diagram of the flight safety analysis module in this invention;

[0028] Figure 9 This is a relational diagram of Embodiment 2 of the present invention. Detailed Implementation

[0029] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0030] Example 1: This example provides an intelligent inspection and acceptance system for power distribution networks using drones based on edge-end collaboration, combined with... Figures 1 to 6 As shown.

[0031] An intelligent inspection and acceptance system for power distribution networks using unmanned aerial vehicles (UAVs) based on edge-end collaboration is disclosed. The system includes an analysis module, a control module, and a communication module. The analysis module analyzes and derives relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the UAV's flight path, and transmits this information to the control module. The control module derives a comprehensive optimization factor for the flight path based on these indicators and transmits it to the communication module. The communication module then transmits the comprehensive optimization factor to the user terminal.

[0032] The system obtains the comprehensive optimization factor for each flight path and selects the flight path with the smallest comprehensive optimization factor to execute the flight mission.

[0033] Optionally, the analysis module includes a flight path selection submodule, a data storage submodule, a meteorological detection submodule, a flight path risk detection submodule, a flight path optimization submodule, and a flight environment detection submodule. The flight path selection submodule is used to select a flight path. The data storage submodule sets and stores the planned values ​​of the flight path and the preset values ​​of the UAV's flight speed based on the selected flight path, and is also used to store the field of view width, and transmits the field of view width, the planned values ​​of the flight path, and the preset values ​​of the UAV's flight speed to the control module. The meteorological detection submodule detects and derives the environmental wind speed rating coefficient, environmental visibility rating coefficient, environmental precipitation rating coefficient, and environmental temperature rating coefficient, and transmits them to the control module. The flight path risk detection submodule detects and derives the obstacle density of the selected flight path, and the control module calculates the obstacle density based on the environmental wind speed rating coefficient, environmental visibility rating coefficient, and environmental temperature rating coefficient. The meteorological condition score index is derived from the precipitation score coefficient and the ambient temperature score coefficient and transmitted to the flight path risk detection submodule. The flight path risk detection submodule derives the flight path risk index based on the meteorological condition score index and the obstacle density of the flight path, and transmits it to the control module. The flight path optimization submodule is used to set the flight target and derive the flight path optimization index, which is then transmitted to the control module. The flight environment detection submodule is used to detect and derive the terrain slope and magnetic field strength based on the selected flight path, derive the flight environment complexity index based on the terrain slope and magnetic field strength, and transmit it to the control module. The control module derives the flight coverage efficiency index based on the preset values ​​of the field of view width and the UAV flight speed, and derives the comprehensive optimization factor of the flight path based on the planned value of the flight path, the flight path risk index, the flight path optimization index, the flight coverage efficiency index, and the flight environment complexity index.

[0034] Optionally, the meteorological detection submodule includes a wind speed detection unit, a visibility detection unit, a precipitation detection unit, and a temperature detection unit; the wind speed detection unit is used to detect the ambient wind speed and obtain an ambient wind speed rating coefficient, and transmit it to the control module; the visibility detection unit is used to detect the ambient visibility and obtain an ambient visibility rating coefficient, and transmit it to the control module; the precipitation detection unit is used to detect the precipitation and obtain an ambient precipitation rating coefficient, and transmit it to the control module; the temperature detection unit is used to detect the ambient temperature and obtain an ambient temperature rating coefficient, and transmit it to the control module.

[0035] Optionally, the flight path risk detection submodule includes an obstacle density analysis unit and a risk detection unit; the obstacle density analysis unit analyzes the selected flight path and obtains the obstacle density of the flight path, and transmits it to the control module and the risk detection unit; the control module transmits the meteorological condition score index to the risk detection unit; the risk detection unit obtains the risk index of the flight path based on the meteorological condition score index and the obstacle density of the flight path, and transmits it to the control module.

[0036] Optionally, the flight environment detection submodule includes a terrain analysis unit, a magnetic field detection unit, and an environment analysis unit; the terrain analysis unit analyzes the selected flight path and obtains the terrain slope, and transmits it to the environment analysis unit; the magnetic field detection unit analyzes the selected flight path and obtains the magnetic field strength, and transmits it to the environment analysis unit; the environment analysis unit obtains the environmental complexity indicators of flight based on the terrain slope and magnetic field strength, and transmits them to the control module.

[0037] Optionally, when calculating the comprehensive optimization factor of the flight path, the control module satisfies the following formula:

[0038]

[0039] Among them, F opt d is the comprehensive optimization factor for the flight path, w is the planned value of the flight path, and α is the risk index of the flight path. The risk index ranges from [1.2, 2.1]. The larger the risk index, the greater the risk of the current path. α is the optimization index of the flight path. The optimization index ranges from [0.7, 1.5]. The value increases in the order of priority: coverage, energy saving, inspection efficiency, data accuracy, or safety. c is the coverage efficiency index of the flight path, and e is the environmental complexity index of the flight path. The environmental complexity index ranges from [1.1, 2.8]. The more unsuitable the environment, the larger the value.

[0040] Preferably, the risk indicators for the flight path have the following values: w = 1.2 or w = 2.1. When w = 1.2, it means that the obstacle density of the flight path is less than 2 obstacles per 100 meters (i.e., less than 2 obstacles within 100 meters) and the meteorological condition score is greater than 80 points. In other cases, w = 2.1. The optimization indicators for the flight path have the following values: α = 0.7, α = 0.8, α = 1.2, α = 1.3, or α = 1.5. When α = 0.7, it means that the flight target is set with coverage priority. When α = 0.8, it means that the flight path is set with coverage priority. The target setting prioritizes energy saving. When α = 1.2, it means that the flight target prioritizes inspection efficiency. When α = 1.3, it means that the flight target prioritizes data accuracy. When α = 1.5, it means that the flight target prioritizes safety. The specific priority is set by the staff in advance according to actual needs. The environmental complexity index of flight has the following values: e = 1.1 or e = 2.8. When e = 1.1, it means that the terrain slope of the flight path is less than 10% and the magnetic field strength of the flight path is less than 80 microtesla. In other cases, e = 2.8.

[0041] Optionally, the control module may perform calculations that satisfy the following formula:

[0042] c = v × fov; where mi is the meteorological condition score index, and fs is the environmental wind speed score coefficient. The environmental wind speed score coefficient has the following values: fs = 100, fs = 90, fs = 80, fs = 70, fs = 50, or fs = 30. When fs = 100, it means the wind speed is 0; when fs = 90, it means the wind speed is greater than 0 and less than 5 meters per second; when fs = 80, it means the wind speed is 5 meters per second; and when fs = 70, it means the wind speed is greater than 5 and less than 1 meter per second. Between 0 and 0 meters per second, when fs = 50 it indicates a wind speed of 10 meters per second, when fs = 30 it indicates a wind speed greater than 10 meters per second, and njd is the environmental visibility rating coefficient. The environmental visibility rating coefficient has the following values: njd = 100, njd = 80, njd = 60, or njd = 50. When njd = 100 it indicates a visibility greater than or equal to 10 kilometers, when njd = 80 it indicates a visibility between 5 and less than 10 kilometers, and when njd = 50 it indicates a visibility between 5 and less than 10 kilometers. =60 indicates visibility between 2 and 5 kilometers; njd=50 indicates visibility less than or equal to 2 kilometers. js is the environmental precipitation score coefficient, which can take the following values: js=90, js=70, or js=50. When js=90, it indicates precipitation less than or equal to 1 mm per hour; when js=70, it indicates precipitation greater than 1 to less than 2 mm per hour; and when js=50, it indicates precipitation greater than or equal to 2 mm per hour. For the hour, wd is the ambient temperature rating coefficient, which can take the following values: wd = 95, wd = 70, or wd = 40. When wd = 95, it means the ambient temperature is greater than or equal to 10 degrees Celsius and less than or equal to 35 degrees Celsius. When wd = 70, it means the ambient temperature is greater than 0 degrees Celsius and less than 10 degrees Celsius or greater than 35 degrees Celsius and less than 40 degrees Celsius. In all other cases, wd = 40. v is the field of view width, and fov is the preset value of the drone's flight speed.

[0043] When the control module calculates the comprehensive optimization factor for the flight path, refer to the following program code:

[0044]

[0045]

[0046] Specifically, the optimal flight path is determined based on the value of the comprehensive optimization factor. A smaller comprehensive optimization factor indicates better performance. Flights are taken along the path with the smallest comprehensive optimization factor to reduce inspection time and ensure mission safety. The planned value of the flight path is in meters, representing the distance traveled from the starting point to the destination. The distance between waypoints is calculated in real-time using the UAV's GPS, representing a pre-set path. Regarding the setting of flight path optimization indicators, one of the fixed values ​​is explained in detail below: when inspection efficiency is prioritized, the corresponding flight path optimization indicator value is higher. Because its high efficiency means collecting more data per unit time, it may cause data backlog during the inspection process, which will burden the edge processing system. Data backlog and lag will affect the real-time performance of the inspection. The unit of field of view width is meters, which is determined by the specifications of the corresponding shooting element of the drone. The unit of the preset value of the drone's flight speed is meters per second. When calculating the meteorological condition score index, since there are many reasons that may affect the flight of the drone or the normal operation of its internal components, multiple coefficients are introduced. The coefficients are given a range, and the specific values ​​of the coefficients are set by those skilled in the art based on actual data and experience.

[0047] This embodiment also provides a method for intelligent inspection and acceptance of distribution network drones based on edge-end collaboration, applied to an intelligent inspection and acceptance system for distribution network drones based on edge-end collaboration, including the following steps: Step S1: The analysis module analyzes and obtains relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the drone flight path, and transmits them to the control module; Step S2: The control module obtains the comprehensive optimization factor of the flight path based on the relevant information on the planning indicators, risk indicators, optimization indicators, coverage efficiency indicators, and complexity indicators of the drone flight path, and transmits it to the communication module; Step S3: The communication module transmits the comprehensive optimization factor of the flight path to the user terminal.

[0048] The above units are just examples. Those skilled in the art can set different units according to actual needs when implementing this solution.

[0049] This embodiment solves the problem of low efficiency in traditional intelligent inspection and acceptance systems. The control module automatically analyzes and obtains the comprehensive optimization factor of the flight path, reducing manual intervention and realizing a high degree of automation in UAV inspection.

[0050] Example 2: This example includes all the content of Example 1, and provides a smart inspection and acceptance system for power distribution networks using drones based on edge-end collaboration, combined with... Figures 7 to 9 As shown.

[0051] An intelligent inspection and acceptance system for power distribution network drones based on edge-end collaboration, which also includes a flight safety analysis module;

[0052] The flight safety analysis module is used to analyze and derive the dynamic value of the safe flight distance of the UAV, and transmit it to the communication module;

[0053] The communication module transmits the dynamic value of the safe flight distance of the drone to the user terminal.

[0054] Optionally, the flight safety analysis module includes an information storage submodule, an obstacle detection submodule, a speed detection submodule, and a calculation submodule;

[0055] The information storage submodule is used to store the drone's minimum flight altitude, default safe distance, maximum obstacle density during drone flight, and maximum drone flight speed, and then transmits them to the calculation submodule;

[0056] The obstacle detection submodule is used to detect and obtain the measured values ​​of obstacle density and obstacle detection radius during drone flight, and transmit them to the calculation submodule;

[0057] The speed detection submodule is used to detect and obtain the measured value of the UAV's flight speed, and then transmit it to the calculation submodule;

[0058] The calculation submodule derives the flight speed impact index based on the measured value and maximum value of the drone's flight speed, derives the obstacle impact index based on the measured value and maximum value of the obstacle density during drone flight, and derives the dynamic value of the drone's flight safety distance based on the drone's minimum flight altitude, obstacle impact index, obstacle detection radius, default safe distance, flight speed impact index, and the measured value of the drone's flight speed, and transmits this information to the communication module.

[0059] Optionally, when calculating the dynamic value of the safe flight distance of the UAV, the calculation submodule satisfies the following formula:

[0060] D safe =max(h min +k1×r,s m +k2×sv o );

[0061]

[0062] Among them, D safe h is the dynamic value of the safe flight distance for drones. min The minimum flight altitude of the UAV is given by k1, the obstacle impact index is given by r, and the obstacle detection radius is given by s. m The default safe distance, k2 is the flight speed impact index, sv oThis represents the measured flight speed of the drone.

[0063] md o The measured value of obstacle density during drone flight, md max This represents the maximum obstacle density during drone flight; sv max This represents the maximum flight speed of the drone.

[0064] When calculating the dynamic value of the safe flight distance for the UAV in the calculation submodule, refer to the following program code:

[0065]

[0066]

[0067] print("Speed ​​influence coefficient k2:",k2)

[0068] Specifically, the dynamic value of the drone's safe flight distance is measured in meters. This dynamic value must be maintained within a preset threshold range. Calculating the dynamic value of the drone's safe flight distance allows for real-time adjustment of the drone's flight data to ensure flight safety. Specifically, based on changes in the dynamic value of the drone's safe flight distance, the drone's flight altitude is adjusted in real-time to maintain it within a safe altitude range. The minimum flight altitude of the drone is measured in meters, primarily representing the vertical height limit to ensure a safe distance between the drone and the ground. This minimum altitude is determined by those skilled in the art based on geographic information data or lidar testing. The obstacle detection radius is measured in meters and is determined by the actual obstacle detection radius during drone flight. The default safe distance is measured in meters. It refers to the minimum horizontal safe distance maintained by the drone during flight, i.e., the minimum horizontal distance between the drone and other objects or the ground when there are no special obstacles. It can be understood as a horizontal distance buffer used to ensure the drone's obstacle avoidance capability on a horizontal surface. The measured value of the drone's flight speed is meters per second. Both the measured value and the maximum value of the obstacle density during drone flight are measured in units per 100 meters. The maximum obstacle density during drone flight is set by those skilled in the art. The maximum drone flight speed is measured in meters per second, and is set by those skilled in the art.

[0069] The above units are just examples. Those skilled in the art can set different units according to actual needs when implementing this solution.

[0070] This embodiment solves the problem of the relatively simple nature of traditional intelligent inspection and acceptance systems. The flight safety analysis module calculates the dynamic flight safety distance of the UAV, enabling the UAV to adjust its distance in real time during the inspection process, ensuring safe flight in complex or high-risk areas and reducing the possibility of collisions or other accidents.

[0071] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A distribution network drone intelligent inspection and acceptance system based on edge-end collaboration, characterized in that: The system includes an analysis module, a control module and a communication module; The analysis module is used to analyze and obtain relevant information of the planning index, risk index, optimization index, coverage efficiency index and complexity index of the UAV flight path, and transmit it to the control module; The control module obtains a comprehensive optimization factor of the flight path based on the relevant information of the planning index, risk index, optimization index, coverage efficiency index and complexity index of the UAV flight path, and transmits it to the communication module; The communication module transmits the comprehensive optimization factor of the flight path to the user terminal; The system obtains the comprehensive optimization factor of each flight path and selects the flight path with the smallest comprehensive optimization factor to perform the flight mission.

2. The distribution network drone intelligent inspection and acceptance system based on edge-end collaboration as described in claim 1 is characterized in that: The analysis module includes a flight path selection submodule, a data storage submodule, a weather detection submodule, a flight path risk detection submodule, a flight path optimization submodule and a flight environment detection submodule; The flight path selection submodule is used to select a flight path; The data storage submodule is configured to store the planned value of the flight path and the preset value of the UAV flight speed according to the selected flight path, and is also configured to store the field of view width, and transmit the field of view width, the planned value of the flight path and the preset value of the UAV flight speed to the control module; The meteorological detection submodule is used to detect and obtain the environmental wind speed scoring coefficient, environmental visibility scoring coefficient, environmental precipitation scoring coefficient and environmental temperature scoring coefficient, and transmit them to the control module; The flight path risk detection submodule detects and obtains the obstacle density of the flight path according to the selected flight path, the control module obtains the meteorological condition scoring index according to the environmental wind speed scoring coefficient, the environmental visibility scoring coefficient, the environmental precipitation scoring coefficient and the environmental temperature scoring coefficient, and transmits the index to the flight path risk detection submodule, the flight path risk detection submodule obtains the risk index of the flight path according to the meteorological condition scoring index and the obstacle density of the flight path, and transmits the index to the control module; The flight path optimization submodule is used to set the flight target and obtain the optimization index of the flight path, and transmit it to the control module; The flight environment detection submodule is used to detect and obtain the terrain slope and magnetic field strength according to the selected flight path, obtain the flight environment complexity index according to the terrain slope and magnetic field strength, and transmit it to the control module; The control module obtains a flight coverage efficiency index according to preset values ​​of the field of view width and the flight speed of the drone, and obtains a comprehensive optimization factor of the flight path according to the planned value of the flight path, the risk index of the flight path, the optimization index of the flight path, the flight coverage efficiency index and the flight environment complexity index.

3. The distribution network drone intelligent inspection and acceptance system based on edge-end collaboration as described in claim 2 is characterized in that: The meteorological detection submodule includes a wind speed detection unit, a visibility detection unit, a precipitation detection unit and a temperature detection unit; The wind speed detection unit is used to detect the ambient wind speed and obtain the ambient wind speed scoring coefficient, and transmit it to the control module; The visibility detection unit is used to detect the environmental visibility and obtain the environmental visibility scoring coefficient, and transmit it to the control module; The precipitation detection unit is used to detect the precipitation and obtain the environmental precipitation scoring coefficient, and transmit it to the control module; The temperature detection unit is used to detect the ambient temperature and obtain the ambient temperature rating coefficient, and transmit it to the control module.

4. The distribution network drone intelligent inspection and acceptance system based on edge-end collaboration as described in claim 2 is characterized in that: The flight path risk detection submodule includes an obstacle density analysis unit and a risk detection unit; The obstacle density analysis unit analyzes and obtains the obstacle density of the flight path according to the selected flight path, and transmits the obtained obstacle density to the control module and the risk detection unit; The control module transmits the meteorological condition score index to the risk detection unit; The risk detection unit obtains a risk index of the flight path according to the meteorological condition score index and the obstacle density of the flight path, and transmits the risk index to the control module.

5. The distribution network drone intelligent inspection and acceptance system based on edge-end collaboration as described in claim 2 is characterized in that: The flight environment detection submodule includes a terrain analysis unit, a magnetic field detection unit and an environment analysis unit; The terrain analysis unit analyzes and obtains the terrain slope according to the selected flight path, and transmits the terrain slope to the environment analysis unit; The magnetic field detection unit analyzes and obtains the magnetic field strength according to the selected flight path, and transmits it to the environment analysis unit; The environmental analysis unit obtains a flight environment complexity index based on the terrain slope and the magnetic field strength, and transmits the index to the control module.

6. The distribution network UAV intelligent inspection and acceptance system based on edge-end collaboration as described in claim 2 is characterized in that: When the control module calculates the comprehensive optimization factor of the flight path, the following formula is satisfied: Among them, F opt is the comprehensive optimization factor of the flight path, d is the planned value of the flight path, w is the risk index of the flight path, the value range of the risk index of the flight path is [1.2, 2.1], the larger the risk index, the greater the risk of the current path, α is the optimization index of the flight path, the value range of the optimization index of the flight path is [0.7, 1.5], the value size is in the order of increasing priority according to the actual flight target setting coverage priority, flight target setting energy saving priority, flight target setting inspection efficiency priority, flight target setting data accuracy priority or safety priority requirements, c is the flight coverage efficiency index, e is the flight environment complexity index, the value range of the flight environment complexity index is [1.1, 2.8], the more unsuitable the environment is for flight, the larger the value is.

7. A distribution network UAV intelligent inspection and acceptance method based on edge-end collaboration, applied to a distribution network UAV intelligent inspection and acceptance system based on edge-end collaboration as claimed in claim 1 or 2, characterized in that: The following steps are involved: Step S1: The analysis module analyzes and obtains relevant information of the planning index, risk index, optimization index, coverage efficiency index and complexity index of the UAV flight path, and transmits it to the control module; Step S2: The control module obtains the comprehensive optimization factor of the flight path according to the relevant information of the planning index, risk index, optimization index, coverage efficiency index and complexity index of the UAV flight path, and transmits it to the communication module; Step S3: The communication module transmits the comprehensive optimization factor of the flight path to the user end.

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