A Distribution Network UAV Intelligent Inspection and Acceptance System and Method Based on Edge-Terminal Collaboration

Abstract

The present invention relates to the technical field of intelligent inspection and acceptance systems for unmanned aerial vehicles (UAVs), and particularly relates to a UAV intelligent inspection and acceptance system and method for distribution networks based on edge-cloud collaboration. The system includes an analysis module, a control module, and a communication module. The analysis module is used to analyze and obtain relevant information on 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 the comprehensive optimization factor of the flight path based on the relevant information on 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 control module automatically analyzes and obtains the comprehensive optimization factor of the flight path, reducing manual intervention and achieving a high degree of automation in UAV inspection.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent inspection and acceptance systems for unmanned aerial vehicles, and particularly to a distribution network unmanned aerial vehicle intelligent inspection and acceptance system and method based on edge-cloud collaboration. Background Art

[0002] The intelligent inspection and acceptance system collects data through unmanned aerial vehicles and intelligently analyzes the inspection results on edge devices, thereby realizing efficient and intelligent inspection and automated acceptance processes. Such a system is suitable for the inspection of distribution networks in complex environments and can improve the real-time performance, accuracy, and automation level of inspection and acceptance.

[0003] The application document with the publication number CN206921161U discloses an intelligent inspection system including inspection management, defect handling, and acceptance. The system management platform includes a server and a database function module. The database function 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 a maintenance record module and a camera module. The management handheld terminal includes a on-site acceptance module and a photography module. The passive NFC radio frequency tag is at least one NFC tag installed at the inspection site. The system management platform is respectively connected to the inspection handheld terminal, the maintenance handheld terminal, and the management handheld terminal.

[0004] The prior art relies on handheld terminals and NFC modules for manual inspection and data collection. Inspectors need to manually record information on-site, resulting in low efficiency and a lot of manual intervention. Summary of the Invention

[0005] The purpose of the present invention is to improve efficiency. In view of the above deficiencies, a distribution network unmanned aerial vehicle intelligent inspection and acceptance system and method based on edge-cloud collaboration are proposed.

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

[0007] A distribution network unmanned aerial vehicle intelligent inspection and acceptance system based on edge-cloud collaboration, the system includes an analysis module, a control module, and a communication module; the analysis module is used to analyze and obtain relevant information on the planning index, risk index, optimization index, coverage efficiency index, and complexity index of the unmanned aerial vehicle flight path, and transmit it to the control module; the control module obtains the comprehensive optimization factor of the flight path according to the relevant information on the planning index, risk index, optimization index, coverage efficiency index, and complexity index of the unmanned aerial vehicle 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;

[0008] The system obtains the comprehensive optimization factor of 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 sub-module, a data storage sub-module, a meteorological detection sub-module, a flight path risk detection sub-module, a flight path optimization sub-module, and a flight environment detection sub-module; the flight path selection sub-module is used to select a flight path; the data storage sub-module sets and stores 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 used 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 sub-module is used to detect and obtain the environmental wind speed scoring coefficient, the environmental visibility scoring coefficient, the environmental precipitation scoring coefficient, and the environmental temperature scoring coefficient, and transmit them to the control module; the flight path risk detection sub-module 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 it to the flight path risk detection sub-module, and the flight path risk detection sub-module 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 it to the control module; the flight path optimization sub-module 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 sub-module is used to detect and obtain the terrain slope and magnetic field intensity according to the selected flight path, and obtain the flight environment complexity index according to the terrain slope and magnetic field intensity and transmit it to the control module; the control module obtains the flight coverage efficiency index according to the field of view width and the preset value of the UAV flight speed, and obtains the 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.

[0010] Optionally, the meteorological detection sub-module 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 environmental wind speed and obtain the environmental 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 environmental temperature and obtain the environmental temperature scoring coefficient and transmit it to the control module.

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

[0012] Optionally, the flight environment detection sub-module includes a terrain analysis unit, a magnetic field detection unit, and an environment analysis unit; the terrain analysis unit analyzes the terrain slope based on the selected flight path and transmits it to the environment analysis unit; the magnetic field detection unit analyzes the magnetic field intensity based on the selected flight path and transmits it to the environment analysis unit; the environment analysis unit obtains the flight environment complexity index based on the terrain slope and the magnetic field intensity and transmits it to the control module.

[0013] Optionally, when the control module calculates the comprehensive optimization factor of the flight path, the following formula is satisfied:

[0014] where, 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], and the value size is set according to the actual flight target, which is in the order of coverage priority, energy consumption saving priority, inspection efficiency priority, data accuracy priority, or safety priority requirements set by the flight target and increases gradually, c is the coverage efficiency index of the flight, e is the flight environment complexity index, the value range of the flight environment complexity index is [1.1, 2.8], and the more unsuitable the environment for flight, the larger the value.

[0015] This embodiment also provides a method for intelligent inspection and acceptance of distribution network unmanned aerial vehicles based on edge-end collaboration, which is applied to a system for intelligent inspection and acceptance of distribution network unmanned aerial vehicles based on edge-end collaboration, and includes the following steps: Step S1: The analysis module analyzes and obtains the relevant information of the planned index, risk index, optimization index, coverage efficiency index, and complexity index of the unmanned aerial vehicle flight path and transmits it to the control module; Step S2: The control module obtains the comprehensive optimization factor of the flight path based on the relevant information of the planned index, risk index, optimization index, coverage efficiency index, and complexity index of the unmanned aerial vehicle 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 the present invention are as follows:

[0017] 1. The control module automatically analyzes and obtains the comprehensive optimization factor of 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" and combines it with indicators such as path planning, risk, and coverage efficiency, enabling intelligent decision-making for the UAV flight path and automatic recording of acceptance data, improving the accuracy and consistency of the acceptance work.

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

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

[0021] Figure 2 It is a schematic diagram of the structure of the meteorological detection sub-module in the present invention;

[0022] Figure 3 It is a schematic diagram of the structure of the flight path risk detection sub-module in the present invention;

[0023] Figure 4 It is a schematic diagram of the structure of the flight environment detection sub-module in the present invention;

[0024] Figure 5 It is a relationship diagram of the present invention;

[0025] Figure 6 It is a method flow chart of the present invention;

[0026] Figure 7 It is a schematic diagram of the overall structure of the second embodiment of the present invention;

[0027] Figure 8 It is a schematic diagram of the structure of the flight safety analysis module in the present invention;

[0028] Figure 9 It is a relationship diagram of the second embodiment of the present invention. Detailed Embodiments

[0029] The following are specific embodiments to illustrate the implementation manners 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. 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. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions. It is hereby stated in advance. The following implementation manners will further detail the relevant technical content of the present invention, but the disclosed content is not intended to limit the protection scope of the present invention.

[0030] Embodiment 1: This embodiment provides a smart inspection and acceptance system for distribution network unmanned aerial vehicles (UAVs) based on edge-cloud collaboration, combined with Figures 1 to 6 as shown.

[0031] A smart inspection and acceptance system for distribution network UAVs based on edge-cloud collaboration, the system includes an analysis module, a control module, and a communication module; the analysis module is used to analyze and obtain the 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 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; the communication module transmits the comprehensive optimization factor of the flight path to the user terminal;

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

[0033] Optionally, the analysis module includes a flight path selection sub-module, a data storage sub-module, a meteorological detection sub-module, a flight path risk detection sub-module, a flight path optimization sub-module, and a flight environment detection sub-module; the flight path selection sub-module is used to select a flight path; the data storage sub-module sets and stores 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 used to store the field of view width, and transmits 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 sub-module is used to detect and obtain the environmental wind speed scoring coefficient, the environmental visibility scoring coefficient, the environmental precipitation scoring coefficient, and the environmental temperature scoring coefficient, and transmits them to the control module; the flight path risk detection sub-module 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 it to the flight path risk detection sub-module, and the flight path risk detection sub-module 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 it to the control module; the flight path optimization sub-module is used to set a flight target and obtain the optimization index of the flight path, and transmits it to the control module; the flight environment detection sub-module is used to detect and obtain the terrain slope and magnetic field strength according to the selected flight path, and obtains the flight environment complexity index according to the terrain slope and magnetic field strength, and transmits it to the control module; the control module obtains the flight coverage efficiency index according to the field of view width and the preset value of the UAV flight speed, and obtains the 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.

[0034] Optionally, the meteorological detection sub-module 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 environmental wind speed and obtain the environmental wind speed scoring coefficient, and transmits it to the control module; the visibility detection unit is used to detect the environmental visibility and obtain the environmental visibility scoring coefficient, and transmits it to the control module; the precipitation detection unit is used to detect the precipitation and obtain the environmental precipitation scoring coefficient, and transmits it to the control module; the temperature detection unit is used to detect the environmental temperature and obtain the environmental temperature scoring coefficient, and transmits it to the control module.

[0035] Optionally, the flight path risk detection sub-module 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 it to the control module and the risk detection unit; the control module transmits the meteorological condition scoring index to the risk detection unit; the risk detection unit 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 it to the control module.

[0036] Optionally, the flight environment detection sub-module 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 it to the environment analysis unit; the magnetic field detection unit analyzes and obtains the magnetic field intensity according to the selected flight path, and transmits it to the environment analysis unit; the environment analysis unit obtains the flight environment complexity index according to the terrain slope and the magnetic field intensity, and transmits it to the control module.

[0037] Optionally, when the control module calculates the comprehensive optimization factor of the flight path, the following formula is satisfied:

[0038]

[0039] where, 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], and the value size is set according to whether the actual flight target is coverage priority, energy consumption saving priority, inspection efficiency priority, data accuracy priority, or safety priority requirements in increasing order, c is the coverage efficiency index of the flight, e is the flight environment complexity index, and the value range of the flight environment complexity index is [1.1, 2.8], and the more unsuitable the environment for flight, the larger the value.

[0040] Preferably, the risk indicators of the flight path have the following values respectively: 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 per 100 meters (i.e., less than 2 obstacles within 100 meters) and the meteorological condition scoring index is greater than 80 points. In other cases, w = 2.1. The optimization indicators of the flight path have the following values respectively: α = 0.7 or α = 0.8 or α = 1.2 or α = 1.3 or α = 1.5. When α = 0.7, it means that the flight target is set to prioritize coverage. When α = 0.8, it means that the flight target is set to prioritize energy conservation. When α = 1.2, it means that the flight target is set to prioritize inspection efficiency. When α = 1.3, it means that the flight target is set to prioritize data accuracy. When α = 1.5, it means that safety is prioritized. Which one is preferred is specifically set by the staff in advance according to actual needs. The complex environment indicators of the flight have the following values respectively: 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 intensity of the flight path is less than 80 microteslas. In other cases, e = 2.8.

[0041] Optionally, when the control module calculates, the following formula is satisfied:

[0042] c = v × fov; where, mi is the meteorological condition scoring index, fs is the environmental wind speed scoring coefficient, and the environmental wind speed scoring coefficient has the following values: fs = 100 or fs = 90 or fs = 80 or fs = 70 or 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 m / s. When fs = 80, it means the wind speed is 5 m / s. When fs = 70, it means the wind speed is greater than 5 and less than 10 m / s. When fs = 50, it means the wind speed is 10 m / s. When fs = 30, it means the wind speed is greater than 10 m / s. njd is the environmental visibility scoring coefficient, and the environmental visibility scoring coefficient has the following values: njd = 100 or njd = 80 or njd = 60 or njd = 50. When njd = 100, it means the visibility is greater than or equal to 10 km. When njd = 80, it means the visibility is greater than or equal to 5 and less than 10 km. When njd = 60, it means the visibility is greater than 2 and less than 5 km. When njd = 50, it means the visibility is less than or equal to 2 km. js is the environmental precipitation scoring coefficient, and the environmental precipitation scoring coefficient has the following values: js = 90 or js = 70 or js = 50. When js = 90, it means the precipitation is less than or equal to 1 mm / h. When js = 70, it means the precipitation is greater than 1 and less than 2 mm / h. When js = 50, it means the precipitation is greater than or equal to 2 mm / h. wd is the environmental temperature scoring coefficient, and the environmental temperature scoring coefficient has the following values: wd = 95 or wd = 70 or wd = 40. When wd = 95, it means the environmental temperature is greater than or equal to 10 °C and less than or equal to 35 °C. When wd = 70, it means the environmental temperature is greater than 0 °C and less than 10 °C or greater than 35 °C and less than 40 °C. In other cases, wd = 40; v is the field of view width, and fov is the preset value of the UAV flight speed.

[0043] When the control module calculates the comprehensive optimization factor of 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 of the flight path. The smaller the value of the comprehensive optimization factor of the flight path, the better it is. By obtaining the flight path with the smallest comprehensive optimization factor for flight, the inspection time is reduced and the task safety is ensured; the unit of the planned value of the flight path is meters, and the planned value is the travel length required from the starting point to the ending point, and the distance between waypoints is calculated in real time through the GPS of the UAV, which belongs to the preset of the path before flight; regarding the setting of the optimization index of the flight path, one of the fixed values will be explained in detail below: when the inspection efficiency is prioritized, the value of the optimization index corresponding to the flight path is relatively high. The reason is that high efficiency means collecting more data per unit time, which may cause data backlog during the inspection of the UAV and bring a burden to the edge processing system. Data backlog and lag will affect the real-time nature of the inspection; the unit of the field of view width is meters, which is determined by the specifications of the shooting components corresponding to the UAV; the preset value of the UAV flight speed is in meters per second; when calculating the meteorological condition scoring index, since there are various reasons that will affect the flight of the UAV or the normal operation of the internal components, multiple coefficients are introduced. The coefficients are given as an interval range, and those skilled in the art will set the specific values of the coefficients according to actual data and experience.

[0047] This embodiment also provides a method for intelligent inspection and acceptance of distribution network UAVs based on edge-end collaboration, which is applied to an intelligent inspection and acceptance system for distribution network UAVs based on edge-end collaboration, and includes the following steps: Step S1: The analysis module analyzes and obtains the relevant information of the planned 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 based on the relevant information of the planned 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 terminal.

[0048] The above units are only examples, and 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 of traditional intelligent inspection and acceptance systems. The control module automatically analyzes and obtains the comprehensive optimization factor of the flight path, reduces manual intervention, and realizes highly automated UAV inspection.

[0050] Embodiment 2: This embodiment includes all the contents of Embodiment 1 and provides an intelligent inspection and acceptance system for distribution network UAVs based on edge-end collaboration, combined with Figures 7 to 9 as shown.

[0051] A power distribution network UAV intelligent inspection and acceptance system based on edge-cloud collaboration, the system further includes a flight safety analysis module;

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

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

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

[0055] The information storage sub-module is used to store the minimum flight altitude of the UAV, the default safety distance, the maximum value of the obstacle density during UAV flight, and the maximum value of the UAV flight speed, and transmit them to the calculation sub-module;

[0056] The obstacle detection sub-module is used to detect and obtain the measured value of the obstacle density and the obstacle detection radius during UAV flight, and transmit them to the calculation sub-module;

[0057] The speed detection sub-module is used to detect and obtain the measured value of the UAV flight speed, and transmit it to the calculation sub-module;

[0058] The calculation sub-module obtains the flight speed influence index according to the measured value of the UAV flight speed and the maximum value of the UAV flight speed, obtains the obstacle influence index according to the measured value of the obstacle density during UAV flight and the maximum value of the obstacle density during UAV flight, and according to the minimum flight altitude of the UAV, the obstacle influence index, the obstacle detection radius, the default safety distance, the flight speed influence index and the measured value of the UAV flight speed, obtains the dynamic value of the UAV flight safety distance, and transmits it to the communication module.

[0059] Optionally, when the calculation sub-module calculates the dynamic value of the UAV flight safety distance, it satisfies the following formula:

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

[0061]

[0062] Wherein, D safe is the dynamic value of the UAV flight safety distance, h min is the minimum flight altitude of the UAV, k1 is the obstacle influence index, r is the obstacle detection radius, s m is the default safety distance, k2 is the flight speed influence index, sv ois the measured value of the flight speed of the drone;

[0063] md o is the measured value of the obstacle density when the drone is flying, md max is the maximum value of the obstacle density when the drone is flying; sv max is the maximum value of the flight speed of the drone.

[0064] When the calculation sub-module calculates the dynamic value of the flight safety distance of the drone, refer to the following program code:

[0065]

[0066]

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

[0068] Specifically, the unit of the dynamic value of the flight safety distance of the drone is meters. The dynamic value of the safety distance needs to be kept within a preset threshold range. Calculating the dynamic value of the flight safety distance of the drone can adjust the flight data of the drone in real time to ensure flight safety. Specifically, according to the change of the dynamic value of the flight safety distance of the drone, the flight altitude of the drone is adjusted in real time to keep the drone within the safe altitude range; the unit of the minimum flight altitude of the drone is meters, mainly the vertical altitude limit to ensure the safe distance between the drone and the ground, which is obtained by those skilled in the art according to geographic information data or lidar testing; the unit of the obstacle detection radius is meters, which is obtained by actual detection during the flight of the drone; the unit of the default safety distance is meters, and the default safety distance refers to the minimum horizontal safety distance that the drone always maintains during flight, that is, the minimum horizontal distance between the drone and other objects or the ground without special obstacles, which can be understood as a horizontal distance buffer to ensure the obstacle avoidance ability of the drone on the horizontal plane; the measured value of the flight speed of the drone is meters per second; the units of the measured value of the obstacle density when the drone is flying and the maximum value of the obstacle density when the drone is flying are both per 100 meters, and the maximum value of the obstacle density when the drone is flying is set by those skilled in the art; the unit of the maximum value of the flight speed of the drone is meters per second, which is set by those skilled in the art.

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

[0070] This embodiment solves the problem that the traditional intelligent inspection and acceptance system is relatively single. The flight safety analysis module enables the drone to adjust the distance in real time during the inspection process by calculating the dynamic flight safety distance of the drone, 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 does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated.

Claims

1. An intelligent inspection and acceptance system for distribution network UAVs 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 on 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 the comprehensive optimization factor of the flight path based on the relevant information on 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 execute the flight mission; The analysis module includes a flight path selection sub-module, a data storage sub-module, a meteorological detection sub-module, a flight path risk detection sub-module, a flight path optimization sub-module, and a flight environment detection sub-module; The flight path selection sub-module is used to select the flight path; The data storage sub-module sets and stores the planning value of the flight path and the preset value of the UAV flight speed according to the selected flight path, and is also used to store the field of view width, and transmits the field of view width, the planning value of the flight path, and the preset value of the UAV flight speed to the control module; The meteorological detection sub-module 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 sub-module 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 based on the environmental wind speed scoring coefficient, environmental visibility scoring coefficient, environmental precipitation scoring coefficient, and environmental temperature scoring coefficient and transmits it to the flight path risk detection sub-module. The flight path risk detection sub-module obtains the risk index of the flight path based on the meteorological condition scoring index and the obstacle density of the flight path, and transmits it to the control module; The flight path optimization sub-module 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 sub-module is used to detect and obtain the terrain slope and magnetic field intensity according to the selected flight path, and obtain the environmental complexity index of the flight based on the terrain slope and magnetic field intensity, and transmit it to the control module; The control module obtains the coverage efficiency index of the flight based on the field of view width and the preset value of the UAV flight speed, and obtains the comprehensive optimization factor of the flight path based on the planning value of the flight path, the risk index of the flight path, the optimization index of the flight path, the coverage efficiency index of the flight, and the environmental complexity index of the flight.

2. The intelligent inspection and acceptance system for distribution network UAVs based on edge-end collaboration according to claim 1, characterized in that, The meteorological detection sub-module 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 environmental wind speed and obtain the environmental 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, obtain the ambient temperature scoring coefficient, and transmit it to the control module.

3. The intelligent inspection and acceptance system for distribution network UAVs based on edge-terminal collaboration according to claim 1, wherein, The flight path risk detection sub-module includes an obstacle density analysis unit and a risk detection unit; The obstacle density analysis unit analyzes the selected flight path to obtain 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 scoring index to the risk detection unit; The risk detection unit obtains the risk index of the flight path based on the meteorological condition scoring index and the obstacle density of the flight path, and transmits it to the control module.

4. The intelligent inspection and acceptance system for distribution network UAVs based on edge-terminal collaboration according to claim 1, wherein, The flight environment detection sub-module includes a terrain analysis unit, a magnetic field detection unit, and an environment analysis unit; The terrain analysis unit analyzes the selected flight path to obtain the terrain slope, and transmits it to the environment analysis unit; The magnetic field detection unit analyzes the selected flight path to obtain the magnetic field intensity, and transmits it to the environment analysis unit; The environment analysis unit obtains the flight environment complexity index based on the terrain slope and the magnetic field intensity, and transmits it to the control module.

5. The intelligent inspection and acceptance system for distribution network UAVs based on edge-end collaboration according to claim 1, wherein When the control module calculates the comprehensive optimization factor of the flight path, the following formula is satisfied: ； Among them, is the comprehensive optimization factor of the flight path, is the planned value of the flight path, 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. 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 increases in the order of the priority requirements of the coverage range first, energy consumption saving first, inspection efficiency first, data accuracy first, or safety first set by the actual flight target. is the coverage efficiency index of the flight, is the environmental complexity index of the flight. The value range of the environmental complexity index of the flight is [1.1, 2.8]. The more unsuitable the environment for flight, the larger the value.

6. A method for intelligent inspection and acceptance of distribution network UAVs based on edge-end collaboration, which is applied to the intelligent inspection and acceptance system of distribution network UAVs based on edge-end collaboration described in claim 1 or 2, and is characterized in that, It includes the following steps: Step S1: The analysis module analyzes and obtains 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 control module; Step S2: The control module obtains the 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; Step S3: The communication module transmits the comprehensive optimization factor of the flight path to the user terminal.

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