Multi-task regulation and control system based on distribution network inspection and working method of multi-task regulation and control system

Abstract

A multi-task regulation and control system based on distribution network inspection comprises a task division and unloading mode selection system, an unmanned aerial vehicle inspection route generation system and multi-model unmanned aerial vehicles. The task division and unloading mode selection system is used for dividing automatically generated tasks and manually input tasks into task types according to a time limit degree, selecting unloading of unmanned aerial vehicle tasks according to the task types and calculating; according to the distribution network inspection-based multi-task regulation and control system and the working method thereof, task types of inspection tasks are refined, proper task unloading modes of the inspection tasks are selected, the current meteorological environment of a task area is obtained through a meteorological remote sensing satellite, and task execution models are selected, so that the task execution efficiency is improved. And in combination with the geographic features of the task area acquired by the land remote sensing satellite and the planning information of the power distribution network, a 3D model is established for route simulation, so that the sailing safety of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network inspection, and in particular to a multi-task control system based on distribution network inspection and a working method thereof. Background Art

[0002] With the continuous development of society and the rise of new energy, the distribution network plays a very important role. The smooth operation of its transmission lines is particularly important. With the continuous development and maturity of transmission technology, the density of the distribution network continues to increase, resulting in an increasing workload for inspection staff. With the gradual maturity of drone technology and the application of 5G communication technology, most areas use drones to replace manual inspections of distribution networks, which not only reduces the workload of inspection staff, but also provides convenience for the monitoring of distribution networks. However, the current drone inspection technology used for distribution networks has some defects, which are specifically reflected in the following aspects: (1) The models selected for current inspection tasks rarely pay attention to the meteorological conditions in the mission area, resulting in the drone's inability to adapt well to the regional meteorological conditions, thereby increasing the drone's operating energy consumption, shortening the inspection range, and reducing the drone's inspection life; (2) During the current mission execution, the pursuit of short time limits is blindly pursued, thereby increasing the drone's wireless transmission energy consumption and reducing the drone's endurance. It ignores the fact that some tasks have lower time requirements and pay more attention to the requirements of the cruising range; (3) In terms of flight obstacle avoidance, the current drones mainly focus on adding various sensors to the drone end to perform real-time obstacle avoidance, and the obstacle avoidance effect is significant, but various obstacle avoidance sensors greatly reduce the drone's endurance, ignoring the need to avoid obstacles in advance during the flight route planning process. In this way, while ensuring the safety of the drone's flight, the installation of obstacle avoidance sensors can be appropriately reduced, thereby enhancing the drone's endurance and inspection range.

[0003] Chinese patent CN 115209379A discloses a power grid cloud-edge collaborative inspection system and method based on 5G intelligent networked drones, including a cloud-based drone intelligent inspection system and a drone, and the two are connected for communication through a network transmission layer. It uses uRLLC slicing in the network transmission layer to meet the needs of low-latency and high-reliability communication, but it shortens the drone's inspection endurance and reduces the range of a single inspection, ignoring the fact that compared to low latency, daily inspection tasks tend to increase the range of a single inspection.

[0004] Chinese patent CN 116700357A discloses an intelligent inspection method for an overhead power transmission line UAV formation, including inspection equipment layout statistics, basic information extraction, basic information pre-analysis, limited inspection UAV formation condition information integration, target inspection overhead power transmission line status identification and status abnormality feedback. The present invention analyzes the inspection suitability of the inspection UAV itself based on the meteorological information of the monitoring area, and can make the actually called inspection UAV coordinate and adapt to the regional meteorological conditions. It will not only reduce the application loss of the inspection UAV, further ensure the service life of the inspection UAV, but also effectively avoid the defects caused by the inability to timely discover the status safety hazards of the overhead power transmission line. However, it does not take into account whether the model is suitable for performing tasks in the current mission environment and whether it can complete obstacle avoidance, thereby causing property losses. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-task control system based on distribution network inspection and its working method. By refining the task type of the inspection task and selecting its appropriate task offloading method, the current meteorological environment of the task area is obtained through meteorological remote sensing satellites, and the task execution model is selected to reduce the energy consumption of the system. In addition, a 3D model is established to simulate the route by combining the geographical features of the task area obtained by land remote sensing satellites with the distribution network planning information, thereby improving the safety of UAV navigation.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a multi-task control system based on distribution network inspection, including: a task division and unloading mode selection system, a drone inspection route generation system and multiple models of drones; The task division and offloading mode selection system is used to divide the automatically generated tasks and manually input tasks into task types according to the time limit, and select the unloading of drone tasks according to the task type and calculate; The UAV inspection route generation system includes a UAV patrol airspace application module, a UAV task management module, and a UAV inspection route simulation and training module; The drone inspection airspace application module is used by drone patrol airspace applicants to declare drone patrol areas; The UAV inspection route simulation and training module includes a route 3D simulation module, an inspection route planning module, a mission execution model selection module, a cruise environment 3D module, and a historical route database; The multiple types of drones include rotor inspection drones, fixed-wing inspection drones and airship computing drones; wherein the airship computing drone is equipped with a mobile edge computing server.

[0007] Preferably, the tasks automatically generated by the system include routine inspections of the entire inspection area at a system-set period and emergency tasks automatically triggered by bad weather or accident alarms.

[0008] Preferably, the task types include routine tasks, emergency tasks and designated tasks, and the corresponding unloading modes are local unloading, airship edge unloading and gap unloading.

[0009] A working method of a multi-task control system based on distribution network inspection comprises the following steps: S1. The system divides the received tasks and selects the unloading method accordingly; S2. After receiving the task, the UAV task management module conducts airspace planning and application, selects the task model through the task model selection module, and reads the historical routes of this task through the historical route database. Then, the inspection route planning module plans the route according to the specific task, and finally simulates the route in the route 3D simulation module to reduce collisions.

[0010] Preferably, step S1 comprises the following steps: S1.1, the system divides the received tasks into routine tasks, urgent tasks and designated tasks, and the routine tasks have a longer time limit, so the local unloading method is selected, while the urgent tasks have the shortest time limit, so the airship edge unloading method is selected, and the designated tasks have a moderate time limit, so the intermittent unloading method is selected; S1.2. After the task division is completed and the unloading mode is selected, the system inputs the results into the drone task management module in the drone cruise route generation module.

[0011] Preferably, the step S2 specifically includes the following steps: S2.1. The applicant for drone patrol airspace shall make a plan in the drone inspection airspace planning module and apply for drone inspection operation airspace; S2.2, after receiving the information from the drone management module, the task execution model selection module obtains the temperature, humidity, wind speed and other meteorological conditions of the task area through meteorological satellite remote sensing technology, and at the same time compares the characteristic parameters of various types of inspection drones in the database to obtain the most suitable model for this task; S2.3, the cruise environment 3D module obtains the soil coverage, soil utilization rate and location information of herbaceous plants in the mission area through the land satellite remote sensing system, and at the same time obtains the location and information of each inspection task point in the mission area through the distribution network planning information stored in the database, and combines the obtained information to establish a 3D model of the mission area; S2.4. The historical route database retrieves historical routes to provide data support for the route planning module, which then optimizes the route to obtain a route suitable for the current task; S2.5, the route 3D simulation module combines the mission aircraft model, route planning, and environmental 3D model to simulate the flight of the route to obtain the optimal route.

[0012] Preferably, in step S1.1, the task offloading mode selection and multiple task offloading methods of the drone are performed according to the time requirements of the task, and the specific contents are as follows: 1) The time limit requirement of a task refers to the time it takes from the generation of the task to the acquisition of the processing result, which includes transmission time, propagation time and processing time. The time limit requirements vary depending on the task, as follows: Routine tasks: As daily inspection tasks, drones are required to collect images of metal parts such as poles and insulators, and line problems such as conductor wear, and then analyze the operation of the distribution network in the area. However, since the poles and towers are too loosely distributed, compared with the short time limit, it pays more attention to the commuting range of drones. Therefore, by reducing the energy consumption of wireless transmission, that is, selecting the local unloading mode, the endurance of the drone can be improved and its inspection range can be expanded; Emergency tasks are tasks that are automatically triggered by harsh environments or even safety accidents. To prevent major production safety accidents, they are more focused on short time limits than commuting ranges. Therefore, the airship edge unloading mode is adopted to minimize the time limit and reduce accident losses. Designated missions are specific missions with specific mission objectives, so the drone can fly directly to the mission point. The time limit requirement is as important as the commuting range, so the intermittent unloading method is selected; 2) Selecting the unloading mode means that the drone selects the mission data unloading mode, which is described as follows: To simplify the model, the energy consumption of the inspection drone described in the present invention includes the operating energy consumption and wireless transmission energy consumption, and no data calculation and processing is performed; the operating energy consumption, including the energy consumption of each type of inspection drone in several time slots in each time domain, is a fixed value. ; The wireless transmission energy consumption includes the downlink traffic of the inspection drone, ignoring its uplink traffic, and its downlink traffic increases with the increase of transmission distance and data; Local offloading means that the inspection drone does not transmit data during the mission execution. After the mission is completed, when the inspection drone flies back to the nest, the data will be transmitted to the tower server; Airship edge unloading means that the inspection drone and the airship computing drone perform tasks at the same time. The airship computing drone is equipped with an edge service processor. The inspection drone enters the mission area to collect data, and then transmits the data to the airship computing drone. Different inspection drones send and receive information through the same channel. The airship drone hovers outside the mission area and waits for the inspection drone to transmit data. After receiving the data, the airship computing drone processes the data and performs simple calculations and analysis, and sends the processed data to the tower through different channels. Intermittent unloading means that the inspection drone sends data intermittently within the mission execution time domain, so as to balance the relationship between time limit requirements and cruising range.

[0013] Preferably, in step S2.2, the most suitable model for this task is obtained as follows: basic information of each model of drone is collected, including characteristic parameters and state stability parameters of the drone, and the most suitable environmental parameters are obtained by analysis. ,in To achieve the best temperature for the task, To achieve the best air pressure for the mission, To perform the mission with the best wind speed, Optimal humidity for mission execution; Meteorological satellite remote sensing technology can provide the current environmental parameters of the mission area. , sent to the task execution model selection module, and compared with the best fit index of the inspection environment of each type of UAV to obtain the best execution model for the current task.

[0014] Preferably, the step S2.3 comprises the following steps: S2.3.1, determine the mission area; S2.3.2. Based on land satellite remote sensing technology, obtain the geomorphic features, soil utilization rate, and location information of vegetation in the mission area, and establish a 3D map model; S2.3.3. Obtain the location information of the task point in the current task area through the distribution network planning information; S2.3.4. Integrate the mission point information with the 3D map model to establish a 3D model of the mission area.

[0015] Preferably, the step S2.5 comprises the following steps: S2.5.1. The route 3D simulation module uses the 3D model of the mission area generated by the cruise environment 3D module as the simulation background; S2.5.2, the route 3D simulation module uses the mission execution aircraft model selected by the mission execution aircraft model selection module as the simulation object; S2.5.3. The route 3D simulation module uses the optimal route planned by the cruise route planning module as the flight route as the simulation path: S2.5.4. During the simulation, if an aircraft collides, the route 3D simulation module will send the collision location information to the cruise route planning module, which will optimize the route; S2.5.5. After the route is optimized, the simulated flight is performed again to obtain the best route.

[0016] The present invention provides a multi-task control system based on distribution network inspection and a working method thereof, which has the following beneficial effects: 1. Compared with the existing technology, different inspection tasks have different time limit requirements and endurance requirements. By adopting detailed task division, the task data unloading mode suitable for the task is selected, thereby reducing unnecessary energy consumption and completing the task better; 2. Considering that the meteorological environment in the mission area has a certain negative impact on the operating energy consumption of different types of drones, the meteorological parameters in the current mission area are obtained through meteorological remote sensing satellites, and compared with the best matching parameters of the inspection environment of each type of drone, so as to obtain the type of drone with the lowest negative impact on the operating energy consumption of the current environment, which will be used as the execution model of this mission; 3. When setting the initial inspection route, the specific number of mission points and the longitude and latitude of the mission points are obtained through the planning information of the distribution network. The soil information of the mission area and the surrounding herbaceous information are obtained through land remote sensing satellites. Various information are further combined to establish a 3D model of the mission area, simulate the UAV flight, optimize the route according to the terrain in the mission area, obtain the optimal mission execution route, and improve the safety of the UAV route flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a structural schematic diagram of the present invention; Figure 2 It is a flowchart of the autonomous triggering and task execution of emergency tasks of the present invention; Figure 3 This is a system model diagram of the airship edge unloading mode of the present invention. DETAILED DESCRIPTION

[0018] like Figure 1 As shown, a multi-task control system based on distribution network inspection includes: a task division and unloading mode selection system, a drone inspection route generation system and multiple models of drones; The task division and offloading mode selection system is used to divide the automatically generated tasks and manually input tasks into task types according to the time limit, and select the unloading of drone tasks according to the task type and calculate; The UAV inspection route generation system includes a UAV patrol airspace application module, a UAV task management module, and a UAV inspection route simulation and training module; The UAV inspection route generation system includes a UAV cruise airspace application module, a UAV task management module, and a UAV inspection route simulation and training module. The UAV task management module is responsible for receiving the task signal sent by the task division and unloading mode selection system, and synchronously sending it to the UAV cruise airspace application module and the UAV inspection route simulation and training module to ensure that the task can be completed efficiently and accurately. The UAV cruise airspace application module is a prerequisite for the UAV inspection task, which provides a legal and safe flight space for the UAV. After receiving the task signal, the UAV inspection route simulation and training module automatically plans the path, and simulates the flight before the actual cruise to optimize the cruise path in real time. The three modules together constitute the UAV inspection route generation system, which are interrelated and supportive of each other to ensure that the UAV can perform the inspection task safely and efficiently.

[0019] The drone inspection airspace application module is used by drone patrol airspace applicants to declare drone patrol areas; The UAV inspection route simulation and training module includes a route 3D simulation module, an inspection route planning module, a mission execution model selection module, a cruise environment 3D module, and a historical route database; The UAV inspection route simulation and training module includes a route 3D simulation module, an inspection route planning module, a task execution model selection module, a cruise environment 3D module, and a historical route database. After the UAV inspection route simulation and training module receives the task, the cruise environment 3D module obtains the topography of the task area by calling the land satellite remote sensing system, and obtains the latitude and longitude of the inspection object in the task area according to the distribution network planning information, so as to provide a simulated environment for the UAV flight; the inspection route planning module matches the current task historical route in the historical route database for reference and plans the cruise route; the task execution model selection module obtains the weather conditions in the task area through meteorological satellite remote sensing technology, and compares the characteristic parameters of each model of inspection UAV in the database to obtain the model most suitable for this task. The route 3D simulation module synchronously receives information from other modules, performs task simulation, and adjusts the route synchronously. Each submodule is interrelated, providing strong support for the execution of actual tasks.

[0020] The multiple types of drones include rotor inspection drones, fixed-wing inspection drones and airship computing drones; wherein the airship computing drone is equipped with a mobile edge computing server.

[0021] Preferably, the tasks automatically generated by the system include routine inspections of the entire inspection area at a system-set period and emergency tasks automatically triggered by bad weather or accident alarms.

[0022] Preferably, the task types include routine tasks, emergency tasks and designated tasks, and the corresponding unloading modes are local unloading, airship edge unloading and gap unloading.

[0023] A working method of a multi-task control system based on distribution network inspection: S1: The system divides the received tasks and selects the unloading method. The process is as follows: S1.1: The system divides the received tasks into routine tasks, urgent tasks and designated tasks. The routine tasks have a longer time limit, so the local unloading method is selected. The urgent tasks have the shortest time limit, so the airship edge unloading method is selected. The designated tasks have a moderate time limit, so the intermittent unloading method is selected. S1.2: After the task division is completed and the unloading mode is selected, the system inputs the results into the UAV task management module in the UAV cruise route generation module; S2: After receiving the task, the UAV task management module performs airspace planning and application, selects the task model through the task model selection module, and reads the historical route of this task through the historical route database. Then, the inspection route planning module performs route planning according to the specific task, and finally performs route simulation in the route 3D simulation module to reduce collisions. The specific process is as follows: S2.1 The applicant for drone patrol airspace shall make plans in the drone inspection airspace planning module and apply for drone inspection operation airspace; S2.2 After receiving the information from the UAV management module, the task execution model selection module obtains the temperature, humidity, wind speed and other meteorological conditions of the task area through meteorological satellite remote sensing technology, and at the same time compares the characteristic parameters of various types of inspection UAVs in the database to obtain the most suitable model for this task; S2.3 The cruise environment 3D module obtains the soil coverage, soil utilization rate and location information of herbaceous plants in the mission area through the land satellite remote sensing system, and at the same time obtains the location and information of each inspection task point in the mission area through the distribution network planning information stored in the database, and combines the obtained information to establish a 3D model of the mission area; S2.4 The historical route database retrieves historical routes to provide data support for route planning by the inspection route planning module, which then optimizes the route to obtain a route suitable for the current task; The S2.5 route 3D simulation module combines the mission aircraft type, route planning, and environmental 3D model to simulate the flight of the route to obtain the optimal route.

[0024] Preferably, in step S1.1, the task offloading mode and multiple task offloading methods of the drone are selected according to the time requirements of the task, and the specific contents are as follows: (1) The time limit requirement of the task refers to the time it takes from the generation of the task to the acquisition of the processing result, which includes transmission time, propagation time and processing time. The time limit requirements vary depending on the task, as described below: Routine tasks: As daily inspection tasks, drones are mainly required to collect images of metal parts such as poles and insulators, and line problems such as conductor wear, and then analyze the operation of the distribution network in the area. However, since the poles and towers are too loosely distributed, they pay more attention to the commuting range of drones than to the short time limit. Therefore, by reducing the energy consumption of wireless transmission, that is, selecting the local unloading mode, the endurance of the drone can be improved and its inspection range can be expanded; Emergency tasks are tasks that are automatically triggered by harsh environments or even safety accidents. To prevent major production safety accidents, they are more focused on short time limits than commuting ranges. Therefore, the airship edge unloading mode is adopted to minimize the time limit and reduce accident losses. Designated tasks are specific tasks with specific mission objectives, so the drone can fly directly to the mission point. The time limit requirement is as important as the commuting range, so the intermittent unloading method is selected.

[0025] (2) The selected unloading mode refers to the drone selecting the task data unloading mode, which is described as follows: To simplify the model, the energy consumption of the inspection drone described in the present invention only includes the operating energy consumption and wireless transmission energy consumption, and does not perform data calculation and processing; the operating energy consumption of each type of inspection drone mentioned in the present invention is a fixed value in several time slots in each time domain. ; In terms of wireless transmission energy consumption, the present invention mainly focuses on the uplink traffic of the inspection drone, ignoring its downlink traffic, and its uplink traffic increases with the increase of transmission distance and data; Local offloading means that the inspection drone does not transmit data during the mission execution. After the mission is completed, when the inspection drone flies back to the nest, the data will be transmitted to the tower server; Airship edge unloading means that the inspection drone and the airship computing drone perform tasks at the same time. The airship computing drone is equipped with an edge service processor. The inspection drone enters the mission area to collect data, and then transmits the data to the airship computing drone. Different inspection drones send and receive information through the same channel. The airship drone hovers outside the mission area and waits for the inspection drone to transmit data. After receiving the data, the airship computing drone processes the data and performs simple calculations and analysis, and sends the processed data to the tower through different channels, such as Figure 3 As shown; Intermittent unloading means that the inspection drone sends data intermittently within the mission execution time domain, so as to balance the relationship between time limit requirements and cruising range.

[0026] Preferably, the downlink traffic increases with the increase of transmission distance and data. The specific contents are as follows: Assume that the inspection time of the drone is ,Will Divide evenly into The length is The set of time slots is defined as , the total amount of task data transmission is D; Assume that the UAV flight altitude is fixed at , in the time slot When the drone Horizontal coordinate , the flight angle is , the flight distance is , then the next time slot and The coordinates are: ; ; Assume the location of the task offloading server is , then the distance between the drone and the task offloading server is: ; The UAV of the present invention adopts an orthogonal frequency division scheme and a line-of-sight link for communication. The loss of other channels is not considered. The path loss between the UAV and the server is ; Where: is the additional attenuation factor added to the free space propagation model of the LoS link, According to the free space path calculation model, we can get: ; In the formula is the system carrier frequency, UAV To the server The communication gain between is: ; remember For drones The transmission power of the drone is , the server bandwidth is , then the speed at which the drone transmits data to the offloading server is: ; The transmission time and energy consumption from the drone to the offloading server are: ; ; That is, the wireless transmission energy consumption of the drone increases as the distance between the drone and the server increases. At the same time, the larger the amount of data, the greater the energy consumption required for transmission, and the transmission speed will also increase.

[0027] Preferably, the specific contents of step S2.2 are as follows: S2.2.1 Collect basic information of each type of drone, including characteristic parameters and state stability parameters of the drone, and analyze them to obtain the most suitable environmental parameters. ,in To achieve the best temperature for the task, To achieve the best air pressure for the mission, To perform the mission with the best wind speed, Optimal humidity for mission execution; S2.2.2 Meteorological satellite remote sensing technology can measure the current environmental parameters of the mission area , sent to the task execution model selection module, and compared with the best matching parameters of the inspection environment of each type of UAV to obtain the best execution model for the current task.

[0028] Preferably, the specific steps of step S2.3 are: S2.3.1 Determine the mission area S2.3.2 Based on land satellite remote sensing technology, obtain information such as geomorphic features, soil utilization, and location of vegetation in the surrounding environment within the mission area, and establish a 3D map model; S2.3.3 Obtain the location information of the task point in the current task area through the distribution network planning information; S2.3.4 Integrate the mission point information with the 3D map model to establish a 3D model of the mission area.

[0029] Preferably, the specific steps of step S2.5 are: S2.5.1 The route 3D simulation module uses the 3D model of the mission area formed by combining the cruise environment 3D modules as the simulation background; S2.5.2 The route 3D simulation module uses the mission execution aircraft model selected by the mission execution aircraft model selection module as the simulation object; S2.5.3 The route 3D simulation module uses the optimal route planned by the cruise route planning module as the flight route as the simulation path: S2.5.4 During the simulation, if an aircraft collides, the route 3D simulation module will send the collision location information to the cruise route planning module, which will optimize the route; S2.5.5 After the route is optimized, re-simulate the flight to obtain the best route.

[0030] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A multi-task control system based on distribution network inspection, characterized in that: include: Task division and unloading method selection system, drone inspection route generation system and multiple drone models; The task division and offloading mode selection system is used to divide the automatically generated tasks and manually input tasks into task types according to the time limit, and select the unloading of drone tasks according to the task type and calculate; The UAV inspection route generation system includes a UAV patrol airspace application module, a UAV task management module, and a UAV inspection route simulation and training module; The drone inspection airspace application module is used by drone patrol airspace applicants to declare drone patrol areas; The UAV inspection route simulation and training module includes a route 3D simulation module, an inspection route planning module, a mission execution model selection module, a cruise environment 3D module, and a historical route database; The multiple types of drones include rotor inspection drones, fixed-wing inspection drones and airship computing drones; wherein the airship computing drone is equipped with a mobile edge computing server.

2. According to claim 1, a multi-task control system based on distribution network inspection is characterized in that: The tasks automatically generated by the system include routine inspections of the entire inspection area at a system-set period and emergency tasks automatically triggered by bad weather or accident alarms.

3. According to claim 2, a multi-task control system based on distribution network inspection is characterized in that: The task types include routine tasks, emergency tasks and designated tasks, and the corresponding unloading modes are local unloading, airship edge unloading and gap unloading.

4. A working method of a multi-task control system based on distribution network inspection as claimed in claim 3, characterized in that: The following steps are involved: S1. The system divides the received tasks and selects the unloading method accordingly; S2. After receiving the task, the UAV task management module conducts airspace planning and application, selects the task model through the task model selection module, and reads the historical routes of this task through the historical route database. Then, the inspection route planning module plans the route according to the specific task, and finally simulates the route in the route 3D simulation module to reduce collisions.

5. The working method of the multi-task control system based on distribution network inspection according to claim 4 is characterized in that: The step S1 comprises the following steps: S1.1, the system divides the received tasks into routine tasks, urgent tasks and designated tasks, and the routine tasks have a longer time limit, so the local unloading method is selected, while the urgent tasks have the shortest time limit, so the airship edge unloading method is selected, and the designated tasks have a moderate time limit, so the intermittent unloading method is selected; S1.

2. After the task division is completed and the unloading mode is selected, the system inputs the results into the drone task management module in the drone cruise route generation module.

6. The working method of the multi-task control system based on distribution network inspection according to claim 4 is characterized in that: The step S2 specifically includes the following steps: S2.

1. The applicant for drone patrol airspace shall make a plan in the drone inspection airspace planning module and apply for drone inspection operation airspace; S2.2, after receiving the information from the drone management module, the task execution model selection module obtains the temperature, humidity, wind speed and other meteorological conditions of the task area through meteorological satellite remote sensing technology, and at the same time compares the characteristic parameters of various types of inspection drones in the database to obtain the most suitable model for this task; S2.3, the cruise environment 3D module obtains the soil coverage, soil utilization rate and location information of herbaceous plants in the mission area through the land satellite remote sensing system, and at the same time obtains the location and information of each inspection task point in the mission area through the distribution network planning information stored in the database, and combines the obtained information to establish a 3D model of the mission area; S2.

4. The historical route database retrieves historical routes to provide data support for the route planning module, which then optimizes the route to obtain a route suitable for the current task; S2.5, the route 3D simulation module combines the mission aircraft model, route planning, and environmental 3D model to simulate the flight of the route to obtain the optimal route.

7. The working method of the multi-task control system based on distribution network inspection according to claim 5 is characterized in that: In step S1.1, the task offloading mode and multiple task offloading methods of the UAV are selected according to the time requirements of the task, and the specific contents are as follows: 1) The time limit requirement of a task refers to the time it takes from the generation of the task to the acquisition of the processing result, which includes transmission time, propagation time and processing time. The time limit requirements vary depending on the task, as follows: Routine tasks: As daily inspection tasks, drones are required to collect images of metal parts such as poles and insulators, and line problems such as conductor wear, and then analyze the operation of the distribution network in the area. However, since the poles and towers are too loosely distributed, compared with the short time limit, it pays more attention to the commuting range of drones. Therefore, by reducing the energy consumption of wireless transmission, that is, selecting the local unloading mode, the endurance of the drone can be improved and its inspection range can be expanded; Emergency tasks are tasks that are automatically triggered by harsh environments or even safety accidents. To prevent major production safety accidents, they are more focused on short time limits than commuting ranges. Therefore, the airship edge unloading mode is adopted to minimize the time limit and reduce accident losses. Designated missions are specific missions with specific mission objectives, so the drone can fly directly to the mission point. The time limit requirement is as important as the commuting range, so the intermittent unloading method is selected; 2) Selecting the unloading mode means that the drone selects the mission data unloading mode, which is described as follows: The energy consumption of the inspection drone includes the operating energy consumption and the wireless transmission energy consumption. The operating energy consumption includes the fixed value of each type of inspection drone in several time slots in each time domain. ; The wireless transmission energy consumption includes the downlink traffic of the inspection drone, ignoring its uplink traffic, and its downlink traffic increases with the increase of transmission distance and data; Local offloading means that the inspection drone does not transmit data during the mission execution. After the mission is completed, when the inspection drone flies back to the nest, the data will be transmitted to the tower server; Airship edge unloading means that the inspection drone and the airship computing drone perform tasks at the same time. The airship computing drone is equipped with an edge service processor. The inspection drone enters the mission area to collect data, and then transmits the data to the airship computing drone. Different inspection drones send and receive information through the same channel. The airship drone hovers outside the mission area and waits for the inspection drone to transmit data. After receiving the data, the airship computing drone processes the data and performs simple calculations and analysis, and sends the processed data to the tower through different channels. Intermittent unloading means that the inspection drone sends data intermittently within the mission execution time domain, so as to balance the relationship between time limit requirements and cruising range.

8. The working method of the multi-task control system based on distribution network inspection according to claim 6 is characterized in that: In step S2.2, the most suitable model for this task is obtained as follows: basic information of each model of drone is collected, including characteristic parameters and state stability parameters of the drone, and the most suitable environmental parameters are obtained by analysis. ,in To achieve the best temperature for the task, To achieve the best air pressure for the mission, To perform the mission with the best wind speed, Optimal humidity for mission execution; Meteorological satellite remote sensing technology can provide the current environmental parameters of the mission area. , sent to the task execution model selection module, and compared with the best fit index of the inspection environment of each type of UAV to obtain the best execution model for the current task.

9. The working method of the multi-task control system based on distribution network inspection according to claim 6 is characterized in that: The step S2.3 comprises the following steps: S2.3.1, determine the mission area; S2.3.

2. Based on land satellite remote sensing technology, obtain the geomorphic features, soil utilization rate, and location information of vegetation in the mission area, and establish a 3D map model; S2.3.

3. Obtain the location information of the task point in the current task area through the distribution network planning information; S2.3.

4. Integrate the mission point information with the 3D map model to establish a 3D model of the mission area.

10. The working method of the multi-task control system based on distribution network inspection according to claim 6 is characterized in that: The step S2.5 comprises the following steps: S2.5.

1. The route 3D simulation module uses the 3D model of the mission area generated by the cruise environment 3D module as the simulation background; S2.5.2, the route 3D simulation module uses the mission execution aircraft model selected by the mission execution aircraft model selection module as the simulation object; S2.5.

3. The route 3D simulation module uses the optimal route planned by the cruise route planning module as the flight route as the simulation path: S2.5.

4. During the simulation, if an aircraft collides, the route 3D simulation module will send the collision location information to the cruise route planning module, which will optimize the route; S2.5.

5. After the route is optimized, the simulated flight is performed again to obtain the best route.

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