A grid-based dispatching method for air-ground integrated unmanned aerial vehicles

Abstract

The present invention provides a method for grid-based dispatching of unmanned aerial vehicles (UAVs) in an integrated air-ground manner, including: dividing the inspection area into grids on a grid-based application platform; integrating information of different grid densities, deploying relevant UAVs and power distribution resource information to generate resource data, integrating surrounding geological and working environment information and information of nearby card machines and camera digital Internet of Things devices to generate environmental data, and transmitting the data to an intelligent inspection and control platform; planning the UAV route based on the intelligent inspection and control platform, and formulating a cruise plan based on resource data and environmental data; the UAV executes the route for autonomous flight, and transmits the inspection data back to the intranet through the grid-based application platform; the defect recognition platform analyzes defects based on the inspection data and optimizes the inspection data; the optimized inspection data is transmitted back to the intelligent inspection and control platform to generate an inspection report. The present invention divides the inspection area into grids, uses UAVs to conduct inspections in each grid, and realizes efficient inspection and monitoring of large areas.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) intelligent inspection, and in particular to an air-ground integrated grid scheduling method for UAVs. Background Art

[0002] Currently, the drone intelligent inspection system has the following defects and challenges:

[0003] Flight time and endurance limitations: The flight time and endurance of drones are limited. A single inspection mission may require multiple drones to fly alternately, or require charging or battery replacement during the mission.

[0004] Flight altitude and remote sensing resolution: The flight altitude of the drone and the resolution of the remote sensing equipment will affect the inspection results. Lower flight altitude and higher resolution can provide more detailed inspection results, but will increase flight time and cost.

[0005] Flight path planning and obstacle avoidance: UAVs need to perform flight path planning and obstacle avoidance during grid inspections to ensure that they can fully cover the inspection area. The current path planning algorithms and obstacle avoidance technologies still have certain limitations and need further improvement and optimization.

[0006] Data processing and analysis: The large amount of data obtained by drone inspections needs to be effectively processed and analyzed to extract useful information and conclusions. This requires the use of advanced data processing and analysis technologies, including image processing, pattern recognition, machine learning, etc.

[0007] Flight safety and regulatory restrictions: Drone inspections must comply with relevant flight safety regulations and regulatory restrictions. In some specific areas or scenarios, drone inspections may be restricted or prohibited, requiring compliance assessment and risk management;

[0008] Cost and economic benefits: The cost of drone grid inspection includes drone equipment, remote sensing equipment, personnel training and maintenance. In some application scenarios, the cost may limit the promotion and application of drone inspection, and economic benefit evaluation and cost optimization are needed;

[0009] To sum up, the current technical background and applications make the UAV intelligent inspection system itself rely on industry defects and challenges that need to be solved and overcome through technological innovation, standardization and experience summary to improve the efficiency and reliability of UAV grid inspection. Summary of the invention

[0010] In order to overcome the above-mentioned deficiencies of the prior art, the present invention discloses a method for air-ground integrated grid scheduling of unmanned aerial vehicles.

[0011] The technical solution provided by the present invention is: a method for air-ground integrated grid scheduling of unmanned aerial vehicles, comprising:

[0012] Divide the inspection area into grids on the grid application platform;

[0013] Integrate information on different grid densities, deploy relevant drones and power distribution resource information to generate resource data, integrate surrounding geological and working environment information and nearby card machines, camera digital IoT equipment information to generate environmental data, and transmit the data to the intelligent inspection and control platform;

[0014] Plan drone routes based on the intelligent inspection and control platform, and formulate patrol plans based on resource data and environmental data;

[0015] The drone executes the route and flies autonomously, transmitting the inspection data back to the intranet through the grid application platform;

[0016] The defect identification platform analyzes defects based on inspection data and optimizes inspection data;

[0017] The optimized inspection data is transmitted back to the intelligent inspection management and control platform to generate an inspection report.

[0018] Preferably, the grid application platform and energy inspection control platform are constructed based on the Internet of Things (IoT) architecture to manage the entire grid inspection drones, take-off and landing platforms, personnel, tasks, and data for centralized management and feedback.

[0019] Further preferably, each grid in the grid application platform is equipped with a corresponding grid member, and the grid members manage their respective inspection areas.

[0020] Further preferably, the intelligent inspection and control platform is provided with flight takeover, automatic airport execution and feedback functions.

[0021] Further preferably, the defect identification platform is assisted by an artificial intelligence (AI) algorithm to assist in defect analysis, while manual defect review is used to ensure accuracy of the analysis results.

[0022] Further preferably, the intelligent inspection and control platform is equipped with a UAV take-off and landing platform, an automated airport and an inspection vehicle combination, which adopts grid-based skipping for unimportant areas and conducts periodic and frequent inspections of important sections.

[0023] Further preferably, the drone is provided with a drone mounting system to perform detailed inspections of poles and towers, inspections of tree barriers in channels, rapid inspections of channels, and external breach and local discharge detection.

[0024] Further preferably, the drone grid application platform is provided with a two-dimensional GIS map module, a grid division module, an equipment management module, a route management module, a data transmission module, and a hardware APi module.

[0025] Further preferably, the UAV intelligent inspection and control platform is equipped with a three-dimensional GIS map module, a route planning module, a defect management module, a tree obstacle analysis module, and a real-time image transmission module.

[0026] Further preferably, the drone is based on the "Airspace" system, and a rule control interface is reserved on the drone.

[0027] The beneficial effects of the present invention are:

[0028] Flight time and endurance limitations: Coordinate and deploy related drones and power distribution resources according to different grid densities to achieve the goal of increasing regional drone operation capabilities and ensure business support and expansion capabilities in temporary key areas;

[0029] Flight altitude and remote sensing resolution: Combined with the surrounding geological and working conditions information, and integrated with nearby card cameras and camera digital IoT devices, the purpose of multi-dimensional monitoring of the operation status can be achieved;

[0030] Flight path planning and obstacle avoidance: flight route re-planning, obstacle estimation and intelligent recognition algorithm, ultimately achieving the purpose of assisting drone flight route planning;

[0031] Data processing and analysis: Integrate multiple algorithms and expert assistance systems, and have the ability to provide comprehensive and systematic processing, parsing, analysis, and response of relevant data;

[0032] Flight safety and regulatory restrictions: Provide an "air border" system to reserve a safe drone rule control interface to facilitate access to platforms such as self-regulatory, government, and third-party supervision, and achieve the system's "managed compliance" capability;

[0033] Cost and economic benefits: We will provide comprehensive assessment capabilities for corresponding route value, navigation quality, and equipment quality to ensure that the estimated data is accurate and authentic;

[0034] The invention of the drone air-ground integrated grid dispatching strategy aims to strengthen the construction of power informatization, realize the rapid integration of data resources, enhance the linkage between ground and air equipment, expand the multi-disciplinary sharing capabilities of drones in the power field, and fundamentally solve the comprehensive industry difficulties of "difficulty in video fusion, difficulty in location fusion, difficulty in unified data visualization, and difficulty in unified dispatching and command" in the multi-disciplinary "transmission, transformation, distribution and use" of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A grid application flow chart of a method for grid-based scheduling of air-ground integrated unmanned aerial vehicles provided by the present invention;

[0036] Figure 2 A grid operation flow chart of a UAV air-ground integrated grid scheduling method provided by the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0038] The present invention provides a method for air-ground integrated grid scheduling of unmanned aerial vehicles, comprising the following steps:

[0039] Divide the inspection area into grids on the grid application platform;

[0040] The grid application platform and energy inspection and control platform are built based on the Internet of Things (IoT) architecture, managing the entire grid inspection’s drones, take-off and landing platforms, personnel, tasks, and data centralized management and feedback. At the same time, each grid in the grid application platform is equipped with a corresponding grid member, who manages their own inspection area. The grid application platform is equipped with a two-dimensional GIS map module, a grid division module, an equipment management module, a route management module, a data transmission module, and a hardware APi module.

[0041] Integrate information on different grid densities, deploy relevant drones and power distribution resource information to generate resource data, integrate surrounding geological and working environment information and nearby card machines, camera digital IoT equipment information to generate environmental data, and transmit the data to the intelligent inspection and control platform;

[0042] Plan drone routes based on the intelligent inspection and control platform, and formulate patrol plans based on resource data and environmental data;

[0043] The intelligent inspection and control platform is equipped with flight takeover, automatic airport execution and feedback functions. It is equipped with a UAV take-off and landing platform, an automated airport and an inspection vehicle combination. It adopts grid-based skipping for unimportant areas and periodically inspects important sections frequently. The UAV intelligent inspection and control platform is equipped with a three-dimensional GIS map module, a route planning module, a defect management module, a tree obstacle analysis module, and a real-time image transmission module.

[0044] The drone executes the route and flies autonomously, transmitting the inspection data back to the intranet through the grid application platform;

[0045] The defect recognition platform analyzes defects based on inspection data and optimizes inspection data. The defect recognition platform uses artificial intelligence (AI) algorithms to assist in defect analysis and uses manual defect review to ensure accurate analysis results.

[0046] The optimized inspection data is transmitted back to the intelligent inspection management and control platform to generate an inspection report.

[0047] Among them, the drone is equipped with a drone mounting system to perform detailed inspections of poles and towers, tree barrier inspections in channels, rapid channel inspections, and external breach and local release detection. A rule control interface is reserved on the drone.

[0048] It should be understood that, inspired by the technical concept of the present invention, those skilled in the art can make various improvements and changes based on the above description without departing from the content of the present invention, which still fall within the protection scope of the present invention.

[0049] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the claims.

[0050] It should be understood that the present invention is not limited to the precise construction shown in the above description and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for air-ground integrated grid scheduling of unmanned aerial vehicles, characterized in that: include: Divide the inspection area into grids on the grid application platform; Integrate information on different grid densities, deploy relevant drones and power distribution resource information to generate resource data, integrate surrounding geological and working environment information and nearby card machines, camera digital IoT equipment information to generate environmental data, and transmit the data to the intelligent inspection and control platform; Plan drone routes based on the intelligent inspection and control platform, and formulate patrol plans based on resource data and environmental data; The drone executes the route and flies autonomously, transmitting the inspection data back to the intranet through the grid application platform; The defect identification platform analyzes defects based on inspection data and optimizes inspection data; The optimized inspection data is transmitted back to the intelligent inspection management and control platform to generate an inspection report.

2. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The grid application platform and energy inspection control platform are built based on the Internet of Things (IoT) architecture to manage the entire grid inspection’s drones, take-off and landing platforms, personnel, tasks, and data for centralized management and feedback.

3. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: Each grid in the grid application platform is equipped with a corresponding grid member, and the grid members manage their respective inspection areas.

4. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The intelligent inspection and control platform is equipped with flight takeover, automatic airport execution and feedback functions.

5. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The defect identification platform is assisted by artificial intelligence (AI) algorithms to assist in defect analysis, while manual defect review is used to ensure accurate analysis results.

6. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The intelligent inspection and control platform is equipped with a UAV take-off and landing platform, an automated airport and an inspection vehicle combination. It adopts grid-based skipping for unimportant areas and conducts periodic and frequent inspections of important sections.

7. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The drone is equipped with a drone mounting system to perform detailed inspections of poles and towers, tree barrier inspections of channels, rapid channel inspections, and external breach and local discharge detections.

8. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The UAV grid application platform is provided with a two-dimensional GIS map module, a grid division module, an equipment management module, a route management module, a data transmission module, and a hardware APi module.

9. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The UAV intelligent inspection and control platform is equipped with a three-dimensional GIS map module, a route planning module, a defect management module, a tree obstacle analysis module, and a real-time image transmission module.

10. The method for air-ground integrated grid scheduling of unmanned aerial vehicles according to claim 1, characterized in that: The drone is based on the "Airspace" system, and a rule control interface is reserved on the drone.

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