Unmanned aerial vehicle inspection method and device

Abstract

The invention provides an unmanned aerial vehicle inspection method and device. The method comprises the following steps: generating a target flight plan according to acquired meteorological condition information, airspace resource use conditions and performance parameters of each unmanned aerial vehicle equipment terminal; a target unmanned aerial vehicle equipment terminal is matched according to the target flight plan, the control terminal executes a flight task, and target area data is collected and sent to the unmanned aerial vehicle inspection control platform; controlling the unmanned aerial vehicle inspection control platform to identify the target area data to obtain inspection target object data; obtaining unmanned aerial vehicle group operation state information and external adjustment information uploaded by the unmanned aerial vehicle inspection control platform; and according to the operation state information of the unmanned aerial vehicle group, the inspection target object data and the external adjustment information, adjusting the target flight plan by using a first preset decision algorithm to obtain an adjusted flight plan, and performing inspection according to the adjusted flight plan. The method can flexibly deal with various emergencies, thereby realizing inspection tasks with continuous time and space.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle inspection, and in particular to a unmanned aerial vehicle inspection method and device. Background Art

[0002] Traditional inspection methods have gradually revealed their limitations in many industries. For example, in power inspection scenarios, manual inspections are often restricted by factors such as the geographical environment and traffic conditions, making it difficult to reach power facilities in some remote or complex terrain areas. In addition, inspections using ordinary instruments or the naked eye have low accuracy and efficiency.

[0003] Currently, drone technology has been introduced into inspection work, but the existing drone inspection system still has some problems: changes in meteorological conditions (such as sudden bad weather), restrictions on airspace resources (such as temporary airspace control due to military activities or special events), and differences in performance between different drones (such as differences in endurance, flight speed, and load capacity). It is difficult to continuously and stably implement long and large inspection plans, which are prone to interruptions or inability to adjust in time, and it is impossible to achieve efficient and time- and space-continuous inspection tasks. Summary of the invention

[0004] The present invention provides a drone inspection method and device to solve the defects of the prior art that it is difficult to continuously and stably execute long inspection plans, and it is easy to be interrupted or unable to adjust in time, so as to realize efficient and time- and space-continuous inspection tasks. The technical solutions proposed by the present invention are as follows: In a first aspect, the present invention provides a drone inspection method, comprising: Generate a target flight plan based on the acquired meteorological conditions, airspace resource usage, and performance parameters of each UAV equipment terminal; Match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to perform the flight mission, collect target area data and send it to the UAV inspection control platform; Controlling the UAV inspection control platform to identify the target area data to obtain inspection target object data; Obtain the drone group operation status information and external adjustment information uploaded by the drone inspection control platform; According to the drone group operation status information, the inspection target object data and the external adjustment information, the target flight plan is adjusted using a first preset decision algorithm to obtain an adjusted flight plan; wherein the external adjustment information includes: weather change information, airspace resource dynamic adjustment information and new inspection task information; Control the UAV equipment terminal to conduct inspections according to the adjusted flight plan.

[0005] Optionally, generating a target flight plan according to the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal includes: Obtain information on meteorological conditions, airspace resource usage, and performance parameters of each drone equipment terminal; Generate multiple initial flight plans according to the meteorological condition information, the airspace resource usage status and the performance parameters of each UAV equipment terminal; A target flight plan is selected from the multiple initial flight plans according to a preset optimization strategy.

[0006] Optionally, generating a plurality of initial flight plans according to the meteorological condition information, the airspace resource usage status and the performance parameters of each UAV device terminal includes: Generate corresponding data structures for the meteorological condition information, the airspace resource usage status, and the performance parameters of the UAV equipment terminal, respectively; wherein the data structures corresponding to the meteorological condition information, the airspace resource usage status, and the performance parameters of the UAV equipment terminal are respectively a first data structure, a second data structure, and a third data structure; Define the parameters, scope, and time window of the flight mission; Querying the meteorological data in the corresponding time period from the first data structure according to the time window of the flight mission, and screening out the time period and area that meet the flight safety requirements; According to the scope and time window of the flight mission, query the airspace resource usage in the corresponding area from the second data structure, and filter out the time period and area without flight restrictions; Generate multiple candidate flight plans based on flight mission requirements within the selected time periods and areas that meet flight safety requirements and have no flight restrictions; each candidate flight plan includes a take-off time, a flight route, and a flight speed; Determining, based on the third data structure, whether each candidate flight plan meets the performance requirements of the drone device terminal; A candidate flight plan that meets the performance requirements of the UAV equipment terminal is used as the initial flight plan.

[0007] Optionally, the method further comprises: When all candidate flight plans do not meet the performance requirements of the UAV device terminal, optimizing the candidate flight plans using an optimization algorithm to obtain an optimized flight plan; The feasibility of the optimized flight plan is judged, and a flight plan that meets the requirements in terms of meteorological conditions, airspace resources and UAV performance parameters is output as the initial flight plan.

[0008] Optionally, the target area data is acquired by an image acquisition device carried by an unmanned aerial vehicle inspection control platform; and the unmanned aerial vehicle inspection control platform is controlled to identify the target area data to obtain inspection target object data, including: The inspection control platform of the drone is controlled to obtain three-dimensional point cloud data through the laser radar equipment carried; Identify the target area data to obtain feature information and status information of the inspection target object; Matching the three-dimensional point cloud data with the characteristic information of the inspection target object to obtain the three-dimensional coordinate information of the inspection target object; The state information of the inspection target object is associated with the three-dimensional coordinate information of the inspection target object to obtain the inspection target object data. Optionally, the step of adjusting the target flight plan using a first preset decision algorithm according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan includes: Detect conflicts in the target flight plan based on the UAV group operation status information and external adjustment information; Determine whether there are any omissions or duplicate inspections based on the inspection target data; Calling a first preset decision algorithm to make a decision based on the detected conflict, omission or duplicate inspection situation to obtain decision information; wherein the first preset decision algorithm comprehensively considers the performance of the UAV, the task priority, the importance level of the inspection target, and the urgency of the external adjustment information, with the goal of minimizing conflicts and maximizing task completion efficiency and safety; The target flight plan is adjusted according to the decision information to obtain an adjusted flight plan.

[0009] In a second aspect, the present invention further provides a drone inspection device, comprising the following modules: The plan generation module is used to generate a target flight plan based on the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal; A data acquisition module is used to match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to perform the flight mission, collect target area data and send it to the UAV inspection control platform; A data identification module is used to control the UAV inspection control platform to identify the target area data to obtain the inspection target object data; The data acquisition module is used to obtain the drone group operation status information uploaded by the drone inspection control platform, as well as external adjustment information; A plan adjustment module, configured to adjust the target flight plan using a first preset decision algorithm according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan; wherein the external adjustment information includes: weather change information, airspace resource dynamic adjustment information and new inspection task information; The inspection control module is used to control the UAV equipment terminal to perform inspections according to the adjusted flight plan.

[0010] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the drone inspection method as described in the first aspect above is implemented.

[0011] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the drone inspection method as described in the first aspect above.

[0012] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the drone inspection method as described in the first aspect above.

[0013] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows: The drone inspection method and device provided by the present invention first generate a target flight plan based on the acquired meteorological condition information. Meteorological factors have been considered in the planning stage, so that flight interruptions caused by sudden bad weather can be avoided or at least reduced. During the flight, the flight plan is adjusted in real time by obtaining the drone group operation status information uploaded by the drone inspection control platform and the external adjustment information (including meteorological change information). This further enhances the adaptability and response speed to changes in meteorological conditions. The present invention also considers the use of airspace resources and uses it as one of the important bases for generating a target flight plan. This helps to avoid problems caused by airspace control in the flight plan formulation stage. By obtaining airspace resource dynamic adjustment information in real time and adjusting the flight plan accordingly, it is possible to flexibly respond to temporary airspace control caused by military activities or special events, ensuring the continuity and stability of the flight mission. The target flight plan is generated according to the performance parameters of each drone equipment terminal. This ensures that the flight plan can fully consider key factors such as the drone's endurance, flight speed and load capacity, thereby avoiding flight interruptions or mission failures caused by performance mismatch. During the flight, by monitoring the operating status information of the drone group, potential problems caused by performance differences, such as insufficient drone power or equipment failure, can be discovered and handled in a timely manner. By comprehensively considering multiple factors such as meteorological conditions, airspace resources, and drone performance, and generating and adjusting flight plans accordingly, this method can ensure the efficient execution of inspection tasks. The ability to obtain and adjust flight plans in real time enables flexible response to various emergencies, thereby achieving inspection tasks that are continuous in time and space.

[0014] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a system architecture diagram of the drone inspection system provided by the present invention.

[0018] Figure 2 It is a flow chart of the UAV inspection method provided by the present invention.

[0019] Figure 3 It is a timing diagram of the workflow provided by the present invention.

[0020] Figure 4 This is an example diagram of the UAV inspection route planning provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the unmanned aerial vehicle inspection device provided by the present invention.

[0022] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The existing drone inspection methods are often interrupted by the weather, airspace and drone performance. For example, bad weather may prevent the drone from taking off. When there is a long inspection plan, two or more drones cannot guarantee continuous monitoring of the target in time and space when they arrive at the sub-task demarcation point, resulting in a blank inspection period. It is difficult to quickly adjust the inspection plan according to the real-time changing environment and resource conditions. Once an emergency (such as airspace control) occurs, the entire inspection work may come to a standstill, resulting in a waste of time and resources. There is a lack of efficient real-time processing methods for video and other data collected by drones, and it is impossible to accurately extract target information from massive data in a timely manner, resulting in potential risks that are difficult to discover in time, affecting the inspection effect.

[0025] Combine the following Figure 1-Figure 5 The drone inspection method and device of the present invention are described.

[0026] In order to overcome the defects of the existing technology, an innovative drone inspection system is provided. By integrating a variety of real-time information, drone inspections can be carried out continuously in time and space, significantly improving inspection efficiency and accuracy, and timely and accurately discovering abnormal conditions of target objects, thereby effectively ensuring the safe and stable operation of various facilities (such as power facilities, infrastructure, etc.), while improving the intelligence level of the entire inspection process and the ability to cope with complex environments. Figure 1As shown, the UAV inspection system includes a remote integrated management and control center, a UAV inspection control platform and a UAV equipment terminal.

[0027] The remote integrated control center is the hub of the entire drone inspection system, responsible for data collection, comprehensive evaluation, flight plan formulation and optimization, as well as monitoring and management of the overall system. Specifically, the remote integrated control center establishes real-time data connections with external data sources such as the meteorological department and the airspace management department to obtain the latest meteorological conditions and airspace resource usage. At the same time, it collects and stores detailed performance parameters of each drone equipment terminal. The remote integrated control center also uses intelligent algorithms to conduct a comprehensive evaluation of the current environment and drone resources, and preliminarily plans multiple feasible flight plan solutions. The remote integrated control center also selects the target flight plan based on the evaluation results and preset optimization strategies, and dynamically adjusts and optimizes the subsequent flight plan based on real-time feedback and external information. The remote integrated control center receives the drone group operation status information uploaded by the drone inspection control platform in real time, and monitors and manages the system as a whole.

[0028] The drone inspection control platform is the core of flight plan execution and data transmission. It is responsible for receiving flight plan instructions, matching target drones, sending tasks and flight mission parameters, and receiving and processing data uploaded by drones (such as equipment performance parameters, coordinate information, and target area data). The drone inspection control platform is used for flight plan reception and task allocation. It receives flight plan instructions issued by the remote integrated control center, accurately matches target drones based on the task requirements in the instructions and the real-time status of each drone, and sends detailed flight mission parameters. The drone inspection control platform is also used for drone status monitoring, real-time monitoring of drone power, location, equipment health status and other status information. The drone inspection control platform is also used for data transmission and processing, receiving data uploaded by drone equipment terminals, starting the built-in AI recognition module to process the data, identifying the characteristics and status of the inspection target, and parsing the coordinate information of the inspection target in three-dimensional space. The drone inspection control platform is also used for data storage and query: the processed data is classified and stored in the local database to form a complete and orderly inspection data record for subsequent query and analysis.

[0029] The UAV equipment terminal is the executor of the inspection task, responsible for receiving the flight mission parameters, and performing tasks such as takeoff, flight, inspection and data collection. The UAV equipment terminal is used for task reception and preparation, receiving the flight mission parameters sent by the UAV inspection control platform, and performing self-inspection and preparation before takeoff, including checking the sensor status, calibrating the flight control system, and confirming that the communication link is normal. It is also used for flight and inspection, taking off on time according to the scheduled time, flying along the planned flight route, obtaining the current precise coordinate information in real time through the high-precision GPS positioning system and inertial navigation system, and using the high-definition camera, infrared thermal imager, laser rangefinder and other advanced sensors to continuously collect video, image and distance data of the target area. It is also used to compress the collected data and current coordinate information in real time, and upload it to the UAV inspection control platform at a high frequency through the wireless communication module (such as 5G network).

[0030] The drone inspection method provided by the present invention is implemented by the above-mentioned drone inspection system. Figure 2 As shown, the drone inspection method includes the following: Step S110: Generate a target flight plan based on the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal.

[0031] First, refer to Figure 3 As shown, the remote integrated control center will obtain real-time meteorological condition information (such as wind speed, wind direction, temperature, precipitation probability, visibility, etc.) and airspace resource usage status (such as no-fly zone range, temporary control area and time, etc.) from external data sources such as the meteorological department and airspace management department. At the same time, detailed performance parameters of each drone equipment terminal will be collected (such as flight speed, endurance, maximum load, sensor type and accuracy, etc.). Based on the collected data, a comprehensive evaluation is performed using intelligent algorithms, taking into account the performance limitations of the drone, the impact of meteorological conditions, and the availability of airspace resources, to generate multiple preliminary flight plan schemes. Further, based on the priority of the inspection task, the geographical characteristics of the target area, the distribution of facilities, and potential risk points, the preliminary plan is optimized to determine the optimal target flight plan. This plan will plan in detail the drone's take-off time, flight route, flight altitude, speed setting, and inspection action instructions. The flight route includes multiple inspection points, such as Figure 4 Inspection point 1, inspection point 2, inspection point 3…inspection point n.

[0032] Step S120: match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to execute the flight mission, collect target area data and send it to the UAV inspection control platform.

[0033] In the UAV inspection system, the target flight plan is the core guiding document for the entire inspection task. The target flight plan lists in detail the specific requirements of the flight mission, including key parameters such as flight altitude, speed setting, and inspection action instructions. These parameters are determined based on the characteristics of the inspection task (such as the type of inspection object, distribution range, inspection accuracy requirements, etc.) and the performance parameters of the UAV (such as flight speed, endurance, sensor type and accuracy, etc.). After receiving the flight plan instructions, the UAV inspection control platform will extract the corresponding flight mission parameters from the target flight plan according to these requirements.

[0034] Similarly, the target flight plan also contains the planning information of the flight route. This route is planned based on the target area of ​​the inspection mission, topography, obstacle distribution, flight safety and other factors. It ensures that the drone can fly along the optimal path to cover all areas that need to be inspected while avoiding potential dangerous areas and obstacles.

[0035] After determining the flight mission parameters and flight route, the UAV inspection control platform will accurately match the appropriate UAV equipment terminal based on this information. The matching process will take into account the current status of the UAV (such as battery power, location, equipment health, etc.) to ensure that the selected UAV is capable of performing the inspection mission. Figure 4 As shown, a drone equipment terminal is matched from drone equipment terminals 1 to n. The present invention adopts a drone inspection task matching algorithm to match a suitable drone equipment terminal. The input of the algorithm is: flight mission parameters (flight altitude, speed setting, inspection action instructions, etc.), flight route information (geographical features of the target area, obstacle distribution, flight safety requirements, etc.), drone equipment terminal list (including performance parameters, current status, etc. of each drone). The output of the algorithm is the matched drone equipment terminal ID. The matching process is as follows: S1201. Read the flight mission parameters and flight route information from the target flight plan. Read the list of drone equipment terminals, including the performance parameters (flight speed, endurance, maximum load, sensor type and accuracy, obstacle avoidance, etc.) and current status (battery level, location, equipment health, etc.) of each drone.

[0036] S1202: Match performance parameters. Specifically, for each UAV device terminal, check whether its flight speed meets the flight speed required by the flight mission. Check whether the UAV's endurance is sufficient to complete the flight mission. Check whether the UAV's maximum load capacity meets the sensor and equipment weight required for the inspection mission.

[0037] S1203: Match the sensor type and accuracy. Specifically, check whether the drone is equipped with the sensor type required for the flight mission and whether the accuracy meets the requirements.

[0038] S1204. Based on the complexity of the flight route and the distribution of obstacles, evaluate whether the obstacle avoidance capability of the UAV is sufficient.

[0039] S1205: Perform current status assessment. Specifically, check whether the current power of the drone is sufficient. Check whether the current location of the drone is convenient for takeoff and mission execution. Check whether the equipment of the drone is in good health and has no faults or damage.

[0040] S1206: Match the task priority with the drone availability. If there are multiple tasks, sort and allocate them according to the task priority and the drone availability. Prioritize the allocation of available drones that meet the performance requirements to tasks with higher priority.

[0041] S1207: According to the above matching results, select a target UAV device terminal that meets all conditions. If there are multiple UAVs that meet the conditions, further screening can be performed based on additional selection criteria (such as cost, maintenance history, etc.).

[0042] S1208. Output the matching drone device terminal ID.

[0043] Once the match is completed, the drone inspection control platform will send detailed flight mission parameters and flight route information in the form of encrypted instructions in real time to the matching target drone equipment terminal through a high-speed communication network (such as a 5G network). Through the above process, the flight mission parameters and flight route in the target flight plan are accurately transmitted to the drone equipment terminal, guiding the drone to complete the entire inspection mission.

[0044] After receiving the mission command, the UAV equipment terminal will perform a series of self-inspections and preparations before takeoff. After passing the self-inspection, the UAV will take off on time according to the scheduled time and fly strictly along the planned flight route. During the flight, the current coordinate information is obtained in real time through the high-precision GPS positioning system and inertial navigation system, and the image acquisition equipment (such as high-definition cameras), infrared thermal imagers, laser rangefinders or lidar equipment and other advanced sensors are used to continuously collect video, image and distance data of the target area. The collected data may include visible light images, infrared images, thermal imaging data, etc. The following is referred to as the target area data, and the collected target area data is uploaded to the UAV inspection control platform in real time.

[0045] Step S130: Control the UAV inspection control platform to identify the target area data to obtain inspection target object data.

[0046] The drone equipment terminal transmits the collected data to the drone inspection control platform in real time. The drone inspection control platform uses image recognition, data analysis and other technologies to process and analyze the received data, identify the inspection targets (such as power lines, towers, buildings, etc.) and their status information (such as whether they are damaged, abnormal, etc.). The identified inspection targets and their status information are stored in the database for subsequent query and analysis.

[0047] Step S140: Obtain the drone group operation status information and external adjustment information uploaded by the drone inspection control platform.

[0048] The drone inspection control platform will continuously monitor the operating status information of the drone group (such as battery level, flight speed, location, etc.), and obtain external adjustment information in real time (such as weather change information, dynamic adjustment information of airspace resources, and new inspection task information, etc.).

[0049] Monitor the operating status of the drone group, specifically the remaining battery of each drone, to ensure that it will not be forced to land due to power exhaustion during flight. Track the flight speed of the drone in real time to ensure that it flies within the specified speed range, neither too fast nor too slow, to maintain a high inspection efficiency. Use GPS or other positioning technologies to accurately record the real-time location of the drone so that the flight route can be adjusted in time or to respond to emergencies.

[0050] Obtain information on meteorological changes, specifically real-time acquisition of meteorological data such as wind speed, wind direction, temperature, and precipitation, in order to predict and respond to weather changes that may affect flight safety. Obtain information on dynamic adjustment of airspace resources, specifically receiving notifications from airspace management departments, understanding changes in no-fly zones, temporary control areas, and time, and ensuring that drones do not fly in restricted areas. Obtain new inspection task information, specifically receiving and processing new inspection task requests based on actual needs, such as adding inspection points, changing inspection priorities, etc.

[0051] Step S150: According to the drone group operation status information, the inspection target object data and the external adjustment information, a first preset decision algorithm is used to adjust the target flight plan to obtain an adjusted flight plan.

[0052] Based on the collected information on the operation status of the drone group, the inspection target data, and the external adjustment information, the first preset decision algorithm is used to adjust and optimize the target flight plan. The algorithm performs comprehensive analysis and evaluation based on the collected information on the operation status of the drone group, the inspection target data, and the external adjustment information. The algorithm considers various factors, such as the remaining power of the drone, the flight speed, the distance between the current position and the target inspection point, the weather conditions, the airspace restrictions, and the requirements of the new task. Based on the evaluation results, the algorithm generates one or more adjusted flight plan schemes that are designed to optimize the inspection efficiency, ensure flight safety, and meet the requirements of the new inspection task. This adjustment process may include changing the flight route, adjusting the flight altitude and speed, and adding or reducing inspection points. Adjust the flight route of the drone according to the real-time weather conditions and airspace restrictions to avoid bad weather or restricted areas. Adjust the flight altitude and speed according to the weather conditions and the performance of the drone to ensure flight safety and inspection efficiency. Add or reduce inspection points according to the new inspection task information to meet actual needs.

[0053] For example, if the weather conditions in a certain area deteriorate, the drone flight route will be adjusted in time to bypass the area; if a target object is found to have potential abnormalities, the inspection frequency of the area will be increased or other drones will be dispatched for coordinated inspection. The adjusted flight plan will be sent to the corresponding drone equipment terminal in real time through a high-speed communication network (such as a 5G network). After receiving the new flight plan, the drone equipment terminal will immediately update it and continue to perform the inspection task according to the new plan.

[0054] Step S160: Control the UAV equipment terminal to perform inspection according to the adjusted flight plan.

[0055] After receiving the adjusted flight plan, the drone equipment terminal will continue to perform the inspection task according to the new plan. During the inspection process, the drone will use the sensors it carries (such as high-definition cameras, infrared thermal imagers, etc.) to continuously collect video, image and other data of the target area. At the same time, the drone will also upload its operating status and collected data to the drone inspection control platform in real time for further analysis and processing.

[0056] The remote integrated control center will continuously monitor the operating status of the drone and changes in the external environment. If it finds that the drone is abnormal (such as insufficient power, equipment failure, etc.) or the external environment changes significantly (such as bad weather, airspace restrictions, etc.), the remote integrated control center will immediately make dynamic adjustments and optimizations to ensure that the drone can complete the inspection task safely and efficiently. Through this dynamic adjustment and optimization cycle, the drone inspection system can flexibly respond to various real-time changes, ensuring that the drone group always maintains an efficient and safe inspection status in a complex and changing environment.

[0057] The unmanned aerial vehicle inspection method provided by the present invention first generates a target flight plan according to the acquired meteorological condition information. Meteorological factors have been considered in the planning stage, so that flight interruptions caused by sudden bad weather can be avoided or at least reduced. During the flight, the flight plan is adjusted in real time by obtaining the operation status information of the unmanned aerial vehicle group uploaded by the unmanned aerial vehicle inspection control platform, as well as the external adjustment information (including meteorological change information). This further enhances the adaptability and response speed to changes in meteorological conditions. The present invention also considers the use of airspace resources and uses it as one of the important bases for generating a target flight plan. This helps to avoid problems caused by airspace control in the flight plan formulation stage. By obtaining the dynamic adjustment information of airspace resources in real time and adjusting the flight plan accordingly, it is possible to flexibly respond to temporary airspace control caused by military activities or special events, and ensure the continuity and stability of the flight mission. The target flight plan is generated according to the performance parameters of each unmanned aerial vehicle equipment terminal. This ensures that the flight plan can fully consider key factors such as the endurance, flight speed and load capacity of the unmanned aerial vehicle, thereby avoiding flight interruptions or mission failures caused by performance mismatch. During the flight, by monitoring the operating status information of the drone group, potential problems caused by performance differences, such as insufficient drone power or equipment failure, can be discovered and handled in a timely manner. By comprehensively considering multiple factors such as meteorological conditions, airspace resources, and drone performance, and generating and adjusting flight plans accordingly, this method can ensure the efficient execution of inspection tasks. The ability to obtain and adjust flight plans in real time enables flexible response to various emergencies, thereby achieving inspection tasks that are continuous in time and space.

[0058] The first preset decision algorithm used in the present invention can adjust the flight plan in real time according to the external adjustment information. This enhances the flexibility and adaptability of the UAV inspection, enabling it to better cope with the complex and changeable inspection environment. By controlling the UAV equipment terminal to inspect according to the adjusted flight plan, it can ensure the smooth completion of the inspection task and maximize the inspection efficiency and accuracy.

[0059] In an optional embodiment, the step S110 described above generates a target flight plan according to the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV device terminal, including: S1101. Obtain weather condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal.

[0060] The remote integrated control center establishes real-time data connections with external data sources such as the meteorological department and airspace management department to obtain the latest meteorological conditions information (including wind speed, wind direction, temperature, precipitation probability, visibility, etc.) and airspace resource usage (no-fly zone range, temporary control area and time, etc.). Meteorological conditions information refers to real-time meteorological data obtained from the meteorological department or other reliable sources, including wind speed, wind direction, temperature, precipitation probability, visibility, etc. These data are essential for assessing flight safety and determining flight altitude and speed. Airspace resource usage refers to obtaining airspace resource usage information such as no-fly zone range, temporary control area and time. This helps to prevent drones from entering restricted areas and ensure the legality and safety of flights. UAV equipment terminal performance parameters refer to cruising range, flight speed limit, maximum load capacity, sensor type and accuracy, etc. These parameters are essential for formulating flight plans that meet the actual capabilities of drones.

[0061] S1102. Generate multiple initial flight plans based on the meteorological condition information, the airspace resource usage status and the performance parameters of each UAV equipment terminal.

[0062] A comprehensive assessment is conducted based on the acquired meteorological information, airspace resource usage, and UAV equipment terminal performance parameters. This includes considering the safety and efficiency of the flight route, the performance limitations of the UAV, and external environmental factors. Using algorithms or software tools, a number of feasible flight plans are preliminarily planned based on the comprehensive assessment results. These plans should cover different take-off times, flight route combinations, and inspection focus area allocations to cope with various possible situations. The preliminarily planned flight plan is converted into specific flight instructions and parameters, including take-off time, flight altitude, speed setting, inspection action instructions, etc. These instructions will serve as the basis for the UAV to perform inspection tasks.

[0063] S1103: Filter out a target flight plan from the multiple initial flight plans according to a preset optimization strategy.

[0064] According to actual needs and constraints, formulate preset optimization strategies. These strategies can include maximizing inspection efficiency, minimizing flight costs, ensuring flight safety, etc. Input multiple initial flight plans into the optimization algorithm, and screen and evaluate them according to the preset optimization strategy. This includes calculating the flight time, cost, safety and other indicators of each plan, and comparing and sorting them. From the screened flight plans, select one or more optimal ones as the target flight plans. These plans should be able to achieve the best inspection effect while meeting all constraints.

[0065] The remote integrated control center obtains meteorological condition information from the meteorological department and communicates with the airspace management department to obtain the status of airspace resource usage. It also collects and records the performance parameters of the UAV equipment terminal. Comprehensive evaluation is performed based on this information. Multiple feasible flight plans are preliminarily planned using algorithms or software tools. The preliminarily planned flight plans are converted into specific flight instructions and parameters. Multiple initial flight plans are screened and evaluated according to the preset optimization strategy. One or more optimal flight plans are selected as the target flight plan. The target flight plan is sent to the UAV inspection control platform in real time in the form of encrypted instructions through the high-speed communication network. The UAV inspection control platform receives and executes the target flight plan. The remote integrated control center continuously monitors the operating status of the UAV and changes in the external environment, and dynamically adjusts and optimizes as needed. Through the above algorithm process, the automatic generation and optimization of the target flight plan in the UAV inspection system can be realized, thereby improving inspection efficiency, reducing flight costs and enhancing flight safety.

[0066] The present invention can significantly improve the efficiency and accuracy of inspections by formulating the optimal flight plan by comprehensively considering factors such as meteorological conditions, airspace resources and drone performance. By optimizing flight routes and parameters, the energy consumption and wear of drones can be reduced, thereby reducing flight costs. By avoiding bad weather and restricted areas, and ensuring that drone performance meets mission requirements, flight safety can be significantly enhanced.

[0067] In an optional embodiment, the step S1102 described above generates multiple initial flight plans according to the meteorological condition information, the airspace resource usage status and the performance parameters of each drone device terminal, including: S11021. Generate corresponding data structures for the meteorological condition information, the airspace resource usage status and the performance parameters of the UAV equipment terminal respectively; wherein the data structures corresponding to the meteorological condition information, the airspace resource usage status and the performance parameters of the UAV equipment terminal are the first data structure, the second data structure and the third data structure respectively.

[0068] The first data structure is used to store and manage meteorological conditions information, such as wind speed, wind direction, temperature, precipitation, etc. It can be a time series database that can store and query meteorological data in chronological order. The second data structure is used to record the dynamic use of airspace resources, including no-fly zones, temporary control areas, flight restriction times, etc. It can be a spatial database that can store and query geospatial information. The third data structure is used to record the detailed performance parameters of each drone equipment terminal, such as range, flight speed limit, maximum load capacity, sensor type, etc. It is a general database used to store and manage the static information of drones.

[0069] S11022. Define the parameters, scope, and time window of the flight mission.

[0070] Clarify the specific requirements of the flight mission, such as flight altitude, flight speed range, required payload, etc. Determine the scope of the flight mission, specifically the geographical scope of the flight, including the take-off point, landing point, and possible transit areas. The time window of the flight mission refers to the time range of the flight mission, and when setting it, factors such as weather changes, airspace usage restrictions, and the availability of drone equipment must be considered.

[0071] S11023. Query the meteorological data in the corresponding time period from the first data structure according to the time window of the flight mission, and select the time period and area that meet the flight safety requirements.

[0072] Using the data structure of meteorological condition information (first data structure), query the meteorological data within the corresponding time period according to the time window of the flight mission. Analyze the meteorological data, evaluate the impact of meteorological factors such as wind speed, wind direction, and precipitation on flight safety, and select time periods and areas that meet flight safety requirements. This usually requires comprehensive consideration of multiple meteorological factors to ensure that the drone will not encounter extreme weather conditions during flight.

[0073] S11024. According to the scope and time window of the flight mission, query the airspace resource usage in the corresponding area from the second data structure, and filter out the time periods and areas without flight restrictions.

[0074] Using the data structure of airspace resource usage (the second data structure), query the airspace resource usage in the corresponding area according to the scope and time window of the flight mission. Analyze the airspace resource usage, identify the no-fly zones, temporary control areas, and any flight restriction times, and filter out the time periods and areas without flight restrictions. This helps ensure that the flight plan complies with local aviation regulations and safety requirements.

[0075] S11025. Generate multiple candidate flight plans based on flight mission requirements within the selected time periods and areas that meet flight safety requirements and have no flight restrictions; each candidate flight plan includes a take-off time, a flight route, and a flight speed.

[0076] Identify the specific requirements of the flight mission, including but not limited to: take-off and landing points, flight altitude and speed, flight time, payload requirements, and flight route. Determine the altitude range and speed range of the flight based on the mission requirements. Choose an appropriate take-off time within the time period that meets safety requirements. Understand the type, weight, and distribution of payload required for the flight mission. Determine if there are specific flight route requirements, such as avoiding certain areas or flying along a specific route.

[0077] After determining the screening criteria and flight mission requirements, you can start generating candidate flight plans. Specifically, determine the take-off and landing times, and select multiple possible take-off times within a time period that meets safety requirements. Determine the corresponding landing time based on the time requirements of the flight mission. Use a geographic information system (GIS) or flight planning software to plan multiple possible flight routes based on take-off and landing points. Consider factors such as flight altitude, speed, and load distribution to optimize each route. Set parameters such as flight altitude, speed, and heading for each flight route based on the requirements of the flight mission. Ensure that these parameters are within the performance range of the drone and meet safety requirements. Conduct a preliminary evaluation of each generated flight plan, including flight time, energy consumption, safety, etc. Eliminate plans that do not meet flight mission requirements or have safety hazards.

[0078] Finally, the generated multiple candidate flight plans are sorted and output. This includes: flight plan list and flight plan chart. The flight plan list lists the numbers, take-off time, landing time, flight routes and flight parameters of all candidate flight plans. The flight plan chart uses a chart to intuitively display the direction, altitude and speed of each flight route.

[0079] S11026. Determine, based on the third data structure, whether each candidate flight plan meets the performance requirements of the UAV equipment terminal.

[0080] Using the data structure of the UAV equipment terminal performance parameters (the third data structure), evaluate whether each candidate flight plan meets the performance requirements of the UAV. The evaluation indicators may include the UAV's cruising range, flight speed limit, maximum load capacity, etc. By comparing the key parameters in the candidate flight plan with the UAV's performance indicators, it can be determined whether the flight plan is feasible. Based on the evaluation results, select the candidate flight plans that meet the UAV performance requirements. These plans will serve as the basis for optimizing and screening the target flight plans in the subsequent steps.

[0081] S11027, taking the candidate flight plans that meet the performance requirements of the UAV equipment terminal as the initial flight plans. These initial flight plans will serve as the basis for optimizing and screening the target flight plans in subsequent steps.

[0082] The present invention comprehensively considers meteorological conditions and airspace resource usage, screens out time periods and areas that meet flight safety requirements, and ensures that the drone will not encounter extreme weather or flight restrictions during flight. It can generate multiple candidate flight plans according to flight mission requirements, and screen and optimize them on the premise of meeting the performance requirements of the drone to find the optimal flight plan and improve flight efficiency. Through reasonable flight plan arrangements, unnecessary flight time and energy consumption are reduced, thereby reducing flight costs. This process can dynamically generate and adjust flight plans according to different flight mission requirements and external environmental changes, enhancing the flexibility and adaptability of drone inspections. By constructing and managing data structures of meteorological condition information, airspace resource usage status, and drone equipment terminal performance parameters, efficient data storage and query are achieved, improving data utilization and the intelligence level of the system.

[0083] In an optional embodiment, the method further includes: S11028. When all candidate flight plans do not meet the performance requirements of the UAV device terminal, optimize the candidate flight plans using an optimization algorithm to obtain an optimized flight plan.

[0084] In the case that all candidate flight plans do not meet the performance requirements of the UAV equipment terminal, further measures need to be taken to generate a feasible flight plan. In this step, an optimization algorithm will be introduced to optimize the candidate flight plan to find a flight plan that meets the performance requirements of the UAV. The optimization algorithm may include but is not limited to heuristic search algorithms (such as genetic algorithms, particle swarm algorithms, etc.), dynamic programming algorithms, linear programming algorithms, etc. These algorithms will adjust and optimize the candidate flight plans based on the performance parameters of the UAV (such as maximum flight speed, maximum flight altitude, maximum load, endurance, etc.) and the requirements of the flight mission (such as take-off point, landing point, flight time window, etc.). The optimization process may involve fine-tuning the flight route, adjusting the flight speed, changing the flight altitude, etc., to ensure that the optimized flight plan can meet the performance requirements of the UAV while being as close to the requirements of the flight mission as possible.

[0085] S11029. Conduct a feasibility assessment on the optimized flight plan, and output a flight plan that meets the requirements in terms of meteorological conditions, airspace resources, and UAV performance parameters as the initial flight plan.

[0086] After obtaining the optimized flight plans, it is necessary to judge the feasibility of these plans. The purpose of this step is to ensure that the optimized flight plans meet the requirements in terms of meteorological conditions, airspace resources and drone performance parameters. Specifically, the meteorological conditions information and airspace resource usage will be queried again, and the optimized flight plans will be evaluated one by one against the performance parameters of the drone. The evaluation process will take into account factors such as take-off time, flight route, flight speed, flight altitude, etc. in the flight plan to ensure that the flight plan meets safety requirements and flight mission needs in all aspects.

[0087] If the optimized flight plan meets all the requirements, it will be output as the initial flight plan; if it still does not meet the requirements, it is necessary to return to step S11028 and continue to use the optimization algorithm for iterative optimization until a feasible flight plan is found.

[0088] The present invention can significantly improve the feasibility of the flight plan by introducing an optimization algorithm to optimize the candidate flight plan and judging its feasibility after optimization. This helps to ensure that the UAV can complete the task safely and smoothly during the flight. The method can dynamically generate and adjust the flight plan according to the performance parameters of the UAV and the requirements of the flight mission. This enhances the flexibility and adaptability of the system, enabling it to cope with various complex situations, such as changes in meteorological conditions, limitations on airspace resources, etc. By optimizing the flight plan, unnecessary flight time and energy consumption can be reduced, thereby improving flight efficiency. This helps to reduce flight costs and improve the operational benefits of the UAV. The method can provide users with a more reliable and efficient flight plan, thereby improving user experience. Users can select a suitable flight plan according to actual needs and monitor the flight process in real time to ensure the smooth completion of the mission.

[0089] In an optional embodiment, the controlling of the drone inspection control platform in step S130 to identify the target area data to obtain the inspection target object data includes: S1301. Control the UAV inspection control platform to obtain three-dimensional point cloud data through the laser radar equipment carried by it.

[0090] Control the drone to take off and navigate to the target area. Use GPS or other positioning technology to ensure that the drone is in the correct position and altitude. Use the image acquisition device (such as a high-definition camera) on the drone inspection control platform to capture image data of the target area. Ensure that the image acquisition device is stably connected to the drone and can transmit image data to the drone inspection control platform in real time.

[0091] At the same time, the target area is scanned by the laser radar device carried by the drone. The laser radar emits a laser beam and receives the reflected signal to calculate the distance between the inspection target and the laser radar. Based on the distance information and the scanning angle of the laser radar, the three-dimensional point cloud data of the target area is generated.

[0092] Specifically, obtain each frame of scanning data from the LiDAR device, including the emission angle of each laser beam (such as horizontal angle and pitch angle) and the corresponding distance information. Ensure that the installation angle and position of the LiDAR device are known, and consider the necessary calibration parameters (such as device tilt, offset, etc.).

[0093] The data measured by the LiDAR is in spherical coordinates (the distance measured by the laser beam , horizontal angle , pitch angle ) represents, convert it into Cartesian coordinate system ( , , ). The conversion formula is as follows: in: is the distance measured by the laser beam. is the horizontal angle (i.e. the azimuth of the lidar scan, with the lidar as the center and the clockwise direction as the positive direction). It is the pitch angle (i.e. the elevation angle of the lidar scan, with the horizontal plane of the radar as the reference, and upward as the positive direction).

[0094] The converted Cartesian coordinate points are stored as a point cloud data structure, each point contains , , If the lidar device provides timestamp information, these timestamps can be managed to support subsequent spatiotemporal analysis or data fusion.

[0095] S1302: Identify the target area data to obtain feature information and status information of the inspection target object.

[0096] The drone inspection control platform pre-processes the collected image data, including denoising, contrast enhancement, distortion correction, etc., to improve the accuracy of subsequent recognition. Computer vision technology, such as deep learning models (such as convolutional neural networks CNN) or traditional image processing algorithms, is used to identify inspection targets on pre-processed images or video data. After identifying the inspection target (such as power poles, transmission lines, etc.), its feature information, such as shape, color, texture, etc., is extracted. At the same time, image segmentation, edge detection and other technologies are used to identify the status information of the inspection target, such as whether it is damaged or whether there are any abnormal signs.

[0097] Specifically, feature points are detected in the preprocessed image through feature point extraction algorithms (such as Harris, Shi-Tomasi, SIFT, SURF, ORB or AKAZE, etc.), and feature descriptors are generated to obtain the above feature information. Taking the Harris feature point extraction method as an example, it detects feature points by analyzing the gradient changes of each pixel in the image. When the gradient changes around a pixel are large in multiple directions, the point is considered to be a feature point.

[0098] S1303: Match the three-dimensional point cloud data with the feature information of the inspection target object to obtain three-dimensional coordinate information of the inspection target object.

[0099] Filter the 3D point cloud data to remove noise points and redundant points, and extract feature points to generate feature descriptors. Statistical filtering, voxel filtering, or RANSAC algorithms can be used. Taking statistical filtering as an example, for each point in the point cloud, the average distance of its k nearest neighboring points is calculated. and standard deviation If the distance between a point and its neighbor is greater than ( If the value is between 1 and 2), the point is considered an outlier and is removed from the point cloud.

[0100] After filtering, feature points of the point cloud data need to be extracted and feature descriptors generated. Feature points refer to points with significant geometric characteristics in the point cloud, such as corner points, edge points, etc. Feature descriptors are used to describe the local geometric features of feature points for subsequent point cloud matching, recognition and other tasks. Harris 3D detection, PCA analysis, ISS (Intrinsic Shape Signatures) algorithm and other methods can be used to extract feature points. Generate feature descriptors such as PFH or FPFH for the extracted feature points. Output the filtered point cloud data, extracted feature points and their feature descriptors.

[0101] Use the calibration plate to calibrate the image acquisition device and obtain the camera's intrinsic and extrinsic matrix for subsequent 3D reconstruction and coordinate transformation. According to the camera calibration results and the laser radar's scanning parameters, establish the transformation relationship between the image coordinate system, the camera coordinate system, and the laser radar coordinate system. Use a feature matching algorithm (such as FLANN matching) to match the feature points in the image with the feature points in the point cloud. Through the matching results, find the pixel position in the image corresponding to the feature point in the point cloud. Using the matched feature point pairs and the coordinate transformation model, convert the pixel points in the image into 3D space to obtain the 3D coordinate information of the inspection target.

[0102] The process of matching feature points in an image with feature points in a point cloud is as follows: Use the FLANN algorithm to construct a KD tree index for the feature descriptors of the point cloud (or feature points of an image). Specify the parameters for FLANN matching, such as the number of recursive traversals (checks), the algorithm used (such as a KD tree), etc. For each feature point in the image (or point cloud), use the FLANN algorithm to search for the nearest neighbor in the KD tree index of the point cloud (or feature point of an image). Calculate the feature descriptor distance between the feature point and the nearest neighbor as a measure of the degree of matching. Filter matching points based on the feature descriptor distance and remove matching points with too large a distance. Based on the filtered matching points, subsequent tasks such as point cloud registration, image stitching, and 3D reconstruction can be performed.

[0103] S1304: Associating the state information of the inspection target object with the three-dimensional coordinate information of the inspection target object to obtain the inspection target object data.

[0104] The status information of the inspection target is associated with the three-dimensional coordinate information to form complete inspection target data. The inspection target data is output to the display interface of the drone inspection control platform or stored in the database, and the status information of the inspection target is associated with the corresponding three-dimensional coordinate information. The status information may include the degree of damage of the target, the type of abnormality, etc.

[0105] The drone equipment terminal can also be controlled to obtain the current coordinate information of the drone through the drone's GPS system, inertial navigation system (INS) or other positioning technology, and send it to the drone inspection control platform. The three-dimensional coordinate information of the inspection target and the status information of the inspection target are associated with the current coordinate information of the drone. This is achieved by marking the location of the inspection target and recording the flight trajectory of the drone. The above-mentioned associated data are integrated together to form complete inspection target data. Data integration can include operations such as merging data tables and mapping fields. The associated inspection target data is stored in the database for subsequent analysis and query. The database can be a relational database or a non-relational database, and it can be selected according to actual needs. The associated data is visualized using visualization tools (such as three-dimensional modeling software, data visualization platform, etc.). Visual display can help operators understand the status and location information of the target more intuitively.

[0106] The present invention can significantly improve the inspection efficiency of the UAV inspection control platform by automatically identifying and parsing the target information. Compared with the traditional manual inspection method, the automated inspection can identify the inspection target and obtain relevant information more quickly, thereby shortening the inspection cycle and increasing the inspection frequency. By using image recognition and spatial analysis technology, the platform can accurately identify the characteristic information and status information of the inspection target, and calculate the precise coordinates of the inspection target in three-dimensional space (i.e., the above-mentioned three-dimensional coordinate information). This helps to reduce false alarms and missed alarms and improve the accuracy of the inspection. By acquiring the current coordinate information of the UAV in real time and associating it with the three-dimensional coordinate information and status information of the inspection target, the platform can realize real-time monitoring and dynamic adjustment of the inspection task. This helps to deal with emergencies or environmental changes and ensure that the inspection task can proceed smoothly.

[0107] In an optional embodiment, the step S150 described above uses a first preset decision algorithm to adjust the target flight plan according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan, including: S1501. Detect conflicts in the target flight plan based on the drone group operation status information and external adjustment information.

[0108] The remote integrated control center will detect whether there are conflicts in the target flight plan based on the operating status information of the drone group (such as location, speed, battery, health status, etc.) and external adjustment information (such as temporary no-fly zones, weather changes, emergency tasks, etc.). These conflicts may include: possible collisions between drones, drones entering no-fly zones or dangerous areas, drones running low on battery and unable to complete the remaining tasks, and tasks that cannot be executed as planned due to external factors.

[0109] Possible collisions between drones are detected in the following way: The relative distance and relative speed between drones are calculated through position information and speed information. Based on the relative distance and relative speed, the risk of collision between drones in the future is evaluated. If a possible collision is predicted, the system will issue an alarm and may adjust the flight trajectory or speed of the drone to avoid a collision.

[0110] The drone’s entry into a no-fly zone or danger zone is detected by comparing the drone’s current position and flight trajectory with the no-fly zone / danger zone information. If the drone’s flight trajectory will cause it to enter a no-fly zone or danger zone, the system will sound an alarm and may re-plan the drone’s flight path to avoid entering these areas.

[0111] The method of detecting that the drone is unable to complete the remaining tasks due to insufficient battery is to calculate whether the drone can complete the remaining tasks based on the current battery level of the drone and the energy consumption requirements of the remaining tasks. If the drone is predicted to be low on battery, the system will issue a low battery alarm and may reallocate tasks to drones with sufficient battery or adjust the flight plan to reduce energy consumption.

[0112] The detection method for the failure of mission execution as planned due to external factors is to analyze the impact of external adjustment information on the UAV's mission execution. According to the status of the UAV and the mission requirements, the UAV is evaluated to see whether it can cope with these external factors. If it is predicted that external factors will cause the mission to fail to be executed as planned, the system will issue an alarm and may re-plan the flight plan, adjust the task allocation, or seek manual intervention.

[0113] S1502. Determine whether there are any missed or repeated inspections based on the inspection target data.

[0114] The remote integrated control center will analyze whether there are any omissions or repeated inspections based on the inspection target data. This involves a comprehensive analysis of the location of the target, the importance level, the number of inspections, and other information. By comparing the target flight plan with the actual inspection needs, it is possible to identify: important targets that have not been scheduled for inspection, targets that have been scheduled for inspection repeatedly, and inefficiencies caused by unreasonable inspection sequences.

[0115] Specifically, collect the location information (such as latitude and longitude, altitude, etc.), importance level (such as high, medium, low) and historical inspection records of all inspection targets. Obtain the current flight plan, including the planned inspection targets, inspection sequence, and estimated inspection time.

[0116] The method of identifying important targets that are not scheduled for inspection is as follows: According to the importance level of the targets, select the targets with higher importance levels. Compare these important targets with the targets in the flight plan to identify the important targets that are not included in the flight plan. For the important targets that are not scheduled for inspection, the system will issue an alarm to prompt the management personnel to make up for the omissions.

[0117] The method for identifying objects that are repeatedly scheduled for inspection is to remove duplicate objects in the flight plan and count the number of times each object appears in the flight plan. Based on the statistical results, the objects that are repeatedly scheduled for inspection are identified. For the identified objects that are repeatedly scheduled for inspection, the system will mark them and prompt the management personnel to verify and adjust them.

[0118] The inefficiency analysis method caused by unreasonable inspection sequence is as follows: According to the location information of the target and the inspection sequence in the flight plan, the flight distance and time between adjacent targets are calculated. Analyze whether the flight distance and time are reasonable, whether there are too many round trips or invalid flight paths. Based on the analysis results, identify the inefficiency caused by unreasonable inspection sequence. For the identified problems, the system will provide optimization suggestions, such as adjusting the inspection sequence, merging inspections of similar targets, etc.

[0119] The analysis results are output in the form of a report, including a list of important objects that have not been scheduled for inspection, a list of objects that have been repeatedly scheduled for inspection, and a summary of inefficiencies caused by unreasonable inspection sequences. Specific optimization suggestions and improvement measures are provided for the identified problems.

[0120] S1503. Call the first preset decision algorithm to make a decision based on the detected conflicts, omissions or repeated inspections to obtain decision information; wherein the first preset decision algorithm comprehensively considers the performance of the UAV, the task priority, the importance level of the inspection target, and the urgency of the external adjustment information, with the goal of minimizing conflicts and maximizing task completion efficiency and safety.

[0121] Once a conflict is detected or it is determined that there is a missed / duplicate inspection, the remote integrated control center will call the first preset decision algorithm to make a decision. The algorithm takes into account multiple factors, including: drone performance, task priority, importance level of inspection target, urgency of external adjustment information, etc. Drone performance refers to the flight speed, endurance, sensor performance, etc. of the drone. Task priority refers to the urgency and importance of different inspection tasks. The importance level of the inspection target refers to the criticality, value and potential risks of the target. The urgency of external adjustment information refers to the urgency of temporary no-fly zones and the urgency of weather changes. The goal of the algorithm is to find an optimal solution to minimize conflicts and maximize task completion efficiency and safety. This involves replanning the drone's flight path, adjusting the inspection sequence, and reallocating task assignments.

[0122] S1504: Adjust the target flight plan according to the decision information to obtain an adjusted flight plan.

[0123] According to the decision information output by the first preset decision algorithm, the remote integrated control center will adjust the target flight plan to obtain an adjusted flight plan. This new flight plan will better adapt to the current environmental conditions, drone status and mission requirements.

[0124] The present invention reduces unnecessary flight time and energy consumption and improves inspection efficiency by optimizing flight paths and inspection sequences. It ensures the safety of drones and inspection tasks by avoiding collisions between drones and avoiding no-fly zones and dangerous areas. It can adjust flight plans in real time according to external adjustment information and drone group status to adapt to complex and changing environments and task requirements. It can reasonably allocate tasks according to drone performance and task priority to ensure efficient use of resources.

[0125] The first preset decision algorithm can be a Multi-Objective Optimization Decision Algorithm (MOODA). The input of the algorithm includes the operation status information of the drone group, the inspection target data, the external adjustment information, the task priority list and the drone performance parameters. The output of the algorithm is the adjusted flight plan. The algorithm steps are as follows: S210, setting algorithm parameters, such as number of iterations, population size, crossover probability, mutation probability, etc.

[0126] S220, population generation: Generate an initial population based on input information, where each individual represents a possible flight plan.

[0127] S230, fitness evaluation: calculate the fitness value of each individual, which comprehensively considers multiple goals such as task completion efficiency, safety, resource allocation, etc.

[0128] S240. Select excellent individuals as parents according to the fitness value to generate the next generation.

[0129] S250, performing crossover and mutation operations on the parent individuals to generate new offspring individuals.

[0130] S260. Repeat steps S230-S250 until a preset number of iterations is reached or other stop conditions are met.

[0131] S270. Select the individual with the highest fitness value from the final population as the optimal solution, that is, the adjusted flight plan.

[0132] The present invention uses intelligent algorithms to generate dynamic flight plans by integrating multi-source real-time data such as meteorology, airspace, and drone performance, and combines accurate drone task allocation and control mechanisms to achieve accurate matching of target drones and accurate setting of task parameters, ensuring seamless connection of inspection tasks in time and space. At the same time, with the help of AI recognition technology, a large amount of data collected by drones is processed and analyzed in real time, and target information is quickly and accurately parsed, providing timely and accurate basis for inspection decisions. In addition, the present invention also establishes an adaptive dynamic adjustment mechanism based on real-time feedback information and changes in the external environment, ensuring that the inspection system can flexibly respond to various complex situations and always maintain the best inspection state. Under the joint action of this series of innovative points, not only uninterrupted, efficient and accurate inspection operations are achieved, the inspection coverage and timeliness are significantly improved, but also the adaptability and flexibility of the system are enhanced, and it can operate stably in a complex and changeable environment, and provide intelligent decision-making support for inspection management and facility maintenance, optimize resource allocation, and greatly improve the overall inspection efficiency and quality.

[0133] The drone inspection device provided by the present invention is described below. The drone inspection device described below and the drone inspection method described above can be referenced to each other.

[0134] The unmanned aerial vehicle inspection device provided by the present invention refers to Figure 5 As shown, it includes the following modules: The plan generation module 310 is used to generate a target flight plan based on the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal; The data acquisition module 320 is used to match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to perform the flight mission, collect target area data and send it to the UAV inspection control platform; The data identification module 330 is used to control the UAV inspection control platform to identify the target area data to obtain the inspection target object data; The data acquisition module 340 is used to obtain the drone group operation status information and external adjustment information uploaded by the drone inspection control platform; The plan adjustment module 350 is used to adjust the target flight plan using a first preset decision algorithm according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan; wherein the external adjustment information includes: weather change information, airspace resource dynamic adjustment information and new inspection task information; The inspection control module 360 ​​is used to control the UAV equipment terminal to perform inspections according to the adjusted flight plan.

[0135] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the drone inspection method.

[0136] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0137] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the drone inspection method provided by the above-mentioned methods.

[0138] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the drone inspection method provided by the above-mentioned methods.

[0139] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone inspection method, characterized in that: include: Generate a target flight plan based on the acquired meteorological conditions, airspace resource usage, and performance parameters of each UAV equipment terminal; Match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to perform the flight mission, collect target area data and send it to the UAV inspection control platform; Controlling the UAV inspection control platform to identify the target area data to obtain inspection target object data; Obtain the drone group operation status information and external adjustment information uploaded by the drone inspection control platform; According to the drone group operation status information, the inspection target object data and the external adjustment information, the target flight plan is adjusted using a first preset decision algorithm to obtain an adjusted flight plan; wherein the external adjustment information includes: weather change information, airspace resource dynamic adjustment information and new inspection task information; Control the UAV equipment terminal to conduct inspections according to the adjusted flight plan.

2. The drone inspection method according to claim 1, characterized in that: The target flight plan is generated based on the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal, including: Obtain information on meteorological conditions, airspace resource usage, and performance parameters of each drone equipment terminal; Generate multiple initial flight plans according to the meteorological condition information, the airspace resource usage status and the performance parameters of each UAV equipment terminal; A target flight plan is selected from the multiple initial flight plans according to a preset optimization strategy.

3. The drone inspection method according to claim 2, characterized in that: The generating of a plurality of initial flight plans according to the meteorological condition information, the airspace resource usage status and the performance parameters of each UAV equipment terminal includes: Generate corresponding data structures for the meteorological condition information, the airspace resource usage status, and the performance parameters of the UAV equipment terminal, respectively; wherein the data structures corresponding to the meteorological condition information, the airspace resource usage status, and the performance parameters of the UAV equipment terminal are respectively a first data structure, a second data structure, and a third data structure; Define the parameters, scope, and time window of the flight mission; Querying the meteorological data in the corresponding time period from the first data structure according to the time window of the flight mission, and screening out the time period and area that meet the flight safety requirements; According to the scope and time window of the flight mission, query the airspace resource usage in the corresponding area from the second data structure, and filter out the time period and area without flight restrictions; Generate multiple candidate flight plans based on flight mission requirements within the selected time periods and areas that meet flight safety requirements and have no flight restrictions; each candidate flight plan includes a take-off time, a flight route, and a flight speed; Determining, based on the third data structure, whether each candidate flight plan meets the performance requirements of the drone device terminal; A candidate flight plan that meets the performance requirements of the UAV equipment terminal is used as the initial flight plan.

4. The drone inspection method according to claim 3, characterized in that: The method further comprises: When all candidate flight plans do not meet the performance requirements of the UAV device terminal, optimizing the candidate flight plans using an optimization algorithm to obtain an optimized flight plan; The feasibility of the optimized flight plan is judged, and a flight plan that meets the requirements in terms of meteorological conditions, airspace resources and UAV performance parameters is output as the initial flight plan.

5. The drone inspection method according to claim 1, characterized in that: The target area data is obtained by an image acquisition device carried by the UAV inspection control platform; the UAV inspection control platform is controlled to identify the target area data to obtain the inspection target object data, including: The inspection control platform of the drone is controlled to obtain three-dimensional point cloud data through the laser radar equipment carried; Identify the target area data to obtain feature information and status information of the inspection target object; Matching the three-dimensional point cloud data with the characteristic information of the inspection target object to obtain the three-dimensional coordinate information of the inspection target object; The state information of the inspection target object is associated with the three-dimensional coordinate information of the inspection target object to obtain the inspection target object data.

6. The drone inspection method according to claim 1, characterized in that: The method of adjusting the target flight plan by using a first preset decision algorithm according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan includes: Detect conflicts in the target flight plan based on the UAV group operation status information and external adjustment information; Determine whether there are any omissions or duplicate inspections based on the inspection target data; Calling a first preset decision algorithm to make a decision based on the detected conflict, omission or duplicate inspection situation to obtain decision information; wherein the first preset decision algorithm comprehensively considers the performance of the UAV, the task priority, the importance level of the inspection target, and the urgency of the external adjustment information, with the goal of minimizing conflicts and maximizing task completion efficiency and safety; The target flight plan is adjusted according to the decision information to obtain an adjusted flight plan.

7. A drone inspection device, characterized in that: include: The plan generation module is used to generate a target flight plan based on the acquired meteorological condition information, airspace resource usage status, and performance parameters of each UAV equipment terminal; A data acquisition module is used to match the target UAV equipment terminal according to the target flight plan, control the target UAV equipment terminal to perform the flight mission, collect target area data and send it to the UAV inspection control platform; A data identification module is used to control the UAV inspection control platform to identify the target area data to obtain the inspection target object data; The data acquisition module is used to obtain the drone group operation status information uploaded by the drone inspection control platform, as well as external adjustment information; A plan adjustment module, configured to adjust the target flight plan using a first preset decision algorithm according to the drone group operation status information, the inspection target object data and the external adjustment information to obtain an adjusted flight plan; wherein the external adjustment information includes: weather change information, airspace resource dynamic adjustment information and new inspection task information; The inspection control module is used to control the UAV equipment terminal to perform inspections according to the adjusted flight plan.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the drone inspection method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the drone inspection method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the drone inspection method according to any one of claims 1 to 6 is implemented.

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