A joint inspection control method, system, device and storage medium

Abstract

A joint inspection control method, system, device and storage medium, wherein the method comprises: acquiring feature data and weather data of a new energy station, and determining a first inspection area according to power generation equipment distribution data in the feature data; determining first inspection parameters according to the feature data, the weather data and the first inspection area, controlling a first inspection unmanned aerial vehicle to inspect the first inspection area according to the first inspection parameters to collect first inspection images; performing fault analysis on the first inspection images to obtain fault analysis results; determining whether secondary inspection is needed according to the fault analysis results; if the secondary inspection is needed, performing the secondary inspection according to the fault analysis results to collect second inspection images; and if the secondary inspection is not needed, ending the inspection. The application can enable the unmanned aerial vehicle to determine the inspection parameters according to the environmental conditions to perform the inspection, ensure the collection quality of the inspection images, and thus improve the quality of the inspection.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) inspection technology, specifically to a joint inspection control method, system, equipment, and storage medium. Background Technology

[0002] With the large-scale construction of photovoltaic (PV) and wind power plants, efficient and safe daily inspections have become a crucial issue. Because PV and wind power plants are typically located in remote and geographically complex areas, traditional manual inspection methods have become increasingly inefficient, lacking in coverage and safety, making them unsuitable for the inspection needs of large-scale sites. To improve inspection efficiency, accuracy, and safety, drone inspection technology has been widely applied in complex environments such as PV and wind power plants, becoming an important tool for equipment monitoring and fault detection.

[0003] Currently, existing drone inspection solutions typically involve taking photos of equipment along a pre-set inspection route and transmitting the collected images to a backend system for analysis and processing. This method improves inspection efficiency to some extent and reduces labor costs.

[0004] However, existing drone inspection solutions often use fixed inspection parameters that do not change with the environment when performing inspection tasks. In complex environments, using fixed inspection parameters for inspection results in different environmental conditions, which affects the quality of inspection image acquisition and thus reduces the overall quality of inspection. Summary of the Invention

[0005] This application provides a joint inspection control method, system, device, and storage medium to solve the problem that UAVs cannot determine inspection parameters based on environmental conditions for inspection, thereby ensuring the quality of inspection image acquisition and improving the quality of inspection.

[0006] The first aspect of this application provides a joint inspection control method applied in a server, the method comprising:

[0007] The system acquires characteristic data and meteorological data of new energy power stations, and determines a first inspection area based on the power generation equipment distribution data in the characteristic data. The new energy power stations include photovoltaic power stations and wind power stations. Based on the characteristic data, meteorological data, and the first inspection area, the system determines first inspection parameters and controls a first inspection drone to inspect the first inspection area according to the first inspection parameters to collect first inspection images. The system performs fault analysis on the first inspection images to obtain fault analysis results. Based on the fault analysis results, the system determines whether a second inspection is needed. If a second inspection is needed, a second inspection is performed based on the fault analysis results to collect second inspection images. If a second inspection is not needed, the inspection ends.

[0008] Optionally, the first inspection parameters are determined based on characteristic data, meteorological data, and the first inspection area, specifically including:

[0009] The flight altitude range of the first inspection drone is determined based on the environmental feature data in the feature data; meteorological data is analyzed to obtain meteorological analysis results; based on the meteorological analysis results, the inspection path of the first inspection area is determined through preset path setting rules; the flight altitude of the first inspection drone is determined based on the meteorological analysis results and the lower limit of the flight altitude range; the flight altitude range, inspection path and flight altitude are set as the first inspection parameters.

[0010] Optionally, the flight altitude of the first inspection UAV can be determined based on meteorological analysis results and the lower limit of the flight altitude range, specifically including:

[0011] When the meteorological analysis result is the first weather condition, the flight altitude of the first inspection drone is reduced to the lower limit of the flight altitude range by a first preset percentage; when the meteorological analysis result is the second weather condition, the flight altitude of the first inspection drone is adjusted to the upper limit of the flight altitude range by a second preset percentage; when the meteorological analysis result is the third weather condition, the inspection is stopped, the return flight altitude is determined according to the meteorological data, the drone returns according to the inspection path and the return flight altitude, and a new inspection time point is determined according to the meteorological data; wherein, the interference of the first weather condition on the inspection is less than that of the second weather condition, and the interference of the second weather condition on the inspection is less than that of the third weather condition.

[0012] Optionally, if a secondary inspection is required, a secondary inspection will be performed based on the fault analysis results to collect a second inspection image, specifically including:

[0013] The second inspection area is determined based on the fault analysis results, and the second inspection area is prioritized according to the fault analysis results. The second inspection parameters are determined based on the meteorological data, environmental feature data in the feature data and the second inspection area. The second inspection UAV is controlled to inspect the second inspection area according to the second inspection parameters and priority order, and the second inspection images are collected.

[0014] Optionally, if a secondary inspection is required, the method further includes performing a secondary inspection based on the fault analysis results to acquire a second inspection image, and then:

[0015] The first inspection image and the second inspection image are compared and analyzed to obtain the comparison results. Based on the comparison results, it is determined whether all fault locations in the first inspection image can be determined. If all fault locations can be determined, the inspection ends. If all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, at which point the inspection ends.

[0016] Optionally, if the location of all faults cannot be determined, a cyclical inspection is triggered until the location of all faults can be determined, at which point the inspection ends. This includes:

[0017] Based on the comparison results, a third inspection area is determined. The third inspection area includes fault areas detected in the first inspection image but not covered in the second inspection image, as well as fault areas detected in the second inspection image but not detected in the first inspection image. The third inspection area is inspected, and third inspection images are collected. Based on the comparison results, the third inspection images are analyzed to determine whether the location of all faults in all equipment in the new energy power station can be determined. If the location of all faults can be determined, the inspection ends. If the location of all faults cannot be determined, a cyclical inspection is triggered until the location of all faults can be determined, at which point the inspection ends.

[0018] Optionally, if a secondary inspection is required, the method further includes performing a secondary inspection based on the fault analysis results, and acquiring a second inspection image, and then:

[0019] The system identifies equipment images of new energy power stations in the second inspection image and segments these images to obtain the images to be inspected. It then performs matching detection on the images to be inspected using a preset standard image library to obtain a matching score. When the matching score is less than a preset threshold, an anomaly is determined in the image to be inspected, an anomaly warning is generated, and the warning is sent to the management system. When the matching score is greater than or equal to the preset threshold, the image to be inspected is determined to be normal, and an inspection record is generated.

[0020] A second aspect of this application provides a joint inspection and control system, comprising:

[0021] The acquisition module is used to acquire characteristic data and meteorological data of new energy power stations, and determine the first inspection area based on the power generation equipment distribution data in the characteristic data. The new energy power stations include photovoltaic power stations and wind power stations.

[0022] The determination module is used to determine the first inspection parameters based on feature data, meteorological data and the first inspection area, and control the first inspection drone to inspect the first inspection area according to the first inspection parameters in order to collect the first inspection image.

[0023] The analysis module is used to perform fault analysis on the first inspection image and obtain the fault analysis results.

[0024] The judgment module is used to determine whether a secondary inspection is needed based on the fault analysis results.

[0025] The secondary inspection module is used to perform a secondary inspection based on the fault analysis results if a secondary inspection is required, so as to collect a second inspection image.

[0026] The end module is used to end the inspection if a second inspection is not required.

[0027] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0028] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the foregoing descriptions.

[0029] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0030] 1. By acquiring characteristic data and meteorological data from new energy power plants, combined with information on the distribution of power generation equipment, inspection areas are accurately determined, and drones are used for efficient inspections according to pre-set parameters. Fault analysis of the inspection images allows for flexible assessment of whether secondary inspections are necessary, thus avoiding unnecessary duplication of work. Simultaneously, during parameter setting, the drone's flight altitude and inspection path are dynamically adjusted based on environmental characteristic data and meteorological analysis results. This enables the drone to determine inspection parameters according to environmental conditions, ensuring the quality of image acquisition and improving the overall quality of the inspection.

[0031] 2. By analyzing meteorological data, the drone's flight altitude and inspection strategy are dynamically adjusted based on the analysis results. This allows the drone to flexibly adapt to different weather conditions during inspections, ensuring the quality of image acquisition. In clear weather, the flight altitude is lowered to improve image detail; in cloudy weather, the flight altitude is increased to expand the inspection coverage; and in severe weather such as heavy rain, the drone proactively stops inspections and safely returns to base. Simultaneously, it intelligently predicts new inspection times, ensuring the safety and scientific rigor of the inspection mission. This precise response to weather conditions effectively improves the accuracy, efficiency, and reliability of inspections, optimizes resource utilization, and solves the problem of insufficient adaptability to weather changes in traditional inspection methods.

[0032] 3. Secondary inspection areas are determined based on fault analysis results, and these areas are prioritized to address areas with higher fault risk, significantly improving inspection efficiency. Secondary inspection parameters are dynamically set by combining meteorological and environmental data to ensure accurate coverage of problem areas, further enhancing inspection precision. By controlling drones to inspect and collect images of secondary inspection areas according to priority, the inspection path and sequence are scientifically planned, effectively reducing redundant tasks and optimizing resource utilization.

[0033] 4. After comparing and analyzing the first and second inspection images, determine whether all fault locations can be determined based on the comparison results, thereby achieving accurate fault location and improving inspection efficiency. If all fault locations cannot be determined, further determine the third inspection area based on the comparison results, and conduct cyclical inspections of the uncovered fault areas until all fault locations are confirmed, at which point the inspection ends. This method can gradually narrow the inspection scope, focusing on unresolved fault areas, avoiding repeated inspections of already covered areas, thereby improving inspection efficiency, reducing resource waste, and significantly enhancing the accuracy and comprehensiveness of equipment inspections in photovoltaic and wind power plants, ensuring the completeness and accuracy of fault location.

[0034] 5. By recognizing and segmenting the equipment image in the second inspection image, an image to be inspected is generated. This image is then matched using a preset standard image library, and the equipment status is determined based on the matching score. When the matching score is below a preset threshold, an anomaly is quickly identified and an anomaly warning is generated. Simultaneously, the warning information is sent to the management system, enabling timely response to faults. When the matching score is above or equal to the preset threshold, the equipment status is confirmed to be normal, and an inspection record is generated. This method, through the combination of image segmentation and intelligent matching detection, improves the accuracy and efficiency of equipment anomaly detection, ensures timely fault warnings, reduces missed and false detections, and enhances the comprehensiveness and standardization of inspection data recording. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a joint inspection control method in an embodiment of this application;

[0036] Figure 2 This is a schematic diagram of the structure of a joint inspection and control system according to an embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0038] Explanation of reference numerals in the attached diagram: 201, Acquisition module; 202, Determination module; 203, Analysis module; 204, Judgment module; 205, Secondary inspection module; 206, End module; 207, Loop module; 208, Early warning module; 301, Processor; 302, Communication bus; 303, User interface; 304, Network interface; 305, Memory. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0040] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0041] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0042] Figure 1 This is a flowchart illustrating a joint inspection control method in an embodiment of this application.

[0043] Please see Figure 1 In this application embodiment, a joint inspection control method is applied to a server, and the method includes:

[0044] S101. Obtain characteristic data and meteorological data of new energy power stations, and determine the first inspection area based on the power generation equipment distribution data in the characteristic data;

[0045] The data management system acquires characteristic data of new energy power stations, including photovoltaic (PV) power stations and wind power stations. This characteristic data includes at least the distribution data of power generation equipment and environmental characteristics. For PV power stations, the system obtains a distribution map of the entire PV panel array, detailing the spatial location of all PV panels. It also acquires the distribution information of all transformers, including the specific installation locations of all box-type and centralized transformers within the station. Additionally, it includes the distribution information of combiner boxes and the laying paths of major cables. In wind power stations, for the distribution data of power generation equipment, it is necessary to obtain a distribution map of all wind turbines within the station, and record in detail the spatial location information of each turbine, including turbine number, installation coordinates (latitude and longitude), and altitude. The specific location and distribution of the substation within the wind power station should also be obtained, including the installation locations of various electrical equipment (such as main transformers and high-voltage switchgear). Simultaneously, the laying path information of the main cables within the station should be obtained, including low-voltage cables from the wind turbines to the combiner boxes, medium-voltage cables from the combiner boxes to the substation, and the routing of high-voltage cables. Regarding environmental characteristic data, the station's Digital Elevation Model (DEM) can be obtained from the station's data management system. The DEM can digitally display the topographic features of the new energy station, including ground undulations and slope changes. The location coordinates and altitude of all buildings within the station should also be obtained. Furthermore, the distribution data of fixed obstacles within the new energy station should be acquired, such as trees, utility poles, and the distribution of high-voltage and low-voltage power lines, while clearly defining the geographical boundaries of the station.

[0046] After acquiring feature data, the system divides the power generation station into several relatively independent blocks based on the spatial distribution characteristics of the power generation equipment. These blocks are divided according to the natural boundaries of the equipment distribution, such as internal roads, fencing, or obvious terrain separation, while also considering the concentration of equipment types, such as the arrangement direction of photovoltaic panel arrays and wind turbine generators, and the functional zoning within the station, such as the special characteristics of the area surrounding the substation. After completing the basic block division, the system merges these blocks to form one or more first inspection areas. For example, adjacent or functionally similar blocks are merged into one or more first inspection areas; special functional areas such as substations and distribution rooms are usually divided into one or more separate inspection areas. During the merging process, it is necessary to ensure that the areas remain geographically continuous, avoiding disjointed and scattered areas, while also ensuring the integrity of functional areas and avoiding artificially dividing complete functional areas. Furthermore, the scope of the inspection area should be suitable for a drone to complete in a single inspection task. Additionally, corresponding drone parking points can be set up in each first inspection area to facilitate drone inspections of the corresponding blocks within signal range.

[0047] For meteorological data, the system can connect to the local weather forecast system to obtain meteorological data for the current period and a certain future time (e.g., 24 hours), including weather conditions, wind speed, wind direction, and other meteorological parameters. Simultaneously, the new energy power stations also have independent meteorological station towers and distributed auxiliary sensors, depending on the scale and actual needs of the photovoltaic and wind power stations. For example, for small photovoltaic power stations (assuming a total installed capacity of less than 50 MW is considered a small photovoltaic power station) or small wind power stations, a single independent meteorological station tower is typically set up in the central area of ​​the photovoltaic power station; while for large photovoltaic power stations (assuming a total installed capacity greater than 50 MW is considered a large photovoltaic power station) or large wind power stations, one main meteorological station tower is set up, with multiple auxiliary sensors or multiple auxiliary meteorological station towers deployed in key areas (e.g., different areas). These independent meteorological station towers and distributed auxiliary sensors collect meteorological data in real time, and the collected meteorological data is uploaded to the meteorological data management system of the new energy power station in real time. Generally, meteorological data that can be collected through independent meteorological station towers and distributed auxiliary sensors includes at least weather conditions, wind speed, wind direction, visibility, cloud cover and rainfall, which affect the operation of power generation equipment in new energy power plants, as well as meteorological data that affects the flight of inspection drones.

[0048] S102. Determine the flight altitude range of the first inspection drone based on the environmental feature data in the feature data;

[0049] In complex terrain, the first inspection UAV needs to dynamically adjust its flight altitude range according to the terrain features and obstacle distribution of the inspection area to ensure the safety and accuracy of the mission.

[0050] Specifically, the system first uses Digital Elevation Model (DEM) data to obtain the terrain elevation (H_terrain) of each point within the inspection area. A DEM is a terrain data model extracted from satellite, aerial surveying, or terrestrial mapping, providing accurate elevation information for each geographical location within the inspection area. Using DEM data, the system can accurately obtain the terrain elevation for each location within the inspection area, serving as the basis for subsequent flight altitude calculations. The terrain elevation is the starting point for calculating the entire altitude range because the height of obstacles is superimposed on the terrain.

[0051] Based on the acquired terrain elevation, the system analyzes the distribution data of all obstacles within the inspection area, including fixed obstacles such as photovoltaic supports, trees, buildings, transformer boxes, and high-voltage power lines. The absolute height (H_obstacle_absolute) of each obstacle is obtained by adding the terrain elevation (H_terrain) of the obstacle's location to the obstacle's relative height (H_obstacle_relative). The formula is: H_obstacle_absolute = H_terrain + H_obstacle_relative, where H_obstacle_absolute is the absolute height of the obstacle, H_terrain is the terrain elevation of the obstacle's location, and H_obstacle_relative is the relative height of the obstacle. Through this calculation, the system can dynamically acquire the absolute height information of all obstacles within the inspection area, constructing a comprehensive obstacle height model and providing a basis for subsequent calculations of the lower limit of flight altitude.

[0052] The system dynamically determines the lower limit (H_abs_min) of the absolute flight altitude range using absolute altitude data of all obstacles within the inspection area. Specifically, the system first performs a comprehensive analysis of the absolute altitude of all obstacles and takes the maximum value as the highest point of any obstacle within the inspection area. To ensure drone flight safety, the system adds a safety threshold (H_margin) to this value to handle fluctuations in flight altitude, navigation errors, or other unforeseen circumstances. The formula is: H_abs_min = max(H_obstacle_absolute) + H_margin, where H_abs_min is the lower limit of the absolute flight altitude range, and max(H_obstacle_absolute) is the maximum absolute altitude of the obstacle. Through this calculation, the system ensures that the drone can safely fly over all obstacles within the inspection area while avoiding potential flight risks caused by the complexity of obstacle distribution.

[0053] The upper limit of the absolute flight altitude range (H_abs_max) is dynamically determined by the system based on the distribution of high-altitude obstacles in the inspection area. High-altitude obstacles (such as high-voltage power lines, communication towers, etc.) usually have a minimum absolute height limit. The system calculates the limit height of high-altitude obstacles by subtracting a safety threshold (H_margin). The system takes the minimum absolute height of the high-altitude obstacles as the upper limit of the flight altitude range to ensure that the UAV does not approach high-altitude obstacles during flight.

[0054] To convert an absolute flight altitude range into a relative flight altitude range, the system needs to obtain the altitude (H_start) of the first inspection drone's landing point. The altitude of the takeoff point can be directly extracted from the feature data, either from DEM data or the drone's GPS system. The altitude of the takeoff point is the benchmark for relative altitude calculation and is therefore crucial data for subsequent calculations of the relative flight altitude range.

[0055] After obtaining the absolute flight altitude range and the altitude of the takeoff point, the system converts them into a relative flight altitude range relative to the takeoff point. The relative flight altitude range is the flight altitude range of the first inspection UAV, and it is dynamically determined. The formula for calculating the relative altitude is as follows:

[0056] Lower limit of relative flight altitude: H_rel_min = H_abs_min - H_start, where H_rel_min is the lower limit of relative flight altitude, H_abs_min is the lower limit of absolute flight altitude range, and H_start is the altitude of the first inspection drone parking point;

[0057] The upper limit of relative flight altitude is: H_rel_max = H_abs_max - H_start, where H_rel_max is the upper limit of relative flight altitude, H_abs_max is the upper limit of absolute flight altitude range, and H_start is the altitude of the first inspection drone parking point.

[0058] This conversion enables drones to control their flight based on the takeoff point, ensuring that the set flight altitude matches the actual operation.

[0059] S103. Analyze meteorological data and obtain meteorological analysis results;

[0060] Wind speed data collected from independent weather station towers and distributed auxiliary sensors is converted to m / s and categorized into light, moderate, and strong winds based on the equipment status of the inspection drone. Fog is classified into dense fog (0.2 km to 1 km visibility), fog (1 km to 5 km visibility), light fog (5 km to 10 km visibility), and clear skies (visibility greater than 10 km) based on visibility. Rainfall intensity is categorized into no rainfall, light rain, and heavy rain. These meteorological parameters are then comprehensively analyzed in conjunction with the actual needs of station inspections, and the results are categorized into three types based on their degree of interference with the inspection:

[0061] First-level weather conditions: Suitable weather conditions for inspection. These conditions typically include sunny or partly cloudy skies with minimal disturbance. Specific requirements include wind speeds less than 5.4 m / s (light breeze), visibility greater than 10 km (sunny), no rainfall or other significant weather impacts, temperatures between 10°C and 30°C, and suitable humidity that does not affect the performance of the drone equipment. Under these first-level weather conditions, the drone flies stably, acquires high-resolution images, and the inspection mission can be executed smoothly.

[0062] Second, weather conditions: Weather conditions that can be adjusted for inspection. These conditions may include light fog (e.g., moderate visibility of 5-10 km), light rain, or high wind speeds (e.g., 5.4-6.9 m / s). Although these conditions have some impact on the flight stability or image clarity of the UAV, the mission can still be completed by adjusting inspection parameters (e.g., increasing flight altitude, optimizing inspection path).

[0063] Thirdly, weather conditions: Unsuitable weather conditions for inspection, such as severe weather. Severe weather includes, but is not limited to, heavy rain, heavy snow, strong winds (e.g., wind speeds greater than 6.9 m / s), sandstorms, and dense fog or mist. Under these conditions, visibility is typically less than 5 kilometers, rainfall may exceed 20 mm / h, temperatures may be below 10°C or above 30°C, and humidity may approach 100%. These weather conditions can severely impact the flight safety of drones and the quality of inspection mission completion.

[0064] S104. Based on the meteorological analysis results, determine the inspection path of the first inspection area through the preset path setting rules;

[0065] The path setting rules for inspection drones differ depending on the weather conditions. For example, under suitable weather conditions (such as sunny days and light winds), the path planning should ensure full coverage of all equipment within the inspection area. Under conditions that have some impact on the inspection mission (such as light fog and light rain), the path planning should prioritize covering densely populated or high-priority areas, while inspections in other areas may be temporarily suspended. Under conditions that seriously affect the safety of the inspection mission, the inspection mission should be terminated, and the return route should be planned according to the shortest path principle.

[0066] Specifically, under the first meteorological condition, the weather is suitable for inspection, with no significant interference factors, such as a sunny day. The system provides comprehensive coverage of all equipment within the inspection area. For all equipment within the inspection area, the system controls the inspection drone to complete the inspection task according to the inspection path determined by the preset path setting rules. The preset path setting rules for photovoltaic power stations and wind power stations in new energy power plants are consistent. In this implementation, the preset path setting rules are explained using a photovoltaic power station as an example. For photovoltaic power stations, the system divides the areas such as photovoltaic panel arrays, centralized and box-type transformers, combiner boxes, and main cable laying paths into blocks, and plans Z-shaped or serpentine paths according to the distribution characteristics of the equipment to ensure that the drone can efficiently cover the entire inspection area without missing any critical equipment. At the same time, the shortest connection path between critical equipment areas is calculated to reduce the drone's flight distance and energy consumption, thereby optimizing inspection efficiency. To ensure the continuity of the inspection task, the system designs backup paths for each main inspection path, such as reverse flight paths or shorter alternative paths, to cope with possible emergencies, such as obstacles or signal interruptions.

[0067] Under the second meteorological condition, weather conditions have a certain impact on the inspection task, such as the possibility of light rain, light fog, or high wind speeds. Therefore, the system will adjust the inspection route to adapt to the current weather conditions while striving to complete the task. Route planning will prioritize areas with dense equipment or high-priority areas, while inspections of non-critical areas may be temporarily suspended. Areas with dense equipment can be obtained from power generation equipment distribution data, and priority areas for power generation equipment will be set according to the importance of their functions. For photovoltaic power plants, the system will focus on inspecting the core equipment around densely packed photovoltaic panel arrays and centralized transformers, while inspections of peripheral areas or auxiliary facilities will not be performed. The system will also optimize the inspection sequence based on the meteorological conditions within the area, for example, prioritizing areas with lower wind speeds and higher visibility, and scheduling areas with slightly worse weather conditions for later stages.

[0068] Under the third weather condition, due to severe weather events such as dense fog, heavy rain, heavy snow, or strong winds, which seriously affect the safety of the inspection mission, the system will suspend the inspection mission and plan a return route to ensure the safe withdrawal of the drone. The return route planning will prioritize the shortest and safest path back to the starting point, while avoiding high-risk areas such as densely wooded areas, high-wind-speed areas, or areas with low visibility. The system will also combine weather forecast data to analyze future weather changes, reset the next inspection time for the first inspection area, and retain the current inspection route plan so that the mission can be quickly resumed once weather conditions improve.

[0069] S105. Determine the flight altitude of the first inspection drone based on the meteorological analysis results and the lower limit of the flight altitude range;

[0070] Specifically, when the meteorological analysis result is the first weather condition, the flight altitude of the first inspection drone is reduced to the lower limit of the flight altitude range by a first preset percentage; when the meteorological analysis result is the second weather condition, the flight altitude of the first inspection drone is adjusted to the upper limit of the flight altitude range by a second preset percentage; when the meteorological analysis result is the third weather condition, the inspection is stopped, the return flight altitude is determined according to the meteorological data, the drone returns according to the inspection path and the return flight altitude, and a new inspection time point is determined according to the meteorological data; wherein, the interference of the first weather condition on the inspection is less than that of the second weather condition, and the interference of the second weather condition on the inspection is less than that of the third weather condition.

[0071] Under the first meteorological condition, the weather is suitable for inspection, with no significant interference factors. This typically includes clear skies and light winds (wind speed less than 5.4 m / s), high visibility (greater than 10 km), and no rainfall or other interference with light and field of vision. Under these conditions, the UAV's flight environment is stable, suitable for high-precision inspections. The system will lower the UAV's flight altitude to the lower limit of the flight altitude range to improve the accuracy of the inspection task. Specifically, the flight altitude setting is based on a "first preset ratio" of the lower limit of the flight altitude range. This ratio is calculated by the system based on a safety threshold to ensure safety while acquiring more accurate image data; it is generally less than 100%. For example, assuming the dynamically determined flight altitude range is 130 meters to 350 meters, and the system-calculated preset ratio is 90%, the flight altitude can be adjusted to 130 meters × 90% = 117 meters. Setting the flight altitude under the first meteorological condition allows the UAV to get closer to ground equipment (such as photovoltaic panels, transformers, wind turbine blades, blade roots, and towers) while ensuring safety, thereby acquiring higher resolution images.

[0072] Under the second weather condition, weather can have a certain impact on inspection tasks. For example, in light fog, light rain, or high wind speeds, to improve the safety of drone flight, the system will adjust the flight altitude to the upper limit of the flight altitude range at a "second preset ratio," similar to the principle used to calculate the first preset ratio. For example, if the flight altitude range is 130 meters to 350 meters, the system will set the drone's flight altitude to 350 meters × 90% = 315 meters. Increasing the drone's flight altitude can effectively reduce the interference of ground obstacles (such as trees and buildings) on the inspection task, while avoiding the risk of collisions caused by wind fluctuations. By appropriately increasing the flight altitude, the drone can avoid areas with significant impact while maintaining sufficient image coverage and minimizing the impact of cloudy weather on image quality.

[0073] Under the third meteorological condition, severe weather conditions, such as heavy rain, heavy snow, strong winds, dense fog, or mist, severely impact the flight safety of drones and prevent the normal execution of inspection missions. In such cases, the system will immediately halt the inspection and dynamically adjust the drone's return flight altitude based on real-time meteorological data to ensure its safe return to the starting point. The return flight altitude setting comprehensively considers the height of obstacles in the current area, safety thresholds, and the impact of severe weather to avoid obstacles that may be obscured by rain and other potential risks.

[0074] S106. Set the flight altitude range, inspection path, and flight altitude as the first inspection parameters.

[0075] The flight altitude range, inspection path, and dynamically adjusted actual flight altitude are integrated into the first inspection parameter, which serves as the core setting for the UAV to perform inspection tasks.

[0076] S107. Control the first inspection drone to inspect the first inspection area according to the first inspection parameters, so as to collect the first inspection image;

[0077] Based on the first inspection parameters generated by S106, the system controls the first inspection drone to conduct a comprehensive inspection of the first inspection area according to the set path and flight altitude, while simultaneously acquiring high-quality image data. The drone first flies along the set inspection path; for example, in a photovoltaic power station, the drone will sequentially cover the photovoltaic panel array, centralized transformer, combiner box, and other critical equipment using a zigzag or serpentine path. During its flight over the corresponding area, the drone maintains the set flight altitude to ensure clear and comprehensive image acquisition of the equipment surfaces and surrounding environment. To cope with potential emergencies, such as obstacles or signal interruptions, the drone can adjust according to a preset backup path to continue completing the mission.

[0078] During inspections, drones use onboard high-definition cameras and sensors to collect real-time images of equipment and environmental data within the inspection area. For example, in photovoltaic power plants, drones can detect dirt, cracks, or hot spots on the surface of photovoltaic panels, inspect the appearance of transformers, and identify potential safety hazards around combiner boxes. The collected data is transmitted in real-time to the backend system via wireless communication modules for storage and preliminary analysis. In wind power plants, drones can use high-resolution cameras and thermal imaging equipment to conduct comprehensive inspections of wind turbine blades, towers, and their internal electrical equipment, detecting cracks, corrosion, wear, or ice buildup on the blade surfaces, identifying coating peeling, rust, or structural damage on the towers, and detecting abnormal overheating in generators, converters, and other equipment.

[0079] If weather conditions change during the inspection (e.g., increased wind speed or rainfall), the system can dynamically adjust the drone's flight altitude and inspection path to adapt to the current environment. For example, when wind speed increases, the drone will increase its flight altitude to avoid obstacles; when visibility decreases, the inspection path may be optimized into a more compact and shorter route. When weather conditions deteriorate to a third weather condition (e.g., heavy rain or strong winds), the system will terminate the inspection mission and plan the safest return path, guiding the drone to evacuate to the starting point.

[0080] After completing the mission, the drone returned to the starting point along the preset path and landed safely. All collected images and data were uploaded to the backend system.

[0081] S108. Perform fault analysis on the first inspection image to obtain the fault analysis results;

[0082] The system performs fault analysis on the first inspection image to identify potential anomalies in the equipment at the new energy power station and generates fault analysis results. By extracting and comparing features of key equipment parts in the inspection image, the system can accurately determine the equipment status.

[0083] For photovoltaic panels, the system focuses on their surface as a key area, extracting texture and temperature distribution features to detect issues such as cracks, contamination, or hot spots. It also analyzes the edges to check for edge damage or splicing anomalies. For wind turbines, the system focuses on the blades, blade roots, and tower, analyzing the shape and edge integrity of the blades to determine for wear, cracks, or defects. It also checks the tightness of the blade roots and inspects the tower exterior for rust, cracks, or coating peeling. For transformers, the system focuses on their casing, wiring ports, and surrounding areas, detecting deformation, leaks, or coating peeling.

[0084] After feature extraction, the system compares the equipment features in the inspection images with the normal equipment status features in the standard database to determine whether there are any abnormalities in the equipment. If an abnormality is detected, such as hot spots on the surface of a photovoltaic panel, defects on the edge of a wind turbine blade, or deformation of the transformer casing, the system will record this abnormal information, including the location and type of abnormality, and obtain the fault analysis results.

[0085] S109. Determine whether a secondary inspection is needed based on the fault analysis results;

[0086] If the fault analysis results indicate that all equipment within the inspection area is in normal condition and no abnormalities or potential hazards are found, the system will determine that the first inspection task is complete, eliminating the need for a second inspection. An inspection report will be generated, recording the equipment's operating status as normal, and the report will be archived in the management system. For minor anomalies, such as slight dirt on the photovoltaic panel surface or minor wear on the wind turbine blade edges, if these anomalies do not directly affect equipment operating safety and site efficiency, the system will record the anomaly information and include it in subsequent maintenance plans without immediately conducting a second inspection.

[0087] When a serious or suspected abnormality is detected, such as cracks on the surface of a photovoltaic panel, damage to wind turbine blades, or leakage in the transformer casing, the system will determine that further confirmation of the specific circumstances of the abnormality or collection of higher-precision data is required, and at this time, a secondary inspection task will be initiated.

[0088] S110. If a second inspection is required, a second inspection shall be carried out based on the fault analysis results to collect a second inspection image.

[0089] Specifically, the second inspection area is determined based on the fault analysis results, and the second inspection area is prioritized according to the fault analysis results; the second inspection parameters are determined based on the meteorological data, environmental feature data in the feature data, and the second inspection area; the second inspection UAV is controlled to inspect the second inspection area according to the second inspection parameters and priority order, and the second inspection images are collected.

[0090] Based on the fault analysis results from the first inspection, the system accurately locates the specific equipment and its area requiring a second inspection. For example, if a crack or hot spot is detected on a photovoltaic panel, the system designates that photovoltaic panel and the surrounding photovoltaic array area as the second inspection area; if wear or cracks appear on the edges of wind turbine blades, the wind turbine equipment and its surrounding area become the inspection focus; and for transformer casing leaks or abnormal wiring ports, the transformer installation point and its surrounding area become the inspection scope.

[0091] After delineating the second inspection area, the system prioritizes the equipment within these areas. The prioritization is based primarily on the severity of the fault, the importance of the equipment, and its potential impact on the station's operation. For example, cracks on the surface of photovoltaic panels can directly affect power generation efficiency, thus receiving a higher priority; damage to wind turbine blades has a higher priority than minor coating peeling; and leakage in the transformer casing has a higher priority than minor damage to the surface coating.

[0092] After determining the priority, the system combines the distribution of the second inspection area, fault analysis results, and environmental characteristic data (such as terrain undulation, obstacle distribution, and weather conditions) to set the second inspection parameters. The setting rules for the second inspection parameters are the same as those for the first inspection parameters, and can be found in steps S102-S106, which will not be repeated here. Based on the second inspection parameters, the system controls the second UAV to perform a secondary inspection task, focusing on covering the area where high-priority equipment is located and collecting second inspection images.

[0093] S111. If a second inspection is not required, the inspection will end.

[0094] If the fault analysis results indicate that all equipment within the inspection area is in normal condition and no faults are found, the system directly marks the inspection task as complete. The report will record the inspection completion time, the normal operating status of the equipment, and specific inspection data. Simultaneously, if the system detects minor anomalies (such as slight contamination of photovoltaic panels or minor scratches on wind turbine blades), but these anomalies do not significantly affect the operational safety or performance of the equipment, the system will record this anomaly information in the inspection report and include it in subsequent maintenance plans. These records will indicate the location of the abnormal equipment, the type of anomaly, and its severity, for subsequent maintenance personnel to conduct regular inspections and handle. Once the inspection report is completed and stored, the system will end the current inspection task and mark the inspection as complete.

[0095] Optional, in Figure 1 After step S110 in the illustrated embodiment, the following steps can be performed:

[0096] Specifically, the first and second inspection images are compared and analyzed to obtain comparison results. Based on the comparison results, it is determined whether all fault locations in the first inspection image can be determined. If all fault locations can be determined, the inspection ends; if not, a cyclical inspection is triggered until all fault locations can be determined, at which point the inspection ends. Specifically, the process of triggering a cyclical inspection until all fault locations can be determined includes: determining a third inspection area based on the comparison results. The third inspection area includes fault areas detected in the first inspection image but not covered in the second inspection image, as well as fault areas detected in the second inspection image but not detected in the first image; inspecting the third inspection area and collecting third inspection images; analyzing the third inspection images based on the comparison results to determine whether all fault locations of all equipment in the new energy power station can be determined; if all fault locations can be determined, the inspection ends; if not, a cyclical inspection is triggered until all fault locations can be determined, at which point the inspection ends.

[0097] When the first and second inspection images are compared and analyzed, the system determines the areas requiring further inspection, i.e., the third inspection area, based on the comparison results. The third inspection area is defined based on the faults detected in the first inspection image, including fault areas not covered or confirmed in the second inspection image, as well as fault areas detected in the second inspection image but not detected in the first. Specifically: some faults detected in the first inspection image may not be clearly visible in the second inspection image due to interference from lighting, angle, or other environmental factors, thus preventing verification of their location or characteristics. These unconfirmed fault areas are extracted separately to form the third inspection area. For example, if a hot spot is detected on a photovoltaic panel in the first inspection, but the second inspection image fails to cover this area due to a poor shooting angle, then the photovoltaic panel and its surrounding area are designated as the third inspection area. Fault areas detected in the second inspection image but not detected in the first image are also considered third inspection areas.

[0098] After identifying the third inspection area, the system formulates inspection parameters based on the distribution and characteristics of the power generation equipment within that area, and controls the drone to conduct precise inspections of the area, acquiring images for the third inspection. Specifically, the system sets the drone's flight altitude, path planning, and task priority based on the equipment distribution, environmental characteristics (such as terrain and obstacle distribution), and current weather conditions within the third inspection area. For example, the flight altitude is adjusted based on the highest point of obstacles within the third inspection area plus a safety threshold; the inspection path prioritizes covering critical parts of faulty equipment; and task priority is focused on equipment with severe faults or high importance. The drone covers the third inspection area step by step according to the set inspection parameters, ensuring that all unconfirmed faulty areas are clearly captured.

[0099] After acquiring the third inspection image, the system analyzes it and compares it with the first and second inspection images to determine whether all fault locations can be confirmed. The system extracts equipment features from the third inspection image (such as photovoltaic panel surface texture, wind turbine blade edge shape, transformer casing condition, etc.) and compares them with corresponding features in the first and second inspection images. It focuses on analyzing fault areas not confirmed in the first two inspections to verify the specific location, type, and severity of the fault. If the third inspection image can verify fault information not confirmed in the first two inspections (such as crack location, hot spot area, etc.), the fault is marked as confirmed; if the third inspection image still cannot provide sufficient information (such as blurry image, unclear abnormal features, etc.), it is marked as unconfirmed.

[0100] The system comprehensively analyzes the results to determine whether all faults have been covered and confirmed. If fault information in all inspection areas is clear, with no omissions or unconfirmed anomalies, then all fault location is considered complete. If some fault information remains unconfirmed, the next round of cyclical inspection needs to be triggered. Based on the analysis results of the third inspection image, the inspection areas requiring further confirmation are redefined. The new inspection areas only include equipment with unconfirmed faults and their surrounding key areas. Based on current weather conditions, equipment distribution, and environmental characteristics, the UAV's flight altitude, path planning, and task priority are further optimized to ensure inspection efficiency and coverage. The system controls the UAV to fly over the updated inspection areas, collect new inspection images, and analyze them according to the above steps to determine whether all faults can be confirmed.

[0101] Once the system confirms that all faults in the first area have been clearly located, the inspection ends.

[0102] Optional, in Figure 1 After step S111 in the illustrated embodiment, the following steps can be performed:

[0103] Specifically, the system identifies equipment images of new energy power stations in the second inspection image and segments these images to obtain the images to be inspected. It then performs matching detection on the images to be inspected using a preset standard image library to obtain a matching score. When the matching score is less than a preset threshold, it determines that the image to be inspected is abnormal, generates an abnormality warning, and sends the warning to the management system. When the matching score is greater than or equal to the preset threshold, it determines that the image to be inspected is normal and generates an inspection record.

[0104] By identifying and segmenting equipment images of photovoltaic and wind power stations collected in the second inspection images, the system extracts the images of the equipment to be inspected and performs matching detection based on a preset standard image library. The standard image library stores image samples of equipment in normal conditions. By calculating and comparing the matching scores between the inspection images and the standard images, the system determines whether the equipment is in normal condition.

[0105] When the matching score falls below a preset threshold, the system determines that the equipment may be malfunctioning, such as cracks or hot spots on the surface of photovoltaic panels, damage to the edges of wind turbine blades, or leaks in the transformer casing. The system will generate an anomaly warning, recording the equipment location, anomaly type, and severity in detail, and send this information to the management system to remind maintenance personnel to take timely maintenance or repair measures. When the matching score is greater than or equal to the preset threshold, the system determines that the equipment is in normal condition, generates an inspection record (e.g., marked "Inspection normal on XX date"), and archives it for future reference.

[0106] This matching and detection logic also applies during the construction phase, but it will focus on potential problems during equipment installation and commissioning. For example, the system can detect whether photovoltaic panels are tilted, whether the spacing does not meet design requirements, or whether there are cracks in the wind turbine foundation. When an anomaly is detected, the system will generate an early warning message for the construction phase, marking the location and type of the problematic equipment, and will link with the management system to notify the construction party to rectify the problem in a timely manner. If the detection results meet the standards, the system will generate a record of "Installation and testing normal on XX date" and archive it as a basis for construction acceptance.

[0107] Please see Figure 2 This is a schematic diagram of a joint inspection and control system provided in an embodiment of this application. The joint inspection and control system 200 specifically includes:

[0108] The acquisition module 201 is used to acquire characteristic data and meteorological data of new energy power stations, and determine the first inspection area based on the power generation equipment distribution data in the characteristic data. The new energy power stations include photovoltaic power stations and wind power stations.

[0109] The determination module 202 is used to determine the first inspection parameters based on the feature data, meteorological data and the first inspection area, and control the first inspection drone to inspect the first inspection area according to the first inspection parameters in order to collect the first inspection image.

[0110] Analysis module 203 is used to perform fault analysis on the first inspection image and obtain fault analysis results;

[0111] The judgment module 204 is used to determine whether a secondary inspection is needed based on the fault analysis results.

[0112] The secondary inspection module 205 is used to perform a secondary inspection based on the fault analysis results if a secondary inspection is required, so as to collect a second inspection image.

[0113] End module 206 is used to end the inspection if a secondary inspection is not required.

[0114] Optionally, module 202 is determined, specifically for:

[0115] The flight altitude range of the first inspection drone is determined based on the environmental feature data in the feature data; meteorological data is analyzed to obtain meteorological analysis results; based on the meteorological analysis results, the inspection path of the first inspection area is determined through preset path setting rules; the flight altitude of the first inspection drone is determined based on the meteorological analysis results and the lower limit of the flight altitude range; the flight altitude range, inspection path and flight altitude are set as the first inspection parameters.

[0116] Optionally, module 202 is also specifically used for:

[0117] When the meteorological analysis result is the first weather condition, the flight altitude of the first inspection drone is reduced to the lower limit of the flight altitude range by a first preset percentage; when the meteorological analysis result is the second weather condition, the flight altitude of the first inspection drone is adjusted to the upper limit of the flight altitude range by a second preset percentage; when the meteorological analysis result is the third weather condition, the inspection is stopped, the return flight altitude is determined according to the meteorological data, the drone returns according to the inspection path and the return flight altitude, and a new inspection time point is determined according to the meteorological data; wherein, the interference of the first weather condition on the inspection is less than that of the second weather condition, and the interference of the second weather condition on the inspection is less than that of the third weather condition.

[0118] Optional, the secondary inspection module 205 is specifically used for:

[0119] The second inspection area is determined based on the fault analysis results, and the second inspection area is prioritized according to the fault analysis results. The second inspection parameters are determined based on the meteorological data, environmental feature data in the feature data and the second inspection area. The second inspection UAV is controlled to inspect the second inspection area according to the second inspection parameters and priority order, and the second inspection images are collected.

[0120] Optionally, the system also includes a loop module 207, specifically used for:

[0121] The first inspection image and the second inspection image are compared and analyzed to obtain the comparison results. Based on the comparison results, it is determined whether all fault locations in the first inspection image can be determined. If all fault locations can be determined, the inspection ends. If all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, at which point the inspection ends.

[0122] Optionally, loop module 207 is also specifically used for:

[0123] The third inspection area is determined based on the comparison results. The third inspection area includes fault areas detected in the first inspection image but not covered in the second inspection image, as well as fault areas detected in the second inspection image but not detected in the first inspection image. The third inspection image is analyzed based on the comparison results to determine whether all fault locations of all equipment in the new energy power station can be determined. If all fault locations can be determined, the inspection ends. If all fault locations cannot be determined, a cyclical inspection is triggered until all fault locations can be determined, at which point the inspection ends.

[0124] Optionally, the system also includes an early warning module 208, specifically used for:

[0125] The system identifies equipment images of new energy power stations in the second inspection image and segments these images to obtain the images to be inspected. It then performs matching detection on the images to be inspected using a preset standard image library to obtain a matching score. When the matching score is less than a preset threshold, an anomaly is determined in the image to be inspected, an anomaly warning is generated, and the warning is sent to the management system. When the matching score is greater than or equal to the preset threshold, the image to be inspected is determined to be normal, and an inspection record is generated.

[0126] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0127] This embodiment also discloses an electronic device, as shown in the reference. Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0128] The communication bus 302 is used to enable communication between these components.

[0129] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0130] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0131] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0132] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a joint inspection control method.

[0133] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a joint inspection control method. When executed by one or more processors 301, the electronic device executes one or more methods as described in the above embodiments.

[0134] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device 305. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage device 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage device 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0140] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A joint inspection control method, characterized in that, When applied to a server, the method includes: Acquire characteristic data and meteorological data of new energy power stations, and determine the first inspection area based on the power generation equipment distribution data in the characteristic data. The new energy power stations include photovoltaic power stations and wind power stations. Based on the feature data, the meteorological data, and the first inspection area, the first inspection parameters are determined, and the first inspection drone is controlled to inspect the first inspection area according to the first inspection parameters in order to collect the first inspection image. Fault analysis is performed on the first inspection image to obtain the fault analysis results; Based on the fault analysis results, determine whether a secondary inspection is required; If a second inspection is required, a second inspection shall be carried out based on the fault analysis results to collect a second inspection image. If a second inspection is not required, the inspection can be ended. The first inspection parameters are determined based on the feature data, the meteorological data, and the first inspection area, specifically including: The flight altitude range of the first inspection drone is determined based on the environmental feature data in the feature data; The meteorological data was analyzed to obtain meteorological analysis results; Based on the meteorological analysis results, the inspection path for the first inspection area is determined by a preset path setting rule. The flight altitude of the first inspection drone is determined based on the meteorological analysis results and the lower limit of the flight altitude range. The flight altitude range, the inspection path, and the flight altitude are set as the first inspection parameters; The terrain elevation of each point within the inspection area is obtained, the distribution data of all obstacles within the inspection area is analyzed, the absolute height of each obstacle is calculated, and the absolute height data of the inspection area is obtained. The absolute height is the sum of the terrain elevation of the obstacle's location and the relative height of the obstacle itself. The flight altitude range is dynamically determined based on the absolute height data.

2. The method according to claim 1, characterized in that, Determining the flight altitude of the first inspection drone based on the meteorological analysis results and the lower limit of the flight altitude range specifically includes: When the meteorological analysis result is the first meteorological condition, the flight altitude of the first inspection drone is reduced to a first preset ratio of the lower limit of the flight altitude range; When the meteorological analysis result is the second meteorological condition, the flight altitude of the first inspection drone is adjusted to the second preset ratio of the upper limit of the flight altitude range; When the meteorological analysis result is the third meteorological condition, the inspection is stopped, the return flight altitude is determined according to the meteorological data, the inspection is carried out according to the inspection path and the return flight altitude, and a new inspection time point is determined according to the meteorological data. Among them, the interference of the first weather condition on the inspection is less than that of the second weather condition, and the interference of the second weather condition on the inspection is less than that of the third weather condition.

3. The method according to claim 1, characterized in that, If a secondary inspection is required, a secondary inspection will be performed based on the fault analysis results to collect a second inspection image, specifically including: The second inspection area is determined based on the fault analysis results, and the second inspection area is prioritized according to the fault analysis results. The second inspection parameters are determined based on the meteorological data, the environmental feature data in the feature data, and the second inspection area; The second inspection drone is controlled to inspect the second inspection area according to the second inspection parameters and priority order, and to collect the second inspection images.

4. The method according to claim 1, characterized in that, After the method further includes performing a secondary inspection based on the fault analysis results to acquire a second inspection image if a secondary inspection is required, the method also includes: The first inspection image and the second inspection image are compared and analyzed to obtain the comparison results; Based on the comparison results, determine whether all fault locations in the first inspection image can be determined. If all the aforementioned fault locations can be determined, the inspection will end. If the location of all the aforementioned faults cannot be determined, a cyclic inspection is triggered until the location of all the aforementioned faults can be determined, at which point the inspection ends.

5. The method according to claim 4, characterized in that, If the location of all the faults cannot be determined, a cyclic inspection is triggered until the location of all the faults can be determined, at which point the inspection ends. Specifically, this includes: The third inspection area is determined based on the comparison results. The third inspection area is the fault area detected in the first inspection image but not covered in the second inspection image, and the fault area detected in the second inspection image but not detected in the first inspection image. The third inspection area is inspected, and third inspection images are collected; Based on the comparison results, the third inspection image is analyzed to determine whether all fault locations of all equipment in the new energy power station can be determined. If all the aforementioned fault locations can be determined, the inspection will end. If the location of all the aforementioned faults cannot be determined, a cyclic inspection is triggered until the location of all the aforementioned faults can be determined, at which point the inspection ends.

6. The method according to claim 1, characterized in that, If a secondary inspection is required, the method further includes performing a secondary inspection based on the fault analysis results to acquire a second inspection image, and then: Identify the equipment images of the new energy power station in the second inspection image, and segment the equipment images to obtain the image to be inspected; The image to be detected is matched and detected using a preset standard image library to obtain a matching score; When the matching score is less than a preset threshold, it is determined that the image to be detected is abnormal, an abnormality warning is generated, and the abnormality warning is sent to the management system. When the matching score is greater than or equal to the preset threshold, the image to be detected is determined to be normal, and an inspection record is generated.

7. A joint inspection and control system, characterized in that, include: The acquisition module is used to acquire characteristic data and meteorological data of new energy power stations, and determine the first inspection area based on the power generation equipment distribution data in the characteristic data. The new energy power stations include photovoltaic power stations and wind power stations. The determination module is used to determine the first inspection parameters based on the feature data, the meteorological data and the first inspection area, and control the first inspection drone to inspect the first inspection area according to the first inspection parameters in order to collect the first inspection image. The analysis module is used to perform fault analysis on the first inspection image and obtain the fault analysis results; The judgment module is used to determine whether a secondary inspection is needed based on the fault analysis results. The secondary inspection module is used to perform a secondary inspection based on the fault analysis results if a secondary inspection is required, so as to collect a second inspection image. The end module is used to end the inspection if a second inspection is not required. The determining module is further specifically configured to: determine the flight altitude range of the first inspection drone based on the environmental feature data in the feature data; analyze the meteorological data to obtain meteorological analysis results; determine the inspection path of the first inspection area based on the meteorological analysis results and a preset path setting rule; determine the flight altitude of the first inspection drone based on the meteorological analysis results and the lower limit of the flight altitude range; and set the flight altitude range, the inspection path, and the flight altitude as the first inspection parameters. The terrain elevation of each point within the inspection area is obtained, the distribution data of all obstacles within the inspection area is analyzed, the absolute height of each obstacle is calculated, and the absolute height data of the inspection area is obtained. The absolute height is the sum of the terrain elevation of the obstacle's location and the relative height of the obstacle itself. The flight altitude range is dynamically determined based on the absolute height data.

8. A joint inspection and control device, characterized in that, include: One or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the joint inspection control device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on a joint inspection control device, the joint inspection control device performs the method as described in any one of claims 1-6.

Images