Joint inspection control method, system and equipment and storage medium

Abstract

A joint inspection control method, system, device and storage medium, the method comprising: acquiring feature data and meteorological data of a new energy station, and determining a first inspection area according to power generation equipment distribution data in the feature data; determining a first inspection parameter according to the feature data, the meteorological data and the first inspection area, and controlling the first inspection unmanned aerial vehicle to inspect the first inspection area according to the first inspection parameter so as to acquire a first inspection image; performing fault analysis on the first inspection image to obtain a fault analysis result; judging whether secondary inspection is needed or not according to the fault analysis result; if secondary inspection is needed, secondary inspection is carried out according to the fault analysis result to collect a second inspection image; and if the secondary inspection is not needed, ending the inspection. According to the invention, the unmanned aerial vehicle can determine the inspection parameters for inspection according to the environment condition, the collection quality of the inspection image is ensured, and the inspection quality is improved.

Description

Technical Field

[0001] The present application relates to the field of drone inspection technology, and specifically to a joint inspection control method, system, device and storage medium. Background Art

[0002] With the large-scale construction of photovoltaic power stations and wind power stations, how to efficiently and safely complete the daily inspection of photovoltaic power stations and wind power stations has become an important issue. Since photovoltaic power stations and wind power stations are usually located in remote areas with complex terrain, traditional manual inspection methods have gradually exposed problems such as low efficiency, insufficient coverage, and poor safety, making it difficult to meet the inspection needs of large-scale sites. In order to improve inspection efficiency, accuracy, and safety, drone inspection technology has been widely used in complex environments such as photovoltaic power stations and wind power stations, becoming an important tool for equipment monitoring and fault detection.

[0003] At present, the existing drone inspection solutions usually take pictures and record the equipment according to the preset inspection route, and transmit the collected images to the background for analysis and processing. This method improves the inspection efficiency to a certain 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. Inspections using fixed inspection parameters in complex environments result in differences in inspection results under different environmental conditions, affecting the acquisition quality of inspection images, thereby reducing the quality of inspections. Summary of the invention

[0005] The present application provides a joint inspection control method, system, device and storage medium, which are used to solve the problem that UAVs are unable to determine inspection parameters for inspection according to environmental conditions, ensure the acquisition quality of inspection images, and thus improve the quality of inspection.

[0006] In a first aspect of the present application, a joint inspection control method is provided, which is applied to a server. The method includes: Acquire characteristic data and meteorological data of a new energy site, and determine a first inspection area according to the distribution data of power generation equipment in the characteristic data, wherein the new energy site includes a photovoltaic power station and a wind power station; determine a first inspection parameter according to the characteristic data, the meteorological data and the first inspection area, control a first inspection drone to inspect the first inspection area according to the first inspection parameter to collect a first inspection image; perform a fault analysis on the first inspection image to obtain a fault analysis result; determine whether a secondary inspection is required according to the fault analysis result; if a secondary inspection is required, perform a secondary inspection according to the fault analysis result to collect a second inspection image; if a secondary inspection is not required, end the inspection.

[0007] Optionally, determining the first inspection parameter according to the characteristic data, the meteorological data and the first inspection area specifically includes: Determine the flight altitude range of the first inspection UAV according to the environmental characteristic data in the characteristic data; analyze the meteorological data to obtain the meteorological analysis results; determine the inspection path of the first inspection area according to the meteorological analysis results through preset path setting rules; determine the flight altitude of the first inspection UAV according to the meteorological analysis results and the lower limit of the flight altitude range; set the flight altitude range, inspection path and flight altitude as the first inspection parameters.

[0008] Optionally, the flight altitude of the first inspection drone is determined according to the meteorological analysis result and the lower limit of the flight altitude interval, specifically including: When the meteorological analysis result is the first meteorological condition, the flight altitude of the first inspection UAV is reduced to a first preset proportion 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 UAV is adjusted to a second preset proportion 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 returned 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 degree of the first meteorological condition on the inspection is less than the interference degree of the second meteorological condition on the inspection, and the interference degree of the second meteorological condition on the inspection is less than the interference degree of the third meteorological condition.

[0009] Optionally, if a secondary inspection is required, a secondary inspection is performed according to the fault analysis result to collect a second inspection image, specifically including: Determine the second inspection area according to the fault analysis result, and prioritize the second inspection area according to the fault analysis result; determine the second inspection parameters according to the meteorological data, environmental characteristic data in the characteristic data, and the second inspection area; control the second inspection drone to inspect the second inspection area according to the priority order based on the second inspection parameters, and collect second inspection images.

[0010] Optionally, if a secondary inspection is required, after performing the secondary inspection according to the fault analysis result to collect a second inspection image, the method further includes: The first inspection image and the second inspection image are compared and analyzed to obtain a comparison result; based on the comparison result, it is determined whether all fault locations in the first inspection image can be determined; if all fault locations can be determined, the inspection is terminated; if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated.

[0011] Optionally, if all faults cannot be located, a cyclic inspection is triggered until all faults can be located, and then the inspection is terminated, which specifically includes: Determine a third inspection area based on the comparison result, the third inspection area being 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; inspect the third inspection area and collect a third inspection image; analyze the third inspection image based on the comparison result to determine whether all fault locations of all equipment in the new energy station can be determined; if all fault locations can be determined, terminate the inspection; if all fault locations cannot be determined, trigger a cyclic inspection until all fault locations can be determined, and then terminate the inspection.

[0012] Optionally, if a secondary inspection is required, the secondary inspection is performed according to the fault analysis result to collect a second inspection image, and the method further includes: Identify the equipment image of the new energy station in the second inspection image, and segment the equipment image to obtain the image to be detected; perform matching detection on the image to be detected through a preset standard image library to obtain a matching score; when the matching score is less than a preset threshold, determine that the image to be detected is abnormal, generate an abnormal warning, and send the abnormal warning to the management system; when the matching score is greater than or equal to the preset threshold, determine that the image to be detected is normal, and generate an inspection record.

[0013] In a second aspect of the present application, a joint inspection control system is provided, comprising: An acquisition module, used to acquire characteristic data and meteorological data of a new energy station, and determine a first inspection area according to distribution data of power generation equipment in the characteristic data, wherein the new energy station includes a photovoltaic power station and a wind power station; A determination module, used to determine a first inspection parameter according to the characteristic 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 parameter to collect a first inspection image; An analysis module, used to perform fault analysis on the first inspection image to obtain a fault analysis result; A judgment module is used to judge whether a secondary inspection is required based on the fault analysis results; A secondary inspection module, used to perform a secondary inspection according to the fault analysis result 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.

[0014] In the third aspect of the present application, an electronic device is provided, 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 so that the electronic device performs any of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the above methods is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring the characteristic data and meteorological data of new energy stations, combined with the distribution information of power generation equipment, the inspection area is accurately determined, and the drone is used to conduct efficient inspections according to the set inspection parameters. Through fault analysis of the inspection image, it is flexible to determine whether a second inspection is required, thereby avoiding unnecessary duplication of work. At the same time, during the parameter setting process, the environmental characteristic data and meteorological analysis results are combined to dynamically adjust the flight altitude and inspection path of the drone, so that the drone can determine the inspection parameters according to the environmental conditions for inspection, ensuring the acquisition quality of the inspection image, thereby improving the quality of the inspection.

[0017] 2. By analyzing meteorological data, the flight altitude and inspection strategy of the drone are dynamically adjusted according to the results of meteorological analysis, so that the drone can flexibly respond to different meteorological conditions during the inspection process to ensure the quality of inspection image acquisition. The flight altitude is lowered on sunny days to improve the fineness of the inspection image, and the flight altitude is increased on cloudy days to expand the inspection coverage. In severe weather such as heavy rain, the inspection is actively stopped and returned safely. At the same time, the new inspection time point is intelligently predicted to ensure the safety and scientific nature of the inspection task. By accurately responding to meteorological conditions, the accuracy, efficiency and reliability of inspections are effectively improved, resource utilization is optimized, and the problem of insufficient adaptability of traditional inspections to meteorological changes is solved.

[0018] 3. Determine the secondary inspection area through the fault analysis results, and prioritize these areas, so as to give priority to areas with higher fault risks and significantly improve inspection efficiency. Dynamically set secondary inspection parameters in combination with meteorological data and environmental characteristic data to ensure that the inspection can accurately cover the problem area and further enhance the accuracy of the inspection. By controlling the drone to inspect the secondary inspection area in order of priority and collect images, the inspection path and sequence are scientifically planned, which effectively reduces redundant tasks and optimizes resource utilization.

[0019] 4. After comparing and analyzing the first inspection image and the second inspection image, 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, the third inspection area is further determined through the comparison results, and the uncovered fault areas are inspected cyclically until all fault locations are confirmed and the inspection is terminated. Through this method, the inspection scope can be gradually narrowed, focusing on unresolved fault areas, avoiding repeated inspections of covered areas, thereby improving inspection efficiency, reducing resource waste, and greatly improving the accuracy and comprehensiveness of equipment inspections of photovoltaic power stations and wind power stations, ensuring the integrity and accuracy of fault location.

[0020] 5. Generate the image to be detected by identifying and segmenting the device image in the second inspection image, and use the preset standard image library for matching detection to determine the device status based on the matching score. When the matching score is lower than the preset threshold, quickly identify the anomaly and generate an abnormal warning, and send the warning information to the management system to achieve a timely response to the fault; when the matching score is higher than or equal to the preset threshold, confirm that the device status is normal and generate an inspection record. This method improves the accuracy and efficiency of equipment anomaly detection by combining image segmentation with intelligent matching detection, ensures the timeliness of fault warnings, reduces the occurrence of missed detections and false detections, and improves the comprehensiveness and standardization of inspection data records. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a joint inspection control method in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a joint inspection control system in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0022] Explanation of the accompanying drawings: 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 DESCRIPTION

[0023] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0024] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0025] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0026] Figure 1 It is a flow chart of a joint inspection control method in an embodiment of the present application.

[0027] See also Figure 1 In an embodiment of the present application, a joint inspection control method is applied to a server, and the method includes: S101, acquiring characteristic data and meteorological data of a new energy station, and determining a first inspection area according to distribution data of power generation equipment in the characteristic data; The characteristic data of new energy stations are obtained through the station data management system. New energy stations include photovoltaic power stations and wind power stations. The characteristic data at least includes power generation equipment distribution data and environmental characteristic data. In photovoltaic power stations, for power generation equipment distribution data, the photovoltaic panel array distribution map of the entire station is obtained. This distribution map records the spatial location information of all photovoltaic panels in detail; at the same time, the distribution information of all transformers is obtained, including the specific installation locations of all box-type transformers and centralized transformers in the station; in addition, the distribution information of junction boxes and the laying paths of major cables are also included. In a wind power station, for the distribution data of power generation equipment, obtain the distribution map of all wind turbines in the station, and record the spatial location information of each wind turbine in detail, including the wind turbine number, installation coordinates (latitude and longitude) and altitude, etc.; obtain the specific location and distribution of the booster station in the wind power station, including the installation location of various electrical equipment (such as main transformers and high-voltage switch cabinets); at the same time, obtain the laying path information of the main cables in the station, including the low-voltage cable from the wind turbine to the junction box, the medium-voltage cable from the junction box to the booster station, and the direction of the high-voltage cable. In terms of environmental feature data, the digital elevation model (DEM) of the station can also be obtained through the station data management system. The digital elevation model can digitally display the topographic features of the new energy station, including ground undulations, slope changes and other data; at the same time, obtain the location coordinates and altitude of all buildings in the station; also obtain the distribution data of fixed obstacles in the new energy station, such as trees, electric poles, and the distribution of high-voltage and low-voltage wires, etc., and at the same time, clarify the geographical boundary range of the station.

[0028] After acquiring the characteristic data, the system divides the station into several relatively independent blocks according to the spatial distribution characteristics of the power generation equipment. The division of these blocks is based on the natural boundaries of the equipment distribution, such as roads, fence facilities or obvious terrain separations within the site, while considering the concentration of equipment types, such as the arrangement direction of photovoltaic panel arrays and wind turbines, and taking into account the functional divisions within the site, such as the particularity of the area around the booster station. After completing the basic block division, the system merges these blocks to form one or more first inspection areas, for example, merging adjacent or functionally similar blocks into one or more first inspection areas; special functional areas such as booster stations and distribution rooms are usually divided into one or more inspection areas separately. In the merging process, it is necessary to ensure that the area remains geographically continuous to avoid the appearance of jump-like scattered areas, while ensuring the integrity of the functional area, avoiding artificial division of the complete functional area, and making the scope of the inspection area suitable for the drone to complete in one inspection task. At the same time, a corresponding inspection drone parking point can be set in each first inspection area to facilitate the inspection drone to inspect the corresponding block within the signal range.

[0029] For meteorological data, the system can connect the local weather forecast system to the inspection system to obtain meteorological data for the current and future period of time (such as 24 hours), including weather conditions, wind speed, wind direction, and other meteorological parameters. At the same time, independent meteorological station towers and distributed auxiliary sensors are also set up in new energy sites according to the scale and actual needs of photovoltaic power stations and wind power stations. For example, for small photovoltaic power stations (assuming that photovoltaic power stations with a total installed capacity of less than 50 megawatts are small photovoltaic power stations) or small wind power stations, an independent meteorological station tower is usually set up in the central area of ​​the photovoltaic power station; and for large photovoltaic power stations (assuming that photovoltaic power stations with a total installed capacity of more than 50 megawatts are large photovoltaic power stations) or large wind power stations, a main meteorological station tower is set up, and multiple auxiliary sensors or multiple auxiliary meteorological station towers are arranged in key areas (such as different areas). These independent meteorological station towers and distributed auxiliary sensors collect meteorological data in real time, and the collected meteorological data will be uploaded to the meteorological data management system of the new energy site in real time. Generally, the meteorological data that can be collected through independent weather station towers and distributed auxiliary sensors include at least weather conditions, wind speed, wind direction, visibility, cloud cover and rainfall, which affect the operating conditions of power generation equipment in new energy sites, and meteorological data that affect the flight of inspection drones.

[0030] S102, determining a flight altitude range of the first inspection drone according to the environmental feature data in the feature data; In complex terrain, the first inspection drone needs to dynamically adjust the flight altitude range according to the terrain characteristics and obstacle distribution of the inspection area to ensure the safety and accuracy of the mission.

[0031] Specifically, the system first uses the digital elevation model (DEM) data to obtain the terrain altitude (H_terrain) of each point in the inspection area. DEM is a terrain data model extracted from satellite, aerial survey or ground mapping, which can provide accurate altitude information for each geographical location in the inspection area. Through DEM data, the system can accurately obtain the terrain height for each location in the inspection area as the basis for subsequent flight altitude calculations. The terrain altitude is the starting point for the calculation of the entire altitude range, because the height of the obstacle is superimposed on the terrain.

[0032] Based on the terrain altitude, the system analyzes the distribution data of all obstacles in the inspection area, including fixed obstacles such as photovoltaic brackets, trees, buildings, box transformers, and high-voltage wires. The absolute height of each obstacle (H_obstacle_absolute) is obtained by superimposing the terrain altitude (H_terrain) where the obstacle is located and the relative height of the obstacle itself (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 altitude where the obstacle is located, and H_obstacle_relative is the relative height of the obstacle itself. Through this calculation, the system can dynamically obtain the absolute height information of all obstacles in the inspection area, build a comprehensive obstacle height model, and provide a basis for the subsequent calculation of the lower limit of the flight altitude.

[0033] The system uses the absolute height data of all obstacles in the inspection area to dynamically determine the lower limit of the absolute flight altitude range (H_abs_min). Specifically, the system first conducts a comprehensive analysis of the absolute heights of all obstacles and takes the maximum value as the highest point of the obstacles in the inspection area. In order to ensure the flight safety of the drone, the system superimposes a safety threshold (H_margin) on this basis to deal with flight altitude fluctuations, navigation errors or other emergencies. 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 height of the obstacle. Through this calculation, the system ensures that the drone can safely fly over all obstacles in the inspection area while avoiding potential flight risks due to the complexity of obstacle distribution.

[0034] 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 wires, communication towers, etc.) usually have a minimum absolute height limit. The system calculates the height limit of high-altitude obstacles by subtracting a safety threshold (H_margin). The system takes the lowest absolute height of high-altitude obstacles as the upper limit of the flight altitude range to ensure that the drone does not approach high-altitude obstacles during flight.

[0035] In order to convert the absolute flight altitude interval into the relative flight altitude interval, the system needs to obtain the altitude (H_start) of the parking point of the first inspection drone. The altitude of the take-off point can be directly extracted from the feature data, which can be provided by DEM data or the GPS system of the drone. The altitude of the take-off point is the basis for the relative altitude calculation, and therefore is the key data for the subsequent calculation of the relative flight altitude interval.

[0036] After obtaining the absolute flight altitude range and the altitude of the take-off point, the system converts it into a relative flight altitude range relative to the take-off point. The relative flight altitude range is the flight altitude range of the first inspection drone, which is determined dynamically. The calculation formula for the relative altitude is as follows: Relative flight altitude lower limit: H_rel_min=H_abs_min-H_start, where H_rel_min is the relative flight altitude lower limit, H_abs_min is the lower limit of the absolute flight altitude range, and H_start is the altitude of the first inspection drone parking point; Relative flight altitude upper limit: H_rel_max=H_abs_max-H_start, where H_rel_max is the relative flight altitude upper limit, H_abs_max is the upper limit of the absolute flight altitude range, and H_start is the altitude of the first inspection drone parking point.

[0037] This conversion allows the drone to perform flight control based on the take-off point, ensuring that the flight altitude setting is consistent with actual operations.

[0038] S103, analyzing meteorological data to obtain meteorological analysis results; The wind speed in the meteorological data collected by the independent meteorological station tower and the distributed auxiliary sensors is converted into m / s, and the wind speed is divided into light wind, moderate wind and strong wind levels according to the equipment status of the inspection drone; fog is divided into heavy fog (such as visibility between 0.2 km and 1 km), fog (such as visibility between 1 km and 5 km), light fog (such as visibility between 5 km and 10 km) and sunny days (such as visibility greater than 10 km) according to visibility; rainfall intensity is divided into no rainfall, light rain and heavy rain according to rainfall. Then these meteorological parameters are combined with the actual needs of the site inspection for a comprehensive analysis, and the meteorological results are divided into the following three categories according to the degree of interference with the inspection: First meteorology: meteorological conditions suitable for inspection. Such conditions usually include sunny days or cloudy days without significant interference. Specific requirements may be wind speed less than 5.4m / s (i.e. light wind), visibility greater than 10km (i.e. sunny day), no rainfall or other significant weather influences, temperature between 10℃ and 30℃, suitable humidity and no performance impact on the UAV equipment. Under the first meteorological conditions, the UAV will fly stably, the image collected will be clear, and the inspection task can be carried out smoothly.

[0039] Second weather: weather conditions that can be adjusted for inspection. Such conditions may include light fog (such as medium visibility 5~10km), light rain, or high wind speed (such as 5.4~6.9m / s). Although these conditions have a certain impact on the flight stability or image clarity of the drone, the task can still be completed by adjusting the inspection parameters (such as increasing the flight altitude and optimizing the inspection path).

[0040] Third weather: weather conditions that are not suitable for inspection, such as bad weather. Bad weather includes but is not limited to heavy rain, heavy snow, strong winds (such as wind speed greater than 6.9m / s), sandstorms, heavy fog or mist, etc. Under such conditions, visibility is usually less than 5 kilometers, rainfall may be greater than 20mm / h, temperature may be lower than 10℃ or higher than 30℃, and humidity is close to 100%. These weather conditions will seriously affect the flight safety of drones and the quality of completion of inspection tasks.

[0041] S104, determining the inspection path of the first inspection area by using a preset path setting rule according to the meteorological analysis result; Under different meteorological conditions, the path setting rules of inspection drones are different. For example, under the first meteorological condition suitable for inspection (such as sunny days, light wind, etc.), the path planning must ensure full coverage of all equipment in the inspection area; under the second meteorological condition that has a certain impact on the inspection task (such as light fog, light rain, etc.), the path planning gives priority to covering equipment-intensive areas or high-priority areas, while the inspection tasks in other areas may be temporarily shelved; under the third meteorological condition that seriously affects the safety of the inspection task, the inspection task is terminated, and the return path is planned according to the shortest path principle.

[0042] Specifically, under the first meteorological condition, the meteorological conditions are suitable for inspection, and there are no significant interference factors, such as sunny days. The system comprehensively covers all equipment in the inspection area. For all equipment in the inspection area, the system will control 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 of photovoltaic power stations and wind power stations in new energy stations are consistent. In this implementation, the preset path setting rules are explained by taking the 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, junction 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 key equipment. At the same time, the shortest connection path between key equipment areas is calculated to reduce the flight distance and energy consumption of the drone, thereby optimizing the inspection efficiency. In order to ensure the continuity of the inspection task, the system will design a backup path for each main inspection path, such as a reverse flight path or a shorter alternative path, to deal with possible emergencies, such as obstacles or signal interruptions.

[0043] Under the second meteorological condition, the meteorological conditions have a certain impact on the inspection tasks, such as light rain, light fog or high wind speed. To this end, the system will adjust the inspection path to adapt to the current meteorological conditions and try to complete the task. Path planning will give priority to covering equipment-intensive areas or high-priority areas, while inspection tasks in non-critical areas may be temporarily shelved. Equipment-intensive areas can be obtained through the distribution data of power generation equipment, and the priority areas of power generation equipment are set according to the importance of the functions of power generation equipment. For photovoltaic power stations, the system will focus on inspecting the core equipment around the dense photovoltaic panel array and the centralized transformer, while the inspection tasks of the marginal areas or auxiliary facilities will not be performed for the time being. The system will also optimize the inspection order according to the meteorological conditions in the area, such as giving priority to areas with low wind speed and high visibility for inspection, and arranging areas with slightly worse meteorological conditions in the subsequent stage.

[0044] Under the third meteorological condition, due to severe weather conditions, such as heavy fog or mist, heavy rain, heavy snow or strong winds, which seriously affect the safety of the inspection mission, the system will stop the inspection mission and plan a return path to ensure the safe evacuation of the drone. The return path planning will give priority to the shortest safe path to the starting point, while avoiding high-risk areas, such as densely treed areas, high wind speed areas or areas with low visibility. The system will also combine weather forecast data, analyze future meteorological changes, and re-set the next inspection time point for the first inspection area, while retaining the current inspection path planning so that the mission can be quickly resumed after weather conditions improve.

[0045] S105, determining the flight altitude of the first inspection drone according to the meteorological analysis result and the lower limit of the flight altitude interval; Specifically, when the meteorological analysis result is the first meteorological condition, the flight altitude of the first inspection UAV is reduced to a first preset proportion 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 UAV is adjusted to a second preset proportion 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 returned 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 degree of the first meteorological condition on the inspection is less than the interference degree of the second meteorological condition on the inspection, and the interference degree of the second meteorological condition on the inspection is less than the interference degree of the third meteorological condition.

[0046] Among them, under the first meteorological condition, the weather is suitable for inspection, and there are no significant interference factors, usually including sunny days and light winds (wind speed less than 5.4m / s), high visibility (greater than 10 kilometers), no rain or other interference to light and vision, etc. Under such conditions, the flight environment of the drone is stable and suitable for high-precision inspections. The system will reduce the flight altitude of the first inspection drone to the lower limit of the flight altitude range to improve the accuracy of the inspection task. Specifically, the setting of the flight altitude is based on the "first preset ratio" of the lower limit of the flight altitude range. The ratio is calculated by the system based on the safety threshold to ensure safety while collecting more accurate image data, which is generally less than 100%. Assuming that the flight altitude range calculated after dynamic determination is 130 meters to 350 meters, and the preset ratio calculated by the system is 90%, the flight altitude can be adjusted to 130 meters × 90% = 117 meters. The flight altitude setting under the first meteorological condition enables the drone to get closer to ground equipment (such as photovoltaic panels, transformers, blades, roots and towers of wind turbines, etc.) while ensuring safety, thereby collecting higher resolution images.

[0047] Under the second meteorological condition, the weather will have a certain impact on the inspection task, such as light fog, slight rainfall, high wind speed, etc. In order to improve the safety of the UAV flight, the system will adjust the flight altitude to the "second preset ratio" of the upper limit of the flight altitude range, similar to the principle of calculating the first preset ratio, and calculate the second preset ratio. For example, if the flight altitude range is 130 meters to 350 meters, the system will set the flight altitude of the UAV to 350 meters × 90% = 315 meters. Increasing the flight altitude of the UAV can effectively reduce the interference of ground obstacles (such as trees, buildings, etc.) on the inspection task, while avoiding the risk of collision caused by wind fluctuations. By appropriately increasing the flight altitude, the UAV can avoid areas with greater impact while maintaining sufficient image coverage and minimizing the impact of cloudy weather on image quality.

[0048] Under the third meteorological condition, the weather conditions are bad, such as heavy rain, heavy snow, strong winds, fog or mist, which seriously affect the flight safety of the drone and the inspection task cannot be performed normally. In this case, the system will directly stop the inspection and dynamically set the return flight altitude of the drone according to the real-time meteorological data to ensure its safe return to the starting point. The setting of the return flight altitude will comprehensively consider the obstacle height, safety threshold and the impact of bad weather in the current area to avoid obstacles that may be blurred by rain and other potential risks.

[0049] S106: Set the flight height range, inspection path and flight height as first inspection parameters.

[0050] 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 drone to perform inspection tasks.

[0051] S107, controlling the first inspection drone to inspect the first inspection area according to the first inspection parameter to collect a first inspection image; 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 collecting high-quality image data. The drone first flies according to the set inspection path. For example, in a photovoltaic power station, the drone will cover the photovoltaic panel array, centralized transformer, junction box and other key equipment in sequence according to a zigzag or serpentine path. The drone maintains the set flight altitude during the flight of the corresponding area to ensure that the image acquisition of the equipment surface and the surrounding environment is clear and the coverage is complete. In order to deal with possible emergencies, such as obstacles or signal interruptions, the drone can adjust according to the preset backup path to continue to complete the task.

[0052] During the inspection process, the drone uses the high-definition camera and sensors on board to collect equipment images and environmental data in the inspection area in real time. For example, for photovoltaic power stations, drones can capture whether there is contamination, cracks or hot spots on the surface of photovoltaic panels, check whether the appearance of the transformer is intact, and whether there are safety hazards around the junction box. The collected data is transmitted to the background system in real time through the wireless communication module for storage and preliminary analysis. For wind power stations, drones can use high-resolution cameras and thermal imaging equipment to conduct comprehensive inspections of wind turbine blades, towers and their internal electrical equipment, capture problems such as cracks, corrosion, wear or ice accumulation on the blade surface, identify whether the tower has coating shedding, rust or structural damage, and detect whether there is abnormal heating in equipment such as generators and converters.

[0053] If weather conditions change during the inspection (such as 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 the wind speed increases, the drone will increase its flight altitude to avoid obstacles; when visibility decreases, the inspection path may be optimized to a more compact short path. When weather conditions deteriorate to the third weather (such as heavy rain or strong winds), the system will terminate the inspection mission and plan the safest return path to guide the drone to evacuate to the starting point.

[0054] After the mission is completed, the drone returns to the starting point along the preset path and lands safely, and all collected images and data are uploaded to the background system.

[0055] S108, performing fault analysis on the first inspection image to obtain a fault analysis result; Perform fault analysis on the first inspection image, identify possible abnormal conditions of the equipment in the new energy station, and generate fault analysis results. By extracting and comparing the features of key equipment parts in the inspection image, the system can accurately determine the equipment status.

[0056] For photovoltaic panels, the system uses their surfaces as key parts to extract texture features and temperature distribution features to detect cracks, stains, or hot spots. At the same time, the edge parts are analyzed to check whether there is edge damage or splicing abnormalities. For wind turbines, the system uses blades, blade roots, and towers as key parts to analyze the shape and edge integrity of the blades to determine whether there is wear, cracks, or defects. At the same time, the system detects the tightness of the blade roots and whether there is rust, cracks, or coating peeling on the outside of the tower. For transformers, the system focuses on its casing, wiring ports, and surrounding areas to detect whether the casing is deformed, leaking, or the coating is peeling off.

[0057] After feature extraction, the system compares the device features in the inspection image with the normal device status features in the standard database, and determines whether the device is abnormal through comparison analysis. If an abnormality is detected, such as hot spots on the surface of photovoltaic panels, defects on the edges of wind turbine blades, or deformation of transformer casings, the system will record these abnormal information, including the location and type of abnormal equipment, and obtain fault analysis results.

[0058] S109, judging whether a secondary inspection is required according to the fault analysis result; If the fault analysis results show that all equipment in the inspection area is in normal condition and no abnormalities or potential hazards are found, the system will determine that the first inspection task has been completed and no secondary inspection is required. The system will generate an inspection report, record the equipment operation status as normal, and archive the report to the management system. For minor abnormalities, such as slight contamination on the surface of photovoltaic panels or slight wear on the edges of wind turbine blades, if these abnormalities do not have a direct impact on the safety of equipment operation and site efficiency, the system will record the abnormal information and include it in the subsequent maintenance plan without the need for an immediate secondary inspection.

[0059] When serious or suspected abnormalities are detected, such as cracks on the surface of photovoltaic panels, damaged wind turbine blades, or leakage in transformer casing, the system will determine that it is necessary to further confirm the specific circumstances of the abnormality or collect more accurate data, and then initiate a secondary inspection task.

[0060] S110, if a second inspection is required, a second inspection is performed according to the fault analysis result to collect a second inspection image; Specifically, the second inspection area is determined according to the fault analysis result, and the second inspection area is prioritized according to the fault analysis result; the second inspection parameters are determined according to the meteorological data, environmental characteristic data in the characteristic data, and the second inspection area; the second inspection drone is controlled to inspect the second inspection area in order of priority according to the second inspection parameters, and collect second inspection images.

[0061] Among them, based on the fault analysis results of the first inspection, the system accurately locates the specific equipment and the area where it is located that needs a second inspection. For example, if a photovoltaic panel is detected with cracks or hot spots, the system sets the photovoltaic panel and the surrounding photovoltaic array area as the second inspection area; if the edge of the wind turbine blade is worn or cracked, the wind turbine equipment and the surrounding area will be the inspection focus; if the transformer casing leaks or the wiring port is abnormal, the transformer installation point and the surrounding area will be the inspection range.

[0062] After defining the second inspection area, the system will prioritize the equipment in these areas. The basis for the ranking is mainly the severity of the fault, the importance of the equipment and its potential impact on the operation of the site. For example, cracks on the surface of photovoltaic panels may directly affect the power generation efficiency, so they have a higher priority; the damage problem of wind turbine blades has a higher priority than the slight peeling of the coating; the leakage problem of the transformer casing has a higher priority than the slight damage to the surface coating.

[0063] After determining the priority, the system sets the second inspection parameters based on the distribution of the second inspection area, the fault analysis results, and environmental characteristic data (such as terrain undulations, obstacle distribution, meteorological conditions, etc.). The setting rules of the second inspection parameters are the same as those of the first inspection parameters. For details, please refer to steps S102 to S106, which will not be repeated here. According to the second inspection parameters, the system controls the second UAV to perform the secondary inspection task, focusing on covering the area where the high-priority equipment is located and collecting the second inspection image.

[0064] S111. If a second inspection is not required, the inspection is terminated.

[0065] If the fault analysis results show that all equipment in the inspection area is in normal condition and no faults are found, the system directly marks the inspection task as completed. The report will record the inspection completion time, the normal operating status of the equipment, and the specific inspection data. At the same time, if the system detects some minor abnormalities (such as slight contamination of photovoltaic panels, tiny scratches on the surface of wind turbine blades, etc.), but these abnormalities do not significantly affect the operating safety or performance of the equipment, the system will record these abnormal information in the inspection report and include them in the subsequent maintenance plan. These records will indicate the location of the abnormal equipment, the type and degree of the abnormality, so that subsequent operation and maintenance personnel can conduct regular inspections and processing. When the inspection report is completed and stored, the system will end the current inspection task and mark the end of the inspection.

[0066] Optional, in Figure 1 After step S110 of the illustrated embodiment, the following steps may be performed: Specifically, the first inspection image and the second inspection image are compared and analyzed to obtain a comparison result; based on the comparison result, it is determined whether all fault locations in the first inspection image can be determined; if all fault locations can be determined, the inspection is terminated; if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated. Among them, if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated, which specifically includes: determining a third inspection area based on the comparison result, the third inspection area is a fault area detected in the first inspection image but not covered in the second inspection image, and a fault area detected in the second inspection image but not detected in the first inspection image; inspecting the third inspection area and collecting a third inspection image; analyzing the third inspection image based on the comparison result to determine whether all fault locations of all equipment in the new energy station can be determined; if all fault locations can be determined, the inspection is terminated; if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated.

[0067] Among them, after the first inspection image is compared and analyzed with the second inspection image, the system will determine the area that needs further inspection based on the comparison results, that is, the third inspection area. The demarcation of the third inspection area is based on the faults detected in the first inspection image, including those fault areas that are not covered or confirmed in the second inspection image, and those fault areas detected in the second inspection image but not detected in the first inspection image. Specifically: some faults detected in the first inspection image may not be captured in the second inspection image with a clear enough image to verify their position or characteristics due to interference from light, angle or other environmental factors. The unconfirmed fault areas are extracted separately to form the third inspection area. For example, a photovoltaic panel detects a hot spot in the first inspection, but the second inspection image fails to cover the area due to poor shooting angles. In this case, the photovoltaic panel and its surrounding area are demarcated as the third inspection area. The fault area detected in the second inspection image but not detected in the first inspection image is also the third inspection area.

[0068] After determining the third inspection area, the system formulates inspection parameters according to the distribution and characteristics of the power generation equipment in the third inspection area, and controls the drone to conduct precise inspections in the area and collect the third inspection images. Specifically, the system sets the flight altitude, path planning and task priority of the drone according to the equipment distribution, environmental characteristic data (such as terrain, obstacle distribution, etc.) and current meteorological conditions in the third inspection area. For example: the flight altitude is adjusted according to the highest point of the obstacle in the third inspection area plus the safety threshold; the inspection path gives priority to covering the key parts of the faulty equipment; the task priority is concentrated on equipment with serious faults or high importance. The drone covers the third inspection area one by one according to the set inspection parameters to ensure that all unconfirmed fault areas can be clearly captured.

[0069] After completing the acquisition of the third inspection image, the system analyzes it and combines the comparison results of the first inspection image and the second inspection image to determine whether all fault locations can be confirmed. The system extracts the equipment features in the third inspection image (such as the surface texture of the photovoltaic panel, the edge shape of the wind turbine blade, the status of the transformer housing, etc.) and compares them with the corresponding features in the first and second inspection images. Focus on analyzing the fault areas that have not been confirmed by the first two inspections to verify the specific location, type and severity of the fault. If the third inspection image can verify the fault information that was 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 enough information (such as blurred image, unclear abnormal features, etc.), it is marked as unconfirmed.

[0070] The system comprehensively analyzes the results to determine whether all faults have been covered and confirmed. If the fault information of all inspection areas is clear and there are no omissions or unconfirmed abnormalities, it is considered that all faults have been located; if some fault information is still not confirmed, it is necessary to continue to trigger the next round of cyclic inspection. According to the analysis results of the third inspection image, the inspection area that needs further confirmation is redefined. The new inspection area only includes those equipments and their surrounding key areas where the faults have not been confirmed. According to the current meteorological conditions, equipment distribution and environmental characteristics data, the flight altitude, path planning and task priority of the drone are further optimized to ensure the inspection efficiency and coverage. The system controls the drone to fly over the updated inspection area, collect new inspection images, and analyze them according to the above steps to determine whether all faults can be confirmed.

[0071] When the system confirms that all faults in the first area have been clearly located, the inspection ends.

[0072] Optional, in Figure 1 After step S111 of the illustrated embodiment, the following steps may be performed: Specifically, the equipment image of the new energy station in the second inspection image is identified, and the equipment image is segmented to obtain the image to be detected; the image to be detected is matched and detected through 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 abnormal warning is generated, and the abnormal warning is sent to the management system; when the matching score is greater than or equal to the preset threshold, it is determined that the image to be detected is normal, and an inspection record is generated.

[0073] By identifying and segmenting the equipment images of the photovoltaic power station and wind power station collected in the second inspection image, the equipment image to be detected is extracted, and matching detection is performed based on the preset standard image library. The standard image library stores image samples of the equipment in normal status. By calculating and comparing the matching scores of the inspection image and the standard image, the system determines whether the equipment status is normal.

[0074] When the matching score is lower than the preset threshold, the system determines that the device may be abnormal, such as cracks or hot spots on the surface of the photovoltaic panel, damage to the edge of the wind turbine blade, leakage in the transformer casing, etc. The system will generate abnormal warning information, record the location of the device, the type of abnormality and the severity in detail, and send the information to the management system to remind the operation and maintenance personnel to take maintenance or repair measures in time. When the matching score is greater than or equal to the preset threshold, the system determines that the device is in normal state and generates an inspection record, such as "XX date inspection is normal", and archives it for subsequent reference.

[0075] This matching detection logic is also applicable during the construction period, but it will focus on potential problems during the equipment installation and commissioning phase. For example, the system can detect whether the photovoltaic panels are tilted, the spacing does not meet the design requirements, or whether there are cracks in the wind turbine foundation. When an abnormality is detected, the system will generate an early warning message for the construction phase, mark the location and type of the problem equipment, and link the management system to notify the construction party to make timely corrections; if the test results meet the standards, the system will generate a record of "installation test normal on XX date" and archive it as the basis for construction acceptance.

[0076] See also Figure 2 , is a structural diagram of a joint inspection control system provided in an embodiment of the present application, a joint inspection control system 200 specifically includes: The acquisition module 201 is used to acquire characteristic data and meteorological data of a new energy station, and determine a first inspection area according to the distribution data of power generation equipment in the characteristic data, wherein the new energy station includes a photovoltaic power station and a wind power station; The determination module 202 is used to determine the first inspection parameter according to the characteristic 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 parameter to collect the first inspection image; The analysis module 203 is used to perform fault analysis on the first inspection image to obtain a fault analysis result; The judgment module 204 is used to judge whether a secondary inspection is required according to the fault analysis result; A secondary inspection module 205 is used to perform a secondary inspection according to the fault analysis result if a secondary inspection is required, so as to collect a second inspection image; The end module 206 is used to end the inspection if a second inspection is not required.

[0077] Optionally, the determination module 202 is specifically configured to: Determine the flight altitude range of the first inspection UAV according to the environmental characteristic data in the characteristic data; analyze the meteorological data to obtain the meteorological analysis results; determine the inspection path of the first inspection area according to the meteorological analysis results through preset path setting rules; determine the flight altitude of the first inspection UAV according to the meteorological analysis results and the lower limit of the flight altitude range; set the flight altitude range, inspection path and flight altitude as the first inspection parameters.

[0078] Optionally, the determination module 202 is further specifically configured to: When the meteorological analysis result is the first meteorological condition, the flight altitude of the first inspection UAV is reduced to a first preset proportion 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 UAV is adjusted to a second preset proportion 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 returned 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 degree of the first meteorological condition on the inspection is less than the interference degree of the second meteorological condition on the inspection, and the interference degree of the second meteorological condition on the inspection is less than the interference degree of the third meteorological condition.

[0079] Optionally, the secondary inspection module 205 is specifically used for: Determine the second inspection area according to the fault analysis result, and prioritize the second inspection area according to the fault analysis result; determine the second inspection parameters according to the meteorological data, environmental characteristic data in the characteristic data, and the second inspection area; control the second inspection drone to inspect the second inspection area according to the priority order based on the second inspection parameters, and collect second inspection images.

[0080] Optionally, the system further includes a circulation module 207, which is specifically used for: The first inspection image and the second inspection image are compared and analyzed to obtain a comparison result; based on the comparison result, it is determined whether all fault locations in the first inspection image can be determined; if all fault locations can be determined, the inspection is terminated; if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated.

[0081] Optionally, the circulation module 207 is further specifically used for: The third inspection area is determined according to the comparison result, and 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 image is analyzed according to the comparison result to determine whether all fault locations of all equipment in the new energy station can be determined; if all fault locations can be determined, the inspection is terminated; if all fault locations cannot be determined, a cyclic inspection is triggered until all fault locations can be determined, and the inspection is terminated.

[0082] Optionally, the system further includes an early warning module 208, which is specifically used to: Identify the equipment image of the new energy station in the second inspection image, and segment the equipment image to obtain the image to be detected; perform matching detection on the image to be detected through a preset standard image library to obtain a matching score; when the matching score is less than a preset threshold, determine that the image to be detected is abnormal, generate an abnormal warning, and send the abnormal warning to the management system; when the matching score is greater than or equal to the preset threshold, determine that the image to be detected is normal, and generate an inspection record.

[0083] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0084] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0085] The communication bus 302 is used to realize the connection and communication between these components.

[0086] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0087] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0088] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0089] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a joint inspection control method.

[0090] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program storing a joint inspection control method in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0091] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0097] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A joint inspection control method, characterized in that: Applied in a server, the method comprises: Acquire characteristic data and meteorological data of a new energy station, and determine a first inspection area according to distribution data of power generation equipment in the characteristic data, wherein the new energy station includes a photovoltaic power station and a wind power station; Determine a first inspection parameter according to the characteristic data, the meteorological data and the first inspection area, and control a first inspection drone to inspect the first inspection area according to the first inspection parameter to collect a first inspection image; Performing fault analysis on the first inspection image to obtain a fault analysis result; Determine whether a secondary inspection is required based on the fault analysis result; If a second inspection is required, a second inspection is performed according to the fault analysis result to collect a second inspection image; If a second inspection is not required, the inspection is terminated.

2. The method according to claim 1, characterized in that The determining of the first inspection parameter according to the characteristic data, the meteorological data and the first inspection area specifically includes: Determining a flight altitude range of the first inspection drone according to the environmental feature data in the feature data; Analyzing the meteorological data to obtain meteorological analysis results; According to the meteorological analysis result, determining the inspection path of the first inspection area by using a preset path setting rule; Determining the flight altitude of the first inspection drone according to the meteorological analysis result and the lower limit of the flight altitude interval; The flight height interval, the inspection path and the flight height are set as the first inspection parameters.

3. The method according to claim 2, characterized in that The determining the flight altitude of the first inspection drone according to the meteorological analysis result and the lower limit of the flight altitude interval specifically includes: When the meteorological analysis result is a first meteorological condition, reducing the flight altitude of the first inspection drone to a first preset proportion of the lower limit of the flight altitude range; When the meteorological analysis result is the second meteorological condition, adjusting the flight altitude of the first inspection drone to a 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, a return flight altitude is determined according to the meteorological data, the inspection is returned 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 degree of the first meteorological condition on the inspection is smaller than the interference degree of the second meteorological condition on the inspection, and the interference degree of the second meteorological condition on the inspection is smaller than the interference degree of the third meteorological condition.

4. The method according to claim 1, characterized in that: If a secondary inspection is required, a secondary inspection is performed according to the fault analysis result to collect a second inspection image, specifically including: Determine a second inspection area according to the fault analysis result, and prioritize the second inspection area according to the fault analysis result; Determining a second inspection parameter according to the meteorological data, the environmental characteristic data in the characteristic 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 in order of priority, and collect second inspection images.

5. The method according to claim 1, characterized in that If a secondary inspection is required, after performing a secondary inspection according to the fault analysis result to collect a second inspection image, the method further includes: Comparing and analyzing the first inspection image and the second inspection image to obtain a comparison result; Determining whether all fault locations in the first inspection image can be determined according to the comparison result; If all the fault locations can be determined, the inspection is terminated; If all the fault locations cannot be determined, a cyclic inspection is triggered until all the fault locations can be determined, and then the inspection is terminated.

6. The method according to claim 5, characterized in that If all the fault locations cannot be determined, triggering a cyclic inspection until all the fault locations can be determined, and then ending the inspection, specifically includes: Determine a third inspection area according to the comparison result, wherein the third inspection area is a fault area detected in the first inspection image but not covered in the second inspection image, and a fault area detected in the second inspection image but not detected in the first inspection image; Inspecting the third inspection area and collecting a third inspection image; Analyze the third inspection image according to the comparison result to determine whether all fault locations of all equipment in the new energy station can be determined; If all the fault locations can be determined, the inspection is terminated; If all the fault locations cannot be determined, a cyclic inspection is triggered until all the fault locations can be determined, and then the inspection is terminated.

7. The method according to claim 1, characterized in that If a secondary inspection is required, a secondary inspection is performed according to the fault analysis result to collect a second inspection image, and the method further includes: Identify the equipment image of the new energy station in the second inspection image, and segment the equipment image to obtain an image to be inspected; Performing matching detection on the image to be detected through 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 has an abnormality, 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, it is determined that the image to be detected is normal, and an inspection record is generated.

8. A joint inspection control system, characterized in that: include: An acquisition module, used to acquire characteristic data and meteorological data of a new energy station, and determine a first inspection area according to distribution data of power generation equipment in the characteristic data, wherein the new energy station includes a photovoltaic power station and a wind power station; a determination module, configured to determine a first inspection parameter according to the characteristic data, the meteorological data and the first inspection area, and control a first inspection drone to inspect the first inspection area according to the first inspection parameter to collect a first inspection image; An analysis module, used to perform fault analysis on the first inspection image to obtain a fault analysis result; A judgment module, used to judge whether a secondary inspection is required according to the fault analysis result; A secondary inspection module, used for performing a secondary inspection according to the fault analysis result 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.

9. A joint inspection control device, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, wherein the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the joint inspection control device to execute the method as described in any one of claims 1-7.

10. 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 is caused to execute the method as described in any one of claims 1-7.

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