Intelligent inspection method and system for power station

Abstract

The invention relates to the technical field of power generation station inspection, and discloses a power generation station intelligent inspection method and system, and the method comprises the steps: obtaining an abnormal feature value of a photovoltaic module in a power generation station, and calculating an abnormal parameter according to the abnormal feature value of the photovoltaic module in the power generation station, establishing an inspection thermodynamic diagram according to the abnormal parameters in combination with the power station layout; performing routing inspection line planning according to the routing inspection thermodynamic diagram, and performing fault routing inspection on the power station according to a routing inspection line planning result; and screening out the fault photovoltaic module according to the fault inspection result, calculating the fault credibility of the fault photovoltaic module, and outputting a final inspection result according to the fault credibility of the fault photovoltaic module. According to the invention, the inspection line of the photovoltaic module in the power station can be optimized, the photovoltaic module with a fault can be rapidly positioned, and the inspection efficiency and accuracy of the power station are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power station inspection, and more specifically, to an intelligent inspection method and system for power stations. Background Art

[0002] In recent years, my country's photovoltaic power generation technology has developed rapidly and has become one of the country's most important energy sources. However, the large-scale construction of photovoltaic power plants has brought with it the challenge of efficiently conducting operation and maintenance inspections of newly built photovoltaic power plants to ensure their safe, stable, and economical operation throughout their 20-25 year operating lifecycle, maximize the energy efficiency of photovoltaic power generation, and recover the initial construction costs within the expected period.

[0003] Existing inspection systems for photovoltaic power plants involve controlling drones to fly over the power plant along a specific inspection route. The drones then scan visible light images of all photovoltaic panels within the plant to identify defects, locate defective panels, and generate a fault inspection report. However, because fault types and severities vary across photovoltaic panels within a power plant, existing inspection technology is unable to focus on critical areas, resulting in low inspection efficiency and inaccurate results. Summary of the Invention

[0004] The present invention provides an intelligent inspection method and system for power stations, which is used to solve the problems of low inspection efficiency and inaccurate inspection results in the prior art for power stations, including: Obtain abnormal characteristic values of photovoltaic modules in power plants, calculate abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power plants, and establish an inspection heat map based on the abnormal parameters and the layout of the power plants; Plan inspection routes based on inspection heat maps, and conduct fault inspections on power plants based on the inspection route planning results; The faulty photovoltaic modules are screened out according to the fault inspection results, the fault reliability of the faulty photovoltaic modules is calculated, and the final inspection results are output according to the fault reliability of the faulty photovoltaic modules.

[0005] Furthermore, the calculation of abnormal parameters according to abnormal characteristic values of photovoltaic modules in the power generation station includes: Obtaining a current fluctuation value of each photovoltaic module within a power generation cycle of the power station, and determining a first abnormal characteristic value based on the current fluctuation value of each photovoltaic module within the power generation cycle of the power station; Obtaining a voltage fluctuation value of each photovoltaic module within a power generation cycle of the power station, and determining a second abnormal characteristic value based on the voltage fluctuation value of each photovoltaic module within the power generation cycle of the power station; Obtaining a discrete rate of each photovoltaic module within a power generation cycle of the power station, and determining a third abnormal characteristic value based on the discrete rate of each photovoltaic module within the power generation cycle of the power station; Determine the correlation coefficient between each abnormal characteristic value and the power generation of the power station based on the historical power generation data of the power station, and assign the characteristic weight of the abnormal characteristic value based on the correlation coefficient; The abnormal characteristic values and characteristic weights of the photovoltaic modules are weighted and summed to obtain the abnormal parameters of the photovoltaic modules. According to the abnormal parameters of the photovoltaic modules and the layout of the power station, an inspection heat map is established.

[0006] Furthermore, the establishment of an inspection heat map based on the abnormal parameters and the layout of the power station includes: The photovoltaic modules are clustered according to abnormal parameters based on the k-means clustering algorithm, and the seed points of the power generation station are selected according to the cluster centers corresponding to each photovoltaic module; Obtain abnormal parameters of the photovoltaic modules adjacent to the seed point, and calculate the difference between the abnormal parameters of the seed point and the adjacent photovoltaic modules; Determine whether the difference between the abnormal parameters of the seed point and the adjacent photovoltaic component is less than a first preset threshold value, and if the difference between the abnormal parameters of the seed point and the adjacent photovoltaic component is less than the first preset threshold value, set the corresponding adjacent photovoltaic component as a new growth seed point; Taking the new growth seed point as the center, continue to detect new adjacent PV panels until the area can no longer grow, and obtain several PV panel abnormal areas; Counting the average values of abnormal parameters of photovoltaic modules in abnormal areas of photovoltaic modules, and grading the abnormality degree of the abnormal areas of photovoltaic modules according to the average values of abnormal parameters of photovoltaic modules; According to the degree of abnormality of the abnormal area of the photovoltaic components, the power station is divided into the area to be inspected and the normal area, and an inspection heat map is established based on the area to be inspected and the normal area.

[0007] Furthermore, the inspection route planning according to the inspection heat map includes: Obtain the preset inspection range, and segment the inspection heat map according to the preset inspection range to obtain several inspection blocks; Collect abnormal parameters of PV modules in each inspection block and calculate the average value of abnormal parameters in the inspection block; The key inspection blocks are selected based on the average values of abnormal parameters of the inspection blocks, and the inspection routes are planned based on the key inspection blocks in the inspection heat map.

[0008] Furthermore, the inspection route planning according to the key inspection blocks of the inspection heat map includes: Generate the shortest path through all key inspection blocks based on the key inspection blocks to obtain the initial inspection route; Collect abnormal parameters of key inspection areas in the initial inspection route and convert them into inspection radius according to the preset ratio; Scan the area to be inspected within the inspection radius of each key inspection block to obtain the auxiliary area of the initial inspection route, and generate an auxiliary route based on the auxiliary area of the initial inspection route; Count the remaining areas to be inspected for which no paths have been generated, generate the shortest path through all the remaining areas to be inspected, and obtain the remaining routes; Connect the initial inspection route, the subsidiary routes and the remaining routes to obtain the inspection route to be revised; Determine whether there is a normal area in the inspection route to be corrected. If so, perform obstacle avoidance correction on the normal area in the inspection route to be corrected to obtain the final inspection route.

[0009] Furthermore, the method of screening out faulty photovoltaic modules according to the fault inspection results and calculating the fault credibility of the faulty photovoltaic modules includes: Determine the inspection image data of each photovoltaic module based on the inspection results of the power station, determine the fault type of the photovoltaic module based on the inspection image data of the photovoltaic module, and screen out the faulty photovoltaic modules; Abnormal parameters of the faulty PV module are obtained, and the fault credibility is determined according to the abnormal parameters of the faulty PV module and the corresponding fault type.

[0010] Furthermore, determining the fault type of the photovoltaic assembly based on the inspection image data of the photovoltaic assembly includes: Obtain historical photovoltaic module inspection image datasets and corresponding fault types at power plants, and preprocess the historical photovoltaic module inspection image datasets and corresponding fault types; A training sample set is established based on the pre-processed historical photovoltaic module inspection image dataset and the corresponding fault types, a fault recognition model is established based on the training sample set, and the fault recognition model is trained to obtain a trained fault recognition model; The inspection image data of the current photovoltaic module is input into the trained fault recognition model to obtain the fault type corresponding to the current photovoltaic module.

[0011] Furthermore, determining the fault credibility based on the abnormal parameters of the faulty photovoltaic module and the corresponding fault type includes: Draw an abnormal parameter change curve according to the abnormal parameter change of the faulty PV module, and determine the abnormal parameter standard change curve according to the fault type of the faulty PV module; Calculate the correlation coefficient between the abnormal parameter change curve and the corresponding abnormal parameter standard change curve to obtain the credibility coefficient; A preset tolerance reliability coefficient is obtained, and a ratio of the reliability coefficient to the preset tolerance reliability coefficient is calculated to obtain the fault reliability of the faulty photovoltaic module.

[0012] Furthermore, outputting the final inspection result according to the fault credibility of the faulty photovoltaic module includes: determining whether the fault credibility of the faulty photovoltaic module is greater than a second preset threshold, and if the fault credibility of the faulty photovoltaic module is greater than the second preset threshold, locating the faulty photovoltaic module and outputting the fault type; If the fault credibility of the faulty photovoltaic component is less than or equal to the second preset threshold, it is determined that the photovoltaic component does not have a fault.

[0013] In order to achieve the above objectives, the present invention also provides an intelligent inspection system for power stations, comprising: Establish a module for obtaining abnormal characteristic values of photovoltaic modules in power generation stations, calculating abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power generation stations, and establishing an inspection heat map based on the abnormal parameters and the layout of the power generation stations; Planning module, used to plan inspection routes based on inspection heat maps and conduct fault inspections on power plants based on the inspection route planning results; The inspection module is used to screen out faulty photovoltaic modules according to the fault inspection results, calculate the fault reliability of the faulty photovoltaic modules, and output the final inspection results according to the fault reliability of the faulty photovoltaic modules.

[0014] The beneficial effects of the present invention are: By applying the above technical solutions, the present invention establishes an inspection heat map of the power station according to the abnormal parameters of different photovoltaic modules, locates the key inspection blocks and the normal blocks that do not need to be inspected through the inspection heat map, thereby optimizing the inspection route to improve the inspection efficiency. At the same time, the inspection results are combined with the abnormal parameters to accurately locate the faulty photovoltaic modules and output the final inspection results, thereby realizing the rapid positioning of the faulty modules, effectively improving the inspection efficiency and accuracy of the power station, discovering problems as quickly as possible, and avoiding serious accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 The figure shows an overall flow chart of an intelligent inspection method for power stations proposed in an embodiment of the present invention; Figure 2 The figure shows a schematic structural diagram of an intelligent inspection system for power plants proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] The present application provides an intelligent inspection method for power station. Figure 1 Shown, including: S101, obtaining abnormal characteristic values of photovoltaic modules in a power station, calculating abnormal parameters based on the abnormal characteristic values of photovoltaic modules in the power station, and establishing an inspection heat map based on the abnormal parameters and the layout of the power station; In some embodiments of the present application, the abnormal parameters are calculated based on the abnormal characteristic values of the photovoltaic components in the power station, including: obtaining the current fluctuation value of each photovoltaic component within a power generation cycle of the power station, and determining a first abnormal characteristic value based on the current fluctuation value of each photovoltaic component within a power generation cycle of the power station; obtaining the voltage fluctuation value of each photovoltaic component within a power generation cycle of the power station, and determining a second abnormal characteristic value based on the voltage fluctuation value of each photovoltaic component within a power generation cycle of the power station; obtaining the discrete rate of each photovoltaic component within a power generation cycle of the power station, and determining a third abnormal characteristic value based on the discrete rate of each photovoltaic component within a power generation cycle of the power station; determining the correlation coefficient between each abnormal characteristic value and the power generation of the power station based on the historical power generation data of the power station, and allocating characteristic weights of the abnormal characteristic values based on the correlation coefficients; performing weighted summation on the abnormal characteristic values and characteristic weights of the photovoltaic components to obtain abnormal parameters of the photovoltaic components, and establishing an inspection heat map based on the abnormal parameters of the photovoltaic components in combination with the layout of the power station.

[0019] In this embodiment, the current and voltage changes of each photovoltaic module during a power generation cycle of the power station are recorded, the current fluctuation value is obtained by calculating the difference between the maximum and minimum current values, and the voltage fluctuation value is obtained by calculating the difference between the maximum and minimum voltage values. The discrete rate of the photovoltaic module is obtained by calculating the standard deviation of the string current to which the photovoltaic module belongs / the average value of the string current. At the same time, the correlation coefficient between the historical abnormal characteristic values of the power station and the power generation of the station is used as the characteristic weight to obtain the abnormal parameters of the photovoltaic module.

[0020] In some embodiments of the present application, the establishment of an inspection heat map based on abnormal parameters in combination with the layout of the power station includes: clustering the photovoltaic components according to the abnormal parameters based on the k-means clustering algorithm, and selecting the seed point of the power station according to the cluster center corresponding to each photovoltaic component; obtaining the abnormal parameters of the photovoltaic components adjacent to the seed point, and calculating the difference between the abnormal parameters of the seed point and the adjacent photovoltaic components; judging whether the difference between the abnormal parameters of the seed point and the adjacent photovoltaic components is less than a first preset threshold value, if the difference between the abnormal parameters of the seed point and the adjacent photovoltaic components is less than the first preset threshold value, setting the corresponding adjacent photovoltaic component as a new growth seed point; with the new growth seed point as the center, continuing to detect new adjacent photovoltaic components until the area can no longer grow, and obtaining a number of photovoltaic component abnormal areas; counting the average value of the abnormal parameters of the photovoltaic components in the abnormal area of the photovoltaic components, and grading the abnormal degree of the photovoltaic component abnormal area according to the average value of the abnormal parameters of the photovoltaic components; dividing the power station into a to-be-inspected area and a normal area according to the abnormal degree grading of the abnormal area of the photovoltaic components, and establishing an inspection heat map according to the to-be-inspected area and the normal area.

[0021] In this embodiment, the photovoltaic modules with the closest abnormal parameters to each cluster center are selected as the seed points of the power generation station to make the seed points evenly distributed. Regional growth is performed by the difference between the abnormal parameters of the seed points and the adjacent photovoltaic modules to obtain several abnormal photovoltaic module regions. The degree of abnormality is graded by the average value of the abnormal parameters of the photovoltaic modules in the abnormal photovoltaic module regions. The higher the average value, the higher the corresponding abnormality level. In this embodiment, the k value is set to 5, thereby dividing 5 abnormal photovoltaic module regions. The abnormal regions are divided into five levels 1-5 by the average value of the abnormal parameters of the photovoltaic modules, where level 1 is the normal area, represented by green, and levels 2-5 are areas to be inspected, represented by yellow, pink, orange, and red, respectively, thereby forming an inspection heat map.

[0022] S102, planning inspection routes based on the inspection heat map, and performing fault inspections on the power station based on the inspection route planning results; In some embodiments of the present application, the inspection route planning based on the inspection heat map includes: obtaining a preset inspection range, dividing the inspection heat map according to the preset inspection range to obtain a number of inspection blocks; collecting abnormal parameters of the photovoltaic components in each inspection block, and calculating the average value of the abnormal parameters of the inspection block; screening out key inspection blocks according to the average value of the abnormal parameters of the inspection block, and planning the inspection route according to the key inspection blocks of the inspection heat map.

[0023] In this embodiment, the preset inspection range is set according to the visual range of the drone, thereby dividing a number of inspection blocks, and the inspection blocks whose average abnormal parameter value is greater than the preset allowable abnormal parameter threshold are set as key inspection blocks.

[0024] In some embodiments of the present application, the inspection route is planned according to the key inspection blocks of the inspection heat map, including: generating the shortest path passing through all key inspection blocks according to the key inspection blocks to obtain an initial inspection route; collecting abnormal parameters of the key inspection blocks in the initial inspection route, and converting the abnormal parameters into an inspection radius according to a preset ratio; scanning the area to be inspected in the inspection radius of each key inspection block to obtain an affiliated area of the initial inspection route, and generating an affiliated route according to the affiliated area of the initial inspection route; counting the remaining areas to be inspected for which no path has been generated, generating a shortest path route passing through all the remaining areas to be inspected, and obtaining a remaining route; connecting the initial inspection route, the affiliated route and the remaining route to obtain an inspection route to be corrected; judging whether there is a normal area in the inspection route to be corrected, and if so, performing obstacle avoidance correction on the normal area in the inspection route to be corrected to obtain a final inspection route.

[0025] In this embodiment, the shortest path passing through all the key inspection blocks is set as the initial inspection route, and the inspection radius is set in the key inspection blocks in the initial inspection route according to the average value of the abnormal parameters of the key inspection blocks. The subsidiary area is obtained by scanning the area to be inspected within the inspection radius. After passing through a key inspection block, a coverage path corresponding to the subsidiary area is generated based on the CCPP full coverage path planning algorithm to obtain the subsidiary route. Then, the shortest path passing through all the remaining areas to be inspected is set as the remaining route. The initial inspection route, the subsidiary route and the remaining routes are merged in sequence to obtain the inspection route to be corrected. Since there may be normal areas that do not need to be inspected in the inspection route to be corrected, the inspection route to be corrected is optimized based on the obstacle avoidance algorithm to obtain the final inspection route. This inspection route achieves a balance between rapid response to high-risk points and full area coverage through hierarchical priority processing and path optimization.

[0026] S103 , screening out faulty photovoltaic modules according to the fault inspection results, calculating the fault reliability of the faulty photovoltaic modules, and outputting a final inspection result according to the fault reliability of the faulty photovoltaic modules.

[0027] In some embodiments of the present application, the method of screening out faulty photovoltaic components based on the fault inspection results and calculating the fault credibility of the faulty photovoltaic components includes: determining the inspection image data of each photovoltaic component based on the inspection results of the power generation station, determining the fault type of the photovoltaic component based on the inspection image data of the photovoltaic component, and screening out the faulty photovoltaic components; obtaining abnormal parameters of the faulty photovoltaic components, and determining the fault credibility based on the abnormal parameters of the faulty photovoltaic components and the corresponding fault type.

[0028] In this embodiment, the drone is controlled to inspect the photovoltaic modules along the final inspection route, and inspection image data of each photovoltaic module is collected, so as to screen out faulty photovoltaic modules.

[0029] In some embodiments of the present application, determining the fault type of a photovoltaic component based on the inspection image data of the photovoltaic component includes: obtaining a historical photovoltaic component inspection image data set and a corresponding fault type of a power generation station, and preprocessing the historical photovoltaic component inspection image data set and the corresponding fault type; establishing a training sample set based on the preprocessed historical photovoltaic component inspection image data set and the corresponding fault type, establishing a fault recognition model based on the training sample set and training the fault recognition model to obtain a trained fault recognition model; inputting the inspection image data of the current photovoltaic component into the trained fault recognition model to obtain the fault type corresponding to the current photovoltaic component.

[0030] In this embodiment, a fault recognition model is established through the historical photovoltaic module inspection image data set of the power station and the corresponding fault types, so that the fault recognition model is used to identify the faults of the inspection image data of the current photovoltaic module. The fault types specifically include hot spot faults, crack faults, obstruction faults and no faults.

[0031] In some embodiments of the present application, the fault credibility is determined based on the abnormal parameters of the faulty photovoltaic component and the corresponding fault type, including: drawing an abnormal parameter change curve based on the abnormal parameter change of the faulty photovoltaic component, and determining an abnormal parameter standard change curve based on the fault type of the faulty photovoltaic component; calculating the correlation coefficient between the abnormal parameter change curve and the corresponding abnormal parameter standard change curve to obtain a credibility coefficient; obtaining a preset tolerance credibility coefficient, calculating the ratio of the credibility coefficient to the preset tolerance credibility coefficient, and obtaining the fault credibility of the faulty photovoltaic component.

[0032] In some embodiments of the present application, the outputting of the final inspection result based on the fault credibility of the faulty photovoltaic component includes: determining whether the fault credibility of the faulty photovoltaic component is greater than a second preset threshold; if the fault credibility of the faulty photovoltaic component is greater than the second preset threshold, locating the faulty photovoltaic component and outputting the fault type; if the fault credibility of the faulty photovoltaic component is less than or equal to the second preset threshold, determining that there is no fault in the photovoltaic component.

[0033] In this embodiment, the fault credibility is calculated by the correlation coefficient between the abnormal parameter change curve of the faulty PV module and the corresponding abnormal parameter standard change curve, and the precise fault probability of the PV module is determined based on the change of the abnormal parameters.

[0034] Based on the same technical concept, such as Figure 2 As shown, the present invention also provides an intelligent inspection system for power stations, comprising: An establishment module is used to obtain abnormal characteristic values of photovoltaic modules in power plants, calculate abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power plants, and establish an inspection heat map based on the abnormal parameters and the layout of power plants; a planning module is used to plan inspection routes based on the inspection heat map, and perform fault inspections on power plants based on the inspection route planning results; an inspection module is used to screen out faulty photovoltaic modules based on the fault inspection results and calculate the fault credibility of the faulty photovoltaic modules, and output the final inspection results based on the fault credibility of the faulty photovoltaic modules.

[0035] By applying the above technical solutions, the present invention obtains abnormal characteristic values of photovoltaic modules in power plants, calculates abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power plants, and establishes an inspection heat map based on the abnormal parameters and the layout of the power plants; plans inspection routes based on the inspection heat map, and performs fault inspections on the power plants based on the inspection route planning results; screens out faulty photovoltaic modules based on the fault inspection results and calculates the fault credibility of the faulty photovoltaic modules; and outputs the final inspection results based on the fault credibility of the faulty photovoltaic modules. The present invention can optimize the inspection routes of photovoltaic modules in power plants, quickly locate faulty photovoltaic modules, and effectively improve the inspection efficiency and accuracy of power plants.

[0036] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent inspection method for power stations, characterized in that: The method comprises: Obtain abnormal characteristic values of photovoltaic modules in power plants, calculate abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power plants, and establish an inspection heat map based on the abnormal parameters and the layout of the power plants; Plan inspection routes based on inspection heat maps, and conduct fault inspections on power plants based on the inspection route planning results; The faulty photovoltaic modules are screened out according to the fault inspection results, the fault reliability of the faulty photovoltaic modules is calculated, and the final inspection results are output according to the fault reliability of the faulty photovoltaic modules.

2. The intelligent inspection method for power plants according to claim 1, characterized in that: The calculating of abnormal parameters according to abnormal characteristic values of photovoltaic modules in the power generation station includes: Obtaining a current fluctuation value of each photovoltaic module within a power generation cycle of the power station, and determining a first abnormal characteristic value based on the current fluctuation value of each photovoltaic module within the power generation cycle of the power station; Obtaining a voltage fluctuation value of each photovoltaic module within a power generation cycle of the power station, and determining a second abnormal characteristic value based on the voltage fluctuation value of each photovoltaic module within the power generation cycle of the power station; Obtaining a discrete rate of each photovoltaic module within a power generation cycle of the power station, and determining a third abnormal characteristic value based on the discrete rate of each photovoltaic module within the power generation cycle of the power station; Determine the correlation coefficient between each abnormal characteristic value and the power generation of the power station based on the historical power generation data of the power station, and assign the characteristic weight of the abnormal characteristic value based on the correlation coefficient; The abnormal characteristic values and characteristic weights of the photovoltaic modules are weighted and summed to obtain the abnormal parameters of the photovoltaic modules. According to the abnormal parameters of the photovoltaic modules and the layout of the power station, an inspection heat map is established.

3. The intelligent inspection method for power plants according to claim 2, characterized in that: The inspection heat map is established based on the abnormal parameters and the power station layout, including: The photovoltaic modules are clustered according to abnormal parameters based on the k-means clustering algorithm, and the seed points of the power generation station are selected according to the cluster centers corresponding to each photovoltaic module; Obtain abnormal parameters of the photovoltaic modules adjacent to the seed point, and calculate the difference between the abnormal parameters of the seed point and the adjacent photovoltaic modules; Determine whether the difference between the abnormal parameters of the seed point and the adjacent photovoltaic component is less than a first preset threshold value, and if the difference between the abnormal parameters of the seed point and the adjacent photovoltaic component is less than the first preset threshold value, set the corresponding adjacent photovoltaic component as a new growth seed point; Taking the new growth seed point as the center, continue to detect new adjacent PV panels until the area can no longer grow, and obtain several PV panel abnormal areas; Counting the average values of abnormal parameters of photovoltaic modules in abnormal areas of photovoltaic modules, and grading the abnormality degree of the abnormal areas of photovoltaic modules according to the average values of abnormal parameters of photovoltaic modules; According to the degree of abnormality of the abnormal area of the photovoltaic components, the power station is divided into the area to be inspected and the normal area, and an inspection heat map is established based on the area to be inspected and the normal area.

4. The intelligent inspection method for power plants according to claim 3, characterized in that: The inspection route planning according to the inspection heat map includes: Obtain the preset inspection range, and segment the inspection heat map according to the preset inspection range to obtain several inspection blocks; Collect abnormal parameters of PV modules in each inspection block and calculate the average value of abnormal parameters in the inspection block; The key inspection blocks are selected based on the average values of abnormal parameters of the inspection blocks, and the inspection routes are planned based on the key inspection blocks in the inspection heat map.

5. The intelligent inspection method for power plants according to claim 4, characterized in that: Planning inspection routes based on key inspection areas in the inspection heat map includes: Generate the shortest path through all key inspection blocks based on the key inspection blocks to obtain the initial inspection route; Collect abnormal parameters of key inspection areas in the initial inspection route and convert them into inspection radius according to the preset ratio; Scan the area to be inspected within the inspection radius of each key inspection block to obtain the auxiliary area of the initial inspection route, and generate an auxiliary route based on the auxiliary area of the initial inspection route; Count the remaining areas to be inspected for which no paths have been generated, generate the shortest path through all the remaining areas to be inspected, and obtain the remaining routes; Connect the initial inspection route, the subsidiary routes and the remaining routes to obtain the inspection route to be revised; Determine whether there is a normal area in the inspection route to be corrected. If so, perform obstacle avoidance correction on the normal area in the inspection route to be corrected to obtain the final inspection route.

6. The intelligent inspection method for power plants according to claim 1, characterized in that: The method of screening out faulty photovoltaic modules according to the fault inspection results and calculating the fault credibility of the faulty photovoltaic modules includes: Determine the inspection image data of each photovoltaic module based on the inspection results of the power station, determine the fault type of the photovoltaic module based on the inspection image data of the photovoltaic module, and screen out the faulty photovoltaic modules; Abnormal parameters of the faulty PV module are obtained, and the fault credibility is determined according to the abnormal parameters of the faulty PV module and the corresponding fault type.

7. The intelligent inspection method for power plants according to claim 6, characterized in that: Determining the photovoltaic component fault type based on the inspection image data of the photovoltaic component includes: Obtain historical photovoltaic module inspection image datasets and corresponding fault types at power plants, and preprocess the historical photovoltaic module inspection image datasets and corresponding fault types; A training sample set is established based on the pre-processed historical photovoltaic module inspection image dataset and the corresponding fault types, a fault recognition model is established based on the training sample set, and the fault recognition model is trained to obtain a trained fault recognition model; The inspection image data of the current photovoltaic module is input into the trained fault recognition model to obtain the fault type corresponding to the current photovoltaic module.

8. The intelligent inspection method for power plants according to claim 6, characterized in that: The determining of the fault credibility according to the abnormal parameters of the faulty photovoltaic module and the corresponding fault type includes: Draw an abnormal parameter change curve according to the abnormal parameter change of the faulty PV module, and determine the abnormal parameter standard change curve according to the fault type of the faulty PV module; Calculate the correlation coefficient between the abnormal parameter change curve and the corresponding abnormal parameter standard change curve to obtain the credibility coefficient; A preset tolerance reliability coefficient is obtained, and a ratio of the reliability coefficient to the preset tolerance reliability coefficient is calculated to obtain the fault reliability of the faulty photovoltaic module.

9. The intelligent inspection method for power stations according to claim 8, characterized in that: Outputting the final inspection result according to the fault credibility of the faulty photovoltaic module includes: determining whether the fault credibility of the faulty photovoltaic module is greater than a second preset threshold, and if the fault credibility of the faulty photovoltaic module is greater than the second preset threshold, locating the faulty photovoltaic module and outputting the fault type; If the fault credibility of the faulty photovoltaic component is less than or equal to the second preset threshold, it is determined that the photovoltaic component does not have a fault.

10. An intelligent inspection system for power stations, characterized in that: include: Establish a module for obtaining abnormal characteristic values of photovoltaic modules in power generation stations, calculating abnormal parameters based on the abnormal characteristic values of photovoltaic modules in power generation stations, and establishing an inspection heat map based on the abnormal parameters and the layout of the power generation stations; Planning module, used to plan inspection routes based on inspection heat maps and conduct fault inspections on power plants based on the inspection route planning results; The inspection module is used to screen out faulty photovoltaic modules according to the fault inspection results, calculate the fault reliability of the faulty photovoltaic modules, and output the final inspection results according to the fault reliability of the faulty photovoltaic modules.

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