Photovoltaic cell panel inspection system and method based on artificial intelligence

Abstract

The invention discloses a photovoltaic cell panel inspection system and method based on artificial intelligence, and relates to the technical field of photovoltaic cell panel inspection. Comprising a real-time data acquisition module, a real-time performance analysis module, a visual parameter acquisition module, a field visual analysis module, a comprehensive inspection condition analysis module, an inspection plan making module, an early warning terminal and a database. The inspection frequency can be ensured to be matched with the actual demand of the photovoltaic cell panel, the real-time data acquisition module can dynamically adjust the inspection period according to the performance parameters such as the power generation power, the energy conversion efficiency and the solar radiation intensity of the photovoltaic cell panel, unnecessary resource waste caused by fixed-period inspection is avoided, and meanwhile, the inspection efficiency of the photovoltaic cell panel is improved. And through continuous data acquisition, the change of the state of the photovoltaic cell panel can be responded in time, and the photovoltaic cell panel is ensured to operate in an optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panel inspection, and in particular to an artificial intelligence-based photovoltaic panel inspection system and method. Background Art

[0002] With the increasing global demand for renewable energy and the rapid development of the photovoltaic industry, the operating efficiency and maintenance of photovoltaic panels have become key issues. Traditional manual inspection methods have many disadvantages such as low efficiency, high cost, and poor real-time performance, and cannot meet the operating needs of large-scale photovoltaic power stations.

[0003] Prior art, such as the invention patent application with announcement number CN114782442A, discloses an intelligent inspection method and system for photovoltaic panels based on artificial intelligence, including acquiring an image of a photovoltaic panel and a corresponding thermal imaging image, performing image processing to obtain an orthophoto image and an orthophoto thermal imaging image of the photovoltaic panel, converting the orthophoto image into a binary image, analyzing dust pixels in the image, obtaining the convex hull area of ​​each dust cluster, obtaining the dust severity according to the maximum temperature of the thermal imaging area corresponding to the convex hull area and the sum of the dust grayscale of the dust cluster, obtaining the aggregated dust severity of each dust cluster and the global dust severity of the photovoltaic panel according to the minimum distance between the dust cluster and the dust cluster within the range of the dust cluster and the dust severity of the convex hull area of ​​the dust cluster, comparing the threshold to determine whether cleaning is needed, and obtaining the time of the next inspection by calculation. Based on artificial intelligence and image processing, it is more accurate and intelligent than traditional methods, saves costs, and reasonably uses resources.

[0004] There are at least the following technical problems with the above scheme: 1. The above scheme lacks detailed design and description of the real-time data acquisition and monitoring module, which will lead to a lack of timely feedback and adjustment capabilities on the operating status of the solar panels. In actual applications, real-time monitoring and data feedback are key factors in the successful implementation of any inspection system. Their absence may lead to a lag in inspection results, making it difficult for photovoltaic panels to take quick countermeasures when failures occur, affecting power generation efficiency.

[0005] 2. The above scheme lacks analysis of the relationship between performance parameters and visual parameters, which will lead to an incomplete evaluation of the comprehensive performance of photovoltaic panels. For example, performance parameters such as power generation, energy conversion efficiency and solar radiation intensity should be interrelated with visual parameters such as dirt coverage, obstruction height and number of cracks. The lack of comprehensive analysis of the relationship between these two types of parameters will make it impossible to achieve a comprehensive evaluation of the status of photovoltaic panels.

[0006] 3. The above scheme lacks the design description of the on-site visual analysis and evaluation module, which will lead to the lack of real-time evaluation of the actual environmental conditions. For example, relying only on the visual parameters obtained by image processing without a real-time analysis mechanism will make the inspection system insufficiently aware of the actual operating status of the site and unable to respond to dynamic environmental changes, thus affecting the timeliness and accuracy of the inspection. Summary of the invention

[0007] The purpose of the present invention is to provide a photovoltaic panel inspection system and method based on artificial intelligence, which solves the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an artificial intelligence-based photovoltaic panel inspection system, including: a real-time data acquisition module, used to set each inspection cycle and collect performance parameters of the specified photovoltaic panels corresponding to each inspection cycle.

[0009] The real-time performance analysis module is used to calculate the real-time performance evaluation coefficient of the specified photovoltaic panel in each inspection cycle according to the performance parameters of the specified photovoltaic panel corresponding to each inspection cycle.

[0010] The visual parameter acquisition module is used to capture images corresponding to designated photovoltaic panels through drones and fixed cameras, and then obtain visual parameters corresponding to the designated photovoltaic panels in each inspection cycle.

[0011] The on-site visual analysis module is used to calculate the on-site visual evaluation coefficient corresponding to the specified photovoltaic panel in each inspection cycle according to the visual parameters corresponding to the specified photovoltaic panel in each inspection cycle.

[0012] The comprehensive inspection condition analysis module is used to analyze and obtain the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle according to the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle.

[0013] The inspection plan formulation module is used to evaluate the inspection level of the specified photovoltaic panels in each inspection cycle, and then formulate the inspection plan for the specified photovoltaic panels in each inspection cycle.

[0014] The early warning terminal is used to issue an early warning prompt when a designated photovoltaic panel needs to be inspected during a certain inspection cycle.

[0015] In a second aspect, the present invention provides an artificial intelligence-based photovoltaic panel inspection method, comprising: step one, real-time data collection: setting each inspection cycle, and collecting performance parameters of the specified photovoltaic panel corresponding to each inspection cycle.

[0016] Step 2: Real-time performance analysis: Based on the performance parameters of the designated photovoltaic panels corresponding to each inspection cycle, the real-time performance evaluation coefficient of the designated photovoltaic panels in each inspection cycle is calculated.

[0017] Step 3: Obtain visual parameters: Use drones and fixed cameras to capture images corresponding to designated photovoltaic panels, and then obtain visual parameters corresponding to designated photovoltaic panels in each inspection cycle.

[0018] Step 4: On-site visual analysis: Based on the visual parameters corresponding to the specified photovoltaic panels in each inspection cycle, the on-site visual evaluation coefficient corresponding to the specified photovoltaic panels in each inspection cycle is calculated.

[0019] Step 5: Comprehensive inspection condition analysis: Based on the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle, the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle is analyzed.

[0020] Step 6: Develop an inspection plan: Evaluate the inspection level of the designated photovoltaic panels in each inspection cycle, and then develop an inspection plan for the designated photovoltaic panels in each inspection cycle.

[0021] Step 7: Early warning: When the designated photovoltaic panel needs to be inspected in a certain inspection cycle, an early warning will be issued.

[0022] The beneficial effects of the present invention are: 1. The present invention provides an artificial intelligence-based photovoltaic panel inspection system and method. During the inspection cycle setting process, a real-time data acquisition module is used to ensure that the inspection frequency matches the actual needs of the photovoltaic panels. The real-time data acquisition module can dynamically adjust the inspection cycle according to performance parameters such as the photovoltaic panel's power generation power, energy conversion efficiency, and solar radiation intensity, thereby avoiding unnecessary waste of resources caused by fixed-cycle inspections. At the same time, continuous data collection is helpful to timely respond to changes in the status of the photovoltaic panels, ensuring that the photovoltaic panels operate in the best condition.

[0023] 2. In the performance evaluation process, the embodiment of the present invention calculates the real-time performance evaluation coefficient of each inspection cycle based on the performance parameters collected in real time, which can quickly reflect the working efficiency and possible problems of the photovoltaic panels, and not only helps to timely discover abnormalities and provide reliable data support for subsequent maintenance measures. In the process of visual parameter acquisition, the use of equipment such as drones and fixed cameras is conducive to comprehensively acquiring the surface status of photovoltaic panels, and acquiring key visual parameters such as dirt coverage, obstruction height and number of cracks, which provides high-quality data input for subsequent on-site visual analysis and helps to improve the accuracy and comprehensiveness of the analysis.

[0024] 3. In the process of comprehensive inspection condition analysis, the embodiment of the present invention passes the comprehensive inspection condition analysis module, which is conducive to generating a comprehensive inspection evaluation index. According to the comprehensive inspection evaluation index, the inspection level of the photovoltaic panel is determined, and then the inspection plan is formulated. By matching the inspection level with the preset inspection resources, such as the number of inspection staff, the number of inspection equipment and the estimated time spent, it can ensure the efficient use of inspection resources and avoid excessive or insufficient allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0027] Figure 2 It is a schematic flow chart of the implementation steps of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 As shown, the present invention provides a photovoltaic panel inspection system based on artificial intelligence, which includes: a real-time data acquisition module, a real-time performance analysis module, a visual parameter acquisition module, an on-site visual analysis module, a comprehensive inspection condition analysis module, an inspection plan formulation module, an early warning terminal and a database.

[0030] The real-time data acquisition module is connected to the real-time performance analysis module, the visual parameter acquisition module is connected to the on-site visual analysis module, the real-time performance analysis module and the on-site visual analysis module are both connected to the comprehensive inspection condition analysis module, the comprehensive inspection condition analysis module is respectively connected to the inspection plan formulation module and the early warning terminal, and the database is respectively connected to the real-time performance analysis module, the on-site visual analysis module and the comprehensive inspection condition analysis module.

[0031] The real-time data acquisition module is used to set each inspection cycle and collect the performance parameters of the specified photovoltaic panels corresponding to each inspection cycle.

[0032] It should be noted that the designated photovoltaic panel is a photovoltaic panel among many photovoltaic panels in a photovoltaic power station.

[0033] In a specific embodiment, the performance parameters include power generation, energy conversion efficiency and solar radiation intensity, and the visual parameters include dirt coverage, shielding height and crack quantity.

[0034] It should be noted that obstructions include trees, weeds, etc.

[0035] In a specific embodiment, the performance parameters of the designated photovoltaic panels corresponding to each inspection cycle are collected, and the specific collection process is as follows: the historical fault frequency corresponding to the designated photovoltaic panel is obtained from the database, and then the inspection cycles corresponding to the designated photovoltaic panel are set, an inverter is set in the designated photovoltaic panel, the inverter is connected to the inspection system through the network, and then the voltage and current in the designated photovoltaic panel corresponding to each inspection cycle are read, and the voltage and current in the designated photovoltaic panel corresponding to each inspection cycle are multiplied and calculated, and the result obtained is the power generation power of the designated photovoltaic panel corresponding to each inspection cycle, which is recorded as Wi, and the solar radiation intensity of the designated photovoltaic panel corresponding to each inspection cycle is measured by the solar radiation sensor, which is recorded as qi, and the collection time period is set as T, and the area corresponding to the designated photovoltaic panel is obtained from the database, which is recorded as S, and the calculation formula is used. The energy conversion efficiency fi of the specified photovoltaic panel in each inspection cycle is obtained.

[0036] It should be noted that the collection time period is used to collect the energy conversion efficiency of the specified photovoltaic panel in the collection time period, and the energy conversion efficiency of the specified photovoltaic panel in the collection time period is used as the energy conversion efficiency of the specified photovoltaic panel corresponding to the specified photovoltaic panel in a certain inspection cycle.

[0037] Low power generation indicates that there are problems with the photovoltaic panels, such as surface dirt, component aging, shading, inverter failure or poor circuit connection. At this time, the photovoltaic panels are not in good operating condition and need to be inspected and maintained. Low energy conversion efficiency indicates that there are problems with the operating condition of the photovoltaic panels, which usually require repair or replacement of components, such as aging of photovoltaic components, surface damage, contamination, excessive temperature or design defects.

[0038] The real-time performance analysis module is used to calculate the real-time performance evaluation coefficient of the specified photovoltaic panel in each inspection cycle according to the performance parameters of the specified photovoltaic panel corresponding to each inspection cycle.

[0039] In a specific embodiment, the real-time performance evaluation coefficient of the specified photovoltaic panel in each inspection cycle is calculated, and the specific calculation process is as follows: obtaining the standard control performance parameters corresponding to the specified photovoltaic panel in normal operation from the database, the standard control performance parameters including the standard control power generation, the standard control energy conversion efficiency and the standard control solar radiation intensity;

[0040] By calculating the formula The real-time performance evaluation coefficient αi of the specified photovoltaic panel in each inspection cycle is obtained, where i is the number corresponding to each inspection cycle, i=1, 2,..., n, n is any integer greater than 2, and n is the total number corresponding to the set inspection cycle, wherein W′, f′, q′ represent the standard control power generation, the standard control energy conversion efficiency, and the standard control solar radiation intensity, respectively, and κ1, κ2, κ3 are the weight factors corresponding to the set power generation, the weight factors corresponding to the energy conversion efficiency, and the weight factors corresponding to the solar radiation intensity, respectively.

[0041] It should be noted that the values ​​of κ1, κ2, and κ3 are all greater than 0 and less than 1.

[0042] It should also be noted that the performance parameters are analyzed in combination with actual operating data and expert opinions to determine the contribution of the performance parameters to the overall performance of a specified photovoltaic panel. For example, if the power generation power is considered to dominate the actual power output, the power generation power weight factor is set to 0.5, and the energy conversion efficiency has a direct impact on the conversion capacity, so the weight factor is set to 0.3. The solar radiation intensity is used as an environmental condition, and its weight factor is set to 0.2. In the specific setting process, by referring to historical operating data and industry standards, a weighted method is used to quantify the impact of performance parameters to form a comprehensive evaluation index. Suppose in a certain project, through data regression analysis, it is concluded that the weight of the power generation power is set to 0.5, the weight of the energy conversion efficiency is set to 0.3, and the weight of the solar radiation intensity is set to 0.2.

[0043] The visual parameter acquisition module is used to capture images corresponding to designated photovoltaic panels through drones and fixed cameras, and then obtain visual parameters corresponding to the designated photovoltaic panels in each inspection cycle.

[0044] In a specific embodiment, the visual parameters corresponding to the designated photovoltaic panel in each inspection cycle are obtained by the following specific acquisition process: the area of ​​the dirt region corresponding to the designated photovoltaic panel in each inspection cycle is photographed from a bird's-eye view angle by a high-definition camera mounted on an unmanned aerial vehicle; the area of ​​the dirt region corresponding to the designated photovoltaic panel in each inspection cycle is divided by the total area of ​​the designated photovoltaic panel according to the total area corresponding to the designated photovoltaic panel; the result obtained is the dirt coverage rate corresponding to the designated photovoltaic panel in each inspection cycle; a fixed camera is set within a set range around the designated photovoltaic panel, and the image of the obstruction corresponding to each inspection cycle is obtained; and the height of the obstruction around the designated photovoltaic panel in each inspection cycle is obtained by image recognition technology;

[0045] The surface images of the designated photovoltaic panels are taken by drones, and the lengths of various cracks on the surface of the designated photovoltaic panels are obtained through image recognition technology. Cracks with lengths greater than the set standard crack length are recorded, and the number of cracks corresponding to the designated photovoltaic panels in each inspection cycle is obtained.

[0046] It should be noted that the number of cracks corresponding to a specified photovoltaic panel in each inspection cycle refers to only counting cracks whose length is greater than the set standard crack length. The dirt coverage rate refers to the proportion of the photovoltaic panel surface blocked by dust, dirt, etc. As the dirt coverage rate increases, the light intensity received by the photovoltaic panel will be significantly reduced. For example, when the dirt coverage rate reaches 20%, the power generation efficiency will drop by 10% to 20%. The height of the obstruction will directly affect the light reception of the photovoltaic panel. When the height of the obstruction is similar to or higher than the photovoltaic panel, it will directly lead to a reduction in the light received by the panel during specific periods, such as morning or evening, thereby reducing power generation. The number of cracks refers to the number of visible cracks on the surface of the photovoltaic panel. Cracks will affect the electrical performance and mechanical strength of the panel. For example, when the number of cracks increases, it will cause local heating, short circuit or breakdown and other problems.

[0047] During the performance evaluation process, the embodiment of the present invention calculates the real-time performance evaluation coefficient of each inspection cycle based on the performance parameters collected in real time, which can quickly reflect the working efficiency and possible problems of the photovoltaic panels, and not only helps to timely discover abnormalities and provide reliable data support for subsequent maintenance measures. In the process of visual parameter acquisition, the use of equipment such as drones and fixed cameras is conducive to comprehensively acquiring the surface state of the photovoltaic panels, acquiring key visual parameters such as dirt coverage, obstruction height and number of cracks, providing high-quality data input for subsequent on-site visual analysis, and helping to improve the accuracy and comprehensiveness of the analysis.

[0048] The on-site visual analysis module is used to calculate the on-site visual evaluation coefficient corresponding to the specified photovoltaic panel in each inspection cycle according to the visual parameters corresponding to the specified photovoltaic panel in each inspection cycle.

[0049] In a specific embodiment, the on-site visual evaluation coefficient corresponding to the designated photovoltaic panel in each inspection cycle is calculated, and the specific calculation process is as follows: the standard control visual parameters corresponding to the external surface status evaluation of the designated photovoltaic panel are obtained from the database, and the standard control visual parameters include the standard control dirt coverage, the standard control obstruction height and the standard control crack number, which are respectively recorded as FG′, Z′ and C′;

[0050] By calculating the formula The on-site visual assessment coefficient βi corresponding to the specified photovoltaic panel in each inspection cycle is obtained, where FGi, Zi, and Ci represent the dirt coverage rate, obstruction height, and number of cracks corresponding to the specified photovoltaic panel in the i-th inspection cycle, respectively, and λ1, λ2, and λ3 are the weight factors corresponding to the set dirt coverage rate, obstruction height, and number of cracks, respectively.

[0051] It should be noted that the values ​​of λ1, λ2, and λ3 are all greater than 0 and less than 1.

[0052] It should also be noted that, for example, the dirt coverage directly affects the light received by the photovoltaic panel, and is set to the highest weight factor, such as 0.6, because a small amount of dirt will significantly reduce the power generation efficiency. Secondly, the height of the obstruction affects the duration and intensity of sunlight, which is set to 0.3. Although the number of cracks will affect the overall structure and safety of the component, it does not directly affect the efficiency as much as the first two in specific cases. Therefore, the weight factor is set to 0.1. Through historical data analysis and expert consultation, combined with actual cases, for example, in a certain project, it was found that high dirt coverage caused a 20% decrease in power generation, high obstruction height reduced light by 15%, and the number of cracks had little effect on power generation. This observation helps verify the rationality of the weight setting and ensures that the comprehensive evaluation model can accurately reflect the performance changes of photovoltaic panels under different environmental and operating conditions.

[0053] The comprehensive inspection condition analysis module is used to analyze and obtain the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle according to the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle.

[0054] In a specific embodiment, the analysis obtains the comprehensive inspection evaluation index corresponding to each inspection cycle of the specified photovoltaic panel. The specific analysis process is as follows: Substitute the real-time performance evaluation coefficient and the on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle into the calculation formula Get the comprehensive inspection evaluation index corresponding to each inspection cycle of the specified photovoltaic panel Among them, κ1 and κ2 are the weight factors corresponding to the set real-time performance evaluation coefficient and the weight factors corresponding to the on-site visual evaluation coefficient respectively.

[0055] It should be noted that the values ​​of κ1 and κ2 are both greater than 0 and less than 1.

[0056] It should also be noted that the real-time performance evaluation coefficient reflects the power generation capacity and efficiency changes of the equipment under actual operating conditions, so it is of high importance and the weight factor is set to 0.7. This is because real-time data can quickly reflect equipment status and environmental changes and adjust operating strategies in a timely manner. The on-site visual evaluation coefficient refers to the external surface problems of the specified photovoltaic panels, which have little impact on the actual operating performance of the specified photovoltaic panels. Therefore, the on-site visual evaluation coefficient weight factor can be set to 0.3.

[0057] The inspection plan formulation module is used to evaluate the inspection level of the specified photovoltaic panels in each inspection cycle, and then formulate the inspection plan for the specified photovoltaic panels in each inspection cycle.

[0058] In a specific embodiment, the evaluation specifies the inspection level of the photovoltaic panel in each inspection cycle, and the specific evaluation process is as follows: the inspection levels corresponding to the specified photovoltaic panel in the inspection process are obtained from the database, and each inspection level corresponds to each comprehensive inspection evaluation index interval. The comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle is compared with each comprehensive inspection evaluation index interval respectively. If the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in a certain inspection cycle belongs to a certain comprehensive inspection evaluation index interval, then the inspection level corresponding to the specified photovoltaic panel in the inspection cycle is the inspection level corresponding to the comprehensive inspection evaluation index interval, thereby obtaining the inspection level of the specified photovoltaic panel in each inspection cycle.

[0059] In a specific embodiment, the inspection plan for the designated photovoltaic panel in each inspection cycle is formulated as follows: the inspection system compares the inspection level of the designated photovoltaic panel in each inspection cycle with the inspection levels corresponding to the preset inspection resources according to the inspection level of the designated photovoltaic panel in each inspection cycle. The inspection resources include the number of inspection staff, the number of inspection equipment and the estimated time spent, so as to obtain the inspection resources corresponding to the designated photovoltaic panel in the inspection cycle. The inspection system sends the inspection resource information corresponding to the inspection process of the designated photovoltaic panel to the staff receiving end, so as to realize the designation of the inspection plan for the designated photovoltaic panel in each inspection cycle.

[0060] In the process of comprehensive inspection condition analysis, the embodiment of the present invention passes through a comprehensive inspection condition analysis module, which is conducive to generating a comprehensive inspection evaluation index. According to the comprehensive inspection evaluation index, the inspection level of the photovoltaic panel is determined, and then an inspection plan is formulated. By matching the inspection level with preset inspection resources, such as the number of inspection staff, the number of inspection equipment and the estimated time spent, it can ensure the efficient use of inspection resources and avoid excessive or insufficient allocation of resources.

[0061] The early warning terminal is used to issue an early warning prompt when a designated photovoltaic panel needs to be inspected during a certain inspection cycle.

[0062] The database is used to store the historical fault frequency corresponding to the specified photovoltaic panel, and also stores the area corresponding to the specified photovoltaic panel, and also stores the standard control performance parameters corresponding to the specified photovoltaic panel during normal operation, the standard control performance parameters include the standard control power generation, the standard control energy conversion efficiency and the standard control solar radiation intensity, and also stores the standard control visual parameters corresponding to the external surface status evaluation of the specified photovoltaic panel, the standard control visual parameters include the standard control dirt coverage, the standard control obstruction height and the standard control crack number, and also stores the various inspection levels corresponding to the specified photovoltaic panel during the inspection process.

[0063] See also Figure 2 A photovoltaic panel inspection method based on artificial intelligence is shown, comprising the following steps:

[0064] Step 1: Real-time data collection: Set each inspection cycle and collect the performance parameters of the specified photovoltaic panels in each inspection cycle.

[0065] Step 2: Real-time performance analysis: Based on the performance parameters of the designated photovoltaic panels corresponding to each inspection cycle, the real-time performance evaluation coefficient of the designated photovoltaic panels in each inspection cycle is calculated.

[0066] Step 3: Obtain visual parameters: Use drones and fixed cameras to capture images corresponding to designated photovoltaic panels, and then obtain visual parameters corresponding to designated photovoltaic panels in each inspection cycle.

[0067] Step 4: On-site visual analysis: Based on the visual parameters corresponding to the specified photovoltaic panels in each inspection cycle, the on-site visual evaluation coefficient corresponding to the specified photovoltaic panels in each inspection cycle is calculated.

[0068] Step 5: Comprehensive inspection condition analysis: Based on the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle, the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle is analyzed.

[0069] Step 6: Develop an inspection plan: Evaluate the inspection level of the designated photovoltaic panels in each inspection cycle, and then develop an inspection plan for the designated photovoltaic panels in each inspection cycle.

[0070] Step 7: Early warning: When the designated photovoltaic panel needs to be inspected in a certain inspection cycle, an early warning will be issued.

[0071] The present invention provides an artificial intelligence-based photovoltaic panel inspection system and method. During the inspection cycle setting process, a real-time data acquisition module is used to ensure that the inspection frequency matches the actual needs of the photovoltaic panels. The real-time data acquisition module can dynamically adjust the inspection cycle according to performance parameters such as the photovoltaic panel's power generation power, energy conversion efficiency, and solar radiation intensity, thereby avoiding unnecessary resource waste caused by fixed-cycle inspections. At the same time, continuous data collection is helpful to timely respond to changes in the status of the photovoltaic panels, ensuring that the photovoltaic panels operate in the best state.

[0072] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A photovoltaic panel inspection system based on artificial intelligence, characterized in that: include: Real-time data acquisition module, used to set each inspection cycle and collect the performance parameters of the specified photovoltaic panels in each inspection cycle; A real-time performance analysis module is used to calculate the real-time performance evaluation coefficient of the specified photovoltaic panel in each inspection cycle according to the performance parameters of the specified photovoltaic panel corresponding to each inspection cycle; A visual parameter acquisition module is used to capture images corresponding to a specified photovoltaic panel through a drone and a fixed camera, and then obtain visual parameters corresponding to the specified photovoltaic panel in each inspection cycle; An on-site visual analysis module is used to calculate the on-site visual evaluation coefficient corresponding to the specified photovoltaic panel in each inspection cycle according to the visual parameters corresponding to the specified photovoltaic panel in each inspection cycle; The comprehensive inspection condition analysis module is used to analyze and obtain the comprehensive inspection evaluation index corresponding to each inspection cycle of the specified photovoltaic panel according to the real-time performance evaluation coefficient and on-site visual evaluation coefficient of each inspection cycle; Inspection plan formulation module, used to evaluate the inspection level of the specified photovoltaic panels in each inspection cycle, and then formulate the inspection plan for the specified photovoltaic panels in each inspection cycle; The early warning terminal is used to issue an early warning prompt when a designated photovoltaic panel needs to be inspected during a certain inspection cycle.

2. The photovoltaic panel inspection system based on artificial intelligence according to claim 1 is characterized in that: The performance parameters include power generation, energy conversion efficiency and solar radiation intensity, and the visual parameters include dirt coverage, obstruction height and number of cracks.

3. The photovoltaic panel inspection system based on artificial intelligence according to claim 2 is characterized in that: The collection of performance parameters of the specified photovoltaic panels in each inspection cycle is as follows: Obtain the historical fault frequency corresponding to the specified photovoltaic panel from the database, and then set the inspection cycles corresponding to the specified photovoltaic panel, set an inverter in the specified photovoltaic panel, connect the inverter to the inspection system through the network, and then read the voltage and current in the specified photovoltaic panel corresponding to each inspection cycle, multiply the voltage and current in the specified photovoltaic panel corresponding to each inspection cycle, and the result is the power generation power of the specified photovoltaic panel in each inspection cycle, denoted as Wi, measure the solar radiation intensity of the specified photovoltaic panel in each inspection cycle through the solar radiation sensor, denoted as qi, set the collection time period as T, obtain the area corresponding to the specified photovoltaic panel from the database, denoted as S, and calculate the formula The energy conversion efficiency fi of the specified photovoltaic panel in each inspection cycle is obtained.

4. The photovoltaic panel inspection system based on artificial intelligence according to claim 3 is characterized in that: The calculation specifies the real-time performance evaluation coefficient of the photovoltaic panel in each inspection cycle. The specific calculation process is as follows: Obtaining from the database the standard control performance parameters corresponding to the normal operation of the specified photovoltaic panel, the standard control performance parameters including the standard control power generation, the standard control energy conversion efficiency and the standard control solar radiation intensity; By calculating the formula The real-time performance evaluation coefficient αi of the specified photovoltaic panel in each inspection cycle is obtained, where i is the number corresponding to each inspection cycle, i=1, 2, ..., n, n is any integer greater than 2, and n is the total number corresponding to the set inspection cycle, wherein W′, f′, q′ represent the standard control power generation, the standard control energy conversion efficiency and the standard control solar radiation intensity, respectively, and κ1, κ2, κ3 are the weight factors corresponding to the set power generation, the weight factors corresponding to the energy conversion efficiency, and the weight factors corresponding to the solar radiation intensity, respectively.

5. The photovoltaic panel inspection system based on artificial intelligence according to claim 4 is characterized in that: The specific acquisition process of obtaining the visual parameters corresponding to the specified photovoltaic panel in each inspection cycle is as follows: The high-definition camera carried by the drone is used to photograph the dirt area corresponding to the designated photovoltaic panel in each inspection cycle from a bird's-eye view angle. According to the total area corresponding to the designated photovoltaic panel, the dirt area corresponding to the designated photovoltaic panel in each inspection cycle is divided by the total area of ​​the designated photovoltaic panel. The result is the dirt coverage rate corresponding to the designated photovoltaic panel in each inspection cycle. The image of the obstruction corresponding to each inspection cycle is obtained by setting a fixed camera within a set range around the designated photovoltaic panel. The height of the obstruction around the designated photovoltaic panel in each inspection cycle is obtained by image recognition technology. The surface images of the designated photovoltaic panels are taken by drones, and the lengths of various cracks on the surface of the designated photovoltaic panels are obtained through image recognition technology. Cracks with lengths greater than the set standard crack length are recorded, and the number of cracks corresponding to the designated photovoltaic panels in each inspection cycle is obtained.

6. The photovoltaic panel inspection system based on artificial intelligence according to claim 5 is characterized in that: The calculation specifies the on-site visual assessment coefficient of the photovoltaic panel corresponding to each inspection cycle. The specific calculation process is as follows: Obtain the standard control visual parameters corresponding to the external surface condition assessment of the specified photovoltaic panel from the database, the standard control visual parameters include the standard control dirt coverage, the standard control shielding height and the standard control crack count, which are recorded as FG′, Z′ and C′ respectively; By calculating the formula The on-site visual assessment coefficient βi corresponding to the specified photovoltaic panel in each inspection cycle is obtained, where FGi, Zi, and Ci represent the dirt coverage rate, obstruction height, and number of cracks corresponding to the specified photovoltaic panel in the i-th inspection cycle, respectively, and λ1, λ2, and λ3 are the weight factors corresponding to the set dirt coverage rate, obstruction height, and number of cracks, respectively.

7. The photovoltaic panel inspection system based on artificial intelligence according to claim 6 is characterized in that: The analysis obtains the comprehensive inspection evaluation index corresponding to each inspection cycle of the specified photovoltaic panel. The specific analysis process is as follows: Substitute the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panel in each inspection cycle into the calculation formula Get the comprehensive inspection evaluation index corresponding to each inspection cycle of the specified photovoltaic panel Among them, κ1 and κ2 are the weight factors corresponding to the set real-time performance evaluation coefficient and the weight factors corresponding to the on-site visual evaluation coefficient respectively.

8. The photovoltaic panel inspection system based on artificial intelligence according to claim 7 is characterized in that: The evaluation specifies the inspection level of the photovoltaic panels in each inspection cycle. The specific evaluation process is as follows: The inspection levels corresponding to the specified photovoltaic panel during the inspection process are obtained from the database. Each inspection level corresponds to a comprehensive inspection evaluation index interval. The comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in each inspection cycle is compared with each comprehensive inspection evaluation index interval. If the comprehensive inspection evaluation index corresponding to the specified photovoltaic panel in a certain inspection cycle belongs to a certain comprehensive inspection evaluation index interval, then the inspection level corresponding to the specified photovoltaic panel in the inspection cycle is the inspection level corresponding to the comprehensive inspection evaluation index interval, thereby obtaining the inspection level of the specified photovoltaic panel in each inspection cycle.

9. The photovoltaic panel inspection system based on artificial intelligence according to claim 8, characterized in that: The specific process of formulating the inspection plan for the designated photovoltaic panels in each inspection cycle is as follows: The inspection system compares the inspection level of the designated photovoltaic panel in each inspection cycle with the inspection levels corresponding to the preset inspection resources according to the inspection level of the designated photovoltaic panel in each inspection cycle. The inspection resources include the number of inspection staff, the number of inspection equipment and the estimated time spent, thereby obtaining the inspection resources corresponding to the designated photovoltaic panel in the inspection cycle. The inspection system sends the inspection resource information corresponding to the inspection process of the designated photovoltaic panel to the staff receiving end, thereby realizing the designation of the inspection plan for the designated photovoltaic panel in each inspection cycle.

10. An artificial intelligence-based photovoltaic panel inspection method for executing the artificial intelligence-based photovoltaic panel inspection system according to any one of claims 1 to 9, characterized in that: The steps include: Step 1: Real-time data collection: Set each inspection cycle and collect the performance parameters of the specified photovoltaic panels in each inspection cycle; Step 2: Real-time performance analysis: Based on the performance parameters of the designated photovoltaic panels corresponding to each inspection cycle, the real-time performance evaluation coefficient of the designated photovoltaic panels in each inspection cycle is calculated; Step 3: Obtain visual parameters: Use drones and fixed cameras to capture images corresponding to designated photovoltaic panels, and then obtain visual parameters corresponding to designated photovoltaic panels in each inspection cycle; Step 4: On-site visual analysis: Based on the visual parameters corresponding to the specified photovoltaic panels in each inspection cycle, the on-site visual evaluation coefficient corresponding to the specified photovoltaic panels in each inspection cycle is calculated; Step 5: Comprehensive inspection condition analysis: Based on the real-time performance evaluation coefficient and on-site visual evaluation coefficient of the specified photovoltaic panels in each inspection cycle, the comprehensive inspection evaluation index corresponding to the specified photovoltaic panels in each inspection cycle is analyzed; Step 6: Inspection plan formulation: Evaluate the inspection level of the designated photovoltaic panels in each inspection cycle, and then formulate the inspection plan for the designated photovoltaic panels in each inspection cycle; Step 7: Early warning: When the designated photovoltaic panel needs to be inspected in a certain inspection cycle, an early warning will be issued.

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