A method, system, terminal, and medium for handling abnormal zoning of solar panel strings.

Abstract

The application relates to a solar panel string abnormal partition processing method and system, a terminal and a medium, and relates to the field of solar panel detection technology. The method comprises the following steps: connecting multiple single solar panels in sequence to form a solar panel string; detecting the solar panel string, and screening out abnormal solar panels from the single solar panels according to the display brightness in a display interface; if the abnormal solar panels are located in a first area, analyzing the abnormal dominant factors of the abnormal solar panels; performing secondary detection on the abnormal solar panels according to the abnormal dominant factors to obtain detection results; if the abnormal solar panels are located in a second area, evaluating the self performance defects of the abnormal solar panels; performing repair processing on the abnormal solar panels according to the self performance defects to obtain repaired solar panels; and performing secondary detection on the repaired solar panels to obtain detection results. The application has the effects of improving detection efficiency and accurately matching quality control resources.

Description

Technical Field

[0001] This application relates to the field of solar panel detection technology, and in particular to a method, system, terminal and medium for handling abnormal partitioning of solar panel strings. Background Technology

[0002] In modern photovoltaic intelligent manufacturing systems, the solar panel string anomaly zoning processing system is a key technological support for improving detection efficiency and accurately matching quality control resources.

[0003] In related technologies, solar panel anomaly detection systems are based on traditional image recognition technology. By analyzing the brightness distribution of a single solar panel in an electroluminescent image, they identify and display abnormal areas where the brightness is below a threshold, mark the abnormal locations, and trigger a manual re-inspection process.

[0004] Regarding the aforementioned technologies, there are issues with insufficient accuracy in risk identification and rigid decision-making processes when delineating abnormal areas, resulting in low production line inspection efficiency and inaccurate matching of maintenance resources. Summary of the Invention

[0005] To improve detection efficiency and accurately match quality control resources, this application provides a method, system, terminal, and medium for handling abnormal partitioning of solar panel strings.

[0006] Firstly, this application provides a method for handling abnormal partitioning of solar panel strings, employing the following technical solution:

[0007] A method for handling abnormal partitioning of solar panel strings includes:

[0008] Multiple individual solar panels are connected in sequence to form a solar panel string;

[0009] The solar panel string is detected, and abnormal solar panels are selected from the individual solar panels based on the display brightness in the display interface. The display brightness of the abnormal solar panels is lower than the brightness threshold.

[0010] If the abnormal solar panel is located in the first region, analyze the dominant factors of the abnormality of the abnormal solar panel. The first region refers to the first or last region of the solar panel string. The dominant factors of the abnormality include column defects and inherent performance defects.

[0011] The abnormal solar panel was subjected to a secondary test based on the aforementioned dominant abnormal factors, and the test results were obtained.

[0012] If the abnormal solar panel is located in the second region, assess the inherent performance defects of the abnormal solar panel, where the second region refers to the middle region of the solar panel string;

[0013] The abnormal solar panel is repaired according to its own performance defects to obtain a repaired solar panel;

[0014] The repaired solar panel was subjected to a second test, and the test results were obtained.

[0015] By employing the above technical solution, the display brightness of multiple individual solar panels in the display interface of a solar panel string is analyzed. Abnormal solar panels are then identified based on brightness thresholds. Differential processing is applied according to the location of the abnormal solar panel. If it is located in the first or last region, its dominant abnormality is analyzed and a secondary inspection is performed. If it is located in the second region (middle region), its performance defects are assessed and repairs are implemented. A secondary inspection is then performed after repair. This solution significantly improves the accuracy of anomaly diagnosis, optimizes the allocation of inspection and maintenance resources, and reduces misjudgments or missed diagnoses.

[0016] Optionally, the electrical parameters of the abnormal solar panel are obtained, including voltage and current;

[0017] The column of the abnormal solar panel is scanned and inspected to identify whether there is external contamination on the surface of the column;

[0018] If so, and the electrical parameters are within the parameter threshold range, then the dominant abnormal factor is determined to be the column defect;

[0019] If not, and the electrical parameters are not within the parameter threshold range, then the dominant factor of the anomaly is determined to be the inherent performance defect.

[0020] By employing the above technical solution, electrical parameters such as voltage and current of abnormal solar panels are obtained. Combined with the scanning and detection results of the column surface, external contamination or damage is accurately identified. The dominant factor of the anomaly is determined based on whether the electrical parameters are within the normal range. If the column is abnormal but the electrical parameters are normal, it is determined to be a column defect; if the column is intact but the electrical parameters are abnormal, it is determined to be an inherent performance defect. This solution significantly improves the accuracy of fault attribution and reduces the rate of incorrect repairs.

[0021] Optionally, a location-failure probability mapping table is constructed, which stores the mapping relationship between the position ordinal number of the individual solar panel in the solar panel string and the historical statistical probability value. The historical statistical probability value includes the historical statistical probability value of column defects and the historical statistical probability value of its own performance defects.

[0022] Based on the location-failure probability mapping table, the historical statistical probability value of the column defect is compared with the first threshold, and the historical statistical probability value of its own performance defect is compared with the second threshold to obtain the comparison result;

[0023] Based on the comparison results, a region division label is generated for the position ordinal number. The region division label includes at least one of the following: self-defect risk area, column defect risk area, and composite defect risk area.

[0024] According to the positional sequence, individual solar panels that are consecutive and have the same area division label are aggregated to form candidate areas;

[0025] The first region and the second region are updated based on the candidate regions.

[0026] By employing the above technical solution, the location sequence of individual solar panels is analyzed, and a location-failure probability mapping table is used to accurately identify defect risk types and generate region division labels. Simultaneously, consecutive modules with the same label are aggregated to form candidate regions, and the first and second regions are updated accordingly. This solution significantly improves the accuracy of risk region identification and optimizes the rationality of detection region division.

[0027] Optionally, obtain maintenance plan information for the columns connected to the solar panel string;

[0028] Based on the maintenance plan information, it is determined whether the position ordinal number is within the impact window period, where the impact window period is the N cycles prior to the column replacement operation;

[0029] If so, the historical statistical probability value of the column defect corresponding to the influence window period is attenuated to obtain the corrected historical statistical probability value of the column defect.

[0030] If the corrected historical statistical probability value of the column defect is greater than the first threshold, then the region division label is generated by combining the historical statistical probability value of its own performance defect.

[0031] By employing the above technical solution, maintenance plan information for the columns is obtained. The historical statistical probability of column defects is dynamically attenuated based on the impact window period. If the probability still exceeds the threshold after correction, the system integrates the column's own performance defect probability to generate region-specific labels. This solution significantly reduces the risk of false alarms regarding column defects caused by impending column replacement.

[0032] Optionally, it is determined whether the position ordinal number satisfies the attenuation conflict condition. The attenuation conflict condition includes the historical statistical probability value of the column defect being higher than the attenuation threshold before the attenuation process is performed, and lower than the attenuation threshold after the attenuation process is performed.

[0033] If satisfied, then generate low-confidence region segmentation labels for the position ordinal numbers;

[0034] Add the location ordinal number with the low-confidence region segmentation label to the priority review queue;

[0035] Based on the priority verification queue, the individual solar panels corresponding to the position number are tested.

[0036] By employing the above technical solution, it is determined whether the position ordinal number of a single solar panel meets the attenuation conflict condition. Based on the change in the historical statistical probability value of column defects before and after attenuation processing, a low-confidence area segmentation label is generated, and the position ordinal number with this label is added to the priority review queue. Simultaneously, the detection operation for the corresponding single solar panel is triggered based on the priority review queue. This solution optimizes the scheduling accuracy of review resources and reduces the risk of anomalies caused by threshold jumps.

[0037] Optionally, the cumulative number of times the test position ordinal number is determined to satisfy the attenuation conflict condition within M consecutive periods is counted;

[0038] Obtain the current load parameters corresponding to the priority review queue;

[0039] If the current load parameter exceeds the load threshold, the load threshold is lowered based on the number of remaining individual solar panels in the priority review queue to obtain the frequency threshold.

[0040] Whether the cumulative number of times is greater than the frequency threshold;

[0041] If so, the individual solar panel corresponding to the test location number is prohibited from entering the priority review queue, and an equipment maintenance warning instruction is generated.

[0042] By adopting the above technical solution, the cumulative number of times the test location ordinal number meets the attenuation conflict condition within M consecutive periods is counted. The frequency threshold is dynamically adjusted based on the current load parameters of the priority review queue, and whether queuing is prohibited is determined according to whether the cumulative number exceeds the frequency threshold. Simultaneously, equipment maintenance early warning instructions are generated when the conditions are met. This solution significantly improves the rationality of review task scheduling, optimizes the load balancing capability of detection resources, and reduces the risk of resource congestion and anomaly omissions caused by duplicate queuing.

[0043] Optionally, a first position ordinal and a second position ordinal are determined in the candidate region, wherein the number of position ordinals between the first position ordinal and the second position ordinal is less than a preset number threshold;

[0044] Determine the position ordinal numbers between the first position ordinal number and the second position ordinal number to form a set of position ordinal numbers;

[0045] Based on the first position ordinal, the second position ordinal, and the set of position ordinals, a position ordinal detection sequence is formed;

[0046] Calculate the difference in the historical statistical probability values ​​of adjacent position ordinal numbers in the position ordinal detection sequence to form a detection difference set;

[0047] If all detection differences in the detection difference set are less than the tolerance threshold, then the individual solar panel corresponding to the detection sequence of the position number is marked as the candidate region.

[0048] By employing the aforementioned technical solution, the first and second position ordinal numbers within the candidate region are determined. Combining the quantitative relationship between these two position ordinal numbers with the trend of differences in adjacent historical statistical probability values, an intermediate set of position ordinal numbers is identified, and a detection sequence is constructed. Simultaneously, based on whether all detection difference sets are less than a tolerance threshold, individual solar panels corresponding to the set of position ordinal numbers that meet the criteria are marked as candidate regions. This solution significantly improves the completeness of candidate region division, optimizes the spatial continuity identification capability of risk areas, and reduces regional breaks and missed detections caused by local fluctuations.

[0049] Secondly, this application provides a solar panel string abnormal zoning processing system, which adopts the following technical solution:

[0050] A solar panel string anomaly zoning processing system includes:

[0051] The acquisition module is used to acquire solar panel strings;

[0052] A memory for storing the program for the solar panel string abnormal partitioning handling method;

[0053] The processor and the program in the memory can be loaded and executed by the processor to implement the solar panel string abnormal partitioning processing method.

[0054] By adopting the above technical solution, the module completes the input reception of the solar panel string, the processor calls the abnormal partitioning processing program stored in the memory and executes the corresponding algorithm logic, and the memory reliably saves the instructions and parameters required for the processing process, realizing the systematic operation of the abnormal partitioning processing method of the solar panel string. While ensuring the accurate connection of each processing link, it significantly improves the automation level of the detection process.

[0055] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0056] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described in any of the above-mentioned embodiments.

[0057] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improved detection efficiency and accurate matching of quality control resources, and adopts the following technical solution:

[0058] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed any of the above-described methods for handling abnormal partitioning of solar panel strings.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] 1. Analyze the display brightness of multiple individual solar panels on the display interface of a solar panel string, filter out abnormal solar panels based on brightness thresholds, and implement differentiated processing according to the location of the abnormal solar panel. If it is located in the first or last region, analyze its dominant abnormality factors and conduct secondary testing; if it is located in the middle second region, assess its performance defects and implement repair processing, followed by secondary testing. This solution significantly improves the accuracy of anomaly diagnosis, optimizes the allocation of testing and maintenance resources, and reduces misjudgments or omissions.

[0061] 2. The algorithm counts the cumulative number of times the test location sequence meets the decay conflict condition within M consecutive periods. It dynamically adjusts the frequency threshold based on the current load parameters of the priority review queue, and determines whether to prohibit queuing based on whether the cumulative number exceeds the frequency threshold. Simultaneously, it generates equipment maintenance warning instructions when the conditions are met. This solution significantly improves the rationality of review task scheduling, optimizes the load balancing capability of detection resources, and reduces the risk of resource congestion and anomaly omissions caused by duplicate queuing.

[0062] 3. Determine the first and second position ordinals within the candidate regions. Combining the quantitative relationship between these two position ordinals with the trend of differences in adjacent historical statistical probability values, identify the intermediate position ordinal set and construct a detection sequence. Simultaneously, based on whether all detection difference sets are less than a tolerance threshold, mark the individual solar panels corresponding to the qualified position ordinal sets as candidate regions. This scheme significantly improves the completeness of candidate region division, optimizes the spatial continuity identification capability of risk areas, and reduces regional breaks and missed detections caused by local fluctuations. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a method for handling abnormal partitioning of solar panel strings provided in an embodiment of this application.

[0064] Figure 2 This is a flowchart illustrating a method for determining the dominant factors of solar panel abnormalities provided in an embodiment of this application.

[0065] Figure 3 This is a flowchart illustrating a partition update method based on a location-fault probability mapping table provided in an embodiment of this application.

[0066] Figure 4This is a flowchart illustrating a method for generating area division labels based on maintenance plan information, as provided in an embodiment of this application.

[0067] Figure 5 This is a flowchart illustrating a method for verifying solar panels after attenuation processing, provided in an embodiment of this application.

[0068] Figure 6 This is a flowchart illustrating a load-adaptive conflict frequency suppression method provided in an embodiment of this application.

[0069] Figure 7 This is a flowchart illustrating a solar panel area optimization method based on probability difference provided in an embodiment of this application.

[0070] Figure 8 This is a schematic diagram of a solar panel string abnormal partitioning processing system provided in an embodiment of this application. Detailed Implementation

[0071] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0072] This application discloses a method for handling abnormal partitioning of solar panel strings. (Refer to...) Figure 1 This includes:

[0073] Step S101: Connect multiple individual solar panels sequentially to form a solar panel string.

[0074] A single solar panel refers to a standard photovoltaic module with independent power generation capability, having both positive and negative electrodes.

[0075] A solar panel string is a continuous circuit structure formed by connecting multiple individual solar panels in series.

[0076] In a linear sequence, the negative electrode of the previous solar panel is connected in series with the positive electrode of the next solar panel by welding.

[0077] Step S102: Detect the solar panel string and filter out abnormal solar panels from the individual solar panels according to the display brightness on the display interface. The display brightness of the abnormal solar panels is lower than the brightness threshold.

[0078] The detection process involves applying a positive voltage to a string of solar panels through a column, using an infrared camera to capture images of each individual solar panel emitting light, analyzing the brightness of each area in the image, and identifying individual solar panels with brightness below a certain threshold as abnormal solar panels.

[0079] The display interface refers to the screen of the testing equipment, which is a display unit used to present the luminous image of the solar panel string. Normal panels emit light evenly and with high brightness, while abnormal panels emit light weakly and appear as dark areas due to defects.

[0080] An abnormal solar panel refers to a single solar panel whose brightness is lower than a preset brightness threshold during the testing process.

[0081] By comparing the luminance of each individual solar panel in the display interface with the luminance threshold, solar panels with luminance below the luminance threshold are identified as abnormal solar panels, thus completing the screening process.

[0082] Step S103: If the abnormal solar panel is located in the first region, analyze the dominant factors of the abnormal solar panel. The first region refers to the first or last region of the solar panel string. The dominant factors of the abnormality include column defects and performance defects of the panel itself.

[0083] The first region refers to the area near the column in a solar panel string. For example, in a string of 10 panels, the first and tenth panels are the first region.

[0084] The dominant abnormal factor refers to the main factor that causes the abnormal decline in the performance of solar panels.

[0085] A column defect refers to a problem where current conduction is obstructed due to foreign impurities or physical damage to the column.

[0086] Inherent performance defects refer to material cracks or contamination in individual solar panels, which are problems inherent to the individual solar panels themselves.

[0087] By determining whether the anomaly is caused by contamination, foreign objects, or poor contact at external connection points, and by checking whether the solar panel itself has internal structural problems such as microcracks or broken grids, the dominant factors of the anomaly can be identified and determined.

[0088] Step S104: Conduct a secondary inspection of the abnormal solar panel based on the dominant abnormal factors to obtain the inspection results.

[0089] Secondary testing refers to performing the same testing operation on the solar panel as in step S102.

[0090] The test result refers to the result obtained by conducting a secondary test on the abnormal solar panel, which is used to determine whether the abnormal solar panel is usable.

[0091] Step S105: If the abnormal solar panel is located in the second region, assess the performance defects of the abnormal solar panel itself. The second region refers to the middle region of the solar panel string.

[0092] The second region refers to the middle part of a solar panel string excluding the beginning and end. For example, in a string of 10 panels, the 2nd to 9th panels are the middle region.

[0093] Step S106: Repair the abnormal solar panel according to its own performance defects to obtain a repaired solar panel.

[0094] Repaired solar panels refer to individual solar panels that have undergone repair treatment, but whose performance indicators are unknown and have not yet been restored to an acceptable range.

[0095] Step S107: Perform a second test on the repaired solar panel and obtain the test results.

[0096] If a second test confirms an abnormality, the solar panel is deemed unusable; if a second test confirms no abnormality, the solar panel is deemed usable.

[0097] By employing the above technical solution, the display brightness of multiple individual solar panels in the display interface of a solar panel string is analyzed. Abnormal solar panels are then identified based on brightness thresholds. Differential processing is applied according to the location of the abnormal solar panel. If it is located in the first or last region, its dominant abnormality is analyzed and a secondary inspection is performed. If it is located in the second region (middle region), its performance defects are assessed and repairs are implemented. A secondary inspection is then performed after repair. This solution significantly improves the accuracy of anomaly diagnosis, optimizes the allocation of inspection and maintenance resources, and reduces misjudgments or missed diagnoses.

[0098] This application discloses a method for determining the dominant factors causing abnormalities in solar panels. (Refer to...) Figure 2 This includes:

[0099] Step S201: Obtain the electrical parameters of the abnormal solar panel, including voltage and current.

[0100] Electrical parameters are physical quantities that reflect the electrical performance status of abnormal solar panels. These parameters are obtained by measuring them under standard test conditions using a digital multimeter or source meter and are used to evaluate the power generation capacity of abnormal solar panels.

[0101] Step S202: Scan the column of the abnormal solar panel to identify whether there is external contamination on the surface of the column.

[0102] Scanning inspection refers to observing the cylindrical surface of an abnormal solar panel using an optical microscope.

[0103] External contamination refers to oil stains, dust, metal debris, or oxides adhering to the surface of a column, including foreign impurities, pollution, or physical damage. The presence of external contamination is determined by the presence of oil stains, foreign matter attachments, or oxidation marks.

[0104] Step S203: If so, and the electrical parameters are within the parameter threshold range, then the dominant abnormal factor is determined to be a column defect.

[0105] For example, if an abnormal solar panel is found to have oil stains on the surface of the column after scanning and detection, confirming the presence of external contamination, and its voltage is 0.63V and current is 9.1A, both within the parameter threshold range, it indicates that its own power generation function is normal. Therefore, the main factor of the abnormality is determined to be the defect of the column.

[0106] Step S204: If not, and the electrical parameters are not within the parameter threshold range, then the main abnormal factor is determined to be its own performance defect.

[0107] For example, if the abnormal solar panel is scanned and no contamination or damage is found on the surface of the column, but the open circuit voltage is measured to be 0.52V and the short circuit current is 7.0A, both of which are outside the parameter threshold range, then the main factor of the abnormality is determined to be its own performance defect.

[0108] If there is contamination on the surface of the column and the electrical parameters exceed the parameter threshold range, it is determined that there is a conflict; if the column has not reached the replacement cycle, it is first determined that the problem is with the column and the column is cleaned; the abnormal solar panel is tested again after cleaning; based on the brightness change in the display interface, the dominant factor of the abnormal solar panel is determined. If there is no change in the brightness in the display interface, the dominant factor of the abnormality is determined to be its own performance defect; if the brightness in the display interface becomes brighter, the dominant factor of the abnormality is determined to be the column defect.

[0109] By employing the above technical solution, electrical parameters such as voltage and current of abnormal solar panels are obtained. Combined with the scanning and detection results of the column surface, external contamination or damage is accurately identified. The dominant factor of the anomaly is determined based on whether the electrical parameters are within the normal range. If the column is abnormal but the electrical parameters are normal, it is determined to be a column defect; if the column is intact but the electrical parameters are abnormal, it is determined to be an inherent performance defect. This solution significantly improves the accuracy of fault attribution and reduces the rate of incorrect repairs.

[0110] This application discloses a partition update method based on a location-fault probability mapping table. (Refer to...) Figure 3 This includes:

[0111] Step S301: Construct a location-failure probability mapping table. The location-failure probability mapping table stores the mapping relationship between the position ordinal number of a single solar panel in the solar panel string and its historical statistical probability value. The historical statistical probability value includes the historical statistical probability value of column defects and the historical statistical probability value of its own performance defects.

[0112] A location-failure probability mapping table is a two-dimensional data structure in which each row corresponds to a location ordinal number and records the historical statistical probability value of the corresponding column defect and the historical statistical probability value of its own performance defect, which is used to reflect the failure tendency of different locations.

[0113] The position number refers to the numbering of individual solar panels in the solar panel string according to the connection order. For example, starting from the first end, they are numbered 1, 2, 3... until the end.

[0114] Historical statistical probability value refers to the probability of a certain type of defect occurring at a certain location, which is statistically derived from historical inspection data. It is expressed in numerical form as the level of risk.

[0115] Step S302: Based on the location-failure probability mapping table, compare the historical statistical probability value of the column defect with the first threshold, and compare the historical statistical probability value of its own performance defect with the second threshold to obtain the comparison result.

[0116] The first threshold refers to a preset standard for judging the risk of column defects, used to identify areas with a high incidence of column defects, for example, set to 0.05. When the historical statistical probability value of a column defect at a certain location is greater than or equal to the first threshold, it is determined that there is a risk of column defect.

[0117] The second threshold refers to a preset standard for judging the risk of self-performance defects, used to identify areas with a high incidence of self-performance defects, for example, set to 0.03. When the historical statistical probability value of self-performance defects at a certain location is greater than or equal to the second threshold, it is determined that there is a risk of self-performance defects.

[0118] The comparison result refers to the judgment output of each position ordinal number on the two types of defects, which is used to generate region division labels in the subsequent process.

[0119] Step S303: Generate region division labels for the position ordinal based on the comparison results. The region division labels include at least one of the following: self-defect risk area, column defect risk area, and composite defect risk area.

[0120] Regional classification labels are risk type identifiers for a specific location number, used to distinguish different defect-dominant regions.

[0121] The self-defect risk zone refers to the area where the historical statistical probability value of the self-performance defect at that location exceeds the second threshold, while the historical statistical probability value of the column defect does not exceed the first threshold, indicating that the anomaly is mainly caused by internal problems of the solar panel.

[0122] The column defect risk zone refers to the area where the historical statistical probability value of the column defect at that location exceeds the first threshold, while the historical statistical probability value of its own performance defect does not exceed the second threshold, indicating that the anomaly is mainly caused by column problems.

[0123] The composite defect risk zone refers to the area where the historical statistical probability values ​​of both types of defects at that location exceed the corresponding threshold, indicating that there is a dual risk of both the column and its own performance.

[0124] Step S304: According to the location sequence, aggregate individual solar panels that are consecutive and have the same area division label to form candidate areas.

[0125] Aggregation refers to the process of merging multiple adjacent individual solar panels with the same label into a single logical region.

[0126] A candidate region is a range consisting of multiple consecutive individual solar panels with the same regional division label, representing a concentrated distribution area of ​​a certain type of defect.

[0127] Step S305: Update the first region and the second region based on the candidate regions.

[0128] Based on the initial division of the first and second regions, the region boundaries are adjusted according to the distribution of candidate regions generated in step S304. If the actual risk range of the candidate region exceeds or is smaller than the initial setting, the coverage of the first region is expanded or contracted accordingly, and the range of the second region is adjusted simultaneously to make the region division more consistent with the actual fault concentration area.

[0129] By employing the above technical solution, the location sequence of individual solar panels is analyzed, and a location-failure probability mapping table is used to accurately identify defect risk types and generate region division labels. Simultaneously, consecutive modules with the same label are aggregated to form candidate regions, and the first and second regions are updated accordingly. This solution significantly improves the accuracy of risk region identification and optimizes the rationality of detection region division.

[0130] This application discloses a method for generating area division labels based on maintenance plan information. (Refer to...) Figure 4 This includes:

[0131] Step S401: Obtain maintenance plan information for the column connected to the solar panel string.

[0132] Maintenance plan information refers to the pre-set schedule or operating cycle record for periodic replacement, cleaning or inspection of the column, provided by the equipment maintenance log, and used to track the usage status of the column.

[0133] Step S402: Based on the maintenance plan information, determine whether the position sequence is within the impact window period. The impact window period is the N cycles before the column replacement operation.

[0134] The impact window period refers to a specific time period before the planned replacement of the column, expressed in terms of the number of operating cycles, usually set as the first N cycles, for example N=3, which is the last 3 cycles before the replacement.

[0135] Based on the maintenance plan information, determine the replacement cycle and the number of cycles already run for the column. Check the position of the current cycle in the column's entire usage cycle and determine whether it falls within the last N cycles before replacement. If so, determine that the position ordinal number is within the influence window period; otherwise, determine that the position ordinal number is not within the influence window period.

[0136] Step S403: If so, the historical statistical probability value of column defects corresponding to the window period is attenuated to obtain the corrected historical statistical probability value of column defects.

[0137] Attenuation processing involves multiplying the original historical statistical probability value of column defects by a dynamic attenuation factor less than 1. This dynamic attenuation factor decreases as replacement approaches. This is done to account for the gradual degradation of column connection performance over time after long-term use, and by reducing the defect probability assessment value near the replacement cycle, the risk assessment more accurately reflects actual operating conditions.

[0138] Step S404: If the corrected historical statistical probability value of the column defect is greater than the first threshold, then generate a region division label by combining the historical statistical probability value of its own performance defect.

[0139] When the corrected historical statistical probability value of column defects is greater than the first threshold, if the historical statistical probability value of its own performance defects is greater than the second threshold, a composite defect risk zone is generated; if it is less than the second threshold, a column defect risk zone is generated.

[0140] By employing the above technical solution, maintenance plan information for the columns is obtained. The historical statistical probability of column defects is dynamically attenuated based on the impact window period. If the probability still exceeds the threshold after correction, the system integrates the column's own performance defect probability to generate region-specific labels. This solution significantly reduces the risk of false alarms regarding column defects caused by impending column replacement.

[0141] This application discloses a method for verifying solar panels after attenuation processing. (Refer to...) Figure 5 This includes:

[0142] Step S501: Determine whether the position ordinal number meets the attenuation conflict condition. The attenuation conflict condition includes the historical statistical probability value of the column defect being higher than the attenuation threshold before attenuation processing and lower than the attenuation threshold after attenuation processing.

[0143] The attenuation conflict condition refers to a situation where, during the attenuation processing of historical statistical probability values ​​of column defects, the attenuation factor leads to contradictory risk assessments. The historical statistical probability value of the column defect corresponding to the position ordinal number is compared with the attenuation threshold before and after the attenuation process. If the value before processing is higher than the threshold and the value after processing is lower than the threshold, then the attenuation conflict condition is satisfied.

[0144] For example, if the historical statistical probability value of a column defect corresponding to a certain position ordinal number is 0.068 and the attenuation threshold is 0.06, the value before attenuation processing is greater than 0.06. After multiplying by the attenuation factor of 0.85, the corrected value is 0.0578, which is less than 0.06, thus satisfying the attenuation conflict condition.

[0145] Step S502: If satisfied, generate low-confidence region segmentation labels for the position ordinal.

[0146] The low-confidence area classification label is a special marker used to indicate that the risk assessment result of the location ordinal number is highly uncertain.

[0147] For example, if a positional ordinal number is determined to meet the decay conflict condition, it is marked as a low-confidence region partitioning label.

[0148] Step S503: Add the position ordinal number of the region with low confidence label to the priority review queue.

[0149] The priority review queue is a data queue used to manage objects that require further confirmation, storing the location ordinal numbers that need further inspection. Location ordinal numbers with low-confidence labels are prioritized in the priority review queue because their risk assessment is uncertain, ensuring that they are processed first in subsequent inspections.

[0150] When adding, the confidence values ​​corresponding to the labels are divided according to the low confidence areas. The lower the confidence value, the less sufficient the judgment basis and the higher the risk uncertainty. Therefore, the position with the smallest confidence value should be regarded as the most serious and the highest priority. Such positions are prioritized and placed at the front of the queue to ensure that the most unstable areas are ranked first in the priority review queue, so as to realize the detection sorted by risk urgency.

[0151] For example, if the confidence level of the third solar panel is 0.42 and that of the seventh panel is 0.58, and both have low confidence level area labels, the third panel is placed at the top of the queue and the seventh panel is placed next, according to the confidence level values ​​from low to high. Both are then added to the priority review queue.

[0152] Step S504: Based on the priority verification queue, perform detection operations on the individual solar panels corresponding to the position sequence number.

[0153] The detection procedure is consistent with the detection method in step S102.

[0154] For example, the 3rd and 7th individual solar panels are reviewed again based on their position number in the priority review queue to confirm their actual defect status.

[0155] By employing the above technical solution, it is determined whether the position ordinal number of a single solar panel meets the attenuation conflict condition. Based on the change in the historical statistical probability value of column defects before and after attenuation processing, a low-confidence area segmentation label is generated, and the position ordinal number with this label is added to the priority review queue. Simultaneously, the detection operation for the corresponding single solar panel is triggered based on the priority review queue. This solution optimizes the scheduling accuracy of review resources and reduces the risk of anomalies caused by threshold jumps.

[0156] This application discloses a collision frequency suppression method based on load adaptation. (Refer to...) Figure 6 This includes:

[0157] Step S601: Count the cumulative number of times the position number to be tested is determined to meet the attenuation conflict condition within M consecutive periods.

[0158] The test location sequence number refers to the sequential number of a single solar panel that meets the attenuation conflict condition within the panel string.

[0159] M consecutive cycles refer to M complete cycles tracing back from the current production cycle as the end point. For example, M=5 means within 5 consecutive cycles.

[0160] The cumulative number of times refers to the total number of times the ordinal number of the position to be measured is repeatedly identified as satisfying the decay conflict condition within a continuous period, and is used to assess the frequency of occurrence of risk judgment instability.

[0161] For each position number to be tested, retrieve the judgment records within M consecutive production cycles, check whether the attenuation conflict condition is met, increment the count by one for each time the condition is met, and finally accumulate the total number of times.

[0162] For example, if M is set to 5, and the third individual solar panel is determined to meet the attenuation conflict condition 1 time, 0 times, 1 time, 1 time and 1 time in 5 consecutive cycles, then the cumulative number of times is 4.

[0163] Step S602: Obtain the current load parameters corresponding to the priority review queue.

[0164] The current load parameter refers to the number of individual solar panels currently pending processing in the priority review queue, reflecting the amount of additional testing resources required and used to measure the workload of the review task.

[0165] For example, if the priority review queue is read and four individual solar panels (numbers 2, 5, 7, and 9) are found to be pending processing, the current load parameter is determined to be 4.

[0166] Step S603: If the current load parameters exceed the load threshold, the load threshold is lowered based on the number of remaining individual solar panels in the priority review queue to obtain the frequency threshold.

[0167] The reduction operation involves multiplying the load threshold by an adjustment coefficient less than 1, and then lowering the original threshold based on the number of remaining tasks to obtain a more stringent frequency threshold. The more individual solar panels in the priority review queue, the smaller the adjustment coefficient, resulting in a greater reduction in the load threshold. This allows for adjustments that are made more drastically as the workload increases.

[0168] The frequency threshold is a new threshold obtained by lowering the load threshold. It is used to replace the original threshold in subsequent judgments, which means that the conditions for entering the priority review queue are more stringent under high load.

[0169] For example, if the current load parameter is 8, which exceeds the load threshold of 6, the load threshold is multiplied by 0.8 to obtain the frequency threshold of 4.8, based on the large number of tasks remaining in the queue. This is then rounded up to 5 for subsequent judgment.

[0170] Step S604: Determine whether the cumulative number of times is greater than the frequency threshold.

[0171] For example, if the fourth board experiences 4 attenuation conflicts within 5 consecutive cycles, and the current frequency threshold is 3, then 4 is greater than 3, and the condition is met.

[0172] Step S605: If yes, prohibit the individual solar panels corresponding to the test location number from entering the priority review queue, and generate equipment maintenance warning instructions.

[0173] Equipment maintenance early warning commands are prompt signals issued by the system to notify relevant personnel that there is an abnormality in the equipment and that it needs to be checked in time to prevent persistent connection defects caused by equipment aging or deviation.

[0174] For example, if the fourth board meets the attenuation conflict condition six times in five consecutive cycles and the current load exceeds the limit, the system will prevent it from entering the priority review queue and send an equipment maintenance warning instruction to prompt the device to be checked.

[0175] By adopting the above technical solution, the cumulative number of times the test location ordinal number meets the attenuation conflict condition within M consecutive periods is counted. The frequency threshold is dynamically adjusted based on the current load parameters of the priority review queue, and whether queuing is prohibited is determined according to whether the cumulative number exceeds the frequency threshold. Simultaneously, equipment maintenance early warning instructions are generated when the conditions are met. This solution significantly improves the rationality of review task scheduling, optimizes the load balancing capability of detection resources, and reduces the risk of resource congestion and anomaly omissions caused by duplicate queuing.

[0176] This application discloses a method for optimizing solar panel areas based on probability differences. (Refer to...) Figure 7 This includes:

[0177] Step S701: Determine the first position ordinal number and the second position ordinal number in the candidate region, wherein the number of position ordinal numbers between the first position ordinal number and the second position ordinal number is less than a preset number threshold.

[0178] The first position ordinal number refers to the position number of an endpoint selected as the starting point of analysis within the candidate region.

[0179] The second position ordinal number refers to the position number of an endpoint selected as the analysis endpoint within the candidate region.

[0180] The positional ordinal number refers to the number of consecutive positions between the first and second positional ordinal numbers, excluding the first and second positional ordinal numbers.

[0181] For example, if in the candidate region of positions 1 to 6, position 1 is selected as the first position number, position 4 is selected as the second position number, and there are positions 2 and 3 in between, for a total of 2 positions, and if the preset quantity threshold is 3, then the condition is met.

[0182] Step S702: Determine the position ordinal between the first position ordinal and the second position ordinal to form a set of position ordinals.

[0183] The position ordinal set refers to the set of all consecutive position numbers between the first position ordinal and the second position ordinal, excluding the first position ordinal and the second position ordinal.

[0184] For example, the first position ordinal number is 1, the second position ordinal number is 4, and positions 2 and 3 are contained in between, forming the position ordinal set {2, 3}.

[0185] Step S703: Based on the first position ordinal, the second position ordinal, and the set of position ordinals, form a position ordinal detection sequence.

[0186] A positional ordinal detection sequence is a continuous sequence formed by arranging the first positional ordinal, the set of positional ordinals, and the second positional ordinal in order, which is used for subsequent analysis of risk differences between adjacent positions.

[0187] For example, the first position number is 1, the second position number is 4, and the position number set is {2, 3}. The positions 1, 2, 3, and 4 are arranged in order to form a position number detection sequence.

[0188] Step S704: Calculate the difference in historical statistical probability values ​​of adjacent position ordinal numbers in the position ordinal detection sequence to form a detection difference set.

[0189] The difference refers to the absolute value of the difference between the historical statistical probability values ​​of column defects or the historical statistical probability values ​​of their own performance defects at two adjacent positions in the position ordinal detection sequence.

[0190] The detection difference set refers to the set of absolute values ​​of the differences between the historical statistical probability values ​​of each pair of adjacent positions in the position ordinal detection sequence.

[0191] For example, the position number detection sequence is 1, 2, 3, 4. The historical statistical probability values ​​of column defects at positions 1 to 4 are 0.070, 0.068, 0.071, and 0.073, respectively, and the adjacent differences are 0.002, 0.003, and 0.002, forming a detection difference set {0.002, 0.003, 0.002}.

[0192] Step S705: If all the detection differences in the detection difference set are less than the tolerance threshold, then the individual solar panel corresponding to the detection sequence of the position number is marked as a candidate region.

[0193] For example, the detection difference set is {0.002, 0.003, 0.002}, the tolerance threshold is 0.005, all differences are less than the threshold, and the individual solar panels corresponding to the location ordinal detection sequences 1, 2, 3, 4 are marked as candidate regions.

[0194] By employing the aforementioned technical solution, the first and second position ordinal numbers within the candidate region are determined. Combining the quantitative relationship between these two position ordinal numbers with the trend of differences in adjacent historical statistical probability values, an intermediate set of position ordinal numbers is identified, and a detection sequence is constructed. Simultaneously, based on whether all detection difference sets are less than a tolerance threshold, individual solar panels corresponding to the set of position ordinal numbers that meet the criteria are marked as candidate regions. This solution significantly improves the completeness of candidate region division, optimizes the spatial continuity identification capability of risk areas, and reduces regional breaks and missed detections caused by local fluctuations.

[0195] Based on the same inventive concept, embodiments of this application provide a solar panel string abnormal zoning processing system. Please refer to [link / reference]. Figure 8 The system includes:

[0196] Step S801: Acquisition module, used to acquire solar panel strings;

[0197] Step S802: Memory, used to store the program for handling abnormal partitioning of solar panel strings;

[0198] Step S803: The processor can load and execute the program in the memory to implement the solar panel string abnormal partitioning handling method.

[0199] By adopting the above technical solution, the module completes the input reception of the solar panel string, the processor calls the abnormal partitioning processing program stored in the memory and executes the corresponding algorithm logic, and the memory reliably saves the instructions and parameters required for the processing process, realizing the systematic operation of the abnormal partitioning processing method of the solar panel string. While ensuring the accurate connection of each processing link, it significantly improves the automation level of the detection process.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0201] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a method for handling abnormal partitioning of solar panel strings.

[0202] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0203] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a method for handling abnormal partitioning of solar panel strings.

[0204] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0205] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for abnormal sectioning of a string of solar panels, characterized in that, The method comprises the following steps: connecting a plurality of monomer solar panels in sequence to form a solar panel string; detecting the solar panel string, and screening out an abnormal solar panel from the monomer solar panels according to display brightness in a display interface, the display brightness of the abnormal solar panel being lower than a brightness threshold value; if the abnormal solar panel is located in a first area, analyzing an abnormal dominant factor of the abnormal solar panel, the first area referring to a first area or a last area of the solar panel string, the abnormal dominant factor including a column defect and a self performance defect; performing secondary detection on the abnormal solar panel according to the abnormal dominant factor to obtain a detection result; if the abnormal solar panel is located in a second area, evaluating the self performance defect of the abnormal solar panel, the second area referring to a middle area of the solar panel string; performing repair processing on the abnormal solar panel according to the self performance defect to obtain a repaired solar panel; performing secondary detection on the repaired solar panel to obtain the detection result.

2. The method of claim 1, wherein, Further comprising: obtaining electrical parameters of the abnormal solar panel, the electrical parameters including voltage and current; performing scanning detection on a column of the abnormal solar panel to identify whether there is external pollution on a surface of the column; if yes, and the electrical parameters are within a parameter threshold range, determining that the abnormal dominant factor is the column defect; if no, and the electrical parameters are not within the parameter threshold range, determining that the abnormal dominant factor is the self performance defect.

3. The method of claim 1, wherein the method further comprises: Further comprising: constructing a position-failure probability mapping table, the position-failure probability mapping table storing a mapping relationship between a position sequence number of the monomer solar panel in the solar panel string and a historical statistical probability value, the historical statistical probability value including a column defect historical statistical probability value and a self performance defect historical statistical probability value; comparing the column defect historical statistical probability value with a first threshold value and comparing the self performance defect historical statistical probability value with a second threshold value according to the position-failure probability mapping table to obtain a comparison result; generating an area division label for the position sequence number according to the comparison result, the area division label including at least one of a self defect risk area, a column defect risk area and a composite defect risk area; aggregating monomer solar panels that are continuous and have the same area division label according to the position sequence number to form a candidate area; updating the first area and the second area according to the candidate area.

4. The method of claim 3, wherein, Before the step of generating an area division label for the position sequence number according to the comparison result, further comprising: obtaining maintenance plan information of a column connected with the solar panel string; judging whether the position sequence number is in an influence window period according to the maintenance plan information, the influence window period being a front N cycle period from a column replacement operation; if yes, performing attenuation processing on the column defect historical statistical probability value corresponding to the influence window period to obtain a modified column defect historical statistical probability value; If the modified column defect history statistical probability value is greater than the first threshold value, the region division label is generated in combination with the self-performance defect history statistical probability value.

5. The method of claim 4, wherein, Further comprising: determining whether the position sequence number satisfies a decay conflict condition, the decay conflict condition including that the column defect history statistical probability value is higher than a decay threshold value before the decay processing is performed and is lower than the decay threshold value after the decay processing is performed; if yes, generating a low-confidence region division label for the position sequence number; adding the position sequence number with the low-confidence region division label to a priority review queue; based on the priority review queue, performing a detection operation on the monomer solar panel corresponding to the position sequence number.

6. The method of claim 5, wherein, Further comprising: counting an accumulated number of times that a to-be-tested position sequence number is determined to satisfy the decay conflict condition in consecutive M periods; obtaining a current load parameter corresponding to the priority review queue; if the current load parameter exceeds a load threshold value, performing a load threshold value lowering operation on the load threshold value to obtain a frequency threshold value according to a number of remaining monomer solar panels in the priority review queue; obtaining whether the accumulated number of times is greater than the frequency threshold value; if yes, prohibiting the monomer solar panel corresponding to the to-be-tested position sequence number from entering the priority review queue and generating a device maintenance warning instruction.

7. The method of claim 3, wherein the method further comprises: Further comprising: determining a first position sequence number and a second position sequence number in the candidate region, a number of position sequence numbers between the first position sequence number and the second position sequence number being less than a preset number threshold value; determining position sequence numbers between the first position sequence number and the second position sequence number to form a position sequence number set; based on the first position sequence number, the second position sequence number, and the position sequence number set, forming a position sequence number detection sequence; calculating a difference value of historical statistical probability values of adjacent position sequence numbers in the position sequence number detection sequence to form a detection difference value set; if each detection difference value in the detection difference value set is less than a tolerance threshold value, marking a monomer solar panel corresponding to the position sequence number detection sequence as the candidate region.

8. A solar panel string anomaly partitioning system, comprising: The system is used to execute the solar panel string anomaly partition processing method in any one of claims 1 to 7, comprising: an acquisition module configured to acquire a solar panel string; a memory configured to store a program of the solar panel string anomaly partition processing method; a processor, the program in the memory being loadable and executable by the processor and realizing the solar panel string anomaly partition processing method.

9. A smart terminal, characterized by The memory and the processor are included, and the memory has stored thereon a computer program loadable and executable by the processor to execute the method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The memory has stored thereon a computer program loadable and executable by the processor to execute the method in any one of claims 1 to 7.

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