An intelligent detection system for photovoltaic components

Abstract

The present invention discloses an intelligent detection system for photovoltaic components, and relates to the field related to photovoltaic component detection. The system includes: a working environment parameter parsing module for parsing working environment parameters of target application scenarios; a performance target information acquisition module for acquiring performance target information; a composite detection index determination module for decomposing and configuring photovoltaic components and determining composite detection indexes; a single detection index set acquisition module for decomposing composite detection indexes and obtaining single detection index sets; an index detection module for performing index detection on photovoltaic components; and a fusion evaluation module for configuring the proportion coefficients of each photovoltaic component and performing fusion evaluation on the detection results. The system solves the technical problem of insufficient reliability and comprehensiveness of detection results in existing photovoltaic component detection, and achieves the technical effect of improving the reliability and comprehensiveness of detection results.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic module detection, and in particular to an intelligent detection system for photovoltaic components. Background Art

[0002] With the increasing attention paid to renewable energy around the world, photovoltaic technology, as an important component, plays a key role in energy transformation and sustainable development. As the core components of photovoltaic systems, the stability and efficiency of photovoltaic components directly affect the power generation capacity and economic benefits of the entire system. Therefore, comprehensive and accurate detection and evaluation of photovoltaic components has become an important prerequisite for ensuring efficient and reliable operation of photovoltaic systems. In the field of detection and evaluation of photovoltaic components, traditional methods mainly rely on standard tests in laboratory environments, and evaluate the performance of components by simulating specific environmental conditions (such as temperature, humidity, light intensity, etc.). This method is difficult to fully simulate the complex and changeable conditions in actual application scenarios, resulting in poor environmental adaptability. At the same time, traditional methods often focus on the testing of a single performance indicator (such as conversion efficiency), ignoring the various performance requirements that photovoltaic components may face in actual applications (such as life, cost-effectiveness, etc.), resulting in one-sided performance evaluation.

[0003] Among the current related technologies, photovoltaic component testing has technical problems such as insufficient reliability and comprehensiveness of test results. Summary of the invention

[0004] The present application provides an intelligent detection system for photovoltaic components, which adopts technical means such as integrated working environment parameter analysis, performance target information acquisition, composite detection index determination, single detection index set acquisition, index detection and fusion evaluation of multiple modules, thereby realizing comprehensive and intelligent detection and evaluation of photovoltaic components in complex actual working environments, and achieving the technical effect of improving the reliability and comprehensiveness of the detection results.

[0005] The present application provides an intelligent detection system for photovoltaic components, comprising:

[0006] A working environment parameter parsing module, the working environment parameter parsing module is used to parse the working environment parameters of the target application scenario, including temperature, humidity, light intensity, dust, and wind speed; a performance target information acquisition module, the performance target information acquisition module is used to acquire performance target information, and the performance target information is used to characterize the setting target requirements of photovoltaic components, including life, energy demand, and target cost; a composite detection index determination module, the composite detection index determination module is used to decompose and configure photovoltaic components according to the performance target information and determine composite detection indicators; a single detection index set acquisition module, the single detection index set acquisition module is used to decompose the composite detection indicators according to the performance target information and the working environment parameters to obtain a single detection index set; an index detection module, the index detection module is used to perform index detection on the photovoltaic components respectively according to the single detection index set to obtain the detection results of each photovoltaic component; a fusion evaluation module, the fusion evaluation module is used to configure the proportion coefficient of each photovoltaic component, perform fusion evaluation on the detection results of each photovoltaic component, and obtain a detection evaluation result.

[0007] In a possible implementation, the photovoltaic components are decomposed and configured based on the performance target information, composite detection indicators are determined, and the following processing is performed:

[0008] Obtain the constraint threshold of the performance target information and determine the weight ratio relationship of each performance target; based on the constraint threshold and the weight ratio relationship, with the goal of minimizing the loss value of the performance target information, optimize and analyze the photovoltaic performance index to obtain the composite detection index, which is a comprehensive index for photovoltaic performance detection and corresponds to multiple single detection indicators.

[0009] In a possible implementation, the composite detection index is decomposed according to the performance target information and the working environment parameter to obtain a single detection index set, and the following processing is performed:

[0010] According to the performance target information and the working environment parameters, an impact relationship analysis is performed based on a historical case data set to determine the impact coefficient of the performance target-environmental parameter; the performance target information and the working environment parameters are used to perform time series alignment screening to obtain environmental distribution characteristics and parameter extreme values; trend compensation is performed on the impact coefficient of the performance target-environmental parameter according to the environmental distribution characteristics and parameter extreme values ​​to obtain a compensated performance target and a compensated probability value; the composite detection indicator is compensated by the compensated performance target and the compensation probability value to generate a compensated composite detection indicator quantity; the compensated composite detection indicator quantity is decomposed into a single indicator to obtain the single detection indicator set.

[0011] In a possible implementation, the compensation composite detection index is decomposed into single indexes to obtain the single detection index set, and the following processing is performed:

[0012] Obtain a relationship list between the composite detection index and the single detection index, wherein the relationship list is obtained by fitting historical sample data; parse the compensated composite detection index according to the relationship list to determine the correlation between each single detection index; and screen according to the correlation between each single detection index to obtain the single detection index set, wherein the single detection index set includes single detection indicators whose correlation reaches a screening threshold, and their detection scalars.

[0013] In a possible implementation, trend compensation is performed on the influence coefficient of the performance target-environmental parameter according to the environmental distribution characteristics and the extreme value of the parameter, a compensation performance target and a compensation probability value are obtained, and the following processing is performed:

[0014] According to the environmental distribution characteristics and parameter extreme values, the cumulative influence of the environmental parameters is fitted; according to the influence coefficient of the performance target-environmental parameters, the performance target influence of the cumulative influence is calculated to determine the influence probability value of each environmental distribution; an association mapping between the influence probability value of each environmental distribution and the performance target is established, and an influence compensation calculation is performed to obtain the compensated performance target and compensation probability value.

[0015] In a possible implementation, an association mapping between the impact probability value of each environment distribution and the performance target is established, an impact compensation calculation is performed, the compensation performance target and the compensation probability value are obtained, and the following processing is performed:

[0016] Obtain the impact probability values ​​of service life, energy conversion efficiency, and maintenance cost respectively; establish an associated mapping between the performance target information and the impact probability value, and fit the probability value curve of each performance target information according to the periodic timing relationship of the service life; use the probability value curve and the indicator quantity baseline of the composite detection indicator to perform difference calculation to determine the compensation probability value.

[0017] In a possible implementation, the photovoltaic performance index is optimized and analyzed with the goal of minimizing the loss value of the performance target information, and the following processing is performed:

[0018] Taking the minimum loss value of the performance target information as the goal, a loss evaluation function is constructed, and its expression is: ,in, is the i-th performance target indicator value, is the target value of the ith performance objective, is the weight of the i-th performance objective, is the constraint threshold of the i-th performance target, n is a positive integer, indicating the total number of performance targets, is the loss evaluation function, The square of the deviation of the ith performance target is ensured to be non-negative and to impose a greater penalty on large deviations; a search space is constructed based on the loss evaluation function, and an initial solution and an optimization direction are randomly determined to optimize the combination of composite performance indicators, so as to obtain the combination of performance indicators with the minimum loss value and obtain the composite detection indicator.

[0019] In a possible implementation, the photovoltaic components are respectively tested for indicators according to the single test indicator set to obtain the test results of each photovoltaic component, and the following processing is performed:

[0020] The detection scalar of each indicator in the single detection indicator set is obtained respectively to determine the detection accuracy requirement and the detection quantification requirement; the detection means are matched according to the detection accuracy requirement and the detection quantification requirement, and the single indicator detection is performed on each photovoltaic component according to the detection requirement of the single detection indicator by matching the detection means to obtain the detection result.

[0021] The intelligent detection system for photovoltaic components proposed in the present application is proposed to parse the working environment parameters of the target application scenario through the working environment parameter parsing module, including temperature, humidity, light intensity, dust, and wind speed; the performance target information is obtained through the performance target information acquisition module, and the performance target information is used to characterize the setting target requirements of the photovoltaic components, including life, energy demand, and target cost; the photovoltaic components are decomposed and configured with the performance target information through the composite detection index determination module, and the composite detection index is determined; the composite detection index is decomposed according to the performance target information and the working environment parameters through the single detection index set acquisition module to obtain a single detection index set; the photovoltaic components are respectively tested according to the single detection index set through the index detection module to obtain the detection result of each photovoltaic component; the proportion coefficient of each photovoltaic component is configured through the fusion evaluation module, and the detection result of each photovoltaic component is fusion evaluated to obtain the detection evaluation result, thereby achieving the technical effect of improving the reliability and comprehensiveness of the detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic structural diagram of an intelligent detection system for photovoltaic components provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of a process for obtaining a single detection index set in an intelligent detection system for photovoltaic components provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: working environment parameter parsing module 10 , performance target information acquisition module 20 , composite detection index determination module 30 , single detection index set acquisition module 40 , index detection module 50 , fusion evaluation module 60 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides an intelligent detection system for photovoltaic components, such as Figure 1 As shown, the system comprises:

[0030] The working environment parameter parsing module 10 is used to parse the working environment parameters of the target application scenario, including temperature, humidity, light intensity, dust, and wind speed. Specifically, through the sensor network (a network composed of multiple sensor nodes, including temperature sensors, humidity sensors, light intensity sensors, dust concentration sensors, and wind speed sensors, etc.) deployed in the target application scenario, data of working environment parameters such as temperature, humidity, light intensity, dust concentration, and wind speed are collected in real time, and historical environmental parameter data can also be queried from the database for trend analysis or as a detection reference. Among them, the working environment parameters refer to external environmental factors that affect the operating efficiency and life of photovoltaic components, and changes in these parameters will affect the performance of photovoltaic components. The collected data is cleaned to remove outliers and noise to ensure the accuracy and reliability of the data, and key environmental parameter values ​​such as current temperature, average humidity, and maximum light intensity are extracted from the preprocessed data. The extracted environmental parameters can be further analyzed, such as calculating the rate of change of parameters, statistical distribution characteristics, etc., to obtain more information about the environmental status. For real-time changing environmental parameters, the records in the database are continuously updated to ensure the timeliness of the data.

[0031] The performance target information acquisition module 20 is used to obtain performance target information, which is used to characterize the target requirements for setting photovoltaic components, including life, energy demand, and target cost. Specifically, communicate with users or project teams to determine the specific performance target requirements of photovoltaic components. Performance targets refer to the performance indicators and requirements that photovoltaic components need to achieve during design, installation or operation and maintenance, including but not limited to expected life (the expected length of time that photovoltaic components can work normally), energy demand (the total amount of electrical energy that the photovoltaic system needs to generate), target cost (the cost level expected to be achieved during the design and production process) and other key indicators. Collect information related to performance targets, including documents, technical specifications, industry standards or historical data provided by users, and verify the collected performance target information to ensure its rationality and feasibility. For example, check whether the energy demand exceeds the theoretical maximum output of photovoltaic components, whether the cost target is consistent with the actual market situation, etc. The verified performance target information is entered into the system database through the user interface, database import or other system interfaces, or saved in other forms (such as configuration files) for use by subsequent modules. During the project execution, if the performance target information changes (such as due to design changes, changes in market conditions, etc.), update the information in the system in a timely manner.

[0032] The composite detection index determination module 30 is used to decompose and configure the photovoltaic components with the performance target information to determine the composite detection index. Specifically, the types, specifications, performance characteristics, etc. of the photovoltaic components are analyzed to determine the differences in their performance under different working environments, and the key factors affecting the performance are determined according to the characteristics and application scenarios of the photovoltaic components. According to the performance target information and the characteristics of the photovoltaic components, a decomposition and configuration strategy for the composite detection index is formulated, wherein the decomposition and configuration strategy refers to the method and steps of decomposing the performance target information into quantifiable detection indicators and associating these indicators with the specific performance parameters of the photovoltaic components. According to the decomposition and configuration strategy, the composite detection index is determined, and the composite detection index refers to a set of multiple detection indicators formulated to comprehensively evaluate the performance of photovoltaic components under specific working environments. These indicators are interrelated and complementary, and are closely related to the performance target information, and together constitute a complete evaluation system. Composite detection indicators include, but are not limited to: conversion efficiency (the ability of photovoltaic components to convert light energy into electrical energy), stability (the ability of photovoltaic components to maintain stable performance during long-term operation), durability (the ability of photovoltaic components to resist damage and maintain performance in harsh working environments), cost-effectiveness ratio (the proportional relationship between the cost of photovoltaic components and the economic benefits they bring), etc. For each composite detection indicator, a corresponding weight is assigned according to its importance and the degree of influence on the performance target to ensure the objectivity and accuracy of the evaluation results. The determined composite detection indicators and their weights are sorted into sets and output to subsequent modules (such as the single detection indicator set acquisition module 40) for use.

[0033] In a possible implementation, photovoltaic components are decomposed and configured based on the performance target information to determine a composite detection index, including: obtaining a constraint threshold of the performance target information to determine a weight ratio relationship among performance targets; based on the constraint threshold and the weight ratio relationship, with the goal of minimizing the loss value of the performance target information, optimizing and analyzing the photovoltaic performance index to obtain the composite detection index, which is a comprehensive index for photovoltaic performance detection and corresponds to a plurality of single detection indicators.

[0034] Specifically, performance target requirements for photovoltaic components are collected from users or design specifications, such as lifespan must reach XX years, energy demand must not be less than YY kWh / year, target cost must be controlled within ZZ yuan, etc. Based on the collected performance target requirements, specific constraint thresholds are set for each performance target. For example, the constraint threshold for lifespan can be XX years ±X%, the constraint threshold for energy demand can be YY kWh / year ±Y%, and the constraint threshold for target cost can be ZZ yuan ±Z%. Based on the specific needs of the application scenario, user preferences, and technical feasibility, the importance of each performance target to the overall performance of the photovoltaic component is evaluated. Based on the importance of the performance target, a weight is assigned to each performance target based on expert opinions, historical data, or statistical analysis. The size of the weight reflects the relative importance of the performance target in the overall performance evaluation.

[0035] Based on the constraint threshold and weight ratio relationship, a multi-objective optimization model is established. The goal of this model is to minimize the loss value of performance target information, that is, to minimize the gap or loss between the actual performance of photovoltaic components and the performance target requirements. According to the complexity and characteristics of the optimization model, optimization algorithms such as genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm are selected for solution. Through iterative calculation, the various performance indicators of photovoltaic components are continuously adjusted to find a solution that meets the constraints and has the smallest performance loss. The results of the optimization process are comprehensively analyzed to obtain a series of composite detection indicators corresponding to multiple single detection indicators. The composite detection indicator is a comprehensive indicator of photovoltaic performance detection, which can fully reflect the performance of photovoltaic components in different aspects. This implementation method comprehensively considers multiple performance objectives of photovoltaic components. Through optimization analysis, a set of composite detection indicators that both meet the constraints and are as close to the optimal performance as possible are obtained, which improves the accuracy and reliability of photovoltaic component performance evaluation. Through the setting of weight ratio relationship and constraint threshold, it can flexibly adapt to changes in different application scenarios and user needs, and improve the flexibility and adaptability of the system.

[0036] In a possible implementation, the photovoltaic performance index is optimized and analyzed with the goal of minimizing the loss value of the performance target information, including: constructing a loss evaluation function with the goal of minimizing the loss value of the performance target information, and the expression is: ,in, is the i-th performance target indicator value, is the target value of the ith performance objective, is the weight of the i-th performance objective, is the constraint threshold of the i-th performance target, n is a positive integer, indicating the total number of performance targets, is the loss evaluation function, The square of the deviation of the ith performance target is ensured to be non-negative and to impose a greater penalty on large deviations; a search space is constructed based on the loss evaluation function, and an initial solution and an optimization direction are randomly determined to optimize the combination of composite performance indicators, so as to obtain the combination of performance indicators with the minimum loss value and obtain the composite detection indicator.

[0037] Specifically, based on the optional performance indicators of photovoltaic components, a multidimensional search space is constructed, one or more initial solutions are randomly selected as the starting point in the search space, and a heuristic algorithm (such as genetic algorithm, particle swarm optimization, etc.) is used to guide the search process, determine the next direction of the search, and continuously adjust the configuration of the performance indicators through iterative search. The loss evaluation function value after each adjustment is calculated to find the performance indicator combination that minimizes the loss value. When the loss value no longer decreases significantly after multiple iterations, or reaches the preset number of iterations, the algorithm is considered to have converged. The performance indicator combination obtained at this time is the optimal solution that minimizes the loss evaluation function value, and the corresponding performance indicator combination is the composite detection indicator. This implementation method integrates multiple performance objectives of photovoltaic components into a quantifiable evaluation indicator by constructing a loss evaluation function, making the optimization process clearer and more operational, and can efficiently find the optimal solution that minimizes the loss value, thereby achieving comprehensive optimization of the performance indicators of photovoltaic components.

[0038] The single detection index set acquisition module 40 is used to decompose the composite detection index according to the performance target information and the working environment parameters to obtain a single detection index set. Specifically, the composite detection index set from the composite detection index determination module 30, the performance target information from the performance target information acquisition module 20, and the working environment parameters from the working environment parameter parsing module 10 are received, and the correlation between each composite detection index and the working environment parameters is analyzed. For example, some composite detection indicators (such as durability) may be greatly affected by parameters such as temperature, humidity and dust. The composite detection index is matched with specific working environment parameters to determine which parameters have a direct impact on which indicators. According to the specific content of the composite detection index and the influence of the working environment parameters, the composite detection index is further refined into a specific single detection index. The single detection index refers to a single detection item formulated to specifically evaluate the performance of photovoltaic components in a certain aspect. It is a further refinement and concretization of the composite detection index, and has a clear measurement standard and method. All refined single detection indicators are organized into a complete single detection indicator set. For each single detection indicator, a reasonable parameter threshold is set according to its importance and the actual situation of the target application scenario, which is used as a basis for judgment when the subsequent indicator detection module 50 performs performance evaluation. The constructed single detection indicator set and its parameter threshold are output to the indicator detection module 50 for actual detection operations.

[0039] like Figure 2 As shown, in a possible implementation, the composite detection index is decomposed according to the performance target information and the working environment parameters to obtain a single detection index set, including: according to the performance target information and the working environment parameters, an impact relationship analysis is performed based on a historical case data set to determine the impact coefficient of the performance target-environmental parameter; the performance target information and the working environment parameters are used for time series alignment screening to obtain environmental distribution characteristics and parameter extreme values; trend compensation is performed on the impact coefficient of the performance target-environmental parameter according to the environmental distribution characteristics and parameter extreme values ​​to obtain a compensated performance target and a compensated probability value; the composite detection index is compensated by the compensated performance target and the compensation probability value to generate a compensated composite detection indicator quantity; the compensated composite detection indicator quantity is decomposed into a single indicator to obtain the single detection indicator set.

[0040] Specifically, historical data including performance targets, working environment parameters and actual test results are collected, and statistical methods (such as regression analysis, correlation analysis, etc.) are used to analyze the relationship between performance targets and working environment parameters, determine the degree and direction of influence of each working environment parameter on each performance target, and assign an influence coefficient to each pair of working environment parameters and performance targets based on the analysis results, indicating the contribution of the change of the working environment parameter to the change of the performance target. The performance target information is aligned with the time series data of the working environment parameters to ensure that the two are one-to-one in time, and the characteristics of the environmental distribution are identified, such as seasonal changes, day and night temperature differences, extreme weather events (such as sandstorms, rainstorms), etc., as well as the extreme values ​​of parameters corresponding to these characteristics. According to the characteristics of environmental distribution and the extreme values ​​of parameters, the change trend of the working environment parameters is analyzed, and according to the trend analysis results, the original performance target-environmental parameter influence coefficient is adjusted to reflect the impact of possible future working environment changes on the performance target. Based on the adjusted performance target-environmental parameter influence coefficient, the change range of the performance target, that is, the compensatory performance target, is predicted, and the probability of achieving the compensatory performance target is calculated. Apply the compensation performance target and compensation probability value to the composite detection index, adjust its expected value and detection threshold, and the adjusted composite detection index is the compensated composite detection index quantity, which more accurately reflects the impact of changes in working environment parameters on performance targets. Determine the various sub-items or dimensions contained in the compensated composite detection index quantity, assign the detection task to the corresponding single detection index according to the degree of influence of each sub-item on the compensated composite detection index quantity, and combine the decomposed single detection indicators into a set, where each single detection indicator detects a specific aspect of the compensated composite detection index quantity. This implementation method ensures that predictions and detections are closer to the actual situation through trend compensation, and achieves the technical effect of ensuring that the composite detection indicators remain effective and accurate under complex and changing environmental conditions.

[0041] In a possible implementation, the compensated composite detection indicator quantity is decomposed into single indicators to obtain the single detection indicator set, including: obtaining a relationship list between the composite detection indicator and the single detection indicator, wherein the relationship list is obtained by fitting historical sample data; parsing the compensated composite detection indicator quantity according to the relationship list to determine the correlation between each single detection indicator; screening according to the correlation between each single detection indicator to obtain the single detection indicator set, wherein the single detection indicator set includes single detection indicators whose correlation reaches a screening threshold, and their detection scalars.

[0042] Specifically, a large amount of historical sample data is collected, which includes a variety of single detection indicators and corresponding composite detection indicators. The collected data is cleaned to remove outliers and missing values ​​to ensure the accuracy and completeness of the data. Statistical methods (such as regression analysis, machine learning algorithms, etc.) are used to fit the relationship between single detection indicators and composite detection indicators, and the fitted relationship is presented in the form of a list, which contains the mathematical expression or mapping relationship between each single detection indicator and the composite detection indicator.

[0043] The compensation composite detection index quantity to be analyzed is taken as input, and the compensation composite detection index quantity is substituted into the corresponding mathematical expression or mapping relationship according to the generated relationship list, and the correlation between each single detection index and the current compensation composite detection index quantity is obtained by calculation or deduction. Among them, the correlation degree can be a specific value directly calculated by a mathematical expression, or it can be an evaluation result based on a certain rule. According to actual needs or experience, a screening threshold is set to determine which single detection indicators have a correlation degree that is important enough to be included in the final single detection indicator set. The correlation degree of each single detection indicator is compared with the screening threshold. If the correlation degree is greater than or equal to the screening threshold, the single detection indicator and its detection scalar (i.e., the standard quantity or accuracy standard of the detection, which is used to measure the accuracy and reliability of the detection result) are added to the single detection indicator set. This implementation method accurately reflects the actual relationship between the composite detection indicator and the single detection indicator through the relationship list obtained by fitting the historical sample data. According to the relationship list, the compensation composite detection indicator quantity is parsed, and the correlation degree of each single detection indicator can be quickly obtained, so that the key indicators that have a greater impact on the composite detection indicator can be quickly screened out, achieving the technical effect of improving the efficiency and accuracy of the single indicator decomposition.

[0044] In one possible implementation, trend compensation is performed on the influence coefficient of the performance target-environmental parameters according to the environmental distribution characteristics and the extreme values ​​of the parameters to obtain a compensated performance target and a compensation probability value, including: fitting the cumulative influence of the environmental parameters according to the environmental distribution characteristics and the extreme values ​​of the parameters; performing a performance target influence calculation on the cumulative influence according to the influence coefficient of the performance target-environmental parameters to determine the influence probability value of each environmental distribution; establishing an association mapping between the influence probability value of each environmental distribution and the performance target, performing an influence compensation calculation, and obtaining the compensated performance target and the compensation probability value.

[0045] Specifically, a statistical model or a machine learning algorithm is used to establish a cumulative impact model of working environment parameters on the performance of photovoltaic components based on environmental distribution characteristics and parameter extreme values, so as to quantify how working environment factors cumulatively affect the performance of photovoltaic components over time and obtain a cumulative impact. The cumulative impact can be a function that changes over time, reflecting the comprehensive effect of the long-term action of working environment parameters.

[0046] The cumulative impact is applied to the corresponding performance target, and the performance target-environmental parameter impact coefficient is used to calculate the impact of the performance target, so as to quantify the expected change of the performance target of photovoltaic components under specific working environment conditions. Based on the environmental distribution characteristics and the calculation results of the cumulative impact, the probability values ​​of each environmental distribution affecting the performance target of photovoltaic components are evaluated. These probability values ​​take into account the volatility and uncertainty of the working environment parameters and reflect the possibility of the performance target being affected.

[0047] Establish an association mapping between the impact probability values ​​of each environmental distribution and the performance targets of photovoltaic components. This mapping can be a multi-dimensional data structure or model used to describe the expected range of changes in performance targets under different environmental conditions. According to the association mapping, the original performance target is compensated for the impact amount, and the performance target is adjusted to reflect the actual impact of the working environment parameters on the performance target, thereby obtaining a more accurate and reliable compensation performance target and compensation probability value. This implementation method accurately quantifies the impact of environmental parameters on performance targets by fitting the cumulative impact of environmental parameters and using the performance target-environmental parameter impact coefficient, achieving the technical effect of improving the accuracy of the compensation performance target.

[0048] In one possible implementation, an association mapping between the impact probability values ​​of the environmental distributions and the performance targets is established, and an impact compensation calculation is performed to obtain the compensated performance targets and compensation probability values, including: obtaining the impact probability values ​​of the service life, energy conversion efficiency, and maintenance cost, respectively; establishing an association mapping between the performance target information and the impact probability value, and fitting the probability value curve of each performance target information according to the periodic timing relationship of the service life; and performing a difference calculation using the probability value curve and the indicator quantity baseline of the composite detection indicator to determine the compensation probability value.

[0049] Specifically, the impact probability values ​​of service life, energy conversion efficiency, and maintenance cost are extracted from the impact probability values ​​of each environmental distribution. The impact probability value reflects the possibility of changes in the performance targets (such as service life, energy demand, and target cost) of photovoltaic components under certain environmental conditions. The impact probability values ​​of each environmental distribution on service life, energy conversion efficiency, and maintenance cost are associated with the corresponding performance target information. This associated mapping relationship describes how the performance targets change with changes in working environment parameters under different environmental conditions.

[0050] Analyze the periodic change pattern of the service life of photovoltaic components, and use statistical models (such as time series analysis, curve fitting algorithms, etc.) to fit the probability value curves of various performance target information according to the periodic time series relationship of the service life. These probability value curves reflect the trend and uncertainty of performance targets changing over time.

[0051] Based on historical data or industry standards, determine the index baseline of the composite detection index, that is, a reference standard or expected value, compare the probability value curve of the fitted performance target information with the index baseline, and calculate the difference between the two. This difference reflects the degree of deviation of the performance target from the reference state under specific environmental conditions. According to the difference calculation result, the impact probability value corresponding to the maximum difference is used as the compensation probability value. The compensation probability value reflects the degree and possibility of adjustment or compensation required to make up for the deviation of the performance target. This implementation method uses the impact probability value corresponding to the maximum difference as the compensation probability value, and compensates with the maximum probability, thereby accurately adjusting the performance target and ensuring that photovoltaic components can operate stably and efficiently in various environments.

[0052] The index detection module 50 is used to perform index detection on the photovoltaic components according to the single detection index set, and obtain the detection results of each photovoltaic component. Specifically, receive the single detection index set and its corresponding parameter threshold from the single detection index set acquisition module 40. Prepare necessary detection equipment and tools, and formulate detailed detection plans and operation procedures according to the characteristics of photovoltaic components and the specific requirements of detection indicators. Obtain samples of photovoltaic components to be tested, ensure that the samples are representative and meet the detection standards, and perform appropriate pretreatment on the samples, such as cleaning, drying, etc., to eliminate the influence of external factors on the detection results. According to the detection plan and operation process, single detection indicators are detected for photovoltaic components one by one, and corresponding detection equipment and methods are used to record the actual measurement value of each detection indicator, compare the actual measurement value with the parameter threshold, and judge whether the photovoltaic component is qualified in the detection indicator. The actual measurement values ​​and judgment results of various detection indicators of each photovoltaic component are recorded to ensure the accuracy and traceability of the data. During the detection process, if abnormal data or non-compliance with the requirements are found, re-inspection or re-testing is carried out in time to ensure the accuracy and reliability of the detection results. The detection results of all photovoltaic components are organized into reports or tables, and the detection results are output to the fusion evaluation module 60 for further fusion evaluation.

[0053] In a possible implementation, the photovoltaic components are respectively tested for indicators according to the single detection indicator set to obtain the test results of each photovoltaic component, including: respectively obtaining the detection scalar of each indicator in the single detection indicator set to determine the detection accuracy requirement and the detection quantification requirement; matching the detection means according to the detection accuracy requirement and the detection quantification requirement, performing single indicator detection on each photovoltaic component according to the detection requirements of the single detection indicator by matching the detection means, and obtaining the test results.

[0054] Specifically, the single detection index set is traversed, and for each single detection index, the corresponding detection scalar is determined. The detection scalar refers to the specific numerical value or physical quantity used to measure and represent the index, such as voltage, current, temperature, etc. For each single detection index, the corresponding detection accuracy requirements and detection quantification requirements are determined based on the performance target information and working environment parameters. Among them, the detection accuracy requirement refers to the accuracy level that the detection result needs to achieve, and the detection quantification requirement refers to the specific numerical range or standard that the detection result needs to meet. According to the determined detection accuracy requirements and detection quantification requirements, appropriate detection means are matched from the predefined detection means library, including sensor measurement, experimental testing, simulation and other methods. According to the matched detection means, each photovoltaic component is tested one by one according to the detection requirements of the single detection index. After completing all single indicator detections, the detection results are collected and sorted as the basic data for subsequent fusion evaluation. This implementation method ensures the accuracy of the detection process by matching the detection means according to the detection accuracy requirements and detection quantification requirements, and achieves the technical effect of improving the accuracy and reliability of the detection results.

[0055] Fusion evaluation module 60, the fusion evaluation module 60 is used to configure the proportion coefficient of each photovoltaic component, perform fusion evaluation on the detection results of each photovoltaic component, and obtain the detection evaluation results. Specifically, the detection results of each photovoltaic component from the index detection module 50 are received, including a series of actual measurement values ​​of single detection indicators and their comparison with parameter thresholds. According to the overall design requirements of the photovoltaic system, the specific needs of the target application scenario, and the importance of photovoltaic components in the system, etc., a corresponding proportion coefficient is configured for each photovoltaic component, and the proportion coefficient reflects the relative importance of different photovoltaic components in the overall system performance evaluation.

[0056] According to the performance target information, working environment parameters and the proportion coefficient of each photovoltaic component, a fusion evaluation model is constructed. The fusion evaluation model is a mathematical model used to comprehensively consider the performance of each photovoltaic component and the interaction between them, and is used to obtain a comprehensive and objective evaluation result. The detection results of each photovoltaic component are substituted into the fusion evaluation model, calculated, and the calculation results are analyzed to determine whether the overall performance of the photovoltaic system meets the expected goals, and to analyze the contribution of each photovoltaic component to the system performance and possible problems and bottlenecks. According to the evaluation results and analysis content, an evaluation report is generated, including the overall performance evaluation of the photovoltaic system, the performance of each photovoltaic component, the existing problems and improvement suggestions. The embodiment of the present application adopts technical means such as integrated working environment parameter analysis, performance target information acquisition, composite detection index determination, single detection index set acquisition, index detection and fusion evaluation of multiple modules to achieve comprehensive and intelligent detection and evaluation of photovoltaic components in complex actual working environments, and achieve the technical effect of improving the reliability and comprehensiveness of the detection results.

[0057] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0058] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An intelligent detection system for photovoltaic components, characterized in that: The intelligent detection system for photovoltaic components comprises: A working environment parameter analysis module, which is used to analyze working environment parameters of a target application scenario, including temperature, humidity, light intensity, dust, and wind speed; A performance target information acquisition module, the performance target information acquisition module is used to acquire performance target information, the performance target information is used to characterize the setting target requirements of photovoltaic components, including life, energy demand, and target cost; A composite detection index determination module, the composite detection index determination module is used to decompose and configure photovoltaic components according to the performance target information to determine composite detection indicators; A single detection indicator set acquisition module, the single detection indicator set acquisition module is used to decompose the composite detection indicator according to the performance target information and the working environment parameters to obtain a single detection indicator set; An index detection module, the index detection module is used to perform index detection on the photovoltaic components respectively according to the single detection index set to obtain the detection results of each photovoltaic component; A fusion evaluation module is used to configure the proportion coefficient of each photovoltaic component, perform fusion evaluation on the detection results of each photovoltaic component, and obtain the detection evaluation result.

2. The intelligent detection system for photovoltaic components according to claim 1, characterized in that: Decompose and configure photovoltaic components based on the performance target information to determine composite detection indicators, including: Obtaining the constraint threshold of the performance target information and determining the weight ratio relationship of each performance target; According to the constraint threshold and the weight ratio relationship, the photovoltaic performance target is optimized and analyzed with the goal of minimizing the loss value of the performance target information to obtain the composite detection index, which is a comprehensive index of photovoltaic performance detection and corresponds to multiple single detection indicators.

3. The intelligent detection system for photovoltaic components according to claim 2, characterized in that: Decomposing the composite detection index according to the performance target information and the working environment parameters to obtain a single detection index set includes: According to the performance target information and the working environment parameters, an impact relationship analysis is performed based on a historical case data set to determine an impact coefficient of a performance target-environmental parameter; Using the performance target information and the working environment parameters to perform time series alignment screening to obtain environmental distribution characteristics and parameter extreme values; Perform trend compensation on the influence coefficient of the performance target-environmental parameter according to the environmental distribution characteristics and the extreme values ​​of the parameters to obtain a compensated performance target and a compensation probability value; The composite detection index is compensated by the compensation performance target and the compensation probability value to generate a compensated composite detection index amount; The compensation composite detection index quantity is decomposed into single indexes to obtain the single detection index set.

4. The intelligent detection system for photovoltaic components according to claim 3, characterized in that: Decomposing the compensation composite detection index into single indexes to obtain the single detection index set includes: Obtaining a relationship list between the composite detection index and the single detection index, wherein the relationship list is obtained by fitting historical sample data; Analyze the compensation composite detection index according to the relationship list to determine the correlation degree of each single detection index; Screening is performed according to the correlation degree of each single detection indicator to obtain the single detection indicator set, wherein the single detection indicator set includes single detection indicators whose correlation degree reaches a screening threshold and their detection scalars.

5. The intelligent detection system for photovoltaic components according to claim 3, characterized in that: Performing trend compensation on the influence coefficient of the performance target-environmental parameter according to the environmental distribution characteristics and the extreme values ​​of the parameters to obtain a compensation performance target and a compensation probability value, including: According to the environmental distribution characteristics and parameter extreme values, fitting the cumulative impact of environmental parameters; According to the influence coefficient of the performance target-environmental parameter, the performance target influence calculation is performed on the cumulative influence amount to determine the influence probability value of each environmental distribution; Establish an association mapping between the impact probability value of each environmental distribution and the performance target, perform impact compensation calculation, and obtain the compensation performance target and compensation probability value.

6. The intelligent detection system for photovoltaic components according to claim 5, characterized in that: Establishing an association mapping between the impact probability value of each environmental distribution and the performance target, performing impact compensation calculation, and obtaining the compensation performance target and compensation probability value, including: Obtain the impact probability values ​​of service life, energy conversion efficiency, and maintenance cost respectively; Establishing an association mapping between the performance target information and the impact probability value, and fitting a probability value curve of each performance target information according to a periodic time series relationship of the service life; The probability value curve and the index value baseline of the composite detection index are used to perform difference calculation to determine the compensation probability value.

7. The intelligent detection system for photovoltaic components according to claim 2, characterized in that: Taking the loss value of the performance target information as the minimum as the goal, the photovoltaic performance target is optimized and analyzed, including: Taking the minimum loss value of the performance target information as the goal, a loss evaluation function is constructed, and its expression is: ,in, is the i-th performance target indicator value, is the target value of the ith performance objective, is the weight of the i-th performance objective, is the constraint threshold of the i-th performance target, n is a positive integer, indicating the total number of performance targets, is the loss evaluation function, The squared deviation of the ith performance objective ensures non-negativity of the error and a larger penalty for large deviations, Represents finding the loss evaluation function The minimum value of A search space is constructed based on the loss evaluation function, and the photovoltaic performance target optimization combination is performed by randomly determining the initial solution and the optimization direction, so as to obtain the performance target combination with the minimum loss value and obtain the composite detection index.

8. The intelligent detection system for photovoltaic components according to claim 1, characterized in that: The photovoltaic components are respectively tested for indicators according to the single test indicator set to obtain the test results of each photovoltaic component, including: Respectively obtain the detection scalar of each indicator in the single detection indicator set to determine the detection accuracy requirement and the detection quantification requirement; The detection means are matched according to the detection accuracy requirements and the detection quantification requirements, and the single indicator detection is performed on each photovoltaic component according to the detection requirements of the single detection indicator through the matching detection means to obtain the detection result.

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