Water-wind-light key component type selection evaluation system and method

Abstract

The invention provides a water-wind-light key component type selection evaluation system and method, and the system can achieve the collection, analysis and recommendation of multi-source reliability data, utilizes the environment-performance data from a multi-environment stress test platform, an on-site monitoring system and historical test records, and achieves the evaluation of the type selection of a water-wind-light key component. And carrying out profile database building according to the unified parameter dimension and segmented interval coding. According to the system, based on target working condition parameters input by a user, a weighted similarity matching algorithm is adopted to quickly position a closest historical environment profile, and corresponding reliability indexes such as life margin, failure rate, performance drift stability and verification coverage are extracted. And generating and maintaining a trusted component library according to a preset rule, and outputting interpretable type selection suggestions with scoring details and recommendation levels in combination with multi-index weighted scoring and rigid elimination conditions. When a data gap under a target working condition is detected, the system can automatically trigger an external test request through a standardized interface, obtain supplementary data, backfill a database and update an evaluation result, and data-driven closed-loop optimization is formed.

Description

Technical Field

[0001] This invention relates to the field of reliability assessment and selection technology for electronic components in hydro-wind-solar power stations, and particularly to a selection and assessment system and method for key components in hydro-wind-solar power stations. Background Technology

[0002] In new energy power generation equipment and industrial control systems, the long-term reliability of key electronic components (such as processors, analog-to-digital converters, sensors, and power semiconductors) directly determines the operational life and stability of the entire system. When these components operate in complex scenarios such as hydropower, wind power, and photovoltaic power, they often face the superposition of multiple environmental stresses, such as high and low temperature cycling, high humidity, salt spray corrosion, strong electromagnetic interference, and mechanical vibration and shock. These stresses may lead to package aging, solder joint fatigue, parameter drift, or even functional failure.

[0003] Current component selection typically relies on component datasheets or standardized test and certification results. However, these data often only cover single stress conditions and are mostly obtained under ideal laboratory conditions, lacking quantitative assessments of performance under multiple stress synergies. As the demands for long lifespan and high reliability in new energy equipment continue to increase, the lack of technical means to systematically integrate multi-source data and provide automated selection recommendations for specific application environments has become a key bottleneck restricting the improvement of design reliability.

[0004] Currently, the closest technical solutions mainly focus on multi-environment stress testing platforms. These platforms can integrate various simulation devices within a sealed chamber to achieve electrical stress loading and temperature rise feedback control of the tested components. They can reproduce complex service conditions through precise synchronization or dynamic switching of multiple stresses and perform statistical analysis on the test data. However, the core of this type of solution is still physical testing capability and stress control accuracy. Its data processing function is mainly focused on the presentation and archiving of test results, rather than on automated matching, reliable component screening, and comprehensive scoring and recommendation based on multi-source historical data and operating condition requirements. Therefore, it cannot directly replace intelligent component selection decision-making systems for the engineering design stage.

[0005] Existing multi-environment stress testing platforms rely on a single data utilization method, with test results often stored in reports or static databases. They lack a dynamic matching mechanism directly linking data to the specific requirements of different project operating conditions, hindering efficient cross-project reuse. Furthermore, they lack operating condition mapping and matching mechanisms, failing to automatically map user target operating conditions to existing test profiles, identify similar conditions, and quickly retrieve corresponding component data. There is no trusted component library management; a unified rule-based approach based on multiple profiles and indicators is not used to generate and maintain a "trusted component library," making it impossible to quickly select a set of components that have been verified and qualified under the target operating conditions. They also lack comprehensive scoring and recommendation functions, failing to weight and integrate multi-dimensional indicators such as life margin, failure rate, performance stability, and verification coverage to form intuitive, rankable recommendation results. Finally, they lack a closed-loop improvement mechanism; when data is missing under the target operating conditions, they cannot automatically identify data gaps and trigger supplementary tests, leading to delayed database updates and incomplete selection criteria. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a selection and evaluation system and method for key components in water, wind and solar power, solving the problems of insufficient data utilization, inability to make intelligent recommendations and closed-loop improvements in the prior art, improving the scientificity, traceability and adaptability of component selection, and reducing reliability risks in the design stage.

[0007] To achieve the above-mentioned objectives, this invention provides a selection and evaluation system for key components in water-wind-solar systems, including a data acquisition module, a profile matching module, a reliability index calculation module, a trusted component library management module, a scoring and recommendation module, and a data gap identification module. The data acquisition module is used to receive environmental stress and performance data from a multi-environmental stress test platform, an on-site sensor database, and historical test records, and to normalize the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. The profile matching module retrieves the closest historical profile from the environmental profile dataset based on the target working condition parameters input by the user. The reliability index calculation module is used to calculate multiple reliability indices such as lifetime margin, failure rate, performance drift stability, and verification coverage based on the matched historical profile data, and to associate and store the indices with component identifiers. The trusted component library management module is used to compare the calculated reliability indicators according to the preset reliability threshold rules, select qualified components to add to the trusted component library, and upgrade, downgrade or eliminate the status of components when the indicators are updated. The scoring and recommendation module is used to comprehensively score the components in the trusted component library and classify the components into different recommendation levels based on the scoring results.

[0008] Preferably, the data gap identification module is used to detect whether the profile data under the target working condition is missing or the number of samples is insufficient during the profile matching and reliability index calculation process. When a gap exists, it generates test request information and triggers the external test data supplementation process.

[0009] Preferably, the profile matching module includes a similarity calculation unit. The similarity calculation unit calculates the matching distance based on the difference between the target working condition parameters and the corresponding parameters of each historical profile, combined with the weight of each parameter dimension, using a weighted Euclidean distance algorithm or an approximate nearest neighbor index algorithm, and selects the profile with the smallest matching distance as the evaluation basis.

[0010] Preferably, the trusted component library management module is also used to record audit information on changes in the status of components, including the type of status change, the reason for the change, and snapshots of indicators before and after the change.

[0011] Preferably, the comprehensive score is a weighted sum of lifetime margin score, failure rate score, performance drift stability score and verification coverage score according to preset weights; before performing the comprehensive score, the scoring and recommendation module also includes a hard elimination judgment unit, which is used to directly mark the component as not recommended and remove it from the recommendation list when any key indicator of the component is lower than a preset lower limit.

[0012] Preferably, the test request information includes target operating condition parameters, test duration, and indicators to be collected; the data gap identification module sends the test request information to an external test platform through a standardized interface, and after receiving supplementary test data, fills the data back into the environmental profile dataset and triggers an update of the trusted component library.

[0013] Preferably, the modules transmit information through a standardized data interface, which includes data format definitions, parameter field specifications, and calling procedures to achieve loosely coupled integration with different external data sources and experimental platforms.

[0014] Another aspect of the present invention provides a method for selecting and evaluating key components for water-wind-solar systems, applied to the aforementioned water-wind-solar key component selection and evaluation system, the method comprising: S1. Data Acquisition and Normalization Processing: Receive environmental stress and performance data from multiple environmental stress test platforms, field sensor databases, and historical test records. Normalize the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. S2, Profile Matching: Based on the target working condition parameters input by the user, retrieve the closest historical profile from the environmental profile dataset; S3. Reliability index calculation: Based on the matched historical profile data, calculate multiple reliability indices such as lifetime margin, failure rate, performance drift stability, and verification coverage, and store the indices in association with component identifiers. S4. Trusted Component Library Construction and Maintenance: Based on the preset reliability threshold rules, the calculated reliability indicators are compared, and qualified components are selected and added to the trusted component library. When the indicators are updated, the status of the components is promoted, downgraded or eliminated. S5. Comprehensive scoring and recommendation: The components in the trusted component library are comprehensively scored, and the components are divided into different recommendation levels according to the scoring results and a recommendation list is output. S6. Data Gap Identification and Closed-Loop Optimization: During the profile matching and reliability index calculation process, it is detected whether the profile data under the target working condition is missing or the number of samples is insufficient. When a gap exists, test request information is generated and the external test data supplementation process is triggered. After the supplemented data is obtained, it is backfilled into the environmental profile dataset and the process returns to step S2 to re-execute the subsequent process.

[0015] Preferably, in step S2, the weighted Euclidean distance algorithm or the approximate nearest neighbor index algorithm is used to calculate the matching distance between the target working condition parameters and each historical profile. Specifically, the matching distance is calculated based on the difference between the corresponding parameters of the target working condition parameters and each historical profile, combined with the weight of each parameter dimension, and the profile with the smallest matching distance is selected as the evaluation basis.

[0016] Preferably, in step S5, the comprehensive score is a weighted sum of lifetime margin score, failure rate score, performance drift stability score and verification coverage score according to preset weights; before performing the comprehensive score, it is first determined whether there are any key indicators of the components that are lower than the preset lower limit. If so, they are directly marked as not recommended and removed from the recommendation list.

[0017] Preferably, in step S6, the test request information includes target operating condition parameters, test duration, and indicators to be collected; the test request information is sent to an external test platform through a standardized interface, supplementary test data returned by the external test platform is received, the data is backfilled into the environmental profile dataset, and the trusted component library is updated.

[0018] The present invention has the following beneficial effects: This invention utilizes a multi-source data profiling and database construction mechanism to standardize and store environmental stress-performance data from different sources, supporting cross-project reuse. Through a parameter-to-profile matching algorithm, it achieves rapid and accurate matching between target operating conditions and historical profiles. A trusted component library generation and maintenance mechanism filters out preferred components that meet reliability requirements. Multi-index comprehensive scoring and recommendation ranking provide users with intuitive and interpretable selection suggestions. A data gap identification and closed-loop improvement mechanism automatically supplements missing data, continuously improving selection accuracy and database completeness. This invention provides intelligent and systematic decision support for component selection under complex operating conditions, significantly improving selection efficiency and reliability, reducing design risks, and possessing strong engineering application value. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Figure 1 This is a system framework diagram of the present invention.

[0021] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0023] Example 1: This invention provides a selection and evaluation system for key components in water, wind, and solar power systems, including a data acquisition module, a profile matching module, a reliability index calculation module, a reliable component library management module, a scoring and recommendation module, and a data gap identification module. The data acquisition module receives environmental stress and performance data from a multi-environmental stress test platform, a field sensor database, and historical test records, and normalizes the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. The profile matching module retrieves the closest historical profile from the environmental profile dataset based on the target operating condition parameters input by the user. The reliability index calculation module is used to calculate multiple reliability indices, including lifetime margin, failure rate, performance drift stability, and verification coverage, based on matched historical profile data, and to associate and store these indices with component identifiers. The trusted component library management module is used to compare the calculated reliability indices according to preset reliability threshold rules, select qualified components to add to the trusted component library, and perform promotion, downgrading, or elimination of component status when indices are updated. The scoring and recommendation module is used to comprehensively score the components in the trusted component library and classify the components into different recommendation levels based on the scoring results.

[0024] Furthermore, the data gap identification module is used to detect whether profile data under the target operating conditions is missing or the sample size is insufficient during the profile matching and reliability index calculation process. When a gap exists, it generates a test request message and triggers an external test data supplementation process. This module, based on data integrity verification logic and a statistical sample size assessment model, identifies missing data items by comparing the target operating condition parameter dimensions with the coverage dimensions of the environmental profile dataset. Simultaneously, it uses confidence interval analysis to determine whether the existing sample size meets the statistical requirements for reliability index calculation; when the sample size is below a threshold, it is determined to be a data gap. This achieves precise matching between data requirements and test resources, avoiding selection and evaluation biases caused by insufficient data, forming a closed loop of "evaluation - gap detection - supplementary testing - optimized evaluation," and continuously improving the credibility of the system's evaluation results.

[0025] Furthermore, the profile matching module includes a similarity calculation unit. This unit calculates the matching distance based on the differences between the target operating condition parameters and the corresponding parameters of each historical profile, combined with the weights of each parameter dimension. It employs either a weighted Euclidean distance algorithm or an approximate nearest neighbor index algorithm, and selects the profile with the smallest matching distance as the evaluation criterion. The weighted Euclidean distance algorithm quantifies the parameter differences between the target operating condition and historical profiles by assigning differentiated weights to different environmental stress parameters (such as temperature, humidity, and salt spray concentration). The weight coefficients are calibrated based on the degree of influence of each stress on the reliability of components under water-wind-light scenarios. The approximate nearest neighbor index algorithm significantly improves the matching efficiency in massive profile data by constructing a high-dimensional data index structure, solving the performance bottleneck of traditional traversal algorithms. This ensures that the matching results not only conform to the stress impact priority in engineering practice but also achieve millisecond-level retrieval in large-scale datasets, significantly improving the timeliness and accuracy of selection and evaluation.

[0026] Furthermore, the trusted component library management module is also used to record audit information on component status changes. This audit information includes the type of status change, the reason for the change, and snapshots of indicators before and after the change. Based on a full lifecycle data traceability mechanism, the system uses the transaction log function of blockchain or relational databases to record the entire process of component entry, promotion, demotion, and obsolescence, establishing an immutable status change chain and associating it with indicator threshold conditions that trigger the change. This achieves traceability in component reliability assessment, facilitating technical personnel to review the basis for selection decisions, and providing data support for industry standard setting and component quality traceability, thereby enhancing the system's engineering application value.

[0027] Furthermore, the comprehensive score is a weighted sum of lifetime margin score, failure rate score, performance drift stability score, and verification coverage score according to preset weights. Before performing the comprehensive score, the scoring and recommendation module also includes a hard elimination judgment unit, which directly marks the component as unrecommended and removes it from the recommendation list if any key indicator of the component is lower than a preset lower limit. The hard elimination judgment unit is based on reliability threshold screening logic, and the preset lower limit of key indicators refers to the long-term service standards of water-wind-solar power plants and industry reliability specifications to ensure that the selected components meet basic reliability requirements. The weighted summation model dynamically adjusts the weight of each indicator according to the reliability requirements priority in different application scenarios. For example, in wind power scenarios, the weight of vibration resistance performance indicator can be increased, and in photovoltaic scenarios, the weight of temperature and humidity cycle resistance indicator can be increased. This avoids unreasonable selection results of "compensating for low-scoring indicators with high-scoring indicators", ensures the basic reliability of recommended components, and achieves scenario-based accurate recommendations through dynamic weight adjustment, improving the adaptability of selection results to actual working conditions.

[0028] Furthermore, the test request information includes target operating condition parameters, test duration, and required data collection indicators. The data gap identification module sends the test request information to the external test platform through a standardized interface. Upon receiving supplementary test data, it backfills the data into the environmental profile dataset and triggers an update to the trusted component library. The standardized interface is built based on RESTful API or MQTT protocol to achieve cross-system data interaction with the external test platform. The data backfilling mechanism adopts an incremental data update strategy, appending only the supplementary test data to the environmental profile dataset and triggering a partial re-evaluation of the trusted component library, rather than a full recalculation. This reduces the integration cost between the system and the external test platform, achieves automatic data backflow and dynamic updates to the component library, and avoids the errors and inefficiencies of manual data entry.

[0029] Furthermore, the modules transmit information through standardized data interfaces, which include data format definitions, parameter field specifications, and calling procedures to achieve loosely coupled integration with different external data sources and experimental platforms. This loosely coupled integration architecture is based on a modular design concept. Each module interacts with data through standardized interfaces, which conform to relevant ISO / IEC data transmission standards and support common data formats such as JSON and XML. Dependencies between modules are decoupled through interfaces, rather than through direct code calls. This enhances the system's scalability and compatibility, allowing for flexible integration with new data sources or experimental platforms without modifying core algorithm modules, reducing system maintenance and upgrade costs, and adapting to the rapid technological iteration needs of the hydro-wind-solar industry.

[0030] The system in Example 1 constructs a multi-source data-driven reliability assessment and selection closed-loop system. Through six core links—data normalization, intelligent profile matching, multi-dimensional index calculation, dynamic library management, accurate recommendation, and gap closed-loop optimization—it overcomes the limitations of traditional selection methods that rely on manuals and experience.

[0031] The data layer eliminates the heterogeneity of multi-source data by unifying parameter dimensions and segmented interval encoding, transforming discrete experimental data and field data into standardized environmental profile datasets, providing a unified data foundation for subsequent analysis.

[0032] The algorithm layer integrates weighted Euclidean distance, approximate nearest neighbor index, accelerated life model (such as Arrhenius model), weighted scoring model and other algorithms to realize intelligent operation of the entire process from working condition matching to index calculation and recommendation ranking, ensuring the scientific and objective nature of the evaluation results.

[0033] The application layer establishes a self-iterative system of "data-evaluation-recommendation-experimentation-data" based on the dynamic management of the trusted component library and the closed-loop optimization of data gaps, so that the system's evaluation capability can be continuously improved with the accumulation of data.

[0034] Example 2: This embodiment provides a method for selecting and evaluating key components for water-wind-solar systems, applied to the aforementioned water-wind-solar key component selection and evaluation system. The method includes: S1. Data Acquisition and Normalization Processing: Receive environmental stress and performance data from multiple environmental stress test platforms, field sensor databases, and historical test records. Normalize the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. S2, Profile Matching: Based on the target working condition parameters input by the user, retrieve the closest historical profile from the environmental profile dataset; S3. Reliability index calculation: Based on the matched historical profile data, calculate multiple reliability indices such as lifetime margin, failure rate, performance drift stability, and verification coverage, and store the indices in association with component identifiers. S4. Trusted Component Library Construction and Maintenance: Based on the preset reliability threshold rules, the calculated reliability indicators are compared, and qualified components are selected and added to the trusted component library. When the indicators are updated, the status of the components is promoted, downgraded or eliminated. S5. Comprehensive scoring and recommendation: The components in the trusted component library are comprehensively scored, and the components are divided into different recommendation levels according to the scoring results and a recommendation list is output. S6. Data Gap Identification and Closed-Loop Optimization: During the profile matching and reliability index calculation process, it is detected whether the profile data under the target working condition is missing or the number of samples is insufficient. When a gap exists, test request information is generated and the external test data supplementation process is triggered. After the supplemented data is obtained, it is backfilled into the environmental profile dataset and the process returns to step S2 to re-execute the subsequent process.

[0035] Example 3: See Figure 1 The system architecture diagram includes key components such as a multi-environment data acquisition module 101, a reliability index extraction module 102, a component reliability database 103, a trusted component library 104, a selection decision engine 105, and a user interface 106. The entire system adopts a modular software architecture, with the front end providing a user interface and the back end consisting of multiple functional modules working collaboratively to achieve a closed-loop process from data acquisition to component recommendation.

[0036] The functional design of each module in the system is as follows: Multi-environment data acquisition module 101: Responsible for collecting performance and lifespan data of components under complex environments from various sources. On one hand, it interfaces with external environmental testing platforms through standardized interfaces to automatically acquire test results of components under different stress combinations; on the other hand, it receives existing data such as industry reliability manuals and laboratory test reports. The collected data includes: component identification (model, batch, etc.), applied environmental stress conditions (temperature range, humidity, salt spray concentration, vibration acceleration, electromagnetic interference level, etc.), test duration and results (whether failure occurred and the failure time), and changes in performance parameters recorded during the test (e.g., parameter drift, changes in electrical performance indicators, etc.). The module preprocesses the raw data (cleaning outliers, aligning the time axis, and converting units) and transmits it to subsequent processing modules and stores it in the database in a standard format.

[0037] The reliability index extraction module 102 performs statistical analysis on the collected multi-source raw data to calculate key reliability indicators of components under various environmental stress conditions. This module incorporates a series of reliability assessment algorithm models: for example, it fits multiple failure time data points to a Weibull distribution to extract mean time between failures (MTBF) and lifetime distribution parameters; it uses the Arrhenius model to estimate lifetime at room temperature based on high-temperature test results, with the acceleration factor determined by activation energy, etc.; it calculates the slope of performance indicators (such as signal noise and communication bit error rate) under vibration stress to quantify immunity; and it statistically analyzes the magnitude of parameter drift under different temperature and humidity conditions to assess environmental sensitivity. Through these analyses, the module outputs lifetime estimates, failure rates, performance stability, and other indicators for each component under various environmental profiles, and writes the results into the reliability database.

[0038] Component Reliability Database 103: Used to store and manage environmental test data and extracted reliability metrics for all components. The database is implemented using a hybrid relational or NoSQL approach, with multiple tables recording information from multiple dimensions, such as: a basic component information table (fields include component ID, model, function type, manufacturer, batch, package, etc.), an environmental profile table (defining several standardized environmental combination profiles, such as "85℃ / 85%RH high temperature and humidity" or "-40~+85℃ cyclic temperature change + EMI level 3 interference," with fields for temperature, humidity, stress type, etc. for each profile), a test data table (recording the stress conditions, duration, failure status, and performance readings of components in a given test), a reliability metric table (stores the metrics calculated for each component under each environmental profile, such as MTBF, lifetime estimate, drift rate, and failure probability distribution parameters), and a failure record table (summarizing the failure modes, failure locations, and environmental conditions of components in each test). The database indexes key fields, such as by component model / type, by environmental profile, and by combination of component and environment, to support rapid query and analysis. It is worth mentioning that this database is similar to a multidimensional reliability datasheet for electronic components, with each component associated with performance data entries across multiple environmental profiles. This method of environmental profile segmentation ensures that the analysis can accurately match the relevant data for specific application scenarios, improving the relevance of selection decisions.

[0039] Trusted Component Library 104: A subset of "preferred components" derived from the aforementioned database. The system evaluates and filters components in the database according to predetermined rules, selecting models that meet specific reliability thresholds for inclusion in the trusted library. Filtering rules can be configured by the system administrator or automatically set based on standards, such as: requiring components to have undergone over 1000 hours of testing without failure under the most stringent environmental profiles; key performance indicators drifting within a certain percentage of their rated values ​​across the entire temperature range; historical field failure rate below a certain threshold; and estimated lifespan not less than X times the target lifespan. Each time the database is updated, the filtering script calculates a score for the new data and updates the trusted library list. The trusted library records component model, passed reliability level, applicable environmental range, and comprehensive score, and can be categorized by application area (e.g., for photovoltaic inverters, wind turbine nacelle controllers, etc.). By establishing the trusted library, engineers can prioritize these verified components during selection, avoiding the selection of potentially unreliable components due to insufficient data.

[0040] Selection Decision Engine 105: The core decision-making module of the system, responsible for filtering and recommending components from the database and trusted component library based on user requirements. The engine first obtains the input application condition parameters and required component type from the user interface, and then internally maps the user's operating conditions to a predefined environmental profile. For example, if the user inputs a temperature range of "-40~85℃", humidity "maximum 90%RH", and electromagnetic interference "level III", the system will match the closest environmental profile record (e.g., industrial-grade temperature and humidity + EMI3). Next, the engine queries the trusted component library to see if there is a list of verified models for that type of component under that profile. If the trusted component library has results, these candidate components are retrieved; if the trusted component library does not have a matching record (e.g., the combined environmental conditions required by the user are rare), the engine then retrieves relevant data for that type of component from the reliability database, and calculates its reliability index under the target combination based on existing single or partial stress data using a model. For example, if a component only has data under high temperature and no humidity conditions, the system can roughly assess its lifespan degradation rate under high temperature and high humidity conditions using a humidity-accelerated model as a reference. Next, the engine calculates a comprehensive reliability score for all candidate components. The score considers factors such as: the margin of predicted lifespan relative to the user's expected lifespan, historical failure rate, environmental tolerance margin (e.g., how much higher the maximum tolerance temperature is compared to the user's requirements), and performance stability indicators. A multi-indicator weighted algorithm can be used to normalize the above factors and sum them to obtain the total score. The engine then sorts the components from highest to lowest score and selects the top N components as the recommended list. For each recommended component, the system prepares a summary description, including its main performance parameters, a summary of reliability indicators, and data sources (test environment and number of tests, etc.) for user reference.

[0041] User Interface 106: Provides an intuitive and user-friendly interactive window, supporting input of operating conditions, viewing of recommended results, and manual adjustment options. Users can fill in application scenario parameters (temperature and humidity range, mechanical vibration level, expected lifespan, etc.) and the required component category or specific model through the interface. After submitting the query, the interface calls the selection engine to obtain a recommendation list and displays the results in an easy-to-read format. For example, the interface lists key information such as the model, manufacturer, applicable environment range, estimated lifespan, and reliability score of recommended components in a table, and uses different colors or icons to indicate which indicators fully meet the requirements. Users can click on a component to view detailed data curves (provided by the database, such as temperature vs. drift curves, accelerated life test statistics, etc.) for further comparison. At the interactive level, the interface also allows users to apply filters or preferences, such as preferring a certain brand, excluding a model that is too expensive, etc., and adjust the filtering conditions to regenerate recommendations. After confirming the final selection, users can archive the decision and have the opportunity to provide feedback to the system on the performance in actual use, continuously improving the accuracy of the recommendations.

[0042] The system's data flow begins with the collection of environmental test data, proceeds sequentially through reliability analysis, database storage, and trusted database filtering, and then the user inputs application requirements, triggering the selection engine to retrieve and output recommended results. The entire process can be divided into the following steps: Multi-environment data acquisition: Periodically or as needed, acquire test data of components under various environmental stress combinations from the test platform and transmit the data to the back-end system.

[0043] Reliability index extraction: Analyze the raw test data, calculate key reliability indices (lifetime, failure rate, drift, etc.), and update the performance index entries of components in various environmental profiles.

[0044] Update the database: Store new experimental data and calculated indicators into the component reliability database, and add new environmental profile entries or expand the data volume of existing entries as needed.

[0045] Filter trusted library: Run preset rules to re-evaluate all components in the database, update the list of components that meet the reliability standards to the trusted component library, and remove entries that no longer meet the conditions.

[0046] User input requirements: Users submit specific operating parameters and selection requirements (such as component type, specifications, target life, etc.) through the interface.

[0047] Candidate matching and retrieval: The decision engine matches the corresponding environmental profile according to the user's operating conditions and retrieves a list of components that meet the conditions of the profile from the trusted database; if there are no results in the trusted database, it searches for components with similar environmental data in the database and calculates their reliability performance under the target operating conditions, and summarizes them to obtain a set of candidate components.

[0048] Scoring and Ranking: Reliability scores are calculated for candidate components, taking into account factors such as life margin, failure rate, and performance stability, and components are ranked according to their scores.

[0049] Output recommendation results: The top-ranked components are presented as a recommendation list to the user interface for browsing and selection. Detailed reliability information and explanations for the recommendations are also provided for each component to support user decision-making.

[0050] Through the aforementioned modular division of labor and process design, the system automates the process from environmental stress test data to selection decisions, significantly reducing the workload of manual data searching and experience-based selection. In this data-driven process, each step ensures the effective transmission and utilization of information: performance data under complex environments is fully collected and mined, transformed into quantifiable reliability indicators, and then used to provide a basis for engineering selection through intelligent rules and algorithms. This architecture gives the system excellent scalability and adaptability; when new components or new environmental conditions emerge, only the corresponding data and analysis models need to be added to integrate them into the existing process.

[0051] Database design (fields and indexes): To support the above functions, the reliability database design follows the principles of high cohesion and scalability. The main table structure and field descriptions are as follows: The Components table includes fields such as ComponentID (primary key, component model or code), Category (e.g., processor, memory, ADC, sensor, etc.), Manufacturer, Specs (key specifications, such as operating voltage, package type, etc.), BaselineMTBF (MTBF under standard conditions, such as 25°C), and Rating (component grade, such as industrial grade, military grade). ComponentID is set as the primary index, and secondary indexes are created for frequently used query fields such as Category and Manufacturer to facilitate quick retrieval of all components of a specific category or manufacturer.

[0052] The Environmental Profile table (EnvProfile) includes fields such as ProfileID (primary key), Description (environmental description, e.g., "85℃ / 85%RH + random vibration + salt spray"), and detailed stress parameters (specific values ​​or codes for temperature range, humidity range, vibration level, electromagnetic interference level, etc.). This table predefines several commonly used profiles and can be expanded as needed to standardize the representation of complex environmental combinations. ProfileID can be referenced in other data tables, significantly reducing storage redundancy. A full-text index is created for fields such as Description, facilitating the search for relevant profiles using descriptive keywords (e.g., "high temperature" or "salt spray").

[0053] The Test Results table (TestData) includes fields such as TestID (primary key), ComponentID (foreign key referencing the component table), ProfileID (foreign key referencing the environment profile table), Duration (test duration, in hours), Result (test result, enumeration: normal completion / failure), FailureTime (records the time of failure if a failure occurred, otherwise empty), FailureMode (failure mode description, such as "open circuit failure"), and ParamTrends (performance parameter change records, which may be stored as a JSON string or referenced to a time-series data table). The test results table data accumulates with each test, and a composite index (ComponentID, ProfileID) can retrieve all historical test records for a specific component under a specific environment. For massive amounts of sensor readings or parameter curves, a separate time-series data table can be created for storage. The ParamTrends field in the TestData table can store reference IDs or statistical summaries (e.g., maximum drift value).

[0054] The ReliabilityMetrics table includes fields such as MetricID, ComponentID, ProfileID, MTBF (Mean Time Between Failures, in hours), LifeEst (Lifetime Estimation, in hours or years), FailRate (Annual Failure Rate / %), DriftRate (Performance Drift Rate / %), Weibull_beta (Weibull shape parameter), and Weibull_eta (Weibull scale parameter). Records in this table are inserted or updated whenever the analysis unit calculates metrics based on new test data. This table can be seen as a summary of key reliability metrics by component and environment. Each record corresponds to a set of reliability parameter values ​​for a specific component and environment, allowing the query engine to directly retrieve these values ​​without temporary calculations. For indexing, (ComponentID, ProfileID) is used as a composite primary key to support precise lookup of metrics for a specific component at a specific profile. A separate index is created on the ProfileID column to facilitate statistical analysis of the performance distribution of all components under specific environmental conditions.

[0055] The TrustedComponents table includes fields such as ComponentID, ApplicableProfiles (a list of verified environment profiles), Score (overall reliability score), and LastUpdate (last evaluation date). Only components that meet the trust screening criteria appear in this table. ComponentID is the primary key, and ApplicableProfiles is indexed in full-text or JSON format (depending on the storage format) to support environment-based queries. For example, when a user queries candidate components in environment Profile-A, the list can be quickly obtained by retrieving records in ApplicableProfiles that contain Profile-A. Furthermore, for specialized trusted libraries in different application areas (such as the aforementioned photovoltaic or wind power libraries), classification can be achieved by adding an Application field to this table, or by creating multiple instances of the TrustedComponents table for physical separation.

[0056] Index Design and Performance Considerations: Since the system needs to query based on multiple combinations of conditions (component type + environment profile + reliability metrics, etc.), database optimization is crucial. In addition to the basic indexes for primary and foreign keys mentioned above, we can also consider designing composite indexes and materialized views. For example, to support filtering components by "environment profile + lifespan threshold," a composite index can be created on the ProfileID and LifeEst fields of the ReliabilityMetrics table, or a materialized view of "a list of components with a lifespan exceeding X under a certain environment" can be generated periodically for querying. In NoSQL implementations (such as MongoDB), indexes can be created on nested fields of JSON documents, and MapReduce can be used to pre-calculate statistical results. To ensure performance as the data scales, a partitioning mechanism can be introduced to horizontally partition the data table by component category or profile, and caches (such as Redis) can be used to store frequently used query results. Through careful field design and index layout, the system can achieve millisecond-level response times for complex queries, which is particularly critical for interactive selection recommendations.

[0057] Test platform interface and data collection specifications: The system integrates seamlessly with external multi-environment testing platforms through standardized data interfaces, enabling automatic triggering of the testing process and data acquisition. The interface design considers both versatility and reliability, including the following aspects: Communication Protocol: The system employs a RESTful API-based web service interface or the MQTT / OPC-UA protocol commonly used in Industrial IoT to exchange data with the testing platform. For example, the system can send test commands to the testing platform via HTTP POST requests, specifying the component ID, the required environmental profile (temperature, humidity, etc.), and the test duration. Upon receiving the command, the testing platform begins execution and periodically uploads data during the test (real-time data can be pushed via HTTP PUT or MQTT messages). Upon completion of the experiment or in the event of a failure, the platform returns a result notification via the interface. The entire communication process utilizes an acknowledgment mechanism to ensure reliable command delivery and prevent data loss, and incorporates a data caching and retransmission strategy in case of network interruptions.

[0058] Data Format: Transmitted data uses a self-describing JSON or XML format for easy parsing and expansion. Specific fields include: component_id (component ID), profile_id (environmental profile ID or directly specifying temperature, humidity, etc.), timestamp (data timestamp), param_values ​​(a list of performance parameter measurements, such as voltage, power consumption, bit error rate, etc.; specific details vary depending on the component type), status (current test status, such as "running", "failed", "completed"), and failure_mode (if status is "failed", the failure mode is specified). For example, an uploaded JSON might look like this: { "component_id":"ADC-XYZ123", "profile":{"temperature":85,"humidity":85,"EMI_level":3}, "timestamp":"2025-08-14T10:00:00Z", "param_values":{"offset_drift":0.5,"gain_drift":1.2}, "status":"running"} In this way, the system's data acquisition module can universally read these fields without needing to know the specific device implementation. For long-term experiments, the platform will also periodically send status heartbeats and interim data, which the system will use to update temporary records in the database.

[0059] Interface Functionality: The interface not only supports data reception but also includes control and management functions for the experiment. For example, the system can remotely set experimental parameters (temperature cycling curves, humidity change rate, etc.), start or stop the test, and query the real-time status of the test via API commands. This is achieved by defining a clear set of API endpoints, such as: / start_test, / stop_test, / query_status?test_id=... etc. Interface access control is also crucial; only authorized requests can control the experimental platform to prevent accidental operation. Furthermore, to ensure experimental safety, the interface design incorporates mechanisms such as stress conflict arbitration and emergency stop. For instance, when multiple stress combinations issued by the system have physical conflicts (such as simultaneous requirements for high temperature and water spray), the platform will return an error or adjust to a safe mode through the interface.

[0060] Data Acquisition Frequency and Synchronization: The system sets an appropriate data acquisition frequency based on the component and stress type. For example, slowly changing parameters such as temperature and voltage can be sampled per minute, while rapidly changing parameters such as vibration acceleration may be sampled per second or even higher. Considering network bandwidth and data processing overhead, the platform can also perform necessary filtering and compression on the raw high-frequency data before uploading (e.g., providing RMS values, peak values, and other statistics). The system's data acquisition module appends a uniform timestamp to the received data and synchronizes it with the clock of the test platform to ensure alignment of data from different sensors. When multiple stress channels are running in parallel, data may arrive asynchronously. The system marks each source and time according to a predefined data format so that the subsequent analysis module can correctly integrate multi-source data.

[0061] Fault and Anomaly Handling: The interface protocol specifies methods for handling anomalies. For example, if a sensor malfunctions or data exceeds a reasonable range during testing, the platform will issue an alarm via the status field. The system will then mark the data as invalid in the database and notify the administrator for repair. Similarly, if a network outage causes a period of data loss, the platform will cache the data and send it in batches after recovery, marking the data with a retransmission flag. The system must check the received data to avoid duplicate recording. For control commands sent by the system to the platform, if the platform does not respond or rejects them (e.g., the test chamber is currently unavailable), the system will retry a certain number of times and log the responses for manual intervention.

[0062] Through the above interface and data acquisition specifications, the system achieves seamless integration with the testing hardware. On the one hand, it ensures the real-time and accurate uploading of experimental data, ensuring that the reliability information in the database is always updated synchronously with the latest test results. On the other hand, the system can automatically initiate the required tests to achieve closed-loop verification (i.e., if a component lacks data for a specific environment, the system can schedule tests to acquire data and then use it for selection decisions). This tightly coupled design significantly improves the efficiency of data acquisition and processing. For example, when a new device of interest to the user has no historical data in a certain extreme environment, the system can prompt and quickly control the testing platform to conduct accelerated aging tests on the device in the corresponding environment through the interface, and automatically supplement the database with the results obtained after a few days or weeks. Compared with the traditional approach of manually contacting tests, waiting for results, and then manually analyzing them, the efficiency is greatly improved. In summary, the standardized and comprehensive interface integrates the selection evaluation system with the testing platform, forming a virtuous cycle in new product introduction testing and abnormal data feedback, continuously enriching the reliability database, and improving the selection decision-making capability.

[0063] This system is a purely software-based component selection and evaluation platform that analyzes multi-source environmental stress-performance data provided by external test benches or historical field databases. The system profiles the externally provided test data to form an environmental stress-performance profile database, and establishes a closed-loop chain for scoring, recommendation, and data updates based on this database. Importantly, this solution does not include any physical test chambers or hardware stress loading devices, nor does it involve hardware components such as temperature and humidity controllers, light / wind speed loading modules, or stress arbitration mechanisms. In other words, this system utilizes data provided by existing environmental test platforms for software analysis, and its technical boundaries are strictly limited to the data processing and algorithm levels, fundamentally different from traditional environmental simulation test platforms (which require sealed environmental chambers, multi-stress loading hardware, etc.). All functional modules of the system are implemented in computer software, completing component reliability assessment and selection recommendations through data standardization and algorithmic calculations, achieving a complete software closed loop. This distinctive feature ensures that this invention does not rely on a dedicated hardware environment, highlighting its innovation in the field of purely data-driven systems.

[0064] Data source and format specifications: The system's data input module supports multi-source heterogeneous data, including: Data exported from the test platform: such as JSON files or CSV / Excel report formats, recording component performance degradation data and environmental stress conditions obtained on a dedicated accelerated aging test platform. Each record includes the test number, component ID, environmental stress parameters (temperature, humidity, light, wind speed, salt spray, etc.) and corresponding performance measurements (such as initial performance, performance after N hours, failure time, etc.).

[0065] Field sensor database: Monitoring data from actual operating sites, obtained through SQL database interface or API. The data format is a structured table, with fields including timestamp, component ID, environmental stress sensing values ​​(such as ambient temperature, humidity, power load), and performance index readings (such as output power, efficiency, drift).

[0066] Historical test records: These may be imported in batches as Excel workbooks or XML files, and contain a summary of stress-performance data from previous test reports. Fields need to be normalized and mapped to match the database schema.

[0067] Data format specifications: To ensure consistent parsing, all data sources must adhere to a predefined format before importing. The JSON format uses a uniform key name, such as {"component_id":"...","test_env":{...},"results":{...}}, where test_env contains nested environmental stress parameters (such as "temp":85,"humidity":95, etc.) and results contains nested performance results (such as "time_to_fail":1000,"param_drift":0.05, etc.).

[0068] CSV / Excel requires a fixed column order and column names, such as test number, component ID, temperature (°C), humidity (%RH), light intensity (W / m²), wind speed (m / s), salt spray concentration (g / m³), test duration (h), performance drift rate, etc. During import, column names are matched and mapped to internal fields.

[0069] The database interface input is read according to the table structure. For example, the sensor database has a table FieldData(component_id,timestamp,temp,humidity,performance_metric,...). The system periodically queries new data and extracts the required fields.

[0070] All input data undergoes format validation and conversion upon entering the system: data that does not conform to the specifications will be rejected or preprocessed (e.g., unit conversion, outlier filtering). The cleaned and standardized data is then written to the profile database, preparing it for subsequent analysis. This unified data format specification ensures that multi-source data can be integrated and processed, facilitating the aggregation of profile information.

[0071] Profile data normalization and database design: Profile data normalization mechanism: This system abstracts environmental stress-performance data into fixed-dimensional profile vectors and normalizes data from different sources. Fixed parameter dimensions: A standard set of environmental stress parameters is defined, with each profile record containing the same number of parameter dimensions. For example, five dimensions are included: temperature cycle amplitude, humidity level, light intensity, wind impact, and salt spray concentration. Each profile data point is mapped to a stress parameter vector of the form (x_1, x_2, x_3, x_4, x_5), where x_i is the quantized value of the corresponding dimension. To ensure compatibility with different data sources, if data for a certain dimension is missing, it is filled with a default value (such as 0 or marked "N / A") and further processed in the missing data identification module.

[0072] Segmented Interval Coding: For continuous stress parameters, an interval discretization coding mechanism is introduced. Each continuous parameter is divided into several level intervals according to its empirical range, and its interval is represented by an integer or a code. For example, temperature cycle ranges are categorized as <40℃, 40℃, etc. 80℃ and >80 are coded as 0 / 1 / 2; humidity is coded as less than 50%RH and 50 90%RH and >90%RH are coded; light intensity is classified based on standard solar radiation of 1000W / m²; wind speed is classified according to whether it contains sand and instantaneous impact; salt spray is classified according to concentration or frequency. Each profile vector is converted into a standardized interval coded vector accordingly, making the stress intensity of different tests comparable on the same scale.

[0073] Numerical normalization: For performance indicators that require continuous values ​​(such as drift rate and lifetime hours), Min-Max or Z-Score normalization is used to map the value range to the 0~1 range, eliminating the influence of dimensions. For example, the performance drift rate can be normalized relative to a certain reference range, so that the drift degree of different components can be directly compared.

[0074] Profile database structure design: Profile databases can be implemented using either relational or document-oriented methods. Below are structure examples for PostgreSQL and MongoDB respectively: Relational schema (PostgreSQL): --Profile Table: Performance Results of Storage Elements Under Certain Environmental Stress Conditions CREATETABLEProfiles( profile_idSERIALPRIMARYKEY, component_idVARCHAR(50)NOTNULL, temp_range_codeINT, -- Temperature cycle amplitude code humidity_codeINT, -- Humidity range code light_codeINT, -- Light intensity code wind_codeINT, -- Wind speed stress code salt_codeINT, -- Salt spray stress coding test_duration_hFLOAT, -- Total test duration (hours) life_hoursFLOAT, -- Equivalent lifespan (hours) drift_rateFLOAT, -- Performance drift rate (e.g., percentage decay per thousand hours) fail_countINT, -- Number of failed samples data_sourceVARCHAR(20), -- Data source identifier (e.g., "Platform Test", "Field") CONSTRAINTfk_compFOREIGNKEY(component_id)REFERENCESComponents(id) ); --Component List: Summary of basic information and reliability indicators of storage components CREATETABLEComponents( idVARCHAR(50)PRIMARYKEY, typeVARCHAR(50), manufacturerVARCHAR(50), base_specsJSONB, -- Basic specification parameters (JSON format) reliability_scoreFLOAT, -- Overall score recommended_levelVARCHAR(10) -- Recommendation level (e.g., "A", "B", "C", "Rejected") ); Document-oriented schema (MongoDB): / / Example of a structure where each component document contains multiple environment profile sub-documents { "_id":"Component123", "type":"IGBT module", "manufacturer":"Company X", "profiles":[ { "profile_id":"Comp123_Profile1", "env_params":{ "temp_range_code":2, "humidity_code":2, "light_code":1, "wind_code":1, "salt_code":1 }, "test_duration_h":500, "life_hours":20000, "drift_rate":0.04, "fail_count":0, "data_source":"Test Platform JSON" }, ... / / Other profile data ], "metrics":{ "MTBF": 1.0e6, / / Mean Time Between Failures (hours) "failure_rate": 1e-3, / / Failure rate (1 / h) "life_margin": 1.5, / / Life margin (relative indicator) "validation_strength": 0.8 / / Historical validation strength (0~1) }, "reliability_score": 85.5, "recommended_level":"A" } In the above profile database design, relational tables store each profile record in a standardized manner, with each field clearly separated; document-based tables nest components with multiple profiles, facilitating direct access to all environmental behaviors of the entire component. In actual implementation, the appropriate database type can be selected based on the data scale and query requirements. The key is to ensure that the profile data structure is consistent and reproducible: any technician with this data can store the data and use it for algorithm calculations based on the above structure. Profile data normalization and storage design provide a standardized data foundation for the entire system.

[0075] Profile similarity matching algorithm: Matching Requirements: During the selection and evaluation process, it is necessary to match the target application environment of the component to be evaluated with the existing data in the profile database based on similarity. This module takes a target environment profile vector as input (a set of environmental stress parameters provided by the user or derived from the requirements) and outputs several historical profiles and corresponding components in the database that are "most similar" to it, for subsequent scoring.

[0076] Matching Algorithm: Employs weighted multidimensional similarity calculation. The default method is weighted Euclidean distance: the profile vector is denoted as... The target profile is Pre-set weights w i To indicate the importance of stress in each dimension, the distance is calculated as follows: ; in w i This can be adjusted based on the application scenario (e.g., if lighting is particularly critical for a certain application, its weight can be increased). A smaller distance *d* indicates a more similar profile. The system calculates the distance between each candidate element and the target profile from multiple profiles in the database, selecting the one with the smallest distance as the element's "closest environmental profile." Then, it filters several elements that meet the threshold based on their distance from smallest to largest for further evaluation. The pseudocode is as follows: functionfindSimilarProfiles(target_profile): similar_profiles=[ forcompinComponents: best_d=INF best_profile=None forprofileincomp.profiles: d=0 foriinrange(len(profile.env_params)): diff=profile.env_params[i]-target_profile[i] d += weight[i] * diff * diff d = sqrt(d) ifd <best_d: best_d=d best_profile=profile ifbest_d <SIMILARITY_THRESHOLD: similar_profiles.append((comp.id,best_profile,best_d)) #Sort by distance from smallest to largest similar_profiles.sort(key=lambdax:x[2]) returnsimilar_profiles In the algorithm described above, each element may have multiple profile data points, and the one that best matches the target environment is selected as the representative. SIMILARITY_THRESHOLD is a predefined matching threshold. When the best matching distance of all elements is greater than this threshold, it indicates that there is not enough close environmental data in the database. This situation will trigger the data gap identification and test request process described later.

[0077] Performance Optimization: When the profile database is large, direct traversal and computation may be inefficient. To address this, locality-sensitive hashing (LSH) and other approximate nearest neighbor search techniques can be introduced to build profile vector indexes. For example, a hash function can be constructed to map similar profiles into the same bucket; during a query, only the corresponding bucket needs to be searched, significantly reducing computation. LSH leverages the property that close points in high-dimensional space remain close in low-dimensional projections to pre-classify data into multiple buckets, thereby performing fast similarity matching within each bucket. This system can construct LSH or KD-tree indexes based on profile vectors during data preprocessing, improving retrieval speed while maintaining matching accuracy.

[0078] Using the aforementioned similarity matching algorithm, the system can automatically identify historical data that best matches the target environmental conditions, laying the foundation for subsequent reliability index calculation and scoring. The entire process is completed in software, without relying on manual comparison, thus ensuring the reproducibility and efficiency of the implementation method.

[0079] Reliability index calculation: After matching similar profiles, the system calculates a series of reliability index fields for each candidate component to quantify its expected reliability and performance stability under the environmental conditions. The main indices and their calculation formulas / methods include: Life conversion: This involves converting life data obtained under accelerated stress in the laboratory to estimate the equivalent life of a component under actual service conditions. A common method is to perform conversions based on accelerated models, such as using the Arrhenius model to calculate the acceleration factor AF under temperature stress. ; in E a To activate energy, kBoltzmann constant, T use This refers to the actual operating temperature (absolute temperature). T test To accelerate the test temperature. By OF Multiply the test time under high stress by OF This is converted to life under normal stress. For other stresses such as humidity and vibration, the Peck model or Miner fatigue accumulation model can be used for conversion. Life margin can be defined as the ratio or difference between the converted life and the design life requirement. For example: if a component obtains a life of 1000 hours in an accelerated test at 85℃, and AF=10, then the converted actual life = 10000 hours; if the design life requirement is 8000 hours, then the life margin = 10000 / 8000 = 1.25 (margin 25%).

[0080] Failure Rate / Reliability Indicators: These are functions that estimate the failure rate and reliability of a component based on experimental or field data. If sufficient failure samples are available, a Weibull or exponential distribution can be fitted to calculate the failure rate function. And Mean Time Between Failures (MTBF). For example, for an exponential distribution. For the Weibull distribution, the shape and scale parameters can be obtained through maximum likelihood estimation, after which the failure rate can be calculated. For small sample sizes, the Bayesian method can be used to infer the distribution prior, and then the reliability R(t) and failure rate can be calculated. The system implementation provides options for commonly used distribution models and automatically calculates the failure rate (taking the stable-period failure probability density) and MTBF from the data. These indicators will be used in the comprehensive scoring (lower failure rate and higher MTBF are preferred).

[0081] Performance drift rate modeling: Calculating the rate of change of component performance parameters with time / stress. For example, drift rates such as capacitor capacity decay and photovoltaic module power reduction can be obtained by fitting experimental data with linear regression or exponential models to obtain the percentage change in performance per thousand hours. For possible nonlinear degradation, stochastic process models (such as Wiener processes and Gamma processes) can be used to model the drift trend and extract drift stability indices. Drift stability can be measured by the standard deviation of the drift rate or the width of the confidence interval: the smaller the drift change (stable), the higher the stability score.

[0082] Sample distribution estimation: For each component, its historical test sample coverage is statistically analyzed, including the number of test samples and the breadth of environmental profile coverage. A larger sample size and more diverse conditions result in higher reliability of the evaluation results. This indicator can be quantified as historical validation strength, for example, a score of 0-1 based on the combined score of sample size and covered environments. Sample distribution can also be used to estimate confidence intervals: if a component has been tested with N samples under similar environments without failure, its upper limit of failure rate at a certain confidence level can be calculated.

[0083] The calculations of the aforementioned indicators are all performed automatically within the software according to preset formulas or models. The system module processes the raw data from the profile database using the aforementioned models, outputting key indicator values ​​such as the predicted lifetime, failure rate, drift rate, and validation strength of each component under the target environment. By providing explicit formulas (as described in the Arrhenius model formula above) and calculation logic, this implementation ensures that technicians can reproduce the calculations of these indicators without relying on specific platform software. When necessary, the system also allows developers to adjust model parameters (e.g., activation energy E). a The values, confidence levels, etc., are selected to accommodate the failure mechanisms of different components. Each reliability index serves as the basis for subsequent scoring and recommendations, and will be stored in a trusted component library or a temporary calculation result set for easy retrieval.

[0084] Trusted Component Library Generation Logic: The Trusted Component Library is a dynamically updated collection of components generated by the system, containing preferred components that have been verified and meet reliability requirements for various environmental profiles. Its generation logic is as follows: Profile + Index Screening: For each component, check whether its data in the profile database covers the stress condition combination required by the target application. If a component has no data records that match or are close to the target profile (i.e., the matching distance in the previous section is higher than the threshold, or data for key stress dimensions is missing), it cannot be temporarily rated as trustworthy. Only components with sufficient data support are eligible to enter the trustworthy library.

[0085] Threshold determination for reliability indicators: Set a minimum threshold combination for reliability indicators. A typical rule is: the calculated service life must not be less than the target design service life. The reliability requirements are as follows: failure rate must not exceed a certain value (e.g., <0.5% per year); performance drift rate must be stable within a certain range (e.g., <2% drift over 1000 hours); and historical verification strength must reach a certain level (e.g., at least 5 samples covering high-temperature summer environments). Only when all indicators of a component meet the predetermined thresholds is it considered to have passed the reliability requirements. Those that do not meet the standards will be eliminated or marked as "not recommended." These thresholds can be adjusted according to the application scenario, thus flexibly controlling the trust library threshold.

[0086] Combination Rules: When evaluating the same component using multiple environmental profiles, the overall reliability must be comprehensively assessed. For example, a component may pass the high-temperature, high-humidity profile but have insufficient data or poor performance in the high-salt-spray profile; in such cases, a decision may need to be made based on the specific circumstances. Common combination rules include the worst-case principle (completion if any critical scenario fails) or the weighted average principle (an overall score is derived by weighting multiple scenario indicators). By default, this system uses the worst-case principle for critical stress scenarios to ensure the component's reliability under all necessary operating conditions.

[0087] Update Mechanism: The trusted component library is dynamically updated as new data is added. If a component's shortcomings are addressed through new experimental data, the system will recalculate its metrics and add it to the trusted library if it meets the criteria. Conversely, if subsequent data shows that a component no longer meets the standards (e.g., early failure reported in the field), it can be removed or downgraded. This mechanism is implemented through predefined trigger rules: whenever there is an update in the database, the trusted status of the corresponding component is reassessed.

[0088] The generated trusted component library can be stored as a dedicated data table or collection, such as TrustedComponents(component_id, valid_profiles, metrics_summary, status), where status indicates whether it is "trusted / to be observed / to be phased out". Through this logic, the system ensures that only components that have undergone sufficient data validation and meet the required metrics are included in the trusted library. This not only provides highly reliable candidates for the final recommendation but also builds a closed-loop, continuously improving knowledge base: the addition of new data continuously enhances the library's trustworthiness and coverage.

[0089] Comprehensive scoring and judgment logic: For candidate components that pass the initial screening, the system further calculates a comprehensive score, which is used to rank and classify the components and make final recommendations. The comprehensive score adopts a weighted scoring model, which linearly combines multiple normalized reliability indicators to obtain the total score. An example formula is as follows: The weights listed above are for illustrative purposes only and can be adjusted according to actual needs (ensuring the sum of the weights is 1.0). Obtaining each sub-rating: Life margin rating: The life margin (such as equivalent life / design life) is mapped to 0100 points. A margin of 1.0 is recorded as the baseline of 60 points. For every 10% exceeding the margin, a certain number of points are added, and vice versa. The upper and lower limits are constrained to 0100.

[0090] Failure Rate Score: Based on the calculated annual failure rate or FIT value, lower failure rates are mapped to higher scores (e.g., 0.1% failure rate corresponds to 100 points, 1% corresponds to 80 points, and >5% is recorded as 0 points, etc., using linear interpolation). It can also reflect MTBF; a higher MTBF corresponds to a lower failure rate and a higher score.

[0091] Drift stability rating: scored based on the fluctuation range of performance drift rate. A full score is awarded if performance degradation is <1% over 1000 hours; 60 points are awarded for a 5% degradation; and a low score or even 0 points are awarded for >10%. Furthermore, drift consistency (small standard deviation) also improves the score.

[0092] Historical validation strength score: scored based on sample size and diversity. For example, 100 points are awarded for test samples >= 10 that cover all key profiles; 70 points are awarded for around 5 samples; and low points are awarded for only 1-2 samples or no on-site validation.

[0093] The system assigns a clear function formula (usually a piecewise linear or S-curve mapping) to each indicator score. In implementation, these scoring functions can be hard-coded or configured in the database for easy adjustment. Below is a brief pseudocode for calculating the comprehensive score: functioncomputeCompositeScore(metrics): / / Metrics include life_margin, fail_rate, drift_stability, validation_strength, etc. life_score=mapToScore(metrics.life_margin,scheme="margin") / / Life margin mapping score fail_score = mapToScore(metrics.fail_rate, scheme="fail_rate") / / Map failure rate to score (low failure rate -> high score) drift_score=mapToScore(metrics.drift_stability,scheme="drift") / / Drift stability mapping score valid_score=mapToScore(metrics.validation_strength,scheme="validation") total_score=0.4*life_score+0.3*fail_score+0.2*drift_score+0.1*valid_score returntotal_score Judgment Logic: After obtaining a comprehensive score, the system determines the recommendation level and eliminates components accordingly. Elimination criteria: If a component fails to meet any key performance indicator (e.g., lifetime margin <1.0 or failure rate higher than the threshold), it will be directly judged as "not recommended" regardless of its overall score. This is achieved by applying a hard rule before scoring: check the indicator set, and if the elimination rule is triggered, mark the status as eliminated, and the overall score can be recorded as 0 to indicate invalidity.

[0094] Tiering Rules: For components that are not eliminated, they are ranked according to their overall score and assigned a recommendation level. For example: a total score of ≥90 is classified as "Level A Recommended", 80-90 as "Level B Alternative", 60-80 as "Level C Considerable", and scores below 60 are labeled "Barely Recommended / Not Recommended". Users can adjust the tier range and level names as needed. Level determination can be implemented using simple if-else logic: iftotal_score>=90:level="A" eliftotal_score>=80:level="B" eliftotal_score>=60:level="C else:level="Not Recommended" Output processing: The elimination and grade information are appended to the component results for use in the final output report. Eliminated components can be filtered out from the final recommendation list, or the reason for not recommending them can be listed separately in the results (e.g., "a certain indicator did not meet the standard").

[0095] Through the aforementioned comprehensive scoring formula and judgment logic, the system achieves a quantitative and objective comparison of the merits of components. This process is executed entirely according to algorithmic rules, allowing technical personnel to clearly see the scoring criteria and elimination standards, thereby enabling them to reproduce the system's recommendation results or adjust and optimize the scoring rules. While the weights and thresholds can be configured during implementation, their operational mechanism conforms to the above description, regardless of the configuration.

[0096] Recommendation result structure and output: The system's final output includes a list of recommended components and their detailed rating information, which can be presented in either a JSON response or a database table record. JSON structure: Suitable for scenarios where results are retrieved via an API interface. The top-level JSON contains an array of elements, each element entry including: { "component_id":"X12345", "best_profile_id":"X12345_profile7", "scores":{ "life_margin":1.25, "life_score":85, "fail_rate":0.001, "fail_score":90, "drift_stability": 0.95, "drift_score":88, "validation_strength":0.8, "validation_score":80, "total_score": 85.4 }, "recommended_level":"A", "status":"Recommended" } Here, `component_id` is the component identifier, `best_profile_id` is the ID of the most similar profile used for evaluation, the `scores` object provides the values ​​of each indicator and their corresponding sub-scores, `total_score` is the overall score, `recommended_level` is the recommendation level, and `status` can be "recommended," "candidate," or "rejected," etc. This structured output facilitates front-end display or further program processing.

[0097] Regardless of the format, the recommendation results clearly state the component ID, the corresponding profile ID (proving the data used for evaluation), detailed scoring, and recommendation level / status. This ensures the traceability and transparency of the system output. The example result structure provided in this embodiment ensures that others can parse and understand the recommendation content according to this format, facilitating direct citation in patent specifications or engineering documents. Unlike traditional black-box platforms that only provide conclusions, this system's output structure comprehensively displays the scoring basis and recommendation reasons, demonstrating the software system's advantages in information completeness and decision interpretation.

[0098] Data Gap Identification and Experiment Request Mechanism: During the matching and evaluation process described above, the system may identify data gaps under certain target environmental conditions: that is, the database lacks profile data for specific stress combinations or the sample size is insufficient, making it impossible to reliably evaluate the relevant components. To address this, this system is designed with an automatic data gap identification and test request mechanism to achieve a recommendation-test closed-loop improvement. Gap identification logic: During the profile matching stage, if the matching distance of all components exceeds a threshold, or if no candidate components have test data covering the range for a certain key stress factor, a data gap is identified. The system records the missing data dimensions and ranges in detail. For example: "Lack of accelerated aging data under conditions of temperature ≥100℃ and humidity ≥90%RH" or "No performance verification data for component X under high salt spray environment". Furthermore, when calculating reliability indicators, if the sample size of a component is too small, resulting in insufficient confidence of the indicator (e.g., only one sample without failure), this can also be considered a data gap, requiring supplementary testing.

[0099] Experiment Request Structure: Once a data gap is identified, the system generates an experiment request message and notifies the external experiment execution platform via an asynchronous API or message queue. The request content describes the required experiment in a structured format, such as JSON. { "request_id":"REQ20250814001", "type":"NEW_TEST", "component_id":"X12345", "required_env_profile":{ "temp":110, "humidity":95, "light":1000, "wind":"sandstorm", "salt":"high" }, "reason":"DataGap:similarprofilenotfound", "parameters":{ "test_duration":500, "metrics":["time_to_fail","drift_rate"] }, "callback_url":"https: / / lab.platform / api / result_upload / REQ20250814001" } The request includes a unique ID, component ID, required environmental profile parameters (specific to stress values ​​or levels), the reason for the request, and the desired metrics and test duration. The arbitration mechanism is entirely software-driven; the system automatically determines when a new test is needed based on the discrepancy and submits the request via a standard interface, requiring no manual intervention.

[0100] Asynchronous Testing and Data Backfilling: After receiving a request, the external testing platform executes the corresponding test when conditions permit. Upon completion of the test, the result data (JSON format) is uploaded back to the system's data interface via a pre-provided callback_url. The uploaded data structure is consistent with the aforementioned input format, such as: { "request_id":"REQ20250814001", "component_id":"X12345", "env_params":{"temp":110,"humidity":95,"light":1000,"wind":"sandstorm","salt":"high"}, "result":{"life_hours":6000,"drift_rate":0.08,"fail_count":0} } After receiving the data, the system verifies and parses it, automatically adding it as a new profile record (generating a new profile_id) and storing it in the profile database. Subsequently, it triggers a re-run of the selection and evaluation process: the new data is considered in the matching and scoring, updating the various indicators of component X12345. This forms a closed loop: system identifies deficiencies -> requests testing -> obtains data -> updates the database -> makes improvement recommendations.

[0101] Workflow Management: The system includes a simple request status tracking module that records the status of each request (sent, in progress, completed) and the result reception status. If no result is received for an extended period, the system can remind the user or prompt them to request again. This module also ensures that duplicate requests for tests already in progress are avoided.

[0102] Closed-loop diagram: The system's recommendation-test closed loop can be summarized as follows: First, the component selection and recommendation are supported by the profile database; if insufficient data is found, the system automatically initiates a test request to the testing platform; after completing the test, the testing platform feeds back the data, enriching the profile database; the system updates its evaluation based on this, achieving more accurate recommendations, and so on, continuously improving the completeness of the component library (the data collection process is entirely completed through software interaction, without any human intervention or arbitration). This closed-loop process ensures the system's self-improvement capability: over time, the database becomes more comprehensive, and the recommendation results become more accurate and reliable. More importantly, the above mechanism is entirely implemented by software logic and is reproducible in specific projects—developers can achieve interface with the remote testing platform and data feedback based on the provided request structure and triggering conditions, building a closed-loop system.

[0103] Example 2: See Figure 2 The following section, using a flowchart, further explains the complete operation process and closed-loop characteristics of this system: 1. Data Integration and Profile Database Construction: After system startup, the data integration module is executed first to collect environmental and performance data from various data sources. After format normalization and profile normalization, the data is written to the profile database. This step ensures that the data required for analysis is ready. (Corresponds to the "Data Sources and Format Standardization" and "Profile Normalization" sections above) 2. Input Target Profile and Candidate List: The user or upper-layer application specifies the profile parameters of the target application environment, as well as a list of candidate components to be evaluated. The target profile can be explicitly provided by the user or automatically generated based on actual site environment sensor data. The candidate list is usually selected by the user as the model of the components to be compared, such as different brands of a certain type of power module.

[0104] 3. Profile Matching: For each candidate element, the system retrieves historical profile data of its closest target environment from the profile database and calculates the similarity distance. As mentioned earlier, weighted Euclidean distance or an LSH-optimized retrieval method may be used. The matching results produce the best matching profile and similarity for each element. (Corresponding to the "Profile Similarity Matching Algorithm" section) 4. Data Gap Check: Check if the matching results are sufficient. If some components cannot find sufficiently similar profiles (distance is higher than the threshold), or if a certain key stress dimension has blank data for all components, a data gap is determined to exist. If a gap exists, proceed to step 5; otherwise, skip to step 6.

[0105] 5. Initiating and Updating Experiment Requests: For identified data gaps, the system generates corresponding experiment requests and sends them to the experiment platform via API, temporarily terminating the current evaluation process. This request can be processed asynchronously; the system enters a waiting state while recording the sent requests. When the experiment platform returns new data, the system automatically updates the profile database and re-executes the matching in step 3 to utilize the latest data for evaluation. This ensures that subsequent scoring is based on the latest and most comprehensive information. (Corresponds to the "Data Gap Identification and Experiment Request Mechanism" section) 6. Reliability Indicator Calculation: For each candidate component and its matching profile data, calculate reliability indicators such as lifetime reduction, failure rate, and drift rate. Results are temporarily stored in memory or written to the component's temporary results table. (Corresponding to the "Reliability Indicator Calculation" section) 7. Comprehensive Scoring and Judgment: The comprehensive scoring module is invoked to calculate the total score for each component and to classify components into elimination and recommendation levels based on threshold rules. The scoring results, along with detailed indicator breakdowns, are summarized. If a component is eliminated, its status is notified with the reason for elimination. (Corresponding to the "Comprehensive Scoring and Judgment Logic" section) 8. Recommendation Results Output: The final evaluation results will be output according to the agreed-upon structure. This includes a recommendation list (sorted by level or score) and detailed scoring breakdowns. Output format may include console logs, file reports, JSON API responses, or database records, depending on application requirements. Users can use this information to make component selection decisions. (Corresponding to the "Recommendation Results Structure and Output" section) 9. Update the Trusted Component Library: Based on the evaluation results, update the internal trusted component library. If a component meets the trust criteria and was not previously in the library, add it; if it already exists, update its metrics and level; components that do not meet the criteria are removed from the trusted library or marked as invalid. The trusted library is thus gradually expanded and improved, providing a more refined candidate pool and data index for future evaluations. (Corresponds to the "Trusted Component Library Generation Logic" section) After completing the above process, the system can repeat the same steps for new environmental profiles or new components. Throughout the process, the system maintains a closed software loop: continuously improving its recommendation capabilities through a data -> analysis -> output -> data cycle. The input-output relationships of each module are clearly defined, allowing experienced engineers to implement and verify the system module by module. For example, profile matching functions can be written independently and their outputs tested using existing data. Independent modules are connected through clear data contracts (such as unified data structures and interfaces). Since there are no hidden dedicated hardware processes, each step can be simulated and reproduced in a conventional computing environment, which is highly beneficial for engineering development and subsequent system maintenance and upgrades.

[0106] Example 3: Implementation and verification process of example application scenarios: The following is a specific application scenario to illustrate how this system works and the benefits it generates. Suppose a new energy company is designing an outdoor photovoltaic inverter control board and needs to select an ADC (Analog-to-Digital Converter) chip to acquire voltage and current signals. The design requirements are as follows: the operating environment is outdoor, with a temperature range of -40~85℃, annual humidity reaching 90%, strong electromagnetic interference (EMI level III), and an expected equipment lifespan of 15 years. It is desired that the ADC used will perform stably and reliably during this period.

[0107] Traditionally, engineers might select an industrial-grade ADC solely based on its datasheet. However, datasheets typically only provide performance parameters at 25°C room temperature or under simple stress, failing to directly demonstrate reliable operation for 15 years in high-temperature, high-humidity, and strong EMI environments. Inadvertently selecting a short-lifespan device (e.g., using a component with only a 5-year lifespan in a system requiring a 20-year lifespan) could lead to premature product failure before reaching its design life, resulting in serious quality risks. This system is designed to avoid such problems.

[0108] In this example, the engineer uses this selection and evaluation system to complete the ADC device selection process as follows: Input requirements: The engineer opens the system user interface, selects the component type as "ADC analog-to-digital converter", fills in the environmental conditions (temperature -40~85℃, humidity up to 90%, water mist condensation present, EMI interference level III) and lifespan requirement of 15 years, and submits the query.

[0109] Matching the environmental profile: After receiving the input, the selection engine finds a similar standard configuration in the environmental profile table—for example, "Harsh Environmental Profile A" (defined as high temperature 85℃ / high humidity 85%RH, accompanied by EMI Level III interference). The system recognizes that this profile basically covers the user's temperature and humidity requirements and the interference level is comparable, so profile A is selected as the search condition (if multiple profiles partially match, they can be combined for filtering or a more stringent one can be selected to cover the requirements).

[0110] The engine first searches the trusted component library for all components marked as ADC type and verified through the "Profile A" test. Assume that three ADC models meet the criteria found in the trusted library: ADC-ModelX and ADC-ModelY from different manufacturers, and a domestically produced ADC-ModelZ. All three ADCs have operated without failure for over 1000 hours under 85℃ / 85%RH conditions, and are therefore verified as "trustworthy" devices. The system then lists these three as preliminary candidates.

[0111] Comprehensive Indicator Comparison: For the three candidate ADCs, the system extracts their detailed indicators under profile A from the reliability database, for example: Model X: A product of a major international manufacturer, it has been running continuously for 2000 hours at 85℃ / 85%RH without failure, with minimal performance drift (gain drift <0.5%, and negligible noise increase), and an estimated lifespan of over 20 years; in historical field applications, the annual failure rate is very low (FIT value is only in the tens).

[0112] Model Y: Another common industrial ADC, which experienced one interruption (soft fault, recovered by restart) after 1500 hours of operation in the same test, with key parameters drifting by about 2%, and an estimated lifespan of nearly 18 years; its failure rate is slightly higher than that of Model X.

[0113] ModelZ: A new domestic ADC model that showed no failures after 800 hours of accelerated aging testing at 85℃ / 85%RH, but exhibited critical performance indicators (such as an increase in offset to near the allowable upper limit). It is estimated that it can support a lifespan of about 15 years. The advantages of this device are low cost and reliable supply, but because it has only recently entered production, the amount of reliability data is relatively small.

[0114] The system calculates reliability scores for the models based on the above data and according to predetermined weights. Assumptions: Model X scores 95 points, Model Y scores 88 points, and Model Z scores 80 points.

[0115] Generate a recommendation list: After compiling the scoring results, the system provides feedback to engineers through the interface. Each row in the interface includes the component model, manufacturer, applicable environment, a summary of reliability indicators (such as lifespan estimates), and the reasons for the recommendation. Engineers can click on ModelX to view its detailed test data curves and failure analysis reports to further confirm its reliability.

[0116] User Decision and Feedback: Based on the information provided by the system, the engineer determined that the Model X fully met the requirements and had sufficient redundancy, so they decided to adopt this ADC. After confirming the selection on the interface, the system recorded and archived this decision. Meanwhile, if the engineer showed interest in domestically produced components for the Model Z but did not adopt them due to slightly lower lifespan, further testing of the Model Z could be planned (the system could even ask the user if they wanted to conduct longer, more demanding tests on the Model Z to obtain more data). After this selection process, the system collected the user's choices and preferences. This feedback data will be used to train and improve future recommendation algorithms—for example, noticing that users prefer components with larger lifespan margins, the algorithm will correspondingly increase the lifespan weight in similar scenarios in the future.

[0117] Long-term validation: Once the project enters the long-term operational phase, if the selected ModelX ADC performs well in real-world field applications, its field operational data can be fed back to the system (imported through periodic maintenance records, etc.). This measured reliability data will further corroborate the superior performance of the ModelX, allowing the system to raise its rating under similar operating conditions. Furthermore, if any unexpected problems arise, the system can promptly detect and adjust the model. In this way, the accuracy of the system's recommendations will continuously improve based on actual usage feedback.

[0118] This example demonstrates that the component selection and evaluation system of this invention significantly improves the scientific rigor and efficiency of electronic component selection under complex operating conditions. On one hand, engineers receive decision recommendations based on massive amounts of reliability data within minutes, drastically reducing the time spent screening and comparing components. On the other hand, the system-recommended devices have data-supported reliability guarantees, reducing the risk of design errors and early product failure. This data-driven, automated selection method is not only faster and more efficient than the previous manual manual review and experience-based judgment, but also more objective and accurate. Especially in fields with extremely high reliability requirements, such as new energy and defense, applying this system can ensure that key components have sufficient environmental adaptability and lifespan redundancy, improving the stability and safety of the entire system from the source.

[0119] In summary, the water-wind-solar key component selection and evaluation system of this invention organically combines environmental stress testing, reliability big data analysis, and component selection decision-making through a sophisticated architecture and algorithm. Its modular design facilitates expansion to different types of components and operating conditions; the standardization of the database and interfaces makes it possible to continuously acquire new data, and the system accuracy will continuously improve over time. With the above-described in-depth design, those skilled in the art can reproduce and apply this system based on the provided technical solution, realizing intelligent decision support for electronic component selection in complex environments in actual engineering projects, thus safeguarding product reliability.

[0120] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

Claims

1. A selection and evaluation system for key components in water-wind-solar systems, characterized in that, It includes a data acquisition module, a profile matching module, a reliability index calculation module, a trusted component library management module, a scoring and recommendation module, and a data gap identification module; The data acquisition module is used to receive environmental stress and performance data from a multi-environmental stress test platform, an on-site sensor database, and historical test records, and to normalize the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. The profile matching module retrieves the closest historical profile from the environmental profile dataset based on the target working condition parameters input by the user. The reliability index calculation module is used to calculate multiple reliability indices such as lifetime margin, failure rate, performance drift stability, and verification coverage based on the matched historical profile data, and to associate and store the indices with component identifiers. The trusted component library management module is used to compare the calculated reliability indicators according to the preset reliability threshold rules, select qualified components to add to the trusted component library, and upgrade, downgrade or eliminate the status of components when the indicators are updated. The scoring and recommendation module is used to comprehensively score the components in the trusted component library and classify the components into different recommendation levels based on the scoring results.

2. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The data gap identification module is used to detect whether the profile data under the target working condition is missing or the number of samples is insufficient during the profile matching and reliability index calculation process. When a gap exists, it generates test request information and triggers the external test data supplementation process.

3. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The profile matching module includes a similarity calculation unit. The similarity calculation unit calculates the matching distance based on the difference between the target working condition parameters and the corresponding parameters of each historical profile, combined with the weight of each parameter dimension, using a weighted Euclidean distance algorithm or an approximate nearest neighbor index algorithm, and selects the profile with the smallest matching distance as the evaluation basis.

4. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The trusted component library management module is also used to record audit information on changes in component status. The audit information includes the type of status change, the reason for the change, and snapshots of indicators before and after the change.

5. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The comprehensive score is a weighted sum of lifetime margin score, failure rate score, performance drift stability score and verification coverage score according to preset weights. Before performing the comprehensive score, the scoring and recommendation module also includes a hard elimination judgment unit, which is used to directly mark the component as not recommended and remove it from the recommendation list when any key indicator of the component is lower than a preset lower limit.

6. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The test request information includes target operating condition parameters, test duration, and indicators to be collected; the data gap identification module sends the test request information to the external test platform through a standardized interface, and after receiving supplementary test data, fills the data back into the environmental profile dataset and triggers an update of the trusted component library.

7. The selection and evaluation system for key components in water-wind-solar energy systems according to claim 1, characterized in that, The modules transmit information through a standardized data interface, which includes data format definitions, parameter field specifications, and calling procedures to achieve loosely coupled integration with different external data sources and testing platforms.

8. A method for selecting and evaluating key components for water-wind-solar systems, characterized in that, The method applied to the selection and evaluation system for key water-wind-solar components according to any one of claims 1-8 includes: S1. Data Acquisition and Normalization Processing: Receive environmental stress and performance data from multiple environmental stress test platforms, field sensor databases, and historical test records. Normalize the data according to a unified parameter dimension and segmented interval encoding to form an environmental profile dataset. S2, Profile Matching: Based on the target working condition parameters input by the user, retrieve the closest historical profile from the environmental profile dataset; S3. Reliability index calculation: Based on the matched historical profile data, calculate multiple reliability indices such as lifetime margin, failure rate, performance drift stability, and verification coverage, and store the indices in association with component identifiers. S4. Trusted Component Library Construction and Maintenance: Based on the preset reliability threshold rules, the calculated reliability indicators are compared, and qualified components are selected and added to the trusted component library. When the indicators are updated, the status of the components is promoted, downgraded or eliminated. S5. Comprehensive scoring and recommendation: The components in the trusted component library are comprehensively scored, and the components are divided into different recommendation levels according to the scoring results and a recommendation list is output. S6. Data Gap Identification and Closed-Loop Optimization: During the profile matching and reliability index calculation process, it is detected whether the profile data under the target working condition is missing or the number of samples is insufficient. When a gap exists, test request information is generated and the external test data supplementation process is triggered. After the supplemented data is obtained, it is backfilled into the environmental profile dataset and the process returns to step S2 to re-execute the subsequent process.

9. The method for selecting and evaluating key components for water-wind-solar systems according to claim 8, characterized in that, In step S2, the weighted Euclidean distance algorithm or the approximate nearest neighbor index algorithm is used to calculate the matching distance between the target working condition parameters and each historical profile. Specifically, the matching distance is calculated based on the difference between the corresponding parameters of the target working condition parameters and each historical profile, combined with the weight of each parameter dimension, and the profile with the smallest matching distance is selected as the evaluation basis.

10. The method for selecting and evaluating key components for water-wind-solar systems according to claim 8, characterized in that, In step S5, the comprehensive score is a weighted sum of lifetime margin score, failure rate score, performance drift stability score and verification coverage score according to preset weights. Before the comprehensive score is performed, it is first determined whether there are any key indicators of the components that are lower than the preset lower limit. If so, they are directly marked as not recommended and removed from the recommendation list.

11. The method for selecting and evaluating key components for water-wind-solar systems according to claim 8, characterized in that, In step S6, the test request information includes target operating condition parameters, test duration, and indicators to be collected; the test request information is sent to an external test platform through a standardized interface, supplementary test data returned by the external test platform is received, the data is backfilled into the environmental profile dataset, and the trusted component library is updated.

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