Climate environment reliability platform quality evaluation method and system based on multi-dimensional indexes

Abstract

The invention relates to the technical field of climate environment evaluation, in particular to a climate environment reliability platform quality evaluation method and system based on multi-dimensional indexes. According to the technical scheme, real-time dynamic quality evaluation is carried out by adopting big data analysis based on temperature operation, humidity operation, temperature metering performance, humidity metering performance and equipment function multi-dimensional indexes aiming at independent equipment in a climate environment reliability platform, so that the laboratory management cost can be remarkably reduced, the working efficiency is improved, and the working efficiency is improved. The platform operation capability and the test data accuracy and reliability are accurately judged in real time, emergencies are handled in time, a key means is provided for long-term stability study and judgment, fine management and proper maintenance of equipment, a traditional manual monitoring mode is replaced, deep operation data of the equipment can be obtained in real time, manual one-by-one routing inspection and data recording are not needed, and the working efficiency is improved. And meanwhile, data errors caused by manual operation are avoided, and the digital intelligent management level and the overall working efficiency of a laboratory are improved.

Description

Technical Field

[0001] This invention relates to the field of climate and environmental assessment technology, and in particular to a method and system for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators. Background Technology

[0002] Climate environment testing is a key technical means that simulates real climate and environmental conditions or reproduces the effects of environmental conditions. It is mainly used to evaluate the performance stability and reliability of products and components during actual use, transportation and storage. It is widely used in important fields such as power and electrical engineering, new energy, intelligent manufacturing and new materials, and is an important support for ensuring the quality and safe operation of products in related industries.

[0003] For climate and environmental laboratories of a certain scale, their testing scenarios are characterized by significant complexity: on the one hand, the climate and environmental reliability equipment is scattered and numerous, covering various types of equipment such as high-temperature test chambers, low-temperature test chambers, and damp heat test chambers; on the other hand, the equipment needs to operate continuously for a long time to meet the needs of long-cycle testing, which places extremely high demands on the accuracy of equipment data and the stability of operation of the testing platform.

[0004] However, existing equipment management and quality evaluation systems for such laboratories have significant shortcomings: Data acquisition limitations: Traditional management models that rely on manual monitoring cannot comprehensively and in real time acquire in-depth data during equipment operation (such as real-time temperature difference between equipment and standard, fluctuation / uniformity of multiple temperature points, etc.), resulting in data collection being lagging and one-sided. Lack of quality evaluation: There is a lack of dynamic quality evaluation methods for the long-term operation of equipment, making it impossible to judge the equipment's operating capacity and the accuracy and reliability of test data in a timely manner, and making it difficult to identify the risk of equipment performance degradation or data deviation in advance; Significant risks and hidden dangers: The above-mentioned defects can easily trigger a chain of problems, which may result in test delays and invalid test results, or even cause abnormal damage to equipment, or even cause laboratory safety accidents due to equipment failure. This seriously restricts the laboratory's test efficiency and management level, and cannot meet the industry's development needs for intelligent and digital test platforms.

[0005] In summary, this application proposes a method and system for evaluating the quality of climate and environmental reliability platforms based on multi-dimensional indicators. Summary of the Invention

[0006] The purpose of this invention is to address the problem in the background technology that the equipment management and quality evaluation system cannot obtain comprehensive and timely in-depth data information, and to propose a quality evaluation method and system for climate environment reliability platforms based on multi-dimensional indicators.

[0007] The technical solution of this invention: a method for evaluating the quality of a climate environment reliability platform based on multi-dimensional indicators, comprising the following steps: For each individual device in the climate environment reliability platform, perform the following: A. Calculate the temperature operation unit score of the device based on real-time temperature data. ; B. Calculate the humidity operation unit score of the device based on real-time humidity data. ; C. Calculate the temperature measurement performance unit score of the device based on the temperature measurement traceability data. ; D. Calculate the humidity measurement performance unit score of the device based on humidity measurement traceability data. ; E. Based on the equipment's functional parameters, including measurement range, failure rate, and maintenance rate, calculate the functional unit score for the equipment. ; Based on the score of the temperature operation unit Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The weighted calculation results yield the total quality evaluation score P of the independent equipment; According to preset rules, independent devices with related functions or consistent test scenarios within the platform are divided into associated device groups. The quality evaluation score of the associated device block is obtained by weighted calculation based on the total quality evaluation score P of each independent device in the group. The overall quality evaluation result of the climate environment reliability platform is obtained by weighting the quality evaluation scores of each associated device block within the platform.

[0008] Optionally, in step A, the real-time temperature data includes the device's own temperature and the corresponding acquisition standard temperature, as well as the temperature operation unit score. The calculation process includes: Obtain real-time temperature data from N independent devices: the temperature of the i-th device at time j. Let i ∈ {1, 2, ..., N}, where N is the total number of independent devices, and j ∈ {1, 2, ..., T}, where T is the total number of time points for temperature data acquisition, and the temperature of the acquisition standard corresponding to the i-th device at the j-th time point. ; Calculate the temperature difference between the i-th device and its corresponding data acquisition standard at time j. Classified by equipment type , Y represents the total number of equipment types, determining the number of categories. Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical temperature difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for the temperature deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. Normalized to [0, 100], the formula is: , in, Scores for the temperature operation unit of the equipment The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance ; In step B, the humidity operation unit score of the device is calculated based on real-time humidity data. Real-time humidity data includes the device's own humidity and the humidity of the corresponding acquisition standard, as well as the humidity operation unit score. The calculation process includes: Obtain real-time humidity data from M independent devices: humidity of device u at time v. Let u∈{1,2,…,N}, where N is the total number of independent devices, and v∈{1,2,…,Q}, where Q is the total number of humidity data collection time points, and the humidity of the data collection standard corresponding to the u-th device at the v-th time point. ; Calculate the humidity difference between the u-th device and its corresponding data acquisition standard at time v. Classified by equipment type , Determine the first Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical humidity difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for humidity deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. Normalized to [0, 100], the formula is: , in, Scores for the humidity operation unit of the equipment. The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance ; Wherein, the specified threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and .

[0009] Optional, specify threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and .

[0010] Optionally, in step C, the temperature measurement traceability data includes temperature fluctuation, temperature uniformity and corresponding thresholds, and the temperature measurement performance unit score. The calculation process includes: Obtain traceability data of temperature values ​​of an independent device at A temperature points, including the temperature fluctuation at the i-th temperature point. Temperature fluctuation threshold and the temperature uniformity at the i-th temperature point Temperature uniformity threshold ; Set the boundary scores for each item. In the temperature fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the temperature uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the temperature fluctuation score for the i-th temperature point. Score for temperature uniformity The formulas are as follows: ; ; in, This is the temperature fluctuation attenuation coefficient. , This is the temperature uniformity attenuation coefficient. ; calculate Normalized to [0, 100], the formula is: , The total number of temperature points covered by the traceability data collection of temperature values ​​from independent devices; D. Calculate the humidity measurement performance unit score of the device based on humidity measurement traceability data. The humidity measurement traceability data includes humidity fluctuation, humidity uniformity and corresponding thresholds, and humidity measurement performance unit score. The calculation process includes: Obtain source data on humidity values ​​of independent devices at B humidity points, including the humidity fluctuation at the i-th humidity point. Humidity fluctuation threshold and the humidity uniformity at the i-th humidity point Humidity uniformity threshold ; Set the boundary scores for each item. In the humidity fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the humidity uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the humidity fluctuation score for the i-th humidity point. Sub-scores for humidity uniformity The formulas are as follows: ; ; in, This is the humidity fluctuation attenuation coefficient. , The humidity uniformity attenuation coefficient is... ; calculate Normalized to [0, 100], the formula is: ; The total number of humidity points covered by the traceability data collection of humidity values ​​from independent devices; Humidity fluctuation sub-item boundary score meets , , and The boundary score for the humidity uniformity component meets the requirements. , , and .

[0011] Optionally, the boundary scores for the temperature fluctuation component satisfy... , , and The boundary score for the temperature uniformity component satisfies... , , and .

[0012] Optional, functional unit score The calculation process includes: Calculate the score for the temperature measurement range item. Obtain the actual temperature measurement range of the equipment. and the initial value of the preset temperature measurement range Based on temperature decay penalty factor , The formula is: , Calculate the score for the humidity measurement range item. Obtain the actual humidity measurement range of the device. and the initial value of the preset humidity measurement range Based on humidity attenuation penalty factor , The formula is: , Calculate the failure rate score Obtain the actual equipment failure rate and preset critical failure rate The formula is: , Calculate the maintenance rate score Obtain the actual equipment repair rate and preset critical maintenance rate Based on the severity coefficient , The formula is: , Based on weighting coefficients , 2, 3, 4 Corresponding to Calculate the functional unit score The formula is: .

[0013] Optional, critical failure rate Based on historical failure statistics of the equipment over the past 3-5 years and the average failure rate of similar equipment in the industry, the critical maintenance rate is determined. The determination is based on the equipment's historical maintenance statistics over the past 3-5 years and the average maintenance rate of similar equipment in the industry.

[0014] Optionally, the formula for calculating the total score P for the quality evaluation of independent equipment is as follows: , in, Scores for temperature operation units Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The corresponding weighting coefficients are i = 1, 2, 3, 4, 5; Weighting coefficients in the formula for calculating the total score P of independent equipment quality evaluation and functional unit scores Weighting coefficients All values ​​are determined using the analytic hierarchy process or the entropy weight method, and the sum of all weight coefficients within the same group is 1.

[0015] Optionally, the preset rules include: dividing associated equipment groups according to equipment function type or test scenario. Equipment function type includes high temperature test chamber, low temperature test chamber, and damp heat test chamber. Test scenario includes power equipment reliability test scenario and new energy material environmental test scenario. The method also includes a dynamic weight adjustment step: Based on the historical total quality evaluation score sequence of the independent equipment, its performance degradation slope is calculated; and its temperature operation unit score is monitored in real time. Humidity operating unit score The short-term mutation rate; When the performance degradation slope exceeds a preset degradation threshold, or the short-term mutation rate exceeds a preset mutation threshold, a weight adjustment strategy is activated: when calculating the total quality evaluation score P of the device, the score of the temperature operation unit is increased. Humidity operating unit score The weighting coefficient of the score is adjusted accordingly, thereby reducing the weighting coefficient of the scores in other units; The weight adjustment strategy remains effective until the performance degradation slope and short-term mutation rate of the device return to the normal threshold range.

[0016] Secondly, this application provides a climate environment reliability platform quality evaluation system based on multi-dimensional indicators, used to implement the method described in the first aspect, including: The test equipment unit includes N climate environment test devices and a temperature acquisition standard for each device, which is used to carry out climate environment tests and output the temperature data of the device itself and the temperature data of the acquisition standard. The data acquisition unit includes a temperature sensor, a data acquisition card, and a parameter acquisition module. It is used to acquire real-time temperature data, traceability data, and functional parameters of the test equipment, and transmit the acquired data to the data processing and storage unit. The traceability data includes fluctuation, uniformity, and threshold, and the functional parameters include measurement range, failure rate, and maintenance rate. The data processing and storage unit includes a built-in processor and storage module. The processor is used to execute the temperature operation unit scoring in the method described in the first aspect. Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The calculation includes the total score P of independent devices, the block score of associated devices, and the overall evaluation of the platform. The storage module is used to store the collected data, calculation process data, and the final evaluation results. The data display unit includes a display screen and interactive visualization software, which is used to display the total score P of independent devices, the block scores of related devices, and the overall evaluation results of the platform in real time. It supports exporting evaluation result reports and querying historical data.

[0017] The communication unit uses wired Ethernet or wireless LoRa protocol for bidirectional data transmission between the test equipment unit, data acquisition unit, and data processing and storage unit.

[0018] Compared with the prior art, this application includes at least one of the following beneficial technical effects: This invention integrates devices with multiple interface types to build a unified management platform, replacing the traditional manual monitoring mode. It can acquire in-depth operational data of devices in real time, eliminating the need for manual inspection and data recording of each device, significantly reducing manpower input, and avoiding data errors caused by manual operation, thereby improving the level of digital management and overall work efficiency of the laboratory.

[0019] The evaluation index is constructed from the temperature operation unit, temperature measurement performance unit, and functional unit. Through the scoring rules of out-of-tolerance penalty and critical reinforcement, the operating status of the equipment and the reliability of the test data can be dynamically judged, the risk of equipment performance degradation can be identified in advance, the test can be invalidated due to data deviation, and the long-term stable operation of the platform can be guaranteed.

[0020] The evaluation results can serve as a key basis for equipment verification and stability assessment. For equipment that has been running continuously for a long time and whose calibration cycle has expired, the equipment performance can be verified through the evaluation results without stopping the machine. At the same time, combined with the scores of failure rate and maintenance rate, the equipment maintenance priority can be accurately determined, so as to realize the refined management and proper maintenance of the equipment, extend the service life of the equipment, and reduce the cost of equipment repair and replacement.

[0021] Real-time dynamic quality evaluation can promptly detect equipment anomalies, providing data support for emergency response and preventing test delays, equipment damage, or even personnel safety accidents caused by equipment failure, thereby further ensuring laboratory test safety and test progress.

[0022] This invention establishes a unified management platform for multi-interface equipment in climate and environmental testing. Based on multiple dimensions of indicators such as temperature operation, temperature measurement performance, and functionality, it employs big data analysis to conduct real-time dynamic quality evaluation. This significantly reduces laboratory management costs, improves work efficiency, accurately assesses platform operational capabilities and the accuracy and reliability of test data in real time, and promptly handles emergencies. It provides key means for long-term equipment stability assessment, refined management, and proper maintenance, effectively promoting the intelligentization and digitalization of testing platforms and mitigating risks such as test delays and failures, equipment damage, and personnel safety. Detailed Implementation

[0023] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0024] Example 1: This embodiment proposes a quality evaluation method for climate and environmental reliability platforms based on multi-dimensional indicators. The method will be described in detail below.

[0025] 1. For each individual device in the climate environment reliability platform, calculate the temperature operation unit score for that device. Calculate the humidity operation unit score of the equipment. Calculate the unit score for the temperature measurement performance of this equipment. Calculate the temperature measurement performance unit score and humidity measurement performance unit score of the equipment. Calculate the functional unit score of the device. The equipment's functional parameters include measurement range, failure rate, and maintenance rate.

[0026] A. Calculate the temperature operation unit score of the device based on real-time temperature data. Real-time temperature data includes the device's own temperature and the corresponding acquisition standard temperature, as well as the temperature operation unit score. The calculation process includes: Obtain real-time temperature data from N independent devices: the temperature of the i-th device at time j. Let i ∈ {1, 2, ..., N}, where N is the total number of independent devices, and j ∈ {1, 2, ..., T}, where T is the total number of time points for temperature data acquisition, and the temperature of the acquisition standard corresponding to the i-th device at the j-th time point. ; Calculate the temperature difference between the i-th device and its corresponding data acquisition standard at time j. Classified by equipment type , Y represents the total number of equipment types, determining the number of categories. Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical temperature difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for the temperature deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. Normalized to [0, 100], the formula is: , in, Scores for the temperature operation unit of the equipment The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance .

[0027] This embodiment calculates based on real-time temperature data from the device's own temperature and the corresponding acquisition standard. Its core value lies in achieving dynamic and accurate monitoring of equipment operating status. On the one hand, by real-time temperature difference statistics and the division of out-of-tolerance, critical, and qualified ranges, it can instantly capture deviations between equipment temperature data and standard values, avoiding the problem of "delayed detection of deviations" in traditional manual monitoring and ensuring the accuracy of temperature data during the test. On the other hand, by using scoring rules for critical enhancement and out-of-tolerance penalties (such as exponential weight decay polynomial formulas), it can highlight the risk level of temperature deviations, allowing managers to quickly identify high-risk equipment (such as equipment with a high number of out-of-tolerance occurrences) and intervene in a timely manner to make adjustments. This effectively avoids invalid test results or test delays caused by distorted temperature data, ensuring the stability and reliability of the test process.

[0028] B. Calculate the humidity operation unit score of the device based on real-time humidity data. Real-time humidity data includes the device's own humidity and the humidity of the corresponding acquisition standard, as well as the humidity operation unit score. The calculation process includes: Obtain real-time humidity data from M independent devices: humidity of device u at time v. Let u∈{1,2,…,N}, where N is the total number of independent devices, and v∈{1,2,…,Q}, where Q is the total number of humidity data collection time points, and the humidity of the data collection standard corresponding to the u-th device at the v-th time point. ; Calculate the humidity difference between the u-th device and its corresponding data acquisition standard at time v. Classified by equipment type , Determine the first Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical humidity difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for humidity deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. Normalized to [0, 100], the formula is: , in, Scores for the humidity operation unit of the equipment. The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance ; Wherein, the specified threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and Specify threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and ; C. Calculate the temperature measurement performance unit score of the device based on the temperature measurement traceability data. Temperature measurement traceability data includes temperature fluctuation, temperature uniformity, and corresponding thresholds, as well as the temperature measurement performance unit score. The calculation process includes: Obtain traceability data of temperature values ​​of an independent device at A temperature points, including the temperature fluctuation at the i-th temperature point. Temperature fluctuation threshold and the temperature uniformity at the i-th temperature point Temperature uniformity threshold ; Set the boundary scores for each item. In the temperature fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the temperature uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the temperature fluctuation score for the i-th temperature point. Score for temperature uniformity The formulas are as follows: , , in, This is the temperature fluctuation attenuation coefficient. , This is the temperature uniformity attenuation coefficient. ; calculate Normalized to [0, 100], the formula is: , The total number of temperature points covered by the traceability data collection of temperature values ​​from independent devices.

[0029] In this embodiment, the boundary score of the temperature fluctuation component satisfies , , and The boundary score for the temperature uniformity component satisfies... , , and .

[0030] It is worth noting that the S3 calculation, based on temperature fluctuation, temperature uniformity, and corresponding threshold temperature traceability data, plays a crucial role in ensuring the compliance and stability of the equipment's core functions from the perspective of "metrological performance." Firstly, by collecting and stratifying fluctuation and uniformity data from multiple temperature points (e.g., fluctuation di corresponds to different scores in different ranges), the metrological accuracy of the equipment under different operating conditions can be comprehensively evaluated, ensuring that the equipment meets national / industry standards and testing requirements, and preventing test data from losing its reference value due to substandard metrological performance. Secondly, this score can serve as an important basis for assessing the long-term stability of the equipment—even in scenarios where the equipment operates continuously for extended periods and the calibration cycle expires, the trend of S3 changes can be used to determine whether the equipment's metrological performance has deteriorated. Equipment status verification can be achieved without shutdown calibration, ensuring metrological accuracy while reducing efficiency losses caused by test interruptions, providing data support for refined equipment maintenance.

[0031] D. Calculate the humidity measurement performance unit score of the device based on humidity measurement traceability data. The humidity measurement traceability data includes humidity fluctuation, humidity uniformity and corresponding thresholds, and humidity measurement performance unit score. The calculation process includes: Obtain source data on humidity values ​​of independent devices at B humidity points, including the humidity fluctuation at the i-th humidity point. Humidity fluctuation threshold and the humidity uniformity at the i-th humidity point Humidity uniformity threshold ; Set the boundary scores for each item. In the humidity fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the humidity uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the humidity fluctuation score for the i-th humidity point. Sub-scores for humidity uniformity The formulas are as follows: , , in, This is the humidity fluctuation attenuation coefficient. , The humidity uniformity attenuation coefficient is... ; calculate Normalized to [0, 100], the formula is: ; The total number of humidity points covered by the traceability data collection of humidity values ​​from independent devices; Humidity fluctuation sub-item boundary score meets , , and The boundary score for the humidity uniformity component meets the requirements. , , and .

[0032] E. Based on the equipment's functional parameters, including measurement range, failure rate, and maintenance rate, calculate the functional unit score for the equipment. Functional unit score The calculation process includes: Calculate the score for the temperature measurement range item. Obtain the actual temperature measurement range of the equipment. and the initial value of the preset temperature measurement range Based on temperature decay penalty factor , The formula is: ; Calculate the score for the humidity measurement range item. Obtain the actual humidity measurement range of the device. and the initial value of the preset humidity measurement range Based on humidity attenuation penalty factor , The formula is: ; Calculate the failure rate score Obtain the actual equipment failure rate and preset critical failure rate The formula is: ; Calculate the maintenance rate score Obtain the actual equipment repair rate and preset critical maintenance rate Based on the severity coefficient , The formula is: ; Based on weighting coefficients , 2, 3, 4 Corresponding to Calculate the functional unit score The formula is: ; Among them, the critical failure rate Based on historical failure statistics of the equipment over the past 3-5 years and the average failure rate of similar equipment in the industry, the critical maintenance rate is determined. The determination is based on the equipment's historical maintenance statistics over the past 3-5 years and the average maintenance rate of similar equipment in the industry.

[0033] In addition, the formula for calculating the total score P of the independent equipment quality evaluation is as follows: ; in, Scores for temperature operation units Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The corresponding weighting coefficients are i = 1, 2, 3, 4, 5; Weighting coefficients in the formula for calculating the total score P of independent equipment quality evaluation and functional unit scores Weighting coefficients All values ​​are determined using the analytic hierarchy process or the entropy weight method, and the sum of all weight coefficients within the same group is 1.

[0034] In this embodiment, calculations are based on equipment functional parameters such as measurement range, failure rate, and maintenance rate. Its core advantage lies in achieving full lifecycle management of equipment from the dimensions of "functional integrity and operational efficiency." From a functional integrity perspective, the scores for temperature / humidity measurement range provide a clear indication of whether the equipment meets the measurement range requirements of the testing scenario, preventing tests from being impossible or data from being incomplete due to insufficient range. From an operational efficiency perspective, the scores for failure rate and repair rate quantify the equipment's failure risk and repair costs, helping managers identify high-failure and high-repair-rate equipment, prioritizing maintenance or replacement, and reducing the risk of test interruptions due to sudden equipment failures. Simultaneously, The comprehensive score can serve as a basis for equipment selection and operation and maintenance resource allocation, promoting the transformation of laboratory equipment management from "passive maintenance" to "proactive prediction", further improving equipment utilization efficiency and reducing overall management costs.

[0035] II. Scores based on the temperature operation unit Temperature measurement performance unit score Functional Unit Score The weighted calculation results yield the total quality score P for the independent equipment, enabling a comprehensive and quantitative assessment of the quality status of a single piece of equipment. On one hand, compared to evaluations based on a single dimension (such as only considering temperature data), weighted calculation integrates the performance of the equipment across three core dimensions: real-time operational stability (S1), metrological accuracy compliance (S3), and functional integrity and operational efficiency (S5). This avoids misjudgments caused by the one-sidedness of a single indicator (for example, if a piece of equipment has small real-time temperature fluctuations but a high failure rate, focusing solely on S1 may overlook its operational risks, while the P score comprehensively reflects this issue). On the other hand, the quantified total score P makes the quality levels of different equipment "comparable," allowing managers to quickly identify equipment with superior quality (high P score) or inferior quality (low P score), accurately pinpointing equipment requiring special attention (e.g., prioritizing maintenance for equipment with low P scores), providing an intuitive and unified basis for the refined management of independent equipment.

[0036] Third, independent devices within the platform with related functions or consistent test scenarios are grouped into associated device groups according to preset rules. A weighted calculation is performed based on the total quality evaluation score P of each independent device within the group to obtain the quality evaluation score for the associated device block. The preset rules include: grouping associated device groups by device function type or by test scenario. Device function types include high-temperature test chambers, low-temperature test chambers, and damp heat test chambers; test scenarios include power equipment reliability test scenarios and new energy material environmental test scenarios. Dividing associated device groups according to preset rules (device function type / test scenario) and weighting the block score based on the P score of devices within the group is crucial for achieving the classification and focused management of the quality status of the "device cluster." From the perspective of equipment function type classification (such as high temperature test chamber group, damp heat test chamber group), the overall quality level of equipment with the same function can be evaluated in a targeted manner. For example, if the block score of the high temperature test chamber group is low, the common problems of this type of equipment can be quickly located (such as measurement accuracy deviation under high temperature conditions), which facilitates batch maintenance or functional optimization. From the perspective of test scenario classification (such as power equipment test scenario group, new energy material test scenario group), the adaptability of equipment clusters can be evaluated to match specific test requirements. For example, power equipment reliability tests have high requirements for equipment stability. Through the block score of this scenario, it can be directly determined whether the equipment in the group meets the quality standards of the test, avoiding test batch failure due to substandard equipment cluster quality. At the same time, block-based evaluation also upgrades the management scope from "single equipment" to "same type / same scenario equipment group", which greatly improves the efficiency and targeting of laboratory equipment management.

[0037] IV. The overall quality evaluation result of the climate environment reliability platform is obtained by weighted calculation based on the quality evaluation scores of each associated equipment block within the platform. The core advantage of obtaining the overall quality evaluation result based on the weighted calculation of the scores of each associated equipment block lies in achieving macro-level control and risk warning of the overall quality status of the climate environment reliability platform. On the one hand, the overall evaluation result can integrate the quality performance of all blocks (such as the high-temperature test chamber group, the power equipment test scenario group, etc.), intuitively reflecting the overall operational level of the platform—if the overall score is high, it indicates that the equipment in each link of the platform can meet the test requirements; if the overall score is low, it is possible to further trace which specific block's score is lagging behind, quickly locating the weak links of the platform as a whole (e.g., if the score of the new energy materials test scenario group is low, the equipment in that scenario needs to be optimized). On the other hand, the results provide data support for the overall planning of the platform. For example, when expanding the laboratory, the overall evaluation results can be used to determine whether the existing platform needs to add a certain type of equipment (e.g., if a certain functional block has a low score, the high-quality equipment of that type can be added first), or to adjust the layout of the test scenarios (e.g., if a certain test scenario block has a high score, the test capacity of that scenario can be appropriately increased). Ultimately, this ensures that the platform as a whole can stably and efficiently match various test needs and avoid the overall operation of the platform being affected by local equipment problems.

[0038] Example 2: This embodiment, based on embodiment 1, also includes a dynamic weight adjustment step: Based on the historical total quality evaluation score sequence of the independent equipment, its performance degradation slope is calculated; and its temperature operation unit score is monitored in real time. Humidity operating unit score The short-term mutation rate; when the performance degradation slope exceeds a preset degradation threshold, or the short-term mutation rate exceeds a preset mutation threshold, a weight adjustment strategy is activated: when calculating the total quality evaluation score P of the device, the score of the temperature operation unit is increased. Humidity operating unit score The weighting coefficient of the assigned unit is adjusted accordingly, reducing the weighting coefficients of other unit scores. This weighting adjustment strategy remains effective until the performance degradation slope and short-term mutation rate of the equipment return to within the normal threshold range. By introducing a dynamic weighting adjustment mechanism, the evaluation system can automatically focus on the equipment operation indicators that have experienced performance degradation or abnormalities. This elevates the evaluation from a static approach to an intelligent system with early fault warning and adaptive diagnostic capabilities, significantly improving the accuracy and foresight of the quality assessment.

[0039] The specific implementation of the dynamic weight adjustment steps is as follows: In this embodiment, the method further includes a dynamic weight adjustment step, used to automatically adjust the weights of each evaluation unit when the device experiences performance degradation or abnormal fluctuations, in order to more accurately reflect the actual operating status of the device. The specific steps are as follows: Step 1: Calculation of Performance Degradation Slope Obtain the historical total quality evaluation score sequence for the most recent K periods (e.g., 30 days) for this independent device: K is used to calculate the size of the time window for the performance degradation slope, i.e., the number of historical periods selected.

[0040] The sequence was fitted using linear regression to obtain the slope. As the slope of performance degradation: ; in, For the historical total score sequence, This represents the first in the time series. The time point or the first Each cycle, setting the attenuation threshold. , ,when At that time, it was believed that the equipment performance showed a significant downward trend.

[0041] Step 2: Short-term mutation rate calculation Real-time monitoring of the temperature operation unit score of the device over the most recent L time points (e.g., 24 hours) Humidity operating unit score sequence; Calculate the coefficient of variation (the ratio of standard deviation to mean) for each unit as the short-term mutation rate: ; Set mutation threshold , When the mutation rate exceeds this threshold, it is considered that there are abnormal fluctuations in the operating status of the equipment.

[0042] Step 3: Triggering the weight adjustment strategy The weight adjustment strategy will be activated when any of the following conditions are met: Performance degradation slope ; Short-term mutation rate .

[0043] Step 4: Weight Adjustment Method Calculate the total quality evaluation score for this equipment At that time, the weight coefficients of each unit are temporarily adjusted. : promote and Weights: ; ; Corresponding reduction Weights: ; ; ; in, The adjusted temporary weighting coefficients, To adjust the range, And all ownership weights still meet the requirements after adjustment. .

[0044] Step 5: Strategy Continuation and Recovery The weight adjustment strategy remains effective, and the system reassesses the equipment status every 24 hours. When the performance degradation slope And short-term mutation rate When that happens, restore the original weighting coefficients.

[0045] By dynamically adjusting weights, the system can more sensitively detect abnormal equipment operation and identify potential faults in advance; avoid distortion of evaluation results due to equipment performance fluctuations, and improve the accuracy and reliability of evaluations; the weight adjustment results can provide data support for equipment maintenance priorities, and achieve precise operation and maintenance.

[0046] Example 3: This embodiment provides a quality evaluation system for a climate environment reliability platform based on multi-dimensional indicators, used to implement the method of Embodiment 1, including a test equipment unit, a data acquisition unit, a data processing and storage unit, a data display unit, and a communication unit.

[0047] The test equipment unit includes N climate environment test equipment and a temperature acquisition standard for each equipment, which is used to carry out climate environment tests and output the temperature data of the equipment itself and the temperature data of the acquisition standard. In addition, the data acquisition unit includes a temperature sensor, a data acquisition card, and a parameter acquisition module, which are used to acquire real-time temperature data, temperature traceability data, and functional parameters of the test equipment, and transmit the acquired data to the data processing and storage unit. The temperature traceability data includes fluctuation, uniformity, and threshold, and the functional parameters include measurement range, failure rate, and maintenance rate. In this embodiment, the data processing and storage unit has a built-in processor and storage module. The processor is used to execute the temperature operation unit scoring in the method described in Embodiment 1. Temperature measurement performance unit score Functional Unit Score The calculation includes the total score P of independent devices, the block score of associated devices, and the overall evaluation of the platform. The storage module is used to store the collected data, calculation process data, and the final evaluation results. Furthermore, the data display unit includes a display screen and interactive visualization software, which is used to display the total score P of independent devices, the block scores of related devices, and the overall evaluation results of the platform in real time, and supports the export of evaluation result reports and the query of historical data.

[0048] It should be noted that the communication unit adopts wired Ethernet or wireless LoRa protocol to realize bidirectional data transmission between the test equipment unit, data acquisition unit, and data processing and storage unit.

[0049] In this embodiment, the test equipment unit ensures the output of basic test data, the data acquisition unit accurately acquires key data such as real-time temperature, temperature value traceability, and functional parameters, the data processing and storage unit efficiently performs multi-dimensional scoring and total score calculation and securely stores the data, and the communication unit ensures stable bidirectional data transmission between units through wired Ethernet or wireless LoRa protocol, avoiding data transmission delays or loss. On the other hand, the data display unit, with the help of a display screen and visualization software, can intuitively present the total score of independent equipment, the block score of related equipment, and the overall evaluation results of the platform. It also supports report export and historical data query, allowing managers to grasp the quality status of equipment and platform in real time and easily trace historical data. Ultimately, it realizes full-process automation and visualization from data acquisition and processing to result display, significantly improving the efficiency, accuracy, and management convenience of the climate environment reliability platform quality evaluation.

[0050] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators, characterized in that, Includes the following steps: For each individual device in the climate environment reliability platform, perform the following: A. Calculate the temperature operation unit score of the device based on real-time temperature data. ; B. Calculate the humidity operation unit score of the device based on real-time humidity data. ; C. Calculate the temperature measurement performance unit score of the device based on the temperature measurement traceability data. ; D. Calculate the humidity measurement performance unit score of the device based on humidity measurement traceability data. ; E. Based on the equipment's functional parameters, including measurement range, failure rate, and maintenance rate, calculate the functional unit score for the equipment. ; Based on the score of the temperature operation unit Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The weighted calculation results yield the total quality evaluation score for the independent equipment. ; Independent devices within the platform that are functionally related or have the same test scenarios are grouped into related device groups according to preset rules, and the overall quality evaluation score of each independent device in the group is used as the basis for the group. A weighted calculation is performed to obtain the quality evaluation score of the associated equipment block; The overall quality evaluation result of the climate environment reliability platform is obtained by weighting the quality evaluation scores of each associated device block within the platform.

2. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 1, characterized in that, In step A, the real-time temperature data includes the device's own temperature and the corresponding acquisition standard temperature, as well as the temperature operation unit score. The calculation process includes: Obtain real-time temperature data from N independent devices: the temperature of the i-th device at time j. , N is the total number of independent devices. T represents the total number of time points for temperature data acquisition, and the temperature of the data acquisition standard corresponding to the i-th device at the j-th time point. ; Calculate the temperature difference between the i-th device and its corresponding data acquisition standard at time j. Classified by equipment type , Y is the total number of equipment types, determining the number of categories. Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical temperature difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for the temperature deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. Normalized to [0, 100], the formula is: ； in, Scores for the temperature operation unit of the equipment The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance ; Specified threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and .

3. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 2, characterized in that, In step B, the humidity operation unit score of the device is calculated based on real-time humidity data. Real-time humidity data includes the device's own humidity and the humidity of the corresponding acquisition standard, as well as the humidity operation unit score. The calculation process includes: Obtain real-time humidity data from M independent devices: humidity of device u at time v. Let u∈{1,2,…,N}, where N is the total number of independent devices, and v∈{1,2,…,Q}, where Q is the total number of humidity data collection time points, and the humidity of the data collection standard corresponding to the u-th device at the v-th time point. ; Calculate the humidity difference between the u-th device and its corresponding data acquisition standard at time v. Classified by equipment type , Determine the first Specified threshold for class devices And combined with risk coefficient Calculate the first Critical threshold for similar devices ; Statistical humidity difference exist out-of-tolerance range Critical interval The counts of the qualified intervals are denoted as follows: And satisfy , This represents the total number of statistical counts for humidity deviation data; Set boundary scores, when hour ,when hour ,when hour The principle of critical reinforcement, qualified weakening, and out-of-tolerance penalty is adopted, and the calculation is performed using an exponential weight decay polynomial formula. and normalized to The formula is: ， in, Scores for the humidity operation unit of the equipment. The benchmark scores for out-of-tolerance, critical, and acceptable ranges. The excess attenuation coefficient, , For critical nonlinear weights, , Secondary penalty for exceeding the tolerance ; Wherein, the specified threshold According to the The national / industry standards, technical specifications, or historical test verification data corresponding to this type of equipment are determined, and the boundary scores meet the requirements. , , ,and .

4. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 1, characterized in that, In step C, the temperature measurement traceability data includes temperature fluctuation, temperature uniformity and corresponding thresholds, and the temperature measurement performance unit score. The calculation process includes: Obtain traceability data of temperature values ​​of an independent device at A temperature points, including the temperature fluctuation at the i-th temperature point. Temperature fluctuation threshold and the temperature uniformity at the i-th temperature point Temperature uniformity threshold ; Set the boundary scores for each item. In the temperature fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the temperature uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the temperature fluctuation score for the i-th temperature point. Score for temperature uniformity The formulas are as follows: ； ； in, This is the temperature fluctuation attenuation coefficient. , This is the temperature uniformity attenuation coefficient. ; calculate and normalized to The formula is: , The total number of temperature points covered by the traceability data collection of temperature values ​​from independent devices; D. Calculate the humidity measurement performance unit score of the device based on humidity measurement traceability data. The humidity measurement traceability data includes humidity fluctuation, humidity uniformity and corresponding thresholds, and humidity measurement performance unit score. The calculation process includes: Obtain source data on humidity values ​​of independent devices at B humidity points, including the humidity fluctuation at the i-th humidity point. Humidity fluctuation threshold and the humidity uniformity at the i-th humidity point Humidity uniformity threshold ; Set the boundary scores for each item. In the humidity fluctuation item, when... Time corresponding score ,when Time corresponding score ,when Time corresponding score In the humidity uniformity sub-item, when Time corresponding score ,when Time corresponding score ,when Time corresponding score ; Calculate the humidity fluctuation score for the i-th humidity point. Sub-scores for humidity uniformity The formulas are as follows: ； ； in, This is the humidity fluctuation attenuation coefficient. , The humidity uniformity attenuation coefficient is... ; calculate and normalized to The formula is: ; The total number of humidity points covered by the traceability data collection of humidity values ​​from independent devices; Humidity fluctuation sub-item boundary score meets , , and The boundary score for the humidity uniformity component meets the requirements. , , and .

5. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 4, characterized in that, Temperature fluctuation component boundary score satisfies , , and The boundary score for the temperature uniformity component satisfies... , , and .

6. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 1, characterized in that, Functional Unit Score The calculation process includes: Calculate the score for the temperature measurement range item. Obtain the actual temperature measurement range of the equipment. and the initial value of the preset temperature measurement range Based on temperature decay penalty factor , The formula is: ； Calculate the score for the humidity measurement range item. Obtain the actual humidity measurement range of the device. and the initial value of the preset humidity measurement range Based on humidity attenuation penalty factor , The formula is: ， Calculate the failure rate score Obtain the actual equipment failure rate and preset critical failure rate The formula is: ， Calculate the maintenance rate score Obtain the actual equipment repair rate and preset critical maintenance rate Based on the severity coefficient , The formula is: ， Based on weighting coefficients , 2, 3, 4 Corresponding to Calculate the functional unit score The formula is: .

7. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 6, characterized in that, Critical failure rate Based on historical failure statistics of the equipment over the past 3-5 years and the average failure rate of similar equipment in the industry, the critical maintenance rate is determined. The determination is based on the equipment's historical maintenance statistics over the past 3-5 years and the average maintenance rate of similar equipment in the industry.

8. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 7, characterized in that, Total score for independent equipment quality evaluation The calculation formula is: ， in, Scores for temperature operation units Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The corresponding weighting coefficients, 2, 3, 4, 5; Weighting coefficients in the formula for calculating the total score P of independent equipment quality evaluation and functional unit scores Weighting coefficients All values ​​are determined using the analytic hierarchy process or the entropy weight method, and the sum of all weight coefficients within the same group is 1.

9. The method for evaluating the quality of a climate and environmental reliability platform based on multi-dimensional indicators according to claim 1, characterized in that, The preset rules include: dividing associated equipment groups according to equipment function type or test scenario. Equipment function type includes high temperature test chamber, low temperature test chamber, and damp heat test chamber. Test scenario includes power equipment reliability test scenario and new energy material environmental test scenario. The method also includes a dynamic weight adjustment step: Based on the historical total quality evaluation score sequence of the independent equipment, its performance degradation slope is calculated; and its temperature operation unit score is monitored in real time. Humidity operating unit score The short-term mutation rate; When the performance degradation slope exceeds a preset degradation threshold, or the short-term mutation rate exceeds a preset mutation threshold, a weight adjustment strategy is activated: when calculating the total quality evaluation score P of the device, the score of the temperature operation unit is increased. Humidity operating unit score The weighting coefficient of the score is adjusted accordingly, thereby reducing the weighting coefficient of the scores in other units; The weight adjustment strategy remains effective until the performance degradation slope and short-term mutation rate of the device return to the normal threshold range.

10. A quality evaluation system for a climate and environmental reliability platform based on multi-dimensional indicators, used to implement the method described in any one of claims 1-9, characterized in that, include: The test equipment unit includes N climate environment test devices and a temperature acquisition standard for each device, which is used to carry out climate environment tests and output the temperature data of the device itself and the temperature data of the acquisition standard. The data acquisition unit includes a temperature sensor, a data acquisition card, and a parameter acquisition module. It is used to acquire real-time temperature data, traceability data, and functional parameters of the test equipment, and transmit the acquired data to the data processing and storage unit. The traceability data includes fluctuation, uniformity, and threshold, and the functional parameters include measurement range, failure rate, and maintenance rate. The data processing and storage unit includes a built-in processor and storage module, wherein the processor is used to execute the temperature operation unit scoring in the method according to any one of claims 1-9. Humidity operation unit score Temperature measurement performance unit score Humidity measurement performance unit score Functional Unit Score The calculation includes the total score P of independent devices, the block score of associated devices, and the overall evaluation of the platform. The storage module is used to store the collected data, calculation process data, and the final evaluation results. The data display unit includes a display screen and interactive visualization software, which is used to display the total score P of independent devices, the block score of related devices, and the overall evaluation results of the platform in real time. It supports exporting evaluation result reports and querying historical data. The communication unit uses wired Ethernet or wireless LoRa protocol for bidirectional data transmission between the test equipment unit, data acquisition unit, and data processing and storage unit.

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