Power equipment quality evaluation method and device, computer device and storage medium

Abstract

The application relates to a power equipment quality evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring power grid material sampling data corresponding to power equipment in a target time period; based on the power grid material sampling data of the power equipment, evaluating index values of a plurality of quality indexes corresponding to a power grid quality dimension, the power grid quality dimension comprising at least one of a quality level dimension, a time stability dimension and a cost efficiency dimension; positively calibrating the index values of the quality indexes to obtain calibrated index values of each quality index; based on the calibrated index values of each quality index corresponding to the power equipment and the index weights of each quality index, calculating a sampling quality score of the power equipment; and based on the sampling quality score, evaluating the power equipment to obtain a quality evaluation result of the power equipment. The method can evaluate the quality of the power equipment in different quality evaluation dimensions.

Description

Technical Field

[0001] This application relates to the field of power grid material quality control technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for evaluating the quality of power equipment. Background Technology

[0002] In the process of quality control of power grid materials, random inspection of power grid materials is an important basis for evaluating the quality level of power equipment.

[0003] Currently, the quality evaluation of various power equipment in power grid materials typically relies on a single indicator such as pass rate, number of non-conformities, or number of defects. However, a single indicator is insufficient to reflect the differences among various power equipment. For power equipment with only a small sample size, occasional pass rates may lead to overestimation, resulting in unsound quality evaluation results.

[0004] Therefore, how to evaluate various power equipment in the power grid from different quality evaluation dimensions is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a power equipment quality evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can evaluate the quality of power equipment from different quality evaluation dimensions, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for evaluating the quality of power equipment, including:

[0007] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0008] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0009] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0010] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0011] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0012] In one embodiment, when the power grid quality indicators include a quality level dimension, the multiple quality indicators corresponding to the quality level dimension include a sampling pass rate indicator, a confidence-corrected pass rate indicator, and a quality recovery indicator after non-conformity; based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including:

[0013] Based on the total number of samples and the total number of qualified samples in the power grid material sampling inspection data, calculate the index value corresponding to the power equipment sampling pass rate index.

[0014] Based on the total number of samples and the total number of non-conforming samples in the power grid material sampling inspection data, the confidence correction pass rate index of power equipment is evaluated using a confidence correction function.

[0015] Based on the sampling results of each batch in the power grid material sampling data, evaluate the index value corresponding to the quality recovery index after the power equipment fails to meet the standards.

[0016] In one embodiment, when the power grid quality indicators include a time stability dimension, the multiple quality indicators corresponding to the time stability dimension include a pass rate fluctuation indicator, a time-corrected pass rate indicator, and a quality trend indicator; based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including:

[0017] Based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple sub-data of power grid material sampling within multiple benchmark time periods.

[0018] For each benchmark time period, the benchmark pass rate is calculated based on the power grid material sampling sub-data in the benchmark time period. Based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined.

[0019] Based on the index value corresponding to the random inspection pass rate index, calculate the index value corresponding to the timeliness-corrected pass rate index.

[0020] Obtain the target time threshold, and take the power grid material sampling data whose sampling time is after the target time threshold as the target sampling data. Based on the target sampling data and the other sampling data in the power grid material sampling data excluding the target sampling data, evaluate the index value corresponding to the quality trend index.

[0021] In one embodiment, when the power grid quality indicators include a cost-efficiency dimension, the multiple quality indicators corresponding to the power grid quality indicators include a sampling inspection cost-to-investment ratio indicator and a problem discovery cost-efficiency indicator; based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including:

[0022] Obtain the cost efficiency evaluation function;

[0023] Based on the sampling cost in the power grid material sampling data, the indicator value corresponding to the sampling cost input ratio is evaluated through the cost efficiency evaluation function.

[0024] Based on the percentage of problems in the power grid material sampling inspection data, the cost efficiency evaluation function is used to assess the corresponding indicator values ​​of the cost efficiency indicators for the problems.

[0025] In one embodiment, the index values ​​of each quality indicator are positively calibrated to obtain a calibrated index value for each quality indicator, including:

[0026] Construct a quality indicator matrix based on the indicator values ​​corresponding to each quality indicator;

[0027] The quality index matrix is ​​positiveized to obtain the positiveized quality index matrix.

[0028] Based on the positively processed quality index matrix, a calibration quality index matrix is ​​obtained through a calibration function. The calibration quality index matrix includes the calibration index values ​​of each quality index.

[0029] In one embodiment, the power equipment is evaluated based on the quality score of the random inspection to obtain the quality evaluation result of the power equipment, including:

[0030] Based on the quality score of the random inspection, the equipment status of the power equipment is evaluated by mapping the quality score to the equipment status.

[0031] Based on the equipment status, generate quality evaluation results for the power equipment.

[0032] Secondly, this application also provides a power equipment quality evaluation device, comprising:

[0033] The acquisition module is used to acquire the power grid material sampling inspection data corresponding to the power equipment within the target time period;

[0034] The evaluation module is used to evaluate the index values ​​of multiple quality indicators corresponding to the power grid quality dimensions based on the power grid material sampling inspection data of power equipment. The power grid quality dimensions include at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0035] The calibration module is used to perform positive calibration on the index values ​​of each quality indicator to obtain the calibration index value of each quality indicator.

[0036] The calculation module is used to calculate the sampling quality score of the power equipment based on the calibration index values ​​of each quality index corresponding to the power equipment and the index weight of each quality index.

[0037] The evaluation module is used to evaluate power equipment based on the quality scores of random inspections, and obtain the quality evaluation results of the power equipment.

[0038] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0039] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0040] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0041] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0042] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0043] Based on the quality scores from random inspections, the power equipment is evaluated to obtain a quality evaluation result. Fourthly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps:

[0044] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0045] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0046] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0047] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0048] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0049] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0050] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0051] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0052] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0053] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0054] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0055] The aforementioned power equipment quality evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product first acquire grid material sampling inspection data corresponding to the power equipment within a target time period. Based on this data, the values ​​of multiple quality indicators corresponding to the grid quality dimensions are evaluated. These grid quality dimensions include at least one of quality level, time stability, and cost efficiency. This process allows for evaluation of the grid material sampling inspection data across quality level, time stability, and cost efficiency dimensions, providing a more comprehensive reflection of the power equipment's quality status. Secondly, by positively calibrating the values ​​of each quality indicator to obtain a calibrated value, values ​​from different performance directions can participate in the comprehensive evaluation and be compared on the same scale. Finally, based on the calibrated values ​​of each quality indicator and their respective weights, a sampling inspection quality score is calculated for the power equipment. Based on this score, the power equipment is evaluated to obtain the final quality evaluation result. The resulting power equipment quality evaluation not only reflects the quality risks of the power equipment but also serves to optimize the allocation of sampling inspection resources. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1This is a diagram illustrating the application environment of a power equipment quality evaluation method in one embodiment.

[0058] Figure 2 This is a flowchart illustrating a power equipment quality evaluation method in one embodiment;

[0059] Figure 3 This is a schematic diagram of multiple quality indicators corresponding to the power grid quality dimension in one embodiment;

[0060] Figure 4 This is a flowchart illustrating the evaluation steps for the indicator value corresponding to the pass rate fluctuation indicator in one embodiment.

[0061] Figure 5 This is a flowchart illustrating the power equipment quality evaluation method in another embodiment;

[0062] Figure 6 This is a structural block diagram of a power equipment quality evaluation device in one embodiment;

[0063] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0066] The power equipment quality evaluation method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Specifically, terminal 102 or server 104 executes a power equipment quality evaluation method, which includes: acquiring grid material sampling inspection data corresponding to power equipment within a target time period; evaluating the index values ​​of multiple quality indicators corresponding to grid quality dimensions based on the grid material sampling inspection data of power equipment, wherein the grid quality dimensions include at least one of quality level dimension, time stability dimension, and cost efficiency dimension; positively calibrating the index values ​​of each quality indicator to obtain a calibrated index value for each quality indicator; calculating the sampling inspection quality score of the power equipment based on the calibrated index values ​​of each quality indicator corresponding to the power equipment and the respective index weights of each quality indicator; and evaluating the power equipment based on the sampling inspection quality score to obtain the power equipment quality evaluation result.

[0067] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0068] In one exemplary embodiment, such as Figure 2 As shown, a method for evaluating the quality of power equipment is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 210. Wherein:

[0069] Step 202: Obtain the power grid material sampling inspection data corresponding to the power equipment within the target time period.

[0070] The data for power grid material sampling inspections should include at least the sampling date, sampling batch, total number of samples, total number of qualified samples, total number of unqualified samples, sampling cost, and sampling results. Power equipment includes various types of electronic equipment such as transformers, cables, switchgear, circuit breakers, surge arresters, instrument transformers, insulators, and fittings. The target time period is several years between the evaluation benchmark month, such as the past three years, the past five years, or other preset timeframes. The evaluation benchmark month refers to the current statistical cutoff month used for quality evaluation and can be dynamically updated over time.

[0071] For example, power grid material sampling data that lacks sampling dates, sampling quantities, or sampling results can be removed or marked as data to be verified.

[0072] For example, an evaluation benchmark month is determined, and based on the evaluation benchmark month, a target time period is determined, and the power grid material sampling inspection data corresponding to the power equipment within the target time period is obtained.

[0073] Step 204: Based on the power grid material sampling inspection data of power equipment, evaluate the index values ​​of multiple quality indicators corresponding to the power grid quality dimension. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0074] Among them, such as Figure 3 As shown, the quality level dimension includes multiple quality indicators that reflect the overall quality level, reliable pass rate, and continued passability status of power equipment after non-compliance. These include the sampling pass rate indicator, the confidence-corrected pass rate indicator, and the quality recovery indicator after non-compliance. The time stability dimension includes multiple quality indicators that reflect the time fluctuation of power equipment's quality performance, the timeliness of historical data, and recent quality trends. These include the pass rate fluctuation indicator, the timeliness-corrected pass rate indicator, and the quality trend indicator. The cost efficiency dimension includes multiple quality indicators that reflect the performance of power equipment in terms of sampling resource consumption and the efficiency of quality problem detection. These include the sampling cost input ratio indicator and the problem detection cost efficiency indicator.

[0075] For example, based on the quality evaluation requirements of power equipment, multiple power grid quality dimensions and multiple quality indicators corresponding to each power grid quality dimension are determined. These quality evaluation requirements may include maintaining the clarity of the meaning of quality indicators, focusing on the interpretability of the process for calculating the indicator values, considering the cost and efficiency of the sampling inspection process, and assessing the quality level of power equipment.

[0076] For example, based on the data from the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated respectively.

[0077] Step 206: Perform positive calibration on the index values ​​of each quality indicator to obtain the calibration index value of each quality indicator.

[0078] Among them, the calibration index value is the value after positive calibration of the index value.

[0079] For example, the index values ​​of each quality indicator are positiveized and calibrated to obtain the calibration index value of each quality indicator.

[0080] Step 208: Calculate the sampling inspection quality score of the power equipment based on the calibration index values ​​of each quality index corresponding to the power equipment and the index weight of each quality index.

[0081] The weights of each quality indicator are pre-set.

[0082] Optionally, the weights of each quality indicator can be determined using the entropy weight method. The entropy weight method is an objective weighting method based on the dispersion of indicator data. Its principle is that the greater the difference between different power equipment, the more distinguishing information the indicator provides; therefore, its entropy value should be smaller, its difference coefficient larger, and its contribution to the sampling quality score should be higher. Conversely, if the difference between a quality indicator and all power equipment is small, it indicates that the indicator's distinguishing ability is weak, and its weight should be correspondingly reduced. Compared to methods that rely entirely on manual experience for weighting, the entropy weight method can utilize the distribution characteristics of historical sampling data to determine weights, reducing subjectivity.

[0083] Furthermore, the calibration index values ​​of each quality indicator are shifted and made positive:

[0084]

[0085] In the formula, It is the minimum value among the j-th calibration index values. To prevent zero values ​​from affecting small positive numbers in logarithmic calculations, it is preferable to take... This translation process does not change the ranking relationship between power equipment under the same index; it only converts the standardized results into non-negative data to meet the requirements of the entropy weight method calculation.

[0086] Specifically, first, calculate the weight of the i-th equipment category on the j-th quality indicator, using the following formula: Then, calculate the information entropy corresponding to the j-th quality indicator, using the following formula: Where n is the number of equipment categories participating in the evaluation. When pij is 0, it is stipulated that... Take 0, or via Make corrections. Then calculate the difference coefficient of the j-th quality indicator, using the following formula: Finally, the entropy weight of the j-th quality index is calculated using the following formula: ,in, Let be the weight of the j-th quality indicator. If the difference coefficients of all indicators are 0, it indicates that there is no significant distinction between the evaluation objects, and equal weights or reserve weights set by experts can be used for processing. Through the above method, the weight of each quality indicator can be objectively determined. The larger the weight, the stronger the role of the calibration indicator value corresponding to that quality indicator in distinguishing the quality status and resource allocation value of different power equipment in the power grid material sampling inspection data.

[0087] For example, based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the respective index weights of each quality indicator, the total quality score of the power equipment is calculated. The total quality score is then normalized to obtain the sampling inspection quality score. Specifically, the total quality score is:

[0088]

[0089] in, To determine the quality score of the random inspection, Let j be the weight of the quality indicator. This refers to the calibration index value corresponding to this power equipment.

[0090] Furthermore, the quality score for the random inspection was:

[0091]

[0092] In the formula, Let be the normalized comprehensive evaluation score for the i-th equipment category. and These are the maximum and minimum values ​​of the total quality score for all electrical equipment, respectively. equal If the sampling quality scores of the equipment in each store are consistent, all electrical equipment can be marked as the same level or enter into manual review.

[0093] Step 210: Based on the quality score of the random inspection, evaluate the power equipment to obtain the quality evaluation result of the power equipment.

[0094] The quality evaluation results include the equipment status of the power equipment and corresponding recommendations for the allocation of sampling inspection resources.

[0095] For example, based on the quality score of the random inspection, the power equipment is evaluated to obtain the equipment status of the power equipment. Based on the equipment status of the power equipment, corresponding random inspection resource configuration suggestions are generated. Based on the random inspection resource configuration suggestions and the equipment status, the quality evaluation result of the power equipment is generated.

[0096] The aforementioned power equipment quality evaluation method first involves acquiring grid material sampling inspection data corresponding to the power equipment within a target time period. Based on this data, the values ​​of multiple quality indicators corresponding to the grid quality dimensions are evaluated. These grid quality dimensions include at least one of the following: quality level, time stability, and cost efficiency. This process allows for evaluation of the grid material sampling inspection data across quality level, time stability, and cost efficiency dimensions, providing a more comprehensive reflection of the power equipment's quality status. Secondly, by positively calibrating the values ​​of each quality indicator to obtain a calibrated value, values ​​from different strengths and weaknesses can participate in the comprehensive evaluation and be compared on the same scale. Finally, based on the calibrated values ​​of each quality indicator and their respective weights, the sampling inspection quality score of the power equipment is calculated. Based on this score, the power equipment is evaluated to obtain the final quality evaluation result. The resulting power equipment quality evaluation not only reflects the quality risks of the power equipment but also serves to optimize the allocation of sampling inspection resources.

[0097] In an exemplary embodiment, when the power grid quality indicators include a quality level dimension, the multiple quality indicators corresponding to the quality level dimension include a sampling pass rate indicator, a confidence-corrected pass rate indicator, and a quality recovery index after non-compliance. Based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including: calculating the indicator value corresponding to the sampling pass rate indicator of power equipment based on the total number of sampling samples and the total number of qualified samples in the power grid material sampling inspection data; evaluating the indicator value corresponding to the confidence-corrected pass rate indicator of power equipment based on the total number of sampling samples and the total number of non-qualified samples in the power grid material sampling inspection data using a confidence correction function; and evaluating the indicator value corresponding to the quality recovery index after non-compliance of power equipment based on the sampling results of each sampling batch in the power grid material sampling inspection data.

[0098] The sampling pass rate index is used to characterize the overall pass rate of power equipment within a target time period. The confidence-corrected pass rate index is used to characterize a conservative estimate of the true pass rate of power equipment at a given confidence level, and is also used to evaluate the credible pass rate of equipment categories considering sample size differences. The post-nonconformity quality recovery index reflects the quality recovery status and continued passability evidence of power equipment after a nonconformity. The larger the index value of the post-nonconformity quality recovery index, the more passable sampling records the power equipment has accumulated since the last nonconformity, and the more recent sampling data is still available, indicating a better recent quality recovery status. The confidence correction function is a function of the index value corresponding to the confidence-corrected pass rate index changing with the sampling nonconformity rate, and is used to evaluate the confidence-corrected pass rate index of power equipment.

[0099] For example, the ratio between the total number of samples inspected and the total number of qualified samples in the power grid material sampling inspection data is calculated to obtain the index value corresponding to the power equipment sampling inspection pass rate index. The specific calculation formula is as follows:

[0100]

[0101] in, The pass rate of the random inspection is an indicator. This represents the total number of samples collected. This represents the total number of qualified samples.

[0102] For example, based on the total number of samples and the total number of non-conforming samples in the power grid material sampling inspection data, the sampling failure rate is calculated. Then, using a confidence correction function, the sampling failure rate is corrected based on the upper confidence limit to evaluate the index value corresponding to the confidence-corrected pass rate of power equipment. Specifically, the formula for calculating the sampling failure rate is:

[0103]

[0104] in, The non-compliance rate of the random inspection This represents the total number of non-compliant samples. This represents the total number of samples collected.

[0105] Furthermore, the confidence correction function is:

[0106]

[0107] in, The non-compliance rate of the random inspection; This represents the standard normal distribution quantile corresponding to the confidence level. For example, when the confidence level is 95%, z is preferably 1.96. As a confidence-corrected pass rate indicator, for power equipment with a relatively small sample size, even if the random inspection pass rate is high, the confidence-corrected pass rate indicator calculated by the confidence correction function will be appropriately reduced to avoid being overestimated.

[0108] For example, based on the sampling results of each sampling batch in the power grid material sampling inspection data, the sampling results corresponding to the target cumulative qualified sampling batches up to the evaluation benchmark month after the most recent non-conformance record of the power equipment are determined, as well as the number of months between the most recent sampling date and the evaluation benchmark month; based on the number of months between the sampling results and the target cumulative qualified sampling batches, the index value corresponding to the quality recovery index indicator after non-conformance of the power equipment is evaluated. The specific calculation formula is as follows:

[0109]

[0110] in, The interval is the number of months; This refers to the quality recovery index after non-compliance. This refers to the sampling results corresponding to the target cumulative number of qualified sampling batches. When the sampling result corresponding to the target cumulative number of qualified sampling batches is unqualified... If the value is 0, and no non-conforming records are found within the target time period, the cumulative number of qualified batches sampled within the target time period can be used as [the target time period]. The power equipment will be marked as continuously qualified.

[0111] In this embodiment, the process of calculating the pass rate index based on data from multiple sampled batches within the target time period can mitigate the impact of randomness in single-batch sampling, serving as a fundamental evaluation indicator for the quality level of power equipment. Furthermore, by calculating the confidence-corrected pass rate using a confidence correction function, the estimated true pass rate can be reasonably reduced when the sample size is small, resulting in a more conservative and reliable evaluation result, avoiding quality misjudgments caused by high pass rates in small samples. In addition, the quality recovery index after non-compliance can effectively quantify the quality recovery capability and current quality status of equipment after non-compliance, further improving the evaluation coverage of the quality level dimension.

[0112] In an exemplary embodiment, when the power grid quality indicators include a time stability dimension, the multiple quality indicators corresponding to the time stability dimension include a pass rate fluctuation indicator, a time-corrected pass rate indicator, and a quality trend indicator. Based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including: dividing the power grid material sampling inspection data into multiple benchmark time periods based on the sampling time in the power grid material sampling inspection data; for each benchmark time period, calculating the benchmark pass rate based on the power grid material sampling inspection sub-data in the benchmark time period; determining the indicator value corresponding to the pass rate fluctuation indicator based on the benchmark pass rate of each benchmark time period; calculating the indicator value corresponding to the time-corrected pass rate indicator based on the indicator value corresponding to the sampling pass rate indicator; obtaining a target time threshold; using the power grid material sampling inspection data whose sampling time is after the target time threshold as target sampling data; and evaluating the indicator value corresponding to the quality trend indicator based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data.

[0113] The pass rate fluctuation index reflects the degree of fluctuation in the pass rate of this equipment category in recent years. The smaller the value of the pass rate fluctuation index, the more stable the quality performance of the power equipment. The larger the value of the pass rate fluctuation index, the more significant the difference in pass rate between different years or periods, indicating poorer quality stability of the power equipment. The time-adjusted pass rate index reflects the effectiveness of historical pass rates in judging current quality. For power equipment with the same historical pass rate, the closer the last sampling inspection is to the evaluation benchmark month, the higher the time-adjusted pass rate index; the further back the last sampling inspection was, the lower the time-adjusted pass rate index. The quality trend index reflects the recent quality trend of the equipment category relative to the previous quality. When the quality trend index is greater than 0, it indicates that the recent pass rate is higher than the previous pass rate, and the quality of the power equipment is improving. When the quality trend index is equal to 0, it indicates that the recent quality is basically stable. When the quality trend index is less than 0, it indicates that the recent pass rate is lower than the previous pass rate, and the equipment quality is deteriorating. The larger the absolute value of the quality trend index, the more obvious the deterioration. The benchmark time period can be an annual period or a statistical period.

[0114] For example, such as Figure 4As shown, based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple benchmark time periods to obtain sub-data for power grid material sampling within each benchmark time period. For each benchmark time period, the benchmark pass rate is calculated based on the sub-data for power grid material sampling within that benchmark time period. Based on the benchmark pass rate of each benchmark time period, the number of benchmark time periods with valid sampling data and the average of each benchmark pass rate are calculated. Based on the number of benchmark time periods with valid sampling data and the average of each benchmark pass rate, the index value corresponding to the pass rate fluctuation index is determined, and the calculation formula is as follows:

[0115]

[0116] in, This refers to the number of baseline time periods for which valid sampling data for power equipment is available within the target time period. Let be the benchmark pass rate for the j-th benchmark time period. This represents the average of the various benchmark pass rates for power equipment. This is an indicator of pass rate fluctuation.

[0117] Optionally, in In this case, it indicates that the historical data period for the power equipment is insufficient. The power equipment can be marked as having insufficient data and excluded from the calculation of the pass rate fluctuation index. In this case, calculate the index value corresponding to the pass rate fluctuation index.

[0118] For example, based on the requirements of random inspection management, the rate of quality change of power equipment, the validity period of historical random inspection data, and the backtesting results of historical samples, a time decay coefficient is determined; based on the index value corresponding to the random inspection pass rate index and the time decay coefficient, the index value corresponding to the time-adjusted pass rate index is calculated. The specific calculation formula is as follows:

[0119]

[0120] in, To adjust the pass rate indicator for timeliness, This refers to the number of months between the last random inspection date of the power equipment and the evaluation benchmark month. The time decay coefficient is preferably... The value is 0.02. This value is not a fixed empirical value, but rather a result that allows the historical pass rate to decrease gently over time. When the time decay period is 12 months, the time decay factor is approximately 0.786; when It is approximately 0.619 when the duration is 24 months; when The value is approximately 0.487 when the timeframe is 36 months. Based on this, the sampling results from one year ago still retain high reference value, while the sampling results from three years ago retain about half of the reference weight. This meets the requirement of power grid material sampling data that "recent data can better reflect the current quality status, while long-term data still has reference significance but with reduced weight."

[0121] Optionally, the time decay coefficient can be corrected by backtesting historical sampling data, expert review, or the quality change cycle of different equipment categories.

[0122] For example, such as Figure 4 As shown, based on the sampling time, the number of valid years m of power equipment is determined. In this case, it indicates that the historical data period for the power equipment is insufficient, and the power equipment can be marked as having insufficient data. In this case, based on the number of valid years *m* and a preset recent proportion, the number of recent years is determined. Based on this number of recent years, the target time threshold is then determined. The number of recent years is:

[0123]

[0124] Where m represents the number of valid years for the power equipment. The preset recent ratio, The preferred value is 0.4. This value ensures that the recent window reflects recent quality changes without being affected by occasional fluctuations in a single year due to an excessively short window. For example, when a certain equipment category has five consecutive years of valid sampling data, If we take 0.4, then k is 2. The specific value can be adjusted adaptively based on the power grid company's sampling cycle, the rate of change in the quality of power equipment, and the results of historical data backtesting.

[0125] For example, power grid material sampling data whose sampling time is after the target time threshold is used as target sampling data. The pass rate of the target sampling data and the pass rate of the remaining sampling data (excluding the target data) are calculated. Based on the pass rates of the target data and the remaining sampling data, the corresponding index value of the quality trend indicator is evaluated. The specific calculation formula is as follows:

[0126]

[0127] in, As a quality trend indicator, This represents the pass rate for the remaining sampled data. The pass rate of the sampling inspection corresponding to the target sampling data.

[0128] In this embodiment, the pass rate fluctuation index can effectively quantify the degree of pass rate fluctuation between different statistical periods, objectively reflecting the time stability of power equipment quality; the time-corrected pass rate index obtained by correcting the original sampling pass rate with a time decay coefficient fully conforms to the evaluation logic that "recent data can better reflect the current quality", which can avoid over-reliance on outdated historical pass records and improve the timeliness of current quality evaluation; and the quality trend index can identify the equipment categories with recent quality decline from a time dimension, providing a basis for stricter sampling and key supervision.

[0129] In an exemplary embodiment, when the power grid quality indicators include a cost efficiency dimension, the multiple quality indicators corresponding to the power grid quality indicators include a sampling cost input ratio indicator and a problem discovery cost efficiency indicator. Based on the power grid material sampling inspection data of power equipment, the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension are evaluated, including: obtaining a cost efficiency evaluation function; evaluating the indicator value corresponding to the sampling cost input ratio indicator based on the sampling cost in the power grid material sampling inspection data using the cost efficiency evaluation function; and evaluating the indicator value corresponding to the problem discovery cost efficiency indicator based on the problem proportion in the power grid material sampling inspection data using the cost efficiency evaluation function.

[0130] The sampling cost input ratio index reflects the proportion of cost consumption of power equipment in all sampling resources. A higher sampling cost input ratio index indicates that the power equipment has consumed more testing fees and resources. The problem discovery cost efficiency index measures the relative efficiency of quality problems discovered by the sampling cost of power equipment. When the problem discovery cost efficiency index is greater than 1, it indicates that the problem discovery ratio of the power equipment is higher than the cost input ratio, and the sampling input has a high problem discovery efficiency; when the problem discovery cost efficiency index is less than 1, it indicates that the problem discovery ratio of the power equipment is lower than the cost input ratio, and the sampling cost efficiency is relatively low. The cost efficiency evaluation function includes the problem discovery cost efficiency index calculation function and the sampling cost input ratio index calculation function. The sampling cost input ratio index calculation function is a function that shows how the index value of the sampling cost input ratio index changes with the sampling cost, and the problem discovery cost efficiency index calculation function is a function that shows how the index value of the problem discovery cost efficiency index changes with the problem proportion.

[0131] For example, based on the sampling cost in the power grid material sampling data, the indicator value corresponding to the sampling cost input ratio index is calculated using the sampling cost input ratio index calculation function. Specifically, the sampling cost input ratio index calculation function is as follows:

[0132]

[0133] in, Let $\frac{i}{i}$ be the sampling cost for the i-th power device. The total cost of sampling inspection for all electrical equipment. This is an indicator representing the percentage of cost invested in random inspections.

[0134] For example, based on the proportion of problems in the power grid material sampling inspection data, the corresponding index value of the problem discovery cost efficiency index is evaluated using a problem discovery cost efficiency index calculation function. Specifically, the problem discovery cost efficiency index calculation function is:

[0135]

[0136] in, As the percentage of problems, , The number of defective electrical equipment. This represents the total number of non-conforming items across all electrical equipment. This represents the percentage of the cost invested in the random inspection of the power equipment. .

[0137] In this embodiment, by calculating multiple quality indicators corresponding to the cost-efficiency dimension, high-cost power equipment for random inspection can be identified, providing a basis for random inspection budget management and testing resource allocation; at the same time, it can support the optimization of random inspection plans, helping to prioritize the allocation of random inspection resources to power equipment with higher problem detection efficiency or more prominent quality risks.

[0138] In one embodiment, the index values ​​of each quality indicator are positively calibrated to obtain the calibration index value of each quality indicator, including: constructing a quality indicator matrix based on the index values ​​corresponding to each quality indicator; performing positive transformation on the quality indicator matrix to obtain a positively transformed quality indicator matrix; and obtaining a calibration quality indicator matrix based on the positively transformed quality indicator matrix through a calibration function, wherein the calibration quality indicator matrix includes the calibration index values ​​of each quality indicator.

[0139] For example, a quality indicator matrix is ​​constructed based on the indicator values ​​corresponding to each quality indicator. The quality indicator matrix is ​​as follows:

[0140]

[0141] Where i represents the i-th power equipment and j represents the j-th quality indicator.

[0142] Optionally, since different quality indicators have different directions of advantage and disadvantage, in order to ensure that all quality indicators have a consistent evaluation direction, it is necessary to positively transform the inverse quality indicators in the quality indicator matrix. This can be achieved by using the negative value method to positively transform the inverse quality indicators in the quality indicator matrix, resulting in a positively transformed quality indicator matrix. The negative value method is a linear transformation method, which is characterized by maintaining the original data's sorting relationship and relative differences, only reversing the direction of the quality indicators, thus avoiding the abnormal amplification of data close to zero caused by the reciprocal method.

[0143] For example, the sampling pass rate, confidence-corrected pass rate, quality recovery after non-conformity, timeliness-corrected pass rate, quality trend, and problem discovery cost efficiency indicators are positive quality indicators. The larger the corresponding indicator value, the better the quality status, data timeliness, quality improvement trend, or sampling efficiency under the corresponding power grid quality dimension. The sampling cost input ratio and pass rate fluctuation indicators are negative quality indicators. A larger pass rate fluctuation indicator indicates greater fluctuation in the sampling pass rate, and a larger sampling cost input ratio indicator indicates higher sampling costs. Therefore, for the negative quality indicators in the positive transformation of the quality indicator matrix, the transformed quality indicators are: For positive quality indicators, the positiveized quality indicators are: Based on this, the quality index matrix after positive transformation is obtained.

[0144] For example, the positiveized indicator data is calibrated. Since the dimensions, numerical ranges, and orders of magnitude of various quality indicators differ—for instance, pass rate indicators are usually expressed as percentages, the pass rate fluctuation index is a fluctuation indicator, and cost ratio and cost-effectiveness index have different orders of magnitude—direct weighting could lead to indicators with larger numerical ranges having an excessive impact on the overall evaluation. Therefore, a calibration function is used to calibrate the indicator values ​​corresponding to each quality indicator in the positiveized quality indicator matrix, resulting in a calibrated quality indicator matrix. This calibrated quality indicator matrix includes the calibrated indicator values ​​for each quality indicator. Specifically, the calibration function is:

[0145]

[0146] In the formula, Let be the calibration index value corresponding to the j-th quality index of the i-th power equipment. These are the index values ​​corresponding to the positiveized quality indicators. Let be the average value of the j-th quality index across all power equipment. Let be the standard deviation of the j-th quality index across all power equipment. When the value is 0, it indicates that the quality indicator is the same across all power equipment. The system can calibrate the corresponding indicator value to 0 or set the corresponding indicator weight to 0.

[0147] In this embodiment, by positively oriented the constructed quality index matrix, all quality indicators in the matrix can be converted into a direction where "larger values ​​are more beneficial to the sampling inspection quality score," providing a consistent data foundation for subsequent comprehensive evaluation. By calibrating the positively oriented quality index matrix, a calibrated quality index matrix is ​​obtained, which transforms the index values ​​corresponding to different quality indicators to a unified scale with a mean of 0 and a standard deviation of 1. Each calibrated index value reflects the degree of deviation of the evaluated object from the overall average level. Positive values ​​of the calibrated index values ​​indicate that the power equipment is better than the overall average in that quality indicator, while negative values ​​indicate that it is lower than the overall average, thus truly reflecting the relative merits and demerits of each power device.

[0148] In one embodiment, evaluating power equipment based on sampling inspection quality scores to obtain a power equipment quality evaluation result includes: assessing the equipment status of the power equipment based on the sampling inspection quality scores and the mapping relationship between the quality scores and equipment status; and generating a power equipment quality evaluation result based on the equipment status.

[0149] The equipment status includes good status, stable status, status requiring attention, and status requiring key control.

[0150] For example, if the sampling quality score is greater than or equal to the first score threshold, the equipment status of the power equipment is stable; if the sampling quality score is greater than or equal to the second score threshold and less than the first score threshold, the equipment status of the power equipment is stable; if the sampling quality score is greater than or equal to the third score threshold and less than the second score threshold, the equipment status of the power equipment is a state requiring attention; if the sampling quality score is greater than or equal to the fourth score threshold and less than the third score threshold, the equipment status of the power equipment is a state requiring key control.

[0151] The first, second, third, and fourth scoring thresholds are preset and can be fixed values. For example, the first scoring threshold could be 80, the second 60, the third 40, and the fourth 20. Alternatively, they can be set based on the quantile of the scoring division. For example, the quality scores of each sampling inspection are sorted from largest to smallest, the product of the number of power equipment and the preset quantile of 20% is calculated to obtain the target ranking rank, the sampling quality score corresponding to the target ranking rank is determined in the ranking results, and the sampling quality score is used as the first scoring threshold.

[0152] For example, based on the equipment status, corresponding sampling resource configuration suggestions are generated; based on the sampling resource configuration suggestions and the equipment status, quality evaluation results of the power equipment are generated.

[0153] In this embodiment, different equipment states are classified by the quality score of random inspection, which enables the matching of differentiated management strategies for power equipment with different quality levels. For power equipment in good and stable states, the regular random inspection ratio can be maintained, while for power equipment in states requiring attention and key control states, the random inspection ratio can be gradually increased, so as to achieve precise allocation of random inspection resources, effectively improve the overall quality control level of power grid equipment, and reduce the quality risks of power grid operation.

[0154] In one of the most specific embodiments, such as Figure 5 As shown, a method for evaluating the quality of power equipment according to this application includes the following steps:

[0155] Step 1: Obtain the power grid material sampling inspection data corresponding to the power equipment.

[0156] The evaluation baseline month is determined, and based on this, the target time period is determined. Then, the power grid material sampling inspection data corresponding to the power equipment within the target time period is obtained. The evaluation baseline month is the current statistical cutoff month used when calculating quality indicators. For example, if the system operates from June 2026, then the evaluation baseline month is June 2026; if it operates from January 2027, then the evaluation baseline month is automatically updated to January 2027. Furthermore, the target time period can be set to 5 years prior to the evaluation baseline month. When a certain equipment category has valid sampling inspection records within 5 years, the system includes that equipment category in the indicator calculation. For scenarios with longer historical data, the historical statistical window can also be set to 3 years, 5 years, 8 years, or other preset years.

[0157] Obtain the power grid material sampling inspection data corresponding to the power equipment within the target time period. Each sampling inspection record should include at least the sampling date, sampling batch, total number of samples sampled, total number of qualified samples, total number of unqualified samples, sampling cost, and sampling results.

[0158] Step 2: Evaluate the values ​​of multiple quality indicators corresponding to the quality level dimension.

[0159] For the quality level dimension, the evaluation focuses on the corresponding index values ​​of the sampling pass rate, the confidence-corrected pass rate, and the quality recovery index after non-conformity. Based on the total number of samples and the total number of qualified samples in the power grid material sampling data, the index value corresponding to the sampling pass rate of power equipment is calculated. Based on the total number of samples and the total number of non-qualified samples in the power grid material sampling data, the index value corresponding to the confidence-corrected pass rate of power equipment is evaluated using a confidence correction function. Finally, based on the sampling results for each batch in the power grid material sampling data, the index value corresponding to the quality recovery index after non-conformity of power equipment is evaluated.

[0160] Step 3: Evaluate the values ​​of multiple quality indicators corresponding to the time stability dimension.

[0161] For the time stability dimension, the average values ​​of the pass rate fluctuation index, the time-adjusted pass rate index, and the quality trend index are evaluated. Based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple benchmark time periods to obtain power grid material sampling sub-data. For each benchmark time period, the benchmark pass rate is calculated based on the power grid material sampling sub-data within the benchmark time period. Based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined. Based on the index value corresponding to the sampling pass rate index, the index value corresponding to the time-adjusted pass rate index is calculated. A target time threshold is obtained, and the power grid material sampling data whose sampling time is after the target time threshold is used as the target sampling data. Based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data, the index value corresponding to the quality trend index is evaluated.

[0162] Step 4: Evaluate the values ​​of multiple quality indicators corresponding to the cost efficiency dimension.

[0163] For the cost efficiency dimension, the corresponding indicator values ​​for the sampling cost input ratio and the problem discovery cost efficiency indicator are evaluated. A cost efficiency evaluation function is obtained; based on the sampling cost in the power grid material sampling data, the indicator value corresponding to the sampling cost input ratio is evaluated using the cost efficiency evaluation function; based on the problem proportion in the power grid material sampling data, the indicator value corresponding to the problem discovery cost efficiency indicator is evaluated using the cost efficiency evaluation function.

[0164] Step 5: Perform positive calibration on the values ​​of each quality indicator.

[0165] Based on the index values ​​corresponding to each quality indicator, a quality indicator matrix is ​​constructed. This matrix is ​​then forward-oriented to obtain a forward-oriented quality indicator matrix. Based on this forward-oriented quality indicator matrix, a calibration function is used to obtain a calibration quality indicator matrix, which includes the calibration values ​​for each quality indicator.

[0166] Step 6: Determine the weight of each quality indicator.

[0167] After shifting and positiveizing the calibration values ​​of each quality indicator, the weight of each quality indicator is determined by the entropy weight method.

[0168] Step 7: Calculate the quality score of the power equipment sampling inspection.

[0169] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the total quality score of the power equipment is calculated, and the total quality score is normalized to obtain the sampling inspection quality score.

[0170] Step 8: Evaluate the power equipment.

[0171] Based on the quality scores from random inspections, power equipment is evaluated to obtain quality assessment results. These results can be applied in various scenarios. For example, they can be used in the preparation of material inspection plans for power grid companies. By evaluating the quality of different power equipment, equipment with poor quality, large fluctuations, recent deterioration trends, or high inspection value can be identified, providing a basis for setting inspection frequency and prioritizing inspection targets. Alternatively, they can be applied to supplier quality evaluation and performance management. For power equipment provided by different suppliers, quantifiable and comparable quality assessment results can be generated based on the corresponding power grid material inspection data and equipment status, providing a reference for supplier performance evaluation, quality negotiations, supply risk identification, and subsequent procurement decisions. Furthermore, they can be applied to optimize the allocation of testing resources. When testing resources, inspection funding, and testing capabilities are limited, cost-efficiency indicators and power equipment quality assessment results can be combined to identify power equipment with high inspection costs but low problem detection efficiency, as well as equipment categories with high problem detection efficiency per unit inspection cost, thereby optimizing the allocation of inspection tasks and improving the utilization efficiency of inspection resources. Finally, they can be used for early warning of power grid material quality risks. By using the pass rate fluctuation index, the time-adjusted pass rate, and the recent quality trend index, equipment categories with significant quality fluctuations, a long-term lack of recent sampling data, or a recent decline in pass rates can be identified. This provides a basis for stricter sampling inspections, key supervision, and early intervention in quality risks. In addition to power grid material sampling inspections, this technology can also be applied to scenarios requiring multi-dimensional quality evaluation, status classification, and resource allocation optimization based on historical sampling data, such as manufacturing product quality inspection results, quality supervision and spot check results, third-party testing agency report analysis, and engineering material entry inspections.

[0172] It should be noted that the above embodiments are all based on power equipment as the evaluation object. In practical applications, the evaluation object can also be expanded to equipment model, supplier, material batch, testing agency or regional unit according to the needs of power grid material management.

[0173] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0174] Based on the same inventive concept, this application also provides a power equipment quality evaluation device for implementing the power equipment quality evaluation method described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more power equipment quality evaluation device embodiments provided below can be found in the limitations of the power equipment quality evaluation method described above, and will not be repeated here.

[0175] In one exemplary embodiment, such as Figure 6 As shown, a power equipment quality evaluation device 600 is provided, including: an acquisition module 602, an evaluation module 604, a calibration module 606, a calculation module 608, and an evaluation module 610, wherein:

[0176] The acquisition module 602 is used to acquire the power grid material sampling inspection data corresponding to the power equipment within the target time period.

[0177] The evaluation module 604 is used to evaluate the index values ​​of multiple quality indicators corresponding to the power grid quality dimension based on the power grid material sampling inspection data of power equipment. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0178] The calibration module 606 is used to perform positive calibration on the index values ​​of each quality indicator to obtain the calibration index value of each quality indicator.

[0179] The calculation module 608 is used to calculate the sampling quality score of the power equipment based on the calibration index values ​​of each quality index corresponding to the power equipment and the index weight of each quality index.

[0180] Evaluation module 610 is used to evaluate power equipment based on the quality score of random inspection and obtain the quality evaluation result of the power equipment.

[0181] In one embodiment, when the power grid quality indicators include a quality level dimension, the multiple quality indicators corresponding to the quality level dimension include a sampling pass rate indicator, a confidence-corrected pass rate indicator, and a quality recovery index after non-compliance. The evaluation module 604 is further used to calculate the indicator value corresponding to the sampling pass rate indicator of power equipment based on the total number of sampling samples and the total number of qualified samples in the power grid material sampling data; to evaluate the indicator value corresponding to the confidence-corrected pass rate indicator of power equipment based on the total number of sampling samples and the total number of non-qualified samples in the power grid material sampling data through a confidence correction function; and to evaluate the indicator value corresponding to the quality recovery index after non-compliance of power equipment based on the sampling results of each sampling batch in the power grid material sampling data.

[0182] In one embodiment, when the power grid quality indicators include a time stability dimension, the multiple quality indicators corresponding to the time stability dimension include a pass rate fluctuation indicator, a time-corrected pass rate indicator, and a quality trend indicator. The evaluation module 604 is further used to divide the power grid material sampling data based on the sampling time in the power grid material sampling data to obtain power grid material sampling sub-data within multiple benchmark time periods. For each benchmark time period, a benchmark pass rate is calculated based on the power grid material sampling sub-data within the benchmark time period. Based on the benchmark pass rate of each benchmark time period, the indicator value corresponding to the pass rate fluctuation indicator is determined. Based on the indicator value corresponding to the sampling pass rate indicator, the indicator value corresponding to the time-corrected pass rate indicator is calculated. A target time threshold is obtained, and the power grid material sampling data whose sampling time is after the target time threshold is used as the target sampling data. Based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data, the indicator value corresponding to the quality trend indicator is evaluated.

[0183] In one embodiment, when the power grid quality indicators include a cost efficiency dimension, the multiple quality indicators corresponding to the power grid quality indicators include the sampling cost input ratio indicator and the problem discovery cost efficiency indicator; the evaluation module 604 is also used to obtain a cost efficiency evaluation function; based on the sampling cost in the power grid material sampling data, the indicator value corresponding to the sampling cost input ratio indicator is evaluated through the cost efficiency evaluation function; based on the problem ratio in the power grid material sampling data, the indicator value corresponding to the problem discovery cost efficiency indicator is evaluated through the cost efficiency evaluation function.

[0184] In one embodiment, the calibration module 606 is further configured to construct a quality index matrix based on the index values ​​corresponding to each quality index; perform forward processing on the quality index matrix to obtain a forward-processed quality index matrix; and obtain a calibration quality index matrix based on the forward-processed quality index matrix through a calibration function, wherein the calibration quality index matrix includes the calibration index values ​​of each quality index.

[0185] In one embodiment, the evaluation module 610 is further configured to assess the equipment status of the power equipment based on the sampling quality score and the mapping relationship between the quality score and the equipment status; and generate a quality evaluation result of the power equipment based on the equipment status.

[0186] Each module in the aforementioned power equipment quality evaluation device 600 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0187] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores power grid material sampling inspection data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a power equipment quality evaluation method.

[0188] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0189] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0190] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0191] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0192] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0193] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0194] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0195] In one embodiment, when the processor executes the computer program, it further performs the following steps: calculating the index value corresponding to the power equipment's pass rate index based on the total number of sampled samples and the total number of qualified samples in the power grid material sampling inspection data; evaluating the index value corresponding to the power equipment's confidence-corrected pass rate index using a confidence correction function based on the total number of sampled samples and the total number of unqualified samples in the power grid material sampling inspection data; and evaluating the index value corresponding to the power equipment's post-unqualification quality recovery index index based on the sampling results of each sampling batch in the power grid material sampling inspection data.

[0196] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple power grid material sampling sub-data within a benchmark time period; for each benchmark time period, based on the power grid material sampling sub-data within the benchmark time period, a benchmark pass rate is calculated; based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined; based on the index value corresponding to the sampling pass rate index, the index value corresponding to the timeliness correction pass rate index is calculated; a target time threshold is obtained; power grid material sampling data whose sampling time is after the target time threshold is used as target sampling data; based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data, the index value corresponding to the quality trend index is evaluated.

[0197] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a cost efficiency evaluation function; evaluating the indicator value corresponding to the sampling cost input ratio indicator based on the sampling cost in the power grid material sampling data using the cost efficiency evaluation function; and evaluating the indicator value corresponding to the problem discovery cost efficiency indicator based on the problem ratio in the power grid material sampling data using the cost efficiency evaluation function.

[0198] In one embodiment, when the processor executes the computer program, it further performs the following steps: constructing a quality index matrix based on the index values ​​corresponding to each quality index; performing a forward-oriented process on the quality index matrix to obtain a forward-oriented quality index matrix; and obtaining a calibration quality index matrix based on the forward-oriented quality index matrix through a calibration function, wherein the calibration quality index matrix includes the calibration index values ​​of each quality index.

[0199] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on the sampling quality score, assessing the equipment status of the power equipment through the mapping relationship between the quality score and the equipment status; and generating a quality evaluation result of the power equipment based on the equipment status.

[0200] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0201] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0202] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0203] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0204] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0205] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0206] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the index value corresponding to the pass rate index of power equipment based on the total number of sampled samples and the total number of qualified samples in the power grid material sampling inspection data; evaluating the index value corresponding to the confidence-corrected pass rate index of power equipment through a confidence correction function based on the total number of sampled samples and the total number of unqualified samples in the power grid material sampling inspection data; and evaluating the index value corresponding to the quality recovery index index after non-conformity of power equipment based on the sampling results of each sampling batch in the power grid material sampling inspection data.

[0207] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple power grid material sampling sub-data within a benchmark time period; for each benchmark time period, based on the power grid material sampling sub-data within the benchmark time period, a benchmark pass rate is calculated; based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined; based on the index value corresponding to the sampling pass rate index, the index value corresponding to the timeliness correction pass rate index is calculated; a target time threshold is obtained; power grid material sampling data whose sampling time is after the target time threshold is used as target sampling data; based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data, the index value corresponding to the quality trend index is evaluated.

[0208] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a cost efficiency evaluation function; evaluating the indicator value corresponding to the sampling cost input ratio indicator based on the sampling cost in the power grid material sampling data using the cost efficiency evaluation function; and evaluating the indicator value corresponding to the problem discovery cost efficiency indicator based on the problem ratio in the power grid material sampling data using the cost efficiency evaluation function.

[0209] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing a quality index matrix based on the index values ​​corresponding to each quality index; performing a forward-oriented process on the quality index matrix to obtain a forward-oriented quality index matrix; and obtaining a calibration quality index matrix based on the forward-oriented quality index matrix through a calibration function, wherein the calibration quality index matrix includes the calibration index values ​​of each quality index.

[0210] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sampling quality score, assessing the equipment status of the power equipment through the mapping relationship between the quality score and the equipment status; and generating a quality evaluation result of the power equipment based on the equipment status.

[0211] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0212] Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period;

[0213] Based on the power grid material sampling inspection data of power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the following: quality level dimension, time stability dimension, and cost efficiency dimension.

[0214] The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator.

[0215] Based on the calibration index values ​​of each quality indicator corresponding to the power equipment and the index weight of each quality indicator, the sampling inspection quality score of the power equipment is calculated.

[0216] Based on the quality scores from random inspections, the power equipment is evaluated to obtain the quality evaluation results.

[0217] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the index value corresponding to the pass rate index of power equipment based on the total number of sampled samples and the total number of qualified samples in the power grid material sampling inspection data; evaluating the index value corresponding to the confidence-corrected pass rate index of power equipment through a confidence correction function based on the total number of sampled samples and the total number of unqualified samples in the power grid material sampling inspection data; and evaluating the index value corresponding to the quality recovery index index after non-conformity of power equipment based on the sampling results of each sampling batch in the power grid material sampling inspection data.

[0218] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple power grid material sampling sub-data within a benchmark time period; for each benchmark time period, based on the power grid material sampling sub-data within the benchmark time period, a benchmark pass rate is calculated; based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined; based on the index value corresponding to the sampling pass rate index, the index value corresponding to the timeliness correction pass rate index is calculated; a target time threshold is obtained; power grid material sampling data whose sampling time is after the target time threshold is used as target sampling data; based on the target sampling data and the remaining sampling data in the power grid material sampling data excluding the target sampling data, the index value corresponding to the quality trend index is evaluated.

[0219] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a cost efficiency evaluation function; evaluating the indicator value corresponding to the sampling cost input ratio indicator based on the sampling cost in the power grid material sampling data using the cost efficiency evaluation function; and evaluating the indicator value corresponding to the problem discovery cost efficiency indicator based on the problem ratio in the power grid material sampling data using the cost efficiency evaluation function.

[0220] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing a quality index matrix based on the index values ​​corresponding to each quality index; performing a forward-oriented process on the quality index matrix to obtain a forward-oriented quality index matrix; and obtaining a calibration quality index matrix based on the forward-oriented quality index matrix through a calibration function, wherein the calibration quality index matrix includes the calibration index values ​​of each quality index.

[0221] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sampling quality score, assessing the equipment status of the power equipment through the mapping relationship between the quality score and the equipment status; and generating a quality evaluation result of the power equipment based on the equipment status.

[0222] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0224] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0225] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating the quality of power equipment, characterized in that, The method includes: Obtain the power grid material sampling inspection data corresponding to power equipment within the target time period; Based on the power grid material sampling inspection data of the power equipment, the index values ​​of multiple quality indicators corresponding to the power grid quality dimension are evaluated. The power grid quality dimension includes at least one of the quality level dimension, time stability dimension, and cost efficiency dimension. The values ​​of each quality indicator are positively calibrated to obtain the calibrated value of each quality indicator. Based on the calibration index values ​​of each quality index corresponding to the power equipment and the index weight of each quality index, the sampling inspection quality score of the power equipment is calculated. Based on the quality score of the random inspection, the power equipment is evaluated to obtain the quality evaluation result of the power equipment.

2. The method according to claim 1, characterized in that, When the power grid quality indicators include a quality level dimension, the multiple quality indicators corresponding to the quality level dimension include a sampling pass rate indicator, a confidence-corrected pass rate indicator, and a quality recovery indicator after non-conformity; the evaluation of the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension based on the power grid material sampling inspection data of the power equipment includes: Based on the total number of samples and the total number of qualified samples in the power grid material sampling inspection data, calculate the index value corresponding to the sampling pass rate index of the power equipment. Based on the total number of samples and the total number of non-conforming samples in the power grid material sampling inspection data, the index value corresponding to the confidence correction pass rate index of the power equipment is evaluated by using a confidence correction function. Based on the sampling results of each batch in the power grid material sampling data, evaluate the index value corresponding to the quality recovery index after the power equipment fails to meet the standards.

3. The method according to claim 2, characterized in that, When the power grid quality indicators include a time stability dimension, the multiple quality indicators corresponding to the time stability dimension include the pass rate fluctuation indicator, the time-corrected pass rate indicator, and the quality trend indicator. The evaluation of multiple quality indicators corresponding to the power grid quality dimension based on the power grid material sampling inspection data of the power equipment includes: Based on the sampling time in the power grid material sampling data, the power grid material sampling data is divided into multiple sub-data of power grid material sampling within a reference time period. For each benchmark time period, the benchmark pass rate is calculated based on the power grid material sampling sub-data in the benchmark time period. Based on the benchmark pass rate of each benchmark time period, the index value corresponding to the pass rate fluctuation index is determined. Based on the index value corresponding to the sampling pass rate index, calculate the index value corresponding to the timeliness-corrected pass rate index. Obtain a target time threshold, and take the power grid material sampling data whose sampling time is after the target time threshold as the target sampling data. Based on the target sampling data and the other sampling data in the power grid material sampling data excluding the target sampling data, evaluate the index value corresponding to the quality trend index.

4. The method according to claim 1, characterized in that, When the power grid quality indicators include a cost-efficiency dimension, the multiple quality indicators corresponding to the power grid quality indicators include the sampling inspection cost-to-input ratio indicator and the problem discovery cost-efficiency indicator; the evaluation of the indicator values ​​of the multiple quality indicators corresponding to the power grid quality dimension based on the power grid material sampling inspection data of the power equipment includes: Obtain the cost efficiency evaluation function; Based on the sampling cost in the power grid material sampling data, the indicator value corresponding to the sampling cost input ratio is evaluated through the cost efficiency evaluation function. Based on the percentage of problems in the power grid material sampling inspection data, the cost efficiency evaluation function is used to evaluate the corresponding index value of the problem discovery cost efficiency index.

5. The method according to claim 1, characterized in that, The process of positively calibrating the values ​​of each quality indicator to obtain the calibration value of each quality indicator includes: Construct a quality indicator matrix based on the indicator values ​​corresponding to each quality indicator; The quality index matrix is ​​positiveized to obtain the positiveized quality index matrix. Based on the positively processed quality index matrix, a calibration quality index matrix is ​​obtained through a calibration function. The calibration quality index matrix includes the calibration index values ​​of each quality index.

6. The method according to claim 1, characterized in that, The evaluation of the power equipment based on the sampling quality score, to obtain the quality evaluation result of the power equipment, includes: Based on the sampling quality score, the equipment status of the power equipment is evaluated through the mapping relationship between the quality score and the equipment status. Based on the equipment status, a quality evaluation result for the power equipment is generated.

7. A power equipment quality evaluation device, characterized in that, The device includes: The acquisition module is used to acquire the power grid material sampling inspection data corresponding to the power equipment within the target time period; The evaluation module is used to evaluate the index values ​​of multiple quality indicators corresponding to the power grid quality dimension based on the power grid material sampling inspection data of the power equipment. The power grid quality dimension includes at least one of the quality level dimension, time stability dimension, and cost efficiency dimension. The calibration module is used to perform positive calibration on the index values ​​of each quality indicator to obtain the calibration index value of each quality indicator. The calculation module is used to calculate the sampling quality score of the power equipment based on the calibration index values ​​of each quality index corresponding to the power equipment and the index weight of each quality index. The evaluation module is used to evaluate the power equipment based on the sampling quality score and obtain the quality evaluation result of the power equipment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images