Power equipment sampling inspection strategy optimization method in combination with cloud supervision and manufacture data

Abstract

The invention relates to a power equipment sampling inspection strategy optimization method in combination with cloud supervision and manufacture data. The method comprises the following steps: collecting power equipment cloud supervision and manufacture data of all suppliers and corresponding sampling inspection data; associating detection items in the power equipment cloud supervision data of all suppliers with corresponding sampling inspection items in the sampling inspection data; and analyzing the overall cloud supervision process of the power equipment and the cloud supervision process of the power equipment of each supplier based on the associated data, and performing targeted optimization on a sampling inspection strategy according to an analysis result.

Description

Technical Field

[0001] The invention relates to a method for optimizing a power equipment sampling inspection strategy in combination with cloud monitoring data, and belongs to the technical field of power equipment detection. Background Art

[0002] The material quality work of State Grid Corporation of China follows the principle of "relying on the owner unit, cooperating with professional departments, and highlighting manufacturers", and mainly adopts the methods of supervision and random inspection to carry out material quality supervision and management work.

[0003] The process between material production and quality sampling is a linked one. As an extension of on-site supervision and an innovation in the supervision model, EIP's "cloud supervision" highly integrates all aspects of production and manufacturing, and uses digital means to automatically collect, structure, record, and standardize the key parameters of power grid materials, so as to achieve online visualization of the production process of all categories of materials. The goal of sampling management is to test the key technical indicators of power grid materials through sampling. Before the installation of power grid materials, the material management department organizes the inspection and testing of the performance parameters of power grid materials supplied by suppliers in a sampling manner to verify their compliance with the contract requirements.

[0004] At present, cloud monitoring and sampling inspection business have generated a large amount of discrete quality data information in the process of work, and relevant majors have also combined their own data for mining and analysis to support the efficient development of their own business, but the interactive relationship between the two data has not been fully utilized. If cloud monitoring data can be used to guide the optimization of sampling inspection strategies, and sampling inspection data can be used to assist in the monitoring of key links of cloud monitoring, this will have great practical significance for the mutual promotion of the two businesses and the joint promotion of the quality improvement of power grid equipment. This will strengthen the traceability of electrical equipment quality issues, clarify quality responsibilities, and promote cloud monitoring personnel and suppliers to strengthen strict control of equipment quality, thereby ensuring that the equipment and materials put into use meet the standard requirements, so as to guide and promote suppliers to further improve the quality of electrical equipment and provide more solid material guarantees for power grid construction and safe and stable operation. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a method for optimizing the random inspection strategy of power equipment combined with cloud monitoring data.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides a method for optimizing a random inspection strategy for electric power equipment in combination with cloud monitoring data, comprising the following steps:

[0008] Collect cloud monitoring data of power equipment from all suppliers and corresponding sampling data;

[0009] Associate the inspection items in the cloud-based monitoring data of all suppliers’ power equipment with the corresponding inspection items in the inspection data;

[0010] Based on the correlated data, the overall cloud monitoring process of power equipment and the cloud monitoring process of power equipment of each supplier are analyzed, and the sampling strategy is optimized according to the analysis results.

[0011] As a preferred embodiment of the present invention, the power equipment cloud monitoring data of all suppliers includes production data, raw material inspection data, factory test data, key point witness result data, quality warning data, high-quality product rate, data application quality score data, and supplier quality evaluation score data;

[0012] The random inspection data of the power equipment includes the sampling time, actual measured values ​​of key parameters, inspection items, inspection sub-items and corresponding inspection results and sample results.

[0013] As a preferred embodiment of the present invention, the quality alarm situation in the production process of power equipment is analyzed based on the quality alarm data of all suppliers, and a sampling inspection strategy for associated detection items based on quality alarms is designed, that is, the sampling inspection items corresponding to the detection items with a quality alarm ratio greater than a threshold are focused on;

[0014] Based on the cloud-based monitoring data of power equipment from all suppliers, the production process data of power equipment is analyzed for benchmarking, and a sampling strategy for related inspection items with differentiated industry values ​​and standard values ​​for cloud-based monitoring tests is designed. That is, the difference between the average industry standard value of each inspection item in the cloud-based monitoring data of power equipment from all suppliers and the standard value of each inspection item is used to determine whether the inspection item corresponding to the inspection item needs to be inspected in a focused manner;

[0015] Based on the cloud monitoring data of power equipment from all suppliers, the stability of the power equipment production process data is analyzed, and a sampling strategy for related inspection items based on the fluctuation of industry values ​​of cloud monitoring tests is designed. That is, the coefficient of variation is calculated through the annual overall average value, monthly average value, and variance of each inspection item in the cloud monitoring data of power equipment from all suppliers. The stability of each production process is judged through the coefficient of variation of each inspection item, and key sampling is carried out on the corresponding sampling items of the inspection items whose stability is lower than the threshold.

[0016] As a preferred embodiment of the present invention, a power equipment quality index is constructed based on three indicators: quality alarm situation, data benchmarking situation, and data stability situation in the production process of power equipment. The specific steps are:

[0017] A third-order judgment matrix is ​​constructed based on the three indicators, and the characteristic vector and weight of each indicator are obtained through the hierarchical analysis method, and then the maximum characteristic root is calculated through the corresponding weight value of each indicator;

[0018] The CI value is calculated using the maximum characteristic root, and then the consistency index value CR is calculated by combining the RI value. The consistency index value CR is used to determine whether the current weight of each indicator is consistent;

[0019] After consistency testing, the power equipment quality index is calculated based on the weight of each indicator and the corresponding indicator value;

[0020] According to the score of the power equipment quality index, one or more of the sampling inspection strategies corresponding to the three indicators are selected as the sampling inspection strategy.

[0021] As a preferred embodiment of the present invention, based on the high-quality product rate of each supplier, suppliers whose high-quality product rate is lower than a threshold are selected for key sampling inspection;

[0022] Based on the quality evaluation score data of each supplier, select the sampling items corresponding to the deducted test items in the quality evaluation score for key sampling inspection;

[0023] Based on the quality alarm data of each supplier, select suppliers whose quality alarm times are greater than the threshold for key spot checks;

[0024] Based on the three indicators of each supplier's high-quality product rate, quality evaluation score deduction, and quality warning, an adjusted sampling strategy based on the supplier's production quality is designed: the hierarchical analysis method is used to calculate the weight of each indicator, and then the supplier's product quality score is calculated based on the weight of each indicator and the corresponding indicator value. Suppliers are graded according to their product quality scores, and different sampling frequencies are adopted for suppliers of different grades.

[0025] As a preferred embodiment of the present invention, a diversified sampling strategy based on the digitalization level of the supplier is designed according to the data quality score data of each supplier: that is, an integrity scoring level is established for each supplier based on the quality score data of the supplier's historical data application, and different sampling strategies are designed for suppliers of different levels.

[0026] As a preferred embodiment of the present invention, the process deviation of the supplier is analyzed based on the cloud monitoring data of the power equipment of each supplier, and a sampling inspection strategy is designed at the same time: the detection mean of each detection item in the cloud monitoring data of the power equipment of each supplier is compared with the detection mean of each detection item of other suppliers in the same industry, and the sampling inspection items corresponding to the detection items lower than the detection mean of each detection item in the same industry are used as the key sampling inspection items of the supplier;

[0027] Analyze the process stability of each supplier's power equipment cloud monitoring data, and design a sampling strategy: that is, calculate the coefficient of variation of each inspection item of each supplier through the average value and standard deviation of each inspection item in the power equipment cloud monitoring data of each supplier, set multiple levels according to the coefficient of variation, and adopt different sampling frequencies for the inspection items corresponding to different levels of inspection items;

[0028] Based on the two indicators of supplier's process deviation and process stability, the supplier's process evaluation index is constructed: weights are set for the two indicators respectively, and the supplier's process evaluation index is calculated according to the weight of each indicator and the corresponding indicator value;

[0029] Design a multiple sampling inspection strategy based on the supplier's production process evaluation, that is, evaluate each supplier through the supplier process evaluation indicators, divide the supplier process level according to the evaluation results, and formulate different inspection strategies for suppliers of different levels.

[0030] As a preferred embodiment of the present invention, based on the key point witness result data of each supplier, the problem situation in the key point witness result of each supplier is analyzed, and the production orders with problems are subject to focused spot checks;

[0031] The supplier production quality index is constructed based on four indicators: supplier's high-quality product rate, quality warning, data application quality score, and problems in key point witness results:

[0032] The weight of each indicator is set by using the hierarchical analysis method, and the supplier production quality index is calculated according to the weight and value of each indicator;

[0033] Calculate the supplier production quality index score for each supplier, and select one or more of the sampling strategies corresponding to the four indicators as the sampling strategy.

[0034] As a preferred embodiment of the present invention, the overall cloud monitoring process of power equipment is evaluated based on the sampling data, and improvement suggestions are put forward. The specific steps are:

[0035] Analyze the qualified rate of each sample corresponding to the sampling item in the sampling data;

[0036] The inspection items are divided into multiple risk levels according to the pass rate of the inspection items;

[0037] Different targeted optimization strategies are designed for the inspection items in the cloud monitoring process corresponding to the sampling items with different risk levels.

[0038] As a preferred implementation mode of the present invention, the cloud monitoring process of power equipment of each supplier is evaluated according to the sampling data, and improvement suggestions are put forward. The specific steps are as follows:

[0039] Suppliers are divided into grades according to the qualification rate of each supplier's power equipment, and different targeted optimization strategies are designed for the cloud monitoring process of power equipment of suppliers of different grades.

[0040] The present invention has the following beneficial effects:

[0041] 1. The present invention integrates and analyzes the cloud monitoring data of power equipment and the corresponding sampling data to realize the "monitoring and inspection linkage" of power equipment, which will promote the quality control of "cloud monitoring" to be more accurate, the supervision to be more powerful and reliable, and at the same time can effectively guide the implementation of factory test sampling work, ensure that the sampling work is more efficient, scientific and practical, and continuously improve the quality control ability of electrical equipment into the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the method of the present invention;

[0043] Figure 2 This is a mapping relationship diagram between the cloud monitoring data and the sampling data of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0046] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0047] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0048] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0049] Embodiment 1:

[0050] See also Figure 1 , a method for optimizing the sampling inspection strategy of power equipment combined with cloud monitoring data, comprising the following steps:

[0051] Collect cloud monitoring data of power equipment from all suppliers and corresponding sampling data;

[0052] Associate the inspection items in the cloud-based monitoring data of all suppliers’ power equipment with the corresponding inspection items in the inspection data;

[0053] Based on the correlated data, the overall cloud monitoring process of power equipment and the cloud monitoring process of power equipment of each supplier are analyzed, and the sampling strategy is optimized according to the analysis results.

[0054] The cloud monitoring data is used to track production orders and conduct intelligent monitoring on the EIP platform, and the sampling inspection data is used to manage the sampling inspection plan, track the sampling inspection process, and upload the statistics of the sampling inspection results on the ECP platform;

[0055] As a preferred implementation of this embodiment, the power equipment cloud monitoring data of all suppliers includes production and manufacturing data, raw material inspection data, factory test data, key point witness result data, quality warning data, high-quality product rate, data application quality score data, and supplier quality evaluation score data;

[0056] The sampling data of the power equipment includes sampling time, actual measured values ​​of key parameters, test items, test sub-items and corresponding test results, sample results, and the sampling pass rate and the number of unqualified items are calculated based on the above data analysis.

[0057] Statistical analysis is performed on various data of the cloud-supervised production process of electrical equipment, as well as the sampling pass rate of different orders and the proportion of unqualified items in the inspection items. The cloud-supervised test data and the sampling data are "multi-coupled" to compare and analyze the correlation between the sampling result data and the cloud-supervised data.

[0058] In this embodiment, the key witness items of the raw material and component inspection, production technology and process inspection, and factory test link based on cloud supervision are analyzed in association with the A, B, and C test items of the sampling quota test items. Figure 2 ,Take high-voltage switchgear as an example, from the results of the correlation analysis between the test items in the cloud supervision link and the random inspection items, it can be seen that the C-type inspection items of the random inspection quota test items are significantly related to the cloud supervision witness items;

[0059] As a preferred implementation of this embodiment, based on the quality alarm data of all suppliers, the quality alarm situation in the production process of power equipment is analyzed, and a sampling inspection strategy 1 is designed: a sampling inspection strategy based on the associated detection items of the quality alarm, that is, focusing on the sampling inspection items corresponding to the detection items with a quality alarm ratio greater than the threshold;

[0060] Based on the cloud-based power equipment monitoring data of all suppliers, the production process data of power equipment is analyzed for benchmarking, and sampling strategy 2 is designed: sampling strategy for related inspection items based on the differentiation of industry values ​​and standard values ​​of cloud-based monitoring tests, that is, judging whether the sampling items corresponding to the inspection items need to be focused on by the difference between the average industry standard value of each inspection item in the cloud-based power equipment monitoring data of all suppliers and the standard value of each inspection item; when the average industry standard value is contrary to the evaluation rule of "the higher the better / the lower the better", it is determined that a certain inspection item has a risk of failure, so it is necessary to list the corresponding sampling inspection items around high-risk cloud-based monitoring inspection items, strengthen the accuracy of sampling inspections, expand the frequency and coverage of sampling inspections, improve the pertinence and targeting of sampling inspections, continuously enhance the quality risk management and control capabilities, and use new working mechanisms to promote new breakthroughs in the efficiency of sampling inspections;

[0061] Based on the cloud monitoring data of power equipment from all suppliers, the stability of the power equipment production process data is analyzed, and sampling strategy 3 is designed: a sampling strategy for related detection items based on the fluctuation of the cloud monitoring test industry value, that is, the coefficient of variation is calculated through the annual overall average value, standard deviation of the monthly average value, and variance of each detection item in the cloud monitoring data of power equipment from all suppliers. The stability of each production process is judged through the coefficient of variation of each detection item, and the corresponding sampling items of the detection items with stability below the threshold are focused on. The calculation formula of the coefficient of variation is:

[0062]

[0063] Here, σ is the standard deviation and μ is the mean.

[0064] By analyzing the coefficient of variation of each test item, the stability of each production process can be judged, so as to infer the potential risks in the cloud monitoring process. The coefficient of variation is divided into different intervals. According to the calculated coefficient of variation, it is compared with the set interval to judge the fluctuation risk level of each test item. Different sampling intensities are adopted according to different risk levels, as shown in the following table:

[0065]

[0066] According to the risk level classification, the risk level of each inspection item can be determined. By associating each cloud monitoring inspection item with the random inspection items, the items that need to be strengthened in random inspection and control can be intuitively determined. Some examples are as follows:

[0067]

[0068] As a preferred implementation of this embodiment, a power equipment quality index is constructed based on three indicators: quality alarm situation, data benchmarking situation, and data stability situation in the production process of power equipment. The specific steps are:

[0069] The lower right diagonal line in the AHP data format is 1, indicating that the importance of oneself is completely equal to that of oneself; the data in the upper right corner and the lower left corner are in a reciprocal symmetric format; the numbers represent the relative importance between the indicators, and the larger the number, the stronger the relative importance. The AHP original data of the three indicators are shown in the following table:

[0070]

[0071] Based on the three indicators, a third-order judgment matrix is ​​constructed, and the characteristic vector A = (1.574, 0.425, 1.001) and weight ω = (52.468%, 14.156%, 33.377%) of each indicator are obtained through the analytic hierarchy process (AHP). Then, the maximum characteristic root is calculated through the corresponding weight value of each indicator:

[0072]

[0073] λ max =3.054, the CI value (0.027) is calculated using the maximum eigenvalue, and the CI value is used for consistency testing:

[0074]

[0075] When using AHP for weight calculation, consistency test analysis is required. The consistency test requires the use of two index values, CI and RI; the CI value has been calculated, and the RI value can be obtained by querying the table above. The random consistency RI value is 0.520, and the RI value is used for consistency test calculation;

[0076] The consistency test result is mainly determined by calculating the consistency index CR value:

[0077]

[0078] Generally speaking, the smaller the CR value, the better the consistency of the judgment matrix. Generally speaking, if the CR value is less than 0.1, the judgment matrix satisfies the consistency test. If the CR value is greater than 0.1, it means that there is no consistency, and the judgment matrix should be properly adjusted and analyzed again. This time, the CI value calculated for the 3rd-order judgment matrix is ​​0.027, and the RI value is 0.520. Therefore, the calculated CR value is 0.520<0.1, indicating that the judgment matrix satisfies the consistency test and the calculated weights are consistent.

[0079] After the consistency test, the indicator weight is rounded to an integer, and the power equipment quality index is calculated based on the weight of each indicator and the corresponding indicator value:

[0080] Electrical equipment quality index = quality alarm situation * 0.5 + production process data benchmarking situation * 0.15 + production process data stability * 0.35;

[0081] According to the score of the electrical equipment quality index and the score of each sub-indicator, select the appropriate sampling strategy to assist in the implementation of the sampling work. From the perspective of electrical equipment, there are 3 sampling strategies, which can be divided into the following according to different matching combinations: There are three ways, as shown in the following table:

[0082]

[0083] As a preferred implementation of this embodiment, based on the quality rate of each supplier, the sampling ratio can be reduced for suppliers with high quality rates, and the sampling frequency can be increased for suppliers with particularly low quality rates;

[0084] Based on the quality evaluation score data of each supplier, select the sampling items corresponding to the deducted test items in the quality evaluation score for key sampling inspection;

[0085] Based on the quality warning data of each supplier, suppliers with quality warning times greater than the threshold are selected for key spot checks. For suppliers that fail to meet the standards twice, the points are accumulated and they are ordered to make rectifications. For suppliers that fail to meet the standards five or more times, all product categories of the supplier will be suspended for one year.

[0086] Based on the three indicators of each supplier's high-quality product rate, quality evaluation score deduction, and quality warning, design sampling strategy 4: Adjustment sampling strategy based on supplier production quality: Use the hierarchical analysis method to calculate the weight of each indicator, as shown in the following table:

[0087]

[0088] Then, based on the weight of each indicator and the corresponding indicator value, the supplier's product quality score is calculated = supplier product quality rate * 0.25 + quality evaluation deduction * 0.1 + quality alarm process frequency * 0.65. Suppliers are graded according to their product quality scores, and different sampling frequencies are adopted for suppliers of different grades, as shown in the following table:

[0089]

[0090] As a preferred implementation of this embodiment, based on the data application quality score data of each supplier, a sampling inspection strategy 5 is designed: a diversified sampling inspection strategy based on the digital intelligence level of the supplier: that is, based on the supplier's historical data application quality score data, an integrity rating level is established for each supplier, and different sampling inspection strategies are designed for suppliers of different levels, as shown in the following table:

[0091]

[0092] As a preferred implementation of this embodiment, based on each supplier's power equipment cloud monitoring

[0093] Data analysis of supplier process deviations is conducted, and sampling inspection strategies are designed: the mean value of each inspection item in the cloud monitoring data of each supplier's power equipment is compared with the mean value of each inspection item of other suppliers in the same industry, and the sampling inspection items corresponding to the inspection items that are lower than the mean value of each inspection item in the same industry are selected as the key sampling inspection items of the supplier;

[0094] Specifically, the actual test values ​​of each test item in the supervision phase of the supplier cloud are counted as a1, a2…a n , industry values ​​are x1, x2…x n , the deviation rate between the actual value and the industry value is recorded as k1, k2…kn, then the deviation rate Based on the benchmarking rule of "the bigger the better / the smaller the better", within the standard value range, the scores of each supplier's test items are calculated according to the k value as shown in the following table:

[0095] Smaller is better:

[0096]

[0097] Bigger is better:

[0098]

[0099] Assign weights to raw material / component inspection, production technology and process inspection, factory test and inspection items in each link, and calculate the overall score of the supplier's process deviation based on the weights.

[0100] Based on the cloud monitoring data of each supplier's power equipment, the process stability of the supplier is analyzed, and a sampling strategy is designed: that is, the coefficient of variation of each inspection item of the supplier is calculated by the average value and standard deviation of each inspection item in the cloud monitoring data of each supplier's power equipment. Specifically, the average inspection value μ of each inspection item is calculated using the following formula: x i Represents a single actual test value, n represents the number of test values, and the standard deviation σ of each test item is calculated as follows:

[0101]

[0102] Set multiple levels according to the coefficient of variation, as shown in the following table:

[0103]

[0104] Different sampling frequencies are adopted for sampling items corresponding to different levels of inspection items;

[0105] Based on the two indicators of supplier's process deviation and process stability, the supplier process evaluation index is constructed: weights are set for the two indicators respectively. Since the two indicators are equally important, the weights of the indicators are both set to 0.5. According to the weight of each indicator and the corresponding indicator value, the supplier process evaluation index is calculated as follows: supplier process deviation*0.5+supplier process stability*0.5;

[0106] Design sampling strategy 6: Multiple sampling sampling strategy based on supplier production process evaluation, that is, evaluate each supplier through supplier process evaluation indicators, divide the supplier process level according to the evaluation results, and formulate different sampling strategies for suppliers of different levels, as shown in the following table:

[0107]

[0108]

[0109] As a preferred implementation mode of this embodiment, based on the key point witness result data of each supplier, the problems in the key point witness results of each supplier are analyzed. The key point witness results of cloud monitoring can reflect the potential risk points in the production process of electrical equipment. According to the problems found in the key point witness and the frequency of occurrence, the abnormal trends in the production process can be identified, which can be used to guide the focus of the sampling work, enhance transparency, improve product quality control, and provide information support for the formulation of sampling strategies. It greatly improves the efficiency and accuracy of the sampling work and realizes accurate sampling. According to the classification and statistics of common problems of key point witness, it can be roughly classified into the following problems: First, the supplier mistransmits data; second, the uploaded parameter specifications are incorrect, and the deduction data is actually within the limit, resulting in the quality score not matching the actual data; third, the automatic data collection is not a mandatory item, and the supplier is not connected; fourth, the supplier has not opened the data acquisition equipment, resulting in the lack of automatic data collection for production process and process inspection. For production orders with problems, key sampling is carried out, which is sampling strategy 7;

[0110] The supplier production quality index is constructed based on four indicators: supplier's high-quality product rate, quality warning, data application quality score, and problems in key point witness results:

[0111] The weight of each indicator is set using the hierarchical analysis method, as shown in the following table:

[0112]

[0113] The supplier production quality index is calculated based on the weight and value of each indicator = supplier product quality score*0.55+data application quality*0.15+supplier process evaluation score*0.25+key point witness result*0.05;

[0114] Calculate the supplier production quality index score of each supplier, as well as the score of each sub-indicator, and select the appropriate sampling strategy to assist in the sampling work. From the supplier dimension, there are 4 sampling strategies, according to different matching combinations. There are three ways, as shown in the following table:

[0115]

[0116]

[0117] As a preferred implementation of this embodiment, the overall cloud monitoring process of the power equipment is evaluated based on the sampling data, and improvement suggestions are put forward. The specific steps are as follows:

[0118] Analyze the qualified rate of each sample corresponding to the sampling item in the sampling data;

[0119] The inspection items are divided into multiple risk levels according to the pass rate of the inspection items;

[0120] Design different targeted optimization strategies for the inspection items in the cloud monitoring process corresponding to the inspection items of different risk levels;

[0121] In this embodiment, the random inspection data of the city and county companies under the State Grid Fujian Electric Power Co., Ltd. from August 2021 to June 2024 are statistically analyzed, and the unqualified inspection items among the unqualified samples found in the random inspections during the three years are statistically analyzed, mainly including two aspects, one is the overall situation of the unqualified inspection items, and the other is what are the main unqualified items, as shown in the following table:

[0122]

[0123]

[0124] After statistical analysis, it was found that the overall sampling pass rate of high-voltage switchgear was 91.23%. Inspection items with low pass rates and a large number of unqualified items were selected, including: temperature rise test (41.67%), primary wiring form, phase sequence, and air clearance inspection (68.75%), cabinet size, thickness, and material inspection (88.12%), and listed as high-risk inspection items. Inspection items with low pass rates and a small number of unqualified items were selected, including: interlocking test (81.25%), main circuit resistance measurement (81.25%), trolley or disconnector test (89.47%), and power frequency voltage test (93.75%), and listed as medium-risk inspection items. Inspection items with a pass rate of 100% were selected as low-risk inspection items, including: insulation test of auxiliary and control circuits, mechanical operation and mechanical property measurement test, mechanical operation and mechanical property test, insulation parts test, lightning impulse voltage test, and trolley or disconnector test silver plating thickness test;

[0125]

[0126]

[0127] Explore the relationship between the sampling inspection pass rate and other factors (such as raw material and component quality, production process parameters, etc.), identify the key factors affecting the sampling inspection pass rate, and use it as an entry point for quality improvement and production optimization. From the correspondence between the sampling inspection items and the cloud manufacturing supervision witness items, the primary wiring form, phase sequence, and air clearance inspection items are high-risk inspection items, and the cloud manufacturing supervision witness items do not have this content. Therefore, it is recommended to expand the scope of cloud manufacturing supervision witness, increase the inspection of this test, and recommend that other test items of Class A and Class B of the sampling inspection items be included in the cloud manufacturing supervision witness items to fully guarantee the remote and all-round supervision and control of various key manufacturing processes of electrical equipment.

[0128] For high-risk categories, such as cabinet material, thickness, electrical interlock, and main circuit resistance value, which are distributed in the raw materials, components, and factory test links, the monitoring frequency and intensity of cloud monitoring can be dynamically adjusted, the scope of key witness points of cloud monitoring can be expanded, and data collection points can be increased, focusing on the deviation values ​​of relevant production process parameters from standards. The key point witness records of cloud monitoring can also be used to trace the specific links where the problem occurred, locate the root cause of the problem, and quickly formulate corrective measures, providing reference for suppliers to adjust raw material selection, improve production processes, and other strategic optimizations.

[0129] For the medium-risk categories, there are five witness items, such as busbar, moving contact, static contact, circuit breaker trolley, and whole cabinet power frequency withstand voltage test, which are distributed in raw materials, components, production technology and process links. Targeted key supervision can be carried out on these important production links, and the control efforts can be enhanced from the aspects of supervision witnessing methods, personnel allocation, supervision process management, etc., so as to improve the overall production efficiency and the quality of electrical equipment.

[0130] In addition, the following three measures can be taken to strengthen the control level of cloud monitoring. First, establish a quality problem information database. Once a problem is found in the random inspection, the quality problems found will be sorted and summarized to form a quality problem information database, and the quality problems will be graded and classified. They can be divided into level I-III quality problems according to the severity of the problem. By making the list of suppliers and the types of products they supply public through the Internet, it can effectively promote suppliers to improve production quality. Second, improve the accuracy of cloud monitoring data. Once data anomalies or authenticity problems are found, the investigation procedure will be immediately initiated, and the supplier will be required to provide additional proof, and continuous monitoring will be implemented to ensure the accuracy, standardization and timeliness of production equipment information recording, statistics and reporting until the data quality is verified. For suppliers with problems in data authenticity, flight inspections will be carried out, and the quality of production equipment will be checked on site. If there are any problems, they will be included in the procurement bad credit list. Third, upgrade the intelligence level of the cloud monitoring platform. Using AI and machine learning algorithms, according to the unqualified rate of the random inspection items, the parameter thresholds and alarm rules of cloud monitoring are dynamically adjusted to feedback abnormal situations immediately and shorten the response time. At the same time, the potential risks in the production process can be predicted based on the sampling pass rate, the accuracy of cloud monitoring data processing and early warning can be improved, and early warning can be given and preventive measures can be taken.

[0131] As a preferred implementation of this embodiment, the cloud monitoring process of power equipment of each supplier is evaluated according to the sampling data, and improvement suggestions are put forward. The specific steps are as follows:

[0132] Suppliers are divided into grades according to the qualification rate of each supplier's power equipment, and different targeted optimization strategies are designed for the cloud monitoring process of power equipment of suppliers of different grades.

[0133] In this embodiment, the sampling data of the city and county companies under the State Grid Fujian Electric Power Co., Ltd. from August 2021 to June 2024 are analyzed, and it is found that there are a total of 13 historical suppliers of high-voltage switchgear. The sampling pass rates of high-voltage switchgear of different suppliers are counted, and the interval range is divided according to the distribution characteristics of the pass rate. For example, the pass rate is divided into several levels (above 95%, 85%-95%, below 85%, etc.). Suppliers are ranked from high to low according to the sampling pass rate, and they are assigned to the corresponding pass rate intervals. Suppliers with a sampling pass rate of more than 95% are classified as excellent suppliers, suppliers with a sampling pass rate in the range of 85%-95% are classified as good suppliers, and suppliers with a sampling pass rate below 85% are classified as qualified suppliers, as shown in the following table:

[0134]

[0135]

[0136] For suppliers of three different levels, namely excellent, good and qualified, we implement different degrees of cloud manufacturing supervision and control to ensure effective resource allocation. For excellent suppliers, we mainly witness key points to ensure that the production process meets the standards. We encourage suppliers to share best practices through the cloud manufacturing supervision platform, establish long-term cooperation mechanisms, and jointly explore technological innovation and cost optimization solutions. For good suppliers, we conduct online real-time Q&A on key parameters and test results through the cloud manufacturing supervision platform to ensure data accuracy. We strictly review test data, reports, etc. to ensure authenticity and effectiveness. For qualified suppliers, we establish a supplier risk library, formulate special control mechanisms, include them in the scope of key manufacturing supervision and control, and even implement full remote monitoring to ensure that every link meets the standards, and assign dedicated personnel or expert teams to track and supervise suppliers throughout the process, immediately require rectification of any problems found, and track the rectification status. If a supplier continues to fail to meet the requirements, consider eliminating it and looking for a better alternative supplier.

[0137] For suppliers with good and qualified grades, we conduct in-depth analysis of the reasons that affect the sampling pass rate, such as raw material quality, production process control, etc., and provide rectification support to suppliers. We evaluate the production capacity of suppliers through the sampling pass rate, communicate with suppliers with low pass rates and propose optimization and improvement of production quality control processes, including process improvement, equipment upgrades, personnel training, etc.; we even provide necessary technical support and resources to help suppliers improve production quality so that they can achieve a higher pass rate.

[0138] Regularly evaluate the production quality and process stability of suppliers with low sampling pass rates, evaluate their improvement, and adjust the control efforts based on the evaluation results. Introduce artificial intelligence technology to intelligently calculate the pass rate of electrical equipment of suppliers within a fixed period, automatically adjust the range of their intervals, and update the supplier risk database on a rolling basis. Establish a feedback mechanism to promptly convey the pass rate ranking and improvement requirements to suppliers, and continuously promote the improvement of their electrical equipment production quality.

[0139] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0140] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0142] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0143] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the sampling inspection strategy of power equipment combined with cloud monitoring data, characterized in that: The following steps are involved: Collect cloud monitoring data of power equipment from all suppliers and corresponding sampling data; Associate the inspection items in the cloud-based monitoring data of all suppliers’ power equipment with the corresponding inspection items in the inspection data; Based on the correlated data, the overall cloud monitoring process of power equipment and the cloud monitoring process of power equipment of each supplier are analyzed, and the sampling strategy is optimized according to the analysis results.

2. According to claim 1, a method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data is characterized in that: The power equipment cloud monitoring data of all suppliers include production and manufacturing data, raw material inspection data, factory test data, key point witness result data, quality warning data, high-quality product rate, data application quality score data, and supplier quality evaluation score data; The random inspection data of the power equipment includes the sampling time, actual measured values ​​of key parameters, inspection items, inspection sub-items and corresponding inspection results and sample results.

3. According to claim 2, a method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data is characterized in that: According to the quality alarm data of all suppliers, the quality alarm situation in the production process of power equipment is analyzed, and a sampling inspection strategy for related detection items based on quality alarms is designed, that is, the sampling inspection items corresponding to the detection items with a quality alarm ratio greater than the threshold are focused on; Based on the cloud-based monitoring data of power equipment from all suppliers, the production process data of power equipment is analyzed for benchmarking, and a sampling strategy for related inspection items with differentiated industry values ​​and standard values ​​for cloud-based monitoring tests is designed. That is, the difference between the average industry standard value of each inspection item in the cloud-based monitoring data of power equipment from all suppliers and the standard value of each inspection item is used to determine whether the inspection item corresponding to the inspection item needs to be inspected in a focused manner; Based on the cloud monitoring data of power equipment from all suppliers, the stability of the power equipment production process data is analyzed, and a sampling strategy for related inspection items based on the fluctuation of industry values ​​of cloud monitoring tests is designed. That is, the coefficient of variation is calculated through the annual overall average value, monthly average value, and variance of each inspection item in the cloud monitoring data of power equipment from all suppliers. The stability of each production process is judged through the coefficient of variation of each inspection item, and key sampling is carried out on the corresponding sampling items of the inspection items whose stability is lower than the threshold.

4. According to claim 3, a method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data is characterized in that: The power equipment quality index is constructed based on the three indicators of quality alarm, data benchmarking and data stability in the power equipment production process. The specific steps are as follows: A third-order judgment matrix is ​​constructed based on the three indicators, and the characteristic vector and weight of each indicator are obtained through the hierarchical analysis method, and then the maximum characteristic root is calculated through the corresponding weight value of each indicator; The CI value is calculated using the maximum characteristic root, and then the consistency index value CR is calculated by combining the RI value. The consistency index value CR is used to determine whether the current weight of each indicator is consistent; After consistency testing, the power equipment quality index is calculated based on the weight of each indicator and the corresponding indicator value; According to the score of the power equipment quality index, one or more of the sampling inspection strategies corresponding to the three indicators are selected as the sampling inspection strategy.

5. According to claim 2, a method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data is characterized in that: Based on the quality rate of each supplier, suppliers with a quality rate below the threshold are selected for key spot checks; Based on the quality evaluation score data of each supplier, select the sampling items corresponding to the deducted test items in the quality evaluation score for key sampling inspection; Based on the quality alarm data of each supplier, select suppliers whose quality alarm times are greater than the threshold for key spot checks; Based on the three indicators of each supplier's high-quality product rate, quality evaluation score deduction, and quality warning, an adjusted sampling strategy based on the supplier's production quality is designed: the hierarchical analysis method is used to calculate the weight of each indicator, and then the supplier's product quality score is calculated based on the weight of each indicator and the corresponding indicator value. Suppliers are graded according to their product quality scores, and different sampling frequencies are adopted for suppliers of different grades.

6. The method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data according to claim 5 is characterized in that: Based on the data quality score data of each supplier, we design diversified sampling strategies based on the supplier's digital intelligence level: that is, based on the supplier's historical data, we apply the quality score data to establish an integrity score level for each supplier, and design different sampling strategies for suppliers of different levels.

7. The method for optimizing the power equipment sampling inspection strategy in combination with cloud monitoring data according to claim 6 is characterized in that: Analyze the process deviation of each supplier based on the cloud monitoring data of each supplier's power equipment, and design a sampling strategy: compare the mean value of each test item in the cloud monitoring data of each supplier's power equipment with the mean value of each test item of other suppliers in the same industry, and select the sampling items corresponding to the test items that are lower than the mean value of each test item in the same industry as the key sampling items of the supplier; Analyze the process stability of each supplier's power equipment cloud monitoring data, and design a sampling strategy: that is, calculate the coefficient of variation of each inspection item of each supplier through the average value and standard deviation of each inspection item in the power equipment cloud monitoring data of each supplier, set multiple levels according to the coefficient of variation, and adopt different sampling frequencies for the inspection items corresponding to different levels of inspection items; Based on the two indicators of supplier's process deviation and process stability, the supplier's process evaluation index is constructed: weights are set for the two indicators respectively, and the supplier's process evaluation index is calculated according to the weight of each indicator and the corresponding indicator value; Design a multiple sampling inspection strategy based on the supplier's production process evaluation, that is, evaluate each supplier through the supplier process evaluation indicators, divide the supplier process level according to the evaluation results, and formulate different inspection strategies for suppliers of different levels.

8. The method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data according to claim 7 is characterized in that: Analyze the problems in the key point witness results of each supplier based on the key point witness result data of each supplier, and conduct key spot checks on production orders with problems; The supplier production quality index is constructed based on four indicators: supplier's high-quality product rate, quality warning, data application quality score, and problems in key point witness results: The weight of each indicator is set by using the hierarchical analysis method, and the supplier production quality index is calculated according to the weight and value of each indicator; Calculate the supplier production quality index score for each supplier, and select one or more of the sampling strategies corresponding to the four indicators as the sampling strategy.

9. The method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data according to claim 1 is characterized in that: According to the sampling data, the overall cloud supervision process of power equipment is evaluated and improvement suggestions are put forward. The specific steps are as follows: Analyze the qualified rate of each sample corresponding to the sampling item in the sampling data; The inspection items are divided into multiple risk levels according to the pass rate of the inspection items; Different targeted optimization strategies are designed for the inspection items in the cloud monitoring process corresponding to the sampling items with different risk levels.

10. The method for optimizing the sampling strategy of electric power equipment combined with cloud monitoring data according to claim 1 is characterized in that: Based on the sampling data, the cloud supervision process of power equipment of each supplier is evaluated and improvement suggestions are put forward. The specific steps are as follows: Suppliers are divided into grades according to the qualification rate of each supplier's power equipment, and different targeted optimization strategies are designed for the cloud monitoring process of power equipment of suppliers of different grades.

Images