A data storage management method and system for a product

Abstract

The application provides a product data storage management method and system, and relates to data management. The method comprises the following steps: obtaining a test flow and a test state of a product; scoring the importance of each test item and test parameter through a knowledge base and expert experience to obtain the importance score of each test item and the importance score of each test parameter; obtaining an abnormality classification; obtaining a first score of each abnormality parameter according to the frequency and influence of abnormality; obtaining a total correlation score of each parameter of each upstream test item according to the correlation degree between the abnormality parameter of a downstream test item and the parameters of each upstream test item; constructing a data query index of the product; setting a test item displayed on a user interaction interface according to the importance score of the test item; determining the storage period of each parameter of each test item according to the parameter importance score, the first score and the total correlation score; and realizing fine management of test data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a product data storage management method and system. BACKGROUND

[0002] In modern production processes, products need to go through a series of strict test procedures to ensure their quality and performance. These test procedures usually include multiple test stations, each of which contains multiple test items for detecting different characteristics and functions of the product. However, with the continuous increase in test items and the massive accumulation of test data, how to efficiently manage and store these data has become a pressing problem.

[0003] Traditional data storage management methods often use a unified storage strategy, i.e., the same period and method of storage for all test data of test items; however, this approach has obvious shortcomings; on the one hand, for test items with higher importance, their test data may need to be saved for a longer period of time for subsequent analysis and quality traceability; on the other hand, for items with lower importance or higher test frequency, their test data may not need to be saved for a long time, and excessive storage will only waste storage space and management resources.

[0004] In addition, during the test process, various abnormal situations often occur, which are often associated with specific test items and test parameters; traditional data storage management methods often ignore the correlation between these abnormal parameters and upstream test item parameters, making it difficult to quickly locate the root cause of the problem during abnormal analysis. SUMMARY

[0005] Based on the above problems, the present application proposes a product data storage management method and system, which determines the storage period of each parameter of each test item by comprehensively considering the importance score, abnormal classification and correlation of test items and test parameters, thereby realizing fine management of test data; both long-term preservation of important data and avoidance of unnecessary storage space waste.

[0006] The purpose of the present application is achieved by adopting the following technical solutions:

[0007] In a first aspect, the present application provides a product data storage management method, comprising:

[0008] S1, obtaining the test procedure and test state of the product, and uniquely numbering each test item of the product at each test station; scoring the importance of each test item and test parameter through a knowledge base and expert experience to obtain the importance score of each test item and the importance score of each test parameter;

[0009] S2, obtaining abnormal classification through historical data; obtaining a first score of each abnormal parameter according to the frequency and influence of abnormal occurrence; obtaining a total correlation score of each parameter of each upstream test item according to the correlation degree between the abnormal parameter of the downstream test item and the parameter of each upstream test item;

[0010] S3, constructing a data query index of the product according to the product serial number, test time, test station, test state and test number; setting the test items displayed on the user interaction interface according to the importance score of the test items;

[0011] S4, determining the storage period of each parameter of each test item according to the parameter importance score, the first score and the total correlation score.

[0012] Preferably, the S1 comprises:

[0013] Obtaining the test flow of the product, the test flow comprising test stations and test sequences of each test item of each test station; the test station comprising product basic performance test, function test and reliability test;

[0014] Obtaining the test state of the product, the test state comprising new test, repeated test and test after rework; marking the test state of the product at each test station;

[0015] Uniquely numbering each test item of the product at each test station;

[0016] Importance scoring of each test item and test parameter in the test item through knowledge base and expert experience.

[0017] Preferably, the S2 comprises:

[0018] Obtaining abnormal classification information through historical data; the abnormal classification information comprising abnormal type, test station of abnormal occurrence, test item of abnormal occurrence and abnormal parameter;

[0019] Obtaining a first score of each abnormal parameter corresponding to each abnormality according to the frequency and influence of abnormal occurrence;

[0020] Obtaining the correlation degree between the abnormal parameter of the downstream test item and the parameter of each upstream test item; obtaining a total correlation score of each parameter of each upstream test item according to the correlation degree.

[0021] Preferably, the obtaining of the correlation degree between the abnormal parameter of the downstream test item and the parameter of each upstream test item; obtaining a total correlation score of each parameter of each upstream test item according to the correlation degree comprises:

[0022] Obtaining a first correlation coefficient between the abnormal parameter of a certain abnormal type and the parameter of each upstream test item;

[0023] Obtain the proportion of the upstream parameter distribution of each abnormal product under this abnormal category in the overall distribution of the corresponding upstream parameters within the statistical period;

[0024] Based on the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameter, obtain the second correlation coefficient of the upstream parameter for the abnormality;

[0025] Based on the first correlation coefficient between the upstream parameter and the anomaly test parameter of a certain anomaly type, and the second correlation coefficient of the upstream parameter for that anomaly, the first correlation score of the upstream parameter for that anomaly is obtained;

[0026] The total correlation score of a parameter is obtained based on the first correlation score between a parameter and multiple anomaly types.

[0027] Preferably, the step of obtaining the second correlation coefficient of the upstream parameter for the anomaly based on the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameters includes:

[0028] If the proportion of a certain upstream parameter distribution of an abnormal product is greater than a first preset threshold, and the larger the parameter, the worse the performance; or,

[0029] If the proportion of a certain upstream parameter distribution of an abnormal product is less than the second preset threshold, and the smaller the parameter, the worse the performance, then the abnormal product is marked; where the first preset threshold is greater than the second preset threshold.

[0030] Obtain the average proportion of the upstream parameter distribution of multiple labeled abnormal products; based on the average proportion and the proportion of labeled abnormal products in the total number of abnormal products, obtain the second correlation coefficient of the upstream parameter for the abnormality;

[0031]

[0032] in, For the first The parameter and the first The second correlation coefficient of the anomaly; For the first The abnormal product was in the first The average percentage of each parameter; For the first The first preset threshold for the distribution of parameters; For the first A second preset threshold for the distribution of parameters; For the first time in the statistical period The first abnormality The number of parameters that are labeled; For the first time in the statistical period Total number of abnormal products; is a constant, 5.

[0033] Preferably, the first correlation coefficient of the upstream parameter and the abnormal parameter of a certain abnormal type and the second correlation coefficient of the abnormality are used to obtain the first correlation degree score of the parameter for the abnormality; including:

[0034]

[0035] wherein, is the first correlation degree score of the th parameter and the th abnormality; is the first correlation coefficient of the th parameter and the th abnormality; is the second correlation coefficient of the th parameter and the th abnormality; is the absolute value, , is a weight coefficient.

[0036] Preferably, the first correlation degree score of a certain parameter and multiple abnormal types is used to obtain the total correlation degree score of the parameter; including:

[0037]

[0038] wherein, is the total correlation degree score of the th parameter; is the first correlation degree score of the th parameter and the th abnormality; is the first score of the abnormal parameter of the th abnormality, is the total number of abnormal types.

[0039] Preferably, the parameter importance score, the first score and the total correlation degree score are used to determine the storage period of each parameter of each test item; including:

[0040]

[0041]

[0042] wherein, is the storage period of the th parameter, is the total correlation degree score of the th parameter; is the first correlation coefficient of the an importance score of the parameter; a first score of the parameter; a first score of the parameter; a preset storage period; a comprehensive score of the jth parameter; a preset comprehensive score; a function relationship.

[0043] In a second aspect, the present application provides a product data storage management system, which comprises:

[0044] an importance score module, configured to acquire a test flow and a test state of a product, and to uniquely number each test item of the product at each test station; and to score the importance of each test item and each test parameter through a knowledge base and expert experience, and to obtain an importance score of each test item and an importance score of each test parameter;

[0045] a correlation degree score module, configured to obtain an abnormality classification through historical data; to obtain a first score of each abnormality parameter according to the frequency and influence of abnormality occurrence; and to obtain a correlation degree total score of each parameter of each upstream test item according to the correlation degree between the abnormality parameter of a downstream test item and the parameters of each upstream test item;

[0046] a query display module, configured to construct a product data query index according to a product serial number, a test time, a test station, a test state and a test number; and to set a test item to be displayed on a user interactive interface according to the importance score of the test item;

[0047] a storage module, configured to determine a storage period of each parameter of each test item according to the importance score of the parameter, the first score and the correlation degree total score.

[0048] In a third aspect, the present application further provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any method of the present application when executing the computer program.

[0049] The beneficial effects of the present application include: the importance of test items and parameters is scored by the knowledge base and expert experience, and the storage period is set combined with the abnormal analysis result, which ensures that the key data is saved preferentially, and improves the effectiveness and pertinence of subsequent data analysis. The content displayed on the user interaction interface is set according to the importance score of the test items, so that the engineers can focus more on important test results, and the problem positioning and solving process is accelerated. By comprehensively evaluating the importance score, the first score and the correlation total score of the parameters, a reasonable storage period is set for each parameter, unnecessary long-term storage is avoided, and the cost of data storage is significantly reduced; the reasonable storage strategy not only saves storage space, but also reduces the maintenance complexity and improves the use efficiency of storage resources. Through in-depth analysis of historical data, different types of abnormalities are identified and classified, including the test station, test item and specific abnormal parameters where the abnormalities occur, which helps better understand the root cause of quality problems; the first correlation score formula comprehensively evaluates the relationship between the parameters and the abnormalities, considering both the direct correlation (through the first correlation coefficient) and the influence of abnormal product distribution (through the second correlation coefficient). The correlation total score formula enables each parameter to obtain a comprehensive score according to its correlation degree with multiple abnormal types, which more comprehensively reflects the importance and potential risk of the parameter in the entire test process; the storage period of each parameter is determined based on its actual importance and influence in the product test process, realizing the fine management of data life cycle; according to the different attributes of the parameters (such as importance, abnormal risk and correlation degree), the system can dynamically adjust the storage period, so that the key data can be saved for a longer time, and the less important data can be cleaned up at the appropriate time point. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 FIG. 1 is a schematic diagram of a product data storage management method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0051] Hereinafter, the present application will be further described in conjunction with the drawings and specific embodiments, and it should be noted that the following described embodiments or technical features can be combined in any manner to form new embodiments without conflict.

[0052] Referring to Figure 1 , the present application provides a product data storage management method, which comprises:

[0053] S1, obtaining the test process and test state of the product, and uniquely numbering each test item of the product at each test station; scoring the importance of each test item and test parameter by the knowledge base and expert experience to obtain the importance score of each test item and the importance score of each test parameter;

[0054] S2, obtaining an abnormality classification through historical data; obtaining a first score of each abnormality parameter according to a frequency and an influence of the abnormality; obtaining a total correlation score of each parameter of each upstream test item according to a correlation degree between an abnormality parameter of a downstream test item and parameters of each upstream test item;

[0055] S3, constructing a data query index of the product according to a product serial number, a test time, a test station, a test state and a test number; setting a test item to be displayed on a user interaction interface according to an importance score of the test item;

[0056] S4, determining a storage period of each parameter of each test item according to the importance score, the first score and the total correlation score.

[0057] In some embodiments, the S1 comprises:

[0058] obtaining a test flow of the product, the test flow comprising test stations and test orders of each test item of each test station; the test stations comprising a product basic performance test, a function test and a reliability test;

[0059] obtaining a test state of the product, the test state comprising a new test, a repeated test and a test after rework; marking the test state of the product at each test station;

[0060] numbering each test item of the product at each test station uniquely;

[0061] performing importance scoring on each test item and a test parameter in the test item through a knowledge base and expert experience. The importance score range is (0, 1) for convenience.

[0062] The working principle of the above technical solution is as follows: first, collect complete test flow information of each product, including different test stations (such as basic performance test, function test, reliability test, etc.) and their test orders, and record the test state (new test, repeated test or test after rework) of each product; each test item is assigned a unique number in each test station, ensuring the traceability of the test item.

[0063] All test items and their parameters are evaluated using a knowledge base and expert experience, and are scored according to their importance to product quality and production process, with a score range of (0, 1); the score reflects the criticality of each test item and parameter.

[0064] Based on historical data, different abnormal situations are identified and classified to determine which test stations, test items and parameters are most likely to have problems. For each abnormality, the importance of the affected parameter is evaluated according to its frequency of occurrence and its impact on the product, resulting in a first score; the range of the first score is also (0, 1).

[0065] The relationship between the abnormal parameters of the downstream test items and the parameters of the upstream test items is analyzed, and a total correlation score of each parameter in the upstream test items with the abnormality is calculated to quantify the influence of these parameters on the subsequent test results.

[0066] In order to facilitate quick retrieval and analysis, a high-efficiency data query index is established using information such as product serial number, test time, test station, test status, and test number. According to the importance score of the test items, the system dynamically adjusts the test items displayed on the user interaction interface, highlights more important test results, and helps technicians focus more on key issues.

[0067] Finally, a reasonable storage period is set for each parameter of the test items in combination with the importance score of the parameter, the first score of the abnormality, and the total correlation score, which not only considers the importance of the parameter itself but also comprehensively considers its role in abnormality detection, ensuring that important data is properly saved while reducing unnecessary storage burden.

[0068] The above technical solutions have the following effects: By importance scoring of test items and parameters and setting storage periods in combination with abnormality analysis results, key data is prioritized for storage, improving the relevance and effectiveness of subsequent data analysis. The user interaction interface displays test items according to importance scores, allowing engineers to focus more on important test results and speed up problem positioning and resolution. Precise determination of the storage period of each parameter avoids unnecessary long-term storage, significantly reducing data storage costs; a reasonable data retention strategy reduces data volume, reducing maintenance complexity and potential risks. The introduction of abnormality classification and total correlation scores helps identify the root cause of problems and supports more effective quality improvement measures. The labeling of different test statuses (such as new tests, repeated tests, and tests after rework) helps track product quality changes and provides strong support for quality control. Using historical data to predict potential quality problems can help take preventive measures and reduce the incidence of defective products. The establishment of a data query index makes it possible to quickly retrieve relevant information from a large amount of test data, providing a solid foundation for continuous optimization of the production process. The unique number of test items ensures traceability of each test step, enhancing the transparency and reliability of the system.

[0069] In some embodiments, the S2 comprises:

[0070] Abnormal classification information is obtained from historical data; the abnormal classification information includes abnormal type, test station where the abnormality occurs, test item where the abnormality occurs, and abnormal parameter;

[0071] According to the frequency and impact consequences of the occurrence of each anomaly, a first score of the anomaly parameter corresponding to each anomaly is obtained;

[0072] The correlation degree between the anomaly parameter of the downstream test item and the parameters of each upstream test item is obtained; and a correlation degree total score of each parameter of each upstream test item is obtained according to the correlation degree.

[0073] The working principle of the above technical solution is as follows: first, a large amount of historical test data is analyzed to identify and classify anomalies; these anomalies can be classified according to different dimensions, such as anomaly type, specific test station where the anomaly occurs (for example, basic performance test station, function test station or reliability test station), test item involved and specific anomaly parameter; for each anomaly, its feature information is extracted, including but not limited to frequency of anomaly occurrence, test stage where the anomaly occurs, test item and parameter affected, etc.

[0074] Based on the above anomaly classification information, especially the frequency of anomaly occurrence and its impact consequences on product quality, a score of the anomaly parameter corresponding to each anomaly is obtained; this score (first score) reflects the importance and potential risk of the parameter in causing the anomaly; the scoring mechanism takes into account the frequency of anomaly occurrence - frequent occurrence of anomalies may indicate a systemic problem; at the same time, the impact consequences of the anomaly are also evaluated - a higher severity of the anomaly will get a higher score; in order to calculate uniformly, the score range is also (0, 1).

[0075] In order to further understand the relationship between different test stages, the correlation degree between the anomaly parameter of the downstream test item and the parameters of the upstream test item is calculated, and this step aims to find out which upstream parameter changes may lead to specific types of anomalies in the downstream.

[0076] According to the calculated correlation degree, a correlation degree total score is given to each parameter of the upstream test item. This score takes into account the correlation coefficient between the parameters and the upstream parameter distribution of the abnormal product, and quantifies the contribution of the upstream parameter to the downstream anomaly.

[0077] The effect of the above technical solution is: through the analysis of a large amount of historical data, different types of anomalies can be accurately identified and classified, including the test station where the anomaly occurs, the test item and the specific anomaly parameter. This detailed classification helps to more accurately locate the root cause of the problem.

[0078] Based on the frequency of anomaly occurrence and its impact consequences on product quality, a first score in the range of (0, 1) is assigned to each anomaly parameter corresponding to each anomaly; this score not only reflects the importance of the parameter, but also helps to identify key factors that may cause serious quality problems. The scoring range is unified in (0, 1), ensuring the consistency and comparability of the scoring results, facilitating subsequent data processing and decision support.

[0079] By calculating the correlation between downstream abnormal parameters and upstream parameters, the hidden causal relationship between different test stages can be revealed, which helps to understand which changes in upstream parameters may trigger a specific type of downstream abnormality, thereby providing directions for preventive measures. By considering the correlation coefficients between parameters and the upstream parameter distribution of abnormal products, a correlation degree overall score is assigned to each upstream parameter, which quantifies the contribution of upstream parameters to downstream abnormalities, enhancing the accuracy of abnormal prediction. Using the above analysis results, high-score parameters can be monitored and improved, reducing potential quality risks. By identifying and understanding the causes of abnormalities, enterprises can optimize test processes, improve production efficiency, and reduce rework and scrap rates.

[0080] In some embodiments, the correlation between the abnormal parameters of the downstream test items and the parameters of each upstream test item is obtained; and a correlation degree overall score of each parameter of each upstream test item is obtained according to the correlation degree, including:

[0081] Obtaining a first correlation coefficient between the abnormal parameters of a certain abnormal type and the parameters of each upstream test item;

[0082] Obtaining the proportion of the upstream parameter distribution of each abnormal product in the corresponding upstream parameter overall distribution in the statistical period under the abnormal classification;

[0083] According to the proportion of the upstream parameter distribution of each abnormal product in the upstream parameter overall distribution, a second correlation coefficient of the upstream parameter for the abnormality is obtained;

[0084] According to the first correlation coefficient of the upstream parameter and the abnormal test parameter of a certain abnormal type and the second correlation coefficient of the upstream parameter for the abnormality, a first correlation degree score of the upstream parameter for the abnormality is obtained;

[0085] According to the first correlation degree scores of a certain parameter and multiple abnormal types, a correlation degree overall score of the parameter is obtained.

[0086] The working principle of the above technical solution is:

[0087] First, for a specific abnormal type, the correlation coefficient between its abnormal parameters (such as a specific test value or state) and each upstream test item parameter is calculated; the correlation coefficient is a statistical index that measures the closeness of the linear relationship between two variables, and its value is between -1 and 1. Positive values indicate positive correlation, negative values indicate negative correlation, and values close to 0 indicate little linear relationship.

[0088] In a statistical period (e.g., a month, a quarter, etc.), collect all abnormal product data belonging to the abnormal category and extract the parameter values of the upstream test items; then, calculate the proportion of the upstream parameter values of these abnormal products in the overall distribution of the corresponding upstream parameter (i.e., the distribution of the parameter values of all products, including normal and abnormal products). This proportion reflects the "abnormal" degree or deviation of abnormal products in a certain upstream parameter. For example, if a certain upstream parameter of a certain abnormal product is distributed at 85% of the upstream parameter, the proportion of the parameter of the product is 0.85; another abnormal product of a certain upstream parameter is distributed at 15% of the upstream parameter, the proportion of the parameter of the product is 0.15.

[0089] Based on the proportion of the upstream parameter distribution of the abnormal product, a "second correlation coefficient" can be further calculated, which not only considers the linear relationship between parameters (such as the first correlation coefficient in the first step), but also considers the position or deviation of abnormal products in the parameter distribution.

[0090] Combined with the first correlation coefficient of the upstream parameter and the abnormal test parameter of a certain abnormal type (i.e., the first correlation coefficient in the first step) and the second correlation coefficient of the upstream parameter for the abnormality, a "first correlation score" is calculated. This score integrates the information of the two coefficients, considering both the direct correlation between parameters and the characteristics of abnormal products in the parameter distribution.

[0091] Finally, for a certain upstream parameter, if it is associated with multiple abnormal types (i.e., it has a first correlation coefficient and a second correlation coefficient with the abnormal test parameters of multiple abnormal types), a "total correlation score" can be calculated according to the first correlation score of each abnormal type, which reflects the comprehensive correlation degree of the upstream parameter in multiple abnormal types, and helps to identify which upstream parameters have the most significant impact on product quality.

[0092] Through the above steps, this embodiment not only considers the direct correlation between parameters, but also considers the characteristics of abnormal products in the parameter distribution, so as to more accurately evaluate the relationship between upstream test item parameters and downstream abnormalities, and provide strong data support for subsequent abnormal processing, quality improvement and decision support.

[0093] The above technical solution achieves the following results: By calculating the first correlation coefficient between the anomaly parameter of a certain anomaly type and the parameters of each upstream test item, it is possible to more accurately identify which upstream parameters are significantly associated with downstream anomalies. Introducing a second correlation coefficient, i.e., the proportion of the upstream parameter of the anomalous product in the overall distribution, further enhances the accuracy of the analysis, as this considers not only the linear relationship between parameters but also the distribution of the anomalous product in the parameter space. By calculating the first correlation score of each upstream parameter for multiple anomaly types and synthesizing the total correlation score, the potential impact of each upstream parameter on multiple anomaly types can be comprehensively assessed; this helps to identify key upstream parameters whose changes may have a significant impact on downstream test items, thus becoming the focus of quality improvement and anomaly prevention.

[0094] In some embodiments, obtaining the second correlation coefficient of the upstream parameter for the anomaly based on the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameters includes:

[0095] If the proportion of a certain upstream parameter distribution of an abnormal product is greater than a first preset threshold, and the larger the parameter, the worse the performance; or,

[0096] If the proportion of a certain upstream parameter distribution of an abnormal product is less than the second preset threshold, and the smaller the parameter, the worse the performance, then the abnormal product is marked; where the first preset threshold is greater than the second preset threshold.

[0097] Obtain the average proportion of the upstream parameter distribution of multiple labeled abnormal products; based on the average proportion and the proportion of labeled abnormal products in the total number of abnormal products, obtain the second correlation coefficient of the upstream parameter for the abnormality;

[0098]

[0099] in, For the first The parameter and the first The second correlation coefficient of the anomaly; For the first The abnormal product was in the first The average percentage of each parameter; For the first The first preset threshold for the distribution of parameters; For the first A second preset threshold for the distribution of parameters; For the first time in the statistical period The first abnormality The number of parameters that are labeled; For the first time in the statistical period Total number of abnormal products; is a constant, 5.

[0100] The working principle of the above technical solution is that for each upstream parameter of the abnormal product, the proportion of the parameter in the overall distribution of the parameter is calculated; according to the preset threshold (the first preset threshold and the second preset threshold) and the performance trend of the parameter (the larger the parameter represents the worse the performance or the smaller the parameter represents the worse the performance), the abnormal product is marked.

[0101] If the proportion of the distribution of a certain upstream parameter of the abnormal product is greater than the first preset threshold, and the larger the parameter represents the worse the performance, the abnormal product is marked.

[0102] If the proportion of the distribution of a certain upstream parameter of the abnormal product is less than the second preset threshold, and the smaller the parameter represents the worse the performance, the abnormal product is also marked.

[0103] It should be noted that the first preset threshold is greater than the second preset threshold to ensure the accuracy of the marking; for example, the first preset threshold is 0.85 and the second preset threshold is 0.15.

[0104] For all marked abnormal products, the mean of the proportion of the distribution of each upstream parameter is calculated.

[0105] The second correlation coefficient is calculated using a given formula, which takes into account multiple factors, including the mean of the proportion, the number of marked abnormal products, the total number of abnormal products, and a constant a.

[0106] The value of the second correlation coefficient reflects the degree of association between the jth upstream parameter and the ith abnormality.

[0107] The effect of the above technical solution is that by setting the first preset threshold and the second preset threshold and combining the performance trend of the upstream parameter (the larger or smaller the parameter represents the worse the performance), the product closely related to the abnormality can be more accurately marked, which helps to reduce the influence of noise data and improve the quality of subsequent analysis.

[0108] The introduction of the second correlation coefficient provides an effective method for quantifying the degree of association between the upstream parameter and the specific abnormality. The coefficient not only considers the mean of the proportion of the abnormal product in the distribution of the upstream parameter, but also considers the proportion of the number of marked abnormal products to the total number of abnormal products, thereby more comprehensively reflecting the relationship between the parameter and the abnormality.

[0109] By calculating the second correlation coefficient between each upstream parameter and each abnormality, it can be clearly identified which parameters have the most significant impact on a specific abnormality.

[0110] By accurately identifying the upstream parameters related to the anomaly, the source of the problem can be located faster, reducing unnecessary investigation and testing work, thereby improving work efficiency and reducing costs; in addition, targeted quality control measures can also help reduce waste and rework rates, further reducing production costs.

[0111] In summary, the method provides more effective means of anomaly diagnosis and quality control for enterprises through precise labeling of abnormal products, quantifying the correlation between upstream parameters and anomalies, enhancing the targeting of quality control, and supporting continuous improvement and decision-making, etc.

[0112] In some embodiments, the first correlation coefficient of the upstream parameter and the abnormal parameter of a certain type of anomaly, and the second correlation coefficient for the anomaly, are used to obtain the first correlation score of the parameter for the anomaly; including:

[0113]

[0114] wherein, is the first correlation score of the th parameter and the th anomaly; is the first correlation coefficient of the abnormal parameter of the th parameter and the th anomaly; is the second correlation coefficient of the th parameter and the th anomaly; is the absolute value; , is a weight coefficient.

[0115] In some embodiments, the first correlation scores of a certain parameter and multiple types of anomalies are used to obtain the total correlation score of the parameter; including:

[0116]

[0117] wherein, is the total correlation score of the th parameter; is the first correlation score of the th parameter and the th anomaly; is the first score of the abnormal parameter of the th anomaly; is the total number of anomaly types.

[0118] The working principle and effect of the above technical solution are as follows: for each upstream parameter (the jth parameter) and a certain specific abnormal type (the ith abnormality), first, the first correlation coefficient and the second correlation coefficient of the parameter and the abnormal parameter of the abnormality are calculated.

[0119] The correlation coefficient generally measures the strength and direction of the linear relationship between two variables, while the second correlation coefficient is quantified based on the proportion of abnormal products in the upstream parameter distribution and the performance trend.

[0120] The two coefficients are integrated into the first correlation degree score through weighted summation, where w1 and w2 are weight coefficients representing the relative importance of the correlation coefficient and the second correlation coefficient in the first correlation degree score. The abs() function is used to take the absolute value of the correlation coefficient to ensure that the score will not be reduced due to negative correlation; the formula considers both the direct linear relationship between the upstream parameter and the abnormal parameter (through the first correlation coefficient) and the performance trend based on the proportion of abnormal products (through the second correlation coefficient), thereby obtaining a comprehensive score; ensuring a comprehensive evaluation of the relationship between the parameter and the abnormality, considering both direct correlation (through the first correlation coefficient) and the influence of abnormal product distribution (through the second correlation coefficient);

[0121] For each upstream parameter, the association between it and multiple abnormal types needs to be considered. In order to obtain a comprehensive evaluation, the sum of the first correlation degree scores of the parameter with all abnormal types is calculated, and the weight is added according to the severity of each abnormality (represented by the first score of the abnormal parameter).

[0122] By multiplying the first correlation degree score of each abnormality with its corresponding first score and summing them up, a correlation degree total score that comprehensively considers multiple abnormal types and the severity of each abnormality can be obtained. By integrating the correlation coefficient and the second correlation coefficient, a comprehensive correlation degree score for each upstream parameter and a specific abnormal type is provided. By considering multiple abnormal types and the severity of each abnormality, a comprehensive correlation degree total score for each upstream parameter is provided; so that each parameter can obtain a comprehensive score according to its association with multiple abnormal types, and the importance and potential risks of the parameter in the entire test process are more comprehensively reflected.

[0123] In some embodiments, the storage period of each parameter of each test item is determined according to the parameter importance score, the first score and the correlation degree total score; comprising:

[0124]

[0125]

[0126] wherein, is the jth upstream parameter, Storage cycle of each parameter For the first Total score for the correlation of each parameter; For the first The importance score of each parameter; For the first The first score for each parameter; Preset storage period; The overall score for the j-th parameter; This is a preset comprehensive score; The relationship is a function; the function can be expressed as a direct addition or a weighted average.

[0127] The working principle of the above technical solution is as follows:

[0128] For each parameter of a test item (the j-th parameter), its comprehensive score needs to be determined by comprehensively considering its importance score, first score, and total relevance score.

[0129] Importance scores reflect the relative importance or influence of a parameter within a test item.

[0130] The first score represents the performance or quality level of the parameter itself.

[0131] The overall correlation score measures the degree of correlation between the parameter and multiple anomaly types.

[0132] The functional relationship f() is used to integrate these three scores into a comprehensive score. This functional relationship can be a direct addition or a weighted average, depending on the assessment objectives and scoring criteria.

[0133] The default storage period is a baseline value that represents the default storage period for parameters without specific evaluation or optimization.

[0134] The preset comprehensive score is a benchmark value used for comparison and normalization. It may represent the comprehensive score level required to reach a certain storage cycle decision threshold.

[0135] The preset storage period is adjusted based on the overall score of the parameter relative to the preset overall score, thus obtaining a personalized storage period for each parameter. If the overall score of a parameter is higher than the preset overall score, it means that the parameter needs a longer storage period to ensure the traceability of its quality and performance; conversely, if the overall score of a parameter is lower than the preset overall score, it means that the parameter can be set to a shorter storage period.

[0136] The effect of the above technical solution is: according to different attributes (such as importance, abnormal risk and correlation degree) of parameters, the system can dynamically adjust its storage period, so that key data is saved for a longer time, and less important data can be cleaned at appropriate time points. By accurately setting the storage period, unnecessary long-term storage is avoided, thereby reducing storage cost and maintenance complexity; reasonable storage strategy ensures efficient use of limited storage resources, supporting faster data retrieval and analysis speed.

[0137] The storage period setting based on the comprehensive score provides strong data support for subsequent quality analysis, helping engineers better understand the trend of product quality. By saving key data, enterprises can continuously review and analyze problems in the production process, and continuously optimize the production process.

[0138] Reasonable setting of the storage period ensures that data analysis can focus on data that has a significant impact on product quality and performance, improving the accuracy and reliability of the analysis results. For key parameters with high comprehensive scores, extending their storage period allows longer trend analysis, which helps to discover potential problem patterns and improvement opportunities.

[0139] In summary, the storage period setting method provided by the present application realizes fine management of the storage period by introducing a scientific mathematical model, combining the importance score, the first score and the correlation degree total score of the parameters, which not only improves the effectiveness of data management and storage strategy, but also provides strong technical support for manufacturing industry, and promotes the double improvement of product quality and production efficiency.

[0140] The present application provides a product data storage management system, which comprises:

[0141] An importance score module is configured to obtain a test process and a test state of a product, and uniquely number each test item of the product at each test station; and score the importance of each test item and each test parameter based on a knowledge base and expert experience to obtain the importance score of each test item and the importance score of each test parameter.

[0142] A correlation degree score module is configured to obtain an abnormal classification based on historical data, score each abnormal parameter based on the frequency and impact of the abnormality, and obtain a correlation degree total score of each parameter of each upstream test item based on the correlation degree between the abnormal parameters of downstream test items and the parameters of each upstream test item.

[0143] A query display module is configured to construct a data query index of the product based on a product serial number, a test time, a test station, a test state and a test number, and set the test items displayed on the user interaction interface based on the importance score of the test items.

[0144] a storage module configured to determine a storage period of each parameter of each test item according to the parameter importance score, the first score, and the correlation degree total score.

[0145] In some embodiments, the importance score module comprises:

[0146] a flow acquisition unit configured to acquire a test flow of the product, the test flow comprising test stations and test orders of each test item of each test station; the test stations comprising product basic performance testing, function testing, and reliability testing;

[0147] a state acquisition unit configured to acquire a test state of the product, the test state comprising new testing, repeated testing, and testing after rework; the test state of the product being marked at each test station;

[0148] a numbering unit configured to uniquely number each test item of the product at each test station;

[0149] an importance score unit configured to score the importance of each test item and test parameters in the test item by means of a knowledge base and expert experience.

[0150] In some embodiments, the correlation degree score module comprises:

[0151] an abnormality classification unit configured to obtain abnormality classification information by means of historical data; the abnormality classification information comprising abnormality types, test stations where abnormality occurs, test items where abnormality occurs, and abnormal parameters;

[0152] a first score unit configured to obtain a first score of each abnormal parameter corresponding to each abnormality according to the frequency of occurrence of each abnormality and the impact consequences;

[0153] a total score acquisition unit configured to acquire the correlation degree between abnormal parameters of a downstream test item and parameters of each upstream test item; and obtain a correlation degree total score of each parameter of each upstream test item according to the correlation degree.

[0154] In some embodiments, the total score acquisition unit comprises:

[0155] a correlation coefficient acquisition subunit configured to obtain a first correlation coefficient of abnormal parameters of a certain abnormality type and parameters of each upstream test item;

[0156] a distribution acquisition subunit configured to obtain a proportion of upstream parameter distribution of each abnormal product under the abnormality classification in the corresponding overall distribution of the upstream parameter within a statistical period;

[0157] a second correlation coefficient subunit configured to obtain a second correlation coefficient of the upstream parameter with respect to the abnormality according to the proportion of upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameter.

[0158] a first correlation degree score sub-unit, configured to obtain a first correlation degree score of an upstream parameter for an abnormality according to a first correlation coefficient of the upstream parameter and an abnormality test parameter of a certain abnormality type and a second correlation coefficient of the upstream parameter for the abnormality;

[0159] a total correlation degree score sub-unit, configured to obtain a total correlation degree score of a certain parameter according to first correlation degree scores of the parameter and multiple abnormality types.

[0160] In some embodiments, the second correlation coefficient sub-unit comprises:

[0161] a marking component, configured to mark an abnormal product if a proportion of a certain upstream parameter distribution of the abnormal product is greater than a first preset threshold, and the greater the parameter, the worse the performance; or,

[0162] a proportion of a certain upstream parameter distribution of the abnormal product is less than a second preset threshold, and the smaller the parameter, the worse the performance; wherein the first preset threshold is greater than the second preset threshold;

[0163] a calculation component, configured to obtain a mean value of the proportion of the upstream parameter distribution of multiple marked abnormal products; and obtain a second correlation coefficient of the upstream parameter for the abnormality according to the mean value of the proportion and a proportion of the marked abnormal products in a total amount of the abnormal products;

[0164]

[0165] wherein, is a second correlation coefficient of an i-th parameter and a j-th abnormality; is a mean value of a proportion of products of an i-th abnormality in an i-th parameter; is a first preset threshold of an i-th parameter distribution; is a second preset threshold of an i-th parameter distribution; is a number of i-th parameters of a j-th abnormality marked in a statistical period; is a total number of products of a j-th abnormality in a statistical period; is a constant, 5. In some embodiments, the first correlation degree score sub-unit comprises:

[0166] In some embodiments, the first correlation degree score sub-unit comprises:

[0167] ​​​​​​​​

[0168] wherein, is a first correlation score of the jth parameter and the ith abnormality; is a first correlation coefficient of the abnormal parameter of the jth parameter and the ith abnormality; is a second correlation coefficient of the jth parameter and the ith abnormality; is a second correlation coefficient of the jth parameter and the ith abnormality; is an absolute value, , is a weight coefficient. In some embodiments, the correlation total score subunit comprises:

[0169]

[0170]

[0171] wherein, is a correlation total score of the jth parameter; is a first correlation score of the jth parameter and the ith abnormality; is a first score of the abnormal parameter of the ith abnormality, is a total number of abnormal types. In some embodiments, the storage module comprises:

[0172]

[0173]

[0174]

[0175] wherein, is a storage period of the jth parameter, is a correlation total score of the jth parameter; is an importance score of the jth parameter; is a first score of the jth parameter; is a preset storage period; is a comprehensive score of the jth parameter; is a preset comprehensive score; is a functional relationship. The working principle and effects of the above technical solutions are the same as those of the method embodiments of the present application, and will not be repeated here.

[0176] The working principle and effects of the above technical solutions are the same as those of the method embodiments of the present application, and will not be repeated here.

[0177] ​​​​​​​​​​​​The application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method according to any one of the application when executing the computer program.

[0178] The application is described from the use purpose, efficiency, progress and novelty, and meets the function improvement and use requirements emphasized by the patent law. The above description and drawings are only the preferred embodiments of the application, and are not limited to the application. Therefore, all similar, similar, equivalent replacement or modification, etc. within the scope of the patent application of the application, shall belong to the scope of the patent application protection of the application.

Claims

1. A data storage management method of products, characterized by, The method comprises: S1, obtaining the test flow and test state of the product, and uniquely numbering each test item of the product at each test station; scoring the importance of each test item and test parameter through a knowledge base and expert experience to obtain the importance score of each test item and the importance score of each test parameter; S2, obtaining abnormal classification through historical data; obtaining a first score of each abnormal parameter according to the frequency and impact of abnormal occurrence; obtaining a total correlation score of each parameter of each upstream test item according to the correlation degree between the abnormal parameters of the downstream test item and the parameters of each upstream test item; S3, constructing a data query index of the product according to the product serial number, test time, test station, test state and test number; setting the test items displayed on the user interaction interface according to the importance score of the test items; S4, determining the storage period of each parameter of each test item according to the parameter importance score, the first score and the total correlation score; The S2 comprises: Obtain abnormal classification information through historical data; the abnormal classification information includes abnormal type, test station of abnormal occurrence, test item of abnormal occurrence and abnormal parameter; Obtain a first score of the abnormal parameter corresponding to each abnormal according to the frequency and impact of the occurrence of each abnormal; Obtain the correlation degree between the abnormal parameters of the downstream test item and the parameters of each upstream test item; obtain the total correlation score of each parameter of each upstream test item according to the correlation degree; The total correlation score of each parameter of each upstream test item is obtained according to the correlation degree, comprising: Obtain the first correlation coefficient of the abnormal parameter of a certain abnormal type and the parameters of each upstream test item; Obtain the proportion of the upstream parameter distribution of each abnormal product in the corresponding upstream parameter overall distribution in a statistical period; Obtain the second correlation coefficient of the upstream parameter for the abnormal according to the proportion of the upstream parameter distribution of each abnormal product in the upstream parameter overall distribution; Obtain the first correlation degree score of the upstream parameter for the abnormal according to the first correlation coefficient of the upstream parameter and the abnormal test parameter of a certain abnormal type and the second correlation coefficient of the upstream parameter for the abnormal; Obtain the total correlation score of the parameter according to the first correlation degree score of a certain parameter and multiple abnormal types; The second correlation coefficient of the upstream parameter for the abnormal is obtained according to the proportion of the upstream parameter distribution of each abnormal product in the upstream parameter overall distribution, comprising: If the proportion of the distribution of a certain upstream parameter of the abnormal product is greater than a first preset threshold, and the larger the parameter is, the worse the performance is; or, The proportion of the distribution of a certain upstream parameter of the abnormal product is less than a second preset threshold, and the smaller the parameter is, the worse the performance is; then mark the abnormal product; wherein the first preset threshold is greater than the second preset threshold; Obtain the mean of the proportion of the distribution of the upstream parameter of multiple marked abnormal products; obtain the second correlation coefficient of the upstream parameter for the abnormal according to the proportion mean and the proportion of the marked abnormal product in the total amount of the abnormal product; Wherein, C ij2 is the second correlation coefficient of the jth parameter and the ith anomaly; P ija is the average proportion of the product of the ith anomaly in the jth parameter; P jy1 is the first preset threshold of the jth parameter distribution; P jy2 is the second preset threshold of the jth parameter distribution; m ijb is the number of the jth parameter of the ith anomaly marked in the statistical period; m iz is the total number of products of the ith anomaly in the statistical period; and α is a constant, 2≤α≤5.

2. The method of claim 1, wherein, The S1 comprises: Obtaining a test flow of the product, the test flow comprising test stations and test orders of each test item of each test station; the test stations comprising product basic performance test, function test and reliability test; Obtaining a test state of the product, the test state comprising new test, repeated test and test after rework; marking the test state of the product at each test station; Uniquely numbering each test item of the product at each test station; Importance scoring of each test item and test parameter in the test item through a knowledge base and expert experience.

3. The method of claim 1, wherein, The first correlation coefficient of an upstream parameter and an abnormal parameter of a certain abnormal type and the second correlation coefficient of the abnormal parameter are obtained to obtain a first correlation degree score of the parameter for the abnormality; Comprising: C ij = w1 x abs(C ij1 ) + w2 x C ij2 wherein C ij is the first correlation degree score of the jth parameter and the ith anomaly; C ij1 is the first correlation coefficient of the anomaly parameter of the jth parameter and the ith anomaly; C ij2 is the second correlation coefficient of the jth parameter and the ith anomaly; abs() is the absolute value, and w1 and w2 are weight coefficients.

4. The method of claim 1, wherein, The first correlation degree scores of a certain parameter and multiple abnormal types are obtained to obtain a total correlation degree score of the parameter; comprising: wherein C j is the total score of the jth parameter correlation degree; C ij is the first correlation degree score of the jth parameter and the ith anomaly; Ai is the first score of the anomaly parameter of the ith anomaly, and n is the total number of anomaly types.

5. The method of claim 1, wherein, The storage period of each parameter of each test item is determined according to the parameter importance score, the first score and the total correlation degree score; Comprising: CZ j = f(Z j ,Aj,C j ) Wherein, T j is the storage period of the jth parameter, C j is the total score of the jth parameter correlation degree; Z j is the importance score of the jth parameter; Aj is the first score of the jth parameter; T y is the preset storage period; CZ j is the comprehensive score of the jth parameter; CZ y is the preset comprehensive score; f() is a function relationship.

6. A data storage management system for implementing the product of any of claims 1-5, characterized by The system comprises: An importance scoring module is configured to obtain a test flow and a test state of the product, uniquely number each test item of the product at each test station, and score the importance of each test item and each test parameter through a knowledge base and expert experience; A correlation degree scoring module is configured to obtain abnormal classification through historical data, obtain a first score of each abnormal parameter according to the frequency and influence of abnormality, and obtain a total correlation degree score of each parameter of each upstream test item according to the correlation degree between the abnormal parameter of a downstream test item and the parameters of each upstream test item; A query display module is configured to construct a data query index of the product according to a product serial number, a test time, a test station, a test state and a test number, and set a test item displayed by a user interaction interface according to the importance score of the test item; A storage module is configured to determine the storage period of each parameter of each test item according to the parameter importance score, the first score and the total correlation degree score.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method of any one of claims 1-5 when executing the computer program.

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