Data storage management method and system for products

Abstract

The invention provides a product data storage management method and system, and relates to data management, and the method comprises the steps: obtaining a test process and a test state of a product; performing importance scoring on each test item and each test parameter through a knowledge base and expert experience to obtain an importance score of each test item and an importance score of each test parameter; obtaining an abnormal classification; obtaining a first score of each abnormal parameter according to the occurrence frequency and influence of the abnormality; obtaining a correlation degree total 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; constructing a data query index of the product; setting the test items displayed by the user interaction interface according to the importance scores of the test items; determining 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; and the fine management of the test data is realized.

Description

Technical Field

[0001] This application relates to the technical field of data management, and particularly to a method and system for data storage management of products. Background Art

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

[0003] Traditional data storage management methods often adopt a unified storage strategy, that is, store the test data of all test items in the same cycle and manner; however, this approach has obvious deficiencies; on the one hand, for test items with higher importance, their test data may need to be stored for a longer time for subsequent analysis and quality traceability; on the other hand, for items with lower importance or higher test frequencies, their test data may not need to be stored for a long time, and excessive storage will only waste storage space and management resources.

[0004] In addition, during the testing process, various abnormal situations often occur, and these abnormal situations are often associated with specific test items and test parameters; traditional data storage management methods often ignore the correlation between these abnormal parameters and the parameters of upstream test items, resulting in difficulty in quickly locating the root cause of problems during abnormal analysis. Summary of the Invention

[0005] Based on the above problems, the present invention proposes a method and system for data storage management of products. By comprehensively considering factors such as importance scores of test items and test parameters, abnormal classification, and correlation degrees, the storage cycle of each parameter of each test item is determined, thereby realizing refined management of test data; ensuring long-term preservation of important data while avoiding unnecessary waste of storage space.

[0006] The objectives of this application are achieved by the following technical solutions:

[0007] In a first aspect, this application provides a method for data storage management of products, and the method includes:

[0008] S1. Obtain the test process and test status of the product, and uniquely number each test item of the product at each test station; perform importance scoring on 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. Obtain the anomaly classification through historical data; obtain the first score of each anomaly parameter according to the frequency and impact of the anomaly; obtain the total correlation score of each parameter of each upstream test item according to the correlation between the anomaly parameters of the downstream test item and the parameters of each upstream test item.

[0010] S3. Construct a data query index for the product based on the product serial number, test time, test station, test status, and test number; set the test items displayed in the user interface according to the importance score of the test items.

[0011] S4. Determine 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 includes:

[0013] Obtain the test process of the product, where the test process includes the test stations and the test sequence of each test item at each test station; the test stations include product basic performance test, function test, and reliability test.

[0014] Obtain the test status of the product, where the test status includes new test, repeated test, and test after rework; mark the test status of the product at each test station.

[0015] Assign a unique number to each test item of the product at each test station.

[0016] Perform importance scoring on each test item and the test parameters in the test item through the knowledge base and expert experience.

[0017] Preferably, the S2 includes:

[0018] Obtain the anomaly classification information through historical data; the anomaly classification information includes the anomaly type, the test station where the anomaly occurs, the test item where the anomaly occurs, and the anomaly parameter.

[0019] Obtain the first score of the anomaly parameter corresponding to each anomaly according to the frequency and impact of each anomaly.

[0020] Obtain the correlation between the anomaly 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.

[0021] Preferably, the obtaining the correlation between the anomaly parameters of the downstream test item and the parameters of each upstream test item; obtaining the total correlation score of each parameter of each upstream test item according to the correlation includes:

[0022] Obtain the first correlation coefficient between the anomaly parameter of a certain anomaly type and the parameters of each upstream test item.

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

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

[0025] According to the first correlation coefficient between the upstream parameter and the abnormal test parameter of a certain abnormal type and the second correlation coefficient of this upstream parameter for this abnormality, obtain the first association degree score of this upstream parameter for this abnormality;

[0026] According to the first association degree scores of a certain parameter with multiple abnormal types, obtain the total association degree score of this parameter.

[0027] Preferably, the step of obtaining the second correlation coefficient of this upstream parameter for this abnormality according to the proportion of the upstream parameter distribution of each abnormal product in the overall upstream parameter distribution includes:

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

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

[0030] Obtain the average proportion of the distribution of this upstream parameter of multiple marked abnormal products; according to the proportion of this average proportion in the total amount of abnormal products, obtain the second correlation coefficient of this upstream parameter for this abnormality;

[0031]

[0032] Among them, is the second correlation coefficient of the th parameter with the th abnormality; is the average proportion of the th abnormal product in the th parameter; is the first preset threshold of the th parameter distribution; is the second preset threshold of the th parameter distribution; is the number of times the th parameter of the th abnormality is marked within the statistical period; is the total number of products of the th abnormality within the statistical period; is a constant, 5.

[0033] Preferably, obtaining the first association degree score of the parameter for the anomaly according to the first correlation coefficient between the upstream parameter and the anomaly parameter of a certain anomaly type and the second correlation coefficient for this anomaly; includes:

[0034]

[0035] Wherein, is the first association degree score of the th parameter and the th anomaly; is the th parameter and the th anomaly parameter of the first correlation coefficient; is the th parameter and the th anomaly of the second correlation coefficient; is to take the absolute value, , are weight coefficients.

[0036] Preferably, obtaining the total association degree score of the parameter according to the first association degree scores of a certain parameter with multiple anomaly types; includes:

[0037]

[0038] Wherein, is the total association degree score of the th parameter; is the th parameter and the th anomaly of the first association degree score; is the first score of the th anomaly parameter, is the total number of anomaly types.

[0039] Preferably, determining the storage period of each parameter of each test item according to the parameter importance score, the first score and the total association degree score; includes:

[0040]

[0041]

[0042] Wherein, is the storage period of the th parameter, is the total association degree score of the th parameter; is the Importance score of a parameter; is the first score for the th parameter; is the preset storage period; is the comprehensive score for the jth parameter; is the preset comprehensive score; is the functional relationship.

[0043] In a second aspect, the present application provides a data storage management system for a product, and the system includes:

[0044] An importance scoring module, configured to obtain the test process and test status of the product, and uniquely number each test item at each test station of the product; perform importance scoring on 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;

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

[0046] A query and display module, configured to construct a data query index of the product according to the product serial number, test time, test station, test status, and test number; set the test items displayed on the user interaction interface according to the importance score of the test items;

[0047] A storage module, 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.

[0048] In a third aspect, the present application further provides an electronic device, and the electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any method described in the present application are implemented.

[0049] The beneficial effects of the present invention include: By scoring the importance of test items and parameters through a knowledge base and expert experience, and setting the storage period in combination with the abnormal analysis results, it ensures that key data is preferentially saved, improving 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, enabling engineers to focus more on important test results and accelerating the problem location and solution process. By comprehensively evaluating the importance score of parameters, the first score, and the total correlation score, a reasonable storage period is set for each parameter, avoiding unnecessary long-term storage, thus significantly reducing the cost of data storage; The reasonable storage strategy not only saves storage space but also reduces maintenance complexity and improves the utilization efficiency of storage resources. Through in-depth analysis of historical data, different types of abnormalities are identified and classified, including the test workstations, test items, and specific abnormal parameters where they occur, helping to better understand the root causes of quality problems; The first correlation score formula comprehensively evaluates the relationship between parameters and abnormalities, considering both direct correlation (through the first correlation coefficient) and the impact of the abnormal product distribution (through the second correlation coefficient). The total correlation score formula enables each parameter to obtain a comprehensive score based on its degree of association with multiple abnormal types, more comprehensively reflecting the importance and potential risks of the parameter throughout the test process; The storage period of each parameter is determined based on its actual importance and impact during the product testing process, realizing refined management of the data life cycle; According to the different attributes of parameters (such as importance, abnormal risk, and correlation), the system can dynamically adjust their storage periods, enabling key data to be saved for a longer time while less important data can be cleared at appropriate time points. Description of the Drawings

[0050] Figure 1 It is a schematic diagram of a data storage management method for a product provided by an embodiment of the present application. Detailed Implementation Manner

[0051] Next, in combination with the drawings and the detailed implementation manner, the present application will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be arbitrarily combined to form new embodiments.

[0052] See Figure 1 , the present application provides a data storage management method for a product, and the method includes:

[0053] S1. Obtain the test process and test status of the product, and assign a unique number to each test item of the product at each test workstation; Score 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;

[0054] S2. Obtain the anomaly classification through historical data; obtain the first score of each anomaly parameter according to the frequency and impact of the anomaly; obtain the total correlation score of each parameter of each upstream test item according to the correlation degree between the anomaly parameters of the downstream test item and the parameters of each upstream test item.

[0055] S3. Construct a data query index for the product according to the product serial number, test time, test station, test status, and test number; set the test items displayed on the user interaction interface according to the importance score of the test items.

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

[0057] In some embodiments, the S1 includes:

[0058] Obtain the test process of the product, where the test process includes the test stations and the test sequence of each test item at each test station; the test stations include product basic performance test, function test, and reliability test.

[0059] Obtain the test status of the product, where the test status includes new test, repeated test, and test after rework; mark the test status of the product at each test station.

[0060] Assign a unique number to each test item of the product at each test station.

[0061] Perform importance scoring on each test item and the test parameters in the test item through the knowledge base and expert experience respectively. Among them, for the convenience of processing, the importance scoring range is (0, 1).

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

[0063] Use the knowledge base and expert experience to evaluate all test items and their parameters, and give a score according to their importance to product quality and production process, and the scoring range is (0, 1); the score reflects the critical degree of each test item and parameter.

[0064] Based on historical data, identify and classify different abnormal situations to determine which test stations, test items, and parameters are most likely to have problems. For each anomaly, evaluate the importance of the affected parameters according to its occurrence frequency and its impact on the product to obtain the first score; the range of the first score is also (0, 1);

[0065] Analyze the relationship between the abnormal parameters of downstream test items and the parameters of upstream test items, and calculate the total correlation score of each parameter in the upstream test item with the abnormality to quantify the impact of these parameters on subsequent test results.

[0066] To facilitate quick retrieval and analysis, use information such as product serial number, test time, test station, test status, and test number to establish an efficient data query index. According to the importance score of the test item, the system dynamically adjusts the test items displayed on the user interface, highlighting the more important test results to help technicians focus more on key issues.

[0067] Finally, combining the importance score of the parameter, the first score of the abnormality, and the total correlation score, set a reasonable storage period for the parameters of each test item, taking into account not only the importance of the parameter itself but also its role in abnormality detection, ensuring that important data is properly stored while reducing unnecessary storage burdens.

[0068] The effects of the above technical solutions are as follows: By scoring the importance of test items and parameters and setting the storage period in combination with the abnormality analysis results, it ensures that key data is preferentially stored, improving the pertinence and effectiveness of subsequent data analysis. The user interface displays test items according to the importance score, enabling engineers to focus more on important test results and accelerating the problem location and solution process. Accurately determining the storage period of each parameter avoids unnecessary long-term storage, thus significantly reducing the cost of data storage; a reasonable data retention strategy reduces the amount of data, lowering the maintenance complexity and potential risks. The introduction of abnormality classification and total correlation score helps identify the root causes of problems and supports more effective quality improvement measures. Marking different test statuses (such as new test, repeated test, and retested after rework) helps track product quality changes and provides strong support for quality control. Using the abnormal information in historical data, potential quality problems can be predicted, preventive measures can be taken in advance, and the incidence of defective products can be reduced. 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 the continuous optimization of the production process. The unique number of the test item ensures the traceability of each test link, enhancing the transparency and reliability of the system.

[0069] In some embodiments, S2 includes:

[0070] Obtain abnormality classification information through historical data; the abnormality classification information includes the type of abnormality, the test station where the abnormality occurs, the test item where the abnormality occurs, and the abnormal parameter;

[0071] Obtain the first score of the abnormal parameter corresponding to each abnormality according to the occurrence frequency and impact consequence of each abnormality.

[0072] Obtain the correlation degree between the abnormal parameters of the downstream test items 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.

[0073] The working principle of the above technical solution is as follows: First, identify and classify abnormalities by analyzing a large amount of historical test data; these abnormalities can be classified according to different dimensions, such as abnormality type, specific test station where the abnormality occurs (for example, basic performance test station, function test station or reliability test station), involved test items, and specific abnormal parameters; for each abnormality, extract its characteristic information, including but not limited to the occurrence frequency of the abnormality, the test stage when it occurs, the affected test items and parameters, etc.

[0074] Based on the above abnormality classification information, especially the occurrence frequency of the abnormality and its impact consequence on product quality, score the abnormal parameter corresponding to each abnormality; this score (the first score) reflects the importance and potential risk of the parameter in triggering the abnormality; the scoring mechanism takes into account the occurrence frequency of the abnormality - frequently occurring abnormalities may indicate systematic problems; at the same time, it will also evaluate the impact consequence of the abnormality - more severe abnormalities will get higher scores; for the sake of unified calculation, the scoring range is also (0, 1).

[0075] To further understand the relationship between different test stages, calculate the correlation degree between the abnormal parameters of the downstream test items and the parameters of the upstream test items. This step aims to find out which changes in the upstream parameters may lead to specific types of abnormalities downstream.

[0076] According to the calculated correlation degree, assign a total correlation score to the parameter of each upstream test item. This score comprehensively considers the correlation coefficient between parameters and the distribution of upstream parameters of abnormal products, and quantifies the contribution degree of upstream parameters to downstream abnormalities.

[0077] The effect of the above technical solution is as follows: Through the analysis of a large amount of historical data, different types of abnormalities can be accurately identified and classified, including the test stations, test items, and specific abnormal parameters where they occur. This detailed classification helps to more precisely locate the root cause of the problem.

[0078] Based on the occurrence frequency of the abnormality and its impact consequence on product quality, assign a first score within the range of (0, 1) to the abnormal parameter corresponding to each abnormality. This score not only reflects the importance of the parameter but also helps to identify the key factors that may lead to serious quality problems. Unifying the scoring range to (0, 1) ensures the consistency and comparability of the scoring results, facilitating subsequent data processing and decision support.

[0079] By calculating the correlation degree between downstream abnormal parameters and upstream parameters, the hidden causal relationships between different test stages can be revealed, which helps to understand which changes in upstream parameters may trigger specific types of downstream abnormalities, thus providing directions for preventive measures. Considering the correlation coefficient between parameters and the distribution of upstream parameters of abnormal products comprehensively, an overall correlation score is assigned to each upstream parameter. This score quantifies the contribution degree of upstream parameters to downstream abnormalities, enhancing the accuracy of abnormality prediction. Using the above analysis results, key monitoring and improvement can be carried out on high-score parameters to reduce potential quality risks. By identifying and understanding the causes of abnormalities, enterprises can optimize the test process, improve production efficiency, and reduce the rework rate and scrap rate.

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

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

[0082] Obtaining the proportion of the distribution of upstream parameters of each abnormal product under this abnormality classification within the overall distribution of the corresponding upstream parameters during the statistical period;

[0083] According to the proportion of the distribution of upstream parameters of each abnormal product within the overall distribution of this upstream parameter, obtaining the second correlation coefficient of this upstream parameter for this abnormality;

[0084] According to the first correlation coefficient between the upstream parameter and the abnormal test parameters of a certain type of abnormality and the second correlation coefficient of this upstream parameter for this abnormality, obtaining the first correlation score of this upstream parameter for this abnormality;

[0085] According to the first correlation scores of a certain parameter for multiple types of abnormalities, obtaining the overall correlation score of this parameter.

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

[0087] First, for a specific type of abnormality, calculate the correlation coefficient between its abnormal parameters (such as a specific test value or status) and the parameters of each upstream test item; the correlation coefficient is a statistical indicator that measures the tightness of the linear relationship between two variables, and its value ranges from -1 to 1. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and a value close to 0 indicates almost no linear relationship.

[0088] Within a statistical period (such as a month, a quarter, etc.), collect all the data of abnormal products belonging to this abnormal classification, and extract the parameter values of their upstream test items; then, calculate the proportion of the upstream parameter values of these abnormal products in the overall distribution of the corresponding upstream parameters (that is, the distribution of this parameter value for all products, including normal and abnormal products). This proportion reflects the "abnormal" degree or deviation degree of abnormal products in a certain upstream parameter. For example, if the distribution of a certain upstream parameter of an abnormal product is at 85% of this upstream parameter, then the proportion of this parameter of this product is 0.85; if the distribution of a certain upstream parameter of another abnormal product is at 15% of this upstream parameter, then the proportion of this parameter of this product is 0.15.

[0089] Based on the proportion of the upstream parameter distribution of abnormal products, a "second correlation coefficient" can be further calculated. This coefficient 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 degree of abnormal products in the parameter distribution.

[0090] Combining the first correlation coefficient between the upstream parameter and the abnormal test parameter of a certain abnormal type (that is, the first correlation coefficient in the first step) and the second correlation coefficient of this upstream parameter for this abnormality, calculate a "first association degree score". This score synthesizes 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 (that is, there are both the first correlation coefficient and the second correlation coefficient with the abnormal test parameters of multiple abnormal types), then a "total association degree score" can be calculated according to its first association degree score with each abnormal type. This total score reflects the comprehensive association degree of this upstream parameter in multiple abnormal types, which 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 be able to more accurately evaluate the relationship between the parameters of upstream test items and downstream abnormalities, providing strong data support for subsequent abnormality handling, quality improvement, and decision-making support.

[0093] The effects of the above technical solutions are as follows: By calculating the first correlation coefficient between the abnormal parameters of a certain abnormal type and the parameters of each upstream test item, it is possible to more accurately identify which upstream parameters have a significant association with the downstream abnormality. Introducing the second correlation coefficient, that is, the proportion of the upstream parameters of the abnormal product in the overall distribution, further enhances the accuracy of the analysis, because this not only considers the linear relationship between the parameters, but also considers the distribution of the abnormal product in the parameter space. By calculating the first association degree score of each upstream parameter for multiple abnormal types and comprehensively obtaining the total association degree score, it is possible to comprehensively evaluate the potential impact of each upstream parameter on multiple abnormal types; it helps to identify key upstream parameters, and changes in these parameters may have a significant impact on downstream test items, thus becoming the focus of quality improvement and abnormality prevention.

[0094] In some embodiments, obtaining the second correlation coefficient of the upstream parameter for the abnormality according to the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameter; includes:

[0095] 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, the worse the performance; or,

[0096] 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, the worse the performance; then mark the abnormal product; where the first preset threshold is greater than the second preset threshold;

[0097] Obtain the average proportion of the distribution of the upstream parameter of multiple marked abnormal products; according to the proportion average and the proportion of the marked abnormal products in the total amount of abnormal products, obtain the second correlation coefficient of the upstream parameter for the abnormality;

[0098]

[0099] Among them, is the second correlation coefficient of the th parameter and the th abnormality; is the average proportion of the th abnormal product in the th parameter; is the first preset threshold of the th parameter distribution; is the second preset threshold of the th parameter distribution; is the number of times the th abnormality of the th parameter is marked during the statistical period; is the total number of products of the th abnormality during the statistical period; is a constant, 5.

[0100] The working principle of the above technical solution is as follows: For each upstream parameter of the abnormal product, calculate its proportion in the overall distribution of the parameter; according to the preset thresholds (the first preset threshold and the second preset threshold) and the performance trend of the parameter (the larger the parameter, the worse the performance, or the smaller the parameter, the worse the performance), mark the abnormal product.

[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, the worse the performance, then mark the abnormal product.

[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, the worse the performance, then also mark the abnormal product.

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

[0104] For all marked abnormal products, calculate the mean value of the proportion of the distribution of each of their upstream parameters.

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

[0106] The value of the second correlation coefficient reflects the degree of association between the j-th upstream parameter and the i-th abnormality;

[0107] The effect of the above technical solution is as follows: 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, the worse the performance), it is possible to more accurately mark the products that are closely related to the abnormality, which helps to reduce the influence of noise data and thus 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 upstream parameters and specific abnormalities. The coefficient not only considers the mean value of the proportion of abnormal products in the distribution of upstream parameters, but also considers the ratio of the number of marked abnormal products to the total number of abnormal products, thus more comprehensively reflecting the relationship between parameters and abnormalities.

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

[0110] By accurately identifying the upstream parameters related to anomalies, the source of problems can be located more quickly, unnecessary investigation and testing work can be reduced, thereby improving work efficiency and reducing costs; in addition, targeted quality control measures also help reduce the scrap rate and rework rate, further reducing production costs.

[0111] In summary, the method provides enterprises with more effective means of anomaly diagnosis and quality control through the beneficial effects of accurately marking abnormal products, quantifying the association between upstream parameters and anomalies, enhancing the pertinence of quality control, and supporting continuous improvement and decision-making.

[0112] In some embodiments, obtaining the first association degree score of a parameter for an anomaly according to the first correlation coefficient between the upstream parameter and the anomaly parameter of a certain anomaly type and the second correlation coefficient for the anomaly includes:

[0113]

[0114] Among them, is the first association degree score of the th parameter for the th anomaly; is the first correlation coefficient between the th parameter and the anomaly parameter of the th anomaly; is the second correlation coefficient between the th parameter and the th anomaly; is taking the absolute value; and are weight coefficients.

[0115] In some embodiments, obtaining the total association degree score of a parameter according to the first association degree scores of the parameter with multiple anomaly types includes:

[0116]

[0117] Among them, is the total association degree score of the th parameter; is the first association degree score of the th parameter for the th anomaly; is the first score of the anomaly parameter of the th anomaly; is the total number of anomaly types.

[0118] The working principle and effects of the above technical solution are as follows: For each upstream parameter (the j-th parameter) and a specific type of anomaly (the i-th anomaly), first calculate the first correlation coefficient and the second correlation coefficient between the parameter and the anomaly parameter of the anomaly.

[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 distribution of upstream parameters and the performance trend.

[0120] These two coefficients are integrated into the first association 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 association degree score respectively. 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 comprehensively considers the direct linear relationship between the upstream parameter and the anomaly parameter (through the first correlation coefficient) and the performance trend based on the proportion of abnormal products (through the second correlation coefficient), so as to obtain a comprehensive score; it ensures a comprehensive evaluation of the relationship between the parameter and the anomaly, considering both the direct correlation (through the first correlation coefficient) and the influence of the abnormal product distribution (through the second correlation coefficient);

[0121] For each upstream parameter, its associations with multiple types of anomalies need to be considered. To obtain a comprehensive evaluation, calculate the sum of the first association degree scores between the parameter and all types of anomalies, and weight it according to the severity of each anomaly (represented by the first score of the anomaly parameter).

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

[0123] In some embodiments, determining the storage period of each parameter of each test item according to the parameter importance score, the first score, and the total association degree score includes:

[0124]

[0125]

[0126] Among them, For the The storage period of a parameter is the total score of the correlation degree of the j-th parameter; is the importance score of the j-th parameter; is the first score of the j-th parameter; is the preset storage period; is the comprehensive score of the j-th parameter; is the preset comprehensive score; is the functional relationship; where the functional relationship can be expressed as direct addition or weighted average.

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

[0128] For the parameters of each test item (the j-th parameter), it is necessary to comprehensively consider its importance score, first score, and total correlation score to determine its comprehensive score.

[0129] The importance score reflects the relative importance or influence of the parameter in the test item.

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

[0131] The total correlation score measures the degree of association between the parameter and multiple abnormal types.

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

[0133] The preset storage period is a reference value, representing the default storage period of the parameter without specific evaluation or optimization.

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

[0135] According to the comprehensive score of the parameter relative to the preset comprehensive score, the preset storage period is adjusted to obtain the personalized storage period of each parameter; if the comprehensive score of the parameter is higher than the preset comprehensive score, it means that the parameter requires a longer storage period to ensure the traceability of its quality and performance; on the contrary, if the comprehensive score of the parameter is lower than the preset comprehensive score, it means that the parameter can be set with a shorter storage period.

[0136] The effects of the above technical solutions are as follows: According to the different attributes of parameters (such as importance, abnormal risk, and correlation), the system can dynamically adjust its storage period, enabling critical data to be stored for a longer time, while less important data can be cleared at appropriate time points. By precisely setting the storage period, unnecessary long-term storage is avoided, thereby reducing storage costs and maintenance complexity; a reasonable storage strategy ensures that limited storage resources are utilized efficiently, supporting faster data retrieval and analysis speeds.

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

[0138] The reasonable setting of the storage period ensures that data analysis can focus on those data that have a significant impact on product quality and performance, improving the accuracy and reliability of the analysis results. For critical parameters with a high comprehensive score, extending their storage period allows for longer-term trend analysis, helping to discover potential problem patterns and improvement opportunities.

[0139] In summary, the storage period setting method provided in this application realizes the refined management of the storage period by introducing a scientific mathematical model, combining the importance score, the first score, and the total correlation score of parameters, not only improving the effectiveness of data management and storage strategies, but also providing strong technical support for the manufacturing industry, promoting the dual improvement of product quality and production efficiency.

[0140] This application provides a data storage management system for a product, and the system includes:

[0141] An importance scoring module, which is used to obtain the test process and test status of the product, and uniquely number each test item at each test station of the product; through the knowledge base and expert experience, perform importance scoring on each test item and test parameter to obtain the importance score of each test item and the importance score of each test parameter;

[0142] A correlation scoring module, which is used to obtain abnormal classifications through historical data; obtain the first score of each abnormal parameter according to the frequency and impact of the occurrence of the abnormality; obtain the total correlation score of each parameter of each upstream test item according to the correlation between the abnormal parameters of the downstream test item and the parameters of each upstream test item;

[0143] A query and display module, which is used to construct a data query index of the product according to the product serial number, test time, test station, test status, and test number; set the test items displayed in the user interaction interface according to the importance score of the test items;

[0144] A storage module, configured to determine the storage period of each parameter of each test item according to the parameter importance score, the first score, and the overall relevance score.

[0145] In some embodiments, the importance scoring module includes:

[0146] A process acquisition unit, configured to acquire the test process of a product, where the test process includes test workstations and the test sequence of each test item of each test workstation; the test workstations include product basic performance test, function test, and reliability test;

[0147] A status acquisition unit, configured to acquire the test status of a product, where the test status includes new test, repeated test, and test after rework; mark the test status of the product at each test workstation;

[0148] An identification number unit, configured to assign a unique identification number to each test item of a product at each test workstation;

[0149] An importance scoring unit, configured to perform importance scoring on each test item and the test parameters in the test item through a knowledge base and expert experience.

[0150] In some embodiments, the relevance scoring module includes:

[0151] An anomaly classification unit, configured to obtain anomaly classification information through historical data; the anomaly classification information includes anomaly type, test workstation where the anomaly occurs, test item where the anomaly occurs, and anomaly parameter;

[0152] A first scoring unit, configured to obtain the first score of the anomaly parameter corresponding to each anomaly according to the occurrence frequency and impact consequence of each anomaly;

[0153] An overall score acquisition unit, configured to obtain the relevance between the anomaly parameter of a downstream test item and the parameters of each upstream test item; obtain the overall relevance score of each parameter of each upstream test item according to the relevance.

[0154] In some embodiments, the overall score acquisition unit includes:

[0155] A correlation coefficient acquisition subunit, configured to obtain the first correlation coefficient between the anomaly parameter of a certain anomaly type and the parameters of each upstream test item;

[0156] A distribution acquisition subunit, configured to obtain the proportion of the upstream parameter distribution of each anomaly product under this anomaly classification within the overall distribution of the corresponding upstream parameter during the statistical period;

[0157] A second correlation coefficient subunit, configured to obtain the second correlation coefficient of this upstream parameter for this anomaly according to the proportion of the upstream parameter distribution of each anomaly product within the overall distribution of this upstream parameter;

[0158] The first correlation degree scoring sub-unit is used to obtain the first correlation degree score of the upstream parameter for the anomaly according to 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 the anomaly;

[0159] The total correlation degree scoring sub-unit is used to obtain the total correlation degree score of a parameter according to the first correlation degree scores of the parameter with multiple anomaly types.

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

[0161] A marking component, which is used to mark an abnormal product 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, the worse the performance; or,

[0162] 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, the worse the performance; then mark the abnormal product; where the first preset threshold is greater than the second preset threshold;

[0163] A calculation component, which is used to obtain the proportion mean of the distribution of the upstream parameter of multiple marked abnormal products; according to the proportion mean and the proportion of the marked abnormal products in the total amount of the abnormal products, obtain the second correlation coefficient of the upstream parameter for the anomaly;

[0164]

[0165] Wherein, is the second correlation coefficient of the th parameter with the th anomaly; is the proportion mean of the th anomaly product in the th parameter; is the first preset threshold of the th parameter distribution; is the second preset threshold of the th parameter distribution; is the number of times the th anomaly of the th parameter is marked during the statistical period; is the total number of products of the th anomaly during the statistical period; is a constant, 5.

[0166] In some embodiments, the first correlation degree scoring sub-unit includes:

[0167]

[0168] Among them, is the first correlation degree score between the th parameter and the th exception; is the first correlation coefficient between the th parameter and the abnormal parameter of the th exception; is the second correlation coefficient between the th parameter and the th exception; is to take the absolute value, , are weight coefficients.

[0169] In some embodiments, the correlation degree total score sub-unit includes:

[0170]

[0171] Among them, is the total correlation degree score of the th parameter; is the first correlation degree score between the th parameter and the th exception; is the first score of the abnormal parameter of the th exception, is the total number of exception types.

[0172] In some embodiments, the storage module includes:

[0173]

[0174]

[0175] Among them, is the storage period of the th parameter, is the total correlation degree score of the th parameter; is the importance score of the th parameter; is the first score of the th parameter; is the preset storage period; is the comprehensive score of the jth parameter; is the preset comprehensive score; is a functional relationship.

[0176] The working principle and effect of the above technical solution are the same as those in the method embodiment of the present application, and will not be elaborated here.

[0177] The present application also provides an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any method described in the present application are implemented.

[0178] The present application is described from the viewpoints of purpose of use, effectiveness, progressiveness, and novelty, and has met the functional enhancement and use requirements emphasized by the patent law. The above description and accompanying drawings of the present application are only preferred embodiments of the present application and do not limit the present application thereto. Therefore, all those that are similar or identical to the structure, device, features, etc. of the present application, that is, all equivalent substitutions or modifications made according to the scope of the patent application of the present application, shall fall within the scope of protection of the patent application of the present application.

Claims

1. A data storage management method for a product, characterized in that: The method comprises: S1. Obtain the product's test process and test status, and uniquely number each test item of the product at each test station; score the importance of each test item and test parameter based on the knowledge base and expert experience, and obtain the importance score of each test item and the importance score of each test parameter; S2. Obtain anomaly classification through historical data; obtain a first score for each anomaly parameter based on the frequency and impact of the anomaly; obtain a total score for the degree of correlation of each parameter of each upstream test item based on the degree of correlation between the anomaly parameter of the downstream test item and the parameters of each upstream test item; S3. Build a data query index for the product based on the product serial number, test time, test station, test status, and test number; set the test items displayed on the user interaction interface based on the importance score of the test items; S4. Determine the storage period of each parameter of each test item according to the parameter importance score, the first score and the total correlation score.

2. The method according to claim 1, characterized in that The S1 includes: Obtaining a product test process, wherein the test process includes test stations and the test sequence of each test item at each test station; the test station includes product basic performance testing, function testing, and reliability testing; Obtaining the test status of the product, including new test, repeated test, and test after rework; marking the test status of the product at each test station; Uniquely number each test item of the product at each test station; The importance of each test item and the test parameters in the test item is scored through the knowledge base and expert experience.

3. The method according to claim 1, characterized in that: The S2 includes: Obtaining abnormality classification information through historical data; the abnormality classification information includes abnormality type, test station where the abnormality occurs, test item where the abnormality occurs, and abnormal parameters; According to the frequency of occurrence and the impact consequence of each abnormality, a first score of the abnormality parameter corresponding to each abnormality is obtained; Obtain the correlation between the abnormal parameters of the downstream test items and the parameters of each upstream test item; and obtain the total correlation score of each parameter of each upstream test item according to the correlation.

4. The method according to claim 3, characterized in that The step of obtaining the correlation between the abnormal parameters of the downstream test items and the parameters of each upstream test item; and obtaining the total correlation score of each parameter of each upstream test item according to the correlation includes: Obtaining a first correlation coefficient between an abnormal parameter of a certain abnormal type and a parameter of each upstream test item; Obtain the proportion of the upstream parameter distribution of each abnormal product under the abnormal classification in the corresponding upstream parameter overall distribution within the statistical period; According to the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameter; obtaining a second correlation coefficient of the upstream parameter for the abnormality; Obtaining a first correlation score of the upstream parameter for the anomaly according to a first correlation coefficient between the upstream parameter and an anomaly test parameter of a certain anomaly type and a second correlation coefficient of the upstream parameter for the anomaly; According to the first correlation scores of a certain parameter and a plurality of abnormal types, a total correlation score of the parameter is obtained.

5. The method according to claim 4, characterized in that The method of obtaining a second correlation coefficient of the upstream parameter for the abnormal product according to the proportion of the upstream parameter distribution of each abnormal product in the overall distribution of the upstream parameter comprises: If the proportion of a certain upstream parameter distribution of abnormal products is greater than the first preset threshold, and the larger the parameter, the worse the performance; or, If the proportion of a certain upstream parameter distribution of an abnormal product is less than a second preset threshold, and the smaller the parameter is, the worse the performance is; the abnormal product is marked; wherein the first preset threshold is greater than the second preset threshold; Obtaining a mean value of the proportion of the upstream parameter distribution of multiple marked abnormal products; obtaining a second correlation coefficient of the upstream parameter for the abnormality according to the mean value of the proportion and the proportion of the marked abnormal product in the total amount of the abnormal product; in, For the Parameters and An abnormal second correlation coefficient; For the The abnormal product is in The mean value of the proportion of parameters; For the a first preset threshold value of a parameter distribution; For the a second preset threshold value of a parameter distribution; The first Anomaly The number of parameters marked; The first Total number of abnormal products; is a constant, 5.

6. The method according to claim 4, characterized in that The step of obtaining a first correlation score of the parameter for the abnormality according to a first correlation coefficient between the upstream parameter and an abnormal parameter of a certain abnormality type and a second correlation coefficient for the abnormality; include: in, For the Parameters and The first relevance score of anomalies; For the Parameters and The first correlation coefficient of the abnormal parameters of the anomalies; For the Parameters and An abnormal second correlation coefficient; To take the absolute value, , is the weight coefficient.

7. The method according to claim 4, characterized in that The step of obtaining a total correlation score of a parameter according to the first correlation scores of a certain parameter and a plurality of abnormal types comprises: in, For the Total score of correlation of parameters; For the Parameters and The first relevance score of anomalies; For the The first score of the anomaly parameter of the anomaly, The total number of exception types.

8. The method according to claim 1, characterized in that: 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; include: in, For the The storage period of the parameters, For the Total score of correlation of parameters; For the Importance score of each parameter; For the The first score of the parameters; is a preset storage period; is the comprehensive score of the jth parameter; Provides a preset comprehensive score; is a functional relationship.

9. A data storage management system for a product, characterized in that: The system comprises: The importance scoring module is used to obtain the product's test process and test status, and uniquely number each test item of the product at each test station; use the knowledge base and expert experience to score the importance of each test item and test parameter, and obtain the importance score of each test item and the importance score of each test parameter; The correlation scoring module is used to obtain anomaly classification through historical data; obtain the first score of each abnormal parameter according to the frequency and impact of the abnormality; and obtain the total correlation score of each parameter of each upstream test item according to the correlation between the abnormal parameters of the downstream test item and the parameters of each upstream test item; The query display module is used to build a product data query index based on the product serial number, test time, test station, test status and test number; and to set the test items displayed in the user interaction interface according to the importance score of the test items; The storage module is used 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 score.

10. An electronic device, characterized in that: The electronic device 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 claims 1 to 8 when executing the computer program.

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