Copper Wire Tensile Quality Detection Method and System for Elongation Analysis

Abstract

This application proposes a copper wire tensile quality detection method and system for elongation analysis. The method includes: obtaining production process record data of copper wire samples that meet the elongation threshold; based on the preset production process, traversing and comparing the production process record data to obtain production process deviation parameters; extracting record data with deviation parameters less than or equal to the threshold according to the deviation parameters for central value evaluation to obtain a production process benchmark space; when the production process monitoring data of the copper wire to be tested falls into the benchmark space, determining that the quality of the copper wire is qualified; when the monitoring data does not fall into the benchmark space, extracting a single-attribute deviation matrix; and retrieving the elongation threshold of abnormal samples that meet the deviation matrix to perform targeted tensile quality actual measurement. By establishing a production process benchmark space, accurate prediction and targeted detection of copper wire tensile quality are realized, effectively reducing the missed detection rate.

Description

Technical Field

[0001] The present invention relates to the field of copper wire quality detection, and in particular to a copper wire tensile quality detection method and system for elongation analysis. Background Art

[0002] Copper wire, as a common conductive material, is widely used in electrical, electronic, and communication fields. The elongation of copper wire is an important indicator for evaluating its mechanical properties, which affects the service life and reliability of the product. In traditional copper wire production quality control, tensile quality testing mainly relies on average sampling, that is, randomly sampling samples from batch-produced copper wire for elongation testing. This random sampling lacks specificity and cannot effectively identify quality problems that may occur under specific production process conditions; at the same time, the number of random inspections is limited, and it is difficult to fully cover various variables and parameter combinations in the production process, resulting in a high missed detection rate, which makes it possible for unqualified copper wire products to flow into the market, bringing safety hazards and economic losses. Summary of the invention

[0003] The present invention aims to solve the technical problem that copper wire stretching quality detection in the prior art relies on average sampling and lacks specificity, thus leading to a high missed detection rate, and provides a copper wire stretching quality detection method and system for elongation analysis to solve the problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a copper wire stretching quality detection method for elongation analysis, comprising: obtaining a plurality of production process record data of a plurality of copper wire samples that meet an elongation threshold; based on a preset production process, traversing the plurality of production process record data for comparison to obtain a plurality of production process deviation parameters; based on the plurality of production process deviation parameters, extracting selected production process record data whose production process deviation parameters are less than or equal to the deviation parameter threshold from the plurality of production process record data for centralized value evaluation to obtain a production process reference space; when the production process monitoring data of the copper wire to be tested falls into the production process reference space, marking the copper wire to be tested as qualified in quality; when the production process monitoring data of the copper wire to be tested does not fall into the production process reference space, extracting a production process single attribute deviation matrix based on a preset deviation threshold of a single attribute of the production process; retrieving the minimum value of the concentrated copper wire elongation of abnormal copper wire samples that meet the production process single attribute deviation matrix, and sending it to the copper wire stretching control end to set the stretching parameters and perform actual stretching quality measurement.

[0006] In a second aspect, the present invention provides a copper wire stretching quality detection system for elongation analysis, comprising: a sample acquisition module for obtaining a number of production process record data of a number of copper wire samples that meet the elongation threshold; a process parameter comparison module for traversing and comparing the number of production process record data based on a preset production process to obtain a number of production process deviation parameters; a reference space construction module for performing a concentration value evaluation on the selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold extracted from the number of production process record data according to the number of production process deviation parameters to obtain a production process reference space; a qualified label determination module for labeling the quality of the copper wire to be tested as qualified when the production process monitoring data of the copper wire to be tested falls into the production process reference space; a deviation matrix extraction module for extracting a single-attribute production process deviation matrix based on a preset deviation threshold for a single production process attribute when the production process monitoring data of the copper wire to be tested does not fall into the production process reference space; a stretching quality actual measurement module for retrieving the minimum value of the concentrated copper wire elongation of abnormal copper wire samples that meet the single-attribute production process deviation matrix and sending it to the copper wire stretching control end to set stretching parameters to perform actual stretching quality measurement.

[0007] The beneficial effects of the present invention are:

[0008] Obtain the production process record data of several copper wire samples that meet the elongation threshold. By collecting the production process record data of copper wire samples that have been proven to meet the elongation quality requirements in history, a basic data set is established for subsequent analysis. Based on the preset production process, traverse the production process record data for comparison to obtain several production process deviation parameters. Compare and analyze the preset production process in the ideal state with the process record data in actual production, calculate the deviation values of each process parameter, and quantify the fluctuations in the production process. According to several production process deviation parameters, extract the selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold from several production process record data for centralized value evaluation to obtain the production process benchmark space. By screening out the production process record data with deviation parameters within the acceptable range and conducting centralized value evaluation on these data, a process benchmark space representing the production conditions of qualified products is constructed, providing a reference standard for subsequent quality judgment. By judging whether the process monitoring data of the currently produced copper wire is within the established process benchmark space, if it is within the space, it can be directly determined to be of qualified quality without actual tensile testing, improving the detection efficiency. When the process monitoring data of the copper wire to be tested does not fall into the production process benchmark space, extract the production process single-attribute deviation matrix based on the preset deviation threshold of the production process single-attribute. For the situation where the process monitoring data is not within the benchmark space, further analyze the deviation of each individual process attribute and construct a deviation matrix to provide a basis for accurately locating potential quality problems. Retrieve the minimum value of the centralized copper wire elongation of abnormal copper wire samples that meet the production process single-attribute deviation matrix and send it to the copper wire stretching control end to set the stretching parameters to perform actual tensile quality testing. By retrieving the abnormal sample records similar to the current deviation matrix in historical data, determine the appropriate elongation threshold and apply it to the tensile testing equipment to perform targeted actual tensile quality testing to ensure that the test parameters match the potential problems and improve the accuracy of detection.

[0009] Through the above technical solutions, the technical upgrade from general sampling inspection to accurate prediction and targeted detection is realized, effectively reducing the missed inspection rate of copper wire tensile quality detection. Description of the Drawings

[0010] Figure 1 It is a flowchart of the copper wire tensile quality detection method for elongation analysis provided by the present invention;

[0011] Figure 2 It is a structural diagram of the copper wire tensile quality detection system for elongation analysis provided by the present invention.

[0012] In the drawings, the components represented by each reference numeral are as follows:

[0013] Sample acquisition module 11, process parameter comparison module 12, reference space construction module 13, qualified mark determination module 14, deviation matrix extraction module 15, and tensile quality measurement module 16. Detailed implementation manners

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0015] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0016] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0017] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a copper wire tensile quality detection method for elongation analysis, including:

[0018] S100: Obtain a number of production process record data of a number of copper wire samples that meet the elongation threshold.

[0019] Specifically, elongation, as a key indicator for measuring the quality of copper wires, directly reflects the deformation ability and material toughness of copper wires under stress, and has a decisive impact on the service performance of copper wires. The elongation threshold refers to the minimum allowable value of the ratio of the elongation before fracture to the original gauge length in the tensile test of copper wires, usually expressed as a percentage. Among them, the elongation threshold is determined based on the statistical analysis of a large amount of historical production data, combined with product standard requirements and actual application needs.

[0020] By screening historical production data and selecting samples with elongation test results reaching or exceeding the elongation threshold, a number of copper wire samples are obtained. These samples represent copper wire products that meet the elongation threshold. For each copper wire sample that meets the elongation threshold, record its complete production process data, including but not limited to key parameters such as the proportion of copper wire raw materials, copper wire rolling process, copper wire drawing process, and heat treatment process. Among them, the copper wire raw material proportion data includes information such as the purity of raw materials and the alloy composition ratio; the copper wire rolling process data records parameters such as temperature, pressure, and speed during the rolling process; the copper wire drawing process data covers factors such as drawing equipment, drawing speed, and tension control; and the heat treatment process data includes process elements such as the heat treatment temperature curve, holding time, and cooling method.

[0021] The obtained production process record data of a number of copper wire samples provides the necessary data support for subsequent process parameter analysis and quality assessment. By collecting these sample data that meet the elongation threshold, the production process benchmark space of copper wires can be determined, providing an accurate reference basis for subsequent quality inspection, thereby achieving targeted quality inspection, effectively reducing the missed inspection rate, and improving the accuracy and efficiency of inspection.

[0022] S200: Based on the preset production process, traverse the above-mentioned production process record data for comparison to obtain a number of production process deviation parameters.

[0023] Specifically, the preset production process refers to the standard process parameter combination determined according to the technical specifications and production experience of copper wire products, usually formulated by the enterprise's technical department as the benchmark process for copper wire production. The preset production process includes the standard formula of the proportion of copper wire raw materials, the standard parameters of the rolling process, the standard settings of the drawing process, and the standard conditions of the heat treatment process, etc.

[0024] Traverse the production process record data of each copper wire sample that meets the elongation threshold in sequence, and compare it one by one with the parameters of the preset production process. During the comparison process, calculate the difference value between the actual production process parameters in the production process record data and the preset parameters in the preset production process to form a number of production process deviation parameters. The production process deviation parameter is a quantitative index to measure the degree of difference between the actual production process and the preset production process, which can be an absolute deviation value, a relative deviation rate or a comprehensive deviation index calculated through a specific mathematical model. These deviation parameters reflect the allowable fluctuation range of the production process on the premise of ensuring the qualified elongation of the copper wire.

[0025] By obtaining a number of production process deviation parameters, quantify the potential impact of the change in the production process on the elongation of the copper wire, establish an association model between the copper wire production process and the elongation, and provide a data basis for determining the process reference space and deviation threshold subsequently, so as to effectively improve the accuracy and reliability of the copper wire stretching quality detection.

[0026] S300: According to the number of production process deviation parameters, extract the selected production process record data with the production process deviation parameters less than or equal to the deviation parameter threshold from the number of production process record data for centralized value evaluation to obtain the production process reference space.

[0027] Specifically, on the basis of obtaining a number of production process deviation parameters, by setting the deviation parameter threshold, screen out the production process record data with smaller deviations, and conduct centralized value evaluation on these data to construct the production process reference space of the copper wire. Among them, the deviation parameter threshold refers to the allowable upper limit value of the production process deviation parameter, which is determined by comprehensively considering the process stability requirements, the tolerance of product quality fluctuations and the actual production situation. For example, through statistical analysis methods, combined with historical production experience, determine a reasonable deviation parameter threshold, which can not only ensure that the selected data is representative, but also ensure that the sample quantity meets the needs of subsequent analysis.

[0028] First, evaluate the production process deviation parameters of each production process record data, and screen out the record data with production process deviation parameters less than or equal to the set deviation parameter threshold, and mark them as selected production process record data. These selected production process record data represent the process parameter combinations with relatively small production process deviations on the premise of ensuring the qualified copper wire elongation rate. Next, conduct a central value evaluation on the selected production process record data. Central value evaluation is a data analysis method aimed at determining the central distribution area of multi-dimensional data. Among them, various statistical methods can be used for central value evaluation, such as principal component analysis, clustering analysis, kernel density estimation, etc., to identify the main distribution characteristics and central tendency of the selected production process record data. Through the central value evaluation, a production process reference space is finally obtained. The production process reference space is a multi-dimensional parameter space that defines the range of process parameter combinations to ensure the qualified copper wire elongation rate. This space can be a geometric area composed of multiple process parameters, or a set of parameter relationships described by a mathematical model.

[0029] By establishing the production process reference space, it provides a basis for subsequent copper wire quality detection. By judging whether the production process monitoring data of the copper wire to be tested falls into this reference space, it can effectively predict whether the elongation rate of the copper wire meets the requirements, thereby realizing the efficient detection of the copper wire stretching quality and overcoming the shortcomings of the traditional sampling inspection method with insufficient pertinence and high missed inspection rate.

[0030] S400: When the production process monitoring data of the copper wire to be tested falls into the production process reference space, mark the copper wire to be tested as qualified in quality.

[0031] Specifically, during the copper wire production process, process parameter data are collected in real time through various sensors, detection instruments, and production control systems, including the copper wire raw material ratio, rolling process parameters, stretching process parameters, and heat treatment process parameters, etc., to obtain the production process monitoring data of the copper wire to be tested, comprehensively reflecting the process conditions of the copper wire to be tested during the production process and providing an important basis for evaluating the copper wire quality. Subsequently, compare the production process monitoring data of the copper wire to be tested with the established production process reference space. Various mathematical methods can be used in the comparison process, such as Euclidean distance calculation, Mahalanobis distance evaluation, multivariate statistical analysis, etc., to judge whether the data point to be tested falls into the production process reference space.

[0032] When the determination result indicates that the production process monitoring data of the copper wire to be tested falls within the production process reference space, it means that the production process conditions of the copper wire to be tested are similar to or consistent with those of the known high-quality copper wire samples, and it can be inferred that the elongation rate of the copper wire to be tested meets the quality requirements. At this time, a quality qualified label is assigned to the copper wire to be tested. Among them, the quality qualified label can be an electronic mark in the production management system or a physical mark on the actual production line, such as a qualified label, a specific color mark, or a bar code, etc. The quality qualified label indicates that the copper wire to be tested does not need to undergo additional elongation rate tests and can directly enter the next production link or finished product inventory, thus significantly improving production efficiency and reducing unnecessary quality inspection costs.

[0033] By assigning a quality qualified label to the copper wire to be tested whose production process monitoring data falls within the production process reference space, rapid determination of the elongation rate quality of the copper wire based on production process parameters is achieved, avoiding the cumbersome process of physical sampling tests for the copper wire to be tested in the traditional method, and improving the efficiency and accuracy of quality inspection. Especially for copper wire products in mass production, this method can significantly reduce the time and labor costs in the quality inspection link while ensuring the reliability of product quality.

[0034] S500: When the production process monitoring data of the copper wire to be tested does not fall within the production process reference space, extract the production process single-attribute deviation matrix based on the preset deviation threshold of the production process single-attribute.

[0035] Specifically, when it is determined that the production process monitoring data of the copper wire to be tested does not fall within the production process reference space, it indicates that there are certain differences between the production process conditions of this copper wire and those of the known high-quality copper wire samples, and its elongation rate may not meet the quality requirements. At this time, it is necessary to further analyze which specific process parameters deviate from the reference range and the degree of deviation.

[0036] Compare each production process monitoring data of the copper wire to be tested with the preset standard values in the preset production process, calculate the actual deviation values of each parameter, and compare these deviation values with the corresponding single-attribute preset deviation thresholds in the production process sheet attributes, so as to determine which parameters exceed the allowable range and the degree of exceeding. These information are organized into a production process sheet attribute deviation matrix. Among them, the production process sheet attribute preset deviation threshold refers to the upper limit value of the allowable deviation set separately for each production process parameter. These deviation thresholds are determined based on historical production data analysis and professional experience, and reflect the influence sensitivity of each process parameter on the elongation rate of the copper wire. For example, the deviation threshold of the purity of the copper wire raw material may be set at ±0.5%, the deviation threshold of the rolling temperature may be set at ±10 °C, the deviation threshold of the heat treatment time may be set at ±5 minutes, etc. The production process sheet attribute deviation matrix is a data structure used to store and display the deviation conditions of each process parameter in the production process monitoring data of the copper wire to be tested. This matrix can contain information such as parameter name, preset standard value, actual monitoring value, deviation value, single-attribute preset deviation threshold, and a mark indicating whether it exceeds the threshold. Through the production process sheet attribute deviation matrix, it is possible to visually identify which process parameters are potential factors causing quality anomalies.

[0037] By constructing a production process sheet attribute deviation matrix for the copper wire to be tested whose production process monitoring data does not fall into the production process reference space, it provides data support for subsequent quality assessment and process adjustment.

[0038] S600: Retrieve the minimum value of the copper wire elongation rate in the concentrated copper wire elongation rate of the abnormal copper wire samples that meet the production process sheet attribute deviation matrix, and send it to the copper wire stretching control end to set the stretching parameters and perform the actual measurement of the stretching quality.

[0039] Specifically, after obtaining the production process sheet attribute deviation matrix, by retrieving the abnormal copper wire sample library, find the abnormal copper wire samples with similar process deviation characteristics to the current copper wire to be tested, obtain their elongation rate data, and set the stretching parameters accordingly to achieve targeted actual measurement of quality.

[0040] First, retrieve historical samples in the abnormal copper wire sample library that match the characteristics of the production process sheet attribute deviation matrix of the current copper wire to be tested to obtain abnormal copper wire samples. These abnormal copper wire samples refer to the copper wire samples in the previous production process whose production process parameter deviation patterns are similar to those of the current copper wire to be tested. Among them, the matching process can adopt methods such as pattern recognition or similarity calculation to ensure finding the most representative similar samples. Since these abnormal copper wire samples are similar to the current copper wire to be tested in terms of production process deviation characteristics, their elongation rate performance has reference value for predicting the elongation rate of the current copper wire to be tested.

[0041] Then, extract the copper wire elongation data of these abnormal copper wire samples, and analyze the distribution characteristics of the obtained copper wire elongation data. For example, use statistical methods such as box plot method, probability density analysis or clustering analysis to identify the concentrated distribution interval of these elongation data. After removing the outliers, obtain the concentrated copper wire elongation, and take the minimum value in the concentrated copper wire elongation as the lowest elongation level that the current copper wire to be tested may reach. The minimum value of the concentrated copper wire elongation refers to the lowest level of elongation in the abnormal copper wire samples produced under similar process conditions. Then, send the minimum value of the concentrated copper wire elongation to the copper wire stretching control end as the basis for setting the stretching parameters. The copper wire stretching control end adjusts the parameter settings of the stretching test equipment, such as stretching rate, initial tension, test temperature, etc., according to the received minimum value of the concentrated copper wire elongation to ensure the accuracy and reliability of the measured stretching quality.

[0042] By obtaining the minimum value of the concentrated copper wire elongation and setting the stretching parameters accordingly, targeted quality inspection of the copper wire produced under abnormal process conditions is achieved, avoiding the inaccurate detection problem caused by using unified stretching parameters in the traditional method. At the same time, since the stretching parameter settings are more in line with the actual characteristics of the copper wire to be tested, the reliability of the detection results is improved, providing more accurate data support for production process control and quality improvement.

[0043] Furthermore, based on the preset production process, traverse the above-mentioned several production process record data for comparison to obtain several production process deviation parameters, including:

[0044] S210: Obtain the first production process record data of the above-mentioned several production process record data;

[0045] S220: Obtain the set of influence weights of production process attributes on elongation;

[0046] S230: Compare the first production process record data with the preset production process for the same attributes to obtain the set of same-attribute deviations;

[0047] S240: According to the set of influence weights of production process attributes on elongation, perform weighted Euclidean distance evaluation on the set of same-attribute deviations to obtain the first production process deviation parameter, and add it to the above-mentioned several production process deviation parameters.

[0048] In a feasible implementation, first, select a production process record data from several acquired production process record data as the first production process record data for processing. The first production process record data refers to any complete production process record data selected from multiple copper wire samples that meet the elongation threshold according to a predetermined sorting rule (such as production time order, batch number order, etc.). This first production process record data contains complete parameter information such as copper wire raw material ratio, rolling process, stretching process, and heat treatment process, and is the basic data for subsequent comparison and analysis. At the same time, obtain a preset set of production process attribute elongation influence weights. This set of production process attribute elongation influence weights refers to a set of quantitative index for the influence degree of each process parameter on the elongation of copper wire. In the process of copper wire production, different process parameters have different influence degrees on the elongation of the final copper wire product. For example, the heat treatment temperature may have a more significant influence on the elongation than the cooling rate. This set of production process attribute elongation influence weights can be determined based on historical data analysis and expert experience, and reflects the sensitivity of the change of each process parameter to the elongation.

[0049] Then, compare each process parameter in the first production process record data with the corresponding standard parameters in the preset production process one by one. The comparison process follows the principle of same-attribute comparison, that is, comparing the purity of copper wire raw material with the standard purity, the actual rolling temperature with the standard rolling temperature, the actual stretching rate with the standard stretching rate, etc. Through the same-attribute comparison, calculate the deviation value of each process parameter to form a same-attribute deviation set. This set contains the deviation information of all process parameters and can be represented as a vector or matrix, where each element corresponds to the deviation value of a specific process parameter. The deviation value can be the absolute difference, relative difference, or other difference measures suitable for the characteristics of specific parameters.

[0050] Subsequently, use the obtained set of production process attribute elongation influence weights to perform a weighted process on the same-attribute deviation set. Specifically, a weighted Euclidean distance evaluation can be adopted, considering the product of the sum of the squares of the deviations of each process parameter and its production process attribute elongation influence weight, so as to comprehensively reflect the degree of the overall process deviation.

[0051] The mathematical expression of the weighted Euclidean distance evaluation can be:

[0052] ;

[0053] where, represents the production process attribute elongation influence weight of the i-th process parameter, Pi represents the actual value of the i-th process parameter, and Si represents the standard value of the i-th process parameter.

[0054] Through the above calculations, the comprehensive deviation parameter of the first production process record data is obtained, that is, the first production process deviation parameter. This first production process deviation parameter is a comprehensive index that quantifies the overall difference degree between the first production process record data and the preset production process. This parameter is added to several production process deviation parameters as the basic data for subsequent analysis.

[0055] The above steps S210 to S240 constitute a cyclic processing flow, which sequentially processes each production process record data in several production process record data, calculates the corresponding production process deviation parameters, forms several production process deviation parameters, and provides data support for the subsequent construction of the production process reference space.

[0056] Furthermore, obtain the influence weight set of the production process attribute elongation rate, including:

[0057] S221: Obtain the first production process attribute;

[0058] S222: Extract the first attribute preset deviation threshold from the preset deviation threshold of the production process sheet attributes;

[0059] S223: Retrieve the number of first abnormal samples in the abnormal copper wire sample library where only the first production process attribute is greater than the first attribute preset deviation threshold;

[0060] S224: Until the Nth production process attribute is obtained;

[0061] S225: Extract the Nth attribute preset deviation threshold from the preset deviation threshold of the production process sheet attributes;

[0062] S226: Retrieve the number of Nth abnormal samples in the abnormal copper wire sample library where only the Nth production process attribute is greater than the Nth attribute preset deviation threshold;

[0063] S227: Calculate the sum value of abnormal samples from the first abnormal sample number to the Nth abnormal sample number, traverse from the first abnormal sample number to the Nth abnormal sample number, and find the ratio with the sum value of abnormal samples to obtain the influence weight set of the production process attribute elongation rate.

[0064] In a preferred embodiment, there are multiple production process attributes in the production process of copper wire. Traverse the multiple production process attributes, and each time select one production process attribute as the first production process attribute, such as the purity of copper wire raw materials, rolling temperature, stretching rate, heat treatment time, etc. At the same time, for the determined first production process attribute, extract its corresponding preset deviation threshold from the preset deviation thresholds of single production process attributes established in advance as the first attribute preset deviation threshold. The preset deviation threshold of single production process attribute refers to the maximum deviation range allowed for each production process attribute, which is determined based on production experience and quality standards. The first attribute preset deviation threshold is the standard line for judging whether the first production process attribute is abnormal. Exceeding this threshold is regarded as an abnormal factor that may affect the elongation rate.

[0065] Then, perform an exact search in the abnormal copper wire sample library to screen out those samples in which only the first production process attribute exceeds the first attribute preset deviation threshold while all other process attributes are within the normal range. Count their number, denoted as the first abnormal sample number, which reflects the frequency of the elongation rate non - compliance cases caused solely by the abnormality of the first production process attribute and is important data for quantifying the influence weight of this attribute. In a similar way, process the second, third, and until the Nth production process attribute in sequence, where N is the number of all production process attributes involved in the copper wire production process. For each production process attribute, perform the operation process of obtaining the production process attribute, extracting the attribute preset deviation threshold, and retrieving the number of single - factor abnormal samples, and finally obtain the first abnormal sample number to the Nth abnormal sample number.

[0066] After obtaining the number of abnormal samples of all production process attributes, first calculate the sum of these numbers, that is, the abnormal sample sum value, which represents the total number of elongation rate non - compliance samples caused by all single - factor abnormalities. Then, traverse the number of abnormal samples of each production process attribute, divide it by the abnormal sample sum value to obtain the relative frequency of the elongation rate abnormality caused by this attribute as the influence weight of this attribute on the elongation rate. These weight values together constitute the set of influence weights of production process attributes on the elongation rate.

[0067] By means of statistical analysis based on abnormal samples, the objective quantification of the influence weights of each production process attribute is realized, avoiding the uncertainty of subjective experience judgment. These influence weights on the elongation rate provide a reliable basis for the subsequent comprehensive evaluation of process deviations, improving the accuracy and reliability of copper wire stretching quality detection.

[0068] Furthermore, extract the selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold from the several production process record data for central value evaluation to obtain the production process reference space, including:

[0069] S310: Pairwise compare the selected production process record data to obtain multiple selected production process deviation parameters;

[0070] S320: Based on the multiple selected production process deviation parameters, traverse the selected production process record data to perform outlier factor statistics and obtain an outlier factor statistics value set;

[0071] S330: Based on an outlier factor threshold, combined with the outlier factor statistics value set, perform sorting on the selected production process record data to obtain concentrated production process record data and construct the production process reference space.

[0072] Specifically, first, from several production process record data, extract the data with production process deviation parameters less than or equal to the deviation parameter threshold, and mark them as selected production process record data. These selected data represent qualified process parameter combinations within a certain deviation tolerance range. Next, perform pairwise comparison and analysis on these selected production process record data. Pairwise comparison means comparing each selected production process record data with every other selected production process record data one by one to calculate the parameter differences between them. Specific comparison methods can use Euclidean distance, Mahalanobis distance, or other distance metrics suitable for multi-dimensional data comparison. Through comprehensive pairwise comparison, multiple selected production process deviation parameters are obtained, quantifying the similarity or difference degree between different process records and providing basic data for subsequent outlier factor analysis.

[0073] After obtaining multiple selected production process deviation parameters, calculate the outlier factor for each selected production process record data. The outlier factor is a statistical indicator used to measure the degree of abnormality or dispersion of a data point from other data points. In specific implementation, traverse each selected production process record data, and statistically analyze the distribution characteristics of its deviation parameters from other selected production process record data, such as average deviation, maximum deviation, standard deviation of deviation, etc., so as to obtain the outlier factor statistics value of this selected production process record data. This process is repeated until all selected production process record data are processed, and finally an outlier factor statistics value set is formed. This set contains the quantification indicators of the outlier degree of each selected production process record data, which can reflect which process records are more consistent with the mainstream process mode and which ones deviate significantly.

[0074] Subsequently, an outlier factor threshold is first determined, which is determined by statistical analysis methods, such as the percentile based on the outlier factor distribution, the clustering analysis result, or the expert experience value, etc. Then, the outlier factor statistical value of each selected production process record data is compared with the set outlier factor threshold. If the outlier factor statistical value of a selected production process record data is less than or equal to the outlier factor threshold, it is determined as the process record data with a concentrated distribution; if it is greater than the outlier factor threshold, it is regarded as an outlier point and not included in the data set for constructing the production process benchmark space. Through this sorting process, the process record data with smaller outlier factors and stronger representativeness are screened out, which are defined as the concentrated production process record data. These data have high mutual consistency and can more accurately reflect the process characteristics of high-quality copper wire production. After that, based on the concentrated production process record data, a production process benchmark space is constructed. This production process benchmark space is a region in the multi-dimensional parameter space, defined by the distribution range of the concentrated data points.

[0075] By constructing the production process benchmark space, the interference of abnormal or atypical process records is excluded, and the process conditions for ensuring the qualified elongation rate of copper wire are more accurately reflected, providing a reliable reference standard for subsequent quality inspection.

[0076] Furthermore, when the production process monitoring data of the copper wire to be tested does not fall into the production process benchmark space, a production process single-attribute preset deviation threshold is used to extract the production process single-attribute deviation matrix, including:

[0077] S510: Obtain the first production process attribute;

[0078] S520: Retrieve the set of copper wire samples with abnormal elongation rate having deviations in the first production process attribute;

[0079] S530: Extract the set of first production process attribute deviation values of the first production process attribute recorded values and the first production process attribute preset values of the set of copper wire samples with abnormal elongation rate;

[0080] S540: Conduct box plot analysis on the set of first production process attribute deviation values to obtain the set of box body deviation values, extract the minimum value, set it as the first production process attribute deviation threshold, and add it to the production process single-attribute preset deviation threshold.

[0081] In a preferred embodiment, first, a production process attribute is selected from multiple production process attributes of the copper wire production process as the first production process attribute. Then, copper wire samples with unqualified elongation and deviation in the first production process attribute are retrieved from the abnormal copper wire sample library to obtain a set of copper wire samples with abnormal elongation. These samples represent historical cases where elongation anomalies are caused when the first production process attribute deviates. By screening these specific abnormal samples, the correlation between process deviation and elongation anomaly can be established.

[0082] For the set of copper wire samples with abnormal elongation selected, the actual recorded values of the first production process attribute therein are extracted and compared with the preset values of the first production process attribute in the preset production process to calculate the deviation values. These deviation values can be absolute deviations, relative deviations, or other difference metrics suitable for the characteristics of this attribute. The deviation values of all samples together constitute the set of deviation values of the first production process attribute, and this set reflects the deviation distribution of this attribute that causes elongation anomalies.

[0083] After obtaining the set of deviation values of the first production process attribute, the box plot analysis method is used to statistically process the data. A box plot is a statistical graph that intuitively shows the distribution characteristics of data and can effectively identify the central tendency and outliers of the data. Through box plot analysis, a set of box body deviation values is obtained, that is, the main data distribution interval after removing outliers. The minimum value is extracted from this interval as the deviation threshold of the first production process attribute. Selecting the minimum value as the threshold reflects the conservative strategy of quality control, that is, as long as the attribute deviation reaches or exceeds the minimum deviation level in the historical abnormal samples, it may cause elongation anomalies. The determined deviation threshold of the first production process attribute is added to the preset deviation threshold of the single production process attribute as a reference standard for subsequent quality assessment. By the same method, other production process attributes are processed in turn, and finally a complete preset deviation threshold of the single production process attribute is constructed.

[0084] Furthermore, retrieving the minimum value of the concentrated copper wire elongation of the abnormal copper wire samples that meet the single production process attribute deviation matrix further includes:

[0085] S610: Obtain a set of elongation influence weights of production process attributes;

[0086] S620: Extract a set of normalized eigenvalue of single production process attribute deviation from the single production process attribute deviation matrix;

[0087] S630: After weighting the set of normalized eigenvalue of single production process attribute deviation according to the set of elongation influence weights of production process attributes, obtain a set of weighted eigenvalues;

[0088] S640: Sort the production process deviation attribute set according to the weighted eigenvalue set from largest to smallest to obtain the production process deviation attribute sorting result;

[0089] S650: Use the copper wire elongation abnormality probability estimator bound to the copper wire model to be measured to process the production process deviation attribute sorting result and the production process single-attribute deviation value set to obtain the elongation abnormality probability;

[0090] S660: When the elongation abnormality probability is greater than or equal to the abnormality probability threshold, perform a full inspection mark on the copper wire to be measured;

[0091] S670: When the elongation abnormality probability is less than the abnormality probability threshold, perform a sampling inspection mark on the copper wire to be measured;

[0092] S680: Combine the full inspection mark or the sampling inspection mark and store it in association with the minimum value of the centralized copper wire elongation.

[0093] In a preferred embodiment, first, obtain the production process attribute elongation influence weight set, which reflects the influence degree of each process parameter on the copper wire elongation and is the basic data for subsequent weighted analysis. Then, extract the deviation values of each process attribute from the production process single-attribute deviation matrix and perform normalization processing to obtain the production process single-attribute deviation normalization eigenvalue set. The purpose of normalization processing is to eliminate the influence brought by the differences in dimension and numerical range of different process attributes, so that the deviation values of each attribute are comparable. Among them, the normalization method can be maximum-minimum normalization, Z-score standardization, etc. Select the normalization method suitable for the characteristics of each attribute to ensure the rationality of data conversion.

[0094] Next, multiply the normalized production process attribute deviation values in the production process single-attribute deviation normalization eigenvalue set by their corresponding influence weights in the production process attribute elongation influence weight set to obtain the weighted eigenvalues. These weighted eigenvalues comprehensively consider the degree of process deviation and the importance of the production process attribute to the elongation, and can more accurately reflect the actual influence degree of each deviation factor on the copper wire elongation. All weighted eigenvalues form a weighted eigenvalue set, providing a basis for the subsequent sorting of the importance of process parameters.

[0095] Subsequently, according to the weighted eigenvalue set, each production process attribute is sorted in descending order of its weighted eigenvalue to form the sorting result of production process deviation attributes. For the production process attributes with higher sorting positions, their deviations have a greater potential impact on the elongation rate of copper wires, which are the key factors that need to be focused on in quality control. The sorting result of production process deviation attributes helps to identify the key process parameters that are most likely to cause abnormal elongation rate for the currently tested copper wires, providing guidance for targeted quality inspection. Then, using a dedicated copper wire elongation rate anomaly probability evaluator bound to the model of the tested copper wire, a comprehensive analysis is performed on the obtained sorting result of production process deviation attributes and the set of single-attribute deviation values of production processes to evaluate the anomaly probability of the elongation rate. Among them, the set of single-attribute deviation values of production processes records the actual deviation values of each single-attribute of production processes. The copper wire elongation rate anomaly probability evaluator is a data model, which can be a prediction model constructed based on machine learning methods such as random forest, support vector machine or neural network, and can output the probability prediction of elongation rate anomaly according to the input deviation characteristics of process parameters. Since copper wires of different models vary in material properties, structural design and application requirements, and their sensitivities to process deviations are also different, the copper wire elongation rate anomaly probability evaluator is bound to a specific copper wire model, which can ensure the pertinence and accuracy of the prediction results.

[0096] According to the obtained anomaly probability of the elongation rate, a differentiated quality inspection strategy is adopted. When the anomaly probability of the elongation rate is greater than or equal to the preset anomaly probability threshold, a full inspection label is given to the tested copper wire, indicating that 100% elongation rate tests need to be carried out for this batch of copper wires to ensure quality and safety. When the anomaly probability of the elongation rate is less than the anomaly probability threshold, a sampling inspection label is given to the tested copper wire, indicating that a sampling inspection method can be adopted to reduce the inspection workload. This differentiated inspection strategy based on risk probability not only ensures the reliability of quality control but also optimizes the allocation of inspection resources, improving the overall quality management efficiency. After that, the full inspection label or sampling inspection label is associated and stored with the minimum value of the obtained centralized copper wire elongation rate. This minimum value is used as a reference index to set the control parameters of the tensile test. The purpose of the associated storage is to establish the corresponding relationship between the inspection strategy and the expected quality level, facilitating subsequent actual measurement of tensile quality and data analysis, and providing a basis for continuously improving the quality control method.

[0097] Through the above steps, the intelligent evaluation of the risk of abnormal elongation rate of copper wires and the formulation of targeted inspection strategies are realized, effectively solving the problems of insufficient pertinence and resource waste in traditional copper wire quality inspection, and improving the scientificity and efficiency of quality control.

[0098] Furthermore, by using the copper wire elongation rate anomaly probability evaluator bound to the model of the tested copper wire, the sorting result of the production process deviation attributes and the set of single-attribute deviation values of production processes are processed to obtain the anomaly probability of the elongation rate, including:

[0099] S651: Limited by the model of the copper wire to be measured, based on the preset deviation threshold of the production process sheet attributes, retrieve the production process sheet attribute deviation record matrix, and obtain the proportion of the number of elongation abnormal samples in the index samples that meet the production process sheet attribute deviation record matrix;

[0100] S652: Perform processing on the production process sheet attribute deviation record matrix to obtain the production process deviation attribute sorting record result;

[0101] S653: Using the proportion of the number of elongation abnormal samples as the supervision, and using the production process sheet attribute deviation record matrix and the production process deviation attribute sorting record result as the input, configure the random forest to obtain the copper wire elongation abnormal probability estimator.

[0102] In a preferred implementation manner, first, limit a specific copper wire model range, and perform data retrieval and analysis for the specific model of the copper wire to be measured. This model limitation ensures that the subsequent constructed copper wire elongation abnormal probability estimator has strong pertinence and can accurately reflect the quality characteristics of a specific model of copper wire. Then, based on the preset deviation threshold of the production process sheet attributes established, retrieve and filter out the sample records with similar process deviation patterns to the current copper wire to be measured in the historical database, and form the production process sheet attribute deviation record matrix. This matrix contains the deviation data of each process attribute in the historical samples and is the basic data set for model training. At the same time, count the number of samples with unqualified elongation and its proportion in the total samples among these index samples that meet the conditions of the deviation record matrix. This proportion data reflects the historical probability of elongation abnormality under specific process deviation conditions and will be used as the supervision signal for model training.

[0103] Subsequently, perform data processing on the production process sheet attribute deviation record matrix, including operations such as data cleaning, feature extraction, and feature selection, to improve the data quality and model training effect. After processing, according to the statistical characteristics of the deviation of each process attribute, such as the deviation mean, deviation variance, and the correlation between the deviation and elongation abnormality, sort the importance of the process attributes to obtain the production process deviation attribute sorting record result. This sorting result reflects the influence degree of different process attributes on elongation and provides prior knowledge of feature importance for subsequent model construction.

[0104] Next, a random forest algorithm is used to construct an abnormal probability estimator for the elongation rate of copper wires. Random forest is an ensemble learning method that can effectively handle high-dimensional features, resist overfitting, and provide good prediction performance by constructing multiple decision trees and combining the obtained prediction results. The production process single-attribute deviation record matrix is used as the input feature, and the proportion of the number of abnormal elongation rate samples is used as the supervision signal or label. Combining the production process deviation attribute sorting record results for feature optimization, the random forest model is trained and configured. The configuration process includes determining parameters such as the number of decision trees, tree depth, and node splitting criteria, and these parameters can be optimized through methods such as cross-validation to improve the model performance. Through the above training and configuration process, an abnormal probability estimator for the elongation rate of a specific copper wire model is finally obtained. This abnormal probability estimator for the elongation rate of copper wires can receive new process deviation data as input and output the probability prediction of elongation rate abnormality, providing a basis for formulating quality inspection strategies.

[0105] By constructing an estimator through a data-driven method, the accurate assessment of the abnormal risk of the elongation rate of copper wires is realized, overcoming the subjectivity and uncertainty of traditional empirical judgments, improving the accuracy and reliability of quality prediction, and providing intelligent technical support for the quality inspection of copper wire stretching.

[0106] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the copper wire stretching quality inspection method for elongation rate analysis provided in Embodiment 1, the embodiment of the present invention also provides a copper wire stretching quality inspection system for elongation rate analysis, including:

[0107] A sample acquisition module 11, configured to obtain a number of production process record data of a number of copper wire samples that meet the elongation rate threshold;

[0108] A process parameter comparison module 12, configured to traverse the number of production process record data for comparison based on a preset production process to obtain a number of production process deviation parameters;

[0109] A reference space construction module 13, configured to perform a concentration value evaluation on the selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold extracted from the number of production process record data according to the number of production process deviation parameters to obtain a production process reference space;

[0110] A qualified label determination module 14, configured to perform a quality qualified label on the copper wire to be tested when the production process monitoring data of the copper wire to be tested falls into the production process reference space;

[0111] A deviation matrix extraction module 15, configured to extract a production process single-attribute deviation matrix based on a preset deviation threshold of a production process single attribute when the production process monitoring data of the copper wire to be tested does not fall into the production process reference space;

[0112] The stretching quality measurement module 16 is used to retrieve the minimum value of the concentrated copper wire elongation rate of abnormal copper wire samples that meet the production process sheet attribute deviation matrix, and send it to the copper wire stretching control end to set stretching parameters to perform stretching quality measurement.

[0113] Furthermore, the process parameter comparison module 12 includes the following execution steps:

[0114] Obtain the first production process record data of the several production process record data;

[0115] Obtain the production process attribute elongation rate influence weight set;

[0116] Perform same-attribute comparison on the first production process record data and the preset production process to obtain a same-attribute deviation set;

[0117] According to the production process attribute elongation rate influence weight set, perform weighted Euclidean distance evaluation on the same-attribute deviation set to obtain the first production process deviation parameter, and add it to the several production process deviation parameters.

[0118] Furthermore, the process parameter comparison module 12 further includes the following execution steps:

[0119] Obtain the first production process attribute;

[0120] Extract the first attribute preset deviation threshold from the production process sheet attribute preset deviation threshold;

[0121] Retrieve the number of first abnormal samples in the abnormal copper wire sample library where only the first production process attribute is greater than the first attribute preset deviation threshold;

[0122] Until the Nth production process attribute is obtained;

[0123] Extract the Nth attribute preset deviation threshold from the production process sheet attribute preset deviation threshold;

[0124] Retrieve the number of Nth abnormal samples in the abnormal copper wire sample library where only the Nth production process attribute is greater than the Nth attribute preset deviation threshold;

[0125] Calculate the sum value of abnormal samples from the number of first abnormal samples to the number of Nth abnormal samples, traverse from the number of first abnormal samples to the number of Nth abnormal samples, and calculate the ratio with the sum value of abnormal samples to obtain the production process attribute elongation rate influence weight set.

[0126] Furthermore, the reference space construction module 13 includes the following execution steps:

[0127] Compare the selected production process record data pairwise to obtain multiple selected production process deviation parameters;

[0128] Based on the multiple selected production process deviation parameters, traverse the selected production process record data for outlier factor statistics to obtain a set of outlier factor statistical values;

[0129] Based on the outlier factor threshold and in combination with the set of outlier factor statistical values, perform sorting on the selected production process record data to obtain concentrated production process record data, and construct the production process reference space.

[0130] Further, the deviation matrix extraction module 15 includes the following execution steps:

[0131] Obtain the first production process attribute;

[0132] Retrieve a set of copper wire samples with abnormal elongation that deviate from the first production process attribute;

[0133] Extract a set of first production process attribute deviation values of the first production process attribute recorded values and the first production process attribute preset values of the set of copper wire samples with abnormal elongation;

[0134] Perform box plot analysis on the set of first production process attribute deviation values to obtain a set of box body deviation values, extract the minimum value, set it as the first production process attribute deviation threshold, and add it to the production process single-attribute preset deviation threshold.

[0135] Further, the measured tensile quality module 16 further includes the following execution steps:

[0136] Obtain a set of weights of the production process attribute elongation influence;

[0137] Extract a set of normalized characteristic values of the production process single-attribute deviation from the production process single-attribute deviation matrix;

[0138] After weighting the set of normalized characteristic values of the production process single-attribute deviation according to the set of weights of the production process attribute elongation influence, obtain a set of weighted characteristic values;

[0139] Sort the production process deviation attribute set in descending order according to the set of weighted characteristic values to obtain a production process deviation attribute sorting result;

[0140] Through a copper wire elongation abnormality probability evaluator bound to the copper wire model to be tested, process the production process deviation attribute sorting result and the set of production process single-attribute deviation values to obtain an elongation abnormality probability;

[0141] When the elongation abnormality probability is greater than or equal to the abnormality probability threshold, perform a full inspection mark on the copper wire to be tested;

[0142] When the abnormal probability of the elongation rate is less than the abnormal probability threshold, a spot check identification is performed on the copper wire to be tested;

[0143] Combined with the full inspection identification or the spot check identification, it is associated and stored with the minimum value of the elongation rate of the centralized copper wire.

[0144] Furthermore, the actual measurement module 16 of the tensile quality further includes the following execution steps:

[0145] Limited by the model of the copper wire to be tested, based on the preset deviation threshold of the production process sheet attributes, retrieve the production process sheet attribute deviation record matrix, and calculate the proportion of the number of elongation abnormal samples in the index samples that meet the production process sheet attribute deviation record matrix;

[0146] Perform processing on the production process sheet attribute deviation record matrix to obtain the production process deviation attribute sorting record result;

[0147] Using the proportion of the number of elongation abnormal samples as supervision, and using the production process sheet attribute deviation record matrix and the production process deviation attribute sorting record result as inputs, configure the random forest to obtain the copper wire elongation abnormal probability evaluator.

[0148] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 each process or multiple processes and / or blocks Figure 1means for the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one Figure 1 or more processes and / or boxes Figure 1 or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one Figure 1 or more processes and / or boxes Figure 1 or more boxes.

[0153] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting the tensile quality of copper wire for elongation analysis, characterized in that, Including: Obtaining a number of production process record data of several copper wire samples that meet the elongation threshold; Based on a preset production process, traversing and comparing the number of production process record data to obtain a number of production process deviation parameters; According to the number of production process deviation parameters, extracting selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold from the number of production process record data for centralized value evaluation to obtain a production process reference space; When the production process monitoring data of the copper wire to be tested falls into the production process reference space, marking the copper wire to be tested as qualified in quality; When the production process monitoring data of the copper wire to be tested does not fall into the production process reference space, extracting a production process single-attribute deviation matrix based on a preset deviation threshold for the production process single attribute; Retrieving the minimum value of the centralized copper wire elongation of abnormal copper wire samples that meet the production process single-attribute deviation matrix, and sending it to the copper wire stretching control end to set stretching parameters and perform actual stretching quality measurement; Among them, based on a preset production process, traversing and comparing the number of production process record data to obtain a number of production process deviation parameters, including: Obtaining the first production process record data of the number of production process record data; Obtaining a set of production process attribute elongation influence weights; Performing same-attribute comparison on the first production process record data and the preset production process to obtain a set of same-attribute deviations; According to the set of production process attribute elongation influence weights, performing weighted Euclidean distance evaluation on the set of same-attribute deviations to obtain a first production process deviation parameter, and adding it to the number of production process deviation parameters; Among them, obtaining a set of production process attribute elongation influence weights includes: Obtaining the first production process attribute; Extracting a first attribute preset deviation threshold from the preset deviation threshold for the production process single attribute; Retrieving the number of first abnormal samples in the abnormal copper wire sample library where only the first production process attribute is greater than the first attribute preset deviation threshold; Until the Nth production process attribute is obtained; Extracting the Nth attribute preset deviation threshold from the preset deviation threshold for the production process single attribute; Retrieving the number of Nth abnormal samples in the abnormal copper wire sample library where only the Nth production process attribute is greater than the Nth attribute preset deviation threshold; Calculating the sum value of abnormal samples from the number of first abnormal samples to the number of Nth abnormal samples, traversing the number of first abnormal samples to the number of Nth abnormal samples, and taking the ratio with the sum value of abnormal samples to obtain the set of production process attribute elongation influence weights.

2. The method according to claim 1, wherein Extracting selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold from the number of production process record data for centralized value evaluation to obtain a production process reference space, including: Performing pairwise comparison on the selected production process record data to obtain a number of selected production process deviation parameters; Based on the number of selected production process deviation parameters, traversing the selected production process record data for outlier factor statistics to obtain a set of outlier factor statistical values; Based on the outlier factor threshold, combined with the set of outlier factor statistical values, perform sorting on the selected production process record data to obtain concentrated production process record data, and construct the production process reference space.

3. The method according to claim 1, wherein When the monitoring data of the production process of the copper wire to be tested does not fall into the production process reference space, extract the production process single-attribute deviation matrix based on the preset deviation threshold of the production process single-attribute, including: Obtain the first production process attribute; Retrieve the set of copper wire samples with abnormal elongation rate with deviation in the first production process attribute; Extract the set of first production process attribute deviation values of the first production process attribute recorded values and the first production process attribute preset values of the set of copper wire samples with abnormal elongation rate; Perform box plot analysis on the set of first production process attribute deviation values to obtain the set of box deviation values, extract the minimum value, set it as the first production process attribute deviation threshold, and add it to the preset deviation threshold of the production process single-attribute.

4. The method according to claim 1, characterized in that Retrieve the minimum value of the concentrated copper wire elongation rate of the abnormal copper wire samples that meet the production process single-attribute deviation matrix, and further include: Obtain the set of production process attribute elongation rate influence weights; Extract the set of production process single-attribute deviation normalized eigenvalue from the production process single-attribute deviation matrix; After weighting the set of production process single-attribute deviation normalized eigenvalue according to the set of production process attribute elongation rate influence weights, obtain the set of weighted eigenvalues; Sort the production process deviation attribute set in descending order according to the set of weighted eigenvalues to obtain the production process deviation attribute sorting result; Through the copper wire elongation rate abnormal probability evaluator bound to the copper wire model to be tested, process the production process deviation attribute sorting result and the set of production process single-attribute deviation values to obtain the elongation rate abnormal probability; When the elongation rate abnormal probability is greater than or equal to the abnormal probability threshold, perform a full inspection mark on the copper wire to be tested; When the elongation rate abnormal probability is less than the abnormal probability threshold, perform a sampling inspection mark on the copper wire to be tested; Combine the full inspection mark or the sampling inspection mark and store it in association with the minimum value of the concentrated copper wire elongation rate.

5. The method according to claim 4, wherein Through the copper wire elongation rate abnormal probability evaluator bound to the copper wire model to be tested, process the production process deviation attribute sorting result and the set of production process single-attribute deviation values to obtain the elongation rate abnormal probability, including: Taking the copper wire model to be tested as the limit, based on the preset deviation threshold of the production process single-attribute, retrieve the production process single-attribute deviation record matrix, and the proportion of the number of abnormal elongation rate samples in the index samples that meet the production process single-attribute deviation record matrix; Process the production process single-attribute deviation record matrix to obtain the production process deviation attribute sorting record result; Taking the proportion of the number of abnormal elongation rate samples as the supervision, taking the production process single-attribute deviation record matrix and the production process deviation attribute sorting record result as the input, configure the random forest to obtain the copper wire elongation rate abnormal probability evaluator.

6. A copper wire drawing quality detection system for elongation analysis, characterized in that, For implementing the method according to any one of claims 1 to 5, including: A sample acquisition module for obtaining a number of production process record data of a number of copper wire samples that meet the elongation rate threshold; A process parameter comparison module, which is used to traverse the several production process record data for comparison based on a preset production process, and obtain several production process deviation parameters; A reference space construction module, which is used to extract the selected production process record data with production process deviation parameters less than or equal to the deviation parameter threshold from the several production process record data according to the several production process deviation parameters for centralized value evaluation, and obtain a production process reference space; A qualified mark determination module, which is used to mark the quality of the copper wire to be tested as qualified when the production process monitoring data of the copper wire to be tested falls into the production process reference space; A deviation matrix extraction module, which is used to extract a production process single-attribute deviation matrix based on a preset deviation threshold of the production process single-attribute when the production process monitoring data of the copper wire to be tested does not fall into the production process reference space; A stretching quality actual measurement module, which is used to retrieve the minimum value of the centralized copper wire elongation rate of the abnormal copper wire samples that meet the production process single-attribute deviation matrix, and send it to the copper wire stretching control end to set stretching parameters to perform stretching quality actual measurement.

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