A method for material tracing and management in an enameled wire production process

Abstract

The application discloses a kind of material tracing and management in enameled wire production process, belong to enameled wire production management technical field, comprising: obtaining the dynamic consumption dataset of production line;According to material type, the grouped material consumption data is obtained by dividing dynamic consumption dataset;According to timestamp, the minute level consumption rate sequence is obtained by sliding window analysis to grouped material consumption data;According to the abnormal score of minute level consumption rate sequence, the dataset with abnormal mark and normal mark is generated;Minute level consumption rate sequence with abnormal mark is associated with production batch number and is mapped, and batch consumption association table is generated;From batch consumption association table, raw material consumption record is extracted, and traceability chain dataset is constructed.The material tracing and management in enameled wire production process solve the problem that material management mode in current enameled wire production is difficult to correspond material consumption data and production batch, and cannot realize whole-process tracing from raw material to finished product.

Description

Technical Field

[0001] The present invention relates to the technical field of enameled wire production management, and in particular to a method for material tracing and management in the enameled wire production process. Background Art

[0002] Enameled wire production is a critical area of ​​the electrical industry, with product quality and production efficiency directly impacting the performance and reliability of equipment such as motors. Efficient material traceability and management are crucial for ensuring product quality and optimizing resource utilization. However, current material management methods in enameled wire production are generally inadequate. Many companies rely on manual record-keeping or simple spreadsheets, resulting in incomplete data, inefficient traceability, and difficulty responding to the dynamic demands of complex production environments. Existing methods, particularly for monitoring raw material consumption and correlating production batches, often lack real-time and accuracy, increasing production costs and quality risks.

[0003] Against this backdrop, material traceability during enameled wire production faces significant challenges. Real-time monitoring of material flow is a primary challenge. Due to the frequent fluctuations in raw material and paint usage on the production line, traditional methods struggle to capture dynamic data, making it impossible to accurately record the actual consumption of each batch of materials. This lack of monitoring further complicates batch correlation. Without precise alignment of material consumption data to specific production batches, companies cannot achieve full traceability from raw materials to finished product, hindering quality control and production optimization. Summary of the Invention

[0004] In order to overcome the defects of the prior art, the present invention provides a method for material tracing and management in the production process of enameled wire to solve the above problems.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for material tracing and management in the enameled wire production process, comprising the following steps:

[0006] S1: Obtain a dynamic consumption dataset of a production line, which includes a timestamp, material type, and material number;

[0007] S2: Divide the dynamic consumption data set according to material type to obtain grouped material consumption data, each grouped material consumption data having a label of material type and material number;

[0008] S3: Perform sliding window analysis on the grouped material consumption data according to the timestamp to obtain a minute-level consumption rate sequence;

[0009] S4: Mark the minute-level consumption rate series as abnormal or normal according to the abnormality score of the minute-level consumption rate series to generate a dataset with abnormal labels and normal labels;

[0010] S5: Associating and mapping the minute-level consumption rate sequence with abnormal labels in the data sets with abnormal labels and normal labels with the production batch numbers to generate a batch consumption association table;

[0011] S6: Extracting raw material consumption records from the batch consumption association table to construct a traceability chain dataset from raw materials to finished products.

[0012] Preferably, in step S1, dynamic consumption data is obtained from production line equipment sensors, the dynamic consumption data including timestamp and material type, and written into the InfluxDB time series database to obtain a structured data set.

[0013] Optionally, in step S2, material consumption records within a specified time range are queried from the structured data set, and grouped by material type to obtain grouped material consumption data, wherein the material type includes enameled wire raw materials and paint.

[0014] Specifically, in step S3, for the grouped material consumption data, the window size and step size are set using the rolling function of Pandas, and the average value of the material consumption in the window is calculated to obtain a minute-level consumption rate sequence.

[0015] It is worth noting that in step S4, the minute-level rate sequence is used as a feature vector and normalized by Z-score to obtain a normalized feature vector;

[0016] The IsolationForest model is trained using the preset pollution parameters to obtain a trained IsolationForest model; the standardized feature vector is input into the trained IsolationForest model to obtain an anomaly score for the standardized feature vector output by the trained IsolationForest model;

[0017] An abnormality threshold is set. If the abnormality score is less than the abnormality threshold, the corresponding sequence is marked as abnormal. If the abnormality score is greater than or equal to the abnormality threshold, the corresponding sequence is marked as normal. A data set with abnormal and normal marks is generated from the minute-level consumption rate sequence with abnormal and normal marks and saved in the database. Both the abnormal and normal marks are labeled with corresponding material type tags.

[0018] Preferably, in step S5, a batch number data set is extracted from a database, the batch number data set including the material number used for the batch, the supplier corresponding to the material number, and the equipment number used for the production batch; then, a minute-level consumption rate sequence with abnormal marks is extracted from the data sets with abnormal marks and normal marks to form an abnormal data set; the abnormal data set is accurately matched with the batch number data set by material number to obtain an initial mapping table of minute-level consumption rate sequences containing abnormal marks and batch numbers;

[0019] Calculate the average value of all elements in each minute-level consumption rate sequence with anomaly marks in the mapping relationship table as the sequence average value. Arrange the sequence average values ​​of all minute-level consumption rate sequences with anomaly marks by timestamp as the feature vector. Perform Z-score normalization to obtain the sequence normalized feature vector.

[0020] The K-means algorithm is used to determine the optimal number of clusters of the sequence normalized feature vector using the silhouette coefficient, and the consumption cluster label corresponding to each minute-level consumption rate sequence with an abnormal mark is obtained as the consumption level;

[0021] According to the association between the batch number and the batch supplier and equipment number fields in the production traceability system, a batch consumption association table including the batch number, the minute-level consumption rate sequence with an abnormal mark, the consumption level, the supplier, and the equipment number is obtained.

[0022] Specifically, in step S5, if there is an unmatched batch in the initial mapping table, the batch number, production start time and production end time are obtained from the production record table, and the start time of the minute-level consumption rate sequence is obtained from the data set with abnormal marks and normal marks; then interval matching is performed; the minute-level consumption rate sequence whose start time is within the interval from the production start time to the production end time is extracted, added to the initial mapping table and associated with the corresponding batch number to obtain a complete mapping relationship table.

[0023] Preferably, in step S6, the raw material consumption record includes a batch number, a minute-level consumption rate sequence with an abnormality mark, and a consumption level;

[0024] According to the batch number, an exact matching operation is performed in the finished product record table to construct a traceability chain data set, where the finished product record table contains the batch number and the finished product quality identifier.

[0025] Specifically, in step S6, the SQL INNER JOIN statement is used to achieve accurate matching of the batch number field. The matching condition is that the batch number field of the batch consumption association table is completely consistent with the batch number field of the finished product record table, thereby obtaining a traceability chain data set; the traceability chain data set includes the batch number, the minute-level consumption rate sequence with an abnormal mark, the consumption level, and the finished product quality identifier.

[0026] The beneficial effects of the present invention are as follows: in the method for material tracing and management during the enameled wire production process, by acquiring the dynamic consumption data of the production line, different material types are grouped and subjected to sliding window analysis to obtain a minute-level consumption rate sequence. An abnormal scoring mechanism is used to mark abnormal or normal data, and the abnormal data is associated with the production batch to construct a batch consumption association table. Finally, the raw material consumption records are extracted from the association table, and a traceability chain from raw materials to finished products is established. The present invention realizes the precise positioning and tracing of material consumption anomalies during the production process, which helps to promptly discover and resolve production problems, and improve production efficiency and product quality. By establishing a complete material consumption traceability chain, the present invention provides strong support for production management and quality control, and is of great significance to improving the overall level of the manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Flowchart of a method for material tracing and management in an enameled wire production process according to one embodiment of the present invention;

[0028] Figure 2 A step-by-step flow chart of step S4 in one embodiment of the present invention;

[0029] Figure 3 FIG. 1 is a step-by-step flow chart of step S5 in one embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0031] like Figure 1-3 As shown, a method for material tracing and management in the enameled wire production process includes the following steps:

[0032] S1: Obtain a dynamic consumption dataset of a production line, which includes a timestamp, material type, and material number;

[0033] S2: Divide the dynamic consumption data set according to material type to obtain grouped material consumption data, each grouped material consumption data having a label of material type and material number;

[0034] S3: Perform sliding window analysis on the grouped material consumption data according to the timestamp to obtain a minute-level consumption rate sequence;

[0035] S4: Mark the minute-level consumption rate series as abnormal or normal according to the abnormality score of the minute-level consumption rate series to generate a dataset with abnormal labels and normal labels;

[0036] S5: Associating and mapping the minute-level consumption rate sequence with abnormal labels in the data sets with abnormal labels and normal labels with the production batch numbers to generate a batch consumption association table;

[0037] S6: Extracting raw material consumption records from the batch consumption association table to construct a traceability chain dataset from raw materials to finished products.

[0038] In the method for material tracing and management during the enameled wire production process, by acquiring dynamic consumption data of the production line, different material types are grouped and subjected to sliding window analysis to obtain a minute-level consumption rate sequence. An abnormal scoring mechanism is used to mark abnormal or normal data, and the abnormal data is associated with the production batch to construct a batch consumption association table. Finally, the raw material consumption records are extracted from the association table to establish a traceability chain from raw materials to finished products. The present invention achieves precise positioning and tracing of material consumption anomalies during the production process, which helps to promptly discover and resolve production problems, and improve production efficiency and product quality. By establishing a complete material consumption traceability chain, the present invention provides strong support for production management and quality control, and is of great significance to improving the overall level of the manufacturing industry.

[0039] It is worth noting that in step S1, dynamic consumption data is obtained from the production line equipment sensor, and the dynamic consumption data includes a timestamp and material type, and is written into the InfluxDB time series database to obtain a structured data set.

[0040] Acquiring dynamic consumption data from sensors on production line equipment involves real-time collection of material consumption information. For example, on a factory production line, sensors record material usage once per second. This data includes timestamps and material types, such as "2025-05-11 08:00:00, enameled wire raw material A, 1000 grams." This data is transmitted to the InfluxDB time series database via an Industrial IoT gateway, generating a structured dataset. InfluxDB's efficient time series storage supports high-frequency writes and queries, ensuring data real-time and integrity.

[0041] Preferably, in step S2, material consumption records within a specified time range are queried from the structured data set, and grouped by material type to obtain grouped material consumption data, wherein the material types include enameled wire raw materials and paints.

[0042] In one possible implementation, query material consumption records within a specified time range, such as those from 8:00 AM to 12:00 PM on May 11, 2025. Grouping by material type yields grouped material consumption data, including enameled wire raw material and paint. This grouping facilitates analysis of material usage, providing a foundation for subsequent analysis.

[0043] Specifically, in step S3, for the grouped material consumption data, the window size and step size are set using the rolling function of Pandas, and the average value of the material consumption in the window is calculated to obtain a minute-level consumption rate sequence.

[0044] For example, we use Pandas' rolling function to calculate minute-level consumption rates for grouped material consumption data. We set a window size of 60 minutes and a step size of 10 minutes, and calculate the average material consumption within the window. For example, for enameled wire raw material A, within the window from 08:00 to 09:00, we slide the step size to 08:10 to 09:10 and calculate the average. This way, within a 60-minute window, we can obtain six sets of minute-level consumption rate averages, forming a six-element minute-level consumption rate series.

[0045] It is worth noting that in step S4, the minute-level rate sequence is used as a feature vector and normalized by Z-score to obtain a normalized feature vector. This Z-score normalization eliminates the dimensionality effect and improves the model training effect. For example, the normalized vector value is [-0.2, 0.0, 1.2…], which facilitates subsequent anomaly detection.

[0046] An IsolationForest model (isolation forest model) is trained using preset pollution parameters to obtain a trained IsolationForest model; the standardized feature vector is input into the trained IsolationForest model to obtain an anomaly score for the standardized feature vector output by the trained IsolationForest model;

[0047] An abnormality threshold is set. If the abnormality score is less than the abnormality threshold, the corresponding sequence is marked as abnormal. If the abnormality score is greater than or equal to the abnormality threshold, the corresponding sequence is marked as normal. A data set with abnormal and normal marks is generated from the minute-level consumption rate sequence with abnormal and normal marks and saved in the database. Both the abnormal and normal marks are labeled with corresponding material type tags.

[0048] For example, train the Scikit-learn IsolationForest model with a contamination parameter of 0.05. Then, input the standardized feature vector into the trained IsolationForest model and obtain the anomaly score for the standardized feature vector output by the trained IsolationForest model. Set the anomaly threshold to -0.6. If the anomaly score of a standardized feature vector is -0.65, which is less than the anomaly threshold of -0.6, the corresponding minute-level consumption rate series is marked as anomaly. If the anomaly score of a standardized feature vector is -0.5, which is greater than the anomaly threshold of -0.6, the corresponding minute-level consumption rate series is marked as normal.

[0049] Preferably, in step S5, a batch number data set is extracted from a database, the batch number data set including the material number used in the batch number, the supplier corresponding to the material number, and the equipment number used in the production batch; then, a minute-level consumption rate sequence with an abnormal mark is extracted from the data set with an abnormal mark and a normal mark to form an abnormal data set; the abnormal data set is precisely matched with the batch number data set by the material number to obtain an initial mapping table of the minute-level consumption rate sequence containing the abnormal mark and the batch number; for example, the batch number data set records the batch number of each production batch and the number of the material used, and the minute-level consumption rate sequence reflects the consumption of the raw material or paint of the enameled wire of the corresponding material number during the production process;

[0050] Calculate the average value of all elements in each minute-level consumption rate sequence with an abnormal mark in the mapping relationship table as the sequence average value, arrange the sequence average values ​​of all minute-level consumption rate sequences with abnormal marks by timestamp as the feature vector, and perform Z-score normalization to obtain the sequence normalized feature vector; specifically, calculate the average value of the material consumption in the window with a window size of 60 minutes and a step length of 10 minutes. The minute-level consumption rate sequence obtained has 6 elements, and the average value of these 6 elements is calculated as the sequence average value of the sequence. Use the starting time of the minute-level consumption rate sequence as the timestamp, arrange all sequence average values ​​to obtain the feature vector, and perform Z-score normalization to obtain the standardized feature vector to eliminate the dimension effect;

[0051] The K-means algorithm uses the silhouette coefficient to determine the optimal number of clusters for the sequence's standardized feature vectors. This results in a consumption cluster label corresponding to each minute-level consumption rate sequence with an abnormality marker, which serves as the consumption level. The K-means algorithm, combined with the silhouette coefficient, is used to evaluate the clustering effect, determining the optimal number of clusters to be 3, generating consumption level labels such as consumption level 1, consumption level 2, and consumption level 3. For example, minute-level consumption rate sequence B1 is labeled "Consumption Level 1," indicating severe abnormal consumption. Specifically, the Euclidean distance between samples in the standardized feature vector is calculated by standardizing the feature vector to determine the initial cluster centers and obtain the initial clustering division. If the silhouette coefficient of the initial clustering division falls below a preset threshold, the K-means algorithm is used to iteratively optimize the cluster centers to obtain an updated clustering division. Based on the updated clustering division, the intra-cluster compactness and inter-cluster separation of each cluster are calculated to obtain a new silhouette coefficient. If the new silhouette coefficient does not reach the preset threshold, the number of clusters is adjusted and the K-means algorithm is re-executed to obtain a new clustering division. By comparing the silhouette coefficients under different numbers of clusters, the number of clusters corresponding to the maximum silhouette coefficient is determined, and the optimal number of clusters is obtained. Using the optimal number of clusters, the final cluster division of the sequence normalized feature vector is output and the cluster labels are determined. This method assists in identifying high-risk batches and optimizing resource allocation by quantifying the degree of anomaly.

[0052] According to the association between the batch number and the batch supplier and equipment number fields in the production traceability system, a batch consumption association table including the batch number, the minute-level consumption rate sequence with an abnormal mark, the consumption level, the supplier, and the equipment number is obtained.

[0053] For example, after linking to the production traceability system, the batch consumption association table integrates the batch number, minute-level consumption rate sequence with anomaly marks, consumption level, supplier, and equipment number. Assuming that B001 corresponds to supplier S1 and equipment E01, analysis finds that batch number B001 has multiple minute-level consumption rate sequences with a consumption level of consumption level 1, which often triggers high consumption anomalies, and E01 has fault records in multiple batches. Based on this, production managers can strengthen raw material testing for supplier S1 or prioritize maintenance for equipment E01. This analytical method, from data extraction to clustering to association tracing, progresses step by step to ensure that abnormal problems are traceable, quantifiable, and optimizable, providing strong support for production decisions.

[0054] Optionally, in step S5, if there is an unmatched batch in the initial mapping table, the batch number, production start time and production end time are obtained from the production record table, and the start time of the minute-level consumption rate sequence is obtained from the data set with abnormal marks and normal marks; then interval matching is performed; the minute-level consumption rate sequence whose start time is within the interval from the production start time to the production end time is extracted, added to the initial mapping table and associated with the corresponding batch number to obtain a complete mapping relationship table.

[0055] The initial mapping table uses precise matching based on material numbers. For example, using material number 001 to associate batch number B001 with the anomaly-marked minute-level consumption rate sequence B1, further processing is required for unmatched batch numbers, such as B002, due to missing database records. Specifically, for unmatched batches, the batch number, production start time, and production end time are obtained from the production record table. For example, if B002's production period is from 2025-05-01 08:00 to 2025-05-01 12:00, the start time of a minute-level consumption rate sequence in the anomaly dataset is 2025-05-01 09:30. Through interval matching, the start time is confirmed to fall within the production interval and added to the initial mapping table, forming a complete mapping relationship table. This table ensures a one-to-one correspondence between all anomaly-marked minute-level consumption rate sequences and batch numbers, laying the foundation for subsequent analysis. This approach effectively traces the source of anomalies and improves the accuracy of production management.

[0056] Specifically, in step S6, the raw material consumption record includes a batch number, a minute-level consumption rate sequence with an abnormality mark, and a consumption level;

[0057] According to the batch number, an exact matching operation is performed in the finished product record table to construct a traceability chain data set, where the finished product record table contains the batch number and the finished product quality identifier.

[0058] For example, in the enameled wire production scenario, raw material consumption records include the batch number, a minute-by-minute consumption rate sequence with an anomaly flag, and the consumption level. The finished product record table records the batch number and finished product quality indicator. Exact matching is achieved using the SQL INNER JOIN statement to ensure batch number consistency and generate a traceability chain dataset. This dataset integrates information on abnormal consumption and finished product quality, providing a foundation for analysis. For example, suppose the consumption record for batch B001 shows a minute-by-minute consumption rate sequence S1 and a consumption level of "Consumption Level 1." Using an INNER JOIN match, the finished product record table indicates that B001's finished product quality indicator is "Unqualified." This association reveals that high consumption may lead to quality issues, providing clues for tracing the root cause of the anomaly.

[0059] It is worth noting that in step S6, the SQL INNER JOIN statement is used to achieve accurate matching of the batch number field. The matching condition is that the batch number field of the batch consumption association table is completely consistent with the batch number field of the finished product record table, thereby obtaining a traceability chain data set; the traceability chain data set includes the batch number, the minute-level consumption rate sequence with an abnormal mark, the consumption level, and the finished product quality mark.

[0060] Specifically, SQL's INNER JOIN operation relies on the complete consistency of the batch number field. For example, managers can filter batches with a consumption level of "consumption level 1" and, combined with the finished product quality identification, calculate the unqualified rate. Assume that among 10 batches, 5 have a consumption level of "consumption level 1", and 4 of them have a finished product quality identification of "unqualified", indicating that consumption level 1 is highly correlated with quality issues. In addition, it is possible to group by supplier to analyze whether the raw materials of a specific supplier cause the anomaly. Assuming that all three batches of supplier S1 show consumption level 1 and the finished products are unqualified, they can be given priority for quality inspection. This multi-dimensional analysis improves the efficiency of problem location.

[0061] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and these changes and modifications still fall within the scope of protection of the present invention.

Claims

1. A method for material tracing and management in the production process of enameled wire, characterized in that: The following steps are involved: S1: Obtain a dynamic consumption dataset of a production line, which includes a timestamp, material type, and material number; S2: Divide the dynamic consumption data set according to material type to obtain grouped material consumption data, each grouped material consumption data having a label of material type and material number; S3: Perform sliding window analysis on the grouped material consumption data according to the timestamp to obtain a minute-level consumption rate sequence; In step S3, for the grouped material consumption data, the window size and step size are set using the rolling function of Pandas, and the average value of the material consumption in the window is calculated to obtain a minute-level consumption rate sequence; S4: Mark the minute-level consumption rate series as abnormal or normal according to the abnormality score of the minute-level consumption rate series to generate a dataset with abnormal labels and normal labels; S5: Associating and mapping the minute-level consumption rate sequence with abnormal labels in the data sets with abnormal labels and normal labels with the production batch numbers to generate a batch consumption association table; In step S5, a batch number dataset is extracted from a database, the batch number dataset including the batch number, the material number used in the batch, the supplier corresponding to the material number, and the equipment number used in the production batch; then, a minute-level consumption rate sequence with the abnormal mark is extracted from the dataset with the abnormal mark and the normal mark to form an abnormal dataset; the abnormal dataset is precisely matched with the batch number dataset by the material number to obtain an initial mapping table of the minute-level consumption rate sequence containing the abnormal mark and the batch number; Calculate the average value of all elements in each minute-level consumption rate sequence with anomaly marks in the mapping relationship table as the sequence average value. Arrange the sequence average values ​​of all minute-level consumption rate sequences with anomaly marks by timestamp as the feature vector. Perform Z-score normalization to obtain the sequence normalized feature vector. The K-means algorithm is used to determine the optimal number of clusters of the sequence normalized feature vector using the silhouette coefficient, and the consumption cluster label corresponding to each minute-level consumption rate sequence with an abnormal mark is obtained as the consumption level; According to the association between the batch number and the batch supplier and equipment number fields in the production traceability system, a batch consumption association table including the batch number, the minute-level consumption rate sequence with an abnormal mark, the consumption level, the supplier, and the equipment number is obtained; S6: Extracting raw material consumption records from the batch consumption association table to construct a traceability chain dataset from raw materials to finished products.

2. The method for material tracing and management in the enameled wire production process according to claim 1, characterized in that: In step S1, dynamic consumption data including timestamp and material type is acquired from production line equipment sensors and written into an InfluxDB time series database to obtain a structured data set.

3. The method for material tracing and management in the enameled wire production process according to claim 2, characterized in that: In step S2, material consumption records within a specified time range are queried from the structured data set, and grouped by material type to obtain grouped material consumption data, wherein the material types include enameled wire raw materials and paints.

4. The method for material tracing and management in the enameled wire production process according to claim 3, characterized in that: In step S4, the minute-level rate sequence is used as a feature vector and normalized by Z-score to obtain a normalized feature vector; The IsolationForest model is trained using the preset pollution parameters to obtain a trained IsolationForest model; the standardized feature vector is input into the trained IsolationForest model to obtain an anomaly score for the standardized feature vector output by the trained IsolationForest model; Set an abnormality threshold. If the abnormality score is less than the abnormality threshold, the corresponding sequence is marked as abnormal. If the abnormality score is greater than or equal to the abnormality threshold, the corresponding sequence is marked as normal. A data set with abnormal and normal labels is generated from a minute-level consumption rate sequence with abnormal and normal labels and saved in a database, wherein both the abnormal and normal labels are labeled with corresponding material types.

5. The method for material tracing and management in the enameled wire production process according to claim 4, characterized in that: In step S5, if there is an unmatched batch in the initial mapping table, the batch number, production start time, and production end time are obtained from the production record table, and the start time of the minute-level consumption rate sequence is obtained from the data set with abnormal and normal marks; Then, interval matching is performed; a minute-level consumption rate sequence with a starting time within the interval from the production start time to the production end time is extracted, added to the initial mapping table and associated with the corresponding batch number to obtain a complete mapping relationship table.

6. The method for material tracing and management in the enameled wire production process according to claim 5, characterized in that: In step S6, the raw material consumption record includes a batch number, a minute-level consumption rate sequence with an abnormality mark, and a consumption level; According to the batch number, an exact matching operation is performed in the finished product record table to construct a traceability chain data set, where the finished product record table contains the batch number and the finished product quality identifier.

7. The method for material tracing and management in the enameled wire production process according to claim 6, characterized in that: In step S6, an SQL INNER JOIN statement is used to achieve an accurate match of the batch number field. The matching condition is that the batch number field of the batch consumption association table is completely consistent with the batch number field of the finished product record table, thereby obtaining a traceability chain data set; the traceability chain data set includes the batch number, a minute-level consumption rate sequence with an abnormal mark, a consumption level, and a finished product quality identifier.

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