An adaptive quality traceability system for multi-process copper busbars in power equipment

Abstract

This invention discloses an adaptive quality traceability system for multi-process copper busbars in power equipment, relating to the field of copper busbar production technology. The system includes: a data acquisition module that collects raw production data from multiple processes of the copper busbar, stores the raw data in a designated space, and assigns raw data codes containing product batch number, process, and quality code according to rules; an information setting module that encodes quality issues into quality codes, combines the product batch number with the raw data codes to form product traceability codes, and overwrites the original codes in the raw data space; an anomaly handling module that copies abnormal data based on the quality codes, stores it in a free space, and generates quality sub-maps using a co-occurrence space; and a process parameter adjustment module that extracts full-process data based on the raw data codes, obtains the equipment parameter adjustment range using a twin model, and adjusts the actual equipment accordingly. Ultimately, this system achieves adaptive traceability and efficient control of multi-process quality in the copper busbar, improving the stability of the production process and the consistency of product quality.

Description

Technical Field

[0001] This invention relates to the field of copper busbar production technology, specifically to an adaptive quality traceability system for multi-process copper busbars in power equipment. Background Technology

[0002] In the field of power equipment manufacturing, copper busbars, as core conductive and structural connection components, directly determine the safe and stable operation and long service life of power equipment. Therefore, quality traceability of the entire multi-process production of copper busbars is of irreplaceable industry necessity. Currently, the copper busbar production process involves multiple continuous steps, including raw material pretreatment, core processing, surface treatment, and testing and assembly. The production data generated by each step is complex and requires correlation with material information, equipment status, and operation records. Existing quality traceability solutions have significant shortcomings in data management efficiency and problem-solving capabilities.

[0003] The existing system lacks a structured design for storing raw production data from multiple processes. Data is not categorized and archived according to unified rules, and a standardized coding system is lacking. This results in production data from different batches and processes being independent, making it difficult to quickly establish data relationships. Subsequent quality traceability is not only time-consuming and labor-intensive but also prone to data omissions or matching errors. When quality issues occur with copper busbars, it is impossible to accurately extract abnormal data associated with the quality problem, nor is a deep correlation established between the quality problem and process data. This makes it difficult for technicians to quickly locate the root cause of the anomaly, and experience in handling similar quality problems cannot be effectively accumulated and reused, requiring repeated investigations when similar problems arise later.

[0004] In summary, existing multi-process quality traceability solutions for copper busbars have significant shortcomings in data standardization, accuracy of anomaly analysis, and parameter adjustment, making it difficult to meet the high quality requirements of power equipment for copper busbars. An adaptive quality traceability system is urgently needed to solve these problems. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive quality traceability system for multi-process copper busbars in power equipment, in order to solve the problems in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive quality traceability system for multi-process copper busbars in power complete sets of equipment, comprising:

[0007] The data acquisition module is used to collect raw production data of copper busbars in complete sets of power equipment, set up raw data space to store the raw production data based on the raw production data, and assign raw data codes to the raw production data according to preset coding rules; wherein, the raw data codes include product batch number codes, process codes and quality codes;

[0008] The information setting module, connected to the data acquisition module, is used to receive the quality problem description of the corresponding product batch number code and edit it into a quality code, and combine the quality code with the original data code according to the product batch number code to form a product traceability code, and store the product traceability code over the corresponding original data code in the original data space;

[0009] An anomaly handling module is used to copy the specific data of the anomaly from the original data space according to the quality code, configure a free space corresponding to the original data space, the free space is used to store the specific data of the anomaly, and set up multiple associated spaces to generate a quality sub-map corresponding to the quality code according to the specific data. Multiple merging points are configured in each associated space, and the multiple associated spaces are merged based on the merging point positions to obtain a quality traceability map of multiple processes of copper busbar of power complete equipment.

[0010] The process parameter adjustment module is used to extract the full-process production data of the product batch number code corresponding to the quality code from the associated space and the original data space according to the original data code, and to simulate the parameter adjustment range of the production equipment based on the preset twin model, and to adjust the actual production equipment according to the parameter adjustment range.

[0011] In a preferred embodiment, the data acquisition module includes:

[0012] The first setting unit is used to set the original data space, divide the original data space into multiple blank subspaces, and divide each subspace into multiple blank data bits;

[0013] The data acquisition unit is used to collect raw production data of multiple processes of copper busbars in complete sets of power equipment. The raw production data is then categorized by product batch and filled into the sub-spaces, with each sub-space corresponding to one product batch and having a unique association with each batch. Within each sub-space, the raw production data of the product batch is filled into the data positions according to the copper busbar production sequence. Each data position corresponds to the entire production data of one copper busbar, and each data position has a unique association with the copper busbar. The entire production data forms a three-layer nested structure according to process - data category - specific data.

[0014] The encoding unit is used to assign an original data code to each data in the entire production data of each data bit according to a preset encoding rule.

[0015] In a preferred embodiment, the preset encoding rule is specifically as follows:

[0016] For each piece of data in the entire production process, it is composed of a product batch number code and a process code. The product batch number code is formed based on the ID, production batch, and equipment number of each copper busbar. The process code is formed based on the production process corresponding to each piece of data, including the process category and the data category. Finally, the batch number code and the process code are combined to form the original data code. The original data code can be in the form of numbers, letters, one / two-dimensional codes, or RFID tag codes.

[0017] In a preferred embodiment, the information setting module includes:

[0018] The information receiving unit is used to obtain the original data code of the problematic copper busbar, and at the same time receive the description of the quality problem of the problematic copper busbar and associate it with the preset code that is the same as the original data code as the quality code to build a unique mapping relationship;

[0019] The information encoding unit combines the quality code with the original data code of the problematic copper busbar to form a product traceability code. The product traceability code covers the original data code corresponding to the problematic copper busbar and hides the original data code corresponding to the problematic copper busbar. The product traceability code can be in the form of numbers, letters, one / two-dimensional codes, or RFID tag codes.

[0020] In a preferred embodiment, the exception handling module includes:

[0021] The second setting unit is used to configure a free space corresponding to the original data space, and to set multiple associated spaces based on the quality code. In each associated space, multiple merging points are configured, and the merging points between the multiple associated spaces have a one-to-one correspondence.

[0022] An abnormal data extraction unit is used to copy the full process data of the problematic copper busbar from the corresponding data bit according to the quality code, compare the full process data with the preset normal specific data range to extract the abnormal specific data as data points, and store the data points in the free space and associate them with the quality code. When the same data point appears under the same quality code, the occurrence count of the data point is marked.

[0023] The first graph generation unit is used to capture data points in the free space that appear more than a preset rule based on the quality code, determine the data category of the data points as child nodes, use the quality code as the parent node, and associate the parent node and child nodes to construct a quality sub-graph. The quality sub-graph is stored in a randomly selected blank co-occurrence space based on the quality code, and the same child nodes are stored at merging points with corresponding relationships.

[0024] The second map generation unit is used to fuse multiple associated spaces based on the data category to obtain a quality traceability map of multiple processes of copper busbars in complete power equipment.

[0025] In a preferred embodiment, the first graph generation unit sets a first counter for each edge of the quality sub-graph to indicate the number of times the quality code is associated with the data category, and sets a second counter for each specific data under the child node to indicate the number of times the specific data is extracted;

[0026] The association frequency is used to represent the strength of the association between the quality problem represented by the quality code and the data category or process, with a higher association frequency indicating a stronger association; and the extraction frequency is used to represent the importance of the specific data, with a higher extraction frequency indicating more important data.

[0027] In the second graph generation unit, the fusion method of multiple associated spaces is as follows: merge the same child nodes and construct the edges of the same child nodes under the same child node. When merging the same child nodes, the same specific data within the same child node is counted and represented by the second counter.

[0028] In a preferred embodiment, the first map generation unit includes:

[0029] The capture subunit is used to capture data points in the free space that appear more than a preset rule based on the quality code, determine the data category of the data point as a child node, take the quality code as a parent node, and associate the parent node and the child node to construct a quality sub-graph.

[0030] The storage sub-unit is used to convert the parent node and multiple child nodes into identifiable data blocks and store them in the companion space, and to convert the data points into identifiable data areas and calculate hash values ​​based on the data areas;

[0031] The tag generation subunit is used to randomly assign randomly generated identifiable category tags to child nodes with the same data category. In multiple co-occurrence spaces, the child nodes with the same category tags are stored in merging points with corresponding relationships. The category tags and hash values ​​are merged and associated as identification tags for data points. The category tags and hash values ​​are merged and associated using an intelligent naming method.

[0032] In a preferred embodiment, the second map generation unit includes:

[0033] The fusion unit is used to establish a transmission channel between merging points that have corresponding relationships and stored data when merging multiple associated spaces to merge data blocks. In the merged data block, a unique data area is retained as a new data area based on the identification mark among multiple identical data areas. The data area is counted using the second counter based on the number of identical hash values. The merged data block is used as a new child node, and the original parent node associated with the child node is used as the new parent node. This completes the fusion of multiple associated spaces and obtains a quality traceability map of multiple processes of copper busbars in power complete equipment.

[0034] In a preferred embodiment, the process parameter adjustment module includes:

[0035] The parameter simulation unit is used to construct a twin model, extract the full-process production data corresponding to the quality code from the associated space and the original data space, and perform physical simulation through the pre-constructed twin model to output the parameter adjustment range of the physical equipment corresponding to the data point.

[0036] The communication unit is used to establish a communication connection with the physical equipment of the copper busbar of the complete set of power equipment, so as to transmit the parameter adjustment range to the physical equipment for equipment adjustment.

[0037] In a preferred embodiment, the parameter simulation unit includes:

[0038] The first extraction subunit is used to selectively extract target data points from the associated space based on the strong or weak correlation relationship;

[0039] The second extraction subunit is used to extract other normal specific data besides the target data point from the original data space, and combine the normal specific data with the data point to form pre-input simulation data;

[0040] The output subunit is adjusted to input the simulation data into a pre-built twin model for physical simulation and output the parameter adjustment range of the physical device corresponding to the data point.

[0041] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0042] This invention solves the problems of data chaos and difficulty in association in traditional solutions by constructing a structured raw data space and assigning standardized raw data codes to achieve orderly storage of raw production data from multiple processes and unique association with the entire process data of individual copper busbars. Combining quality codes with raw data codes to form product traceability codes enables rapid location of problematic copper busbars and their corresponding entire process data, significantly improving the efficiency and accuracy of quality traceability. By setting up a free space to store abnormal data and a co-existing space to generate quality sub-maps and merge them to form a quality traceability map, the correlation between quality problems and process data can be clearly presented, helping to quickly locate the root cause of anomalies and effectively accumulate experience in handling similar quality problems. Furthermore, by using a twin model to physically simulate the entire process production data, a scientific range for adjusting equipment parameters is output, avoiding the subjectivity and uncertainty of traditional experience-based adjustments and ensuring the reliability of parameter adjustments. Ultimately, adaptive traceability and efficient control of copper busbar quality across multiple processes are achieved, fully meeting the high standards required for copper busbar quality in power equipment and improving the stability of the production process and the consistency of product quality. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0044] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 As shown, the adaptive quality traceability system for multi-process copper busbars in power equipment according to the present invention includes:

[0047] The data acquisition module is used to collect raw production data of copper busbars in power complete sets of equipment, set up raw data space to store the raw production data based on the raw production data, and assign raw data codes to the raw production data according to preset coding rules; wherein, the raw data codes include product batch number codes and process codes;

[0048] The information setting module, connected to the data acquisition module, is used to receive the quality problem description of the corresponding product batch number code and edit it into a quality code, and combine the quality code with the original data code according to the product batch number code to form a product traceability code, and store the product traceability code over the corresponding original data code in the original data space;

[0049] An anomaly handling module is used to copy the specific data of the anomaly from the original data space according to the quality code, configure a free space corresponding to the original data space, the free space is used to store the specific data of the anomaly, and set up multiple associated spaces to generate a quality sub-map corresponding to the quality code according to the specific data. Multiple merging points are configured in each associated space, and the multiple associated spaces are merged based on the merging point positions to obtain a quality traceability map of multiple processes of copper busbar of power complete equipment.

[0050] The process parameter adjustment module is used to extract the full-process production data of the product batch number code corresponding to the quality code from the associated space and the original data space according to the original data code, and to simulate the parameter adjustment range of the production equipment based on the preset twin model, and to adjust the actual production equipment according to the parameter adjustment range.

[0051] It should be noted that the existing system lacks a structured design for storing the original production data of copper busbars in multiple processes of complete sets of power equipment. The data is not classified and archived according to unified rules, and there is a lack of a standardized coding system. As a result, the production data of different batches and different processes are independent of each other, making it difficult to quickly establish data association. Subsequent quality traceability is not only time-consuming and laborious, but also prone to data omissions or matching errors. This invention is based on the collection of raw production data from multiple processes. This raw production data comprises all data from the copper busbar production process, such as basic material information (raw material specifications, weight, etc.), shearing process data (preset parameters, testing equipment information, etc.), and bending process data (preset parameters, actual results, testing methods, etc.). This data covers the entire production process of the produced copper busbars. This data is enormous and complex; therefore, each specific piece of data is encoded to form a raw data code stored in the raw data space. This includes a product batch number code and a process code. The product batch number code represents which specific copper busbar production data belongs to which batch, and can be encoded using numbers, letters, or other formats. The process code represents which specific process and category of data the data belongs to (the raw production data of a copper busbar is complete process data, including a three-layer nested structure of process → data category → specific data), and can also be encoded using numbers, letters, or other formats. Combining these two codes allows for a sequential arrangement to form a string of codes as the raw data code. This allows the reader to interpret the specific characteristics of the raw data code to determine which batch, copper busbar, and process the data belongs to.

[0052] On the other hand, when quality problems occur in copper busbars, existing systems cannot accurately extract abnormal data associated with the quality problems, nor have they established a deep correlation between quality problems and process data. This makes it difficult for technicians to quickly locate the root cause of the abnormality, and experience in handling similar quality problems cannot be effectively accumulated and reused, requiring repeated investigations when similar problems are encountered later. This invention edits the description of quality problems in copper busbars (such as localized exposure of copper in the tin plating layer, cracks on the inner side of the copper busbar bending point, etc.) into quality codes. The quality codes are edited in the same format as the original data codes (if the original data codes are numbers, then the quality codes are also numbers). Finally, the quality codes are combined with the original data codes (which can be placed before or after) to form a product traceability code. Some copper busbars without quality problems will not generate product traceability codes. Copper busbars that generate product traceability codes will have their original data codes overwritten with the product traceability codes, indicating that the copper busbars have quality problems. Based on the quality code, the specific data of the anomaly (at the lowest level) can be copied from the original data space. The specific data of the anomaly is stored in a free space. A quality sub-map is generated based on the specific data and stored in the associated space (one associated space corresponds to a quality sub-map of a quality problem). The generation method of the quality sub-map will be described in detail in the following section. From the quality sub-map, the strength of the correlation between the process or data category corresponding to the quality problem can be viewed intuitively, so as to quickly locate the root cause of the anomaly.

[0053] Finally, based on the original data code, the full-process production data corresponding to the quality code is extracted from the associated space and the original data space (the specific data of the abnormalities that need to be adjusted is extracted from the associated space, and the normal data other than the abnormal data is extracted from the original data space to form the full-process production data). Based on the preset twin model (the twin model is a model based on virtual-real mapping and physical simulation in the prior art, which can accurately locate the root cause of the abnormality and calculate the safe and effective parameter adjustment range without repeated trial and error on the physical equipment), the parameter adjustment range of the production equipment is simulated to obtain the parameter adjustment range, and the actual production equipment is adjusted according to the parameter adjustment range.

[0054] In one embodiment, the data acquisition module includes:

[0055] The first setting unit is used to set the original data space, divide the original data space into multiple blank subspaces, and divide each subspace into multiple blank data bits;

[0056] The data acquisition unit is used to collect raw production data of multiple processes of copper busbars in complete sets of power equipment. The raw production data is then categorized by product batch and filled into the sub-spaces, with each sub-space corresponding to one product batch and having a unique association with each batch. Within each sub-space, the raw production data of the product batch is filled into the data positions according to the copper busbar production sequence. Each data position corresponds to the entire production data of one copper busbar, and each data position has a unique association with the copper busbar. The entire production data forms a three-layer nested structure according to process - data category - specific data.

[0057] The encoding unit is used to assign an original data code to each data in the entire production data of each data bit according to a preset encoding rule.

[0058] It should be noted that the original data space is divided into multiple blank subspaces. Each subspace corresponds to the original production data of a copper busbar in a production batch. Each subspace also contains multiple data bits, and each data bit corresponds to the complete production data of a single copper busbar in a production batch. That is, a data bit contains the production data of all processes of the copper busbar. However, because the complete production data has a three-layer nested structure of process-data category-specific data format, meaning that the complete production data includes a large amount of data, each data includes a specific data value, its data category, and the process it belongs to (e.g., passivating agent concentration of 11% is a specific data value, its data category is passivation process data, and its process is surface treatment process), each specific data also includes its corresponding data category and process. Finally, this massive amount of original production data is structured and stored, and a standardized coding system is set up to enable data associations to be quickly established between data, facilitating data extraction and application during subsequent quality traceability and avoiding matching errors. The original data space, subspaces, and data bits are all storage spaces, and their size depends on the amount of data and can be adjusted.

[0059] In one embodiment, the preset encoding rule is specifically as follows:

[0060] For each piece of data in the entire production process, it is composed of a product batch number code and a process code. The product batch number code is formed based on the ID, production batch, and equipment number of each copper busbar. The process code is formed based on the production process corresponding to each piece of data, including the process category and the data category. Finally, the batch number code and the process code are combined to form the original data code. The original data code can be in the form of numbers, letters, one / two-dimensional codes, or RFID tag codes.

[0061] It should be noted that for each specific data in the entire production process, a product batch number code (e.g., based on the product batch number, setting 00001 indicates that the specific data belongs to the first batch of copper busbar product data) and a process code (e.g., 01 indicates that the data belongs to the raw material stage, and 01 indicates the data of the incoming quality inspection category under the raw material stage) are assigned according to the ID, production batch, and equipment number of each copper busbar. Finally, the batch number code and process code are combined to form the original data code (e.g., sequential combination to form 000010101). The content of the specific data is obtained by interpreting the original data code according to the rules.

[0062] In one embodiment, the information setting module includes:

[0063] The information receiving unit is used to obtain the original data code of the problematic copper busbar, and at the same time receive the description of the quality problem of the problematic copper busbar and associate it with the preset code that is the same as the original data code as the quality code to build a unique mapping relationship;

[0064] The information encoding unit combines the quality code with the original data code of the problematic copper busbar to form a product traceability code. The product traceability code covers the original data code corresponding to the problematic copper busbar and hides the original data code corresponding to the problematic copper busbar. The product traceability code can be in the form of numbers, letters, one / two-dimensional codes, or RFID tag codes.

[0065] It should be noted that the process involves receiving a description of a quality issue with the copper busbar (e.g., localized exposure of copper in the tin plating layer) and assigning it a quality code (e.g., 001, representing localized exposure of copper in the tin plating layer). This quality code is then combined with the original data code to form a product traceability code. For example, the quality code might be placed after the original data code, such as 000010101001. This is just an example; in practice, it can be set to letters, a QR code, or an RFID tag code depending on the requirements. Ultimately, the copper busbar with the product traceability code will have the traceability code overlaid on and hidden from the original data code for future use.

[0066] In one embodiment, the exception handling module includes:

[0067] The second setting unit is used to configure a free space corresponding to the original data space, and to set multiple associated spaces based on the quality code. In each associated space, multiple merging points are configured, and the merging points between the multiple associated spaces have a one-to-one correspondence.

[0068] An abnormal data extraction unit is used to copy the full process data of the problematic copper busbar from the corresponding data bit according to the quality code, compare the full process data with the preset normal specific data range to extract the abnormal specific data as data points, and store the data points in the free space and associate them with the quality code. When the same data point appears under the same quality code, the occurrence count of the data point is marked.

[0069] The first graph generation unit is used to capture data points in the free space that appear more than a preset rule based on the quality code, determine the data category of the data points as child nodes, use the quality code as the parent node, and associate the parent node and child nodes to construct a quality sub-graph. The quality sub-graph is stored in a randomly selected blank co-occurrence space based on the quality code, and the same child nodes are stored at merging points with corresponding relationships.

[0070] The second map generation unit is used to fuse multiple associated spaces based on the data category to obtain a quality traceability map of multiple processes of copper busbars in complete power equipment.

[0071] It should be noted that, based on the product traceability code corresponding to the quality code, the entire process data of the problematic copper busbar can be quickly extracted from the data bits. By comparing the specific data in the entire process data with the preset normal specific data range, the specific abnormal data can be obtained and used as data points. After these data points are extracted, they will be distributed in the free space and associated with the corresponding quality code to determine which abnormal data corresponds to which quality code. At the same time, multiple copper busbars in the same batch may have the same quality problem, or copper busbars in different batches may have the same quality problem. The specific abnormal data corresponding to these quality problems may be the same or different. All of them are extracted and distributed in the free space (the free space is also a storage space, and its size can be adjusted according to the amount of data). Identical data is counted to mark the number of occurrences. When the number of occurrences of specific data under the same quality problem is too high (set an occurrence threshold, or set to extract the top-ranked data based on the overall frequency distribution), it may be a significant cause of the quality problem. Therefore, the data category of this data is extracted as a child node (the child node is the data category, and different specific data are distributed under the data category), and the quality code corresponding to the quality problem is used as the parent node. The parent node and child node are associated as edges to generate a quality sub-graph of the quality problem, describing the cause of the anomaly under this quality problem. The quality sub-graph corresponding to a quality code is stored in a companion space (the companion space is also a storage space, and its size can be adjusted according to the amount of data). Multiple merging points are configured in each companion space, and the merging points between multiple companion spaces are set to have a one-to-one correspondence. When storing the quality sub-graph, each child node is stored at a merging point, and based on the category label of the child node (see the subsequent explanation of random generation of category labels), the same child nodes in multiple quality sub-graphs are stored at the corresponding merging points.

[0072] In the context of storage systems, a merge point is specifically represented as an addressable logical identifier. It can be a network entry point for storage space (such as an IP address and port number), a file system mount path (such as / mnt / storage), an object storage access endpoint and bucket name (such as https: / / ...com), or a unique node ID in a distributed storage cluster. This point does not directly point to a specific physical disk sector, but rather serves as a logical anchor point for establishing communication, executing data routing, and managing policies, providing a precise operational address for data migration or spatial merging. Therefore, in scenarios requiring spatial merging, corresponding merge points can establish a transmission channel.

[0073] Furthermore, a resource scheduler is set up in the companion space. When the quality graph is stored in the companion space, the resource scheduler selects a corresponding number of physical locations based on the number of child nodes, registers the logical identifier of the location as a merge point, initializes the data structure, registers association relationships with merge points in other companion spaces, updates the merge point status and registers it with the service discovery system, binds the logical address to the physical resource, and delivers it to the access endpoint. When the quality subgraph is deleted, i.e., when the data in the companion space is cleared, the resource scheduler updates the merge point status to block writing, clears all logical relationships and identifiers of the merge point, completely deletes the record of the point in service discovery, and completes the unbinding of the logical identifier and physical resource to adapt to other storage needs of the companion space or the need for the quality subgraph to be displayed only and not merged. When the companion space is used again and there is a need for merging, the resource scheduler reallocates merge points and configures logical identifiers and association relationships based on the number of child nodes in the newly stored quality subgraph. When there are too many child nodes in the quality graph and not enough merge points, the resource scheduler selects the best physical location in the companion space, then registers the logical identifier of the new point and initializes the data structure, connects the new merge point to the service discovery system and updates the load balancing route, so as to realize the dynamic binding and immediate availability of logical addresses and physical resources.

[0074] Furthermore, different batches of copper busbars may exhibit different quality issues. Therefore, multiple associated spaces correspond one-to-one with multiple different quality issues, each describing the cause of the anomaly under multiple quality issues. However, the specific data of the anomalies under multiple quality issues may be the same. When there are enough associated spaces, it is not convenient to view them. Therefore, multiple selected associated spaces are merged to form a larger storage space to store the quality traceability map formed after fusion. The specific fusion process will be described in detail later. Ultimately, it is convenient to view the cause points of anomalies under multiple quality issues, and also convenient to check whether they have the same cause points.

[0075] In one embodiment, the first graph generation unit sets a first counter for each edge of the quality sub-graph to indicate the number of times the quality code is associated with the data category, and sets a second counter for each specific data under the child node to indicate the number of times the specific data is extracted;

[0076] The association frequency is used to represent the strength of the association between the quality problem represented by the quality code and the data category or process, with a higher association frequency indicating a stronger association; and the extraction frequency is used to represent the importance of the specific data, with a higher extraction frequency indicating more important data.

[0077] In the second graph generation unit, the fusion method of multiple associated spaces is as follows: merge the same child nodes and construct the edges of the same child nodes under the same child node. When merging the same child nodes, the same specific data within the same child node is counted and represented by the second counter.

[0078] It should be noted that when a quality problem arises again (which may be the same as or different from the existing quality problem), a new quality sub-graph is generated based on the newly generated quality problem. If the parent node in the quality sub-graph already exists, the new quality sub-graph is merged with the original quality sub-graph. The child nodes corresponding to the two parent nodes may be the same or different. If they have the same child nodes, the same child nodes are merged, and the specific data under the same child nodes are also merged. At the same time, a second counter is used to count the specific data to indicate the importance of the specific data (one child node in each quality sub-graph represents that the specific data has been extracted from the free space once. When merging child nodes, the second counter can measure the number of times the specific data has been extracted). The more times the counter counts, the more important it is. A counter is also preset for each edge of the quality sub-graph to indicate the number of times the quality code is associated with the data category. When the same child nodes are merged, the first counter is used to count. The more times the counter counts, the stronger the association between the quality problem and the data category or process. The first and second counters can use positive signs, or different colors, line thicknesses, etc., to clearly indicate the relationship between different counts. When merging multiple associated spaces, the same method is used to merge identical child nodes, and the second counter is used to count identical data within the child nodes, ultimately forming a quality traceability map. The quality traceability map shows the strong or weak correlation between multiple quality problems and data categories or processes, as well as the importance of specific data within the data category. This facilitates viewing the root causes of anomalies under multiple quality problems and helps determine the importance of the root causes.

[0079] In one embodiment, the first map generation unit includes:

[0080] The capture subunit is used to capture data points in the free space that appear more than a preset rule based on the quality code, determine the data category of the data point as a child node, take the quality code as a parent node, and associate the parent node and the child node to construct a quality sub-graph.

[0081] The storage sub-unit is used to convert the parent node and multiple child nodes into identifiable data blocks and store them in the companion space, and to convert the data points into identifiable data areas and calculate hash values ​​based on the data areas;

[0082] The tag generation subunit is used to randomly assign randomly generated identifiable category tags to child nodes with the same data category. In multiple co-occurrence spaces, the child nodes with the same category tags are stored in merging points with corresponding relationships. The category tags and hash values ​​are merged and associated as identification tags for data points. The category tags and hash values ​​are merged and associated using an intelligent naming method.

[0083] In one embodiment, the second map generation unit includes:

[0084] The fusion unit is used to establish a transmission channel between merging points that have corresponding relationships and stored data when merging multiple associated spaces to merge data blocks. In the merged data block, a unique data area is retained as a new data area based on the identification mark among multiple identical data areas. The data area is counted using the second counter based on the number of identical hash values. The merged data block is used as a new child node, and the original parent node associated with the child node is used as the new parent node. This completes the fusion of multiple associated spaces and obtains a quality traceability map of multiple processes of copper busbars in power complete equipment.

[0085] It should be noted that when storing the mass sub-map in the associated space, the parent and child nodes in the mass sub-map are respectively converted into data blocks and stored in the associated space. For example, if a mass sub-map contains one parent node and three child nodes, the child nodes and parent nodes are stored as four data blocks. Additionally, data points are converted into identifiable data areas, and multiple data areas constitute a data block. The specific form of a data block and data area is a continuous physical storage space filled with microscopic physical states representing binary 0 and 1—in a hard disk drive, this is the magnetic pole direction of a specific region on a magnetic material, while in a solid-state drive, it is the number of electrons trapped in a floating-gate transistor. Data blocks and data areas are used as objects to calculate hash values. The system reads the binary content of the data block or data area as input, calculates it using a hash function, and generates a fixed-length hash value that uniquely corresponds to the content of the data block or data area.

[0086] Running a hash algorithm on the data area calculates its hash value, which serves as a computer-recognizable digital fingerprint to represent the data point content. The same data content will always have the same hash value. For child nodes representing data blocks, they are represented by randomly generated identifiable category tags. These identifiable category tags are unique identifiers in various forms that the computer system can directly recognize (such as UUIDs, auto-incrementing IDs, and random strings). The identifiable category tags and the hash values ​​of the data area are merged and associated using intelligent naming (encoding the identifiable category tags in file paths or names, such as through the directory structure / @batches / <identifiable category tags> / <hash value>.data), thus obtaining a specific representation of the data area that the computer system can recognize. When two or more companion spaces are merged, data with corresponding relationships and already stored... A transmission channel is established between merging points (where at least one merging point stores data) to merge data blocks. If there is a corresponding relationship and only one data block stores data, then that data block is directly used as the merged data block, forming a child node of the merged quality traceability map. In the merged data block, the same data area is directly identified based on the hash value of the data area, and it is determined whether the data area is under the same child node according to the identifiable category label corresponding to the data area. If so, it is directly merged, and the unique data area is retained as the new data area. The same data area is counted using a second counter, and redundant data areas are directly deleted. Finally, the association relationship between the merged data blocks (child nodes and parent nodes) is obtained according to the original association relationship of the quality sub-map to complete the fusion of multiple associated spaces and finally generate a multi-process quality traceability map of copper busbars for complete power equipment.

[0087] In one embodiment, the process parameter adjustment module includes:

[0088] The parameter simulation unit is used to construct a twin model, extract the full-process production data corresponding to the quality code from the associated space and the original data space, and perform physical simulation through the pre-constructed twin model to output the parameter adjustment range of the physical equipment corresponding to the data point.

[0089] The communication unit is used to establish a communication connection with the physical equipment of the copper busbar of the complete set of power equipment, so as to transmit the parameter adjustment range to the physical equipment for equipment adjustment.

[0090] It should be noted that the twin model is a full-dimensional digital replica of a physical entity (such as copper busbar production equipment and copper busbar products). By collecting operating parameters and status data from the physical end (such as temperature, pressure, dimensional deviations, and quality defects during the process), a precise mapping between the physical state and the virtual model is established. This ensures that the virtual model is highly consistent with the physical entity in terms of geometric structure (such as the bending shape and punching position of the copper busbar), physical properties (such as the plasticity and conductivity of copper), and operational behavior (such as the stress and temperature changes during equipment processing). By injecting abnormal data (such as specific data on quality problems such as cracks at the bending point of the copper busbar and exposed copper in the tin plating layer), and using built-in physical mechanism algorithms (such as material plastic deformation formulas and heat conduction models) and historical normal data, the model simulates and analyzes the root causes of abnormalities (such as excessive bending speed and incomplete pre-plating treatment). It also supports multi-scenario parameter adjustment simulation, testing the effects of different parameter combinations in the virtual environment, and outputting a scientific parameter adjustment range based on equipment safety constraints and process quality standards. Finally, a communication connection is established with the physical equipment of the copper busbar of the complete set of equipment through the communication unit, so as to transmit the parameter adjustment range to the physical equipment for equipment adjustment.

[0091] In one embodiment, the parameter simulation unit includes:

[0092] The first extraction subunit is used to selectively extract target data points from the associated space based on the strong or weak correlation relationship;

[0093] The second extraction subunit is used to extract other normal specific data besides the target data point from the original data space, and combine the normal specific data with the data point to form pre-input simulation data;

[0094] The output subunit is adjusted to input the simulation data into a pre-built twin model for physical simulation and output the parameter adjustment range of the physical device corresponding to the data point.

[0095] It should be noted that, based on the quality traceability map, target data points to be simulated are selectively extracted from the associated space, and normal data other than the selected target data points are extracted from the original data space as simulation data. Simultaneously, fixed physical parameters are preset for the twin model (such as the inherent factory parameters of the processing equipment, the inherent accuracy of the sensors, the quality qualification standards for each process of the copper busbar, fixed process constraints of the processing, fixed parameters of the copper busbar bending die, fixed structural parameters of the tin plating fixture, fixed hole diameter and positioning reference of the punching die, etc.). Finally, the simulation data is input into the pre-built twin model for physical simulation, and the parameter adjustment range of the physical equipment corresponding to the data points is output, thereby adjusting the physical equipment. In this way, simulation can be selectively performed based on the importance of the data corresponding to the quality problem, avoiding repeated testing of multiple data points and improving efficiency.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-process self-adaptive quality tracing system for copper bars of power equipment sets, characterized in that, The method comprises the following steps: A data acquisition module is configured to acquire original production data of copper bars of power equipment in multiple processes, set an original data space based on the original production data to store the original production data, and assign original data codes to the original production data according to a preset coding rule; wherein the original data codes comprise product batch number codes and process codes; An information setting module connected to the data acquisition module is configured to receive quality problem descriptions corresponding to the product batch number codes and edit them into quality codes, combine the quality codes with the original data codes to form product traceability codes according to the product batch number codes, and store the product traceability codes in the original data space by covering the corresponding original data codes; An abnormality processing module is configured to copy specific abnormal data from the original data space according to the quality codes, configure a free space in the original data space, store the specific abnormal data in the free space, and set multiple associated spaces to generate quality sub-maps corresponding to the quality codes based on the specific data; the abnormality processing module comprises: A second setting unit is configured to configure a free space in the original data space, set multiple associated spaces based on the quality codes, configure multiple merging points in each associated space, and set a one-to-one correspondence between the merging points of the multiple associated spaces; An abnormal data extraction unit is configured to copy full-process data of problematic copper bars from corresponding data bits according to the quality codes, compare the full-process data with a preset normal specific data range to extract specific abnormal data as data points, store the data points in the free space in association with the quality codes, and mark the data points when the same data points appear under the same quality code; A first map generation unit is configured to capture data points with an occurrence frequency exceeding a preset rule in the free space based on the quality codes, determine data categories of the data points as child nodes, take the quality codes as parent nodes, associate the parent nodes and child nodes to construct quality sub-maps, and store the quality sub-maps in a randomly selected blank associated space based on the quality codes, and store the same child nodes on merging points with a corresponding relationship; A second map generation unit is configured to fuse multiple associated spaces based on the data categories to obtain a quality traceability map of copper bars of power equipment in multiple processes; A process parameter adjustment module is configured to extract full-process production data of product batch number codes corresponding to the quality codes from the associated spaces and the original data space based on the original data codes, simulate a parameter adjustment range of production equipment based on a preset twin model, and adjust actual production equipment according to the parameter adjustment range.

2. The adaptive quality traceability system for copper bars of power equipment package in multiple sequences according to claim 1, characterized in that, The data acquisition module comprises: A first setting unit is configured to set an original data space, divide the original data space into multiple blank sub-spaces, and divide each sub-space into multiple blank data bits. The collection unit is used for collecting original production data of copper bars of the complete set of power equipment in multiple processes, and filling the original production data into the subspace according to product batches respectively after the original production data is classified according to product batches, one subspace corresponding to one product batch, and each subspace having a unique association with the product batch; the original production data of the product batch is filled into the data bit according to the production sequence of the copper bar in each subspace, one data bit corresponding to the full-process production data of one copper bar, and each data bit having a unique association with the copper bar; wherein the full-process production data forms a three-layer nested structure according to process-data category-specific data. The encoding unit is used for assigning original data code to each data in the full-process production data on each data bit according to a preset encoding rule.

3. The adaptive quality traceability system for copper bars of power equipment package in multiple sequences according to claim 2, characterized in that, The preset encoding rule is specifically: For each data in the full-process production data, a product batch number code + process code is used to form, the product batch number code is formed according to the ID, production batch and equipment number of each copper bar, the process code is formed according to the production process flow corresponding to each data, including process category and data category, finally the batch number code and process code are combined to form the original data code, the form of the original data code includes numbers, letters, one / two-dimensional code or RFID tag code.

4. The adaptive quality tracing system for copper bars of power equipment package multi-processes according to claim 1, characterized in that, The information setting module includes: The information receiving unit is used for obtaining the original data code of the problem copper bar, receiving the description of the quality problem of the problem copper bar, and associating the preset code with the same form as the original data code as a quality code to build a unique mapping relationship; The information encoding unit combines the quality code and the original data code of the problem copper bar to form a product traceability code, covers the original data code of the problem copper bar with the product traceability code, and hides the original data code of the problem copper bar, the form of the product traceability code includes numbers, letters, one / two-dimensional code or RFID tag code.

5. The adaptive quality tracing system for copper bars of power equipment package multi-processes according to claim 1, wherein, In the first graph generation unit, a first counter is set for each edge of the quality subgraph to represent the association frequency of the quality code and the data category, and a second counter is set for each specific data under the subnode to represent the extraction frequency of the specific data; The association frequency is used to represent the strong and weak association relationship between the quality problem represented by the quality code and the data category or process, the more the association frequency is, the stronger the association relationship is; and the extraction frequency is used to represent the importance of the specific data, the more the extraction frequency is, the more important the data is; In the second graph generation unit, the fusion mode of the multiple associated spaces is to merge the same subnodes and construct the edges of the same subnodes under the same subnode, and when the same subnodes are merged, the same specific data in the same subnode is counted and represented by the second counter.

6. The adaptive quality traceability system for copper bars of power equipment package in multiple sequences according to claim 5, characterized in that, The first graph generation unit includes: The grabbing subunit is configured to grab data points whose quality codes exceed a preset rule in the free space based on the quality codes, determine data categories of the data points as child nodes, associate the quality codes with the child nodes as parent nodes, and construct a quality subgraph based on the parent nodes and the child nodes; The storage subunit is configured to convert the parent nodes and the child nodes into identifiable data blocks and store the data blocks in the companion space, convert the data points into identifiable data areas, and calculate hash values based on the data areas; The label generation subunit is configured to randomly assign an identifiable category label to child nodes of the same data category, store the child nodes with the same category label in a merging point with a corresponding relationship in the multiple companion spaces, and merge and associate the category label with the hash values as an identification label of the data points, wherein the category label and the hash values are merged and associated in an intelligent naming manner.

7. The adaptive quality traceability system for copper bars of power equipment kit multi-processes according to claim 6, characterized in that, The second graph generation unit includes: The fusion unit is configured to establish a transmission channel between merging points with a corresponding relationship and stored data to merge data blocks when fusing the multiple companion spaces, reserve a unique data area as a new data area in multiple same data areas based on an identification label in the merged data blocks, count the data area based on a number of same hash values using the second counter, take the merged data blocks as new child nodes, take an original parent node associated with the child nodes as a new parent node, complete fusion of the multiple companion spaces, and obtain a quality traceability graph of the copper bar multi-process of the power complete equipment.

8. The adaptive quality tracing system for copper bars of power equipment kit multiple sequences according to claim 1, characterized in that, The process parameter adjustment module includes: The parameter simulation unit is configured to construct a twin model, extract full-process production data corresponding to the quality codes from the companion space and the original data space, perform physical simulation on the twin model, and output a parameter adjustment range of a physical device corresponding to the data points; The communication unit is configured to establish a communication connection with the physical device of the copper bar multi-process of the power complete equipment, and transmit the parameter adjustment range to the physical device for device adjustment.

9. The adaptive quality tracing system for copper bars of power equipment kit multi-processes according to claim 8, characterized in that, The parameter simulation unit includes: The first extraction subunit is configured to selectively extract target data points from the companion space based on strong and weak correlation relationships; The second extraction subunit is configured to extract other normal specific data other than the target data points from the original data space, and combine the normal specific data with the data points to form pre-input simulation data; The adjustment output subunit is configured to input the simulation data into the pre-constructed twin model for physical simulation, and output a parameter adjustment range of a physical device corresponding to the data points.

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