Material batch whole-process traceability system based on production process

Abstract

The invention discloses a material batch full-process traceability system based on a production process, particularly relates to the field of production and manufacturing traceability, is used for solving the problems of continuity and accuracy of material batch full-process traceability, and is characterized in that batch tracking coordinates are generated by constructing a process mapping matrix and an assembly topological index; detecting circulation holes in real time and inserting placeholder marks; judging broken chain credibility based on path integrity and structural complexity; compressing traceable chain segments; executing field dynamic alignment to generate batch circulation patches; a material batch whole-process continuous tracing view is established; and furthermore, the abnormity positioning time is remarkably shortened, the real-time performance is improved, the influenced batches are accurately locked, the recall range is reduced, the production line decision-making efficiency is optimized, and efficient and reliable technical support is provided for quality management and batch tracking in the production process.

Description

Technical Field

[0001] The present invention relates to the field of production and manufacturing traceability, and more specifically, to a full-process traceability system for material batches based on production processes. Background Art

[0002] Production workshops use batches as units to advance continuous links such as cutting, forming, testing, and assembly. After entering the assembly stage, materials are broken down into multiple sub-components and then pass through different processes in sequence. Each transfer relies on tags, logs, and interfaces to describe its destination. Traceability solutions need to connect all nodes in series to quickly locate the source and scope of impact in the event of quality anomalies later. In actual operations, assembly stations and testing stations are often set up alternately, with a high frequency of information writing and a topological depth that increases geometrically with the product structure. The mapping relationship between the data persistence layer and the real-time interface becomes complex and dynamic, and the traceability link is subsequently lengthened.

[0003] The continuity of batch tracking in the multi-branch assembly-inspection-reassembly chain is vulnerable to three factors: First, transfers between workstations rely on manual labeling and code scanning. Label detachment, damage, or misapplication can leave breakpoints deep within the topology. Second, different workstations use independent log templates, lacking uniform field meanings and time granularity, resulting in a misalignment between the writing order and the actual flow order. Third, when repair or modification operations are performed after assembly is completed, the original parent-child relationship must be rewritten. If the real-time interface is not synchronized in time, the traceability algorithm cannot reconstruct the complete path. The combined effect of these factors leads to gaps in the flow trajectory of batches from raw materials to subassemblies and then to the complete machine. When quality risks arise, it is impossible to quickly identify the affected batches, which expands the scope of recalls and delays production line decisions.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a full-process traceability system for material batches based on production processes, which generates batch tracking coordinates by constructing a process mapping matrix and an assembly topology index, detects flow holes in real time and inserts placeholder marks, determines the credibility of broken chains based on path integrity and structural complexity, compresses traceable chain segments, performs dynamic field alignment to generate batch flow patches, writes to a distributed ledger to refresh the global batch flow map, locates historical abnormal batches and outputs an assembly risk warning list, and establishes a continuous traceability view of the entire process of material batches; thereby significantly shortening the abnormality positioning time, improving real-time performance, accurately locking the affected batches, reducing the recall scope, optimizing production line decision-making efficiency, and providing efficient and reliable technical support for quality management and batch tracking in the production process to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The whole-process traceability system of material batches based on production process includes:

[0008] Mapping index module: Builds a process mapping matrix at the first-order workstation and instantly derives the assembly topology index as the batch tracking coordinate;

[0009] Chain break detection module: The chain break catcher monitors the tag event stream in the process mapping matrix in real time, inserts placeholders after detecting holes, and records the triggering time;

[0010] Chain segment repair module: The placeholder mark triggers the construction of a local batch flow diagram, and the path integrity measurement and structural complexity measurement are combined to form the credibility of the chain break. Based on this, it is determined whether the parent-child relationship at both ends of the gap is compressed to form a traceable chain segment;

[0011] Semantic alignment module: The semantic fusion engine dynamically aligns the traceable segment fields based on the workstation context, generates batch flow patches, and uniformly describes log differences.

[0012] Path update module: After the batch flow patch is written into the distributed ledger, the path reconstructor is triggered to refresh the global batch flow graph;

[0013] Abnormal location module: The global batch flow chart locates historical abnormal batches through the playback module, and outputs an assembly risk warning list for quality decision-making.

[0014] In a preferred embodiment, the mapping index module includes the following contents:

[0015] At the first transfer station, a process mapping matrix in the form of a two-dimensional table is constructed for the material batch to record the flow relationship of the material batch between stations. Initially, the elements of the process mapping matrix are zero, and the flow state occurs by updating the elements to one; a tree-structured assembly topology index is derived to record the hierarchical relationship of the material batch. The nodes in the assembly topology index represent material batches or sub-components, and the connecting lines represent the parent-child relationship. Initially, the assembly topology index only contains the root node; batch tracking coordinates are generated, and the uniqueness of the material batch is ensured by integrating the flow path information and hierarchical structure information and using a hash function to generate a unique identifier of a fixed length.

[0016] In a preferred embodiment, the link break detection module includes the following contents:

[0017] The broken chain catcher monitors the label event flow in the process mapping matrix in real time, inserts a placeholder mark in the process mapping matrix after detecting a hole, and records the triggering time. The technical features can be summarized as follows: continuously monitor the dynamic changes of the label event flow in the process mapping matrix. When it is identified that the label event flow is interrupted to form a hole, it immediately inserts a placeholder mark at the corresponding position of the process mapping matrix, and records the triggering time of the corresponding hole detection.

[0018] In a preferred embodiment, the segment repair module includes the following:

[0019] The construction of the local batch flow graph is triggered by placeholder marking. The flow events related to the workstations before and after the hole are extracted from the process mapping matrix and converted into directed edges. At the same time, the missing edges at the hole position are marked.

[0020] In a preferred embodiment, the segment repair module further includes the following:

[0021] The calculation of the segment coherence index is based on identifying all the paths from the workstation before the void to the workstation after the void in the local batch flow graph, calculating the coherence of each path by multiplying the inverse of the time interval, and taking the geometric mean of the coherence of all paths to quantify the completeness of the flow path in the local batch flow graph;

[0022] The calculation of the topological chaos index is based on the ratio of the sum of the inverse of the shortest path lengths between all node pairs in the local batch flow graph to the sum of the node degrees, which is used to quantify the structural complexity of the local batch flow graph.

[0023] In a preferred embodiment, the segment repair module further includes the following:

[0024] The credibility of the chain break is generated by integrating the chain segment coherence index and the topological chaos index through a logical function. When the credibility of the chain break is higher than the preset threshold, topological compression is performed to merge the workstations before the void, after the void, and in the middle into virtual nodes to generate a traceable chain segment.

[0025] In a preferred embodiment, the semantic alignment module includes the following:

[0026] The semantic fusion engine loads the workstation log template, field definition and time granularity standard from the preconfigured workstation information library and stores them as an internal mapping table; for the flow events in the traceable chain segment, the workstation set is extracted and the workstation fields are converted into a standard field set using the predefined field semantic mapping table to generate aligned flow events; the time granularity unification operation is performed on the aligned flow events, and truncation or interpolation adjustment is performed according to the standard time granularity; the aligned flow events and the unified timestamps are organized into a JSON format batch flow patch; the details of the field alignment operation and the time adjustment operation are recorded, a difference report is generated and added to the batch flow patch metadata field.

[0027] In a preferred embodiment, the path update module includes the following contents:

[0028] The batch flow patch is serialized into a data packet in JSON format and sent to the distributed ledger writing node. The writing node verifies the integrity and digital signature of the data packet and then records the data packet in the distributed ledger. The distributed ledger uses blockchain technology to store the hash value and timestamp of the data packet; the path reconstructor subscribes to the write event of the distributed ledger, receives and parses the batch identifier, workstation pair and alignment flow event set in the newly written batch flow patch, and updates the subgraph part of the corresponding batch in the global batch flow graph according to the parsed alignment flow event set. The global batch flow graph is refreshed by adding new edges or updating edge attributes. The path reconstructor records the update time and the hash value of the batch flow patch to generate a version log, and provides a real-time query interface to access the latest global batch flow graph data.

[0029] In a preferred embodiment, the abnormality location module includes the following contents:

[0030] The replay module loads the latest snapshot of the global batch flow graph from the distributed ledger and obtains the predefined quality exception standards. It uses the depth-first search algorithm to reversely traverse the flow path of the global batch flow graph, records the batches that trigger the quality exception standards into the abnormal batch list, and then uses the Dijkstra algorithm to calculate the shortest path length from the abnormal batch to the downstream batch. The path-dependent attenuation model is used to calculate the risk propagation factor and generate an assembly risk warning list.

[0031] In a preferred embodiment, the abnormality location module further includes the following contents:

[0032] The assembly risk warning list includes batch identification, risk value, abnormality type and recommended measures in a tabular form. Finally, the playback module transmits the assembly risk warning list to the quality decision module to support the technical features of quality control operations.

[0033] The technical effects and advantages of the present invention's full-process traceability system for material batches based on production processes are as follows:

[0034] By constructing a process mapping matrix and assembly topology index to generate batch tracking coordinates, the system detects flow holes in real time and inserts placeholders. It determines the credibility of chain breaks based on path integrity and structural complexity, compresses traceable segments, performs dynamic field alignment to generate batch flow patches, writes to the distributed ledger to refresh the global batch flow map, locates historical abnormal batches, and outputs a list of assembly risk warnings, establishing a continuous traceability view of the entire material batch process. This significantly shortens abnormality location time, improves real-time performance, accurately identifies affected batches, reduces recall scope, optimizes production line decision-making efficiency, and provides efficient and reliable technical support for quality management and batch tracking during production. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1It is a structural schematic diagram of the whole-process traceability system of material batches based on production process of the present invention.

[0036] Figure 2 This is a schematic diagram of the steps of the anomaly locating module of the full-process traceability system for material batches based on the production process of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Example 1: Figure 1 The present invention provides a full-process traceability system for material batches based on production processes, including:

[0039] Mapping index module: Builds a process mapping matrix at the first-transfer station and instantly derives the assembly topology index as the batch tracking coordinate.

[0040] Chain break detection module: The chain break catcher monitors the tag event stream in the process mapping matrix in real time, inserts placeholders after detecting holes, and records the triggering time.

[0041] Chain segment repair module: The placeholder mark triggers the construction of a local batch flow diagram, and the path integrity measurement and structural complexity measurement are combined to form the credibility of the chain break, and based on this, it is determined whether the parent-child relationship at both ends of the gap is compressed into a traceable chain segment.

[0042] Semantic alignment module: The semantic fusion engine dynamically aligns the traceable segment fields based on the workstation context, generates batch flow patches, and uniformly describes log differences.

[0043] Path update module: After the batch flow patch is written to the distributed ledger, the path reconstructor is triggered to refresh the global batch flow graph to reflect the assembly relationship in real time.

[0044] Abnormal location module: The global batch flow chart locates historical abnormal batches through the playback module, and outputs an assembly risk warning list for quality decision-making.

[0045] In modern manufacturing, full-process traceability of material batches is crucial for ensuring product quality and optimizing production management. Production workshops utilize batches to carry out continuous processes such as cutting, forming, testing, and assembly. Once assembled, materials are broken down into multiple subassemblies and flow through various processes, creating a complex flow path.

[0046] Traditional traceability methods rely on manual labeling, code scanning, and logging, but are prone to interruptions due to factors such as label detachment, inconsistent log formats, and untimely synchronization of repair operations. When quality anomalies occur, traditional traceability methods struggle to quickly locate the affected batch, leading to expanded recalls and delayed production line decisions.

[0047] Therefore, a full-process traceability method for material batches based on production processes is urgently needed. By building a continuous and reliable traceability system, the efficiency and accuracy of locating abnormal batches can be improved. The proposed full-process traceability system for material batches based on production processes is precisely targeted at this scenario. By constructing a process mapping matrix and assembly topology index at the first transfer station, it lays the foundation for subsequent traceability and ensures the traceability of batch flow.

[0048] The processing goal of the mapping index module is to establish the initial data structure for the flow tracking of material batches at the first transfer station (the first station where the material batch enters the production process). Specifically, it includes building a process mapping matrix, deriving an assembly topology index, and generating batch tracking coordinates to record the flow relationship and hierarchical structure of material batches in the production process, providing a data basis for subsequent steps.

[0049] The mapping index module includes the following:

[0050] S1.1, Construction of process mapping matrix:

[0051] At the first transfer station in the production process, a process mapping matrix is ​​constructed for each material batch to record the flow relationship between the material batches between various stations.

[0052] The process mapping matrix is ​​a two-dimensional table whose rows and columns correspond to all workstations in the production process. For example, if the production process includes workstations A, B, and C, the rows and columns of the matrix are named after workstations A, B, and C. Each element in the matrix represents the flow status from the workstation corresponding to the row to the workstation corresponding to the column. Initially, all elements in the matrix are set to zero, indicating that the material batch has not yet flowed between any workstations. When a material batch flows from one workstation to another, the system updates the element at the corresponding position in the matrix to 1, indicating that the flow relationship has occurred. For example, when a material batch flows from workstation A to workstation B, the element at the intersection of row workstation A and column workstation B in the matrix changes from zero to 1. By continuously updating the matrix elements, the system can fully record the flow path of the material batch in the production process.

[0053] The process mapping matrix uses a two-dimensional table format to record flow relationships. Its structure is intuitive, easy to implement, and understand. By mapping rows and columns, the system can quickly locate the flow status between any workstations, facilitating real-time query and analysis. Updating matrix elements simply involves replacing zeros with ones, which reduces computational complexity and makes it suitable for efficient execution during production.

[0054] S1.2, Derivation of assembly topology index:

[0055] While constructing the process mapping matrix, an assembly topology index is derived for each material batch to represent the hierarchical relationship and structural changes of the material batch during the assembly process.

[0056] The assembly topology index is recorded in a tree structure, where each node represents a material batch or its subassembly, and the connecting lines between the nodes indicate the parent-child relationship between them. At the first transition station, the assembly topology index tree structure contains only one root node, which corresponds to the initial material batch. As the production process progresses, if the material batch is split into multiple subassemblies in subsequent steps, the system adds a new node in the tree structure for each subassembly and connects it to the parent node representing the original material batch via a connecting line.

[0057] For example, if a material batch is split into subassembly X and subassembly Y during a particular process, two subnodes are added to the root node in the tree structure, corresponding to subassembly X and subassembly Y, respectively. These subnodes are connected to the root node via connecting lines. By continuously expanding the tree structure, the system can accurately record the hierarchical changes of the material batch during the assembly process.

[0058] The assembly topology index uses a tree structure to record hierarchical relationships, clearly reflecting the structural characteristics of material batches during the splitting or assembly process. The tree structure's nodes and connecting lines are intuitively designed, suitable for representing complex parent-child relationships. It also offers good scalability to adapt to the complexity requirements of different assembly processes. By maintaining the tree structure, the system can track the associations between material batches and their subcomponents, providing structured data support for analyzing assembly process integrity or locating anomalies. Furthermore, the tree structure facilitates the extraction of hierarchical information through traversal operations, enhancing data processing flexibility.

[0059] S1.3, Generation of batch tracking coordinates:

[0060] To uniquely identify and locate each material batch in the production process, the system generates a batch tracking coordinate. The batch tracking coordinate generation process is divided into three stages.

[0061] First, the flow path information of the material batch is extracted from the process mapping matrix, that is, the sequence of all workstations that the material batch passes through is recorded, such as the order from workstation A to workstation B and then to workstation C.

[0062] Secondly, the hierarchical structure information of the material batch is extracted from the assembly topology index, that is, the path from the root node to the current node in the tree structure, such as the connection relationship from the root node to the sub-component X.

[0063] In the third step, the flow path information and the hierarchical structure information are sequentially concatenated into a string, and the string is processed through a hash function to generate a unique identifier of a fixed length.

[0064] Hash functions map input strings to fixed-length outputs, ensuring that output identifiers remain unique even with similar input information. For example, even if two material batches have highly similar flow paths and hierarchical structures, the hashing properties of hash functions can generate different identifiers. The resulting batch tracking coordinates serve as a global identifier for the material batch throughout the entire production process.

[0065] Batch tracking coordinates integrate flow path and hierarchical information and use a hash function to generate a unique identifier, ensuring that each material batch is uniquely identified within the system. This approach avoids identifier conflicts and maintains identifier differentiation even in large-scale production. The fixed-length identifiers generated by the hash function are easy to store and transmit, making them suitable for data association and querying in distributed systems. Furthermore, batch tracking coordinates compress multidimensional information into a single identifier, simplifying data management and improving the efficiency and consistency of the system in tracking and locating material batches.

[0066] The mapping index module has constructed a process mapping matrix at the first transfer station and derived an assembly topology index as the basic coordinate for batch tracking. However, transfers between stations rely on manual labeling and code scanning. Labels that fall off, become damaged, or are misapplied can result in missing flow information and form breakpoints in the traceability chain. If these breakpoints are not detected and addressed in a timely manner, the continuity of batch tracking will be impaired, affecting the efficiency and accuracy of subsequent anomaly location. Therefore, the chain break detection module focuses on real-time monitoring of the label event stream in the process mapping matrix, detecting breakpoints and taking measures to ensure the integrity of the traceability chain.

[0067] The processing goal of the broken chain detection module is to detect holes in the flow path by monitoring the label event flow in the process mapping matrix in real time. After the hole is detected, a placeholder marker is inserted and the triggering time is recorded to ensure the continuity of the material batch traceability link.

[0068] The link break detection module includes the following:

[0069] S2.1, Real-time monitoring mechanism:

[0070] The Broken Chain Catcher is a real-time monitoring module responsible for subscribing to and parsing the tag event stream in the process mapping matrix. This tag event stream is the sequence of records generated by scanning devices as a material batch moves between production stations. Each record contains the batch number, the starting station, the destination station, and the time the record was generated. By subscribing to update events in the process mapping matrix, the Broken Chain Catcher acquires and parses these records in real time to verify the integrity of the material batch flow path. This parsing process involves reading each record individually, confirming that each record logically connects to the previous one in terms of time and station.

[0071] S2.2, Void Detection:

[0072] A void refers to a missing link in the material batch flow path, which makes the traceability link discontinuous.

[0073] The broken chain catcher identifies holes by analyzing the continuity of the tag event stream. The specific process is to construct a flow path graph for each material batch, which uses workstations as nodes and flow records as edges connecting the nodes. Starting from the time the material batch enters the first workstation, the broken chain catcher checks each record one by one in chronological order to verify whether there is a direct connection from the current workstation to the next workstation. If it is found that the starting workstation of a record does not match the target workstation of the previous record, or if there is a lack of records from one workstation to another, it is determined that there is a hole in the flow path, and the specific location of the hole is recorded, that is, the missing connected workstation pair.

[0074] By constructing a flow path diagram and verifying the connectivity of each record, the system can clearly identify breakpoints in the flow path. This approach leverages the chronological order of records and the relationships between workstations to intuitively and accurately locate gaps. Using a flow path diagram facilitates automated analysis of flow records.

[0075] S2.3, insert a placeholder marker and record the triggering time:

[0076] When a hole is detected, the chain break catcher inserts a placeholder marker into the process mapping matrix to identify the missing flow record. This placeholder marker contains the following information: the material batch number, the starting and destination stations of the missing flow record, and the specific time when the hole was detected and the marker was inserted. Insertion involves writing the placeholder marker into the process mapping matrix at the location corresponding to the missing flow record. The time of hole detection is recorded as the trigger moment and stored in the system for subsequent analysis and repair.

[0077] The purpose of inserting a placeholder marker is to maintain the continuity of the flow path by temporarily identifying the missing flow record. This method ensures that even if there is missing data, the system can still track the location of the material batch and prevent the traceability chain from being completely interrupted.

[0078] In the production process, the mapping index module has completed the construction of the process mapping matrix, which records the flow relationship between material batches and workstations. Based directly on the data in the process mapping matrix, the chain break detection module uses a chain break catcher to monitor the tag event stream in real time, detect holes in the flow path, insert placeholders when a hole is detected, and record the trigger time. The chain segment repair module uses these placeholders and trigger time information to construct a local batch flow diagram and perform chain break repair, thereby ensuring the continuity of material batch tracking and the credibility of the data.

[0079] The chain break detection module enables continuous monitoring of material batch flow paths through a real-time monitoring mechanism, hole detection methods, and recording of placeholder marker insertion and triggering times. Real-time monitoring by the chain break detector ensures the system can quickly respond to changes in flow records; the construction and verification of the flow path map accurately locates holes; and the insertion of placeholder markers and recording of triggering times maintain the integrity of the traceability chain and provide essential information for subsequent analysis. This systematic processing logic significantly improves the reliability and efficiency of material batch tracking during the production process.

[0080] The broken chain detection module uses a broken chain catcher to monitor the label event stream in real time. After detecting an information hole, it inserts a placeholder marker and records the triggering time, providing a clear broken chain location identifier for subsequent processing. However, the existence of information holes threatens the continuity of the batch flow path, especially in the multi-branch assembly-inspection-reassembly link. Factors such as label detachment, inconsistent log format or unsynchronized rework operations may lead to the interruption of the traceability link. The processing goal of the segment repair module is to use the placeholder markers inserted in the broken chain detection module to construct a local batch flow graph, analyze the integrity and structural complexity of the flow path, generate the broken chain credibility, and perform topological compression based on the judgment results to optimize the flow path structure.

[0081] The chain segment repair module includes the following:

[0082] S3.1, construct a local batch flow diagram:

[0083] The placeholders inserted in the broken chain detection module trigger the construction of a local batch flow graph. The local batch flow graph uses workstations as nodes and the flow events of material batches between workstations as edges connecting the nodes. The graph focuses on the empty locations indicated by the placeholders and their adjacent workstations. The construction process includes the following steps:

[0084] First, the flow events related to the workstations before and after the hole are extracted from the process mapping matrix. Second, the extracted flow events are converted into directed edges to generate a local batch flow graph. Finally, the missing edges are marked at the hole position to clearly identify the broken chain location.

[0085] S3.2, calculate the segment coherence index:

[0086] The segment coherence index is used to quantify the integrity of the flow path in the local batch flow diagram, reflecting the path continuity from the station before the void to the station after the void. The calculation process includes the following steps:

[0087] First, all possible paths from the workstation before the void to the workstation after the void are identified in the local batch flow graph. Second, for each path, its coherence is calculated based on the time interval between flow events. Coherence is defined as the product of the reciprocals of the time intervals between adjacent workstations on the path. Finally, the segment coherence index is the geometric mean of the coherence of all paths. If no path exists, the segment coherence index is zero.

[0088] The segment coherence index, calculated by multiplying the reciprocals of time intervals, emphasizes the temporal compactness of each link along a path. Higher values ​​indicate a more continuous path. The application of the geometric mean balances the coherence of multiple paths, avoiding the influence of bias from a single path and ensuring a comprehensive assessment. This calculation method intuitively reflects the integrity of the flow path and provides a key quantitative basis for determining the credibility of chain breaks.

[0089] S3.3, calculate the topological chaos index:

[0090] The topological chaos index is used to quantify the structural complexity of the local batch flow graph and reflects the degree of disorder in the connections between nodes. The calculation process includes the following steps:

[0091] First, the sum of the reciprocals of the shortest path lengths between all pairs of nodes in the local batch flow graph is calculated. Second, the sum of the degrees of all nodes is calculated, where the node degree is defined as the number of edges directly connected to the node. Finally, the topological chaos index is the ratio of the sum of the reciprocals of the shortest path lengths to the sum of the node degrees.

[0092] The topological chaos index measures graph compactness by summing the inverse of the shortest path length and connectivity density by summing the node degrees. This ratio reflects the structural complexity of the graph. Higher values ​​indicate greater structural complexity and a greater likelihood of chain breaks. This quantitative approach integrates both global and local characteristics of the graph, providing a structural basis for assessing the credibility of chain breaks.

[0093] S3.4, generate broken link credibility:

[0094] The chain break credibility is assessed by combining the segment coherence index and the topological chaos index through a chain break confidence model to assess the authenticity of the chain break at the void location. The chain break confidence model uses a logistic function to process the segment coherence index and the topological chaos index as inputs. The chain break credibility is calculated using pre-trained parameters. The output range is limited to zero to one, with values ​​closer to one indicating a higher probability of chain break.

[0095] For example, the calculation can be done in the following way:

[0096] Broken chain confidence model:

[0097] Using a logical function:

[0098] Among them, β0, β1, and β2 are pre-training parameters, representing the baseline value, the influence coefficient of SCI, and the influence coefficient of TCI, respectively.

[0099] Parameter explanation:

[0100] SCI: Segmental coherence index, lower values ​​indicate more incomplete paths;

[0101] TCI: Topological Chaos Index, the higher the value, the more complex the structure;

[0102] β0: model base offset;

[0103] β1, β2: Coefficients obtained through training with historical broken link data, which regulate the impact of SCI and TCI.

[0104] The chain break confidence model converts the chain segment coherence index and topological chaos index into chain break credibility through logical functions, realizing intelligent judgment of the authenticity of the chain break.

[0105] S3.5, determine whether to perform topology compression:

[0106] The system determines whether to perform topology compression on the flow path at the hole location based on the comparison between the broken link credibility and the preset threshold. The specific process is as follows:

[0107] If the reliability of a link break is higher than a preset threshold, the hole is considered a real link break and topology compression is performed. If the reliability is lower than or equal to the preset threshold, the current path structure is retained. Topology compression combines the workstations before and after the hole and the intermediate workstations into a single virtual node, generating a traceable chain segment and recording the parent-child relationships before and after compression.

[0108] By comparing and judging against preset thresholds, the system can intelligently choose whether to perform topology compression, avoiding unnecessary computational operations and improving resource utilization efficiency. Topology compression simplifies the flow path structure by merging intermediate workstations.

[0109] The segment repair module intelligently analyzes broken links and optimizes their paths by constructing a local batch flow graph, calculating segment coherence and topological chaos indices, generating break credibility, and performing topological compression. This improves the reliability and efficiency of material batch tracking, optimizes the flow path structure, and provides strong support for the overall system performance.

[0110] The segment repair module constructs a local batch flow graph and performs a credibility check on broken links to generate traceable segments. However, different workstations use independent log templates with inconsistent field meanings and time granularity, resulting in semantic differences in the traceable segments, which directly affects the traceability accuracy of batch flow information. To address this issue, the semantic alignment module uses a semantic fusion engine to dynamically align the fields of the traceable segments, generate batch flow patches, and unify log differences, providing a consistent semantic view for the path update module to reconstruct the global batch flow graph.

[0111] The semantic alignment module includes the following:

[0112] S4.1, Initialization of semantic fusion engine:

[0113] As the core processing module of the semantic alignment module, the semantic fusion engine is responsible for semantically aligning the fields in the traceable chain segments. During the initialization phase, the semantic fusion engine loads the contextual information of each workstation from the preconfigured workstation information library. The contextual information includes the log template, field definition, and time granularity standard for each workstation. The log template consists of a set of fields that have clear semantic meanings, such as material number, processing status, etc., and are associated with specific data types, such as strings or integers. The time granularity standard defines the accuracy of recording time events for each workstation, such as in seconds or milliseconds. After loading is complete, the semantic fusion engine stores this contextual information as an internal mapping table. The internal mapping table organizes data in the form of key-value pairs, where the key is the workstation identifier and the value is the corresponding log template, field definition, and time granularity standard, so as to enable fast query and comparison in subsequent processing.

[0114] S4.2, dynamic alignment of fields:

[0115] The semantic fusion engine performs dynamic field alignment on the traceable segments generated by the segment repair module to ensure semantic consistency across different workstations. A traceable segment contains information about the flow of a batch between production workstations and consists of a set of flow events. Each flow event records several field values, such as material number or processing status. The dynamic field alignment process involves three steps. First, all workstations involved in the traceable segment are identified to form a workstation set. Second, for each workstation, the corresponding log template and field set are extracted from an internal mapping table to clarify the semantic meaning and data type of each field. Finally, based on a predefined field semantic mapping table, the workstation-specific fields are converted to a unified standard field set. The field semantic mapping table is a pre-built dictionary that records the correspondence between workstation fields and standard fields. For example, the "Material ID" field for a particular workstation is converted to the standard field "Material_ID." Through this conversion, the semantic fusion engine adjusts each flow event in the traceable segment to an aligned flow event, generating an aligned traceable segment.

[0116] Dynamic field alignment achieves field standardization through a predefined semantic mapping table, eliminating the differences in field meanings between different workstations and ensuring the consistency of flow events at the semantic level.

[0117] S4.3, unified time granularity:

[0118] The semantic fusion engine performs a time granularity unification operation on the timestamps in the aligned traceable segments, ensuring that the time records of all flow events are recorded with consistent accuracy. The time granularity unification process includes the following steps. First, the standard time granularity used by the system is determined. Second, for each flow event in the aligned traceable segments, the timestamp granularity is checked to ensure consistency with the standard time granularity. If any inconsistency is found, adjustments are made based on the granularity difference.

[0119] Specifically, if the timestamp granularity of a flow event is finer than the standard granularity, such as in milliseconds, the millisecond portion is removed through truncation and the timestamp is converted to seconds. If the timestamp granularity of a flow event is coarser than the standard granularity, such as in minutes, an intermediate timestamp that meets the standard granularity is generated through linear interpolation. The adjusted timestamp is updated to the corresponding flow event, generating a traceable chain segment with a unified time granularity.

[0120] The unified time granularity eliminates the accuracy differences of time records between different workstations through truncation and linear interpolation methods, ensuring the consistency of timestamps of all flow events.

[0121] S4.4, generate batch transfer patches:

[0122] The semantic fusion engine generates batch flow patches based on the traceable chain segments with unified time granularity, which are used to correct and unify the log differences in the flow path. The batch flow patch contains the aligned flow event set and the timestamp with unified time granularity, and uses JSON format to store relevant information, including batch number, workstation pair, aligned flow event list and unified timestamp. The generation process includes the following: The semantic fusion engine organizes the flow events and timestamps in the traceable chain segments with unified time granularity into batch flow patches, and ensures that the fields and timestamps recorded therein meet the standardization requirements. The generated batch flow patch is used as input for subsequent steps to correct the global flow path.

[0123] Batch flow patches provide standardized flow path correction data by integrating aligned flow events and unified timestamps. This allows the system to quickly apply patches to correct log discrepancies in flow paths, ensuring the accuracy and consistency of the global batch flow map.

[0124] S4.5, unified description of log differences:

[0125] The semantic fusion engine uses batch flow patches to uniformly describe the log differences in the traceable chain segments, ensuring that subsequent processing uses a consistent semantic view. The unified description process includes the following steps: First, compare the original flow events with the aligned flow events, and analyze the specific operations of field mapping and time adjustment. Secondly, record the mapping rules used in the field alignment process and the truncation or interpolation method used in the time granularity unification process to form a difference report. The difference report contains a detailed description of the field alignment rules and time adjustment methods, and is added to the metadata field of the batch flow patch. Finally, the batch flow patch contains not only the aligned data, but also a complete description of the alignment and adjustment process.

[0126] The semantic alignment module uses a semantic fusion engine to dynamically align fields and unify time granularity on traceable segments, successfully generating batch flow patches and eliminating discrepancies in field meaning and time granularity between log templates at different workstations. The initialization phase ensures accurate loading of workstation context information; dynamic field alignment achieves data standardization through semantic mapping; time granularity unification adjusts timestamps through truncation and interpolation; and the generation of batch flow patches and difference descriptions provides standardized data support for the reconstruction of the global batch flow graph, ensuring semantic consistency and traceability accuracy across flow paths.

[0127] The semantic alignment module generates batch flow patches through the semantic fusion engine, completing the unification of data fields of different workstations and alignment of time granularity; the path update module uses these batch flow patches, writes them into the distributed ledger and triggers the path reconstructor to refresh the global batch flow graph, realizing real-time update of flow relationships; the anomaly location module performs anomaly location and quality decision analysis based on the updated global batch flow graph.

[0128] The path update module includes the following:

[0129] S5.1, batch transfer patches are written to the distributed ledger:

[0130] In the full-process traceability system for material batches based on production processes, the batch transfer patch generated by the semantic alignment module is first written to the distributed ledger. The batch transfer patch is standardized data containing aligned transfer events and a unified timestamp. The distributed ledger is a decentralized data storage system that ensures data immutability and traceability. The writing process includes the following:

[0131] First, the batch of circulating patches is converted into a JSON-formatted data packet. This data packet is then transmitted to the distributed ledger's write node. Upon receiving the data packet, the write node verifies its integrity and the validity of the digital signature. Once verified, the write node records the data packet in the distributed ledger. The distributed ledger uses blockchain technology, with each block storing the hash values ​​and timestamps of multiple batches of circulating patches. Blocks are interconnected via a hash chain to ensure data continuity and security.

[0132] The process of writing batches of patches to a distributed ledger ensures data security and traceability. The decentralized nature of the distributed ledger prevents the risk of data being tampered with by a single node, while the use of hash chains and digital signatures ensures data integrity and authenticity.

[0133] S5.2, triggering the path reconstructor:

[0134] The path reconstructor is the system's module responsible for real-time updates to the global batch flow graph. Its triggering process relies on write operations to the distributed ledger. When a batch flow patch is successfully written to the distributed ledger, the distributed ledger generates an event notification. By subscribing to the distributed ledger's write event, the path reconstructor receives the newly written batch flow patch data and uses it as a basis for initiating subsequent updates.

[0135] By subscribing to write events in the distributed ledger, the path reconstructor can promptly obtain the latest batch flow patches, ensuring the timely update of material batch flow relationships. Compared to periodic polling methods that check the ledger, this event-driven mechanism reduces unnecessary resource consumption and processing delays, improves system response speed and operational efficiency, and provides technical support for real-time management.

[0136] S5.3, refresh the global batch flow chart:

[0137] The global batch flow graph is a directed graph structure, where nodes represent workstations in the production process and edges represent flow events between batches. It is used to reflect the flow relationship of all material batches. The process of refreshing the global batch flow graph includes the following:

[0138] First, based on the information in the batch flow patch, locate the subgraph part related to the batch in the global batch flow graph; then, for each alignment event in the batch flow patch, check whether the corresponding edge already exists in the global batch flow graph; if there is no corresponding edge, add a new edge to the global batch flow graph; if there is a corresponding edge, update the attributes of the edge, such as modifying the timestamp or supplementing the event description; after completing the processing of all alignment events, ensure that the updated subgraph can accurately reflect the latest flow relationship recorded in the batch flow patch.

[0139] Refreshing the global batch flow graph allows the system to promptly update the flow relationships of material batches, ensuring the integrity and accuracy of the traceability chain. By incrementally updating subgraphs, the system avoids the computational overhead of full graph reconstruction while supporting rapid query and analysis needs.

[0140] S5.4, real-time reflection of assembly relationships:

[0141] After refreshing the global batch flow diagram, the assembly relationships of material batches are updated in real time. After the path reconstructor completes the global batch flow diagram refresh, it records the update time and the hash value of the batch flow patch, generating a version log to support historical data backtracking. The system also provides a real-time query interface, allowing users and quality control modules to access the latest global batch flow diagram data to meet real-time monitoring needs during the production process.

[0142] Real-time reflection of assembly relationship processing ensures the timeliness and accuracy of material batch flow relationships during the production process. Version log generation facilitates tracking of historical update records, supports data auditing and exception backtracking, and enhances the system's traceability. The real-time query interface provides convenient data access for users and quality control modules, enhancing the system's interactivity and application value.

[0143] The path update module writes batch flow patches to the distributed ledger and refreshes the global batch flow graph, forming a complete view reflecting real-time assembly relationships. However, during the production process, quality anomalies may occur due to historical batches (such as processing time exceeding the standard or test results deviating from the normal range). It is necessary to accurately locate abnormal batches through a backtracking mechanism and evaluate their impact on downstream assembly to support quality decision-making. Based on the global batch flow graph generated by the path update module, the anomaly positioning module locates historical abnormal batches through the replay module and outputs a list of assembly risk warnings, providing an accurate basis for production line quality control.

[0144] The process of the abnormal location module is as follows Figure 2 As shown, including the following:

[0145] S6.1, initialization of playback module:

[0146] First, the replay module initializes by loading the latest version of the global batch flow diagram and obtaining predefined quality exception criteria. The global batch flow diagram records the flow path and related attributes of material batches through the production process. The latest version is updated by the path update module. Quality exception criteria are represented by a set of exception patterns, each of which includes the exception type and corresponding threshold, such as processing time exceeding the normal range or test results deviating from the preset standard. The replay module reads the latest snapshot of the global batch flow diagram from the distributed ledger, ensuring that analysis is based on real-time updated data. After initialization, the replay module is capable of traversing and analyzing the global batch flow diagram.

[0147] The replay module loads the latest version of the global batch flow chart and predefined quality anomaly criteria, ensuring anomaly location is based on the latest production data, thereby ensuring accurate and timely analysis. Reading snapshot data from the distributed ledger maintains data integrity and consistency, avoiding data omissions or errors, and providing reliable support for subsequent anomaly detection and risk assessment.

[0148] S6.2, locate historical abnormal batches:

[0149] The replay module reversely traverses the flow path in the global batch flow graph to identify batches that trigger quality exception standards. The specific process is as follows:

[0150] Starting from the end station of the global batch flow chart, the system works backwards through each batch's flow events at each station to determine whether their attributes meet any of the criteria in the set of abnormal patterns. For example, for processing time anomalies, if the processing time of a flow event exceeds the preset maximum allowable time, the corresponding batch will be marked as an abnormal batch. For test result anomalies, if the test value of a flow event exceeds the preset normal range, the corresponding batch will also be marked as an abnormal batch. All batches that trigger abnormal criteria are recorded in the abnormal batch list.

[0151] By traversing the global batch flow diagram backwards, from the end of the production process back to its source, the system can comprehensively and accurately identify batches causing quality anomalies. By comparing each item against predefined anomaly criteria, the system automatically detects anomalies, avoiding the inefficiencies and subjective biases of manual judgment.

[0152] S6.3, generate assembly risk warning list:

[0153] Based on the abnormal batch list, the playback module analyzes the impact of the abnormal batch in the global batch flow diagram and generates an assembly risk warning list. The specific process is as follows:

[0154] For each batch in the outlier batch list, its downstream batches—those subsequent batches affected by the outlier batch in the production process—are identified and a risk propagation factor is calculated to quantify the extent of the impact. This risk propagation factor is calculated using a path-dependent attenuation model, taking into account the shortest path length from the outlier batch to the downstream batch in the global batch flow diagram and the severity of the outlier. As the shortest path length increases, the risk value decreases exponentially. Finally, all downstream batches are sorted from high to low by risk value to generate an assembly risk alert list. This list includes the outlier batch ID, the ID of the affected downstream batches, the risk value, and a description of the outlier.

[0155] For example, the processing can be as follows:

[0156] For each abnormal batch B k ∈L abnormal , identify its downstream batch B dourn (i.e., subject to B k and calculate the risk propagation factor R k,down To quantify the extent of the impact.

[0157] Using the path-dependent decay model:

[0158] in:

[0159] d k,down Indicates B k to B down The shortest path length in the global batch flow graph;

[0160] α is the attenuation coefficient (value range 0<α<1, for example 0.8), which indicates the attenuation of risk with path length;

[0161] I k B k The severity of the anomaly is predefined based on the anomaly type and degree of deviation (e.g., the percentage of processing time exceeding the standard);

[0162] Labnormal is a list of exception batches.

[0163] By calculating risk propagation factors using a path-dependent attenuation model, the system objectively quantifies the impact of abnormal batches on downstream batches, reflecting the propagation and attenuation patterns of risk within the production process. This facilitates prioritization of high-risk downstream batches and optimizes resource utilization. The generation of an assembly risk alert list allows for rapid identification of high-risk areas, mitigating the further impact of quality anomalies on the production process.

[0164] S6.4, output assembly risk warning list:

[0165] The replay module outputs the generated assembly risk alert list to the quality decision module to support exception handling and production line decision-making. The assembly risk alert list is presented in a table format, with each row including the batch ID, risk value, exception type, and recommended action, such as "quarantine batch" or "retest." Based on the risk value and recommended action in the list, the quality decision module takes appropriate quality control actions, such as suspending production of the relevant batch or arranging for further inspection.

[0166] The anomaly location module accurately identifies and assesses quality anomalies through initialization of the replay module, locating historical anomaly batches, and generating and outputting an assembly risk warning list. Based on the latest version of the global batch flow diagram, the replay module reversely traverses the flow path, accurately marking anomaly batches. It then quantifies the impact of anomalies on downstream batches using a path-dependent attenuation model, ultimately generating structured risk warning information. This step, along with real-time data updates from the path update module, ensures accurate anomaly location and timely risk assessment, providing technical support for production workshops to quickly identify the impact area and implement effective control measures when quality anomalies occur.

[0167] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0168] It should be noted that the system of the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting a variety of hardware environments and usage requirements.

[0169] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0170] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0171] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The whole process traceability system of material batches based on production process is characterized by: include: Mapping index module: Builds a process mapping matrix at the first-order workstation and instantly derives the assembly topology index as the batch tracking coordinate; Chain break detection module: The chain break catcher monitors the tag event stream in the process mapping matrix in real time, inserts placeholders after detecting holes, and records the triggering time; Chain segment repair module: The placeholder mark triggers the construction of a local batch flow diagram, and the path integrity measurement and structural complexity measurement are combined to form the credibility of the chain break. Based on this, it is determined whether the parent-child relationship at both ends of the gap is compressed to form a traceable chain segment; Semantic alignment module: The semantic fusion engine dynamically aligns the traceable segment fields based on the workstation context, generates batch flow patches, and uniformly describes log differences. Path update module: After the batch flow patch is written into the distributed ledger, the path reconstructor is triggered to refresh the global batch flow graph; Abnormal location module: The global batch flow chart locates historical abnormal batches through the playback module, and outputs an assembly risk warning list for quality decision-making.

2. The whole-process traceability system for material batches based on production process according to claim 1 is characterized in that: The mapping index module includes the following: At the first transfer station, a process mapping matrix in the form of a two-dimensional table is constructed for the material batch to record the flow relationship of the material batch between stations. Initially, the elements of the process mapping matrix are zero, and the flow state occurs by updating the elements to one; a tree-structured assembly topology index is derived to record the hierarchical relationship of the material batch. The nodes in the assembly topology index represent material batches or sub-components, and the connecting lines represent the parent-child relationship. Initially, the assembly topology index only contains the root node; batch tracking coordinates are generated, and the uniqueness of the material batch is ensured by integrating the flow path information and hierarchical structure information and using a hash function to generate a unique identifier of a fixed length.

3. The whole-process traceability system for material batches based on production processes according to claim 2 is characterized in that: The link break detection module includes the following: The broken chain catcher monitors the label event flow in the process mapping matrix in real time, inserts a placeholder mark in the process mapping matrix after detecting a hole, and records the triggering time. The technical features can be summarized as follows: continuously monitor the dynamic changes of the label event flow in the process mapping matrix. When it is identified that the label event flow is interrupted to form a hole, it immediately inserts a placeholder mark at the corresponding position of the process mapping matrix, and records the triggering time of the corresponding hole detection.

4. The whole-process traceability system for material batches based on production process according to claim 3 is characterized in that: The chain segment repair module includes the following: The construction of the local batch flow graph is triggered by placeholder marking. The flow events related to the workstations before and after the hole are extracted from the process mapping matrix and converted into directed edges. At the same time, the missing edges at the hole position are marked.

5. The whole-process traceability system for material batches based on production process according to claim 4 is characterized in that: The segment repair module also includes the following: The calculation of the segment coherence index is based on identifying all the paths from the workstation before the void to the workstation after the void in the local batch flow graph, calculating the coherence of each path by multiplying the inverse of the time interval, and taking the geometric mean of the coherence of all paths to quantify the completeness of the flow path in the local batch flow graph; The calculation of the topological chaos index is based on the ratio of the sum of the inverse of the shortest path lengths between all node pairs in the local batch flow graph to the sum of the node degrees, which is used to quantify the structural complexity of the local batch flow graph.

6. The whole-process traceability system for material batches based on production process according to claim 5 is characterized in that: The segment repair module also includes the following: The credibility of the chain break is generated by integrating the chain segment coherence index and the topological chaos index through a logical function. When the credibility of the chain break is higher than the preset threshold, topological compression is performed to merge the workstations before the void, after the void, and in the middle into virtual nodes to generate a traceable chain segment.

7. The whole-process traceability system for material batches based on production process according to claim 6 is characterized in that: The semantic alignment module includes the following: The semantic fusion engine loads workstation log templates, field definitions, and time granularity standards from the preconfigured workstation information library and stores them as an internal mapping table. For flow events in the traceable chain segment, it extracts the workstation set and uses the predefined field semantic mapping table to convert the workstation fields into a standard field set, generating aligned flow events. Perform time granularity unification operations on the aligned flow events, and perform truncation or interpolation adjustments based on the standard time granularity; Organize the aligned flow events and unified timestamps into JSON format batch flow patches; Record the details of field alignment and time adjustment operations, generate a difference report and add it to the batch flow patch metadata field.

8. The whole-process traceability system for material batches based on production process according to claim 7 is characterized in that: The path update module includes the following: Batch flow patches are serialized into data packets in JSON format and sent to the distributed ledger writing node. The writing node verifies the integrity and digital signature of the data packet and records the data packet to the distributed ledger. The distributed ledger uses blockchain technology to store the data packet's hash value and timestamp. The path reconstructor subscribes to the write events of the distributed ledger, receives and parses the batch identifier, workstation pair and alignment flow event set in the newly written batch flow patch, and updates the subgraph part of the corresponding batch in the global batch flow graph according to the parsed alignment flow event set. The global batch flow graph is refreshed by adding new edges or updating edge attributes. The path reconstructor records the update time and the hash value of the batch flow patch to generate a version log, and provides a real-time query interface to access the latest global batch flow graph data.

9. The whole-process traceability system for material batches based on production process according to claim 8 is characterized in that: The anomaly location module includes the following: The replay module loads the latest snapshot of the global batch flow graph from the distributed ledger and obtains the predefined quality exception standards. It uses the depth-first search algorithm to reversely traverse the flow path of the global batch flow graph, records the batches that trigger the quality exception standards into the abnormal batch list, and then uses the Dijkstra algorithm to calculate the shortest path length from the abnormal batch to the downstream batch. The path-dependent attenuation model is used to calculate the risk propagation factor and generate an assembly risk warning list.

10. The whole-process traceability system for material batches based on production process according to claim 9 is characterized in that: The anomaly location module also includes the following: The assembly risk warning list includes batch identification, risk value, abnormality type and recommended measures in a tabular form. Finally, the playback module transmits the assembly risk warning list to the quality decision module to support the technical features of quality control operations.

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