A lace fabric production traceability management system and method

Abstract

The application discloses a lace fabric production traceability management system and method, and belongs to the technical field of production traceability management, and specifically comprises the following steps: lace fabric production orders containing material quality description, pattern code and production batch identification are received, historical production data is searched according to the pattern code, and production process records of the same pattern code are obtained, an initial traceability path defining production node device access sequence and data collection points is generated based on the historical data, real-time production events such as raw material input events, processing operation events and device state change events are monitored, the initial traceability path is dynamically adjusted to skip non-key production node devices or combine data collection points, an optimized traceability path is formed, finally, production data is collected according to the optimized path, and a traceability report containing production process integrity and abnormal point identification is generated by integration, and dynamic optimization and accurate traceability of the lace fabric production process are realized.

Description

Technical Field

[0001] This invention relates to the field of production traceability management technology, specifically to a traceability management system and method for lace fabric production. Background Technology

[0002] Lace fabric, as an important category in the textile industry, involves multiple stages in its production process, including raw material preparation, weaving, dyeing, and finishing. It is characterized by complex processes and high quality control requirements. Against the backdrop of the textile industry's intelligent transformation, establishing a comprehensive production traceability system has become a key requirement for improving product quality management. Systematically recording raw material information, process parameters, and inspection data during the production process helps to quickly locate quality problems and clearly define production responsibilities.

[0003] Existing traceability methods for lace fabric production mainly employ a fixed-path data collection model. These methods typically set up data collection points according to a pre-defined process sequence, constructing a one-way traceability chain from raw materials to finished products by collecting data related to raw material input, equipment operation, and product quality. Some methods monitor the production process by installing sensors on production equipment to collect operating parameters and comparing them with standard process parameters.

[0004] However, existing fixed-path traceability methods are ill-suited to the dynamic characteristics of lace fabric production. When the pattern specifications of production orders change, the established traceability paths cannot be adjusted accordingly to the actual production situation, potentially leading to a mismatch between data collection point settings and the actual production process. Some non-critical processes may experience excessive data collection, while data collection for critical processes may be insufficient. Furthermore, there is a lag in response to unexpected events during production, preventing timely optimization of traceability paths based on real-time production status, thus impacting traceability efficiency and data utilization value. Summary of the Invention

[0005] The purpose of this invention is to provide a traceability management system and method for lace fabric production, solving the following technical problems:

[0006] The existing fixed-path tracing method cannot dynamically adapt to the actual process of lace fabric production, resulting in low data collection efficiency and insufficient flexibility in the tracing path.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for traceability management in lace fabric production includes the following steps:

[0009] S1. Receive production orders for lace fabrics. The production orders include the material description, pattern code, and production batch identifier of the lace fabrics.

[0010] S2. Based on the pattern code of the production order, retrieve the historical production data of the lace fabric and obtain the production process record of the lace fabric with the same pattern code.

[0011] S3. Based on historical production data, generate an initial traceability path for the lace fabric. The initial traceability path defines the access order of production node equipment and data collection points.

[0012] S4. Monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events.

[0013] S5. Based on real-time production events, dynamically adjust the initial traceability path, skip non-critical production node equipment or merge data collection points to generate an optimized traceability path;

[0014] S6. Following the optimized traceability path, collect production data of lace fabric from production node equipment, integrate and generate a production traceability report of lace fabric, including the integrity of the production process and the identification of anomalies.

[0015] As a further aspect of the present invention: in step S1, the process of receiving the production order for lace fabric is as follows:

[0016] Parse the material description in the production order, match the material description with the raw material type in the raw material inventory record, and parse the pattern code in the production order, matching the pattern code with the equipment processing capacity in the production equipment specification table.

[0017] Based on the matching results, a production task allocation plan is generated. The production task allocation plan includes a raw material allocation plan and an equipment scheduling plan. The raw material allocation plan is allocated to the raw material storage node, and the equipment scheduling plan is allocated to the corresponding production node equipment.

[0018] As a further aspect of the present invention: in step S2, the process of retrieving historical production data for lace fabric is as follows:

[0019] Extract the pattern code content from the production order, use the pattern code content as the search condition to query the historical production database, and obtain historical production task records with the same pattern code content. The historical production task records include raw material allocation records, process execution records and equipment scheduling records.

[0020] Analyze the task execution time series in historical production task records, identify key process nodes in the process execution records, calculate the time interval distribution of key process nodes, and select historical production task records with time interval distribution within a predetermined range as reference datasets.

[0021] As a further aspect of the present invention: in step S3, the process of generating the initial traceability path for the lace fabric is as follows:

[0022] Analyze the execution sequence of processes in historical production task records, establish the process flow relationship between production node equipment, and construct the node set and connecting edge of the process topology diagram. The node set represents the production process stage, and the connecting edge represents the process flow direction.

[0023] Determine the quality control node locations for each production process stage. These quality control node locations include raw material inspection locations, semi-finished product inspection locations, and finished product inspection locations. Calculate the connection weights between nodes, which are based on the flow frequency in historical production task records.

[0024] Adjust the direction of the connecting edges in the process topology graph according to the connection weight, establish an alternative path set, which includes the main production path and the backup production path, associate the quality control node position with the corresponding production process stage, generate the initial traceability path and save it to the path database.

[0025] As a further aspect of the present invention: in step S4, the process of monitoring real-time production events of the lace fabric is as follows:

[0026] Monitor the production status signals of production node equipment, receive the raw material allocation status, process execution status and equipment operation status from the production status signals, and establish a classification index of status signals. The classification index includes signal type identifier and signal strength parameters.

[0027] The process type code and status update timestamp in the production status signal are analyzed to establish the temporal relationship of the status signal, which reflects the progress of the production process.

[0028] Production status signals are categorized into the status queues of the corresponding production batches. The frequency of status updates in the status queues is counted. The difference between the status update frequency and the baseline frequency data is compared, and production batches whose status update frequency exceeds the predetermined range are marked.

[0029] As a further aspect of the present invention: in step S5, the process of dynamically adjusting the initial tracing path to generate the optimized tracing path is as follows:

[0030] Compare the real-time production events with the expected event sequence of the initial traceability path, extract the event types and event timestamps from the real-time production events, construct the mapping relationship between event types and production node devices, calculate the event occurrence frequency of each production node device, compare the difference between the event occurrence frequency and the expected event frequency, mark the production node devices whose event occurrence frequency is below the threshold range, verify whether the marked production node devices include quality control node locations, and confirm the list of production node devices that have not experienced critical events.

[0031] Identify adjacent data collection points with the same data collection type, calculate the physical distance between adjacent data collection points, compare the physical distance with a preset distance threshold, merge adjacent data collection points whose physical distance is less than the preset distance threshold, recalculate the access order of production node devices, generate an optimized traceability path, and update the path database.

[0032] As a further aspect of the present invention: the process of confirming the list of production node equipment where no critical events have occurred is as follows:

[0033] Traverse the marked production node devices, extract the historical event records of each production node device, analyze the occurrence patterns of key events in the historical event records, calculate the average time interval of key events, compare the difference between the current time interval and the average time interval, mark the production node devices whose difference exceeds the preset range, check the event records of the marked production node devices in the current production cycle, confirm the production node devices without key event records, and generate a list of production node devices that have not experienced key events.

[0034] As a further aspect of the present invention: in step S6, the process of collecting production data of lace fabric and integrating it to generate a production traceability report is as follows:

[0035] According to the optimized traceability path, data acquisition instructions are sent to the production node equipment, and raw material characteristic data, processing technology data and inspection result data are received from the production node equipment;

[0036] Compare the degree of matching between production data and material descriptions in production orders, identify data points in production data that deviate from the material descriptions, and mark these deviating data points as outliers.

[0037] The number of production node devices visited in the optimized traceability path is counted, the ratio of visited production node devices to the total number of production node devices in the initial traceability path is calculated, and a production process integrity index is generated.

[0038] Integrate the list of abnormal data points and production process integrity indicators to generate a production traceability report.

[0039] The present invention also includes a traceability management system for lace fabric production, for implementing the above-described traceability management method for lace fabric production, comprising:

[0040] The order receiving module is used to receive production orders for lace fabrics. The production order includes the material description, pattern code, and production batch identifier of the lace fabric.

[0041] The historical data retrieval module is used to retrieve historical production data of lace fabrics based on the pattern code of the production order, and to obtain the production process records of lace fabrics with the same pattern code.

[0042] The initial path generation module is used to generate an initial traceability path for lace fabric based on historical production data. The initial traceability path defines the access order of production node equipment and data collection points.

[0043] The real-time event monitoring module is used to monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events.

[0044] The dynamic path optimization module is used to dynamically adjust the initial traceability path based on real-time production events, skipping non-critical production node equipment or merging data collection points to generate an optimized traceability path.

[0045] The traceability report generation module is used to collect production data of lace fabric from production node equipment according to the optimized traceability path, and integrate it to generate a production traceability report of lace fabric, including the integrity of the production process and the identification of anomalies.

[0046] The beneficial effects of this invention are:

[0047] This invention achieves intelligent management of the lace fabric production traceability process by establishing an initial traceability path based on historical production data and dynamically adjusting it to an optimized traceability path according to real-time production events. By skipping non-critical production node equipment and merging adjacent data collection points, redundant data collection operations are significantly reduced, improving traceability efficiency. Monitoring raw material input events, processing operation events, and equipment status change events ensures that the traceability path remains synchronized with the actual production process. Pattern coding matching and historical production task record analysis provide data support for traceability path generation. Establishing a set of alternative paths, including the main production path and backup production paths, enhances the adaptability of the traceability system. Production process integrity indicators and anomaly markers comprehensively reflect the production status of the lace fabric. The entire method, by optimizing the traceability path generation and adjustment mechanism, improves the targeting and timeliness of data collection while ensuring traceability integrity, effectively solving the problem of insufficient flexibility in fixed-path traceability methods. Attached Figure Description

[0048] The invention will now be further described with reference to the accompanying drawings.

[0049] Figure 1 This is a flowchart illustrating a method for traceability management of lace fabric production according to the present invention.

[0050] Figure 2 This is a schematic diagram of a module for a traceability management system for lace fabric production according to the present invention. Detailed Implementation

[0051] 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, and 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.

[0052] Please see Figure 1 As shown, this invention is a method for traceability management of lace fabric production, comprising the following steps:

[0053] S1. Receive production orders for lace fabrics. The production orders include the material description, pattern code, and production batch identifier of the lace fabrics.

[0054] S2. Based on the pattern code of the production order, retrieve the historical production data of the lace fabric and obtain the production process record of the lace fabric with the same pattern code.

[0055] S3. Based on historical production data, generate an initial traceability path for the lace fabric. The initial traceability path defines the access order of production node equipment and data collection points.

[0056] S4. Monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events.

[0057] S5. Based on real-time production events, dynamically adjust the initial traceability path, skip non-critical production node equipment or merge data collection points to generate an optimized traceability path;

[0058] S6. Following the optimized traceability path, collect production data of lace fabric from production node equipment, integrate and generate a production traceability report of lace fabric, including the integrity of the production process and the identification of anomalies.

[0059] In S1, the process of receiving the production order for lace fabric is as follows:

[0060] The order data parsing module is activated. This module uses an XML / JSON parsing engine to perform structured parsing of the order data, extracting the material description and pattern code content contained in the order. For the parsing of the material description, key parameters such as raw material composition, yarn specifications, yarn density, and fiber fineness need to be extracted. For example, from a material description like "pure cotton combed yarn 40S / 2 density 28 stitches / cm," specific parameter values ​​for the raw material composition (pure cotton), yarn count (40S / 2), and weaving density (28 stitches / cm) are extracted. Subsequently, the parsed material description parameters are matched against the raw material type records in the raw material inventory management system database. The raw material inventory database contains fields such as "raw material type code," "raw material composition," "yarn count," "yarn density," "inventory quantity," and "warehouse location number." A multi-field fuzzy matching algorithm is used to verify whether there are raw material types in the inventory that are consistent with or compatible with the order's material requirements. If multiple compatible raw materials exist, candidate raw materials are further determined based on inventory quantity priority and storage duration priority. If no matching raw materials are found in the inventory, a raw material procurement warning signal is triggered.

[0061] Parsing the pattern code requires identifying information such as pattern complexity level, weaving process type, pattern repeat size, and pattern density. Pattern codes typically use structured coding rules; for example, in "HX-08-120-50", "HX" represents the lace type, "08" represents the pattern complexity level, "120" represents the pattern repeat size (unit: mm), and "50" represents the pattern density (unit: clusters / 10cm). After parsing, the process parameters corresponding to the pattern code are matched with the production equipment specification table. The production equipment specification table stores the technical parameters of each production equipment, including maximum weaving width, maximum pattern repeat size, supported process types, stitch length range, and maximum pattern density. By comparing these parameters, the set of equipment capable of processing the pattern is determined. For example, for a pattern with a repeat size of 120mm, it is necessary to match equipment in the equipment specification table whose "maximum pattern repeat size" field value is not less than 120mm. Simultaneously, it is verified whether the equipment's "supported process type" field includes the weaving process corresponding to the pattern.

[0062] Based on the material and equipment matching results, the production task allocation engine is activated to generate a production task allocation plan. The raw material allocation plan must specify the candidate raw material's storage location number, outbound quantity, outbound time, transportation route (logistics route from the storage location to the production node), and raw material pretreatment requirements (such as pre-humidification and pre-stretching treatment). It must also be linked to the raw material batch number to ensure traceability. The equipment scheduling plan must determine the processing equipment number, equipment usage period, equipment preheating time (the preheating time must match the process requirements depending on the equipment type; for example, heat-setting equipment needs to be preheated to the set temperature 30 minutes in advance), and process connection intervals (such as the interval between the completion of weaving equipment and the start of dyeing equipment). It must also consider the current load status of the equipment to avoid equipment conflicts. After the plan is generated, the raw material allocation plan is sent to the warehouse management system (WMS) of the raw material storage node via industrial Ethernet. The WMS generates a raw material outbound order according to the plan and drives the warehouse robot to complete the raw material picking and outbound. The equipment scheduling plan is sent to the corresponding production node equipment management system. The equipment management system updates the equipment production calendar according to the plan and sets the pre-configuration instructions for equipment operating parameters to ensure that the equipment is in a ready-to-produce state during the specified period.

[0063] In step S2, the process of retrieving historical production data for lace fabric is as follows:

[0064] Pattern codes are extracted from parsed production orders via an order data interface. Using these pattern codes as the core search keywords, a structured query (SQL) is constructed, targeting the historical production database. This historical production database employs a relational database architecture and comprises four core tables: Production Batch Table, Raw Material Allocation Table, Process Execution Table, and Equipment Scheduling Table. Each table is linked by a foreign key using the "Production Batch Number" and "Pattern Code" fields. Specifically, the Production Batch Table stores basic information about production batches, including batch number, pattern code, production time, order number, and finished product quantity; the Raw Material Allocation Table stores raw material usage information for the corresponding batch, including batch number, raw material batch number, raw material type, raw material usage, and raw material pretreatment records; the Process Execution Table stores execution information for each process, including batch number, process number, process name, start time, end time, executing equipment number, and process parameters (such as weaving speed and dyeing temperature); and the Equipment Scheduling Table stores equipment usage information, including batch number, equipment number, equipment runtime, equipment fault records, and equipment maintenance records.

[0065] After executing the query, all historical production task records matching the target pattern code are retrieved, forming an initial historical dataset. Subsequently, the time series analysis module is activated to process the process execution records in the initial historical dataset using time series analysis. The start and end times of each historical batch are extracted from the process execution table, sorted in ascending order by timestamp, and a process execution time series for each historical batch is constructed, clarifying the sequence and time intervals of each process. Based on the time series, a process importance assessment model is used to identify key process nodes. This model determines key processes through two dimensions: first, the impact coefficient of the process on product quality. The frequency of product defects caused by abnormalities in each process is extracted from historical quality inspection data; the higher the frequency, the greater the impact coefficient. For example, if the frequency of color fastness defects caused by abnormalities in the dyeing process accounts for 40% of the total defect frequency, then the impact coefficient of this process is set to 0.4. Second, the irreplaceability of the process. If a process has no alternative execution plan (e.g., the weaving of a specific pattern requires specialized equipment, and no other equipment can replace it), then it is determined to be an irreplaceable process. Combining the two dimensions, the process sequence with an impact coefficient higher than 0.3 and that is irreplaceable is identified as a key process node. For example, the weaving process, dyeing process, and setting process in lace production are usually identified as key process nodes.

[0066] For the identified key process nodes, the time interval distribution between adjacent key process nodes is calculated. Specifically, the time difference between adjacent key processes (such as the end time of the weaving process and the start time of the dyeing process) is extracted from the time series of each historical batch to form a time interval dataset. Statistical analysis is performed on this dataset to calculate the mean, median, and standard deviation of the time intervals, determining the normal distribution range of the time intervals. For example, if the mean time interval from weaving to dyeing is 2 hours and the standard deviation is 0.5 hours, the normal distribution range is set to the interval corresponding to the mean plus or minus one standard deviation. Based on this distribution range, historical production task records in the initial historical dataset whose time intervals are all within the normal distribution range are selected, and abnormal batches with time intervals exceeding the range (such as batches with a weaving-to-dyeing interval as long as 5 hours due to equipment failure) are excluded, ultimately forming a reference dataset. The reference dataset must contain complete raw material allocation records, process execution records, and equipment scheduling records, and the time intervals of each key process node must conform to normal production patterns, providing data support for the generation of subsequent initial traceability paths.

[0067] In step S3, the process of generating the initial traceability path for the lace fabric is as follows:

[0068] A process flow relationship analysis was performed on the historical production task records in the reference dataset obtained from S2. The process sequence of each historical batch was extracted from the process execution table of the reference dataset, and a process flow relationship model was constructed using a directed graph modeling tool. The set of nodes in the model corresponds to each process stage of lace production, including the raw material preparation stage, weaving stage, dyeing stage, setting stage, semi-finished product inspection stage, and finished product inspection stage. Each node needs to be labeled with the corresponding process name, execution equipment type, and core process parameter range. The connecting edges in the model correspond to the process flow direction, pointing from the node of the preceding process stage to the node of the following process stage. For example, the node of the raw material preparation stage points to the node of the weaving stage, and the node of the weaving stage points to the node of the dyeing stage, which intuitively reflects the sequential relationship between processes.

[0069] Based on the process flow relationship model, the locations of quality control nodes corresponding to each process stage are determined. The raw material inspection node is set at the pre-raw material input stage during the raw material preparation phase, specifically at the quality inspection station between the raw material storage area and the production area. This node must complete inspection items such as raw material composition verification, yarn strength testing, and yarn evenness detection. Inspection data is linked to the raw material batch number in real time. The semi-finished product inspection node is set at the transfer station after the weaving stage is completed. This node must complete items such as pattern clarity detection, weaving density measurement, and semi-finished product width detection. Inspection data is linked to the equipment number and production time of the weaving process. The finished product inspection node is set at the inspection station after the setting stage is completed and before packaging. This node must complete items such as color fastness testing, dimensional stability testing, and appearance defect inspection. Inspection data is linked to the finished product batch number. Each quality control node must be marked with a specific physical location code and inspection item list in the model to ensure accurate location of the inspection data source during traceability.

[0070] The connection weights between nodes in the model are then calculated based on the frequency of process flows recorded in the historical production task data of the reference dataset. The number of flows between adjacent process stages is counted from the process execution table of the reference dataset, such as the number of flows from the raw material preparation stage to the weaving stage, and the number of flows from the weaving stage to the dyeing stage. The connection weight of a given edge is obtained by dividing the number of flows of a particular edge by the total number of flows of all edges with the same starting point. The weight ranges from 0 to 1, with a higher weight indicating that the flow path appears more frequently in historical production. For example, if the number of flows from the raw material preparation stage to the weaving stage is 100, and the number of flows from the raw material preparation stage to other stages is 0, then the weight of this edge is 1. If the number of flows from the weaving stage to the dyeing stage is 95, and the number of flows from the weaving stage to the rework stage is 5, then the weight of the edge from weaving to dyeing is 0.95, and the weight of the edge from weaving to rework is 0.05.

[0071] The connection paths in the process flow topology diagram are adjusted according to the connection weights. Connections with a weight value greater than 0.8 are designated as the main flow direction and marked with a thick solid line in the topology diagram. Connections with a weight value between 0.2 and 0.8 are designated as alternative flow directions and marked with a thin solid line. Connections with a weight value less than 0.2 are not included in the topology diagram and are only archived as historical data. An alternative path set is established based on the adjusted topology diagram. The main production path consists of the connection edge with the highest weight and follows the conventional flow sequence of "raw material preparation → weaving → dyeing → setting → semi-finished product inspection → finished product inspection" to ensure that the path covers all key process stages and quality control nodes. The backup production path is constructed based on the alternative flow direction and includes flow sequences to deal with abnormal situations, such as "raw material preparation → weaving → rework → dyeing → setting → semi-finished product inspection → finished product inspection", which is used for rework scenarios when pattern deviations occur in the weaving process of the main path, or "raw material preparation → weaving → dyeing → rework → setting → semi-finished product inspection → finished product inspection", which is used for rework scenarios when the color fastness of the dyeing process does not meet the standard.

[0072] Based on the set of alternative paths, the locations of each quality control node are associated with the corresponding process stage nodes. The triggering time for each quality control node is clearly marked in the path (e.g., the raw material inspection node is triggered at the end of the raw material preparation stage, and the semi-finished product inspection node is triggered at the end of the weaving stage) and the data collection requirements (e.g., the raw material inspection node requires the collection of the component analysis report number, and the semi-finished product inspection node requires the collection of the pattern clarity detection value). The final generated initial traceability path must include information such as a unique path identifier, corresponding pattern code, node sequence (including process stage nodes and quality control nodes), connecting edge weights, main path identifier, and backup path identifier. This information is stored in the "Traceability Path Table" of the path database using a structured query language. This table is associated with the "Pattern Code" field of the historical production database, facilitating the rapid retrieval of the corresponding initial traceability path based on the pattern code in subsequent operations.

[0073] In S4, the process of monitoring real-time production events of the lace fabric is as follows:

[0074] A distributed sensing and monitoring system is constructed, configuring suitable sensors for each production node device. Infrared position sensors and weight sensors are deployed in the raw material allocation stage. The infrared position sensors capture the position signals of the raw material conveying device, and the weight sensors collect real-time weight data from the raw material storage unit. In the process execution stage, photoelectric sensors and speed sensors are installed. The photoelectric sensors identify the status of materials entering and leaving the processing area, and the speed sensors collect the operating speed of equipment such as weaving and dyeing equipment. In the equipment operation stage, current sensors, temperature sensors, and vibration sensors are configured to monitor the equipment's power supply current, the temperature of key equipment components, and the vibration amplitude of the equipment. All sensors are connected to an edge computing gateway via an industrial bus. The gateway uses the Modbus TCP protocol to receive the raw signals output by the sensors, converts them into standardized digital signals, and then uploads them to the real-time data processing platform via the MQTT protocol.

[0075] The real-time data processing platform classifies and analyzes the received signals, extracting the raw material allocation status, process execution status, and equipment operating status. The raw material allocation status is determined by the arrival signal from the infrared position sensor and the threshold value from the weight sensor, distinguishing between raw material arrival, raw material shortage, and raw material delivery interruption. The process execution status combines the material detection signal from the photoelectric sensor and the operating speed signal from the speed sensor to determine the start, pause, and completion of the process. The equipment operating status is based on the threshold range of current, temperature, and vibration parameters, identifying normal operation, fault warnings, fault shutdowns, and standby modes.

[0076] Based on the above classification, the platform establishes a classification index for status signals. The signal type identifier uses a 10-character code, with the first 3 characters distinguishing the major status category, the middle 4 characters identifying the sub-status, and the last 3 characters indicating the equipment number. The signal strength parameter maps the analog signal to an integer range of 0-100, which is associated with the raw material inventory, process completion, and equipment health.

[0077] The process type code and status update timestamp in the signal are then analyzed. The process type code is an 8-character structure containing the process category number and process parameter identifier; the timestamp uses UTC format and is accurate to milliseconds, with consistency ensured by NTP synchronization. Signals from the same batch are arranged in ascending order of timestamp to construct a time sequence relationship reflecting the progress of the production process.

[0078] Finally, the signals are categorized into the Redis status queue corresponding to the production batch, and the status update frequency is statistically analyzed according to the time window set by the process cycle. This frequency is compared with the baseline frequency extracted from the historical production database, and production batches whose actual frequency exceeds the preset range are marked, written into the abnormal batch record table, and pushed to the production management terminal.

[0079] In step S5, the process of dynamically adjusting the initial tracing path to generate the optimized tracing path is as follows:

[0080] The system retrieves the real-time production event sequence for the current production batch from the real-time data processing platform and retrieves the expected event sequence from the initial traceability path corresponding to this batch from the path database. The expected event sequence includes the standard event type and occurrence order for each production node device in the initial path, such as the raw material inspection node corresponding to the raw material inspection completion event, the weaving equipment corresponding to the weaving start and weaving completion events, and the dyeing equipment corresponding to the dyeing start and dyeing completion events. The comparison module uses a sequence comparison algorithm to compare the event type and occurrence order of the real-time event sequence with the expected event sequence position by position, identifying sequence differences. For example, if the expected sequence shows a dyeing start event following the weaving completion event, but the real-time sequence shows a device failure event following the weaving completion event, then a sequence difference is determined.

[0081] Event types and timestamps are extracted from real-time production events. Event types use the same encoding rules as the status signal classification index, and event timestamps use the same UTC millisecond format as status update timestamps. Based on the extracted information, a mapping relationship between event types and production node equipment is constructed. The mapping relationship table is stored in the equipment event association database. The table includes fields such as event type code, equipment number, equipment type, and process affiliation. For example, the GZX-0001 process start event is mapped to the weaving equipment number ZZ-003, and the SBY-0003 equipment failure event is mapped to the dyeing equipment number RS-005. Through this mapping relationship, the event occurrence frequency of each production node equipment within a set time window is statistically analyzed. The time window length is consistent with the frequency statistics window in S4 to ensure data dimension consistency.

[0082] The platform compares the actual event frequency of each production node device with the expected event frequency. The expected event frequency, derived from the design parameters of the initial traceability path, reflects the frequency of normal events occurring at that device in the standard production process and is stored in the expected parameter table of the path database. When the actual event frequency is lower than the threshold range of the expected event frequency, the platform marks the production node device. The threshold range is determined by historical production data and is set based on the event frequency fluctuation range of the same equipment and the same process. After marking, the quality control node verification module is activated. It retrieves the list of quality control node locations in the initial traceability path from the path database. The list contains the device number and node type corresponding to all quality control nodes, such as raw material inspection node device JY-001, semi-finished product inspection node device JY-002, and finished product inspection node device JY-003. The verification module matches the marked device number with the quality control node device number, removes devices belonging to quality control nodes, and retains the marked devices that are not quality control nodes as a candidate list of non-critical event devices to be confirmed.

[0083] Next, the data acquisition point optimization module is activated to identify adjacent data acquisition points with the same data acquisition type. The data acquisition type is defined based on the acquisition parameters, such as temperature acquisition, pressure acquisition, humidity acquisition, and pattern accuracy acquisition. Acquisition points of the same type have the same parameter code; for example, CJ-001 represents temperature acquisition, and CJ-002 represents pattern accuracy acquisition. The module retrieves the physical location coordinates of each data acquisition point from the path database. The coordinates are based on the workshop's planar coordinate system and include X-axis and Y-axis coordinate values. The physical distance between adjacent and similar acquisition points is calculated using a distance calculation method in a Cartesian coordinate system to determine the spatial distance between each acquisition point. The calculated physical distance is compared with a preset distance threshold, which is set according to the workshop layout and acquisition accuracy requirements. For example, the preset distance threshold for temperature acquisition points is set to the average level of equipment spacing in the workshop, and the preset distance threshold for pattern accuracy acquisition points is set to the minimum detection spacing required by the process. When the physical distance between adjacent similar acquisition points is less than the preset distance threshold, the module merges these two acquisition points. The merging principle is to retain the acquisition point with higher data acquisition frequency and better data integrity, and to cancel the path node qualification of the other acquisition point.

[0084] After the merger, the access order of production node equipment is recalculated. The access order adjustment follows the dual principles of process logic sequence and spatial distance optimization. While ensuring that the process sequence relationship is not disrupted, the access order is adjusted according to the principle of proximity of equipment spatial location to reduce the round-trip distance of the path. The adjusted path is used as the optimized traceability path and is written to the path database through the path update interface, overwriting the original initial traceability path. At the same time, the path adjustment time, the reason for the adjustment, and the node changes before and after the adjustment are recorded to form a path adjustment log, which is stored in the path change record table.

[0085] When confirming the list of production node equipment for which no critical events have occurred, the equipment traversal module is first activated. Equipment numbers are extracted from the candidate list of non-critical event equipment to be confirmed, and historical event records for each piece of equipment are retrieved one by one. These historical event records come from the equipment event log database and contain information such as event type, event timestamp, and event processing results for all production batches of that equipment over the past 12 months. The occurrence patterns of critical events in the historical event records are analyzed. Critical events include equipment failure recovery events, quality control data exceeding standards events, and process parameter adjustment events. The occurrence pattern is determined by the frequency of occurrence of event types, the time interval between occurrences, and the time interval between adjacent events. For example, a failure recovery event for a certain weaving equipment typically occurs once every three production batches, and often occurs after the equipment has been running continuously for four hours.

[0086] The average time interval between critical events is calculated by extracting the time differences of all adjacent critical events from historical event records and averaging them using an arithmetic method. This average time interval is then stored in the equipment's critical event parameter table. The current time interval for critical events within the current production cycle is compared to the average time interval. The current time interval is the length of time from the last critical event occurring on that equipment to the current moment. When the difference between the current time interval and the average time interval exceeds a preset range, the platform flags the equipment. The preset range is set based on the fluctuation range of historical time intervals to ensure that normal time interval deviations are covered.

[0087] The system checks the event records of the marked production node equipment within the current production cycle. The current production cycle is calculated from the start time of this batch of production. It queries the equipment event log database to check all event records for this equipment within the current production cycle to confirm the existence of any critical event records. If the query results show that the equipment has no critical event records within the current production cycle, then the equipment is added to the list of production node equipment without critical events. This list includes information such as equipment number, equipment type, the type of critical event that did not occur in the corresponding process, and the current production cycle duration. After generation, it is pushed to the production scheduling system via an interface as the basis for subsequent path optimization and production adjustments.

[0088] In step S6, the process of collecting production data for lace fabric and integrating it to generate a production traceability report is as follows:

[0089] First, based on the optimized traceability path, data acquisition commands are issued through the communication interfaces of production node equipment. These commands clearly specify the parameter types and time ranges for data acquisition, ensuring coverage of key data at each node. The received raw material characteristic data includes raw material composition, yarn count, and fiber density; processing technology data includes process parameters such as weaving speed, dyeing temperature, and setting time; and inspection result data includes test parameters such as the component qualification status of raw materials, pattern clarity of semi-finished products, and color fastness of finished products.

[0090] The collected production data was then compared item by item with the material descriptions in the production orders. For raw material characteristics, the composition was checked to ensure it matched the order requirements and the yarn count met specifications. Processing technology data was matched against the implicit process parameter ranges in the orders. Inspection results were compared against the corresponding quality standards in the orders, identifying data points that exceeded the standard range or did not conform to the order descriptions, and these data points were marked as abnormal data points.

[0091] Next, the number of production node devices accessed in the optimized traceability path is counted. Data collection logs are used to confirm whether each node returns valid data, thus determining whether a node has been accessed. The number of accessed nodes is compared with the total number of production node devices in the initial traceability path, and the ratio between the two is calculated; this ratio is the production process integrity indicator.

[0092] Finally, the list of abnormal data points and production process integrity indicators are integrated to generate a production traceability report. The list of abnormal data points must include the node number and data type deviation of the abnormal data. The production process integrity indicators must specify the exact values ​​and calculation basis, and supplement basic information such as the data collection time range and data source nodes to form a structured production traceability report, providing a basis for production quality analysis and traceability.

[0093] Please see Figure 2 As shown, the present invention also includes a traceability management system for lace fabric production, used to implement the above-described method for traceability management of lace fabric production, comprising:

[0094] The order receiving module is used to receive production orders for lace fabrics. The production order includes the material description, pattern code, and production batch identifier of the lace fabric.

[0095] The historical data retrieval module is used to retrieve historical production data of lace fabrics based on the pattern code of the production order, and to obtain the production process records of lace fabrics with the same pattern code.

[0096] The initial path generation module is used to generate an initial traceability path for lace fabric based on historical production data. The initial traceability path defines the access order of production node equipment and data collection points.

[0097] The real-time event monitoring module is used to monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events.

[0098] The dynamic path optimization module is used to dynamically adjust the initial traceability path based on real-time production events, skipping non-critical production node equipment or merging data collection points to generate an optimized traceability path.

[0099] The traceability report generation module is used to collect production data of lace fabric from production node equipment according to the optimized traceability path, and integrate it to generate a production traceability report of lace fabric, including the integrity of the production process and the identification of anomalies.

[0100] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for traceability management in the production of lace fabrics, characterized in that, Includes the following steps: S1. Receive production orders for lace fabrics. The production orders include the material description, pattern code, and production batch identifier of the lace fabrics. S2. Based on the pattern code of the production order, retrieve the historical production data of the lace fabric and obtain the production process record of the lace fabric with the same pattern code. S3. Based on historical production data, generate an initial traceability path for the lace fabric. The initial traceability path defines the access order of production node equipment and data collection points. S4. Monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events. S5. Based on real-time production events, dynamically adjust the initial traceability path, skip non-critical production node equipment or merge data collection points to generate an optimized traceability path; S6. Following the optimized traceability path, collect production data of lace fabric from production node equipment, integrate and generate a production traceability report of lace fabric, including production process integrity and anomaly identification. In step S5, the process of dynamically adjusting the initial tracing path to generate the optimized tracing path is as follows: Compare the real-time production events with the expected event sequence of the initial traceability path, extract the event types and event timestamps from the real-time production events, construct the mapping relationship between event types and production node devices, calculate the event occurrence frequency of each production node device, compare the difference between the event occurrence frequency and the expected event frequency, mark the production node devices whose event occurrence frequency is below the threshold range, verify whether the marked production node devices include quality control node locations, and confirm the list of production node devices that have not experienced critical events. Identify adjacent data collection points with the same data collection type, calculate the physical distance between adjacent data collection points, compare the physical distance with a preset distance threshold, merge adjacent data collection points whose physical distance is less than the preset distance threshold, recalculate the access order of production node devices, generate an optimized traceability path and update the path database; The process of confirming the list of production node equipment where no critical events have occurred is as follows: Traverse the marked production node devices, extract the historical event records of each production node device, analyze the occurrence patterns of key events in the historical event records, calculate the average time interval of key events, compare the difference between the current time interval and the average time interval, mark the production node devices whose difference exceeds the preset range, check the event records of the marked production node devices in the current production cycle, confirm the production node devices without key event records, and generate a list of production node devices that have not experienced key events.

2. The method for traceability management of lace fabric production according to claim 1, characterized in that, In step S1, the process of receiving the production order for lace fabric is as follows: Parse the material description in the production order, match the material description with the raw material type in the raw material inventory record, and parse the pattern code in the production order, matching the pattern code with the equipment processing capacity in the production equipment specification table. Based on the matching results, a production task allocation plan is generated. The production task allocation plan includes a raw material allocation plan and an equipment scheduling plan. The raw material allocation plan is allocated to the raw material storage node, and the equipment scheduling plan is allocated to the corresponding production node equipment.

3. The method for traceability management of lace fabric production according to claim 1, characterized in that, In step S2, the process of retrieving historical production data for lace fabric is as follows: Extract the pattern code content from the production order, use the pattern code content as the search condition to query the historical production database, and obtain historical production task records with the same pattern code content. The historical production task records include raw material allocation records, process execution records and equipment scheduling records. Analyze the task execution time series in historical production task records, identify key process nodes in the process execution records, calculate the time interval distribution of key process nodes, and select historical production task records with time interval distribution within a predetermined range as reference datasets.

4. The method for traceability management of lace fabric production according to claim 1, characterized in that, In step S3, the process of generating the initial traceability path for the lace fabric is as follows: Analyze the execution sequence of processes in historical production task records, establish the process flow relationship between production node equipment, and construct the node set and connecting edge of the process topology diagram. The node set represents the production process stage, and the connecting edge represents the process flow direction. Determine the quality control node locations for each production process stage. These quality control node locations include raw material inspection locations, semi-finished product inspection locations, and finished product inspection locations. Calculate the connection weights between nodes, which are based on the flow frequency in historical production task records. Adjust the direction of the connecting edges in the process topology graph according to the connection weight, establish an alternative path set, which includes the main production path and the backup production path, associate the quality control node position with the corresponding production process stage, generate the initial traceability path and save it to the path database.

5. The method for traceability management of lace fabric production according to claim 1, characterized in that, In step S4, the process of monitoring real-time production events of the lace fabric is as follows: Monitor the production status signals of production node equipment, receive the raw material allocation status, process execution status and equipment operation status from the production status signals, and establish a classification index of status signals. The classification index includes signal type identifier and signal strength parameters. The process type code and status update timestamp in the production status signal are analyzed to establish the temporal relationship of the status signal, which reflects the progress of the production process. Production status signals are categorized into the status queues of the corresponding production batches. The frequency of status updates in the status queues is counted. The difference between the status update frequency and the baseline frequency data is compared, and production batches whose status update frequency exceeds the predetermined range are marked.

6. The method for traceability management of lace fabric production according to claim 1, characterized in that, In step S6, the process of collecting production data of lace fabric and integrating it to generate a production traceability report is as follows: According to the optimized traceability path, data acquisition instructions are sent to the production node equipment, and raw material characteristic data, processing technology data and inspection result data are received from the production node equipment; Compare the degree of matching between production data and material descriptions in production orders, identify data points in production data that deviate from the material descriptions, and mark these deviating data points as outliers. The number of production node devices visited in the optimized traceability path is counted, the ratio of visited production node devices to the total number of production node devices in the initial traceability path is calculated, and a production process integrity index is generated. Integrate the list of abnormal data points and production process integrity indicators to generate a production traceability report.

7. A traceability management system for lace fabric production, used to implement the traceability management method for lace fabric production as described in any one of claims 1-6, characterized in that, include: The order receiving module is used to receive production orders for lace fabrics. The production order includes the material description, pattern code, and production batch identifier of the lace fabric. The historical data retrieval module is used to retrieve historical production data of lace fabrics based on the pattern code of the production order, and to obtain the production process records of lace fabrics with the same pattern code. The initial path generation module is used to generate an initial traceability path for lace fabric based on historical production data. The initial traceability path defines the access order of production node equipment and data collection points. The real-time event monitoring module is used to monitor real-time production events of lace fabrics. Real-time production events include raw material input events, processing operation events, and equipment status change events. The dynamic path optimization module is used to dynamically adjust the initial traceability path based on real-time production events, skipping non-critical production node equipment or merging data collection points to generate an optimized traceability path. The traceability report generation module is used to collect production data of lace fabric from production node equipment according to the optimized traceability path, and integrate it to generate a production traceability report of lace fabric, including the integrity of the production process and the identification of anomalies.

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