Laser marking data tracing method and system

Abstract

The present application relates to the technical field of data tracing, in particular to a laser etching laser marking data tracing method and system, comprising the following steps: analyzing and capturing the molding temperature and the injection molding cycle to obtain an adaptive tracing code assignment parameter set, performing local foolproof verification to intercept identity overlapping sequences to generate a digital entity binding matrix, driving a marking engine to perform laser etching and triggering a code reader to scan and sort to obtain an analysis rating data set, relying on network communication to capture equipment status to extract core variable nodes to obtain an associated detection link set. In the present application, the generation of trace codes by fusing mold instruction and production environment parameters effectively solves the problems of mistakes and omissions, strengthens the absolute space-time mapping reliability of digital base and physical entity, synchronously drives the code reading interception logic to realize physical isolation of defective products, realizes closed-loop overall quality improvement control, reversely traces variable optimization, constructs a path for cross-process defect transmission topology, and enhances the completeness and accurate investigation ability of single-piece flow history.

Description

Technical Field

[0001] This invention relates to the field of data traceability technology, and in particular to a method and system for tracing data traceability using laser engraving and marking. Background Technology

[0002] The field of data traceability technology mainly covers systems that record and link information and material flows at various stages of the production and manufacturing cycle. Its core focus is on assigning identity, collecting information, and storing and linking information for parts from raw material warehousing, processing and forming, assembly to delivery. In the field of precision automotive parts automated production lines, a unique identification code is established for each part based on the industrial identification system. The identification code is read at each workstation of the production line using barcode scanners or fixed barcode readers. The collected production batch, operator, equipment number, and quality inspection judgment information are synchronously written into a central database in time sequence to establish a product information archive. The traditional laser marking data traceability method refers to the technical matter of binding permanent markings on the surface of precision automotive parts with underlying data. It involves an industrial control computer sending a marking instruction containing a specific plaintext string to a laser marking machine, which then engraves the corresponding two-dimensional matrix code on the surface of the automotive panel or body. Subsequently, an industrial camera fixed above the marking station takes a picture of the two-dimensional matrix code, and a parsing program extracts the string from the image. Then, the extracted string is concatenated with the marking instruction issued by the industrial control computer and the equipment clock information, packaged, and transmitted via Ethernet to a relational database on the workshop server to create a new data table row record for physical storage.

[0003] Traditional laser marking traceability mainly relies on directly engraving specific plaintext strings, combined with industrial camera photography to extract images and store them in a database along with clock information. This single, one-way flow model severs the strong binding relationship between digital identifiers and physical entities, which can easily lead to duplicate production sequences and identity conflicts. Furthermore, the lack of a post-processing static storage mechanism for synchronous verification of marking quality means that defective products cannot be intercepted in real time. The delayed data records cannot be mapped back to source processing parameters such as forming temperature, which severely restricts the accuracy of cross-equipment traceability and the efficiency of root cause analysis of defective products. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for tracing data from laser engraving and marking, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a laser marking data traceability method, comprising the following steps: S1: An automated injection molding and assembly production line for automotive parts intercepts mold action instructions through input / output monitoring and communication with a programmable logic controller. It analyzes the molding temperature, injection cycle, and mold base number captured by the target industrial network communication protocol to obtain a set of DMC traceability coding parameters. S2: Based on the adapted DMC traceability coding parameter set, local error prevention interlock verification is performed in the dedicated laser marking engine of the industrial control computer. The comparison is performed in the existing database to intercept serial number sequences with overlapping identities, lock the correspondence between physical component entities and unique digital information, and obtain the error prevention interlock digital entity binding matrix. S3: Call the anti-fool interlocking digital entity binding matrix, drive the visual marking template engine to guide the laser engraving machine to perform laser engraving action in the preset physical area, and simultaneously trigger the professional visual barcode reader to perform barcode scanning and rating logic, intercept and analyze defective samples with quality lower than the set level and drive the robot to perform real-time sorting to obtain entity laser engraving analysis and rating dataset. S4: Based on the entity laser engraving analysis and rating dataset, rely on the upper-layer network communication interface to capture the production status packets of the associated external equipment in the corresponding module cycle, extract the core variable nodes that cause the analysis rating to decline, and obtain the traceability rating associated detection link set.

[0005] As a further embodiment of the present invention, the adapted DMC traceability coding parameter set includes a module-related attribute identifier, a DMC structured string, and a 60-bit information character length limit vector; the error-proof interlocking digital entity binding matrix transmission includes a serial number anti-collision verification bit, an entity flow servo pointer, and a single-item binding certificate sequence; the entity laser engraving analysis rating dataset includes B-level and above compliant scanning records, reader optical contrast parameters, and defective product sorting isolation signal sources; and the traceability rating associated detection link set includes injection molding parameter deterioration transmission weights, cross-process network communication verification frames, and a single-item product complete history chain.

[0006] As a further aspect of the present invention, the step of adapting the DMC traceability coding parameter set specifically includes: S101: Based on the underlying input / output monitoring architecture and programmable logic controller communication network, it listens to the modular flow of automotive panels on the automated production line, extracts the timing of trigger signals and interlock status of six-axis and four-axis industrial robots during the grasping and handling process, and obtains the servo mechanical cooperative action sequence. S102: Based on the servo mechanical cooperative action sequence, the external injection molding equipment is connected through the Net100 industrial network communication protocol to capture the molding temperature fluctuation curve, injection cycle sequence and corresponding mold base physical number of the current processing mold, and obtain the mold production environment parameter package. S103: Call the module production environment parameter package, follow the data matrix code (DMC) combination generation rules, and encapsulate the product type identifier, current real-time date and time, specified number of incrementing serial number and servo system code in series, identify the independent identification code string, and generate an adapted DMC traceability coding parameter set.

[0007] As a further aspect of the present invention, the steps of the foolproof interlocking digital entity binding matrix are as follows: S201: Based on the adapted DMC traceability coding parameter set, in the memory domain inside the industrial control computer, the string to be coded and the hash address domain are subjected to concurrent collision test to identify the conflict sequence that causes identity overlap and obtain the foolproof conflict candidate list. S202: Based on the aforementioned error-proof conflict candidate list, trigger the error-proof and leakage-proof safety interlock mechanism of the control architecture intermediate layer, stop the laser engraving action of the repeated risk sequence, calculate the consistency between the incremental step size of adjacent legal codewords and the production cycle, strip away non-sequential isolated sequences, and obtain the confidence-based coding admission queue. S203: Call the confidence-based admission queue, assign a fixed internal storage pointer to each admission sequence that passes the safety interlock, and forcibly lock the fixed-length logic code and the physical components on the pipeline that have been aligned in the early stage in the spatiotemporal coordinates to generate a foolproof interlock digital entity binding matrix.

[0008] As a further aspect of the present invention, the steps for obtaining the entity laser engraving analysis and rating dataset are as follows: S301: Call the anti-foolproof interlocking digital entity binding matrix, import the characters to be marked into the visual marking template engine, direct the industrial robot to complete the product alignment, drive the laser engraving machine to perform deep ultraviolet or fiber optic marking in the set area, and obtain the physical semi-finished product to be inspected by laser engraving. S302: Based on the laser-engraved physical semi-finished product to be inspected, activate the professional visual barcode reader at the back end to perform synchronous scanning. Relying on the built-in mandatory quality assessment benchmark, the symbol contrast and axial deviation rating of the data matrix code DMC on the physical surface are performed. Samples that meet the required indicators are selected to obtain B-level or above qualified barcode scanning records. S303: Call the B-level and above qualified barcode scanning records, map the interception signals of substandard products to the on-site programmable logic controller, drive the sorting mechanism to perform real-time sorting and physical isolation of defective products, merge the parsing logs of qualified panels, and generate a physical laser engraving parsing rating dataset.

[0009] As a further aspect of the present invention, the steps of the traceability rating association detection link set are specifically as follows: S401: Based on the physical laser engraving analysis and rating dataset, the processing records of the corresponding parts are traced back through the upper-level closed-loop information flow architecture. The correspondence between the visual code reading rating result and the communication timestamps at the moment of marking and the moment of injection molding is locked. The smoothness of the process flow across equipment is calculated, and the process timing flow fit vector is obtained. S402: Based on the process sequence flow fit vector, the implicit correspondence between abnormal fluctuations in molding temperature during the injection molding stage and the degradation of laser marking analysis is explored to obtain a cross-process parameter defect transmission topology diagram. S403: Based on the cross-process parameter defect transmission topology diagram, isolated records with network communication breaks are eliminated, and the continuous physical handling flow is linked with the qualified processing events determined by the complete underlying IO. The complete digital process of a single part from injection molding to barcode scanning and warehousing is mapped, and a traceability rating associated detection link set is generated.

[0010] As a further aspect of the present invention, the coherent physical transport flow refers to connecting part processing events with continuous timestamp associations in the topology graph, eliminating isolated nodes with network communication breaks exceeding a set threshold, and obtaining a coherent physical transport flow.

[0011] As a further aspect of the present invention, the method further includes step S5: S5: Call the traceability rating associated detection link set, and according to the mandatory traceability setting with a retention period of one year, perform block archiving of logs in a local storage medium with a capacity not less than a preset threshold. Based on the physical query frequency of the QR code traceability link, establish an error prevention and omission index mapping architecture to obtain the full life cycle error prevention and omission traceability log table. The full lifecycle error prevention and traceability log table includes a one-year mandatory retention status code, the distribution of sectors hit by tens of millions of searches, and the after-sales traceability addressing path.

[0012] As a further aspect of the present invention, the steps of the full lifecycle error prevention and traceability log table are as follows: S501: Call the traceability rating associated detection link set, access the underlying archive database configured in the industrial control computer, and calculate the original distribution weight based on the trigger frequency of the component after-sales traceability and defective product interception instructions to obtain the traceability node heat addressing mapping table. S502: According to the traceability node hot addressing mapping table, execute the tagging data retention period constraint rule, reside the frequently accessed pipeline traceability logs in the solid-state storage area, and migrate the old DMC quality files that have exceeded the target period to the high-capacity disk sector to obtain a cold and hot log isolated storage architecture. S503: Based on the aforementioned cold and hot log isolation storage architecture, record the query traversal path, solidify the retrieval rules for single-item traceability codes and traceability source parameters issued through deep integration of software and hardware, and generate a full lifecycle error-proof and omission-proof traceability log table.

[0013] The laser marking data traceability system includes: The customized coding generation module is based on the automated injection molding production line of automotive parts. It obtains the DMC traceability coding parameter set by combining the product identifier and servo system code with the DMC variable generation rules through input and output monitoring and PLC capture of module network parameters. Based on the adapted DMC traceability coding parameter set, the digital entity binding module performs local error prevention verification and intercepts duplicate sequences in the existing original database, locks the corresponding identity of the unique traceability code of the physical product, and obtains the error prevention interlocking digital entity binding matrix. The laser engraving and scanning analysis module, based on the foolproof interlocking digital entity binding matrix, drives the visual marking template engine to perform laser engraving within a preset grid. It intercepts and analyzes defective products below grade B through a professional barcode reader and guides the hardware isolation of defective products to obtain a physical laser engraving analysis and rating dataset. The closed-loop rating association module, based on the entity laser engraving analysis rating dataset, relies on the upper-layer information flow architecture to trace the physical transmission link between injection molding temperature and code reading rating results across devices, and associates the product's inbound and outbound lifecycle to obtain a set of traceability rating association detection links; The full lifecycle log archiving module, based on the traceability rating associated detection link set, performs hierarchical management and retrieval addressing optimization on an industrial control computer storage medium with a preset capacity of gigabytes to obtain a full lifecycle error-proof and omission-proof traceability log table.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a fault-proof traceability code is generated by fusing dynamic interception module action instructions with production parameters. Local high-concurrency collision verification is performed to strip isolated conflict sequences, effectively solving the hidden dangers of identity overlap and error binding. This strengthens the absolute spatiotemporal mapping reliability between the digital base and physical entities, synchronously drives the visual code reading and rating logic for bottom-level interception, realizes real-time sorting and physical isolation of defective products, improves the quality control of the on-site manufacturing process, and relies on the upper-level network architecture to trace back core initiating variables such as molding temperature change curves. This optimizes the cross-process defect transmission topology construction path, enhances the completeness and accurate investigation capability of single-piece flow history, and ensures efficient retrieval and secure retention of the underlying data traceability chain throughout its entire lifecycle. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a method for tracing data from laser engraving and marking, comprising the following steps: S1: Based on the automated injection molding and assembly production line for automotive parts, the system intercepts the module action instructions through input / output monitoring and communication with the programmable logic controller. It analyzes the molding temperature, injection cycle and mold number captured by the target industrial network communication protocol, and combines the data matrix code variable generation rules to splice the product type identifier and servo system code to obtain the DMC traceability coding parameter set. S2: Based on the DMC traceability coding parameter set, local error prevention interlock verification is performed in the dedicated laser marking engine of the industrial control computer. The comparison is performed in the existing database to intercept serial number sequences with overlapping identities, lock the correspondence between physical component entities and unique digital information, and obtain the error prevention interlock digital entity binding matrix. S3: Call the foolproof interlocking digital entity binding matrix, drive the visual marking template engine to guide the laser engraving machine to perform laser engraving action in the preset physical area, and simultaneously trigger the professional visual barcode reader to perform barcode scanning and rating logic, intercept and analyze defective samples with quality lower than the set level and drive the robot to perform real-time sorting to obtain the entity laser engraving analysis and rating dataset. S4: Based on the physical laser engraving analysis and rating dataset, rely on the upper-layer network communication interface to capture the production status package of the corresponding external equipment in the mold cycle, construct a multi-dimensional correlation closed loop between the underlying injection molding parameters and the laser engraving analysis quality level, extract the core variable nodes that cause the analysis rating to decline, and obtain the traceability rating correlation detection link set. S5: Call the traceability rating associated detection link set, and perform block archiving of logs in a local storage medium with a capacity not less than the preset threshold according to the mandatory traceability setting of a retention period of one year. Based on the physical query frequency of the QR code traceability link, establish an error prevention and omission index mapping architecture to obtain the full life cycle error prevention and omission traceability log table.

[0020] The DMC traceability coding parameter set includes module-related attribute identifiers, DMC structured strings, and a 60-character information character limit vector. The error-proof interlocking digital entity binding matrix transmission includes serial number anti-collision verification bits, entity flow servo pointers, and single-item binding certificate sequences. The entity laser engraving analysis and rating dataset includes B-level and above compliant barcode scanning records, barcode reader optical contrast parameters, and defective product sorting isolation signal sources. The traceability rating associated detection link set includes injection molding parameter deterioration transmission weights, cross-process network communication verification frames, and a complete single-item product history chain. The full lifecycle error-proof and omission-proof traceability log table includes a one-year mandatory retention status code, tens of millions of search hit sector distributions, and after-sales traceability addressing paths.

[0021] Please see Figure 2 The specific steps for adapting the DMC traceability coding parameter set are as follows: S101: Based on the underlying input / output monitoring architecture and programmable logic controller communication network, it listens to the modular flow of automotive panels on the automated production line, extracts the timing of trigger signals and interlock status of six-axis and four-axis industrial robots during the grasping and handling process, and obtains the servo mechanical cooperative action sequence. By monitoring the flow of data packets on the industrial Ethernet communication bus through the underlying input / output monitoring architecture, the status messages of automotive panels transmitted between workstations on the automated production line are captured with a sampling period of 10 milliseconds. The coordinate sequence of the 6-axis industrial robot, the rotation angle sequence of the 4-axis industrial robot, the grasping action trigger timestamp, the handling end timestamp, and the interlock level identifier of the programmable logic controller are extracted. The difference between the end-of-transfer timestamp and the trigger timestamp of the grabbing action is calculated. This difference is then added to the interlock waiting compensation value to determine the servo-mechanical coordinated action delay parameter. This parameter is compared to the rated cycle time baseline. The grabbing action trigger timestamp is 200 milliseconds, the end-of-transfer timestamp is 1500 milliseconds, and the interlock waiting compensation value is 50 milliseconds. Subtracting 200 milliseconds from 1500 milliseconds yields 1300 milliseconds. Adding this to 50 milliseconds gives a servo-mechanical coordinated action delay parameter of 1350 milliseconds. The rated cycle time baseline is 1500 milliseconds. Since 1350 milliseconds is less than 1500 milliseconds, the captured coordinate sequence and timestamps are combined and encapsulated to output the servo-mechanical coordinated action sequence.

[0022] S102: Based on the servo mechanical cooperative action sequence, it connects to external injection molding equipment through the Net100 industrial network communication protocol to capture the molding temperature fluctuation curve, injection cycle sequence and corresponding mold base physical number of the current processing mold, and obtain the mold production environment parameter package. The timestamp node in the servo mechanical cooperative action sequence is called as the communication synchronization trigger condition. Data read request frames are sent to external injection molding equipment through the industrial network communication protocol interface. The initial melt temperature sampling point set, the mold cavity holding pressure temperature sampling point set, the injection duration and the 12-bit mold base physical number are captured. A sliding window mechanism with five consecutive sampling points is used to traverse the initial melt temperature sampling point set. The arithmetic mean of the temperature of each sampling point within the window is calculated to replace the original sampling point value in the center of the window. The square of the difference between the temperature value of each sampling point after cleaning and the target set temperature value is calculated. All squared values ​​are summed and divided by the total number of sampling points to obtain the molding temperature fluctuation variance parameter. The target set temperature of the injection molding equipment is 210 degrees Celsius. The temperature values ​​of the five sampling points are 210 degrees Celsius, 212 degrees Celsius, 208 degrees Celsius, 215 degrees Celsius, and 209 degrees Celsius. The differences between each point and 210 degrees Celsius are 0 degrees Celsius, 2 degrees Celsius, -2 degrees Celsius, 5 degrees Celsius, and -1 degrees Celsius, respectively. The squares are calculated to obtain 0, 4, 4, 25, and 1. The sum of the squared values ​​is 34. Dividing 34 by 5, the molding temperature fluctuation variance parameter is obtained as 6.8. The molding temperature fluctuation variance parameter, the injection duration, and the physical number of the mold base are concatenated into an array to obtain the mold production environment parameter package.

[0023] S103: Call the module production environment parameter package, follow the data matrix code (DMC) combination generation rules, and encapsulate the product type identifier, current real-time date and time, specified number of incrementing serial number and servo system code in series, identify the independent identification code string, and generate an adaptive DMC traceability coding parameter set; The system reads the physical number of the mold base from the production environment parameter package, retrieves the real-time date and time using the clock, extracts the 8-bit servo system code assigned to the industrial robot by the underlying control architecture, and extracts a 6-bit incrementing serial number from the local counter register. The physical number of the mold base, the real-time date and time, the incrementing serial number, and the servo system code are concatenated sequentially to form an initial text of an independent identification code string with a length not exceeding 60 characters. The decimal American Standard Code (USSC) value corresponding to each character in the initial text is extracted one by one. All decimal values ​​are summed to obtain the total character value. This total character value is divided by a constant 10, and the remainder is extracted. The remainder is used as a check digit and appended to the end of the initial text of the independent identification code string. After summing the decimal values ​​corresponding to each character in the initial text of the independent identification code string, the total character value is 245. Dividing 245 by a constant 10 yields a quotient of 24 and a remainder of 5. The remainder 5 is appended to the end of the initial text. The text string with the check digit and the servo system code are encapsulated into a structure dictionary format to generate a DMC traceability coding parameter set.

[0024] Please see Figure 3 The specific steps for the foolproof interlocking digital entity binding matrix are as follows: S201: Based on the adapted DMC traceability coding parameter set, the string to be coded and the hash address field are concurrently collided in the memory domain inside the industrial control computer to identify the conflict sequence that causes identity overlap and obtain the foolproof conflict candidate list. The code retrieves the text content to be coded from the DMC traceability coding parameter set. A concurrent thread pool is started in the 250 gigabyte memory domain of the industrial control computer. The text content to be coded is mapped to a hash address domain. The decimal values ​​of each character in the text content are extracted one by one. Each character's decimal value is multiplied by the prime base 31. All products are summed, and the sum is divided by the total hash table capacity of 1,000,000, and the remainder is extracted to obtain the hash feature value. The total sum of the products in the text content is 3548902. This is divided by 1,000,000, and the remainder 548902 is used as the hash feature value. The pointer is positioned at index 548902 in the hash address domain, and the value at that position is read. The read value indicates an occupancy status of 1, indicating that there is duplicate code in the text content. The text content to be coded and the servo system code are stored in a sequential list to obtain a candidate list for error prevention conflicts.

[0025] S202: Based on the error-proof conflict candidate list, trigger the error-proof and leakage-proof safety interlock mechanism of the control architecture middle layer, stop the laser engraving action of repeated risk sequences, calculate the consistency between the incremental step size of adjacent legal codewords and the production cycle, strip away non-sequential isolated sequences, and obtain the confidence-based coding admission queue. The system monitors the capacity status of the candidate list for error prevention and determines that when the total number of data entries in the list is greater than 0, it sends a 0 voltage level signal to the programmable logic controller through the input / output interface to cut off the power supply circuit of the relay that enables the laser engraving machine's marking action, thereby terminating the sequence of physical actions. Extract the set of legal codewords that have not entered the error-proofing conflict candidate list. Read the incremental serial number within two adjacent modules. Subtract the incremental serial number of the previous module from the current module's incremental serial number to obtain the sequence incremental step size parameter. Obtain the actual flow module increment constant of the record. Calculate the absolute value of the difference between the incremental step size parameter and the actual flow module increment constant to obtain the beat consistency deviation parameter. The current module incremental serial number is 1505, and the previous legal module incremental serial number is 1502. Subtracting 1502 from 1505 gives the sequence incremental step size parameter as 3. The flow module increment constant is 1. Subtracting 1 from 3 gives 2. The absolute value is 2, so the beat consistency deviation parameter is 2. The isolated sequence tolerance threshold is 3. Since 2 is less than 3, it is determined that the sequence has not left the pipeline beat tolerance. Sequence records with a beat consistency deviation parameter greater than 3 are removed from the set. The remaining sequence text set is packaged and output as a confidence-based coding admission queue.

[0026] S203: Call the confidence-based coding admission queue, allocate a fixed internal storage pointer to each admission sequence that passes the safety interlock, and forcibly lock the fixed-length logic code with the physical components on the pipeline that have been aligned in the early stage in the spatiotemporal coordinates to generate a foolproof interlock digital entity binding matrix; Iterate through each admission sequence in the confidence-coded admission queue, allocate a 64-bit decimal internal storage pointer address for the current sequence in the memory stack, call the pixel data of the center coordinate position of the physical component transmitted through the previous alignment station, and obtain the actual X-axis and Y-axis pixel coordinates of the grab center point, and the theoretical X-axis and Y-axis reference coordinates of the reference center point. The X-axis difference is obtained by subtracting the X-axis reference coordinate from the actual X-axis pixel coordinate of the grabbing center point, and the Y-axis difference is obtained by subtracting the Y-axis reference coordinate from the actual Y-axis pixel coordinate of the grabbing center point. The squares of the X-axis and Y-axis differences are calculated and added together. The square root of the sum of the squares is then used to obtain the spatial position offset. The actual grabbing center point has an X-axis pixel coordinate of 105 mm and a Y-axis pixel coordinate of 205 mm. The theoretical reference center point has an X-axis reference coordinate of 100 mm and a Y-axis reference coordinate of 200 mm. The X-axis difference is 5 mm and the Y-axis difference is 5 mm. The squares of the X-axis and Y-axis differences are 25 square millimeters, and the sum is 50 square millimeters. The square root of 50 square millimeters gives a spatial position offset of 7.07 mm. The 7.07 mm spatial position offset parameter, the 64-bit decimal internal storage pointer address, and the current admission sequence are assembled into a multi-dimensional structure and written into the memory storage area in a two-dimensional array format to generate a foolproof interlocking digital entity binding matrix.

[0027] Please see Figure 4 The specific steps for analyzing and rating datasets using physical laser engraving are as follows: S301: Call the foolproof interlocking digital entity binding matrix, import the characters to be marked into the visual marking template engine, direct the industrial robot to complete the product alignment, drive the laser engraving machine to perform deep ultraviolet or fiber optic marking in the set area, and obtain the physical semi-finished product to be inspected. Multidimensional structures are extracted sequentially from the error-proof interlocking digital entity binding matrix. The admission sequence string within the structure is transmitted to the text data cache of the visual marking template engine. Spatial position offset parameters within the structure are read, converted into compensation pulse signals, and sent to the 6-axis industrial robot controller to drive the robot's end effector for physical alignment and correction compensation. The operating control parameters of the laser engraving machine are extracted, including the average laser power, scanning galvanometer movement speed, and spot diameter. The laser energy density parameter is calculated by dividing the average laser power by the product of the scanning galvanometer movement speed and the spot diameter. The average laser power is 20 watts, the scanning galvanometer moving speed is 1000 mm / s, and the spot diameter is 0.05 mm. Multiplying 1000 by 0.05 gives 50 square millimeters / s. Dividing 20 watts by 50 square millimeters / s yields a laser energy density parameter of 0.4 joules per square millimeter. The energy density is determined to meet the requirements of the deep ultraviolet fiber marking energy release range. A trigger command is sent to the laser engraving machine to control the galvanometer to perform surface ablation marking operations within a 10 mm square area according to the access sequence string trajectory. The output is a physical semi-finished product with surface texture and data matrix code to be inspected.

[0028] S302: Based on the physical semi-finished product to be inspected by laser engraving, activate the professional visual barcode reader at the back end to scan the barcode synchronously. Relying on the built-in mandatory quality assessment benchmark, the symbol contrast and axial deviation rating of the data matrix code DMC on the physical surface are performed. Samples that meet the required indicators are selected to obtain B-level or above compliant barcode scanning records. When the laser-engraved physical semi-finished product to be inspected flows through the inspection station, a 5-volt synchronous pulse signal is sent to the visual barcode reader through a hardware trigger cable. This controls the complementary metal-oxide-semiconductor image sensor to perform synchronous exposure and capture, extract the grayscale matrix array returned by the image sensor, and count the grayscale reflectance values ​​of the pixels in the matrix array to find the highest and lowest reflectance values ​​in the matrix. The highest reflectance value is subtracted from the lowest reflectance value to obtain the sign contrast parameter. The sign contrast evaluation score is multiplied by a weighting coefficient of 0.6, and the axial deviation evaluation score is multiplied by a weighting coefficient of 0.4 to calculate the comprehensive rating score. The highest reflectance in the image is 85%, and the lowest reflectance is 40%. Subtracting 40% from 85% gives a sign contrast parameter of 45%. Querying the evaluation benchmark mapping table, the score corresponding to 45% sign contrast is 80 points. Image processing extracts an axial deviation evaluation score of 70 points. Multiplying 80 points by 0.6 gives 48 points, and multiplying 70 points by 0.4 gives 28 points. Adding 48 points and 28 points gives a comprehensive rating score of 76 points. Comparing 76 points with the B-level quality interception threshold of 75 points, 76 points is greater than 75 points. The semi-finished product access sequence string and scanned image are judged to meet the requirements, stored in the local compliance database, and a B-level or higher compliance scanned record is generated.

[0029] S303: Call the scanning records of grade B and above that meet the standards, map the interception signals of substandard products to the programmable logic controller on site, drive the sorting mechanism to perform real-time sorting and physical isolation of defective products, merge the parsing logs of qualified panels, and generate a physical laser engraving parsing rating dataset. When monitoring the comprehensive rating score output stream and encountering a low-quality barcode scanning event with a comprehensive rating score less than 75, the system extracts the position parameters of the belt conveyor corresponding to the product in that event. It then subtracts the network communication latency from the total physical flow time of transporting the product to the waste sorting port, and further subtracts the rated extension time of the physical isolation pusher cylinder to calculate the sorting action response margin parameter. The total physical flow time of the product arriving at the waste sorting port is 1200 milliseconds, the network communication latency is 50 milliseconds, and the rated extension time of the pusher cylinder is 200 milliseconds. Subtracting 50 milliseconds and 200 milliseconds from 1200 milliseconds yields a sorting action response margin parameter of 950 milliseconds. Comparing 950 milliseconds with the safety isolation response margin threshold of 300 milliseconds, since 950 milliseconds is greater than 300 milliseconds, a 500-millisecond continuous 24-volt intercept signal is output to the control port of the field programmable logic controller, driving the cylinder to perform the physical isolation push-out action. For event records with a comprehensive rating score of 75 or higher, the plaintext string, the timestamp of the QR code scan, and the comprehensive rating score are extracted and appended to the daily summary log text in chronological order to generate an entity laser engraving analysis and rating dataset.

[0030] Please see Figure 5The specific steps for tracing the rating and associated detection link set are as follows: S401: Based on the physical laser engraving analysis and rating dataset, the processing records of the corresponding parts are traced back through the upper-level closed-loop information flow architecture. The correspondence between the visual code reading rating result and the communication timestamps at the moment of marking and the moment of injection molding is locked. The smoothness of the process flow across equipment is calculated, and the process timing flow fit vector is obtained. The plaintext string in the entity laser engraving parsing and rating dataset is extracted, and a query request is sent to the distributed database within the closed-loop information flow architecture. The timestamps associated with the strings are matched and locked: the injection molding completion timestamp, the laser engraving start timestamp, and the barcode reader scanning completion timestamp. The injection molding completion timestamp is subtracted from the barcode reader scanning completion timestamp to calculate the total process flow time span. The optimal total flow time parameter is divided by the total process flow time span, and the quotient is multiplied by the network communication latency penalty coefficient to calculate the cycle smoothness index. The injection molding completion timestamp is 0 milliseconds, and the barcode scanning completion timestamp is 16500 milliseconds. Subtracting the two gives the total process flow time span as 16500 milliseconds. The optimal total turnaround time parameter is 15000 milliseconds, and the network communication latency penalty coefficient is 0.95. Dividing 15000 milliseconds by 16500 milliseconds yields a quotient of 0.909. Multiplying 0.909 by 0.95 gives a cycle time smoothness index of 0.863. The total turnaround time span of the process and the cycle time smoothness index are integrated to construct a 1D data array, generating a process timing flow fit vector.

[0031] S402: Based on the process sequence flow fit vector, the implicit correspondence between abnormal fluctuations in molding temperature during the injection molding stage and the degradation of laser marking analysis is explored to obtain a cross-process parameter defect transmission topology diagram. The molding temperature fluctuation variance parameter and the beat smoothness index parameter are used as input features. Feature processing is performed using a feedforward fully connected neural network architecture consisting of one input layer, two hidden layers, and one output layer. The input layer has six neurons that receive the input variables. The first hidden layer (16 neurons) and the second hidden layer (8 neurons) perform weight matrix multiplication and bias addition using a fully connected topology. A corrected linear unit is applied to cut off negative signals. The output layer has one neuron that applies a sigmoid activation function to output a predicted probability in the 0-1 range. The input molding temperature fluctuation variance parameter is 6.8, and the beat smoothness index parameter is 0.86. The first hidden layer neuron receives weights of 0.5 and -0.2, with a bias of 0.1. Multiplying 6.8 by 0.5 yields 3.4, and multiplying 0.86 by -0.2 yields -0.172. Summing 3.4, -0.172, and 0.1 gives 3.328. The corrected linear unit determines that 3.328 is greater than 0, and the neuron outputs a value of 3.328. If the predicted probability of the output layer is greater than the classification threshold of 0.5, it is determined that there is a defect transmission relationship. A two-class cross-entropy loss function is constructed by calculating the product of the logarithm of the predicted probability and the true binary label. The network node weights are adjusted using the adaptive moment estimation algorithm with a first-order moment exponential decay parameter of 0.9 and a second-order moment exponential decay parameter of 0.999. The weight matrix is ​​solidified, and a cross-process parameter defect transmission topology map is generated.

[0032] S403: Based on the cross-process parameter defect transmission topology, it eliminates isolated records of network communication breaks, links the continuous physical handling flow with the qualified processing events determined by the complete underlying IO, maps the complete digital process of a single part from injection molding to barcode scanning and warehousing, and generates a set of traceability rating associated detection links; Coherent physical transport flow refers to connecting part processing events with continuous timestamp associations in the topology graph, eliminating isolated nodes with network communication breaks exceeding a set threshold, and obtaining coherent physical transport flow. Read the communication interaction timestamp records of processing nodes within the cross-process parameter defect propagation topology diagram. Check the time interval between adjacent nodes sequentially. Subtract the previous node's sending timestamp from the current node's receiving timestamp to obtain the first difference. Subtract the rated network transmission delay constant from the first difference to calculate the communication fault judgment difference. Compare the communication fault judgment difference with the island fault tolerance upper limit threshold. The current laser engraving node's receiving timestamp is 56000 milliseconds, the previous injection molding node's sending timestamp is 50000 milliseconds, and the rated network transmission delay constant is 100 milliseconds. Subtracting 50000 milliseconds from 56000 milliseconds yields 6000 milliseconds. Subtracting 100 milliseconds from 6000 milliseconds yields a communication fault judgment difference of 5900 milliseconds. The island fault tolerance upper limit threshold is 5000 milliseconds. Since 5900 milliseconds is greater than 5000 milliseconds, the record containing the communication fault is judged as an isolated record and deleted from the topology dataset. For retaining continuous event node records, they are arranged in series according to the actual timeline of physical handling, mapping the entire digital footprint of a single component from robotic arm gripping, visual alignment, laser deep ultraviolet marking to code reading and warehousing, and outputting a set of traceability rating associated detection links.

[0033] Please see Figure 6 The specific steps for establishing a full lifecycle error prevention and traceability log table are as follows: S501: Call the traceability rating associated detection link set, access the underlying archive database configured in the industrial control computer, and calculate the original distribution weight based on the trigger frequency of the component after-sales traceability and defective product interception instructions to obtain the traceability node heat addressing mapping table. The traceability rating associated detection link set is invoked, and a full table traversal operation is initiated in the archived relational database within the industrial control computer. For 1,000,000 historical stored records of products, the total number of query actions triggered by the statistical matrix code within 30 days is calculated. The number of defective product associated warning queries is multiplied by the warning penalty multiplier constant to obtain the first product. This first product is added to the total number of regular after-sales traceability queries to obtain the first sum. The first sum is divided by the basic access base constant to calculate the node heat weight value parameter. The component code within 30 days... The total number of regular after-sales traceability queries is 15, the number of defective product association warning queries is 2, the warning penalty multiplier constant is 3, and the basic access base constant is 10. Multiplying 2 by 3 gives 6 queries, adding 15 and 6 gives 21 queries, and dividing 21 by 10 gives a node heat weight value parameter of 2.1. Comparing 2.1 with the heat threshold of 2.0, 2.1 is greater than 2.0, so the corresponding storage area is marked as a high heat addressing node, and a heat addressing mapping table containing the corresponding heat weight traceability node is generated.

[0034] S502: Based on the traceability node hot addressing mapping table, execute the tagging data retention period constraint rules, reside the frequently accessed pipeline traceability logs in the solid-state storage area, and migrate the outdated DMC quality files that have exceeded the target period to the high-capacity disk sector to obtain a cold and hot log isolated storage architecture. Extract the node heat weight value parameter from the traceability node heat addressing mapping table, combine it with the clock parameter, multiply the node heat weight value parameter by the time decay coefficient based on the data survival days to obtain the first product, subtract the storage space constant from the first product, and calculate the storage migration priority score. The node heat weight value parameter is 2.1, the record survival days is 60 days, the mapping time decay coefficient is 0.8, and the storage space constant is 0.5. Multiply 2.1 by 0.8 to get 1.68, subtract 0.5 from 1.68 to get 1.18, and obtain the storage migration priority score of 1.18. Compare 1.18 with the migration retention watershed threshold of 1.0. If the score is greater than 1.18, the data retention period constraint rule is triggered, and the records will be kept in the solid-state storage area. For archive data with a score less than 1.0 and a time since the date of the record being more than 90 days but within the 1-year retention period, a block transfer instruction is issued to the file management architecture to cut and transfer the data to the mechanical disk sector, thus completing the physical storage boundary division and obtaining a cold and hot log isolated storage architecture.

[0035] S503: Based on a cold and hot log isolated storage architecture, it records the query traversal path, solidifies the retrieval rules for single-item traceability codes and traceability source parameters issued through deep integration of software and hardware, and generates a full lifecycle error-proof and omission-proof traceability log table. Based on a cold and hot log isolated storage architecture, query index trees are built for datasets in different physical storage media. For records in solid-state storage areas, a key-value pair hash index is used to point to physical memory addresses; for records in mechanical disk sectors, a B-tree hierarchical search directory is built. The average hit probability of the solid-state storage area index is multiplied by the average seek time of the solid-state area to obtain the first product, and the average hit probability of the disk sector index is multiplied by the average seek time of the disk sector to obtain the second product. The first and second products are added together to evaluate the comprehensive retrieval response time parameter. The average hit probability of the solid-state storage area index is 80%, and the average seek time is 2 milliseconds; the average hit probability of the disk sector index is 20%, and the average seek time is 15 milliseconds. Multiplying 0.8 by 2 milliseconds gives 1.6 milliseconds, and multiplying 0.2 by 15 milliseconds gives 3.0 milliseconds. Adding 1.6 milliseconds and 3.0 milliseconds gives a comprehensive retrieval response time parameter of 4.6 milliseconds. Comparing 4.6 milliseconds to the maximum query latency threshold of 50 milliseconds, 4.6 milliseconds is lower than 50 milliseconds. The index allocation logic and cold / hot storage path parameters are solidified into a static retrieval rule file. Combined with the error-proof interlocking digital entity binding matrix and the single-item traceability code, the database structure is encapsulated and merged to generate a full lifecycle error-proof and omission-proof traceability log table.

[0036] Please see Figure 7 The laser engraving and marking data traceability system includes: The customized coding generation module is based on the automated injection molding production line of automotive parts. It obtains the DMC traceability coding parameter set by combining the product identifier and servo system code with the DMC variable generation rules through input and output monitoring and PLC capture of module network parameters. The digital entity binding module, based on the DMC traceability coding parameter set, performs local error prevention verification and intercepts duplicate sequences in the existing original database, locks the corresponding identity of the unique traceability code of the physical product, and obtains the error prevention interlocking digital entity binding matrix. The laser engraving and scanning analysis module is based on the foolproof interlocking digital entity binding matrix, which drives the visual marking template engine to perform laser engraving within the preset squares. It uses a professional barcode reader to intercept and analyze defective products below grade B and guides the hardware isolation of defective products to obtain the entity laser engraving analysis and rating dataset. The closed-loop rating association module is based on the physical laser engraving analysis rating dataset. Relying on the upper-layer information flow architecture, it traces the physical transmission link between injection molding temperature and code reading rating results across devices, and associates the product's inbound and outbound life cycle to obtain a set of traceability rating association detection links. The full lifecycle log archiving module, based on the traceability rating association detection link set, performs hierarchical management and retrieval addressing optimization on an industrial control computer storage medium with a preset capacity of gigabytes, to obtain a full lifecycle error-proof and omission-proof traceability log table.

[0037] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A method for tracing data from laser engraving and marking, characterized in that, Includes the following steps: S1: An automated injection molding and assembly production line for automotive parts intercepts mold action instructions through input / output monitoring and communication with a programmable logic controller. It analyzes the molding temperature, injection cycle, and mold base number captured by the target industrial network communication protocol to obtain a set of DMC traceability coding parameters. S2: Based on the adapted DMC traceability coding parameter set, local error prevention interlock verification is performed in the dedicated laser marking engine of the industrial control computer. The comparison is performed in the existing database to intercept serial number sequences with overlapping identities, lock the correspondence between physical component entities and unique digital information, and obtain the error prevention interlock digital entity binding matrix. S3: Call the anti-fool interlocking digital entity binding matrix, drive the visual marking template engine to guide the laser engraving machine to perform laser engraving action in the preset physical area, and simultaneously trigger the professional visual barcode reader to perform barcode scanning and rating logic, intercept and analyze defective samples with quality lower than the set level and drive the robot to perform real-time sorting to obtain entity laser engraving analysis and rating dataset. S4: Based on the entity laser engraving analysis and rating dataset, rely on the upper-layer network communication interface to capture the production status packets of the associated external equipment in the corresponding module cycle, extract the core variable nodes that cause the analysis rating to decline, and obtain the traceability rating associated detection link set.

2. The laser marking data traceability method according to claim 1, characterized in that, The adapted DMC traceability coding parameter set includes module-related attribute identifiers, DMC structured strings, and a 60-bit information character length limit vector. The error-proof interlocking digital entity binding matrix transmission includes serial number anti-collision verification bits, entity flow servo pointers, and single-item binding certificate sequences. The entity laser engraving analysis and rating dataset includes B-level and above compliant barcode scanning records, barcode reader optical contrast parameters, and defective product sorting isolation signal sources. The traceability rating associated detection link set includes injection molding parameter deterioration transmission weights, cross-process network communication verification frames, and a complete traceability chain for a single product.

3. The laser marking data traceability method according to claim 1, characterized in that, The specific steps for adapting the DMC traceability coding parameter set are as follows: S101: Based on the underlying input / output monitoring architecture and programmable logic controller communication network, it listens to the modular flow of automotive panels on the automated production line, extracts the timing of trigger signals and interlock status of six-axis and four-axis industrial robots during the grasping and handling process, and obtains the servo mechanical cooperative action sequence. S102: Based on the servo mechanical cooperative action sequence, the external injection molding equipment is connected through the Net100 industrial network communication protocol to capture the molding temperature fluctuation curve, injection cycle sequence and corresponding mold base physical number of the current processing mold, and obtain the mold production environment parameter package. S103: Call the module production environment parameter package, follow the data matrix code (DMC) combination generation rules, and encapsulate the product type identifier, current real-time date and time, specified number of incrementing serial number and servo system code in series, identify the independent identification code string, and generate an adapted DMC traceability coding parameter set.

4. The laser marking data traceability method according to claim 3, characterized in that, The specific steps of the error-proof interlocking digital entity binding matrix are as follows: S201: Based on the adapted DMC traceability coding parameter set, in the memory domain inside the industrial control computer, the string to be coded and the hash address domain are subjected to concurrent collision test to identify the conflict sequence that causes identity overlap and obtain the foolproof conflict candidate list. S202: Based on the aforementioned error-proof conflict candidate list, trigger the error-proof and leakage-proof safety interlock mechanism of the control architecture intermediate layer, stop the laser engraving action of the repeated risk sequence, calculate the consistency between the incremental step size of adjacent legal codewords and the production cycle, strip away the non-sequential isolated sequence, and obtain the confidence-based coding admission queue. S203: Call the confidence-based admission queue, allocate a fixed internal storage pointer to each admission sequence that passes the safety interlock, and forcibly lock the fixed-length logic code and the physical components on the pipeline that have been aligned in the early stage in the spatiotemporal coordinates to generate a foolproof interlock digital entity binding matrix.

5. The laser marking data traceability method according to claim 4, characterized in that, The specific steps for obtaining the entity laser engraving analysis and rating dataset are as follows: S301: Call the foolproof interlocking digital entity binding matrix, import the characters to be marked into the visual marking template engine, direct the industrial robot to complete the product alignment, drive the laser engraving machine to perform deep ultraviolet or fiber optic marking in the set area, and obtain the physical semi-finished product to be inspected by laser engraving. S302: Based on the laser-engraved physical semi-finished product to be inspected, activate the professional visual barcode reader at the back end to perform synchronous barcode scanning. Relying on the built-in mandatory quality assessment benchmark, the symbol contrast and axial deviation rating of the data matrix code DMC on the physical surface are performed. Samples that meet the required indicators are selected to obtain B-level or above compliant barcode scanning records. S303: Call the B-level and above qualified barcode scanning records, map the interception signals of substandard products to the on-site programmable logic controller, drive the sorting mechanism to perform real-time sorting and physical isolation of defective products, merge the parsing logs of qualified panels, and generate a physical laser engraving parsing rating dataset.

6. The laser marking data traceability method according to claim 5, characterized in that, The specific steps of the traceability rating associated detection link set are as follows: S401: Based on the physical laser engraving analysis and rating dataset, the processing records of the corresponding parts are traced back through the upper-level closed-loop information flow architecture. The correspondence between the visual code reading rating result and the communication timestamps at the moment of marking and the moment of injection molding is locked. The smoothness of the process flow across equipment is calculated, and the process timing flow fit vector is obtained. S402: Based on the process sequence flow fit vector, the implicit correspondence between abnormal fluctuations in molding temperature during the injection molding stage and the degradation of laser marking analysis is explored to obtain a cross-process parameter defect transmission topology diagram. S403: Based on the cross-process parameter defect transmission topology diagram, isolated records with network communication breaks are eliminated, and the continuous physical handling flow is linked with the qualified processing events determined by the complete underlying IO. The complete digital process of a single part from injection molding to barcode scanning and warehousing is mapped, and a traceability rating associated detection link set is generated.

7. The laser marking data traceability method according to claim 6, characterized in that, The coherent physical transport flow refers to connecting part processing events with continuous timestamp associations in the topology graph, eliminating isolated nodes with network communication breaks exceeding a set threshold, and obtaining a coherent physical transport flow.

8. The laser marking data traceability method according to claim 1, characterized in that, The method also includes step S5: S5: Call the traceability rating associated detection link set, and according to the mandatory traceability setting with a retention period of one year, perform block archiving of logs in a local storage medium with a capacity not less than a preset threshold. Based on the physical query frequency of the QR code traceability link, establish an error prevention and omission index mapping architecture to obtain the full life cycle error prevention and omission traceability log table. The full lifecycle error prevention and traceability log table includes a one-year mandatory retention status code, the distribution of sectors hit by tens of millions of searches, and the after-sales traceability addressing path.

9. The laser marking data traceability method according to claim 8, characterized in that, The specific steps for establishing the full lifecycle error prevention and traceability log table are as follows: S501: Call the traceability rating associated detection link set, access the underlying archive database configured in the industrial control computer, and calculate the original distribution weight based on the trigger frequency of the component after-sales traceability and defective product interception instructions to obtain the traceability node heat addressing mapping table. S502: According to the traceability node hot addressing mapping table, execute the tagging data retention period constraint rule, reside the frequently accessed pipeline traceability logs in the solid-state storage area, and migrate the old DMC quality files that have exceeded the target period to the high-capacity disk sector to obtain a cold and hot log isolated storage architecture. S503: Based on the aforementioned cold and hot log isolation storage architecture, record the query traversal path, solidify the retrieval rules for single-item traceability codes and traceability source parameters issued through deep integration of software and hardware, and generate a full lifecycle error-proof and omission-proof traceability log table.

10. A laser engraving and marking data traceability system, characterized in that, The system is used to implement the laser marking data traceability method according to any one of claims 1-9, and the system includes: The customized coding generation module is based on the automated injection molding production line of automotive parts. It obtains the DMC traceability coding parameter set by monitoring input and output and capturing module network parameters by PLC, and combining product identifier and servo system code with DMC variable generation rules. Based on the adapted DMC traceability coding parameter set, the digital entity binding module performs local error prevention verification and intercepts duplicate sequences in the existing original database, locks the corresponding identity of the unique traceability code of the physical product, and obtains the error prevention interlocking digital entity binding matrix. The laser engraving and scanning analysis module, based on the foolproof interlocking digital entity binding matrix, drives the visual marking template engine to perform laser engraving within a preset grid. It intercepts and analyzes defective products below grade B through a professional barcode reader and guides the hardware isolation of defective products to obtain a physical laser engraving analysis and rating dataset. The closed-loop rating association module is based on the entity laser engraving analysis rating dataset. Relying on the upper-layer information flow architecture, it traces the physical transmission link between injection molding temperature and code reading rating results across devices, and associates the product's entry and exit life cycle to obtain a set of traceability rating association detection links. The full lifecycle log archiving module, based on the traceability rating association detection link set, performs hierarchical management and retrieval addressing optimization on an industrial control computer storage medium with a preset capacity of gigabytes to obtain a full lifecycle error-proof traceability log table.

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