Garment production process flow identification coding method based on Internet of Things coding and tracing system thereof

Abstract

The invention provides a garment production process flow identification coding method based on Internet of Things coding and a tracing system thereof, and the method comprises the steps: responding to an initialization instruction of a garment production order, and generating a main identification code according to an order attribute, carrying out physical mapping binding on the main identification code and an Internet of Things carrier bearing a plurality of clothes materials of the same batch; acquiring real-time operation data of the production process nodes; judging whether the real-time operation data triggers a preset material discrete condition or not; if the main identification code is triggered, executing the code splitting logic, keeping the circulation path of the main identification code unchanged, and generating a virtual shadow traceability code in the traceability system based on the characteristic data of the discrete material; establishing a logic interlocking relationship between the main identification code and the shadow traceability code, and adding an unclosed-loop warning mark in metadata of the main identification code; and monitoring the circulation state of the shadow traceability code, and when a regression confirmation signal is received, executing the topology reconstruction logic and releasing the logic interlocking relationship.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and industrial Internet of Things (IoT) data processing technology, and in particular to a method for identifying and encoding garment production processes based on IoT coding and its traceability system. Background Technology

[0002] In the production management system of the modern garment manufacturing industry, to adapt to the needs of large-scale industrial manufacturing, factories generally adopt a group-based workflow model. This involves using RFID tags or other IoT carriers to bind dozens of identical and colored cut pieces into a single work unit, which serves as the smallest production batch and circulates between various processes such as cutting, sewing, ironing, and finishing. This management model can efficiently record production progress and calculate worker wages within a standardized linear production process, ensuring the real-time nature and accuracy of data collection.

[0003] However, in the complex real-world workshop environment, the production process often exhibits non-linear and dynamic characteristics. For example, during sewing or quality inspection, individual garment pieces may be temporarily separated from their original physical carriers due to fabric defects, oil stains during cleaning, dimensional deviations requiring rework, or destructive laboratory sampling. At this point, the physical materials have become discrete, but existing digital traceability systems often lack flexible response mechanisms, typically treating the entire batch as an indivisible data unit.

[0004] This leads to a mismatch between physical production data and digital information. If the system mandates that the entire batch of materials must wait for the individual components to be returned before continuing its flow, it will severely slow down the overall production line pace, causing production congestion. Conversely, if the system allows the main batch to continue flowing downstream even with missing quantities, the discrete materials will lose their identification and become detached from digital monitoring, becoming data blind spots. This not only makes inventory reconciliation difficult when finished products are received into the warehouse, but also prevents the accurate recording of the special processing history of these materials, resulting in gaps in the final product's lifecycle quality archives and making it difficult to meet the requirements of refined quality traceability management. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying and encoding the garment production process based on Internet of Things (IoT) coding, and a traceability system thereof, in order to solve the problems mentioned in the background art.

[0006] In a first aspect, the present invention provides a method for identifying and encoding garment production process flow based on Internet of Things (IoT) coding, the method comprising the following steps: In response to the initialization command of the garment production order, a main identification code is generated based on the order attributes, and the main identification code is physically mapped and bound to the Internet of Things carrier carrying multiple garment materials of the same batch. When the garment materials are transferred to the production process node, the real-time operation data of the production process node is acquired; Determine whether the real-time operation data triggers a preset material dispersion condition; If the material discrete condition is triggered, the code splitting logic is executed: the flow path of the main identifier code remains unchanged, and a virtual shadow traceability code is generated in the traceability system based on the characteristic data of the discrete material; Establish a logical interlock relationship between the main identifier code and the shadow traceability code, mark the shadow traceability code as pending regression, and add an unclosed loop warning mark to the metadata of the main identifier code; The circulation status of the shadow traceability code is monitored. When a return confirmation signal of the discrete material is received, the topology reconstruction logic is executed to merge the data branch of the shadow traceability code into the traceability chain of the main identifier code and release the logical interlock relationship.

[0007] Optionally, generating the main identifier code based on order attributes specifically includes: Extract the style number, color number, and production batch number from the garment production order; A preset encoding obfuscation algorithm is invoked to perform calculations on the style number field, color number field, and production batch number to generate a unique basic feature segment; Based on the standard quantity values ​​of the multiple garment materials from the same batch, a capacity check bit is added after the basic feature segment to generate the main identification code.

[0008] Optionally, the step of physically mapping and binding the main identifier code to the IoT carrier carrying multiple garment materials from the same batch specifically includes: Read the unique hardware identifier of the IoT carrier; Create a mapping table in the system's core database and write the correspondence between the main identifier code and the hardware unique identifier; Activate the read and write permissions of the IoT carrier and write the hash value of the main identifier code into the user data area of ​​the IoT carrier.

[0009] Optionally, determining whether the real-time operation data triggers a preset material dispersion condition specifically includes: The current actual quantity of materials passing through the station is obtained through a counting device or manual input interface set at the production process node. The actual quantity of materials passing through the station is compared with the theoretical quantity recorded in the main identification code; If the actual quantity of material passing through the station is less than the theoretical quantity, then the material dispersion condition is triggered.

[0010] Optionally, the real-time operation data may also include the process type code of the current process node; The method further includes: determining whether the current node is an irreversible processing node based on the process type code; If so, when the material dispersion condition is triggered, the access permission of the main identifier code in the next process node is locked simultaneously until a forced release instruction is received from the management terminal.

[0011] Optionally, the generation of a virtual shadow traceability code in the traceability system based on the characteristic data of discrete materials specifically includes: Obtain discrete timestamps, discrete process node IDs, and discrete material defect reason codes; Using the preset shadow generation rules, a virtual data object with a lifecycle countdown attribute is created as the shadow traceability code, with the main identifier code as the root node; The shadow traceability code does not correspond to any physical IoT carrier; it is only stored in the logical operation layer of the traceability system.

[0012] Optionally, the lifecycle countdown attribute is set based on the physical properties of the garment material. If the lifecycle countdown of the shadow traceability code reaches zero and the return confirmation signal is not received, the system automatically converts the shadow traceability code into a scrap status code and triggers the quantity reduction logic for the main identifier code.

[0013] Optionally, establishing the logical interlocking relationship between the master identifier code and the shadow tracing code specifically includes: A semaphore pointer is set in the flow status table of the main identifier code, and the semaphore pointer points to the memory address of the shadow tracing code; Configure the final inspection checkpoint rules of the traceability system, and force a check of the state of the semaphore pointer when the main identifier code is scanned; If the address pointed to by the semaphore pointer still contains a valid shadow tracing code, then an instruction to prohibit entry into the database is output.

[0014] Optionally, the execution of the topology reconstruction logic specifically includes: Verify the repaired material parameters carried in the regression confirmation signal; After successful verification, all process history data recorded by the shadow traceability code will be appended to the history record list of the main identifier code; Cancel the virtual object of the shadow traceability code and clear the unclosed-loop warning mark in the main identifier code.

[0015] Secondly, the present invention provides a garment production process identification and traceability system based on Internet of Things (IoT) coding, which is used to implement the method described in any one of the first aspects, characterized in that the system includes: The encoding initialization module is used to generate the main identifier code based on the order attributes and complete the physical mapping and binding with the IoT carrier; The data monitoring module is used to acquire real-time operational data of production process nodes and determine whether material dispersion conditions are triggered. The dynamic splitting module is used to generate a virtual shadow traceability code and execute the encoding splitting logic when the material discrete conditions are triggered. An interlock control module is used to establish a logical interlock relationship between the main identifier code and the shadow traceability code, and to add an unclosed-loop warning mark; The reconstructed closed-loop module is used to execute topology reconstruction logic, merge data branches, and release the logical interlocking relationship when a regression confirmation signal is received.

[0016] The present invention has achieved the following beneficial effects: This invention effectively resolves the contradiction between batch flow efficiency and individual refined management in garment production by constructing a coding system based on dynamic splitting and topology reconstruction. The system employs a virtual shadow traceability code mechanism, allowing the main production process to continue even when materials become discrete, without interrupting the main production flow. Simultaneously, it assigns independent logical identities to discrete materials for full-process monitoring, thus ensuring the efficient operation of the production line. By establishing a logical interlocking relationship between the main identification code and the shadow traceability code, this invention enforces closed-loop management of materials at the data level, ensuring that all discrete semi-finished products must undergo compliant return or scrapping before final warehousing, mitigating the risks of inventory discrepancies and material loss. Furthermore, the topology reconstruction logic of this invention can completely stitch back the special process data generated during the independent flow of discrete materials into the main traceability chain, achieving a complete restoration of the true production history of each garment, providing detailed and reliable data support for enterprise quality traceability and process optimization.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for identifying and encoding garment production processes based on Internet of Things (IoT) coding, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the composition structure of a garment production process identification and traceability system based on Internet of Things coding, as described in an embodiment of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] This embodiment provides a method for identifying and encoding garment production process based on Internet of Things (IoT) coding. This method is applied to a garment intelligent manufacturing and digital traceability system built on an Industrial Internet of Things (IIoT) architecture.

[0022] The traceability system mainly consists of a perception layer, an edge layer, a network layer, and a platform layer. The perception layer primarily includes IoT read / write terminals deployed at various process nodes of the garment production line (such as cutting and packaging stations, sewing stations, intermediate ironing tables, post-finish quality inspection stations, and finished product packaging lines). These IoT read / write terminals are composite intelligent terminals integrating UHF RFID (Ultra-High Frequency Radio Frequency Identification) read / write modules, high-precision infrared counting sensors, industrial-grade human-machine interface panels, and visual recognition cameras. The edge layer mainly consists of edge computing gateways deployed in the workshop, used to de-jitter, clean, format, and locally cache the massive amounts of raw data collected by the perception layer, reducing the computing load on the cloud and ensuring data security in weak network environments. The platform layer is deployed in a private or hybrid cloud server cluster, running the core algorithm engine of this invention, including an encoding generation service, a state machine transition engine, a shadow traceability calculation module, and a topology reconstruction service module. The core database adopts a hybrid storage architecture of relational databases (such as MySQL Cluster), time-series databases (such as InfluxDB), and key-value cache databases (such as Redis) to meet the needs of high-concurrency read and write operations and complex relational queries in the garment production process.

[0023] Based on the above system architecture, such as Figure 1 As shown, the method in this embodiment specifically includes the following steps: Step S101: In response to the initialization instruction of the garment production order, generate a master identifier code based on the order attributes, and physically map and bind the master identifier code to the Internet of Things carrier that carries multiple garment materials of the same batch.

[0024] In actual production scenarios, after the enterprise's APS (Advanced Planning and Scheduling) system completes the production scheduling, it will send a JSON format data packet containing production task details to this traceability system. The system listens for and captures the data packet, and identifies it as the initialization instruction for the garment production order.

[0025] Specifically, the system first calls the parser to extract key business attribute fields from the initialization instructions. These fields constitute the seed data for generating a unique code. The order attributes include at least: a style ID field (e.g., "2025-SPR-JKT001"), a color code field (e.g., "Pantone-19-4052"), a batch number (e.g., "PO20250509-A"), and a preset bundle number.

[0026] To enhance data security (preventing reverse engineering of plaintext encoding) and prevent unauthorized tampering of the encoding during wireless transmission, this embodiment does not use plaintext concatenation to generate the encoding. Instead, it invokes a preset encoding obfuscation algorithm. In a preferred embodiment, the execution logic of the encoding obfuscation algorithm is as follows: First, the extracted style number field, color number field, and production batch number are converted into a standard UTF-8 binary stream.

[0027] Secondly, a random salt value (Salt) is introduced based on the current microsecond-level timestamp and the server MAC address. This salt value is inserted into a specific position in the binary stream (such as the header or middle split point) to ensure that even identical order attributes can result in completely different basic feature segments when generated at different times.

[0028] Next, the salted binary stream is subjected to SHA-256 or SM3 hash operation to generate a 256-bit hash value. To accommodate the capacity limitations of the RFID tag EPC storage area (usually 96 or 128 bits), the system truncates and compresses the hash value, preferably extracting the middle 32 or 64 bits with the highest dispersion as the basic feature segment.

[0029] Finally, to reflect the quantity attribute of multiple items in the same batch and support offline verification, the system reads the standard quantity value of the bundle of materials specified in the order (e.g., 50 pieces), converts it into hexadecimal code, and calculates a 2-digit checksum using the CRC-16 algorithm. The system appends the quantity code and checksum to the end of the basic feature segment to generate the final main identifier code. This encoding structure possesses both global uniqueness and embedded quantity self-verification capability.

[0030] After the code generation is completed, the system performs a physical mapping and binding operation. This process is usually completed at the subcontracting station of the cutting bed or on the smart card issuing machine. The operator places a bundle of cut pieces (i.e., a bundle containing multiple garment materials from the same batch) on an IoT carrier (such as a bundle number tag, turnover box, or smart hanger with an embedded RFID chip).

[0031] The system controls the RFID reader to transmit radio frequency signals and read the unique hardware identifier embedded in the TID (TagIdentifier) ​​area of ​​the IoT carrier. This identifier is pre-set by the chip manufacturer and has the characteristic of being physically unclonable.

[0032] Subsequently, the system creates a new record in the carrier mapping table of the core database, using the software-generated master identifier code as the primary key and the read hardware unique identifier as the foreign key, and records the binding timestamp and operator ID.

[0033] It is worth noting that, in order to build a closed-loop offline authenticity verification mechanism, the system will use the random salt value generated on the server side mentioned above when performing physical mapping binding. Using the encrypted write command of the reader, a specific protected address range (e.g., address block 0x10-0x18 of the User MemoryBank) is synchronously written into the User area (user data area) of the IoT carrier (RFID tag). This write operation enables the handheld terminal to obtain the salt value by reading and decrypting the User area in a network-free environment, and then reproduce the hash operation by combining it with the public data in the tag's EPC area. At this point, the logical verification formula for offline verification is defined as: in, This represents the hash feature digest value generated locally by the offline terminal; This indicates the system's preset standard digest algorithm; in this embodiment, the SHA-256 algorithm is used. This indicates that the terminal reader directly reads the main identifier encoded plaintext string from the tag EPC storage area; This represents a string concatenation operation; This represents the random salt value (32-bit hexadecimal string) that the terminal reads and decrypts from the protected address segment of the User tag.

[0034] Only when locally calculated The terminal determines that the tag is genuine only when it is completely consistent with the feature segments pre-stored in the tag.

[0035] To ensure data consistency between the cloud, edge, and endpoint, the system further activates write permissions for the User area (user data area) of the IoT carrier. The complete value of the master identifier code (or its compressed index) is written to this area via an over-the-air encrypted channel, and a readback verification command is immediately executed. Only when the readback data is completely consistent with the written data does the system confirm successful binding and control the indicator light on the card issuer to turn green or unlock the mechanical device, allowing the bundle of materials to enter the production flow process.

[0036] Step S102: When the garment material flows to the production process node, obtain the real-time operation data of the production process node.

[0037] Once the bound materials enter the production line, their flow process is discretized into a series of inbound-processing-outbound events. Whenever the material physically moves to a new production process node (such as a sewing machine table or ironing table), the IoT sensing terminal at that node automatically captures the carrier signal, triggering the data acquisition process.

[0038] The acquisition described in this step is a fusion and sensing process of multi-source heterogeneous data. The real-time job data acquired by the system is a structured data object, specifically containing information in the following dimensions: Identification and location information: The main identification code parsed from the RFID reader, and the process node ID currently bound to the reader. The system uses this information to locate the material's real-time position on a digital map.

[0039] Time dimension information: Inbound and outbound timestamps accurate to milliseconds, used to calculate process time.

[0040] Quantity information: The system obtains the current actual quantity of materials passing through the station through various means. For highly automated equipment (such as automatic bagging machines), the system reads the counter value in the PLC register via the OPC UA protocol; for manually assisted equipment, the system counts the obstruction pulses of the infrared photoelectric sensor; for purely manual workstations, the system prompts the worker to confirm or correct the quantity via a pop-up window on the workstation panel. To prevent false alarms, the system has a built-in filtering algorithm to eliminate abnormal pulses caused by garment shaking or repeated scanning.

[0041] Process status information: The system also collects the equipment operating parameters of the current node (such as motor speed, temperature, and pressure) and the process type code. The process type code is directly related to the subsequent discrete judgment logic, especially for the identification of critical or irreversible processes.

[0042] Step S103: Determine whether the real-time operation data triggers the preset material dispersion condition.

[0043] In traditional MES systems, materials are typically assuming to flow in bundles, meaning that if 50 pieces enter the station, 50 pieces should also leave. However, this invention addresses frequent exceptions in actual production, such as rework, spot checks, loss, and replenishment.

[0044] Specifically, the system incorporates a real-time stream processing engine to execute discrete condition judgment logic. The specific judgment process is as follows: First, the system queries the core database or local cache to obtain the theoretical number of iterations of the main identifier code at the end of the previous process. Note that this theoretical number of iterations is dynamically updated and equals the number of outbound iterations of the code at the previous qualified node.

[0045] Secondly, the system compares the current actual material quantity passing through the station obtained in step S102 with the theoretical flow quantity.

[0046] If the two are equal, it is determined to be a normal flow, the system updates the current position status of the main identifier code, and allows it to proceed to the next node.

[0047] If the actual quantity of materials passing through the station is less than the theoretical flow quantity, the system enters the second-level verification logic. The system will verify this by combining the manual input signals on the workstation tablet. If the worker does not click the scrap or rework button on the tablet, but the sensor data shows a shortage, the system will first trigger a verification alarm, prompting the worker to recount. If the worker confirms that the quantity has indeed decreased (for example, 3 pieces of clothing were removed due to oil stains), or the worker actively enters a command to remove 3 pieces on the tablet, the system will determine that the preset material dispersion condition has been triggered.

[0048] Specifically, the real-time operation data also includes the process type code of the current process node. The system will determine whether the current node is an irreversible processing node (such as bag opening, buttonhole, dyeing, etc., which cannot be reversed once processed) based on this code.

[0049] If it's an irreversible processing node and triggers material dispersion conditions (e.g., 100 pieces enter the dyeing vat, 98 pieces exit), the system, in addition to performing regular dispersion processing, will simultaneously trigger an access control mechanism. The system will immediately issue a blacklist instruction to the next process node on the main identifier's flow path, locking its access permissions. This means that until management intervenes to determine the whereabouts of the two missing pieces (whether they are completely scrapped or merely left inside the processing equipment) and clears the alarm in the system, the remaining 98 pieces, even if they arrive at the next station, will trigger an alarm and refuse to begin operation. This mechanism effectively prevents materials with inconsistent process states from being mixed into the finished product in subsequent stages, causing serious quality incidents.

[0050] Step S104: If the material discrete condition is triggered, the code splitting logic is executed: the flow path of the main identifier code remains unchanged, and a virtual shadow traceability code is generated in the traceability system based on the characteristic data of the discrete material.

[0051] When discrete conditions are triggered, this invention introduces a code splitting and shadow tracing mechanism.

[0052] The specific execution logic is as follows: First, the main process remains unchanged: For materials that pass through the station normally (e.g., 47 out of 50), the system maintains the corresponding master identifier code's flow path, priority, and process parameters completely unchanged. The system only updates the current quantity attribute of the master identifier code in the database (from 50 to 47). This ensures that the main production line's flow rhythm is unaffected, and the reader can still identify and release materials normally, thus effectively maintaining the production line's flow efficiency.

[0053] Second, shadow code generation: For the three discrete materials, which have physically separated from their original RFID carriers, the system dynamically instantiates a new data object, namely the shadow traceability code, based on the characteristic data of the discrete materials in the logic operation layer (memory and database).

[0054] The generation of shadow traceability codes based on the feature data of discrete materials specifically includes: The system extracts the timestamp of the discrete event, the process node ID of the discrete event, and the specific defect reason code of the discrete material (such as "Code_03: oil stain cleaning" and "Code_09: dimension verification").

[0055] To reflect the topological relationships between data, shadow tracing codes typically use a format of primary identifier code + split suffix. For example, if the primary code is BATCH2025-001, the shadow code is generated as BATCH2025-001_SHADOW_01. This naming convention allows the system to quickly locate its parent node with a time complexity of O(1) during subsequent retrievals.

[0056] It is worth noting that this shadow traceability code is initially a purely virtual object, and it is not bound to any physical tag at the moment (unless the discrete material is placed in a dedicated smart rework box). It is merely stored in the logical operation layer of the traceability system as a task handle to be processed.

[0057] Third, lifecycle management: In order to prevent shadow codes from occupying system resources indefinitely or causing data redundancy, the system assigns a key attribute to shadow tracking codes, namely lifecycle countdown (TTL, Time To Live).

[0058] The TTL is not a fixed value, but is dynamically calculated by the system based on the physical properties of the garment material. The system maintains a fabric property database that records the physical properties (such as moisture regain, heat shrinkage rate, and elastic recovery time) of different fabrics (such as pure cotton, polyester, silk, and shape memory metal yarn).

[0059] For example, for memory metal wire fabrics, which require a longer cooling and setting time after repair and ironing, the system will query parameters and automatically set the TTL to 4 hours; while for ordinary cotton fabrics, the TTL may be set to 24 hours.

[0060] The system's background daemon process will decrement the TTL in real time. If the countdown reaches zero and the shadow code has not returned, the system will determine that the material has been substantially lost, automatically change its status to a scrap status code, and trigger the quantity reduction logic for the main identifier code (sending a loss report to the ERP), thereby achieving a fully automated abnormal closed loop.

[0061] Step S105: Establish a logical interlock relationship between the main identifier code and the shadow traceability code, mark the shadow traceability code as pending regression, and add an unclosed loop warning mark to the metadata of the main identifier code.

[0062] Generating only shadow codes is insufficient; it is essential to prevent the primary material from being incorrectly completed and entered into inventory when it is missing. Therefore, this step establishes an interlocking relationship. The specific implementation method is as follows: In the primary identifier encoding record structure of the core database, the system allocates a dedicated pointer field (or list head). When a shadow code is generated, this pointer points to the memory address of the shadow code or the database primary key ID. This creates a strong reference in the data structure, where a parent node points to a child node.

[0063] The system updates the status bit of the shadow tracking code to "pending regression".

[0064] The system adds a high-priority incomplete loop warning flag to the metadata of the master identifier code. This flag is a Boolean value (True / False). This flag is linked to the final inspection checkpoint rules of the traceability system. When the master identifier code (carrying 47 materials) reaches the end of the production line, namely the finished product warehousing stage, the warehousing scanning device will read the code and request server verification. The server's logic engine will forcibly check the incomplete loop warning flag of this code.

[0065] If the mark is True, and the corresponding shadow code is found to still exist and not closed through the semaphore pointer, the system will strictly execute the logic interlock: send the "Do Not Open" command to the warehouse gate, and pop up a red warning box on the operation terminal to indicate that there are still 3 shadow materials in this batch that have not returned, and the warehouse entry is prohibited.

[0066] This mechanism forces production managers to process discrete materials (either by retrieving them or by confirming their scrapping in the system) before completing the warehousing of the master order, thus ensuring the accuracy of inventory data.

[0067] Step S106: Monitor the circulation status of the shadow traceability code. When a return confirmation signal of the discrete material is received, execute the topology reconstruction logic, merge the data branch of the shadow traceability code into the traceability chain of the main identifier code, and release the logical interlock relationship.

[0068] The specific operation procedure for this step is as follows: After the three discrete materials are processed in the rework area, the workers return them to the production line (usually back to the node where the discreteness occurred, or directly to the packaging area). The workers scan the master identification code using a PDA and select the shadow regression function, or scan the temporary document attached to the rework material. The system receives this signal, which is then considered a regression confirmation signal.

[0069] The system then initiates topology reconstruction logic, which includes the following sub-steps: The system first verifies the repaired material parameters carried in the regression confirmation signal. For example, it checks whether the quality grade after rework is acceptable and whether the regression quantity matches the quantity recorded in the shadow code. If the quantities do not match (e.g., 3 items were reworked, only 2 were returned, and 1 was defective), the system will require the worker to perform a scrap operation before reworking.

[0070] Shadow traceability codes may generate their own process data (e.g., stain removal at 14:00, secondary ironing at 14:30). The system doesn't simply delete the shadow code; instead, it appends all its recorded historical data as a timestamped sub-branch to the main identifier's process route traceability tree. This way, in future quality traceability, users can clearly see that most products in this batch underwent the standard process, but three pieces were discrete at node A, underwent processing at nodes B and C, and then reverted at node D.

[0071] After data merging, the system deregisters the virtual object of the shadow traceability code (releasing memory resources), adds back the number of main identifier codes (47+3=50), and clears the unclosed-loop warning flag. At this point, the logical interlock is released, the main identifier code returns to normal, and can successfully pass the final inspection and be stored in the warehouse.

[0072] Furthermore, this invention provides a garment production process identification and traceability system based on Internet of Things (IoT) coding. For example... Figure 2 As shown, the system includes the following core functional modules: Encoding Initialization Module 201: This module integrates a Hardware Abstraction Layer (HAL) to adapt to RFID reader / writer devices from different brands. Internally, it runs a high-efficiency concurrent queue to handle large batch card issuance requests from the MES (Management Execution System). This module also includes a Key Management Service (KMS) for storing and rotating the salt and key required for the round-robin encoding obfuscation algorithm, ensuring the security of the generated encoding.

[0073] Data Monitoring Module 202: This module is the system's data throughput center. It adopts an NIO (Non-blocking I / O) communication model, capable of maintaining long-term connections with thousands of IoT terminals simultaneously. Internally, this module deploys a CEP (Complex Event Processing) engine for real-time data stream analysis. For example, when receiving continuous RFID read events, the CEP engine aggregates them according to a preset time window (e.g., 500ms), identifies the business semantics of inbound and outbound traffic, and filters out invalid signal jitter.

[0074] Dynamic Splitting Module 203: Upon receiving a discrete trigger signal, it dynamically constructs a shadow object in memory and allocates an independent thread for its lifecycle monitoring. This module maintains a real-time connection with the fabric property database to quickly obtain TTL parameters and start a countdown timer when the shadow object is instantiated.

[0075] Interlock control module 204: This module maintains a global state table, recording the association relationships between all master codes and shadow codes. It employs distributed lock technology (such as the Redis-based RedLock algorithm) to ensure atomicity in querying and modifying interlock states during concurrent operations across multiple terminals, preventing inconsistencies caused by data contention.

[0076] Reconstruction Closed-Loop Module 205: This module is responsible for the final archiving of data. It includes an ETL (Extract, Transform, Load) job engine. When performing topology reconstruction, this engine converts hot data (shadow data in memory) into cold data format (JSON or XML) and persists it to the historical database. Simultaneously, this module is responsible for generating final production reports and quality traceability reports, which are pushed to the enterprise's ERP system via API.

[0077] Furthermore, in actual factories, wireless networks may experience localized blind spots or temporary malfunctions. To prevent network outages from causing production stoppages, this invention introduces an offline shadow mechanism.

[0078] When the handheld PDA detects that it cannot connect to the server, it will automatically switch to local mode. At this time, if material discrepancies occur (such as workers picking out defective products), the PDA will call a local lightweight algorithm to generate a temporary shadow code and store it in the PDA's local SQLite database. At the same time, the PDA will use the RFID write function to write a specific offline discrete marker bit to the User area of ​​the tag where the main identification code is located.

[0079] In this way, even if the main tag moves to the next workstation, the reader will know that there is unsynchronized discrete data in this batch when it reads the tag bit, and thus issue a local warning. When the network is restored, the PDA will automatically trigger the data synchronization service, upload the local shadow code to the server, and the server will then execute the subsequent standard interlocking logic.

[0080] Furthermore, in certain extreme cases, the discretized material (shadow code) may discretize again during the rework process. For example, out of three garments being reworked, one may have a serious quality issue requiring destructive testing in a laboratory, making rework impossible.

[0081] To address this situation, the system's dynamic splitting module supports recursive splitting. That is, the shadow tracing code can act as a parent node to further split into secondary shadow codes.

[0082] In terms of data structure, this is represented by a multi-branch tree. The primary identifier is the root, first-level shadows are branches, and second-level shadows are leaves. The system's interlock control module traverses the entire tree, and the interlock of the root node is only released after all leaf nodes have been returned or discarded (i.e., all leaves in the tree have been pruned or merged). This recursive design enables the system to adapt well to arbitrarily complex production flow paths.

[0083] In actual garment production workshops, especially in high-density overhead conveyor lines (where workstation spacing is often less than 1.2 meters) or mixed-flow production modes, simple RFID reading often faces problems such as bypass crosstalk, multipath effects, and human occlusion. If these interferences cannot be accurately eliminated, real-time operational data will be distorted, leading to false triggering of discrete decisions. To ensure data accuracy, this invention constructs a filtering and cleaning mechanism based on temporal sliding windows and spatial fingerprints at the edge computing layer. This mechanism specifically includes the following rigorous signal processing steps: The first step is to establish a timing sliding window. When the edge gateway detects an RSSI (Received Signal Strength Indicator) signal with a master identifier code, it does not immediately trigger an inbound event, as this is very likely a momentary reflected signal. The system initiates a sliding window of length... A time window (e.g., 800 milliseconds) is used. Within this window, the gateway continuously collects the raw data packets reported by the reader at a sampling period of 20 milliseconds, forming a four-dimensional feature sequence containing [timestamp, RSSI value, phase angle, antenna port number].

[0084] The second step is to perform ownership arbitration based on spatial fingerprints. In densely populated workstation scenarios, two adjacent workstations (such as workstation A and workstation B) may simultaneously read the same moving material tag. In this case, the system calls upon a pre-built spatial fingerprint database. This fingerprint database is a signal model generated during the equipment deployment phase using standard tags calibrated at different positions, heights, and speeds. The system inputs the collected four-dimensional feature sequence into the fingerprint matching algorithm to calculate the Euclidean distance between this sequence and the standard fingerprints of workstations A and B.

[0085] Since the collected feature data contains different dimensions, this system adopts the Min-Max normalized weighted Euclidean distance algorithm.

[0086] First, the system normalizes the collected feature data using the following formula: in, The value is the normalized value; These are real-time measured values; and These are the minimum and maximum boundary constants of the feature in the system's historical database (for example, the boundaries of RSSI are set to -95dBm and -30dBm).

[0087] Subsequently, the weighted distance between the real-time feature vector and the standard fingerprint vector is calculated. The formula is as follows: in, This represents the dimensionless matching distance between the real-time signal and the standard fingerprint of the workstation; the smaller the value, the higher the matching degree. These are preset weighting coefficients, which are set in this embodiment. (Intensity weight) (Volatility weight) (Phase weight), and satisfy ; This represents the arithmetic mean of the RSSI signals within the real-time acquisition time window, calculated using the normalization formula described above. This represents the normalized value of the arithmetic mean of the RSSI signals in the standard fingerprint preset at this workstation. This represents the normalized value of the standard deviation of the RSSI signal (reflecting the degree of signal jitter) within the real-time acquisition time window. This represents the normalized value of the standard deviation of the RSSI signal in a standard fingerprint; This represents the cumulative absolute value of the phase angle change within the real-time acquisition time window, and the normalized value. This represents the cumulative absolute value of phase angle changes in a standard fingerprint, plus the normalized value.

[0088] Specifically, if the mean RSSI value of station A remains between -45dBm and -55dBm, and the phase angle exhibits a continuous change pattern of approaching, dwelling, and moving away; while the mean RSSI value of station B is below -70dBm and has extremely large fluctuation variance (manifesting as multipath reflection characteristics), the system will determine that the physical location of the material is within the effective working area of ​​station A, and will determine that the signal from station B is crosstalk interference and discard it. This logic effectively solves the data drift problem caused by inadequate physical isolation.

[0089] The third step is de-jitter filtering for false signals. Data stability is crucial for determining whether material discrepancies have been triggered. The system introduces state-preservation logic: when a master identifier code is determined to be in the station, the system establishes a heartbeat session for it in memory. As long as the reader captures any valid signal for that code again within a preset survival threshold (e.g., 5 seconds), the last active time of the session is refreshed. If there are consecutive signal losses (e.g., a worker briefly obscuring the tag), as long as the duration does not exceed the threshold, the system maintains the material's in-station status and will not falsely report abnormal discrepancies. Only when the signal loss duration exceeds the threshold, and the auxiliary infrared counting sensor confirms that there are no obstructions in front of the workstation, does the system officially generate an outgoing event and trigger subsequent quantity verification logic. This ensures that the system maintains extremely high data accuracy even in harsh electromagnetic environments, providing a solid data foundation for subsequent discrepancy judgments.

[0090] In traditional garment manufacturing execution systems (MES), production codes often use a simple plaintext rule of year, month, day + serial number (e.g., 202505090001). This approach presents security risks and performance bottlenecks in high-frequency turnover scenarios such as flexible manufacturing: First, plaintext transmission is vulnerable to external interception and data parsing, resulting in low system information security; second, simple incremental sequences are prone to primary key conflicts when generated by distributed multi-server architecture, leading to database deadlocks; and third, the EPC storage area of ​​RFID tags has limited capacity, and excessively long uncompressed codes significantly reduce the reader's inventory counting speed.

[0091] To address the aforementioned problems, this invention proposes a three-layer architecture for encoding generation based on feature extraction, salting obfuscation, and capacity verification. The following are the detailed execution steps of this method at the system computation level: The system first receives raw order data objects from the Enterprise Resource Planning (ERP) system. The system's parsing engine extracts three core feature fields from this data object: Extract the style number and color number fields. These two fields represent the visual-physical attributes of the garment. The system removes special characters and converts them into a continuous ASCII code stream.

[0092] Extract the production batch number. This field represents a time-related attribute.

[0093] Extract the batch number. This is the smallest physical unit in garment production, representing a collection of the same layer of cut pieces.

[0094] To eliminate calculation errors caused by differences in field lengths, the system calls a preset padding alignment algorithm. If the extracted field length is less than the preset number of bits, the system uses the PKCS#7 standard for zero-byte padding; if it exceeds the preset number of bits, a sliding window sampling method is used to extract the most distinguishable character fragments. Subsequently, the system concatenates the processed fields in the order of style number-color number-batch number-bundle number to form a fixed-length basic feature binary block.

[0095] To completely disrupt the regularity of the data and prevent reverse engineering, the system introduces a dynamic salt value. The salt value is generated by XORing the current millisecond-level timestamp, the last four bits of the server's network card MAC address, and the current thread ID. This salt value generation mechanism leverages the physical unpredictability and temporal non-repeatability of the hardware environment, ensuring that even when two servers simultaneously process the exact same order attributes, the generated intermediate variables will be completely different.

[0096] The system inserts the generated salt value into a specific bit offset position in the basic feature binary block, performing a bit-level obfuscation operation. Subsequently, the system calls the SHA-256 digest algorithm to process the obfuscated data block, generating a 256-bit hash value. Since the EPC storage area of ​​UHF RFID tags has limited capacity (typically 96 or 128 bits), and excessively long encoding increases the bit error rate of radio frequency transmission, this invention introduces a high-low bit folding compression logic: the 256-bit hash value is divided into high 128 bits and low 128 bits; these two parts are XORed to obtain a 128-bit intermediate value; this 128-bit intermediate value is then divided into high 64 bits and low 64 bits and XORed again; finally, a 64-bit basic feature segment is obtained. This feature segment has extremely high dispersion; any small change in the original order information will cause a significant change in this feature segment, thus mathematically guaranteeing the global uniqueness of the encoding and effectively masking business meaning, achieving data anonymization.

[0097] In garment production, the quantity carried by a bundle of cut pieces (i.e., an IoT carrier) is standardized, but the quantity can change due to losses during circulation. To support offline terminals (such as handheld devices in a network-free state) in quickly verifying whether the current carrier has been reduced or its data has been tampered with, this invention embeds a capacity check bit in the encoding.

[0098] The system reads the standard quantity value from the order and converts it into an 8-digit hexadecimal number. Then, the system introduces a modulo-based check factor generation formula, which uses a preset large prime number and the current day's date offset for calculation.

[0099] Specifically, the capacity check bit The formula for generating it is: in, This represents the calculated 8-bit capacity check code value (decimal integer). The decimal integer value representing the standard quantity of the bundle of materials specified in the order (e.g., 50); This represents the first weighted prime number, which is preferably 31 in this embodiment; This represents arithmetic multiplication. This represents the bitwise XOR logical operator; This represents the date offset, specifically the day-in-day ordinal number of the current date in the current year according to the server's system time (UTC+8) (range 1-366). represents the second weighted prime number, which is preferably 17 in this embodiment; mod represents the modulo division operation; This represents the radix for the modulo operation. To accommodate the 8-bit parity bit length, it is set to 256 in this embodiment.

[0100] This formula, through the introduction of prime number weighting and time offset, ensures that even if the quantities are the same, the check codes for different dates will undergo non-linear transitions.

[0101] The calculated verification result is appended to the end of the aforementioned 64-bit basic feature segment. The final combined main identifier encoding structure includes a prefix identifier, basic feature segment, capacity check bit, and redundancy check bit.

[0102] The dual finite state machine in this embodiment runs in server memory and ensures that every discrete material is under digital supervision through precise state transition control. Specifically, it includes: Master Object State Machine: This state machine manages the lifecycle of the master identifier code and includes the following key states: Initialization state: In response to the initialization instruction of claim 1, it is encoded and generated but not written into the carrier.

[0103] Bound status: Physical mapping binding completed, waiting to enter the production line.

[0104] In flow state: The quantity of material is consistent with the theoretical value, and it flows normally between various process nodes.

[0105] Interlock Alert State: When step S103 determines that the material dispersion condition has been triggered, and step S104 generates the shadow traceability code, the main object state machine is forcibly transitioned to this state. In this state, a dangling pointer to the list of shadow objects is implanted into the metadata of the main identifier coded object. The system will implement access restrictions on codes in this state: when the code reaches a critical node (such as dyeing into the vat, finished product into the warehouse), the reading device will initiate a status query to the server. The interlock alert state code returned by the server will directly trigger the PLC controller to lock the physical channel (such as lowering the barrier), strictly prohibiting it from continuing to flow until the interlock is released.

[0106] Closed-loop archive state: The state machine transitions to this state only after all associated shadow objects have returned and the topology reconstruction is completed, marking the completion of the order.

[0107] Shadow Object State Machine: This state machine manages virtual shadow objects. Its state transitions are more complex and time-sensitive, and include the following key states: Derivative state: generated by cloning the main object at the instant the discrete event occurs.

[0108] Countdown State: Corresponds to lifecycle management. When a shadow object enters this state, the system starts a countdown task based on a time wheel. The duration of this task is determined by the physical properties of the fabric. If the state does not change before the countdown reaches zero, an expiration event will be automatically triggered.

[0109] Processing state: When discrete materials are scanned by a handheld terminal in the rework area or testing laboratory, the state transitions to processing state. At this time, the countdown is paused or reset to a specific rework duration.

[0110] Regression State: Upon receiving a regression confirmation signal and undergoing quality verification, the state transitions to the regression state. This state is a necessary condition for triggering the main object to release the interlock.

[0111] Scrapped state: If the material cannot be repaired or the timeout period expires, the state will transition to scrapped state and trigger the loss deduction interface of the ERP system.

[0112] The main object and shadow objects maintain real-time communication through dangling pointers. For example, when a shadow object transitions from the processing state to the returning state, it sends a notification to the main object via the observer pattern. Upon receiving the notification, the main object iterates through all its dangling pointers. Only when all pointers point to shadow objects in the returning or discarded state does the main object automatically restore its own state from the interlocked alert state to the transition state, thereby releasing the physical interlock.

[0113] The execution topology reconstruction logic in this embodiment uses a dynamic directed acyclic graph as the underlying data storage model.

[0114] In the system's graph database, each process event (such as cutting at the cutting table, sewing entering the station, and quality inspection leaving the station) is defined as a graph node, while the material flow relationship is defined as a directed edge.

[0115] During normal operation, the graph generated by the main identifier encoding is a single chain. When a discrete condition is triggered at a node (e.g., 3 out of 50 items need to be reworked), the system performs a node splitting operation: a new directed edge is drawn from that node, pointing to a new virtual node. This edge is marked as a splitting edge and carries the discrete quantity and cause attribute. At this point, the main chain continues to extend, while the branch chains begin to grow independently, forming a subgraph structure.

[0116] When performing topology reconstruction, the system needs to seamlessly merge the aforementioned subgraphs back into the main graph. The specific algorithm steps are as follows: The system extracts the timestamps of all nodes in the subgraph and compares them with the timeline after the split point in the main graph. The system checks for logical paradoxes of time reversal (e.g., the rework end time is earlier than the discrete start time). If such paradoxes exist, an error is reported and regression is blocked.

[0117] The system locates the corresponding node in the main graph based on the physical location of the regression confirmation signal. The algorithm establishes a directed edge from the end node of the subgraph to the anchor node of the main graph, which is marked as the merge edge.

[0118] The algorithm starts from the merge point and traverses all subsequent nodes in the main graph. For each node, the system recalculates its cumulative processing quantity attribute, adds back the regressed material quantity, and appends the process parameters generated in the subgraph (such as the secondary high-temperature setting temperature) as extended attributes to the main graph node.

[0119] The system serializes the reconstructed complete DAG graph, calculates its root hash value, and stores it in a write-once, read-many storage medium.

[0120] Furthermore, when implementing the lifecycle countdown based on the physical properties of the garment fabric, the system incorporates a fabric property expert knowledge base, mapping fabric attributes to lifecycle countdown (TTL) calculation factors. The TTL calculation formula comprehensively considers the following factors: Basic buffer time: The default value is a multiple of the standard workshop cycle time, such as 4 hours.

[0121] Elasticity factor: For highly elastic fabrics (such as Lycra and spandex). These fabrics require resting to recover after stretching during repair, otherwise dimensional deviations will occur. For example, for fabrics containing more than a certain proportion of spandex, this factor is positive, thus extending the countdown and giving the fabric sufficient physical recovery time.

[0122] Recovery factor: For memory fabrics or fabrics prone to fading (such as silk and Tencel). These fabrics require cooling and shaping after high-temperature repair, necessitating additional cooling time.

[0123] Process compensation: The difficulty of rework varies depending on the process. For example, the compensation time is longer for a complex bag opening process, while the compensation time is shorter for a simple button sewing process.

[0124] When the system generates a shadow traceability code, it automatically reads the fabric composition ratio from the order's bill of materials and substitutes it into the formula to calculate the TTL value accurate to the minute. The TTL (Time To Live) calculation formula is specifically defined as a linear weighted compensation model: in, This indicates the calculated lifespan of the shadow tracking code, in minutes. This indicates the standard cycle time for the current process, which is read from the production scheduling parameter table of the MES system (e.g., 240 minutes). The recovery weighting factor of the high-elasticity fabric is used to compensate for the shrinkage time; in this embodiment, it is preset to 0.5. Indicates multiplication operation; This indicates the fabric's elasticity coefficient, which is the spandex content in the order BOM. The value is 1.0 if the condition is met, and 0 otherwise. The cooling and shaping weighting factor for memory fabric is preset to 0.8 in this embodiment; This indicates the characteristics of memory fabric. The value is 1.0 when the fabric contains memory yarn or Tencel, and 0 otherwise. This represents a mathematical addition operation; This indicates the total number of rework processes required for discrete materials. Indicates the first The index of each rework process (values ​​from 1 to N); Indicates the first The standard operating hours for each rework process are obtained from the system's Standard Operating Hours (GST) database, in minutes.

[0125] For example, a batch of silk stretch satin women's clothing was lost during the ironing process. The system identified that the fabric contained a high proportion of silk and a small amount of spandex. The TTL value calculated by the system will be significantly longer than that of ordinary cotton fabric. The system sets a countdown accordingly. If the shadow code does not return after this time, the system determines that the material may have undergone irreversible physical deformation or been lost, and automatically triggers a scrap warning.

[0126] In actual production, special processes such as washing, printing, and singeing often require transporting semi-finished products out of the factory and sending them to third-party outsourcing factories for processing. Maintaining the logical connection between the main identification code and the shadow traceability code when the physical location is changed is another core technical problem that this invention addresses.

[0127] When a container carrying semi-finished products passes through the factory's outbound gate, the system automatically identifies the IoT carrier on it. At this point, the system's workflow engine determines whether the next process is an outsourced process. If so, the system does not simply record the shipment, but instead executes the digital passport generation logic.

[0128] The system packages the batch's main identification code, the current quantity of materials attached, and all unclosed shadow traceability code information into an encrypted outsourcing transfer token. This token is then pushed to the receiving system at the outsourcing factory via a secure network channel, or a QR code is generated and printed on the handover document.

[0129] During outsourced processing, if the outsourcing factory reports losses (e.g., damaged printing plates), the operator of the outsourcing system scans the QR code on the handover form or receives a token to record the loss information. At this time, the cloud platform of this invention receives a cross-domain signal and remotely triggers remote splitting logic. Although the physical carrier is not in the factory, the system will still generate a new shadow traceability code in the core database for the main identifier and mark the source as outsourced loss.

[0130] More importantly, when outsourced processing is completed and materials are returned to the factory for warehousing, the warehousing reader will read the IoT carrier again. At this time, the system will trigger a two-way handshake verification: on the one hand, it verifies whether the unique identifier of the physical tag is consistent with that when it was shipped out, preventing the carrier from being replaced without authorization; on the other hand, the system will download the list of remote shadow codes generated during outsourcing and force the factory's receiving personnel to physically confirm the outsourcing loss. Only when the receiving personnel click to confirm on the terminal that the loss is correct or the physical item has been replenished will the system perform topology reconstruction, stitching together the data breakpoints during outsourcing and allowing the batch to be reintegrated into the factory's production flow.

[0131] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for identifying and encoding garment production process flows based on Internet of Things (IoT) coding, characterized in that, The method includes the following steps: In response to the initialization command of the garment production order, a main identification code is generated based on the order attributes, and the main identification code is physically mapped and bound to the Internet of Things carrier carrying multiple garment materials of the same batch. When the garment materials are transferred to the production process node, the real-time operation data of the production process node is acquired; Determine whether the real-time operation data triggers a preset material dispersion condition; If the material discrete condition is triggered, the code splitting logic is executed: the flow path of the main identifier code remains unchanged, and a virtual shadow traceability code is generated in the traceability system based on the characteristic data of the discrete material; Establish a logical interlock relationship between the main identifier code and the shadow traceability code, mark the shadow traceability code as pending regression, and add an unclosed loop warning mark to the metadata of the main identifier code; The circulation status of the shadow traceability code is monitored. When a return confirmation signal of the discrete material is received, the topology reconstruction logic is executed to merge the data branch of the shadow traceability code into the traceability chain of the main identifier code and release the logical interlock relationship.

2. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The process of generating the main identifier code based on order attributes specifically includes: Extract the style number, color number, and production batch number from the garment production order; A preset encoding obfuscation algorithm is invoked to perform calculations on the style number field, color number field, and production batch number to generate a unique basic feature segment; Based on the standard quantity values ​​of the multiple garment materials from the same batch, a capacity check bit is added after the basic feature segment to generate the main identification code.

3. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The step of physically mapping and binding the main identifier code with the IoT carrier carrying multiple garment materials from the same batch specifically includes: Read the unique hardware identifier of the IoT carrier; Create a mapping table in the system's core database and write the correspondence between the main identifier code and the hardware unique identifier; Activate the read and write permissions of the IoT carrier and write the hash value of the main identifier code into the user data area of ​​the IoT carrier.

4. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The determination of whether the real-time operation data triggers a preset material dispersion condition specifically includes: The current actual quantity of materials passing through the station is obtained through a counting device or manual input interface set at the production process node. The actual quantity of materials passing through the station is compared with the theoretical quantity recorded in the main identification code; If the actual quantity of material passing through the station is less than the theoretical quantity, then the material dispersion condition is triggered.

5. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The real-time operation data also includes the process type code of the current process node; The method further includes: determining whether the current node is an irreversible processing node based on the process type code; If so, when the material dispersion condition is triggered, the access permission of the main identifier code in the next process node is locked simultaneously until a forced release instruction is received from the management terminal.

6. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The generation of a virtual shadow traceability code in the traceability system based on the characteristic data of discrete materials specifically includes: Obtain discrete timestamps, discrete process node IDs, and discrete material defect reason codes; Using the preset shadow generation rules, a virtual data object with a lifecycle countdown attribute is created as the shadow traceability code, with the main identifier code as the root node; The shadow traceability code does not correspond to any physical IoT carrier; it is only stored in the logical operation layer of the traceability system.

7. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 6, characterized in that, The lifecycle countdown attribute is set based on the physical properties of the fabric of the garment material; If the lifecycle countdown of the shadow traceability code reaches zero and the return confirmation signal is not received, the system automatically converts the shadow traceability code into a scrap status code and triggers the quantity reduction logic for the main identifier code.

8. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The establishment of the logical interlock relationship between the main identifier code and the shadow traceability code specifically includes: A semaphore pointer is set in the flow status table of the main identifier code, and the semaphore pointer points to the memory address of the shadow tracing code; Configure the final inspection checkpoint rules of the traceability system, and force a check of the state of the semaphore pointer when the main identifier code is scanned; If the address pointed to by the semaphore pointer still contains a valid shadow tracing code, then an instruction to prohibit entry into the database is output.

9. The method for identifying and encoding garment production process flow based on Internet of Things coding according to claim 1, characterized in that, The execution of the topology reconstruction logic specifically includes: Verify the repaired material parameters carried in the regression confirmation signal; After successful verification, all process history data recorded by the shadow traceability code will be appended to the history record list of the main identifier code; Cancel the virtual object of the shadow traceability code and clear the unclosed-loop warning mark in the main identifier code.

10. A garment production process identification and traceability system based on Internet of Things (IoT) coding, used to implement the method described in any one of claims 1 to 9, characterized in that, The system includes: The encoding initialization module is used to generate the main identifier code based on the order attributes and complete the physical mapping and binding with the IoT carrier; The data monitoring module is used to acquire real-time operational data of production process nodes and determine whether material dispersion conditions are triggered. The dynamic splitting module is used to generate a virtual shadow traceability code and execute the encoding splitting logic when the material discrete conditions are triggered. An interlock control module is used to establish a logical interlock relationship between the main identifier code and the shadow traceability code, and to add an unclosed-loop warning mark; The reconstructed closed-loop module is used to execute topology reconstruction logic, merge data branches, and release the logical interlocking relationship when a regression confirmation signal is received.

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