A tamper-proofing mechanism for textile supply chain data and a low-inventory guarantee system

Abstract

The application discloses a kind of textile supply chain data tamper-proofing mechanism and low inventory guarantee system method, belongs to blockchain traceability technical field, is related to supply chain management technology and method, based on blockchain traceability platform, provide a kind of dynamic credible symbol link network mechanism, realize to textile production process data block storage and data encryption reorganization high-strength password protection, data encryption processing function is realized to the data to be protected encryption, block and data reorganization using preset encryption algorithm;Pipeline production progress management module is constructed intelligent fault diagnosis model, blockchain traceability platform joins blockchain traceability mechanism, guarantee system data is not tampered with by human, avoid supply chain system guarantee raw material low inventory capacity failure.The application is improved in the realization of textile industry low inventory raw material purchase and efficient order management, and also can be transplanted appropriately, to other related and similar manufacturing field play inspiration and promotion role.

Description

Technical Field

[0001] This invention belongs to the field of blockchain traceability technology, and relates to supply chain management technology and methods, specifically to a method for a textile supply chain data anti-tampering mechanism and a low inventory guarantee system. Background Technology

[0002] In modern industrial production, avoiding the existence of inventory in the form of stock can eliminate a series of problems associated with warehousing, such as warehouse construction and management costs, inventory maintenance, storage, loading and unloading, and handling costs, as well as the problems of working capital tied up in inventory and the aging, loss, and spoilage of inventory. The concept of zero inventory can be traced back to the 1960s and 70s, when Toyota Motor Corporation in Japan implemented Just-in-Time (JIT) production. In terms of management methods, they adopted Kanban management and implemented pull manufacturing using technologies such as cellular production to achieve a situation where there was virtually no stockpiling of raw materials and semi-finished products during the production process.

[0003] Today, Toyota Motor Corporation is renowned worldwide for its zero-inventory, streamlined production management system, pioneering a production method driven by orders and demand. Toyota's supply chain management model originates from the Toyota Production System (TPS), also known as Lean Production (LPS) or Just-in-Time (JIT) production. This demand-driven manufacturing process not only significantly reduces inventory and capital tied up in production but also improves management efficiency in the process of achieving JIT. Toyota only produces "what is needed, when needed, and in the required quantity," effectively producing high-quality products by completely eliminating waste, inconsistencies, and unreasonable demands (referred to as "muda," "mura," and "muri" in Japanese) on the production line. To fulfill customer orders as quickly as possible, vehicles are manufactured efficiently by adhering to the following principles within the shortest possible time: In other words, upon receiving a vehicle order, a production order must be issued to the starting point of the vehicle production line as quickly as possible. The assembly line must stock all necessary parts in the required quantity so that any type of ordered vehicle can be assembled. The assembly line must replace the parts used by retrieving the same number of parts from the parts production process (the previous process). The previous process must store a small number of all types of parts and only generate the number of parts that the operator retrieves from the next process.

[0004] In operational terms, zero inventory in production means that materials (including raw materials, semi-finished products, and finished products) are not stored in warehouses during one or more operational stages such as procurement, production, and sales, but are constantly in circulation. In other words, the key to zero inventory is not whether it's appropriate or not; it has nothing to do with whether or not inventory is held. The crucial issue is whether the products are stored or in circulation. Therefore, the benefits of zero inventory are obvious. If a company can achieve zero inventory at different stages, it will benefit from reduced capital tied up in inventory; optimized accounts receivable and payable; faster capital turnover; lower inventory management costs; and mitigation of the risks of price reductions and unsold inventory caused by market changes and product updates.

[0005] The core concept of TPS logistics lies in zero inventory and leveling. Zero inventory aims to control costs by eliminating all forms of waste throughout the supply chain and ensuring a high degree of matching between parts and the finished vehicle. Toyota implements leveling in its production orders and logistics transportation, coordinating the work of upstream and downstream suppliers and logistics providers.

[0006] Analysis of the above case study on Toyota's "zero inventory" production and manufacturing supply chain reveals that successfully adapting Toyota's zero-inventory production philosophy to the textile manufacturing industry is no easy task. Before this process can begin, the following conditions must be met: achieving order- and demand-driven production in the textile industry; ensuring quality assurance and process / progress traceability throughout the entire production process from raw material input to product output; and ensuring reliable cash flow at each stage of raw material procurement and order fulfillment. Achieving these goals relies on implementing low-inventory raw material procurement and order management methods. In the textile industry, the main reasons for raw material inventory buildup are as follows: ①The raw material data for textile production was falsified, resulting in significant deviations in the procurement of raw materials.

[0007] ② A temporary malfunction occurred on the production line in the textile production workshop, which prevented the production process from proceeding smoothly, resulting in a backlog of raw materials.

[0008] ③ Unexpected events in textile order management and product transportation, such as data being destroyed or tampered with, can easily lead to inventory backlogs throughout the entire production chain and process, resulting in supply chain system failure and collapse.

[0009] References: [1] Tarun Kumar Agrawal, Vijay Kumar, Rudrajeet Pal, Lichuan Wang,Yan Chen. Blockchain-based framework for supply chain traceability: A caseexample of textile and clothing industry. Comput. Ind. Eng. 154: 107130(2021). [2]Petri Helo, Yuqiuge Hao. Blockchains in operations and supplychains: A model and reference implementation. Comput. Ind. Eng. 136: 242-251(2019) [3] JJB Pérez, Queiruga-Dios A , VG Martínez, et al. Traceability ofReady-to-Wear Clothing through Blockchain Technology[J]. Sustainability,2020, 12. [4] Agrawal, T. K., Kalaiarasan, R., & Wiktorsson, M. (2020).Blockchain-based secured collaborative model for supply chain resourcesharing and visibility. In B. Lalic, V. Majstorovic, U. Marjanovic, G. vonCieminski, & D. Romero (Eds.), Advances in production management systems. Thepath to digital transformation and innovation of production managementsystems (pp. 259–266). [5] Agrawal, T. K., & Pal, R. (2019). Traceability in textile andclothing supply chains:Classifying implementation factors and informationsets via Delphi study. Sustainability, 11(6), 1698. [6] Azzi, R., Chamoun, R. K., & Sokhn, M. (2019). The power of ablockchain-based supply chain. Computers & Industrial Engineering, 135, 582–592. [7] Bull´on P´erez, J. J., Queiruga-Dios, A., Gayoso Martínez, V., &Martín del Rey, ´A. (2020). Traceability of ready-to-wear clothing throughblockchain technology. Sustainability, 12(18), 7491. [8] Helo, P., & Hao, Y. (2019). Blockchains in operations and supplychains: A model and reference implementation. Computers & IndustrialEngineering, 136, 242–251. [9] Kumar, V., Hallqvist, C., & Ekwall, D. (2017). Developing aframework for traceability implementation in the textile supply chain.Systems, 5(2), 33.

[10] Pal, K., & Yasar, A.-U.-H. (2020). Internet of things and blockchain technology in apparel manufacturing supply chain data management. Procedia Computer Science, 170, 450–457. Summary of the Invention

[0010] In view of the technical problems existing in the prior art, the purpose of this invention is to provide a method for a data anti-tampering mechanism and a low inventory guarantee system for the textile supply chain, so as to achieve significant technical improvement in low inventory raw material procurement and efficient order management in the textile industry. At the same time, these technologies and methods can also be appropriately transferred to other related and similar production and manufacturing fields, which can provide good inspiration and promotion.

[0011] To achieve the above-mentioned objectives, the present invention provides a method for a textile supply chain data anti-tampering mechanism and a low inventory assurance system, characterized in that the textile supply chain data anti-tampering mechanism and low inventory assurance system is based on a blockchain traceability platform and includes an order management subsystem, a material procurement subsystem, a production line process management subsystem, and a logistics and transportation subsystem. The order management subsystem includes a mobile distributed order entry module and a textile production material ratio calculation module. The material procurement subsystem includes a material procurement order tracking module and a material procurement progress tracking module. The production line process management subsystem includes a production line progress management module and a production line process management module. The production line progress management module includes adaptive diagnosis of production line faults, self-service repair of production line faults, fault uploading, and manual repair. The logistics and transportation subsystem includes a logistics and transportation management module and a material progress tracking module.

[0012] Furthermore, the method of the textile supply chain data anti-tampering mechanism and low inventory guarantee system is characterized in that the textile supply chain data anti-tampering mechanism includes a dynamic and trusted symbolic link network mechanism to achieve high-strength cryptographic protection for the partitioned storage and encrypted reassembly of textile production process data. The data encryption processing function uses a preset encryption algorithm to encrypt, partition, and reassemble the data to be protected. The entire process of encrypting, segmenting, and reassembling the data to be protected consists of three parts: a key generation function, a blockchain network, and a user set; after the data is decrypted by the decryption function, the previously segmented data is decrypted and reassembled according to the blockchain's link address.

[0013] Furthermore, the method for the textile supply chain data anti-tampering mechanism and low inventory assurance system includes the following steps, wherein the assembly line production progress management module includes constructing an intelligent fault diagnosis model.

[0014] Furthermore, the method for the textile supply chain data anti-tampering mechanism and low inventory guarantee system includes the following steps: on the basis of the data anti-tampering mechanism, a blockchain traceability mechanism is added to construct a complete set of traceability code generation models to ensure that the system data is not tampered with by humans and to prevent the supply chain system from failing to guarantee the low inventory of raw materials.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: ① The entire data flow in the textile industry supply chain is managed in a distributed and decentralized manner, thereby increasing data tamper-proof capabilities and the reliability of data transmission, retrieval, and application across the entire chain. This invention achieves low inventory assurance in the textile industry through mechanisms that prevent human input errors or illegal tampering during transmission.

[0016] ② The entire process of data in the textile industry supply chain is transparent and traceable, enabling all parties to track the progress of the entire process from order taking, raw material purchase, assembly line production, to packaging, transportation and sales in real time on a trusted blockchain platform.

[0017] ③ According to the pre-set material ratio module for textile production on the blockchain platform, apart from trusted management personnel who can modify the pre-set material ratio data, other parties cannot read or modify it, thereby increasing the reliability of low-inventory raw material procurement throughout the textile production process.

[0018] ④ On the trusted blockchain platform, self-service troubleshooting and repair of production line faults can be achieved, along with automatic activation of backup production lines and distributed management and repair of anticipated faults on the production line by professional technicians. Therefore, even if a production line experiences a temporary malfunction, it can be repaired immediately, thus preventing raw material inventory buildup in the production process. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall implementation framework of the present invention.

[0020] Figure 2 This is a schematic diagram of the textile industry supply chain according to the present invention.

[0021] Figure 3 This is a schematic diagram of the blockchain-based supply chain traceability framework of the present invention (for the textile industry). Detailed Implementation

[0022] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for explanation and illustration only and are not intended to limit the present invention.

[0023] like Figure 1 The diagram shows the overall implementation framework of this invention: a textile supply chain data anti-tampering mechanism and low inventory assurance system. The textile supply chain data anti-tampering mechanism and low inventory assurance system is based on a blockchain traceability platform and includes an order management subsystem, a material procurement subsystem, a production line process management subsystem, and a logistics and transportation subsystem. The order management subsystem includes a mobile distributed order entry module and a textile production material ratio calculation module. The material procurement subsystem includes a material procurement order tracking module and a material procurement progress tracking module. The production line process management subsystem includes a production line progress management module and a production line process management module. The production line progress management module includes adaptive diagnosis of production line faults, self-service repair of production line faults, fault uploading, and manual repair. The logistics and transportation subsystem includes a logistics and transportation management module and a material progress tracking module.

[0024] like Figure 2 The diagram shows the supply chain of the textile industry, which consists of fiber manufacturers, yarn manufacturers, fabric manufacturers, garment manufacturers, buyers / retailers, and consumers.

[0025] like Figure 3 The diagram shows a blockchain-based supply chain traceability framework for the textile industry. The entire supply chain, from the raw material procurement stage of fiber supply to the stage where consumers purchase textile products at the retail level, is managed using a blockchain platform. Figure 3The fiber supply chain requires the procurement of raw materials including wool, polyester fiber, organic cotton, and propylene. Therefore, the blockchain for this segment records the procurement data for these raw materials, which is then transmitted to the spinning manufacturing segment via the blockchain platform. The spinning manufacturing segment, comprised of several spinning mills, records raw material ratio data from the raw material supply chain, as well as the operational status of equipment, the spinning process, and results. This data is then further transmitted to the fabric manufacturing segment, which consists of weaving mills, knitting mills, and nonwoven fabric mills. The blockchain for this segment receives and records data from the spinning segment, as well as equipment status, production processes, and results from weaving, knitting, and nonwoven fabric manufacturing. This data is then further transmitted to the garment manufacturing segment. The garment manufacturing segment, as a textile manufacturer on the blockchain platform, is comprised of several textile manufacturers. In addition to recording data from the fabric manufacturing segment, this segment also records equipment status, production processes, and results within the textile manufacturing segment. This recording and transmission of data continues throughout the entire blockchain platform's production and manufacturing process, ultimately reaching the consumer. At the same time, data throughout the entire supply chain can be traced back and confirmed. In other words, the data communication on the entire blockchain platform, in addition to recording the entire process and workflow data in the ledger, also has error correction functions to ensure data traceability and confirm error-free data transmission. Figure 3 The presentation of textile traceability information at the top shows that each round of data transmission and backtracking in the block data can be distinguished by sending ID and receiving ID, and the traceability ID can be used to identify and confirm the information transmission status and location.

[0026] Preferably, the method of the textile supply chain data anti-tampering mechanism and low inventory guarantee system of the present invention can fully utilize the decentralized, highly secure, and tamper-proof ledger characteristics and advantages of the blockchain platform, and simultaneously design a dynamic and trusted symbolic link network mechanism to achieve a high-strength cryptographic protection mechanism for the partitioned storage of raw material data for textile production and data encryption and reassembly. The data encryption processing function can consider using some pre-built encryption algorithms to encrypt, partition, and reassemble the data to be protected.

[0027] First, a set of encryption methods based on paired content, blockchain attributes, and bilinear mappings is defined. The specific implementation steps are as follows: Step 1: Define two loop groups: Priority of loop groups .

[0028] Step 2: For any pair of content to be encrypted (plaintext) ,satisfy ,in To meet priority The file content stored in blocks on the blockchain.

[0029] Step 3: Use the encryption key The file content is encrypted as shown in Formula 1. (1) The content to be encrypted and paired (plaintext) With data Using it as a carrier, construct a data storage function. This content is stored in a certain block. The next step is to construct a tree structure for each block on the blockchain. The constructed tree structure is as follows: The structure is stored on the blockchain in encrypted form and is generated by randomly generated values. Ensure the dynamism of blocks.

[0030] The encryption process is described as follows: First, an encryption function needs to be constructed. This can lead to the first Ciphertext on each block The ciphertext content can be constructed as follows: ; Ciphertext The content is explained as follows: Let each represent a random number, which are the constructs of the first random number. Public key on each block One of the important components; the public key The other part is composed of Composition; among which, private key Depend on generate.

[0031] Step 4: Private Key The generation process is described as follows: First, a private key generation function needs to be constructed. ;in, Indicates user The user attributes, which are uploaded by the user to the private key generation function, and then linked to the master key generation function. and random numbers Together, we can generate users. private key .

[0032] Step 5: The decryption process is described below: First, construct the decryption function. Then, the decryption process can be completed based on the following formula 2; (2) Based on the above process, the entire process of encrypting, segmenting, and reassembling the data to be protected can be divided into three parts: a key generation function, a blockchain network, and a user set. After the data is decrypted by the decryption function, the previously segmented data can be decrypted and reassembled according to the blockchain's link addresses.

[0033] Preferably, the method of the textile supply chain data anti-tampering mechanism and low inventory assurance system of the present invention can construct an intelligent fault diagnosis model to deal with temporary failures in the textile production line. The specific steps are as follows: Step 1: Define the set of operational status identifiers for the production line in the textile production workshop. The failure level of the production line is further defined based on its operating status. The range is 0-10, where 0 represents 1-5 indicates that the production line is operating normally; 1-5 represent This indicates a partial operational malfunction in the production line; 6-9 represent... 10 indicates a serious operational malfunction in the production line; 10 represents This indicates a complete failure of the production line, i.e., paralysis; when In such cases, a self-diagnostic and repair procedure can be initiated for the production line; these types of faults are generally software or network failures. When this happens, it is necessary to initiate an automatic switchover to a backup production line. This type of fault is generally a hardware failure and cannot be automatically repaired in a short time. In such cases, it is necessary to call in a professional engineer to the site to troubleshoot the problem, including updating or replacing the production line. The method proposed in this invention is mainly aimed at... To resolve such issues, which primarily involve self-diagnosis and repair of software or network faults, it is essential to first identify the problem type and pinpoint its location. Therefore, solutions can be tailored to the specific problem type. and the location of the problem Define it as follows: , .in, These represent software problems and network problems, respectively. These represent the workshop number and the production line number, respectively.

[0034] Step 2 involves constructing an intelligent fault diagnosis model for the production line, incorporating historical operational data. It is generally believed that once the fault type and location are determined, self-diagnosis and repair can be achieved. Therefore, the calculation process in Formula 3 is used to predict the joint probability of potential faults in future production line operations. , (3) in, This represents the combined probability of a faulty production line occurring within the workshop where the fault occurred. It can be calculated based on the fact that the fault occurred in the workshop. probability and occurs on the assembly line probability joint probability The calculation yielded the result. This solves the problem of probabilistic prediction of self-diagnosable and repairable faults on the production line. To differentiate fault types, the corresponding fault types can be added to Formula 3, resulting in Formula 4.

[0035] (4) The identifier used in Formula 4 This allows you to distinguish whether a computational failure is a software failure or a network failure.

[0036] Therefore, while malfunctions in textile production lines are unavoidable, contingency plans can be implemented. If a malfunction occurs and can be resolved quickly without affecting the production schedule, then an adaptive self-healing module should be considered for fault repair (e.g., ...). Figure 1 The process ①—②—③) or the fault is repaired manually by technicians (such as...) Figure 1 The process can be done in the manner of ①—②—③ˊ—④; if it cannot be repaired in a short time, the fault repair process of the original faulty pipeline can be completed in parallel while starting and automatically switching to the backup pipeline.

[0037] Preferably, regarding the issues of anti-tampering mechanisms and traceability for textile supply chain data, this invention can incorporate a blockchain traceability mechanism into the existing anti-tampering mechanisms for textile supply chain data. The steps include the following: Constructing a traceability code generation model. Ensuring the uniqueness of the traceability code is a prerequisite for blockchain traceability. Only by ensuring the uniqueness of the traceability code can the uniqueness of all operations and processes on the blockchain be achieved, thus ensuring that every record's operation is traceable. The traceability code generation model is constructed as shown in Formula 5.

[0038] (5) in, , This represents the key generation function. This represents the set of key primary keys, used to uniquely identify the generated key. This represents the session key set, used to generate encryption keys for human-computer interaction content. These represent the sets of asymmetric key pairs consisting of a public key and a private key, respectively. , This represents the signature generation function. This represents a random number generation function. This indicates the unique identifier of the block. , This represents the function for generating source tags. The meaning is the same as above. This represents the set of encryption keys generated for session keywords. Based on Formula 5, a complete traceability code generation model can be constructed, thereby ensuring that system data is not tampered with and preventing the supply chain system from failing to maintain low raw material inventory levels.

[0039] The present invention relates to a textile supply chain data anti-tampering mechanism and a low-inventory guarantee system. To achieve and guarantee low inventory levels, two conditions must be met: first, raw material ratios must comply with standardized requirements; and second, data at any stage must not be illegally tampered with. In other words, data at every stage of production and manufacturing cannot be tampered with manually. Otherwise, whether due to human input errors or illegal tampering during transmission, low inventory levels will be impossible to achieve. Therefore, ensuring low inventory ultimately depends on preventing illegal data tampering or human input errors. This application implements a textile low-inventory guarantee method through a supply chain data anti-tampering mechanism.

[0040] It's important to clarify that achieving low inventory and ensuring low inventory are different. The "zero inventory" system used by Toyota in the background technology is a method for achieving low inventory, not a method for ensuring low inventory. If Toyota's zero inventory data is tampered with or contains input errors, it will also affect its achievement of zero inventory. This application implements a method for ensuring low inventory. That is, ensuring low inventory means that data that may affect the achievement of low inventory in each step of the manufacturing process should be protected from human input errors or illegal tampering. Otherwise, the blockchain mechanism will prevent input errors, and the encryption / decryption mechanism will prevent illegal tampering.

[0041] The present invention provides a method for preventing data tampering in the textile supply chain and a low-inventory guarantee system, which will achieve significant technological improvements in realizing low-inventory raw material procurement and efficient order management in the textile industry. At the same time, these technologies and methods can also be appropriately adapted to inspire and promote other related and similar manufacturing fields.

Claims

1. A method for a data anti-tampering mechanism and low inventory assurance system in the textile supply chain, characterized in that, The textile supply chain data anti-tampering mechanism and low inventory guarantee system are based on a blockchain traceability platform, including an order management subsystem, a material procurement subsystem, a production line process management subsystem, and a logistics and transportation subsystem. The order management subsystem includes a mobile distributed order entry module and a textile production material ratio calculation module. The material procurement subsystem includes a material procurement order tracking module and a material procurement progress tracking module. The production line process management subsystem includes a production line progress management module and a production line process management module. The production line progress management module includes adaptive diagnosis of production line faults, self-service repair of production line faults, fault uploading, and manual repair. The logistics and transportation subsystem includes a logistics and transportation management module and a material progress tracking module; The textile supply chain data anti-tampering mechanism includes a dynamic and trusted symbolic link network mechanism to achieve high-strength cryptographic protection for the partitioned storage and encrypted reassembly of textile production process data. The data encryption processing function uses a preset encryption algorithm to encrypt, partition, and reassemble the data to be protected. Define a set of cryptographic methods based on paired content and blockchain attributes, and bilinear mappings, including the following steps: Step 1: Define two loop groups: Priority of loop groups ; Step 2: For any pairing content to be encrypted ,satisfy ,in To meet priority The file content stored in the partitioned blocks on the blockchain; Step 3: Use the encryption key The file content is encrypted as shown in Formula 1. （1） Among them, the content to be encrypted for pairing With data Using it as a carrier, construct a data storage function. This content is stored in a certain block. Next, a tree structure is constructed for each block on the blockchain, resulting in the following tree structure: The structure is stored on the blockchain in encrypted form and is generated by randomly generated values. Ensure the dynamism of blocks; The encryption process is as follows: An encryption function needs to be constructed. This leads to the first Ciphertext on each block The ciphertext content is constructed as follows ciphertext The content is explained as follows: Let each represent a random number, which are the constructs of the first random number. Public key on each block One of the important components is the public key. The other part is composed of Composed of, among which, private key Depend on generate; Step 4: Private Key The generation process is as follows: A private key generation function needs to be constructed. ,in, Indicates user The user attributes, which are uploaded by the user to the private key generation function, and then linked to the master key generation function. and random numbers Together, we can generate users. private key ; Step 5: The decryption process is as follows: Construct the decryption function. Then, the decryption process is completed based on the following formula 2. （2） Based on the above process, the entire process of encryption, segmentation, and data reassembly for the data to be protected consists of three parts: key generation function, blockchain network, and user set; after the data is decrypted by the decryption function, the previously segmented data is decrypted and reassembled according to the blockchain link address.

2. The method for the textile supply chain data anti-tampering mechanism and low inventory assurance system according to claim 1 includes the following steps, wherein the assembly line production progress management module includes constructing an intelligent fault diagnosis model, including the following steps: Step 1: Define the set of operational status identifiers for the production line in the textile production workshop. The failure level of the production line is further defined based on its operating status. The range is 0-10, where 0 represents This indicates that the production line is operating normally; 1-5 represent This indicates that there is a partial operational malfunction in the production line; 6-9 represent This indicates a serious operational malfunction in the production line; 10 represents This indicates a complete failure of the production line, i.e., paralysis; when When this occurs, a self-diagnostic and repair procedure is initiated on the production line; such faults are generally software or network problems. When this occurs, the automatic switch to a backup production line is initiated. This type of fault is generally a hardware failure and cannot be automatically repaired in a short time. In such cases, a professional engineer is called to the site to troubleshoot the problem, including updating or replacing the production line. According to the type of problem and the location of the problem Define it as follows: , ,in, These represent software problems and network problems, respectively. These represent the workshop number and the production line number, respectively. Step 2: Construct an intelligent fault diagnosis model for the production line. By combining historical operation data of the production line, if the fault type and location can be determined, the self-diagnosis and repair of the fault are completed. The calculation process of Formula 3 is used to predict the joint probability of future faults in the production line. , （3） in, This represents the combined probability of a faulty production line occurring within a workshop where the fault occurred. It utilizes the fact that the fault occurred in the workshop... probability and occurs on the assembly line probability joint probability The calculation yields the result; to differentiate between fault types, the corresponding fault type is added to Formula 3, resulting in Formula 4. （4） The identifier used in Formula 4 This means distinguishing whether a computational failure is a software failure or a network failure.

3. The method for the textile supply chain data anti-tampering mechanism and low inventory assurance system according to claim 1 includes the following steps: A blockchain traceability mechanism is added to the data anti-tampering mechanism, including the following steps: A traceability code generation model is constructed to ensure the uniqueness of the traceability code, thereby achieving the uniqueness of all operations and processes on the blockchain and ensuring that every record is traceable. The traceability code generation model is constructed as shown in Formula 5. （5） in, , This represents the key generation function. This represents the set of primary keys used to uniquely identify the generated keys. This represents the session key set, used to generate encryption keys for the human-computer interaction content. These represent the sets of asymmetric key pairs consisting of a public key and a private key, respectively. , This represents the signature generation function. This represents a random number generation function. Indicates the unique identifier of a block; , This represents the function for generating source tags. The meaning is the same as above. This represents the set of encryption keys generated for session keywords; based on Formula 5, a complete set of traceability code generation models are constructed to ensure that system data is not tampered with and to prevent the supply chain system from failing to maintain low raw material inventory.

Images