Garment supply chain transparent purchasing and collaborative management method and system based on Internet of Things and block chain

Abstract

The invention discloses a clothing supply chain transparent purchasing and collaborative management method and system based on the Internet of Things and a block chain. The method comprises the following steps: monitoring and collecting state data of raw materials in real time through Internet of Things equipment; when the state data meets a preset purchase triggering condition, a purchase demand transaction is automatically generated and broadcasted on the alliance block chain network; a purchase smart contract deployed on the alliance block chain network responds to the purchase demand, screens candidate suppliers, and quantitatively scores the candidate suppliers based on multi-dimensional historical performance data obtained from a distributed account book to automatically determine a target supplier; the purchase smart contract automatically generates a purchase order transaction and performs consensus evidence storage on the alliance block chain network; and continuously tracking order state update on the alliance block chain network, and automatically executing a settlement process when a preset payment condition is satisfied. According to the invention, the problems of opaque information, lagging purchasing decision, low process efficiency and difficulty in multi-party collaboration of the traditional clothing supply chain are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology and supply chain management, and particularly relates to a clothing supply chain transparent procurement and collaborative management method and system based on Internet of Things and block chain. BACKGROUND

[0002] The clothing industry is one of the important industries in the world, and its supply chain has the characteristics of long chain, multiple links, complex participants, short product life cycle, and fast changing market demand. The traditional clothing supply chain management largely relies on centralized enterprise resource planning (ERP) system, supply chain management (SCM) system and manual communication and coordination. However, with the intensification of market competition and the improvement of consumers' demand for personalization and rapid response, this traditional management mode gradually exposes its inherent limitations.

[0003] Firstly, the information island phenomenon is serious, which leads to low supply chain transparency. In the clothing supply chain, from raw material suppliers, fabric processing plants, auxiliary material suppliers, to clothing manufacturers, logistics transporters, distributors and retailers, each participant usually has and maintains its own independent information system. Due to technical standards, data formats, business barriers and other reasons, these systems often cannot achieve seamless data connection and sharing. Key business information, such as the real raw material inventory of upstream suppliers, whether the production workshop environment (such as temperature and humidity) meets the standard, the real-time transportation location of goods, the authenticity of quality inspection reports, etc. cannot be timely, transparently and credibly transmitted between upstream and downstream enterprises in the supply chain. The delay, distortion and even loss of information make it difficult for the purchaser to make quick and accurate decisions, often leading to insufficient material procurement or excessive inventory, increasing operating costs and risks.

[0004] Secondly, the cost of establishing trust between enterprises is high. Due to the lack of a neutral and credible information verification mechanism, the collaboration trust between enterprises mainly depends on long-term cooperation, complicated contract terms and complex manual audit processes. For example, the purchaser has difficulty in verifying whether the qualification certification documents claimed by the supplier are real and effective, and cannot be sure that the product quality certificate provided by the supplier has not been tampered with. The fragility of such trust makes the parties in the supply chain hold a reserved attitude when collaborating, and the collaborative efficiency is greatly discounted, and it also brings huge time and economic cost to solve business disputes.

[0005] Thirdly, the procurement process is inefficient and has management risks. The traditional procurement process, including initiating a procurement application, finding a supplier, inquiring about prices, comparing prices, signing a contract, tracking orders, checking goods into storage, reconciling accounts, and making payments, is full of manual operations and paper document circulation. This process not only takes time and effort, is prone to errors due to human error, but also the opacity of the process provides the possibility for commercial fraud, behind-closed-door operations, and other non-compliant behaviors, posing challenges to the compliance management of enterprises.

[0006] In recent years, in order to improve the traceability of the supply chain, some enterprises have begun to try to apply RFID or QR code technology. For example, some public technical solutions describe adding electronic tags to clothing to track their flow in the logistics and sales stages, thereby achieving product anti-counterfeiting and traceability. However, such solutions have some shortcomings. On the one hand, they mostly focus on the end of the supply chain, i.e. the stage after the finished product is shipped, and pay little attention to the procurement and management of raw materials that determine the final quality of the clothing, failing to solve the problem from the source. On the other hand, although some solutions introduce blockchain technology to record data, the initial data entry process still relies on manual operation, and the objectivity and original credibility of the data are not fundamentally guaranteed, and these solutions mainly stay at the level of information recording and querying, and have not been able to go deep into the automation and intelligent transformation of core business processes, especially in the procurement decision-making process.

[0007] Therefore, there is an urgent need for a new technical solution in the field of clothing supply chain to overcome the lack of trust, information opacity, low efficiency of the process, and other problems existing in the prior art, so as to build a more transparent, efficient, and intelligent collaborative management system. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a clothing supply chain transparent procurement and collaborative management method and system based on the Internet of Things and blockchain, in view of the defects of information silos, lack of trust, low efficiency of the procurement process, and difficulty in quality risk control in the traditional clothing supply chain management mode mentioned in the background art.

[0009] To solve the above technical problems, an aspect of the present application provides a clothing supply chain transparency procurement and collaborative management method based on Internet of Things and blockchain. The method realizes the full-process automation and transparency from the generation of procurement demand to the final settlement by constructing an alliance blockchain network composed of core participants of the supply chain (such as purchasers, suppliers, logistics parties, quality inspection parties, etc.) and combining Internet of Things technology. The core technical solution of the method is that, first, through the Internet of Things devices deployed at the front end of the supply chain, such as the warehouses or transportation tools of raw material suppliers, real-time and objective monitoring and collection of raw material related state data are realized. These Internet of Things devices can be RFID readers for identifying inventory, temperature and humidity sensors for monitoring the environment, or GPS locators for tracking location, etc. The system sets a preset procurement trigger condition for key state data, for example, sets a threshold for the safety stock level of a certain raw material. When the real-time inventory level data collected by the Internet of Things device is lower than the threshold, the condition is met.

[0010] Once the procurement trigger condition is met, the system will automatically generate and broadcast a procurement demand transaction on the alliance blockchain network. This transaction, as an unalterable signal triggered by a real physical world event, is consensus by all related nodes in the network. Subsequently, one or more procurement smart contracts deployed on the alliance blockchain network will listen to the procurement demand transaction and be activated. A smart contract is a computer program that defines and executes a business agreement in the form of code, which runs on a blockchain and has the characteristics of automatic execution and unalterability.

[0011] The activated procurement smart contract first filters out all candidate suppliers with corresponding supply qualifications from the supplier directory stored in the alliance blockchain network according to the material information contained in the procurement demand. Then, in order to make the best choice from these candidate suppliers, the smart contract will perform a key quantitative scoring step. It will safely and reliably obtain multi-dimensional historical performance data associated with each candidate supplier from the distributed ledger of the alliance blockchain. These data are objective records of the past transaction behaviors of the supplier, such as the transaction price of historical orders, the on-time rate of delivery, the overall performance success rate of orders, and the quality inspection pass rate of delivery batches, etc. The procurement smart contract has a supplier scoring model, such as a multi-dimensional weighted scoring model, which will use these reliable historical data to calculate a comprehensive score for each candidate supplier.

[0012] After the scoring is completed, the procurement smart contract will automatically determine the target supplier for the current procurement based on the scoring results, for example, by selecting the supplier with the highest score. Subsequently, the contract will immediately generate a new procurement order transaction. This transaction contains clear procurement information, such as the purchaser, target supplier, material details, quantity, price, expected delivery date, etc. This procurement order transaction is also broadcast to the consortium blockchain network, verified and consensus by network nodes, and permanently recorded on the distributed ledger, forming a legally binding, irrefutable electronic order.

[0013] After the order is generated, the method of the present application also includes continuous tracking of the order lifecycle. Each key state node of the order, such as the target supplier confirming the order, shipping, the logistics provider updating the transportation location, the purchaser confirming receipt, the quality inspection provider submitting the inspection report, etc., will be recorded as a new transaction on the chain, forming a complete, transparent, and traceable order fulfillment track. Finally, during the order fulfillment process, the smart contract continuously checks whether the pre-set payment conditions are met. This condition can be set as a logical combination of multiple sub-conditions, for example, the order's goods receipt status must be "signed for" and the quality inspection result status must be "qualified". Once all the pre-set conditions are met, the smart contract will automatically execute the settlement process associated with the procurement order, for example, trigger the payment instruction to the target supplier, or generate a trusted accounts payable voucher.

[0014] In an optional embodiment, in order to ensure the accuracy and real-time nature of the supplier score, the state data can include not only inventory levels, but also environmental and transportation process data collected by devices such as temperature and humidity sensors, vibration sensors, etc. These data can be used as a basis for evaluating the quality of the supplier's warehouse and logistics services, dynamically affecting their quality score.

[0015] In an optional embodiment, the scoring model for the supplier intelligent matching can be more complex. The weight coefficients in this multi-dimensional weighted scoring model can be dynamically adjusted by the purchaser according to the focus of different procurement tasks (for example, for urgent orders, the weight of on-time delivery rate can be increased), thereby achieving a more flexible and intelligent supplier selection strategy.

[0016] In an optional embodiment, to strengthen the source management of suppliers, the method of the present application also designs a strict access process when the supplier joins the consortium blockchain network. The process requires the supplier to be admitted to submit various qualification documents (such as business license, system certification, etc.). The system will perform a hash calculation on these documents to generate a unique digital fingerprint that can represent the content of the document, i.e. the qualification document hash value. The hash value together with the identity information of the supplier will be submitted to the consortium blockchain for multi-party consensus review. Only after passing the review, a dedicated identity management smart contract will generate a globally unique decentralized digital identity (DID) for the supplier. This digital identity is firmly bound to the qualification document hash value, which constitutes the trusted root basis for all business activities in the network.

[0017] Another aspect of the present application also provides an Internet of Things and blockchain-based transparent procurement and collaborative management system for clothing supply chain. The system can be a software system deployed on a server or cloud platform, and its internal structure and functional modules are designed to execute the above-mentioned method. The system should at least include one or more processors and a memory. The memory stores computer program instructions which, when executed by the processor, can cooperatively complete all or part of the steps of the method.

[0018] In a specific system architecture implementation, the system can be divided into several logical functional modules. Among them, the Internet of Things monitoring module is responsible for communicating with the front-end Internet of Things devices, performing real-time monitoring and data collection functions, and generating and broadcasting procurement demand transactions when conditions are met. The core function of the supplier matching module is to execute the supplier screening and intelligent scoring logic in the procurement smart contract, which interacts with the distributed ledger of the consortium blockchain, reads the historical data of the candidate supplier, and calculates the final target supplier according to the scoring model. The order management module is responsible for executing the automatic generation of procurement orders and the related contract logic of on-chain consensus evidence. The state tracking and settlement module continuously monitors the state flow of the order on the chain, and is responsible for triggering and executing the automated settlement smart contract when the payment conditions are met. These modules interact with each other through the calling of smart contracts deployed on the consortium blockchain or through API, work together, and jointly constitute the system of the present application.

[0019] Optionally, the system can also include a supplier access module. The module provides a user interface for suppliers to register and submit qualification documents, and performs a series of functions in the back end, such as hash calculation on the documents, submission to the chain for consensus review, and calling of the identity management smart contract to generate a digital identity, to ensure that each supplier joining the network is strictly authenticated and trusted.

[0020] In summary, the present application monitors and collects the state data of raw materials in real time through the Internet of Things device; when the state data meets the preset procurement trigger condition, a procurement demand transaction is automatically generated and broadcast on the alliance blockchain network; the procurement smart contract deployed on the alliance blockchain network responds to the procurement demand, screens candidate suppliers, and quantitatively scores the candidate suppliers based on the multi-dimensional historical performance data obtained in the distributed ledger, to automatically determine the target supplier; the procurement smart contract automatically generates a procurement order transaction and consensus stores it on the alliance blockchain network; the order status is continuously tracked on the alliance blockchain network, and the settlement process is automatically executed when the preset payment condition is met. The present application solves the problems of information opacity, procurement decision lag, low process efficiency and difficulty in multi-party cooperation in the traditional clothing supply chain by deeply integrating the objective data collection of the Internet of Things with the trust mechanism of the blockchain and the automatic execution capability of the smart contract, and builds a data-driven, process-automatic and fully-trustworthy supply chain collaborative management system.

[0021] Compared with the background art, the present application has many beneficial effects. First, the present application improves the transparency of the clothing supply chain and the trust between the participants. All key data and transaction records are stored on a multi-party consensus distributed ledger, and the non-tamperable feature fundamentally eliminates the risk of information being manipulated by one party, builds a fair and transparent trust environment for all participants, and significantly reduces the collaboration cost between enterprises. Second, the present application realizes the high automation and intelligence of the procurement core process, thereby greatly improving the operation efficiency. From demand perception, supplier selection, order generation to final settlement, the entire process is driven by code and preset rules, minimizing time-consuming and error-prone manual intervention, which is expected to shorten the procurement cycle by more than 30% and reduce the corresponding human management cost. Third, the present application strengthens the whole-process quality risk control from the source of the supply chain. Through real-time monitoring of the raw material storage and transportation environment by the Internet of Things, the quality management is changed from passive inspection after the event to proactive risk warning and prevention before the event, and all process data are recorded on the chain for traceability, providing a clear basis for responsibility definition for possible quality disputes. Finally, the present application establishes a fair, dynamic and completely objective data-driven supplier management and evaluation system. This not only helps the procurement party to select partners more scientifically and accurately, but also encourages all suppliers to continuously improve their service level and product quality, thereby promoting the benign competition and continuous optimization of the entire supply chain ecosystem. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings described below only illustrate some of the embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings are within the scope of the present application.

[0023] Figure 1 is a schematic diagram of the overall architecture of the system according to an embodiment of the present application.

[0024] Figure 2 is a general business flowchart of the procurement and collaborative management method according to an embodiment of the present application.

[0025] Figure 3 is a schematic diagram of the core smart contract interaction logic according to an embodiment of the present application.

[0026] Figure 4 is a flowchart of the supplier access and digital identity generation according to an embodiment of the present application.

[0027] Figure 5 is a detailed flowchart of the automated procurement decision-making logic according to an embodiment of the present application.

[0028] Figure 6 is a schematic diagram of the hardware deployment of the system in a specific application scenario according to an embodiment of the present application.

[0029] Figure 7 is a comparison chart of the key performance indicators of the method of the present application and the prior art according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0031] It should be noted that in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0032] As Figure 1 The system environment architecture and the specific structure and functions of each layer applied to the present application are described in detail as shown in the system overall architecture diagram: 1. Internet of Things perception layer This layer is the data source of the system, responsible for the automated collection of state information of physical entities and environment in the supply chain. The main deployed devices include: RFID / NFC reader: deployed at the entrance and exit of the supplier's warehouse and the entrance of the purchaser's warehouse. A unique RFID / NFC electronic tag is attached to each batch (or roll) of raw materials, and the reader is used to achieve fast and batch identification of materials and automatic recording of warehouse information.

[0033] Temperature and humidity sensor: deployed in the warehouse storing raw materials and the cargo box of the transport vehicle, used to monitor the environmental temperature and humidity in real time, and ensure that it is maintained within the appropriate range to prevent the fabric from deteriorating or being damaged due to improper environment.

[0034] GPS locator: installed on the vehicle responsible for transporting raw materials, used to obtain the latitude and longitude information of the transport tool in real time, and realize continuous tracking of the location of the materials in transit.

[0035] Vibration sensor: also installed on the transport vehicle, used to monitor abnormal vibration or impact during transportation, as a basis for evaluating logistics service quality and judging the risk of damage.

[0036] 2. Edge computing and network transmission layer This layer serves as a bridge between the perception layer and the platform layer, responsible for preliminary processing and secure transmission of data.

[0037] Internet of Things gateway / edge computing node: deployed at the data collection site, such as inside the warehouse. Its main functions include: 1) data cleaning: filtering out abnormal or redundant data collected by sensors; 2) data aggregation: aggregating high-frequency collected data according to pre-set strategies (such as calculating the average temperature and humidity in 5 minutes) to reduce the transmission and storage burden of the upper network; 3) data formatting and encryption: encapsulating the processed data into a unified JSON format and ensuring the confidentiality of the data during transmission through encryption algorithms (such as AES).

[0038] Network transmission: according to the deployment cost and communication needs of different scenarios, low-power wide-area network technologies such as Narrowband Internet of Things (NB-IoT) and LoRaWAN, or cellular network technologies such as 5G / 4G LTE can be selected to securely and reliably transmit encrypted data to the blockchain platform layer.

[0039] 3. Blockchain platform layer This layer is the core of the system, responsible for building a multi-party trusted distributed business environment and executing automated business logic.

[0040] The present application preferentially adopts Hyperledger Fabric as the underlying alliance chain framework. The reason is that Fabric has high performance, modularization, and pluggable consensus mechanism, and provides a "channel" mechanism to isolate data of different businesses, supports role-based permission control, and is very suitable for enterprise-level multi-party alliance application scenarios.

[0041] Network node composition: the participants of the network, including at least one garment manufacturer (purchaser), several raw material suppliers, logistics companies and third-party quality inspection agencies, each of which deploys and maintains its own peer node (Peer Node). These nodes jointly maintain a distributed ledger, execute smart contracts, and endorse transactions. The network's transaction ordering service is provided by the ordering node (Orderer Node).

[0042] Data on-chain and off-chain storage mechanism: In order to balance the non-tamperability of the blockchain and the performance requirements of the system in processing high-throughput Internet of Things data, the system adopts a chain-on-chain and off-chain collaborative storage scheme. Massive and continuous raw sensor data is stored in a high-performance off-chain database (such as the time series database InfluxDB or the distributed file system IPFS). Key event data collected by the Internet of Things (such as inventory alarm, environmental alarm), transactions submitted by each party (such as order creation, delivery confirmation, quality inspection report submission), and the hash digest (Hash Value) of the off-chain raw data packet are written into the distributed ledger of the blockchain. This mechanism not only ensures the credibility and traceability of core data, but also avoids the excessive expansion of the blockchain ledger.

[0043] 4. Application service layer This layer is the user interface of the system, which provides visual function services for users of different roles by calling the APIs or SDKs provided by the blockchain platform layer.

[0044] Purchasing management cockpit: for the management personnel of the purchaser (garment manufacturer), to display the material inventory level of each supplier's warehouse, the logistics status of the in-transit order, the comprehensive score ranking of the supplier, etc. in the form of charts in real time, and to provide manual approval and management functions for purchase orders.

[0045] Supplier portal: the supplier completes the authentication and on-chain of enterprise qualifications, manages material information, receives and responds to purchase orders, updates delivery status, and views the record of remittance through this portal.

[0046] Logistics tracking interface: Logistics company personnel or the purchaser can intuitively view the real-time position and historical trajectory of the transport vehicle bound to a specific order on the map through this interface.

[0047] Supervision audit interface: Provide read-only interface for possible regulatory agencies or internal audit departments, so that they can query and verify the transaction records on the chain according to the authority, and realize penetrating supervision.

[0048] Embodiment one The embodiment provides a specific implementation of a clothing supply chain transparent procurement and collaborative management method based on Internet of Things and block chain. The method aims to solve many problems in the existing clothing supply chain management through an integrated technical platform. Please refer to Figure 2 , which shows the overall business process of the method provided by the embodiment of the application. The method can run in an environment supported by the system architecture shown in Figure 1 , and the specific steps are as follows.

[0049] First, through the Internet of Things device deployed at the front end of the supply chain, the state data related to the raw materials is monitored and collected in real time.

[0050] In the embodiment, the so-called front end of the supply chain mainly refers to the warehousing link of the raw material supplier and the logistics transportation link from the supplier to the purchaser. In order to realize the comprehensive, objective and automatic collection of the state of the raw materials, the system deploys various types of Internet of Things devices in this link.

[0051] Specifically, an RFID (Radio Frequency Identification) access control or channel type reader is deployed in each certified supplier warehouse. Each batch of raw materials, such as each roll of fabric, will be attached with a passive ultra-high frequency (UHF) RFID electronic tag containing a unique identification code when entering the warehouse. When the raw materials enter or exit the warehouse, the RFID reader will automatically and non-contactly read the tag information in batches, so as to realize the accurate and real-time update of the inventory quantity, and avoid the inefficiency and error of manual inventory. At the same time, industrial-grade temperature and humidity sensors are also deployed in the key areas of the warehouse. These sensors are configured to record the current environmental temperature and humidity readings, for example, every 5 minutes, to ensure that the raw materials are stored in suitable environmental conditions and prevent the quality of the fabric from being reduced due to abnormal environment.

[0052] In the logistics transportation link, a temperature and humidity sensor is also installed in the cargo box of each truck responsible for transporting raw materials to monitor the in-transit environment. In addition, the transportation vehicle is also equipped with a GPS (Global Positioning System) locator and a vibration sensor. The GPS locator is configured to report the latitude and longitude coordinates of the vehicle to the system, for example, once every 1 minute, to realize the whole-process visual tracking of the order goods. The vibration sensor is used to record abnormal jolts or impact events during transportation, and these data can be used as an objective basis for evaluating the quality of logistics services and judging the potential damage risk of goods.

[0053] All these Internet of Things devices aggregate data through the Internet of Things gateway. The gateway is an edge computing device deployed on site, which is responsible for preliminary processing of raw sensor data, such as filtering out obvious noise data, aggregating high-frequency data into average values of specified time intervals, and then sending these structured data to the system's back-end platform in real time and continuously through a secure communication protocol such as MQTT (Message Queue Telemetry Transport) via a wireless network such as 5G or NB-IoT (Narrowband Internet of Things).

[0054] To ensure the original authenticity of the collected data, the selected Internet of Things devices need to have the corresponding industrial protection level. For example, the temperature and humidity sensor adopts a design with a protective cover and a filter circuit to reduce the impact of electromagnetic interference and physical impact on the readings. For RFID readers, a multi-antenna layout and adaptive frequency hopping technology are used to overcome signal shielding and multi-tag collision problems to ensure the accuracy of tag reading. All sensors need to be calibrated by a third-party institution with CMA / CNAS qualification before deployment, and a calibration certificate will be generated. The hash value of this certificate can also be chained as part of the device digital identity.

[0055] Second, when the state data meets the pre-set procurement trigger condition, a procurement demand transaction is automatically generated and broadcast on the alliance blockchain network.

[0056] A monitoring service in the system background continuously receives and analyzes the state data sent from the front-end Internet of Things gateway. For inventory data, the purchaser can set a safe inventory threshold (e.g. 1000 meters) for each key raw material (e.g. mulberry silk fabric with material ID "M-Silk-01") according to its production plan and material consumption model. The monitoring service will continuously compare the real-time received inventory level (e.g. 950 meters) with this threshold.

[0057] Once the monitoring service detects that the real-time inventory level is below the preset safety stock threshold, this constitutes the "procurement trigger condition" in this step. At this point, the service will immediately and automatically construct a standard-form "procurement demand" transaction. This transaction is a digitally signed data package whose payload contains at least the following information: a clear event type identifier "LowStockWarning"; the unique ID of the material that triggered the demand "M-Silk-01"; the current inventory level 950; the set threshold 1000; and a timestamp accurate to the millisecond.

[0058] Subsequently, the system submits this procurement demand transaction to one or more peer nodes in the consortium blockchain network through the blockchain client SDK (software development kit). After the node receives the transaction, it will verify it, such as verifying whether the signature of the transaction is valid and the format is correct. After verification, the transaction will be broadcast to other nodes in the network and eventually packaged into a new block by the ordering service (Orderer Service), so that it is permanently and tamper-proof recorded on the distributed ledger jointly maintained by all participants. The on-chain transaction of this transaction marks the creation of a procurement demand originating from the real physical world and reaching consensus across the network.

[0059] It should be understood that using inventory level below threshold as procurement trigger condition is only a preferred embodiment of the present application. In other application scenarios, the preset procurement trigger condition can be set in other forms and can also be supported by the system.

[0060] For example, an alternative trigger condition is based on predictive maintenance. Internet of Things sensors installed on the supplier's production equipment (such as textile machines) can monitor parameters such as equipment vibration and temperature, and use machine learning models to predict the probability of equipment failure. When the health of a core spare part (such as a special bearing) is predicted to be below a threshold, the system can automatically trigger a procurement demand transaction for the spare part.

[0061] Another alternative trigger condition is based on direct monitoring of the quality status of raw materials. For example, for fabrics that need to be stored in a specific environment, when the Internet of Things temperature and humidity sensor detects that the environmental indicators have been continuously outside the preset safety range for a certain period of time (such as 2 hours), the system can determine that the batch of materials has a quality risk, and automatically trigger a new procurement demand transaction to replenish the potentially damaged inventory.

[0062] Third, the procurement smart contract deployed on the consortium blockchain network selects at least one candidate supplier from the consortium blockchain network in response to the procurement demand transaction.

[0063] In this embodiment, a set of Chaincode, i.e. smart contract, written in Go language or Node.js is pre-deployed on the consortium blockchain platform. Among them, a contract called "Procurement Dispatch Smart Contract" has an event listener implemented inside, which is specifically used to listen to a specific type of transaction on the chain, i.e. the "Procurement Demand Transaction" generated in the previous step.

[0064] When a new block containing the "LowStockWarning" event is added to the ledger, the listener of the smart contract will be activated and parse the material ID in the transaction. Then, the smart contract will call another "Identity Management Smart Contract". Please refer to Figure 4 , which maintains the trusted digital identity (DID) of all admitted suppliers and their business scope. The Procurement Dispatch Smart Contract queries the Identity Management Smart Contract to obtain a list containing all suppliers authorized to supply the specified material ID ("M-Silk-01") and currently in the "active" state. This list constitutes the "candidate supplier" list in this step.

[0065] Optionally, the screening conditions can be further refined, for example, the supplier must have a specific quality certification (e.g. ISO9001) or its registered address must be within a certain geographical area, and this information can be found in the associated attributes of its digital identity.

[0066] Fourthly, the procurement smart contract quantitatively scores the at least one candidate supplier based on multi-dimensional historical performance data related to the at least one candidate supplier obtained from the distributed ledger of the consortium blockchain network, and automatically determines a target supplier according to the scoring result.

[0067] This step is the core of realizing intelligent procurement decision-making. After obtaining the candidate supplier list, the Procurement Dispatch Smart Contract will perform a scoring calculation on each supplier in the list. Please refer to Figure 5 , which details the internal logic of this scoring decision.

[0068] For each candidate supplier, the smart contract queries the distributed ledger of the consortium blockchain using its unique DID as an index. Since all historical transactions are recorded on the chain, the smart contract can safely and efficiently retrieve all past performance records of the supplier. These data are multi-dimensional, specifically including at least the following four dimensions: 1. Historical transaction price data: Query all successful orders of this material in the history of this supplier, calculate the average transaction price or the latest transaction price, and compare it with the prices of all candidate suppliers to form a price score (P_score). Generally, the lower the price, the higher the score.

[0069] 2. Historical delivery on time rate data: Query the "promised delivery date" in the historical order and the "actual arrival date" confirmed by the purchaser node on the chain, calculate the average delay days, and form a delivery score (D_score). The less the delay, the higher the score.

[0070] 3. Historical order fulfillment success rate data: Count the total number of orders received by the supplier in the history and the number of orders finally successfully completed (i.e. no return or cancellation), calculate the fulfillment success rate, and form a fulfillment score (R_score).

[0071] 4. Historical inspection pass rate data of delivery batches: Query the on-chain inspection results of all orders related to this supplier in the history, count the proportion of "qualified" batches to total delivery batches, and form a quality score (Q_score).

[0072] After obtaining these raw data, the smart contract will apply a multi-dimensional weighted scoring model preset in the contract code to calculate the comprehensive score (S) of each supplier. The formula of the model can be expressed as: S = w_p * P_score + w_d * D_score + w_r * R_score + w_q * Q_score. Where w_p, w_d, w_r, w_q represent the weight coefficients of price, delivery, fulfillment and quality respectively, and their sum is 1. They can be pre-configured by the purchaser according to their own procurement strategy, for example, set the price weight to 0.4 and the others to 0.2.

[0073] To ensure the scientificity and fairness of the score, the design and implementation of the multi-dimensional weighted scoring model are detailed in this embodiment: 1) Determination method of weight coefficients: The weight coefficients w_p, w_d, w_r, w_q are not randomly set. The purchaser can input their preferences for different procurement goals through the Analytic Hierarchy Process (AHP) or expert scoring method in the system front-end interface, and the system will automatically calculate and generate a set of weight coefficients accordingly. For example, for urgent replenishment tasks, the system will guide the user to evaluate the importance of delivery on time as the highest, thereby automatically increasing the weight of w_d. These weight coefficients are chained as transaction metadata together with the procurement demand, ensuring the transparency and traceability of each decision-making basis.

[0074] where w_p, w_d, w_r, w_q are weight coefficients, and their sum is 1. These weight coefficients can be dynamically determined to adapt to different procurement task requirements. Specifically, the procurement demand transaction can contain a procurement strategy field (e.g., cost priority, speed priority, quality priority). The smart contract internally predefines multiple sets of weight coefficient combinations corresponding to these strategy labels. When the smart contract parses a specific strategy label, it automatically loads and applies the corresponding weight coefficients for scoring calculation. This mechanism makes the adjustment of weight coefficients no longer an arbitrary manual operation, but an automatic and conditional selection based on pre-set business rules, ensuring flexibility while maintaining the transparency and fairness of the process.

[0075] 2) Normalization of different dimensional data: In order to compare different dimensional and directional data such as price (yuan), delay days (days), and success rate (%) uniformly, the smart contract will perform Min-Max Normalization before calculating each sub-score. For example, for the negative indicator of price, the calculation formula of price score P_score is P_score = (MaxPrice - CurrentPrice) / (MaxPrice - MinPrice), where MaxPrice and MinPrice are the maximum and minimum values of all candidate supplier prices in this transaction. For positive indicators such as on-time delivery rate, a similar positive normalization formula is used. All sub-scores are uniformly mapped to the interval.

[0076] 3) Processing mechanism for sparse or missing historical performance data: To address the issue of unfair scoring for new suppliers or occasional transaction suppliers due to insufficient historical data, the smart contract introduces a cold start and decay mechanism.

[0077] a) Cold start: For new suppliers with fewer than a pre-set threshold (e.g., 5) of historical transactions, their historical data score will be composed of a default base score (e.g., 0.7) and an adjustment score based on their on-chain qualification level (e.g., whether they have passed ISO9001 or are strategic partners).

[0078] b) Data time decay: When calculating historical data, the model introduces a time decay factor. That is, the more recent the transaction data, the higher its weight in calculating the average; the more distant the transaction data, the lower its weight. This ensures that the score more accurately reflects the supplier's recent performance.

[0079] Finally, the smart contract calculates the comprehensive score S for each supplier in the candidate list. After all calculations are complete, the contract sorts all scores and automatically selects the supplier with the highest score as the target supplier for this procurement.

[0080] To make the mechanism of the collaboration between the various core smart contracts clearer, please refer to the attached Figure 3 , which shows a diagram of the interaction logic of the core smart contracts. The entire automated procurement process is completed by a group of functionally decoupled, logically interconnected smart contracts.

[0081] Specifically, the starting point of the entire process is the "procurement demand event", i.e. the transaction recorded on the chain in the aforementioned step due to the inventory falling below the threshold. This event serves as the initial trigger, first activating the "procurement scheduling smart contract" as the central coordinator.

[0082] After being activated, the "procurement scheduling smart contract" performs a series of predefined function calls. A query call will be initiated to the "identity management smart contract", passing the material ID as a parameter, to obtain a list of candidate supplier DIDs that have corresponding supply qualifications and are in the "active" state. This is one of the data bases for decision-making: determining "who is qualified".

[0083] After obtaining the list of candidate suppliers, the "procurement scheduling smart contract" needs to obtain objective basis for decision-making. Therefore, a series of rich queries for historical order data will be initiated to the "distributed ledger" itself. The distributed ledger broadly includes all archived historical transaction data. The contract will accurately read all past performance data of each candidate supplier, including price, delivery, quality inspection, etc. according to their DID. This is the second data base for decision-making: understanding "who does better".

[0084] After obtaining all necessary data and completing the internal quantitative scoring and sorting logic (i.e. the detailed process of the fourth step), the "procurement scheduling smart contract" determines the optimal target supplier. At this point, it enters the execution phase of the process, calling the "order management smart contract". It passes all the calculated and decided information, such as the selected supplier DID, procurement quantity, agreed price, etc., as parameters to the order management contract's create order function, to generate a new procurement order and store it on the chain.

[0085] Finally, when the order's state meets the pre-set payment conditions (e.g. goods receipt and quality inspection) under the life cycle management of the "order management smart contract", the "order management smart contract" will further automatically trigger the "settlement smart contract", passing the order ID and settlement amount information to the settlement contract to execute the final automated payment process.

[0086] Step 5, the procurement smart contract automatically generates a procurement order transaction containing procurement information, and stores the procurement order transaction on the consortium blockchain network for consensus and storage.

[0087] After the target supplier is determined, the procurement scheduling smart contract will immediately call another "order management smart contract". It will pass all the necessary information of this procurement, including the material specifications automatically brought out according to the material ID, the recommended procurement quantity calculated according to the trigger condition (for example, the difference between the safety threshold and the current inventory), the latest quotation obtained from the target supplier chain information, and the transaction subject information composed of the procurement party DID and the target supplier DID, as parameters to the "create order" function of the order management smart contract.

[0088] The order management smart contract will construct a structured "procurement order" transaction based on these parameters. The data structure of the transaction contains a unique order ID, a creation timestamp, an order status (initial state "to be confirmed"), and all the procurement details mentioned above. According to the business rules set by the system, this order creation transaction may need the endorsement of the procurement party node, for example, if the total order amount exceeds a certain limit, the procurement manager's node needs to digitally sign for confirmation.

[0089] After meeting the endorsement strategy, this procurement order transaction will be submitted to the ordering service and finally packaged into a new block and written into the distributed ledger. At this point, a procurement order triggered automatically by the system, with intelligent decision-making and multi-party consensus, is officially generated and takes effect. The target supplier will receive a notification of the new order through its client application (supplier portal).

[0090] Further, after determining the target supplier, the procurement smart contract needs to determine a fair, transparent, and automated transaction price for the procurement order it automatically generates. The present invention provides the following several optional pricing mechanisms, and the specific one to be used can be pre-configured in the smart contract: a) Latest quotation priority mechanism: the smart contract will automatically read the standard quotation of the target supplier for this material, which is the latest registration and on-chain in its digital identity associated information, as the transaction price. This mechanism is suitable for relatively stable materials.

[0091] b) Quotation-request matching mechanism: after the procurement demand transaction is triggered, the procurement scheduling smart contract will broadcast a chain quotation request to all eligible candidate suppliers in addition to the supplier scoring. Suppliers can submit encrypted quotations through their portals within a preset time window (such as 30 minutes). After the time window closes, the smart contract automatically decrypts all quotations and, combined with the comprehensive score of the supplier, executes a price-quality optimal or weighted optimal algorithm to determine the final transaction supplier and price. For example, the supplier with the highest comprehensive score is selected, and its quotation is adopted as the final price.

[0092] c) Historical Weighted Average Price Mechanism: In markets where price fluctuates frequently, the contract can automatically query all on-chain transaction records about this material in the past period (e.g. 30 days) of the target supplier, calculate a weighted average price (which can be weighted by transaction volume), and take it as the transaction price of this order, to stabilize market fluctuations.

[0093] Step 6: Continuously track order status updates related to the procurement order transaction on the consortium blockchain network, and automatically execute the settlement process associated with the procurement order transaction when the preset payment conditions are met.

[0094] After the order is on-chain, its life cycle management is also completely on the consortium blockchain network. The order management smart contract defines a series of functions for updating the order status, which can only be called by the participating nodes with corresponding permissions.

[0095] Specifically, when the target supplier accepts the order on the portal application, its node will call the confirmOrder function to update the order status from "to be confirmed" to "confirmed". When the supplier finishes stocking and hands it over to the logistics party for transportation, its node will call the shipOrder function and attach the logistics information to update the order status to "shipped". During transportation, the nodes of the authorized logistics company can periodically call the updateLogistics function to continuously update the logistics status of the order by writing the GPS data digest or key logistics node information (such as "arrived at the distribution center") on-chain. When the goods arrive at the procurement party's warehouse, the warehouse personnel complete the receipt of the goods through the RFID device, and the procurement party's node will call the receiveOrder function to update the order status to "arrived". Subsequently, an independent third-party inspection agency conducts inspection and writes the inspection result (e.g. "qualified" or "unqualified") and the hash value of the detailed inspection report associated with the order on-chain through its node by calling the submitInspectionResult function.

[0096] Throughout the process, a payment check logic inside the order management smart contract is continuously triggered. This logic strictly defines the "preset payment conditions" in this step. In this embodiment, the condition is set as an "and" logic: the latest status of the order must be "arrived" AND the latest inspection result associated must be "qualified".

[0097] Once the smart contract detects that both conditions are met, it will automatically invoke another "settlement smart contract". The settlement smart contract will execute an automated settlement process according to the amount and payment terms recorded in the order. This can be to directly initiate a transfer transaction in a system integrated with digital currency, or in a more common business scenario, to generate an encrypted and signed "electronic invoice" that cannot be tampered with. The electronic invoice can be automatically obtained and verified by the financial system of the purchaser (such as ERP), as a reliable instruction to trigger bank payment, thus completing the entire procurement process.

[0098] Embodiment Two The present embodiment provides a specific implementation of a clothing supply chain transparency procurement and collaborative management system based on Internet of Things and blockchain. The system is a product form for carrying out and executing the method described in Embodiment One. Referring to Figure 1 , the system can be logically divided into different levels and functional modules, and the functions and implementation methods of the main modules will be described in detail below. The system can be deployed on a series of physical servers, virtual machines or cloud infrastructure, and the nodes of each participant are distributed in their respective IT environments, together forming a decentralized business network.

[0099] Firstly, the system of the present invention includes an Internet of Things monitoring module. The function of this module is to serve as a bridge between the physical world and the digital world, responsible for objective and automated data collection on the status of physical entities at the front end of the supply chain, and for converting this physical event into a chain-based, trusted digital transaction when the data meets the preset conditions.

[0100] In a specific implementation, the Internet of Things monitoring module is composed of hardware and software. The hardware part is the various Internet of Things devices described in Embodiment One, such as ultra-high frequency RFID readers and writers, industrial-grade temperature and humidity sensors, GPS positioning modules, etc. The software part includes: 1) embedded software running on the Internet of Things gateway, using a lightweight operating system (such as Linux Yocto), responsible for device drivers, implementation of data collection protocols (such as the LLRP protocol for RFID), and edge-side preprocessing of data (such as filtering and aggregation algorithms written in Python or C++); 2) a backend data receiving and analysis service deployed on a cloud server, which can receive data streams from a large number of gateways through a high-concurrency message middleware (such as RabbitMQ or Kafka), and use a monitoring engine based on a rule engine (such as Drools) or a stream processing framework (such as Apache Flink) to match and judge procurement trigger conditions in real time. When the conditions are met, the service will use the client SDK of Hyperledger Fabric to construct and submit a procurement demand transaction to the blockchain network. ​

[0101] Further, to prevent data from being tampered with during transmission from the sensor to the Internet of Things gateway and then to the blockchain platform, the system uses end-to-end encryption and signature mechanisms. Specifically, some advanced sensors or microcontroller units (MCUs) connected to them have the ability to generate asymmetric key pairs and can digitally sign data packets at the source of data collection. After receiving the data, the Internet of Things gateway first verifies the signature, then signs the aggregated data packet again using the gateway's own private key, and transmits it to the cloud through the TLS 1.3 protocol. The backend service in the cloud will verify the signatures of the gateway and the source sensor in sequence to ensure the integrity of the data and the non-repudiation of the source, and then construct a transaction and submit it to the blockchain.

[0102] Secondly, the system of the present application includes a supplier matching module. The core function of this module is to perform an automated, trusted data-based supplier screening and selection logic after receiving the on-chain procurement demand, replacing the traditional procurement process of selecting suppliers based on human experience and subjective judgment.

[0103] In a specific implementation, the functions of the supplier matching module are completely embedded in the "procurement scheduling smart contract" deployed on the consortium blockchain network. This smart contract can be developed using the Go language and deployed as chaincode on the peer nodes of all relevant participants. Its internal implementation is as follows: 1) Define a chain-based data structure for storing supplier qualifications, service scope, and other information; 2) Include a queryQualifiedSuppliers function that receives a material ID as input and queries the DID list of all qualified suppliers in the state database (such as CouchDB); 3) The core is a rankAndSelectSupplier function that iterates through the list of candidate suppliers and, for each supplier, performs a series of distributed ledger-rich query operations to obtain its multi-dimensional historical performance data. For example, query all historical orders with docType as order and supplierDID as the target DID. After obtaining the data, the function calls internal private functions to calculate price points and delivery points, respectively, and finally calculates the comprehensive score based on the pre-set weighting formula and returns the supplier DID with the highest score. The entire process is executed on the blockchain node, ensuring the transparency, consistency of the calculation logic, and the non-tamperability of the results.

[0104] Thirdly, the system of the present application includes an order management module. The function of this module is responsible for the entire lifecycle management of procurement orders, from automatic creation, state transition to final archiving, ensuring that all related information and operations of the order are performed on the chain, forming a complete and auditable chain of evidence.

[0105] In a specific implementation, the functions of the order management module are also implemented by a chaincode named "Order Management Smart Contract". This contract defines a core Order asset data structure, which contains order ID, transaction parties' DID, material list, total amount, status (using an enumeration type such as PENDING, CONFIRMED, SHIPPED, RECEIVED, COMPLETED, CANCELED), timestamp, and other fields. The contract exposes a series of public functions for state transition, such as createOrder, confirmOrder, shipOrder, etc. Each function contains strict permission control logic, for example, the confirmOrder function will first verify whether the initiator of the transaction is the specified supplier in the order, and only after verification, the order status field will be modified. This way of solidifying business logic and permission control in on-chain code ensures the standardization and security of order management.

[0106] Finally, the system of the present application includes a state tracking and settlement module. The function of this module is to closely link the order performance status and the financial settlement process, and to realize automatic and conditional payment based on the performance facts.

[0107] In a specific implementation, the state tracking and settlement module is implemented by the order management smart contract and another "settlement smart contract". After each state update, the order management smart contract internally calls a checkPaymentConditions function. This function checks whether the current order status satisfies, for example, status == RECEIVED and inspectionResult == QUALIFIED. If it does, the order management smart contract calls the triggerPayment function of the settlement smart contract through cross-chain code calling, and passes the order ID and amount as parameters. The settlement smart contract is responsible for handling specific payment logic. For example, it can generate an electronic payment voucher that meets specific financial standards and contains digital signatures of all parties, and record it on the chain. The financial system of the purchaser can automatically obtain this trusted payment voucher by subscribing to on-chain events, and seamlessly integrate it into its internal payment gateway to complete the final bank transfer.

[0108] Specifically, the 'electronic invoice' is an XML or JSON file in a certain data specification (such as UBL standard), which contains the order details, amount, payment information and digital signatures of all parties. The system provides a set of secure RESTful API interfaces for the ERP system of the purchaser to call. The ERP system can periodically poll or subscribe to on-chain events through Webhook. Once a new valid invoice is generated, the system can obtain the invoice through the API and parse it, automatically generate a bill to be paid in the financial module, and then trigger the subsequent payment instruction.

[0109] Optionally, the system of the present application can further include a supplier access module. The function of this module is to ensure that all suppliers participating in the supply chain collaboration network are strictly reviewed and certified at the source, providing a trusted identity basis for all subsequent on-chain behaviors. In a specific implementation, this module includes a front-end web application (i.e. supplier portal) for suppliers to upload their qualification files. After receiving the files, the back-end service does not directly chain the original files, but calculates their SHA-256 hash values. The hash value is submitted together with the supplier's other information to call the registerSupplier function of the 'identity management smart contract'. This function records the access application in a 'to be reviewed' state on the chain. The governance rules of the consortium chain stipulate that, for example, the nodes of the procurement department and the legal department need to jointly endorse the transaction to make it effective. Once it is effective, the approveSupplier function of the contract is called to generate a DID for the supplier and update its status to 'active'. This way not only takes advantage of the consensus mechanism of the blockchain to ensure the public credibility of the review, but also protects the privacy of business files and saves on-chain storage space by storing the original files off-chain and the hash on-chain.

[0110] To protect the privacy of the commercial data of all participants, the system makes full use of the channel (Channel) mechanism of Hyperledger Fabric. Specifically, the purchaser establishes an independent and private communication channel with each supplier. All transactions related to inventory, quotations, and orders are only recorded and agreed upon within this channel, and other supplier nodes cannot access these data because they are not in this channel. For order fulfillment steps that require the participation of multiple parties (such as logistics and quality inspection), the Private Data Collection function of Fabric can be used to hash the core public information of the order on the main channel, while sensitive detailed data (such as prices) are distributed and stored among the participants who need to know in a peer-to-peer manner, achieving on-demand visibility of data and ensuring commercial confidentiality.

[0111] Example Three This embodiment will combine a specific apparel supply chain application scenario to further elaborate the technical solutions of the present invention, in order to demonstrate its application process and effects in actual business.

[0112] A high-end fashion clothing brand (the purchaser) needs to purchase a batch of high-quality mulberry silk fabric (Material ID: M-Silk-01) for making limited edition silk dresses from its suppliers. The purchaser has very high quality requirements for the fabric, especially for the temperature and humidity of the storage environment, and because it is a "fast fashion" model, it also has strict requirements for the procurement cycle and the response speed of the suppliers. Its cooperating suppliers include A, B, and C, all of which have joined the supply chain collaboration system described in the present invention.

[0113] Technical requirements and hardware selection: 1. Internet of Things sensing layer: In order to achieve accurate inventory and environmental monitoring of M-Silk-01 fabric, the purchaser requires all suppliers to deploy FX9600 fixed RFID readers in the constant temperature and humidity warehouse where the fabric is stored, and to paste Avery Dennison AD-381u8 inlay electronic tags on each roll of fabric. At the same time, SHT3x series high-precision digital temperature and humidity sensors are installed in the warehouse. The transportation vehicles use FMB920 GPS trackers. The specific frequency and accuracy of data collection are configured according to the monitored material properties and business requirements. In this embodiment, for high-value silk fabric, the RFID inventory checking frequency is set to no less than once every 10 minutes to ensure the quasi-real-time nature of inventory data. The temperature and humidity sensor acquisition frequency is once every minute, and the data accuracy requirement is temperature ±0.5°C and humidity ±3% RH. The GPS locator reports every 30 seconds, and the positioning accuracy requirement is within 5 meters. These technical parameters are written into the on-chain access agreement of the suppliers, and the system can automatically audit the data collection compliance of the suppliers by analyzing the time stamp interval and value fluctuation of the on-chain data.

[0114] 2. Blockchain platform layer: The entire system is built on the Hyperledger Fabric v2.2 LTS version of the consortium chain network based on the Linux Foundation. The network is maintained by the purchaser, suppliers A, B, and C, as well as a designated third-party logistics company and an authoritative quality inspection agency. Each participant has its own peer nodes (Peers) and ordering nodes (Orderers) deployed in the cloud (such as Alibaba Cloud or AWS). The smart contract (chain code) is written in Go language.

[0115] 3. Application Service Layer: The procurement team uses a web-based procurement management cockpit that interacts with the blockchain network through the Fabric SDK, visualizing all suppliers' inventories and in-transit orders in real-time. Suppliers, in turn, use a dedicated supplier portal web application to manage orders and shipments.

[0116] The safety stock threshold for material M-Silk-01 is set to 5000 meters in the buyer's system.

[0117] Storage environment requirements: Temperature 18-22°C, humidity 45-55% RH. Any reading outside this range for more than 30 minutes will be recorded as an "environmental anomaly" event by the IoT monitoring module and will be chained.

[0118] The supplier scoring model weights for the procurement scheduling smart contract are set as follows: quality weight (wq) = 0.5, delivery weight (wd) = 0.3, price weight (wp) = 0.1, and performance weight (wr) = 0.1. This weight configuration highlights the buyer's high emphasis on quality and delivery speed.

[0119] 1. Inventory alert and automatic procurement: The buyer's production line consumes M-Silk-01 fabric, causing the virtual total inventory (the sum of inventories at all suppliers) recorded in the ERP system to drop to 4800 meters. At the same time, the RFID system in Supplier A's warehouse shows a corresponding decrease in actual inventory. After the system's IoT monitoring module chains this inventory change, the aggregated inventory data falls below the 5000-meter safety threshold, triggering an on-chain procurement demand transaction for 2000 meters of M-Silk-01 fabric.

[0120] 2. Intelligent supplier matching: The procurement scheduling smart contract is activated. It filters out Suppliers A, B, and C as candidates. Then, it fetches historical data from the on-chain ledger to score them: Supplier A: 100% historical quality inspection pass rate, 98% on-time delivery rate, but highest bid.

[0121] Supplier B: 99% historical quality inspection pass rate, 90% on-time delivery rate, average bid.

[0122] Supplier C: 95% historical quality inspection pass rate, 95% on-time delivery rate, lowest bid.

[0123] According to the pre-set weights (quality 0.5, delivery 0.3), the smart contract calculates that Supplier A has the highest overall score. Therefore, the system automatically generates a procurement order for 2000 meters of fabric from Supplier A and sends it to Supplier A through an on-chain transaction.

[0124] 3. Order fulfillment and transparent tracking: Supplier A receives the order on its portal, clicks to confirm, and the order status is updated on-chain from "pending confirmation" to "confirmed". When the goods are shipped, the fabric is loaded onto a GPS-enabled transport vehicle, and supplier A links the order to the shipping waybill on-chain, updating the order status to "shipped". During the next 48 hours of transport, the purchasing manager of the buyer can see the real-time location of the vehicle on a map in their cockpit. One night, due to a temporary malfunction in the vehicle's refrigeration system, the temperature inside the cargo compartment reached 25°C for 40 minutes. This "environmental anomaly" event was captured by the temperature and humidity sensors inside the vehicle and recorded on-chain as an unalterable record, linked to the order.

[0125] 4. Receiving, quality inspection, and exception handling: The goods arrive at the buyer's warehouse. Before entering the warehouse, the quality inspection agency conducts a priority inspection of the batch of fabric. Noticing the "environmental anomaly" record on-chain, they add a test of the strength of the silk protein fibers. The test report shows that some physical performance indicators of the fabric have decreased slightly, although still within the usable range, but do not meet the highest standards of the limited edition dress. The quality inspection agency uploads the "conditionally qualified" test results and report hash on-chain.

[0126] 5. Collaborative processing and settlement based on trusted data: The purchasing manager of the buyer receives the updated quality inspection results on-chain. Due to the transparent data and solid evidence throughout the process, he immediately negotiates online with supplier A through the system. Based on the abnormal events recorded on-chain, the two parties quickly reach an agreement: supplier A agrees to provide a 15% price discount for this batch of orders and promises to improve the management of its logistics partners. This negotiation result is signed by both parties as a supplementary agreement to the order and uploaded on-chain. Subsequently, the system automatically triggers the settlement process based on the new price agreed upon, and completes the payment after confirming the order status as "arrived" and "conditionally qualified".

[0127] Please refer to the attached Figure 6 , which shows in the form of a schematic diagram the deployment relationship of various types of hardware devices required to implement the system in the specific application scenario of this embodiment and their data interaction links.

[0128] The entire physical deployment architecture can be divided into three core scenario areas: the supplier scenario, the logistics scenario, and the blockchain cloud platform as the brain of the system.

[0129] In the supplier scenario, the core is the raw material warehouse. In order to achieve accurate perception of inventory and environment, RFID readers and temperature and humidity sensors are deployed inside the warehouse. RFID readers are usually installed at the entrances and exits of the warehouse to cover and automatically identify the incoming and outgoing materials; temperature and humidity sensors are built into key storage areas in the warehouse. The raw data collected by these two sensors will not be directly uploaded to the cloud, but first connected to the Internet of Things gateway deployed on the warehouse site through wired or wireless (such as Wi-Fi, Zigbee) methods. The gateway is responsible for local aggregation, cleaning, formatting and encryption of data, and plays the role of edge computing.

[0130] In the logistics scenario, the core entity is the "transport vehicle". The vehicle is equipped with a "GPS locator" to track the real-time geographic location. These on-board devices are connected to a "vehicle gateway" through an internal bus. The vehicle gateway is similar to a mobile Internet of Things gateway, responsible for collecting in-transit state data of vehicles and goods.

[0131] All data from the supplier scenario and the logistics scenario ultimately need to be uploaded to the blockchain cloud platform. The communication link in Figure 6 shows this. The Internet of Things gateway can upload data through the low-power wide-area network (NB-IoT) or 5G network, while the vehicle gateway usually uses 5G or 4G cellular networks. The data is uploaded to the cloud server cluster deployed in the cloud, where it is received, distributed and deeply analyzed. Above the cloud server cluster, the core of the invention is the consortium chain node maintained by all participants, which communicates at high speed through an internal network, executes smart contracts, and maintains a distributed ledger.

[0132] Please refer to Figure 7 , which shows the significant technical effects that can be achieved compared to traditional supply chain management methods after adopting the disclosed method and system in a combination chart composed of four sub-charts from multiple dimensions and in detail. Figure 7 The technical effect comparison data shown in 1) Test environment: A Hyperledger Fabric simulation network containing 1 purchaser node, 10 supplier nodes, 2 logistics nodes and 1 quality inspection node was built and deployed on 4 cloud servers configured with 8-core CPU and 16GB memory.

[0133] 2) Comparison benchmark: The data of the traditional method is derived from the statistical and average of key performance indicators (KPI) of a three-month actual procurement process (involving 100 purchase orders) of a medium-sized clothing enterprise. Its process relies on email, telephone and manual ERP entry.

[0134] 3) Data sources of the method: In the simulation network, the same number (100) and type of procurement requirements as the traditional method were simulated by writing an automated script. Internet of Things data (inventory changes, GPS trajectories) were simulated by a data generator, with fluctuation patterns referencing real-world statistical distributions.

[0135] 4) Test method: The script automatically recorded the time stamp consumed by each procurement process from triggering to settlement completion, the time consumption of each link, the execution cost of the smart contract (simulated value of gas consumption), and the response and processing results of the system when introducing simulated abnormalities (such as logistics delay, quality inspection disqualification). Appendix Figure 7 The data in subgraphs (a) and (b) are the results obtained after statistical averaging and normalization of the KPIs of the 100 simulated processes and 100 actual processes. Appendix Figure 7 The effect of suppressing the bullwhip effect in subgraph (c) is obtained by inputting a normal distribution of actual demand into the simulator and observing the fluctuation variance of the procurement quantity under the two modes. Appendix Figure 7 The comprehensive capability evaluation in subgraph (d) is obtained by averaging the Likert scale scores of five industry experts based on the detailed processes of the two schemes.

[0136] Specifically, Figure 7 Subgraph (a) compares and analyzes the total cycle of the entire procurement process and its internal composition in the form of a grouped column chart. As can be seen from the graph, when using the traditional method, the four main stages from "demand identification and decision-making" to "accounting and settlement" take a long time, with a total cycle of about 18 days. After using the method of the invention, the total cycle is significantly shortened to 7 days. The most significant improvement is in the "demand identification and decision-making" stage, which is shortened from 5 days to 0.5 days, thanks to the real-time nature of Internet of Things inventory monitoring and the automated triggering mechanism of smart contracts, which almost eliminates manual perception delay and decision-making processes; the time of "order processing and confirmation" and "accounting and settlement" is also greatly reduced, which is due to the automated generation of orders and the automated execution of settlement processes, respectively.

[0137] Figure 7The neutron diagram (b) compares the differences in key operating costs and risks between the two methods, which are illustrated using a relative cost index (based on 100 for each item of the traditional method). After adopting the method of the present application, the "inventory holding cost" is reduced from 100 to 75, because real-time transparent inventory data and rapid procurement response enable enterprises to adopt a lower safety stock strategy, thereby improving inventory turnover; the "manual processing cost" is significantly reduced from 100 to 40, directly reflecting the efficiency improvement brought by the present application through replacing a large number of manual operations with automated processes; and the "quality problem cost" (including losses such as returns, claims, production delays, etc.) is significantly reduced from 100 to 30, which is mainly due to the process quality monitoring capability of the Internet of Things in the warehouse and logistics links, realizing early warning and rapid and accurate traceability of quality risks, and avoiding a large number of potential losses.

[0138] Figure 7 The neutron diagram (c) shows the technical effects of the present application in improving the stability of the supply chain and suppressing the "bullwhip effect" in the form of a line graph. The bullwhip effect refers to the distortion of demand information in the supply chain, which is amplified at each level, resulting in much larger fluctuations in the orders of upstream suppliers than in the demand of end consumers. In the figure, the green dotted line of "actual demand" represents the real demand fluctuation of the market. When the traditional method is adopted, the "purchase quantity" of the purchaser shows a much larger and more dramatic fluctuation than the actual demand, which is a typical consequence of information delay and inaccurate prediction. However, when the method of the present application is adopted, the "purchase quantity" closely follows the change curve of the actual demand, and the fluctuation amplitude is effectively suppressed. This shows that the system of the present application greatly enhances the response sensitivity of the supply chain to real demand through real-time data-driven, small-batch, and high-frequency automated procurement, thereby improving the stability and resilience of the entire chain.

[0139] Finally, ​ The neutron diagram (d) evaluates the comprehensive capabilities of the two methods in some key aspects that are difficult to quantify with a single index (full score of 10). It can be seen that the traditional method scores lower in "data transparency", "decision intelligence", and "risk traceability". However, the method of the present application shows great advantages in all evaluation dimensions, with the blue area formed by it much larger than the gray area of the traditional method, especially in "data transparency", "decision intelligence", "coordination efficiency", and "risk traceability", which are close to full marks. This directly proves that the present application is not a local optimization of a single link, but a systematic and all-round improvement of the entire supply chain collaborative management.

[0140] In summary, the accompanying drawings of the specification ​The four complementary subgraphs fully and deeply prove the technical solution proposed by the application from four aspects of time, cost, stability and comprehensive ability, and compared with the prior art, have many, significant, quantifiable technical advantages and beneficial effects.

[0141] The application constructs a new paradigm of supply chain collaborative management which deeply couples and closes the loop of objective perception of the physical world, decentralized trust of the digital world and automatic execution of business logic. It is not simply a functional superposition of Internet of Things and blockchain technology, but designs a trinity of automatic business architecture of "perception-records-execution". In this architecture, the Internet of Things technology is deployed as a front-end sensory system at the source of the supply chain (raw material end), which is responsible for converting the physical state (such as inventory level, storage environment, transportation process) that is difficult to quantify and opaque in traditional mode into objective, continuous and automatic raw data flow. Then, the alliance blockchain network is used as the trust cornerstone and shared hub of the whole system, which receives and processes these raw data, and through the distributed consensus mechanism, it upgrades them from isolated, perishable and single-sided tamperable signals to digital facts that are witnessed by all network participants, tamper-proof and fully traceable. The application uses the smart contract deployed on the chain as an automatic business execution engine, and directly uses these consensus-authorized trusted digital facts as triggering conditions with contractual force. For example, an Internet of Things perceived low inventory event is no longer just an information reminder, but directly activates the smart contract to automatically start a complex business process of supplier intelligent matching, order generation, status tracking and even final settlement based on multi-dimensional historical fulfillment data on the chain, which is completely transparent and fair. Thus, the application fundamentally constructs a self-driven, data-closed collaborative ecosystem, which changes the supply chain management from a passive response mode relying on artificial experience and delayed information to an active and automatic governance mode based on objective facts and preset rules.

[0142] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer programs are loaded on a computer and executed, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer programs can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer programs can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as high-density digital video disc (digital video disc, DVD)), or semiconductor media (such as solid state disk (solid state disk, SSD)) and the like.

[0143] Those of ordinary skill in the art can understand that the first, second, and the like various numerical designations involved in the present disclosure are only for the convenience of description and do not limit the scope of the embodiments of the present disclosure, nor represent the order of precedence.

[0144] At least one of the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, which is not limited by the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", and there is no order or size order between the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0145] The correspondence relationship shown in each table in the present disclosure can be configured or predefined. The values of the information in each table are merely examples, and other values can be configured, and the present disclosure is not limited. When configuring the correspondence relationship between the information and each parameter, it is not necessarily required to configure all the correspondence relationships shown in each table. For example, the correspondence relationship shown in some rows in the table in the present disclosure can also not be configured. For another example, the above tables can be appropriately deformed, for example, split, merged, and the like. The names of the parameters shown in the titles of the above tables can also use other names understandable by the communication device, and the values or representations of the parameters can also use other values or representations understandable by the communication device. The above tables can also use other data structures when implemented, for example, arrays, queues, containers, stacks, linear tables, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables, or the like.

[0146] The predefinition in the present disclosure can be understood as definition, predefinition, storage, prestorage, prenegotiation, preconfiguration, solidification, or pre-burning. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present disclosure.

[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0148] The above is merely a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present disclosure, which should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method for transparent procurement and collaborative management of the apparel supply chain based on the Internet of Things and blockchain, characterized in that, Includes the following steps: By deploying IoT devices at the front end of the supply chain, real-time monitoring and collection of status data related to raw materials are achieved. The status data includes at least one of raw material inventory level data, warehousing environment data, and transportation process data. When the status data meets the preset procurement triggering conditions, a procurement demand transaction is automatically generated and broadcast on the consortium blockchain network. The procurement smart contract deployed on the consortium blockchain network, in response to the procurement demand transaction, selects at least one candidate supplier from the consortium blockchain network; The procurement smart contract quantifies and scores the at least one candidate supplier based on multi-dimensional historical performance data related to the at least one candidate supplier obtained from the distributed ledger of the consortium blockchain network, and automatically determines a target supplier based on the scoring results. The procurement smart contract automatically generates a procurement order transaction containing procurement information, and the procurement order transaction is consensused and stored on the consortium blockchain network. as well as, The system continuously tracks order status updates related to the purchase order transaction on the consortium blockchain network, and automatically executes the settlement process associated with the purchase order transaction when preset payment conditions are met.

2. The method according to claim 1, characterized in that, The status data includes real-time inventory level data of raw materials, and the procurement trigger condition is that the real-time inventory level is lower than a preset safety stock threshold.

3. The method according to claim 1, characterized in that, The multi-dimensional historical performance data includes at least: historical transaction price data, historical on-time delivery rate data, historical order fulfillment success rate data, and historical quality inspection pass rate data for delivered batches. The step of quantitatively scoring the at least one candidate supplier specifically includes: For each dimension of the multi-dimensional historical performance data, a weighting coefficient is determined; and Based on the weighting coefficients and the multi-dimensional historical performance data, a comprehensive score for each candidate supplier is calculated using a multi-dimensional weighted scoring model, which serves as the quantitative scoring result.

4. The method according to claim 1, characterized in that, The order status update includes: order confirmation status and order shipment status initiated by the target supplier's node, in-transit logistics status initiated by the logistics participant's node, goods receipt status initiated by the purchaser's node, and quality inspection result status initiated by the quality inspection participant's node; the preset payment condition is that the order status update simultaneously satisfies the goods receipt status as signed and the quality inspection result status as qualified.

5. The method according to claim 1, characterized in that, Before selecting at least one candidate supplier, the process also includes: Receive qualification documents submitted by suppliers seeking admission; Perform a hash calculation on the qualification document to generate a qualification document hash value; The hash value of the qualification document is consensus-based and stored on the consortium blockchain network; and Through identity management smart contracts, a decentralized digital identity, unique across the entire network and bound to the hash value of the qualification document, is generated for suppliers who have passed the consensus review.

6. A transparent procurement and collaborative management system for the apparel supply chain based on the Internet of Things and blockchain, characterized in that, include: At least one processor; as well as A memory having stored instructions executable by the at least one processor, wherein when executed, the instructions cause the system to implement the method as described in any one of claims 1 to 5.

7. A transparent procurement and collaborative management system for the apparel supply chain based on the Internet of Things and blockchain, characterized in that, include: The IoT monitoring module is configured to monitor and collect status data related to raw materials in real time through IoT devices deployed at the front end of the supply chain, and automatically generate and broadcast a procurement demand transaction on the consortium blockchain network when the status data meets the preset procurement trigger conditions. The status data includes at least one of raw material inventory level data, warehousing environment data, and transportation process data. The supplier matching module is configured to, in response to the procurement demand transaction, filter at least one candidate supplier from the consortium blockchain network, and quantitatively score the at least one candidate supplier based on multi-dimensional historical performance data related to the at least one candidate supplier obtained from the distributed ledger of the consortium blockchain network, so as to automatically determine a target supplier based on the scoring results. The order management module is configured to automatically generate a purchase order transaction containing procurement information and to perform consensus and notarization of the purchase order transaction on the consortium blockchain network. as well as, The status tracking and settlement module is configured to continuously track order status updates related to the purchase order transaction on the consortium blockchain network, and automatically execute the settlement process associated with the purchase order transaction when preset payment conditions are met.

8. The system according to claim 7, characterized in that, The status data includes real-time inventory level data of raw materials, and the procurement trigger condition is that the real-time inventory level is lower than a preset safety stock threshold.

9. The system according to claim 7, characterized in that, The multi-dimensional historical performance data includes: historical transaction price data, historical on-time delivery rate data, historical order fulfillment success rate data, and historical quality inspection pass rate data for delivered batches; the supplier matching module is specifically configured to: determine a weight coefficient for each dimension of the multi-dimensional historical performance data; and calculate the comprehensive score of each candidate supplier based on the weight coefficient and the multi-dimensional historical performance data through a multi-dimensional weighted scoring model, as a quantitative scoring result.

10. The system according to claim 7, characterized in that, Also includes: The supplier admission module is configured to receive qualification documents submitted by suppliers seeking admission, perform hash calculations on the qualification documents to generate a qualification document hash value, conduct consensus and storage of the qualification document hash value on the consortium blockchain network, and generate a decentralized digital identity that is unique across the entire network and bound to the qualification document hash value for suppliers that pass the consensus review through an identity management smart contract.

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