Distributed supply chain traceability system and method supported by block chain

Abstract

The invention discloses a distributed supply chain traceability system and method supported by a block chain, and relates to the field of data analysis, and the system comprises a collection module which is used for capturing the original data of a supply chain in real time through an Internet of Things sensor, and cleaning the data; the processing module is used for constructing a distributed account book based on a HyperledgerFabric architecture, performing hash encryption on the data, performing uplink storage on the data, packaging the data into blocks according to a time sequence, and completing data verification between nodes through a BFT consensus mechanism; according to the invention, full-link data of production, logistics and the like are captured in real time through the Internet of Things sensor and encrypted and chained, so that the data cannot be tampered and can be traced in the whole process, and the problems of easy data tampering and many breakpoints in traditional traceability are solved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a distributed supply chain traceability system and method supported by blockchain. Background Art

[0002] Supply chain traceability is a system that uses technologies like the Internet of Things and blockchain to record and track information throughout the entire product process, from raw material procurement to production, distribution, and sales. This system enables transparency of product information, facilitates quality control, anti-counterfeiting traceability, and accountability, improving supply chain efficiency and trust.

[0003] The invention patent application with application number 202310393922.2 discloses an efficient traceability method for confidential data in a supply chain, which includes: using blockchain technology to establish each supplier node as a supply chain system; after the key generation mechanism initializes the supply chain system, it generates and distributes keys to each supplier node; each supplier node includes at least a data requester node and a data holder node; the data holder node initially encrypts the supplier data, stores the initially encrypted data ciphertext in the IPFS file system, and uploads the IPFS file system address and data index to the blockchain: locally generates a proxy re-encryption key and an access list: the access list includes the data index, the ciphertext address, and a set of data requester nodes with access rights; the data requester node initiates a traceability request to the proxy server; The proxy server searches the access list based on the public key to verify whether the data requester node has access rights. If the identity authentication is passed, the IPFS file system ciphertext address of the corresponding data is obtained according to the data index query, and then the corresponding data ciphertext is obtained according to the ciphertext address. The corresponding data ciphertext is proxy re-encrypted using the proxy re-encryption key. After completion, the proxy re-encrypted ciphertext is forwarded to the data requester node, and the full process history of this data flow is uploaded to the chain. This application aims to solve the problem that "due to a large amount of interaction and collaboration between members such as suppliers, manufacturers, distributors, retailers and end consumers, various types of information generated during the operation of the entire supply chain are discretely stored in the respective systems of each link, and the information flow lacks transparency, which makes it difficult to establish trust between enterprises in the supply chain, data information is fragmented, and responsibility tracing is difficult."

[0004] However, for such scenarios, it is difficult to fully cover supply chain smart contract execution, node authority management, multi-condition traceability query, and data security protection;

[0005] To this end, a distributed supply chain traceability system and method supported by blockchain is proposed. Summary of the Invention

[0006] In response to the above-mentioned shortcomings of the existing technology, the present invention provides a distributed supply chain traceability system and method supported by blockchain, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a distributed supply chain traceability system supported by blockchain, comprising:

[0009] The acquisition module is used to capture the original data of the supply chain in real time through IoT sensors and clean the data; the processing module is used to build a distributed ledger based on the Hyperledger Fabric architecture, hash the data and store it on the chain, and package the data into blocks in chronological order and complete the data verification between nodes through the BFT consensus mechanism; the contract management module is used to deploy supply chain business contracts in the EVM virtual machine environment. When the preset conditions are triggered, the contract code is automatically executed to complete data verification, fund transfer and permission change, and the execution results are stored on the chain; the node management module is used to use the CA certification center to generate and manage identity keys for user nodes, and assign data access levels to different nodes through the RBAC permission model; the traceability module is used to provide a GraphQL interface to support multi-condition combination queries, quickly locate on-chain data through Elasticsearch indexes, and present the query results in the form of timeline maps and three-dimensional topology maps; the maintenance module is used to encrypt the transport layer data using the national secret SM4 algorithm and desensitize sensitive information through ZKP

[0010] Furthermore, the IoT sensors include RFID tags and GPS devices, and the raw supply chain data includes: product production information, logistics and transportation tracks, warehouse entry and exit records, and transaction vouchers. When cleaning the data, ETL tools are used to remove duplicates and standardize the data format;

[0011] The acquisition module integrates the IoT device interface, collects sensor data in real time, and performs integrity verification on the data. The verification logic is:

[0012] Hash=SHA-256(Data||Timestamp||Kay);

[0013] Where: Hash is the generated hash value; Data is the collected original data; Timestamp is the data collection timestamp; Kay is the node private key.

[0014] Furthermore, the processing module uses the SHA-256 algorithm to hash the data and then stores it on the chain, while maintaining the P2P communication of nodes in the blockchain network through the gRPC framework;

[0015] The block generation time in the processing module complies with:

[0016]

[0017] Where: T is the block generation time; T0 is the basic time parameter; α is the adjustment coefficient; n is the number of nodes participating in the consensus in the network; m is the minimum number of nodes required to reach consensus;

[0018] Among them, the BFT consensus mechanism dynamically adjusts the block generation time through the above formula, and the adjustment coefficient α ranges from (0, 1].

[0019] Furthermore, the supply chain business contracts in the contract management module include order settlement contracts and logistics receipt contracts, and the preset conditions include logistics node scanning and receipt;

[0020] The contract management module supports custom contract templates, allowing users to quickly deploy smart contracts based on different supply chain business scenarios, and also has contract version management capabilities;

[0021] The contract management module sets a dynamic gas fee adjustment mechanism to optimize contract execution efficiency:

[0022]

[0023] Where: Gas adjusted Gas fee after adjustment; Gas base is the basic Gas fee parameter; β is the load sensitivity coefficient; Load is the current blockchain network load rate; Load max is the maximum network load threshold;

[0024] Among them, when the network load is higher than 70%, the gas fee is increased based on the above formula, and the load sensitivity coefficient β is within the range of 0.1 to 0.5.

[0025] Furthermore, during the operation phase of the node management module, user nodes that perform identity key generation and management include manufacturers, logistics companies, and distributors, and simultaneously monitor the consensus node voting approval when nodes join, the data permission recovery when nodes exit, and the abnormal offline status of nodes;

[0026] The node management module adopts a hierarchical authority control architecture, dividing nodes into core nodes, ordinary nodes and consumer nodes. Nodes at different levels have different data access and operation permissions. Core nodes can participate in blockchain consensus, ordinary nodes can query all data, and consumer nodes can only query traceability information related to themselves.

[0027] Furthermore, the multi-condition combination query in the traceability module includes: product batch + logistics time interval search; specification parameter + logistics time interval search; product batch + specification parameter;

[0028] The timeline graph and three-dimensional topology graph support interactive zooming and key data highlighting;

[0029] The traceability module supports traceability data association analysis based on time series. The analysis operations are:

[0030] Production raw material procurement records, processing data, logistics transfer tracks, warehousing and outbound information, and sales transaction vouchers are indexed and stored by timestamp in a time series database. When a user initiates a traceability query, the system automatically extracts the time fields contained in the data of each link with millisecond accuracy. The system calculates the time correlation of data in different links through a dynamic time warping algorithm, identifies key nodes with time series dependencies, including raw material entry and production feeding, finished product delivery and logistics order acceptance, and uses the product's unique identifier as an index to map the cleaned structured data to an interactive timeline map drawn by D3.js. The map displays the sequence of nodes in the entire process horizontally using the date-time axis and is layered and labeled vertically according to the supply chain links. Each data node presents key information in the form of a bubble chart. The nodes are connected by arrow curves and the time interval is marked. Users can click on a node to view the detailed data hash value and chain block height of that link. It also supports filtering data for a specific time period using a time slider.

[0031] Among them, structured data includes operation subject, geographic location, and data hash value.

[0032] Furthermore, the sensitive information in the maintenance module includes transaction amounts. During the operation phase of the maintenance module, a firewall is deployed to monitor abnormal operations on the chain in real time and trigger access blocking mechanisms.

[0033] The maintenance module uses homomorphic encryption to process sensitive data, allowing data calculation and verification in ciphertext state. The encryption process is expressed as:

[0034]

[0035] Where: E is the encryption function; a and b are the original data;

[0036] The maintenance module monitors supply chain data changes in real time during operation. When data anomalies are detected, an early warning mechanism is automatically triggered and relevant nodes are notified through smart contracts to handle the anomaly.

[0037] Among them, data anomalies include: logistics track interruption, inconsistent transaction data, nodes frequently initiating invalid data chain requests, data timestamps that do not conform to business logic, and missing data fields.

[0038] Furthermore, the access blocking mechanism is:

[0039] Real-time interception: Through WAF or API gateway, requests that meet abnormal rules are immediately intercepted and the 403 Forbidden or 503 Service Temporarily Unavailable status code is returned;

[0040] Node isolation: If abnormal behavior comes from a specific blockchain node, the node will be marked as a "suspicious node" through the BFT consensus mechanism, and its communication link with other nodes will be temporarily cut off;

[0041] Dynamic blacklist: Add the IP address, node ID, or operating account that triggers the block to the dynamic blacklist, set the blocking time limit, and reject all requests from the subject during the blocking period. The blacklist supports manual review and removal;

[0042] The early warning mechanism is:

[0043] When the blocking operation is triggered, an early warning notification will be sent to the system administrator simultaneously, and it can be pushed via SMS, email or internal messaging system;

[0044] Linked with the contract management module, if the blocking reason is abnormal contract execution, the execution permission of the contract will be automatically suspended and a scan for contract vulnerabilities will be performed;

[0045] Among them, exception rules include:

[0046] When the number of query requests from the same IP address in a unit of time exceeds the threshold, or a large number of non-standard API calls appear; when ordinary nodes attempt to access unauthorized data layers, or obtain super-authorized operations by forging identity tokens; abnormal transactions are detected in the blockchain network.

[0047] Furthermore, the acquisition module is interactively connected with the processing module and the contract management module through a wireless network, the contract management module is interactively connected with the node management module through a wireless network, the node management module and the contract management module are interactively connected with the traceability module through a wireless network, and the traceability module and the processing module are interactively connected with the maintenance module through a wireless network.

[0048] On the other hand, a distributed supply chain traceability method supported by blockchain includes:

[0049] The original data of the supply chain is captured in real time through IoT sensors, and ETL tools are used to perform deduplication and format unification processing, and data integrity verification is completed through specific verification logic; a distributed ledger is built based on the Hyperledger Fabric architecture, and the data is hashed and encrypted using the SHA-256 algorithm and stored on the chain. The data is packaged into blocks in chronological order, and the data between nodes is verified through the BFT consensus mechanism and dynamic adjustment formula. At the same time, the node P2P communication is maintained through the gRPC framework; order settlement and logistics receipt supply chain business contracts are deployed in the EVM virtual machine environment. When the logistics node is triggered to scan the receipt preset conditions, the contract code is automatically executed to complete data verification and fund transfer, and the execution efficiency is optimized through the dynamic gas fee adjustment mechanism; the CA certification center is used to provide production enterprises with , the user nodes of the logistics company generate and manage identity keys, adopt the RBAC permission model and hierarchical control architecture, assign different data access and operation permissions to core nodes, ordinary nodes and consumer nodes, and monitor the node joining, exiting and abnormal offline status; perform combined queries through the GraphQL interface, use the Elasticsearch index to quickly locate the on-chain data, and present the query results in the form of timeline graphs and three-dimensional topology graphs, supporting interactive zooming, key data highlighting and time series-based traceability data association analysis; use the national secret SM4 algorithm and homomorphic encryption to encrypt the transport layer data and sensitive information, combine with ZKP to desensitize sensitive information, deploy firewalls to monitor abnormal operations in real time and trigger access blocking and early warning mechanisms, and simultaneously monitor data changes and handle anomalies.

[0050] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0051] The present invention provides a distributed supply chain traceability system and method supported by blockchain. During execution, the system and method captures production, logistics and other full-link data in real time through IoT sensors and encrypts and uploads them to the blockchain, ensuring that the data cannot be tampered with and is fully traceable. This solves the problems of easy data tampering and multiple breakpoints in traditional traceability.

[0052] The consensus mechanism dynamically adjusts block generation time, and combines smart contracts to automatically execute order settlement, logistics receipt, and other services, significantly improving supply chain collaboration efficiency and reducing manual intervention costs. The use of hierarchical authority control and national secret algorithm encryption ensures data security while enabling differentiated access to traceability information by different roles.

[0053] By visualizing traceability data through timeline graphs and three-dimensional topology graphs, combined with time-series correlation analysis, the entire process information can be presented in an intuitive and interactive manner, making it easy to quickly locate problem nodes. This provides innovative solutions for transparent management and risk prevention and control in all links of the supply chain, effectively promoting the development of the supply chain towards intelligence and trustworthiness. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0055] Figure 1 This is a schematic diagram of the structure of a distributed supply chain traceability system supported by blockchain;

[0056] Figure 2 The figure is a flowchart of a distributed supply chain traceability method supported by blockchain. DETAILED DESCRIPTION

[0057] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] The present invention will be further described below with reference to the embodiments.

[0059] Example 1:

[0060] A distributed supply chain traceability system supported by blockchain in this embodiment, such as Figure 1 Shown, including:

[0061] The acquisition module is used to capture raw supply chain data in real time through IoT sensors and clean the data;

[0062] IoT sensors include RFID tags and GPS devices. Supply chain raw data includes: product production information, logistics and transportation tracks, warehouse inbound and outbound records, and transaction vouchers. When cleaning data, ETL tools are used to remove duplicates and standardize the data format.

[0063] The acquisition module integrates the IoT device interface, collects sensor data in real time, and performs integrity verification on the data. The verification logic is as follows:

[0064] Hash=SHA-256(Data||Timestamp||Kay);

[0065] Where: Hash is the generated hash value; Data is the collected original data; Timestamp is the data collection timestamp; Kay is the node private key;

[0066] The processing module is used to build a distributed ledger based on the Hyperledger Fabric architecture, hash the data and store it on the chain, package the data into blocks in chronological order, and complete data verification between nodes through the BFT consensus mechanism;

[0067] The processing module uses the SHA-256 algorithm to hash and encrypt the data before storing it on the chain. At the same time, the gRPC framework is used to maintain P2P communication between nodes in the blockchain network.

[0068] The block generation time in the processing module follows:

[0069]

[0070] Where: T is the block generation time; T0 is the basic time parameter; α is the adjustment coefficient; n is the number of nodes participating in the consensus in the network; m is the minimum number of nodes required to reach consensus;

[0071] Among them, the BFT consensus mechanism dynamically adjusts the block generation time through the above formula, and the adjustment coefficient α ranges from (0, 1];

[0072] The contract management module is used to deploy supply chain business contracts in the EVM virtual machine environment. When the preset conditions are triggered, the contract code is automatically executed to complete data verification, fund transfer and permission changes, and the execution results are stored on the chain;

[0073] The supply chain business contracts in the contract management module include order settlement contracts and logistics receipt contracts, and the preset conditions include scanning and receipt at the logistics node;

[0074] The contract management module supports custom contract templates, allowing users to quickly deploy smart contracts based on different supply chain business scenarios, and also has contract version management capabilities;

[0075] The contract management module sets up a dynamic gas fee adjustment mechanism to optimize contract execution efficiency:

[0076]

[0077] Where: Gas adjusted Gas fee after adjustment; Gas base is the basic Gas fee parameter; β is the load sensitivity coefficient; Load is the current blockchain network load rate; Load max is the maximum network load threshold;

[0078] Among them, when the network load is higher than 70%, the gas fee is increased based on the above formula, and the load sensitivity coefficient β is within the range of 0.1 to 0.5;

[0079] The node management module is used to generate and manage identity keys for user nodes using the CA certification center, and to assign data access levels to different nodes through the RBAC permission model;

[0080] During the node management module operation phase, user nodes that generate and manage identity keys include manufacturers, logistics companies, and distributors. It also monitors the consensus node voting approval when a node joins, the data permission recovery when a node exits, and abnormal node offline status.

[0081] The node management module adopts a hierarchical permission control architecture, dividing nodes into core nodes, ordinary nodes, and consumer nodes. Nodes at different levels have different data access and operation permissions. Core nodes can participate in blockchain consensus, ordinary nodes can query all data, and consumer nodes can only query traceability information related to themselves.

[0082] The traceability module is used to provide a GraphQL interface to support multi-condition combination queries, quickly locate on-chain data through Elasticsearch indexes, and present query results in the form of timeline graphs and three-dimensional topology graphs;

[0083] The multi-condition combination query in the traceability module includes: product batch + logistics time interval search; specification parameter + logistics time interval search; product batch + specification parameter;

[0084] Timeline graphs and 3D topology graphs support interactive zooming and highlighting of key data;

[0085] The traceability module supports traceability data association analysis based on time series. The analysis operations are:

[0086] Production raw material procurement records, processing data, logistics transfer tracks, warehousing and outbound information, and sales transaction vouchers are indexed and stored by timestamp in a time series database. When a user initiates a traceability query, the system automatically extracts the time fields contained in the data of each link with millisecond accuracy. The system calculates the time correlation of data in different links through a dynamic time warping algorithm, identifies key nodes with time series dependencies, including raw material entry and production feeding, finished product delivery and logistics order acceptance, and uses the product's unique identifier as an index to map the cleaned structured data to an interactive timeline map drawn by D3.js. The map displays the sequence of nodes in the entire process horizontally using the date-time axis and is layered and labeled vertically according to the supply chain links. Each data node presents key information in the form of a bubble chart. The nodes are connected by arrow curves and the time interval is marked. Users can click on a node to view the detailed data hash value and chain block height of that link. It also supports filtering data for a specific time period using a time slider.

[0087] Among them, structured data refers to the operation subject, geographic location, and data hash value;

[0088] The maintenance module is used to encrypt transport layer data using the national secret SM4 algorithm and desensitize sensitive information through ZKP;

[0089] Sensitive information in the maintenance module includes transaction amounts. A firewall is deployed simultaneously during the operation phase of the maintenance module to monitor abnormal operations on the chain in real time and trigger access blocking mechanisms.

[0090] The maintenance module uses homomorphic encryption to process sensitive data, allowing data calculation and verification in ciphertext state. The encryption process is expressed as:

[0091]

[0092] Where: E is the encryption function; a and b are the original data;

[0093] During the operation phase of the maintenance module, changes in supply chain data are monitored in real time. When data anomalies are detected, an early warning mechanism is automatically triggered, and relevant nodes are notified through smart contracts to handle the anomaly.

[0094] Data anomalies include: logistics track interruption, inconsistent transaction data, nodes frequently initiating invalid data on-chain requests, data timestamps that do not conform to business logic, and missing data fields;

[0095] The access blocking mechanism is:

[0096] Real-time interception: Through WAF or API gateway, requests that meet abnormal rules are immediately intercepted and the 403 Forbidden or 503 Service Temporarily Unavailable status code is returned;

[0097] Node isolation: If abnormal behavior comes from a specific blockchain node, the node will be marked as a "suspicious node" through the BFT consensus mechanism, and its communication link with other nodes will be temporarily cut off;

[0098] Dynamic blacklist: Add the IP address, node ID, or operating account that triggers the block to the dynamic blacklist, set the blocking time limit, and reject all requests from the subject during the blocking period. The blacklist supports manual review and removal;

[0099] The early warning mechanism is:

[0100] When the blocking operation is triggered, an early warning notification will be sent to the system administrator simultaneously, and it can be pushed via SMS, email or internal messaging system;

[0101] Linked with the contract management module, if the blocking reason is abnormal contract execution, the execution permission of the contract will be automatically suspended and a scan for contract vulnerabilities will be performed;

[0102] Among them, exception rules include:

[0103] When the number of query requests from the same IP address in a unit of time exceeds the threshold, or a large number of non-standard API calls appear; when ordinary nodes attempt to access unauthorized data layers, or obtain super-authorized operations by forging identity tokens; when abnormal transactions are detected in the blockchain network;

[0104] The acquisition module is interactively connected with the processing module and the contract management module through a wireless network. The contract management module is interactively connected with the node management module through a wireless network. The node management module and the contract management module are interactively connected with the traceability module through a wireless network. The traceability module and the processing module are interactively connected with the maintenance module through a wireless network.

[0105] In this embodiment, the acquisition module uses IoT sensors to capture supply chain raw data in real time and cleans the data. The processing module then runs to build a distributed ledger based on the Hyperledger Fabric architecture, hashes the data and stores it on the chain, packages the data into blocks in chronological order, and completes data verification between nodes through the BFT consensus mechanism. The contract management module is then used to deploy supply chain business contracts in the EVM virtual machine environment. When the preset conditions are triggered, the contract code is automatically executed to complete data verification, fund transfers, and permission changes, and the execution results are stored on the chain. The node management module further uses the CA certification center to generate and manage identity keys for user nodes, assigns data access levels to different nodes through the RBAC permission model, and uses the traceability module to provide a GraphQL interface to support multi-condition combination queries, quickly locates on-chain data through the Elasticsearch index, and presents the query results in the form of a timeline map and a three-dimensional topology map. Finally, the maintenance module uses the national secret SM4 algorithm to encrypt the transport layer data and desensitizes sensitive information through ZKP.

[0106] It should be noted that:

[0107] Dynamic interaction logic and multi-terminal adaptation of data visualization in the traceability module:

[0108] WebGL technology is introduced to enable 360-degree rotation of the 3D topology map and node-penetrating query. When the user drags the 3D model, the system automatically highlights the upstream and downstream links related to the current node, and displays the hash value and transaction timestamp of the related data in a pop-up window.

[0109] A lightweight visualization component is developed for mobile devices, which uses Canvas drawing technology to compress the horizontal display dimension of the timeline graph, supports two-finger zooming operations, and adds a node long-press recognition function in touch interaction. Long press can quickly retrieve the hash chain records of attached files such as quality inspection reports and logistics receipts for that link.

[0110] Example 2:

[0111] In terms of specific implementation, based on Example 1, this example refers to Figure 2 A distributed supply chain traceability system supported by blockchain in Example 1 is further described in detail:

[0112] A distributed supply chain traceability method supported by blockchain, including:

[0113] Step 1: Use IoT sensors to capture raw supply chain data in real time, use ETL tools to remove duplicates and standardize the format, and complete data integrity verification through specific verification logic;

[0114] Step 2: Build a distributed ledger based on the Hyperledger Fabric architecture, use the SHA-256 algorithm to hash and encrypt the data before storing it on the blockchain, package the data into blocks in chronological order, complete data verification between nodes through the BFT consensus mechanism and dynamic adjustment formula, and maintain node P2P communication through the gRPC framework;

[0115] Step 3: Deploy order settlement and logistics receipt supply chain business contracts in the EVM virtual machine environment. When the logistics node is triggered to scan the preset conditions for receipt, the contract code is automatically executed to complete data verification and fund transfer, and the execution efficiency is optimized through the dynamic gas fee adjustment mechanism.

[0116] Step 4: Use the CA certification center to generate and manage identity keys for user nodes of manufacturing enterprises and logistics companies. Adopt the RBAC permission model and hierarchical control architecture to assign different data access and operation permissions to core nodes, ordinary nodes, and consumer nodes. At the same time, monitor node joining, exiting, and abnormal offline status.

[0117] Step 5: Perform combined queries through the GraphQL interface, quickly locate on-chain data with the help of Elasticsearch indexes, and present the query results in the form of timeline graphs and three-dimensional topology graphs, supporting interactive zooming, key data highlighting, and time-series-based traceability data association analysis.

[0118] Step 6: Use the national secret SM4 algorithm and homomorphic encryption to encrypt the transport layer data and sensitive information, combine it with ZKP to desensitize sensitive information, deploy firewalls to monitor abnormal operations in real time and trigger access blocking and early warning mechanisms, and simultaneously monitor data changes and handle anomalies.

[0119] In summary, during the execution of the systems and methods in the above embodiments, IoT sensors are used to capture data from the entire chain, including production and logistics, in real time and encrypt it on the chain, ensuring that the data cannot be tampered with and is traceable throughout the entire process, solving the problems of easy tampering and multiple breakpoints in traditional traceability. The block generation time is dynamically adjusted based on the consensus mechanism, and order settlement, logistics receipt and other services are automatically executed in combination with smart contracts, which greatly improves the collaborative efficiency of the supply chain and reduces the cost of manual intervention. The hierarchical authority control and national secret algorithm encryption are used to ensure data security while achieving differentiated access to traceability information for different roles. The traceability data is visualized through timeline graphs and three-dimensional topological graphs, combined with time-series correlation analysis, so that the entire process information can be presented in an intuitive and interactive manner, facilitating the rapid location of problem nodes, and providing innovative solutions for transparent management and risk prevention and control in all links of the supply chain, effectively promoting the development of the supply chain towards intelligence and trustworthiness.

[0120] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distributed supply chain traceability system supported by blockchain, characterized by: include: The acquisition module is used to capture raw supply chain data in real time through IoT sensors and clean the data; The processing module is used to build a distributed ledger based on the Hyperledger Fabric architecture, hash the data and store it on the chain, package the data into blocks in chronological order, and complete data verification between nodes through the BFT consensus mechanism; The contract management module is used to deploy supply chain business contracts in the EVM virtual machine environment. When the preset conditions are triggered, the contract code is automatically executed to complete data verification, fund transfer and permission changes, and the execution results are stored on the chain; The node management module is used to generate and manage identity keys for user nodes using the CA certification center, and to assign data access levels to different nodes through the RBAC permission model; The traceability module is used to provide a GraphQL interface to support multi-condition combination queries, quickly locate on-chain data through Elasticsearch indexes, and present query results in the form of timeline graphs and three-dimensional topology graphs; The maintenance module is used to encrypt transport layer data using the national secret SM4 algorithm and desensitize sensitive information through ZKP.

2. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: The IoT sensors include RFID tags and GPS devices. The raw supply chain data includes: product production information, logistics and transportation tracks, warehouse entry and exit records, and transaction vouchers. When cleaning the data, ETL tools are used to remove duplicates and unify the data format. The acquisition module integrates the IoT device interface, collects sensor data in real time, and performs integrity verification on the data. The verification logic is: Hash=SHA-256(Data||Timestamp||Kay); Where: Hash is the generated hash value; Data is the collected original data; Timestamp is the data collection timestamp; Kay is the node private key.

3. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: The processing module uses the SHA-256 algorithm to hash the data and then stores it on the chain, while maintaining the P2P communication of nodes in the blockchain network through the gRPC framework; The block generation time in the processing module complies with: Where: T is the block generation time; T0 is the basic time parameter; α is the adjustment coefficient; n is the number of nodes participating in the consensus in the network; m is the minimum number of nodes required to reach consensus; Among them, the BFT consensus mechanism dynamically adjusts the block generation time through the above formula, and the adjustment coefficient α ranges from (0, 1].

4. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: The supply chain business contracts in the contract management module include order settlement contracts and logistics receipt contracts, and the preset conditions include logistics node scanning and receipt; The contract management module supports custom contract templates, allowing users to quickly deploy smart contracts based on different supply chain business scenarios, and also has contract version management capabilities; The contract management module sets a dynamic gas fee adjustment mechanism to optimize contract execution efficiency: Where: Gas adjusted Gas fee after adjustment; Gas base is the basic Gas fee parameter; β is the load sensitivity coefficient; Load is the current blockchain network load rate; Load max is the maximum network load threshold; Among them, when the network load is higher than 70%, the gas fee is increased based on the above formula, and the load sensitivity coefficient β is within the range of 0.1 to 0.

5.

5. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: During the operation phase of the node management module, user nodes that perform identity key generation and management include manufacturing companies, logistics companies, and distributors. It also monitors the consensus node voting approval when a node joins, the data permission recovery when a node exits, and the abnormal offline status of a node. The node management module adopts a hierarchical authority control architecture, dividing nodes into core nodes, ordinary nodes and consumer nodes. Nodes at different levels have different data access and operation permissions. Core nodes can participate in blockchain consensus, ordinary nodes can query all data, and consumer nodes can only query traceability information related to themselves.

6. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: The multi-condition combination query in the traceability module includes: product batch + logistics time interval search; specification parameter + logistics time interval search; product batch + specification parameter; The timeline graph and three-dimensional topology graph support interactive zooming and key data highlighting; The traceability module supports traceability data association analysis based on time series. The analysis operations are: Production raw material procurement records, processing data, logistics transfer tracks, warehousing and outbound information, and sales transaction vouchers are indexed and stored by timestamp in a time series database. When a user initiates a traceability query, the system automatically extracts the time fields contained in the data of each link with millisecond accuracy. The system calculates the time correlation of data in different links through a dynamic time warping algorithm, identifies key nodes with time series dependencies, including raw material entry and production feeding, finished product delivery and logistics order acceptance, and uses the product's unique identifier as an index to map the cleaned structured data to an interactive timeline map drawn by D3.js. The map displays the sequence of nodes in the entire process horizontally using the date-time axis and is layered and labeled vertically according to the supply chain links. Each data node presents key information in the form of a bubble chart. The nodes are connected by arrow curves and the time interval is marked. Users can click on a node to view the detailed data hash value and chain block height of that link. It also supports filtering data for a specific time period using a time slider. Among them, structured data includes operation subject, geographic location, and data hash value.

7. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: Sensitive information in the maintenance module includes transaction amounts. A firewall is deployed during the operation phase of the maintenance module to monitor abnormal operations on the chain in real time and trigger access blocking mechanisms; The maintenance module uses homomorphic encryption to process sensitive data, allowing data calculation and verification in ciphertext state. The encryption process is expressed as: Where: E is the encryption function; a and b are the original data; The maintenance module monitors supply chain data changes in real time during operation. When data anomalies are detected, an early warning mechanism is automatically triggered and relevant nodes are notified through smart contracts to handle the anomaly. Among them, data anomalies include: logistics track interruption, inconsistent transaction data, nodes frequently initiating invalid data chain requests, data timestamps that do not conform to business logic, and missing data fields.

8. A distributed supply chain traceability system supported by blockchain according to claim 7, characterized in that: The access blocking mechanism is: Real-time interception: Through WAF or API gateway, requests that meet abnormal rules are immediately intercepted and the 403 Forbidden or 503 Service Temporarily Unavailable status code is returned; Node isolation: If abnormal behavior originates from a specific blockchain node, the node will be marked as a "suspicious node" through the BFT consensus mechanism, and its communication links with other nodes will be temporarily cut off; Dynamic blacklist: Add the IP address, node ID, or operating account that triggers the block to the dynamic blacklist, set the blocking time limit, and reject all requests from the subject during the blocking period. The blacklist supports manual review and removal; The early warning mechanism is: When the blocking operation is triggered, an early warning notification will be sent to the system administrator simultaneously, and it can be pushed via SMS, email or internal messaging system; Linked with the contract management module, if the blocking reason is abnormal contract execution, the execution permission of the contract will be automatically suspended and a scan for contract vulnerabilities will be performed; Among them, exception rules include: When the number of query requests from the same IP address in a unit of time exceeds the threshold, or a large number of non-standard API calls appear; when ordinary nodes attempt to access unauthorized data layers, or obtain super-authorized operations by forging identity tokens; abnormal transactions are detected in the blockchain network.

9. A distributed supply chain traceability system supported by blockchain according to claim 1, characterized in that: The acquisition module is interactively connected with the processing module and the contract management module through a wireless network, the contract management module is interactively connected with the node management module through a wireless network, the node management module and the contract management module are interactively connected with the traceability module through a wireless network, and the traceability module and the processing module are interactively connected with the maintenance module through a wireless network.

10. A distributed supply chain traceability method supported by blockchain, the method being an implementation method of a distributed supply chain traceability system supported by blockchain as claimed in any one of claims 1 to 9, characterized in that: include: Step 1: Use IoT sensors to capture raw supply chain data in real time, use ETL tools to remove duplicates and standardize the format, and complete data integrity verification through specific verification logic; Step 2: Build a distributed ledger based on the Hyperledger Fabric architecture, use the SHA-256 algorithm to hash and encrypt the data before storing it on the blockchain, package the data into blocks in chronological order, complete data verification between nodes through the BFT consensus mechanism and dynamic adjustment formula, and maintain node P2P communication through the gRPC framework; Step 3: Deploy order settlement and logistics receipt supply chain business contracts in the EVM virtual machine environment. When the logistics node is triggered to scan the preset conditions for receipt, the contract code is automatically executed to complete data verification and fund transfer, and the execution efficiency is optimized through the dynamic gas fee adjustment mechanism. Step 4: Use the CA certification center to generate and manage identity keys for user nodes of manufacturing enterprises and logistics companies. Adopt the RBAC permission model and hierarchical control architecture to assign different data access and operation permissions to core nodes, ordinary nodes, and consumer nodes. At the same time, monitor node joining, exiting, and abnormal offline status. Step 5: Perform combined queries through the GraphQL interface, quickly locate on-chain data with the help of Elasticsearch indexes, and present the query results in the form of timeline graphs and three-dimensional topology graphs, supporting interactive zooming, key data highlighting, and time-series-based traceability data association analysis. Step 6: Use the national secret SM4 algorithm and homomorphic encryption to encrypt the transport layer data and sensitive information, combine it with ZKP to desensitize sensitive information, deploy firewalls to monitor abnormal operations in real time and trigger access blocking and early warning mechanisms, and simultaneously monitor data changes and handle anomalies.

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