CN-121998666-A - Supply chain full-flow data tracing and collaboration system based on block chain

CN121998666ACN 121998666 ACN121998666 ACN 121998666ACN-121998666-A

Abstract

The invention discloses a block chain-based supply chain full-flow data tracing and collaborative system, which relates to the technical field of supply chain digital management and comprises a supply chain collaborative management platform, wherein the supply chain collaborative management platform is in communication connection with a twin edge simulation monitoring module which is used for integrating digital twin and edge calculation technologies, constructing a real-time behavior simulation model at each supply chain link point, dynamically monitoring on-site operation behaviors and identifying anomalies in real time through time sequence mode analysis. According to the invention, the digital twin and edge calculation are integrated at key nodes of the supply chain, the entity behavior simulation model is constructed, the actual and expected operation behaviors are compared in real time, potential fraud and abnormal behaviors are identified, the abnormal data uplink is directly blocked at the edge side, the abnormal identification and blocking are pre-arranged in the data acquisition stage, the pollution of the block chain data source by false, repeated or malicious operation data is fundamentally prevented, and the authenticity, timeliness and integrity of the uplink data are ensured.

Inventors

CAO LIULI
ZHANG YAN

Assignees

霍州市星坤供应链科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260206

Claims (10)

1. The supply chain full-flow data tracing and collaboration system based on the block chain comprises a supply chain collaboration management platform, and is characterized in that the supply chain collaboration management platform is in communication connection with the following modules: the twin edge simulation monitoring module is used for integrating digital twin and edge calculation technology, constructing a real-time behavior simulation model at each supply chain link point, dynamically monitoring on-site operation behaviors, and identifying anomalies in real time through time sequence mode analysis; The intelligent contract interception and purification module automatically triggers the intelligent contract to intercept and clean abnormal field operation behavior data in real time based on the twinning edge simulation monitoring result to acquire a clean blockchain data set; the multi-agent coordination engine is used for constructing multi-role agents based on the clean block chain data set and adjusting a data synchronization strategy by combining the real-time state of each link of the supply chain; the dynamic path optimization module is used for continuously optimizing a data tracing path and cooperative logic in the data synchronization strategy through reinforcement learning; and the privacy security collaborative traceability module is used for introducing a differential privacy mechanism in the cross-link supply chain data sharing and adding controllable noise to the sensitive service field.
2. The block chain-based supply chain full-flow data tracing and cooperative system of claim 1, wherein said twin edge simulation monitoring module comprises a dynamic behavior simulation unit and a timing anomaly identification unit; the dynamic behavior simulation unit is used for deploying an entity behavior simulation model at an edge node by combining a digital twin and edge computing technology, simulating and comparing actual field operation behavior data with expected operation in real time, and identifying behavior deviation and potential fraud; The time sequence abnormality identification unit is used for monitoring the time sequence consistency of the on-site operation behavior in real time based on time sequence data analysis and automatically triggering an early warning mechanism.
3. The system for traceback and collaboration of supply chain full flow data based on blockchain as in claim 2, wherein the dynamic behavior simulation unit performs the steps of: disposing edge computing equipment at each supply chain node, and constructing a digital twinning-based entity behavior simulation model by combining entity physical parameters and a business process model, wherein the entity behavior simulation model comprises real-time mapping relations of vehicle positioning, weighing sensor data flow and loading and unloading action time sequences; Based on the entity behavior simulation model, real-time receiving actual field operation behavior data from the sensor cluster, executing expected operation simulation in parallel at the edge side, and calculating the deviation degree between the actual behavior and the simulation behavior through a behavior track comparison algorithm; When the deviation exceeds a preset deviation threshold, the on-site operation behavior is judged to be abnormal, a behavior deviation report is automatically generated, the abnormal event is marked as potential fraudulent behavior, and meanwhile, the transmission of original data corresponding to the abnormal operation to the blockchain platform is blocked.
4. The full-process data traceback and collaboration system of a supply chain based on a blockchain as set forth in claim 2, wherein the timing anomaly identification unit performs the steps of: Receiving actual field operation behavior data, and extracting multidimensional time sequence characteristics including a vehicle in-out time sequence, a continuous weighing numerical sequence and a loading operation interval; Applying a time sequence pattern recognition algorithm to analyze the multi-dimensional time sequence characteristics in real time, and detecting whether a time sequence pattern violating service logic exists or not, wherein the time sequence pattern includes repeated weighing in a short time, inverted loading and weighing sequence and abnormal vehicle residence time; When the abnormal time sequence mode is identified, a real-time early warning signal is automatically triggered, and an early warning event, an associated time stamp and an operation node identifier are sent to the intelligent contract interception and purification module so as to start a data interception flow.
5. The blockchain-based supply chain full-flow data traceback and collaboration system as in claim 2, wherein the intelligent contract interception and purification module comprises a contract trigger interception unit and a data cleaning archiving unit; The contract triggering interception unit is used for automatically executing intelligent contracts, marking abnormal data and blocking the abnormal data from being written into the blockchain when abnormal field operation behaviors are monitored; the data cleaning and archiving unit is used for cleaning, marking and archiving the intercepted abnormal data, recording the abnormal type, time and operator information, forming an abnormal data log, and integrating to obtain a clean block chain data set.
6. The system for full-process data traceback and collaboration of a supply chain based on a blockchain as set forth in claim 5, wherein the contract trigger interceptor unit performs the steps of: Monitoring an abnormal event signal from the twinning edge simulation monitoring module, and automatically calling an interception intelligent contract preset on a blockchain platform when an abnormal early warning is received; Analyzing abnormal event content by utilizing the interception intelligent contract, judging an abnormal level according to a predefined rule set, marking a to-be-checked state identifier by the to-be-checked data record generated by the current operation; executing interception logic in the intelligent contract, preventing the abnormal data record marked as the state to be checked from being written into a new block of the blockchain, generating an interception log containing an abnormal abstract, interception time and a triggering contract address, and storing the interception log in an under-chain database.
7. The full-process data traceback and collaboration system of a supply chain based on a blockchain as in claim 5, wherein the data cleansing and archiving unit performs the steps of: reading an interception log generated by the contract triggering interception unit from a link database, and extracting a corresponding original abnormal data record; calling a corresponding data cleaning rule according to the abnormal type, repairing, removing or marking the original abnormal data, generating a clean data version, and associating with the abnormal metadata; and archiving the clean data version, the associated abnormal metadata and the cleaning process record to an under-chain audit database together to form a structured abnormal data log, and synchronizing the clean data version to the blockchain platform to update the clean data version into an effective clean blockchain data set capable of being uplinked.
8. The blockchain-based supply chain full-flow data traceback and collaboration system of claim 5, wherein the multi-agent collaboration engine performs the steps of: Based on the clean blockchain data set output by the intelligent contract interception and purification module, respectively instantiating corresponding agents for warehouse management, transportation scheduling and financial settlement links, and packaging business logic and data access interfaces of the links of each agent; each agent monitors the state update event related to the link on the blockchain in real time, and dynamically adjusts the priority and frequency of data synchronization through a negotiation protocol according to the overall performance progress of the supply chain; And constructing a global order performance view through message transmission and state sharing among the intelligent agents, and recording key collaborative events on a blockchain.
9. The system for traceback and synergy of supply chain full flow data based on blockchain as in claim 8, wherein the dynamic path optimization module performs the steps of: The method comprises the steps of taking historical collaborative data of a full link of a supply chain and a real-time state as input, and constructing a reinforcement learning environment, wherein a state space comprises synchronous delay of data of each link, resource load and emergency degree of an order, and an action space is used for selecting a data tracing path and adjusting synchronous strategy parameters; The intelligent agent explores and executes actions in the reinforcement learning environment, action effects are evaluated through a reward function, and a strategy network is continuously and iteratively optimized so as to learn an optimal strategy which is suitable for a dynamic supply chain scene and comprises optimal data tracing and a cooperative path; and deploying the optimal strategy obtained by training to the multi-agent collaborative engine to guide each agent to dynamically adjust the data flow direction and the processing logic under the complex scene.
10. The full-flow data traceback and collaboration system of a supply chain based on a blockchain as set forth in claim 9, wherein the privacy security collaboration traceback module performs the steps of: before cross-link data sharing, identifying sensitive business fields in the data, including specific transaction amounts, provider costs and specific product inventory details; And adding Laplacian noise or Gaussian noise conforming to a preset privacy budget to the sensitive service field by applying a differential privacy mechanism, generating disturbed data meeting epsilon-differential privacy, and using the disturbed data for cross-agent collaborative traceability and global analysis.

Description

Supply chain full-flow data tracing and collaboration system based on block chain Technical Field The invention relates to the technical field of supply chain digital management, in particular to a block chain-based supply chain full-flow data tracing and collaboration system. Background With the increase of market competition and the increase of consumer demand for product quality and source transparency, enterprises are faced with pressure from all parties, and the optimization of supply chain management is urgently needed, so that a data-driven decision support system becomes the core of modern supply chain management, and can monitor and analyze data of all links in real time, thereby improving efficiency and reducing cost, and meanwhile, cooperation of a plurality of links such as logistics, production, sales and the like is helpful for realizing optimal allocation of resources, and all participants of a supply chain, such as suppliers, manufacturers and distributors, need to ensure information sharing through transparent data flow so as to respond to market changes and customer demands rapidly. For example, the core technology related to the product supply chain traceability system and method based on the industrial block chain of CN118520511A mainly comprises a traceability method of user roles, full-flow operation behaviors, full-period supply chain data flow supervision, full-flow states and processes related to the product supply chain. The prior art relies on blockchain data comparison to identify tampering, but can only carry out post verification after data is uplinked, abnormal behaviors cannot be identified in real time in a data acquisition stage, and in field links such as logistics transportation and the like, vehicles report data falsely by repeated weighing and other means to cheat fraudulent actions of subsidy, once the abnormal data are uplinked, the whole traceability system is polluted, the authenticity and timeliness of the data are destroyed, and after the abnormal data are blocked, a supply chain is related to multi-role and multi-link data asynchronous updating and isolation management such as warehouse, transportation, finance and the like, a collaborative traceability path is difficult to dynamically construct based on real-time and credible data, so that transparency of an order form implementing process is insufficient, cross-link collaborative efficiency is low, real-time and reliable supply chain decision and state tracking cannot be supported. Disclosure of Invention In order to solve the technical problems, the invention is realized by the following technical scheme that the supply chain full-flow data tracing and cooperative system based on the block chain comprises a supply chain cooperative management platform, wherein the supply chain cooperative management platform is in communication connection with the following modules: The twin edge simulation monitoring module is used for integrating digital twin and edge calculation technology, constructing a real-time behavior simulation model at each supply chain link point, dynamically monitoring on-site operation behaviors, and identifying anomalies in real time through time sequence mode analysis to ensure the reality and credibility of a data source; The intelligent contract interception and purification module automatically triggers the intelligent contract to intercept and clean abnormal field operation behavior data in real time based on the twinning edge simulation monitoring result to acquire a clean blockchain data set, prevent abnormal data from being linked up and ensure the integrity and timeliness of blockchain data; The multi-agent cooperative engine is used for constructing multi-role agents covering warehouse, transportation and finance based on the clean blockchain data set, and adjusting a data synchronization strategy by combining the real-time state of each link of the supply chain, so that the data synchronization of the links is realized, and the order performance transparency and the cooperative response speed are improved; The dynamic path optimization module is used for continuously optimizing a data tracing path and cooperative logic in a data synchronization strategy through reinforcement learning, adapting to dynamic change of a supply chain, supporting real-time and reliable supply chain state tracing and decision making, and improving the full-link operation efficiency; the privacy security collaborative traceability module is used for introducing a differential privacy mechanism in the cross-link supply chain data sharing, adding controllable noise to sensitive service fields, and protecting business privacy of each participant while guaranteeing data availability and traceability consistency. Preferably, the twin edge simulation monitoring module comprises a dynamic behavior simulation unit and a time sequence abnormality recognition unit; The dynamic behavior simulation unit is used for deplo