Blockchain-Driven Supply Chain Collaborative Management Method

Abstract

The present application provides a blockchain-driven supply chain collaborative management method, which relates to the technical field of supply chain management. It collects sensing data of multiple nodes in the supply chain; uploads the sensing data of multiple nodes to the blockchain in real time to generate multiple blocks; optimally configures the multiple blocks to generate a resource configuration result, and based on the resource configuration result, shares and updates the blockchain to generate a platform shared data set of the blockchain; conducts multi-party collaboration through the platform shared data set to formulate collaborative management decisions; simulates the execution of the collaborative management decisions to monitor the risks of the supply chain, activates the early warning mechanism to give feedback responses to the collaborative management decisions, and generates collaborative management optimization strategies. The present application solves the technical problems in the prior art that due to the lack of reliable data sharing and collaborative decision-making mechanisms, the data sharing efficiency between nodes in the supply chain is low and the collaborative decision-making efficiency is not high, and improves the real-time performance, transparency and collaborative decision-making efficiency of supply chain management.

Description

Technical Field

[0001] This application relates to the technical field of supply chain management, and particularly to a blockchain-driven supply chain collaborative management method. Background Art

[0002] Supply chain management is a key activity that coordinates resources and information among various nodes in the supply chain, such as suppliers, manufacturers, distributors, and retailers. The increasingly complex multi-node collaboration and dynamic market demands in the supply chain pose higher requirements for supply chain management.

[0003] Existing supply chain management methods usually rely on centralized systems or decentralized data processing, where each node separately collects, stores, and processes information. Due to the lack of a unified data sharing mechanism, the collaboration efficiency between the upstream and downstream of the supply chain is low, the problem of information silos is serious, resulting in an increase in communication costs between nodes. Secondly, existing methods mostly focus on static configuration and lack the ability to optimize dynamic resources, making it difficult to achieve real-time and secure data sharing. Data lag often leads to uneven resource allocation and slow supply chain response, and it is unable to quickly adapt to demand fluctuations or emergencies. Summary of the Invention

[0004] This application provides a blockchain-driven supply chain collaborative management method, which solves the technical problems in the prior art that due to the lack of a reliable data sharing and collaborative decision-making mechanism, the data sharing efficiency between nodes in the supply chain is low and the collaborative decision-making efficiency is not high, and achieves the technical effects of improving the real-time performance, transparency, and collaborative decision-making efficiency of supply chain management.

[0005] In view of the above problems, this application provides a blockchain-driven supply chain collaborative management method, and the method includes: traversing multiple supply chain nodes in the supply chain for data collection to obtain multiple node sensing data; uploading the multiple node sensing data to the blockchain in real time to generate multiple blocks; optimizing and configuring the multiple node sensing data of the supply chain according to the multiple blocks to generate a resource configuration result, and based on the resource configuration result, sharing and updating the blockchain to generate a platform shared data set of the blockchain; based on the multiple supply chain nodes, conducting multi-party joint through the platform shared data set of the blockchain to formulate a collaborative management decision; simulating the execution of the collaborative management decision to monitor the risks of the supply chain to generate an abnormal data set, and according to the abnormal data set, activating an early warning mechanism to give a feedback response to the collaborative management decision to generate a collaborative management optimization strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By collecting sensing data from multiple nodes, the original information of each link in the supply chain can be obtained, providing the most basic data source for the management of the entire supply chain. The multiple node sensing data is uploaded in real time through the blockchain to generate multiple blocks, establishing a reliable infrastructure for data sharing and management. The multiple node sensing data of the supply chain is optimally configured according to the multiple blocks to generate a resource allocation result, improving the resource utilization efficiency. Based on the resource allocation result, the blockchain is shared and updated to generate a platform shared data set of the blockchain, enabling the optimized resource allocation result to be shared among the nodes of the supply chain, further breaking the information silos, promoting the collaborative cooperation among the nodes, and improving the synergy of the entire supply chain. Based on the multiple supply chain nodes, a multi-party union is carried out through the platform shared data set of the blockchain to formulate collaborative management decisions, which can integrate the interests and needs of each link in the supply chain, make the decisions more scientific and reasonable, and improve the decision-making efficiency and quality of the overall supply chain. By simulating the execution of the collaborative management decision to monitor the risks of the supply chain, abnormal situations in the operation process of the supply chain can be detected in time to generate an abnormal data set. According to the abnormal data set, the early warning mechanism is activated to give a feedback response to the collaborative management decision, generating a collaborative management optimization strategy, thereby continuously improving the collaborative management decision and enhancing the supply chain's ability to respond to risks.

[0008] In summary, through the optimization of data configuration, the formulation of collaborative decisions, and the dynamic optimization mechanism, this application reduces information asymmetry and decision-making errors in the supply chain, thereby improving the overall operation efficiency of the supply chain. The data security ensured by blockchain technology and the dynamic optimization of decisions enable the supply chain to adjust decisions in a timely manner when facing internal and external risks, ensuring the efficient and stable operation of the supply chain, enhancing the stability and reliability of the supply chain, and improving the overall competitiveness of the supply chain and its ability to respond to complex environments.

[0009] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. In order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of the blockchain-driven supply chain collaborative management method provided by the embodiment of this application.

[0011] Figure 2 It is a schematic flowchart of generating multiple blocks in the blockchain-driven supply chain collaborative management method provided by the embodiment of this application.

[0012] Figure 3Schematic flowchart of making collaborative management decisions in the blockchain-driven supply chain collaborative management method provided by the embodiments of the present application. Detailed implementation manners

[0013] By providing the blockchain-driven supply chain collaborative management method, the embodiments of the present application solve the technical problems in the prior art that due to the lack of a reliable data sharing and collaborative decision-making mechanism, the data sharing efficiency between supply chain nodes is low and the collaborative decision-making efficiency is not high, and achieve the technical effects of improving the real-time performance, transparency, and collaborative decision-making efficiency of supply chain management.

[0014] As Figure 1 shown, the embodiments of the present application provide a blockchain-driven supply chain collaborative management method, and the method includes:

[0015] Step S1: Traverse multiple supply chain nodes in the supply chain for data collection to obtain multiple node sensing data.

[0016] Specifically, supply chain nodes are each link participating in information flow and resource interaction in the supply chain. For example, raw material suppliers, manufacturers, wholesalers, warehousing management, logistics companies, retailers, etc. are all supply chain nodes. Node sensing data refers to the data obtained through sensors or other data collection devices at each supply chain node. These data can reflect various state information of the nodes, such as production quantity, inventory level, location and speed of transport vehicles (if it is a transport node), etc.

[0017] Data collection is performed on each node through sensors or data collection devices deployed on each supply chain node. These sensors can be physical sensors, such as temperature sensors, humidity sensors, pressure sensors, etc., or software-level data collection tools, such as monitoring software for collecting the operating data of production equipment. Exemplarily, in a fresh food supply chain, there will be soil humidity sensors and weather stations (collecting data such as temperature, humidity, rainfall, etc.) at the farm node; there will be GPS sensors (collecting vehicle location and driving speed) at the transport node; and there will be an inventory management system at the warehouse node to collect inventory quantity and other data. These different types of data collection devices collect the node sensing data of each node.

[0018] By traversing multiple supply chain nodes in the supply chain for data collection, each link of the supply chain can be monitored in real time, ensuring the accuracy and integrity of the data, providing basic data support for subsequent data uploading, optimization configuration, and collaborative management decisions, and ensuring that the subsequent steps can be based on accurate and comprehensive data.

[0019] Step S2: Real-time upload the multiple node sensing data through the blockchain to generate multiple blocks.

[0020] Specifically, blockchain is a distributed ledger technology that stores data on multiple nodes (computers), and each node holds a complete or partial copy of the ledger. Data exists in the form of blocks, each block contains certain transaction information or data, and they are linked together through cryptographic techniques. A block is the basic data structure unit in the blockchain. Each block contains certain data (in this supply chain management solution, it is node sensing data), a timestamp, the hash value of the previous block, and other information.

[0021] The multiple node sensing data collected are first encrypted and then automatically triggered to be uploaded to the blockchain network through a smart contract. These data are packed into new blocks. Each block contains a series of data records and is linked to the previous block through the hash value, forming a continuously growing blockchain. The blockchain ensures the security and reliability of the node sensing data, providing a decentralized and tamper-proof data recording system for the supply chain.

[0022] Step S3: Optimally allocate the multiple node sensing data of the supply chain according to the multiple blocks to generate a resource allocation result, and based on the resource allocation result, perform a shared update on the blockchain to generate a platform shared dataset of the blockchain.

[0023] Specifically, the resource allocation result is the result of reasonably allocating various resources (such as human, material, and financial resources) in the supply chain according to the sensing data of each node in the supply chain. The platform shared dataset is a dataset formed after a shared update on the blockchain. This dataset contains supply chain-related data after optimal allocation and can be accessed and used by multiple nodes in the supply chain.

[0024] Analyze the node sensing data in each block, which reflects the status of each node in the supply chain. Use deep learning and optimization algorithms to optimize the allocation of resources according to factors such as the needs and supply capabilities of each node to generate a resource allocation result, so as to improve resource utilization efficiency, reduce costs, and enhance the response ability of the supply chain. Then, update the optimized resource allocation result to the blockchain, making the dataset on the blockchain become a platform shared dataset containing the latest resource allocation information for access and use by each node in the supply chain.

[0025] For example, in a furniture manufacturing supply chain, according to the inventory data of the wood supplier node (node sensing data in the block), the production capacity and order demand data of each production workshop, determine resource allocation results such as how much wood to allocate to each workshop and how many workers to arrange for overtime through data analysis algorithms. Then update this result to the blockchain, and supply chain nodes such as furniture manufacturers and sellers can obtain these shared data through the blockchain platform.

[0026] Step S4: Based on the platform shared data set of the multiple supply chain nodes through the blockchain, conduct multi-party collaboration and formulate collaborative management decisions.

[0027] Specifically, collaborative management decisions are decisions regarding supply chain management jointly made by multiple nodes in the supply chain. This decision takes into account various factors such as the interests of multiple nodes, resource status, market demand, etc., aiming to achieve the optimal operation of the entire supply chain. Multiple supply chain nodes utilize the shared data set on the blockchain, conduct games and analyses through a multi-party collaboration model, generate joint game results, and formulate collaborative management decisions to promote cooperation among all parties in the supply chain, improve the efficiency of decision-making, and enhance the collaborative effect.

[0028] Step S5: Simulate the execution of the collaborative management decision to conduct risk monitoring on the supply chain, generate an abnormal data set, activate the early warning mechanism based on the abnormal data set to give a feedback response to the collaborative management decision, and generate a collaborative management optimization strategy.

[0029] Specifically, the abnormal data set is a data set that does not conform to the normal situation discovered through risk monitoring of the supply chain during the simulation execution of the collaborative management decision. These data may indicate risks in the supply chain, such as production delays, inventory shortages, transportation failures, etc. The collaborative management optimization strategy is a management strategy generated after adjusting and optimizing the collaborative management decision according to the abnormal situation. This strategy aims to eliminate or mitigate the impact of risks on the supply chain and restore the normal operation of the supply chain.

[0030] Use supply chain simulation software or establish a mathematical model of the supply chain, input the formulated collaborative management decision into the model or software for simulation execution. During the simulation, by comparing the actual node sensing data (obtained from the blockchain) and the simulated expected data, find the differences, and collect these difference data (i.e., the abnormal data set) as the abnormal data set. After discovering the abnormal data set, activate the early warning mechanism to notify relevant supply chain nodes to take temporary measures. The early warning mechanism can notify relevant personnel by automatically sending text messages, emails, or popping up a prompt box in the management system, etc. At the same time, transmit the abnormal data and early warning results to the collaborative management decision-making system through the feedback mechanism, and dynamically adjust the supply chain strategy based on the abnormal analysis results, such as reconfiguring resources, modifying transportation routes, updating production schedules, etc., to respond to risks and restore the normal operation of the supply chain.

[0031] By conducting risk monitoring through the simulation execution of decisions, potential risks in the supply chain operation process can be discovered in advance, an abnormal data set can be generated, and the early warning mechanism can be activated in a timely manner. Giving a feedback response to the collaborative management decision based on the early warning information and generating an optimization strategy can continuously adjust and improve the decision, ensure the stable and efficient operation of the supply chain, and reduce the impact of risks on the supply chain.

[0032] Further, as Figure 2 shown, step S2 of the embodiment of the present application includes:

[0033] Step S21: Construct a smart contract, automatically trigger a transmission instruction through the smart contract, and synchronize the multiple node sensing data into the blockchain for encapsulation through the transmission instruction to generate multiple data packets.

[0034] Step S22: Perform mapping calculation on the multiple data packets by using a hash function to generate multiple hash values, and there is a corresponding relationship between the multiple hash values and the multiple data packets.

[0035] Step S23: Aggregate the multiple data packets according to the multiple hash values to generate multiple initial blocks.

[0036] Step S24: Verify and check the multiple initial blocks through a consensus mechanism to obtain a block verification result.

[0037] Step S25: Chain-store the multiple initial blocks in chronological order according to the block verification result to generate the multiple blocks.

[0038] Specifically, a smart contract is a set of automatically executed contract terms that exist in the form of code on the blockchain. This contract defines the conditions and processes for uploading data to the blockchain. First, deploy the smart contract on the blockchain and set the trigger conditions. For example, upload data once every certain period of time, or trigger when the sensor data is updated. When the trigger condition is met, the smart contract automatically generates a transmission instruction to synchronize the data into the blockchain network. These data are encapsulated into multiple data packets for the next step of processing. Through the smart contract, the automation of data upload can be achieved, reducing manual intervention and improving efficiency and accuracy.

[0039] A hash function is a function that maps data of any length to a hash value of a fixed length. Use the hash function to process each data packet to generate a unique hash value. This hash value is an encrypted representation of the data packet content and can be used to verify the integrity and authenticity of the data. Each data packet has a corresponding hash value, ensuring the corresponding relationship between the data and providing a secure identity for the data packet to prevent the data from being tampered with during transmission.

[0040] Aggregate the multiple data packets according to the multiple hash values to generate multiple initial blocks. Each initial block contains a certain number of data packets and their corresponding hash values. Through the aggregation of data packets, the storage efficiency is improved, enabling more data to be stored on the blockchain.

[0041] The consensus mechanism is an algorithm in the blockchain that ensures all nodes reach an agreement on the order and content of the blockchain data. The consensus mechanisms include Proof of Work (PoW), Proof of Stake (PoS), etc. Multiple nodes in the network use the consensus mechanism to verify and check the initial block, including checking the hash value of the block, verifying the legality of data packets, etc. After each verification, a corresponding verification result is generated, indicating whether the block passes the verification. Only when the block passes the verification will it be accepted and added to the blockchain. By verifying and checking multiple initial blocks through the consensus mechanism, the security and reliability of the blockchain are ensured, preventing invalid or malicious data from being added to the chain.

[0042] Chain-store multiple initial blocks in chronological order according to the block verification results to generate multiple new blocks. Each new block contains the hash value of the previous block, forming a chain structure, ensuring the immutability of the data in the blockchain and providing a reliable data record for the supply chain.

[0043] Through the above data storage, the security and integrity of the data in the supply chain on the blockchain can be ensured, and at the same time, the real-time update and sharing of data can be realized, improving the efficiency and transparency of supply chain management.

[0044] Furthermore, in step S3 of the embodiment of the present application, the sensing data of the multiple nodes of the supply chain is optimized and configured according to the multiple blocks to generate a resource configuration result, including:

[0045] Step S31: Introduce the resource environment parameters of the supply chain, and based on the multiple blocks and in combination with the resource environment parameters, perform traversal configuration analysis on the sensing data of the multiple nodes to generate a pre-configuration plan.

[0046] Step S32: Perform expected optimization analysis according to the sensing data of the multiple nodes and set an optimization configuration target.

[0047] Step S33: Perform resource allocation calculation on the pre-configuration plan according to the optimization configuration target, and perform configuration feasibility analysis according to the allocation result to generate resource configuration feasible information.

[0048] Step S34: Perform deep reinforcement learning according to the resource configuration feasible information in combination with the resource environment parameters to generate the resource configuration result.

[0049] Specifically, the resource environment parameters refer to the external environmental factors in the supply chain that affect resource allocation and configuration, such as production capacity, transportation capacity, market demand, inventory level, etc. The pre-configuration plan is a preliminary plan generated through analysis results, describing the preliminary resource allocation and configuration plan.

[0050] First, introduce the resource environment parameters that affect the operation of the supply chain, such as market demand forecasting, warehouse capacity limitations, transportation costs, etc. Then, combine the sensing data of multiple nodes in the blockchain to perform traversal configuration analysis on multiple nodes of the supply chain. Use data analysis software to evaluate the sensing data of each node to determine how to allocate resources to meet current and expected demands, and generate a preliminary resource allocation plan, that is, a pre-configuration plan based on the analysis results. This plan is the basis for subsequent optimization and provides a feasible framework for the preliminary allocation of resources. For example, in the electronic device supply chain, if the resource environment parameters indicate that the supply of rare metals in a certain region may be limited, combined with the equipment production capacity and raw material inventory data in the sensing data of each production workshop node, an initial plan for adjusting the allocation of rare metals among different workshops is analyzed.

[0051] The optimization configuration goal refers to the performance indicators for resource configuration optimization, such as cost minimization, service level maximization, etc. The setting of the optimization configuration goal is to guide the subsequent resource allocation calculation to ensure that the final resource configuration result can meet the business requirements of the supply chain. Based on the real-time sensing data of each node in the supply chain, analyze the current state and set the expected optimization goal. For example, in the clothing supply chain, by analyzing the production progress of each production workshop and the inventory data of the warehouse, it is found that the production progress is lagging and the inventory is overstocked. The expected optimization configuration goal set after the optimization analysis may be to increase the production efficiency by 20% within a month while reducing the inventory level by 30%.

[0052] According to the pre-configuration plan and the optimization configuration goal, perform resource allocation calculation. A mathematical model (such as a linear programming model) can be used to perform resource allocation calculation, and various resources in the pre-configuration plan are quantitatively allocated according to the optimization configuration goal. For example, according to the set goal of improving production efficiency, calculate how many workers and how much raw materials should be allocated to each workshop. Then, through simulation operation or actual experience evaluation, perform configuration feasibility analysis on the allocation results to check whether the configuration meets the actual constraint conditions, such as inventory levels, production capacity, logistics limitations, etc., and generate resource configuration feasibility information to judge whether the configuration plan is practically executable. This resource configuration feasibility information is the feedback information generated through feasibility analysis to confirm the operability and effect of the resource configuration plan. For example, if the allocation result causes the number of workers in a certain workshop to exceed its space capacity (not meeting the actual operating conditions), it is considered infeasible. Through resource allocation calculation and feasibility analysis, the feasibility and effectiveness of the resource configuration plan in actual operation are ensured.

[0053] Combine the generated feasible resource configuration information with resource environment parameters as the input for deep reinforcement learning. Through a deep reinforcement learning model (such as a deep Q-network or a deep reinforcement learning algorithm), continuously optimize the resource configuration plan. The model learns how to reasonably allocate resources in different environments through a trial-and-error process to achieve maximum benefits or meet the optimization goal, and generate the final resource configuration result. This resource configuration result is a plan optimized by deep reinforcement learning and can be effectively executed in practice. The introduction of deep reinforcement learning improves the intelligence and self-adaptability of the resource configuration plan, enabling the supply chain resource configuration to not only be optimized based on static data but also self-adjust according to dynamic changes.

[0054] The above steps combine sensing data, resource environment parameters, and deep reinforcement learning to systematically optimize the resource configuration of the supply chain, ensure the reasonable allocation of resources, improve the efficiency and flexibility of supply chain management, and meet the dynamic market demands.

[0055] Furthermore, in step S3 of the embodiment of the present application, based on the resource configuration result, the blockchain is shared and updated to generate a platform shared dataset of the blockchain, including:

[0056] Step S35: Encrypt the multiple node sensing data to generate multiple encrypted data.

[0057] Step S36: Synchronize the multiple encrypted data to the blockchain according to the resource configuration result to generate chained synchronization data.

[0058] Step S37: Determine whether the chained synchronization data meets the expected synchronization threshold.

[0059] Step S38: When the chained synchronization data does not meet the expected synchronization threshold, generate a first prompt, set the priority of the chained synchronization data to 0 according to the first prompt, and add the chained synchronization data to the pending data column.

[0060] Step S39: When the chained synchronization data meets the expected synchronization threshold, generate a second prompt, set the priority of the chained synchronization data to 1 according to the second prompt, and generate the platform shared dataset of the blockchain.

[0061] Specifically, use an encryption algorithm (such as AES, RSA, or the unique encryption technology of the blockchain) to encrypt the sensing data of each node to generate corresponding encrypted data. The encrypted data is stored as ciphertext to ensure that it will not be tampered with or leaked during data transmission and storage.

[0062] Determine the storage location, update order, etc. of each encrypted data in the blockchain according to the resource allocation result, and then allocate the encrypted data to different blocks in the blockchain. The synchronization of the encrypted data is realized through the distributed ledger mechanism of the blockchain to ensure data consistency. The synchronized encrypted data forms chained synchronized data, which is structured and stored on the blockchain and becomes the basis for subsequent sharing and verification.

[0063] The expected synchronization threshold is a preset value during the data synchronization process, used to judge whether the data synchronization has achieved the expected effect. This threshold can be set according to different supply chain requirements, such as quantitative indicators in aspects like the integrity ratio of data and the timeliness requirement of data updates. According to the specific indicators of the set expected synchronization threshold, use a data verification algorithm (such as a Merkle tree or a hash function) to check the chained synchronized data and verify the integrity and consistency of the chained synchronized data. Compare the verification result of the synchronized data with the expected synchronization threshold. Output the judgment result of compliance or non-compliance to determine the next operation.

[0064] The first prompt is a notification message used to inform relevant systems or personnel that the chained synchronized data has not reached the expected synchronization threshold. The pending data column is a data storage area for storing those chained synchronized data that temporarily do not meet the processing requirements (i.e., do not meet the expected synchronization threshold) and need further processing. When it is judged that the chained synchronized data does not meet the expected synchronization threshold, generate the first prompt to inform relevant personnel of the problem reason (such as missing data or incorrect data format). This prompt can be a log record, a system message, or a notification sent to the administrator. Then, set the priority of this chained synchronized data to 0 and suspend further operations on the blockchain. And add the abnormal data to the pending data column, which is added to a special pending data column and waits for subsequent correction or re-verification. In data processing, priority is an attribute that determines the order of data processing. Setting the priority to 0 means that this chained synchronized data is given the lowest priority in the current processing queue, which means that these data will be processed after other data with higher priorities are processed.

[0065] The second prompt is similar to the first prompt and is a notification message used to inform relevant systems or personnel that the chained synchronized data has reached the expected synchronization threshold. When the chained synchronized data meets the expected synchronization threshold, generate the second prompt, which can also be a log record, a system message, or a notification. Then, set the priority of this data to 1 and grant it the permission to be stored and shared in the blockchain. Integrate these compliant chained synchronized data to form the platform shared data set of the blockchain and provide access and use to each node in the supply chain. Setting the priority of the chained synchronized data to 1 means that this data has a higher priority in the processing queue and can be preferentially used for subsequent operations such as generating the platform shared data set.

[0066] The above steps encrypt the node sensing data, ensuring the security and privacy of the data during transmission and storage, and preventing the data from being accessed and tampered with by unauthorized parties. Through the expected synchronization threshold verification, abnormal data can be prevented from entering the blockchain, ensuring that only high-quality data is stored and shared, enhancing the credibility and reliability of supply chain data, and providing a high-quality data foundation for subsequent collaborative management.

[0067] Further, step S35 includes:

[0068] Step S351: Traverse the multiple node sensing data for sensitive analysis to obtain multiple data sensitivities.

[0069] Step S352: Arrange the multiple node sensing data in descending order according to the multiple data sensitivities to generate a sensing data sequence.

[0070] Step S353: Divide the multiple node sensing data based on the sensing data sequence to determine multiple data to be encrypted.

[0071] Step S354: Perform homomorphic encryption calculation on the multiple data to be encrypted to generate the multiple encrypted data.

[0072] Specifically, traverse the sensing data uploaded by supply chain nodes for sensitivity analysis to identify and classify the sensitivity of the data. Determine the sensitivity level of each node sensing data according to the sensitivity analysis results and generate a data sensitivity for it. This data sensitivity is a measure of the importance and security requirements of the data and can be represented by a score or a grade. Sensitivity analysis can be determined through preset rules or by using machine learning algorithms to establish a sensitivity evaluation model. Exemplarily, different sensitivity levels can be assigned to different types, different data volumes, and different sources of data in the preset rules. Match each node sensing data according to the preset rules to generate the corresponding data sensitivity. The sensitivity evaluation model can collect the labeled historical node sensing data and use machine learning algorithms such as decision trees, random forests, and neural networks for training, enabling the model to continuously learn the characteristics of sensitive data in the historical node sensing data. Use the test data set to evaluate the model, calculate metrics such as accuracy, recall, and F1 score, and adjust the internal parameters of the model using an optimizer according to the evaluation results, enabling the model to generate the corresponding data sensitivity value according to the input node sensing data.

[0073] Bind the calculated sensitivity value to the corresponding node sensing data, and use a sorting algorithm (such as bubble sort, quick sort, etc.) to arrange the multiple node sensing data in descending order of sensitivity to generate a sensing data sequence.

[0074] Based on the pre-set partitioning rules, the node sensing data is partitioned according to the sensing data sequence, and multiple node sensing data is divided into "data to be encrypted" and "data not requiring encryption", determining the data range to be encrypted and organizing it into a list of data to be encrypted. The data outside the encryption range can be stored in a normal format or lightly encrypted. For example, a sensitivity threshold can be set, and the data above this threshold is determined to be data to be encrypted, and the remaining data is data not requiring encryption. Or according to a certain ratio, such as the top 50% of the data with the highest sensitivity is determined to be data to be encrypted. The data partitioning optimizes the allocation of encryption resources, ensures the priority protection of important data, and at the same time reduces the overall computing cost.

[0075] Apply the homomorphic encryption algorithm to the list of data to be encrypted one by one. Use homomorphic encryption tools (such as PyCrypto, SEAL library) to encrypt the data, generate encrypted data in ciphertext format, and store or transmit it to the blockchain. While protecting data privacy, homomorphic encryption allows direct processing of encrypted data, achieving a balance between privacy protection and data utilization.

[0076] The above steps gradually achieve the priority partitioning and secure encrypted storage of the supply chain node sensing data through sensitive analysis, data sorting, partitioning, and homomorphic encryption, while protecting data privacy and maintaining the availability of the data.

[0077] Furthermore, as Figure 3 shown, step S4 of the embodiment of the present application includes:

[0078] Step S41: Construct a multi-party joint model, synchronize the platform shared dataset of the blockchain to the multi-party joint model according to the multiple supply chain nodes, and generate a joint game result.

[0079] Step S42: Perform correlation analysis on the multiple supply chain nodes based on the joint game result to generate multiple correlation coefficients.

[0080] Step S43: Trace the data of the multiple supply chain nodes according to the multiple correlation coefficients, and identify the multiple supply chain nodes according to the tracing result to generate multiple associated identification information.

[0081] Step S44: Perform collaborative partitioning on the multiple supply chain nodes according to the multiple associated identification information to generate multiple collaborative node groups.

[0082] Step S45: Formulate the collaborative management decision according to the multiple collaborative node groups in combination with the joint game result.

[0083] Specifically, the multi-party joint model is a mathematical model used to simulate and analyze the interaction behaviors among multiple supply chain nodes. The joint game result refers to the result of the strategic interaction among the parties in the multi-party joint model, which is used to guide actual decision-making. A multi-party joint model is constructed using the cooperative game model in game theory, and multiple supply chain nodes are defined as game participants. Then, the platform shared data set on the blockchain is synchronized into this model to reflect the status and behaviors of each node. The model is analyzed using game theory methods (such as Nash equilibrium). By simulating the strategic choices and interactions of each node, joint game results are generated, which reflect the benefits and risks of each node under different strategies, providing decision-making support for the collaborative management of the supply chain.

[0084] Based on the generated joint game results, evaluate the collaboration patterns and interdependent relationships among each supply chain node. Calculate the correlation coefficients among each node in the supply chain using statistical analysis or correlation algorithms (such as Pearson correlation coefficient or mutual information) to quantify the cooperation potential and synergy effects among the nodes. For example, if the correlation coefficient between two warehouse nodes is relatively high, it indicates that their cooperation relationship in the transportation task is relatively close, and collaborative optimization needs to be considered preferentially.

[0085] Based on the calculated correlation coefficients, conduct data tracing for the supply chain nodes, track and record the data sources, transmission paths, and change processes to determine the specific connections and influence paths among the nodes. Then, according to the tracing results, label the role and responsibility of each node in the supply chain, such as key nodes, collaborative nodes, etc., and generate the associated identification information corresponding to each node. This information can be used to identify key nodes and optimize the supply chain structure.

[0086] Based on the associated identification information, group the supply chain nodes according to similarity and relevance to form collaborative node groups. Each collaborative node group contains multiple nodes that have common goals or cooperation relationships in the supply chain. Through collaborative partitioning, the effective cooperation among supply chain nodes is promoted, and the overall collaborative efficiency of the supply chain is improved.

[0087] Combining the characteristics of the collaborative node groups and the joint game results, formulate specific management decisions for each collaborative group, namely collaborative management decisions. These collaborative management decisions include specific management measures in aspects such as resource allocation, task assignment, and risk management, aiming to maximize the overall efficiency of the supply chain.

[0088] Furthermore, step S41 includes:

[0089] Step S411: Based on the multiple supply chain nodes and the platform shared data set combined with the blockchain, make strategic choices to generate multiple strategy sets.

[0090] Step S412: Introduce a payment function and calculate the multiple strategy sets through the payment function to generate a strategy payment matrix.

[0091] Step S413: Use the Nash equilibrium method to deduce the platform shared dataset in combination with the strategy payment matrix to generate multiple equilibrium solution sets.

[0092] Step S414: Perform a game on the multiple supply chain nodes according to the multiple equilibrium solution sets to generate the joint game result.

[0093] Specifically, for each supply chain node, relevant data is first obtained from the platform shared dataset of the blockchain. For example, a manufacturer may obtain data such as raw material prices and market demand forecasts; a retailer may obtain data such as product supply conditions and consumer preferences. Then, based on this data, combined with historical behavior and expected goals, all possible strategy options are listed to form the strategy set of this node. Taking the manufacturer as an example, if its goal is to maximize profit and it knows from the platform shared dataset that the market demand is strong and the raw material prices are stable, it may choose the strategy of increasing production quantity and appropriately raising the price. Finally, the strategies selected by each supply chain node are combined together to form multiple strategy sets.

[0094] The payment function is a function used to measure the benefits obtained or costs incurred by each participant (supply chain node) when choosing a specific strategy. The payment function of each participant is determined according to its own characteristics. For example, the payment function of a manufacturer includes parameters such as sales quantity, cost, and market demand, and the payment function of a retailer includes parameters such as product pricing, inventory cost, and order quantity. The strategy payment matrix is a two-dimensional array calculated based on the payment function, representing the benefit situation of each node under different strategy combinations. Among them, the rows represent the strategy choices of different participants, the columns also represent the strategy choices of different participants, and each element in the matrix represents the benefit or cost of each participant under the corresponding strategy combination. Determine the payment function of each supply chain node. Exemplarily, the payment function of a manufacturer can be set as S = (P - C) × Q, where P is the product price, C is the unit cost, and Q is the sales quantity. The payment function of a retailer can be set as S = (R - P)x - hx, where R is the retail price, P is the purchase price, x is the sales quantity, and h is the unit inventory cost. For each strategy set, substitute the strategies of each supply chain node in it into the corresponding payment function for calculation. Organize the payment results under all possible strategy combinations into a strategy payment matrix.

[0095] Using the strategic payoff matrix as input, analyze the profit changes of each node under different strategy combinations. Through the Nash equilibrium method, find the Nash equilibrium points, deduce the strategy combinations that satisfy the Nash equilibrium conditions for all nodes, and generate the equilibrium solution set. Each equilibrium solution represents a stable strategy combination, and the nodes will not easily change their strategies. For each participant, given the strategies of other participants, find the strategy that maximizes their payoff. For example, for the manufacturer, given the specific pricing and inventory management strategies chosen by the retailer, find the production quantity and pricing strategies that maximize its own payoff (profit); for the retailer, given the specific production quantity and pricing strategies chosen by the manufacturer, find the pricing and inventory management strategies that maximize its own payoff. Through iterative calculations, determine all the strategy combinations that satisfy the Nash equilibrium conditions, and these combinations form multiple equilibrium solution sets. The equilibrium solution sets provide multiple possible cooperation or competition results for each node in the supply chain, facilitating the selection of strategy combinations that meet the overall optimization goals.

[0096] Each node in the supply chain conducts actual games according to the strategies in the equilibrium solution. During the game process, each node adjusts its own strategy based on market dynamics and the behaviors of other nodes, records data such as the payoffs and costs of each node, as well as the impacts on aspects such as the overall efficiency and stability of the supply chain. These data together constitute the results of the joint game.

[0097] The above steps, from strategy selection to game deduction, comprehensively analyze the interaction behaviors and optimal cooperation methods of supply chain nodes. The finally generated joint game results provide a clear cooperation model and stable strategy combinations for the supply chain, optimize the resource utilization efficiency, and enhance the collaborative ability and operational stability of the overall supply chain.

[0098] Furthermore, step S45 includes:

[0099] Step S451: Based on the multiple collaborative node groups, conduct matching analysis according to the strategic payoff matrix to generate a matching result.

[0100] Step S452: Based on the matching result, conduct screening and verification according to the multiple equilibrium solution sets to determine multiple collaborative strategies, and there is a corresponding relationship between the multiple collaborative strategies and the multiple collaborative node groups.

[0101] Step S453: Map the multiple collaborative strategies according to the joint game results to formulate the collaborative management decision.

[0102] Specifically, for multiple collaborative node groups, use the strategic payoff matrix to conduct matching analysis on each node group to evaluate the cooperation potential of different nodes after selecting strategies. Through calculation and comparison, generate a matching result, that is, the performance of each collaborative node group under different strategy combinations.

[0103] According to the generated matching results, combined with multiple equilibrium solution sets, screening and verification are carried out. Through comparison, determine which strategy combinations can ensure the maximization of the benefits of each collaborative node group and be consistent with the equilibrium solution set. These screened strategies are the collaborative strategies, and each collaborative node group has an optimal strategy corresponding to it. Through screening and verification, ensure that the finally determined collaborative strategies can achieve resource optimization and benefit maximization among nodes, thereby improving the overall efficiency of the supply chain.

[0104] According to the multiple collaborative strategies screened out, map these strategies to the results of the joint game to ensure that the decision-making follows the optimal solution in game theory. Make the final collaborative management decision, and transform these optimal strategies into actual supply chain operation decisions, such as inventory management, transportation arrangement, order processing, etc. By mapping the collaborative strategies to the game results, finally formulate stable and optimal collaborative management decisions to ensure that each node in the supply chain can cooperate efficiently, thereby improving the overall operation efficiency, reducing costs, and enhancing service quality.

[0105] In summary, the blockchain-driven supply chain collaborative management method provided by the embodiments of the present application has the following technical effects:

[0106] The embodiments of the present application, through the combination of blockchain and the multi-party collaboration model, adopt means such as distributed data collection, encrypted storage, game analysis, and risk warning to achieve real-time data sharing, resource optimization allocation, and collaborative decision-making in supply chain management. First, through blockchain technology, the sensing data of supply chain nodes is encrypted, hashed, and stored in a chain to ensure the security and integrity of data transmission; then, based on resource environment parameters and deep reinforcement learning methods, the collected data is optimized and configured to generate resource configuration results and synchronously update them to the blockchain to form a shared data set. Subsequently, using the joint game model, the collaborative strategies among nodes are screened and verified through the strategy payment matrix and the equilibrium solution set, collaborative management decisions are made, and feedback optimization strategies are generated through simulation execution and anomaly monitoring. Finally, the information transparency, maximum resource utilization, and risk management intelligence among supply chain nodes are realized, the overall response efficiency and collaborative ability of the supply chain are improved, and the security and stability of the supply chain are enhanced.

[0107] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A blockchain-driven supply chain collaborative management method, characterized in that, The method includes: Traversing multiple supply chain nodes in the supply chain to collect data, and obtaining multiple node sensing data; Real-time uploading the multiple node sensing data through the blockchain to generate multiple blocks; Optimally configuring the multiple node sensing data of the supply chain according to the multiple blocks to generate a resource allocation result, and based on the resource allocation result, sharing and updating the blockchain to generate a platform shared dataset of the blockchain; Based on the multiple supply chain nodes, conducting multi-party collaboration through the platform shared dataset of the blockchain to formulate a collaborative management decision; Simulating the execution of the collaborative management decision to monitor the risks of the supply chain, generating an abnormal dataset, and activating an early warning mechanism according to the abnormal dataset to give a feedback response to the collaborative management decision, generating a collaborative management optimization strategy; The optimally configuring the multiple node sensing data of the supply chain according to the multiple blocks to generate a resource allocation result includes: Introducing the resource environment parameters of the supply chain, and based on the multiple blocks and in combination with the resource environment parameters, traversing and configuring and analyzing the multiple node sensing data to generate a pre-configuration plan; Conducting expected optimization analysis according to the multiple node sensing data to set an optimization configuration target; Performing resource allocation calculation on the pre-configuration plan according to the optimization configuration target, and conducting configuration feasibility analysis according to the allocation result to generate resource allocation feasible information; Performing deep reinforcement learning according to the resource allocation feasible information in combination with the resource environment parameters to generate the resource allocation result; The conducting multi-party collaboration through the platform shared dataset of the blockchain based on the multiple supply chain nodes to formulate a collaborative management decision includes: Constructing a multi-party collaboration model, and synchronizing the platform shared dataset of the blockchain to the multi-party collaboration model according to the multiple supply chain nodes to generate a joint game result; Conducting correlation analysis on the multiple supply chain nodes based on the joint game result to generate multiple correlation coefficients; Tracing the data of the multiple supply chain nodes according to the multiple correlation coefficients, and identifying the multiple supply chain nodes according to the tracing result to generate multiple associated identification information; Collaboratively dividing the multiple supply chain nodes according to the multiple associated identification information to generate multiple collaborative node groups; Formulating the collaborative management decision according to the multiple collaborative node groups in combination with the joint game result.

2. The blockchain-driven supply chain collaborative management method according to claim 1, wherein, Real-time uploading the multiple node sensing data through the blockchain to generate multiple blocks, and the method includes: Constructing a smart contract, automatically triggering a transmission instruction through the smart contract, and synchronizing the multiple node sensing data to the blockchain for encapsulation through the transmission instruction to generate multiple data packets; Performing mapping calculation on the multiple data packets by using a hash function to generate multiple hash values, and there is a corresponding relationship between the multiple hash values and the multiple data packets; Aggregating the multiple data packets according to the multiple hash values to generate multiple initial blocks; Verifying and checking the multiple initial blocks through a consensus mechanism to obtain a block verification result; Chain-store the multiple initial blocks in chronological order according to the block verification result to generate the multiple blocks.

3. The blockchain-driven supply chain collaborative management method according to claim 1, characterized in that Based on the resource configuration result, perform shared updates on the blockchain to generate a platform shared dataset of the blockchain. The method includes: Encrypt the multiple node sensing data to generate multiple encrypted data; Synchronize the multiple encrypted data to the blockchain according to the resource configuration result to generate chained synchronization data; Determine whether the chained synchronization data meets the expected synchronization threshold; When the chained synchronization data does not meet the expected synchronization threshold, generate a first prompt, set the priority of the chained synchronization data to 0 according to the first prompt, and add the chained synchronization data to the to-be-processed data column; When the chained synchronization data meets the expected synchronization threshold, generate a second prompt, set the priority of the chained synchronization data to 1 according to the second prompt, and generate the platform shared dataset of the blockchain.

4. The blockchain-driven supply chain collaborative management method according to claim 3, characterized in that, Encrypt the multiple node sensing data to generate multiple encrypted data. The method includes: Traverse the multiple node sensing data for sensitive analysis to obtain multiple data sensitivities; Arrange the multiple node sensing data in descending order according to the multiple data sensitivities to generate a sensing data sequence; Based on the sensing data sequence, divide the multiple node sensing data to determine multiple data to be encrypted; Perform homomorphic encryption calculation on the multiple data to be encrypted to generate the multiple encrypted data.

5. The blockchain-driven supply chain collaborative management method according to claim 1, wherein, Synchronize the platform shared dataset of the blockchain to the multi-party joint model according to the multiple supply chain nodes to generate a joint game result. The method includes: Based on the multiple supply chain nodes, combine the platform shared dataset of the blockchain to perform strategy selection to generate multiple strategy sets; Introduce a payment function and calculate the multiple strategy sets through the payment function to generate a strategy payment matrix; Use the Nash equilibrium method to combine the strategy payment matrix to deduce the platform shared dataset to generate multiple equilibrium solution sets; Based on the multiple equilibrium solution sets, perform a game on the multiple supply chain nodes to generate the joint game result.

6. The blockchain-driven supply chain collaborative management method according to claim 5, wherein Formulate the collaborative management decision according to the multiple collaborative node groups in combination with the joint game result. The method includes: Based on the multiple collaborative node groups, perform matching analysis according to the strategy payment matrix to generate a matching result; Based on the matching result, perform screening and verification according to the multiple equilibrium solution sets to determine multiple collaborative strategies, and the multiple collaborative strategies have a corresponding relationship with the multiple collaborative node groups; Map the multiple collaborative strategies according to the joint game result to formulate the collaborative management decision.

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