Multi-organization collaboration method and system for supply chain management

Abstract

This invention provides a multi-organization collaboration method and system for supply chain management, which relates to the field of multi-organization collaboration technology. This involves collecting multi-organization data to build a supply chain collaboration network model, and using distributed ledger technology to establish a trusted data interaction platform. Matrix calculation node indicators are analyzed to formulate differentiated collaboration rules. Smart contracts are deployed to automatically trigger collaboration processes, monitor and optimize collaboration strategies in real time, and record interactions and decision-making rationales on a blockchain. This invention achieves efficient collaboration and full traceability among multiple organizations, improving supply chain management efficiency and reducing collaboration risks.

Description

Technical Field

[0001] The present invention relates to multi-organization collaboration technology, and in particular to a multi-organization collaboration method and system for supply chain management. Background Art

[0002] Supply chain management is a crucial component of modern business operations, involving complex collaboration across multiple organizations. With the advancement of globalization and digitalization, supply chain management faces increasing challenges, requiring more efficient, transparent, and flexible collaborative approaches. Traditional supply chain management methods, which primarily rely on centralized information systems and manual decision-making, struggle to adapt to rapidly changing market environments and the complex demands of multi-organizational collaboration.

[0003] In recent years, the development of emerging technologies such as big data, artificial intelligence, and blockchain has provided new solutions for supply chain management. However, existing supply chain collaboration methods still have some flaws and shortcomings:

[0004] First, existing supply chain collaboration methods lack comprehensive analysis and modeling of the complex relationships between multiple organizations. Most methods focus solely on organizational relationships across a single dimension, failing to effectively capture the multidimensional and multi-level relationships within the supply chain network. This makes it difficult for collaborative decision-making to fully consider the interests and influences of all parties.

[0005] Second, existing approaches present trust and security issues with data sharing and interaction. Due to the lack of a reliable data exchange mechanism, participating organizations are often reluctant to share business data, hindering the accuracy and efficiency of collaborative decision-making. Furthermore, centralized data storage and management methods increase the risk of data leakage and tampering.

[0006] Finally, existing supply chain collaboration methods lack intelligent and automated collaborative process management. Most methods still rely on manual intervention and decision-making, failing to achieve real-time, automated triggering and optimization of collaborative processes. Furthermore, the decision-making basis and data interaction records during the collaborative process are difficult to effectively trace, hindering the transparency and auditability of collaboration. Summary of the Invention

[0007] The embodiments of the present invention provide a multi-organization collaboration method and system for supply chain management, which can solve the problems in the prior art.

[0008] According to a first aspect of the embodiments of the present invention,

[0009] Provides a multi-organizational collaborative approach to supply chain management, including:

[0010] Collect inventory data, production plan data, and logistics and distribution data from multiple supply chain participating organizations, construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data, and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and use distributed ledger technology to assign a unique identifier and corresponding data access rights to each supply chain participating organization, thereby building a cross-organizational trusted data interaction platform;

[0011] Analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform to calculate the node connectivity, node centrality, and node influence of each supply chain participating organization; classify the node connectivity, node centrality, and node influence; and formulate differentiated collaboration rules for supply chain participating organizations at different levels;

[0012] Based on the collaboration rules, a smart contract is deployed on the cross-organization trusted data interaction platform. The smart contract automatically triggers the cross-organization collaboration process according to the real-time business data of the supply chain participating organizations, and monitors the cross-organization collaboration process in real time by calculating the collaboration efficiency index and the risk warning index. When the collaboration efficiency index is lower than the preset efficiency threshold, the smart contract automatically initiates a collaboration strategy optimization request to the supply chain participating organizations whose node influence is higher than the policy preset threshold, and records the data interaction records and decision-making basis in the collaboration process to the blockchain network, so as to realize the full traceability of the collaboration process.

[0013] Collect inventory data, production plan data, and logistics and distribution data from multiple supply chain participating organizations; construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data; and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and use distributed ledger technology to assign a unique identifier and corresponding data access rights to each supply chain participating organization to build a cross-organizational trusted data interaction platform, including:

[0014] Collecting business data of multiple supply chain participating organizations, building a supply chain collaborative network model with a dual-matrix structure based on the business data, and generating a node hierarchical relationship matrix by calculating the business dependence and resource complementarity between the supply chain participating organizations. The node hierarchical relationship matrix is ​​used to represent the hierarchical association relationship between the supply chain participating organizations;

[0015] By analyzing the material flow, information flow and capital flow between the organizations participating in the supply chain, an inter-node business flow relationship matrix is ​​generated. The inter-node business flow relationship matrix is ​​used to characterize the business interaction relationship between the organizations participating in the supply chain, and a cross-organizational trusted data interaction platform is constructed using distributed ledger technology.

[0016] Analyzing the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform to calculate the node connectivity, node centrality, and node influence of each supply chain participating organization; and grading each supply chain participating organization according to the calculated node connectivity, node centrality, and node influence includes:

[0017] Analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, calculate the node connectivity of the supply chain participating organizations based on the node hierarchical relationship matrix, and accumulate the matrix element values ​​directly connected to the target supply chain participating organizations in the node hierarchical relationship matrix to obtain the direct connection strength;

[0018] The matrix element values ​​indirectly connected to the target supply chain participating organization in the node hierarchical relationship matrix are weighted by the path distance attenuation coefficient to obtain the indirect connection strength, and the direct connection strength and the indirect connection strength are added to obtain the node connectivity; the node hierarchical relationship matrix is ​​subjected to eigenvalue decomposition to obtain the main eigenvector, and the component value corresponding to the main eigenvector is used as the node centrality of the supply chain participating organization;

[0019] Based on the business flow relationship matrix between the nodes, a business information propagation model is constructed. The SI propagation algorithm is used to calculate the propagation range of business information, and a time decay function is introduced to correct the propagation timeliness to obtain the node influence of the supply chain participating organizations;

[0020] The entropy weight method is used to calculate the weight coefficients of the node connectivity, the node centrality and the node influence respectively, and the weight coefficients are weightedly calculated with the corresponding indicator values ​​to obtain the comprehensive score of the supply chain participating organizations; based on the comprehensive score and combined with the hierarchical constraint relationship in the node hierarchical relationship matrix, the K-means clustering algorithm is used to classify the supply chain participating organizations.

[0021] Differentiated collaboration rules are formulated for supply chain participating organizations at different levels. The collaboration rules are used to determine the data sharing scope, collaborative decision weight, and business process triggering conditions of each supply chain participating organization, including:

[0022] Collecting assessment data of supply chain participating organizations, calculating comprehensive assessment scores of supply chain participating organizations based on the assessment data, classifying the supply chain participating organizations into core level, important level, and general level based on the comprehensive assessment scores, and generating level information and corresponding assessment scores for each supply chain participating organization;

[0023] The level information of the supply chain participating organizations is input into a preset data access control model, and a data sharing scope is set for each supply chain participating organization based on the data access control model, and the data sharing scope is used to define the data access rights of each supply chain participating organization; the comprehensive evaluation score is substituted into the data access control model to generate the collaborative decision-making weight of each supply chain participating organization, and the collaborative decision-making weight is used to determine the influence weight of each supply chain participating organization in supply chain decision-making; and business process trigger conditions are set based on the level information of the supply chain participating organizations.

[0024] Deploy a smart contract on the cross-organization trusted data interaction platform based on the collaboration rules, receive real-time business data uploaded by supply chain participating organizations, extract business indicators from the real-time business data, compare the business indicators with the trigger conditions preset in the collaboration rules, and automatically trigger the cross-organization collaboration process when the trigger conditions are met, and distribute collaboration tasks to relevant supply chain participating organizations according to the decision weight sequence preset in the collaboration rules;

[0025] The smart contract calculates the collaborative efficiency index and the risk warning index, monitors the cross-organizational collaborative process in real time, uses a time series analysis method to analyze the changing trends of the collaborative efficiency index and the risk warning index, and mines historical monitoring data through a machine learning algorithm to identify potential problem patterns.

[0026] When the collaborative efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy, and records the data interaction and decision basis during the collaborative process to the blockchain network, realizing the full traceability of the collaborative process, including:

[0027] When the collaborative efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy. The collaborative strategy optimization request includes the location analysis, cause diagnosis and optimization suggestions of the efficiency anomaly. The optimization request is distributed to the most relevant and influential nodes according to the type of efficiency anomaly.

[0028] The smart contract automatically writes data interaction records during the collaborative strategy optimization process into the blockchain network. The data interaction records include business data exchange between nodes, collaborative instruction transmission, and status information update. The smart contract also simultaneously records the decision basis to the blockchain network. The decision basis includes decision trigger conditions, decision rules, reasoning process, and decision results.

[0029] The smart contract ensures the integrity and non-tamperability of the data interaction records and the decision-making basis through the distributed ledger mechanism and consensus mechanism of the blockchain network, supports collaborative process traceability queries based on the time dimension, participant dimension and business dimension, and realizes trusted traceability of the entire process from efficiency warning, optimized decision-making to execution feedback.

[0030] According to a second aspect of the embodiments of the present invention,

[0031] Provides a multi-organization collaboration system for supply chain management, including:

[0032] The first unit is configured to collect inventory data, production plan data, and logistics and distribution data from multiple supply chain participating organizations, construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data, and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and employ distributed ledger technology to assign a unique identifier and corresponding data access rights to each supply chain participating organization, thereby constructing a cross-organizational trusted data interaction platform;

[0033] The second unit is used to analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, calculate the node connectivity, node centrality and node influence of each supply chain participating organization; classify the supply chain participating organizations according to the calculated node connectivity, node centrality and node influence; formulate differentiated collaboration rules for supply chain participating organizations at different levels, and the collaboration rules are used to determine the data sharing scope, collaborative decision weight and business process triggering conditions of each supply chain participating organization;

[0034] The third unit is used to deploy smart contracts on the cross-organizational trusted data interaction platform based on the collaboration rules. The smart contract automatically triggers the cross-organizational collaboration process according to the real-time business data of the supply chain participating organizations, and monitors the collaboration process in real time by calculating the collaboration efficiency index and the risk warning index. When the collaboration efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaboration strategy optimization request to the supply chain participating organizations whose node influence is higher than the policy preset threshold, and records the data interaction records and decision-making basis in the collaboration process to the blockchain network, so as to realize the full traceability of the collaboration process.

[0035] According to a third aspect of the embodiments of the present invention,

[0036] An electronic device is provided, comprising:

[0037] processor;

[0038] a memory for storing processor-executable instructions;

[0039] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0040] According to a fourth aspect of the embodiments of the present invention,

[0041] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0042] The beneficial effects of this application are as follows:

[0043] This invention achieves efficient collaboration and secure data sharing among multiple organizations by building a supply chain collaborative network model and a cross-organizational trusted data exchange platform. Supply chain organizations are classified based on node characteristics and differentiated collaboration rules are formulated, improving the accuracy and flexibility of collaborative decision-making. By automatically triggering and monitoring cross-organizational collaborative processes through smart contracts, real-time optimization and full traceability of the collaborative process are achieved, significantly improving the overall operational efficiency of the supply chain.

[0044] This invention uses distributed ledger technology to assign unique identifiers and data access rights to each participating organization, ensuring the security of sensitive business information while fostering trust and deeper collaboration among organizations. The differentiated collaboration rules fully consider the status and influence of different organizations, protecting the interests of core enterprises while encouraging the active participation of small and medium-sized enterprises, ultimately achieving healthy development of the supply chain ecosystem.

[0045] This invention automates collaborative processes through smart contracts, significantly reducing manual intervention and the risk of errors, thereby improving the efficiency and accuracy of supply chain management. Real-time monitoring and automated optimization mechanisms enable the supply chain to rapidly adapt to market changes, enhancing overall risk resilience. Full traceability provides a reliable basis for subsequent performance evaluation and continuous improvement, contributing to the long-term improvement of collaborative supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of a multi-organization collaboration method for supply chain management according to an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the structure of a multi-organization collaborative system for supply chain management according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, 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 shall fall within the scope of protection of the present invention.

[0049] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0050] Figure 1 FIG. 1 is a flow chart of a multi-organization collaboration method for supply chain management according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0051] Collect inventory data, production plan data, and logistics and distribution data from multiple supply chain participating organizations, construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data, and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and use distributed ledger technology to assign a unique identifier and corresponding data access rights to each supply chain participating organization, thereby building a cross-organizational trusted data interaction platform;

[0052] The node hierarchical relationship matrix and the inter-node business flow relationship matrix are analyzed through the cross-organizational trusted data interaction platform to calculate the node connectivity, node centrality, and node influence of each supply chain participating organization; the node connectivity, node centrality, and node influence are graded; and differentiated collaboration rules are formulated for different levels of supply chain participating organizations, and the collaboration rules are used to determine the data sharing scope, collaborative decision weight, and business process triggering conditions of each supply chain participating organization;

[0053] Based on the collaboration rules, a smart contract is deployed on the cross-organization trusted data interaction platform. The smart contract automatically triggers the cross-organization collaboration process according to the real-time business data of the supply chain participating organizations, and monitors the cross-organization collaboration process in real time by calculating the collaboration efficiency index and the risk warning index. When the collaboration efficiency index is lower than the preset efficiency threshold, the smart contract automatically initiates a collaboration strategy optimization request to the supply chain participating organizations whose node influence is higher than the policy preset threshold, and records the data interaction records and decision-making basis in the collaboration process to the blockchain network, so as to realize the full traceability of the collaboration process.

[0054] In an optional embodiment, inventory data, production plan data, and logistics and distribution data of multiple supply chain participating organizations are collected; a node hierarchical relationship matrix and an inter-node business flow relationship matrix are constructed based on the inventory data, the production plan data, and the logistics and distribution data; and a supply chain collaborative network model is established based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and distributed ledger technology is used to assign a unique identifier and corresponding data access rights to each supply chain participating organization, thereby constructing a cross-organizational trusted data interaction platform, including:

[0055] Collecting business data of multiple supply chain participating organizations, building a supply chain collaborative network model with a dual-matrix structure based on the business data, and generating a node hierarchical relationship matrix by calculating the business dependence and resource complementarity between the supply chain participating organizations. The node hierarchical relationship matrix is ​​used to represent the hierarchical association relationship between the supply chain participating organizations;

[0056] By analyzing the material flow, information flow and capital flow between the organizations participating in the supply chain, an inter-node business flow relationship matrix is ​​generated. The inter-node business flow relationship matrix is ​​used to characterize the business interaction relationship between the organizations participating in the supply chain, and a cross-organizational trusted data interaction platform is constructed using distributed ledger technology.

[0057] This embodiment provides a method for building a supply chain collaborative network model and a cross-organizational trusted data interaction platform. The method first collects business data from multiple supply chain participating organizations, including inventory data, production planning data, logistics and distribution data, and sales forecast data.

[0058] When collecting inventory data, we extract information about raw material inventory, work-in-progress inventory, and finished goods inventory from each organization's warehouse management system. For example, the inventory data for manufacturer A might include: 1,000 pieces of raw material X, 500 pieces of work-in-progress Y, and 2,000 pieces of finished goods Z.

[0059] When collecting production planning data, obtain production schedule and capacity information for a future period (e.g., three months) from the production scheduling system. For example, Manufacturer A's production plan data might include: production of 5,000 units of product P in June, with a capacity utilization rate of 80%; and production of 4,000 units of product Q in July, with a capacity utilization rate of 75%.

[0060] When collecting logistics and delivery data, we extract delivery route and capacity information from the logistics management system. For example, data from logistics service provider B might include: the delivery route from Factory 1 to Warehouse 2 is Highway H1-H2 and Provincial Road S1, with an average daily capacity of 100 tons.

[0061] When collecting sales forecast data, extract historical sales data and market trend analysis results from the sales management system. For example, Retailer C's data might include: Product R's average monthly sales volume for the past 12 months was 10,000 units, and the projected monthly growth rate for the next six months is 5%.

[0062] Next, a dual-matrix supply chain collaboration network model is constructed based on the collected business data. First, a node hierarchical relationship matrix is ​​generated to represent the hierarchical relationships between organizations participating in the supply chain. The specific method is to calculate the degree of business dependence between organizations, such as the proportion of raw material supply and product sales, and the degree of resource complementarity between organizations, such as production capacity matching and inventory complementarity. The calculated results are then comprehensively scored to determine the strength of the hierarchical relationships between organizations.

[0063] Then, a business flow relationship matrix between nodes is generated to represent the business interactions between organizations involved in the supply chain. The specific method is to analyze the material flow between organizations, such as raw material supply and product delivery; information flow, such as order delivery and demand forecast sharing; and capital flow, such as payment and advance payments. The analysis results are then comprehensively scored to determine the strength of business interactions between organizations.

[0064] Based on the constructed dual-matrix model, a cross-organizational trusted data exchange platform is constructed using distributed ledger technology. First, each supply chain participant is assigned a unique digital identity. For example, digital certificates based on public-key cryptography can be used to generate a public-private key pair for each organization, with the public key serving as the organization's digital identity.

[0065] Then, establish multi-level data access permissions based on digital identity. Based on the node hierarchy relationship matrix, organizations are divided into different tiers, with higher-level organizations able to access some of the data of lower-level organizations. Based on the inter-node business flow relationship matrix, determine the scope of data that organizations with direct business dealings can access. For example, a manufacturer can access the inventory and production capacity data of its direct suppliers, but not the data of its second-tier suppliers.

[0066] Data access permission rules are recorded in the distributed ledger. When an organization requests data access, the system automatically determines whether authorization is granted based on the rules. All data access operations are recorded in the distributed ledger, ensuring the traceability and immutability of data interactions.

[0067] Through the above method, a supply chain collaborative network model based on actual business data can be constructed, and cross-organizational trusted data interaction can be achieved to provide support for supply chain optimization and collaborative decision-making.

[0068] The solution of this application can:

[0069] By collecting multi-dimensional actual business data, this method constructs a supply chain collaborative network model that is closer to reality, improving the model's accuracy and practicality. The dual-matrix structure reflects both the hierarchical relationships between organizations and the closeness of business interactions, comprehensively depicting the characteristics of the supply chain network. The cross-organizational data interaction platform, built using distributed ledger technology, ensures the security and credibility of data sharing. The design based on digital identity and multi-level access rights not only promotes data sharing between organizations, but also protects commercial secrets, achieving a balanced relationship between openness and protection. This method provides a reliable data foundation and technical support for supply chain optimization and collaborative decision-making. By integrating data from multiple parties, global inventory visualization, demand forecasting collaboration, and capacity allocation optimization can be achieved, improving the operational efficiency and responsiveness of the entire supply chain.

[0070] In an optional embodiment, analyzing the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform to calculate the node connectivity, node centrality, and node influence of each supply chain participating organization; and grading each supply chain participating organization according to the calculated node connectivity, node centrality, and node influence includes:

[0071] Analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, calculate the node connectivity of the supply chain participating organizations based on the node hierarchical relationship matrix, and accumulate the matrix element values ​​directly connected to the target supply chain participating organizations in the node hierarchical relationship matrix to obtain the direct connection strength;

[0072] The matrix element values ​​indirectly connected to the target supply chain participating organization in the node hierarchical relationship matrix are weighted by the path distance attenuation coefficient to obtain the indirect connection strength, and the direct connection strength and the indirect connection strength are added to obtain the node connectivity; the node hierarchical relationship matrix is ​​subjected to eigenvalue decomposition to obtain the main eigenvector, and the component value corresponding to the main eigenvector is used as the node centrality of the supply chain participating organization;

[0073] Based on the business flow relationship matrix between the nodes, a business information propagation model is constructed. The SI propagation algorithm is used to calculate the propagation range of business information, and a time decay function is introduced to correct the propagation timeliness to obtain the node influence of the supply chain participating organizations;

[0074] The entropy weight method is used to calculate the weight coefficients of the node connectivity, the node centrality and the node influence respectively, and the weight coefficients are weightedly calculated with the corresponding indicator values ​​to obtain the comprehensive score of the supply chain participating organizations; based on the comprehensive score and combined with the hierarchical constraint relationship in the node hierarchical relationship matrix, the K-means clustering algorithm is used to classify the supply chain participating organizations.

[0075] This embodiment provides a method for grading supply chain participating organizations. This method first analyzes the node hierarchical relationship matrix and the inter-node business flow relationship matrix using a cross-organizational trusted data exchange platform to calculate the node connectivity, node centrality, and node influence of each supply chain participating organization. Supply chain participating organizations are then graded based on these calculation results.

[0076] First, we use the cross-organizational trusted data exchange platform to obtain the node hierarchical relationship matrix and the inter-node business flow relationship matrix. The node hierarchical relationship matrix reflects the hierarchical relationship between the participating organizations in the supply chain, while the inter-node business flow relationship matrix describes the business flow between organizations.

[0077] Next, the node connectivity of the supply chain participating organizations is calculated based on the node hierarchical relationship matrix. For the target supply chain participating organizations, the matrix element values ​​directly connected to them are first accumulated to obtain the direct connection strength. For example, an organization A is directly connected to organizations B, C, and D, and the corresponding matrix element values ​​are 0.8, 0.6, and 0.5, respectively. The direct connection strength of A is 1.9. Then, for indirectly connected organizations, the path distance attenuation coefficient is introduced for weighted calculation. Assuming the attenuation coefficient is 0.8, A is indirectly connected to E through B, and the matrix element value between B and E is 0.7, then the indirect connection strength between A and E is 0.8 0.7 = 0.56. Add up all direct and indirect connection strengths to get the node connectivity.

[0078] Then, perform eigenvalue decomposition on the node hierarchy relationship matrix to obtain the main eigenvector. The component value corresponding to the main eigenvector is used as the node centrality of the supply chain participating organization. After eigenvalue decomposition of the node hierarchical relationship matrix of 5, the main eigenvector is (0.4, 0.3, 0.2, 0.1, 0.0), so the node centrality of the five organizations is 0.4, 0.3, 0.2, 0.1, and 0.0 respectively.

[0079] Next, a business information dissemination model is constructed based on the business flow relationship matrix between nodes. The SI propagation algorithm is used to calculate the dissemination range of business information, and the time decay function is introduced to correct the timeliness of the dissemination to obtain the node influence of the supply chain participating organizations. For example, if an organization influences 100 other organizations within 10 days, assuming the time decay function is e^(-0.1t), its node influence can be expressed as 100 e^(-0.1 10)≈36.8.

[0080] Subsequently, the entropy weight method is used to calculate the weight coefficients of node connectivity, node centrality, and node influence. Assume that the calculated weight coefficients are 0.4, 0.3, and 0.3 respectively. The weight coefficients are weighted with the corresponding indicator values ​​to obtain the comprehensive score of the supply chain participating organizations. For example, if the node connectivity of an organization is 0.8, the node centrality is 0.6, and the node influence is 0.7, then its comprehensive score is 0.8. 0.4+0.6 0.3+0.7 0.3=0.71.

[0081] Finally, based on the comprehensive scores and the hierarchical constraints in the node hierarchy matrix, a K-means clustering algorithm is used to rank the supply chain organizations. Assuming the organizations are divided into three levels, three initial cluster centers can be selected, such as 0.9, 0.6, and 0.3. The distance from each organization to the cluster center is then iteratively calculated, and the cluster centers are continuously adjusted to ultimately determine the level of each organization.

[0082] The solution of this application can:

[0083] This method comprehensively assesses the importance of supply chain organizations within a network by comprehensively considering node connectivity, node centrality, and node influence, improving the accuracy and rationality of the grading results. The introduction of a path distance decay coefficient and a time decay function more accurately characterizes indirect relationships and the impact of timeliness within the supply chain network, making the assessment results more realistic. The entropy weighting method is used to determine the weights of each indicator, avoiding the potential bias caused by subjective weighting. The grading is combined with the K-means clustering algorithm to ensure the objectivity and interpretability of the grading results.

[0084] In an optional embodiment, differentiated collaboration rules are formulated for supply chain participating organizations at different levels. The collaboration rules are used to determine the data sharing scope, collaborative decision weight, and business process triggering conditions of each supply chain participating organization, including:

[0085] Collecting assessment data of supply chain participating organizations, calculating comprehensive assessment scores of supply chain participating organizations based on the assessment data, classifying the supply chain participating organizations into core level, important level, and general level based on the comprehensive assessment scores, and generating level information and corresponding assessment scores for each supply chain participating organization;

[0086] The level information of the supply chain participating organizations is input into a preset data access control model, and a data sharing scope is set for each supply chain participating organization based on the data access control model, and the data sharing scope is used to define the data access rights of each supply chain participating organization; the comprehensive evaluation score is substituted into the data access control model to generate the collaborative decision-making weight of each supply chain participating organization, and the collaborative decision-making weight is used to determine the influence weight of each supply chain participating organization in supply chain decision-making; and business process trigger conditions are set based on the level information of the supply chain participating organizations.

[0087] In this embodiment, the assessment data for supply chain participating organizations includes organizational size data, operating status data, and collaboration performance data. Organizational size data includes annual turnover and asset size, operating status data includes credit ratings and financial indicators, and collaboration performance data includes historical collaboration times and collaboration evaluation scores. A standardized processing method is used for annual turnover and asset size, mapping the values ​​to a range of 0-100 points. Credit ratings are divided into six levels: AAA, AA, A, BBB, BB, and B, with scores corresponding to 100, 80, 60, 40, 20, and 0, respectively. Financial indicators include the weighted results of the debt-to-asset ratio, current ratio, and quick ratio. Historical collaboration times are scored based on the number of collaborations within the past year, and the collaboration evaluation score is a comprehensive score based on the timeliness, accuracy, and degree of cooperation during the collaboration process.

[0088] When calculating the comprehensive evaluation score for supply chain participating organizations, different weights are assigned to each evaluation dimension. Organizational size is weighted 0.3, operating performance is weighted 0.4, and collaboration performance is weighted 0.3. For example, a supply chain participating organization scored 85 points for annual turnover, 90 points for asset size, 80 points for an AA credit rating, 75 points for financial indicators, 95 points for historical collaborations, and 88 points for collaboration evaluation. After weighted calculation, the organization's comprehensive evaluation score is 84.9.

[0089] Supply chain organizations are categorized into different levels based on their comprehensive assessment scores. The specific criteria are: comprehensive assessment scores of 85 or greater are classified as core, scores between 70 and 85 are classified as important, and scores below 70 are classified as general. In practice, a supply chain consists of 50 organizations, of which 8 are classified as core, 22 as important, and 20 as general.

[0090] The data access control model establishes a matrix of access permissions for different data types. Data types are categorized into planning data and execution data. Planning data includes content such as demand forecasts, inventory plans, and procurement plans; execution data includes order information, logistics information, and inventory information. Core-level organizations have access to all data types, while important-level organizations have access to both planning and execution data. General-level organizations only have access to the execution data directly related to their business.

[0091] During the collaborative decision-making weight generation process, the comprehensive evaluation score is substituted into the weight calculation model. For example, for a supply chain inventory optimization decision, a core-level organization's comprehensive evaluation score is 88, resulting in a decision weight of 0.25; an important-level organization's comprehensive evaluation score is 82, resulting in a decision weight of 0.15; and a general-level organization's comprehensive evaluation score is 65, resulting in a decision weight of 0.05.

[0092] The setting of business process trigger conditions is configured differently based on level information. In the procurement process, core-level organizations can directly initiate procurement applications; important-level organizations must meet the requirements of a credit rating of no less than AA and a recent collaboration evaluation score of no less than 80 points; general-level organizations must meet the requirements of a credit rating of no less than AA and a recent collaboration evaluation score of no less than 85 points, and are also required to provide a corresponding performance bond. In the production plan adjustment process, core-level organizations can make independent adjustments within a plan change range of no more than 30%; important-level organizations can make independent adjustments within a plan change range of no more than 20%; and general-level organizations' plan changes must be approved. In the inventory allocation process, core-level organizations can directly allocate inventory; important-level organizations can make independent adjustments when the inventory allocation amount is less than 1 million yuan; and general-level organizations' inventory allocation must be approved.

[0093] In actual operation, when supply chain organizations request data access, permissions are verified using the data access control model. When supply chain decisions are made, the opinions of each participating organization are collected and weighted results are calculated based on their collaborative decision-making weights. When a supply chain organization requests to initiate a business process, it is determined whether it meets the triggering conditions at the corresponding level. This differentiated collaborative rule setting enables orderly collaboration among all supply chain organizations.

[0094] In an optional embodiment, a smart contract is deployed on the cross-organization trusted data interaction platform based on the collaboration rules. The smart contract automatically triggers the cross-organization collaboration process based on the real-time business data of the supply chain participating organizations, and the collaboration process is monitored in real time by calculating the collaboration efficiency index and the risk warning index.

[0095] Deploy a smart contract on the cross-organization trusted data interaction platform based on the collaboration rules, receive real-time business data uploaded by supply chain participating organizations, extract business indicators from the real-time business data, compare the business indicators with the trigger conditions preset in the collaboration rules, and automatically trigger the cross-organization collaboration process when the trigger conditions are met, and distribute collaboration tasks to relevant supply chain participating organizations according to the decision weight sequence preset in the collaboration rules;

[0096] The smart contract calculates the collaborative efficiency index and the risk warning index, monitors the cross-organizational collaborative process in real time, uses a time series analysis method to analyze the changing trends of the collaborative efficiency index and the risk warning index, and mines historical monitoring data through a machine learning algorithm to identify potential problem patterns.

[0097] This technical solution provides a method for deploying smart contracts and monitoring collaborative processes based on a cross-organizational trusted data exchange platform. This method first deploys smart contracts on the cross-organizational trusted data exchange platform, including a data access control contract, a decision weight allocation contract, and a business process triggering contract. These contracts are generated based on pre-defined collaborative rules and are used to implement functions such as data sharing, weight allocation, and process triggering.

[0098] Specifically, data access control contracts are generated based on the data sharing rules in the collaboration rules and are used to control data access rights between different organizations. For example, suppliers can be limited to accessing order data related to them, while being unable to view sensitive information from other suppliers. Decision weight allocation contracts are generated based on the weight allocation rules in the collaboration rules and are used to assign decision weights to each participant in the collaborative decision-making process. For example, different decision weights can be set based on factors such as the business scale and credit rating of each organization. Business process trigger contracts are generated based on the trigger condition rules in the collaboration rules and are used to define the specific conditions that trigger cross-organizational collaborative processes.

[0099] Once deployed, the smart contract begins receiving real-time business data uploaded by supply chain organizations. This data may include inventory levels, order status, production progress, and other information. The smart contract extracts business indicators from this data, such as current inventory levels, number of outstanding orders, and production plan completion rates. These indicators are then compared with pre-set trigger conditions.

[0100] For example, if the preset trigger condition is "inventory levels fall below 80% of safety stock," when an organization uploads inventory data showing that its inventory levels have fallen to 75% of safety stock, the cross-organizational collaboration process is automatically triggered. Once triggered, the smart contract distributes collaborative tasks to relevant supply chain organizations based on a pre-defined decision weight sequence. For example, it might send a replenishment recommendation to an organization experiencing insufficient inventory, while simultaneously sending a production increase request to an upstream supplier.

[0101] During the execution of a collaborative process, smart contracts calculate collaborative efficiency and risk warning indicators in real time to monitor the process. Collaborative efficiency indicators include response time, decision execution rate, and resource utilization. Response time is calculated by recording the time interval from the fulfillment of a trigger condition to the completion of process execution, for example, the time from the detection of insufficient inventory to the completion of replenishment. Decision execution rate is calculated by calculating the ratio of executed decision tasks to the total number of decision tasks, such as the number of completed replenishment tasks divided by the total number of replenishment tasks. Resource utilization is calculated by analyzing the occupancy of various resources, such as the efficiency of transport vehicles and storage space.

[0102] Risk warning indicators include data consistency, decision conflict, and process anomaly. The data consistency indicator assesses data synchronization by comparing data versions and content across different nodes, for example, comparing order information across organizational systems. The decision conflict indicator identifies potential conflicts by analyzing the interactions between parallel decision tasks, such as detecting whether multiple organizations are simultaneously requesting increased production of the same raw material. The process anomaly indicator assesses risk by monitoring abnormal events during process execution, such as detecting abnormalities like long-term unprocessed orders and frequent changes to production plans.

[0103] Smart contracts continuously monitor these indicators and analyze their trends using time series analysis. For example, a moving average can be used to analyze the response time indicator's trends to identify any persistent decline in response speed. Simultaneously, machine learning algorithms can be used to mine historical monitoring data to identify potential problem patterns. For example, clustering algorithms can be used to analyze historical data to identify which combinations of factors are most likely to lead to decreased collaborative efficiency or increased risk.

[0104] In this way, this technical solution realizes the intelligent triggering and real-time monitoring of cross-organizational collaborative processes, which can timely discover and solve efficiency and risk problems in the collaborative process, and improve the collaborative efficiency and stability of the entire supply chain.

[0105] The solution of this application can:

[0106] This technical solution, by deploying smart contracts on a cross-organizational trusted data exchange platform, enables automated triggering and intelligent monitoring of supply chain collaboration processes, significantly improving the efficiency and reliability of cross-organizational collaboration. Smart contracts automatically trigger collaborative processes based on real-time business data, eliminating the delays associated with manual judgment and operation, enabling supply chain participants to more quickly respond to market changes and business needs. This solution also comprehensively monitors cross-organizational collaborative processes by calculating and analyzing collaborative efficiency and risk warning indicators in real time. This monitoring mechanism promptly identifies inefficiencies or increased risks, providing decision support for managers to take appropriate measures to optimize collaborative processes and improve overall operational efficiency. Furthermore, this technical solution utilizes machine learning algorithms to mine and analyze historical monitoring data, identifying potential problem patterns and risk factors. This predictive analysis provides strong support for supply chain risk management, enabling participating organizations to proactively prevent potential issues and enhancing the resilience and stability of the entire supply chain.

[0107] In an optional implementation, when the collaborative efficiency index falls below a preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to supply chain participating organizations whose node influence exceeds the preset strategy threshold, and records the data interaction and decision-making basis during the collaborative process to the blockchain network, achieving full traceability of the collaborative process, including:

[0108] When the collaborative efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy. The collaborative strategy optimization request includes the location analysis, cause diagnosis and optimization suggestions of the efficiency anomaly. The optimization request is distributed to the most relevant and influential nodes according to the type of efficiency anomaly.

[0109] The smart contract automatically writes data interaction records during the collaborative strategy optimization process into the blockchain network. The data interaction records include business data exchange between nodes, collaborative instruction transmission, and status information update. The smart contract also simultaneously records the decision basis to the blockchain network. The decision basis includes decision trigger conditions, decision rules, reasoning process, and decision results.

[0110] The smart contract ensures the integrity and non-tamperability of the data interaction records and the decision-making basis through the distributed ledger mechanism and consensus mechanism of the blockchain network, supports collaborative process traceability queries based on the time dimension, participant dimension and business dimension, and realizes trusted traceability of the entire process from efficiency warning, optimized decision-making to execution feedback.

[0111] This embodiment provides a method for optimizing supply chain collaboration efficiency based on blockchain and smart contracts. This method first establishes a supply chain collaboration efficiency evaluation model and regularly calculates a collaboration efficiency index. When the collaboration efficiency index falls below a preset threshold, the smart contract automatically triggers the collaboration strategy optimization process.

[0112] First, the smart contract analyzes the specific manifestations and impact of efficiency anomalies. For example, it could be that a certain node's response time is too long, or that inventory turnover is low at a certain stage. Based on pre-set rules, the smart contract locates and diagnoses the anomaly. For example, an abnormal response time could be due to an information system failure or insufficient staffing, while a low inventory turnover could be caused by inaccurate demand forecasts or irrational production plans.

[0113] Next, the smart contract automatically generates targeted optimization recommendations based on the anomaly type. For example, for information system failures, it recommends upgrading the system or adding a backup system; for demand forecasting issues, it recommends optimizing the forecasting algorithm or increasing historical data samples. The smart contract sends a collaborative strategy optimization request, including anomaly location, cause diagnosis, and optimization recommendations, to supply chain organizations whose node influence exceeds a preset threshold.

[0114] Node influence can be comprehensively assessed across multiple dimensions, including transaction volume, credit rating, and financial strength. For example, a retailer with an annual transaction volume exceeding 1 billion yuan, an A-level credit rating, and sufficient funds would have a node influence score of 95, exceeding the preset threshold of 80 points, and would therefore receive an optimization request.

[0115] Smart contracts distribute optimization requests to the most relevant, high-impact nodes based on the specific type of efficiency anomaly. For example, production planning issues are primarily sent to manufacturers, while logistics and distribution issues are primarily sent to logistics service providers. This targeted distribution improves the pertinence and efficiency of problem solving.

[0116] During the collaborative strategy optimization process, smart contracts automatically record relevant data interactions. This includes business data exchanged between nodes, such as sales forecasts, inventory levels, and production plans; the transmission of collaborative instructions, such as adjusting production plans and increasing inventory; and updates to node status information, such as inventory changes and production progress. All of these data interaction records are written to the blockchain network.

[0117] Smart contracts also record the basis and process for decision-making. This includes the conditions that trigger the decision, such as when an efficiency indicator falls below a threshold; the rules used for decision-making, such as selecting an optimization solution based on the type of anomaly; the reasoning process, such as analyzing the cause of the anomaly and generating recommendations; and the final decision outcome, such as the specific optimization measures. These decision-making bases are also recorded on the blockchain network.

[0118] Blockchain networks use distributed ledger technology and consensus mechanisms to ensure the integrity and immutability of all records. Each participant maintains a complete copy of the ledger, and any attempts to tamper with it are detected and rejected by other nodes. This mechanism ensures the credibility and transparency of the collaborative process.

[0119] Data recorded on the blockchain enables multi-dimensional traceability of collaborative processes. From a temporal perspective, collaborative activities within any timeframe can be queried; from a participant perspective, all relevant operations at a specific node can be traced; and from a business perspective, the entire process record of a specific business process can be viewed. This comprehensive traceability capability covers the entire collaborative process, from efficiency early warning and optimized decision-making to execution feedback.

[0120] For example, a manufacturer discovered that its inventory turnover rate was consistently lower than expected, triggering a collaborative efficiency optimization process. Smart contract analysis revealed that this was due to inaccurate demand forecasts from retailers. The contract automatically sent requests to several influential large retailers to optimize their demand forecasts. The retailers then adjusted their forecasting models and transmitted the new sales forecasts back to the manufacturer. All data interactions and decision-making processes during this process were recorded on the blockchain, enabling subsequent traceability and auditability.

[0121] The solution of this application can:

[0122] This solution automatically triggers and executes collaborative strategy optimization through smart contracts, speeding up the detection and resolution of supply chain collaboration efficiency anomalies, reducing human intervention and delays, and effectively improving the operational efficiency of the entire supply chain. A targeted distribution mechanism based on node influence ensures that efficiency optimization requests quickly reach the most relevant and capable parties, improving the targeted and efficient nature of problem solving and fostering effective collaboration among all parties in the supply chain. By utilizing blockchain technology to record data interactions and decision-making basis during the collaborative process, this enables reliable traceability throughout the entire process, enhancing the transparency and auditability of supply chain collaboration, helping to build mutual trust among all parties, reduce disputes, and provide data support for continuous optimization.

[0123] Figure 2 FIG. 1 is a schematic diagram of a multi-organization collaborative system for supply chain management according to an embodiment of the present invention. Figure 2 As shown, the system includes:

[0124] The first unit is configured to collect inventory data, production plan data, and logistics and distribution data from multiple supply chain participating organizations, construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data, and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; and employ distributed ledger technology to assign a unique identifier and corresponding data access rights to each supply chain participating organization, thereby constructing a cross-organizational trusted data interaction platform;

[0125] The second unit is used to analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, calculate the node connectivity, node centrality and node influence of each supply chain participating organization; classify the supply chain participating organizations according to the calculated node connectivity, node centrality and node influence; formulate differentiated collaboration rules for supply chain participating organizations at different levels, and the collaboration rules are used to determine the data sharing scope, collaborative decision weight and business process triggering conditions of each supply chain participating organization;

[0126] The third unit is used to deploy smart contracts on the cross-organizational trusted data interaction platform based on the collaboration rules. The smart contract automatically triggers the cross-organizational collaboration process according to the real-time business data of the supply chain participating organizations, and monitors the collaboration process in real time by calculating the collaboration efficiency index and the risk warning index. When the collaboration efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaboration strategy optimization request to the supply chain participating organizations whose node influence is higher than the policy preset threshold, and records the data interaction records and decision-making basis in the collaboration process to the blockchain network, so as to realize the full traceability of the collaboration process.

[0127] According to a third aspect of the embodiments of the present invention,

[0128] An electronic device is provided, comprising:

[0129] processor;

[0130] a memory for storing processor-executable instructions;

[0131] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0132] According to a fourth aspect of the embodiments of the present invention,

[0133] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0134] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-organizational collaborative approach for supply chain management, characterized by: include: Collecting inventory data, production planning data, and logistics distribution data from multiple supply chain participating organizations, constructing a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production planning data, and the logistics distribution data, and establishing a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; Distributed ledger technology is used to assign unique identifiers and corresponding data access rights to each supply chain participant, building a cross-organizational trusted data interaction platform; Analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform to calculate the node connectivity, node centrality and node influence of each supply chain participating organization; The supply chain participating organizations are graded according to the calculated node connectivity, node centrality and node influence, including: Analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, calculate the node connectivity of the supply chain participating organizations based on the node hierarchical relationship matrix, and accumulate the matrix element values ​​directly connected to the target supply chain participating organizations in the node hierarchical relationship matrix to obtain the direct connection strength; The matrix element values ​​indirectly connected to the target supply chain participating organization in the node hierarchical relationship matrix are weighted by the path distance attenuation coefficient to obtain the indirect connection strength, and the direct connection strength and the indirect connection strength are added to obtain the node connectivity; the node hierarchical relationship matrix is ​​subjected to eigenvalue decomposition to obtain the main eigenvector, and the component value corresponding to the main eigenvector is used as the node centrality of the supply chain participating organization; Based on the business flow relationship matrix between the nodes, a business information propagation model is constructed. The SI propagation algorithm is used to calculate the propagation range of business information, and a time decay function is introduced to correct the propagation timeliness to obtain the node influence of the supply chain participating organizations; The entropy weight method is used to calculate the weight coefficients of the node connectivity, the node centrality, and the node influence, and the weight coefficients are weighted with the corresponding index values ​​to obtain a comprehensive score of the supply chain participating organizations; based on the comprehensive score and combined with the hierarchical constraint relationship in the node hierarchical relationship matrix, the K-means clustering algorithm is used to classify the supply chain participating organizations; Formulate differentiated collaboration rules for different levels of supply chain participating organizations; Based on the collaboration rules, a smart contract is deployed on the cross-organization trusted data interaction platform. The smart contract automatically triggers the cross-organization collaboration process according to the real-time business data of the supply chain participating organizations, and monitors the cross-organization collaboration process in real time by calculating the collaboration efficiency index and the risk warning index. When the collaboration efficiency index is lower than the preset efficiency threshold, the smart contract automatically initiates a collaboration strategy optimization request to the supply chain participating organizations whose node influence is higher than the policy preset threshold, and records the data interaction records and decision-making basis in the collaboration process to the blockchain network, so as to realize the full traceability of the collaboration process.

2. The method according to claim 1, characterized in that Collecting inventory data, production planning data, and logistics distribution data from multiple supply chain participating organizations, constructing a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production planning data, and the logistics distribution data, and establishing a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; Distributed ledger technology is used to assign unique identifiers and corresponding data access rights to each supply chain participant, building a cross-organizational trusted data interaction platform including: Collecting business data of multiple supply chain participating organizations, building a supply chain collaborative network model with a dual-matrix structure based on the business data, and generating a node hierarchical relationship matrix by calculating the business dependence and resource complementarity between the supply chain participating organizations. The node hierarchical relationship matrix is ​​used to represent the hierarchical association relationship between the supply chain participating organizations; By analyzing the material flow, information flow and capital flow between the organizations participating in the supply chain, an inter-node business flow relationship matrix is ​​generated. The inter-node business flow relationship matrix is ​​used to characterize the business interaction relationship between the organizations participating in the supply chain, and a cross-organizational trusted data interaction platform is constructed using distributed ledger technology.

3. The method according to claim 1, characterized in that Differentiated collaboration rules are formulated for supply chain participating organizations at different levels. The collaboration rules are used to determine the data sharing scope, collaborative decision weight, and business process triggering conditions of each supply chain participating organization, including: Collecting assessment data of supply chain participating organizations, calculating comprehensive assessment scores of supply chain participating organizations based on the assessment data, classifying the supply chain participating organizations into core level, important level, and general level based on the comprehensive assessment scores, and generating level information and corresponding assessment scores for each supply chain participating organization; The level information of the supply chain participating organizations is input into a preset data access control model, and a data sharing scope is set for each supply chain participating organization based on the data access control model, and the data sharing scope is used to define the data access rights of each supply chain participating organization; the comprehensive evaluation score is substituted into the data access control model to generate the collaborative decision-making weight of each supply chain participating organization, and the collaborative decision-making weight is used to determine the influence weight of each supply chain participating organization in supply chain decision-making; and business process trigger conditions are set based on the level information of the supply chain participating organizations.

4. The method according to claim 1, wherein Based on the collaboration rules, a smart contract is deployed on the cross-organization trusted data interaction platform. The smart contract automatically triggers the cross-organization collaboration process based on the real-time business data of the supply chain participating organizations, and monitors the collaboration process in real time by calculating the collaboration efficiency index and risk warning index. Deploy a smart contract on the cross-organization trusted data interaction platform based on the collaboration rules, receive real-time business data uploaded by supply chain participating organizations, extract business indicators from the real-time business data, compare the business indicators with the trigger conditions preset in the collaboration rules, and automatically trigger the cross-organization collaboration process when the trigger conditions are met, and distribute collaboration tasks to relevant supply chain participating organizations according to the decision weight sequence preset in the collaboration rules; The smart contract calculates the collaborative efficiency index and the risk warning index, monitors the cross-organizational collaborative process in real time, uses a time series analysis method to analyze the changing trends of the collaborative efficiency index and the risk warning index, and mines historical monitoring data through a machine learning algorithm to identify potential problem patterns.

5. The method according to claim 1, wherein When the collaborative efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy, and records the data interaction and decision basis during the collaborative process to the blockchain network, realizing the full traceability of the collaborative process, including: When the collaborative efficiency index is lower than the preset threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy. The collaborative strategy optimization request includes the location analysis, cause diagnosis and optimization suggestions of the efficiency anomaly. The optimization request is distributed to the most relevant and influential nodes according to the type of efficiency anomaly. The smart contract automatically writes data interaction records during the collaborative strategy optimization process into the blockchain network. The data interaction records include business data exchange between nodes, collaborative instruction transmission, and status information update. The smart contract also simultaneously records the decision basis to the blockchain network. The decision basis includes decision trigger conditions, decision rules, reasoning process, and decision results. The smart contract ensures the integrity and non-tamperability of the data interaction records and the decision-making basis through the distributed ledger mechanism and consensus mechanism of the blockchain network, supports collaborative process traceability queries based on the time dimension, participant dimension and business dimension, and realizes trusted traceability of the entire process from efficiency warning, optimized decision-making to execution feedback.

6. A multi-organization collaborative system for supply chain management, configured to implement the method according to any one of claims 1 to 5, characterized in that: include: The first unit is configured to collect inventory data, production plan data, and logistics and distribution data of multiple supply chain participating organizations, construct a node hierarchical relationship matrix and an inter-node business flow relationship matrix based on the inventory data, the production plan data, and the logistics and distribution data, and establish a supply chain collaborative network model based on the node hierarchical relationship matrix and the inter-node business flow relationship matrix; Distributed ledger technology is used to assign unique identifiers and corresponding data access rights to each supply chain participant, building a cross-organizational trusted data interaction platform; The second unit is used to analyze the node hierarchical relationship matrix and the inter-node business flow relationship matrix through the cross-organizational trusted data interaction platform, and calculate the node connectivity, node centrality and node influence of each supply chain participating organization; Classifying the node connectivity, the node centrality, and the node influence; Formulate differentiated collaboration rules for different levels of supply chain participating organizations; A third unit is configured to deploy a smart contract on the cross-organization trusted data interaction platform based on the collaboration rules. The smart contract automatically triggers the cross-organization collaboration process based on the real-time business data of the supply chain participating organizations, and monitors the cross-organization collaboration process in real time by calculating collaboration efficiency indicators and risk warning indicators. When the collaborative efficiency index is lower than the preset efficiency threshold, the smart contract automatically initiates a collaborative strategy optimization request to the supply chain participating organizations whose node influence is higher than the preset threshold of the strategy, and records the data interaction records and decision-making basis in the collaborative process to the blockchain network, thereby realizing full traceability of the collaborative process.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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