Supplier contract security management system and method based on blockchain

Abstract

This invention belongs to the technical field of contract security management. It discloses a blockchain-based supplier contract security management system and method. It performs encrypted, fragmented storage and multi-node verification of supplier contracts to ensure that data cannot be tampered with. Based on this, it conducts multi-dimensional supplier qualification assessments and credit score records. It implements contract clause coding and trigger condition setting to form a self-executing contract control chain. It reaches a consensus on the contract execution status through cross-organizational collaboration and distributed consensus. It uses data hierarchical encryption and refined permission control to ensure privacy and security. It combines compliance requirements mapping and real-time risk assessment to generate a risk situation map. It builds a contract performance traceability chain based on historical data indexing and correlation analysis. It implements abnormal behavior detection and security incident classification processing. Finally, it generates optimization suggestions and performance evaluation reports. This invention significantly improves the security, efficiency, and transparency of supplier contract management.

Description

Technical Field

[0001] The present invention relates to the technical field of contract security management, and more particularly, to a blockchain-based supplier contract security management system and method. Background Art

[0002] With the increasing complexity of the global business environment and the continuous expansion of supply chain networks, supplier contract management has become a critical component of business operations. Traditional supplier contract management relies primarily on paper documents and basic electronic filing systems, but these methods have shown many limitations in today's digital, fast-paced business environment.

[0003] Currently, the supplier contract management systems available on the market are primarily categorized into three types: document management, process management, and relationship management. Document management systems focus on the storage and retrieval of contract documents and typically utilize a centralized database architecture. While these systems enable basic electronic management, they present a single point of failure risk in terms of data security and lack effective anti-tampering mechanisms. Process management systems focus on contract approval and execution oversight, incorporating workflow engines. However, their rigid, pre-set processes struggle to adapt to complex and ever-changing business scenarios, and manual oversight of execution remains dominant. Relationship management systems focus on maintaining supplier relationships and integrate basic evaluation capabilities, but their evaluation metrics are single and static, making it difficult to dynamically reflect the true status of suppliers.

[0004] In current supplier contract management practices, traditional paper-based contract management systems suffer from issues such as easy data tampering, difficulty accessing data, and chaotic version control. While electronic systems have seen some improvements, they still lack effective anti-tampering mechanisms, making it difficult to ensure contract authenticity and often leading to difficulties in identifying evidence in commercial disputes. Supplier evaluation systems often suffer from single-dimensionality, data silos, and static evaluations, making it difficult to fully reflect suppliers' actual performance capabilities and creditworthiness. This leads companies to repeatedly make mistakes in supplier selection, encountering risks such as substandard quality and delivery delays. Contract execution relies heavily on manual oversight and intervention, which is not only inefficient and costly, but also prone to errors or omissions in clause implementation due to human negligence. Information asymmetry is prevalent in cross-departmental and cross-organizational contract collaboration scenarios, resulting in different understandings of contract status among various parties, often leading to buck-passing and communication barriers. Protection mechanisms for sensitive business information are imperfect, making it difficult to balance data sharing and privacy protection, resulting in frequent leaks of trade secrets. Compliance risk monitoring often lags behind the actual occurrence of risks, forcing companies to passively respond to violations, resulting in high compliance costs and reputational damage. The difficulty of tracing the entire contract lifecycle and incomplete historical change records make it difficult to clarify responsibilities in performance disputes and prolong dispute resolution cycles. The lack of a systematic detection and response mechanism for abnormal behavior and security incidents leads to untimely remediation of security vulnerabilities, which provides opportunities for criminals to exploit. Performance evaluations are superficial, lacking in-depth analysis and targeted improvement recommendations, making it difficult to substantially improve contract management, ultimately impacting the stability and competitiveness of corporate supply chains.

[0005] In view of this, the present invention proposes a blockchain-based supplier contract security management system and method to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides a blockchain-based supplier contract security management system and method, which improves the security, efficiency and transparency of contract management.

[0007] In a first aspect, the present application provides a blockchain-based supplier contract security management method, comprising: step 1, encrypting and sharding the supplier contract and performing multi-node verification to obtain a tamper-proof contract data structure;

[0008] Step 2: Based on the tamper-proof contract data structure, perform a multi-dimensional supplier qualification assessment and credit score record to obtain a supplier credit assessment network;

[0009] Step 3: Encode contract terms and set trigger conditions based on the supplier credit evaluation network to obtain a self-executing contract control chain;

[0010] Step 4: Based on the self-executing contract control chain, cross-organizational collaboration and distributed consensus are achieved to obtain a contract execution status consensus ledger;

[0011] Step 5: Perform hierarchical data encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy-preserving access control matrix;

[0012] Step 6: Perform compliance requirement mapping and real-time risk assessment based on the privacy-preserving access control matrix to obtain a contract compliance risk situation map;

[0013] Step 7: Perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain;

[0014] Step 8: Perform abnormal behavior detection and security incident classification based on the contract performance traceability chain to obtain a security response decision tree;

[0015] Step 9: Based on the security response decision tree and the contract performance traceability chain, generate optimization suggestions and perform performance evaluation on contract management to obtain a comprehensive report on supplier contract management.

[0016] In a second aspect, the present application provides a blockchain-based supplier contract security management system, including: a contract data distributed storage module for encrypted shard storage and multi-node verification of supplier contracts to obtain a tamper-proof contract data structure;

[0017] The supplier qualification chain authentication module is used to perform multi-dimensional supplier qualification evaluation and credit score records based on the tamper-proof contract data structure to obtain a supplier credit evaluation network;

[0018] A smart contract automatic execution module, configured to encode contract terms and set trigger conditions based on the supplier credit assessment network to obtain a self-executing contract control chain;

[0019] A multi-party collaborative consensus mechanism module is used to conduct cross-organizational collaboration and reach distributed consensus based on the self-executing contract control chain to obtain a consensus ledger on the contract execution status;

[0020] A data privacy protection module is used to perform hierarchical data encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy protection access control matrix;

[0021] A compliance risk dynamic monitoring module, configured to perform compliance requirement mapping and real-time risk assessment based on the privacy-preserving access control matrix to obtain a contract compliance risk situation map;

[0022] A contract lifecycle tracing module is used to perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain;

[0023] A security incident intelligent response module is used to detect abnormal behavior and classify security incidents based on the contract performance traceability chain to obtain a security response decision tree;

[0024] An intelligent decision support module is used to generate optimization suggestions and perform performance evaluation on contract management based on the security response decision tree and the contract performance traceability chain, and obtain a comprehensive report on supplier contract management; each module is connected through a blockchain network to achieve secure data transmission and consistency maintenance between modules.

[0025] The technical effects and advantages of the blockchain-based supplier contract security management system and method of the present invention are as follows:

[0026] The present invention improves the security, efficiency and transparency of contract management. It effectively prevents the risk of data tampering, ensures the authenticity and integrity of contract information, and greatly enhances data privacy protection capabilities to ensure that sensitive information is not leaked or abused. By establishing a dynamic, multi-dimensional supplier credit assessment mechanism, an accurate assessment of supplier qualifications and performance capabilities is achieved, providing a scientific basis for enterprises to select reliable partners. The intelligent contract automatic execution mechanism not only reduces the frequency of manual intervention and reduces execution deviations, but also significantly improves contract performance efficiency and reduces management costs. Cross-organizational collaboration barriers are effectively eliminated, promoting information sharing and collaborative decision-making among all participants, and establishing a closer supply chain cooperation relationship. Real-time risk monitoring and early warning functions enable enterprises to promptly discover and respond to potential risks, improving the foresight and initiative of risk management. The complete contract performance traceability mechanism provides a reliable basis for responsibility identification and dispute resolution, reducing dispute handling costs. The performance evaluation and optimization suggestion function based on historical data promotes the continuous improvement of contract management and helps enterprises establish long-term and stable supplier relationships. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the blockchain-based supplier contract security management method of the present invention;

[0028] Figure 2 Detailed implementation flow chart of step 8 and step 9 of the present invention;

[0029] Figure 3 Schematic diagram of the blockchain-based supplier contract security management system of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] This application example provides a blockchain-based supplier contract security management system and method. The execution entities of this blockchain-based supplier contract security management system and method include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that are equipped with this system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of: a contract management system, a supplier information management system, and a blockchain management system.

[0033] See also Figure 1 The present invention provides a supplier contract security management method based on blockchain, comprising the following steps:

[0034] Step 1: Encrypt the supplier contract in sharded storage and perform multi-node verification to obtain a tamper-proof contract data structure;

[0035] Step 2: Based on the tamper-proof contract data structure, perform multi-dimensional supplier qualification evaluation and credit score records to obtain the supplier credit evaluation network;

[0036] Step 3: Encode contract terms and set trigger conditions based on the supplier credit evaluation network to obtain a self-executing contract control chain;

[0037] Step 4: Based on the self-executing contract control chain, cross-organizational collaboration and distributed consensus are achieved to obtain the contract execution status consensus ledger;

[0038] Step 5: Perform hierarchical data encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy-preserving access control matrix;

[0039] Step 6: Perform compliance requirement mapping and real-time risk assessment based on the privacy-preserving access control matrix to obtain a contract compliance risk landscape map;

[0040] Step 7: Perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain the contract performance traceability chain;

[0041] Step 8: Based on the contract fulfillment traceability chain, perform abnormal behavior detection and security incident classification and processing to obtain a security response decision tree;

[0042] Step 9: Based on the security response decision tree and contract fulfillment traceability chain, generate optimization suggestions and perform performance evaluation on contract management to obtain a comprehensive report on supplier contract management.

[0043] The present invention ensures the integrity and security of contract data through encrypted shard storage and multi-node verification technology, constructs a tamper-proof contract data structure to provide a reliable data basis for subsequent operations, and conducts multi-dimensional supplier qualification evaluation and credit score records based on the tamper-proof contract data structure to achieve a comprehensive evaluation of supplier qualifications and reputation. Generating a supplier credit evaluation network helps identify high-risk suppliers and optimize partner selection. Traditional contracts are converted into smart contracts through contract clause coding and trigger condition setting. The self-executing contract control chain reduces human intervention and improves contract execution efficiency. Cross-organizational collaboration and distributed consensus-reaching mechanisms ensure consensus among multiple parties on the contract execution status. The contract execution status consensus ledger provides transparent and tamper-proof execution. Records, data hierarchical encryption and refined permission control ensure the security of sensitive contract data. The privacy protection access control matrix ensures that all parties can only access information within the authorized scope. Compliance requirements mapping and real-time risk assessment continuously monitor compliance risks during contract execution. The contract compliance risk situation map intuitively displays risk distribution and trends. Historical data indexing and correlation analysis support the traceability of the entire life cycle of the contract. The contract performance traceability chain helps resolve disputes and provide complete audit evidence. Abnormal behavior detection and security incident classification processing improve system security. The security response decision tree realizes rapid and effective security incident response. Optimization suggestion generation and performance evaluation promote continuous improvement of contract management. The supplier contract management comprehensive report provides comprehensive support for decision-making.

[0044] In the embodiment of the present invention, the detailed implementation steps of step 1 include:

[0045] Perform semantic analysis and structural processing on the supplier contract text to obtain a set of key contract elements, and then perform multiple hash encryption on the set of key contract elements to obtain the contract data fingerprint;

[0046] The contract data fingerprint is constructed into a Merkle tree to obtain a contract verification tree, and data sharding is performed based on the contract verification tree to obtain multiple contract data shards;

[0047] Multiple contract data shards are distributed and stored in different blockchain nodes to obtain a distributed storage structure. The distributed storage structure is then verified by a consensus algorithm to obtain verified block data.

[0048] The timestamp is embedded in the verified block data to obtain a time-series link structure, and a contract version control mechanism is constructed based on the time-series link structure to obtain a version tracking model;

[0049] Create a cross-chain data reference index for the version tracking model to obtain a cross-chain index table, and then build a tamper-proof contract data structure based on the cross-chain index table and the verified block data.

[0050] In this embodiment, the supplier contract text is first collected, including electronic documents (such as Word, PDF) and scanned versions of paper documents to ensure the integrity and clarity of the text. Natural language processing (NLP) technology is used to perform semantic analysis on the contract text, including word segmentation, part-of-speech tagging, named entity recognition, etc., to identify key entities, clauses and semantic relationships in the contract. The unstructured contract text is converted into a structured data format, such as JSON, XML or a relational database table, to facilitate subsequent processing and analysis. The key elements of the contract are extracted from the structured data, including: contract number, signing date, information of the two parties to the contract, contract subject, contract amount, performance period, payment terms, breach of contract liability, etc., to form a set of key elements of the contract. , apply multiple hash algorithms, such as SHA-256, SHA-3 or Blake2, to the set of key elements of the contract, generate a hash value for each key element, and then hash all the hash values again to form a multi-level hash structure to generate a contract data fingerprint. This fingerprint uniquely identifies the contract content. Any slight change will cause the fingerprint to change. A Merkle tree is constructed based on the contract data fingerprint. The hash value of each key element is used as a leaf node. The hash value of the upper node is generated by pairwise combination, and finally a root hash value is formed to construct a complete contract verification tree. The tree supports efficient data verification and partial data verification. Data sharding is performed according to the contract verification tree, and the contract data is divided into multiple logically related data blocks. Each data block contains part of the contract content and its path proof in the Merkle tree, generating multiple contract data shards that can be independently verified and stored. A suitable blockchain network (such as Ethereum, Hyperledger Fabric, EOS, etc.) is selected as the storage platform. Considering the security, performance and cost of the network, the contract data shards are distributed and stored on different blockchain nodes. Distributed storage technologies such as IPFS (InterPlanetary File System) are used to ensure high availability and fault tolerance of data and form a distributed storage structure. According to the characteristics of the blockchain network, a suitable consensus algorithm (such as PoW, PoS, PBFT, Raft, etc.) is selected to verify the distributed storage structure and ensure data consistency between nodes. It is to prevent single-point tampering, generate verified block data, embed timestamps in the verified block data, record the generation time of each block and the creation and modification time of the data, use trusted time sources (such as NTP servers or timestamp services) to ensure the accuracy and credibility of timestamps, form a time-series link structure, build a contract version control mechanism based on the time-series link structure, record each modification and update of the contract, support version backtracking and difference comparison, similar to the code version control system (such as Git), form a version tracking model, create a cross-chain data reference index, and realize data intercommunication and reference between different blockchain networks, such as the main chain stores the contract summary and the side chain stores the detailed content, establish a link relationship through the hash pointer, and form a cross-chain index table.Integrate cross-chain index tables, verified block data, time-series link structures, and version tracking models to build a tamper-proof contract data structure. This structure ensures the authenticity, integrity, and traceability of contract data, providing a reliable data foundation for subsequent supplier evaluation and contract management.

[0051] In the embodiment of the present invention, the detailed implementation steps of step 2 include:

[0052] Perform multi-dimensional feature extraction on the supplier qualification information in the tamper-proof contract data structure to obtain the supplier's initial feature vector, and establish a qualification evaluation index system based on the supplier's initial feature vector to obtain a multi-dimensional evaluation framework;

[0053] Conduct historical performance data correlation analysis on the multi-dimensional evaluation framework to obtain supplier historical performance metrics, and design a credit scoring algorithm based on the supplier historical performance metrics to obtain an initial credit scoring model;

[0054] A third-party verification mechanism is introduced into the initial credit scoring model to obtain credit data verified by multiple parties. This credit data is then written into a dedicated credit block to obtain a credit score chain.

[0055] Analyze the time series characteristics of the credit score chain to obtain the supplier credit fluctuation pattern, and establish a credit risk prediction model based on the supplier credit fluctuation pattern to obtain a set of risk warning indicators;

[0056] The risk warning indicator set and credit score chain are modeled into a network structure to obtain a supplier relationship network. Based on the supplier relationship network, the correlation between different suppliers is calculated to obtain a supplier credit evaluation network.

[0057] In this embodiment, supplier qualification information is extracted from the tamper-proof contract data structure, including basic enterprise information (such as registered capital, establishment time), qualification certificates (such as business license, industry qualification certificate), financial status (such as asset-liability ratio, profitability), personnel composition (such as the proportion of technical personnel, management team background), etc., and multi-dimensional feature extraction is performed on the extracted supplier qualification information, including: numerical features (such as registered capital, number of employees, financial indicators, etc.), categorical features (such as enterprise type, industry classification, etc.), text features (such as enterprise description, product introduction, etc.), and feature engineering techniques (such as feature selection, feature conversion, feature encoding, etc.) are used to process the original features to form the supplier's initial feature vector. Based on the supplier's initial feature vector, the supplier's initial feature vector is extracted. To evaluate the quality of suppliers, a multi-dimensional qualification evaluation indicator system should be established, including: basic qualification dimension (such as enterprise scale, qualification level), financial health dimension (such as debt repayment ability, profitability), technical capability dimension (such as R&D investment, number of patents), social responsibility dimension (such as environmental compliance, labor rights), etc. Evaluation indicators and calculation methods should be designed for each dimension to form a multi-dimensional evaluation framework. The supplier's historical performance data should be extracted from the tamper-proof contract data structure, including: contract completion rate, delivery timeliness, product / service quality, response speed, customer satisfaction, etc., and correlation analysis should be conducted on the historical performance data to identify the correlation and causal relationship between the data. Statistical methods (such as correlation analysis, regression analysis) or machine learning methods (such as decision trees, random forests) should be used to analyze the supplier's historical performance data. Analyze the performance of suppliers and generate historical performance metrics. Based on the historical performance metrics of suppliers, design a scientific and reasonable credit scoring algorithm. Consider the weight distribution of different dimensions. Use weighted average, hierarchical analysis method (AHP) or machine learning models (such as logistic regression, neural network) to generate an initial credit scoring model. This model integrates the evaluation results of multiple dimensions into a credit score. Introduce a third-party verification mechanism, such as industry associations, regulatory agencies, credit rating agencies, etc., to conduct multi-party verification of supplier credit data. Design a data verification protocol to ensure the fairness and effectiveness of the verification process, form multi-party verified credit data, and write the multi-party verified credit data into a dedicated credit block. Each block contains supplier ID, credit score, score Time, scoring basis and other information are used to form a credit score chain, which records the change history of the supplier's credit score. The credit score chain is analyzed for its time series characteristics. Time series analysis methods (such as ARIMA, exponential smoothing, wavelet analysis, etc.) are used to identify supplier credit fluctuation patterns, such as stable rise, fluctuating decline, seasonal fluctuation, etc. Based on the supplier credit fluctuation pattern, a credit risk prediction model is established. Machine learning methods (such as support vector machines, random forests, deep learning, etc.) are used to predict future credit risks, set risk warning thresholds, generate risk warning indicator sets, perform network structure modeling on the risk warning indicator set and the credit score chain, and use graph theory algorithms and social network analysis methods to construct a supplier relationship network. The nodes in this network represent suppliers.Edges represent relationships between suppliers (such as cooperative relationships, competitive relationships, and supply chain relationships). The degree of association between different suppliers is calculated, and network centrality metrics (such as degree centrality, betweenness centrality, and eigenvector centrality) are used to assess the importance and influence of suppliers in the network. Key suppliers and high-risk suppliers are identified, forming a supplier credit assessment network that provides data support for subsequent contract clause coding and risk management.

[0058] In the embodiment of the present invention, the detailed implementation steps of step 3 include:

[0059] The contract terms in the supplier credit evaluation network are formally described and converted to obtain a machine-readable contract template. The terms logic is then extracted based on the machine-readable contract template to obtain a terms logic graph.

[0060] Perform smart contract coding on the clause logic diagram to obtain the initial smart contract code, and then formally verify the initial smart contract code to obtain a smart contract that has passed security verification;

[0061] Set execution trigger conditions and thresholds for smart contracts that have passed security verification to obtain a conditional trigger execution framework. Based on the conditional trigger execution framework, a multi-level trigger network is constructed to obtain an event-driven execution chain.

[0062] Configure the external data source interface for the event-driven execution chain to obtain the data feeding channel, and design the oracle verification mechanism based on the data feeding channel to obtain a trusted data input mechanism;

[0063] Integrate the trusted data input mechanism with the event-driven execution chain to obtain a complete execution environment, and build a self-executing contract control chain based on the complete execution environment and security-verified smart contracts.

[0064] In this embodiment, contract terms are extracted from the supplier credit assessment network, including key terms such as payment terms, delivery requirements, quality standards, and liability for breach of contract. These contract terms are then formalized and converted from natural language descriptions into a machine-readable structured format, such as JSON, XML, or DSL (domain-specific language). Natural language processing techniques (such as semantic analysis and knowledge graphs) are used to assist in this conversion, forming a machine-readable contract template. The logical relationship between the terms is then extracted from the machine-readable contract template to identify logical relationships between the terms, such as prerequisites ("if...then..."), mutually exclusive conditions ("choose one of two"), and dependencies ("only if...").”, etc., use logical reasoning and knowledge representation technology to construct a clause logic diagram, which represents the logical structure and execution order between clauses. Based on the clause logic diagram, use smart contract programming languages (such as Solidity, Chaincode, Plutus, etc.) to encode the smart contract, convert the contract terms into executable code, including data structure definition, function implementation, event triggers, etc., generate the initial smart contract code, and formally verify the initial smart contract code. Use formal verification tools (such as Coq, Isabelle, K framework, etc.) or static analysis tools (such as Mythril, Slither, etc.) to check whether the code has logical vulnerabilities, security vulnerabilities, or performance issues, verify the correctness, security, and reliability of the smart contract, generate a smart contract that passes security verification, and set execution trigger conditions and thresholds for smart contracts that pass security verification, such as payment conditions ("payment within 7 days after receipt of goods"), delivery conditions ("delivery within 30 days after signing the contract"), and quality acceptance conditions ("qualification rate ≥ 98%"), etc. Define the judgment logic and threshold parameters of the trigger conditions to form a conditional trigger execution framework. Based on the conditional trigger execution framework, build a multi-level trigger network to process Complex conditional judgments and event responses enable conditional cascade triggering (one condition triggers another) and multi-condition combination triggering (a trigger is triggered only when multiple conditions are met simultaneously), forming an event-driven execution chain. Interfaces for external data sources, such as bank payment systems (providing payment confirmation data), logistics systems (providing delivery status data), and quality inspection systems (providing quality inspection data), are configured. Data interface protocols and data format specifications are designed to ensure data interoperability and consistency, forming a data feeding channel. Oracle verification mechanisms are designed to ensure the authenticity and reliability of external data. Methods such as multi-source data cross-validation, data signature verification, and reputation mechanisms can be used to prevent data tampering or forgery. This creates a trusted data input mechanism and integrates it with the event-driven execution chain to build a complete smart contract execution environment, ensuring that contract terms are automatically executed based on real-world conditions. This includes the entire process from data input, conditional judgment, event triggering, and execution operations. By integrating the complete execution environment with security-verified smart contracts, a self-executing contract control chain is constructed. This chain automatically executes contract terms based on preset conditions, reducing human intervention, improving execution efficiency and reliability, and providing a technical foundation for subsequent cross-organizational collaboration and consensus-building.

[0065] In the embodiment of the present invention, the detailed implementation steps of step 4 include:

[0066] Classify the organization nodes of the self-executing contract control chain to obtain a multi-level organization node structure, and design an authority classification mechanism based on the multi-level organization node structure to obtain an organization authority tree;

[0067] A multi-layer consensus protocol is designed based on the organizational authority tree to obtain a hybrid consensus mechanism. Consensus voting is performed on the execution status of key contracts based on the hybrid consensus mechanism to obtain a status confirmation ticket.

[0068] Perform multi-party digital signatures on the state confirmation ticket to obtain a multi-signature authentication record, and establish state transition verification rules based on the multi-signature authentication record to obtain a state transition security gateway;

[0069] The state transition security gateway records the contract execution process in real time to obtain the execution state flow, and builds a state snapshot storage mechanism based on the execution state flow to obtain the state snapshot library;

[0070] The distributed ledger technology is integrated into the state snapshot library to obtain the execution state distributed ledger, and the contract execution state consensus ledger is constructed based on the execution state distributed ledger and multi-signature authentication records.

[0071] In this embodiment, the participating organizations in the self-executing contract control chain are classified, such as suppliers, purchasers, regulatory agencies, financial institutions, etc., and classified according to factors such as organizational type, scale, and role, to construct a multi-level organizational node structure. This structure reflects the hierarchical relationship and division of responsibilities between organizations. Based on the multi-level organizational node structure, a permission classification mechanism is designed to define the access rights of different organizations to contract data and operations, such as read permission, write permission, approval permission, etc., and a role-based access control (RBAC) or attribute-based access control (ABAC) model is used to form an organizational permission tree. This tree clearly defines the scope of authority and hierarchical relationship of each organization. Based on the organizational permission tree, a consensus protocol suitable for multi-organization collaboration is designed, such as PBFT (Practical Byzantine Fault Tolerance), Raft, PoA (Proof of Authority), etc., adopt different consensus mechanisms for different types of decisions, such as using a stricter consensus mechanism for high-value transactions and a more efficient consensus mechanism for daily operations, forming a hybrid consensus mechanism that takes into account both efficiency and security. The hybrid consensus mechanism is used to conduct consensus voting on key contract execution status (such as payment confirmation, delivery acceptance, quality inspection, etc.), requiring relevant parties to participate in the voting according to preset rules to ensure that all parties reach a consensus on the execution status, generate a status confirmation note, which records the voting results and consensus process, and conducts multi-party digital signatures on the status confirmation note, requiring relevant parties (such as suppliers, purchasers, regulators, etc.) to use their respective private keys to digitally sign the key status. The multi-signature confirmation adopts multi-signature technology (such as m-of-n signature) to ensure the security and validity of the signature, forming a multi-signature authentication record, which proves the recognition of the execution status by all parties. Based on the multi-signature authentication record, the state transition verification rules are established to define the legal path and verification method of the state transition during the contract execution process, such as what conditions and verification steps need to be met for the transition from "pending payment" to "paid", forming a state transition security gateway, which ensures the legality and security of the state transition. The state transition security gateway records the real-time status of the contract execution process, tracks the entire process from signing to fulfillment, records detailed information of each state, such as state value, transition time, trigger condition, operator, etc., and generates an execution status. The state flow records the complete process of contract execution. Based on the execution state flow, a state snapshot storage mechanism is built to generate snapshots of the contract execution status regularly (such as daily, weekly, or after each state change). The snapshot contains complete information about the current state, which is convenient for historical backtracking and auditing. A state snapshot library is formed, and the execution state flow and state snapshot library are integrated into the distributed ledger. Blockchain technology is used to ensure the immutability and consistency of data. Each state change forms a transaction, which is recorded in a block to form an execution state distributed ledger. The execution state distributed ledger and multi-signature authentication records are integrated to build a contract execution status consensus ledger. This ledger records the contract execution status confirmed by consensus by all parties, providing reliable data for subsequent permission control and risk assessment.

[0072] In the embodiment of the present invention, the detailed implementation steps of step 5 include:

[0073] Perform sensitivity analysis on the data in the contract execution status consensus ledger to obtain a data sensitivity classification table. Based on the data sensitivity classification table, an encryption strategy is formulated to obtain a multi-level encryption scheme.

[0074] Applying zero-knowledge proof technology to the multi-level encryption scheme, we obtain a verifiable privacy data structure, and design an attribute-based encryption mechanism based on the verifiable privacy data structure to obtain an attribute encryption framework.

[0075] Perform user role mapping on the attribute encryption framework to obtain a role-based encryption access control model, and formulate dynamic authorization rules based on the role-based encryption access control model to obtain a dynamic access control policy;

[0076] Construct a key management and distribution model to obtain a secure key distribution channel, and implement a secure multi-party computing protocol based on the secure key distribution channel to obtain a privacy computing network;

[0077] The privacy computing network and dynamic access control strategy are integrated to obtain a complete privacy protection framework, and the access permission matrix is constructed based on the complete privacy protection framework to obtain a privacy protection access control matrix.

[0078] In this embodiment, a sensitivity analysis is performed on the data in the contract execution status consensus ledger. According to the sensitivity and importance of the data, the data is divided into different levels, such as public data (such as basic contract information), internal data (such as execution progress), confidential data (such as pricing strategy), highly confidential data (such as business secrets), etc. Data classification methods (such as data labels, data dictionaries) are used to perform sensitivity assessment to form a data sensitivity classification table. According to the data sensitivity classification table, corresponding encryption strategies are formulated. Encryption algorithms and key management schemes of different strengths are used for data of different levels. For example, lightweight encryption or no encryption can be used for public data, and high-strength encryption (such as AES-256, RSA-2048, etc.) can be used for confidential data to form a multi-level encryption system. Multi-level encryption scheme, applying zero-knowledge proof technology to the multi-level encryption scheme, so that the data verifier can verify the authenticity and integrity of the data without knowing the original data, such as using zero-knowledge proof protocols such as zk-SNARK, zk-STARK or Bulletproofs to form a verifiable privacy data structure, which protects data privacy while ensuring data verifiability. Based on the verifiable privacy data structure, an attribute-based encryption (ABE) mechanism is designed to control access to encrypted data according to user attributes (such as organizational role, responsibilities, permissions, etc.), including key policy attribute-based encryption (KP-ABE) and cipher policy attribute-based encryption (CP-ABE) modes, forming an attribute encryption framework, which associates user roles (such as procurement manager) with user attributes. The key management and distribution model is constructed, including key generation, storage, distribution, update and revocation, etc. Hardware security modules (HSM) or trusted execution environments (TEE) are used to protect key security, and secure communication protocols (such as TLS and SSH) are used to transmit keys to form a secure key management system. Key distribution channel, based on the secure key distribution channel, implements a secure multi-party computing protocol, enabling multiple parties to jointly calculate results without disclosing their respective data, such as using technologies such as homomorphic encryption, secret sharing or obfuscated circuits to form a privacy computing network. This network supports multi-party collaborative computing without disclosing the original data, integrates the privacy computing network and dynamic access control strategy, and builds a complete privacy protection framework to ensure the security and privacy of data during sharing and use, including multi-level protection mechanisms such as data encryption, access control, privacy computing, and audit tracking. Based on the complete privacy protection framework, an access permission matrix is constructed to clearly define each user's access permissions for each type of data, such as reading, writing, modifying, deleting, etc., to form a privacy protection access control matrix.This matrix provides security for subsequent compliance management and risk assessment.

[0079] In the embodiment of the present invention, the detailed implementation steps of step 6 include:

[0080] Collect legal and regulatory requirements from multiple regions and industries to obtain a compliance requirements knowledge base. Build a compliance rule engine based on the compliance requirements knowledge base to obtain an executable compliance rule set.

[0081] Mapping analysis is performed on the executable compliance rule set and the privacy protection access control matrix to obtain the contract terms compliance assessment results. Based on the contract terms compliance assessment results, real-time monitoring indicators are established to obtain a compliance monitoring indicator system.

[0082] Perform risk quantification calculations on the compliance monitoring indicator system to obtain a risk scorecard, and build a multi-dimensional risk assessment model based on the risk scorecard to obtain a dynamic risk assessment engine;

[0083] Utilize a dynamic risk assessment engine to continuously monitor the contract execution status, obtain risk monitoring data streams, and perform risk trend analysis based on the risk monitoring data streams to obtain a risk evolution trend report;

[0084] The risk evolution trend report is visualized to obtain a risk heat map, and a contract compliance risk situation map is constructed based on the risk heat map and risk monitoring data flow.

[0085] In this embodiment, legal and regulatory requirements related to supplier contracts are collected, including contract law, bidding law, antitrust law, data protection law, industry regulatory provisions, etc., covering laws and regulations in multiple regions (such as domestic provinces and cities, major international markets) and multiple industries (such as manufacturing, IT, finance, etc.), to form a compliance requirements knowledge base. This knowledge base covers compliance requirements in multiple regions and industries. Based on the compliance requirements knowledge base, a compliance rule engine is built to convert legal and regulatory requirements into executable rules, such as "IF-THEN" rules, decision trees, etc., and use rule engine technology (such as Drools, CLIPS, etc.) or natural language processing technology to convert The text rules are converted into executable code to form an executable compliance rule set. The executable compliance rule set is mapped and analyzed with the privacy protection access control matrix to evaluate the consistency of contract terms with legal and regulatory requirements, identify potential compliance risk points, such as insufficient data privacy protection, antitrust risks, and non-compliance with information disclosure, and form contract terms compliance assessment results. Based on the contract terms compliance assessment results, real-time monitoring indicators are established, such as compliance risk index and illegal operation detector, and indicator calculation methods and monitoring frequencies are designed to form a compliance monitoring indicator system. This system can reflect the compliance status during the contract execution process in real time and perform risk quantification calculations on compliance monitoring indicators. Convert qualitative compliance risks into quantitative risk scores, such as using the risk matrix method (risk = possibility × impact) or fuzzy comprehensive evaluation method, to form a risk score card that reflects the severity and possibility of different compliance risks. Based on the risk score card, a multidimensional risk assessment model is established, taking into account multiple dimensions such as legal risk, financial risk, operational risk, and reputation risk. Machine learning methods (such as decision trees, random forests, neural networks, etc.) or statistical models are used to build a risk assessment model to form a dynamic risk assessment engine that can dynamically assess risk levels based on real-time data. The dynamic risk assessment engine is used to continuously monitor the status of contract execution, effectively Compliance risk levels are assessed in real time, and the monitoring frequency is adjusted according to the risk level. High-risk areas may require more frequent monitoring. A risk monitoring data stream is generated, which records the real-time changes in risk indicators. Time series analysis is performed on the risk monitoring data stream to identify risk trends and patterns. Time series analysis methods (such as moving average, exponential smoothing, ARIMA, etc.) are used to predict possible future risk changes and generate a risk evolution trend report. The report describes the development trend of the risk and the prediction results. The risk evolution trend report is visualized using heat maps, radar maps, trend maps, etc. to intuitively display the risk distribution and change trends. Data visualization technology (such as D3.js, ECharts, etc.) to generate interactive visualization charts, forming a risk heat map. This integrates the risk heat map and risk monitoring data stream to construct a contract compliance risk situation map. This situation map comprehensively displays the compliance risk status during the contract execution process, including multi-dimensional information such as risk distribution, risk level, risk trend, and risk correlation, providing an intuitive reference for risk management and decision-making.

[0086] In the embodiment of the present invention, the detailed implementation steps of step 7 include:

[0087] The contract compliance risk situation map is indexed by time dimension to obtain a time series event index table. Based on the time series event index table, a timeline of key contract events is created to obtain a contract lifecycle timeline.

[0088] Perform correlation analysis on the data of each node in the contract lifecycle timeline to obtain an event correlation network. Based on the event correlation network, a contract change tracking strategy is formulated to obtain a change management framework.

[0089] Compare historical versions of contracts based on the change management framework to obtain a version difference report. Build a decision point tracing mechanism based on the version difference report to obtain a decision basis chain.

[0090] Map the responsible parties to the decision-making basis chain to obtain a responsibility attribution network, and design a dispute resolution evidence collection mechanism based on the responsibility attribution network to obtain an evidence chain;

[0091] Integrate the evidence chain with the contract life cycle timeline to obtain a complete historical tracing structure, and build a visual tracing interface based on the complete historical tracing structure to obtain the contract performance tracing chain.

[0092] In this embodiment, a time dimension index is constructed for the data in the contract compliance risk situation map, and contract-related events, such as contract signing, modification, execution, payment, delivery, etc., are organized and indexed in chronological order. A time index structure, such as a B+ tree or a time series database, is designed to support efficient time range queries and form a time series event index table. This table facilitates rapid positioning of the contract status and risk situation at a specific time point. Based on the time series event index table, a timeline of key contract events is created, marking important nodes in the contract life cycle, such as signing, effectiveness, payment, delivery, acceptance, change, termination, etc. The timeline visualization technology is used to display the time sequence and intervals of key events to form a contract life cycle timeline, and the timelines on the contract life cycle timeline are visualized. Perform association analysis on the data of each node, identify the causal relationship, dependency and influence relationship between events, use association rule mining algorithms (such as Apriori, FP-Growth, etc.) or graph analysis algorithms to discover the association patterns between events, and build an event association network that reflects the internal connection between events in the contract execution process. Based on the event association network, formulate a contract change tracking strategy, record and manage the entire process of contract changes, including the reason for the change, the content of the change, the approval process, etc., design change management processes and tools, such as version control systems, change request forms, etc., to form a change management framework. Based on the change management framework, compare the differences of historical versions of the contract, identify the changes and modifications between different versions, and make the contract more efficient. Use text difference comparison algorithms (such as Levenshtein distance, Myers difference algorithm, etc.) to compare content and form a version difference report. This report records the evolution of the contract content in detail. Based on the version difference report, a decision point tracing mechanism is established to record key decisions and their basis during the contract execution process, such as change decisions, payment decisions, dispute resolution decisions, etc., and record the decision time, decision content, decision basis and decision results to form a decision basis chain. This chain helps to understand the rationality and effectiveness of historical decisions. The decision basis chain is mapped to the responsible party, clarifying the person or department responsible for each decision and operation, and establishing a responsibility-decision mapping table to record "who made what decision and when" to form a responsibility The attribution network clearly defines the responsibilities and obligations of each party during the execution of the contract. Based on the responsibility attribution network, a dispute resolution evidence collection mechanism is designed to systematically collect and preserve evidence that may be used to resolve contract disputes, such as electronic communication records, operation logs, payment vouchers, delivery certificates, etc. Evidence classification standards and storage strategies are designed to ensure the integrity and reliability of evidence, forming an evidence chain that provides evidence support for potential contract disputes. The evidence chain is integrated with the contract life cycle timeline to build a complete historical tracing structure to achieve traceability of the entire contract life cycle, including multi-dimensional tracing of contract content, execution status, risk events, responsible parties, etc. Based on the complete historical tracing structure, a visual tracing interface is constructed.Interactive visualization technology (such as timeline diagrams, relationship diagrams, and flow charts) is used to intuitively display the complete process and key nodes of contract performance, supporting multi-dimensional query and drill-down analysis to form a contract performance traceability chain, which provides a historical basis for abnormal behavior detection and security incident handling.

[0093] In the embodiment of the present invention, see Figure 2 , is a flowchart of the detailed implementation steps of step 8 and step 9. The detailed implementation steps of step 8 and step 9 include:

[0094] Apply anomaly detection algorithms to the contract performance traceability chain to obtain an abnormal behavior feature library, and build a behavior pattern recognition model based on the abnormal behavior feature library to obtain an abnormal behavior classifier;

[0095] Use the abnormal behavior classifier to conduct real-time analysis of the behaviors during contract execution to obtain security incident classification results. Based on the security incident classification results, a hierarchical response strategy is formulated to obtain a response strategy library.

[0096] Conduct decision tree modeling on the response strategy library to obtain a security response decision tree, and establish an automated response mechanism based on the security response decision tree to obtain a security incident handling process;

[0097] Conduct contract management performance analysis based on the security incident handling process and contract fulfillment traceability chain to obtain a performance evaluation report. Based on the performance evaluation report, identify optimization opportunities and obtain a list of improvement suggestions.

[0098] Conduct a cost-benefit analysis on the list of improvement suggestions to obtain a priority list, and integrate the priority list and performance evaluation report to form a comprehensive supplier contract management report, which includes risk assessment, compliance status, performance, optimization suggestions and long-term strategies.

[0099] In this embodiment, anomaly detection algorithms are applied to the data in the contract performance traceability chain, such as statistical analysis (such as Z-score, box plot), machine learning (such as isolation forest, single-class SVM), deep learning (such as autoencoder, LSTM), etc., to identify abnormal behavior patterns and features, such as abnormal access patterns, abnormal operation sequences, abnormal transactions, etc., to form an abnormal behavior feature library, which contains known abnormal behavior patterns and feature descriptions. Based on the abnormal behavior feature library, a behavior pattern recognition model is constructed, such as support vector machine, random forest, neural network, etc., to train the model to identify normal behavior and abnormal behavior, set model parameters and thresholds, balance the sensitivity and accuracy of detection, and form an abnormal behavior analysis model. Classifier, use abnormal behavior classifier to analyze the behavior in contract execution in real time, monitor user operations, system activities and data flows, determine whether the behavior is abnormal, and classify abnormal behaviors, such as fraud, breach of contract, operational errors, etc., to generate security event classification results. Based on the security event classification results, formulate a graded response strategy. According to the event type and severity, security events are divided into different levels (such as low risk, medium risk, high risk, emergency). For events of different levels, design corresponding processing procedures and response measures to form a response strategy library. Decision tree modeling is performed on the response strategy library, and the response strategy is organized into a decision tree structure to clarify the decision path and processing process, such as "IF event type = fraud AND risk level = high THEN Execute the freeze operation and notify the security team", form a security response decision tree, which guides the system on how to respond to different types of security incidents. Based on the security response decision tree, establish an automated response mechanism to automate the processing of common security incidents, reduce manual intervention, and improve response efficiency. Design response workflows and automated scripts to form a security incident handling process, which standardizes the handling process of security incidents. Based on the security incident handling process and the contract performance traceability chain, analyze the contract management performance, evaluate the efficiency of contract execution (such as cycle time, response time), quality (such as error rate, satisfaction), cost (such as management cost, transaction cost) and risk control (such as risk occurrence rate, risk handling efficiency), and use balanced scorecards or key The performance evaluation report comprehensively evaluates the effectiveness of contract management, identifies improvement opportunities and optimization space from the performance evaluation report, such as process bottlenecks, duplication of work, communication barriers, etc., and puts forward specific improvement suggestions, such as process optimization, risk control, supplier management, etc., to form a list of improvement suggestions, conduct a cost-benefit analysis on the list of improvement suggestions, evaluate the investment (such as time, manpower, and funds) and expected benefits (such as efficiency improvement, cost reduction, and risk reduction) of each improvement measure, calculate the return on investment (ROI) or cost-benefit ratio, and prioritize the improvement suggestions based on the analysis results to form a priority ranking table. The priority ranking table and the performance evaluation report are integrated to form a comprehensive report on supplier contract management.The report includes risk assessment (such as risk distribution and risk trends), compliance status (such as compliance level and violations), contract fulfillment performance (such as completion rate and timeliness), optimization suggestions (such as short-term improvements and long-term planning), and long-term strategies (such as supplier relationship management and risk prevention and control system construction), providing comprehensive support for corporate decision-making.

[0100] The above describes the supplier contract security management method based on blockchain in the embodiment of the present application. The following describes the supplier contract security management system based on blockchain in the embodiment of the present application. Figure 3 In the embodiment of the present application, an embodiment of the supplier contract security management system based on blockchain includes:

[0101] The contract data distributed storage module is used to encrypt and fragment the supplier contract and perform multi-node verification to obtain a tamper-proof contract data structure;

[0102] The supplier qualification chain authentication module is used to conduct multi-dimensional supplier qualification evaluation and credit score records based on the tamper-proof contract data structure to obtain the supplier credit evaluation network;

[0103] Smart contract automatic execution module, used to encode contract terms and set trigger conditions based on the supplier credit evaluation network to obtain a self-executing contract control chain;

[0104] A multi-party collaborative consensus mechanism module is used to achieve cross-organizational collaboration and distributed consensus based on a self-executing contract control chain, and obtain a consensus ledger on the contract execution status;

[0105] The data privacy protection module is used to perform hierarchical data encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy protection access control matrix;

[0106] A dynamic compliance risk monitoring module is used to map compliance requirements and conduct real-time risk assessment based on a privacy-preserving access control matrix, generating a contract compliance risk landscape.

[0107] The contract lifecycle tracing module is used to index and analyze historical data based on the contract compliance risk situation map to obtain the contract performance traceability chain;

[0108] The security incident intelligent response module is used to detect abnormal behavior and classify security incidents based on the contract performance traceability chain to obtain a security response decision tree;

[0109] The intelligent decision support module is used to generate optimization suggestions and perform performance evaluation for contract management based on the security response decision tree and contract fulfillment traceability chain, and obtain a comprehensive report on supplier contract management; each module is connected through a blockchain network to achieve secure data transmission and consistency maintenance between modules.

[0110] This invention ensures the integrity and immutability of contract data through encrypted sharded storage and multi-node verification, comprehensively assesses supplier reputation using multi-dimensional qualification assessments and credit score records, enables intelligent coding and automatic execution of contract terms, establishes cross-organizational collaboration and distributed consensus mechanisms, uses hierarchical data encryption and refined permission control to ensure data security, conducts real-time compliance risk assessment and visual display, builds a traceability chain for the entire contract lifecycle, enables intelligent detection of abnormal behavior and automatic response to security incidents, and generates optimization recommendations and comprehensive management reports. This invention significantly improves the security, efficiency, and transparency of supplier contract management, reduces contract risks and management costs, and provides strong technical support for enterprise supply chain management.

[0111] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will be able to modify the technical solutions described in the foregoing embodiments or to substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0112] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0113] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0114] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0115] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0116] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0117] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A blockchain-based supplier contract security management method, characterized by: include: Step 1: Encrypt the supplier contract, store it in shards, and verify it on multiple nodes to obtain a tamper-proof contract data structure. Step 2: Based on the tamper-proof contract data structure, perform a multi-dimensional supplier qualification assessment and credit score record to obtain a supplier credit assessment network; Step 3: Encode contract terms and set trigger conditions based on the supplier credit evaluation network to obtain a self-executing contract control chain; Step 4: Based on the self-executing contract control chain, cross-organizational collaboration and distributed consensus are achieved to obtain a contract execution status consensus ledger; Step 5: Perform sensitivity analysis on the data in the contract execution status consensus ledger to obtain a data sensitivity classification table, and formulate an encryption strategy based on the data sensitivity classification table to obtain a multi-level encryption scheme; Applying zero-knowledge proof technology to the multi-level encryption scheme to obtain a verifiable privacy data structure, and designing an attribute-based encryption mechanism based on the verifiable privacy data structure to obtain an attribute encryption framework; Performing user role mapping on the attribute encryption framework to obtain a role-based encryption access control model, and formulating dynamic authorization rules according to the role-based encryption access control model to obtain a dynamic access control policy; Constructing a key management and distribution model to obtain a secure key distribution channel, and implementing a secure multi-party computing protocol based on the secure key distribution channel to obtain a privacy computing network; Integrating the privacy computing network and the dynamic access control policy to obtain a complete privacy protection framework, and constructing an access permission matrix based on the complete privacy protection framework to obtain a privacy protection access control matrix; Step 6: Perform compliance requirement mapping and real-time risk assessment based on the privacy-preserving access control matrix to obtain a contract compliance risk situation map; Step 7: construct a time dimension index for the contract compliance risk situation map to obtain a time series event index table, and create a contract key event timeline based on the time series event index table to obtain a contract life cycle timeline; Performing correlation analysis on the data of each node of the contract lifecycle timeline to obtain an event correlation network, and formulating a contract change tracking strategy based on the event correlation network to obtain a change management framework; Compare historical versions of contracts based on the change management framework to obtain a version difference report, and build a decision point tracing mechanism based on the version difference report to obtain a decision basis chain; Mapping the decision-making basis chain to the responsible subject to obtain a responsibility attribution network, and designing a dispute resolution evidence collection mechanism based on the responsibility attribution network to obtain an evidence chain; Integrate the evidence chain with the contract life cycle timeline to obtain a complete historical tracing structure, and build a visual tracing interface based on the complete historical tracing structure to obtain the contract performance tracing chain; Step 8: Perform abnormal behavior detection and security incident classification based on the contract performance traceability chain to obtain a security response decision tree; Step 9: Based on the security response decision tree and the contract performance traceability chain, generate optimization suggestions and perform performance evaluation on contract management to obtain a comprehensive report on supplier contract management.

2. The blockchain-based supplier contract security management method according to claim 1 is characterized in that: The supplier contract is encrypted, sharded, stored, and verified on multiple nodes to obtain a tamper-proof contract data structure, including: Perform semantic analysis and structural processing on the supplier contract text to obtain a set of key contract elements, and perform multiple hash encryption on the set of key contract elements to obtain a contract data fingerprint; Constructing a Merkle tree for the contract data fingerprint to obtain a contract verification tree, and performing data sharding processing based on the contract verification tree to obtain multiple contract data shards; Distributed storage of the multiple contract data shards to different blockchain nodes to obtain a distributed storage structure, and consensus algorithm verification of the distributed storage structure to obtain verified block data; Embed the timestamp of the verified block data to obtain a time sequence link structure, and build a contract version control mechanism based on the time sequence link structure to obtain a version tracking model; A cross-chain data reference index is created for the version tracking model to obtain a cross-chain index table, and a tamper-proof contract data structure is constructed based on the cross-chain index table and the verified block data.

3. The blockchain-based supplier contract security management method according to claim 2 is characterized in that: The multi-dimensional supplier qualification evaluation and credit score record based on the tamper-proof contract data structure is performed to obtain a supplier credit evaluation network, including: Performing multi-dimensional feature extraction on the supplier qualification information in the tamper-proof contract data structure to obtain an initial supplier feature vector, and establishing a qualification evaluation index system based on the initial supplier feature vector to obtain a multi-dimensional evaluation framework; Performing a correlation analysis on historical performance data of the multi-dimensional evaluation framework to obtain a supplier's historical performance metric, and designing a credit scoring algorithm based on the supplier's historical performance metric to obtain an initial credit scoring model; Introducing a third-party verification mechanism into the initial credit scoring model to obtain credit data verified by multiple parties, and writing the credit data verified by multiple parties into a dedicated credit block to obtain a credit score chain; Performing a time series feature analysis on the credit score chain to obtain a supplier credit fluctuation pattern, and establishing a credit risk prediction model based on the supplier credit fluctuation pattern to obtain a risk warning indicator set; A network structure model is performed on the risk warning indicator set and the credit score chain to obtain a supplier relationship network, and the correlation between different suppliers is calculated based on the supplier relationship network to obtain a supplier credit evaluation network.

4. The blockchain-based supplier contract security management method according to claim 3 is characterized in that: The contract clause encoding and trigger condition setting based on the supplier credit evaluation network to obtain a self-executing contract control chain includes: Performing formal description conversion on the contract terms in the supplier credit evaluation network to obtain a machine-readable contract template, and performing clause logic extraction based on the machine-readable contract template to obtain a clause logic graph; Performing smart contract coding on the clause logic diagram to obtain an initial smart contract code, and performing formal verification on the initial smart contract code to obtain a smart contract that has passed security verification; Setting execution trigger conditions and thresholds for the smart contract that has passed the security verification to obtain a conditional trigger execution framework, and constructing a multi-level trigger network based on the conditional trigger execution framework to obtain an event-driven execution chain; An external data source interface is configured for the event-driven execution chain to obtain a data feeding channel, and an oracle verification mechanism is designed based on the data feeding channel to obtain a trusted data input mechanism; The trusted data input mechanism is integrated with the event-driven execution chain to obtain a complete execution environment, and a self-executing contract control chain is constructed based on the complete execution environment and the security-verified smart contract.

5. The blockchain-based supplier contract security management method according to claim 4 is characterized in that: The cross-organizational collaboration and distributed consensus based on the self-executing contract control chain are performed to obtain a contract execution status consensus ledger, including: Classifying the organization nodes of the self-executing contract control chain to obtain a multi-level organization node structure, and designing an authority classification mechanism based on the multi-level organization node structure to obtain an organization authority tree; Designing a multi-layer consensus protocol based on the organizational authority tree to obtain a hybrid consensus mechanism, and performing consensus voting on the execution status of key contracts based on the hybrid consensus mechanism to obtain a status confirmation ticket; Perform multi-party digital signatures on the state confirmation ticket to obtain a multi-signature authentication record, and establish state transition verification rules based on the multi-signature authentication record to obtain a state transition security gateway; The state transition security gateway records the contract execution process in real time to obtain an execution state stream, and constructs a state snapshot storage mechanism based on the execution state stream to obtain a state snapshot library; The state snapshot library is integrated with distributed ledger technology to obtain an execution state distributed ledger, and a contract execution state consensus ledger is constructed based on the execution state distributed ledger and the multi-signature authentication record.

6. The blockchain-based supplier contract security management method according to claim 5 is characterized in that: The compliance requirements mapping and real-time risk assessment based on the privacy protection access control matrix are performed to obtain a contract compliance risk situation map, including: Collect legal and regulatory requirements from multiple regions and industries to obtain a compliance requirements knowledge base, and build a compliance rule engine based on the compliance requirements knowledge base to obtain an executable compliance rule set; Performing a mapping analysis on the executable compliance rule set and the privacy-preserving access control matrix to obtain a contract terms compliance assessment result, and establishing real-time monitoring indicators based on the contract terms compliance assessment result to obtain a compliance monitoring indicator system; Performing risk quantification calculation on the compliance monitoring indicator system to obtain a risk score card, and establishing a multi-dimensional risk assessment model based on the risk score card to obtain a dynamic risk assessment engine; Utilizing the dynamic risk assessment engine to continuously monitor the contract execution status to obtain a risk monitoring data stream, and performing risk trend analysis based on the risk monitoring data stream to obtain a risk evolution trend report; The risk evolution trend report is visualized to obtain a risk heat map, and a contract compliance risk situation map is constructed based on the risk heat map and the risk monitoring data flow.

7. The blockchain-based supplier contract security management method according to claim 6 is characterized in that: The abnormal behavior detection and security incident classification processing based on the contract performance traceability chain are performed to obtain a security response decision tree; and optimization suggestions and performance evaluation are performed on contract management based on the security response decision tree and the contract performance traceability chain to obtain a comprehensive supplier contract management report, including: Applying an anomaly detection algorithm to the contract performance traceability chain to obtain an abnormal behavior feature library, and constructing a behavior pattern recognition model based on the abnormal behavior feature library to obtain an abnormal behavior classifier; Utilizing the abnormal behavior classifier to analyze the behaviors in the contract execution in real time, obtaining security event classification results, and formulating hierarchical response strategies based on the security event classification results to obtain a response strategy library; Performing decision tree modeling on the response strategy library to obtain a security response decision tree, and establishing an automated response mechanism based on the security response decision tree to obtain a security incident handling process; Conduct contract management performance analysis based on the security incident handling process and the contract fulfillment traceability chain to obtain a performance evaluation report, and identify optimization opportunities based on the performance evaluation report to obtain a list of improvement suggestions; Conduct a cost-benefit analysis on the list of improvement suggestions to obtain a priority list, and integrate the priority list and the performance evaluation report to form a comprehensive supplier contract management report, which includes risk assessment, compliance status, performance, optimization suggestions and long-term strategies.

8. A blockchain-based supplier contract security management system, which is used to implement the blockchain-based supplier contract security management method according to any one of claims 1 to 7, characterized in that: include: The contract data distributed storage module is used to encrypt and fragment the supplier contract and perform multi-node verification to obtain a tamper-proof contract data structure; The supplier qualification chain authentication module is used to perform multi-dimensional supplier qualification evaluation and credit score records based on the tamper-proof contract data structure to obtain a supplier credit evaluation network; A smart contract automatic execution module, configured to encode contract terms and set trigger conditions based on the supplier credit assessment network to obtain a self-executing contract control chain; A multi-party collaborative consensus mechanism module is used to conduct cross-organizational collaboration and reach distributed consensus based on the self-executing contract control chain to obtain a consensus ledger on the contract execution status; A data privacy protection module is used to perform hierarchical data encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy protection access control matrix; A compliance risk dynamic monitoring module, configured to perform compliance requirement mapping and real-time risk assessment based on the privacy-preserving access control matrix to obtain a contract compliance risk situation map; A contract lifecycle tracing module is used to perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain; A security incident intelligent response module is used to detect abnormal behavior and classify security incidents based on the contract performance traceability chain to obtain a security response decision tree; An intelligent decision support module is used to generate optimization suggestions and perform performance evaluation on contract management based on the security response decision tree and the contract performance traceability chain, and obtain a comprehensive report on supplier contract management; each module is connected through a blockchain network to achieve secure data transmission and consistency maintenance between modules.

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