Supplier contract safety management system and method based on block chain

Abstract

The invention belongs to the technical field of contract security management, and discloses a supplier contract security management system and method based on a block chain. Encryption fragmentation storage and multi-node verification are carried out on the supplier contract to ensure that data cannot be tampered; based on this, supplier multi-dimensional qualification evaluation and credit point recording are carried out; contract term coding and trigger condition setting are realized, and a self-execution contract control chain is formed; contract execution state consensus is achieved through cross-organization cooperation and distributed consensus; the privacy security is guaranteed by adopting data hierarchical encryption and authority fine control; generating a risk situation map in combination with compliance requirement mapping and real-time risk assessment; constructing a contract performance traceability chain based on historical data indexes and association analysis; performing abnormal behavior detection and security event classification processing; and finally, an optimization suggestion and a performance evaluation report are generated. According to the method, the safety, efficiency and transparency of supplier contract management are remarkably improved.

Description

Technical Field

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

[0002] With the increasingly complex global business environment and the continuous expansion of the supply chain network, supplier contract management has become a crucial link in enterprise operations. Traditional supplier contract management mainly relies on paper documents and basic electronic file systems, and these methods show many limitations in today's digital and rapidly developing business environment.

[0003] Currently, the supplier contract management systems existing on the market are mainly divided into three categories: document management type, process management type, and relationship management type. The document management type system focuses on the storage and retrieval of contract documents and usually adopts a centralized database architecture. Although it realizes basic electronic management, there is a risk of single-point failure in terms of data security and there is a lack of an effective anti-tampering mechanism. The process management type system pays attention to contract approval circulation and execution supervision and introduces a workflow engine, but its preset rigid process is difficult to adapt to complex and changeable business scenarios, and the manual supervision and execution link still dominates. The relationship management type system focuses on the maintenance of supplier relationships and integrates basic evaluation functions, but the evaluation indicators are single and static and it is difficult to dynamically reflect the true situation of suppliers.

[0004] In current supplier contract management practices, traditional paper-based contract management systems have pain points such as easy data tampering, difficult querying, and chaotic version control. Although electronic systems have improved, they still lack an effective anti-tampering mechanism, making it difficult to guarantee the authenticity of contracts and often leading to difficulties in evidence recognition in commercial disputes. The supplier evaluation system generally has problems such as a single dimension, data silos, and static evaluation, making it difficult to comprehensively reflect the actual performance capabilities and credit status of suppliers. As a result, enterprises often encounter risks such as substandard quality and delayed delivery when selecting suppliers. The contract execution process highly relies on manual supervision and intervention, which is not only inefficient and costly but also prone to deviations or omissions in clause execution due to human negligence. In cross-departmental and cross-organizational contract collaboration scenarios, information asymmetry is common, and there are differences in the understanding of the contract status among all parties, often leading to responsibility shifting and communication barriers. The protection mechanism for sensitive business information is imperfect, and it is difficult to balance data sharing and privacy protection, with commercial secrets leakage incidents occurring from time to time. Compliance risk monitoring often lags behind the occurrence of risks, and enterprises passively respond to violations, bearing high compliance costs and reputation losses. It is difficult to trace the entire life cycle of the contract, and the historical change records are incomplete, resulting in difficulties in clarifying responsibilities in performance disputes and prolonging the dispute resolution cycle. In the face of abnormal behaviors and security incidents, there is a lack of a systematic detection and response mechanism, and security vulnerabilities are not repaired in a timely manner, giving criminals an opportunity. Performance evaluation becomes a mere formality, lacking in-depth analysis and targeted improvement suggestions, making it difficult to substantially improve the contract management level and ultimately affecting the stability and competitiveness of the enterprise's supply chain.

[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] To overcome the above defects of the prior art and to achieve the above object, 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, including: Step 1, encrypting and sharding the storage of the supplier contract and performing multi-node verification to obtain an anti-tampering contract data structure; Step 2, performing multi-dimensional qualification evaluation and credit score recording of the supplier based on the anti-tampering contract data structure to obtain a supplier credit evaluation network; Step 3, performing contract clause coding and trigger condition setting based on the supplier credit evaluation network to obtain a self-executing contract control chain; Step 4, performing cross-organizational collaboration and distributed consensus reaching based on the self-executing contract control chain to obtain a contract execution status consensus ledger; Step 5: Perform data hierarchical encryption and refined permission control based on the contract execution status consensus ledger to obtain a privacy protection access control matrix; Step 6: Perform compliance requirement mapping and real-time risk assessment based on the privacy protection access control matrix to obtain a contract compliance risk situation map; Step 7: Perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain; Step 8: Perform abnormal behavior detection and security event classification processing based on the contract performance traceability chain to obtain a security response decision tree; Step 9: Generate optimization suggestions and perform performance evaluation on contract management based on the security response decision tree and the contract performance traceability chain to obtain a comprehensive report on supplier contract management.

[0008] In a second aspect, the present application provides a blockchain-based supplier contract security management system, including: a contract data distributed storage module for encrypting and sharding the storage of supplier contracts and performing multi-node verification to obtain a tamper-proof contract data structure; A supplier qualification on-chain certification module for performing multi-dimensional qualification assessment and credit score recording of suppliers based on the tamper-proof contract data structure to obtain a supplier credit assessment network; An intelligent contract automatic execution module for encoding contract terms and setting trigger conditions based on the supplier credit assessment network to obtain a self-executing contract control chain; A multi-party collaboration consensus mechanism module for performing cross-organization collaboration and distributed consensus reaching based on the self-executing contract control chain to obtain a contract execution status consensus ledger; A data privacy protection module for performing data hierarchical 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 for performing compliance requirement mapping and real-time risk assessment based on the privacy protection access control matrix to obtain a contract compliance risk situation map; A contract full life cycle traceability module for performing historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain; A security event intelligent response module for performing abnormal behavior detection and security event classification processing based on the contract performance traceability chain to obtain a security response decision tree; An intelligent decision support module for generating optimization suggestions and performing performance evaluation on contract management based on the security response decision tree and the contract performance traceability chain to 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.

[0009] Technical effects and advantages of the blockchain-based supplier contract security management system and method of the present invention: The present invention improves the security, efficiency, and transparency of contract management. It effectively guards against the risk of data tampering, safeguards the authenticity and integrity of contract information, and significantly enhances data privacy protection to ensure that sensitive information is not leaked or misused. By establishing a dynamic and multi-dimensional supplier credit assessment mechanism, it realizes the accurate assessment of supplier qualifications and performance capabilities, providing a scientific basis for enterprises to select reliable partners. The intelligent contract automatic execution mechanism not only reduces the frequency of manual intervention, decreases 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. The real-time risk monitoring and early warning function enables enterprises to promptly detect and respond to potential risks, enhancing the forward-looking and initiative of risk management. The complete contract performance traceability mechanism provides a reliable basis for liability determination and dispute resolution, reducing the cost of dispute handling. 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

[0010] Figure 1 It is a schematic diagram of the blockchain-based supplier contract security management method of the present invention; Figure 2 It is a schematic diagram of the detailed implementation steps of Steps 8 and 9 of the present invention; Figure 3 It is a schematic diagram of the blockchain-based supplier contract security management system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0012] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0013] The embodiments of the present application provide a blockchain-based security management system and method for supplier contracts. The execution entities of the blockchain-based security management system and method for supplier contracts include, but are not limited to, the following devices equipped with the system: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be regarded as general computing nodes of the present 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.

[0014] Please refer to Figure 1 , the present invention provides a blockchain-based security management method for supplier contracts, including the following steps: Step 1: Encrypt and fragment the storage of the supplier contract and perform multi-node verification to obtain a tamper-proof contract data structure; Step 2: Based on the tamper-proof contract data structure, conduct multi-dimensional qualification assessment and credit score recording of the supplier to obtain a supplier credit assessment network; Step 3: Based on the supplier credit assessment network, perform contract clause encoding and trigger condition setting to obtain a self-executing contract control chain; Step 4: Based on the self-executing contract control chain, conduct cross-organization collaboration and distributed consensus reaching to obtain a contract execution status consensus ledger; Step 5: Based on the contract execution status consensus ledger, perform data hierarchical encryption and refined permission control to obtain a privacy protection access control matrix; Step 6: Based on the privacy protection access control matrix, perform compliance requirement mapping and real-time risk assessment to obtain a contract compliance risk situation map; Step 7: Based on the contract compliance risk situation map, perform historical data indexing and correlation analysis to obtain a contract performance traceability chain; Step 8: Based on the contract performance traceability chain, conduct abnormal behavior detection and security event classification processing 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 performance evaluation for contract management to obtain a comprehensive report on supplier contract management.

[0015] The present invention ensures the integrity and security of contract data through encrypted sharding storage and multi-node verification technology, constructs a tamper-proof contract data structure to provide a reliable data foundation for subsequent operations, conducts multi-dimensional qualification assessment and credit score recording of suppliers based on the tamper-proof contract data structure to achieve a comprehensive assessment of supplier qualifications and reputation, generates a supplier credit assessment network to help identify high-risk suppliers and optimize partner selection, converts traditional contracts into smart contracts through contract clause coding and trigger condition settings, the self-executing contract control chain reduces human intervention and improves contract execution efficiency, cross-organization collaboration and distributed consensus reaching mechanisms ensure multi-party consensus on the contract execution status, the contract execution status consensus ledger provides a transparent and tamper-proof execution record, data hierarchical encryption and refined permission control guarantee the security of sensitive contract data, the privacy protection access control matrix ensures that each party can only access information within the authorized scope, compliance requirement mapping and real-time risk assessment continuously monitor compliance risks during the contract execution process, the contract compliance risk situation map intuitively displays the risk distribution and trend, historical data indexing and correlation analysis support the traceability of the entire contract life cycle, the contract performance traceability chain helps resolve disputes and provides complete audit evidence, abnormal behavior detection and security event classification processing improve system security, the security response decision tree enables a fast and effective security event response, optimized recommendation generation and performance evaluation promote the continuous improvement of contract management, and the comprehensive supplier contract management report provides comprehensive support for decision-making.

[0016] In the embodiment of the present invention, the detailed implementation steps of step 1 include: Perform semantic analysis and structured processing on the supplier contract text to obtain a set of contract key elements, and perform multiple hash encryptions on the set of contract key elements to obtain a contract data fingerprint; Construct a Merkle tree for the contract data fingerprint to obtain a contract verification tree, and perform data sharding processing based on the contract verification tree to obtain multiple contract data shards; Distributively store multiple contract data shards in different blockchain nodes to obtain a distributed storage structure, and perform a consensus algorithm verification on the distributed storage structure to obtain verified block data; Embed a time stamp into the verified block data to obtain a time sequence link structure, and construct a contract version control mechanism based on the time sequence link structure to obtain a version tracking model; Create a cross-chain data reference index for the version tracking model to obtain a cross-chain index table, and integrally construct a tamper-proof contract data structure based on the cross-chain index table and the verified block data.

[0017] In this embodiment, first, collect supplier contract texts, including electronic documents (such as Word, PDF) and scanned versions of paper documents, to ensure the integrity and clarity of the texts. Use natural language processing (NLP) techniques to perform semantic analysis on the contract texts, including word segmentation, part-of-speech tagging, named entity recognition, etc., to identify key entities, clauses, and semantic relationships in the contracts. Convert the unstructured contract texts into structured data formats, such as JSON, XML, or relational database tables, for subsequent processing and analysis. Extract the key elements of the contracts from the structured data, including: contract number, signing date, information of both parties to the contract, contract subject matter, contract amount, performance period, payment terms, liability for breach of contract, etc., to form a set of contract key elements. Apply multiple hash algorithms, such as SHA-256, SHA-3, or Blake2, to the set of contract key elements to generate hash values for each key element, and then hash all the hash values again to form a multi-level hash structure, generating a contract data fingerprint. This fingerprint uniquely identifies the contract content, and any minor change will cause the fingerprint to change. Based on the contract data fingerprint, construct a Merkle tree, using the hash values of each key element as leaf nodes, and generating the hash values of the upper-level nodes by pairwise combination, finally forming a root hash value to construct a complete contract verification tree. This tree supports efficient data verification and partial data verification. Perform data sharding processing according to the contract verification tree, divide the contract data 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. These shards can be independently verified and stored. Select a suitable blockchain network (such as Ethereum, Hyperledger Fabric, EOS, etc.) as the storage platform, considering the security, performance, and cost of the network, and distribute the contract data shards to different blockchain nodes. Adopt distributed storage technologies such as IPFS (InterPlanetary File System) to ensure the high availability and fault tolerance of the data, forming a distributed storage structure. According to the characteristics of the blockchain network, select a suitable consensus algorithm (such as PoW, PoS, PBFT, Raft, etc.) to verify the distributed storage structure, ensure the consistency of data among nodes, and prevent single-point tampering, generating verified block data. Embed timestamps into the verified block data to record the generation time of each block and the creation and modification times of the data. Use a trusted time source (such as an NTP server or a timestamp service) to ensure the accuracy and credibility of the timestamps, forming a time-sequential link structure. Based on the time-sequential link structure, construct a contract version control mechanism to record each modification and update of the contract, support version backtracking and difference comparison, similar to a code version control system (such as Git), forming a version tracking model. Create a cross-chain data reference index to achieve data interoperability and reference between different blockchain networks. For example, the main chain stores the contract summary, and the side chain stores the detailed content, and establish a link relationship through hash pointers to form a cross-chain index table.Integrate the cross-chain index table, verified block data, time-sequential link structure, and version tracking model to construct a tamper-proof contract data structure, which ensures the authenticity, integrity, and traceability of contract data and provides a reliable data foundation for subsequent supplier evaluation and contract management.

[0018] In the embodiment of the present invention, the detailed implementation steps of step 2 include: Extract multi-dimensional features from the supplier qualification information in the tamper-proof contract data structure to obtain the initial supplier feature vector, and establish a qualification evaluation index system based on the initial supplier feature vector to obtain a multi-dimensional evaluation framework; Conduct historical performance data correlation analysis on the multi-dimensional evaluation framework to obtain the supplier historical performance metric, and design a credit scoring algorithm based on the supplier historical performance metric to obtain the initial credit scoring model; Introduce a third-party verification mechanism into the initial credit scoring model to obtain multi-party verified credit data, and write the multi-party verified credit data into a dedicated credit block to obtain a credit integral chain; Conduct time-sequential feature analysis on the credit integral 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 risk warning index set; Conduct network structure modeling on the risk warning index set and the credit integral chain to obtain the supplier relationship network, and calculate the correlation degree between different suppliers based on the supplier relationship network to obtain the supplier credit evaluation network.

[0019] 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 proportion of technical personnel, management team background), etc. Multidimensional 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.). The original features are processed using feature engineering techniques (such as feature selection, feature transformation, feature encoding, etc.) to form an initial supplier feature vector. Based on the initial supplier feature vector, a multi-dimensional qualification evaluation index system is established, including: basic qualification dimension (such as enterprise scale, qualification level), financial health dimension (such as debt repayment ability, profitability), technical ability dimension (such as R & D investment, number of patents), social responsibility dimension (such as environmental protection compliance, labor rights), etc. Evaluation indicators and calculation methods are designed for each dimension to form a multi-dimensional evaluation framework. Supplier historical performance data is extracted from the tamper-proof contract data structure, including: contract completion rate, delivery timeliness, product / service quality, response speed, customer satisfaction, etc. Correlation analysis is performed 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 tree, random forest) are used for analysis to generate a measure of the supplier's historical performance. Based on the measure of the supplier's historical performance, a scientific and reasonable credit scoring algorithm is designed, considering the weight allocation of different dimensions. Methods such as weighted average, analytic hierarchy process (AHP), or machine learning models (such as logistic regression, neural network) can be used to generate an initial credit scoring model. This model synthesizes the multi-dimensional evaluation results into a credit score. A third-party verification mechanism is introduced, such as industry associations, regulatory agencies, credit rating agencies, etc., to conduct multi-party verification of the supplier's credit data. A data verification protocol is designed to ensure the fairness and effectiveness of the verification process, forming multi-party verified credit data. The multi-party verified credit data is written into a dedicated credit block. Each block contains information such as supplier ID, credit score, scoring time, scoring basis, etc., forming a credit score chain. This chain records the change history of the supplier's credit score. Time series feature analysis is performed on the credit score chain, and time series analysis methods (such as ARIMA, exponential smoothing, wavelet analysis, etc.) are used to identify the supplier's credit fluctuation patterns, such as stable increase, fluctuating decrease, seasonal fluctuation, etc. Based on the supplier's credit fluctuation patterns, a credit risk prediction model is established, and machine learning methods (such as support vector machine, random forest, deep learning, etc.) are used to predict future credit risks. A risk warning threshold is set to generate a set of risk warning indicators. Network structure modeling is performed on the set of risk warning indicators and the credit score chain, and graph theory algorithms and social network analysis methods are used to construct a supplier relationship network. In this network, nodes represent suppliers,Edges represent the relationships between suppliers (such as cooperation relationships, competition relationships, supply chain relationships, etc.). Calculate the correlation degree between different suppliers, and use network centrality indicators (such as degree centrality, betweenness centrality, eigenvector centrality, etc.) to evaluate the importance and influence of suppliers in the network, identify key suppliers and high-risk suppliers, and form a supplier credit assessment network. This network provides data support for subsequent contract clause coding and risk management.

[0020] In the embodiment of the present invention, the detailed implementation steps of step 3 include: Perform a formal description conversion on the contract clauses in the supplier credit assessment network to obtain a machine-readable contract template, and extract the clause logic according to the machine-readable contract template to obtain a clause logic diagram; Perform intelligent contract coding on the clause logic diagram to obtain initial intelligent contract code, and perform formal verification on the initial intelligent contract code to obtain an intelligent contract that passes the security verification; Set execution trigger conditions and thresholds for the intelligent contract that passes the security verification to obtain a condition-triggered execution framework, and construct a multi-level trigger network according to the condition-triggered execution framework to obtain an event-driven execution chain; Configure the external data source interface for the event-driven execution chain to obtain a data feeding channel, and design an oracle verification mechanism according to the data feeding channel to obtain a trusted data input mechanism; Integrate the trusted data input mechanism with the event-driven execution chain to obtain a complete execution environment, and construct a self-executing contract control chain according to the complete execution environment and the intelligent contract that passes the security verification.

[0021] In this embodiment, contract terms are extracted from the supplier credit assessment network, including key terms such as payment terms, delivery requirements, quality standards, liability for breach of contract, etc. The contract terms are subjected to a formal description conversion to transform the terms described in natural language into a machine-readable structured format, such as JSON, XML, or DSL (Domain-Specific Language). Natural language processing techniques (such as semantic analysis, knowledge graph) are used to assist in the conversion to form a machine-readable contract template. The clause logic of the machine-readable contract template is extracted to identify the logical relationships between the clauses, such as prerequisite conditions ("if... then..."), mutually exclusive conditions ("either... or..."), dependency relationships ("only when...For example, "payment due within 7 days after receiving the goods"), delivery conditions ("delivery within 30 days after signing the contract"), quality acceptance conditions ("qualified rate ≥ 98%"), etc. Use logical reasoning and knowledge representation techniques 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 smart contracts, convert contract clauses into executable code, including data structure definitions, function implementations, event triggers, etc., to generate initial smart contract code. Perform formal verification on 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, and generate a smart contract that passes the security verification. Set execution trigger conditions and thresholds for the smart contract that passes the security verification, such as payment conditions ("payment due within 7 days after receiving the goods"), delivery conditions ("delivery within 30 days after signing the contract"), quality acceptance conditions ("qualified rate ≥ 98%"), etc., define the judgment logic and threshold parameters of the trigger conditions, and form a conditional trigger execution framework. Based on the conditional trigger execution framework, construct a multi-level trigger network to handle complex conditional judgments and event responses, implement conditional cascade triggering (one condition triggers another condition) and multi-condition combination triggering (multiple conditions are satisfied simultaneously to trigger), and form an event-driven execution chain. Configure external data source interfaces, such as bank payment systems (providing payment confirmation data), logistics systems (providing delivery status data), quality inspection systems (providing quality inspection data), etc., design data interface protocols and data format specifications to ensure data interoperability and consistency, and form a data feeding channel. Design an oracle verification mechanism to ensure the authenticity and reliability of external data. Methods such as multi-source data cross-verification, data signature verification, and reputation mechanisms can be used to prevent data from being tampered with or forged, and form a trusted data input mechanism. Integrate the trusted data input mechanism with the event-driven execution chain to build a complete smart contract execution environment, ensure that contract clauses can be automatically executed according to the actual situation, including complete processes such as data input, conditional judgment, event triggering, and execution operations. Integrate the complete execution environment and the smart contract that passes the security verification to build a self-executing contract control chain, which can automatically execute contract clauses according to preset conditions, reduce human intervention, improve execution efficiency and reliability, and provide a technical basis for subsequent cross-organization collaboration and consensus reaching.

[0022] In the embodiment of the present invention, the detailed implementation steps of step 4 include: Classify the organization nodes of the self-executing contract control chain to obtain a multi-level organization node structure, and design a permission grading mechanism according to the multi-level organization node structure to obtain an organization permission tree; Design a multi - layer consensus protocol based on the organizational permission tree to obtain a hybrid consensus mechanism, and conduct a consensus vote on the execution status of key contracts according to the hybrid consensus mechanism to obtain status confirmation bills; Conduct multi - party digital signatures on the status confirmation bills to obtain multi - signature authentication records, and establish status conversion verification rules according to the multi - signature authentication records to obtain a status conversion security gateway; Record the real - time status of the contract execution process through the status conversion security gateway to obtain an execution status stream, and construct a status snapshot storage mechanism according to the execution status stream to obtain a status snapshot library; Integrate the status snapshot library with distributed ledger technology to obtain an execution status distributed ledger, and construct a contract execution status consensus ledger according to the execution status distributed ledger and the multi - signature authentication records.

[0023] 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 organization type, scale, role, etc., to construct a multi-level organizational node structure, which reflects the hierarchical relationship and division of responsibilities among organizations. Based on the multi-level organizational node structure, a permission grading mechanism is designed to define the access permissions of different organizations to contract data and operations, such as read permissions, write permissions, approval permissions, etc. The role-based access control (RBAC) or attribute-based access control (ABAC) model is adopted to form an organizational permission tree, which clarifies the permission scope 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. Different consensus mechanisms are adopted for different types of decisions. For example, a more strict consensus mechanism is used for high-value transactions, and a more efficient consensus mechanism is used for daily operations, forming a hybrid consensus mechanism that takes into account both efficiency and security. The hybrid consensus mechanism is used to conduct a consensus vote on the key contract execution status (such as payment confirmation, delivery acceptance, quality inspection, etc.), requiring relevant parties to participate in the vote according to preset rules to ensure that all parties reach an agreement on the execution status, generating a status confirmation ticket, which records the voting results and the consensus process. The status confirmation ticket is digitally signed by multiple parties, requiring relevant parties (such as suppliers, purchasers, regulators, etc.) to use their respective private keys to digitally sign and confirm the key status. The multi-signature technology (such as m-of-n signature) is adopted to ensure the security and effectiveness 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, status transition verification rules are established to define the legal paths and verification methods for status transitions during the contract execution process. For example, what conditions and verification steps are required for the transition from "pending payment" to "paid". A status transition security gateway is formed, which ensures the legality and security of status transitions. The contract execution process is recorded in real-time through the status transition security gateway, tracking the entire process of the contract from signing to completion of performance, and recording the detailed information of each status, such as status value, transition time, trigger condition, operator, etc., generating an execution status flow, which records the complete history of the contract execution. Based on the execution status flow, a status snapshot storage mechanism is constructed to generate a snapshot of the contract execution status regularly (such as daily, weekly, or after each status change). The snapshot contains the complete information of the current status, facilitating historical traceability and auditing, forming a status snapshot library. The execution status flow and the status snapshot library are integrated into a distributed ledger, using blockchain technology to ensure the immutability and consistency of data. Each status change forms a transaction and is recorded in a block, forming an execution status distributed ledger. The execution status distributed ledger and the multi-signature authentication record are integrated to construct a contract execution status consensus ledger, which records the contract execution status confirmed by all parties through consensus, providing reliable data for subsequent permission control and risk assessment.

[0024] In the embodiment of the present invention, the detailed implementation steps of step 5 include: Perform a sensitivity analysis on the data in the contract execution status consensus ledger to obtain a data sensitivity classification table, and formulate an encryption policy based on the data sensitivity classification table to obtain a multi-level encryption scheme; Apply the zero-knowledge proof technology to the multi-level encryption scheme to 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; Perform user role mapping on the attribute encryption framework to obtain a role-based encrypted access control model, and formulate dynamic authorization rules based on the role-based encrypted access control model to obtain a dynamic access control policy; Construct a key management and distribution model to obtain a secure key distribution channel, and implement a secure multi-party computation protocol based on the secure key distribution channel to obtain a privacy computing network; Integrate the privacy computing network and the dynamic access control policy to obtain a complete privacy protection framework, and construct an access permission matrix based on the complete privacy protection framework to obtain a privacy protection access control matrix.

[0025] In this embodiment, 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 contract basic information), internal data (such as execution progress), confidential data (such as pricing strategy), highly confidential data (such as trade secrets), etc. Data classification methods (such as data tags, data dictionaries) are used for sensitivity assessment to form a data sensitivity classification table. According to the data sensitivity classification table, corresponding encryption policies are formulated. Different encryption algorithms and key management schemes are adopted for different levels of data. For example, lightweight encryption or no encryption can be used for public data, and high-strength encryption (such as AES-256, RSA-2048, etc.) is used for confidential data to form a multi-level encryption scheme. The zero-knowledge proof technology is applied to the multi-level encryption scheme, enabling the data verification party to verify the authenticity and integrity of the data without knowing the original data. For example, zero-knowledge proof protocols such as zk-SNARK, zk-STARK, or Bulletproofs are used to form a verifiable privacy data structure. This structure 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 the attributes of users (such as organizational roles, responsibilities, permissions, etc.), including two modes: key-policy attribute-based encryption (KP-ABE) and ciphertext-policy attribute-based encryption (CP-ABE), to form an attribute encryption framework. Map user roles (such as procurement managers, supplier representatives, financial personnel, etc.) into the attribute encryption framework, assign corresponding attributes and access permissions according to user roles, construct a role-attribute mapping table, and form a role-based encrypted access control model. Formulate dynamic authorization rules to dynamically adjust user permissions according to contract execution status, user behavior, risk assessment results, etc. For example, increase the data access threshold at the critical stage of contract execution and reduce user permissions when abnormal behavior is detected to form a dynamic access control strategy. Construct a key management and distribution model, including key generation, storage, distribution, update, and revocation. Use a hardware security module (HSM) or a trusted execution environment (TEE) to protect key security, and use a secure communication protocol (such as TLS, SSH) to transmit keys to form a secure key distribution channel. Based on the secure key distribution channel, implement a secure multi-party computation protocol, enabling multiple parties to jointly calculate results without revealing their respective data. For example, use technologies such as homomorphic encryption, secret sharing, or garbled circuits to form a privacy computing network. This network supports multi-party collaborative computing without revealing the original data. Integrate the privacy computing network and the dynamic access control strategy to construct 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, construct an access permission matrix to clearly define the access permissions of each user to each type of data, such as read, write, modify, delete, etc., to form a privacy protection access control matrix.This matrix provides security for subsequent compliance management and risk assessment.

[0026] In the embodiments of the present invention, the detailed implementation steps of step 6 include: Collect the legal and regulatory requirements in multiple regions and industries to obtain a compliance requirement knowledge base, and build a compliance rule engine based on the compliance requirement knowledge base to obtain an executable compliance rule set; Perform mapping analysis on the executable compliance rule set and the privacy protection access control matrix to obtain the contract clause compliance assessment result, and establish real-time monitoring indicators based on the contract clause compliance assessment result to obtain a compliance monitoring indicator system; Perform risk quantification calculation on the compliance monitoring indicator system to obtain a risk scorecard, and establish a multi-dimensional risk assessment model based on the risk scorecard to obtain a dynamic risk assessment engine; Use the dynamic risk assessment engine to continuously monitor the contract execution status to obtain a risk monitoring data stream, and perform risk trend analysis based on the risk monitoring data stream to obtain a risk evolution trend report; Perform visualization processing on the risk evolution trend report to obtain a risk heat map, and construct a contract compliance risk situation map based on the risk heat map and the risk monitoring data stream.

[0027] In this embodiment, legal and regulatory requirements related to supplier contracts are collected, including: the Contract Law, the Bidding Law, the Anti-Monopoly Law, the Data Protection Law, industry regulatory regulations, etc., covering the laws and regulations in multiple regions (such as provinces and cities in China, major international markets) and multiple industries (such as manufacturing, IT, finance, etc.), to form a compliance requirement knowledge base. This knowledge base covers the compliance requirements in multiple regions and industries. Based on the compliance requirement knowledge base, a compliance rule engine is constructed to convert legal and regulatory requirements into executable rules, such as "IF-THEN" rules, decision trees, etc. Use rule engine technologies (such as Drools, CLIPS, etc.) or natural language processing technologies to convert text rules into executable code to form an executable compliance rule set. Map and analyze the executable compliance rule set with the privacy protection access control matrix to evaluate the consistency between contract terms and legal and regulatory requirements, and identify potential compliance risk points, such as insufficient data privacy protection, anti-monopoly risks, non-compliance with information disclosure, etc., to form the contract term compliance evaluation result. Based on the contract term compliance evaluation result, real-time monitoring indicators are established, such as compliance risk indices, violation operation detectors, etc., and the index 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. Quantitatively calculate the risks of the compliance monitoring indicators to convert qualitative compliance risks into quantitative risk scores, such as using the risk matrix method (risk = probability × impact) or the fuzzy comprehensive evaluation method, etc., to form a risk scorecard. This scorecard reflects the severity and probability of different compliance risks. Based on the risk scorecard, a multi-dimensional risk assessment model is established, considering multiple dimensions such as legal risks, financial risks, operational risks, reputation risks, etc. Use machine learning methods (such as decision trees, random forests, neural networks, etc.) or statistical models to construct a risk assessment model to form a dynamic risk assessment engine. This engine can dynamically evaluate the risk level according to real-time data. Use the dynamic risk assessment engine to continuously monitor the contract execution status, and evaluate the compliance risk level in real time. The monitoring frequency is adjusted according to the risk level. Higher-risk areas may require more frequent monitoring to generate a risk monitoring data stream. This data stream records the real-time changes of risk indicators. Conduct time series analysis on the risk monitoring data stream to identify risk trends and patterns. Use time series analysis methods (such as moving average, exponential smoothing, ARIMA, etc.) to predict possible future risk changes to form a risk evolution trend report. This report describes the development trend and prediction results of the risks. Visualize the risk evolution trend report and use heat maps, radar charts, trend charts, etc. to intuitively display the risk distribution and change trends. Adopt data visualization technologies (such as D3.Generate interactive visualization charts (such as js, ECharts, etc.) to form a risk heat map, integrate the risk heat map and the risk monitoring data stream, and 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 association, providing an intuitive reference for risk management and decision-making.

[0028] In the embodiment of the present invention, the detailed implementation steps of step 7 include: 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; Conduct correlation analysis on the node data of the contract life cycle timeline to obtain an event correlation network, and formulate a contract change tracking strategy based on the event correlation network to obtain a change management framework; Based on the change management framework, perform a difference comparison on the historical version contracts to obtain a version difference report, and construct a decision point traceability mechanism based on the version difference report to obtain a decision basis chain; Map the responsible entities of the decision 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; Integrate the evidence chain with the contract life cycle timeline to obtain a complete historical traceability structure, and construct a visual traceability interface based on the complete historical traceability structure to obtain a contract performance traceability chain.

[0029] In this embodiment, time - dimension indexing is constructed for the data in the contract compliance risk situation map. Contract - related events, such as contract signing, modification, execution, payment, delivery, etc., are organized and indexed in chronological order. A time - indexing structure, such as a B + tree or a time - series database, is designed to support efficient time - range queries, forming a time - series event index table. This table facilitates quickly locating the contract status and risk situation at a specific time point. Based on the time - series event index table, a contract key - event timeline is created, marking important nodes in the contract life cycle, such as signing, coming into effect, payment, delivery, acceptance, change, termination, etc. Time - line visualization technology is used to display the chronological order and intervals of key events, forming a contract life - cycle time axis. Correlation analysis is performed on the data of each node on the contract life - cycle time axis to identify causal relationships, dependencies, and impact relationships between events. Association - rule mining algorithms (such as Apriori, FP - Growth, etc.) or graph - analysis algorithms are used to discover the association patterns between events, constructing an event - association network. This network reflects the internal connections between events during the contract execution process. Based on the event - association network, a contract - change tracking strategy is formulated to record and manage the entire process of contract changes, including the reasons for changes, change contents, approval processes, etc. Change - management processes and tools, such as version - control systems, change - request forms, etc., are designed to form a change - management framework. Based on the change - management framework, a comparison of differences is made for historical versions of the contract to identify the changes and modifications between different versions. Text - difference comparison algorithms (such as Levenshtein distance, Myers difference algorithm, etc.) are used for content comparison to form a version - difference report. This report details the evolution process of the contract content. Based on the version - difference report, a decision - point traceability mechanism is constructed to record the key decisions and their bases during the contract execution process, such as change decisions, payment decisions, dispute - resolution decisions, etc. Record the decision time, decision content, decision basis, and decision result 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 entities to clarify the responsible person or responsible department for each decision and operation, establishing a responsibility - decision mapping table to record "who made what decision at what time", forming a responsibility - attribution network. This network clearly defines the responsibilities and obligations of all parties during the contract execution process. Based on the responsibility - attribution network, an evidence - collection mechanism for dispute resolution 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 criteria and storage strategies are designed to ensure the integrity and reliability of evidence, forming an evidence chain. This chain provides evidence support for potential contract disputes. The evidence chain is integrated with the contract life - cycle time axis to construct a complete historical - traceability structure, realizing the traceability of the entire contract life cycle, including multi - dimensional traceability of contract content, execution status, risk events, responsible entities, etc. Based on the complete historical - traceability structure, a visual traceability interface is constructedIntuitively display the complete process and key nodes of contract performance using interactive visualization techniques (such as timeline charts, relationship charts, flowcharts, etc.), support multi-dimensional query and drill-down analysis, and form a contract performance traceability chain, which provides historical basis for abnormal behavior detection and security event handling.

[0030] In the embodiment of the present invention, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of steps 8 and 9. The detailed implementation steps of steps 8 and 9 include: Apply an anomaly detection algorithm to the contract performance traceability chain to obtain an abnormal behavior feature library, and construct a behavior pattern recognition model based on the abnormal behavior feature library to obtain an abnormal behavior classifier; Use the abnormal behavior classifier to perform real-time analysis on the behaviors during contract execution to obtain the classification results of security events, and formulate a hierarchical response strategy based on the classification results of security events to obtain a response strategy library; Perform 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 event handling process; Conduct contract management performance analysis based on the security event handling process and the contract performance 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 ranking table, and integrate and form a comprehensive supplier contract management report based on the priority ranking table and the performance evaluation report, including risk assessment, compliance status, performance, optimization suggestions, and long-term strategies.

[0031] In this embodiment, an anomaly detection algorithm is applied to the data in the contract execution traceability chain, such as statistical analysis (e.g., Z-score, box plot), machine learning (e.g., isolation forest, one-class SVM), deep learning (e.g., autoencoder, LSTM), etc., to identify abnormal behavior patterns and features, such as abnormal access patterns, abnormal operation sequences, abnormal transactions, etc., and form an abnormal behavior feature library. This library contains known abnormal behavior patterns and feature descriptions. Based on the abnormal behavior feature library, a behavior pattern recognition model is constructed, such as a support vector machine, random forest, neural network, etc., and the model is trained to identify normal and abnormal behaviors. Model parameters and thresholds are set to balance the sensitivity and accuracy of detection, forming an abnormal behavior classifier. The abnormal behavior classifier is used to perform real-time analysis on the behaviors during contract execution, monitor user operations, system activities, and data flows, determine whether the behavior is abnormal, and classify the abnormal behaviors, such as fraud behaviors, default behaviors, operation errors, etc., to generate a security event classification result. According to the security event classification result, a hierarchical response strategy is formulated. According to the event type and severity, the security events are divided into different levels (e.g., low risk, medium risk, high risk, urgent). For events of different levels, corresponding processing procedures and countermeasures are designed to form a response strategy library. A decision tree model is built for the response strategy library to organize the response strategies into a decision tree structure, clarifying the decision path and processing procedure, such as "IF event type = fraud AND risk level = high THEN execute the freezing operation and notify the security team", forming a security response decision tree. This tree guides the system on how to respond to different types of security events. Based on the security response decision tree, an automated response mechanism is established to automate the handling of common security events, reduce manual intervention, and improve the response efficiency. Response workflows and automated scripts are designed to form a security event handling process. This process standardizes the handling process of security events. Based on the security event handling process and the contract execution traceability chain, the performance of contract management is analyzed, evaluating the performance in aspects such as the efficiency of contract execution (e.g., cycle time, response time), quality (e.g., error rate, satisfaction), cost (e.g., management cost, transaction cost), and risk control (e.g., risk occurrence rate, risk handling efficiency), etc., using the balanced scorecard or key performance indicator (KPI) method for evaluation, forming a performance evaluation report. This report comprehensively evaluates the effectiveness of contract management. Improvement opportunities and optimization spaces are identified from the performance evaluation report, such as process bottlenecks, repetitive work, communication barriers, etc., and specific improvement suggestions are put forward, such as process optimization, risk control, supplier management, etc., forming a list of improvement suggestions. A cost-benefit analysis is performed on the list of improvement suggestions to evaluate the input (e.g., time, manpower, funds) and expected benefits (e.g., efficiency improvement, cost reduction, risk reduction) of each improvement measure, calculate the return on investment (ROI) or cost-benefit ratio, and prioritize the improvement suggestions according to 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 assessments (such as risk distribution, risk trends), compliance status (such as compliance level, violations), performance (such as completion rate, timeliness rate), optimization suggestions (such as short-term improvements, long-term plans), and long-term strategies (such as supplier relationship management, construction of risk prevention and control systems), providing comprehensive support for enterprise decision-making.

[0032] The above describes the blockchain-based supplier contract security management method in the embodiments of the present application. Next, the blockchain-based supplier contract security management system in the embodiments of the present application will be described. Please refer to Figure 3 An embodiment of the blockchain-based supplier contract security management system in the embodiments of the present application includes: A contract data distributed storage module for encrypting and sharding the storage of supplier contracts and performing multi-node verification to obtain a tamper-proof contract data structure; A supplier qualification on-chain certification module for performing multi-dimensional qualification assessment and credit score recording of suppliers based on the tamper-proof contract data structure to obtain a supplier credit assessment network; An intelligent contract automatic execution module for encoding contract terms and setting trigger conditions based on the supplier credit assessment network to obtain a self-executing contract control chain; A multi-party collaboration consensus mechanism module for performing cross-organization collaboration and distributed consensus reaching based on the self-executing contract control chain to obtain a contract execution status consensus ledger; A data privacy protection module for performing data classification 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 for mapping compliance requirements and performing real-time risk assessment based on the privacy protection access control matrix to obtain a contract compliance risk situation map; A contract full life cycle traceability module for indexing historical data and performing correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain; A security event intelligent response module for detecting abnormal behaviors and classifying and processing security events based on the contract performance traceability chain to obtain a security response decision tree; An intelligent decision support module for generating optimization suggestions and performing performance evaluation on contract management based on the security response decision tree and the contract performance traceability chain to 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.

[0033] The present invention ensures the integrity and immutability of contract data through encrypted sharding storage and multi-node verification, comprehensively evaluates the reputation of suppliers using multi-dimensional qualification assessment and credit score records, realizes intelligent coding and automatic execution of contract terms, establishes cross-organizational collaboration and distributed consensus mechanisms, adopts data hierarchical encryption and refined permission control to ensure data security, conducts real-time compliance risk assessment and visual display, constructs a traceability chain for the entire contract life cycle, realizes intelligent detection of abnormal behaviors and automatic response to security incidents, and generates optimization suggestions and comprehensive management reports. The present 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.

[0034] The above are only the preferred embodiments of the present invention and are 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 can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0035] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0036] In the description of the present invention, it should be understood that terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0037] In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0038] In the description of the present invention, the meaning of "several" is one or more, and the meaning of "a large number" is two or more.

[0039] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0040] For the formulas in this specification, the dimension is removed and only the numerical values are calculated. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0041] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and purposes of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A blockchain-based secure management method for supplier contracts, characterized in that Including: Step 1: Encrypt and shard the storage of the supplier contract and conduct multi-node verification to obtain a tamper-proof contract data structure; Step 2: Based on the tamper-proof contract data structure, conduct multi-dimensional qualification assessment and credit score recording of the supplier to obtain a supplier credit assessment network; Step 3: Based on the supplier credit assessment network, conduct contract clause coding and trigger condition setting to obtain a self-executing contract control chain; Step 4: Based on the self-executing contract control chain, conduct cross-organization collaboration and distributed consensus reaching to obtain a contract execution status consensus ledger; Step 5: Based on the contract execution status consensus ledger, conduct data hierarchical encryption and refined permission control to obtain a privacy protection access control matrix; Step 6: Based on the privacy protection access control matrix, conduct compliance requirement mapping and real-time risk assessment to obtain a contract compliance risk situation map; Step 7: Based on the contract compliance risk situation map, conduct historical data indexing and correlation analysis to obtain a contract performance traceability chain; Step 8: Based on the contract performance traceability chain, conduct abnormal behavior detection and security event classification processing 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 conduct performance evaluation for contract management to obtain a comprehensive report on supplier contract management.

2. The method for secure management of supplier contracts based on blockchain according to claim 1, wherein The encrypting and sharding the storage of the supplier contract and conducting multi-node verification to obtain a tamper-proof contract data structure includes: Conduct semantic analysis and structured processing on the supplier contract text to obtain a set of contract key elements, and conduct multiple hash encryptions on the set of contract key elements to obtain a contract data fingerprint; Construct a Merkle tree for the contract data fingerprint to obtain a contract verification tree, and conduct data sharding processing according to the contract verification tree to obtain multiple contract data shards; Distributively store the multiple contract data shards on different blockchain nodes to obtain a distributed storage structure, and conduct consensus algorithm verification on the distributed storage structure to obtain verified block data; Embed a time stamp into the verified block data to obtain a time sequence link structure, and construct a contract version control mechanism according to the time sequence link structure to obtain a version tracking model; Create a cross-chain data reference index for the version tracking model to obtain a cross-chain index table, and integrally construct a tamper-proof contract data structure according to the cross-chain index table and the verified block data.

3. The method for secure management of supplier contracts based on blockchain according to claim 2, characterized in that, The conducting multi-dimensional qualification assessment and credit score recording of the supplier based on the tamper-proof contract data structure to obtain a supplier credit assessment network includes: Extract multi-dimensional features from the supplier qualification information in the tamper-proof contract data structure to obtain an initial supplier feature vector, and establish a qualification assessment index system according to the initial supplier feature vector to obtain a multi-dimensional assessment framework; Conduct historical performance data correlation analysis on the multi-dimensional assessment framework to obtain a measurement of the supplier's historical performance, and design a credit scoring algorithm according to the measurement of the supplier's historical performance to obtain an initial credit scoring model; Introduce a third-party verification mechanism into the initial credit scoring model to obtain multi-party verified credit data, and write the multi-party verified credit data into a dedicated credit block to obtain a credit score chain; Conduct time-series feature analysis on the credit score chain to obtain the credit fluctuation pattern of suppliers, and establish a credit risk prediction model based on the credit fluctuation pattern of suppliers to obtain a risk warning index set; Conduct network structure modeling on the risk warning index set and the credit score chain to obtain a supplier relationship network, and calculate the correlation degree between different suppliers based on the supplier relationship network to obtain a supplier credit evaluation network.

4. The method for secure management of supplier contracts based on blockchain according to claim 3, wherein Based on the supplier credit evaluation network, perform contract clause coding and trigger condition setting to obtain a self-executing contract control chain, including: Perform formal description conversion on the contract clauses in the supplier credit evaluation network to obtain a machine-readable contract template, and extract clause logic based on the machine-readable contract template to obtain a clause logic diagram; Perform intelligent contract coding on the clause logic diagram to obtain initial intelligent contract code, and perform formal verification on the initial intelligent contract code to obtain an intelligent contract that passes security verification; Set execution trigger conditions and thresholds for the intelligent contract that passes security verification to obtain a condition-triggered execution framework, and build a multi-level trigger network based on the condition-triggered execution framework to obtain an event-driven execution chain; Configure an external data source interface for the event-driven execution chain to obtain a data feeding channel, and design an oracle verification mechanism based on the data feeding channel to obtain a trusted data input mechanism; 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 the intelligent contract that passes security verification.

5. The method for secure management of supplier contracts based on blockchain according to claim 4, wherein Based on the self-executing contract control chain, conduct cross-organizational collaboration and distributed consensus reaching to obtain a contract execution status consensus ledger, including: Classify organizational nodes of the self-executing contract control chain to obtain a multi-level organizational node structure, and design a permission grading mechanism based on the multi-level organizational node structure to obtain an organizational permission tree; Design a multi-layer consensus protocol based on the organizational permission tree to obtain a hybrid consensus mechanism, and conduct consensus voting on the key contract execution status based on the hybrid consensus mechanism to obtain a status confirmation bill; Perform multi-party digital signature on the status confirmation bill to obtain a multi-signature authentication record, and establish a status conversion verification rule based on the multi-signature authentication record to obtain a status conversion security gateway; Record the real-time status of the contract execution process through the status conversion security gateway to obtain an execution status stream, and build a status snapshot storage mechanism based on the execution status stream to obtain a status snapshot library; Integrate the status snapshot library with distributed ledger technology to obtain an execution status distributed ledger, and build a contract execution status consensus ledger based on the execution status distributed ledger and the multi-signature authentication record.

6. The method for secure management of supplier contracts based on blockchain according to claim 5, characterized in that, Based on the contract execution status consensus ledger, conduct data classification encryption and refined permission control to obtain a privacy protection access control matrix, including: Perform sensitivity analysis on the data in the consensus ledger of the contract execution status to obtain a data sensitivity classification table, and formulate an encryption policy based on the data sensitivity classification table to obtain a multi-level encryption scheme; Apply zero-knowledge proof technology to the multi-level encryption scheme to obtain a verifiable private data structure, and design an attribute-based encryption mechanism based on the verifiable private data structure to obtain an attribute encryption framework; Perform user role mapping on the attribute encryption framework to obtain a role-based encrypted access control model, and formulate dynamic authorization rules based on the role-based encrypted access control model to obtain a dynamic access control policy; 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 private computing network; Integrate the private computing network and the dynamic access control policy to obtain a complete privacy protection framework, and construct an access permission matrix based on the complete privacy protection framework to obtain a privacy protection access control matrix.

7. The method for secure management of supplier contracts based on blockchain according to claim 6, characterized in that, Perform compliance requirement mapping and real-time risk assessment based on the privacy protection access control matrix to obtain a contract compliance risk situation map, including: Collect legal and regulatory requirements in multiple regions and industries to obtain a compliance requirement knowledge base, and construct a compliance rule engine based on the compliance requirement knowledge base to obtain an executable compliance rule set; Perform mapping analysis on the executable compliance rule set and the privacy protection access control matrix to obtain a contract clause compliance assessment result, and establish real-time monitoring indicators based on the contract clause compliance assessment result to obtain a compliance monitoring indicator system; Perform risk quantification calculation on the compliance monitoring indicator system to obtain a risk scorecard, and establish a multi-dimensional risk assessment model based on the risk scorecard to obtain a dynamic risk assessment engine; Use the dynamic risk assessment engine to continuously monitor the contract execution status to obtain a risk monitoring data stream, and perform risk trend analysis based on the risk monitoring data stream to obtain a risk evolution trend report; Perform visualization processing on the risk evolution trend report to obtain a risk heat map, and construct a contract compliance risk situation map based on the risk heat map and the risk monitoring data stream.

8. The blockchain-based supplier contract security management method according to claim 7, wherein, Perform historical data indexing and correlation analysis based on the contract compliance risk situation map to obtain a contract performance traceability chain, including: 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 time axis; Perform correlation analysis on the data of each node of the contract life cycle time axis to obtain an event correlation network, and formulate a contract change tracking strategy based on the event correlation network to obtain a change management framework; Perform difference comparison on historical version contracts based on the change management framework to obtain a version difference report, and construct a decision point traceability mechanism based on the version difference report to obtain a decision basis chain; Perform responsible entity mapping on the decision 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; Integrate the evidence chain with the contract life cycle timeline to obtain a complete historical traceability structure, and construct a visual traceability interface based on the complete historical traceability structure to obtain a contract execution traceability chain.

9. The method for secure management of supplier contracts based on blockchain according to claim 8, wherein Perform abnormal behavior detection and security event classification processing based on the contract execution traceability chain to obtain a security response decision tree, and based on the security response decision tree and the contract execution traceability chain, generate optimization suggestions and performance evaluations for contract management to obtain a comprehensive supplier contract management report, including: Apply an anomaly detection algorithm to the contract execution traceability chain to obtain an abnormal behavior feature library, and construct a behavior pattern recognition model based on the abnormal behavior feature library to obtain an abnormal behavior classifier; Use the abnormal behavior classifier to perform real-time analysis on the behaviors during contract execution to obtain security event classification results, and formulate a hierarchical response strategy based on the security event classification results to obtain a response strategy library; Perform 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 event handling process; Conduct contract management performance analysis based on the security event handling process and the contract execution 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 ranking table, and integrate the priority ranking table and the performance evaluation report to form a comprehensive supplier contract management report, including risk assessment, compliance status, performance, optimization suggestions, and long-term strategies.

10. A blockchain-based supplier contract security management system for implementing the blockchain-based supplier contract security management method according to any one of claims 1 to 9, characterized in that, Including: A contract data distributed storage module for encrypting and sharding the storage of supplier contracts and performing multi-node verification to obtain a tamper-proof contract data structure; A supplier qualification on-chain certification module for performing multi-dimensional qualification assessment and credit score recording of suppliers based on the tamper-proof contract data structure to obtain a supplier credit assessment network; An intelligent contract automatic execution module for encoding contract terms and setting trigger conditions based on the supplier credit assessment network to obtain a self-executing contract control chain; A multi-party collaboration consensus mechanism module for performing cross-organization collaboration and distributed consensus reaching based on the self-executing contract control chain to obtain a contract execution status consensus ledger; A data privacy protection module for performing data grading 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 for mapping compliance requirements and performing real-time risk assessment based on the privacy protection access control matrix to obtain a contract compliance risk situation map; A contract full life cycle traceability module for indexing historical data and performing correlation analysis based on the contract compliance risk situation map to obtain a contract execution traceability chain; A security event intelligent response module for performing abnormal behavior detection and security event classification processing based on the contract execution traceability chain to obtain a security response decision tree; An intelligent decision-making support module, which 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, so as to 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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