Enterprise service supply chain traceability and intelligent contract management system based on block chain

Abstract

The invention discloses an enterprise service supply chain traceability and intelligent contract management system based on a block chain, and relates to the technical field of block chain and supply chain management. According to the invention, by using the data encryption and certificate generation module and the cross-enterprise trust mechanism establishment module, the problems of data islands and high trust cost are solved, and the collaboration efficiency and integration capability of the supply chain are significantly improved; through a zero-knowledge proof algorithm and a block chain technology, the balance between data security and privacy protection is guaranteed, and the security of supply chain management is enhanced; by means of the intelligent contract execution module, the resource scheduling optimization module, the data loop optimization module and the like, intelligence and dynamic optimization of the supply chain are achieved, the cooperation efficiency and the intelligent level are continuously improved, and the market competitiveness and the sustainable development capacity of enterprises are enhanced.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of blockchain and supply chain management, in particular to an enterprise service supply chain traceability and smart contract management system based on blockchain. BACKGROUND

[0002] As a core component of the modern business ecosystem, the enterprise service supply chain directly affects the operational efficiency and market competitiveness of enterprises. With the deepening of digital transformation, service supply chain management has become a key factor determining the success or failure of enterprises, and its management level is directly related to the sustainable development ability of enterprises.

[0003] The current mainstream service supply chain management scheme generally has the problem of data island, and there is a lack of effective information sharing mechanism between each participant, resulting in low coordination efficiency of the entire supply chain. The traditional centralized management mode often restricts the depth of cooperation due to high trust cost when facing complex business scenarios involving multiple parties. Although the existing digital platform has improved the processing efficiency of some links, it is difficult to find a balance point between ensuring data authenticity and maintaining business secrets. This structural contradiction seriously hinders the deep integration of the supply chain. SUMMARY

[0004] The purpose of the present application is to provide an enterprise service supply chain traceability and smart contract management system based on blockchain, which solves the problems of data island, high trust cost and insufficient intelligence in traditional service supply chain management by building a cross-enterprise trust mechanism, ensuring the balance between data security and privacy protection, and realizing the intelligentization and dynamic optimization of the supply chain, significantly improving the coordination efficiency, security and intelligence level of the supply chain.

[0005] The purpose of the present application can be achieved by the following technical solutions: The present application provides an enterprise service supply chain traceability and smart contract management system based on blockchain, comprising: A data encryption and certificate generation module obtains service delivery data and performance record of each participant in the enterprise service supply chain, encrypts the service delivery data through a zero-knowledge proof algorithm, and generates a verification certificate containing data authenticity proof but not leaking specific content; A cross-enterprise trust mechanism establishment module establishes a cross-enterprise data interconnection trust mechanism according to the verification certificate, and uses a multi-party secure computation algorithm to jointly verify the performance data submitted by each participant. If the verification result shows that the data consistency reaches a preset threshold, a unified trusted data view is generated for subsequent collaborative processing; An intelligent contract execution module analyzes the service delivery state changes in the trusted data view through an intelligent contract execution algorithm, and automatically generates an execution instruction when a specific performance condition is triggered; The resource scheduling optimization module obtains the service flow conversion rule in the execution instruction, combines the real-time state information of each node of the current supply chain, judges whether there is resource conflict or scheduling exception, and generates an optimized execution scheme; The cross-enterprise collaborative operation module triggers cross-enterprise collaborative operation according to the optimized execution scheme, and distributes relevant instructions to each participant system through a pre-established secure channel; The real-time monitoring and correction module adopts a real-time monitoring mechanism to track the execution progress of the collaborative operation, and automatically records abnormal information and generates correction suggestions when the system detects execution abnormalities or deviations.

[0006] Further, the data encryption and credential generation module includes a zero-knowledge encryption unit, a credential identification and signature unit, and a storage and access control unit, The zero-knowledge encryption unit obtains the service delivery data and performance record of each participant in the enterprise service supply chain, encrypts it through a zero-knowledge proof algorithm, generates encrypted data, and retains the authenticity feature of the original data. The verification credential containing the data authenticity proof is generated by using the commitment scheme in the zero-knowledge proof algorithm, and the specific content of the service delivery data is not disclosed; The credential identification and signature unit identifies the credential through a hash function if the generation of the verification credential meets the preset authenticity threshold, obtains an identification credential, and then signs the credential using digital signature technology to generate a signed credential; The storage and access control unit stores the signed credential to generate a distributed storage record through blockchain technology, and if the access request of the distributed storage record comes from a supply chain participant, it judges the request legality through a zero-knowledge verification protocol to obtain an authorized access result.

[0007] Further, the cross-enterprise trust mechanism establishment module includes a joint verification unit, a data view generation and distribution unit, and a verification and trust record unit, The joint verification unit establishes a cross-enterprise data interconnection trust mechanism according to the verification credential, obtains performance data from each participant, pre-processes the data using data encryption technology to obtain an encrypted performance data set, performs joint verification on the encrypted performance data set using a multi-party secure computing algorithm to obtain a data consistency score, and if the data consistency score reaches a preset threshold, decrypts the encrypted performance data set to obtain a trusted data set; The data view generation and distribution unit generates a unified trusted data view according to the trusted data set, generates a view identifier using data indexing technology, distributes the data view through the view identifier, saves it to each participant node using distributed storage technology, and obtains distribution confirmation information; The check and trust record unit checks a data view by using a consistent hash algorithm to obtain a check result, updates a trust mechanism according to the check result, records a trust state by using a block chain technology, and generates a trust log.

[0008] Further, the smart contract execution module comprises an execution instruction generation unit, a resource allocation analysis unit and an instruction publishing and tracking unit, The execution instruction generation unit acquires a trusted data view through a block chain platform, analyzes a service delivery state change to obtain a state change sequence, generates an execution instruction containing a service flow rule through a smart contract if the state change sequence meets a preset performance condition, and determines a service flow path. The resource allocation analysis unit acquires available resource information from a resource pool according to the service flow rule in the execution instruction, generates a resource allocation scheme, analyzes the resource allocation scheme and historical service delivery data by using a linear regression algorithm, and judges resource allocation rationality. The instruction publishing and tracking unit automatically generates an execution instruction through a smart contract if the resource allocation rationality score is higher than a preset threshold, triggers service flow and resource allocation, acquires state update data after service flow, analyzes performance condition completion degree, and obtains a performance state report.

[0009] Further, the resource scheduling optimization module comprises a panoramic view construction unit, a conflict detection unit, a scheduling optimization unit and a scheme verification unit, The panoramic view construction unit acquires a service flow rule and real-time state data of a supply chain node, extracts rule parameters and node state indicators from a preset database, and obtains a supply chain operation panoramic view. The conflict detection unit analyzes the supply chain operation panoramic view by using a state monitoring module, calculates resource occupation rate and task scheduling of each node, judges that there is resource conflict or scheduling anomaly if the resource occupation rate exceeds a preset threshold or the task scheduling overlaps, and obtains a conflict detection result. The scheduling optimization unit extracts a conflict node and an abnormal scheduling task, calls a linear programming algorithm to generate a resource reallocation scheme, adjusts the resource allocation priority of the conflict node according to the allocation scheme and a node coordination mechanism, generates a resource allocation scheme after node coordination, generates an optimized execution scheme in combination with the service flow rule, and obtains a final task scheduling sequence. The scheme verification unit verifies the stability of the optimized execution scheme by simulating the final task scheduling sequence, returns the linear programming algorithm for recalculation if the verification result shows scheduling anomaly, and obtains an updated optimized execution scheme.

[0010] Further, the cross-enterprise collaborative operation module comprises an instruction encryption and verification unit, an instruction distribution and task allocation unit and a collaborative execution and check unit, The instruction encryption and verification unit obtains the cross-enterprise collaboration instruction through a preset secure channel, processes the instruction data by using an end-to-end encryption technology, analyzes the instruction content, verifies the instruction integrity by using a digital signature, and determines the legality of the instruction. The instruction distribution and task allocation unit distributes the encrypted instruction set to each participant system through an instruction distribution mechanism if the legality of the instruction is verified, obtains a distribution confirmation state, triggers an optimized execution scheme according to the distribution confirmation state, allocates a collaborative operation task by using a load balancing algorithm, and obtains a task allocation result. Further, the collaborative execution and verification unit extracts the task of each participant system from the task allocation result, performs inter-system collaborative operation by using an asynchronous communication protocol, determines an operation completion state, obtains a data transmission log, verifies the integrity of the transmission data by using a hash verification algorithm, judges the data consistency, updates the cross-enterprise collaboration log from the data consistency judgment result, records the operation sequence by using a time stamp, and generates a collaborative operation record.

[0011] Further, the real-time monitoring and correction module includes an exception handling unit and a correction suggestion generation unit, The exception handling unit obtains collaborative operation progress data through a real-time monitoring mechanism, determines an execution state, triggers an exception detection function if the execution state deviates from a preset threshold, extracts exception information and stores it in an exception information database, analyzes the exception reason by using a decision tree algorithm in combination with historical performance data, and generates a reason classification result. The correction suggestion generation unit generates a correction suggestion by using a rule engine to process the reason classification result and the current business rule, adjusts the collaborative operation parameters, updates the execution progress data, detects the adjustment effect by using time series analysis from the updated execution progress data, and judges the system stability.

[0012] Further, it further includes a data cycle optimization module that updates the service delivery state and performance record by using the correction suggestion, re-inputs the updated data into the zero-knowledge proof algorithm for verification processing, forms a new round of trusted data cycle, and continuously optimizes the collaborative efficiency and intelligent development level of the entire supply chain.

[0013] Further, the data cycle optimization module includes a data cleaning and fusion unit and a cycle verification unit, The data cleaning and fusion unit obtains the service delivery state and performance record, extracts real-time data from the database, removes abnormal values by using data cleaning technology, obtains a standardized data set, combines the correction suggestion data, updates the service delivery state and performance record by using data fusion technology, and obtains an updated data set. The circulating verification unit inputs a zero-knowledge proof algorithm if the updated data set meets the preset integrity threshold, processes the algorithm verification result through encryption verification technology, and distributes the data passing the verification to the supply chain collaboration platform by using a trusted data circulating mechanism.

[0014] The application has the following advantages: The application uses a zero-knowledge proof algorithm to encrypt service delivery data and generate verification credentials, and uses a multi-party secure calculation algorithm to jointly verify performance data, effectively solving the data island problem and the problem of high trust cost between parties. The effect is to significantly improve the collaboration efficiency of the supply chain, break down information barriers, promote the deep integration of the supply chain, and provide strong support for efficient operation of enterprises. The zero-knowledge proof algorithm and blockchain technology are used to encrypt service delivery data and generate verification credentials, and the distributed storage and tamper-proof features of the blockchain are used to store the signature credentials, and the legality of access requests is judged by the zero-knowledge verification protocol, solving the problem of balancing data authenticity and maintaining business secrets in existing digital platforms, greatly enhancing the security of supply chain management, ensuring the authenticity and integrity of data, and strictly protecting the business secrets of all parties, providing a solid and reliable security guarantee for the stable operation of the supply chain. By constructing a full-process intelligent management system and continuously improving collaboration efficiency through a data circulation optimization mechanism, the problem of insufficient intelligent level of traditional service supply chain management is effectively solved, and intelligent management and dynamic optimization of the supply chain are realized. The system can automatically generate and adjust the execution plan according to real-time data, ensure the rational allocation and efficient use of resources, continuously improve the collaboration efficiency and intelligent level of the entire supply chain, and enhance the market competitiveness and sustainable development ability of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to better understand and implement, the technical solutions of the present application are described in detail below with reference to the accompanying drawings.

[0016] Fig. 1 The structural diagram of the enterprise service supply chain traceability and smart contract management system based on blockchain provided for Embodiment 1 of the present application; Fig. 2 The structural diagram of the data encryption and credential generation module of the enterprise service supply chain traceability and smart contract management system based on blockchain provided for Embodiment 1 of the present application; Fig. 3 The structural diagram of the cross-enterprise trust mechanism establishment module of the enterprise service supply chain traceability and smart contract management system based on blockchain provided for Embodiment 1 of the present application. DETAILED DESCRIPTION

[0017] To further clarify the technical hand means and effects taken by the present application to achieve the predetermined inventive purpose, hereinafter the exemplary embodiments will be described in detail, which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they only describe methods and systems consistent with some aspects of the present application, as detailed in the appended claims.

[0018] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in this application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0019] The following describes the specific embodiments, features and effects according to the present application in detail in combination with the drawings and preferred embodiments.

[0020] Embodiment 1

[0021] Please refer to Figs. 1-3 The present embodiment provides a blockchain-based enterprise service supply chain traceability and smart contract management system, which comprises: A data encryption and credential generation module obtains service delivery data and performance record of each participant in the enterprise service supply chain, encrypts the service delivery data through a zero-knowledge proof algorithm, and generates a verification credential containing data authenticity proof but not leaking specific content, which guarantees data credibility and commercial secret protection requirements.

[0022] Among them, through the zero-knowledge proof algorithm, the verification credential can verify the authenticity of the data without leaking the specific content of the data, so as to guarantee the credibility of the data while meeting the needs of commercial secret protection.

[0023] Further, the data encryption and credential generation module comprises a zero-knowledge encryption unit, a credential identification and signature unit and a storage and access control unit, The zero-knowledge encryption unit obtains the service delivery data and performance record of each participant in the enterprise service supply chain, encrypts through the zero-knowledge proof algorithm, generates encrypted data, the encrypted data retains the authenticity characteristics of the original data, adopts the commitment scheme in the zero-knowledge proof algorithm, generates a verification credential containing data authenticity proof, and the credential does not leak the specific content of the service delivery data; The credential identification and signature unit uniquely identifies the credential by a hash function if the generation of the verification credential meets the preset authenticity threshold, and generates a signed credential by using a digital signature technology to sign the credential; the authenticity threshold is preset according to the integrity and consistency of the data, and is used to ensure that the generated verification credential has sufficient credibility, and the hash function generates a unique hash value by encrypting the content of the credential, which is used as the unique identifier of the credential to ensure the identifiable and tamper-proof nature of each credential.

[0024] The storage and access control unit stores the signed credential by a blockchain technology to generate a distributed storage record, and judges the legality of the request by a zero-knowledge verification protocol if the access request of the distributed storage record comes from a supply chain participant, and obtains an authorized access result.

[0025] The zero-knowledge verification protocol verifies the identity and authority of the requestor to ensure that only authorized supply chain participants can access the distributed storage record, thereby protecting the security and privacy of the data.

[0026] Specifically, the data encryption and credential generation module uses a zero-knowledge proof algorithm to encrypt data and generate a verification credential, which solves the contradiction between data security and privacy protection in traditional service supply chains, realizes the authenticity verification of data and the protection of commercial secrets, enhances the trust between participants, and improves the security and collaborative efficiency of supply chain management.

[0027] The cross-enterprise trust mechanism establishment module establishes a cross-enterprise data interconnection trust mechanism according to the verification credential, and uses a multi-party secure computation algorithm to jointly verify the performance data submitted by each participant, and generates a unified trusted data view for subsequent collaborative processing if the verification result shows that the data consistency reaches a preset threshold.

[0028] Further, the cross-enterprise trust mechanism establishment module includes a joint verification unit, a data view generation and distribution unit, and a verification and trust record unit, The joint verification unit establishes a cross-enterprise data interconnection trust mechanism according to the verification credential, obtains performance data from each participant, pre-processes the data by using a data encryption technology to obtain an encrypted performance data set, performs joint verification on the encrypted performance data set by using a multi-party secure computation algorithm to obtain a data consistency score, and decrypts the encrypted performance data set if the data consistency score reaches a preset threshold to obtain a trusted data set. The trust mechanism for cross-enterprise data intercommunication is established by using the verification credentials as the basis for data authenticity; the data consistency score is obtained by jointly verifying the encrypted performance data set through a multi-party secure calculation algorithm, and then comprehensively scoring according to the consistency and integrity of the data; and the preset threshold is set in advance according to the industry standard and the data quality requirement, and is used to ensure the reliability and consistency of the data.

[0029] The data view generation and distribution unit generates a unified trusted data view according to the trusted data set, generates a view identifier by using a data indexing technology, distributes the data view through the view identifier, saves to each participant node by using a distributed storage technology, and obtains distribution confirmation information; The data indexing technology indexes the trusted data set to generate a unique view identifier, which is used for quickly locating and distributing the data view. The verification and trust record unit verifies the data view by using a consistent hashing algorithm, obtains a verification result, updates the trust mechanism according to the verification result, records the trust state by using a blockchain technology, and generates a trust log.

[0030] The consistent hashing algorithm verifies the data view for consistency to ensure the integrity and consistency of the data, and according to the verification result, the system updates the trust mechanism and adjusts the trust level of each participant.

[0031] Specifically, the cross-enterprise trust mechanism establishment module solves the problems of lack of trust between participants in the supply chain and difficulty in ensuring data consistency, realizes trusted sharing and collaborative processing of data, enhances the trust relationship between enterprises, and improves the overall collaborative efficiency of the supply chain and the security of data management.

[0032] The smart contract execution module analyzes the service delivery state changes in the trusted data view by using a smart contract execution algorithm, automatically generates an execution instruction when a specific performance condition is triggered, and the execution instruction contains a service flow transfer rule and a resource allocation scheme, thereby avoiding operation risks and efficiency losses caused by manual intervention.

[0033] The execution instruction generates a service flow transfer rule according to a state change sequence and a performance condition, and generates a resource allocation scheme in combination with available resource information in a resource pool.

[0034] Further, the smart contract execution module includes an execution instruction generation unit, a resource allocation analysis unit, and an instruction publishing and tracking unit, The execution instruction generation unit obtains the trusted data view through a blockchain platform, analyzes the service delivery state changes to obtain a state change sequence, and if the state change sequence meets a preset performance condition, generates an execution instruction containing a service flow transfer rule through a smart contract, and determines a service flow transfer path. The resource allocation analysis unit obtains available resource information from the resource pool according to the service flow rule in the execution instruction, generates a resource allocation scheme, and analyzes the resource allocation scheme and historical service delivery data by using a linear regression algorithm to determine the rationality of resource allocation.

[0035] The instruction issuing and tracking unit automatically generates an execution instruction through the smart contract if the rationality of resource allocation score is higher than a preset threshold (the threshold is preset according to historical data and business requirements to ensure the rationality of resource allocation), triggers service flow and resource allocation, obtains state update data after service flow (obtained by real-time monitoring of the service flow process), analyzes the completion degree of the performance condition (obtained by comparing and analyzing the state update data with the preset performance condition), and obtains a performance status report.

[0036] The state update data is obtained by real-time monitoring of the service flow process, and the completion degree of the performance condition is obtained by comparing and analyzing the state update data with the preset performance condition.

[0037] Specifically, the smart contract execution module solves the operation risk and efficiency loss problem caused by manual intervention in the traditional service supply chain by automatically generating an execution instruction, analyzing the rationality of the resource allocation scheme, and tracking the performance status in real time, realizes the automation of service flow and resource allocation, improves the operation efficiency and the accuracy of data processing, enhances the stability and reliability of the supply chain, and thus improves the intelligent management level of the entire supply chain.

[0038] The resource scheduling optimization module obtains the service flow rule in the execution instruction, combines the real-time state information of each node in the current supply chain, determines whether there is a resource conflict or scheduling exception, and if there is a conflict, starts an intelligent scheduling mechanism to redistribute resources and generates an optimized execution scheme.

[0039] The intelligent scheduling mechanism redistributes resources by calling a linear programming algorithm in combination with a node coordination mechanism.

[0040] Further, the resource scheduling optimization module includes a panoramic view construction unit, a conflict detection unit, a scheduling optimization unit, and a scheme verification unit, The panoramic view construction unit obtains the service flow rule and the real-time state data of the supply chain nodes, extracts rule parameters and node state indicators from a preset database, and obtains a panoramic view of the supply chain operation. The preset database contains service flow rule parameters and node state indicators to support the construction of a panoramic view. The conflict detection unit analyzes the supply chain operation panoramic view through the state monitoring module, calculates the resource occupation rate and task scheduling of each node, and if the resource occupation rate exceeds the preset threshold or the task scheduling overlaps, it is determined that there is a resource conflict or scheduling anomaly, and a conflict detection result is obtained. The state monitoring module analyzes the data in the panoramic view in real time, calculates the resource occupation rate and task scheduling of each node.

[0041] The scheduling optimization unit extracts the conflict nodes and abnormal scheduling tasks, calls the linear programming algorithm, generates a resource reallocation scheme, adjusts the resource allocation priority of the conflict nodes according to the allocation scheme combined with the node coordination mechanism, generates a resource allocation scheme after coordination between nodes, and then generates an optimized execution scheme combined with the service flow conversion rule to obtain a final task scheduling sequence. The scheme verification unit verifies the stability of the optimized execution scheme by simulating the final task scheduling sequence, and if the verification result shows scheduling anomalies, it returns to the linear programming algorithm for recalculation to obtain an updated optimized execution scheme.

[0042] The simulation operation is based on the optimized task scheduling sequence and tests its stability through a virtual environment.

[0043] Specifically, the resource scheduling optimization module solves the problems of resource conflict and scheduling anomaly in traditional service supply chains by constructing a panoramic view, detecting resource conflicts, optimizing resource allocation, and verifying the stability of the scheme, realizes intelligent scheduling and optimized configuration of resources, improves the operation efficiency and stability of the supply chain, and enhances the intelligent management level of the entire supply chain.

[0044] The cross-enterprise collaborative operation module triggers cross-enterprise collaborative operations according to the optimized execution scheme, distributes relevant instructions to each participant system through a pre-established secure channel, and the instruction transmission process adopts end-to-end encryption protection to ensure that business secrets are not leaked during transmission.

[0045] Further, the cross-enterprise collaborative operation module includes an instruction encryption and verification unit, an instruction distribution and task allocation unit, and a collaborative execution and verification unit, The instruction encryption and verification unit obtains cross-enterprise collaborative instructions through a preset secure channel (established through a pre-configured secure communication protocol to ensure the security of instruction transmission), processes instruction data using end-to-end encryption technology, parses the instruction content, verifies the integrity of the instruction using digital signature (encrypts the instruction content to ensure that the instruction has not been tampered with during transmission and verifies the legality of the instruction source), and determines the legality of the instruction. The instruction distribution and task allocation unit distributes the encrypted instruction set to each participant system through an instruction distribution mechanism (distributes the encrypted instruction set to each participant system according to the system state and load condition of each participant) if the instruction legality is verified, obtains a distribution confirmation state, triggers an optimized execution scheme according to the distribution confirmation state, allocates a collaborative operation task using a load balancing algorithm, and obtains a task allocation result. The collaborative execution and verification unit extracts the task of each participant system from the task allocation result, executes inter-system collaborative operation using an asynchronous communication protocol (allows each participant system to execute the task at different times to ensure the flexibility and efficiency of collaborative operation), determines the operation completion state, obtains a data transmission log, verifies the data integrity using a hash verification algorithm, judges the data consistency, updates the cross-enterprise collaboration log from the data consistency judgment result, records the operation sequence using a timestamp, and generates a collaborative operation record.

[0046] Specifically, the cross-enterprise collaborative operation module solves the problems of instruction transmission security and operation efficiency in traditional service supply chain cross-enterprise collaborative operation. It protects business secrets using end-to-end encryption, optimizes task allocation using a load balancing algorithm, improves collaboration flexibility using an asynchronous communication protocol, and ensures data consistency using a hash verification algorithm, thereby improving the collaborative efficiency and security of the supply chain and enhancing the reliability of cross-enterprise cooperation.

[0047] The real-time monitoring and correction module uses a real-time monitoring mechanism to track the execution progress of the collaborative operation, and automatically records abnormal information and generates a correction suggestion when the system detects an execution anomaly or deviation. The correction suggestion is intelligently generated based on historical performance data and current business rules.

[0048] Further, the real-time monitoring and correction module includes an exception handling unit and a correction suggestion generation unit, The exception handling unit obtains collaborative operation progress data through a real-time monitoring mechanism (continuously tracks the execution progress data of the collaborative operation and evaluates the execution state in real time), determines the execution state, triggers an exception detection function if the execution state deviates from a preset threshold, extracts abnormal information and stores it in an abnormal information database, analyzes the cause of the exception using a decision tree algorithm (generates a classification result of the cause of the exception by analyzing the abnormal information and historical performance data), and generates a cause classification result; The correction suggestion generation unit generates a correction suggestion by processing the cause classification result and the current business rules using a rule engine, adjusts the collaborative operation parameters, updates the execution progress data, detects the adjustment effect and judges the system stability using time series analysis (detects the adjustment effect and judges the system stability by analyzing the updated execution progress data).

[0049] Specifically, the real-time monitoring and correction module solves the problem of abnormal or deviated operation of traditional service supply chain by tracking the progress of collaborative operation in real time, detecting abnormalities and generating correction suggestions. It uses real-time monitoring mechanism to discover abnormalities in time, combines historical data and business rules to intelligently generate correction suggestions, adjusts operation parameters, and verifies the adjustment effect through time series analysis to ensure the smooth progress of collaborative operation, thereby improving the stability and reliability of the supply chain and enhancing the intelligent management level.

[0050] The data cycle optimization module updates the service delivery status and performance record by correction suggestions, re-enters the updated data into the zero-knowledge proof algorithm for verification processing, forms a new round of trusted data cycle, and continuously optimizes the collaborative efficiency and intelligent development level of the entire supply chain.

[0051] Further, the data cycle optimization module comprises a data cleaning and fusion unit and a cycle verification unit, The data cleaning and fusion unit obtains the service delivery status and performance record, extracts real-time data from the database (extracted from the supply chain database for data cleaning and fusion with the service delivery status and performance record), removes abnormal values through data cleaning technology to obtain a standardized data set, and then combines the correction suggestion data to update the service delivery status and performance record to obtain an updated data set; The cycle verification unit inputs the updated data set into the zero-knowledge proof algorithm if the updated data set meets the preset integrity threshold (the integrity threshold is preset according to data quality requirements and business needs to ensure data integrity and credibility), processes it through encryption verification technology to obtain an algorithm verification result, adopts a trusted data cycle mechanism (ensures that verified data can be timely distributed to the supply chain collaboration platform to form a new round of trusted data cycle), and distributes the verified data to the supply chain collaboration platform.

[0052] Specifically, the data cycle optimization module solves the problem of delayed data update and low data quality in traditional service supply chain. It can update the service delivery status and performance record in time to ensure data integrity and credibility, and continuously optimize the collaborative efficiency and intelligent level of the supply chain through the trusted data cycle mechanism, improving the operation quality and stability of the entire supply chain.

[0053] Embodiment 2

[0054] The application scenario of this embodiment is that an automobile manufacturing enterprise (main plant) closely cooperates with multiple raw material suppliers, component manufacturers, logistics service providers and assembly plants through a blockchain-based enterprise service supply chain traceability and smart contract management system to ensure efficient production and delivery of components.

[0055] Specifically, after the automobile manufacturing enterprise (main plant) and the parts supplier reach a procurement agreement in modern manufacturing, the supplier begins to produce parts. During the production process, the supplier uploads service delivery data such as production progress, quality inspection report, and performance record of the parts to the enterprise service supply chain traceability and smart contract management system based on blockchain. The data encryption and credential generation module in the system starts immediately, encrypts these data using zero-knowledge proof algorithm, generates verification credentials containing data authenticity proof but not revealing specific content, uniquely identifies these credentials through hash function, and signs them using digital signature technology to ensure data credibility and business secret protection. Subsequently, the signed credentials are stored in the distributed ledger by means of blockchain technology, forming an unalterable distributed storage record. Only authorized participants can access these data.

[0056] When the parts production is completed and ready for delivery, the cross-enterprise trust mechanism establishment module starts to work, obtains performance data from each supplier, and pre-processes the data using data encryption technology to obtain an encrypted performance data set. Subsequently, the encrypted performance data set is verified by multi-party secure computing algorithm to obtain a data consistency score. If the score reaches the preset threshold, it indicates that the data provided by each supplier has high consistency. The system will then decrypt the encrypted performance data set to generate a trusted data set, generate a unified trusted data view according to the trusted data set, and generate a view identifier using data indexing technology. Through the view identifier, the system distributes the data view to each participant node and saves it to each node using distributed storage technology, ensuring that each participant can obtain the latest and trusted data in a timely manner. Consistent hash algorithm is used to verify the data view to obtain the verification result. According to the verification result, the system updates the trust mechanism, records the trust status using blockchain technology, generates a trust log, and thus establishes a solid trust foundation among all participants.

[0057] According to the delivery progress and quality of the parts, the host factory needs to automatically trigger payment and subsequent production processes. At this time, the intelligent contract execution module obtains a trusted data view through the blockchain platform, analyzes the service delivery state changes, and generates a state change sequence. If the state change sequence meets the preset performance conditions, such as timely delivery of parts and quality, the intelligent contract will automatically generate an execution instruction containing service flow rules. Combined with the available resource information in the resource pool, a resource allocation scheme is generated, and a linear regression algorithm is used to analyze the resource allocation scheme and historical service delivery data to determine the rationality of resource allocation. If the rationality score of resource allocation is higher than the preset threshold, the intelligent contract will automatically generate an execution instruction to trigger service flow and resource allocation. After the host factory receives the parts, the system obtains the state update data after service flow, analyzes the performance condition completion degree, generates a performance status report, and provides data support for subsequent production decisions.

[0058] In the production process of parts, if a parts manufacturer is delayed due to equipment failure, the resource scheduling optimization module in the system will start. The panoramic view construction unit obtains the real-time state data of the service flow rules and the supply chain nodes, extracts the rule parameters and node state indicators from the preset database, and generates a supply chain operation panoramic view. The conflict detection unit analyzes the panoramic view through the state monitoring module, calculates the resource occupancy rate and task scheduling of each node, and if it finds that the resource occupancy rate of a node exceeds the preset threshold or the task scheduling overlaps, it indicates that there is a resource conflict or scheduling anomaly. The scheduling optimization unit extracts the conflict nodes and abnormal scheduling tasks, calls the linear programming algorithm, and generates a resource reallocation scheme. According to the allocation scheme combined with the node coordination mechanism, the resource allocation priority of the conflict node is adjusted, a resource allocation scheme after node coordination is generated, and an optimized execution scheme is generated combined with the service flow rules to obtain the final task scheduling sequence. The scheme verification unit simulates the final task scheduling sequence to verify the stability of the optimized execution scheme. If the verification result shows scheduling anomalies, the linear programming algorithm is recalculated to obtain an updated optimized execution scheme to ensure that the production of parts can be completed on time.

[0059] In order to ensure that the parts are delivered on time, the main machine factory needs to coordinate multiple parts suppliers and logistics service providers. The cross-enterprise collaborative operation module triggers collaboration according to the optimized execution plan, distributes relevant instructions to each participant system through a pre-established secure channel, the instruction encryption and verification unit obtains the cross-enterprise collaborative instruction through the pre-set secure channel, processes the instruction data using end-to-end encryption technology, parses the instruction content, verifies the instruction integrity using digital signature, and determines the instruction legality. If the instruction legality passes the verification, the instruction distribution and task allocation unit distributes the encrypted instruction set to each participant system, obtains the distribution confirmation state; according to the distribution confirmation state, trigger the optimized execution plan, use load balancing algorithm to allocate collaborative operation tasks, get task allocation result, collaborative execution and verification unit extracts the task of each participant system from the task allocation result, uses asynchronous communication protocol to execute inter-system collaborative operation, determines the operation completion state, obtains the data transmission log, uses hash check algorithm to verify the integrity of the transmission data, judges the data consistency, updates the cross-enterprise collaboration log from the data consistency judgment result, uses timestamp to record the operation sequence, generates the collaborative operation record, ensures that the parts can be delivered to the main machine factory on time and safely.

[0060] In the process of delivering parts, if the transport vehicle of a logistics service provider breaks down, causing delivery delay, the execution progress of the collaborative operation is continuously tracked through the real-time monitoring mechanism. When the system detects execution abnormalities or deviations, the exception handling unit will trigger the exception detection function, extract the exception information and store it in the exception information database, analyze the cause of the exception using the decision tree algorithm based on historical performance data, and generate a cause classification result. The correction suggestion generation unit generates correction suggestions based on the cause classification result and the current business rules using a rule engine, adjusts the collaborative operation parameters, and updates the execution progress data. From the updated execution progress data, the system stability is judged by using time series analysis to detect the adjustment effect. If the adjustment effect is not good, the system will generate a new correction suggestion until the delivery of parts returns to normal, ensuring the stable operation of the supply chain.

[0061] The main machine factory needs to update the parts delivery status and performance record through the data cycle optimization module based on the actual delivery situation and the correction suggestion.

[0062] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the preferred embodiment of the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the present application, and any equivalent embodiments with equivalent changes and modifications are still within the scope of the present application.

Claims

1. A blockchain-based enterprise service supply chain traceability and smart contract management system, characterized in that: The application relates to a cross-enterprise service supply chain data encryption and trust mechanism establishment method. The data encryption and credential generation module obtains service delivery data and performance record of each participant in an enterprise service supply chain, encrypts the service delivery data through a zero-knowledge proof algorithm, and generates a verification credential containing data authenticity proof but not leaking specific content. The cross-enterprise trust mechanism establishment module establishes a cross-enterprise data intercommunication trust mechanism according to the verification credential, jointly verifies the performance data submitted by each participant by using a multi-party secure calculation algorithm, and generates a unified trusted data view for subsequent collaborative processing if the verification result shows that the data consistency reaches a preset threshold. The intelligent contract execution module analyzes the service delivery state change in the trusted data view through an intelligent contract execution algorithm, and automatically generates an execution instruction when a specific performance condition is triggered. The resource scheduling optimization module obtains the service flow rule in the execution instruction, judges whether there is resource conflict or scheduling exception in combination with the real-time state information of each node in the current supply chain, and generates an optimized execution scheme. The cross-enterprise collaborative operation module triggers cross-enterprise collaborative operation according to the optimized execution scheme, and distributes relevant instructions to each participant system through a pre-established secure channel. The real-time monitoring and correction module adopts a real-time monitoring mechanism to track the execution progress of the collaborative operation, and automatically records abnormal information and generates a correction suggestion when the system detects execution abnormality or deviation.

2. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The data encryption and credential generation module comprises a zero-knowledge encryption unit, a credential identification and signature unit and a storage and access control unit. The zero-knowledge encryption unit obtains service delivery data and performance record of each participant in an enterprise service supply chain, encrypts the data through a zero-knowledge proof algorithm, generates encrypted data, retains the authenticity feature of the original data, adopts a commitment scheme in the zero-knowledge proof algorithm, generates a verification credential containing data authenticity proof, and the credential does not leak the specific content of the service delivery data. The credential identification and signature unit uniquely identifies the credential through a hash function if the generation of the verification credential satisfies a preset authenticity threshold, obtains an identified credential, and adopts a digital signature technology to sign the credential, thereby generating a signed credential. The storage and access control unit stores the signed credential to generate a distributed storage record through a blockchain technology, judges the request legality through a zero-knowledge verification protocol if the access request of the distributed storage record comes from a supply chain participant, and obtains an authorized access result.

3. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The cross-enterprise trust mechanism establishment module comprises a joint verification unit, a data view generation and distribution unit and a check and trust record unit. The joint verification unit establishes a cross-enterprise data intercommunication trust mechanism according to the verification credential, obtains performance data from each participant, pre-processes the data through a data encryption technology to obtain an encrypted performance data set, executes joint verification on the encrypted performance data set through a multi-party secure calculation algorithm, obtains a data consistency score, decrypts the encrypted performance data set if the data consistency score reaches a preset threshold, and obtains a trusted data set. The data view generation and distribution unit generates a unified trusted data view according to a trusted data set, generates a view identifier by using a data indexing technology, distributes the data view through the view identifier, saves to each participant node by using a distributed storage technology, and obtains distribution confirmation information; The check and trust record unit checks the data view by using a consistent hashing algorithm, obtains a check result, updates a trust mechanism according to the check result, records a trust state by using a block chain technology, and generates a trust log.

4. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The smart contract execution module includes an execution instruction generation unit, a resource allocation analysis unit and an instruction publishing and tracking unit, The execution instruction generation unit obtains a trusted data view through a block chain platform, analyzes service delivery state changes to obtain a state change sequence, generates an execution instruction including a service flow rule through a smart contract if the state change sequence meets a preset performance condition, and determines a service flow path; The resource allocation analysis unit obtains available resource information from a resource pool according to the service flow rule in the execution instruction, generates a resource allocation scheme, analyzes the resource allocation scheme and historical service delivery data by using a linear regression algorithm, and judges resource allocation rationality; The instruction publishing and tracking unit automatically generates an execution instruction through a smart contract if the resource allocation rationality score is higher than a preset threshold, triggers service flow and resource allocation, obtains state update data after service flow, analyzes performance condition completion degree, and obtains a performance state report.

5. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The resource scheduling optimization module includes a panoramic view construction unit, a conflict detection unit, a scheduling optimization unit and a scheme verification unit, The panoramic view construction unit obtains service flow rules and real-time state data of supply chain nodes, extracts rule parameters and node state indicators from a preset database, and obtains a supply chain operation panoramic view; The conflict detection unit analyzes the supply chain operation panoramic view through a state monitoring module, calculates resource occupation rates and task scheduling of each node, judges that there is a resource conflict or a scheduling anomaly if the resource occupation rate exceeds a preset threshold or the task scheduling overlaps, and obtains a conflict detection result; The scheduling optimization unit extracts conflict nodes and abnormal scheduling tasks, calls a linear programming algorithm, generates a resource reallocation scheme, adjusts the resource allocation priority of the conflict nodes according to the allocation scheme and a node coordination mechanism, generates a resource allocation scheme after node coordination, generates an optimized execution scheme in combination with the service flow rule, and obtains a final task scheduling sequence; The scheme verification unit verifies the stability of the optimized execution scheme by simulating the final task scheduling sequence, returns to the linear programming algorithm for recalculation if the verification result shows scheduling anomaly, and obtains an updated optimized execution scheme.

6. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The cross-enterprise collaborative operation module includes an instruction encryption and verification unit, an instruction distribution and task allocation unit and a collaborative execution and check unit, The instruction encryption and verification unit obtains cross-enterprise collaborative instructions through a preset security channel, processes instruction data by using end-to-end encryption technology, analyzes instruction content, verifies instruction integrity by using digital signature, and determines instruction legality; The instruction distribution and task allocation unit distributes the encrypted instruction set to each participant system through an instruction distribution mechanism if the instruction legality is verified, obtains a distribution confirmation state, triggers an optimized execution scheme according to the distribution confirmation state, allocates a collaborative operation task by using a load balancing algorithm, and obtains a task allocation result.

7. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 6, wherein: The collaborative execution and verification unit extracts the task of each participant system from the task allocation result, performs inter-system collaborative operation by using an asynchronous communication protocol, determines an operation completion state, obtains a data transmission log, verifies the integrity of the transmission data by using a hash verification algorithm, judges data consistency, updates the cross-enterprise collaboration log from the data consistency judgment result, records the operation sequence by using a timestamp, and generates a collaborative operation record.

8. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 1, wherein: The real-time monitoring and correction module includes an exception handling unit and a correction suggestion generation unit, The exception handling unit obtains collaborative operation progress data through a real-time monitoring mechanism, determines an execution state, triggers an exception detection function if the execution state deviates from a preset threshold, extracts exception information and stores it in an exception information database, analyzes the exception reason by using a decision tree algorithm in combination with historical performance data, and generates a reason classification result; The correction suggestion generation unit generates a correction suggestion by using a rule engine process based on the reason classification result and the current business rules, adjusts the collaborative operation parameters, updates the execution progress data, detects the adjustment effect by using time series analysis from the updated execution progress data, and judges the system stability.

9. The blockchain-based enterprise service supply chain provenance and smart contract management system of claim 1, wherein: Further comprising: A data cycle optimization module updates the service delivery state and performance record by using the correction suggestion, re-inputs the updated data into the zero-knowledge proof algorithm for verification processing, forms a new round of trusted data cycle, and continuously optimizes the collaborative efficiency and intelligent development level of the entire supply chain.

10. The blockchain-based enterprise service supply chain traceability and smart contract management system of claim 9, wherein: The data cycle optimization module includes a data cleaning and fusion unit and a cycle verification unit, The data cleaning and fusion unit obtains the service delivery state and performance record, extracts real-time data from the database, removes abnormal values by using data cleaning technology, obtains a standardized data set, combines the correction suggestion data, updates the service delivery state and performance record by using data fusion technology, and obtains an updated data set; The cycle verification unit inputs the updated data set into the zero-knowledge proof algorithm if the updated data set meets the preset integrity threshold, processes it by using encryption verification technology, obtains an algorithm verification result, and distributes the verified data to the supply chain collaboration platform by using a trusted data cycle mechanism.

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