Medicine supply chain distribution settlement big data distributed management method and system

Abstract

This application provides a distributed management method and system for big data in pharmaceutical supply chain distribution and settlement. Through distributed ledger storage and cross-verification, it establishes a trusted data foundation for workers from various parties involved in multi-source heterogeneous settlement data. Specifically, steps S131-S134 ensure the effectiveness of cross-verification, and random verification combined with exponential expansion of verification objects ensures the security of the verification process itself and effectively prevents verification errors. By integrating secure multi-party computation with a consensus-based global settlement rule model, it achieves privacy-preserving intelligent settlement where data is usable but not visible. While protecting core business secrets such as prices and sales volume, it ensures the automatic and accurate calculation of complex rules, enhancing the value of multi-party data fusion.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a distributed management method and system for big data in pharmaceutical supply chain distribution and settlement. Background Technology

[0002] In the pharmaceutical supply chain, distribution and settlement management is a core element in ensuring the efficient and stable operation of the supply chain. Traditional settlement management systems often adopt a centralized architecture, with core enterprises such as large pharmaceutical companies setting up a central server. All participating parties upload settlement data to this center for centralized processing, reconciliation, and payment instruction issuance.

[0003] However, this centralized model requires participants to share their sensitive core business data, which often leads to a lack of willingness among participants, resulting in a loss of data integrity. Furthermore, if the central node fails or is attacked, the entire settlement process will be paralyzed, and the centralized database is at risk of internal data tampering, resulting in insufficient credibility of the settlement results.

[0004] In recent years, the industry has attempted to introduce blockchain technology to upload key settlement hashes to the chain to improve immutability. However, this method only solves the problem of result notarization and fails to address the issues of the authenticity of the original data source and the privacy of the calculation process. Data is still generated and aggregated off-chain, failing to form an end-to-end trusted closed loop. Furthermore, existing methods lack the ability to securely and efficiently process massive amounts of multi-source settlement data in a distributed manner, and cannot achieve automatic verification and execution of settlement rules while protecting data privacy. Summary of the Invention

[0005] This application aims to achieve distributed management of pharmaceutical supply chain distribution and settlement data that can balance data privacy and security, trusted cross-entity collaboration, and intelligent automatic execution, and provides a distributed management method and system for pharmaceutical supply chain distribution and settlement big data.

[0006] To achieve this objective, the following technical solution is adopted in this application: A distributed management method for big data in pharmaceutical supply chain distribution and settlement is provided, comprising the following steps: S1 generates a privacy-preserving digest of the local settlement raw data of each participant node in the supply chain and uploads it to the distributed ledger network for storage and sharing. Then, based on the stored shared digest, the participating nodes cross-verify the data. S2, based on the local settlement raw data that has been cross-verified by all participants, uses the trained global settlement rule model to perform distributed, privacy-preserving intelligent calculations on the transactions to be settled, and generates settlement results.

[0007] Preferably, step S1 specifically includes the following steps: S11, perform structured processing on the original local settlement data from each of the participating nodes; S12, Generate a privacy-preserving digest of the structured data unit and submit it to the distributed ledger for evidence storage; S13, each participant performs distributed cross-verification based on the shared on-chain digest. Preferably, in step S13, the method for distributed cross-verification by each participant based on the on-chain shared digest includes the following steps: S131, the data proxy module of the participating party continuously monitors all on-chain evidence stored that are associated with the locally generated privacy protection summary and adds them to the on-chain summary filter set. "Association" includes that at least one specified data element in the local privacy protection summary is consistent with the data element in the on-chain privacy protection summary of the evidence stored. S132, randomly select a monitored on-chain privacy-preserving digest from the on-chain digest filter set, and perform a data consistency comparison between any specified data element specified in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. If the comparison is successful, proceed to step S133; If the comparison fails, it is determined that the verification of the local privacy summary and the selected on-chain privacy summary has failed, and the process jumps to step S134. S133: Obtain the second on-chain privacy-protected digests stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy-protected digest that has been successfully compared. Then, perform a consistency comparison with all the specified data elements in the local privacy-protected digest one by one. If the comparison is successful, the first on-chain privacy-preserving digest and the second on-chain privacy-preserving digest that participated in the comparison will be added to the comparison result set; If the comparison fails, the verification with the second on-chain privacy digest is determined to have failed and the first on-chain privacy digest currently being compared is filtered out. S134: Filter out the on-chain privacy protection digests that failed verification in step S132 from the on-chain privacy protection digests monitored in step S131, and then determine whether the on-chain digest filter set is empty. If so, terminate the distributed cross-verification process; If not, return to step S132.

[0008] Preferably, step S2 specifically includes the following steps: S21, the participants initiating the settlement send a calculation request to other participants whose cross-verification in step S1 has been passed; S22, each participating node retrieves the corresponding local settlement raw data that has been cross-verified in step S1 from its local database based on the unique transaction identifier carried in the request; S23, each participant converts the local settlement raw data retrieved in step S22 into ciphertext through a pre-trained global settlement rule model, and sends it to a designated secure computing node for settlement calculation, outputting the settlement result.

[0009] This application also provides a distributed management system for big data in pharmaceutical supply chain distribution and settlement, including: The privacy summary generation module generates privacy summaries from the raw local settlement data of each participant in the supply chain. The summary storage and sharing module connects to the privacy-protected summary generation module and is used to generate privacy-protected summaries and upload them to the distributed ledger network for storage and sharing. The cross-verification module connects the digest storage and sharing module, and is used for cross-verification among participating nodes based on the shared digest of the stored evidence. The transaction data settlement module, connected to the cross-verification module, is used to perform distributed, privacy-preserving intelligent calculations on the transactions to be settled based on the local settlement raw data that has been cross-verified by each participant, and to generate settlement results using a trained global settlement rule model.

[0010] Preferably, the cross-verification module includes: The on-chain digest filter set generation and update unit is used to add or update all on-chain evidence associated with locally generated privacy-preserving digests that are continuously monitored by the data proxy module of the participating party to the on-chain digest filter set. The first consistency comparison unit, connected to the on-chain digest filter set generation and update unit, is used to randomly select a monitored on-chain privacy-preserving digest from the latest updated on-chain digest filter set, and perform a data consistency comparison between any specified data element in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. The first judgment unit, connected to the first consistency comparison unit, is used to determine whether the consistency comparison was successful. If so, send a second on-chain privacy-preserving digest retrieval instruction to the digest retrieval unit; If not, the verification of the local privacy digest and the selected on-chain privacy digest is deemed to have failed, and an on-chain digest filtering instruction is sent to the digest filtering unit. The summary acquisition unit, connected to the first judgment unit, is used to acquire, according to the second on-chain privacy protection summary acquisition instruction, the second on-chain privacy protection summary stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy protection summary that has been successfully compared with the first consistency comparison unit; The second consistency comparison unit, which connects to the digest acquisition unit, is used to perform a consistency comparison on each of the acquired second-chain privacy protection units with all specified data elements in the local privacy protection digest, which is the comparison object of the first consistency comparison unit. If the comparison is successful, the first on-chain privacy-preserving digest and the second on-chain privacy-preserving digest that participated in the comparison will be added to the comparison result set; If the comparison fails, an on-chain summary filtering instruction is sent to the summary filtering unit; The digest filtering unit, connected to the first consistency comparison unit and the second consistency comparison unit, is used to filter out the first on-chain privacy protection digest or the second on-chain privacy protection digest carried in the received digest filtering instruction from the on-chain digest filtering set. The second judgment unit is used to determine whether the on-chain digest filter set is empty, based on the instruction that the first consistency comparison unit failed to match. If so, the system will be prompted to terminate the distributed cross-verification process; If not, a continue comparison instruction is sent to the first consistency comparison unit.

[0011] Preferably, the transaction data settlement module includes: The settlement calculation request initiation unit is used to provide calculation requests to other participants who have passed the cross-verification of the initiating settlement direction; The local settlement raw data retrieval unit is deployed locally on the participant's premises and connected to the settlement calculation request initiation unit. It is used to retrieve the corresponding local settlement raw data that has passed cross-verification from the local database based on the unique transaction identifier carried in the received request. The ciphertext conversion unit, connected to the local settlement raw data retrieval unit, is used to convert the retrieved local settlement raw data into ciphertext and output it through a pre-coordinated global settlement rule model. The settlement calculation unit, connected to the ciphertext conversion unit, is used to send the ciphertext output by the ciphertext conversion unit to the designated secure computing node for settlement calculation and output the settlement result.

[0012] This application has the following beneficial effects: 1. This application establishes a trusted data foundation for workers from various parties in multi-source heterogeneous settlement data through distributed ledger notarization and cross-verification. Specifically, steps S131-S134 ensure the effectiveness of cross-verification, and random verification combined with the fission-style expansion of verification objects ensures the security of the verification process itself and effectively prevents verification errors. By integrating secure multi-party computation with a consensus-based global settlement rule model, it achieves privacy-preserving intelligent settlement where data is usable but not visible. While protecting core business secrets such as prices and sales volumes, it ensures the automatic and accurate calculation of complex rules, enhancing the value of multi-party data fusion.

[0013] 2. In step S1, during distributed cross-validation, step S132 specifies any given data element for data consistency comparison. Only after a successful comparison does the process proceed to step S133, which involves expanding the comparison object. This not only improves the effectiveness of subsequent expansion and comparison verification but also increases the overall efficiency of distributed cross-validation. Furthermore, the closed-loop process of steps S132-S133-S134-S132 significantly reduces errors and omissions in distributed cross-validation. Moreover, for different application scenarios, users only need to specify the type of the specified data element in step S132 and the number of specified data elements in step S133 to ensure the effectiveness and efficiency of cross-validation, giving the distributed management method provided in this application good scalability.

[0014] 3. In step S2, through the three sub-steps S21-S23, the trusted data provided in step S1 is inherited, and the global settlement rule model directly performs rule calculations on the encrypted data, ensuring the confidentiality of the calculation process; and the settlement calculation is performed on the designated secure computing node, further enhancing the confidentiality of the settlement calculation process. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly described below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a diagram illustrating the implementation steps of the distributed management method for big data in pharmaceutical supply chain distribution and settlement provided in this application embodiment. Detailed Implementation

[0017] The technical solution of this application will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of this application, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0019] In the accompanying drawings of the embodiments of this application, the same or similar reference numerals correspond to the same or similar components. In the description of this application, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting this application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0020] In the description of this application, unless otherwise expressly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The distributed management method for big data in pharmaceutical supply chain distribution and settlement provided in this application embodiment, such as Figure 1 As shown, the steps include: S1 generates a privacy-preserving digest of the local settlement raw data of each participant node in the supply chain and uploads it to the distributed ledger network for storage and sharing. Then, based on the stored shared digest, the participating nodes cross-verify the data. Step S1 specifically includes the following steps: S11 involves structuring the raw local settlement data from each participating node, specifically as follows: When settlement-related business events occur in the internal systems of participating parties such as pharmaceutical manufacturers, wholesalers, logistics companies, and hospitals, such as ERP systems, a data proxy module deployed on the server of that participating party captures the raw local settlement data in real time or in batches. Based on pre-agreed and mutually agreed-upon data standards and specifications, the data proxy module transforms the raw, inconsistently formatted data into structured data units.

[0022] For example, the transportation management system of logistics company a records an original message, expressed as: December 12, 2025, 14:20, license plate number Jing AX, freight bill number P202512121001, leaving the Beijing warehouse. After this original data is standardized and encapsulated, the formed structured data unit is expressed, for example, as {Event type: transportation node update; Timestamp: 2025-1212T14:20; Operator: logistics company a; Bill of lading number: P202512121001; Node type: leaving; Location: Beijing Central Warehouse; Carrier: license plate number Jing AX}.

[0023] S12. Generate a privacy protection summary for the structured data unit and submit it to the distributed ledger for deposit; The data proxy module calculates the structured data unit formed in step S11 using a cryptographic hash function such as SHA-256 to generate a fixed-length and unique digital fingerprint, that is, the privacy protection summary.

[0024] For example, after the structured data unit {Event type: transportation node update; Timestamp: 2025-1212T14:20; Operator: logistics company a; Bill of lading number: P202512121001; Node type: leaving; Location: Beijing Central Warehouse; Carrier: license plate number Jing AX} is processed by the hash function, a fixed-length hexadecimal string is output, for example, e5f678901234567890abcdef1234567890abcdefa. The process of the hash function generating a string for the data unit adopts existing methods and will not be elaborated.

[0025] Subsequently, the data proxy module packs the privacy protection summary, the type identifier of the data unit, the timestamp, and the digital identity identifier of the corresponding participating party into a deposit transaction and submits it to a permissioned distributed ledger network, such as a consortium chain. Nodes in the network record this transaction into a new and immutable block through a consensus mechanism.

[0026] For example, the packed deposit transaction is: {Privacy protection summary: e5f678901234567890abcdef1234567890abcdefa; Data type: logistics track; Depositor: logistics company a; Time: 2025-12-12T14:20} is submitted to the chain. All nodes on the chain record this deposit information, but only know that logistics company a generated a track voucher for the transaction referred to by e5f678901234567890abcdef1234567890abcdefa at 2025-12-12T14:20, but do not know other specific information.

[0027] S13, Distributed verification based on on-chain shared digests, specifically includes the following steps: S131, the data proxy module of the participating party continuously monitors all on-chain evidence stored that are associated with the locally generated privacy protection summary and adds them to the on-chain summary filter set. "Association" includes that at least one specified data element in the local privacy protection summary is consistent with the data element in the on-chain privacy protection summary of the evidence stored. S132, randomly select a monitored on-chain privacy-preserving digest from the on-chain digest filter set, and perform a data consistency comparison between any specified data element specified in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. If the comparison is successful, proceed to step S133; If the comparison fails, the verification of the local privacy digest and the selected on-chain privacy digest is deemed to have failed, and the process jumps to step S134. S133: Obtain the second on-chain privacy-protected digests stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy-protected digest that has been successfully compared. Then, perform a consistency comparison on each second on-chain privacy-protected digest with all specified data elements in the local privacy-protected digest. If the comparison is successful, the first and second on-chain privacy-preserving digests that participated in the comparison will be added to the comparison result set. If the comparison fails, the verification of the privacy-protected digest on the second chain is deemed to have failed. S134: Filter out the on-chain privacy protection digests that failed verification in step S132 from the on-chain privacy protection digests monitored in step S131, and then determine whether the on-chain digest filter set is empty. If so, terminate the distributed cross-verification process; If not, return to step S132.

[0028] For example, when a transaction involves multiple parties, the data proxy modules of the relevant participants continuously monitor the on-chain evidence events related to themselves on the distributed ledger. For instance, when a hospital anticipates the delivery of a batch of medicines, its system continuously monitors all on-chain evidence associated with the purchase order number for that batch of medicines. Once it receives the pharmaceutical company's shipment summary, the logistics tracking summary, and the final delivery request summary, the hospital system automatically initiates a distributed cross-verification process. A brief description of the verification process is as follows: First, based on its own delivery receipt data, the system generates a privacy-protected digest of the receipt data locally. Then, it compares this local digest with the delivery request digest submitted by the on-chain logistics provider. For example, it compares whether the purchase order number (a specified data element) recorded in the two digests matches. If they match, it proves that the delivery receipt reported by the logistics provider is consistent with the actual delivery receipt by the hospital. Similarly, the pharmaceutical company verifies whether the trajectory digest submitted by the logistics provider is logically consistent with its own shipping information. If any inconsistency occurs, such as a discrepancy in the shipping location between the two digests, the system automatically triggers a pre-defined objection and collaborative processing procedure.

[0029] In this application, step S132 specifies any given data element for data consistency comparison. Only after a successful comparison does the process proceed to step S133, which involves expanding the comparison object. This not only improves the effectiveness of subsequent expansion and comparison verification but also increases the overall efficiency of distributed cross-verification. Furthermore, the closed-loop process of steps S132-S133-S134-S132 significantly reduces errors and omissions in distributed cross-verification. Moreover, for different application scenarios, users only need to specify the type of the specified data element in step S132 and the number of specified data elements in step S133 to ensure the effectiveness and efficiency of cross-verification, giving the distributed management method provided in this application excellent scalability.

[0030] After completing distributed cross-verification among the participating nodes in step S1, as follows: Figure 1 As shown in the embodiment of this application, the steps of the distributed management method for big data in pharmaceutical supply chain distribution and settlement are as follows: S2, based on the locally settled raw data that has been cross-verified by all participants, utilizes the trained global settlement rule model to perform distributed, privacy-preserving intelligent computation on the transactions to be settled, generating preliminary settlement results. Specific steps include: S21, the participants initiating the settlement send a calculation request to other participants whose cross-verification in step S1 has been passed; S22, each participating node retrieves the corresponding local settlement raw data that has been cross-verified in step S1 from its local database based on the unique transaction identifier carried in the request; For example, when a specific transaction needs to be settled, the party initiating the settlement, such as a pharmaceutical company, sends a calculation request to relevant parties such as distributors and hospitals. Upon receiving the request, each participating node retrieves the corresponding local settlement raw data, which has been cross-verified in step S1, from its local database based on the unique transaction identifier, such as the order number, carried in the request.

[0031] For example, when calculating the sales rebate for drug X by distributor B this quarter, pharmaceutical company A initiates a calculation request, attaching an order number such as ORD-2025-001. Distributor B finds the cross-verified sales record corresponding to that order number in its own system, extracts the local settlement raw data of "sales volume of 10,000 boxes," and confirms that its hash value has been stored on the blockchain. Hospital D similarly finds the corresponding inbound record, such as the receipt volume of 9,800 boxes, and confirms its hash.

[0032] S23, each participant converts the raw local computation data retrieved in step S22 into ciphertext through a pre-trained global settlement rule model, and sends it to a designated secure computing node for settlement calculation, outputting the settlement result.

[0033] Each participating node loads a pre-trained and consensus-reached global settlement rule model locally. Instead of directly exchanging raw data, the parties employ a secure multi-party computation protocol to convert the local raw settlement data retrieved in step S22 into ciphertext or data fragments using cryptographic techniques and isomorphic encryption. These ciphertexts or fragments are sent to at least one designated secure computation node, or directly exchanged and computed among the participants using a specific protocol, outputting the settlement result.

[0034] For example, a pre-defined rule in a pre-trained global settlement rule model is: for sales exceeding 5000 boxes, a rebate of 0.5 yuan per box is given. Distributor B encrypts 10000 boxes sold as ciphertext a1, and Hospital D encrypts 9800 boxes received as ciphertext a2. A secure computing node, using a secure multi-party computation protocol, directly performs the logical operation (a1 + a2 - 5000) × 0.5 on the ciphertexts without decryption, ultimately obtaining the ciphertext b representing the rebate amount. The party receiving the settlement result decrypts this ciphertext b using a pre-defined decryption algorithm to obtain the specific rebate amount. During this process, neither party knows the other's specific sales volume.

[0035] In step S2, this application takes over the trusted data provided in step S1 through three sub-steps S21-S23, and the global settlement rule model performs rule calculations directly on the encrypted data, ensuring the confidentiality of the calculation process; and the settlement calculation is performed on a designated secure computing node, further enhancing the confidentiality of the settlement calculation process.

[0036] This application also provides a distributed management system for big data in pharmaceutical supply chain distribution and settlement, including: The privacy summary generation module generates privacy summaries from the raw local settlement data of each participant in the supply chain. The summary storage and sharing module connects to the privacy-protected summary generation module and is used to generate privacy-protected summaries and upload them to the distributed ledger network for storage and sharing. The cross-verification module connects the digest storage and sharing module, and is used for cross-verification among participating nodes based on the shared digest of the stored evidence. The transaction data settlement module, connected to the cross-verification module, is used to perform distributed, privacy-preserving intelligent calculations on the transactions to be settled based on the local settlement raw data that has been cross-verified by each participant, and to generate settlement results using a trained global settlement rule model.

[0037] The cross-validation module specifically includes: The on-chain digest filter set generation and update unit is used to add or update all on-chain evidence associated with locally generated privacy-preserving digests that are continuously monitored by the data proxy module of the participating party to the on-chain digest filter set. The first consistency comparison unit, connected to the on-chain digest filter set generation and update unit, is used to randomly select a monitored on-chain privacy-preserving digest from the latest updated on-chain digest filter set, and perform a data consistency comparison between any specified data element in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. The first judgment unit, connected to the first consistency comparison unit, is used to determine whether the consistency comparison was successful. If so, send a second on-chain privacy-preserving digest retrieval instruction to the digest retrieval unit; If not, the verification of the local privacy digest and the selected on-chain privacy digest is deemed to have failed, and an on-chain digest filtering instruction is sent to the digest filtering unit. The summary acquisition unit, connected to the first judgment unit, is used to acquire, according to the second on-chain privacy protection summary acquisition instruction, the second on-chain privacy protection summary stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy protection summary that has been successfully compared with the first consistency comparison unit; The second consistency comparison unit, which connects to the digest acquisition unit, is used to perform a consistency comparison on each of the acquired second-chain privacy protection units with all specified data elements in the local privacy protection digest, which is the comparison object of the first consistency comparison unit. If the comparison is successful, the first on-chain privacy-preserving digest and the second on-chain privacy-preserving digest that participated in the comparison will be added to the comparison result set; If the comparison fails, an on-chain summary filtering instruction is sent to the summary filtering unit; The digest filtering unit, connected to the first consistency comparison unit and the second consistency comparison unit, is used to filter out the first on-chain privacy protection digest or the second on-chain privacy protection digest carried in the received digest filtering instruction from the on-chain digest filtering set. The second judgment unit is used to determine whether the on-chain digest filter set is empty, based on the instruction that the first consistency comparison unit failed to match. If so, the system will be prompted to terminate the distributed cross-verification process; If not, a continue comparison instruction is sent to the first consistency comparison unit.

[0038] Preferably, the transaction data settlement module includes: The settlement calculation request initiation unit is used to provide calculation requests to other participants who have passed the cross-verification of the initiating settlement direction; The local settlement raw data retrieval unit is deployed locally on the participant's premises and connected to the settlement calculation request initiation unit. It is used to retrieve the corresponding local settlement raw data that has passed cross-verification from the local database based on the unique transaction identifier carried in the received request. The ciphertext conversion unit, connected to the local settlement raw data retrieval unit, is used to convert the retrieved local settlement raw data into ciphertext and output it through a pre-coordinated global settlement rule model. The settlement calculation unit, connected to the ciphertext conversion unit, is used to send the ciphertext output by the ciphertext conversion unit to the designated secure computing node for settlement calculation and output the settlement result.

[0039] In summary, this application establishes a trusted data foundation for workers from various parties in multi-source heterogeneous settlement data through distributed ledger notarization and cross-verification. Specifically, steps S131-S134 ensure the effectiveness of cross-verification, while random verification combined with exponential expansion of verification objects ensures the security of the verification process itself and effectively prevents verification errors. By integrating secure multi-party computation with a consensus-based global settlement rule model, it achieves privacy-preserving intelligent settlement where data is usable but not visible. While protecting core business secrets such as prices and sales volumes, it ensures the automatic and accurate calculation of complex rules, enhancing the value of multi-party data fusion.

[0040] It should be stated that the above-described specific embodiments are merely preferred embodiments and technical principles applied in this application. Those skilled in the art should understand that various modifications, equivalent substitutions, and variations can be made to this application. However, such variations, as long as they do not depart from the spirit of this application, should be within the scope of protection of this application. Furthermore, some terminology used in this application's specification and claims is not limiting but merely for ease of description.

Claims

1. A distributed management method for big data in pharmaceutical supply chain distribution and settlement, characterized by the following steps: include: S1 generates a privacy-preserving digest of the local settlement raw data of each participant node in the supply chain and uploads it to the distributed ledger network for storage and sharing. Then, based on the stored shared digest, the participating nodes cross-verify the data. S2, based on the local settlement raw data that has been cross-verified by all participants, uses the trained global settlement rule model to perform distributed, privacy-preserving intelligent calculations on the transactions to be settled, and generates settlement results.

2. The distributed management method for big data in pharmaceutical supply chain distribution and settlement according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11, perform structured processing on the original local settlement data from each of the participating nodes; S12, Generate a privacy-preserving digest of the structured data unit and submit it to the distributed ledger for evidence storage; S13, each participant performs distributed cross-verification based on the shared on-chain digest.

3. The distributed management method for big data in pharmaceutical supply chain distribution and settlement according to claim 2, characterized in that, In step S13, the method for distributed cross-verification by each participant based on the on-chain shared digest includes the following steps: S131, the data proxy module of the participating party continuously monitors all on-chain evidence stored that are associated with the locally generated privacy protection summary and adds them to the on-chain summary filter set. "Association" means that at least one specified data element in the local privacy protection summary is consistent with the data element in the on-chain privacy protection summary of the evidence stored. S132, randomly select a monitored on-chain privacy-preserving digest from the on-chain digest filter set, and perform a data consistency comparison between any specified data element specified in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. If the comparison is successful, proceed to step S133; If the comparison fails, it is determined that the verification of the local privacy summary and the selected on-chain privacy summary has failed, and the process jumps to step S134. S133: Obtain the second on-chain privacy-protected digests stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy-protected digest that has been successfully compared. Then, perform a consistency comparison with all the specified data elements in the local privacy-protected digest one by one. If the comparison is successful, the first on-chain privacy-preserving digest and the second on-chain privacy-preserving digest that participated in the comparison will be added to the comparison result set; If the comparison fails, the verification with the second on-chain privacy digest is determined to have failed and the first on-chain privacy digest currently being compared is filtered out. S134: Filter out the on-chain privacy protection digests that failed verification in step S132 from the on-chain privacy protection digests monitored in step S131, and then determine whether the on-chain digest filter set is empty. If so, terminate the distributed cross-verification process; If not, return to step S132.

4. The distributed management method for big data in pharmaceutical supply chain distribution and settlement according to claim 1, characterized in that, Step S2 specifically includes the following steps: S21, the participants initiating the settlement send a calculation request to other participants whose cross-verification in step S1 has been passed; S22, each participating node retrieves the corresponding local settlement raw data that has been cross-verified in step S1 from its local database based on the unique transaction identifier carried in the request; S23, each participant converts the local settlement raw data retrieved in step S22 into ciphertext through a pre-trained global settlement rule model, and sends it to a designated secure computing node for settlement calculation, outputting the settlement result.

5. A distributed management system for big data in pharmaceutical supply chain distribution and settlement, characterized in that, include: The privacy summary generation module generates privacy summaries from the raw local settlement data of each participant in the supply chain. The summary storage and sharing module connects to the privacy-protected summary generation module and is used to generate privacy-protected summaries and upload them to the distributed ledger network for storage and sharing. The cross-verification module connects the digest storage and sharing module, and is used for cross-verification among participating nodes based on the shared digest of the stored evidence. The transaction data settlement module, connected to the cross-verification module, is used to perform distributed, privacy-preserving intelligent calculations on the transactions to be settled based on the local settlement raw data that has been cross-verified by each participant, and to generate settlement results using a trained global settlement rule model.

6. The distributed management system for pharmaceutical supply chain distribution and settlement big data as described in claim 5, characterized in that, The cross-verification module includes: The on-chain digest filter set generation and update unit is used to add or update all on-chain evidence associated with locally generated privacy-preserving digests that are continuously monitored by the data proxy module of the participating party to the on-chain digest filter set. The first consistency comparison unit, connected to the on-chain digest filter set generation and update unit, is used to randomly select a monitored on-chain privacy-preserving digest from the latest updated on-chain digest filter set, and perform a data consistency comparison between any specified data element in the local privacy-preserving digest and the on-chain data element in the randomly selected on-chain privacy-preserving digest. The first judgment unit, connected to the first consistency comparison unit, is used to determine whether the consistency comparison was successful. If so, send a second on-chain privacy-preserving digest retrieval instruction to the digest retrieval unit; If not, the verification of the local privacy digest and the selected on-chain privacy digest is deemed to have failed, and an on-chain digest filtering instruction is sent to the digest filtering unit. The summary acquisition unit, connected to the first judgment unit, is used to acquire, according to the second on-chain privacy protection summary acquisition instruction, the second on-chain privacy protection summary stored by each second participant that has a transaction trajectory relationship with the first participant corresponding to the first on-chain privacy protection summary that has been successfully compared with the first consistency comparison unit; The second consistency comparison unit, which connects to the digest acquisition unit, is used to perform a consistency comparison on each of the acquired second-chain privacy protection units with all specified data elements in the local privacy protection digest, which is the comparison object of the first consistency comparison unit. If the comparison is successful, the first on-chain privacy-preserving digest and the second on-chain privacy-preserving digest that participated in the comparison will be added to the comparison result set; If the comparison fails, an on-chain summary filtering instruction is sent to the summary filtering unit; The digest filtering unit, connected to the first consistency comparison unit and the second consistency comparison unit, is used to filter out the first on-chain privacy protection digest or the second on-chain privacy protection digest carried in the received digest filtering instruction from the on-chain digest filtering set. The second judgment unit is used to determine whether the on-chain digest filter set is empty, based on the instruction that the first consistency comparison unit failed to match. If so, the system will be prompted to terminate the distributed cross-verification process; If not, a continue comparison instruction is sent to the first consistency comparison unit.

7. The distributed management system for pharmaceutical supply chain distribution and settlement big data as described in claim 5, characterized in that, The transaction data settlement module includes: The settlement calculation request initiation unit is used to provide calculation requests to other participants who have passed the cross-verification of the initiating settlement direction; The local settlement raw data retrieval unit is deployed locally on the participant's premises and connected to the settlement calculation request initiation unit. It is used to retrieve the corresponding local settlement raw data that has passed cross-verification from the local database based on the unique transaction identifier carried in the received request. The ciphertext conversion unit, connected to the local settlement raw data retrieval unit, is used to convert the retrieved local settlement raw data into ciphertext and output it through a pre-coordinated global settlement rule model. The settlement calculation unit, connected to the ciphertext conversion unit, is used to send the ciphertext output by the ciphertext conversion unit to the designated secure computing node for settlement calculation and output the settlement result.

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