Supply chain quality collaboration method and system based on block chain

Abstract

The invention discloses a supply chain quality collaboration method and system based on a block chain, and relates to the technical field of block chain and supply chain quality management, and the method comprises the steps: collecting quality events according to a batch quality collaboration baseline table, generating a credible collection proof package and a to-be-verified quality event queue, executing dynamic endorsement verification, and writing the dynamic endorsement verification into a quality event account book, outputting a batch state table and an under-chain evidence pointer; performing rapid detection and judgment based on the batch state table and the under-chain evidence pointer, generating a pre-release report and establishing a pre-release mapping table; and triggering a difference correction contract according to the pre-release mapping table, updating the batch state table, generating a collaborative work order, pushing a quality collaborative strategy instruction, and outputting a quality collaborative closed-loop record. According to the method, the risk feedback analysis result table is generated by auditing and evaluating the quality collaborative closed-loop record, the risk layering strategy table is revised, and the batch quality collaborative baseline table is updated, so that the key field constraint is continuously calibrated, and the traceability and consistency of collaborative processing are improved.

Description

Technical Field

[0001] This invention relates to the fields of blockchain and supply chain quality management technology, and in particular to a blockchain-based collaborative method and system for supply chain quality. Background Technology

[0002] In supply chain quality management, a common practice is to issue bearing quality requirements items according to orders, and record and trace quality events online around the production, inspection and circulation links to form batch-level ledgers. In order to achieve cross-entity sharing and traceability, a permissioned access consortium blockchain can also be used to upload key event summaries and indexes to the chain. After multi-node consensus confirmation, traceable records are generated, and release decisions and collaborative handling are carried out based on the batch status at the factory or warehousing stage.

[0003] The above methods tend to separate quality requirements from event evidence, lack fixed fields in the bearing batch digital object and batch quality collaborative baseline table, and make it difficult to align granularity with verification frequency; in addition, they lack the difference correction contract to drive the iteration of risk feedback analysis result table, making it difficult to provide feedback on the handling results. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a blockchain-based supply chain quality collaboration method to address the problems of difficulty in unifying and solidifying quality requirements and event evidence, as well as the difficulty in providing feedback and iterative updates on handling results.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a blockchain-based supply chain quality collaboration method, which includes collecting node identity and qualification certificate information of participating entities, completing registration and verification in the consortium blockchain, configuring dynamic endorsement rules according to risk stratification strategy, and generating a node identity list and a risk stratification strategy table. Based on the node identity list and risk stratification strategy table, publish bearing quality requirement items and bind order numbers, create bearing batch digital objects, and generate batch quality collaborative baseline tables. Based on the batch quality collaboration baseline table, quality events are collected, a trusted collection proof package and a queue of true quality events to be verified are generated, dynamic endorsement verification is performed and written into the quality event ledger, and the batch status table and off-chain evidence pointer are output. Rapid detection and judgment are performed based on batch status table and off-chain evidence pointers to generate pre-release reports and establish pre-release mapping tables; Trigger the difference correction contract based on the pre-release mapping table, update the batch status table, generate a collaborative work order and push quality collaboration strategy instructions, and output quality collaboration closed-loop record. Audit and evaluate the closed-loop records of quality collaboration, revise the risk stratification strategy table, and update the batch quality collaboration baseline table.

[0007] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of collecting the node identity and qualification certificate information of participating entities, completing registration and verification on the consortium blockchain, and configuring dynamic endorsement rules according to a risk stratification strategy are as follows: Collect the node identity and qualification certificate information of the participating entities, and perform field standardization and integrity verification to obtain the node identity certificate information set; Based on the node identity credential information set, a registration verification request is submitted to the consortium blockchain. According to the registration verification result, the identity entries of the participating entities that pass the verification are solidified and the corresponding qualification credential status is bound, and a node identity list is generated. After mapping the risk grading conditions and endorsement thresholds in the risk stratification strategy configuration parameters to consortium blockchain endorsement strategy fields and matching them with node roles and endorsement permissions in the node identity list, an executable rule configuration is generated.

[0008] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps for generating the node identity list and risk stratification strategy table are as follows: The system retrieves historical endorsement records and historical quality event ledgers from the consortium blockchain, summarizes endorsement consistency and dispute resolution timeliness, and generates node endorsement credibility. By combining the node identity list and node endorsement credibility, the scope and verification strength of endorsing nodes are determined and written into the executable rule configuration to generate a risk stratification strategy table.

[0009] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of publishing bearing quality requirement items and binding order numbers, creating bearing batch digital objects, and generating a batch quality collaboration baseline table are as follows: Based on the node identity list and risk stratification strategy table, the participating entities involved in the order are screened and the risk level combination is determined, and a risk configuration record of the participating entities is generated. Based on the risk configuration records of the participating entities, constraint content is set to form bearing quality requirement items, and each bearing quality requirement item is bound to an order number to generate an order quality requirement binding list. Based on the order quality requirements binding list, the order number, bearing quality requirement item and the participant identifier in the node identity list are associated and encoded to create a bearing batch digital object; Based on the bearing quality control dimensions, select the fields corresponding to each bearing quality control dimension as key fields in the bearing batch digital object, and summarize and solidify the bearing quality requirement items and participating entity identity information associated with the key fields to generate a batch quality collaborative baseline table.

[0010] As a preferred embodiment of the blockchain-based supply chain quality collaboration method of the present invention, the steps of collecting quality events based on the batch quality collaboration baseline table and generating a trusted collection proof package and a queue of genuine quality events to be verified are as follows: Online recording of quality events generated during the batch production, inspection, and circulation of bearings, generating a quality event record set; Generate a trusted data collection certificate package according to the participating entity's identity, timestamp, and data collection device identifier as specified in the batch quality collaboration baseline table, and write the set of quality event records associated with the trusted data collection certificate package into the queue of quality events to be verified in sequence.

[0011] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of performing dynamic endorsement verification and writing to the quality event ledger, and outputting a batch status table and off-chain evidence pointers are as follows: Each quality event in the queue of true quality events to be verified is combined with a trusted collection proof package, dynamic endorsement verification is performed and the trust level of the quality event is marked. Quality events that pass the dynamic endorsement verification are sequentially written into the quality event ledger. Generate a batch status table reflecting the real-time quality status of the bearing batch based on the quality event records already written in the quality event ledger, and record off-chain evidence pointers pointing to the corresponding quality event records in the quality event ledger.

[0012] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of performing rapid detection and judgment based on batch status tables and off-chain evidence pointers, generating pre-release reports, and establishing pre-release mapping tables are as follows: Based on the batch status table, rule-based calculations are performed to determine whether each bearing batch meets the rapid detection and judgment conditions, thus generating a rapid detection and judgment result. By locating the associated quality event records in the quality event ledger one by one using off-chain evidence pointers, the key quality events of each bearing batch are summarized and combined with the rapid detection and judgment results to generate a pre-release report. Based on the pre-release report, a one-to-one mapping relationship is established between the batch identifier and order number of each bearing batch and the corresponding pre-release conclusion and off-chain evidence pointer, which are then summarized to form a pre-release mapping table.

[0013] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of triggering a difference correction contract based on the pre-release mapping table, updating the batch status table, generating a collaboration work order and pushing quality collaboration strategy instructions, and outputting a quality collaboration closed-loop record are as follows. Based on the comparison between the batch status field in the pre-release mapping table and the batch status table, the batch identifiers with status differences and the pre-release conclusions are extracted to form a difference correction request. Based on the difference correction request, the difference correction contract updates the status field and risk flag field of the corresponding batch in the batch status table to generate an updated batch status table; Based on batch status information and order number, a collaborative work order is generated, and quality handling actions are determined through rule matching to form a quality collaborative strategy instruction. Execute quality collaboration strategy instructions and collect quality collaboration handling status information. Associate the quality collaboration handling status information with batch identifiers and order numbers and archive it to finally generate a quality collaboration closed-loop record.

[0014] As a preferred embodiment of the blockchain-based supply chain quality collaboration method described in this invention, the steps of auditing and evaluating the quality collaboration closed-loop records, revising the risk stratification strategy table, and updating the batch quality collaboration baseline table are as follows: Based on the closed-loop record of quality collaboration, the correspondence between the status information of quality collaboration and the occurrence of quality anomalies is statistically analyzed, and a risk feedback analysis result table is generated. Based on the risk feedback analysis results table, adjust the risk levels and control parameters in the risk stratification strategy table to generate a revised risk stratification strategy table. Based on the revised risk stratification strategy table, update the risk level field and constraint content field in the batch quality collaborative baseline table to generate the updated batch quality collaborative baseline table.

[0015] Secondly, this invention provides a blockchain-based supply chain quality collaboration system, comprising: The node admission module collects the node identity and qualification certificate information of participating entities, completes registration and verification on the consortium blockchain, configures dynamic endorsement rules according to the risk stratification strategy, and generates a node identity list and risk stratification strategy table. The batch modeling module publishes bearing quality requirement items and binds them to order numbers based on the node identity list and risk stratification strategy table, creates bearing batch digital objects, and generates a batch quality collaborative baseline table. The event verification module collects quality events based on the batch quality collaboration baseline table, generates a trusted collection proof package and a queue of true quality events to be verified, performs dynamic endorsement verification and writes them into the quality event ledger, and outputs a batch status table and off-chain evidence pointers. The pre-release determination module performs rapid detection and determination based on the batch status table and off-chain evidence pointers, generates a pre-release report, and establishes a pre-release mapping table. The collaborative processing module triggers a difference correction contract based on the pre-release mapping table, updates the batch status table, generates a collaborative work order, pushes quality collaborative strategy instructions, and outputs a quality collaborative closed-loop record. The strategy iteration module audits and evaluates the quality collaboration closed-loop records, revises the risk stratification strategy table, and updates the batch quality collaboration baseline table.

[0016] The beneficial effects of this invention are as follows: By retrieving historical endorsement records and historical quality event ledgers from the consortium blockchain, the credibility of node endorsements is generated by summarizing endorsement consistency and dispute resolution timeliness. The scope and verification strength of endorsement nodes are determined by combining the node identity list and written into the executable rule configuration, generating a risk stratification strategy table to achieve adaptive orchestration of endorsement strategies. The quality collaboration closed-loop records are audited and evaluated to generate a risk feedback analysis result table. The risk stratification strategy table is revised and the batch quality collaboration baseline table is updated, so that the constraints of key fields are continuously calibrated, improving the traceability and consistency of collaborative handling. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a blockchain-based supply chain quality collaboration method.

[0019] Figure 2 This is a schematic diagram of a blockchain-based supply chain quality collaboration system.

[0020] Figure 3 A flowchart generated for node access and risk stratification strategies.

[0021] Figure 4 This is a flowchart of the closed-loop process for pre-release determination and collaborative handling. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a blockchain-based supply chain quality collaboration method, comprising the following steps: S1. Collect the node identity and qualification certificate information of the participating entities, complete the registration and verification on the consortium blockchain, configure dynamic endorsement rules according to the risk stratification strategy, and generate a node identity list and risk stratification strategy table. Collect the node identity and qualification certificate information of the participating entities, and perform field standardization and integrity verification to obtain the node identity certificate information set.

[0026] Furthermore, after collecting the node identity and qualification certificate information of the participating entities, fields are extracted according to the entity identifier, node public key identifier, role type, permission scope, qualification type identifier, certificate number, issuer identifier, validity period, and verification signature to form a field set; the field set is normalized according to the encoding rules and enumeration mapping to form a normalized field set; integrity verification is performed based on the normalized field set to generate integrity verification records; qualified entries are selected and matched according to the integrity verification records to obtain the node identity certificate information set.

[0027] The node identity of a participating entity refers to an identity entry that uniquely identifies a certain supply chain participant (such as a steel supplier or a bearing manufacturer) on the consortium blockchain. It consists of the entity identifier, node public key identifier, role type, and scope of permissions.

[0028] Qualification certificate information refers to verifiable evidence that proves the participating entity has the qualifications for the corresponding quality activities, such as the testing qualification certificate of a third-party testing agency, the special process qualification certificate of a heat treatment outsourcing party, the transportation and warehousing compliance qualification certificate of a logistics and warehousing party, and the quality system qualification certificate of a bearing manufacturer.

[0029] Based on the node identity credential information set, a registration verification request is submitted to the consortium blockchain. According to the registration verification result, the identity entries of the participating entities that pass the verification are solidified and the corresponding qualification credential status is bound, thereby generating a node identity list.

[0030] Furthermore, based on the node identity credential information set, each participating entity record is read. The entity identifier field, node public key identifier field, role type field, and permission scope field are written into the identity information section. The qualification type identifier field, credential number field, issuer identifier field, validity period field, and verification signature are written into the qualification credential information section. The identity information section and the qualification credential information section are encapsulated in the order of the consortium blockchain registration verification fields to form a registration verification request and submitted to the consortium blockchain. After the consortium blockchain returns the registration verification result, the participating entity identity entries that pass the verification are filtered, the participating entity identity entries that pass the verification are solidified and bound to the corresponding qualification credential status, and a node identity list is generated.

[0031] A consortium blockchain is a blockchain network jointly maintained by relevant participants in the supply chain and accessible by permission. On-chain data is confirmed by consensus among multiple nodes and forms an immutable shared ledger.

[0032] After mapping the risk grading conditions and endorsement thresholds in the risk stratification strategy configuration parameters to consortium blockchain endorsement strategy fields and matching them with node roles and endorsement permissions in the node identity list, an executable rule configuration is generated.

[0033] Furthermore, risk grading conditions and endorsement thresholds are extracted based on the risk stratification strategy configuration parameters. The risk grading conditions are converted into trigger conditions in the consortium blockchain endorsement strategy fields, and the endorsement thresholds are converted into endorsement quantity constraints and verification strength constraints in the consortium blockchain endorsement strategy fields, forming an endorsement strategy field set. Node roles and endorsement permissions are extracted based on the node identity list. Matching operations are performed on the endorsement strategy field set according to the node role adaptation rules and endorsement permission satisfaction rules to obtain a set of endorsement nodes that satisfy the endorsement strategy field set. The set of endorsement nodes, endorsement quantity constraints, and verification strength constraints are encapsulated together into a consortium blockchain-parsable endorsement strategy configuration file, and mandatory verification content and processing actions when failure occurs are written in, generating an executable rule configuration.

[0034] It should be noted that the risk classification conditions refer to the judgment rules and conditions used to determine the risk level of a quality event or the risk level of a bearing batch. These conditions are formed by setting basic judgment items in combination with bearing product standards and process control specifications, and by revising them based on the statistical results of the quality event ledger and risk feedback analysis results table.

[0035] The endorsement threshold refers to the threshold requirement for the constraint on the number of endorsements and the constraint on the verification strength in the endorsement policy field of the consortium blockchain. It is obtained by mapping the risk level requirements of the risk stratification policy configuration parameters to the endorsement policy constraint items, and calibrating them in combination with the node endorsement credibility distribution corresponding to the node identity list.

[0036] The endorsement permission fulfillment rule is a verification rule used to determine whether the endorsement permissions in the node identity list meet the requirements of the consortium blockchain endorsement policy field. When the scope of the endorsement permissions covers the quality event endorsement operation permissions corresponding to the endorsement policy field and meets the access permissions of the mandatory verification content, and the node role and endorsement permissions are in a valid state and the qualification certificate status passes the verification, the endorsement permission fulfillment rule is determined to be valid.

[0037] The system retrieves historical endorsement records and historical quality event ledgers from the consortium blockchain, summarizes endorsement consistency and dispute resolution timeliness, and generates node endorsement credibility using the following expression: ; in, It is the first The credibility of each node's endorsement. It is an endorsement consistency indicator. It is the endorsement consistency weighting coefficient. It is the time limit for dispute resolution. Weighting coefficients; It should be noted that historical endorsement records refer to the endorsement process data formed and retained during the endorsement verification process of quality events that have occurred on the consortium blockchain. These records include the identity entries of the endorsement nodes, the version identifier of the endorsement policy, the endorsement signature verification results, the endorsement timestamp, and the endorsement conclusion, and are used to trace back endorsement behavior and consistency performance.

[0038] The historical quality event ledger refers to the quality event ledger data written to and stored on the consortium blockchain in chronological order. It contains quality event records, trusted collection proof package summaries, quality event trust levels, off-chain evidence pointers, and batch identifiers and order number association information, which are used to trace the evolution of quality events and the basis for the formation of batch status.

[0039] Dispute resolution timeliness refers to the time span from the dispute acceptance timestamp to the dispute conclusion being written into the consortium blockchain and confirmed through consensus, which is used to characterize the response efficiency of endorsing nodes to dispute resolution and serve as the basis for calculating the credibility of node endorsement.

[0040] The weight coefficients for endorsement consistency and dispute resolution timeliness are set based on the risk grading conditions to determine the focus. They are calibrated by the endorsement consistency index of historical endorsement records and the dispute resolution timeliness distribution of historical quality event ledgers. The results are iteratively adjusted by combining the statistical results of the risk feedback analysis results table and finally written into the risk stratification strategy configuration parameters.

[0041] By combining the node identity list and node endorsement credibility, the scope and verification strength of endorsing nodes are determined and written into the executable rule configuration to generate a risk stratification strategy table.

[0042] Furthermore, based on the node identity list, the node roles and endorsement permissions corresponding to the participating entity identity entries are read, and based on the node endorsement credibility, the credibility value corresponding to the participating entity identity entries is read. After determining the risk level corresponding to the quality event according to the risk classification conditions in the risk stratification strategy configuration parameters, the participating entity identity entries in the node identity list are filtered using the endorsement threshold corresponding to the risk level as a constraint to obtain the range of endorsement nodes that meet the endorsement permissions and node endorsement credibility requirements. Based on the range of endorsement nodes and the endorsement threshold, the required number of endorsements is determined and the verification requirements of the mandatory verification content are determined to form the verification strength. The range of endorsement nodes and the verification strength are written into the executable rule configuration and solidified into the endorsement strategy configuration file. Based on the endorsement strategy configuration file, the risk level, the range of endorsement nodes, and the verification strength are summarized to generate a risk stratification strategy table.

[0043] It should be noted that executable rule configuration refers to converting the scope of endorsement nodes and verification strength into an endorsement policy configuration file that the consortium blockchain can directly execute. This file specifies the set of endorsement nodes corresponding to each type of quality event, the required number of endorsements, the mandatory verification content, and the handling actions when the event fails.

[0044] S2. Based on the node identity list and risk stratification strategy table, publish bearing quality requirement items and bind order numbers, create bearing batch digital objects, and generate batch quality collaborative baseline tables. Based on the node identity list and risk stratification strategy table, the participating entities involved in the order are screened and the risk level combination is determined, generating a risk configuration record for the participating entities of the order. Furthermore, based on the node identity list, the participating entity identifier associated with the order number is read. The participating entity identity entry is located based on the participating entity identifier, and a risk level determination is performed on the participating entity identity entry according to the risk stratification strategy table. For example, when the qualification certificate bound to the participating entity identity entry is verified and the node endorsement credibility is not less than the endorsement threshold, it is determined to be a lower risk level; when the qualification certificate is not verified or the node endorsement credibility is less than the endorsement threshold, it is determined to be a higher risk level. A risk level combination is then formed, and the order number, participating entity identifier, and risk level combination are written into the participating entity risk configuration record for the order. The participating entity risk configuration record for the order retains the correspondence between participating entity identity entries and risk level combinations for use in setting constraint content.

[0045] Based on the risk configuration records of the participating entities, constraint content is set to form bearing quality requirement items, and each bearing quality requirement item is bound to an order number to generate an order quality requirement binding list. Furthermore, based on the risk configuration records of participating entities, the order number and risk level combination are read. Control parameters and verification intensity are located in the risk stratification strategy table according to the risk level combination. The control parameters are mapped to compliance judgment rules for key fields according to the bearing quality control dimension, and the record granularity and verification frequency are determined simultaneously. The verification intensity is mapped to mandatory verification content rules, and verification trigger conditions are solidified. These are summarized to form constraint content and written into the bearing quality requirement entries. Based on the order number and bearing quality requirement entries, binding entries are generated, and after completing the uniqueness verification of the order number, the bearing quality requirement entries are bound one-to-one with the order number and written into the order quality requirement binding list. The order quality requirement binding list maintains a unique mapping from the order number to the bearing quality requirement entry and supports the creation of bearing batch digital objects through associated coding.

[0046] Based on the order quality requirements binding list, the order number, bearing quality requirement items and participating entity identifiers in the node identity list are associated and encoded to create a bearing batch digital object that represents the quality control scope and participating entity scope of a single order production batch.

[0047] Furthermore, based on the order quality requirements binding list, the order number and bearing quality requirement items are read. Based on the order number, the participating entity identifier associated with the order number is located in the node identity list. The order number identifier segment, bearing quality requirement item identifier segment, and participating entity identifier segment are sequentially concatenated according to the association coding rules, and a consistency check is performed to obtain the association coding relationship. This association coding relationship is written to the coding field of the bearing batch digital object. The order number is written to the order number field of the bearing batch digital object, the bearing quality requirement item is written to the quality control scope field of the bearing batch digital object, and the participating entity identifier is written to the participating entity scope field of the bearing batch digital object. This completes the creation of the bearing batch digital object, which is used subsequently to locate fields and determine key fields based on the bearing quality control dimensions.

[0048] Based on the bearing quality control dimensions, select the fields corresponding to each bearing quality control dimension as key fields in the bearing batch digital object, and summarize and solidify the bearing quality requirement items and participating entity identity information associated with the key fields to generate a batch quality collaborative baseline table. Furthermore, based on the bearing quality control dimensions, the bearing quality control dimension name, corresponding record granularity, and verification frequency are read item by item. In the bearing batch digital object, the fields corresponding to the bearing quality control dimensions are located according to the field mapping relationship, and key fields are marked. Based on the key fields, the associated coding relationship is located in the bearing batch digital object to obtain the bearing quality requirement items. Based on the associated coding relationship, the participating entity identity information corresponding to the participating entity identifier is located in the node identity list. The key fields, bearing quality requirement items, participating entity identity information, record granularity, and verification frequency are summarized into baseline record items and uniqueness verification is performed. After the uniqueness verification is passed, the baseline record items are solidified and written into the batch quality collaborative baseline table. The batch quality collaborative baseline table is used to generate a trusted collection proof package according to the participating entity identity, timestamp, and collection device identifier, and to constrain the collection of quality events.

[0049] It should be noted that the bearing quality control dimensions are set after statistical analysis based on the risk level requirements in the bearing product standards, process control specifications, and risk stratification strategy table. They are used to guide the selection of key quality fields in the bearing batch digital object and to determine the corresponding record granularity and verification frequency.

[0050] S3. Collect quality events based on the batch quality collaboration baseline table, generate a trusted collection proof package and a queue of true quality events to be verified, perform dynamic endorsement verification and write them into the quality event ledger, and output the batch status table and off-chain evidence pointer. Online recording of quality events generated during the batch production, inspection, and circulation of bearings, generating a quality event record set; Furthermore, when recording quality events generated during the batch production and inspection processes of bearings online, quality event record entries are established based on the order number and batch identifier. Each quality event record entry includes the identity of the participating entity, a timestamp, the identifier of the data acquisition device, and the values ​​of key fields corresponding to the bearing quality control dimension, as well as the event type identifier. The quality event record entries are then sorted and summarized according to their timestamps to form a quality event record set. This quality event record set maintains a continuous association between the batch identifier and the quality event record entries. The quality event record set is used to associate the trusted data acquisition proof package with the queue of verifiable quality events.

[0051] Generate a trusted data collection certificate package according to the participating entity's identity, timestamp, and data collection device identifier as specified in the batch quality collaboration baseline table, and write the set of quality event records associated with the trusted data collection certificate package into the queue of quality events to be verified in sequence. Furthermore, based on the participating entity identities, timestamps, and acquisition device identifiers specified in the batch quality collaboration baseline table, quality event record entries that meet the requirements of matching participating entity identities, timestamps falling within the record granularity, and consistent acquisition device identifiers are selected from the quality event record set to form a subset to be packaged; a summary is calculated for the subset to be packaged and a verification signature is generated in conjunction with the participating entity identities, and a trusted acquisition proof package is obtained; the trusted acquisition proof package is bound to the subset to be packaged to generate queue entries, and the queue entries are written into the queue of quality events to be verified according to the order of timestamps. The queue of quality events to be verified is used for dynamic endorsement verification and reading.

[0052] Each quality event in the queue of true quality events to be verified is combined with a trusted collection proof package, dynamic endorsement verification is performed and the trust level of the quality event is marked. Quality events that pass the dynamic endorsement verification are sequentially written into the quality event ledger. Furthermore, after reading the queue of quality events to be verified one by one, a trusted collection proof package is extracted from each queue entry, and the quality event record entries associated with the queue entries are extracted. Based on the risk stratification strategy table, the scope of endorsement nodes and the verification strength corresponding to the quality event record entries are determined, and then dynamic endorsement verification is performed. Dynamic endorsement verification completes the verification of the number of endorsements and the verification of the mandatory verification content, and outputs the verification conclusion. Based on the verification conclusion, the credibility level of the quality event is marked, and the quality event record entries that have passed the verification conclusion, together with the credibility level of the quality event, are sequentially written into the quality event ledger. The quality event ledger is used for batch status table generation.

[0053] Generate a batch status table reflecting the real-time quality status of the bearing batch based on the quality event records already written in the quality event ledger, and record off-chain evidence pointers pointing to the corresponding quality event records in the quality event ledger.

[0054] Furthermore, based on the quality event record entries already written in the quality event ledger, the credibility level of quality events is aggregated by batch identifier, and the compliance results of bearing quality control dimension are calculated. Combined with the risk level rules of the risk stratification strategy table, a bearing risk level is formed and a bearing anomaly mark is generated. The batch status table entries are summarized and a batch status table is formed. For each batch status table entry, the corresponding quality event ledger record index is extracted. The quality event ledger record index is written into the off-chain evidence pointer field to form an off-chain evidence pointer. The off-chain evidence pointer is used to locate the quality event ledger record in the pre-release report.

[0055] The batch status table includes bearing risk level, bearing quality control dimension compliance results, and bearing anomaly markers (status fields used to identify abnormalities in each bearing quality control dimension of the bearing batch).

[0056] S4. Perform rapid detection and judgment based on the batch status table and off-chain evidence pointers, generate a pre-release report and establish a pre-release mapping table; Based on the batch status table, rule-based calculations are performed to determine whether each bearing batch meets the rapid detection criteria, thus generating a rapid detection result.

[0057] Furthermore, based on the batch status table, the bearing risk level, bearing quality control compliance results, and bearing anomaly markers corresponding to each batch identifier are read one by one. For each batch identifier, a rule-based algorithm for rapid detection and judgment conditions is constructed. The rule-based algorithm performs a full-dimensional compliance judgment on the bearing quality control compliance results and a no-anomaly judgment on the bearing anomaly markers. At the same time, it performs a release restriction judgment in conjunction with the bearing risk level. When the full-dimensional compliance judgment is valid, the no-anomaly judgment is valid, and the release restriction judgment is met, the batch identifier is written into the rapid detection and judgment result and the pre-release conclusion is written as "pre-release possible". When any judgment is invalid, the batch identifier is written into the rapid detection and judgment result and the pre-release conclusion is written as "rapid detection and judgment conditions are not met". The rapid detection and judgment result is used to generate a pre-release report by summarizing the quality event ledger record summary located by the off-chain evidence pointer.

[0058] The rapid detection and judgment result refers to the batch conclusion given after calculation based on the batch status table. For example, a certain bearing batch is marked as "pre-releaseable" when it complies with all bearing quality control dimensions and has no abnormal records.

[0059] By locating related quality event records in the quality event ledger one by one using off-chain evidence pointers, the key quality events of each bearing batch are summarized and combined with the rapid detection and judgment results to generate a pre-release report.

[0060] Furthermore, based on the batch status table, the batch identifier is read and associated with the off-chain evidence pointer. The associated quality event records in the quality event ledger are located one by one according to the quality event ledger record index pointed to by the off-chain evidence pointer. Key quality events are filtered in the associated quality event records according to the key fields marked in the batch quality collaboration baseline table. The timestamp, participant identity, quality event trust level, and key field values ​​are extracted from the key quality events to form summary entries. These summary entries are sorted by timestamp and summarized into a summary of key quality events. Based on the rapid detection and judgment results, the pre-release conclusion corresponding to the batch identifier is read. The summary of key quality events is combined with the pre-release conclusion and written into the report entry corresponding to the batch identifier, forming a pre-release report. The pre-release report is used to establish a pre-release mapping table and support the triggering of difference correction contracts.

[0061] It should be noted that a critical quality event refers to a quality event record in the quality event ledger that corresponds to a key field marked in the batch quality collaboration baseline table and directly affects the compliance results of the bearing quality control dimension or the marking of bearing anomalies.

[0062] Based on the pre-release report, a one-to-one mapping relationship is established between the batch identifier and order number of each bearing batch and the corresponding pre-release conclusion and off-chain evidence pointer, which are then summarized to form a pre-release mapping table.

[0063] Furthermore, based on the pre-release report, the pre-release conclusions and key quality event summaries corresponding to each batch identifier are read one by one. The order number is obtained by reverse locating the batch status table based on the key quality event summary and associated with it to obtain the off-chain evidence pointer. A mapping entry is generated using the batch identifier as a unique key. The mapping entry is written with the order number, the pre-release conclusion, and the off-chain evidence pointer. After performing batch identifier uniqueness verification, the mapping entry is solidified. All mapping entries are summarized to form a pre-release mapping table. The pre-release mapping table maintains a one-to-one mapping relationship between the batch identifier, the order number, the pre-release conclusion, and the off-chain evidence pointer and is used for difference correction request extraction.

[0064] The pre-release mapping table includes the batch identifier, order number, pre-release conclusion, and off-chain evidence pointers used to locate relevant quality event ledger records for the bearing batch.

[0065] S5. Trigger the difference correction contract based on the pre-release mapping table, update the batch status table, generate a collaborative work order and push the quality collaboration strategy instruction, and output the quality collaboration closed-loop record.

[0066] Based on the comparison between the batch status field in the pre-release mapping table and the batch status table, the batch identifiers with status differences and the pre-release conclusions are extracted to form a difference correction request. Furthermore, the pre-release conclusions corresponding to batch identifiers are read one by one from the pre-release mapping table, and the batch status field matching the batch identifier is located in the batch status table based on the batch identifier. The pre-release conclusions are converted into pre-release judgment conditions, and the batch status fields are converted into quick detection judgment conditions and a consistency comparison operation is performed. When the pre-release conclusions and the quick detection judgment conditions corresponding to the batch status fields are inconsistent, a status difference is determined. The batch identifiers and pre-release conclusions with status differences are written into a difference correction request, which serves as the input for the difference correction contract to update the batch status table.

[0067] Based on the difference correction request, the difference correction contract updates the status field and risk flag field of the corresponding batch in the batch status table to generate an updated batch status table; Furthermore, after receiving a difference correction request, the difference correction contract reads the batch identifier and pre-release conclusion from the difference correction request. Based on the batch identifier, it locates the corresponding batch record in the batch status table and reads the status field and risk flag field. According to the consistency rule between the pre-release conclusion and the status field, it calculates the target status field value and the target risk flag field value. It writes the target status field value and the target risk flag field value into the corresponding batch record in the batch status table and generates an updated batch status table. The updated batch status table serves as the basis for batch status information in subsequent generation of collaborative work orders and push of quality collaborative strategy instructions.

[0068] It should be noted that the difference correction contract refers to the executable contract rules deployed on the consortium blockchain, which perform consistency correction on the status field and risk flag field of the corresponding batch in the batch status table according to the difference correction request and generate an updated batch status table.

[0069] Based on batch status information (bearing risk level, bearing quality control compliance results) and order number, a collaborative work order is generated, and quality handling actions are determined through rule matching to form a quality collaborative strategy instruction; Furthermore, based on the updated batch status table, the batch status information corresponding to the batch identifier is read, and the bearing risk level and bearing quality control dimension compliance results are extracted. A collaborative work order is generated by combining the order number and written with the batch identifier, order number, bearing risk level, and bearing quality control dimension compliance results. Based on the risk stratification strategy table, the quality handling action rules corresponding to the bearing risk level are read. Based on the batch quality collaboration baseline table, the constraint content corresponding to the bearing quality control dimension compliance results is read. Rule matching is performed on the collaborative work order to determine the quality handling action. For example, when the constraint content corresponding to the bearing quality control dimension compliance result requires re-inspection and the bearing quality control dimension compliance result recorded in the collaborative work order is non-compliant, the rule matching result determines the quality handling action as re-inspection. When the bearing quality control dimension compliance result is compliant, the rule matching result determines the quality handling action as release. Based on the node identity list, the participating entity identifier associated with the order number is located, and the quality handling action, along with the participating entity identifier, is solidified and written into the quality collaboration strategy instruction, forming a deployable quality collaboration strategy instruction.

[0070] Execute quality collaboration strategy instructions and collect quality collaboration handling status information. Associate the quality collaboration handling status information with batch identifiers and order numbers and archive it to finally generate a quality collaboration closed-loop record.

[0071] Furthermore, the quality collaboration strategy instructions are read to obtain the quality handling action, participating entity identifier, batch identifier, and order number. Based on the participating entity identifier, the quality handling action is issued to the participating entity and the execution start timestamp is recorded. After the participating entity completes the quality handling action, it returns the handling conclusion, handling evidence summary, and execution end timestamp, which are summarized to form quality collaboration handling status information. The quality collaboration handling status information is bound with the batch identifier and order number to generate archived entries. The archived entries solidify the correspondence between the handling conclusion and the handling evidence summary and are written into the quality collaboration closed-loop record. The quality collaboration closed-loop record is used for the generation of subsequent audit assessment and risk feedback analysis result tables.

[0072] It should be noted that the quality collaborative handling status information refers to the handling process and result record information generated during the execution of quality collaborative strategy instructions, including batch identifier, order number, participating entity identifier, quality handling action, execution start timestamp, execution end timestamp, handling conclusion, and handling evidence summary.

[0073] S6. Audit and evaluate the closed-loop records of quality collaboration, revise the risk stratification strategy table, and update the batch quality collaboration baseline table.

[0074] Based on the closed-loop records of quality collaboration, the correspondence between the status information of quality collaboration and the occurrence of quality anomalies is statistically analyzed, and a risk feedback analysis result table is generated.

[0075] Furthermore, based on the quality collaboration closed-loop record, the quality collaboration handling status information is read one by one, and batch identifiers, order numbers, quality handling actions, handling conclusions, and handling evidence summaries are extracted. Based on the batch identifiers and order numbers, related records are located in the quality event ledger and batch status table, and the quality anomaly occurrence corresponding to the bearing anomaly marker is extracted, forming a set of related samples from quality collaboration handling status information to quality anomaly occurrence. The related sample set is grouped and statistically analyzed according to quality handling actions and handling conclusions, and the distribution characteristics of quality anomaly occurrence are calculated to generate corresponding statistical results. The corresponding statistical results are solidified into a risk feedback analysis result table, which is used to subsequently adjust the risk level and control parameters in the risk stratification strategy table.

[0076] It should be noted that quality anomalies refer to abnormal status records in the quality event ledger and batch status table that indicate non-compliance conclusions or trigger bearing anomaly markers in the bearing quality control dimension of the bearing batch.

[0077] Based on the risk feedback analysis results table, adjust the risk levels and control parameters in the risk stratification strategy table to generate a revised risk stratification strategy table.

[0078] Furthermore, based on the risk feedback analysis results table, the distribution characteristics of quality anomalies corresponding to quality handling actions and conclusions are read. Based on these distribution characteristics, matching risk level entries are located in the risk stratification strategy table, and control parameters are extracted. When the distribution characteristics indicate insufficient coverage of current control parameters, the risk level entries are increased and the constraint range of the control parameters is tightened. When the distribution characteristics indicate redundant control parameters, the risk level entries are decreased and the constraint range of the control parameters is relaxed. The adjusted risk level entries and adjusted control parameters are written back to the risk stratification strategy table, generating a revised risk stratification strategy table. This revised risk stratification strategy table is used to subsequently update the risk level field and constraint content field in the batch quality collaboration baseline table.

[0079] Based on the revised risk stratification strategy table, update the risk level field and constraint content field in the batch quality collaborative baseline table to generate the updated batch quality collaborative baseline table.

[0080] Furthermore, based on the revised risk stratification strategy table, risk level entries and control parameters are read one by one. The control parameters are converted into constraints, and a correspondence between risk levels and constraints is formed. Based on the batch quality collaborative baseline table, the risk level field in each baseline record entry is read one by one. The correspondence between risk levels and constraints is matched based on the risk level field to obtain the target risk level entry and the target constraint content. The target risk level entry is written into the risk level field, and the target constraint content is written into the constraint content field. After being fixed back into the batch quality collaborative baseline table, all updated baseline record entries are summarized to generate the updated batch quality collaborative baseline table.

[0081] This embodiment also provides a blockchain-based supply chain quality collaboration system, including: a node access module, which collects node identity and qualification certificate information of participating entities, completes registration and verification on the consortium blockchain, configures dynamic endorsement rules according to the risk stratification strategy, and generates a node identity list and a risk stratification strategy table; The batch modeling module publishes bearing quality requirement items and binds them to order numbers based on the node identity list and risk stratification strategy table, creates bearing batch digital objects, and generates a batch quality collaborative baseline table. The event verification module collects quality events based on the batch quality collaboration baseline table, generates a trusted collection proof package and a queue of true quality events to be verified, performs dynamic endorsement verification and writes them into the quality event ledger, and outputs a batch status table and off-chain evidence pointers. The pre-release determination module performs rapid detection and determination based on the batch status table and off-chain evidence pointers, generates a pre-release report, and establishes a pre-release mapping table. The collaborative processing module triggers a difference correction contract based on the pre-release mapping table, updates the batch status table, generates a collaborative work order, pushes quality collaborative strategy instructions, and outputs a quality collaborative closed-loop record. The strategy iteration module audits and evaluates the quality collaboration closed-loop records, revises the risk stratification strategy table, and updates the batch quality collaboration baseline table.

[0082] In summary, this invention achieves the following: by retrieving historical endorsement records and historical quality event ledgers from the consortium blockchain, summarizing endorsement consistency and dispute resolution timeliness to generate node endorsement credibility, determining the scope and verification strength of endorsement nodes based on the node identity list and writing them into executable rule configuration, generating a risk stratification strategy table, and realizing adaptive orchestration of endorsement strategies; by auditing and evaluating the quality collaboration closed-loop records to generate a risk feedback analysis result table, revising the risk stratification strategy table and updating the batch quality collaboration baseline table, the invention continuously calibrates key field constraints, and improves the traceability and consistency of collaborative handling.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A blockchain-based supply chain quality collaboration method, characterized in that: include, Collect the node identity and qualification certificate information of participating entities, complete the registration and verification on the consortium blockchain, configure dynamic endorsement rules according to the risk stratification strategy, and generate a node identity list and risk stratification strategy table; Based on the node identity list and risk stratification strategy table, publish bearing quality requirement items and bind order numbers, create bearing batch digital objects, and generate batch quality collaborative baseline tables. Based on the batch quality collaboration baseline table, quality events are collected, a trusted collection proof package and a queue of true quality events to be verified are generated, dynamic endorsement verification is performed and written into the quality event ledger, and the batch status table and off-chain evidence pointer are output. Rapid detection and judgment are performed based on batch status table and off-chain evidence pointers to generate pre-release reports and establish pre-release mapping tables; Trigger the difference correction contract based on the pre-release mapping table, update the batch status table, generate a collaborative work order and push quality collaboration strategy instructions, and output quality collaboration closed-loop record. Audit and evaluate the closed-loop records of quality collaboration, revise the risk stratification strategy table, and update the batch quality collaboration baseline table.

2. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps for collecting the node identity and qualification certificate information of participating entities, completing registration and verification on the consortium blockchain, and configuring dynamic endorsement rules according to the risk stratification strategy are as follows. Collect the node identity and qualification certificate information of the participating entities, and perform field standardization and integrity verification to obtain the node identity certificate information set; Based on the node identity credential information set, a registration verification request is submitted to the consortium blockchain. According to the registration verification result, the identity entries of the participating entities that pass the verification are solidified and the corresponding qualification credential status is bound, and a node identity list is generated. After mapping the risk grading conditions and endorsement thresholds in the risk stratification strategy configuration parameters to consortium blockchain endorsement strategy fields and matching them with node roles and endorsement permissions in the node identity list, an executable rule configuration is generated.

3. The blockchain-based supply chain quality collaboration method as described in claim 2, characterized in that: The steps for generating the node identity list and risk stratification strategy table are as follows: The system retrieves historical endorsement records and historical quality event ledgers from the consortium blockchain, summarizes endorsement consistency and dispute resolution timeliness, and generates node endorsement credibility. By combining the node identity list and node endorsement credibility, the scope and verification strength of endorsing nodes are determined and written into the executable rule configuration to generate a risk stratification strategy table.

4. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps for publishing bearing quality requirement items and binding order numbers, creating bearing batch number objects, and generating batch quality collaborative baseline tables are as follows. Based on the node identity list and risk stratification strategy table, the participating entities involved in the order are screened and the risk level combination is determined, and a risk configuration record of the participating entities is generated. Based on the risk configuration records of the participating entities, constraint content is set to form bearing quality requirement items, and each bearing quality requirement item is bound to an order number to generate an order quality requirement binding list. Based on the order quality requirements binding list, the order number, bearing quality requirement item and the participant identifier in the node identity list are associated and encoded to create a bearing batch digital object; Based on the bearing quality control dimensions, select the fields corresponding to each bearing quality control dimension from the bearing batch digital object as key fields, and summarize and solidify the bearing quality requirement items and participating entity identity information associated with the key fields to generate a batch quality collaborative baseline table.

5. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps for collecting quality events based on the batch quality collaborative baseline table and generating a trusted collection proof package and a queue of true quality events to be verified are as follows. Online recording of quality events generated during the batch production, inspection, and circulation of bearings, generating a quality event record set; Generate a trusted data collection certificate package according to the participating entity's identity, timestamp, and data collection device identifier as specified in the batch quality collaboration baseline table, and write the set of quality event records associated with the trusted data collection certificate package into the queue of quality events to be verified in sequence.

6. The blockchain-based supply chain quality collaboration method as described in claim 5, characterized in that: The steps for performing dynamic endorsement verification and writing to the quality event ledger, outputting the batch status table and off-chain evidence pointers are as follows. Each quality event in the queue of true quality events to be verified is combined with a trusted collection proof package, dynamic endorsement verification is performed and the trust level of the quality event is marked. Quality events that pass the dynamic endorsement verification are sequentially written into the quality event ledger. Generate a batch status table reflecting the real-time quality status of the bearing batch based on the quality event records already written in the quality event ledger, and record off-chain evidence pointers pointing to the corresponding quality event records in the quality event ledger.

7. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps for rapid detection and judgment based on batch status table and off-chain evidence pointers, generating pre-release reports and establishing pre-release mapping tables are as follows. Based on the batch status table, rule-based calculations are performed to determine whether each bearing batch meets the rapid detection and judgment conditions, thus generating a rapid detection and judgment result. By locating the associated quality event records in the quality event ledger one by one using off-chain evidence pointers, the key quality events of each bearing batch are summarized and combined with the rapid detection and judgment results to generate a pre-release report. Based on the pre-release report, a one-to-one mapping relationship is established between the batch identifier and order number of each bearing batch and the corresponding pre-release conclusion and off-chain evidence pointer, which are then summarized to form a pre-release mapping table.

8. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps are as follows: triggering the difference correction contract based on the pre-release mapping table, updating the batch status table, generating a collaborative work order and pushing quality collaboration strategy instructions, and outputting a quality collaboration closed-loop record. Based on the comparison between the batch status field in the pre-release mapping table and the batch status table, the batch identifiers with status differences and the pre-release conclusions are extracted to form a difference correction request. Based on the difference correction request, the difference correction contract updates the status field and risk flag field of the corresponding batch in the batch status table to generate an updated batch status table; Based on batch status information and order number, a collaborative work order is generated, and quality handling actions are determined through rule matching to form a quality collaborative strategy instruction. Execute quality collaboration strategy instructions and collect quality collaboration handling status information. Associate the quality collaboration handling status information with batch identifiers and order numbers and archive it to finally generate a quality collaboration closed-loop record.

9. The blockchain-based supply chain quality collaboration method as described in claim 1, characterized in that: The steps for auditing and evaluating the quality collaboration closed-loop records, revising the risk stratification strategy table, and updating the batch quality collaboration baseline table are as follows. Based on the closed-loop record of quality collaboration, the correspondence between the status information of quality collaboration and the occurrence of quality anomalies is statistically analyzed, and a risk feedback analysis result table is generated. Based on the risk feedback analysis results table, adjust the risk levels and control parameters in the risk stratification strategy table to generate a revised risk stratification strategy table. Based on the revised risk stratification strategy table, update the risk level field and constraint content field in the batch quality collaborative baseline table to generate the updated batch quality collaborative baseline table.

10. A blockchain-based supply chain quality collaboration system, based on the blockchain-based supply chain quality collaboration method according to any one of claims 1 to 9, characterized in that: include, The node admission module collects the node identity and qualification certificate information of participating entities, completes registration and verification on the consortium blockchain, configures dynamic endorsement rules according to the risk stratification strategy, and generates a node identity list and risk stratification strategy table. The batch modeling module publishes bearing quality requirement items and binds them to order numbers based on the node identity list and risk stratification strategy table, creates bearing batch digital objects, and generates a batch quality collaborative baseline table. The event verification module collects quality events based on the batch quality collaboration baseline table, generates a trusted collection proof package and a queue of true quality events to be verified, performs dynamic endorsement verification and writes them into the quality event ledger, and outputs a batch status table and off-chain evidence pointers. The pre-release determination module performs rapid detection and determination based on the batch status table and off-chain evidence pointers, generates a pre-release report, and establishes a pre-release mapping table. The collaborative processing module triggers a difference correction contract based on the pre-release mapping table, updates the batch status table, generates a collaborative work order, pushes quality collaborative strategy instructions, and outputs a quality collaborative closed-loop record. The strategy iteration module audits and evaluates the quality collaboration closed-loop records, revises the risk stratification strategy table, and updates the batch quality collaboration baseline table.

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