An agricultural product supply chain traceability data integrity verification method based on internet of things coding

Abstract

The application discloses a kind of agricultural product supply chain traceability data integrity verification method based on internet of things coding, comprising the following steps: collecting traceability data and internet of things coding and generating coding analysis result by parsing;Identify batch, packaging, aggregation, logistics, coding flow function from detection to sales;Traceability data is formed with the responsibility of code role element and is formed into event responsibility element;Build coding relationship conservation chart and complete conservation pre-check;Write responsibility channel MMR, relationship closure MMR and batch responsibility total MMR to form commitment value;Generate bidirectional relationship conservation proof and output integrity verification result.The present application uses GS1 analysis and layered channel MMR, builds agricultural product coding conservation verification chain, with the advantages of high integrity, accurate traceability and fast abnormal positioning.

Description

Technical Field

[0001] This invention relates to the field of agricultural product supply chain traceability technology, and in particular to a method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding. Background Technology

[0002] Agricultural product supply chain traceability typically uses QR codes, RFID tags, or unified identification codes to record data from production, harvesting, packaging, storage, transportation, testing, and sales. This data is then combined with databases, blockchain, hash digests, or Merkle trees to store and verify the traceability records. Existing methods can, to some extent, prove whether a single record has been modified and can also display the origin, distribution points, and testing information of agricultural products through barcode scanning, enabling regulators or consumers to obtain basic traceability query results.

[0003] However, agricultural products exhibit continuous coding relationships during actual circulation, including batch splitting, packaging acceptance, aggregate loading, logistics transportation, testing coverage, and sales release. Existing methods often use codes as query indexes or evidence entry points, lacking structured verification of the coding flow function, responsibility transfer relationships, and conservation relationship paths. Ordinary hashing or Merkle trees can typically only verify the consistency of record content, making it difficult to determine whether testing vouchers are misbound, whether cold chain segments are covered, or whether sales codes can be traced back to the batch master code, resulting in insufficient granularity for verifying the integrity of traceability data.

[0004] Therefore, how to provide a method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding. This invention utilizes GS1 parsing and hierarchical MMR to construct an agricultural product coding conservation verification chain, which has the advantages of high integrity, accurate traceability, and fast anomaly location.

[0006] A method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding, according to an embodiment of the present invention, includes the following steps: Collect agricultural product supply chain traceability data and its associated agricultural product IoT codes, and generate code parsing results through GS1 DigitalLink structured parsing; Based on the coding parsing results, identify the coding flow functions of agricultural products from batch formation, packaging acceptance, aggregate loading, logistics transportation, testing coverage to sales release, and generate a set of coding role elements; By unifying the responsibility of agricultural product supply chain traceability data with the set of coded role elements, an event responsibility element set is generated. A coding relationship conservation graph is constructed based on the coding role set and the event responsibility set, and a conservation pre-check is performed to generate the conservation relationship pre-check results; Based on the pre-verification results of the conservation relationship, the event responsibility element set is written into the responsibility sub-channel MMR set, the conservation relationship path in the coded relationship conservation diagram is written into the relationship closure MMR, and the responsibility sub-channel MMR set and the relationship closure MMR are written into the batch responsibility total MMR to generate the batch responsibility total commitment value. Based on the IoT code of the agricultural product to be verified, the code relationship conservation diagram, the responsibility route MMR set, the relationship closure MMR, and the total commitment value of batch responsibility, a two-way relationship conservation proof is generated. The MMR inclusion proof and the additional consistency proof are executed to generate the traceability data integrity verification result.

[0007] Optionally, the generation of the encoding parsing result specifically includes: Identify node records carrying agricultural product IoT codes from agricultural product supply chain traceability data, and bind the agricultural product IoT codes with the event time, event subject, and event source in the node records to generate a set of code-bound records; Perform coding normalization on the agricultural IoT codes in the coding binding record set, retain the binding relationship between the normalized agricultural IoT codes and node records, and generate a standardized coding record set; According to the GS1 Digital Link structured parsing rules, the primary identifier key, key qualifiers and attribute fields of the agricultural product IoT codes in the standardized coded record set are parsed to generate the GS1 parsing field set; Write the GS1 parsed field set into the corresponding standardized coded record set to generate the coded parsing result.

[0008] Optionally, the generation of the encoded role meta set specifically includes: Starting with the batch identifier in the code parsing result, the agricultural product IoT codes traceable to the same batch identifier are arranged according to the event time in the node record to form a code flow function chain; Based on the connection, coverage, and release relationships between adjacent agricultural IoT codes in the coding flow function chain, a set of coding flow relationship fragments is generated; Based on the set of code flow relationship fragments, determine the receiving position and coverage of each agricultural product IoT code in the code flow functional chain, and generate role assignment results; The set of fragments relating role attribution to encoding flow is encapsulated to generate a set of encoded role meta-sets.

[0009] Optionally, the generation of the event responsibility element set specifically includes: According to the agricultural product IoT codes in the set of coded role elements, the node records in the agricultural product supply chain traceability data are assigned to the corresponding coded role elements, and role event binding results are generated. Based on the coding flow function in the role event binding result, determine the responsibility assignment type and generate the responsibility assignment result; Based on the role event binding results and responsibility assignment results, determine the main responsibility code, upstream responsibility code, and downstream responsibility code, and generate the responsibility code assignment results; The event business summary is formed from the business content in the role event binding result, and the responsibility acceptance amount, responsibility release amount, voucher coverage summary and environment fragment summary are written according to the responsibility classification type to generate the responsibility content normalization result; The results of responsibility coding, responsibility route allocation, and responsibility content normalization are encapsulated into a set of event responsibility elements.

[0010] Optionally, the main responsibility code is the agricultural product IoT code in the coding role element that generates the current node record, the upstream responsibility code is the upstream code in the coding role element, and the downstream responsibility code is the downstream code in the coding role element. For the node record corresponding to the detection coverage, the agricultural product IoT code corresponding to the detection certificate code role is determined as the main responsibility code, and the agricultural product IoT code corresponding to the covered batch master code role or packaging acceptance code role is determined as the upstream responsibility code and the downstream responsibility code.

[0011] Optionally, the generation of the pre-verification result of the conservation relationship specifically includes: Convert the set of coded role elements into coded role nodes; convert the set of event responsibility elements into responsibility records to be connected. Based on the upstream responsibility code, main responsibility code, and downstream responsibility code in the responsibility record to be connected, establish conservation relationship edges between the coded role nodes to generate a coded relationship conservation graph; Tracing the code flow process under the same batch identifier along the conservation relationship edges in the code relationship conservation graph forms a set of conservation relationship paths; Perform a conservation pre-check on the coding relationship conservation diagram to generate the responsibility route pre-check results; Based on the pre-verification results of responsibility zoning, mark the event responsibility element set and the conservation relationship path set, and generate the conservation relationship pre-verification results.

[0012] Optionally, each conservation relationship path in the conservation relationship path set starts from the coding role node corresponding to the batch master code role, passes through the batch splitting relationship, packaging aggregation relationship or logistics carrier relationship in sequence, and ends at the coding role node corresponding to the sales release code role. When the conservation relationship path has a detection coverage relationship, the coding role node corresponding to the detection voucher code role is merged into the conservation relationship path. When the conservation relationship path has an environment coverage relationship, the environment coverage relationship corresponding to the environment fragment summary is merged into the conservation relationship path.

[0013] Optionally, the generation of the total commitment value for batch responsibility specifically includes: Extract the pre-verified event responsibility elements from the conservation relationship pre-verification results, convert them into channel MMR leaf records, and form a responsibility channel writing sequence according to the responsibility channel type; The responsibility route is written into the sequence and appended to the corresponding responsibility route MMR. In each responsibility route MMR, adjacent leaf nodes are merged layer by layer to form route merging nodes. Nodes that cannot be merged further are identified as route peak nodes, and a set of responsibility route MMRs is generated. For each lane MMR, extract the node summary of the lane peak node, splice the node summary according to the formation order of the lane peak node, and perform summary calculation to generate the set of lane commitment values; Extract the conservation relationship paths that have passed the pre-verification from the conservation relationship pre-verification results, and establish a correspondence between the conservation relationship paths that have passed the pre-verification and the leaf node positions, peak node positions, and commitment values ​​of the responsible event elements they pass through in the responsibility route MMR set, and generate relationship closure leaf records; Using the leaf records of the relational closure as leaf nodes, construct the relational closure MMR, forming relational closure leaf nodes, relational closure merge nodes, and relational closure peak nodes, and generate relational closure commitment values ​​based on the relational closure peak nodes; The set of responsibility channel commitment values ​​and relational closure commitment values ​​under the same batch identifier are encapsulated into a batch responsibility total leaf record, and the batch responsibility total MMR is constructed using the batch responsibility total leaf record as the leaf node write object, thereby generating the batch responsibility total commitment value.

[0014] Optionally, the generation of the traceability data integrity verification result specifically includes: Locate the unverified coding role node and the unverified batch identifier corresponding to the IoT coding of the agricultural product to be verified in the coding relationship conservation diagram; Based on the coded role node to be verified and the batch identifier to be verified, a reverse backtracking proof and a forward coverage proof are generated along the coded relationship conservation graph and encapsulated as a bidirectional relationship conservation proof. Based on the bidirectional relationship conservation proof, extract the event responsibility element, leaf node position, branch peak node position and responsibility branch commitment value, perform MMR inclusion proof in the responsibility branch MMR set, and generate branch inclusion verification results; Based on the bidirectional relation conservation proof, the corresponding relation closure leaf record is found in the relation closure MMR, the conservation relation path verification is performed, and the relation closure verification result is generated. Perform appended consistency proofs on the responsibility route MMR set and relation closure MMR, and generate appended consistency verification results; The batch total commitment verification results are generated by combining the verification results of the sub-path, the verification results of the relational closure, and the additional consistency verification results with the total commitment value of the batch responsibility. Based on the verification results of the path inclusion verification results, the relationship closure verification results, the supplementary consistency verification results, and the batch total commitment verification results, the traceability data integrity verification results are generated, including the complete pass results, the results not included in the responsibility path, the results of the non-closed conservation relationship path, the results of the supplementary consistency failure results, and the results of the batch responsibility total commitment inconsistency results.

[0015] The beneficial effects of this invention are: By employing GS1 Digital Link structured parsing, the IoT codes for agricultural products are converted into parsing results that include the product entity, production entity, batch, packaging, aggregation, logistics, testing certificates, supply chain nodes, and sales terminals. Furthermore, it identifies the coded flow of agricultural products from batch formation, packaging acceptance, aggregation loading, logistics transportation, testing coverage to sales release. This transforms the agricultural product IoT code from merely a scanning query entry point into a unified identity foundation for subsequent responsibility unification, relationship mapping, and integrity verification. This reduces issues such as inconsistent coding rules among different supply chain entities, scattered node records, and unclear association between testing certificates and batches, improving the correlation and verifiability of traceability data across multiple stages.

[0016] By constructing a set of coded role elements, a set of event responsibility elements, and a coded relationship conservation graph, ordinary business records in the agricultural product supply chain are transformed into responsibility proof units with main responsibility codes, upstream responsibility codes, downstream responsibility codes, responsibility channel types, responsibility acceptance quantities, responsibility release quantities, voucher coverage summaries, and environmental fragment summaries. Conservation pre-verification is performed on source inheritance relationships, batch splitting relationships, packaging aggregation relationships, logistics and transportation relationships, testing coverage relationships, environmental coverage relationships, and sales release relationships. This process can identify coding chain breaks, packaging acceptance anomalies, logistics and transportation mismatches, incorrect binding of testing vouchers, missing cold chain coverage, and abnormal sales release sources before writing them into the integrity proof structure. This elevates traceability integrity verification from verifying whether a single data entry has been tampered with to verifying whether the supply chain coding relationships are closed.

[0017] By writing pre-verified event responsibility elements into the responsibility tier MMR set, the conservation relationship path into the relationship closure MMR, and further writing the responsibility tier MMR set and relationship closure MMR into the batch responsibility total MMR, a hierarchical tier integrity proof structure with the batch responsibility total commitment value as the entry point is formed. Subsequently, bidirectional relationship conservation proofs are generated for the IoT codes of agricultural products to be verified, which can simultaneously verify their reverse batch origin and forward supply chain coverage. By using MMR inclusion proofs and additional consistency proofs, it is determined whether the corresponding responsibility tier, conservation relationship path, and batch total commitment are consistent, thereby improving the fine-grainedness, traceability, and anomaly location accuracy of traceability data integrity verification. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding, as proposed in this invention. Figure 2 This is a flowchart illustrating the construction of the coding relationship conservation graph for a data integrity verification method for agricultural product supply chain traceability based on Internet of Things coding proposed in this invention. Figure 3 This is a flowchart of the hierarchical MMR commitment and conservation proof of an agricultural product supply chain traceability data integrity verification method based on Internet of Things coding proposed in this invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0020] refer to Figures 1-3 A method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding includes the following steps: Collect agricultural product supply chain traceability data and its associated agricultural product IoT codes, and generate code parsing results through GS1 DigitalLink structured parsing; Based on the coding parsing results, identify the coding flow functions of agricultural products from batch formation, packaging acceptance, aggregate loading, logistics transportation, testing coverage to sales release, and generate a set of coding role elements; By unifying the responsibility of agricultural product supply chain traceability data with the set of coded role elements, an event responsibility element set is generated. A coding relationship conservation graph is constructed based on the coding role set and the event responsibility set, and a conservation pre-check is performed to generate the conservation relationship pre-check results; Based on the pre-verification results of the conservation relationship, the event responsibility element set is written into the responsibility sub-channel MMR set, the conservation relationship path in the coded relationship conservation diagram is written into the relationship closure MMR, and the responsibility sub-channel MMR set and the relationship closure MMR are written into the batch responsibility total MMR to generate the batch responsibility total commitment value. Based on the IoT code of the agricultural product to be verified, the code relationship conservation diagram, the responsibility route MMR set, the relationship closure MMR, and the total commitment value of batch responsibility, a two-way relationship conservation proof is generated. The MMR inclusion proof and the additional consistency proof are executed to generate the traceability data integrity verification result.

[0021] In this embodiment, the generation of the encoding parsing result specifically includes: Identify node records carrying agricultural product IoT codes from agricultural product supply chain traceability data, and bind the agricultural product IoT codes with the event time, event subject, and event source in the node records to generate a set of code-bound records; Perform coding normalization on the agricultural IoT codes in the coding binding record set, retain the binding relationship between the normalized agricultural IoT codes and node records, and generate a standardized coding record set; According to the GS1 Digital Link structured parsing rules, the primary identifier key, key qualifiers and attribute fields of the agricultural product IoT codes in the standardized coded record set are parsed to generate the GS1 parsing field set; Write the GS1 parsed field set into the corresponding standardized coded record set to generate the coded parsing result; The coding and parsing results include the main identifier of agricultural products, the identifier of the production entity, the batch identifier, the packaging identifier, the aggregation identifier, the logistics identifier, the testing certificate identifier, the supply chain node identifier, and the sales terminal identifier.

[0022] In this embodiment, the generation of the encoded role meta set specifically includes: Starting with the batch identifier in the code parsing result, the agricultural product IoT codes traceable to the same batch identifier are arranged according to the event time in the node record to form a code flow function chain; The coding flow function chain represents the continuous flow process of the IoT code for agricultural products under the same batch identifier from batch formation to sales release. The batch identifier serves as the starting point of the flow, the packaging identifier represents the packaging acceptance after batch formation, the aggregation identifier represents the aggregation loading after packaging acceptance, the logistics identifier represents the logistics transportation after aggregation loading, the inspection certificate identifier represents the inspection coverage of the batch identifier or packaging identifier, and the sales terminal identifier represents the sales release after logistics transportation. Based on the connection, coverage, and release relationships between adjacent agricultural IoT codes in the coding flow function chain, a set of coding flow relationship fragments is generated; Each coding flow relationship fragment in the set of coding flow relationship fragments includes upstream coding, downstream coding, and coding flow function. The coding flow function is determined from batch formation, packaging acceptance, aggregate loading, logistics transportation, inspection coverage, and sales release. Both upstream and downstream coding are derived from the agricultural product Internet of Things coding in the coding parsing results. Based on the set of code flow relationship fragments, determine the receiving position and coverage of each agricultural product IoT code in the code flow functional chain, and generate role assignment results; The role attribution results will determine the agricultural product IoT code that serves as the starting point of circulation as the batch master code role, the agricultural product IoT code that receives the batch as the packaging acceptance code role, the agricultural product IoT code that carries the packaging acceptance code role as the aggregated carrier code role, the agricultural product IoT code that is bound to the packaging acceptance code role or the aggregated carrier code role and enters the logistics carrier role as the logistics carrier code role, the agricultural product IoT code that covers the batch master code role or the packaging acceptance code role as the inspection certificate code role, and the agricultural product IoT code that is released for sale as the sales release code role. The set of fragments relating role attribution to coding flow is encapsulated to generate a set of coded role meta-sets; Each coding role element in the coding role element set includes an agricultural product IoT code, role type, batch identifier, upstream code, downstream code, and code transfer function.

[0023] In this embodiment, the generation of the event responsibility element set specifically includes: According to the agricultural product IoT codes in the set of coded role elements, the node records in the agricultural product supply chain traceability data are assigned to the corresponding coded role elements, and role event binding results are generated. The role event binding result uses the agricultural product IoT code in the coded role element as the binding object, and associates the event time, event subject, event source and business content in the node record with the corresponding coded role element, retaining the role type, batch identifier, upstream code, downstream code and code transfer function in the coded role element; Based on the coding flow function in the role event binding result, determine the responsibility assignment type and generate the responsibility assignment result; The types of responsibility zoning include source responsibility zoning, split responsibility zoning, aggregation responsibility zoning, carrier responsibility zoning, document responsibility zoning, environmental responsibility zoning, and sales release responsibility zoning. Batch formation corresponds to source responsibility zoning, packaging acceptance corresponds to split responsibility zoning, aggregation loading corresponds to aggregation responsibility zoning, logistics carrier corresponds to carrier responsibility zoning, testing coverage corresponds to document responsibility zoning, cold chain monitoring during logistics carrier corresponds to environmental responsibility zoning, and sales release corresponds to sales release responsibility zoning. Based on the role event binding results and responsibility assignment results, determine the main responsibility code, upstream responsibility code, and downstream responsibility code, and generate the responsibility code assignment results; The event business summary is formed from the business content in the role event binding result, and the responsibility acceptance amount, responsibility release amount, voucher coverage summary and environment fragment summary are written according to the responsibility classification type to generate the responsibility content normalization result; The event business summary is generated from the business content in the node record. The responsibility acceptance quantity is formed by the quantity, weight or packaging unit of agricultural products entering the current coded role element in the node record. The responsibility release quantity is formed by the quantity, weight or packaging unit of agricultural products transferred out, sold, sampled, lost or returned from the current coded role element in the node record. For the certificate responsibility channel, the batch identifier, packaging identifier, test items and test conclusions covered by the test certificate are written into the certificate coverage summary. For the environmental responsibility channel, the temperature monitoring segment, humidity monitoring segment and monitoring time segment in the logistics and transportation process are written into the environmental segment summary. The results of responsibility coding, responsibility route allocation, and responsibility content normalization are encapsulated into a set of event responsibility elements.

[0024] In this embodiment, the main responsibility code is the agricultural product IoT code in the coding role element that generates the current node record, the upstream responsibility code is the upstream code in the coding role element, and the downstream responsibility code is the downstream code in the coding role element. For the node record corresponding to the detection coverage, the agricultural product IoT code corresponding to the detection certificate code role is determined as the main responsibility code, and the agricultural product IoT code corresponding to the covered batch master code role or packaging acceptance code role is determined as the upstream responsibility code and the downstream responsibility code.

[0025] In this embodiment, the generation of the conservation relationship pre-verification result specifically includes: Convert the set of coded role elements into coded role nodes; convert the set of event responsibility elements into responsibility records to be connected. The coding role node inherits the agricultural product IoT code, role type, batch identifier, upstream code, downstream code, and code transfer function from the corresponding coding role element. The responsibility record to be connected inherits the main responsibility code, upstream responsibility code, downstream responsibility code, responsibility route type, event business summary, responsibility acceptance volume, responsibility release volume, voucher coverage summary, and environmental fragment summary from the corresponding event responsibility element. Based on the upstream responsibility code, main responsibility code, and downstream responsibility code in the responsibility record to be connected, establish conservation relationship edges between the coded role nodes to generate a coded relationship conservation graph; The conservation relationship includes source inheritance relationship, batch splitting relationship, packaging aggregation relationship, logistics and transportation relationship, testing coverage relationship, environmental coverage relationship, and sales release relationship. Among them, the source responsibility division corresponds to the source inheritance relationship, the splitting responsibility division corresponds to the batch splitting relationship, the aggregation responsibility division corresponds to the packaging aggregation relationship, the transportation responsibility division corresponds to the logistics and transportation relationship, the document responsibility division corresponds to the testing coverage relationship, the environmental responsibility division corresponds to the environmental coverage relationship, and the sales release responsibility division corresponds to the sales release relationship. Tracing the code flow process under the same batch identifier along the conservation relationship edges in the code relationship conservation graph forms a set of conservation relationship paths; Perform a conservation pre-check on the coding relationship conservation diagram to generate the responsibility route pre-check results; The conservation pre-verification includes: verifying whether the batch parent code role has the corresponding event responsibility element for the source inheritance relationship; verifying whether the upstream code of the packaging acceptance code role points to the batch parent code role for the batch splitting relationship; verifying whether the upstream code of the aggregation carrier code role points to the packaging acceptance code role for the packaging aggregation relationship; verifying whether the logistics carrier code role is bound to the packaging acceptance code role or the aggregation carrier code role for the logistics carrier relationship; verifying whether the testing certificate code role covers the batch parent code role or the packaging acceptance code role for the testing coverage relationship; verifying whether the environmental fragment summary corresponds to the logistics carrier relationship for the environmental coverage relationship; and verifying whether the sales release code role can be traced back to the batch parent code role along the conservation relationship edge for the sales release relationship. Based on the pre-verification results of responsibility zoning, mark the event responsibility element set and the conservation relationship path set, and generate the conservation relationship pre-verification results; The pre-verification results of conservation relationships include event liability elements that passed the pre-verification, event liability elements that failed the pre-verification, conservation relationship paths that passed the pre-verification, and conservation relationship paths that failed the pre-verification.

[0026] In this embodiment, each conservation relationship path in the conservation relationship path set starts from the coding role node corresponding to the batch master code role, passes through the batch splitting relationship, packaging aggregation relationship or logistics carrier relationship in sequence, and ends at the coding role node corresponding to the sales release code role. When the conservation relationship path has a detection coverage relationship, the coding role node corresponding to the detection voucher code role is merged into the conservation relationship path. When the conservation relationship path has an environment coverage relationship, the environment coverage relationship corresponding to the environment fragment summary is merged into the conservation relationship path.

[0027] In this embodiment, the generation of the total batch liability commitment value specifically includes: Extract the pre-verified event responsibility elements from the conservation relationship pre-verification results, convert them into channel MMR leaf records, and form a responsibility channel writing sequence according to the responsibility channel type; The leaf records of the channel MMR are generated from the main responsibility code, upstream responsibility code, downstream responsibility code, responsibility channel type, event business summary, responsibility acceptance amount, responsibility release amount, voucher coverage summary and environmental fragment summary in the event responsibility element. As the leaf nodes of the responsibility channel MMR, the channel MMR leaf records under the same responsibility channel type are arranged according to the event time in the node record. The channel MMR leaf records with the same event time are arranged according to the collection order of the node record in the agricultural product supply chain traceability data. The responsibility route is written into the sequence and appended to the corresponding responsibility route MMR. In each responsibility route MMR, adjacent leaf nodes are merged layer by layer to form route merging nodes. Nodes that cannot be merged further are identified as route peak nodes, and a set of responsibility route MMRs is generated. The responsibility route MMR set includes source responsibility route MMR, split responsibility route MMR, aggregate responsibility route MMR, carrier responsibility route MMR, certificate responsibility route MMR, environmental responsibility route MMR, and sales release responsibility route MMR. Each responsibility route MMR includes leaf nodes, route merging nodes, and route peak nodes. Leaf nodes store a summary of the leaf records of the route MMR. Route merging nodes store a summary of the merged adjacent lower-level nodes. Route peak nodes represent the highest-level node that cannot be merged further in the current append state of the responsibility route MMR. For each lane MMR, extract the node summary of the lane peak node, splice the node summary according to the formation order of the lane peak node, and perform summary calculation to generate the set of lane commitment values; The responsibility route commitment value is a fixed-length integrity summary formed by the corresponding responsibility route MMR under the current leaf node content, leaf node number and leaf node writing order. It serves as the verification benchmark for subsequent MMR inclusion proof and appended consistency proof. When the leaf records of the responsibility route MMR change in content, number or writing order, the route peak node is re-determined and the corresponding responsibility route commitment value is updated. Extract the conservation relationship paths that have passed the pre-verification from the conservation relationship pre-verification results, and establish a correspondence between the conservation relationship paths that have passed the pre-verification and the leaf node positions, peak node positions, and commitment values ​​of the responsible event elements they pass through in the responsibility route MMR set, and generate relationship closure leaf records; The leaf record of the relation closure consists of the conservation relation path, the conservation relation edge traversed by the conservation relation path, the corresponding responsibility route type, the corresponding responsibility route commitment value, the leaf node position, and the route peak node position, indicating that the conservation relation path from batch formation to sales release under the same attribution batch identifier has been received by the corresponding responsibility route MMR set; Using the leaf records of the relational closure as leaf nodes, construct the relational closure MMR, forming relational closure leaf nodes, relational closure merge nodes, and relational closure peak nodes, and generate relational closure commitment values ​​based on the relational closure peak nodes; The relational closure MMR stores the correspondence between the conserved relational paths and the responsibility-based branch MMR set. The relational closure leaf node stores the summary of the relational closure leaf record. The relational closure merge node stores the summary after merging adjacent lower-level nodes. The relational closure peak node represents the highest-level node that the relational closure MMR cannot continue to merge in the current write state. The relational closure commitment value is a fixed-length integrity summary formed by the relational closure MMR under the current content of the conserved relational path, the number of conserved relational paths, and the write order of the conserved relational paths. Encapsulate the set of responsibility route commitment values ​​and relational closure commitment values ​​under the same batch identifier into a batch responsibility total leaf record, and construct the batch responsibility total MMR using the batch responsibility total leaf record as the leaf node write object to generate the batch responsibility total commitment value; The total batch responsibility leaf record includes the batch identifier, the responsibility commitment value of the source responsibility channel MMR, the responsibility commitment value of the split responsibility channel MMR, the responsibility commitment value of the aggregate responsibility channel MMR, the responsibility commitment value of the carrier responsibility channel MMR, the responsibility commitment value of the document responsibility channel MMR, the responsibility commitment value of the environmental responsibility channel MMR, the responsibility commitment value of the sales release responsibility channel MMR, and the relationship closure commitment value. The total batch responsibility commitment value is a fixed-length integrity summary of the total batch responsibility MMR under the current set of responsibility channel commitment values ​​and the relationship closure commitment value.

[0028] In this embodiment, the generation of the traceability data integrity verification result specifically includes: Locate the unverified coding role node and the unverified batch identifier corresponding to the IoT coding of the agricultural product to be verified in the coding relationship conservation diagram; Based on the coded role node to be verified and the batch identifier to be verified, a reverse backtracking proof and a forward coverage proof are generated along the coded relationship conservation graph and encapsulated as a bidirectional relationship conservation proof. The reverse backtracking proof starts from the coding role node to be verified and traces back along the sales release relationship, logistics carrier relationship, packaging aggregation relationship and batch splitting relationship to the coding role node corresponding to the batch parent code role, proving that the agricultural product IoT code to be verified has a batch origin; The positive coverage proof starts from the batch parent code role corresponding to the batch identifier to be verified, and covers the code role node to be verified along the batch splitting relationship, packaging aggregation relationship, logistics carrier relationship, testing coverage relationship, environmental coverage relationship and sales release relationship, proving that the supply chain traceability data corresponding to the IoT code of the agricultural product to be verified has entered the scope of integrity verification. Based on the bidirectional relationship conservation proof, extract the event responsibility element, leaf node position, branch peak node position and responsibility branch commitment value, perform MMR inclusion proof in the responsibility branch MMR set, and generate branch inclusion verification results; The MMR includes a summary of the leaf record of the sub-channel MMR that is regenerated based on the event responsibility element, and the summary of the sub-channel merging node is recalculated layer by layer from the leaf node position to the sub-channel peak node position. The consistency of the recalculated sub-channel peak node summary with the sub-channel peak node summary corresponding to the responsibility sub-channel commitment value is checked to obtain the sub-channel inclusion verification result. Based on the bidirectional relation conservation proof, the corresponding relation closure leaf record is found in the relation closure MMR, the conservation relation path verification is performed, and the relation closure verification result is generated. The conservation relationship path verification involves checking the consistency between the conservation relationship edge, responsibility branch type, responsibility branch commitment value, leaf node position, and branch peak node position in the bidirectional relationship conservation proof and the corresponding content stored in the leaf record of the relationship closure. The relationship closure commitment value is then recalculated based on the leaf node, merge node, and peak node of the relationship closure to obtain the relationship closure verification result. Perform appended consistency proofs on the responsibility route MMR set and relation closure MMR, and generate appended consistency verification results; The appended consistency proof determines the previous write state and the current write state in the responsibility route MMR set and the relation closure MMR respectively. The previous integrity summary is generated based on the peak node of the previous write state, and the current integrity summary is generated based on the peak node of the current write state. The current integrity summary is then checked to see if it is formed by appending new leaf nodes to the previous integrity summary, and the appended consistency verification result is obtained. The batch total commitment verification results are generated by combining the verification results of the sub-path, the verification results of the relational closure, and the additional consistency verification results with the total commitment value of the batch responsibility. The batch total commitment verification process involves re-encapsulating the set of responsibility sub-commitment values ​​and relational closure commitment values ​​under the batch identifier to be verified into a batch total responsibility leaf record. The fixed-length integrity summary is then recalculated according to the leaf nodes, merge nodes, and peak nodes of the batch total responsibility MMR. The consistency of the recalculated fixed-length integrity summary with the batch total responsibility commitment value is checked to obtain the batch total commitment verification result. Based on the verification results of the path inclusion verification results, the relationship closure verification results, the supplementary consistency verification results, and the batch total commitment verification results, the traceability data integrity verification results are generated, including the complete pass results, the results not included in the responsibility path, the results of the non-closed conservation relationship path, the results of the supplementary consistency failure results, and the results of the batch responsibility total commitment inconsistency results.

[0029] Example 1: To verify the feasibility of this invention in practice, it was applied to a provincial-level fruit and vegetable supply chain traceability scenario. This scenario covers stages such as harvesting at the production site, sorting and packaging, pallet aggregation, cold chain transportation, warehousing and outbound, third-party testing, and supermarket sales. The implementation location is between a major fruit and vegetable production area in East China and the corresponding city's sales terminal, and the implementation time is selected during the peak harvest and cross-regional distribution period of the season. In this scenario, after harvesting, fruits and vegetables enter the sorting line according to batches. The same batch is split into multiple packaging units, which are then aggregated into turnover baskets and pallets, and subsequently transported by cold chain vehicles to the warehousing center and sales stores. Although existing traceability methods can view the origin, testing, and transportation information through QR codes, problems easily arise during batch splitting, packaging aggregation, testing report binding, and cold chain segment supplementation, such as downstream packaging codes failing to prove the origin, testing certificates being incorrectly bound, and the complete page still being displayed even when transportation segments are missing.

[0030] In this scenario, the harvesting, packaging, warehousing, testing, logistics, and sales ends upload agricultural product supply chain traceability data and their associated IoT codes. The system first generates code parsing results using GS1 Digital Link structured parsing, unifying the product entity, production entity, batch, packaging, aggregation, logistics, testing certificates, supply chain nodes, and sales terminals within the code into a single parsing structure. To ensure stable operation of the code role recognition, the code flow function recognition algorithm is trained using historical traceability records before implementation. The training samples consist of manually verified batch formation records, packaging acceptance records, aggregation loading records, logistics transportation records, testing coverage records, and sales release records. During training, the algorithm learns the correspondence between different identifier fields and supply chain functions, and corrects misjudgment rules in testing coverage, environmental coverage, and sales release scenarios using manually reviewed abnormal samples. After training, the system identifies the encoding parsing results as a set of encoded role elements, and then normalizes the node records with the set of encoded role elements to form an event responsibility element set, so that each harvesting, packaging, testing, transportation and sales record has a main responsibility code, upstream responsibility code, downstream responsibility code and responsibility route type.

[0031] In actual operation, the system constructs a coding relationship conservation graph based on the set of coded role elements and the set of event responsibility elements, and performs conservation pre-verification on source inheritance relationships, batch splitting relationships, packaging aggregation relationships, logistics and transportation relationships, detection coverage relationships, environmental coverage relationships, and sales release relationships. Event responsibility elements that pass the pre-verification are written into the responsibility channel MMR set, and conservation relationship paths that pass the pre-verification are written into the relationship closure MMR. The responsibility channel MMR set and the relationship closure MMR then form the total batch responsibility MMR and the total batch responsibility commitment value. When regulatory personnel or the sales end verify a packaging code, the system generates a two-way relationship conservation proof based on the IoT code of the agricultural product to be verified. It then traces back to see if the packaging code can be traced back to the batch parent code, and forward verifies whether the batch has completed packaging acceptance, aggregation loading, logistics and transportation, detection coverage, environmental coverage, and sales release. The on-site operation log shows that in scenarios such as incorrect binding of testing certificates, missing cold chain monitoring segments, and failure to trace sales release codes back to the batch master code, the system can locate the anomalies to the corresponding responsibility channels and conservation relationship paths, rather than just prompting that the page information is missing. This improves the interpretability, closed-loop nature, and anomaly location capabilities of the integrity verification of agricultural product supply chain traceability data.

[0032] Table 1 Comparison of Data Integrity Verification Performance in Agricultural Product Supply Chain Traceability

[0033] As shown in Table 1, the present invention achieves an accuracy of 90.7% in identifying coded link closures, a 7.8 percentage point improvement compared to the 82.9% accuracy of the MerkleTree batch integrity verification method. This improvement is mainly attributed to the introduction of the coded role set and the coded relationship conservation graph. This ensures that batch identifiers, packaging identifiers, aggregation identifiers, logistics identifiers, inspection certificate identifiers, and sales terminal identifiers are no longer isolated indices for verification, but are organized into a conservation relationship path with upstream coding, downstream coding, and coding flow functions. Therefore, when multiple packaging codes are formed after batch splitting, and these packaging codes then enter aggregation loading and logistics transportation, the system can determine whether the sales release end code can trace back to the batch parent code role along the conservation relationship edge, thereby improving the ability to identify broken coding chains and abnormal sources.

[0034] Regarding the event tampering detection rate, this invention achieves 94.2%, a 2.6 percentage point improvement compared to the 91.6% of the Merkle Tree batch integrity verification method, maintaining a reasonable improvement range. This improvement is not due to simply increasing the strength of the digest algorithm, but rather because the event responsibility element is simultaneously written into the main responsibility code, upstream responsibility code, downstream responsibility code, responsibility channel type, and event business digest. This means that tampered data not only needs to pass single record digest verification but also simultaneously satisfy the commitment consistency among the responsibility channel MMR set, the relationship closure MMR, and the total batch responsibility MMR. When a detection record, transportation record, or sales record is replaced, its corresponding channel MMR leaf record digest, relationship closure leaf record, and total batch responsibility commitment value will all become inconsistent, thereby improving the stability of tampering detection.

[0035] In terms of the identification rates of mis-bound certificates, missing cold chain segments, and abnormal sales release sources, this invention achieves rates of 85.6%, 83.1%, and 88.4%, respectively, representing improvements of 11.8, 13.7, and 10.9 percentage points compared to the Merkle Tree batch integrity verification method. This indicates that this invention provides a more significant improvement in handling relationship-related anomalies. The reason is that the Merkle Tree batch integrity verification method primarily verifies the existence of records within a specific batch dataset, making it difficult to determine whether the detected certificate covers the correct batch or packaging label, or whether the cold chain monitoring segment corresponds to a logistics carrier relationship. This invention records detection coverage, environmental coverage, and sales release relationships through certificate responsibility channels, environmental responsibility channels, and sales release responsibility channels, respectively. Furthermore, it preserves the correspondence between the conserved relationship path and the responsibility channel MMR set through relationship closure (MMR), thus enabling more granular localization of mis-bound certificates, cold chain gaps, and abnormal sales sources.

[0036] From the perspective of verification efficiency, the average time for anomaly localization in this invention is 1.74 seconds, lower than the 2.46 seconds of the Merkle Tree batch integrity verification method. This indicates that the responsibility-based channel MMR set and relational closure MMR can reduce the scope of anomaly investigation, enabling the system to directly locate the responsibility channel, the conserved relation path, and the corresponding coded role node. However, the average time for batch-level integrity verification in this invention is 1.21 seconds, higher than the 1.08 seconds of the Merkle Tree batch integrity verification method. The average proof data volume is 14.8KB per batch, also higher than the 10.3KB per batch of the Merkle Tree batch integrity verification method. This result is consistent with the actual overhead of the hierarchical channel verification structure, because in addition to performing single record inclusion proof, this invention also needs to perform relational closure verification, appended consistency proof, and batch total commitment verification. However, the increased verification overhead is exchanged for higher relational anomaly identification capability and more accurate anomaly localization results.

[0037] In summary, this invention, through the collaborative processing of GS1 Digital Link structured parsing, coded role set, event responsibility set, coded relationship conservation graph, responsibility route MMR set, relationship closure MMR, and batch responsibility total MMR, elevates the integrity verification of agricultural product supply chain traceability data from single-record tamper-proofing to full-link relationship closure verification encompassing batch origin, packaging acceptance, aggregate loading, logistics transportation, testing coverage, environmental coverage, and sales release. While maintaining reasonable verification time and proof data volume, it improves the ability to identify coded link closures, mis-binding of testing certificates, missing cold chain segments, and abnormal sales release origins, making the traceability data integrity verification results more suitable for agricultural product batch supervision, anomaly tracing, and terminal verification scenarios.

[0038] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for verifying the integrity of agricultural product supply chain traceability data based on Internet of Things (IoT) coding, characterized in that, Includes the following steps: Collect agricultural product supply chain traceability data and its associated agricultural product IoT codes, and generate code parsing results through GS1 Digital Link structured parsing; Based on the coding parsing results, identify the coding flow functions of agricultural products from batch formation, packaging acceptance, aggregate loading, logistics transportation, testing coverage to sales release, and generate a set of coding role elements; By unifying the responsibility of agricultural product supply chain traceability data with the set of coded role elements, an event responsibility element set is generated. A coding relationship conservation graph is constructed based on the coding role set and the event responsibility set, and a conservation pre-check is performed to generate the conservation relationship pre-check results; Based on the pre-verification results of the conservation relationship, the event responsibility element set is written into the responsibility sub-channel MMR set, the conservation relationship path in the coded relationship conservation diagram is written into the relationship closure MMR, and the responsibility sub-channel MMR set and the relationship closure MMR are written into the batch responsibility total MMR to generate the batch responsibility total commitment value. Based on the IoT code of the agricultural product to be verified, the code relationship conservation diagram, the responsibility route MMR set, the relationship closure MMR, and the total commitment value of batch responsibility, a two-way relationship conservation proof is generated. The MMR inclusion proof and the additional consistency proof are executed to generate the traceability data integrity verification result.

2. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the encoding parsing result specifically includes: Identify node records carrying agricultural product IoT codes from agricultural product supply chain traceability data, and bind the agricultural product IoT codes with the event time, event subject, and event source in the node records to generate a set of code-bound records; Perform coding normalization on the agricultural IoT codes in the coding binding record set, retain the binding relationship between the normalized agricultural IoT codes and node records, and generate a standardized coding record set; According to the GS1 Digital Link structured parsing rules, the primary identifier key, key qualifiers and attribute fields of the agricultural product IoT codes in the standardized coded record set are parsed to generate the GS1 parsing field set; Write the GS1 parsed field set into the corresponding standardized coded record set to generate the coded parsing result.

3. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the encoded role meta set specifically includes: Starting with the batch identifier in the code parsing result, the agricultural product IoT codes traceable to the same batch identifier are arranged according to the event time in the node record to form a code flow function chain; Based on the connection, coverage, and release relationships between adjacent agricultural IoT codes in the coding flow function chain, a set of coding flow relationship fragments is generated; Based on the set of code flow relationship fragments, determine the receiving position and coverage of each agricultural product IoT code in the code flow functional chain, and generate role assignment results; The set of fragments relating role attribution to encoding flow is encapsulated to generate a set of encoded role meta-sets.

4. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the event responsibility set specifically includes: According to the agricultural product IoT codes in the set of coded role elements, the node records in the agricultural product supply chain traceability data are assigned to the corresponding coded role elements, and role event binding results are generated. Based on the coding flow function in the role event binding result, determine the responsibility assignment type and generate the responsibility assignment result; Based on the role event binding results and responsibility assignment results, determine the main responsibility code, upstream responsibility code, and downstream responsibility code, and generate the responsibility code assignment results; The event business summary is formed from the business content in the role event binding result, and the responsibility acceptance amount, responsibility release amount, voucher coverage summary and environment fragment summary are written according to the responsibility classification type to generate the responsibility content normalization result; The results of responsibility coding, responsibility route allocation, and responsibility content normalization are encapsulated into a set of event responsibility elements.

5. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 4, characterized in that, The primary responsibility code is the agricultural product IoT code in the coding role element that generates the current node record. The upstream responsibility code is the upstream code in the coding role element, and the downstream responsibility code is the downstream code in the coding role element. For the node record corresponding to the detection coverage, the agricultural product IoT code corresponding to the detection certificate code role is determined as the primary responsibility code, and the agricultural product IoT code corresponding to the covered batch master code role or packaging acceptance code role is determined as the upstream responsibility code and the downstream responsibility code.

6. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the pre-verification results of the conservation relationship specifically includes: Convert the set of coded role elements into coded role nodes; convert the set of event responsibility elements into responsibility records to be connected. Based on the upstream responsibility code, main responsibility code, and downstream responsibility code in the responsibility record to be connected, establish conservation relationship edges between the coded role nodes to generate a coded relationship conservation graph; Tracing the code flow process under the same batch identifier along the conservation relationship edges in the code relationship conservation graph forms a set of conservation relationship paths; Perform a conservation pre-check on the coding relationship conservation diagram to generate the responsibility route pre-check results; Based on the pre-verification results of responsibility zoning, mark the event responsibility element set and the conservation relationship path set, and generate the conservation relationship pre-verification results.

7. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 6, characterized in that, Each conservation relationship path in the set of conservation relationship paths starts with the coding role node corresponding to the batch master code role, passes through batch splitting relationship, packaging aggregation relationship or logistics carrier relationship in sequence, and ends with the coding role node corresponding to the sales release code role. When the conservation relationship path has a detection coverage relationship, the coding role node corresponding to the detection voucher code role is merged into the conservation relationship path. When the conservation relationship path has an environment coverage relationship, the environment coverage relationship corresponding to the environment fragment summary is merged into the conservation relationship path.

8. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the total commitment value for batch responsibility specifically includes: Extract the pre-verified event responsibility elements from the conservation relationship pre-verification results, convert them into channel MMR leaf records, and form a responsibility channel writing sequence according to the responsibility channel type; The responsibility route is written into the sequence and appended to the corresponding responsibility route MMR. In each responsibility route MMR, adjacent leaf nodes are merged layer by layer to form route merging nodes. Nodes that cannot be merged further are identified as route peak nodes, and a set of responsibility route MMRs is generated. Extract node summaries from the peak nodes of each responsible lane in the MMR, assemble the node summaries according to the formation order of the peak nodes of the lanes, and perform summary calculation to generate a set of responsible lane commitment values; Extract the conservation relationship paths that have passed the pre-verification from the conservation relationship pre-verification results, and establish a correspondence between the conservation relationship paths that have passed the pre-verification and the leaf node positions, peak node positions, and commitment values ​​of the responsible event elements they pass through in the responsibility route MMR set, and generate relationship closure leaf records; Using the leaf records of the relational closure as leaf nodes, construct the relational closure MMR, forming relational closure leaf nodes, relational closure merge nodes, and relational closure peak nodes, and generate relational closure commitment values ​​based on the relational closure peak nodes; The set of responsibility channel commitment values ​​and relational closure commitment values ​​under the same batch identifier are encapsulated into a batch responsibility total leaf record, and the batch responsibility total MMR is constructed using the batch responsibility total leaf record as the leaf node write object, thereby generating the batch responsibility total commitment value.

9. The method for verifying the integrity of agricultural product supply chain traceability data based on IoT coding according to claim 1, characterized in that, The generation of the traceability data integrity verification result specifically includes: Locate the unverified coding role node and the unverified batch identifier corresponding to the IoT coding of the agricultural product to be verified in the coding relationship conservation diagram; Based on the coded role node to be verified and the batch identifier to be verified, a reverse backtracking proof and a forward coverage proof are generated along the coded relationship conservation graph and encapsulated as a bidirectional relationship conservation proof. Based on the bidirectional relationship conservation proof, extract the event responsibility element, leaf node position, branch peak node position and responsibility branch commitment value, perform MMR inclusion proof in the responsibility branch MMR set, and generate branch inclusion verification results; Based on the bidirectional relation conservation proof, the corresponding relation closure leaf record is found in the relation closure MMR, the conservation relation path verification is performed, and the relation closure verification result is generated. Perform appended consistency proofs on the responsibility route MMR set and relation closure MMR, and generate appended consistency verification results; The batch total commitment verification results are generated by combining the verification results of the sub-path, the verification results of the relational closure, and the additional consistency verification results with the total commitment value of the batch responsibility. Based on the verification results of the path inclusion verification results, the relationship closure verification results, the supplementary consistency verification results, and the batch total commitment verification results, the traceability data integrity verification results are generated, including the complete pass results, the results not included in the responsibility path, the results of the non-closed conservation relationship path, the results of the supplementary consistency failure results, and the results of the batch responsibility total commitment inconsistency results.

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