Logistics information tracking system and method based on block chain

Abstract

The invention provides a logistics information tracking system and method based on a block chain, and relates to the technical field of logistics tracking, and the method comprises the steps: capturing an interaction tracking identifier of a logistics participant in a logistics distribution process, and carrying out the cross verification of the interaction tracking identifier and logistics list data, obtaining path verification nodes on different sub-chain structures in a logistics information tracking process; performing dynamic fragmentation on the tracking feedback label to obtain an ordered tracking signature on a corresponding copy in each sub-chain, and further determining a tracking attribute index on each sub-chain in a logistics information tracking process according to the ordered tracking signature; and comparing the path verification node with the tracking attribute index to obtain logistics point location information in a logistics information tracking process, and storing the logistics point location information on a block chain node of a logistics participant. According to the method and the device, the logistics information can be subjected to joint distributed integration tracking in a multi-subchain heterogeneous block chain environment, so that the inter-chain consistency in a logistics information tracking process is improved.

Description

Technical Field

[0001] This application relates to the technical field of logistics tracking. More specifically, this application relates to a logistics information tracking system and method based on blockchain. Background Art

[0002] Logistics tracking refers to the technical process of real-time recording, dynamic monitoring, and visualization management of key nodes such as the location, status, transfer links, and responsible parties of goods throughout the entire process of logistics transportation and distribution based on information technology means. Traditional logistics tracking mainly relies on centralized databases and manual uploading methods, suffering from problems such as data latency, inconsistency, easy tampering, and information silos, and it is difficult to meet the requirements of modern logistics for transparency, timeliness, and security. With the development of blockchain technology, its technical features such as decentralization, immutability, traceability, and multi-party consensus provide new solutions for logistics tracking.

[0003] However, there are generally defects in the existing blockchain-based logistics information tracking, such as weak cross-chain collaboration capabilities and difficult dynamic reconstruction of tracking paths, making it difficult for all parties involved in logistics to achieve efficient collaboration and data consistency verification in a multi-chain environment during the processes of data uploading, path verification, and information tracing. This leads to problems of structural fragmentation, verification lag, and location link breakpoints in logistics tracking information between different sub-chains, thus making it difficult to ensure the trustworthy transparency and real-time traceability of the entire logistics chain. Therefore, how to perform joint distributed integrated tracking of logistics information in a multi-sub-chain heterogeneous blockchain environment to improve the inter-chain consistency during the logistics information tracking process is an issue faced by the industry. Summary of the Invention

[0004] This application provides a logistics information tracking system and method based on blockchain, which can perform joint distributed integrated tracking of logistics information in a multi-sub-chain heterogeneous blockchain environment to improve the inter-chain consistency during the logistics information tracking process.

[0005] In a first aspect, this application provides a logistics information tracking method based on blockchain. The tracking method includes the following steps:

[0006] Read the distributed ledger of the logistics distribution link, and extract the logistics list data containing the location of the goods from the distributed ledger;

[0007] Capture the interaction tracking identifiers of logistics participants during the logistics distribution process, and perform cross-verification on the interaction tracking identifiers and the logistics list data to obtain path verification nodes on different sub-chain structures during the logistics information tracking process;

[0008] Cross-chain access to the order tracking information in the logistics information tracking process to obtain a tracking feedback tag, dynamically fragment the tracking feedback tag to obtain an ordered tracking signature on the corresponding replicas within each sub-chain, and then determine the tracking attribute index on each sub-chain in the logistics information tracking process from the ordered tracking signature;

[0009] Compare the path verification node with the tracking attribute index to obtain the logistics location information in the logistics information tracking process, and then store the logistics location information on the blockchain nodes of logistics participants.

[0010] In this embodiment, the distributed ledger refers to a data ledger system maintained by multiple logistics participants respectively, each having independent chain nodes, and keeping the data consistent through a consensus mechanism.

[0011] In this embodiment, extracting the logistics list data containing the goods location from the distributed ledger specifically includes:

[0012] Determine the original logistics list log containing the goods location according to the distributed ledger;

[0013] Determine the circulation permission record corresponding to each logistics location coordinate according to the original logistics list log;

[0014] Determine the logistics list data containing the goods location through the circulation permission record.

[0015] In this embodiment, capturing the interaction tracking identifier of logistics participants in the logistics distribution process specifically includes:

[0016] Determine the participation node attribute of logistics participants when performing interaction operations in the distribution path;

[0017] Determine the state migration trajectory when performing interaction operations in the distribution path according to the participation node attribute;

[0018] Identify the interaction operations of logistics participants in the logistics distribution process based on the state migration trajectory to obtain the interaction tracking identifier of logistics participants in the logistics distribution process.

[0019] In this embodiment, cross-chain access to the order tracking information in the logistics information tracking process to obtain a tracking feedback tag specifically includes:

[0020] Construct an order tracking path through a cross-chain routing protocol and the logistics sub-chain topology relationship;

[0021] Resolve the consensus tracking feature of cross-chain logistics information based on the routing identifier of the order tracking path;

[0022] Determine the tracking feedback tag according to the consensus tracking feature.

[0023] In this embodiment, each corresponding copy within the sub-chain represents multiple node copies storing the same logistics data on the sub-chain.

[0024] In this embodiment, determining the tracking attribute index on each sub-chain during the logistics information tracking process by the ordered tracking signature specifically includes:

[0025] Extracting the tracking feature information during the logistics sub-chain tracking operation from the ordered tracking signature;

[0026] Determining the tracking classification keyword on each sub-chain during the logistics information tracking process through the tracking feature information;

[0027] Determining the tracking attribute index on each sub-chain during the logistics information tracking process by the tracking classification keyword.

[0028] In this embodiment, comparing the path verification node and the tracking attribute index to obtain the logistics point information during the logistics information tracking process specifically includes:

[0029] Determining the dynamic similarity period during the logistics information tracking process according to the path verification node and the tracking attribute index;

[0030] Matching the dynamic similarity period with a preset threshold to screen out a candidate node set that meets the logistics path consistency;

[0031] Determining the logistics point information during the logistics information tracking process according to the candidate node set.

[0032] In this embodiment, the blockchain node of the logistics participant refers to a distributed ledger running node for storing, verifying, and synchronizing logistics data.

[0033] In a second aspect, the present application provides a blockchain-based logistics information tracking system for executing a blockchain-based logistics information tracking method. The tracking system includes:

[0034] A data reading module, configured to read the distributed ledger of the logistics distribution link and extract logistics list data including the location of goods from the distributed ledger;

[0035] A cross-verification module, configured to capture the interactive tracking identifier of the logistics participant during the logistics distribution process, perform cross-verification on the interactive tracking identifier and the logistics list data, and obtain path verification nodes on different sub-chain structures during the logistics information tracking process;

[0036] A dynamic sharding module, which is used to cross-chain access the order tracking information in the process of logistics information tracking, obtain a tracking feedback tag, perform dynamic sharding on the tracking feedback tag to obtain an ordered tracking signature on the corresponding replica within each sub-chain, and then determine the tracking attribute index on each sub-chain in the process of logistics information tracking from the ordered tracking signature;

[0037] An information storage module, which is used to compare the path verification node and the tracking attribute index to obtain the logistics point information in the process of logistics information tracking, and then store the logistics point information on the blockchain node of the logistics participant.

[0038] The technical solution provided by the disclosed embodiments of the present application has the following beneficial effects:

[0039] Read the distributed ledger in the logistics distribution link, extract the logistics list data containing the location of the goods from the distributed ledger; capture the interactive tracking identifier of the logistics participant in the logistics distribution process, perform cross-verification on the interactive tracking identifier and the logistics list data to obtain the path verification node on different sub-chain structures in the process of logistics information tracking; cross-chain access the order tracking information in the process of logistics information tracking, obtain a tracking feedback tag, perform dynamic sharding on the tracking feedback tag to obtain an ordered tracking signature on the corresponding replica within each sub-chain, and then determine the tracking attribute index on each sub-chain in the process of logistics information tracking from the ordered tracking signature; compare the path verification node and the tracking attribute index to obtain the logistics point information in the process of logistics information tracking, and then store the logistics point information on the blockchain node of the logistics participant.

[0040] It can be seen that in the present application, the structured availability of logistics information tracking can be improved in the case of isolated node verification during logistics information tracking; among them, by extracting the logistics list data, the accurate positioning and structured expression of the logistics list data in the multi-source chain ledger are ensured; by determining the path verification node, the on-chain interaction identification and path consistency verification of logistics behavior nodes can be realized, so as to establish an accurate path verification mechanism in a multi-sub-chain environment and effectively solve the problems of node behavior breakage and data isolation in the tracking chain; by determining the tracking attribute index, the flexibility and consistency of cross-chain data processing can be enhanced, and through the dynamic sharding of feedback tags and the sorting of tracking signatures, the standardized expression of tracking features in heterogeneous sub-chains can be realized, and the comparability and structured depth of tracking information between sub-chains can be improved; by determining the logistics point information, the deep integration of the tracking path and the attribute index can be realized, the real position points in the whole logistics process can be accurately restored, and the persistence and verifiability of data can be guaranteed through the on-chain storage mechanism, thereby improving the availability of the full-link tracking result.

[0041] In summary, the technical solution adopted in this application can perform joint distributed integration and tracking of logistics information in a multi-sub-chain heterogeneous blockchain environment to improve the inter-chain consistency during the logistics information tracking process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 is an exemplary flowchart of a logistics information tracking method based on a blockchain provided by the present application;

[0044] Figure 2 is a schematic flowchart of determining a path verification node provided by the present application;

[0045] Figure 3 is a schematic flowchart of determining an ordered tracking signature provided by the present application;

[0046] Figure 4 is a module structure diagram of a logistics information tracking system based on a blockchain provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0048] The embodiment of the present application provides a blockchain-based logistics information tracking system and method. Its core is to read the distributed ledger in the logistics distribution link, and extract logistics list data containing the location of goods from the distributed ledger; capture the interaction tracking identifiers of logistics participants during the logistics distribution process, cross-verify the interaction tracking identifiers and the logistics list data to obtain path verification nodes on different sub-chain structures during the logistics information tracking process; cross-chain access the order tracking information during the logistics information tracking process to obtain a tracking feedback tag, perform dynamic sharding on the tracking feedback tag to obtain an ordered tracking signature on the corresponding copy within each sub-chain, and then determine the tracking attribute index on each sub-chain during the logistics information tracking process from the ordered tracking signature; compare the path verification nodes and the tracking attribute index to obtain the logistics point information during the logistics information tracking process, and then store the logistics point information on the blockchain nodes of logistics participants.

[0049] Embodiment 1. To better understand the above technical solution, the following will combine the description drawings of the specification and specific implementation manners to elaborate on the above technical solution in detail. Refer to Figure 1 As shown in the figure, it is an exemplary flowchart of a blockchain-based logistics information tracking method according to this embodiment of the present application. The tracking method includes the following steps:

[0050] In step S1, read the distributed ledger in the logistics distribution link, and extract logistics list data containing the location of goods from the distributed ledger.

[0051] Specifically, reading the distributed ledger in the logistics distribution link can be implemented in the following way: First, it is necessary to determine the chain node positions and ledger access interfaces maintained by each logistics participant. Multiple nodes can be deployed based on the consortium chain architecture (such as Hyperledger Fabric or FISCO BCOS), and each node records the logistics operation data of its own party; define the logistics data structure template through the on-chain smart contract, including fields such as goods ID, location, timestamp, processing actions, etc.; then, the reading process is initiated by a unified data extraction module, which calls the RESTful API or gRPC interface provided by the chain node, specifies the query conditions (such as goods ID or time range), and the ledger returns the structured data containing the target block or transaction. And use a data parser to extract the original block data into a structured format (such as JSON), and perform field verification and hash verification to confirm the data integrity, and cache the extraction result in the intermediate database.

[0052] It should be noted that in the present application, the distributed ledger refers to a data ledger system maintained by multiple logistics participants respectively, each having an independent chain node, and maintaining data consistency through a consensus mechanism.

[0053] In this embodiment, the extraction of logistics list data containing the location of goods from the distributed ledger can be implemented by the following steps:

[0054] Determine the original logistics list log containing the location of goods according to the distributed ledger;

[0055] Determine the circulation permission record corresponding to each logistics location coordinate according to the original logistics list log;

[0056] Determine the logistics list data containing the location of goods through the circulation permission record.

[0057] Specifically, in implementation, first, access the distributed ledger node in the logistics alliance chain through authentication and authorization; call the log query interface in the on-chain smart contract, and initiate a retrieval request according to the specified goods identifier (such as goods ID, batch number) or time range; the blockchain node returns the original transaction data containing the changes in the goods status, and these data include fields such as coordinate information, operation time, operation type, etc.; the reading module parses the fields of the returned data, maps the fields such as location_x, location_y, operation_time, etc. in the transaction data into a structured log format, and takes the result mapped into the log format as the original logistics list log. Then, parse each location coordinate extracted from the original logistics list log one by one; for the operation subject identity field in each coordinate data, initiate an on-chain permission contract call to query whether it has the logistics operation permission for this location; the permission contract matches according to the preset permission table, and the permission table lists the geographical areas or warehousing facilities that each logistics participant can operate; if the match is successful, mark this coordinate as a "legitimate circulation location", and generate a permission verification record, and take the permission verification record as the circulation permission record, and this circulation permission record includes information such as the operator ID, permission level, permission validity period, etc. Finally, traverse the corresponding relationship between the original logistics list log and the circulation permission record; for the location records that have been verified as "legitimate", extract them from the original log as the cleaned logistics list items; attach permission information to each list item, including permission source, operator identity signature, permission verification time, etc.; the cleaned logistics list is encapsulated in a block structure to generate a uniquely identified list block (Logistics Ledger Block), and submit it to the chain for on-chain storage through the on-chain contract; at the same time, generate list data summary information, including goods path summary, geographical distribution summary, etc., and take the list data summary information as the logistics list data containing the location of goods, which will not be elaborated here.

[0058] It should be noted that in this application, the goods location represents the specific location point of the goods during the logistics process; the original logistics list log refers to the initial transaction log data stored on the chain by the logistics participant nodes and recording the changes in the goods status in chronological order of events; the logistics location coordinates refer to the longitude and latitude coordinates of the specific location of the goods during the logistics process; the circulation authority record refers to the authority verification information associated with each logistics location coordinate, used to determine whether the current location is a legal transfer or storage node; the logistics list data refers to the set of verified and compliant goods location and behavior records after authority verification.

[0059] In step S2, capture the interaction tracking identifiers of the logistics participants during the logistics distribution process, and perform cross-verification on the interaction tracking identifiers and the logistics list data to obtain the path verification nodes on different sub-chain structures during the logistics information tracking process.

[0060] In this embodiment, capturing the interaction tracking identifiers of the logistics participants during the logistics distribution process can be implemented by the following steps:

[0061] Determine the participation node attributes of the logistics participants when performing interaction operations in the distribution path;

[0062] Determine the state transition trajectory when performing interaction operations in the distribution path according to the participation node attributes;

[0063] Identify the interaction operations of the logistics participants during the logistics distribution process based on the state transition trajectory to obtain the interaction tracking identifiers of the logistics participants during the logistics distribution process.

[0064] Specifically, first, extract all the logistics event data involving interaction operations in the distribution path from the chain log, and identify the node participants involved in the events. By parsing the subject identity field and operation type field of each transaction in the block data, obtain the attribute information such as the role type, operation authority, affiliated organization, and node number of the participating nodes, and use all the obtained attributes as the participation node attributes. Then, construct a state transition model. The construction of the state transition model can be constructed by machine learning methods, which is not limited here. Then, connect different logistics events through timestamps and goods IDs to form a state change sequence from "warehouse out - transportation - receipt", and use each state change sequence as a state transition trajectory. Finally, analyze the interaction logic between nodes in the state transition trajectory, such as "node A sends - node B receives" or "node C inspects - node D signs", and identify the state transitions that constitute a complete interaction operation according to the preset interaction mode template library. For each type of state transition trajectory segment that conforms to the interaction logic, generate a set of interaction operation records, and extract the core fields such as the operator, recipient, timestamp, and location identifier, and combine them to generate a unique interaction tracking identifier.

[0065] It should be noted that in this application, the participating node attributes represent information reflecting the operation types, permission levels, and role identities assumed by logistics participants at a specific node; the state migration trajectory refers to the sequence of paths of changes in the logistics system state caused by the interaction operations performed by logistics participants; the interaction operations of logistics participants represent the process in which the interaction operations of logistics participants are represented in the form of pairs of state transfer events between nodes; the interaction tracking identifier refers to an identifying data unit that can identify the specific interaction behaviors performed by logistics participants in the distribution path.

[0066] Preferably, in this embodiment, cross-verification is performed on the interaction tracking identifier and the logistics list data to obtain path verification nodes on different sub-chain structures during the logistics information tracking process. Refer to Figure 2 As shown, this figure is a schematic flowchart of determining path verification nodes in some embodiments of this application. The path verification nodes in this embodiment can be implemented by the following steps:

[0067] In step S21, the tracking attribute sets on different sub-chain structures during the logistics information tracking process are determined according to the interaction tracking identifier;

[0068] In step S22, logistics interaction information matching the sub-chain structure is generated based on all the logistics list data;

[0069] In step S23, the tracking attribute sets are classified and compared with the logistics interaction information to obtain the tracking consensus trend during the logistics information tracking process;

[0070] In step S24, the path verification nodes on different sub-chain structures during the logistics information tracking process are determined through the tracking consensus trend.

[0071] In the specific implementation, first, the generated interactive tracking identification is structurally parsed to extract the key fields, including the operator identification, operation type, operation time, interaction location, and the involved goods identification; then, according to the extracted fields, combined with the chain distribution relationship of the current logistics network in the multi-subchain structure, the subchain number, subchain role, subchain time window and other information of each interactive behavior are annotated, and the interactive behaviors with operation continuity and node consistency on the same subchain structure are summarized and organized into a set of attribute sets, namely, tracking attribute sets, where each tracking attribute set represents the traceable interactive behavior characteristics in a certain logistics path on the subchain, and is accompanied by chain ID, participant ID, authority level and behavior time window. Then, the cargo status change records are read one by one from the cleaned and verified logistics list data, and each record contains fields such as cargo location coordinates, operation time, operator, and operation type. By comparing the read fields with the registration information of each node in the blockchain subchain structure, each inventory record is mapped to the corresponding subchain structure, that is, each logistics operation record is attached with a chain number, chain role, and time tag. Subsequently, all mapped inventory records are aggregated into structured information blocks according to the subchain dimension. Each structured information block is a logistics interaction information set on the subchain, which can be used to restore the real logistics operation track on the chain. Then, by comparing each tracking attribute set with the logistics interaction information block on its subchain, its behavior matching degree is calculated; the matching degree calculation includes indicators such as operation time proximity, participant consistency, and behavior type similarity, and is performed by combining rule matching with semantic comparison. If an interactive operation in a tracking attribute set finds multiple consistent operation records in the corresponding logistics interaction information, it is judged that the behavior reaches an "intra-chain behavior consensus". By counting the consensus events on all subchains, a set of trend vectors are generated based on time trends, participant behavior cycles, and inter-chain interaction relationships, and the generated trend vectors are used as the final tracking consensus trend. Finally, find the interaction point with the highest degree of consensus on each sub-chain structure, and evaluate the continuity and behavioral consistency of its upstream and downstream operations; if the state transfer of a node is uninterrupted and is consistently recorded in the interaction identifier and inventory data, it will be marked as a path verification node. The path verification node must meet the following conditions: appear in the interaction tracking identifier, have a corresponding record in the logistics interaction information, be at the sub-chain time sequence center, and have an on-chain identity and authority signature. These path verification nodes are formed into a chain structure, stored in an index that can be used for path verification, and verification credentials are dynamically generated by the smart contract.

[0072] It should be noted that in this application, the tracking attribute set refers to a data set with differentiability, comparability, and path guidance functions during the logistics information tracking process; the logistics interaction information refers to the logistics behavior information reflecting the goods executed by specific participants at specific time points; the tracking consensus trend refers to a set of patterns for judging the consistency degree of on-chain behaviors and the path credibility; the path verification node refers to an on-chain node that can support the authenticity of the path, the consistency of behaviors, and traceability through cross-verification during the logistics information tracking process.

[0073] In step S3, cross-chain access is performed on the order tracking information during the logistics information tracking process to obtain a tracking feedback tag. The tracking feedback tag is dynamically sharded to obtain an ordered tracking signature on the corresponding replicas within each sub-chain, and then the tracking attribute index on each sub-chain during the logistics information tracking process is determined by the ordered tracking signature.

[0074] In this embodiment, cross-chain access to the order tracking information during the logistics information tracking process to obtain a tracking feedback tag can be implemented by the following steps:

[0075] Construct an order tracking path through the cross-chain routing protocol and the logistics sub-chain topological relationship;

[0076] Resolve the consensus tracking characteristics of the cross-chain logistics information based on the routing identifier of the order tracking path;

[0077] Determine the tracking feedback tag according to the consensus tracking characteristics.

[0078] In specific implementation, first, call the cross-chain communication module to establish a connection with the current chain network environment through the registered cross-chain routing protocol. The commonly used cross-chain protocol can select the multi-chain message relay architecture, such as the inter-chain bridging module constructed based on the inter-chain communication protocol IBC or the light client mechanism. While establishing communication, the system synchronously loads the topological relationship graph of the logistics sub-chain. This topological relationship records the dependency paths, access priorities, and on-chain business relationships between different logistics sub-chains in the form of a directed graph. And parse the life cycle link of the order according to the unique order identifier, such as "production chain → warehousing chain → transportation chain → receipt chain", etc., map it to a complete inter-chain path, and use this path as the order tracking path. This order tracking path includes the cross-chain forwarding order, the target chain identifier, and the path encoding rule. Then, after obtaining the order tracking path, call the routing identifier for each jump segment in the path to access each chain one by one. During the process of accessing each sub-chain, locate the transaction record related to the order on this chain according to the order number, and parse the key fields from it, including the status change type, the identity of the processing node, the timestamp, and the digital signature. Subsequently, the system aggregates the status change records of the same order on different sub-chains in a timeline, eliminates duplicate and redundant behaviors, and retains the operation records verified by most sub-chain nodes. For these logistics status records signed and confirmed by multi-chain nodes, construct a structured multi-chain tracking event set and calculate the consensus weight among them. For example, for the same transportation behavior, if three sub-chains all record this behavior and the signatures are consistent, it is regarded as achieving "cross-chain behavior consensus", and all cross-chain behavior consensuses are used as the consensus tracking features of cross-chain logistics information. Finally, after obtaining the consensus tracking features, encode them in the link order to generate a tracking feedback label. The encoding rules include the order number, the hash of the behavior type, the timestamp sequence, and the signature summary of the chain node. Then judge the trust level of the tracking feedback label according to the path integrity scoring mechanism. If a certain order has valid consensus on all expected sub-chains, it is scored as a high trust level, and a complete tracking feedback label is marked; if there is a missing chain segment or the degree of consensus is not high, mark the tracking risk site and the missing node index in the tracking feedback label.

[0079] It should be noted that in this application, cross-chain access refers to the process of data exchange and operation between different blockchain networks to achieve information sharing and interoperability; order tracking information refers to a detailed data set that records the logistics status, location changes, and operations of relevant participants throughout the process from the initiation to the completion of an order; the cross-chain routing protocol refers to a communication mechanism used to forward information requests and return responses between multiple blockchain networks; the topological relationship of the logistics sub-chain refers to the connection structure and access dependency relationship map between each logistics sub-chain; the order tracking path refers to the inter-chain sequential path for an order to be transmitted and transferred between multiple logistics sub-chains; the routing identifier represents the number information used to identify each cross-chain target node in the order tracking path; the consensus tracking feature refers to a set of logistics status information that is consistent with the evolution of the order status in multiple sub-chains and jointly confirmed by multiple on-chain nodes; the tracking feedback label refers to the structured feedback information reflecting the order tracking result.

[0080] Preferably, in this embodiment, the tracking feedback label is dynamically sharded to obtain an ordered tracking signature on the corresponding replica within each sub-chain. Refer to Figure 3 As shown in the figure, which is a schematic flowchart for determining the ordered tracking signature in some embodiments of this application. The determination of the ordered tracking signature in this embodiment can be implemented by the following steps:

[0081] In step S31, the load balancing constraint during dynamic sharding is determined;

[0082] In step S32, according to the load balancing constraint, a sharding strategy matching is performed on the tracking feedback label to generate an initial sharding set;

[0083] In step S33, according to the initial sharding set, a trusted tracking vector on the corresponding replica within each sub-chain is determined;

[0084] In step S34, according to the trusted tracking vector, an ordered tracking signature on the corresponding replica within each sub-chain is determined.

[0085] In specific implementation, first, collect the running load data of the current multi-subchain network, including indicators such as the storage capacity, computing power, network bandwidth, and current task queue length of each subchain node. Based on the collected indicators, construct a load balancing model, adopt a weight allocation algorithm, such as weighted round-robin or a dynamic adjustment strategy based on resource prediction, and set the maximum receiving shard size and request frequency threshold for each subchain and its replicas. The load balancing constraint also includes the principle of minimizing the shard size to ensure that a single shard is convenient for transmission and processing while taking into account the system response speed. Next, cut the integrated tracking feedback tag sequence according to the preset sharding strategy. This sharding strategy is continuously sliced according to the weight of the load balancing model, either by the timestamp or the path order of the tags, to ensure the order and logical integrity of the data. The sliding window algorithm is used in the sharding process to divide the tag sequence into evenly sized and independent interval segments. Establish metadata records for each shard, including the start and end tag indexes, shard size, corresponding target subchain and replica information, and use the combination of all interval segments as the initial shard set. Then, distribute the initial shard set to the corresponding replicas of each subchain. The replica nodes perform integrity verification on the received shard data, using digital signature and hash chain technology to verify that the tags have not been tampered with. Subsequently, the replica nodes construct a tracking vector structure according to the tag order. This tracking vector structure uses a vector array to achieve an ordered arrangement of tags and attaches timestamp and signature information to form a trusted tracking vector. Finally, the replica nodes sequentially call the private key for digital signature on each tag data in the trusted tracking vector to form the corresponding tracking signature. The elliptic curve digital signature algorithm (ECDSA) or the national standard SM2 signature algorithm is used in the signature process to ensure the security and non-forgery of the signature. All signatures are arranged in the order of the tracking tags to form an ordered tracking signature chain, which is then stored in the distributed ledger within the blockchain subchain to ensure the anti-tampering and public verification of the signed data.

[0086] It should be noted that in this application, dynamic sharding refers to the process of dividing the tracking feedback tags into multiple parts according to the system runtime state and data characteristics to achieve load balancing and optimal resource allocation; the load balancing constraint refers to the allocation principles and limiting conditions agreed upon during the sharding process to ensure the even distribution of data and requests among each subchain and its replicas and avoid single-point overload; sharding strategy matching refers to the process of dividing the tracking feedback tags into multiple subsets according to the strategy rules based on the load balancing constraint; the initial shard set refers to the set of multiple data chunks generated after the first application of the sharding strategy; the trusted tracking vector refers to an ordered vector containing tracking tags that has been verified and confirmed by each subchain and its replicas based on the initial shard set; the ordered tracking signature refers to the digital signature sequence generated from the logistics tracking tags arranged in chronological order; each corresponding replica within a subchain represents multiple node replicas on that subchain that store the same logistics data.

[0087] In this embodiment, the tracking attribute index on each sub-chain during the logistics information tracking determined by the ordered tracking signature can be implemented by the following steps:

[0088] Extract the tracking feature information during the logistics sub-chain tracking operation from the ordered tracking signature;

[0089] Determine the tracking classification keywords on each sub-chain during the logistics information tracking through the tracking feature information;

[0090] Determine the tracking attribute index on each sub-chain during the logistics information tracking from the tracking classification keywords.

[0091] In specific implementation, first, access the stored ordered tracking signature sequence within each sub-chain. Combine the tracking label data corresponding to the signature, and use the digital signature verification algorithm to verify the validity of the signature. Through the analysis of the logistics tracking label data corresponding to the signature, extract the key tracking fields included, such as operation time, operation type (such as loading, unloading, transshipment, etc.), operation node identifier, and participant identity information. Take the key tracking fields as the tracking feature information. Among them, the extraction process uses the structured data parsing method, combined with the predefined label format and metadata description, to ensure the accuracy and integrity of the extracted data. Then, use the rule engine or machine learning model to analyze the tracking feature information and automatically generate classification keywords. The rule engine generates corresponding classification labels based on preset business rules, such as combinations of "operation type + time interval + participant category", etc. The machine learning model can use natural language processing and clustering analysis methods to perform pattern recognition on the tracking features, extract high-frequency words or behavior patterns as keywords, that is, obtain the tracking classification keywords on each sub-chain during the logistics information tracking. Finally, based on the generated tracking classification keywords, construct a hierarchical index structure, such as a B+ tree or a Trie tree, to support fast keyword matching and range query. Take the hierarchical index structure as the tracking attribute index. This tracking attribute index structure is stored in the blockchain node database corresponding to each sub-chain to ensure the distributed synchronization and consistency of the index. The content of the tracking attribute index includes the mapping relationship between the keywords and the corresponding ordered tracking signatures, which is convenient for quickly locating specific tracking records.

[0092] It should be noted that in this application, the tracking feature information refers to the key information characterizing the logistics tracking operation during the logistics information tracking; the tracking classification keywords refer to the set of keywords used for classifying and indexing the logistics tracking operations; the tracking attribute index refers to the index structure used for locating and accessing the relevant logistics tracking information within the sub-chain.

[0093] In step S4, compare the path verification node with the tracking attribute index to obtain the logistics location information during the logistics information tracking, and then store the logistics location information on the blockchain node of the logistics participant.

[0094] In this embodiment, comparing the path verification node with the tracking attribute index to obtain the logistics point information during the logistics information tracking process can be achieved by the following steps:

[0095] Determine the dynamic similarity period during the logistics information tracking process according to the path verification node and the tracking attribute index;

[0096] Match the dynamic similarity period with a preset threshold to filter out a set of candidate nodes that meet the logistics path consistency;

[0097] Determine the logistics point information during the logistics information tracking process according to the set of candidate nodes.

[0098] Specifically, first, by traversing the tracking records corresponding to the path verification node and the tracking attribute index, a time series similarity calculation method (such as the Dynamic Time Warping algorithm) is used to perform matching analysis on the time series data of both. The specific implementation includes: first, performing standardization preprocessing on the corresponding tracking timestamps and status data in the path verification node and the tracking attribute index, then calculating their similarity scores on the time axis, and dynamically adjusting the matching interval in combination with the window sliding technology to obtain the dynamic similarity period. This dynamic similarity period reflects the synchronous change trend and the repeatedly occurring time period of both on the logistics path. Then, compare the similarity of the dynamic similarity period with the preset threshold to filter out the path verification nodes with a similarity higher than the threshold. The specific approach is: store the similarity results in a temporary data structure, and use a threshold filtering algorithm to eliminate the nodes that do not meet the conditions, and retain the nodes with a higher degree of compliance as candidate nodes. The determination of the preset threshold is based on the complexity of the logistics path, the characteristics of historical data, and the system error tolerance, and supports dynamic adjustment to adapt to different scenarios. Finally, according to the set of candidate nodes, using the node coordinate information, timestamps, and node attributes, a spatial clustering algorithm (such as DBSCAN or K-means) is used to aggregate the candidate nodes, eliminate noise and duplicate nodes, and finally generate the logistics point information. Subsequently, associate this logistics point information with the corresponding logistics events and cargo statuses to form structured tracking data. This structured tracking data is broadcast and synchronized through blockchain nodes to ensure that all logistics participant nodes can access and verify it.

[0099] It should be noted that in this application, the dynamic similarity period refers to the period during which the path verification node and the tracking attribute index exhibit similar time series characteristics or state change periods during the logistics tracking process; the candidate node set refers to the set of path verification nodes that meet the dynamic similarity period matching conditions and have path consistency after screening; the preset threshold represents the lower limit value of the dynamic similarity period matching degree set according to the logistics business requirements and system performance tuning experience, which is used to distinguish effective matches from invalid matches; the logistics location information refers to the real logistics location and status information represented by the candidate nodes that have been verified and screened.

[0100] In addition, in specific implementation, storing the logistics location information on the blockchain nodes of logistics participants can be achieved in the following way: First, structure the generated logistics location information into a data format recognizable by the blockchain, including location coordinates, timestamps, node identity identifiers, and associated logistics event summaries. Subsequently, according to the sub-chain structure to which the participant belongs, select its blockchain node as the storage target node, and use a distributed consensus mechanism such as PBFT or RAFT to ensure the consistency and legality of the writing process. In specific execution, call the blockchain interface to package the logistics location information into a transaction request, generate a new block after permission verification and format verification through a smart contract, broadcast it to the whole network, and have the nodes of the corresponding logistics participants complete the writing.

[0101] It should be noted that in this application, the blockchain nodes of logistics participants refer to the distributed ledger running nodes used to store, verify, and synchronize logistics data.

[0102] Thus, in this application, the structured availability of logistics information tracking can be improved in the case of isolated node verification during logistics information tracking; among them, by extracting logistics list data, ensuring the accurate positioning and structured expression of logistics list data in the multi-source chain ledger; by determining the path verification node, the on-chain interaction recognition and path consistency verification of logistics behavior nodes can be realized, so as to establish an accurate path verification mechanism in a multi-sub-chain environment and effectively solve the problems of node behavior breakage and data isolation in the tracking chain; by determining the tracking attribute index, the flexibility and consistency of cross-chain data processing can be enhanced, and through the dynamic sharding of feedback tags and the sorting of tracking signatures, the standardized expression of tracking features in heterogeneous sub-chains can be realized, improving the comparability and structured depth of tracking information between sub-chains; by determining the logistics location information, the deep integration of the tracking path and the attribute index can be realized, accurately restoring the real location points in the whole logistics process, and ensuring the persistence and verifiability of data through the on-chain storage mechanism, thereby improving the availability of the full-link tracking results.

[0103] In summary, the technical solution adopted in this application can jointly and distributively integrate and trace logistics information in a multi-sub-chain heterogeneous blockchain environment to improve the inter-chain consistency in the process of logistics information tracing.

[0104] Embodiment 2. This application provides a logistics information tracing system based on a blockchain. Referring to Figure 4 as shown, this figure is a module structure diagram of a logistics information tracing system based on a blockchain according to this embodiment of this application. The tracing system includes:

[0105] A data reading module 100, configured to read the distributed ledger of the logistics distribution link and extract logistics list data including the location of goods from the distributed ledger;

[0106] A cross-verification module 200, configured to capture the interactive tracking identifiers of logistics participants during the logistics distribution process, perform cross-verification on the interactive tracking identifiers and the logistics list data, and obtain path verification nodes on different sub-chain structures during the logistics information tracing process;

[0107] A dynamic sharding module 300, configured to cross-chain access the order tracking information during the logistics information tracing process, obtain a tracking feedback label, perform dynamic sharding on the tracking feedback label to obtain an ordered tracking signature on the corresponding replica within each sub-chain, and further determine the tracking attribute index on each sub-chain during the logistics information tracing process from the ordered tracking signature;

[0108] An information storage module 400, configured to compare the path verification nodes and the tracking attribute index to obtain the logistics point information during the logistics information tracing process, and further store the logistics point information on the blockchain nodes of logistics participants.

[0109] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0110] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0111] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in such a process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. A blockchain-based logistics information tracking method, characterized in that, The tracking method includes the following steps: Read the distributed ledger in the logistics distribution link, and extract the logistics list data containing the location of the goods from the distributed ledger; Capture the interactive tracking identifiers of logistics participants during the logistics distribution process, cross-verify the interactive tracking identifiers and the logistics list data, and obtain the path verification nodes on different sub-chain structures during the logistics information tracking process; Cross-chain access the order tracking information during the logistics information tracking process, obtain the tracking feedback label, perform dynamic sharding on the tracking feedback label, obtain the ordered tracking signatures on the corresponding replicas within each sub-chain, and then determine the tracking attribute index on each sub-chain during the logistics information tracking process from the ordered tracking signatures; Compare the path verification nodes with the tracking attribute index to obtain the logistics point information during the logistics information tracking process, and then store the logistics point information on the blockchain nodes of logistics participants.

2. The logistics information tracking method based on blockchain according to claim 1, wherein The distributed ledger mentioned refers to a data ledger system maintained separately by multiple logistics participants, each having an independent chain node, and keeping the data consistent through a consensus mechanism.

3. The logistics information tracking method based on blockchain according to claim 1, characterized in that, Extracting the logistics list data containing the location of the goods from the distributed ledger specifically includes: Determine the original logistics list log containing the location of the goods according to the distributed ledger; Determine the circulation permission record corresponding to each logistics location coordinate according to the original logistics list log; Determine the logistics list data containing the location of the goods through the circulation permission record.

4. The logistics information tracking method based on blockchain according to claim 1, wherein, Capturing the interactive tracking identifiers of logistics participants during the logistics distribution process specifically includes: Determine the participation node attributes of logistics participants when performing interactive operations in the distribution path; Determine the state migration trajectory when performing interactive operations in the distribution path according to the participation node attributes; Identify the interactive operations of logistics participants during the logistics distribution process based on the state migration trajectory, and obtain the interactive tracking identifiers of logistics participants during the logistics distribution process.

5. The logistics information tracking method based on blockchain according to claim 1, wherein Cross-chain access the order tracking information during the logistics information tracking process and obtain the tracking feedback label specifically includes: Construct an order tracking path through a cross-chain routing protocol and the topological relationship of the logistics sub-chain; Analyze the consensus tracking features of cross-chain logistics information based on the routing identifier of the order tracking path; Determine the tracking feedback label according to the consensus tracking features.

6. The logistics information tracking method based on blockchain according to claim 1, characterized in that The corresponding replicas within each sub-chain mentioned refer to multiple node replicas on the sub-chain that store the same logistics data.

7. The logistics information tracking method based on blockchain according to claim 1, wherein, Determining the tracking attribute index on each sub-chain during the logistics information tracking process from the ordered tracking signatures specifically includes: Extract the tracking feature information when performing logistics sub-chain tracking operations from the ordered tracking signatures; Determine the tracking classification keywords on each sub-chain during the logistics information tracking process through the tracking feature information; Determine the tracking attribute index on each sub-chain during the logistics information tracking process from the tracking classification keywords.

8. A blockchain-based logistics information tracking method according to claim 1, characterized in that, Comparing the path verification nodes with the tracking attribute index to obtain the logistics point information during the logistics information tracking process specifically includes: Determine the dynamic similarity period during the logistics information tracking process according to the path verification nodes and the tracking attribute index; Match the dynamic similarity period with a preset threshold to screen out a set of candidate nodes that meet the consistency of the logistics path; Determine the logistics point information during the logistics information tracking process according to the set of candidate nodes.

9. The logistics information tracking method based on blockchain according to claim 1, characterized in that The blockchain node of the logistics participant refers to a distributed ledger operating node used to store, verify, and synchronize logistics data.

10. A blockchain-based logistics information tracking system for implementing a blockchain-based logistics information tracking method according to any one of claims 1 to 9, characterized in that, The tracking system includes: A data reading module, configured to read the distributed ledger of the logistics distribution link and extract logistics list data including the location of goods from the distributed ledger; A cross-verification module, configured to capture the interactive tracking identifiers of logistics participants during the logistics distribution process, perform cross-verification on the interactive tracking identifiers and the logistics list data, and obtain path verification nodes on different sub-chain structures during the logistics information tracking process; A dynamic sharding module, configured to cross-chain access the order tracking information during the logistics information tracking process, obtain a tracking feedback label, perform dynamic sharding on the tracking feedback label to obtain an ordered tracking signature on the corresponding replica within each sub-chain, and further determine the tracking attribute index on each sub-chain during the logistics information tracking process from the ordered tracking signature; An information storage module, configured to compare the path verification nodes with the tracking attribute index to obtain the logistics point information during the logistics information tracking process, and then store the logistics point information on the blockchain nodes of logistics participants.

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