Smart factory supply chain tracing method and system based on block chain

Abstract

The invention relates to the technical field of smart factory supply chain traceability, and discloses a smart factory supply chain traceability method and system based on a block chain, and the method comprises the steps: recording the geographic position and logistics trajectory data of a supply chain node through the block chain, constructing a network topological graph, carrying out the merging optimization of the node, calculating a shortest path, and recognizing a key link; and the supply chain traceability path is determined. According to the method, firstly, the data credibility is ensured by using a block chain technology, then network optimization is performed based on node attributes and a service dependency relationship, and finally, a key traceability path is determined through an improved path algorithm and a node importance score. According to the method, the supply chain network structure can be effectively simplified, important nodes and abnormal links can be identified, reliable technical support is provided for supply chain management and risk control, and the transparency and traceability of the supply chain are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart factory supply chain traceability, and in particular to a smart factory supply chain traceability method and system based on a blockchain. BACKGROUND

[0002] The application of blockchain technology in the field of smart factory supply chain traceability is crucial. It ensures data transparency and tamper resistance through a distributed ledger, providing a new trust mechanism for supply chain management.

[0003] However, existing traceability methods have significant limitations in dealing with complex supply chain networks, directly affecting the real-time and accuracy of traceability. The main challenges are: first, the complex topology of the supply chain network, the difficulty of unified modeling of the geographical location and logistics trajectory data between nodes, leading to low efficiency of traceability path analysis. Second, there are multiple cross-supply relationships in the network supply chain, and it is difficult to merge data of similar links, which can easily cause data redundancy and increase computational burden. Finally, existing graph algorithms are difficult to quickly identify the shortest traceability path when dealing with dynamic supply chain networks, affecting the positioning efficiency of key links. These technical difficulties hinder the construction of an efficient and accurate traceability system.

[0004] Therefore, how to design a traceability method based on a blockchain to avoid repeated data calculation and quickly locate key links has become a key problem in the field of smart factory supply chain traceability. SUMMARY

[0005] The present application provides a smart factory supply chain traceability method and system based on a blockchain to achieve efficient positioning of key links.

[0006] In the first aspect, to solve the above technical problems, the present application provides a smart factory supply chain traceability method based on a blockchain, comprising: Obtain the geographical location and logistics trajectory of the supply chain nodes and generate an original data set, and determine the connection weight and direction between nodes according to the original data set; Calculate the similarity between nodes. If the similarity is greater than the preset threshold based on the throughput weighted adjustment and the business dependency relationship strength meets the conditions, merge the nodes and obtain a simplified topology graph; Determine the weight and identification of the merged nodes according to the simplified topology graph, obtain an optimized path set, calculate the importance score of the nodes according to the path set, and if the importance score of the nodes is higher than the preset score threshold, determine it as a key link, obtain the traceability path and running state data set containing the key link; Obtain the deviation of the geographical location and logistics trajectory of each node according to the running state data set. If the deviation is greater than the preset deviation threshold, determine it as an abnormal link and obtain the traceability record of the abnormal link; A dynamic dependency graph of the supply chain network is generated based on the traceability records, and real-time update data of the key links is obtained to determine the location results of the key links.

[0007] Preferably, determining the connection weights and directions between nodes based on the original dataset includes: Calculate the physical distance and flow time between nodes to obtain the initial matrix; The shortest path between nodes is calculated based on the initial matrix, and the connection direction of the logistics trajectory is determined by combining transportation costs and time windows. If the shortest path is greater than a preset path threshold, then the connection weight is calculated by combining the flow time and the transportation cost.

[0008] Preferably, the similarity between the computing nodes, if the similarity is greater than a preset threshold based on throughput weighting and the business dependency strength meets the condition, then the nodes are merged and a simplified topology graph is obtained, including: Calculate the similarity between each pair of nodes. If the similarity is greater than a preset threshold based on throughput weighting, extract the dependency strength data between the nodes to obtain the node pairs that meet the conditions. The data of the nodes that meet the conditions are merged and the network topology is updated to obtain the simplified topology graph.

[0009] Preferably, determining the weight and identifier of the merged node based on the simplified topology graph includes: The throughput rate, dependency strength, and interaction frequency of the merged nodes are obtained from the simplified topology graph, and the initial feature vector is calculated to obtain the preliminary feature representation of the merged nodes. Based on the preliminary feature representation and business priority, a weight coefficient is determined. If the business priority is higher than a preset priority threshold, the weight coefficient is increased to determine the weight of the merged node. The nodes are grouped according to the weights to obtain the node identifiers after grouping.

[0010] Preferably, if the importance score of the node is higher than a preset score threshold, it is determined to be a key link, and the tracing path containing the key link is obtained, including: If the importance score of the node is located in a high-value cluster center, it is determined to be a key link, and a set of key links is obtained; By using the set of key links, the paths and data flows containing the key links are obtained, and the paths are sorted according to their weights and business priorities. The paths with the highest rankings are determined as the source tracing paths.

[0011] Preferably, if the deviation is greater than a preset deviation threshold, it is determined to be an abnormal step, and the source tracing record of the abnormal step is obtained, including: Calculate the deviation between the node and the preset standard position to obtain the geographic location deviation dataset of the node and obtain the corresponding logistics trajectory; Calculate the deviation between the logistics trajectory and the standard path. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and a set of abnormal links is obtained. Based on the set of abnormal links, obtain the interaction frequency and status update data of each abnormal link, calculate the fluctuation characteristics of the interaction frequency, and determine the source identifier of the abnormal link.

[0012] Preferably, generating a dynamic dependency graph of the supply chain network based on the traceability records includes: Obtain node identifiers and interaction data from the source tracing records, calculate the state update frequency of the interaction data, obtain the interaction frequency, and calculate the dependencies between nodes; If the interaction frequency is higher than a preset frequency threshold, it is determined to be a strong dependency relationship, the node weight of the dependency relationship is determined, and a node dependency relationship dataset is obtained. Based on the node dependency dataset, logistics status and time series are obtained. Node weights are adjusted using the time series to obtain a dynamic network topology dataset and path deviation and anomaly classification data, generating a dynamic dependency graph of the supply chain network.

[0013] Preferably, obtaining the real-time update data of the key link and determining the location result of the key link includes: Obtain real-time update data of nodes from the dynamic dependency graph and calculate the interaction frequency of nodes; If the interaction frequency is higher than the preset frequency threshold, it is determined to be a high-activity node, and a high-activity node dataset is obtained, along with node weight and topology dynamics data. By updating edge weights through changes in node states, we obtain an adjusted network topology dataset and acquire path deviation and logistics timeliness data. We then calculate the shortest path and adjust the logistics priority between nodes through path optimization to obtain a real-time shortest path dataset. Abnormal nodes and supply chain bottleneck data are obtained from the real-time shortest path dataset. If the abnormal node appears at the bottleneck position of multiple paths, it is determined to be a critical link, and the location result of the critical link is obtained.

[0014] Secondly, the present invention provides a blockchain-based smart factory supply chain traceability system, comprising: The determination module is used to obtain the geographical location and logistics trajectory of the supply chain nodes and generate a raw dataset, and determine the connection weight and direction between nodes based on the raw dataset; The merging module is used to calculate the similarity between nodes. If the similarity is greater than a preset threshold based on throughput weighting and the strength of business dependency meets the conditions, then the nodes are merged and a simplified topology graph is obtained. The first acquisition module is used to determine the weight and identifier of the merged nodes according to the simplified topology graph, obtain the optimized path set, calculate the importance score of the nodes according to the path set, and if the importance score of the node is higher than the preset score threshold, it is determined to be the key link, and acquire the traceability path and running status dataset containing the key link. The second acquisition module is used to acquire the geographical location and logistics trajectory deviation of each node based on the running status dataset. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and the source traceability record of the abnormal link is acquired. The positioning module is used to generate a dynamic dependency graph of the supply chain network based on the traceability records and obtain real-time updated data of the key links to determine the positioning results of the key links.

[0015] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the blockchain-based smart factory supply chain traceability method described in any one of the above.

[0016] Compared to existing technologies, this invention provides a blockchain-based smart factory supply chain traceability method and system. It records the geographical locations and logistics trajectories of supply chain nodes using blockchain, constructs a network topology map, optimizes node merging, calculates the shortest path, and identifies key links to determine the supply chain traceability path. This invention first utilizes blockchain technology to ensure data credibility, then optimizes the network based on node attributes and business dependencies, and finally determines key traceability paths through an improved path algorithm and node importance scoring. This method effectively simplifies the supply chain network structure, identifies important nodes and abnormal links, provides reliable technical support for supply chain management and risk control, and improves the transparency and traceability of the supply chain. Attached Figure Description

[0017] Figure 1 This is a flowchart of a blockchain-based smart factory supply chain traceability method provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a blockchain-based smart factory supply chain traceability system provided in the second embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a flowchart of a blockchain-based smart factory supply chain traceability method, including the following steps: S11, Obtain the geographical location and logistics trajectory of the supply chain nodes and generate the original dataset, and determine the connection weight and direction between the nodes based on the original dataset; S12, calculate the similarity between nodes. If the similarity is greater than a preset threshold based on throughput weighting and the business dependency strength meets the condition, then merge the nodes and obtain a simplified topology graph. S13. Determine the weight and identifier of the merged nodes based on the simplified topology graph to obtain the optimized path set. Calculate the importance score of the nodes based on the path set. If the importance score of a node is higher than a preset score threshold, it is determined to be a key link. Obtain the traceability path and running status dataset containing the key link. S14. Obtain the deviation between the geographical location and logistics trajectory of each node based on the running status dataset. If the deviation is greater than the preset deviation threshold, it is determined to be an abnormal link, and the traceability record of the abnormal link is obtained. S15, Generate a dynamic dependency graph of the supply chain network based on the traceability records and obtain real-time update data of the key links to determine the location result of the key links.

[0020] In step S11, the geographical location and logistics trajectory of the supply chain nodes are obtained and an original dataset is generated. The connection weights and directions between nodes are determined based on the original dataset.

[0021] The step of determining the connection weights and directions between nodes based on the original dataset includes: Calculate the physical distance and flow time between nodes to obtain the initial matrix; The shortest path between nodes is calculated based on the initial matrix, and the connection direction of the logistics trajectory is determined by combining transportation costs and time windows. If the shortest path is greater than a preset path threshold, then the connection weight is calculated by combining the flow time and the transportation cost.

[0022] The system acquires geographical location and logistics trajectory data of supply chain nodes. Real-time latitude and longitude coordinates and transportation route information are collected using pre-set sensor devices. The data is labeled with timestamps and node identifiers to obtain structured initial data records. If the initial data record passes format verification, a hash encryption algorithm is used to process it, generating a unique hash value. This hash value is then combined with the initial data to form data blocks, resulting in a set of data blocks containing hash verification data.

[0023] For example, the collection of geographical location and logistics trajectory data of supply chain nodes is a core aspect of modern logistics management, aiming to achieve transparency and traceability of the transportation process through technological means.

[0024] For example, sensor devices such as GPS modules can collect the latitude and longitude coordinates of transport vehicles in real time.

[0025] For example, a truck transporting cold chain food is equipped with a GPS device that records its location data every 5 seconds, generating latitude and longitude coordinates such as 31.23°N, 121.47°E, along with a timestamp such as 2025-04-24 08:00:00. This high-frequency data collection ensures the accuracy of the trajectory data, providing a reliable foundation for subsequent analysis.

[0026] In one possible implementation, node identifiers are used to distinguish different links in the supply chain.

[0027] For example, warehouses, distribution centers, and retail outlets in cold chain transportation are labeled as nodes A, B, and C, respectively. Whenever a truck arrives at node B, sensors record the node identifier B, the arrival time (2025-04-24 09:00:00), and coordinate data, forming a structured initial data record. This labeling method facilitates tracking the flow of goods in the supply chain and improves scheduling efficiency.

[0028] It should be noted that the initial data records must undergo format validation to ensure data integrity.

[0029] For example, the system checks whether the latitude and longitude are within a reasonable range, whether the timestamps are continuous, and whether the node identifiers are valid. If data is missing or incorrectly formatted, the system will mark it as an anomaly and notify the administrator. This verification mechanism effectively reduces the impact of data errors on subsequent processing.

[0030] Specifically, hash encryption algorithms are used to ensure data security.

[0031] In one embodiment, the initial data record generates a unique hash value using the SHA-256 algorithm.

[0032] For example, a record containing a timestamp of 2025-04-24 08:00:00, coordinates of 31.23°N, 121.47°E, and node identifier A, after processing by an algorithm, generates a hash value such as "7b3c...f9a2". This hash value is combined with the initial data to form a data block, which is then stored in a data block set. This encryption method ensures that the data is tamper-proof, enhancing the credibility of supply chain data.

[0033] Preferably, the data block set can be used to construct a blockchain structure to further enhance data transparency.

[0034] For example, whenever a truck completes a transport segment, the system generates a new data block containing the hash value of the previous data block, the current data, and the new hash value. This chain-like structure ensures that any attempt at tampering will be detected.

[0035] For example, if someone attempts to modify the arrival time of node B, the hash value will not match, and the system will issue an alert. This mechanism provides a highly secure data management solution for the supply chain.

[0036] In one embodiment, the data block set can support real-time querying and analysis.

[0037] For example, retailers can use the system to track the complete transportation route of a batch of cold chain food, view the arrival time and location of each node, and confirm whether the goods have maintained cold chain conditions within the specified time. This transparent management helps increase consumer trust and reduce food safety risks.

[0038] Understandably, the aforementioned solutions offer significant technical benefits. First, real-time data collection and node identification improve logistics efficiency and facilitate rapid response to anomalies. Second, hash encryption and data block structures ensure data security and traceability, making them suitable for scenarios requiring high trust, such as cold chain logistics. Furthermore, the combination of format verification and blockchain technology further reduces the risk of data errors and tampering, providing reliable technical support for supply chain management. These measures collectively promote the intelligentization and standardization of the logistics process.

[0039] In one possible implementation, the shortest path is calculated using Dijkstra's algorithm based on the initial matrix. Dijkstra's algorithm iteratively compares the distances between nodes and prioritizes the path with the shortest cumulative distance.

[0040] Specifically, a network topology is constructed based on a weight matrix and connection directions, and a weighted directed graph is generated using visualization tools. The network topology uses nodes as vertices, connections as directed edges, and weights labeled on the edges.

[0041] For example, the tool can draw an edge from node A to node B, labeled with a weight of 0.75, and the arrows indicate the direction of logistics. The generated graph visually displays the supply chain network structure, such as the preferred path from the warehouse to the distribution center, making it easier for managers to identify bottlenecks or optimize routes.

[0042] In one embodiment, the weighted directed graph supports dynamic adjustment.

[0043] For example, if node B is delayed due to traffic control, the system can update the weight matrix based on real-time data, recalculate the route, and recommend an ADC route. This flexibility enhances the logistics system's ability to respond to unforeseen circumstances.

[0044] Preferably, visual charts can also assist in decision-making.

[0045] For example, by using a chart to identify that multiple input paths to node C have similar weights, managers can prioritize the path with the lower cost, thus reducing overall expenses. This intuitive analysis enhances scheduling efficiency.

[0046] Understandably, the methods described above, through data analysis, path calculation, and visualization, form a complete chain from data to decision-making. Precise processing at each stage ensures the optimization of logistics trajectories, while the construction of weight matrices and directed graphs provides a clear network perspective, offering strong technical support for supply chain management.

[0047] In step S12, the similarity between nodes is calculated. If the similarity is greater than a preset threshold based on throughput weighting and the business dependency strength meets the condition, the nodes are merged and a simplified topology graph is obtained.

[0048] If the similarity between the computing nodes is greater than a preset threshold based on throughput weighting and the strength of the business dependency relationship meets the conditions, then the nodes are merged and a simplified topology graph is obtained, including: Calculate the similarity between each pair of nodes. If the similarity is greater than a preset threshold based on throughput weighting, extract the dependency strength data between the nodes to obtain the node pairs that meet the conditions. The data of the nodes that meet the conditions are merged and the network topology is updated to obtain the simplified topology graph.

[0049] For example, when obtaining node throughput and business dependency data from a network topology diagram, the throughput of each node can be extracted by analyzing the cargo flow records of each node in the supply chain network.

[0050] For example, one logistics center handles an average of 10,000 parcels per day, while another sorting station handles 3,000. This data can be directly retrieved from the database. Business dependencies are determined by analyzing the flow of logistics orders between nodes.

[0051] For example, goods from warehouse A need to pass through transit station B to reach sorting station C, forming a dependency chain from A to B and from B to C. Using database query tools, logistics trajectory information between nodes can be extracted, such as the average transportation time from warehouse A to transit station B being 12 hours, and the number of freight batches being twice daily.

[0052] In one possible implementation, a vector encoding tool is used to combine node throughput, business dependencies, and logistics trajectory information into a first feature vector.

[0053] Specifically, node throughput can be standardized to a range of 0 to 1.

[0054] For example, a throughput of 10,000 items is normalized to 1, and 3,000 items is normalized to 0.3. Business dependencies can be encoded as binary values, with 1 representing the existence of a dependency and 0 representing the absence of a dependency. Logistics tracking information is quantified through transportation time and batch frequency.

[0055] For example, a 12-hour transport time is normalized to 0.5, and two batches per day are denoted as 0.4. These numerical combinations form an eigenvector, such as [1,1,0.5,0.4], which is used for subsequent analysis.

[0056] It should be noted that when using the cosine similarity calculation tool to calculate the similarity score between nodes for the first feature vector, the angle between the feature vectors of the two nodes can be compared.

[0057] For example, the vector angle between warehouse A and transit station B is small, with a score of 0.9, indicating that they are highly similar in terms of throughput and dependency. If the similarity score is greater than a preset threshold (such as 0.8) based on throughput weighting, dependency strength data is further extracted using a business dependency strength assessment tool.

[0058] For example, if the dependency strength from A to B is 0.7, it means that 70% of the goods from A need to be transshipped through B. The node pair that meets this condition is A and B.

[0059] Specifically, when using a node merging tool to merge eligible node pairs, A and B can be considered as a single virtual node. The merged throughput is 13,000 items, and the average value of the logistics trajectory information is taken. The topology reconstruction tool then updates the network topology, generating a first simplified topology diagram.

[0060] For example, the original topology graph has 10 edges, which are reduced to 8 after merging, simplifying the network structure.

[0061] Preferably, when using the K-means algorithm to cluster nodes in the first simplified topology graph, the nodes can be divided into 3 categories based on the feature vectors.

[0062] For example, high-throughput nodes, low-throughput nodes, and relay nodes. Clustering results are used to adjust connection relationships, such as prioritizing connections between nodes of the same cluster.

[0063] For example, higher-throughput nodes form tighter connections, generating a final, simplified topology. This approach optimizes the efficiency of supply chain network analysis, facilitating the identification of key nodes and paths.

[0064] Understandably, the final simplified topology diagram, through clustering and connection adjustments, clearly reveals the core structure of the supply chain network.

[0065] For example, clustering can identify three nodes in a certain region that can be merged into one virtual node, reducing analytical complexity. This method helps to quickly locate bottleneck nodes in the supply chain and improve the targeting of logistics planning.

[0066] In step S13, the weights and identifiers of the merged nodes are determined according to the simplified topology graph to obtain the optimized path set. The importance score of the nodes is calculated according to the path set. If the importance score of the node is higher than the preset score threshold, it is determined to be a key link. The source path and running status dataset containing the key link are obtained.

[0067] The step of determining the weight and identifier of the merged node based on the simplified topology graph includes: The throughput rate, dependency strength, and interaction frequency of the merged nodes are obtained from the simplified topology graph, and the initial feature vector is calculated to obtain the preliminary feature representation of the merged nodes. Based on the preliminary feature representation and business priority, a weight coefficient is determined. If the business priority is higher than a preset priority threshold, the weight coefficient is increased to determine the weight of the merged node. The nodes are grouped according to the weights to obtain the node identifiers after grouping.

[0068] For example, when obtaining the throughput rate, dependency strength, and interaction frequency of merged nodes in a simplified topology diagram of a supply chain network, operational data from logistics centers can be analyzed.

[0069] For example, a merge node represents the combination of warehouse A and transit station B, and its throughput rate can be retrieved from the database as the daily parcel processing volume, such as 13,000 parcels / day. Dependency strength is determined by analyzing cargo flow.

[0070] For example, if 70% of the goods need to flow to sorting station C through this merging node, the dependency strength is 0.7. The interaction frequency is based on the daily transport batches between nodes; for example, three freight trips per day would be recorded as 0.6. This data is extracted from the logistics management system to ensure accuracy.

[0071] In one possible implementation, when calculating the initial feature vector using a weighted average method, the throughput rate, dependency strength, and interaction frequency can be normalized and combined.

[0072] For example, throughput of 13,000 items / day is normalized to 1, dependency strength of 0.7 is normalized to 0.7, and interaction frequency of 0.6 is normalized to 0.6, forming a vector [1, 0.7, 0.6]. The weighted average can be set with initial weights, such as throughput of 40%, dependency strength of 30%, and interaction frequency of 30%, to calculate a preliminary feature representation. This vector clearly describes the logistical characteristics of the merged nodes.

[0073] It should be noted that when determining the weight coefficient based on the preliminary feature representation and business priority, it can be evaluated through business rules.

[0074] For example, if a merge node involves a high-priority customer order with a priority score of 0.9, which is higher than the preset threshold of 0.8, then the weight coefficient is increased, such as from 1 to 1.2. The weight is determined in conjunction with the feature vector.

[0075] For example, the adjusted weight is 1.2, which is used for subsequent analysis. This method highlights the business importance of key nodes.

[0076] Specifically, when obtaining connection density and processing latency based on weights and data flow direction, the logistics network interactions of merged nodes can be analyzed.

[0077] For example, connection density is determined by statistically merging the number of transport routes between a node and its downstream nodes; for instance, 5 routes result in a density of 0.5. Processing latency is calculated by querying the database for the average dwell time of goods at a node; for example, 2 hours, normalized to 0.2. These data reflect the role of nodes in the network, facilitating the optimization of logistics scheduling.

[0078] In one embodiment, when using the K-means clustering algorithm to group nodes, the nodes can be divided into three groups: high load, medium load, and low load, based on feature vectors, weights, connection density, and processing latency.

[0079] For example, merged nodes are grouped into a high-load group, labeled H1, due to their high throughput and connection density. The clustering process iterates multiple times to ensure that the grouping results reflect the characteristics of the nodes, facilitating the identification of key nodes in the network.

[0080] Preferably, the above method ensures accurate node identification after grouping through multi-dimensional data integration and analysis.

[0081] For example, nodes in high-load groups can be prioritized for resource allocation, optimizing logistics efficiency. The implementation methods for each theme support each other, forming a complete analysis chain from data extraction to feature representation, weight adjustment, and clustering, clearly revealing the supply chain network structure and improving the targeting of logistics planning.

[0082] If the importance score of the node is higher than a preset score threshold, it is determined to be a key link, and a tracing path containing the key link is obtained, including: If the importance score of the node is located in a high-value cluster center, it is determined to be a key link, and a set of key links is obtained; By using the set of key links, the paths and data flows containing the key links are obtained, and the paths are sorted according to their weights and business priorities. The paths with the highest rankings are determined as the source tracing paths.

[0083] The weight data and data flow direction between nodes are obtained from the simplified topology graph. The path length from the starting node to the target node is calculated using the improved Dijkstra algorithm to obtain the initial path set. For the initial path set, the connection density and interaction frequency are obtained. If the connection density is higher than a preset threshold, redundant nodes are removed through a path disambiguation mechanism to obtain the optimized path set.

[0084] For example, when obtaining weight data between nodes and data flow direction in a simplified topology diagram of a supply chain network, the transportation records of the logistics network can be analyzed.

[0085] For example, the weighted data for the distance from warehouse A to sorting station B is based on daily transportation costs and distance. If the daily transportation cost is 5,000 yuan and the distance is 100 kilometers, the weight is normalized to 0.5. The data flow direction is determined by analyzing cargo flow.

[0086] For example, 60% of the goods flow from warehouse A to sorting station B, with a flow intensity of 0.6. This data is extracted from the logistics management system to ensure accuracy.

[0087] In one possible implementation, when calculating the path length from the starting node to the target node using the improved Dijkstra algorithm, it can be based on weight data and flow intensity.

[0088] For example, the path from warehouse A to customer C passes through sorting station B and transfer station D. An improved Dijkstra's algorithm considers transportation time and cost when calculating the path length.

[0089] For example, the path length from warehouse A to sorting station B is 2 hours, with a cost weight of 0.5; the path length from sorting station B to transfer station D is 1 hour, with a weight of 0.3. The algorithm iteratively generates an initial set of paths, such as path ABDC, with a total length of 4 hours.

[0090] Specifically, when obtaining connection density and interaction frequency for the initial path set, the transportation routes and number of interactions between nodes can be analyzed.

[0091] For example, sorting station B has 4 transport routes to downstream nodes, with a connection density of 0.4; it interacts with transfer station D 5 times a day, with an interaction frequency of 0.5. If the connection density exceeds a preset threshold of 0.3, a path disambiguation mechanism is triggered.

[0092] For example, there is a redundant node E between sorting station B and transfer station D. Analysis shows that the interaction frequency of node E is only 0.2, which is lower than the threshold of 0.3. Therefore, node E is removed and the optimized path is ABDC.

[0093] Preferably, the path disambiguation mechanism eliminates redundant nodes by analyzing data flow direction and interaction frequency.

[0094] For example, node E accounts for only 10% of the cargo flow and has a transportation delay of 3 hours, indicating that it contributes little to the path. After removing it, the transportation time of the optimized path set is reduced from 4 hours to 3.5 hours, and the cost is reduced by 10%. This method ensures efficient paths and reasonable resource utilization.

[0095] In one embodiment, the optimized path set can be further adjusted in conjunction with business priority.

[0096] For example, if customer C is a high-priority customer with a priority score of 0.9, which is higher than the threshold of 0.8, then route ABDC will be prioritized, and additional transportation resources will be allocated, such as increasing the number of daily transport batches from 2 to 3. This adjustment improves the logistics efficiency of key customers.

[0097] Understandably, the above method integrates multi-dimensional data, from weighted data extraction to path optimization, forming a complete analysis chain. This approach facilitates the identification of efficient paths, optimizes logistics scheduling, and improves the responsiveness and resource allocation efficiency of the supply chain network.

[0098] From the optimized path set, obtain path weights and node identifiers. Calculate the comprehensive weight of each path using the formula [W=αL+βD](attachment: / / weight_formula.png) (where W is the path weight, L is the path length, D is the business dependency strength, and α and β are preset coefficients), resulting in a comprehensive weight set. For this comprehensive weight set, obtain the node throughput and interaction frequency. If the node throughput is higher than a preset threshold and the interaction frequency is greater than a preset value, calculate the node importance score using the formula [S=γT+δF](attachment: / / score_formula.png) (where S is the importance score, T is the throughput, F is the interaction frequency, and γ and δ are preset coefficients), resulting in an importance score set. Based on the importance score set, classify the nodes using the K-means clustering algorithm. If the importance score of a node is located in a high-value cluster center, it is determined to be a key link, resulting in a key link set. Using the key link set, obtain the paths containing key links and their data flow directions. Sort the paths based on their weights and business priorities, and determine the paths ranked higher as the source tracing paths.

[0099] For example, when obtaining path weights and node identifiers in an optimized path set of a supply chain network, the transportation cost and node function of the path can be determined by analyzing logistics records.

[0100] For example, the weights of the ABDC path are based on transportation distance and time, and node B, as a sorting station, is identified as a "sorting center".

[0101] In one possible implementation, a comprehensive weight is calculated based on a path weight formula, with a path length of 3 hours, a business dependency intensity of 0.7, and preset coefficients α of 0.6 and β of 0.4. The comprehensive weight reflects the logistics efficiency of the path.

[0102] It should be noted that the strength of business dependence can be determined through order correlation.

[0103] For example, the high correlation between node D and customer C's orders indicates a strong dependency.

[0104] Specifically, when obtaining node throughput and interaction frequency for a comprehensive weight set, the cargo processing volume and daily interaction count of the nodes can be analyzed.

[0105] For example, sorting station B processes 5,000 items daily, with a throughput of 0.5; it interacts with transit station D 6 times daily, with an interaction frequency of 0.6. If the throughput exceeds the threshold of 0.4 and the interaction frequency is higher than 0.5, a score is calculated using the importance scoring formula. The score reflects the node's pivotal role in the network.

[0106] For example, a higher score for sorting station B indicates its importance.

[0107] In one embodiment, when using the K-means clustering algorithm to classify nodes based on the importance score set, nodes can be divided into three categories: high, medium, and low.

[0108] For example, sorting station B's score is located at the center of a high-value cluster, thus it is identified as a critical link. Transfer station D, with a slightly lower score, is classified as a secondary link. The set of critical links includes sorting station B and similar nodes, reflecting the core hubs of the logistics network.

[0109] Preferably, when obtaining the path containing the key links and their data flow through the set of key links, cargo flow can be analyzed.

[0110] For example, in path ABDC, 60% of the goods flow to customer C, and the data flow intensity is 0.6. Based on path weight and business priority ranking, customer C has a priority of 0.9, which is higher than the threshold of 0.8. Path ABDC is ranked higher because it contains the key link B and has a high weight, and is therefore identified as the traceability path.

[0111] Understandably, generating a set of traceability paths facilitates tracking the core paths in the logistics network.

[0112] For example, ABDC (Availability-Based Costing) can be used to analyze the efficiency of goods transportation from warehouses to customers and optimize scheduling resources.

[0113] In one embodiment, if customer C is of high priority, additional shipping batches can be added to ensure timely delivery. This method improves supply chain efficiency by forming a complete chain through multi-dimensional analysis, from weight calculation to path sequencing.

[0114] Geographic location data and logistics trajectory data of key links are obtained from the traceability path set. These are then filtered using a preset positioning accuracy threshold. If the positioning error of the geographic location is lower than the preset threshold and the trajectory integrity of the logistics trajectory is higher than the preset threshold, the geographic location and logistics trajectory are matched using a geographic information system to obtain a mapping dataset of geographic location and logistics trajectory. For this mapping dataset, time series data is obtained, and time-granularity features are extracted using a time series decomposition method. The time series data is then smoothed using an autoregressive moving average algorithm to obtain a smoothed time series dataset. Based on the smoothed time series dataset, the flow rate and status update frequency are calculated using a sliding window method. If the flow rate or status update frequency is higher than a preset threshold, the dynamic flow features are classified using a feature clustering method to obtain a dynamic flow feature set. Using this dynamic flow feature set, combined with node identifiers and interaction frequency, the operating status is classified using a data fusion method. If the interaction frequency is higher than a preset threshold, a weighted average algorithm is used to calculate the operating status score, resulting in an operating status dataset for key links.

[0115] For example, in the field of logistics optimization in supply chain networks, obtaining geographical location data and logistics trajectory data of key links from the set of traceability paths is an important step in improving scheduling efficiency.

[0116] For example, geolocation data can be obtained via a GPS device.

[0117] For example, the latitude and longitude coordinates of sorting center B are 39.9042, 116.4074, with a positioning error of 5 meters. Logistics trajectory data records the transportation path of goods from warehouse A to customer C, such as the trajectory points along path ABDC. When filtering using preset positioning accuracy thresholds, the positioning error threshold is set to 10 meters, and the trajectory integrity threshold is set to 0.9. Sorting center B's positioning error is less than 10 meters, and its trajectory integrity is 0.95, meeting the conditions. Through geographic information system matching, a mapping dataset containing the coordinates of point B and trajectory points is generated, reflecting the dwell time and flow of goods at point B.

[0118] Specifically, time-series data is obtained from the mapped dataset, recording the arrival and departure times of goods at node B, such as arriving at 8:00 AM and departing at 9:30 AM daily. Time-series decomposition methods are used to extract time-granularity features.

[0119] For example, the hourly peak volume of cargo processing is decomposed. An autoregressive moving average algorithm is used to smooth the data, eliminating noise in the time series, resulting in a smoothed dataset that shows the daily processing peak at point B is between 9:00 and 10:00.

[0120] In one possible implementation, the flow rate and state update frequency are calculated using a sliding window method based on the smoothed time-series dataset. The flow rate reflects the processing efficiency of goods at point B; for example, processing 1000 goods per hour results in a rate of 0.8, exceeding the threshold of 0.7. The state update frequency refers to the number of interactions between point B and downstream node D; for example, updating 5 times per hour exceeds the threshold of 4 times. The dynamic flow features are classified using a feature clustering method, categorizing the features of point B into high-efficiency flow types to generate a dynamic flow feature set, highlighting its pivotal role.

[0121] Preferably, a data fusion method is used to classify the operational status by combining node identification and interaction frequency. Sorting center B is identified as a "core hub," and its interaction frequency with node D is 0.6, higher than the threshold of 0.5. An operational status score is calculated using a weighted average algorithm. Considering the combined flow rate of 0.8 and interaction frequency of 0.6, point B's score is 0.85, indicating efficient operation. The generated key process operational status dataset can be used for scheduling optimization.

[0122] For example, increase the number of transport batches at point B to improve efficiency.

[0123] Understandably, the above method, through multi-dimensional analysis, forms a complete data processing chain, from geographical location to dynamic flow characteristics. Each step supports the others, ensuring accurate assessment of the operational status of key links.

[0124] For example, the high turnover rate and high score of point B jointly demonstrate its core position and provide a basis for resource allocation in the logistics network. This approach facilitates the tracking of dynamic changes in key links, improving the responsiveness and stability of the supply chain.

[0125] In one embodiment, if customer C's order has a higher priority, the transportation plan can be adjusted based on the operational status dataset of point B to ensure timely delivery of goods.

[0126] For example, increasing nighttime batches and shortening wait times at point C. This expansion approach broadens the applicability of the core methodology, ensuring the flexibility and efficiency of the supply chain network.

[0127] In step S14, the deviation between the geographical location and the logistics trajectory of each node is obtained based on the running status dataset. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and the traceability record of the abnormal link is obtained.

[0128] If the deviation exceeds a preset deviation threshold, it is determined to be an abnormal process, and the source tracing record of the abnormal process is obtained, including: Calculate the deviation between the node and the preset standard position to obtain the geographic location deviation dataset of the node and obtain the corresponding logistics trajectory; Calculate the deviation between the logistics trajectory and the standard path. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and a set of abnormal links is obtained. Based on the set of abnormal links, obtain the interaction frequency and status update data of each abnormal link, calculate the fluctuation characteristics of the interaction frequency, and determine the source identifier of the abnormal link.

[0129] For example, in the field of logistics optimization in supply chain networks, the identification and traceability analysis of abnormal nodes are key to ensuring transportation efficiency and stability.

[0130] For example, when extracting node identifiers and geographic location data of abnormal nodes from the operational status dataset, node identifiers can be obtained through database queries. For instance, the identifier for sorting center E is "E001," and its geographic location data is latitude and longitude coordinates of 40.7128, -74.0060, with a positioning accuracy of 8 meters. A geographic information system (GIS) is used for coordinate transformation, converting the WGS84 coordinate system to a local plane coordinate system to facilitate deviation calculation. Using a coordinate matching algorithm, the deviation between point E and a preset standard position is calculated. For example, if the standard position is 40.7129, -74.0059, the deviation is 15 meters, exceeding the threshold of 10 meters. A geographic location deviation dataset is then generated, recording the deviation information of point E.

[0131] Specifically, when acquiring logistics trajectory data for a geographic location deviation dataset, the trajectory of goods from warehouse F to customer G can be extracted, such as the trajectory points of path FEHG. A trajectory fitting algorithm is then used to compare the deviation between the actual trajectory and the standard path.

[0132] For example, the standard path is a straight line FEHG, but the actual trajectory deviates by 20 meters due to a detour, exceeding the threshold of 15 meters. Using an anomaly classification algorithm, point E is determined to be an anomaly and added to the anomaly set.

[0133] It should be noted that anomaly classification can be based on decision tree algorithms, combined with deviation values ​​and trajectory integrity, to accurately distinguish between abnormal and normal processes.

[0134] In one possible implementation, interaction frequency and state update data are obtained based on the set of abnormal links.

[0135] For example, the interaction frequency between point E and downstream node H is 3 times per hour, lower than the normal threshold of 5 times, and the status update data records that the goods' dwell time at point E is 2 hours. Using time series analysis, the fluctuation characteristics of the interaction frequency were calculated, revealing that the frequency at point E significantly decreases between 10:00 and 12:00 daily. Through feature extraction algorithms, the traceability identifier for point E was determined to be "abnormal dwell time," generating a traceability identifier dataset and recording the reasons for the anomaly.

[0136] Preferably, for the traceability identification dataset, a data fusion method is used to integrate node identification, geographical location, and logistics trajectory data.

[0137] For example, the identifier "E001" of point E, its coordinates 40.7128, -74.0060, and its trajectory FEHG are fused together. Using an association rule mining algorithm, the source tracing records are matched, revealing that the stagnation at point E is related to equipment failure. A source tracing record dataset is then generated, recording the failure time and its impact range.

[0138] Understandably, this multi-dimensional integration ensures comprehensive traceability records, providing a basis for subsequent optimization.

[0139] In one embodiment, if customer G's order requires urgent delivery, the equipment maintenance plan at point E can be adjusted based on the traceability record dataset to shorten the delay time. This extended solution improves the responsiveness of the supply chain and ensures the stability of the logistics network.

[0140] In step S15, a dynamic dependency graph of the supply chain network is generated based on the traceability record, and real-time update data of the key links is obtained to determine the location result of the key links.

[0141] The step of generating a dynamic dependency graph of the supply chain network based on the traceability records includes: Obtain node identifiers and interaction data from the source tracing records, calculate the state update frequency of the interaction data, obtain the interaction frequency, and calculate the dependencies between nodes; If the interaction frequency is higher than a preset frequency threshold, it is determined to be a strong dependency relationship, the node weight of the dependency relationship is determined, and a node dependency relationship dataset is obtained. Based on the node dependency dataset, logistics status and time series are obtained. Node weights are adjusted using the time series to obtain a dynamic network topology dataset and path deviation and anomaly classification data, generating a dynamic dependency graph of the supply chain network.

[0142] For example, in the field of logistics optimization in supply chain networks, the source analysis of abnormal links and the construction of dynamic dependencies are key to improving transportation efficiency and network resilience. The following analysis and examples address each technical topic, focusing on supply chain network scenarios while maintaining logical rigor and business consistency.

[0143] For example, node identifiers and interaction data can be obtained from the traceability records of abnormal processes. This can be achieved by querying a database to extract node identifier M002 of sorting center M and its interaction data with upstream warehouse N, such as daily interaction records. Interaction data includes goods handover timestamps and status update logs. Using time series analysis, the status update frequency of M002 is calculated; for example, the average number of interactions from 8:00 to 20:00 daily is 4 times, decreasing to 1 time at night, thus forming a node interaction frequency dataset.

[0144] Understandably, time series analysis extracts frequency trends through smoothing processes, making it easier to detect abnormal fluctuations.

[0145] Specifically, for the node interaction frequency dataset, an association rule mining algorithm is used to calculate the dependencies between nodes.

[0146] For example, analyzing the interaction frequency between M002 and warehouse N reveals that the four daily interactions significantly exceed the threshold of two, indicating a strong dependency. Further calculation of the node weights for this dependency relationship, based on interaction frequency and cargo volume, assigns M002 a weight of 0.8 to N, generating a node dependency dataset.

[0147] It should be noted that association rule mining measures dependency strength using support and confidence metrics to ensure that weights reflect actual business relationships.

[0148] In one possible implementation, logistics status and time-series data, such as the cargo processing status and timestamp of M002, are obtained based on a node dependency dataset. A graph generation algorithm is used to construct the supply chain network topology, connecting M002, N, and other nodes, such as delivery point P, into a directed graph. Taking into account the fluctuation characteristics of the time series, for example, if the interaction frequency of M002 increases by 10% during peak periods, its weight is dynamically adjusted to 0.9, generating a dynamic network topology dataset.

[0149] Preferably, the graph generation algorithm represents node relationships using an adjacency matrix, and the dynamic weights reflect real-time changes in the network.

[0150] In one embodiment, path deviation and anomaly classification data are obtained for a dynamic network topology dataset.

[0151] For example, if the actual path of goods from N to P via M002 deviates by 12 kilometers due to traffic congestion, exceeding the threshold of 8 kilometers, the anomaly classification data marks M002 as congestion anomaly. A visualization algorithm is used to generate a dynamic dependency graph of the supply chain network, showing the strong dependency edges between M002 and N, as well as the red highlighted areas of path deviation.

[0152] Understandably, visualization algorithms visually present network bottlenecks through heatmaps and node connections, making it easier for managers to quickly locate problems.

[0153] For example, dynamic dependency graphs can further reveal the correlation between the fluctuations in the interaction frequency of M002 during peak periods and path deviations, suggesting the need to optimize its scheduling plan. This multi-dimensional analysis forms a closed loop from tracing to visualization, providing a basis for the dynamic adjustment of the supply chain network and significantly improving response speed and stability.

[0154] The step of obtaining real-time updated data of the key links and determining the location result of the key links includes: Obtain real-time update data of nodes from the dynamic dependency graph and calculate the interaction frequency of nodes; If the interaction frequency is higher than the preset frequency threshold, it is determined to be a high-activity node, and a high-activity node dataset is obtained, along with node weight and topology dynamics data. By updating edge weights through changes in node states, we obtain an adjusted network topology dataset and acquire path deviation and logistics timeliness data. We then calculate the shortest path and adjust the logistics priority between nodes through path optimization to obtain a real-time shortest path dataset. Abnormal nodes and supply chain bottleneck data are obtained from the real-time shortest path dataset. If the abnormal node appears at the bottleneck position of multiple paths, it is determined to be a critical link, and the location result of the critical link is obtained.

[0155] For example, in the field of logistics optimization in supply chain networks, obtaining real-time updated data on node status from dynamic dependency graphs is a key step.

[0156] For example, the dynamic dependency graph records the real-time interaction status between sorting center M002 and warehouse N, such as goods handover timestamps and status update logs. Time series analysis is used to calculate the node interaction frequency, which works by using a sliding window to count the number of interactions per unit time and extracting frequency trends.

[0157] Specifically, from the interaction data of M002, the number of interactions per hour was counted. It was found that there were an average of 6 interactions from 8:00 to 20:00 during the day, which was 4 times higher than the preset threshold. Therefore, M002 was determined to be a high-activity node, and a high-activity node dataset was generated.

[0158] It should be noted that time series analysis filters out noise through smoothing to ensure that frequency data reflects true business activity. Obtaining node weights and topology dynamics data from the high-activity node dataset is a subsequent step.

[0159] Preferably, the node weights are calculated based on the interaction frequency and the amount of goods. For example, the weight of M002 to N is 0.75, and the topology dynamics data reflects the fluctuation range of the interaction frequency.

[0160] In one possible implementation, an incremental update mechanism is used to adjust the network topology. When the interaction frequency of M002 increases to 8 times / hour, the edge weight is dynamically adjusted to 0.85. The adjusted network topology dataset is represented by an adjacency matrix, reflecting the changes in relationships between nodes in real time.

[0161] Understandably, the incremental update mechanism only processes nodes whose state has changed, reducing computational overhead. For the adjusted network topology dataset, path deviation and logistics timeliness data are obtained to optimize the paths.

[0162] For example, in the path from warehouse N to delivery point P, the actual path of M002 deviates by 10 kilometers due to congestion, resulting in a 2-hour delay in delivery time. The Dijkstra algorithm is used to calculate the shortest path, which works by traversing nodes through a priority queue to find the path with the minimum total weight.

[0163] For example, the algorithm selects an alternative path to bypass M002, reducing the deviation to 5 kilometers, and generates a real-time shortest path dataset.

[0164] It should be noted that Dijkstra's algorithm prioritizes routes with higher timeliness by adjusting logistics priorities. Obtaining data on abnormal nodes and supply chain bottlenecks from the real-time shortest path dataset is crucial for problem localization.

[0165] In one embodiment, the K-means clustering method is used to identify key links. The principle is to cluster nodes according to path deviation and time delay. It was found that M002 appeared multiple times in the high deviation path and was determined to be a key link.

[0166] For example, M002 is a bottleneck in all three paths, and the clustering results mark it as a key link location result dataset.

[0167] Specifically, clustering methods use multidimensional feature analysis to ensure that the identification results reflect the actual bottleneck location.

[0168] Understandably, identifying key components provides a basis for subsequent optimization and scheduling, significantly improving network efficiency.

[0169] In summary, this invention discloses a blockchain-based smart factory supply chain traceability method. It records the geographical location and logistics trajectory data of supply chain nodes using blockchain, constructs a network topology map, optimizes node merging, calculates the shortest path, and identifies key links to determine the supply chain traceability path. This invention first utilizes blockchain technology to ensure data credibility, then optimizes the network based on node attributes and business dependencies, and finally determines key traceability paths through an improved path algorithm and node importance scoring. This method effectively simplifies the supply chain network structure, identifies important nodes and abnormal links, provides reliable technical support for supply chain management and risk control, and improves the transparency and traceability of the supply chain.

[0170] Reference Figure 2 The second embodiment of the present invention provides a schematic diagram of a blockchain-based smart factory supply chain traceability system, including: The determination module 201 is used to obtain the geographical location and logistics trajectory of the supply chain nodes and generate an original dataset, and determine the connection weight and direction between nodes based on the original dataset; The merging module 202 is used to calculate the similarity between nodes. If the similarity is greater than a preset threshold based on throughput weighting and the strength of business dependency meets the conditions, then the nodes are merged and a simplified topology graph is obtained. The first acquisition module 203 is used to determine the weight and identifier of the merged nodes according to the simplified topology graph, obtain the optimized path set, calculate the importance score of the nodes according to the path set, and if the importance score of the node is higher than the preset score threshold, it is determined to be the key link, and acquire the traceability path and running status dataset containing the key link. The second acquisition module 204 is used to acquire the geographical location and logistics trajectory deviation of each node according to the running status dataset. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and the source record of the abnormal link is acquired. The positioning module 205 is used to generate a dynamic dependency graph of the supply chain network based on the traceability record and obtain real-time update data of the key links to determine the positioning result of the key links.

[0171] It should be noted that the blockchain-based smart factory supply chain traceability system provided in this embodiment of the invention is used to execute all process steps of the blockchain-based smart factory supply chain traceability method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0172] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a blockchain-based smart factory supply chain traceability program. When the processor executes the computer program, it implements the steps described in the various blockchain-based smart factory supply chain traceability method embodiments above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the determination module.

[0173] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0174] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0175] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0176] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0177] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0178] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0179] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A blockchain-based smart factory supply chain traceability method, characterized in that, include: Obtain the geographical location and logistics trajectory of supply chain nodes and generate a raw dataset. Determine the connection weight and direction between nodes based on the raw dataset. Calculate the similarity between nodes. If the similarity is greater than a preset threshold based on throughput weighting and the business dependency strength meets the condition, then merge the nodes and obtain a simplified topology graph. The weights and identifiers of the merged nodes are determined based on the simplified topology graph to obtain an optimized path set. The importance score of the nodes is calculated based on the path set. If the importance score of a node is higher than a preset score threshold, it is determined to be a key link. The source path and running status dataset containing the key link are then obtained. The deviation between the geographical location and the logistics trajectory of each node is obtained based on the operational status dataset. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and the traceability record of the abnormal link is obtained. A dynamic dependency graph of the supply chain network is generated based on the traceability records, and real-time update data of the key links is obtained to determine the location results of the key links.

2. The blockchain-based smart factory supply chain traceability method according to claim 1, characterized in that, The step of determining the connection weights and directions between nodes based on the original dataset includes: Calculate the physical distance and flow time between nodes to obtain the initial matrix; The shortest path between nodes is calculated based on the initial matrix, and the connection direction of the logistics trajectory is determined by combining transportation costs and time windows. If the shortest path is greater than a preset path threshold, then the connection weight is calculated by combining the flow time and the transportation cost.

3. The blockchain-based smart factory supply chain traceability method according to claim 1, characterized in that, If the similarity between the computing nodes is greater than a preset threshold based on throughput weighting and the strength of the business dependency relationship meets the conditions, then the nodes are merged and a simplified topology graph is obtained, including: Calculate the similarity between each pair of nodes. If the similarity is greater than a preset threshold based on throughput weighting, extract the dependency strength data between the nodes to obtain the node pairs that meet the conditions. The data of the nodes that meet the conditions are merged and the network topology is updated to obtain the simplified topology graph.

4. The blockchain-based smart factory supply chain traceability method according to any one of claims 1, characterized in that, The step of determining the weight and identifier of the merged node based on the simplified topology graph includes: The throughput rate, dependency strength, and interaction frequency of the merged nodes are obtained from the simplified topology graph, and the initial feature vector is calculated to obtain the preliminary feature representation of the merged nodes. Based on the preliminary feature representation and business priority, a weight coefficient is determined. If the business priority is higher than a preset priority threshold, the weight coefficient is increased to determine the weight of the merged node. The nodes are grouped according to the weights to obtain the node identifiers after grouping.

5. The blockchain-based smart factory supply chain traceability method according to any one of claims 1, characterized in that, If the importance score of the node is higher than a preset score threshold, it is determined to be a key link, and a tracing path containing the key link is obtained, including: If the importance score of the node is located in a high-value cluster center, it is determined to be a key link, and a set of key links is obtained; By using the set of key links, the paths and data flows containing the key links are obtained, and the paths are sorted according to their weights and business priorities. The paths with the highest rankings are determined as the source tracing paths.

6. The blockchain-based smart factory supply chain traceability method according to any one of claims 1-5, characterized in that, If the deviation exceeds a preset deviation threshold, it is determined to be an abnormal process, and the source tracing record of the abnormal process is obtained, including: Calculate the deviation between the node and the preset standard position to obtain the geographic location deviation dataset of the node and obtain the corresponding logistics trajectory; Calculate the deviation between the logistics trajectory and the standard path. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and a set of abnormal links is obtained. Based on the set of abnormal links, obtain the interaction frequency and status update data of each abnormal link, calculate the fluctuation characteristics of the interaction frequency, and determine the source identifier of the abnormal link.

7. The blockchain-based smart factory supply chain traceability method according to any one of claims 1-5, characterized in that, The step of generating a dynamic dependency graph of the supply chain network based on the traceability records includes: Obtain node identifiers and interaction data from the source tracing records, calculate the state update frequency of the interaction data, obtain the interaction frequency, and calculate the dependencies between nodes; If the interaction frequency is higher than a preset frequency threshold, it is determined to be a strong dependency relationship, the node weight of the dependency relationship is determined, and a node dependency relationship dataset is obtained. Based on the node dependency dataset, logistics status and time series are obtained. Node weights are adjusted using the time series to obtain a dynamic network topology dataset and path deviation and anomaly classification data, generating a dynamic dependency graph of the supply chain network.

8. The blockchain-based smart factory supply chain traceability method according to any one of claims 1-5, characterized in that, The step of obtaining real-time updated data of the key links and determining the location result of the key links includes: Obtain real-time update data of nodes from the dynamic dependency graph and calculate the interaction frequency of nodes; If the interaction frequency is higher than the preset frequency threshold, it is determined to be a high-activity node, and a high-activity node dataset is obtained, along with node weight and topology dynamics data. By updating edge weights through changes in node states, we obtain an adjusted network topology dataset and acquire path deviation and logistics timeliness data. We then calculate the shortest path and adjust the logistics priority between nodes through path optimization to obtain a real-time shortest path dataset. Abnormal nodes and supply chain bottleneck data are obtained from the real-time shortest path dataset. If the abnormal node appears at the bottleneck position of multiple paths, it is determined to be a critical link, and the location result of the critical link is obtained.

9. A blockchain-based smart factory supply chain traceability system, characterized in that, include: The determination module is used to obtain the geographical location and logistics trajectory of the supply chain nodes and generate a raw dataset, and determine the connection weight and direction between nodes based on the raw dataset; The merging module is used to calculate the similarity between nodes. If the similarity is greater than a preset threshold based on throughput weighting and the strength of business dependency meets the conditions, then the nodes are merged and a simplified topology graph is obtained. The first acquisition module is used to determine the weight and identifier of the merged nodes according to the simplified topology graph, obtain the optimized path set, calculate the importance score of the nodes according to the path set, and if the importance score of the node is higher than the preset score threshold, it is determined to be the key link, and acquire the traceability path and running status dataset containing the key link. The second acquisition module is used to acquire the geographical location and logistics trajectory deviation of each node based on the running status dataset. If the deviation is greater than a preset deviation threshold, it is determined to be an abnormal link, and the source traceability record of the abnormal link is acquired. The positioning module is used to generate a dynamic dependency graph of the supply chain network based on the traceability records and obtain real-time update data of the key links to determine the positioning result of the key links.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the blockchain-based smart factory supply chain traceability method as described in any one of claims 1 to 8.

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