A cloud-based e-commerce cloud aggregation supervision method and system for foreign trade.

Abstract

This invention discloses a cloud-based method for the integrated supervision of cross-border e-commerce, comprising the following steps: S1, real-time collection of cross-border e-commerce transaction data, logistics and distribution data, warehousing data, and customs supervision data, and standardization processing using a distributed cleaning method; S2, construction of a multi-source heterogeneous graph based on the standardized data; S3, annotation of node and edge types, relationship types, and timestamps in the graph, and extraction of high-dimensional features using a relation-enhanced graph Transformer; S4, local and global aggregation using a multi-layer attention mechanism to generate an aggregated view; S5, construction of a multi-objective supervision task model and generation of a resource scheduling scheme; S6, identification of transaction, logistics, and supervision anomalies, and generation of risk warning and intervention strategies; S7, dynamic optimization of the relationship structure and Transformer parameters based on execution feedback. This invention, based on high-dimensional graph representation and optimized scheduling mechanism, achieves integrated supervision and risk warning of cross-domain multi-source data in cross-border e-commerce.

Description

Technical Field

[0001] This invention relates to the field of intelligent supervision technology for cross-border e-commerce, and in particular to a cloud-based method and system for cloud-network aggregated supervision of foreign trade e-commerce. Background Technology

[0002] With the rapid development of cross-border e-commerce, transaction activities are becoming increasingly complex in terms of spatial distribution, business processes, and data sources. A highly heterogeneous business network has formed among cross-border e-commerce platforms, third-party logistics companies, warehousing service providers, and customs regulatory agencies, generating a large amount of real-time, high-frequency, and structurally complex multi-source data. This data covers multiple dimensions, including commodity flow, capital flow, information flow, and regulatory flow, forming a typical cross-domain heterogeneous data fusion and regulatory scenario. To achieve digital governance and risk control in the cross-border e-commerce industry, establishing an efficient, accurate, and scalable cloud-network integrated regulatory system has become a key focus for the industry.

[0003] Currently, existing technologies primarily employ field-rule-oriented matching logic, static rule engines, or single-task scheduling frameworks to process and monitor cross-border e-commerce data. These methods largely rely on structured data formats and manually defined rule bases, making them ill-suited to the challenges posed by data heterogeneity and dynamic changes in business scenarios. To enhance data organization and modeling capabilities, some research has attempted to introduce graph neural networks or graph-based recommendation algorithms to model e-commerce relationships. However, most of these efforts remain at the level of static graphs or single-layer adjacency modeling, failing to fully express the higher-order interaction characteristics during the spatiotemporal evolution of nodes.

[0004] In terms of task allocation and resource scheduling, traditional scheduling methods mainly rely on fixed threshold mechanisms or scheduling strategies centered on single-objective optimization, making it difficult to achieve multi-objective coordination between ensuring system real-time performance, accuracy, and resource utilization. In recent years, some optimization algorithms, such as genetic algorithms and particle swarm optimization, have been used for scheduling modeling, but they still suffer from slow convergence speed and poor adaptability in complex cross-border scenarios, especially in handling resource allocation problems under highly dynamic and heterogeneous characteristics.

[0005] Furthermore, current anomaly detection and early warning systems are mostly based on static rule matching or classification models for risk identification, lacking the ability to model business topology and making it difficult to predict the spread path of abnormal behavior and optimize intervention strategies. Faced with multi-dimensional and complex scenarios, existing models have not effectively integrated graph structure learning and multi-objective optimization methods, making it difficult to support the adaptive evolution of regulatory strategies and online system updates.

[0006] Therefore, how to provide a cloud-based e-commerce cloud network aggregation supervision method and system is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] One objective of this invention is to propose a cloud-based e-commerce cloud network aggregation supervision method and system for foreign trade. This invention has the technical effects of achieving efficient aggregation of multi-source heterogeneous data, dynamic identification of abnormal behavior and optimal generation of intervention strategies, and adaptive updating of the supervision model.

[0008] A cloud-based e-commerce cloud network aggregation supervision method according to an embodiment of the present invention includes the following steps:

[0009] S1. Real-time collection of transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and data standardization processing through distributed data cleaning methods to obtain a standardized data pool;

[0010] S2. Based on the standardized data pool, construct a multi-source heterogeneous graph for cross-border e-commerce;

[0011] S3. Label the nodes and edges in the multi-source heterogeneous graph of cross-border e-commerce with node type, relationship type and timestamp respectively, and perform multi-element feature encoding through relationship enhancement graph Transformer to generate a high-dimensional feature representation of the heterogeneous graph.

[0012] S4. Based on the high-dimensional feature representation of the heterogeneous graph, local and global feature information is extracted through the multi-layer attention mechanism of the relation enhancement graph Transformer to form an aggregated view of cross-border e-commerce multi-source data;

[0013] S5. Based on the aggregated view, determine the dynamic monitoring resource scheduling scheme through the multi-objective honey badger optimization algorithm;

[0014] S6. Based on the aggregated view, identify cross-border e-commerce transaction anomalies, logistics anomalies and regulatory anomalies, and generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm;

[0015] S7. Implement dynamic monitoring resource scheduling schemes and risk warning and intervention strategies, and update the relationship structure and parameter weights of the relationship enhancement graph Transformer based on the execution results and feedback information on data fusion quality.

[0016] Optionally, S2 specifically includes:

[0017] S21. Based on a standardized data pool, e-commerce platforms, sellers, buyers, products, logistics service providers, warehousing centers, and customs regulatory agencies are mapped to multi-dimensional spatial coordinates with unique data identifiers to express node attributes in real time.

[0018] S22. Based on the transaction data, extract the transaction time, transaction frequency and transaction volume information between buyers, sellers and products, and generate a dynamic association path with the direction of transaction flow in a multi-dimensional spatial coordinate system.

[0019] S23. Based on logistics and distribution data, collect logistics process data between logistics service providers, distributed goods and warehousing centers, construct multi-segment dynamic delivery paths based on logistics stage markers in real time, and cross-integrate them with dynamic associated paths.

[0020] S24. Based on warehousing data, assign spatial density weights to the storage location, inventory duration, and frequency of goods access within the warehousing center in real time, generate dynamic warehousing nodes with continuously adjusted spatial density weights, and integrate them with dynamic delivery routes.

[0021] S25. Based on customs supervision data, capture changes in the supervision status of import and export of goods in real time, generate spatial constraint boundaries of supervision status in multi-dimensional spatial coordinates, and integrate them with dynamic association paths and dynamic delivery paths.

[0022] S26. Dynamically aggregate the spatial and temporal characteristics of dynamic association paths, dynamic delivery paths, dynamic warehouse nodes, and regulatory status constraint boundaries to construct a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution of topological relationships.

[0023] Optionally, S3 specifically includes:

[0024] S31. Based on the differences in business attributes of e-commerce platform nodes, seller nodes, buyer nodes, product nodes, logistics service provider nodes, warehousing center nodes and customs regulatory agency nodes in the cross-border e-commerce multi-source heterogeneous graph, define a discrete feature code to express the node type, and map each node to a node spatial location vector of the discrete feature code.

[0025] S32. Based on the differences in the types and characteristics of transaction data, logistics data and regulatory data, construct a three-dimensional relationship type identification system, generate three-dimensional relationship vector identifiers representing the transaction relationship dimension, logistics relationship dimension and regulatory relationship dimension respectively, and assign an absolute timestamp to the relationship edge;

[0026] S33. Based on the node spatial position vector and the three-dimensional relation vector identifier, calculate the spatial relative position relation matrix between the node and the edge, which represents the spatial topological relative relation between the nodes;

[0027] S34. Extract the temporal interaction features of e-commerce platform nodes, seller nodes and buyer nodes to obtain the interaction feature vector;

[0028] S35. For commodity nodes, logistics service provider nodes, and warehousing center nodes, extract dynamic attribute features of commodities, logistics service stage features, and real-time change features of warehousing inventory based on real-time status data, and generate node dynamic attribute feature vectors.

[0029] S36. Using node spatial location vectors, node interaction feature vectors, node dynamic attribute feature vectors, and spatial relative position relationship matrix as multi-dimensional inputs, and performing joint encoding through a relationship enhancement graph Transformer, a high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph is obtained.

[0030] Optionally, S4 specifically includes:

[0031] S41. Based on the high-dimensional feature representation of multi-source heterogeneous graphs in cross-border e-commerce, construct corresponding independent topological spatial regions respectively;

[0032] S42. Within each independent topological space region, calculate the spatial density index of topological connections between nodes within the region. The spatial density index is defined as the local topological weighting coefficient of the nodes in the topological space region.

[0033] S43. Based on the local topological weighting coefficients of each topological space region, perform topological space weighted encoding on the initial features of each node in the topological space region to generate a local topological feature representation vector for each topological space region.

[0034] S44. On the topology of the entire cross-border e-commerce multi-source heterogeneous graph, calculate the path weight of topology information propagation between any nodes in real time. The path weight is defined as the topology propagation coefficient of a node in the global topology.

[0035] S45. Based on the topology propagation coefficient of the node, dynamically adjust the global topology feature weight of the node in real time to generate the global topology feature representation vector of the entire heterogeneous graph topology.

[0036] S46. Based on the local topological feature representation vector and the global topological feature representation vector, a dual cross-association encoding is performed through the topological association attention mechanism of the relation enhancement graph Transformer to form an aggregated view containing topological spatial structure features and topological information propagation path features.

[0037] Optionally, the relationship enhancement graph Transformer specifically includes a topological space feature initialization module, a local topological feature encoding module, a global topological feature encoding module, a topological feature cross-fusion module, a high-dimensional feature representation generation module, and an aggregated view generation module:

[0038] The topology space feature initialization module receives node spatial location vectors, node interaction feature vectors, and node dynamic attribute feature vectors, and maps them uniformly to the corresponding topology space feature initial sequence according to the discrete feature encoding of the node type.

[0039] The local topological feature encoding module receives the initial sequence of topological spatial features and the three-dimensional relation vector identifier. Based on the spatial relative position relation matrix, it calculates the spatial density index of the topological connections between nodes in each independent topological spatial region. Using the spatial density index as the local topological weighting coefficient, it performs topological spatial weighted encoding on the feature vectors of nodes in the topological spatial region one by one to form the local topological feature representation vector of each region.

[0040] The global topology feature encoding module receives the initial sequence of topology space features, and uses the topology propagation coefficient of the node in the global topology structure as the global topology weighting coefficient to perform weighted aggregation encoding on all node feature vectors to generate the global topology feature representation vector of the entire cross-border e-commerce multi-source heterogeneous graph.

[0041] The topological feature cross-fusion module takes the local topological feature representation vector and the global topological feature representation vector as dual inputs, calculates the topological association weight of the local topological representation vector to the global topological representation vector and the topological association weight of the global topological representation vector to the local topological representation vector through the topological association attention mechanism, and performs cross-weighted fusion of feature vectors based on the topological association weights to generate a topological cross-association fused feature vector.

[0042] The high-dimensional feature representation generation module takes the topological cross-association fusion feature vector as input and obtains the high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph through feature joint encoding of a multi-layer attention mechanism.

[0043] The aggregated view generation module takes the high-dimensional feature representation as input and performs adaptive feature encoding through a topological association attention mechanism to form an aggregated view that includes topological spatial structure features and topological information propagation path features.

[0044] Optionally, S5 specifically includes:

[0045] S51. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, construct a spatial distribution field of regulatory resource demand. Define the coordinate position of the node in the spatial distribution field using the node spatial location vector, and calculate the resource demand density value of each node according to the real-time interaction frequency of the node interaction feature vector and the change rate of the node dynamic attribute feature vector.

[0046] S52. Based on the resource demand density value of each node, establish a multi-objective regulatory task prediction matrix. By analyzing the historical trend sequence and current feature changes of the dynamic attribute feature vectors of the nodes, calculate the predicted resource demand value within the future set time window for each node through multidimensional linear regression.

[0047] S53. Using the real-time resource demand density of nodes and the predicted future resource demand as constraint variables, construct a regulatory task space field with multi-dimensional objective constraints.

[0048] S54. Within the regulatory task space field, for each regulatory resource and a specific regulatory task, calculate the ease of connection and execution cost of the topological path from the resource to the task, and combine the ease of connection and execution cost to form a dynamic resource-task execution reachability coefficient for comprehensively evaluating the real-time execution capability of the regulatory resource for the task.

[0049] S55. Based on the reachability coefficient of the dynamic resource task, initialize the spatial search population set of the multi-objective honey badger optimization algorithm, and perform optimization operations on individuals in the population through a hybrid elite framework. Use a circular segmented screening mechanism to iteratively generate the spatial configuration solution with optimal dynamic reachability.

[0050] S56. Based on the optimal spatial configuration solution for dynamic reachability, determine the optimal path execution order and execution time constraints for each regulatory resource in the spatial field, and generate a dynamic regulatory resource scheduling scheme that includes spatial coordinates, execution order, and execution time.

[0051] Optionally, S6 specifically includes:

[0052] S61. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, calculate the transaction data change ratio, logistics delivery delay change ratio, and regulatory status change frequency index for each node relative to the historical period, and define them as the node abnormal feature change index.

[0053] S62. Based on the statistical distribution law of the node abnormal characteristic change index, determine the transaction abnormal threshold, logistics abnormal threshold and regulatory abnormal threshold respectively, and perform threshold matching analysis on the transaction data change ratio, logistics delivery delay change ratio and regulatory status change frequency index of each node to identify transaction abnormal nodes, logistics abnormal nodes and regulatory abnormal nodes.

[0054] S63. For each abnormal transaction node, abnormal logistics node, and abnormal regulatory node, determine the spatial coordinates of the central abnormal node based on the node spatial location vector and spatial relative position relationship matrix in the aggregated view, and track the associated nodes that have real-time topological path connection with the central abnormal node to construct an abnormal node topology diffusion network starting from the central abnormal node.

[0055] S64. In the abnormal node topology diffusion network, calculate the topology connection strength value and path abnormal diffusion probability value of the topology connection path between the central abnormal node and each associated node, and multiply the topology connection strength value and the path abnormal diffusion probability value to generate a path abnormality risk index for quantifying the risk level of path abnormal propagation.

[0056] S65. Using the path anomaly risk index as input, establish a multi-objective anomaly risk early warning matrix that includes transaction anomaly risk, logistics anomaly risk, and regulatory anomaly risk. Use the multi-objective honey badger optimization algorithm to initialize the spatial search population set, and use the hybrid elite framework and circular segmented screening mechanism to iteratively screen and evaluate individuals in the population to generate a multi-objective optimal risk early warning solution with the lowest path anomaly risk index as the optimization objective.

[0057] S66. Based on the multi-objective optimal risk warning solution, determine the spatial location of the topological connection path corresponding to the path anomaly risk index, the optimal scheduling path of dynamic monitoring resources, the type of intervention action required, the specific execution sequence of the intervention action, and the intervention intensity, and form a dynamic risk intervention strategy for execution.

[0058] Optionally, S7 specifically includes:

[0059] S71. Based on the dynamic monitoring resource scheduling scheme, determine the spatial location coordinates, topological path execution order, and specific execution time constraints of the monitoring resources in the spatial distribution field, and generate a resource scheduling instruction composed of spatial coordinate identifiers, path sequence identifiers, and time constraint identifiers;

[0060] S72. Based on the dynamic risk intervention strategy, determine the specific intervention type, execution intensity value, execution location coordinates, and time sequence of the intervention actions corresponding to regulatory resources, and generate a risk intervention instruction composed of intervention type identifier, execution intensity value, spatial location coordinates, and time sequence identifier.

[0061] S73. Based on resource scheduling instructions and risk intervention instructions, conduct real-time quantitative evaluation of the spatial coordinate positioning accuracy, topology path execution order, execution time deviation, intervention action type matching degree, and execution intensity control accuracy of the regulatory resource execution task, and form an execution result quantitative matrix.

[0062] S74. By collecting real-time transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and based on the accuracy of spatial coordinate matching, topological path reconstruction, temporal synchronization and data fusion noise levels during the data fusion process, a data fusion quality index matrix is ​​generated.

[0063] S75. Input the execution result quantization matrix and data fusion quality index matrix into the relation enhancement graph Transformer respectively. Through topological feature initialization, local topological weighted encoding and global topological weighted encoding, calculate the topological encoding deviation matrix to describe the optimization degree of the current state of the relation enhancement graph Transformer.

[0064] S76. Using the topology coding deviation matrix as input, the relation structure parameters and feature coding weight parameters in the relation enhancement graph Transformer are iteratively optimized using the multi-objective honey badger optimization algorithm.

[0065] Optionally, a cloud-based e-commerce cloud-network aggregation and supervision system for foreign trade includes the following modules:

[0066] The data acquisition module is used to collect transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms in real time, and to perform data standardization processing through distributed data cleaning methods to obtain a standardized data pool.

[0067] The heterogeneous graph construction module is used to construct a multi-source heterogeneous graph for cross-border e-commerce based on a standardized data pool, and to label the nodes and edges in the heterogeneous graph with node type, relationship type and timestamp respectively;

[0068] The topology feature encoding module is used to initialize and encode the node spatial location vector, node interaction feature vector, node dynamic attribute feature vector, and spatial relative position relationship matrix in the multi-source heterogeneous graph of cross-border e-commerce. It generates local topology feature representation vector and global topology feature representation vector, and performs double cross-association encoding based on the topology association attention mechanism to obtain high-dimensional feature representation and aggregated view.

[0069] The regulatory task scheduling module is used to construct a spatial distribution field of regulatory resource demand based on the aggregated view, determine the spatial location coordinates, topological path execution order and specific execution time constraints of regulatory resources, and calculate the dynamic resource task execution reachability coefficient according to the multi-objective honey badger optimization algorithm to generate a dynamic regulatory resource scheduling scheme.

[0070] The risk warning and intervention module is used to identify abnormal nodes in cross-border e-commerce transactions, logistics, and supervision based on the aggregated view, calculate the path abnormality risk index, generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm, and determine the specific intervention action type, execution intensity, location coordinates, and execution sequence based on the risk warning and intervention strategy.

[0071] The feedback optimization module is used to collect real-time data on the execution results of resource scheduling schemes and risk warning and intervention strategies, as well as data fusion quality indicators. It calculates the topology coding deviation matrix and uses the topology coding deviation matrix to iteratively update the relation structure parameters and feature coding weight parameters of the relation enhancement graph Transformer through a multi-objective honey badger optimization algorithm.

[0072] The beneficial effects of this invention are:

[0073] (1) This invention uses the relation enhancement graph Transformer to jointly encode the node type, relation type and timestamp in the multi-source heterogeneous graph of cross-border e-commerce, and generates a high-dimensional feature representation. This realizes the structured representation and association modeling of e-commerce, logistics, warehousing and regulatory data, effectively improves the accuracy and timeliness of heterogeneous data fusion, and enhances the modeling ability of dynamic e-commerce network structure.

[0074] (2) This invention constructs a multi-objective regulatory task model and introduces a multi-objective honey badger optimization algorithm to optimize the real-time scheduling path, execution order and time constraints of regulatory resources. It can achieve optimal scheduling of regulatory tasks under multiple objectives of real-time performance, accuracy, load balancing and resource consumption, and significantly improve the resource allocation efficiency of the cross-border e-commerce regulatory system in complex dynamic scenarios.

[0075] (3) In terms of abnormal behavior identification and early warning, this invention constructs an abnormal node topology diffusion network and calculates the path abnormal risk index, and combines the multi-objective honey badger optimization algorithm to generate intervention strategies, thereby achieving accurate identification of abnormal behavior propagation paths and optimal generation of intervention instructions, effectively solving the technical problems of untimely response to abnormal handling and lack of accuracy of intervention instructions in the prior art.

[0076] (4) By constructing a feedback optimization mechanism, this invention dynamically generates a topology coding deviation matrix using resource scheduling execution results and data fusion quality indicators, and iteratively optimizes the relational structure parameters and feature coding weights of the relation enhancement graph Transformer, thereby realizing real-time adaptive updates of the model during task execution. This breaks through the limitations of existing graph modeling mechanisms that are static and lack evolutionary capabilities, and improves the system's adaptive monitoring capabilities for scene changes. Attached Figure Description

[0077] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0078] Figure 1 This is a flowchart illustrating a cloud-based e-commerce cloud aggregation and supervision method for foreign trade proposed in this invention. Detailed Implementation

[0079] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0080] refer to Figure 1 A cloud-based e-commerce cloud aggregation supervision method for foreign trade includes the following steps:

[0081] S1. Real-time collection of transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and data standardization processing through distributed data cleaning methods to obtain a standardized data pool;

[0082] S2. Based on the standardized data pool, construct a multi-source heterogeneous graph for cross-border e-commerce;

[0083] S3. Label the nodes and edges in the multi-source heterogeneous graph of cross-border e-commerce with node type, relationship type and timestamp respectively, and perform multi-element feature encoding through relationship enhancement graph Transformer to generate a high-dimensional feature representation of the heterogeneous graph.

[0084] S4. Based on the high-dimensional feature representation of the heterogeneous graph, local and global feature information is extracted through the multi-layer attention mechanism of the relation enhancement graph Transformer to form an aggregated view of cross-border e-commerce multi-source data;

[0085] S5. Based on the aggregated view, determine the dynamic monitoring resource scheduling scheme through the multi-objective honey badger optimization algorithm;

[0086] S6. Based on the aggregated view, identify cross-border e-commerce transaction anomalies, logistics anomalies and regulatory anomalies, and generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm;

[0087] S7. Implement dynamic monitoring resource scheduling schemes and risk warning and intervention strategies, and update the relationship structure and parameter weights of the relationship enhancement graph Transformer based on the execution results and feedback information on data fusion quality.

[0088] By collecting and standardizing multi-source heterogeneous data from cross-border e-commerce platforms, logistics, warehousing, and customs supervision in real time, the system extracts and aggregates local and global features based on the multi-layer attention mechanism of the relation-enhanced graph Transformer. Combined with the multi-objective honey badger optimization algorithm, it achieves multi-objective optimization scheduling of dynamic regulatory resources. On this basis, it generates risk warning and intervention strategies for transaction, logistics, and regulatory anomalies. Finally, based on the scheduling and warning execution feedback, it adaptively updates the relational structure and parameter weights of the Transformer. This enables the system to achieve real-time and accurate data fusion, balanced and flexible resource scheduling, and timely anomaly identification and intervention in complex and ever-changing cross-border e-commerce scenarios.

[0089] In this embodiment, S1 specifically includes:

[0090] S11. Through the transaction data interface of the cross-border e-commerce platform, collect the buyer's and seller's account identifier, order number, product category, transaction amount, payment method, order placement time, payment completion time, and order status, and assign a unique data identifier to the data to form a structured transaction data record;

[0091] S12. Through the logistics distribution system interface, collect logistics service provider identification, cargo identification, delivery route information, spatial coordinates of logistics dispatch nodes and receiving nodes, delivery initiation time and arrival time, transportation vehicle type, logistics document number and logistics status change events to form real-time updated logistics distribution data records.

[0092] S13. Through the warehouse information system interface, collect in real time the warehouse center identifier, the goods' inbound time, outbound time, inventory quantity, goods inventory location coordinates, storage location temperature and humidity, warehouse operation record number, and information on the status changes of goods when entering and leaving the warehouse, forming real-time updated warehouse data records.

[0093] S14. Through the customs supervision platform interface, collect customs agency identification, customs declaration number, customs declaration company identification, cargo type, supervision and clearance status, customs declaration approval time, customs clearance release time, abnormal inspection status and customs clearance related document data in real time to form a real-time updated customs supervision data record.

[0094] S15. Based on the distributed data cleaning and processing framework, perform deduplication, missing value filling, data format unification conversion, outlier detection and correction, and data item consistency checks on the above-collected transaction data records, logistics and distribution data records, warehousing data records, and customs supervision data records respectively, to form a standardized data pool with unified data fields, standardized identification, and consistent format.

[0095] In this embodiment, S2 specifically includes:

[0096] S21. Based on a standardized data pool, e-commerce platforms, sellers, buyers, products, logistics service providers, warehousing centers, and customs regulatory agencies are mapped to multi-dimensional spatial coordinates with unique data identifiers to express node attributes in real time.

[0097] S22. Based on the transaction data, extract the transaction time, transaction frequency and transaction volume information between buyers, sellers and products, and generate a dynamic association path with the direction of transaction flow in a multi-dimensional spatial coordinate system.

[0098] S23. Based on logistics and distribution data, collect logistics process data between logistics service providers, distributed goods and warehousing centers, construct multi-segment dynamic delivery paths based on logistics stage markers in real time, and cross-integrate them with dynamic associated paths.

[0099] S24. Based on warehousing data, assign spatial density weights to the storage location, inventory duration, and frequency of goods access within the warehousing center in real time, generate dynamic warehousing nodes with continuously adjusted spatial density weights, and integrate them with dynamic delivery routes.

[0100] S25. Based on customs supervision data, capture changes in the supervision status of import and export of goods in real time, generate spatial constraint boundaries of supervision status in multi-dimensional spatial coordinates, and integrate them with dynamic association paths and dynamic delivery paths.

[0101] S26. Dynamically aggregate the spatial and temporal characteristics of dynamic association paths, dynamic delivery paths, dynamic warehousing nodes, and regulatory status constraint boundaries to construct a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution of topological relationships. By mapping cross-border e-commerce platforms, sellers, buyers, goods, logistics service providers, warehousing centers, and customs regulatory agencies to multi-dimensional spatial coordinates with unique data identifiers, transaction flow association paths are dynamically generated based on transaction data. Multi-segment delivery paths are constructed based on logistics stage markers and integrated with association paths. Warehouse dynamic nodes are assigned spatial density weights in real time based on warehousing data and integrated with delivery paths. Spatial constraint boundaries are generated based on customs regulatory data and integrated with the above paths. Finally, the spatiotemporal characteristics of various dynamic paths, nodes, and constraint boundaries are dynamically aggregated to construct a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution capabilities. Compared with existing static graph construction or single data source modeling methods, this invention can reflect the spatial-temporal evolution of multi-source entity attributes and multi-stage interaction flows in real time, improving the consistency and accuracy of heterogeneous data representation.

[0102] In this embodiment, S3 specifically includes:

[0103] S31. Based on the differences in business attributes of e-commerce platform nodes, seller nodes, buyer nodes, product nodes, logistics service provider nodes, warehousing center nodes and customs regulatory agency nodes in the cross-border e-commerce multi-source heterogeneous graph, define a discrete feature code to express the node type, and map each node to a node spatial location vector of the discrete feature code.

[0104] S32. Based on the differences in the types and characteristics of transaction data, logistics data and regulatory data, construct a three-dimensional relationship type identification system, generate three-dimensional relationship vector identifiers representing the transaction relationship dimension, logistics relationship dimension and regulatory relationship dimension respectively, and assign an absolute timestamp to the relationship edge;

[0105] S33. Based on the node spatial position vector and the three-dimensional relation vector identifier, calculate the spatial relative position relation matrix between the node and the edge, which represents the spatial topological relative relation between the nodes;

[0106] S34. Extract the temporal interaction features of e-commerce platform nodes, seller nodes and buyer nodes to obtain the interaction feature vector;

[0107] S35. For commodity nodes, logistics service provider nodes, and warehousing center nodes, extract dynamic attribute features of commodities, logistics service stage features, and real-time change features of warehousing inventory based on real-time status data, and generate node dynamic attribute feature vectors.

[0108] S36. Using node spatial location vectors, node interaction feature vectors, node dynamic attribute feature vectors, and spatial relative position relationship matrix as multi-dimensional inputs, and performing joint encoding through a relationship enhancement graph Transformer, a high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph is obtained.

[0109] By performing discrete feature encoding on nodes in the multi-source heterogeneous graph of cross-border e-commerce, constructing a three-dimensional relationship vector for the relationship between transactions, logistics and supervision and assigning it a timestamp, representing the spatial relative positional relationship between nodes and edges in a matrix, and extracting the temporal interaction features and dynamic attribute features of nodes, and jointly inputting the above multi-dimensional spatiotemporal features into the relationship enhancement graph Transformer for encoding, a refined spatiotemporal correlation modeling of complex multi-source data of cross-border e-commerce is realized, which effectively improves the accuracy and consistency of high-dimensional feature representation of heterogeneous graphs.

[0110] In this embodiment, S4 specifically includes:

[0111] S41. Based on the high-dimensional feature representation of multi-source heterogeneous graphs in cross-border e-commerce, construct corresponding independent topological spatial regions respectively;

[0112] S42. Within each independent topological space region, calculate the spatial density index of topological connections between nodes within the region. The spatial density index is defined as the local topological weighting coefficient of the nodes in the topological space region.

[0113] S43. Based on the local topological weighting coefficients of each topological space region, perform topological space weighted encoding on the initial features of each node in the topological space region to generate a local topological feature representation vector for each topological space region.

[0114] S44. On the topology of the entire cross-border e-commerce multi-source heterogeneous graph, calculate the path weight of topology information propagation between any nodes in real time. The path weight is defined as the topology propagation coefficient of a node in the global topology.

[0115] S45. Based on the topology propagation coefficient of the node, dynamically adjust the global topology feature weight of the node in real time to generate the global topology feature representation vector of the entire heterogeneous graph topology.

[0116] S46. Based on the local topological feature representation vector and the global topological feature representation vector, a dual cross-association encoding is performed through the topological association attention mechanism of the relation enhancement graph Transformer to form an aggregated view containing topological spatial structure features and topological information propagation path features.

[0117] By dividing the high-dimensional feature representation of the heterogeneous graph from multiple sources in cross-border e-commerce into independent topological spatial regions, and calculating and encoding the spatial density weights between nodes in each region to generate local topological feature representations, while simultaneously calculating the topological propagation coefficients of nodes in real time on the overall topological structure to generate global topological feature representations, the local and global features are finally fused through the dual cross-association attention mechanism of the relation-enhancing graph Transformer to form an aggregated view containing topological spatial structure and information propagation path features. This approach captures graph structure information at multiple scales, significantly improving the consistency and accuracy of heterogeneous graph representations and effectively enhancing the model's ability to model topological associations in complex multi-source data.

[0118] In this embodiment, the relationship enhancement graph Transformer specifically includes a topological space feature initialization module, a local topological feature encoding module, a global topological feature encoding module, a topological feature cross-fusion module, a high-dimensional feature representation generation module, and an aggregated view generation module:

[0119] The topology space feature initialization module receives node spatial location vectors, node interaction feature vectors, and node dynamic attribute feature vectors, and maps them uniformly to the corresponding topology space feature initial sequence according to the discrete feature encoding of the node type.

[0120] The local topological feature encoding module receives the initial sequence of topological spatial features and the three-dimensional relation vector identifier. Based on the spatial relative position relation matrix, it calculates the spatial density index of the topological connections between nodes in each independent topological spatial region. Using the spatial density index as the local topological weighting coefficient, it performs topological spatial weighted encoding on the feature vectors of nodes in the topological spatial region one by one to form the local topological feature representation vector of each region.

[0121] The global topology feature encoding module receives the initial sequence of topology space features, and uses the topology propagation coefficient of the node in the global topology structure as the global topology weighting coefficient to perform weighted aggregation encoding on all node feature vectors to generate the global topology feature representation vector of the entire cross-border e-commerce multi-source heterogeneous graph.

[0122] The topological feature cross-fusion module takes the local topological feature representation vector and the global topological feature representation vector as dual inputs, calculates the topological association weight of the local topological representation vector to the global topological representation vector and the topological association weight of the global topological representation vector to the local topological representation vector through the topological association attention mechanism, and performs cross-weighted fusion of feature vectors based on the topological association weights to generate a topological cross-association fused feature vector.

[0123] The high-dimensional feature representation generation module takes the topological cross-association fusion feature vector as input and obtains the high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph through feature joint encoding of a multi-layer attention mechanism.

[0124] The aggregated view generation module takes the high-dimensional feature representation as input and performs adaptive feature encoding through a topological association attention mechanism to form an aggregated view that includes topological spatial structure features and topological information propagation path features.

[0125] By uniformly mapping node spatial location vectors, interaction feature vectors, and dynamic attribute feature vectors to an initial sequence of topological spatial features, a local topological feature encoding module generates node features within a region based on spatial density weighting, and a global topological feature encoding module generates global node features based on topological propagation coefficient weighting. A topological feature cross-fusion module calculates the correlation weights between local and global features. A high-dimensional feature representation generation module and an aggregated view generation module complete joint encoding and aggregated view output through a multi-layer attention mechanism. This achieves comprehensive capture and fusion of multi-scale topological structure information in multi-source heterogeneous graphs of cross-border e-commerce, effectively improving the consistency of graph representation and the accuracy of information transmission.

[0126] In this embodiment, S5 specifically includes:

[0127] S51. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, construct a spatial distribution field of regulatory resource demand. Define the coordinate position of the node in the spatial distribution field using the node spatial location vector, and calculate the resource demand density value of each node according to the real-time interaction frequency of the node interaction feature vector and the change rate of the node dynamic attribute feature vector.

[0128] S52. Based on the resource demand density value of each node, establish a multi-objective regulatory task prediction matrix. By analyzing the historical trend sequence and current feature changes of the dynamic attribute feature vectors of the nodes, calculate the predicted resource demand value within the future set time window for each node through multidimensional linear regression.

[0129] S53. Using the real-time resource demand density of nodes and the predicted future resource demand as constraint variables, construct a regulatory task space field with multi-dimensional objective constraints.

[0130] S54. Within the regulatory task space field, for each regulatory resource and a specific regulatory task, calculate the ease of connection and execution cost of the topological path from the resource to the task, and combine the ease of connection and execution cost to form a dynamic resource-task execution reachability coefficient for comprehensively evaluating the real-time execution capability of the regulatory resource for the task.

[0131] S55. Based on the reachability coefficient of the dynamic resource task, initialize the spatial search population set of the multi-objective honey badger optimization algorithm, and perform optimization operations on individuals in the population through a hybrid elite framework. Use a circular segmented screening mechanism to iteratively generate the spatial configuration solution with optimal dynamic reachability.

[0132] S56. Based on the optimal spatial configuration solution for dynamic reachability, determine the optimal path execution order and execution time constraints for each regulatory resource in the spatial field, and generate a dynamic regulatory resource scheduling scheme that includes spatial coordinates, execution order, and execution time.

[0133] By constructing a spatial distribution field of regulatory resource demand based on an aggregated view, and combining the real-time resource demand density of nodes with future demand predictions to construct a multi-dimensional target-constrained regulatory task spatial field, a dynamic resource task execution reachability coefficient is generated by comprehensively evaluating the topological path connection difficulty and execution cost of regulatory resources and tasks. This coefficient is used as the population fitness function of the multi-objective honey badger optimization algorithm, which realizes the accurate determination of the optimal spatial configuration path and execution sequence of regulatory resources in complex cross-border e-commerce scenarios, thereby providing high-precision decision support for resource scheduling schemes in dynamic business environments.

[0134] In this embodiment, S6 specifically includes:

[0135] S61. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, calculate the transaction data change ratio, logistics delivery delay change ratio, and regulatory status change frequency index for each node relative to the historical period, and define them as the node abnormal feature change index.

[0136] S62. Based on the statistical distribution law of the node abnormal characteristic change index, determine the transaction abnormal threshold, logistics abnormal threshold and regulatory abnormal threshold respectively, and perform threshold matching analysis on the transaction data change ratio, logistics delivery delay change ratio and regulatory status change frequency index of each node to identify transaction abnormal nodes, logistics abnormal nodes and regulatory abnormal nodes.

[0137] S63. For each abnormal transaction node, abnormal logistics node, and abnormal regulatory node, determine the spatial coordinates of the central abnormal node based on the node spatial location vector and spatial relative position relationship matrix in the aggregated view, and track the associated nodes that have real-time topological path connection with the central abnormal node to construct an abnormal node topology diffusion network starting from the central abnormal node.

[0138] S64. In the abnormal node topology diffusion network, calculate the topology connection strength value and path abnormal diffusion probability value of the topology connection path between the central abnormal node and each associated node, and multiply the topology connection strength value and the path abnormal diffusion probability value to generate a path abnormality risk index for quantifying the risk level of path abnormal propagation.

[0139] S65. Using the path anomaly risk index as input, establish a multi-objective anomaly risk early warning matrix that includes transaction anomaly risk, logistics anomaly risk, and regulatory anomaly risk. Use the multi-objective honey badger optimization algorithm to initialize the spatial search population set, and use the hybrid elite framework and circular segmented screening mechanism to iteratively screen and evaluate individuals in the population to generate a multi-objective optimal risk early warning solution with the lowest path anomaly risk index as the optimization objective.

[0140] S66. Based on the multi-objective optimal risk warning solution, determine the spatial location of the topological connection path corresponding to the path anomaly risk index, the optimal scheduling path of dynamic monitoring resources, the type of intervention action required, the specific execution sequence of the intervention action, and the intervention intensity, and form a dynamic risk intervention strategy for execution.

[0141] By using node spatial location vectors, node interaction feature vectors, and node dynamic attribute feature vectors in the aggregated view, the transaction data change rate, logistics delivery delay change rate, and regulatory status change frequency are calculated for each node. Based on statistical distribution patterns, anomaly thresholds are precisely set, enabling accurate identification of abnormal transaction nodes, logistics nodes, and regulatory nodes. Furthermore, an anomaly node topology diffusion network is constructed, calculating the topological connection strength and diffusion probability between the central anomaly node and related nodes, and generating a path anomaly risk index. Combined with a multi-objective honey badger optimization algorithm, the lowest-risk multi-objective optimal early warning solution is selected, achieving quantitative assessment and early warning decision-making for anomaly propagation path risks. Finally, based on the optimal risk early warning solution, a dynamic risk intervention strategy is automatically generated, including spatial coordinates, scheduling paths, intervention action types, execution sequences, and intervention intensity. This provides cross-border e-commerce supervision with precise risk management capabilities based on real-time topology and multi-source data.

[0142] In this embodiment, S7 specifically includes:

[0143] S71. Based on the dynamic monitoring resource scheduling scheme, determine the spatial location coordinates, topological path execution order, and specific execution time constraints of the monitoring resources in the spatial distribution field, and generate a resource scheduling instruction composed of spatial coordinate identifiers, path sequence identifiers, and time constraint identifiers;

[0144] S72. Based on the dynamic risk intervention strategy, determine the specific intervention type, execution intensity value, execution location coordinates, and time sequence of the intervention actions corresponding to regulatory resources, and generate a risk intervention instruction composed of intervention type identifier, execution intensity value, spatial location coordinates, and time sequence identifier.

[0145] S73. Based on resource scheduling instructions and risk intervention instructions, conduct real-time quantitative evaluation of the spatial coordinate positioning accuracy, topology path execution order, execution time deviation, intervention action type matching degree, and execution intensity control accuracy of the regulatory resource execution task, and form an execution result quantitative matrix.

[0146] S74. By collecting real-time transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and based on the accuracy of spatial coordinate matching, topological path reconstruction, temporal synchronization and data fusion noise levels during the data fusion process, a data fusion quality index matrix is ​​generated.

[0147] S75. Input the execution result quantization matrix and data fusion quality index matrix into the relation enhancement graph Transformer respectively. Through topological feature initialization, local topological weighted encoding and global topological weighted encoding, calculate the topological encoding deviation matrix to describe the optimization degree of the current state of the relation enhancement graph Transformer.

[0148] S76. Using the topology coding deviation matrix as input, the relation structure parameters and feature coding weight parameters in the relation enhancement graph Transformer are iteratively optimized using the multi-objective honey badger optimization algorithm.

[0149] By using spatial coordinate identifiers, path sequence identifiers, and time constraint identifiers generated by dynamic regulatory resource scheduling schemes and risk intervention strategies as resource scheduling instructions and risk intervention instructions, the system quantifies positioning accuracy, path execution deviation, and intervention matching degree in real time during execution, forming an execution result quantification matrix. Simultaneously, based on the spatial coordinate matching accuracy, path reconstruction accuracy, temporal synchronization accuracy, and noise level of cross-border e-commerce multi-source data fusion, a data fusion quality index matrix is ​​generated. This quantification matrix is ​​then input into a relation enhancement graph Transformer for topology feature initialization, local weighted encoding, and global weighted encoding to calculate the topology encoding deviation matrix. Finally, a multi-objective honey badger optimization algorithm iteratively adjusts the relational structure parameters and feature encoding weights of the Transformer, achieving closed-loop adaptive optimization of model parameters and scheduling schemes. Compared to existing static updates or offline tuning methods, this significantly improves the real-time responsiveness and adaptability of the regulatory system in complex dynamic environments.

[0150] In this embodiment, a cloud-based e-commerce cloud network aggregation and supervision system for foreign trade includes the following modules:

[0151] The data acquisition module is used to collect transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms in real time, and to perform data standardization processing through distributed data cleaning methods to obtain a standardized data pool.

[0152] The heterogeneous graph construction module is used to construct a multi-source heterogeneous graph for cross-border e-commerce based on a standardized data pool, and to label the nodes and edges in the heterogeneous graph with node type, relationship type and timestamp respectively;

[0153] The topology feature encoding module is used to initialize and encode the node spatial location vector, node interaction feature vector, node dynamic attribute feature vector, and spatial relative position relationship matrix in the multi-source heterogeneous graph of cross-border e-commerce. It generates local topology feature representation vector and global topology feature representation vector, and performs double cross-association encoding based on the topology association attention mechanism to obtain high-dimensional feature representation and aggregated view.

[0154] The regulatory task scheduling module is used to construct a spatial distribution field of regulatory resource demand based on the aggregated view, determine the spatial location coordinates, topological path execution order and specific execution time constraints of regulatory resources, and calculate the dynamic resource task execution reachability coefficient according to the multi-objective honey badger optimization algorithm to generate a dynamic regulatory resource scheduling scheme.

[0155] The risk warning and intervention module is used to identify abnormal nodes in cross-border e-commerce transactions, logistics, and supervision based on the aggregated view, calculate the path abnormality risk index, generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm, and determine the specific intervention action type, execution intensity, location coordinates, and execution sequence based on the risk warning and intervention strategy.

[0156] The feedback optimization module is used to collect real-time data on the execution results of resource scheduling schemes and risk warning and intervention strategies, as well as data fusion quality indicators. It calculates the topology coding deviation matrix and uses the topology coding deviation matrix to iteratively update the relation structure parameters and feature coding weight parameters of the relation enhancement graph Transformer through a multi-objective honey badger optimization algorithm.

[0157] By modularizing real-time collected and standardized cross-border e-commerce transaction, logistics, warehousing, and customs supervision data into a standardized data pool, and constructing a multi-source heterogeneous graph based on this data pool, a topological feature encoding module is used to achieve joint encoding and multi-layer cross-fusion of node spatial location, interaction features, and dynamic attributes, thereby accurately obtaining high-dimensional feature representations. Subsequently, the regulatory task scheduling module calculates resource execution reachability coefficients and generates dynamic scheduling schemes under multi-dimensional objective constraints. Combined with the risk warning and intervention module, transaction, logistics, and regulatory anomalies are quantitatively identified and optimal intervention strategies are generated. Finally, the feedback optimization module iteratively updates the graph model parameters based on execution results and data fusion quality indicators, realizing real-time fusion of multi-source heterogeneous data, multi-objective optimization of resource scheduling, accurate identification of anomaly warnings, and adaptive updating of the system model.

[0158] Example 1:

[0159] To verify the feasibility of this invention in practice, the method was applied to a cross-border e-commerce data fusion and supervision platform in a comprehensive bonded zone. This platform utilizes multi-source real-time data from multiple e-commerce platforms, logistics companies, warehousing service providers, and customs regulatory agencies to achieve real-time monitoring of the transaction process, dynamic monitoring of logistics and distribution, efficient coordination of warehousing management, and refined management of customs supervision. In practical applications, traditional methods rely on manually set rules and static data interfaces, leading to difficulties in cross-platform data integration, low efficiency in heterogeneous data fusion, delayed regulatory response, and untimely anomaly identification. Especially during peak transaction periods, data response delays can reach several minutes or even tens of minutes, making it difficult for regulatory authorities to detect abnormal transaction behavior, logistics anomalies, and regulatory violations in real time, posing significant challenges to platform supervision.

[0160] In practice, cross-border e-commerce transaction data is first collected in real time through standardized interfaces, including transaction details, transaction amounts, product numbers, and purchase frequency for both buyers and sellers. Logistics and delivery data are also collected, including shipping time, location in transit, transit time, and delivery status. Simultaneously, warehousing data is collected, including product inventory location, inventory duration, and inbound / outbound frequency. Customs supervision data is also collected, including product import / export status, customs declaration information, and regulatory release status. This heterogeneous data undergoes distributed cleaning and standardization to form a unified, standardized data pool, serving as the foundation for data analysis and graph structure construction.

[0161] Next, utilizing the constructed standardized data pool, e-commerce platforms, buyers, sellers, products, logistics service providers, warehousing centers, and customs regulatory agencies are uniformly identified according to their business attributes, and mapped to multi-dimensional spatial coordinates of unified data identifiers to ensure consistent, clear, and highly distinguishable data representations across different nodes. Then, based on real-time transaction data, the frequency, volume, and time of transactions between buyers and sellers are extracted to dynamically construct dynamic association paths with clearly defined real-time transaction flow directions. Based on logistics and delivery data, multi-segment dynamic delivery paths with clearly defined stage markers are constructed in real-time. Simultaneously, based on warehousing data, the spatial density weights of warehousing nodes are updated in real-time, generating warehousing nodes with continuously dynamically adjusted spatial density. Based on customs supervision data, spatial constraint boundaries for the import and export supervision status of goods are generated in real-time and integrated with transaction, logistics, and warehousing paths. Finally, the Transformer model for relationship enhancement graphs is used to jointly encode the spatial location, temporal interaction, and dynamic attribute features of the multi-source heterogeneous graph, forming a real-time updated high-dimensional feature representation.

[0162] Then, to further improve the regulatory effectiveness, the platform refines the high-dimensional feature representation into multiple topological spatial regions. It calculates the spatial density index of topological connections between nodes within each region, defining it as the local topological weighting coefficient of the node, and performs feature weighting encoding to form a local topological feature representation vector. Simultaneously, it calculates the path weights for topological information propagation between any nodes in real time, defining them as global topological propagation coefficients, and dynamically adjusts the global topological feature weights of nodes to form a global topological feature representation vector. Finally, based on the topological association attention mechanism of the relation-enhancing graph Transformer, it cross-links and fuses the local and global feature representations to generate an aggregated view with topological spatial structure and information propagation path characteristics.

[0163] Subsequently, a spatial distribution field of regulatory resource demand is constructed in real time based on the aggregated view. Resource demand density is calculated for each node according to the real-time interaction frequency and dynamic change rate of the nodes. Multidimensional linear regression is used to predict resource demand over a future period, thereby constructing a multi-objective regulatory task prediction matrix and a regulatory task spatial field with multi-dimensional objective constraints. Within this spatial field, the platform evaluates the accessibility and execution cost of regulatory resources for tasks in real time. A spatial search population set using a multi-objective honey badger optimization algorithm is used for iterative optimization with a hybrid elite framework and a circular segmented selection mechanism to determine the optimal resource scheduling scheme for each regulatory task. In a real-world scenario, after 20 iterations, the spatial accessibility coefficient converged to below 0.05, achieving precise and dynamic resource scheduling.

[0164] In terms of real-time identification and handling of abnormal events, the system calculates the rate of change in current transaction data, the rate of change in logistics delivery delay, and the frequency index of regulatory status changes for each node through an aggregated view, defining a node abnormal characteristic change index. Based on historical statistical data, anomaly thresholds are determined, and abnormal transaction, logistics, and regulatory nodes are identified in real time, with the real-time topological diffusion network of abnormal nodes tracked. The platform calculates the topological connection strength and anomaly diffusion probability between nodes in real time, forming a path anomaly risk index. A multi-objective honey badger optimization algorithm is used for iterative screening to determine the optimal multi-objective risk warning scheme with the lowest risk. In practical applications, the accuracy rate of abnormal node identification reaches 97.8%, and the accuracy rate of risk diffusion prediction reaches 95.6%, significantly outperforming traditional static rule models (anomaly identification accuracy rate of 81.5%).

[0165] Finally, the platform generates specific scheduling and risk intervention instructions based on the resource scheduling scheme and risk warning strategy, collects execution results in real time, and forms a quantitative matrix of execution results. Simultaneously, it collects various quality indicators from the data fusion process, generates a data fusion quality indicator matrix, and calculates the topology coding deviation matrix to dynamically optimize the parameters of the relationship enhancement graph Transformer. During actual operation, the model parameters are adaptively adjusted every hour, and the optimization step size of the relationship structure parameters decreases to below 0.001 after each update, achieving continuous optimization and efficient adaptive updates of the model. The following table shows the comparison data of the actual effect of abnormal event risk prediction during system implementation:

[0166] Table 1 Comparison of Actual and Predicted Data for Cross-border E-commerce Abnormal Event Risk Prediction

[0167]

[0168]

[0169] As shown in Table 1, the cross-border e-commerce supervision platform of this invention exhibits an average error of 2.3% in risk index prediction and an average error of only 4.6 seconds in risk intervention timeliness prediction, significantly outperforming traditional static rule-based methods (which have an average risk index prediction error of 11.2% and an average intervention timeliness prediction error of 20 seconds). Specifically, the measured risk index for event EVT-004 was 0.90, while the system prediction was 0.88, with an error of only 2.2%, and a delay of only 2 seconds in actual intervention, effectively ensuring timely handling of abnormal risks. The risk prediction error for event EVT-022 was 2.2%, and the intervention timeliness prediction error was only 2 seconds, further demonstrating the platform's accurate early warning and efficient response capabilities. This fully verifies the significant technical advantages and practical application value of this invention in real-time cross-border e-commerce data fusion supervision, multi-objective dynamic resource optimization scheduling, and intelligent early warning of risk anomalies.

[0170] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A cloud-based e-commerce cloud network aggregation supervision method for foreign trade, characterized in that, Includes the following steps: S1. Real-time collection of transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and data standardization processing through distributed data cleaning methods to obtain a standardized data pool; S2. Based on the standardized data pool, construct a multi-source heterogeneous graph for cross-border e-commerce; S2 specifically includes: S21. Based on a standardized data pool, e-commerce platforms, sellers, buyers, products, logistics service providers, warehousing centers, and customs regulatory agencies are mapped to multi-dimensional spatial coordinates with unique data identifiers to express node attributes in real time. S22. Based on the transaction data, extract the transaction time, transaction frequency and transaction volume information between buyers, sellers and products, and generate a dynamic association path with the direction of transaction flow in a multi-dimensional spatial coordinate system. S23. Based on logistics and distribution data, collect logistics process data between logistics service providers, distributed goods and warehousing centers, construct multi-segment dynamic delivery paths based on logistics stage markers in real time, and cross-integrate them with dynamic associated paths. S24. Based on warehousing data, assign spatial density weights to the storage location, inventory duration, and frequency of goods access within the warehousing center in real time, generate dynamic warehousing nodes with continuously adjusted spatial density weights, and integrate them with dynamic delivery routes. S25. Based on customs supervision data, capture changes in the supervision status of import and export of goods in real time, generate spatial constraint boundaries of supervision status in multi-dimensional spatial coordinates, and integrate them with dynamic association paths and dynamic delivery paths. S26. Dynamically aggregate the spatial and temporal characteristics of dynamic association paths, dynamic delivery paths, dynamic warehouse nodes, and regulatory status constraint boundaries to construct a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution of topological relationships. S3. Label the nodes and edges in the multi-source heterogeneous graph of cross-border e-commerce with node type, relationship type and timestamp respectively, and perform multi-element feature encoding through relationship enhancement graph Transformer to generate a high-dimensional feature representation of the heterogeneous graph. S3 specifically includes: S31. Based on the differences in business attributes of e-commerce platform nodes, seller nodes, buyer nodes, product nodes, logistics service provider nodes, warehousing center nodes and customs regulatory agency nodes in the cross-border e-commerce multi-source heterogeneous graph, define a discrete feature code to express the node type, and map each node to a node spatial location vector of the discrete feature code. S32. Based on the differences in the types and characteristics of transaction data, logistics data and regulatory data, construct a three-dimensional relationship type identification system, generate three-dimensional relationship vector identifiers representing the transaction relationship dimension, logistics relationship dimension and regulatory relationship dimension respectively, and assign an absolute timestamp to the relationship edge; S33. Based on the node spatial position vector and the three-dimensional relation vector identifier, calculate the spatial relative position relation matrix between the node and the edge, which represents the spatial topological relative relation between the nodes; S34. Extract the temporal interaction features of e-commerce platform nodes, seller nodes and buyer nodes to obtain the interaction feature vector; S35. For commodity nodes, logistics service provider nodes, and warehousing center nodes, extract dynamic attribute features of commodities, logistics service stage features, and real-time change features of warehousing inventory based on real-time status data, and generate node dynamic attribute feature vectors. S36. Using node spatial location vector, node interaction feature vector, node dynamic attribute feature vector and spatial relative position relationship matrix as multidimensional inputs, joint encoding is performed through relationship enhancement graph Transformer to obtain high-dimensional feature representation of cross-border e-commerce multi-source heterogeneous graph; S4. Based on the high-dimensional feature representation of the heterogeneous graph, local and global feature information is extracted through the multi-layer attention mechanism of the relation enhancement graph Transformer to form an aggregated view of cross-border e-commerce multi-source data; S4 specifically includes: S41. Based on the high-dimensional feature representation of multi-source heterogeneous graphs in cross-border e-commerce, construct corresponding independent topological spatial regions respectively; S42. Within each independent topological space region, calculate the spatial density index of topological connections between nodes within the region. The spatial density index is defined as the local topological weighting coefficient of the nodes in the topological space region. S43. Based on the local topological weighting coefficients of each topological space region, perform topological space weighted encoding on the initial features of each node in the topological space region to generate a local topological feature representation vector for each topological space region. S44. On the topology of the entire cross-border e-commerce multi-source heterogeneous graph, calculate the path weight of topology information propagation between any nodes in real time. The path weight is defined as the topology propagation coefficient of a node in the global topology. S45. Based on the topology propagation coefficient of the node, dynamically adjust the global topology feature weight of the node in real time to generate the global topology feature representation vector of the entire heterogeneous graph topology. S46. Based on the local topological feature representation vector and the global topological feature representation vector, a dual cross-association encoding is performed through the topological association attention mechanism of the relation enhancement graph Transformer to form an aggregated view containing topological spatial structure features and topological information propagation path features. S5. Based on the aggregated view, determine the dynamic monitoring resource scheduling scheme through the multi-objective honey badger optimization algorithm; S6. Based on the aggregated view, identify cross-border e-commerce transaction anomalies, logistics anomalies and regulatory anomalies, and generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm; S7. Implement dynamic monitoring resource scheduling schemes and risk warning and intervention strategies, and update the relationship structure and parameter weights of the relationship enhancement graph Transformer based on the execution results and feedback information on data fusion quality.

2. The cloud-based e-commerce cloud network aggregation supervision method for foreign trade as described in claim 1, characterized in that, The relationship enhancement graph Transformer specifically includes a topological space feature initialization module, a local topological feature encoding module, a global topological feature encoding module, a topological feature cross-fusion module, a high-dimensional feature representation generation module, and an aggregated view generation module. The topology space feature initialization module receives node spatial location vectors, node interaction feature vectors, and node dynamic attribute feature vectors, and maps them uniformly to the corresponding topology space feature initial sequence according to the discrete feature encoding of the node type. The local topological feature encoding module receives the initial sequence of topological spatial features and the three-dimensional relation vector identifier. Based on the spatial relative position relation matrix, it calculates the spatial density index of the topological connections between nodes in each independent topological spatial region. Using the spatial density index as the local topological weighting coefficient, it performs topological spatial weighted encoding on the feature vectors of nodes in the topological spatial region one by one to form the local topological feature representation vector of each region. The global topology feature encoding module receives the initial sequence of topology space features, and uses the topology propagation coefficient of the node in the global topology structure as the global topology weighting coefficient to perform weighted aggregation encoding on all node feature vectors to generate the global topology feature representation vector of the entire cross-border e-commerce multi-source heterogeneous graph. The topological feature cross-fusion module takes the local topological feature representation vector and the global topological feature representation vector as dual inputs, calculates the topological association weight of the local topological representation vector to the global topological representation vector and the topological association weight of the global topological representation vector to the local topological representation vector through the topological association attention mechanism, and performs cross-weighted fusion of feature vectors based on the topological association weights to generate a topological cross-association fused feature vector. The high-dimensional feature representation generation module takes the topological cross-association fusion feature vector as input and obtains the high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph through feature joint encoding of a multi-layer attention mechanism. The aggregated view generation module takes the high-dimensional feature representation as input and performs adaptive feature encoding through a topological association attention mechanism to form an aggregated view that includes topological spatial structure features and topological information propagation path features.

3. The cloud-based e-commerce cloud network aggregation supervision method for foreign trade as described in claim 1, characterized in that, S5 specifically includes: S51. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, construct a spatial distribution field of regulatory resource demand. Define the coordinate position of the node in the spatial distribution field using the node spatial location vector, and calculate the resource demand density value of each node according to the real-time interaction frequency of the node interaction feature vector and the change rate of the node dynamic attribute feature vector. S52. Based on the resource demand density value of each node, establish a multi-objective regulatory task prediction matrix. By analyzing the historical trend sequence and current feature changes of the dynamic attribute feature vectors of the nodes, calculate the predicted resource demand value within the future set time window for each node through multidimensional linear regression. S53. Using the real-time resource demand density of nodes and the predicted future resource demand as constraint variables, construct a regulatory task space field with multi-dimensional objective constraints. S54. Within the regulatory task space field, for each regulatory resource and a specific regulatory task, calculate the ease of connection and execution cost of the topological path from the resource to the task, and combine the ease of connection and execution cost to form a dynamic resource-task execution reachability coefficient for comprehensively evaluating the real-time execution capability of the regulatory resource for the task. S55. Based on the reachability coefficient of the dynamic resource task, initialize the spatial search population set of the multi-objective honey badger optimization algorithm, and perform optimization operations on individuals in the population through a hybrid elite framework. Use a circular segmented screening mechanism to iteratively generate the spatial configuration solution with optimal dynamic reachability. S56. Based on the optimal spatial configuration solution for dynamic reachability, determine the optimal path execution order and execution time constraints for each regulatory resource in the spatial field, and generate a dynamic regulatory resource scheduling scheme that includes spatial coordinates, execution order, and execution time.

4. The cloud-based e-commerce cloud network aggregation supervision method for foreign trade as described in claim 1, characterized in that, S6 specifically includes: S61. Based on the node spatial location vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregated view, calculate the transaction data change ratio, logistics delivery delay change ratio, and regulatory status change frequency index for each node relative to the historical period, and define them as the node abnormal feature change index. S62. Based on the statistical distribution law of the node abnormal characteristic change index, determine the transaction abnormal threshold, logistics abnormal threshold and regulatory abnormal threshold respectively, and perform threshold matching analysis on the transaction data change ratio, logistics delivery delay change ratio and regulatory status change frequency index of each node to identify transaction abnormal nodes, logistics abnormal nodes and regulatory abnormal nodes. S63. For each abnormal transaction node, abnormal logistics node, and abnormal regulatory node, determine the spatial coordinates of the central abnormal node based on the node spatial location vector and spatial relative position relationship matrix in the aggregated view, and track the associated nodes that have real-time topological path connection with the central abnormal node to construct an abnormal node topology diffusion network starting from the central abnormal node. S64. In the abnormal node topology diffusion network, calculate the topology connection strength value and path abnormal diffusion probability value of the topology connection path between the central abnormal node and each associated node, and multiply the topology connection strength value and the path abnormal diffusion probability value to generate a path abnormality risk index for quantifying the risk level of path abnormal propagation. S65. Using the path anomaly risk index as input, establish a multi-objective anomaly risk early warning matrix that includes transaction anomaly risk, logistics anomaly risk, and regulatory anomaly risk. Use the multi-objective honey badger optimization algorithm to initialize the spatial search population set, and use the hybrid elite framework and circular segmented screening mechanism to iteratively screen and evaluate individuals in the population to generate a multi-objective optimal risk early warning solution with the lowest path anomaly risk index as the optimization objective. S66. Based on the multi-objective optimal risk warning solution, determine the spatial location of the topological connection path corresponding to the path anomaly risk index, the optimal scheduling path of dynamic monitoring resources, the type of intervention action required, the specific execution sequence of the intervention action, and the intervention intensity, and form a dynamic risk intervention strategy for execution.

5. The cloud-based e-commerce cloud network aggregation supervision method for foreign trade as described in claim 1, characterized in that, Specifically, S7 includes: S71. Based on the dynamic monitoring resource scheduling scheme, determine the spatial location coordinates, topological path execution order, and specific execution time constraints of the monitoring resources in the spatial distribution field, and generate a resource scheduling instruction composed of spatial coordinate identifiers, path sequence identifiers, and time constraint identifiers; S72. Based on the dynamic risk intervention strategy, determine the specific intervention type, execution intensity value, execution location coordinates, and time sequence of the intervention actions corresponding to regulatory resources, and generate a risk intervention instruction composed of intervention type identifier, execution intensity value, spatial location coordinates, and time sequence identifier. S73. Based on resource scheduling instructions and risk intervention instructions, conduct real-time quantitative evaluation of the spatial coordinate positioning accuracy, topology path execution order, execution time deviation, intervention action type matching degree, and execution intensity control accuracy of the regulatory resource execution task, and form an execution result quantitative matrix. S74. By collecting real-time transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms, and based on the accuracy of spatial coordinate matching, topological path reconstruction, temporal synchronization and data fusion noise levels during the data fusion process, a data fusion quality index matrix is ​​generated. S75. Input the execution result quantization matrix and data fusion quality index matrix into the relation enhancement graph Transformer respectively. Through topological feature initialization, local topological weighted encoding and global topological weighted encoding, calculate the topological encoding deviation matrix to describe the optimization degree of the current state of the relation enhancement graph Transformer. S76. Using the topology coding deviation matrix as input, the relation structure parameters and feature coding weight parameters in the relation enhancement graph Transformer are iteratively optimized using the multi-objective honey badger optimization algorithm.

6. A cloud-based e-commerce cloud network aggregation supervision system for foreign trade, implementing the cloud-based e-commerce cloud network aggregation supervision method for foreign trade as described in any one of claims 1 to 5, characterized in that, Includes the following modules: The data acquisition module is used to collect transaction data, logistics and distribution data, warehousing data and customs supervision data from cross-border e-commerce platforms in real time, and to perform data standardization processing through distributed data cleaning methods to obtain a standardized data pool. The heterogeneous graph construction module is used to construct a multi-source heterogeneous graph for cross-border e-commerce based on a standardized data pool, and to label the nodes and edges in the heterogeneous graph with node type, relationship type and timestamp respectively; The topology feature encoding module is used to initialize and encode the node spatial location vector, node interaction feature vector, node dynamic attribute feature vector, and spatial relative position relationship matrix in the multi-source heterogeneous graph of cross-border e-commerce. It generates local topology feature representation vector and global topology feature representation vector, and performs double cross-association encoding based on the topology association attention mechanism to obtain high-dimensional feature representation and aggregated view. The regulatory task scheduling module is used to construct a spatial distribution field of regulatory resource demand based on the aggregated view, determine the spatial location coordinates, topological path execution order and specific execution time constraints of regulatory resources, and calculate the dynamic resource task execution reachability coefficient according to the multi-objective honey badger optimization algorithm to generate a dynamic regulatory resource scheduling scheme. The risk warning and intervention module is used to identify abnormal nodes in cross-border e-commerce transactions, logistics, and supervision based on the aggregated view, calculate the path abnormality risk index, generate risk warning and intervention strategies for abnormal behavior through the multi-objective honey badger optimization algorithm, and determine the specific intervention action type, execution intensity, location coordinates, and execution sequence based on the risk warning and intervention strategy. The feedback optimization module is used to collect real-time data on the execution results of resource scheduling schemes and risk warning and intervention strategies, as well as data fusion quality indicators. It calculates the topology coding deviation matrix and uses the topology coding deviation matrix to iteratively update the relation structure parameters and feature coding weight parameters of the relation enhancement graph Transformer through a multi-objective honey badger optimization algorithm.

Images