Foreign trade e-commerce cloud network aggregation supervision method and system based on cloud service

Abstract

The invention discloses a foreign trade e-commerce cloud network aggregation supervision method based on cloud service, and the method comprises the following steps: S1, collecting cross-border e-commerce transaction data, logistics distribution data, storage data and customs supervision data in real time, and carrying out the standardization processing through a distributed cleaning method; s2, constructing a multi-source heterogeneous graph according to the standardized data; s3, marking types, relation types and timestamps for nodes and edges in the graph, and extracting high-dimensional features through a relation enhancement graph Transform; s4, performing local and global aggregation through a multi-layer attention mechanism to generate an aggregation view; s5, constructing a multi-target supervision task model, and generating a resource scheduling scheme; s6, transaction, logistics and supervision abnormity is identified, and a risk early warning and intervention strategy is generated; and S7, dynamically optimizing the relation structure and Transform parameters according to the execution feedback. According to the method, fusion supervision and risk early warning of foreign trade e-commerce cross-domain multi-source data are realized based on high-dimensional graph expression and an optimization scheduling mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cross-border e-commerce intelligent supervision, and particularly relates to a foreign trade e-commerce cloud network aggregation supervision method and system based on cloud services. BACKGROUND

[0002] With the rapid development of cross-border e-commerce business, transaction activities are becoming increasingly complex in terms of spatial distribution, business processes and data sources. A highly heterogeneous business network is formed among cross-border e-commerce platforms, third-party logistics enterprises, warehousing service providers and customs supervision agencies, generating a large amount of real-time, high-frequency and complex-structured multi-source data. These data cover multiple dimensions of commodity flow, capital flow, information flow and supervision flow, forming a typical cross-domain heterogeneous data fusion and supervision scenario. In order to realize the digital governance and risk control of the cross-border e-commerce industry, it has become a focus in the industry to establish an efficient, accurate and scalable cloud network fusion supervision system.

[0003] At present, the existing technology mainly uses field-oriented matching logic, static rule engine or single task scheduling framework to process and supervise cross-border e-commerce data. These methods mostly rely on structured data formats and manually set rule libraries, and are difficult to adapt to the challenges brought by data heterogeneity and dynamic changes in business scenarios. In order to improve data organization and modeling capabilities, some research attempts to introduce graph neural networks or graph structure-based recommendation algorithms to model e-commerce relationships. However, most of them remain at the level of static graphs or single-layer adjacency modeling, and cannot fully express the high-order interaction characteristics in the spatio-temporal evolution process between nodes.

[0004] In terms of supervision 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 among system real-time, accuracy and resource utilization. In recent years, some optimization algorithms such as genetic algorithm and particle swarm algorithm have been used for scheduling modeling, but they still have problems such as slow convergence speed and poor adaptability in complex cross-border scenarios, especially in handling resource allocation problems under high dynamic and strong heterogeneous characteristics.

[0005] In addition, the current abnormality detection and early warning system mainly relies on static rule matching or classification models for risk identification, lacks modeling capability for business topology structure, and is difficult to realize the prediction of the diffusion path of abnormal behavior and the optimization of intervention strategies. In the face of multi-dimensional 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 supervision strategies and online updates of the system.

[0006] Therefore, how to provide a foreign trade e-commerce cloud network aggregation supervision method and system based on cloud services is a problem that needs to be solved by those skilled in the art. SUMMARY

[0007] One purpose of the present application is to propose a foreign trade e-commerce cloud network aggregation supervision method and system based on cloud services, which has the technical effects of efficient aggregation of multi-source heterogeneous data, dynamic identification of abnormal behavior, optimal generation of intervention strategies, and adaptive updating of supervision models.

[0008] The foreign trade e-commerce cloud network aggregation supervision method based on cloud services according to an embodiment of the present application comprises the following steps:

[0009] S1, real-time collection of cross-border e-commerce platform transaction data, logistics distribution data, warehouse data and customs supervision data, and data standardization processing through a distributed data cleaning method to obtain a standardized data pool;

[0010] S2, constructing a cross-border e-commerce multi-source heterogeneous graph according to the standardized data pool;

[0011] S3, labeling node types, relationship types and timestamps in the cross-border e-commerce multi-source heterogeneous graph, and performing multi-element feature encoding through a relationship-enhanced graph Transformer to generate a high-dimensional feature representation of the heterogeneous graph;

[0012] S4, extracting local and global feature information through the multi-layer attention mechanism of the relationship-enhanced graph Transformer according to the high-dimensional feature representation of the heterogeneous graph to form an aggregated view of cross-border e-commerce multi-source data;

[0013] S5, determining a dynamic supervision resource scheduling scheme through a multi-objective meerkat optimization algorithm according to the aggregated view;

[0014] S6, identifying cross-border e-commerce transaction abnormalities, logistics abnormalities and supervision abnormalities based on the aggregated view, and generating risk warning and intervention strategies for abnormal behavior through a multi-objective meerkat optimization algorithm;

[0015] S7, executing the dynamic supervision resource scheduling scheme and the risk warning and intervention strategies, and updating the relationship structure and parameter weights of the relationship-enhanced graph Transformer according to the execution results and feedback information of data fusion quality.

[0016] Optionally, the S2 specifically comprises:

[0017] S21, based on the standardized data pool, mapping the e-commerce platform, the seller, the buyer, the commodity, the logistics service provider, the warehouse center and the customs supervision agency into a multi-dimensional space coordinate with a unique data identifier to express the node attributes in real time;

[0018] S22, extracting transaction time, transaction frequency and transaction volume information among buyers, sellers and commodities according to transaction data, and generating a dynamic correlation path with a transaction flow direction in the multi-dimensional space coordinate;

[0019] S23, according to the logistics distribution data, collecting the logistics process data between the logistics service providers, the distribution goods and the warehouse centers, and constructing the multi-section dynamic distribution path based on the logistics stage marks in real time, and cross-fusing with the dynamic associated path;

[0020] S24, according to the warehouse data, giving the spatial density weight value to the storage location, the inventory duration and the goods access frequency of the goods in the warehouse center in real time, generating the warehouse dynamic node with the continuously adjusted spatial density weight value, and fusing with the dynamic distribution path;

[0021] S25, based on the customs supervision data, capturing the change of the import and export supervision state of the goods in real time, generating the spatial constraint boundary of the supervision state in the multi-dimensional space coordinates, and fusing with the dynamic associated path and the dynamic distribution path;

[0022] S26, dynamically aggregating the space and time characteristics of the dynamic associated path, the dynamic distribution path, the warehouse dynamic node and the supervision state constraint boundary, and constructing the cross-border e-commerce multi-source heterogeneous graph with the spatio-temporal continuity and the topological relationship adaptive evolution.

[0023] Optionally, the S3 specifically comprises:

[0024] S31, according to the business attribute difference of the e-commerce platform node, the seller node, the buyer node, the goods node, the logistics service provider node, the warehouse center node and the customs supervision agency node in the cross-border e-commerce multi-source heterogeneous graph, defining the discrete feature code for expressing the node type, and mapping each node to the node space position vector of the discrete feature code;

[0025] S32, according to the type and characteristic difference of the transaction data, the logistics data and the supervision data, constructing a three-dimensional relationship type identification system, respectively generating three-dimensional relationship vector identifiers representing the transaction relationship dimension, the logistics relationship dimension and the supervision relationship dimension, and giving the absolute time stamp to the relationship edge;

[0026] S33, according to the node space position vector and the three-dimensional relationship vector identifier, calculating the spatial relative position relationship matrix between the nodes and the edges, representing the spatial topological relative relationship between the nodes;

[0027] S34, extracting the time sequence interaction features of the e-commerce platform node, the seller node and the buyer node to obtain the interaction feature vector;

[0028] S35, for the goods node, the logistics service provider node and the warehouse center node, extracting the goods dynamic attribute features, the logistics service stage features and the warehouse inventory real-time change features according to the real-time state data respectively, and generating the node dynamic attribute feature vector;

[0029] S36. Using the node spatial position vector, node interaction feature vector, node dynamic attribute feature vector and spatial relative position relationship matrix as multi-dimensional input, a relationship-enhanced graph transformer is used for joint encoding to obtain a high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph.

[0030] Optionally, the S4 specifically includes:

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

[0032] S42. In each independent topological space region, calculate the spatial density index of the topological connections between the nodes in the region, where the spatial density index is defined as the local topological weight coefficient of the nodes in the topological space region;

[0033] S43, performing topological space weighted coding on the initial features of each node in the topological space region according to the local topological weighted coefficient of each topological space region, and generating a local topological feature representation vector of each topological space region;

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

[0035] S45. According to the topological propagation coefficient of the node, the global topological feature weight of the node is dynamically adjusted in real time to generate a global topological feature representation vector of the entire heterogeneous graph topology structure;

[0036] S46. Based on the local topological feature representation vector and the global topological feature representation vector, double cross-correlation encoding is performed through the topological correlation attention mechanism of the relationship enhanced graph Transformer to form an aggregated view that includes 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 aggregate view generation module:

[0038] The topological space feature initialization module receives the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector, and uniformly maps them to the corresponding topological space feature initial sequence according to the discrete feature coding of the node type;

[0039] The local topology feature encoding module receives a topology space feature initial sequence and a three-dimensional relationship vector identifier, calculates a space density index of the topology connection between nodes in each independent topology space region according to a space relative position relationship matrix, and performs topology space weighted coding on the feature vectors of the nodes in the topology space region one by one by taking the space density index as a local topology weighting coefficient, to form a local topology feature representation vector of each region.

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

[0041] The topology feature cross-fusion module takes the local topology feature representation vector and the global topology feature representation vector as double inputs, calculates a topology correlation weight of the local topology representation vector to the global topology representation vector and a topology correlation weight of the global topology representation vector to the local topology representation vector through a topology correlation attention mechanism, performs cross-weighted fusion of the feature vectors by taking the topology correlation weights, and generates a topology cross-correlation fusion feature vector.

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

[0043] The aggregated view generation module takes the high-dimensional feature representation as input, and performs feature adaptive coding processing through the topology correlation attention mechanism to form an aggregated view containing topology space structure features and topology information propagation path features.

[0044] Optionally, the S5 specifically includes:

[0045] S51, based on the node space position vector, the node interaction feature vector and the node dynamic attribute feature vector in the aggregated view, a supervision resource demand space distribution field is constructed, the node space position vector is used to define the coordinate position of the node in the space distribution field, and the resource demand density value of each node is calculated 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, a multi-objective supervision task prediction matrix is established according to the resource demand density value of each node, the historical trend sequence and the current feature change of the node dynamic attribute feature vector are analyzed, and the resource demand prediction value in a future set time window is calculated node by node through multi-dimensional linear regression;

[0047] S53, taking the node real-time resource demand density value and the future resource demand prediction value as constraint variables, constructing a regulatory task space field with multi-dimensional objective constraint conditions;

[0048] S54, in the regulatory task space field, for the spatial position relationship between each regulatory resource and a specific regulatory task, calculating the connection difficulty and execution cost of the resource to the task, and combining the connection difficulty and execution cost to form a dynamic resource task execution accessibility coefficient for comprehensive evaluation of the real-time execution capability of the regulatory resource to the task;

[0049] S55, initializing a spatial search population set of the multi-objective meerkat optimization algorithm according to the dynamic resource task execution accessibility coefficient, and performing preferred operation on individuals in the population through a mixed elite framework, and screening the dynamic accessibility optimal spatial configuration solution through a circular segmented screening mechanism;

[0050] S56, according to the dynamic accessibility optimal spatial configuration solution, determining the optimal path execution order and execution time constraint of each regulatory resource in the spatial field, and generating a dynamic regulatory resource scheduling scheme including spatial coordinates, execution order and execution time.

[0051] Optionally, the S6 specifically comprises:

[0052] S61, based on the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector in the aggregated view, calculating the transaction data change rate, the logistics distribution time delay change rate and the regulatory state change frequency index of each node in the current period relative to the historical period, and defining them as node abnormal feature change indexes;

[0053] S62, respectively according to the statistical distribution law of the node abnormal feature change index, determining the transaction abnormal threshold, the logistics abnormal threshold and the regulatory abnormal threshold, and performing threshold matching analysis on the transaction data change rate, the logistics distribution time delay change rate and the regulatory state change frequency index of each node one by one, identifying the transaction abnormal node, the logistics abnormal node and the regulatory abnormal node;

[0054] S63, for each transaction abnormal node, logistics abnormal node and regulatory abnormal node, determining the spatial coordinate position of the central abnormal node according to the node spatial position vector and the spatial relative position relationship matrix in the aggregated view, and tracking the associated nodes having real-time topological path connection relationship with the central abnormal node, and constructing an abnormal node topological diffusion network with the central abnormal node as the starting point;

[0055] S64, in the abnormal node topology diffusion network, the topological connection strength value of the topological connection path between the central abnormal node and each associated node is calculated, and the path abnormal diffusion probability value is calculated, and the topological connection strength value is multiplied by the path abnormal diffusion probability value to generate a path abnormal risk index for quantifying the risk degree of path abnormal propagation;

[0056] S65, inputting the path abnormal risk index, a multi-objective abnormal risk early warning matrix including transaction abnormal risk, logistics abnormal risk and supervision abnormal risk is established, a multi-objective meerkat optimization algorithm is used to initialize a space search population set, and a hybrid elite framework and a circular segmented screening mechanism are used to iteratively screen and evaluate individuals in the population to generate a multi-objective optimal risk early warning solution with the lowest path abnormal risk index as the optimization target;

[0057] S66, according to the multi-objective optimal risk early warning solution, the spatial position of the topological connection path corresponding to the path abnormal risk index, the optimal scheduling path of the dynamic supervision resource, the type of the required intervention action, the specific execution time sequence of the intervention action, and the intervention intensity are determined to form a dynamic risk intervention strategy for execution.

[0058] Optionally, the S7 specifically comprises:

[0059] S71, according to the dynamic supervision resource scheduling scheme, the spatial position coordinates of the supervision resource in the spatial distribution field, the topological path execution order and the specific execution time constraint are determined to generate a resource scheduling instruction composed of spatial coordinate identification, path sequence identification and time constraint identification;

[0060] S72, according to the dynamic risk intervention strategy, the specific intervention type of the supervision resource corresponding to the intervention action, the execution intensity value, the execution position coordinates and the time sequence of the intervention action are determined to generate a risk intervention instruction composed of intervention type identification, execution intensity value, spatial position coordinates and time sequence identification;

[0061] S73, according to the resource scheduling instruction and the risk intervention instruction, the spatial coordinate positioning accuracy, the topological path execution order, the execution time deviation, the intervention action type matching degree and the execution intensity control accuracy of the supervision resource performing the task are respectively quantitatively evaluated in real time to form an execution result quantitative matrix;

[0062] S74, by real-time acquisition of the fusion results of cross-border e-commerce platform transaction data, logistics distribution data, warehouse data and customs supervision data, based on the spatial coordinate matching accuracy, the topological correlation path reconstruction accuracy, the time sequence synchronization accuracy and the data fusion noise level in the data fusion process, a data fusion quality index matrix is generated;

[0063] S75, input the execution result quantization matrix and the data fusion quality index matrix into the relationship enhanced graph Transformer respectively, and through topology feature initialization, local topology weighted coding and global topology weighted coding, calculate a topology coding bias matrix for describing the optimization degree of the current state of the relationship enhanced graph Transformer;

[0064] S76, input the topology coding bias matrix, and through the multi-objective meerkat optimization algorithm, iteratively optimize the relationship structure parameters and feature coding weight parameters in the relationship enhanced graph Transformer.

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

[0066] A data acquisition module is configured to acquire cross-border e-commerce platform transaction data, logistics distribution data, warehouse data and customs supervision data in real time, and perform data normalization processing through a distributed data cleaning method to obtain a standardized data pool;

[0067] A heterogeneous graph construction module is configured to construct a cross-border e-commerce multi-source heterogeneous graph according to the standardized data pool, and label node types, relationship types and timestamps in the heterogeneous graph respectively;

[0068] A topology feature coding module is configured to perform feature initialization and topology feature coding on a node spatial position vector, a node interaction feature vector, a node dynamic attribute feature vector and a spatial relative position relationship matrix in the cross-border e-commerce multi-source heterogeneous graph, generate a local topology feature representation vector and a global topology feature representation vector, and perform double cross-linking coding based on a topology correlation attention mechanism to obtain a high-dimensional feature representation and an aggregated view;

[0069] A supervision task scheduling module is configured to construct a supervision resource demand space distribution field according to the aggregated view, determine spatial position coordinates, topology path execution sequences and specific execution time constraints of the supervision resources, calculate a dynamic resource task execution accessibility coefficient according to a multi-objective meerkat optimization algorithm, and generate a dynamic supervision resource scheduling scheme;

[0070] A risk early warning intervention module is configured to identify cross-border e-commerce transaction abnormal nodes, logistics abnormal nodes and supervision abnormal nodes based on the aggregated view, calculate a path abnormal risk index, generate a risk early warning and intervention strategy for abnormal behavior through a multi-objective meerkat optimization algorithm, and determine a specific intervention action type, execution intensity, position coordinates and execution timing according to the risk early warning and intervention strategy;

[0071] A feedback optimization module is configured to collect resource scheduling scheme and risk early warning and intervention strategy execution result data and data fusion quality indicators in real time, calculate a topology coding bias matrix, and use the topology coding bias matrix to iteratively update relationship structure parameters and feature coding weight parameters of a relationship enhanced graph Transformer through a multi-objective meerkat optimization algorithm.

[0072] The present application has the following advantages:

[0073] (1) The present application uses a relationship enhanced graph Transformer to jointly encode node types, relationship types and timestamps in a cross-border e-commerce multi-source heterogeneous graph, generates high-dimensional feature representations, realizes the structured representation and association modeling of e-commerce, logistics, warehousing and supervision data, effectively improves the precision and timeliness of heterogeneous data fusion, and enhances the modeling capability of dynamic e-commerce network structure.

[0074] (2) The present application builds a multi-objective supervision task model and introduces a multi-objective meerkat optimization algorithm to optimize the real-time scheduling path, execution order and time constraints of supervision resources, which can realize the optimal scheduling of supervision tasks under the multi-dimensional objectives of real-time, accuracy, load balancing and resource consumption, and significantly improve the resource allocation efficiency of the cross-border e-commerce supervision system in complex dynamic scenarios.

[0075] (3) In the aspect of abnormal behavior recognition and early warning, the present application builds an abnormal node topology diffusion network and calculates a path abnormal risk index, generates an intervention strategy in combination with a multi-objective meerkat optimization algorithm, realizes the accurate identification of abnormal behavior propagation paths and the optimal generation of intervention instructions, and effectively solves the technical problems of existing technologies, such as untimely abnormal disposal response and lack of precision of intervention instructions.

[0076] (4) The present application builds a feedback optimization mechanism, dynamically generates a topology coding bias matrix using resource scheduling execution results and data fusion quality indicators, and iteratively optimizes the relationship structure parameters and feature coding weights of the relationship enhanced graph Transformer, realizes the real-time adaptive update of the model during task execution, breaks through the limitations of the existing graph modeling mechanism which is static and does not have evolution capability, and improves the adaptive supervision capability of the system to scenario changes. BRIEF DESCRIPTION OF DRAWINGS

[0077] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and are used to explain the present application, and do not constitute a limitation of the present application. In the drawings:

[0078] Figure 1 A flowchart of a foreign trade e-commerce cloud network aggregation supervision method based on cloud services is provided. DETAILED DESCRIPTION

[0079] The application will now be described in further detail with reference to the drawings. These drawings are simplified schematic illustrations which only show the basic structure of the application in a schematic manner and therefore only show the components which are relevant to the application.

[0080] Reference Figure 1 A cloud service-based foreign trade e-commerce cloud network aggregation supervision method includes the following steps:

[0081] S1, real-time collection of cross-border e-commerce platform transaction data, logistics distribution data, warehouse data and customs supervision data, and data standardization processing through a distributed data cleaning method to obtain a standardized data pool;

[0082] S2, constructing a cross-border e-commerce multi-source heterogeneous graph according to the standardized data pool;

[0083] S3, labeling node types, relationship types and timestamps for nodes and edges in the cross-border e-commerce multi-source heterogeneous graph, and performing multi-element feature encoding through a relationship-enhanced graph Transformer to generate a high-dimensional feature representation of the heterogeneous graph;

[0084] S4, extracting local and global feature information through the multi-layer attention mechanism of the relationship-enhanced graph Transformer based on the high-dimensional feature representation of the heterogeneous graph to form an aggregated view of cross-border e-commerce multi-source data;

[0085] S5, determining a dynamic supervision resource scheduling scheme through a multi-objective meerkat optimization algorithm based on the aggregated view;

[0086] S6, identifying cross-border e-commerce transaction abnormalities, logistics abnormalities and supervision abnormalities based on the aggregated view, and generating risk warning and intervention strategies for abnormal behavior through a multi-objective meerkat optimization algorithm;

[0087] S7, executing the dynamic supervision resource scheduling scheme and the risk warning and intervention strategies, and updating the relationship structure and parameter weights of the relationship-enhanced graph Transformer according to the execution results and feedback information on data fusion quality.

[0088] By real-time collection and standardized processing of multi-source heterogeneous data such as cross-border e-commerce platforms, logistics, warehousing and customs supervision, the multi-layer attention mechanism of the relationship-enhanced graph Transformer extracts and aggregates local and global features, and the multi-objective meerkat optimization algorithm is used to realize the multi-objective optimization scheduling of dynamic supervision resources, and on this basis, the risk warning and intervention strategy for transaction, logistics and supervision abnormalities are generated, and finally the relationship structure and parameter weight of the Transformer are updated adaptively according to the scheduling and warning execution feedback, so that the system can realize the real-time and accuracy of data fusion, the balance and flexibility of resource scheduling, and the timeliness of abnormal identification and intervention in the complex cross-border e-commerce scene.

[0089] In this embodiment, S1 specifically includes:

[0090] S11, through the transaction data interface of the cross-border e-commerce platform, the account identifier of the buyer and the seller, the order number, the commodity category, the transaction amount, the payment method, the order time, the payment completion time, the order status are collected, and the data is assigned a unique data identifier to form a structured transaction data record;

[0091] S12, through the logistics distribution system interface, the logistics service provider identifier, the cargo identifier, the distribution path information, the spatial coordinates of the logistics delivery node and the receiving node, the distribution initiation time and arrival time, the transportation tool type, the logistics document number and the logistics state change event are collected to form real-time updated logistics distribution data records;

[0092] S13, through the warehouse information system interface, real-time collection of warehouse center identifier, commodity warehousing time, warehousing time, inventory quantity, commodity inventory location coordinates, storage location temperature and humidity, storage operation record number and commodity warehouse entry and exit state change information, forming real-time updated warehousing data records;

[0093] S14, through the customs supervision platform interface, real-time collection of customs agency identifier, customs declaration number, customs declaration enterprise identifier, cargo type, supervision customs status, customs declaration approval time, customs clearance time, abnormal inspection status and customs-related voucher data, forming real-time updated customs supervision data records;

[0094] S15, based on the distributed data cleaning processing framework, respectively, the above-mentioned transaction data records, logistics distribution data records, warehousing data records and customs supervision data records are executed to perform de-duplication operation, missing value filling, data format unified conversion, abnormal value detection and correction, and data item consistency check, forming a standardized data pool with unified data fields, standardized identifiers and consistent formats.

[0095] In this embodiment, S2 specifically includes:

[0096] S21, based on the standardized data pool, mapping the e-commerce platform, the seller, the buyer, the commodity, the logistics service provider, the warehouse center and the customs supervision agency to a multi-dimensional space coordinate with a unique data identifier respectively to express the node attributes in real time;

[0097] S22, according to the transaction data, extracting the transaction time, transaction frequency and transaction volume information among the buyer, the seller and the commodity, and generating a dynamic correlation path with a transaction flow direction in the multi-dimensional space coordinate;

[0098] S23, according to the logistics distribution data, collecting the logistics process data between the logistics service provider, the distributed commodity and the warehouse center, and constructing a multi-section dynamic distribution path based on the logistics stage mark in real time, and cross-fusing with the dynamic correlation path;

[0099] S24, according to the warehouse data, giving the space density weight value to the storage location, inventory duration and commodity access frequency of the commodity in the warehouse center in real time, generating a warehouse dynamic node with continuously adjusted space density weight value, and fusing with the dynamic distribution path;

[0100] S25, based on the customs supervision data, capturing the change of the import and export supervision state of the commodity in real time, generating a space constraint boundary of the supervision state in the multi-dimensional space coordinate, and fusing with the dynamic correlation path and the dynamic distribution path;

[0101] S26, dynamically aggregating the space and time characteristics of the dynamic correlation path, the dynamic distribution path, the warehouse dynamic node and the supervision state constraint boundary, and constructing a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution of topological relationship. By mapping the cross-border e-commerce platform, the seller, the buyer, the commodity, the logistics service provider, the warehouse center and the customs supervision agency to a multi-dimensional space coordinate with a unique data identifier, dynamically generating a transaction flow correlation path according to the transaction data, constructing a multi-section distribution path according to the logistics stage mark and fusing with the correlation path, giving a warehouse dynamic node a space density weight value in real time according to the warehouse data and fusing with the distribution path, generating a space constraint boundary according to the customs supervision data and fusing with the above paths, and finally dynamically aggregating the spatiotemporal characteristics of the multiple dynamic paths, nodes and constraint boundaries, a cross-border e-commerce multi-source heterogeneous graph with spatiotemporal continuity and adaptive evolution ability is constructed. Compared with the existing static graph construction or single data source modeling method, the present application can reflect the space-time evolution of multi-source entity attributes and multi-stage interaction flow in real time, and improve the consistency and accuracy of heterogeneous data representation.

[0102] In the embodiment, the 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, storage center nodes, and customs regulatory agency nodes in the cross-border e-commerce multi-source heterogeneous graph, define a discrete feature code for expressing node types, and map each node to a node spatial position vector of the discrete feature code;

[0104] S32. Based on the differences in types and characteristics of transaction data, logistics data, and regulatory data, a three-dimensional relationship type identification system is constructed. Three-dimensional relationship vector identifiers representing the transaction relationship dimension, logistics relationship dimension, and regulatory relationship dimension are generated, and absolute timestamps are assigned to relationship edges.

[0105] S33. Calculate the spatial relative position relationship matrix between the nodes and the edges based on the node spatial position vectors and the three-dimensional relationship vector identifiers to represent the spatial topological relative relationship between the nodes;

[0106] S34, extracting time series interaction features of the e-commerce platform node, the seller node, and the buyer node to obtain an interaction feature vector;

[0107] S35. For the product node, logistics service provider node, and storage center node, extract the product dynamic attribute characteristics, logistics service stage characteristics, and storage inventory real-time change characteristics based on the real-time status data, and generate a node dynamic attribute feature vector;

[0108] S36. Using the node spatial position vector, node interaction feature vector, node dynamic attribute feature vector and spatial relative position relationship matrix as multi-dimensional input, a relationship-enhanced graph transformer is used for joint encoding to obtain a high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph.

[0109] By discretely encoding the nodes in the multi-source heterogeneous graph of cross-border e-commerce, constructing three-dimensional relationship vectors for transaction, logistics and supervision relationships and assigning timestamps, matrixing the spatial relative position relationship between nodes and edges, 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, we have achieved refined spatiotemporal correlation modeling of complex multi-source data of cross-border e-commerce, effectively improving the accuracy and consistency of high-dimensional feature representation of heterogeneous graphs.

[0110] In this embodiment, the S4 specifically includes:

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

[0112] S42, calculate the spatial density index of the topological connection between nodes in each independent topological space region, and the spatial density index is defined as the local topological weighted coefficient of the nodes in the topological space region;

[0113] S43, topologically weight code the initial features of each node in the topological space region according to the local topological weighted coefficient of each topological space region, to generate a local topological feature representation vector of each topological space region;

[0114] S44, calculate the path weight of the topological information propagation between any nodes in the whole cross-border e-commerce multi-source heterogeneous graph topological structure in real time, and the path weight is defined as the topological propagation coefficient of the nodes in the global topological structure;

[0115] S45, dynamically adjust the global topological feature weight of the nodes in real time according to the topological propagation coefficient of the nodes, to generate a global topological feature representation vector of the whole heterogeneous graph topological structure;

[0116] S46, based on the local topological feature representation vector and the global topological feature representation vector, perform double cross-correlation coding through the topological correlation attention mechanism of the relationship enhanced graph Transformer, to form an aggregated view containing topological space structure features and topological information propagation path features.

[0117] By dividing the high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph into independent topological space regions, calculating and coding the spatial density weight between nodes in each region to generate a local topological feature representation, and calculating the topological propagation coefficient of the nodes in the whole topological structure to generate a global topological feature representation in real time, finally fusing the local and global features through the double cross-correlation attention mechanism of the relationship enhanced graph Transformer to form an aggregated view containing topological space structure and information propagation path features, the graph structure information is captured in multiple scales, the consistency and accuracy of the heterogeneous graph representation are significantly improved, and the topological correlation modeling capability of the model for complex multi-source data is effectively enhanced.

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

[0119] The topological space feature initialization module receives the node spatial position vector, the node interaction feature vector, and the node dynamic attribute feature vector, and uniformly maps them to the corresponding topological space feature initial sequence according to the discrete feature coding of the node type;

[0120] The local topology feature encoding module receives a topology space feature initial sequence and a three-dimensional relationship vector identifier, calculates a space density index of the topology connection between nodes in each independent topology space region according to a space relative position relationship matrix, and performs topology space weighted coding on the feature vectors of the nodes in the topology space region one by one by taking the space density index as a local topology weighting coefficient, to form a local topology feature representation vector of each region.

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

[0122] The topology feature cross-fusion module takes the local topology feature representation vector and the global topology feature representation vector as double inputs, calculates a topology correlation weight of the local topology representation vector to the global topology representation vector and a topology correlation weight of the global topology representation vector to the local topology representation vector through a topology correlation attention mechanism, performs cross-weighted fusion of the feature vectors by taking the topology correlation weights, and generates a topology cross-correlation fusion feature vector.

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

[0124] The aggregated view generation module takes the high-dimensional feature representation as input, and forms an aggregated view containing topology space structure features and topology information propagation path features through feature adaptive coding processing of the topology correlation attention mechanism.

[0125] By uniformly mapping the node space position vector, the interaction feature vector and the dynamic attribute feature vector into the topology space feature initial sequence, generating the regional node features based on the space density weighting by the local topology feature encoding module, generating the global node features based on the topology propagation coefficient weighting by the global topology feature encoding module, and calculating the correlation weights between the local and global features by the topology feature cross-fusion module, the high-dimensional feature representation generation module and the aggregated view generation module complete joint coding and aggregated view output through the multi-layer attention mechanism, the multi-scale topology structure information in the cross-border e-commerce multi-source heterogeneous graph is comprehensively captured and fused, and the graph representation consistency and information transmission accuracy are effectively improved.

[0126] In the embodiment, the S5 specifically includes:

[0127] S51, constructing a supervision resource demand space distribution field based on the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector in the aggregated view, defining the coordinate position of the node in the space distribution field by the node spatial position vector, and calculating 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, establishing a multi-objective supervision task prediction matrix according to the resource demand density value of each node, calculating the resource demand prediction value in a future set time window node by node through multi-dimensional linear regression by analyzing the historical trend sequence and the current feature change of the node dynamic attribute feature vector;

[0129] S53, constructing a supervision task space field with multi-dimensional objective constraint conditions by taking the real-time resource demand density value and the future resource demand prediction value of the node as constraint variables;

[0130] S54, in the supervision task space field, calculating the connection difficulty and execution cost of the resource to the task for the spatial position relationship between each supervision resource and a specific supervision task, and combining the connection difficulty and the execution cost to form a dynamic resource task execution accessibility coefficient for comprehensively evaluating the real-time execution capability of the supervision resource to the task;

[0131] S55, initializing the space search population set of the multi-objective meerkat optimization algorithm according to the dynamic resource task execution accessibility coefficient, and performing optimization operation on the individuals in the population through the mixed elite framework, and screening the dynamic accessibility optimal space configuration solution generated by iteration through the circular segmented screening mechanism;

[0132] S56, determining the optimal path execution order and execution time constraint of each supervision resource in the space field according to the dynamic accessibility optimal space configuration solution, and generating a dynamic supervision resource scheduling scheme containing space coordinates, execution order and execution time.

[0133] By constructing a supervision resource demand space distribution field based on an aggregated view, and constructing a multi-dimensional objective constraint supervision task space field combining the real-time resource demand density and future demand prediction of the node, further comprehensively evaluating the connection difficulty and execution cost of the topological path of the supervision resource and the task, generating a dynamic resource task execution accessibility coefficient, taking the coefficient as the population fitness function of the multi-objective meerkat optimization algorithm, the optimal space configuration path and the accurate determination of the execution time sequence of the supervision resource in the complex cross-border e-commerce scene are realized, thereby providing high-precision decision support for the resource scheduling scheme in the dynamic business environment.

[0134] In the embodiment, the S6 specifically comprises:

[0135] S61, based on the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector in the aggregated view, node-by-node calculation of the transaction data change ratio, the logistics distribution time delay change ratio and the regulatory state change frequency index of the current period relative to the historical period, and defined as the node abnormal feature change index;

[0136] S62, according to the statistical distribution rule of the node abnormal feature change index, determine the transaction abnormal threshold, the logistics abnormal threshold and the regulatory abnormal threshold, and perform threshold matching analysis on the transaction data change ratio, the logistics distribution time delay change ratio and the regulatory state change frequency index of each node one by one, identify the transaction abnormal node, the logistics abnormal node and the regulatory abnormal node;

[0137] S63, for each transaction abnormal node, logistics abnormal node and regulatory abnormal node, determine the spatial coordinate position of the central abnormal node according to the node spatial position vector and the spatial relative position relationship matrix in the aggregated view, and track the associated nodes that have real-time topological path connection relationship with the central abnormal node, and construct an abnormal node topological diffusion network with the central abnormal node as the starting point;

[0138] S64, in the abnormal node topological diffusion network, calculate the topological connection strength value and the path abnormal diffusion probability value of the topological connection path between the central abnormal node and each associated node, multiply the topological connection strength value and the path abnormal diffusion probability value, and generate a path abnormal risk index for quantifying the risk degree of path abnormal propagation;

[0139] S65, taking the path abnormal risk index as input, establish a multi-objective abnormal risk early warning matrix containing transaction abnormal risk, logistics abnormal risk and regulatory abnormal risk, initialize the spatial search population set using the multi-objective meerkat optimization algorithm, and use the hybrid elite framework and the circular segmented screening mechanism to perform iterative screening and evaluation on the individuals in the population, and generate a multi-objective optimal risk early warning solution with the lowest path abnormal risk index as the optimization target;

[0140] S66, according to the multi-objective optimal risk early warning solution, determine the spatial position of the topological connection path corresponding to the path abnormal risk index, the optimal scheduling path of the dynamic regulatory resources, the type of required intervention action, the specific execution time sequence of the intervention action, and the intervention intensity, form a dynamic risk intervention strategy for execution.

[0141] The transaction abnormal node, the logistics abnormal node and the supervision abnormal node are accurately identified by calculating the transaction data change rate, the logistics distribution delay change rate and the supervision state change frequency based on the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector in the aggregated view, and accurately setting the abnormal threshold according to the statistical distribution law; further, the abnormal node topology diffusion network is constructed, the topology connection strength and diffusion probability between the central abnormal node and the associated node are calculated, and the path abnormal risk index is generated, the multi-objective meerkat optimization algorithm is combined to screen out the multi-objective optimal early warning solution with the lowest risk, and the quantitative evaluation and early warning decision of the abnormal propagation path risk are realized; finally, according to the optimal risk early warning solution, the dynamic risk intervention strategy including the spatial coordinates, the scheduling path, the intervention action type, the execution time sequence and the intervention intensity is automatically generated, which provides the accurate risk management ability based on the real-time topology structure and multi-source data for the cross-border e-commerce supervision.

[0142] In the embodiment, the S7 specifically includes:

[0143] S71, according to the dynamic supervision resource scheduling scheme, the spatial position coordinates of the supervision resource in the spatial distribution field, the topology path execution sequence and the specific execution time constraint are determined, and the resource scheduling instruction composed of the spatial coordinate identifier, the path sequence identifier and the time constraint identifier is generated;

[0144] S72, according to the dynamic risk intervention strategy, the specific intervention type of the intervention action of the supervision resource, the execution intensity value of the intervention action, the execution position coordinates and the time sequence are determined, and the risk intervention instruction composed of the intervention type identifier, the execution intensity value, the spatial position coordinates and the time sequence identifier is generated;

[0145] S73, according to the resource scheduling instruction and the risk intervention instruction, the spatial coordinate positioning accuracy, the topology path execution sequence, the execution time deviation, the intervention action type matching degree and the execution intensity control accuracy of the supervision resource executing the task are respectively quantitatively evaluated in real time, and the execution result quantitative matrix is formed;

[0146] S74, through real-time acquisition of the fusion results of the cross-border e-commerce platform transaction data, the logistics distribution data, the warehouse data and the customs supervision data, based on the spatial coordinate matching accuracy, the topology correlation path reconstruction accuracy, the time sequence synchronization accuracy and the data fusion noise level in the data fusion process, the data fusion quality index matrix is generated;

[0147] S75, the execution result quantitative matrix and the data fusion quality index matrix are respectively input into the relationship enhanced graph Transformer, the topology feature initialization, the local topology weighted coding and the global topology weighted coding are performed, and the topology coding deviation matrix used to describe the optimization degree of the current state of the relationship enhanced graph Transformer is calculated;

[0148] S76, taking the topological coding deviation matrix as input, iteratively optimizing the relationship structure parameters and feature coding weight parameters in the relationship enhanced graph Transformer through the multi-objective meerkat optimization algorithm.

[0149] By taking the spatial coordinate identifier, path sequence identifier and time constraint identifier generated by the dynamic supervision resource scheduling scheme and risk intervention strategy as resource scheduling instructions and risk intervention instructions, the real-time positioning accuracy, path execution deviation and intervention matching degree are quantified during execution to form an execution result quantization matrix; at the same time, based on the spatial coordinate matching accuracy, path reconstruction accuracy, time sequence synchronization accuracy and noise level of cross-border e-commerce multi-source data fusion, a data fusion quality index matrix is generated, and the above quantization matrix is input into the relationship enhanced graph Transformer for topological feature initialization, local weighted coding and global weighted coding, and the topological coding deviation matrix is calculated; finally, the relationship structure parameters and feature coding weights of the Transformer are iteratively adjusted through the multi-objective meerkat optimization algorithm, realizing the closed-loop adaptive optimization of model parameters and scheduling scheme, which significantly improves the real-time responsiveness and adaptability of the supervision system in complex dynamic environment compared with the existing static update or offline optimization mode.

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

[0151] The data acquisition module is used to acquire cross-border e-commerce platform transaction data, logistics distribution data, warehouse data and customs supervision data in real time, and to process the data through a distributed data cleaning method to obtain a standardized data pool;

[0152] The heterogeneous graph construction module is used to construct a cross-border e-commerce multi-source heterogeneous graph according to the standardized data pool, and to label the node types, relationship types and timestamps in the heterogeneous graph respectively;

[0153] The topological feature coding module is used to initialize and code the node spatial position vector, node interaction feature vector, node dynamic attribute feature vector and spatial relative position relationship matrix in the cross-border e-commerce multi-source heterogeneous graph, generate local topological feature representation vectors and global topological feature representation vectors, and perform double cross-correlation coding based on the topological correlation attention mechanism to obtain high-dimensional feature representation and aggregation view;

[0154] The supervision task scheduling module is used to construct a supervision resource demand spatial distribution field according to the aggregation view, determine the spatial position coordinates, topological path execution order and specific execution time constraints of the supervision resources, and calculate the dynamic resource task execution accessibility coefficient according to the multi-objective meerkat optimization algorithm to generate a dynamic supervision resource scheduling scheme;

[0155] The risk early warning intervention module is configured to identify cross-border e-commerce transaction abnormal nodes, logistics abnormal nodes and supervision abnormal nodes based on the aggregated view, calculate a path abnormal risk index, generate a risk early warning and intervention strategy for abnormal behavior through a multi-objective meerkat optimization algorithm, and determine a specific intervention action type, execution intensity, location coordinates and execution timing according to the risk early warning and intervention strategy.

[0156] The feedback optimization module is configured to collect execution result data and data fusion quality indicators of the resource scheduling scheme and the risk early warning and intervention strategy in real time, calculate a topological coding bias matrix, and use the topological coding bias matrix to iteratively update relationship structure parameters and feature coding weight parameters of a relationship enhanced graph Transformer through a multi-objective meerkat 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 the data pool, the topological feature coding module is used to realize joint coding and multi-layer cross-fusion of node spatial position, interaction features and dynamic attributes, so as to accurately obtain high-dimensional feature representation. Then, the resource execution reachability coefficient is calculated under multi-dimensional target constraints by the supervision task scheduling module to generate a dynamic scheduling scheme, and the risk early warning intervention module is used to quantitatively identify transaction, logistics and supervision abnormalities and generate optimal intervention strategies. Finally, the feedback optimization module iteratively updates the graph model parameters based on the 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 abnormal early warning and adaptive updating of the system model.

[0158] Embodiment 1

[0159] To verify the feasibility of the present application in implementation, the method of the present application is applied to a cross-border e-commerce data fusion supervision platform in a certain comprehensive bonded zone. For multi-source real-time data from multiple e-commerce platforms, logistics enterprises, warehousing service providers and customs supervision agencies, the method realizes real-time supervision of the transaction process, dynamic monitoring of logistics distribution, efficient coordination of warehousing management and fine management of customs supervision. In actual application, due to the dependence of traditional methods on manual rule setting and static data interfaces, it is difficult to connect cross-platform data, the efficiency of heterogeneous data fusion is low, the supervision response is lagging, and the abnormal identification is not timely, etc. Especially during the peak transaction period, the data response delay is as high as several minutes or even tens of minutes, making it difficult for the supervision department to discover abnormal transaction behavior, logistics abnormal events and supervision violation events in real time, causing great difficulty in platform supervision.

[0160] In actual operation, first, cross-border e-commerce transaction data is collected in real time through a standardized interface, including transaction details of both buyers and sellers, transaction amounts, product numbers, purchase frequencies, etc.; logistics distribution data is collected, including shipping times, in-transit locations, transportation durations, and distribution statuses; at the same time, warehouse data is collected, including product inventory locations, inventory durations, and warehouse entry and exit frequencies; and customs supervision data is collected, including product import and export statuses, customs declaration information, and supervision release statuses. After distributed cleaning and standardization processing of these heterogeneous data, a unified standardized data pool is formed, serving as the basis for data analysis and graph structure construction.

[0161] Next, using the constructed standardized data pool, the e-commerce platform, buyers, sellers, products, logistics service providers, warehouse centers, and customs supervision agencies are uniformly identified according to business attribute differences and mapped to a multi-dimensional space coordinate of uniform data identifiers to ensure consistent, clear, and highly distinguishable data representation of different nodes. Then, according to real-time transaction data, transaction frequencies, transaction volumes, and times of both buyers and sellers are extracted to dynamically construct dynamic correlation paths with clear real-time transaction flow directions; according to logistics distribution data, multi-segment dynamic distribution paths with clear stage markers are constructed in real time; at the same time, according to warehouse data, warehouse node spatial density weights are updated in real time to generate warehouse nodes with continuously dynamically adjusted spatial densities; and according to customs supervision data, spatial constraint boundaries of product import and export supervision statuses are generated in real time and fused with transaction, logistics, and warehouse paths in real time. Finally, multi-source heterogeneous graphs are jointly encoded for spatial position, time-series interaction, and dynamic attribute characteristics using a relationship-enhanced graph Transformer model to form a real-time updated high-dimensional feature representation.

[0162] Then, to further improve supervision effectiveness, the platform divides the high-dimensional feature representation into multiple topological space regions, calculates the spatial density index of topological connections between nodes in the region, defines it as the local topological weighted coefficient of the node, and performs feature weighted coding to form a local topological feature representation vector; at the same time, the path weight of topological information propagation between any nodes in the overall structure is calculated in real time, defined as the global topological propagation coefficient, and the global topological feature weight of the node is dynamically adjusted to form a global topological feature representation vector. Based on the topological association attention mechanism of the relationship-enhanced graph Transformer, the local and global feature representations are cross-correlated and fused to generate an aggregated view with topological spatial structure and information propagation path features.

[0163] Subsequently, based on the aggregated view, a real-time regulatory resource demand spatial distribution field is constructed, the resource demand density is calculated node by node according to the real-time interaction frequency and dynamic change rate of the nodes, and the future resource demand in a period of time is predicted through multi-dimensional linear regression, so as to construct a multi-objective regulatory task prediction matrix and a multi-dimensional target constrained regulatory task space field. In this space field, the platform real-time evaluates the accessibility and execution cost of regulatory resources to tasks, and through the spatial search population set of the multi-objective meerkat optimization algorithm, the hybrid elite framework and the circular segmented screening mechanism are used for iterative optimization to determine the optimal resource scheduling scheme for each regulatory task. In actual scenarios, after 20 iterations of optimization, the spatial accessibility coefficient converges to below 0.05, achieving the precision and dynamic of resource scheduling.

[0164] In terms of real-time identification and processing of abnormal events, the node abnormal feature change index is defined by aggregating the view to calculate the current transaction data change rate, the logistics distribution time delay change rate and the regulatory state change frequency index. Based on historical statistical data, the abnormal threshold is determined, the transaction abnormal nodes, logistics abnormal nodes and regulatory abnormal nodes are identified in real time, and the real-time topology diffusion network of abnormal nodes is tracked. The platform real-time calculates the topology connection strength and abnormal diffusion probability between nodes, forms the path abnormal risk index, and determines the multi-objective optimal risk warning scheme with the lowest risk through iterative screening of the multi-objective meerkat optimization algorithm. In practical applications, the accuracy rate of abnormal node identification reaches 97.8%, and the accuracy rate of risk diffusion prediction reaches 95.6%, which is much better than the traditional static rule model (the accuracy rate of abnormal identification is 81.5%).

[0165] Finally, the platform generates specific scheduling and risk intervention instructions according to the resource scheduling scheme and risk warning strategy, real-time collects the execution effect, and forms the execution result quantization matrix; at the same time, it collects various quality indicators in the data fusion process, generates a data fusion quality indicator matrix, calculates a topology coding bias matrix, and dynamically optimizes the parameters of the relationship enhancement graph Transformer. During actual operation, the model parameters are adaptively adjusted every 1 hour, and the optimization step of the relationship structure parameters is reduced to below 0.001 after each update, realizing the continuous optimization and efficient adaptive update of the model. The following table is the actual effect comparison data of abnormal event risk prediction in the system implementation process:

[0166] Table 1 Comparison of measured and predicted data of cross-border e-commerce abnormal event risk prediction

[0167]

[0168]

[0169] From the results shown in Table 1, it can be seen that the average error of the cross-border e-commerce supervision platform of the present application in risk index prediction is 2.3%, and the average error of risk intervention timeliness prediction is only 4.6 seconds, which is significantly better than the traditional static rule method (the average error of risk index prediction is 11.2%, and the average error of intervention timeliness prediction is 20 seconds). Specifically, the actual risk index of event EVT-004 is 0.90, while the system predicts 0.88, with an error of only 2.2%, and only delays 2 seconds in actual intervention, effectively ensuring the timely processing of abnormal risks; the risk prediction error of event EVT-022 is 2.2%, and the intervention timeliness prediction error is only 2 seconds, further demonstrating the precise early warning and efficient response capability of the platform. This fully verifies the significant technical advantages and practical application value of the present application in real-time cross-border e-commerce data fusion supervision, multi-target dynamic resource optimization scheduling and risk abnormal intelligent early warning.

[0170] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art within the technical scope disclosed by the present application, according to the technical scheme and inventive concept of the present application, equivalent replacement or change, should be covered within the protection scope of the present application.

Claims

1. A cloud service-based foreign trade e-commerce cloud network aggregation supervision method, characterized in that: The steps include: S1. Real-time collection of cross-border e-commerce platform transaction data, logistics and distribution data, warehousing data, and customs supervision data. This data is then normalized using a distributed data cleaning method to obtain a standardized data pool. S2. Construct a multi-source heterogeneous graph of cross-border e-commerce based on the standardized data pool; S3. Label the nodes and edges in the cross-border e-commerce multi-source heterogeneous graph with node type, relationship type, and timestamp, and perform multi-element feature encoding through the relationship-enhanced graph transformer to generate a high-dimensional feature representation of the heterogeneous graph; S4. Based on the high-dimensional feature representation of heterogeneous graphs, the multi-layer attention mechanism of the relation-enhanced graph transformer is used to extract local and global feature information, forming an aggregated view of multi-source data of cross-border e-commerce. S5. Based on the aggregated view, a dynamic supervision resource scheduling scheme is determined through a multi-objective honey badger optimization algorithm; S6. Identify cross-border e-commerce transaction anomalies, logistics anomalies, and regulatory anomalies based on aggregated views, and generate risk warnings and intervention strategies for abnormal behaviors through a multi-objective honey badger optimization algorithm; S7. Execute dynamic supervision resource scheduling plans 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. A cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1, characterized in that: The 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 multidimensional spatial coordinates with unique data identifiers to express node attributes in real time. S22. Extract transaction time, frequency, and volume information between buyers, sellers, and products based on the transaction data, and generate dynamic association paths with transaction flow directions in multidimensional space coordinates; S23. Based on the logistics distribution data, the logistics process data between the logistics service provider, the distribution products, and the storage center is collected to construct a multi-segment dynamic distribution path based on the logistics stage markers in real time, and cross-integrate it with the dynamic associated path; S24. Based on the warehouse data, spatial density weights are assigned in real time to the storage location, inventory duration, and access frequency of the goods in the warehouse center. Dynamic warehouse nodes with continuously adjusted spatial density weights are generated and integrated with the dynamic distribution paths. S25. Based on customs supervision data, real-time capture of changes in commodity import and export supervision status is performed, generating spatial constraint boundaries of the supervision status in multi-dimensional space coordinates, and integrating them with dynamic association paths and dynamic distribution paths; S26. Dynamically aggregate the spatial and temporal characteristics of dynamic association paths, dynamic distribution 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.

3. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is characterized in that: The 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, storage center nodes, and customs regulatory agency nodes in the cross-border e-commerce multi-source heterogeneous graph, define a discrete feature code for expressing node types, and map each node to a node spatial position vector of the discrete feature code; S32. Based on the differences in types and characteristics of transaction data, logistics data, and regulatory data, a three-dimensional relationship type identification system is constructed. Three-dimensional relationship vector identifiers representing the transaction relationship dimension, logistics relationship dimension, and regulatory relationship dimension are generated, and absolute timestamps are assigned to relationship edges. S33. Calculate the spatial relative position relationship matrix between the nodes and the edges based on the node spatial position vectors and the three-dimensional relationship vector identifiers to represent the spatial topological relative relationship between the nodes; S34, extracting time series interaction features of the e-commerce platform node, the seller node, and the buyer node to obtain an interaction feature vector; S35. For the product node, logistics service provider node, and storage center node, extract the product dynamic attribute characteristics, logistics service stage characteristics, and storage inventory real-time change characteristics based on the real-time status data, and generate a node dynamic attribute feature vector; S36. Using the node spatial position vector, node interaction feature vector, node dynamic attribute feature vector and spatial relative position relationship matrix as multi-dimensional input, a relationship-enhanced graph transformer is used for joint encoding to obtain a high-dimensional feature representation of the cross-border e-commerce multi-source heterogeneous graph.

4. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is characterized in that: The S4 specifically includes: S41. Based on the high-dimensional feature representation of cross-border e-commerce multi-source heterogeneous graphs, corresponding independent topological space regions are constructed respectively; S42. In each independent topological space region, calculate the spatial density index of the topological connections between the nodes in the region, where the spatial density index is defined as the local topological weight coefficient of the nodes in the topological space region; S43, performing topological space weighted coding on the initial features of each node in the topological space region according to the local topological weighted coefficient of each topological space region, and generating a local topological feature representation vector of each topological space region; S44. Calculate in real time the path weight of topological information propagation between any nodes in the topological structure of the entire cross-border e-commerce multi-source heterogeneous graph. The path weight is defined as the topological propagation coefficient of the node in the global topological structure. S45. According to the topological propagation coefficient of the node, the global topological feature weight of the node is dynamically adjusted in real time to generate a global topological feature representation vector of the entire heterogeneous graph topology structure; S46. Based on the local topological feature representation vector and the global topological feature representation vector, double cross-correlation encoding is performed through the topological correlation attention mechanism of the relationship enhanced graph Transformer to form an aggregated view that includes topological spatial structure features and topological information propagation path features.

5. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is 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 topological space feature initialization module receives the node spatial position vector, the node interaction feature vector and the node dynamic attribute feature vector, and uniformly maps them to the corresponding topological space feature initial sequence according to the discrete feature coding of the node type; The local topological feature encoding module receives the initial sequence of topological space features and the three-dimensional relationship vector identifier, calculates the spatial density index of the topological connection between nodes in each independent topological space area according to the spatial relative position relationship matrix, and performs topological space weighted encoding on the feature vectors of the nodes in the topological space area one by one using the spatial density index as the local topological weighting coefficient to form a local topological feature representation vector of each area; The global topology feature encoding module receives an initial sequence of topological space features, performs weighted aggregation encoding on all node feature vectors using the topological propagation coefficient of the node in the global topological structure as the global topological weight coefficient, and generates a global topological 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 correlation weights of the local topological representation vector to the global topological representation vector and the topological correlation weights of the global topological representation vector to the local topological representation vector through a topological correlation attention mechanism, and performs cross-weighted fusion of feature vectors using the topological correlation weights to generate a topological cross-correlation fused feature vector. The high-dimensional feature representation generation module takes the topological cross-correlation 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 the multi-layer attention mechanism; The aggregate view generation module takes the high-dimensional feature representation as input, performs feature adaptive encoding processing through a topological association attention mechanism, and forms an aggregate view that includes topological spatial structure features and topological information propagation path features.

6. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is characterized in that: The S5 specifically includes: S51. Based on the node spatial position vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregate view, a regulatory resource demand spatial distribution field is constructed. The node spatial position vector is used to define the coordinate position of the node in the spatial distribution field. Based on the real-time interaction frequency of the node interaction feature vector and the change rate of the node dynamic attribute feature vector, the resource demand density value of each node is calculated. S52. Based on the resource demand density value of each node, a multi-objective supervision task prediction matrix is ​​established. By analyzing the historical trend sequence and current feature changes of the node dynamic attribute feature vector, the resource demand prediction value within the future set time window is calculated node by node through multidimensional linear regression; S53. Using the node real-time resource demand density value and the future resource demand forecast value as constraint variables, a supervision task space field with multi-dimensional target constraint conditions is constructed; S54. Within the supervision task space, for each supervision resource and the spatial location relationship between the specific supervision task, calculate the topological path connection difficulty and execution cost between the resource and the task, and combine the connection difficulty and execution cost to form a dynamic resource task execution accessibility coefficient for comprehensively evaluating the real-time execution capability of the supervision resource for the task; S55, according to the dynamic resource task execution accessibility coefficient, 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, and use a circular segmented screening mechanism to iteratively screen and generate a spatial configuration solution with the best dynamic accessibility; S56. Determine the optimal path execution order and execution time constraints of each supervision resource in the spatial field based on the dynamic reachability optimal spatial configuration solution, and generate a dynamic supervision resource scheduling solution including spatial coordinates, execution order, and execution time.

7. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is characterized in that: The S6 specifically includes: S61. Based on the node spatial position vector, node interaction feature vector, and node dynamic attribute feature vector in the aggregate view, calculate the transaction data change ratio, logistics delivery delay change ratio, and regulatory status change frequency index for each node in the current period relative to the historical period, and define this as the node abnormal feature change index. S62. Determine transaction anomaly thresholds, logistics anomaly thresholds, and regulatory anomaly thresholds based on the statistical distribution patterns of the node anomaly feature change indexes. Perform threshold matching analysis on each node's transaction data change ratio, logistics delivery delay change ratio, and regulatory status change frequency index to identify transaction anomaly nodes, logistics anomaly nodes, and regulatory anomaly nodes. S63. For each transaction abnormality node, logistics abnormality node, and regulatory abnormality node, determine the spatial coordinate position of the central abnormality node based on the node spatial position vector and the spatial relative position relationship matrix in the aggregate view, and track the associated nodes that have a real-time topological path connection relationship with the central abnormality node, and construct an abnormal node topological diffusion network with the central abnormality node as the starting point; S64. In the abnormal node topology diffusion network, calculate the topological connection strength value of the topological connection path between the central abnormal node and each associated node, and the path abnormality diffusion probability value, multiply the topological connection strength value by the path abnormality diffusion probability value to generate a path abnormality risk index for quantifying the degree of path abnormality propagation risk; S65. Using the path anomaly risk index as input, a multi-objective anomaly risk warning matrix is ​​established that includes transaction anomaly risk, logistics anomaly risk, and regulatory anomaly risk. A multi-objective honey badger optimization algorithm is used to initialize the spatial search population set. A hybrid elite framework and a circular segmented screening mechanism are then used to iteratively screen and evaluate individuals within the population, generating a multi-objective optimal risk 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 position of the topological connection path corresponding to the path anomaly risk index, the optimal scheduling path of the dynamic supervision resources, the type of required intervention action, the specific execution timing of the intervention action, and the intervention intensity, and form a dynamic risk intervention strategy for execution.

8. The cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to claim 1 is characterized in that: The S7 specifically includes: S71. Determine the spatial location coordinates, topological path execution order, and specific execution time constraints of the supervision resources in the spatial distribution field according to the dynamic supervision resource scheduling plan, and generate a resource scheduling instruction consisting of a spatial coordinate identifier, a path sequence identifier, and a time constraint identifier; 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 action corresponding to the supervisory resource, and generate a risk intervention instruction consisting of the intervention type identifier, execution intensity value, spatial location coordinates, and time sequence identifier; S73. Based on the resource scheduling instructions and risk intervention instructions, conduct real-time quantitative assessments of the spatial coordinate positioning accuracy, topological path execution order, execution time deviation, intervention action type matching, and execution intensity control accuracy of the supervisory resource execution tasks, forming a quantitative matrix of execution results. S74. By collecting the fusion results of cross-border e-commerce platform transaction data, logistics distribution data, warehousing data, and customs supervision data in real time, a data fusion quality indicator matrix is ​​generated based on the spatial coordinate matching accuracy, topological association path reconstruction accuracy, time series synchronization accuracy, and data fusion noise level during the data fusion process; S75, inputting the execution result quantization matrix and the data fusion quality indicator matrix into the relationship enhancement graph transformer respectively, and calculating the topology coding deviation matrix used to describe the optimization degree of the current state of the relationship enhancement graph transformer through topology feature initialization, local topology weighted coding and global topology weighted coding; S76. Using the topological encoding bias matrix as input, the multi-objective honey badger optimization algorithm is used to iteratively optimize the relationship structure parameters and feature encoding weight parameters in the relationship enhancement graph Transformer.

9. A cloud service-based foreign trade e-commerce cloud network aggregation supervision system, which implements a cloud service-based foreign trade e-commerce cloud network aggregation supervision method according to any one of claims 1 to 8, characterized in that: Includes the following modules: The data collection module is used to collect cross-border e-commerce platform transaction data, logistics distribution data, warehousing data, and customs supervision data in real time, and normalize the data through distributed data cleaning methods to obtain a standardized data pool; A heterogeneous graph construction module is used to construct a cross-border e-commerce multi-source heterogeneous graph based on a standardized data pool, and to label the nodes and edges in the heterogeneous graph with node types, relationship types, and timestamps. The topological feature encoding module is used to initialize and encode the node spatial position vectors, node interaction feature vectors, node dynamic attribute feature vectors, and spatial relative position relationship matrices in the cross-border e-commerce multi-source heterogeneous graph. It generates local topological feature representation vectors and global topological feature representation vectors, and performs dual cross-correlation encoding based on the topological correlation attention mechanism to obtain high-dimensional feature representation and aggregation views. The supervision task scheduling module is used to construct the spatial distribution field of supervision resource demand based on the aggregated view, determine the spatial location coordinates of supervision resources, the topological path execution order and the specific execution time constraints, and calculate the dynamic resource task execution accessibility coefficient based on the multi-objective honey badger optimization algorithm to generate a dynamic supervision resource scheduling plan; The risk warning and intervention module is used to identify abnormal nodes in cross-border e-commerce transactions, logistics, and supervision based on aggregated views, calculate the path abnormality risk index, and generate risk warning and intervention strategies for abnormal behaviors through a multi-objective honey badger optimization algorithm. The module also determines the specific intervention action type, execution intensity, location coordinates, and execution timing based on the risk warning and intervention strategies. The feedback optimization module is used to collect the execution result data and data fusion quality indicators of resource scheduling plans and risk warning and intervention strategies in real time, calculate the topological coding deviation matrix, and use the topological coding deviation matrix to iteratively update the relationship structure parameters and feature encoding weight parameters of the relationship enhancement graph Transformer through the multi-objective honey badger optimization algorithm.

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