Cross-border e-commerce retail risk supervision method and system

Abstract

The invention relates to the technical field of e-commerce supervision and prediction, in particular to a cross-border e-commerce retail risk supervision method and system. Comprising the following steps: acquiring cross-border e-commerce multi-source e-commerce data in real time by using the Internet of Things and an API interface; identifying heterogeneous risk factors by using a deep belief migration network; constructing a risk evolution model and generating a dynamic graph based on the spatial state transition type Markov convolutional network; adopting an abnormal subspace clustering algorithm to carry out self-adaptive clustering and real-time grading early warning on the risk modes; and finally, combining a reinforcement learning algorithm, an intelligent decision and a dynamic optimization risk intervention strategy with resource allocation, and outputting a whole-process risk management and control scheme. The intelligent, automatic and precise levels of cross-border e-commerce retail risk monitoring, early warning and response are improved, risk disposal lag and resource waste are effectively reduced, and the industry supervision safety guarantee capability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce supervision prediction, and particularly to a cross-border e-commerce retail risk supervision method and system. Background Art

[0002] With the continuous improvement of the global trade digitization level, the cross-border e-commerce retail business has shown a high-speed development trend. Various platform-based enterprises have built a global logistics and trading network through methods such as overseas warehouses, bonded areas, and direct mail channels. However, with the increasing complexity of cross-border business models, risk supervision issues have gradually emerged, especially in multiple dimensions such as abnormal commodity transactions, payment fraud, logistics delays, inflated inventory, and abnormal user behavior. These risk factors have characteristics such as cross-region, multi-source heterogeneity, and dynamic evolution. The traditional method relying on rule bases and manual reviews has been difficult to meet the requirements of supervision timeliness and accuracy. Currently, there are still the following problems: Traditional risk supervision methods are difficult to effectively integrate data from different sources and different structures. The inconsistent characteristics between heterogeneous data lead to low accuracy in identifying risk factors, and problems such as missed judgments and misjudgments are likely to occur; Existing methods mostly stay at the static risk identification level, lacking the modeling of the dynamic evolution relationship of risk factors in the spatial and temporal dimensions, and unable to achieve dynamic prediction and forward-looking early warning of risks in multiple scenarios such as cross-border logistics and commodity circulation; Traditional supervision measures mostly rely on manual formulation and static rules, lacking intelligent response strategies and dynamic optimization of resource allocation based on real-time risk grading, resulting in untimely risk intervention, resource waste, and low response efficiency. Summary of the Invention

[0003] To solve the above problems, the present invention provides a cross-border e-commerce retail risk supervision method and system, which solves the problems of how to achieve efficient integration and deep fusion of cross-border e-commerce multi-source heterogeneous business data, improve the accuracy of risk factor identification, dynamically model and predict the risk evolution process in the space-time dimension, intelligently identify complex potential risk events, and automatically optimize supervision decisions and resource allocation based on risk grading, thereby effectively improving the comprehensiveness, intelligence, and automation levels of cross-border e-commerce risk supervision.

[0004] To achieve the above object, the technical solution adopted by the present invention is: On the one hand, a cross-border e-commerce retail risk supervision method includes the following steps: S1: Use the Internet of Things and API interfaces to collect cross-border e-commerce multi-source e-commerce data in real time; the multi-source e-commerce data includes commodity transaction data, logistics track data, payment settlement data, user behavior logs, and the dynamic of bonded warehouse inventory; S2: Based on the multi-source e-commerce data, use the deep belief transfer network algorithm for heterogeneous data feature alignment and cross-domain risk factor identification to generate a risk factor set; S3: Based on the set of risk factors, use the spatio-temporal state-transition Markov convolutional network algorithm to construct a risk evolution model, model the spatial distribution and temporal evolution relationship of risk factors, output the prediction results of high-risk evolution paths in the cross-border logistics route and under the commodity dimension, and generate a dynamic risk evolution map; S4: Based on the dynamic risk evolution map, use the abnormal subspace clustering algorithm based on probabilistic logical relationships to adaptively cluster the risk patterns in different spatial and temporal sections, identify potential cross-link composite risk events, and dynamically output risk grading and response warning information; S5: Based on the risk grading and response warning information, use the risk response decision-making algorithm based on reinforcement learning to dynamically optimize supervision measures, automatically match risk intervention strategies and control resource allocation, and output a cross-border e-commerce risk control plan.

[0005] Further, the commodity transaction data includes commodity category, commodity batch, commodity expiration date, commodity packaging temperature and humidity, production date, storage temperature, transportation environment temperature and humidity, and commodity unique identification code; The logistics trajectory data includes logistics transportation path, geographical location of logistics nodes, transportation duration, environmental temperature and humidity along the way, cargo vibration and shock data, real-time positioning information, and abnormal environment exposure records; The payment settlement data includes payment channel type, payment timestamp, geographical location information of the payment terminal, environmental temperature and humidity collected by the terminal device during the payment process, network connection quality, and device unique identification code; The user behavior log includes user access geographical location, real-time environmental parameters of the access terminal, local meteorological data at the time of placing an order, device temperature and humidity, and external environment change information during the user operation process; The dynamic of the bonded warehouse inventory includes warehouse environmental temperature and humidity, warehouse geographical location, change record of batch environmental parameters of the inventory goods, environmental monitoring data during the inbound and outbound process, inventory abnormal alarm events, and device operating environment data.

[0006] Further, the step S3 includes the following steps: Based on the commodity identification code and transportation node sequence in the commodity transaction data and the logistics trajectory data, construct a commodity-logistics bidirectional mapping graph. The nodes of the commodity-logistics bidirectional mapping graph represent commodity batches and logistics nodes, and the edges represent the actual transfer paths and time relationships of the commodity between different logistics nodes; Map the set of risk factors to the corresponding nodes and edges in the commodity-logistics bidirectional mapping graph to form a multi-attribute graph structure including risk categories, timestamps, and spatial positions; Based on the multi-attribute graph structure, a risk evolution model is constructed using a spatial state transition type Markov convolutional network algorithm, which integrates the spatial state transition matrix and time convolution features to simulate the dynamic evolution path of risk factors in the cross-border logistics chain; Through the risk evolution model, high-risk nodes, key commodity combinations and potential risk conduction paths within a future time period are predicted, and a dynamic risk evolution map in the three-way tensor structure of commodity dimension - node dimension - time dimension is generated.

[0007] Furthermore, the formula of the risk evolution model is as follows:

[0008] Where, represents the dynamic risk score of the commodity with the unique identification code i at time t on the logistics node j; represents the activation function; K represents the number of types of risk factors; represents the convolution weight of the k-th type of risk factor; represents the set of previous logistics nodes that have a direct state transition relationship with the logistics node j under the influence of the k-th type of risk factor; represents the attenuation degree of the risk influence caused by the spatial distance propagated from node l to node j; represents at time the historical risk intensity of commodity i on the previous node l; represents the time convolution weight coefficient, that is, the memory weight of the past s time periods; represents the state transition probability of the risk factor k among logistics nodes ; and respectively represent the connection degree quantities of commodity i and node j under the risk factor k.

[0009] Further, the step S4 includes the following steps: Based on the dynamic risk evolution map, the distribution characteristics of risk factors and their correlation relationships in different spatio-temporal sections are extracted, and a risk feature subspace including risk categories, spatial positions, time sections and risk evolution trajectories is constructed; Based on the risk feature subspace, an abnormal subspace clustering algorithm based on probabilistic logical relationships is used for adaptive clustering to identify high-risk aggregation areas that are spatially adjacent or time-series related; During the clustering process, the joint probability and abnormal correlation intensity of risk events in different subspaces are calculated to identify the potential conduction paths and superposition effects of high-risk composite events; According to the adaptive clustering results, cross-link composite risk event labels are dynamically generated to quantify the risk levels, event triggering probabilities and risk influence ranges within each clustering unit; Based on the results of abnormal subspace clustering, real-time output of multi-level risk classification and response warning information including spatial location, commodity dimension, time period, and risk type.

[0010] Furthermore, the formula of the abnormal subspace clustering algorithm is as follows:

[0011] Wherein, represents the optimal composite risk clustering intensity; n represents different composite risk types or event labels; represents the probability logic weighting coefficient of the nth type of clustering; represents the number of samples in the nth type of clustering category; y represents the risk event sample index; represents the multi-dimensional fitness of sample y at spatial location p, commodity batch q, and time period ; represents the comprehensive risk anomaly score of sample y within the time period ; H represents the total number of dimensions of multi-source risk factors; represents the factor triggering probability of sample y in the hth-dimensional risk factor and within the time period ; represents the abnormal event spatial mutual information weight of the nth type of clustering; represents the joint mutual information index of risk events of the nth type of clustering at spatial location p, commodity batch q, and time period ;

[0012] Further, the step S5 includes the following steps: Construct a risk response state space based on the risk classification and response warning information, where the risk response state space uses dynamic risk level, spatial location, commodity dimension, time period, and control resource availability as state variables; Based on the risk response state space, adopt a risk response decision-making algorithm based on deep reinforcement learning, and use the policy network to evaluate the impact of different response actions on risk suppression efficiency and resource utilization in real time; <s Continuously collect the feedback information of risk events after implementation, and based on the state-action value iteration mechanism of reinforcement learning, continuously optimize the parameters of the risk intervention strategy, and output a cross-border e-commerce risk control plan, including a combination of response measures, a resource allocation plan, and the priority of control implementation.

[0013] Furthermore, the risk response decision algorithm specifically adopts a temporal difference strategy optimization mechanism, combines real-time risk levels, resource availability, and regulatory intervention history records, dynamically adjusts risk intervention measures and the allocation path of control resources, aims to minimize overall risk exposure, maximize resource scheduling efficiency, and achieve the shortest response delay, and automatically matches the optimal risk intervention strategy, including measures such as adjusting the goods circulation path, intercepting the payment process, restricting user behavior, and controlling the storage environment.

[0014] Further, in step S2, the risk factor set includes abnormal commodity transactions, logistics delays, abnormal payment behaviors, abnormal user operations, and inventory fluctuations.

[0015] On the other hand, a cross-border e-commerce retail risk supervision system includes an e-commerce data collection module, a risk factor identification module, a risk prediction module, a risk grading and early warning module, and a decision response module that are sequentially communicatively connected; The e-commerce data collection module is used to collect multi-source e-commerce data of cross-border e-commerce in real time by using the Internet of Things and API interfaces; The risk factor identification module is used to perform heterogeneous data feature alignment and cross-domain risk factor identification based on the multi-source e-commerce data by using a deep belief transfer network algorithm, and generate a risk factor set; The risk prediction module is used to construct a risk evolution model based on the risk factor set by using a spatial state transition type Markov convolution network algorithm, and generate a dynamic risk evolution map; The risk grading and early warning module is used to identify cross-link potential composite risk events by using an abnormal subspace clustering algorithm based on probability logic relationships, and dynamically output risk grading and response early warning information; The decision response module is used to adopt a risk response decision algorithm based on reinforcement learning, dynamically optimize supervision measures, and output a cross-border e-commerce risk control plan.

[0016] The beneficial effects of the present invention are as follows: The present invention collects multi-source heterogeneous e-commerce data covering commodities, logistics, payment, users, warehousing, etc. in real time through the Internet of Things and API interfaces, realizes the comprehensive perception of risk factors in the whole process of cross-border e-commerce operation, improves the timeliness and integrity of data acquisition, and lays a solid foundation for subsequent risk identification. By adopting the deep belief transfer network algorithm, the feature distribution differences between multi-source heterogeneous data are effectively solved, the accurate identification ability of cross-domain risk factors is improved, the discovery ability of compound risks such as abnormal transactions, logistics delays, and abnormal payments is significantly enhanced, and the risk is prevented from being missed due to data fragmentation. Through the spatial state transition type Markov convolution network algorithm, the spatial distribution and time evolution relationship of risk factors are accurately modeled, and the high-risk evolution paths in multiple scenarios such as cross-border logistics routes and commodity dimensions can be dynamically predicted, potential risks can be warned in advance, and the foresight and initiative of risk monitoring can be improved. The abnormal subspace clustering algorithm based on probability logic relationship is introduced to realize the adaptive clustering analysis of risk patterns in different spatial and time sections, and the potential compound risk events across links and nodes can be effectively identified, and the comprehensive perception and hierarchical response ability of the system to new, frequent, and sudden risks can be improved. The risk response decision-making algorithm based on reinforcement learning can dynamically optimize supervision measures according to risk grading and real-time early warning, automatically match the optimal risk intervention strategy and resource allocation plan, realize the intelligent decision-making and efficient execution of risk response, and significantly improve the scientific and automated level of cross-border e-commerce risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic flow chart of a cross-border e-commerce retail risk supervision method of the present invention.

[0018] Figure 2 is a schematic flow chart of step S3 provided by an embodiment of the present invention.

[0019] Figure 3 is a schematic module diagram of a cross-border e-commerce retail risk supervision system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Please refer to Figures 1-3 as shown, the present invention relates to a cross-border e-commerce retail risk supervision method and system.

[0021] Embodiment 1

[0022] A cross-border e-commerce retail risk supervision method includes the following steps: S1: Collect multi-source e-commerce data of cross-border e-commerce in real time by using the Internet of Things and API interfaces; the multi-source e-commerce data includes commodity transaction data, logistics track data, payment settlement data, user behavior logs, and the dynamic of bonded warehouse inventory; Among them, the commodity transaction data includes commodity category, commodity batch, commodity expiration date, commodity packaging temperature and humidity, production date, storage temperature, transportation environment temperature and humidity, and commodity unique identification code; The logistics track data includes logistics transportation path, logistics node geographical location, transportation duration, environmental temperature and humidity along the way, cargo vibration and shock data, real-time positioning information, and abnormal environment exposure records; The payment settlement data includes payment channel type, payment timestamp, payment terminal geographical location information, environmental temperature and humidity collected by the terminal device during the payment process, network connection quality, and device unique identification code; The user behavior log includes user access geographical location, real-time environmental parameters of the access terminal, local meteorological data at the time of placing an order, device temperature and humidity, and external environment change information during the user operation process; The bonded warehouse inventory dynamics includes warehouse environment temperature and humidity, warehouse geographical location, record of batch environmental parameter changes of the inventory goods, environmental monitoring data during the inbound and outbound process, inventory abnormal alarm events, and device operation environment data.

[0023] It should be noted that cross-border e-commerce enterprises deploy Internet of Things terminals with environmental sensors (such as temperature and humidity sensors, vibration sensors, GPS positioning modules, etc.) in their various business links (commodity warehousing, transportation, payment, user side, etc.), and at the same time dock with the data APIs of e-commerce platforms, logistics companies, and third-party payment platforms.

[0024] Commodity transaction data collection: Whenever a commodity enters or exits the warehouse, is sorted, shipped, or reaches a node, the system automatically records the commodity's unique identification code, category, batch, expiration date, packaging temperature and humidity, production date, storage temperature, and transportation environment temperature and humidity. All data is uploaded to the cloud platform in real time through wireless networks.

[0025] Logistics track data collection: Multimode GPS and vibration sensors are equipped on logistics vehicles to collect and upload in real time the transportation path, node geographical location, transportation duration of each section, environmental temperature and humidity along the way, and cargo vibration and shock conditions. If there is an abnormal environment exposure (such as too high temperature or severe vibration), the device will immediately record the event and report it.

[0026] Payment settlement data collection: When a user makes a payment, the system automatically obtains the payment channel type, timestamp, geographical location of the payment terminal (such as a mobile phone or POS machine), and at the same time, the environmental sensors built into the terminal synchronously collect temperature and humidity. If there are serious network fluctuations during the payment, the network connection quality is recorded, and all data is uploaded with a unique device code.

[0027] User Behavior Log Collection: When a user accesses the platform or places an order, the system records their access geographical location, real-time environmental parameters of the device used (such as temperature, humidity, air pressure), local meteorological data (obtained through a third-party API), and tracks changes in environmental parameters during the user's operation (such as moving outdoors, etc.).

[0028] Dynamic Collection of Bonded Warehouse Inventory: The internal environment of the warehouse is monitored for temperature and humidity in real time through a sensor grid deployed at multiple points. All environmental parameter data of each batch of goods, environmental monitoring data during the inbound and outbound process, abnormal alarms (such as excessive temperature and humidity, equipment failures), and dynamic changes in inventory are automatically recorded and reported.

[0029] All collection devices and terminals transmit data in an encrypted manner, support data desensitization and hierarchical management, and comply with cross-border data compliance requirements. All types of data are aggregated through the data access middleware, associated according to dimensions such as unified timestamps and commodity IDs, providing a data foundation for the subsequent risk analysis across the entire link and all times and spaces.

[0030] S2: Based on the multi-source e-commerce data, use the deep belief transfer network algorithm to perform heterogeneous data feature alignment and cross-domain risk factor identification, generating a set of risk factors; the set of risk factors includes abnormal commodity transactions, logistics delays, abnormal payment behaviors, abnormal user operations, and inventory fluctuations.

[0031] Specifically, format the collected multi-type data (such as commodity, logistics, payment, user behavior, warehouse data), organize various types of data according to a unified data structure standard, such as encoding using multi-dimensional tensors or table structures, to ensure effective docking of data from different sources. Through the data timestamp, align the multi-source data of commodities, logistics, payment, users, and inventory that occur within the same time period in time sequence to achieve a closed-loop of the entire process data link. For example, the transaction records of a certain commodity, the corresponding logistics node information, payment behavior, and warehouse inbound and outbound data are automatically associated through a unique identification code and time axis. Mark the outliers in the data (such as extreme environmental parameters, invalid geographical coordinates, equipment failure records, etc.), and use interpolation, deletion, or expert rules to repair missing values to ensure the quality of the input data.

[0032] For different types of original data, perform processing using normalization (such as continuous variables like temperature and humidity, transportation duration, etc.) and one-hot encoding (such as categorical variables like commodity categories, payment channel types, etc.) to facilitate subsequent unified identification by the model. The system automatically extracts information cross-features with high correlations. For example, combine "commodity transportation environment temperature and humidity" with "logistics node geographical location" to form a feature of "abnormal logistics nodes in high temperature and high humidity", which helps to identify logistics risks caused by climate. Use historical event data to label known risk cases (such as historical delays, abnormal payments, etc.) to provide effective supervision signals for subsequent model training.

[0033] For cross-border e-commerce scenarios with different locations, different times, and different operations (such as new national markets, different warehouse environments), the model uses a transfer learning mechanism to transfer the recognition experience of existing high-risk scenarios to the new environment, improving the model's generalization ability. The deep belief network automatically extracts high-order and abstract features from the data and learns the potential associations between multi-source data in each layer to solve complex risk factors that are difficult to discover by conventional algorithms. During the model operation, the risk feature score of each data stream or sample is automatically calculated. The system can intelligently mine the following types of high-risk factors and automatically classify and output them: Abnormal commodity transactions (such as frequent transactions of a single batch, abnormally short commodity validity period, transactions under over-limit temperature and humidity, etc.); Logistics delays (such as the continuous transportation duration at a certain node exceeding the standard, the transportation risk increasing in a specific climate zone); Abnormal payment behaviors (such as frequently changing terminals, high-risk payment channels in different locations, abnormal temperature and humidity of the device during payment); Abnormal user operations (such as a drastic change in the user's order placement environment, a sudden change in the operation path, abnormal movement of the device, etc.); Inventory fluctuations (such as a short-term drastic fluctuation in the bonded warehouse inventory, a high incidence of abnormal alarms, etc.); All risk factors identified by the model will be tagged and classified in real time and pushed to the backend risk database, and synchronized to the subsequent risk modeling link.

[0034] S3: Based on the set of risk factors, use the spatial state transition type Markov convolutional network algorithm to construct a risk evolution model, model the spatial distribution and time evolution relationship of risk factors, output the prediction results of high-risk evolution paths on the cross-border logistics route and under the commodity dimension, and generate a dynamic risk evolution map; Among them, the step S3 includes the following steps: Based on the commodity identification codes and transportation node sequences in the commodity transaction data and logistics trajectory data, construct a commodity-logistics two-way mapping graph. The nodes of the commodity-logistics two-way mapping graph represent commodity batches and logistics nodes, and the edges represent the actual transfer paths and time relationships of the commodity between different logistics nodes; It should be noted that the system uses commodity batches and logistics nodes as the nodes of the mapping graph to establish a two-way connection between commodities and logistics nodes.

[0035] The edges of the mapping graph represent the actual transfer of a certain commodity between different logistics nodes, including: entry and exit nodes, arrival and departure times, transportation time, passing geographical information, etc.

[0036] The system continuously receives new data and automatically expands and updates the structure of the commodity-logistics two-way mapping graph to ensure that the full-process logistics relationships of all batches are visible and traceable.

[0037] Map the set of risk factors to the corresponding nodes and edges in the commodity-logistics two-way mapping graph to form a multi-attribute graph structure including risk categories, timestamps, and spatial locations. Specifically, the system first establishes a hierarchical index library for the unique commodity identification code and logistics nodes. For example: establish a unique index table for each batch of commodities, and establish a geographical / serial number index for logistics node information to ensure that each risk factor can be quickly and accurately retrieved to the specific commodity-logistics node combination. For the original records of each risk factor, the system automatically queries key information such as its commodity identification code, batch number, node serial number, and specific time.

[0038] For each node (such as a logistics node, a commodity batch node), assign risk attributes (such as whether there is delay / anomaly / fluctuation), the timestamp of the risk occurrence, the occurrence location, etc. For each edge (representing the actual transfer between two nodes), assign risk labels (such as cargo damage and abnormal temperature and humidity exposure during transportation), time intervals, and environmental parameters involved in this transfer process. If there are multiple risk factors for the same node or edge (such as "delay" and "abnormal payment"), a multi-label storage structure (such as an array or bitmask) is adopted to represent all risk categories and attributes simultaneously, which is convenient for subsequent composite risk analysis.

[0039] Whenever a new risk event or new logistics node / commodity batch data is generated, the system immediately inserts a new node or edge in the multi-attribute graph, appends new risk labels, and updates the timestamp. The system periodically processes historical data in batches to complete the past unmapped abnormal events into the graph structure to ensure the integrity of risk traceability.

[0040] Based on the multi-attribute graph, use a visualization platform (such as Gephi, Neo4j, Graphviz, etc.) to dynamically display the risk distribution of the entire link of commodity transfer, and support multi-dimensional interactive queries such as node color, edge thickness, and label highlighting. Operation and maintenance or management personnel can quickly view the details of all risk events for a certain batch of commodities or a certain path through multi-condition filtering such as commodity identification code and node number.

[0041] Based on the multi-attribute graph structure, adopt a spatial state transition type Markov convolutional network algorithm to construct a risk evolution model, fuse the spatial state transition matrix and time convolution features, and simulate the dynamic evolution path of risk factors in the cross-border logistics chain. Specifically, based on the commodity-logistics multi-attribute graph, the system statistically analyzes the actual transfer paths, frequencies, and historical risk occurrence probabilities of each commodity batch between different logistics nodes to form a transfer probability matrix between nodes (such as the probability from node A to node B). When modeling, the system not only considers the actual geographical transfer between logistics nodes but also fuses the risk states of the nodes (such as an anomaly in the upstream node increases the probability of the downstream node being affected).

[0042] For each node and edge, the system counts the occurrence frequency and type of risk events according to different time windows (such as hours, days, weeks) to form a time series feature vector. Applying a one-dimensional convolution operation to the above time series features extracts the periodic changes, burst patterns, or delay effects of risk events in the time dimension. For example, node Y has frequent delays during the daily high-temperature period within a week, and the high-risk characteristics during this period can be highlighted after convolution.

[0043] Taking the nodes and edges of the multi-attribute graph as the network input, the input includes node attributes (risk category, time, spatial location) and edge attributes (transfer time, abnormal environmental parameters, etc.). First, capture the risk diffusion relationship between nodes through the spatial transfer matrix, and then use a convolutional neural network to extract the time series features of each node. Finally, fuse the spatial and time features through the network to realize the dynamic evolution modeling of risks across logistics nodes and time periods. The network model periodically receives new data streams, automatically calibrates the spatial transfer probability and time feature weights, realizes model self-learning and adaptive adjustment, and ensures the timeliness and accuracy of risk prediction.

[0044] The system provides a basis for early warning and scheduling decisions by outputting the nodes with high future risks, the key risk transmission paths, and the potential commodity batch combinations through the model. The output results can intuitively display the risk evolution trend in multiple dimensions of space-time-commodity on the dynamic graph of the three-way tensor, assisting in decision-making management for multiple roles and multiple business scenarios.

[0045] Predict the high-risk nodes, key commodity combinations, and potential risk conduction paths within the future time period through the risk evolution model, and generate a dynamic risk evolution graph with a three-way tensor structure of commodity dimension-node dimension-time dimension.

[0046] Furthermore, the formula of the risk evolution model is as follows:

[0047] Among them, represents the dynamic risk score of the commodity with the unique identification code i at time t on the logistics node j; represents the activation function; K represents the number of types of risk factors; represents the convolution weight of the k-th type of risk factor; represents the set of previous logistics nodes that have a direct state transfer relationship with the logistics node j under the influence of the k-th type of risk factor; represents the attenuation degree of the risk impact caused by the spatial distance from node l to node j; represents at time the historical risk intensity of commodity i on the previous node l; Denote the time convolution weight coefficient, i.e., the memory weight of the past s time periods; Denote the state transition probability of risk factor k among logistics nodes ; and respectively denote the connection degree quantity between commodity i and node j under risk factor k.

[0048] The calculation formulas are as follows:

[0049] Among them, denotes the geographical distance between logistics nodes l and j, which can be calculated from the node geographical location coordinates in the logistics trajectory data; denotes the average temperature of the area passed through during transportation between nodes; denotes the risk space attenuation coefficient; denotes the standard reference temperature (such as 25°C) for normalization.

[0050] The calculation formulas are as follows:

[0051] Among them, denotes the historical transfer frequency of the commodity from node l to j, which comes from the edge weight of the commodity-logistics bi-directional mapping graph; denotes the incidence rate of the k-th type of risk factor observed among nodes, for example, the proportion of logistics delays comes from the deviation analysis of the transportation duration and the planned time.

[0052] S4: Based on the dynamic risk evolution graph, use the abnormal subspace clustering algorithm based on probabilistic logical relationships to adaptively cluster the risk patterns in different spatial and temporal sections, identify cross-link potential composite risk events, and dynamically output risk grading and response warning information; Among them, the step S4 includes the following steps: Based on the dynamic risk evolution graph, extract the distribution characteristics of risk factors and their correlation relationships in different spatio-temporal sections, and construct a risk characteristic subspace including risk categories, spatial positions, time sections, and risk evolution trajectories; Specifically, slice the risk factors according to dimensions of space (such as country, logistics node, warehouse, sales area), time (such as hour, day, week), and product (such as batch, category) to form multi-dimensional sub-spaces. Extract the occurrence frequency, evolution trajectory, spatio-temporal distribution of risk factors within each sub-space, and their correlation with other risk factors (such as the synergy between product anomalies and logistics delays). Construct a risk feature sub-space structure, including metadata such as risk category, spatial location, time period, evolution path, etc., laying the foundation for subsequent clustering analysis.

[0053] Based on the risk feature sub-space, adopt an abnormal sub-space clustering algorithm based on probabilistic logical relationships to perform adaptive clustering and identify high-risk aggregation areas with adjacent space or related time series. Specifically, combining multi-dimensional features (risk category, spatial node, product batch, time period, risk evolution index, etc.), the system identifies the most active feature subset of the risk pattern in the current scenario through an automatic feature selection mechanism. For example, the risk frequency surges within a certain period, at a specific logistics node, and for a specific product type. Each sub-space is defined as a four-tuple of "spatial node - time period - product batch - risk type", such as "node A - the first week of June - batch B - logistics delay". All risk data are divided into corresponding sub-spaces according to the four-tuple attributes.

[0054] The system statistically analyzes the probability distribution of the occurrence of risk factors (such as Poisson distribution, beta distribution, etc.) for the risk event sequence within each sub-space, and dynamically adjusts the anomaly detection threshold according to the historical baseline. For the co-occurrence logic of different risk factors (such as logistics delay and abnormal payment often occurring simultaneously), a probabilistic graph model (such as Bayesian network, Markov Random Field, etc.) is used to model the conditional probability and causal influence between risk factors.

[0055] The specific process of adaptive clustering is as follows: Initial clustering: Initially cluster all sub-spaces according to the risk distribution characteristics into high-risk, low-risk, and normal areas.

[0056] Dynamic adjustment of the clustering center: If real-time new data causes a significant deviation in the risk distribution of a certain sub-space, automatically adjust the clustering center and the sub-space belonging (such as the expansion of the high-risk area and the shrinkage of the low-risk area).

[0057] Multi-scale clustering: Support clustering at different granularities (such as different products at the same node, different nodes for the same product), and capture risk diffusion through an adaptive window.

[0058] Through the above steps, the system identifies high-risk aggregation sub-spaces with adjacent space or continuous time, providing a basis for subsequent composite risk analysis.

[0059] During the clustering process, calculate the joint probability of risk events in different subspaces and the abnormal correlation intensity, and identify the potential transmission paths and superposition effects of high-risk compound events; Specifically, within each high-risk aggregation subspace, the system counts the frequencies and joint probabilities of the co-occurrence of two or more types of risk factors (such as logistics delays, inventory anomalies, payment anomalies). The formula can be used: P(A∩B) = P(A|B)×P(B), where A and B are different risk events.

[0060] Use indicators such as correlation coefficient, mutual information, and lift to quantify the abnormal correlation intensity between risk factors. For example, when event A occurs, whether the probability of event B increases significantly, and whether this increase exceeds the historical expectation.

[0061] The system tracks the "adjacency relationship" or chronological order between high-risk subspaces, and uses methods such as graph traversal and sequence analysis to reveal the full-link trajectory of risk diffusion and superposition from one node / batch / time period to other nodes. For the identified risk event aggregation chain, conduct scenario simulation: such as the logistics delay at node A, superimposed with the inventory warning at node B, ultimately leading to a sharp increase in the user complaint rate of product C. The system accumulates the multi-dimensional risk impacts and quantifies the "superimposed risk level".

[0062] Once the system determines that a certain subspace or cross-space link has a compound risk with high joint probability and high abnormal correlation intensity, it automatically generates a compound risk label, and the label content includes: involved nodes, product batches, time periods, main risk factors, joint probability values, superimposed risk levels, etc. As new data flows in, the system dynamically updates the joint probability and abnormal intensity models to achieve adaptive iteration of risk discovery and label assignment.

[0063] According to the adaptive clustering results, dynamically generate cross-link compound risk event labels, and quantify the risk levels, event trigger probabilities, and risk impact ranges within each clustering unit; Specifically, for each identified high-risk clustering unit, automatically generate an event label and explain the specific types of risk factors involved (such as "temperature and humidity anomaly + logistics delay + payment failure").

[0064] The system automatically evaluates the overall risk level based on the types, occurrence frequencies, combination situations, historical loss data, etc. of the risk factors within the clustering unit, and classifies it as high, medium, or low. For example, a risk combination with high frequency and across multiple links will be automatically judged as a high-risk.

[0065] Based on the clustering analysis and historical cases, the system intelligently calculates the probability of each type of compound risk event occurring in the future for a certain period of time, and estimates the possible spatial range it may affect (such as affected warehouses, cities, product batches, etc.).

[0066] Finally, the labels of all composite risk events and their risk quantitative indicators will be structured and output, written into the risk database, providing a data basis for subsequent response and supervision measures, and supporting real-time display on the supervision dashboard.

[0067] Based on the abnormal subspace clustering results, multi-level risk classification and response warning information including spatial location, commodity dimension, time period, and risk type are output in real time.

[0068] Furthermore, the formula of the abnormal subspace clustering algorithm is as follows:

[0069] Wherein, represents the optimal composite risk clustering intensity, used to output risk classification and composite warning; n represents different composite risk types or event labels; represents the probability logic weighting coefficient of the nth type of clustering, determined according to the joint probability of risk factors that occurred in the historical data of this clustering category (such as the probability of abnormal commodity transactions, the probability of logistics delays, the probability of abnormal payment behaviors, the probability of inventory fluctuations, etc., obtained through multi-source e-commerce data statistics); represents the number of samples of the nth type of clustering category; y represents the risk event sample index; represents the multi-dimensional fitness of sample y in the spatial position p, commodity batch q, and time period ; represents the comprehensive risk anomaly score of sample y within the time period ; H represents the total number of multi-source risk factor dimensions; represents the factor triggering probability of sample y in the hth-dimensional risk factor and within the time period ; represents the abnormal event spatial mutual information weight of the nth type of clustering, reflecting the risk coupling degree between different nodes, commodities, and times in the same category; represents the joint mutual information index of risk events of the nth type of clustering in the spatial position p, commodity batch q, and time period ;

[0070] S5: Based on the risk classification and response warning information, adopt a risk response decision-making algorithm based on reinforcement learning to dynamically optimize supervision measures, automatically match risk intervention strategies and control resource allocation, and output a cross-border e-commerce risk control plan.

[0071] Among them, the step S5 includes the following steps: Based on the risk classification and response warning information, construct a risk response state space, and the risk response state space takes dynamic risk level, spatial location, commodity dimension, time period, and control resource availability as state variables; Specifically, the system assigns each logistics node, commodity batch, and time period to different risk levels according to risk grading information (such as high, medium, low).

[0072] Spatial location: Indexed by geographical information (such as country, port, warehouse, distribution center), each spatial node is a state component.

[0073] Commodity dimension: The unique identification code, batch number, category, etc. of the commodity are used to accurately locate the risk object.

[0074] Time period: Such as daily / hourly / minutely granularity, corresponding to the specific time window for risk occurrence or intervention.

[0075] Availability of control resources: Includes the currently dispatchable manual inspection teams, emergency logistics vehicles, warehouse temperature control systems, payment risk control strategy quotas, etc.

[0076] Each risk event can be mapped to a multi - tuple of S = {risk level, spatial node, commodity ID, time period, resource availability}.

[0077] The risk monitoring platform continuously updates the above - mentioned state variables. Once any change occurs in the risk level, spatial node, resource margin, etc., the state space is immediately refreshed, providing the latest scenario for the decision - making algorithm.

[0078] Based on the risk response state space, a risk response decision algorithm based on deep reinforcement learning is adopted to real - time evaluate the impact of different response actions on risk suppression efficiency and resource utilization rate through the policy network; Specifically, for actual business requirements, the system pre - defines and continuously expands multiple types of response actions, including but not limited to: Logistics measures: Such as changing the goods transfer path, designating a temporary transfer node, adjusting the transportation schedule or priority, shortening the transportation duration, etc.; Payment measures: Such as temporarily intercepting high - risk payments, increasing the payment verification process, restricting high - risk terminal payments, etc.; User measures: Such as setting limits on high - risk user orders, triggering manual review, temporarily freezing suspicious accounts, etc.; Warehousing measures: Such as urgently starting temperature and humidity control, isolating high - risk batches in different zones, temporarily strengthening video surveillance, etc.; Resource allocation: Such as dynamically allocating emergency resources such as personnel, vehicles, and warehousing equipment, and setting different levels of response priorities.

[0079] The structure of the policy network and the input - output process are specifically as follows: Input layer: It includes state variables such as dynamic risk levels (e.g., high / medium / low), spatial location encoding (node numbers or geographical coordinates), product attributes (batches, categories), current time window, available resource vectors, etc., which are encoded as multi-dimensional vector inputs to the network.

[0080] Policy network (deep neural network): Extract state features through a multi-layer neural network. The network structure can adopt convolutional layers (for spatial relationship modeling), fully connected layers, and attention mechanisms (to focus on important risk areas) to enhance the robustness of decision-making.

[0081] Output layer: Output comprehensive effectiveness indicators such as "expected risk suppression value", "resource consumption", and "response delay" for each action combination.

[0082] The system calculates the expected rewards for all candidate actions for each risk state scenario and automatically screens out the overall optimal action combination, allowing single or multiple actions to be jointly implemented. The system scans the current risk state space periodically (e.g., every 5 minutes), inputs it into the policy network, and automatically generates optimal action instructions; for high-level risks or rapidly deteriorating scenarios, the "emergency decision-making" mode can be triggered to dynamically amplify the resource response intensity, prioritize the allocation of critical resources, and minimize risk exposure to the greatest extent; the system automatically records the context, selected actions, and expected effects of each decision to provide data support for subsequent learning.

[0083] Continuously collect feedback information on risk events after implementation, and based on the state-action value iteration mechanism of reinforcement learning, continuously optimize the parameters of the risk intervention strategy and output a cross-border e-commerce risk control plan, including a combination of response measures, resource allocation plans, and control implementation priorities.

[0084] The risk response decision algorithm specifically adopts a temporal difference strategy optimization mechanism. Combining real-time risk levels, resource availability, and historical records of regulatory interventions, it dynamically adjusts risk intervention measures and control resource allocation paths, aiming to minimize overall risk exposure, maximize resource scheduling efficiency, and achieve the shortest response delay, and automatically matches the optimal risk intervention strategy, including measures such as adjusting the goods flow path, intercepting the payment process, restricting user behavior, and controlling the storage environment.

[0085] Specifically, temporal difference (TD) and adaptive learning are optimized as follows: The system performs temporal difference updates on each decision-implementation-feedback process in real time. By leveraging the difference between the current reward and the future expected reward, it dynamically adjusts the parameters of the policy network, enhancing the model's ability to fit the optimal long-term policy. The continuously collected empirical data is used to train the model in batches according to priority, with a focus on optimizing the decision weights in special scenarios such as "high value" and "extreme risk" to prevent the model from falling into local optima. As new risk patterns and new resource constraints emerge, the system periodically fine-tunes the neural network structure and parameters, enabling the decision-making model to always adapt to the actual changes in the business and avoiding the risk of "lagging old experience". Historical extreme cases or simulated abnormal samples are regularly used to conduct robustness tests on the decision-making effect of the model, improving the algorithm's emergency decision-making ability for sudden risk scenarios.

[0086] Embodiment 2

[0087] A cross-border e-commerce retail risk supervision system includes an e-commerce data collection module, a risk factor identification module, a risk prediction module, a risk grading and early warning module, and a decision response module that are sequentially communicatively connected. The e-commerce data collection module is used to collect multi-source e-commerce data of cross-border e-commerce in real time using the Internet of Things and API interfaces. The risk factor identification module is used to perform heterogeneous data feature alignment and cross-domain risk factor identification based on the multi-source e-commerce data using a deep belief transfer network algorithm, generating a set of risk factors. The risk prediction module is used to construct a risk evolution model based on the set of risk factors using a spatial state transition type Markov convolutional network algorithm and generate a dynamic risk evolution map. The risk grading and early warning module is used to identify cross-link potential composite risk events using an abnormal subspace clustering algorithm based on probability logic relationships and dynamically output risk grading and response early warning information. The decision response module is used to adopt a risk response decision algorithm based on reinforcement learning to dynamically optimize supervision measures and output a cross-border e-commerce risk control plan.

[0088] In this embodiment, a cross-border e-commerce retail risk supervision system is applied to a cross-border e-commerce retail risk supervision method described in Embodiment 1, which will not be elaborated here.

[0089] In summary, the present invention uses Internet of Things terminals and API interfaces to collect multi-source data such as commodity transactions, logistics tracks, payment settlements, user behaviors, and warehouse inventories in real time and throughout the process, greatly improving the coverage, timeliness, and accuracy of the data. Compared with the traditional method that relies on a single link or lagging data, this method realizes the dynamic perception of the entire link of commodities from outbound, transportation, payment to users and warehousing, providing a solid data foundation for subsequent risk analysis and traceability.

[0090] The present invention uses a deep belief transfer network algorithm to achieve feature alignment of multi-source heterogeneous data and cross-domain risk factor identification, and can automatically discover core risk factors such as abnormal commodity transactions, logistics delays, abnormal payments, abnormal user operations, and inventory fluctuations, and supports the migration and generalization of the model in different markets or new environments. This not only improves the intelligence and accuracy of risk identification, but also enhances the adaptability to emerging and complex risk scenarios.

[0091] The present invention realizes the dynamic evolution modeling of risk factors in the dimensions of commodity batches, logistics nodes, and time through a spatial state transition type Markov convolutional network algorithm, combined with commodity-logistics bidirectional mapping and multi-attribute graph modeling. The system can accurately predict high-risk nodes and key risk conduction paths, and timely output dynamic graphs and risk evolution trends, facilitating managers to quickly locate potential risks and conduct targeted prevention and control.

[0092] The present invention uses an abnormal subspace clustering algorithm based on probabilistic logical relationships to adaptively cluster multi-temporal and multi-dimensional risk factors. The system can automatically identify cross-link and cross-link composite risk events, dynamically output hierarchical warning information, quantify the risk superposition effect and conduction chain, and provide a more scientific decision-making basis for warning and intervention.

[0093] The present invention integrates a risk response decision algorithm based on reinforcement learning. The system can intelligently evaluate and dynamically optimize regulatory intervention measures in combination with risk levels, spatial locations, commodity attributes, time windows, and resource availability, automatically generate the optimal risk response plan, and achieve a full-process closed-loop of risk monitoring, identification, warning, and response, significantly improving the risk prevention and control ability and operational safety in the field of cross-border e-commerce.

[0094] The above embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary engineering and technical personnel in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A cross-border e-commerce retail risk supervision method, characterized in that It includes the following steps: S1: Utilize the Internet of Things and API interfaces to collect real-time multi-source e-commerce data of cross-border e-commerce; the multi-source e-commerce data includes commodity transaction data, logistics track data, payment settlement data, user behavior logs, and the dynamic of bonded warehouse inventory; S2: Based on the multi-source e-commerce data, adopt the deep belief transfer network algorithm to perform heterogeneous data feature alignment and cross-domain risk factor identification, and generate a risk factor set; S3: Based on the risk factor set, adopt the spatial state transition type Markov convolution network algorithm to construct a risk evolution model, model the spatial distribution and time evolution relationship of risk factors, output the prediction results of high-risk evolution paths in the cross-border logistics route and under the commodity dimension, and generate a dynamic risk evolution map; S4: Based on the dynamic risk evolution map, use the abnormal subspace clustering algorithm based on probability logic relationships to adaptively cluster the risk patterns in different spatial and time segments, identify potential cross-link composite risk events, and dynamically output risk grading and response warning information; S5: Based on the risk grading and response warning information, adopt a risk response decision algorithm based on reinforcement learning to dynamically optimize supervision measures, automatically match risk intervention strategies and control resource allocation, and output a cross-border e-commerce risk control plan.

2. The cross-border e-commerce retail risk supervision method according to claim 1, characterized in that The commodity transaction data includes commodity categories, commodity batches, commodity expiration dates, commodity packaging temperature and humidity, production dates, storage temperatures, transportation environment temperature and humidity, and commodity unique identification codes; The logistics track data includes the logistics transportation path, the geographical location of logistics nodes, transportation duration, the temperature and humidity of the passing environment, cargo vibration and shock data, real-time positioning information, and abnormal environment exposure records; The payment settlement data includes payment channel types, payment timestamps, payment terminal geographical location information, the temperature and humidity of the environment collected by the terminal device during the payment process, network connection quality, and device unique identification codes; The user behavior logs include the geographical location of user access, the real-time environmental parameters of the access terminal, local meteorological data at the time of placing an order, device temperature and humidity, and external environment change information during the user operation process; The dynamic of bonded warehouse inventory includes warehouse environment temperature and humidity, warehouse geographical location, batch environment parameter change records of the inventory goods, environmental monitoring data during the inbound and outbound process, inventory abnormal alarm events, and device operation environment data.

3. The cross-border e-commerce retail risk supervision method according to claim 1, characterized in that The step S3 includes the following steps: Based on the commodity identification codes and transportation node sequences in the commodity transaction data and logistics track data, construct a commodity-logistics two-way mapping graph, where the nodes of the commodity-logistics two-way mapping graph represent commodity batches and logistics nodes, and the edges represent the actual transfer paths and time relationships of commodities between different logistics nodes; Map the risk factor set to the corresponding nodes and edges in the commodity-logistics two-way mapping graph to form a multi-attribute graph structure containing risk categories, timestamps, and spatial positions; Based on the multi-attribute graph structure, adopt the spatial state transition type Markov convolution network algorithm to construct a risk evolution model, fuse the spatial state transition matrix and time convolution features, and simulate the dynamic evolution path of risk factors in the cross-border logistics chain; Predict high-risk nodes, key commodity combinations, and potential risk conduction paths within a future time period through the risk evolution model, and generate a dynamic risk evolution map in a three-way tensor structure of commodity dimension - node dimension - time dimension.

4. The cross-border e-commerce retail risk supervision method according to claim 3, wherein The formula of the risk evolution model is as follows: Among them, represents the dynamic risk score of the commodity with the unique identification code i at the logistics node j at time t; represents the activation function; K represents the number of types of risk factors; represents the convolution weight of the k-th type of risk factor; represents the set of previous logistics nodes that have a direct state transition relationship with the logistics node j under the influence of the k-th type of risk factor; represents the attenuation degree of the risk impact of the spatial distance propagated from node l to node j; represents at time the historical risk intensity of commodity i on the previous node l; represents the time convolution weight coefficient, that is, the memory weight of the past s time periods; represents the risk factor k among logistics nodes the state transition probability; and respectively represent the connection degree quantity of commodity i and node j under the risk factor k.

5. The cross-border e-commerce retail risk supervision method according to claim 1, characterized in that Step S4 includes the following steps: Based on the dynamic risk evolution map, extract the distribution characteristics of risk factors and their correlation relationships in different space-time segments, and construct a risk characteristic subspace including risk categories, spatial locations, time segments, and risk evolution trajectories; Based on the risk characteristic subspace, use an abnormal subspace clustering algorithm based on probabilistic logical relationships to perform adaptive clustering and identify high-risk aggregation areas that are spatially adjacent or time-series related; During the clustering process, calculate the joint probability and abnormal correlation strength of risk events in different subspaces, and identify the potential conduction paths and superposition effects of high-risk compound events; According to the adaptive clustering results, dynamically generate cross-link compound risk event labels, and quantify the risk level, event trigger probability, and risk impact range within each clustering unit; Based on the abnormal subspace clustering results, real-time output multi-level risk classification and response warning information including spatial location, commodity dimension, time segment, and risk type.

6. The cross-border e-commerce retail risk supervision method according to claim 5, wherein The formula of the abnormal subspace clustering algorithm is as follows: Among them, represents the optimal composite risk clustering intensity; n represents different composite risk types or event labels; represents the probability logic weighting coefficient of the nth class of clustering; represents the number of samples of the nth class of clustering category; y represents the risk event sample index; represents the multi-dimensional fitness of sample y at spatial position p, commodity batch q, and time period ; represents the comprehensive risk anomaly score of sample y within the time period ; H represents the total number of dimensions of multi-source risk factors; represents the factor triggering probability of sample y in the hth dimension of risk factor and within the time period ; represents the spatial mutual information weight of abnormal events in the nth class of clustering; represents the joint mutual information index of risk events in the nth class of clustering at spatial position p, commodity batch q, and time period .

7. A cross-border e-commerce retail risk supervision method according to claim 1, characterized in that, Step S5 includes the following steps: Based on the risk classification and response warning information, construct a risk response state space, where the risk response state space uses dynamic risk level, spatial location, commodity dimension, time segment, and control resource availability as state variables; Based on the risk response state space, use a risk response decision-making algorithm based on deep reinforcement learning to evaluate the impact of different response actions on risk suppression efficiency and resource utilization rate in real time through a policy network; Continuously collect the feedback information of risk events after implementation, and based on the state-action value iteration mechanism of reinforcement learning, continuously optimize the parameters of the risk intervention strategy, and output a cross-border e-commerce risk control plan, including a combination of response measures, a resource allocation plan, and the priority of control implementation.

8. A cross-border e-commerce retail risk supervision method according to claim 7, characterized in that, The risk response decision-making algorithm specifically adopts a temporal difference strategy optimization mechanism, combines real-time risk levels, resource availability, and regulatory intervention historical records, dynamically adjusts risk intervention measures and control resource allocation paths, and aims to minimize overall risk exposure, maximize resource scheduling efficiency, and minimize response latency, and automatically matches the optimal risk intervention strategy, including measures such as adjusting the goods flow path, intercepting the payment process, restricting user behavior, and controlling the warehouse environment.

9. A cross-border e-commerce retail risk supervision method according to claim 1, characterized in that, In step S2, the risk factor set includes abnormal commodity transactions, logistics delays, abnormal payment behaviors, abnormal user operations, and inventory fluctuations.

10. A cross-border e-commerce retail risk supervision system, characterized in that, The system is applied to a cross-border e-commerce retail risk supervision method as described in any one of claims 1-9, including an e-commerce data collection module, a risk factor identification module, a risk prediction module, a risk classification and warning module, and a decision-making response module that are sequentially communicatively connected; The e-commerce data collection module is used to collect multi-source e-commerce data of cross-border e-commerce in real time using the Internet of Things and API interfaces; The risk factor identification module is used to perform heterogeneous data feature alignment and cross-domain risk factor identification based on the multi-source e-commerce data by using the deep belief transfer network algorithm, and generate a risk factor set; The risk prediction module is used to construct a risk evolution model based on the risk factor set by using the spatial state transition type Markov convolution network algorithm, and generate a dynamic risk evolution map; The risk grading and early warning module is used to identify cross-link potential composite risk events by using the abnormal subspace clustering algorithm based on the probabilistic logical relationship, and dynamically output risk grading and response early warning information; The decision-making response module is used to adopt a risk response decision algorithm based on reinforcement learning to dynamically optimize supervision measures and output a cross-border e-commerce risk control plan.

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