Intelligent trade compliance early warning system based on cross-border supply chain state risk assessment

Abstract

The invention discloses an intelligent trade compliance early warning system based on cross-border supply chain state risk assessment. Comprising a multi-source data acquisition module, a data semantic analysis and standardization module, a supply chain graph modeling module, a multi-dimensional risk index construction module, a graph neural network situation modeling module, a time sequence anomaly detection module and a compliance early warning and strategy linkage module. According to the method, the cross-border supply chain topological graph is constructed by fusing multi-national customs policies, real-time logistics states and geographical political event multi-source heterogeneous data, and dynamic propagation modeling and foresight recognition of compliance risks are realized based on the graph neural network and time sequence anomaly detection, so that multi-national policy differences and superimposed effects thereof can be depicted in a unified manner; and policy mutation and non-stationary risk of control and diffusion can be early warned in advance, so that the real-time performance, the accuracy and the overall prevention and control capability of cross-border trade compliance management are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent trade technology, and in particular to an intelligent trade compliance early warning system based on cross-border supply chain risk assessment. Background Technology

[0002] As global trade becomes increasingly complex, countries frequently adjust export control policies, sanctions lists, and compliance requirements. Cross-border supply chains are characterized by multiple nodes, multiple paths, and high dynamism. Existing trade compliance systems mostly rely on rule matching or static blacklists for verification, which has the following shortcomings: There is a lack of unified modeling capabilities for the differences in policies across countries, the frequency of updates, and the cumulative effects of these policies. The failure to integrate unstructured data such as real-time logistics status and geopolitical events resulted in delayed risk perception. The lack of systematic modeling of the supply chain topology makes it difficult to identify compliance risks that are transmitted across nodes. It is impossible to provide early warnings of sudden policy changes and the unstable risks of the spread of sanctions; In light of the above, this application proposes an intelligent trade compliance early warning system based on cross-border supply chain risk assessment. Summary of the Invention

[0003] Based on the technical problems existing in the background technology, this invention proposes an intelligent trade compliance early warning system based on cross-border supply chain risk assessment.

[0004] The present invention proposes an intelligent trade compliance early warning system based on cross-border supply chain risk assessment, which includes a multi-source data acquisition module, a data semantic parsing and standardization module, a supply chain graph modeling module, a multi-dimensional risk indicator construction module, a graph neural network situation modeling module, a time-series anomaly detection module, and a compliance early warning and strategy linkage module. The multi-source data acquisition module includes a multi-country customs policy database interface unit, a real-time logistics tracking unit, and a geopolitical event stream access unit. The data semantic parsing and standardization module includes a policy clause semantic encoding unit, a logistics status structured mapping unit, and an event sentiment intensity and impact domain annotation unit; The supply chain diagram modeling module is used to construct cross-border supply chain topology diagrams; The multidimensional risk indicator construction module is used to construct a risk indicator vector; The graph neural network situational modeling module uses GNN to model node risk propagation. The time-series anomaly detection module is used to detect the risk time series of nodes; The compliance warning and strategy linkage module is used to trigger warnings and output compliance handling suggestions.

[0005] Preferably, the multi-country customs policy database interface unit is used to connect to the policy databases of customs, export control agencies, and sanctions release platforms of various countries, automatically obtain export control regulations, sanctions lists, commodity code restriction rules, and their updates from various countries, and achieve unified management of policy differences and version evolution. Its specific operating logic is as follows: S1011: Establish interface connections with customs policy databases of multiple countries to obtain raw policy data sets. ,in A single policy text; S1012: Perform structured parsing on the acquired policy text, extracting commodity codes, regulatory types, applicable countries, and constraint elements to form a policy feature vector: ,in Indicates the product code. Indicates the applicable country. Indicates the type of regulation. Indicates constraints. Indicates the update time; S1013: Constructing a Policy Update Increment Set ,when When it is marked as a high-frequency update state, where Denotes the policy set at time t. Denotes the policy set at time t-1. This indicates that the policy is being updated frequently, and the threshold for judgment is being updated. S1014: Write the parsed policy elements into the policy semantic library and push them to the multi-dimensional risk indicator construction module; The real-time logistics tracking unit is used to collect dynamic operational data on order status, path nodes, time delays, and abnormal delays during the cross-border cargo flow process, providing a basis for the timeliness analysis of compliance risks. Its specific operating logic is as follows: S1021: Accessing logistics trajectory data streams from logistics service platforms and IoT terminals: ,in Indicates the order ID. Represents a path node. Represents a timestamp. Indicates the transportation status; S1022: Reconstruct the logistics path to form a path sequence: ,in This refers to the k-th node in the path; S1023: Calculate logistics delay and deviation characteristics: ,when Nodes marked as potentially non-compliant during time processing, among which To delay logistics time, Let i be the actual arrival time of node i. Let i be the expected arrival time. The threshold for determining abnormal logistics delays; S1024: Mapping route characteristics and delay indicators to logistics risk factors ; The geopolitical event stream access unit is used to access global news, government announcements, and sanctions dynamics, and to perceive and quantify unstructured information on sudden political events, trade conflicts, and escalating sanctions. Its specific operating logic is as follows: S1031: Real-time reception of geopolitical event text streams ; S1032: Perform named entity recognition and sentiment analysis on the event text to extract the country, organization, sanctions, and severity of the event. ,in For entities involved, As an action type, For emotional tendencies, To determine the level of influence; S1033: Constructing an event impact weight model: ; S1034: Weighting the impact of events Mapped as geopolitical risk factors And associate and label them with nodes in the supply chain.

[0006] Preferably, the policy clause semantic encoding unit is used to perform semantic understanding, unified encoding, and feature vectorization processing on unstructured clauses in customs policy texts, export control regulations, and sanctions lists from multiple countries, providing standardized policy feature inputs for subsequent risk modeling. Its specific operating logic is as follows: S2011: Receive raw policy data sets from the interface unit of the multi-country customs policy database. Furthermore, natural language sentence segmentation and keyword extraction are performed on policy texts to identify the objects of regulation, restricted actions, and scope of application; S2012: Constructing Policy Semantic Encoding Vectors ,in Indicates the product code. Indicates the type of regulation. Indicates the applicable country or region. Indicates the restriction level. Indicates the effective date; S2013: Write the semantic encoding results into the unified policy semantic database and update it synchronously to the multi-dimensional risk indicator construction module; The logistics status structured mapping unit is used to map discrete logistics information such as orders, nodes, timestamps, and transportation status into a unified structured logistics status vector, eliminating data format differences between different logistics platforms. Its specific operating logic is as follows: S2021: Receive logistics tracking data stream: ; S2022: Based on a predefined mapping rule table, convert the status identifiers of different platforms into a unified status code. ; S2023: Constructing the logistics state vector: ,in This refers to the lag time at logistics nodes; S2024: Output structured logistics status characteristics to the multi-dimensional risk indicator construction module; The event sentiment intensity and impact domain labeling unit is used to transform geopolitical news and sanctions announcements into quantifiable sentiment intensity indicators, and to label the scope of their impact on countries, industries, and supply chain nodes. Its specific operating logic is as follows: S2031: Receive event text stream It also performs named entity recognition on the event text to extract the country, organization, and sanctioned entities; S2032: Calculating the emotional intensity of an event based on a sentiment analysis model Where -1 represents extreme negativity and 1 represents extreme positivity; S2033: Construct an influence domain vector by combining event type and involved entities. ,in In order to influence the country, To influence the industry, To affect nodes in the supply chain; S2034: Write the emotional intensity and influence domain labeling results into the event risk mapping table and push them to the multidimensional risk indicator construction module.

[0007] Preferably, the specific operating logic of the supply chain diagram modeling module is as follows: S301: Based on the structured results output by the data semantic parsing and standardization module, construct a node set: ,in Represents an enterprise node. Indicates a port node. Represents a country node. This represents a product node, and a unique identifier (ID) is assigned to each type of node; S302: Construct an edge set based on logistics routes, trade relations, and policy constraints: ,in This indicates the trade relationship between a company and its goods. This indicates the logistics path of goods between ports and national nodes. This indicates the policy constraint relationship between national nodes and commodity nodes; S303: Construct a node feature matrix by using the semantic encoding of policy clauses, logistics status vectors, and the mapping results of event sentiment intensity and influence domain as node attributes: ,in This represents the semantic encoding vector of the fusion policy. Let the logistics state vector be... The intensity of the emotion associated with the event; S304: Assign weights to edges based on logistics delays, policy restriction levels, and event impact weights: ,in , , All are weighting coefficients. For nodes Logistics delays As a policy restriction level, Weighting the impact of events; S305: Constructing a cross-border supply chain topology using a combination of node sets, edge sets, and edge weights: , where W is the edge weight matrix and X is the node feature matrix; S306: When any node or edge attribute changes, a topological increment set is generated. ,in The topology at time t. The topology graph at time t-1 is generated and updated synchronously to the graph neural network situation modeling module to achieve real-time perception of the supply chain situation.

[0008] Preferably, the specific operational logic steps of the multidimensional risk indicator construction module are as follows: S401: Receive the set of node attributes from the data semantic parsing and standardization module and the supply chain graph modeling module: Where i represents the supply chain node number, These represent the policy, logistics, and event attributes of node i, respectively. S402: Calculate the policy risk factor based on the policy restriction level, number of countries covered, and update frequency corresponding to the node: ,in To accommodate the number of applicable countries, , , For coefficients, Policy update frequency; S403: Calculate logistics risk factors based on logistics node delay time, route deviation, and number of anomalies. ,in , , For coefficients, This is the path offset. Number of abnormal occurrences; S404: Calculating geopolitical risk factors based on the intensity of event sentiment and the weight of its impact. ,in The weight of the influence of the k-th event. The intensity of emotion for the k-th event; S405: Construct historical risk correction items based on historical violation records, audit results, and penalty frequency: ,in , For coefficients, This refers to the number of historical violations. Frequency of penalties; S406: Combining the above factors to form a node risk indicator vector: ,in This represents the risk index vector of node i at time t; S407: Normalize and time-decay the risk vector: , where k is the time decay coefficient.

[0009] Preferably, the specific operational logic steps of the graph neural network situation modeling module are as follows: S501: Load the topology diagram generated by the supply chain diagram modeling module: It also receives the node risk vector output by the multidimensional risk indicator construction module. ; S502: Concatenate the original node features with risk indicators to form the initial embedding. ,in Let i be the initial embedding vector for node i. This is a normalized and time-decayed risk indicator vector; S503: Based on edge weights Disseminate risk information: ,in The neighborhood risk information received by node i at layer l; The set of neighboring nodes of node j; S504: Update the hidden state of a node via a nonlinear mapping: , For the hidden state of node i at level l+1, For activation function, Let l be the weight matrix of the l-th layer. This is the bias term for the l-th layer; S505: Repeat steps S503 to S504 to complete the L-layer risk propagation model and obtain the final node embedding vector. ; S506: Map the final embedded vector to a node situational risk score: ,in ( ) is the risk scoring function. This is the final embedding vector of the Lth layer; S507: Aggregate the status of all nodes on the map to generate an overall supply chain status index. ,in This represents the total number of nodes.

[0010] Preferably, the specific logical steps of the timing anomaly detection module are as follows: S601: Receives the node situation risk score output by the graph neural network situation modeling module. And construct a time series of node risks in chronological order. For node i in Risk score at any time; S602: Establish a time-based prediction model based on historical risk sequences to obtain the predicted risk value of a node at the next time step: ,in ( ) is a time series prediction function; S603: Calculate the residual between the actual risk value and the predicted value: ,in This represents the actual risk value. To predict risk values; S604: Dynamically generate anomaly thresholds based on historical residual distribution: ,in This represents the mean of the residuals. Indicates standard deviation, This is the threshold adjustment coefficient; S605: When satisfied If node i is marked as having a compliance risk anomaly at time t; S606: Calculate the anomaly level based on the residual amplitude and duration: , For the duration of the abnormality, This is a function for calculating the grade. S607: Push the abnormal node, abnormal level and occurrence time to the compliance early warning and strategy linkage module as an important basis for triggering trade compliance early warning.

[0011] Preferably, the specific logical steps of the compliance warning and strategy linkage module are as follows: S701: Receives a set of abnormal events output from the timing anomaly detection module: ,in Indicates the abnormal node number. Indicates the level of abnormality; S702: Situational Risk Score of Fusion Node Abnormal level Based on the historical violation weights of nodes, a comprehensive early warning score is constructed. ,in , , For coefficients; S703: Early warning level is generated based on comprehensive score: ; S704: Based on the supply chain topology map, reverse trace the upstream and downstream paths of abnormal nodes to form a set of risk impact paths: ; S705: Retrieve corresponding handling rules from the strategy knowledge base based on the warning level and the type of impact path: ; S706: Execute the matching handling strategy, including freezing orders, adjusting routes, initiating manual review or pushing early warning notifications, and generating a standardized compliance recommendation report to push to the business system; S707: Write the handling results and subsequent execution effects into the historical database to update the risk weight and strategy matching model parameters, forming a self-learning closed loop.

[0012] Compared with existing technologies, the beneficial effects of this invention are: By using the multi-country customs policy database interface unit and the policy clause semantic coding unit, export control policies of different countries and different versions are uniformly semantically parsed and feature-coded. Quantitative indicators such as policy restriction level, scope of application, and update frequency are introduced into the multi-dimensional risk indicator construction module to achieve unified modeling of the differences in policies of multiple countries and their superimposed effects, avoiding the problems of difficulty in maintaining manual rules and difficulty in identifying cross-border policy conflicts. By integrating real-time logistics tracking data with geopolitical event flows, unstructured information such as logistics delays, route deviations, event sentiment intensity, and impact domain is transformed into quantifiable risk factors. This enables the system to detect potential compliance risks at the first moment of logistics anomalies or sudden political events, significantly improving the real-time and forward-looking nature of risk identification. Based on enterprises, ports, countries and commodities, a cross-border supply chain topology map is constructed, and a graph neural network is used to model risk propagation. This enables compliance risks generated at local nodes to be transmitted and analyzed along the supply chain path, thereby effectively identifying implicit cross-node transmission compliance risks that are difficult to detect by traditional systems. By modeling and residual analysis of the node risk score time series through the time series anomaly detection module, abnormal trends can be identified in the early stages of non-stationary risks such as policy changes and escalation of sanctions. The compliance early warning and strategy linkage module can then trigger early warning and response strategies in advance, realizing the transformation from "post-event compliance" to "pre-event prevention". This invention integrates multi-source heterogeneous data on customs policies of multiple countries, real-time logistics status, and geopolitical events to construct a cross-border supply chain topology map. Based on graph neural networks and time-series anomaly detection, it achieves dynamic propagation modeling and forward-looking identification of compliance risks. This not only uniformly depicts the differences in policies of multiple countries and their superimposed effects, but also provides early warnings of policy changes and non-stationary risks of sanction spread, thereby significantly improving the real-time performance, accuracy, and overall prevention and control capabilities of cross-border trade compliance management. Attached Figure Description

[0013] Figure 1 This is a block diagram of an intelligent trade compliance early warning system based on cross-border supply chain risk assessment proposed in this invention. Detailed Implementation

[0014] The present invention will be further explained below with reference to specific embodiments. Example

[0015] Reference Figure 1 This embodiment proposes an intelligent trade compliance early warning system based on cross-border supply chain risk assessment, including a multi-source data acquisition module, a data semantic parsing and standardization module, a supply chain graph modeling module, a multi-dimensional risk indicator construction module, a graph neural network situation modeling module, a time-series anomaly detection module, and a compliance early warning and strategy linkage module. The multi-source data acquisition module includes a multi-country customs policy database interface unit, a real-time logistics tracking unit, and a geopolitical event stream access unit; The multi-country customs policy database interface unit is used to connect with the policy databases of customs, export control agencies, and sanctions release platforms of various countries. It automatically obtains export control regulations, sanctions lists, commodity code restriction rules, and their updates from various countries, and achieves unified management of policy differences and version evolution. Its specific operating logic is as follows: S1011: Establish interface connections with customs policy databases of multiple countries to obtain raw policy data sets. ,in A single policy text; S1012: Perform structured parsing on the acquired policy text, extracting commodity codes, regulatory types, applicable countries, and constraint elements to form a policy feature vector: ,in Indicates the product code. Indicates the applicable country. Indicates the type of regulation. Indicates constraints. Indicates the update time; S1013: Constructing a Policy Update Increment Set ,when When it is marked as a high-frequency update state, where Denotes the policy set at time t. Denotes the policy set at time t-1. This indicates that the policy is being updated frequently, and the threshold for judgment is being updated. S1014: Write the parsed policy elements into the policy semantic library and push them to the multi-dimensional risk indicator construction module; The real-time logistics tracking unit is used to collect dynamic operational data on order status, path nodes, time delays, and abnormal delays during the cross-border cargo flow process, providing a basis for the timeliness analysis of compliance risks. Its specific operational logic is as follows: S1021: Accessing logistics trajectory data streams from logistics service platforms and IoT terminals: ,in Indicates the order ID. Represents a path node. Represents a timestamp. Indicates the transportation status; S1022: Reconstruct the logistics path to form a path sequence: ,in This refers to the k-th node in the path; S1023: Calculate logistics delay and deviation characteristics: ,when Nodes marked as potentially non-compliant during time processing, among which To delay logistics time, Let i be the actual arrival time of node i. Let i be the expected arrival time. The threshold for determining abnormal logistics delays; S1024: Mapping route characteristics and delay indicators to logistics risk factors ; The geopolitical event stream access unit is used to access global news, government announcements, and sanctions dynamics, and to perceive and quantify unstructured information on sudden political events, trade conflicts, and escalating sanctions. Its specific operational logic is as follows: S1031: Real-time reception of geopolitical event text streams ; S1032: Perform named entity recognition and sentiment analysis on the event text to extract the country, organization, sanctions, and severity of the event. ,in For entities involved, As an action type, For emotional tendencies, To determine the level of influence; S1033: Constructing an event impact weight model: ; S1034: Weighting the impact of events Mapped as geopolitical risk factors And associate and label them with supply chain nodes; The data semantic parsing and standardization module includes a policy clause semantic encoding unit, a logistics status structured mapping unit, and an event sentiment intensity and impact domain annotation unit; The policy clause semantic encoding unit is used to perform semantic understanding, unified encoding, and feature vectorization of unstructured clauses in customs policy texts, export control regulations, and sanctions lists from multiple countries, providing standardized policy feature inputs for subsequent risk modeling. Its specific operating logic is as follows: S2011: Receive raw policy data sets from the interface unit of the multi-country customs policy database. Furthermore, natural language sentence segmentation and keyword extraction are performed on policy texts to identify the objects of regulation, restricted actions, and scope of application; S2012: Constructing Policy Semantic Encoding Vectors ,in Indicates the product code. Indicates the type of regulation. Indicates the applicable country or region. Indicates the restriction level. Indicates the effective date; S2013: Write the semantic encoding results into the unified policy semantic database and update it synchronously to the multi-dimensional risk indicator construction module; The logistics status structured mapping unit is used to map discrete logistics information such as orders, nodes, timestamps, and transportation status into a unified structured logistics status vector, eliminating data format differences between different logistics platforms. Its specific operating logic is as follows: S2021: Receive logistics tracking data stream: ; S2022: Based on a predefined mapping rule table, convert the status identifiers of different platforms into a unified status code. ; S2023: Constructing the logistics state vector: ,in This refers to the lag time at logistics nodes; S2024: Output structured logistics status characteristics to the multi-dimensional risk indicator construction module; The event sentiment intensity and impact domain labeling unit is used to transform geopolitical news and sanctions announcements into quantifiable sentiment intensity indicators, and to label the scope of their impact on countries, industries, and supply chain nodes. Its specific operating logic is as follows: S2031: Receive event text stream It also performs named entity recognition on the event text to extract the country, organization, and sanctioned entities; S2032: Calculating the emotional intensity of an event based on a sentiment analysis model Where -1 represents extreme negativity and 1 represents extreme positivity; S2033: Construct an influence domain vector by combining event type and involved entities. ,in In order to influence the country, To influence the industry, To affect nodes in the supply chain; S2034: Write the emotional intensity and influence domain labeling results into the event risk mapping table and push them to the multidimensional risk indicator construction module; The supply chain diagram modeling module is used to construct cross-border supply chain topology diagrams; Its specific operating logic is as follows: S301: Based on the structured results output by the data semantic parsing and standardization module, construct a node set: ,in Represents an enterprise node. Indicates a port node. Represents a country node. This represents a product node, and a unique identifier (ID) is assigned to each type of node; S302: Construct an edge set based on logistics routes, trade relations, and policy constraints: ,in This indicates the trade relationship between a company and its goods. This indicates the logistics path of goods between ports and national nodes. This indicates the policy constraint relationship between national nodes and commodity nodes; S303: Construct a node feature matrix by using the semantic encoding of policy clauses, logistics status vectors, and the mapping results of event sentiment intensity and influence domain as node attributes: ,in This represents the semantic encoding vector of the fusion policy. Let the logistics state vector be... The intensity of the emotion associated with the event; S304: Assign weights to edges based on logistics delays, policy restriction levels, and event impact weights: ,in , , All are weighting coefficients. For nodes Logistics delays As a policy restriction level, Weighting the impact of events; S305: Constructing a cross-border supply chain topology using a combination of node sets, edge sets, and edge weights: , where W is the edge weight matrix and X is the node feature matrix; S306: When any node or edge attribute changes, a topological increment set is generated. ,in The topology at time t. The topology graph at time t-1 is generated and updated synchronously to the graph neural network situation modeling module to achieve real-time perception of the supply chain situation. The multidimensional risk indicator construction module is used to construct risk indicator vectors; Its specific operational logic steps are as follows: S401: Receive the set of node attributes from the data semantic parsing and standardization module and the supply chain graph modeling module: Where i represents the supply chain node number, These represent the policy, logistics, and event attributes of node i, respectively. S402: Calculate the policy risk factor based on the policy restriction level, number of countries covered, and update frequency corresponding to the node: ,in To accommodate the number of applicable countries, , , For coefficients, Policy update frequency; S403: Calculate logistics risk factors based on logistics node delay time, route deviation, and number of anomalies. ,in , , For coefficients, This is the path offset. Number of abnormal occurrences; S404: Calculating geopolitical risk factors based on the intensity of event sentiment and the weight of its impact. ,in The weight of the influence of the k-th event. The intensity of emotion for the k-th event; S405: Construct historical risk correction items based on historical violation records, audit results, and penalty frequency: ,in , For coefficients, This refers to the number of historical violations. Frequency of penalties; S406: Combining the above factors to form a node risk indicator vector: ,in This represents the risk index vector of node i at time t; S407: Normalize and time-decay the risk vector: , where k is the time decay coefficient; The graph neural network situational modeling module uses GNN to model node risk propagation. Its specific operational logic steps are as follows: S501: Load the topology diagram generated by the supply chain diagram modeling module: It also receives the node risk vector output by the multidimensional risk indicator construction module. ; S502: Concatenate the original node features with risk indicators to form the initial embedding. ,in Let i be the initial embedding vector for node i. This is a normalized and time-decayed risk indicator vector; S503: Based on edge weights Disseminate risk information: ,in The neighborhood risk information received by node i at layer l; The set of neighboring nodes of node j; S504: Update the hidden state of a node via a nonlinear mapping: , For the hidden state of node i at level l+1, For activation function, Let l be the weight matrix of the l-th layer. This is the bias term for the l-th layer; S505: Repeat steps S503 to S504 to complete the L-layer risk propagation model and obtain the final node embedding vector. ; S506: Map the final embedded vector to a node situational risk score: ,in ( ) is the risk scoring function. This is the final embedding vector of the Lth layer; S507: Aggregate the status of all nodes on the map to generate an overall supply chain status index. ,in The total number of nodes; The time-series anomaly detection module is used to detect node risk time series. The specific logical steps are as follows: S601: Receives the node situation risk score output by the graph neural network situation modeling module. And construct a time series of node risks in chronological order. For node i in Risk score at any time; S602: Establish a time-based prediction model based on historical risk sequences to obtain the predicted risk value of a node at the next time step: ,in ( ) is a time series prediction function; S603: Calculate the residual between the actual risk value and the predicted value: ,in This represents the actual risk value. To predict risk values; S604: Dynamically generate anomaly thresholds based on historical residual distribution: ,in This represents the mean of the residuals. Indicates standard deviation, This is the threshold adjustment coefficient; S605: When satisfied If node i is marked as having a compliance risk anomaly at time t; S606: Calculate the anomaly level based on the residual amplitude and duration: , For the duration of the abnormality, This is a function for calculating the grade. S607: Push the abnormal node, abnormal level and occurrence time to the compliance early warning and strategy linkage module as an important basis for triggering trade compliance early warning; The compliance warning and strategy linkage module is used to trigger warnings and output compliance handling suggestions; The specific logical steps are as follows: S701: Receives a set of abnormal events output from the timing anomaly detection module: ,in Indicates the abnormal node number. Indicates the level of abnormality; S702: Situational Risk Score of Fusion Node Abnormal level Based on the historical violation weights of nodes, a comprehensive early warning score is constructed. ,in , , For coefficients; S703: Early warning level is generated based on comprehensive score: ; S704: Based on the supply chain topology map, reverse trace the upstream and downstream paths of abnormal nodes to form a set of risk impact paths: ; S705: Retrieve corresponding handling rules from the strategy knowledge base based on the warning level and the type of impact path: ; S706: Execute the matching handling strategy, including freezing orders, adjusting routes, initiating manual review or pushing early warning notifications, and generating a standardized compliance recommendation report to push to the business system; S707: Write the handling results and subsequent execution effects into the historical database to update the risk weight and strategy matching model parameters, forming a self-learning closed loop; This embodiment integrates multi-source heterogeneous data on customs policies of multiple countries, real-time logistics status, and geopolitical events to construct a cross-border supply chain topology map. Based on graph neural networks and time-series anomaly detection, it achieves dynamic propagation modeling and forward-looking identification of compliance risks. This not only uniformly depicts the differences in policies of multiple countries and their superimposed effects, but also provides early warnings of policy changes and non-stationary risks of sanction spread, thereby significantly improving the real-time performance, accuracy, and overall prevention and control capabilities of cross-border trade compliance management.

[0016] In this embodiment, firstly, through the multi-country customs policy database interface unit, real-time logistics tracking unit, and geopolitical event flow access unit, export control regulations, sanctions lists, logistics trajectories, and geopolitical events of various countries are continuously collected to achieve multi-dimensional perception of the policy environment, cargo flow trends, and external political shocks. Secondly, through the data semantic parsing and standardization module, policy clauses, logistics status and event texts from different sources and in different formats are uniformly semantically encoded and structurally mapped to form standardized policy feature vectors, logistics status vectors and event impact factors, thereby solving the problem of difficulty in integrating multi-source heterogeneous data; Then, the supply chain graph modeling module uses enterprises, ports, countries and commodities as nodes and trade relations, logistics routes and policy constraints as edges to construct a cross-border supply chain topology graph. It also assigns weights to the edges based on logistics delays, policy restriction levels and event impact weights to achieve a networked depiction of the compliance risk transmission path. Meanwhile, the multi-dimensional risk indicator construction module forms node risk indicator vectors from multiple dimensions such as policy compliance, logistics operation status, geopolitical impact, and historical violations, and performs time decay and normalization processing to provide stable input for subsequent risk propagation modeling. The graph neural network situation modeling module further performs multi-level weighted aggregation and node status updates on the supply chain topology graph, models the propagation of local abnormal risks along upstream and downstream paths, and outputs a risk score that reflects the current compliance status of the node, so as to achieve accurate identification of implicit transmission risks. Subsequently, the time-series anomaly detection module performs predictive modeling and residual analysis on the risk score time series of each node. When a risk mutation that deviates significantly from the historical pattern is detected, it is automatically identified as an anomaly event and the anomaly level is assessed. Finally, the compliance early warning and strategy linkage module generates an early warning level by comprehensively considering the anomaly level, node situation risk, and historical violation weight. It also traces the impact path of abnormal nodes, matches the corresponding compliance handling strategies, and automatically outputs linkage measures such as freezing orders, adjusting routes, or initiating manual review, forming a closed-loop intelligent trade compliance management mechanism from "risk perception - situation modeling - anomaly identification - early warning handling".

[0017] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart trade compliance early warning system based on cross-border supply chain risk assessment, characterized in that, It includes a multi-source data acquisition module, a data semantic parsing and standardization module, a supply chain graph modeling module, a multi-dimensional risk indicator construction module, a graph neural network situation modeling module, a time series anomaly detection module, and a compliance early warning and strategy linkage module; The multi-source data acquisition module includes a multi-country customs policy database interface unit, a real-time logistics tracking unit, and a geopolitical event stream access unit. The data semantic parsing and standardization module includes a policy clause semantic encoding unit, a logistics status structured mapping unit, and an event sentiment intensity and impact domain annotation unit; The supply chain diagram modeling module is used to construct cross-border supply chain topology diagrams; The multidimensional risk indicator construction module is used to construct a risk indicator vector; The graph neural network situational modeling module uses GNN to model node risk propagation. The time-series anomaly detection module is used to detect the risk time series of nodes; The compliance warning and strategy linkage module is used to trigger warnings and output compliance handling suggestions.

2. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment as described in claim 1, characterized in that, The multi-country customs policy database interface unit is used to connect with the policy databases of customs, export control agencies, and sanctions release platforms of various countries. It automatically obtains export control regulations, sanctions lists, commodity code restriction rules, and their updates from various countries, and achieves unified management of policy differences and version evolution. Its specific operating logic is as follows: S1011: Establish interface connections with customs policy databases of multiple countries to obtain raw policy data sets. ,in A single policy text; S1012: Perform structured parsing on the acquired policy text, extracting commodity codes, regulatory types, applicable countries, and constraint elements to form a policy feature vector: ,in Indicates the product code. Indicates the applicable country. Indicates the type of regulation. Indicates constraints. Indicates the update time; S1013: Constructing a Policy Update Increment Set ,when When it is marked as a high-frequency update state, where Denotes the policy set at time t. Denotes the policy set at time t-1. This indicates that the policy is being updated frequently, and the threshold for judgment is being updated. S1014: Write the parsed policy elements into the policy semantic library and push them to the multi-dimensional risk indicator construction module; The real-time logistics tracking unit is used to collect dynamic operational data on order status, path nodes, time delays, and abnormal delays during the cross-border cargo flow process, providing a basis for the timeliness analysis of compliance risks. Its specific operating logic is as follows: S1021: Accessing logistics trajectory data streams from logistics service platforms and IoT terminals: ,in Indicates the order ID. Represents a path node. Represents a timestamp. Indicates the transportation status; S1022: Reconstruct the logistics path to form a path sequence: ,in This refers to the k-th node in the path; S1023: Calculate logistics delay and deviation characteristics: ,when Nodes marked as potentially non-compliant during time processing, among which Due to logistics delays, Let i be the actual arrival time of node i. Let i be the expected arrival time. The threshold for determining abnormal logistics delays; S1024: Mapping route characteristics and delay indicators to logistics risk factors ; The geopolitical event stream access unit is used to access global news, government announcements, and sanctions dynamics, and to perceive and quantify unstructured information on sudden political events, trade conflicts, and escalating sanctions. Its specific operating logic is as follows: S1031: Real-time reception of geopolitical event text streams ; S1032: Perform named entity recognition and sentiment analysis on the event text to extract the country, organization, sanctions, and severity of the event. ,in For entities involved, As an action type, For emotional tendencies, To determine the level of influence; S1033: Constructing an event impact weight model: ; S1034: Weighting the impact of events Mapped as geopolitical risk factors And associate and label them with nodes in the supply chain.

3. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment as described in claim 1, characterized in that, The policy clause semantic encoding unit is used to perform semantic understanding, unified encoding, and feature vectorization processing on unstructured clauses in customs policy texts, export control regulations, and sanctions lists from multiple countries, providing standardized policy feature inputs for subsequent risk modeling. Its specific operating logic is as follows: S2011: Receive raw policy data sets from the interface unit of the multi-country customs policy database. Furthermore, natural language sentence segmentation and keyword extraction are performed on policy texts to identify the objects of regulation, restricted actions, and scope of application; S2012: Constructing Policy Semantic Encoding Vectors ,in Indicates the product code. Indicates the type of regulation. Indicates the applicable country or region. Indicates the restriction level. Indicates the effective date; S2013: Write the semantic encoding results into the unified policy semantic database and update it synchronously to the multi-dimensional risk indicator construction module; The logistics status structured mapping unit is used to map discrete logistics information such as orders, nodes, timestamps, and transportation status into a unified structured logistics status vector, eliminating data format differences between different logistics platforms. Its specific operating logic is as follows: S2021: Receive logistics tracking data stream: ； S2022: Based on a predefined mapping rule table, convert the status identifiers of different platforms into a unified status code. ； S2023: Constructing the logistics state vector: ,in This refers to the lag time at logistics nodes; S2024: Output structured logistics status characteristics to the multi-dimensional risk indicator construction module; The event sentiment intensity and impact domain labeling unit is used to transform geopolitical news and sanctions announcements into quantifiable sentiment intensity indicators, and to label the scope of their impact on countries, industries, and supply chain nodes. Its specific operating logic is as follows: S2031: Receive event text stream It also performs named entity recognition on the event text to extract the country, organization, and sanctioned entities; S2032: Calculating the emotional intensity of an event based on a sentiment analysis model Where -1 represents extreme negativity and 1 represents extreme positivity; S2033: Construct an influence domain vector by combining event type and involved entities. ,in In order to influence the country, To influence the industry, To impact supply chain nodes; S2034: Write the emotional intensity and influence domain labeling results into the event risk mapping table and push them to the multidimensional risk indicator construction module.

4. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment according to claim 1, characterized in that, The specific operating logic of the supply chain diagram modeling module is as follows: S301: Based on the structured results output by the data semantic parsing and standardization module, construct a node set: ,in Represents an enterprise node. Indicates a port node. Represents a country node. This represents a product node, and a unique identifier (ID) is assigned to each type of node; S302: Construct an edge set based on logistics routes, trade relations, and policy constraints: ,in This indicates the trade relationship between a company and its goods. This indicates the logistics path of goods between ports and national nodes. This indicates the policy constraint relationship between national nodes and commodity nodes; S303: Construct a node feature matrix by using the semantic encoding of policy clauses, logistics status vectors, and the mapping results of event sentiment intensity and influence domain as node attributes: ,in This represents the semantic encoding vector of the fusion policy. Let the logistics state vector be... The intensity of the emotion associated with the event; S304: Assign weights to edges based on logistics delays, policy restriction levels, and event impact weights: ,in , , All are weighting coefficients. For nodes Logistics delays As a policy restriction level, Weight the impact of the event; S305: Constructing a cross-border supply chain topology using a combination of node sets, edge sets, and edge weights: , where W is the edge weight matrix and X is the node feature matrix; S306: When any node or edge attribute changes, a topological increment set is generated. ,in The topology at time t. The topology graph at time t-1 is generated and updated synchronously to the graph neural network situation modeling module to achieve real-time perception of the supply chain situation.

5. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment according to claim 1, characterized in that, The specific operational logic steps of the multidimensional risk indicator construction module are as follows: S401: Receive the set of node attributes from the data semantic parsing and standardization module and the supply chain graph modeling module: Where i represents the supply chain node number, These represent the policy, logistics, and event attributes of node i, respectively. S402: Calculate the policy risk factor based on the policy restriction level, number of countries covered, and update frequency corresponding to the node: ,in To accommodate the number of applicable countries, , , For coefficients, Policy update frequency; S403: Calculate logistics risk factors based on logistics node delay time, route deviation, and number of anomalies. ,in , , For coefficients, This is the path offset. Number of abnormal occurrences; S404: Calculating geopolitical risk factors based on the intensity of event sentiment and the weight of its impact. ,in The weight of the influence of the k-th event. The intensity of emotion for the k-th event; S405: Construct historical risk correction items based on historical violation records, audit results, and penalty frequency: ,in , For coefficients, This refers to the number of historical violations. Frequency of penalties; S406: Combining the above factors to form a node risk indicator vector: ,in This represents the risk index vector of node i at time t; S407: Normalize and time-decay the risk vector: , where k is the time decay coefficient.

6. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment according to claim 1, characterized in that, The specific operational logic steps of the graph neural network situation modeling module are as follows: S501: Load the topology diagram generated by the supply chain diagram modeling module: It also receives the node risk vector output by the multidimensional risk indicator construction module. ; S502: Concatenate the original node features with risk indicators to form the initial embedding. ,in Let i be the initial embedding vector for node i. This is a normalized and time-decayed risk indicator vector; S503: Based on edge weights Disseminate risk information: ,in The neighborhood risk information received by node i at layer l; for The set of neighboring nodes of node j; S504: Update the hidden state of a node via a nonlinear mapping: , For the hidden state of node i at level l+1, For activation function, Let l be the weight matrix of the l-th layer. For the l-th layer bias term; S505: Repeat steps S503 to S504 to complete the L-layer risk propagation model and obtain the final node embedding vector. ; S506: Map the final embedded vector to a node situational risk score: ,in ( ) is the risk scoring function. This is the final embedding vector of the Lth layer; S507: Aggregate the status of all nodes on the map to generate an overall supply chain status index. ,in This represents the total number of nodes.

7. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment according to claim 1, characterized in that, The specific logical steps of the timing anomaly detection module are as follows: S601: Receives the node situation risk score output by the graph neural network situation modeling module. And construct a time series of node risks in chronological order. For node i in Risk score at any moment; S602: Establish a time-based prediction model based on historical risk sequences to obtain the predicted risk value of a node at the next time step: ,in ( ) is a time series prediction function; S603: Calculate the residual between the actual risk value and the predicted value: ,in This represents the actual risk value. To predict risk values; S604: Dynamically generate anomaly thresholds based on historical residual distribution: ,in This represents the mean of the residuals. Indicates standard deviation, This is the threshold adjustment coefficient; S605: When satisfied If node i is marked as having a compliance risk anomaly at time t; S606: Calculate the anomaly level based on the residual amplitude and duration: , For the duration of the abnormality, This is a function for calculating grades; S607: Push the abnormal node, abnormal level and occurrence time to the compliance early warning and strategy linkage module as an important basis for triggering trade compliance early warning.

8. The intelligent trade compliance early warning system based on cross-border supply chain risk assessment according to claim 1, characterized in that, The specific logical steps of the compliance warning and strategy linkage module are as follows: S701: Receives a set of abnormal events output from the timing anomaly detection module: ,in Indicates the abnormal node number. Indicates the level of abnormality; S702: Situational Risk Score of Fusion Node Abnormal level Based on the historical violation weights of nodes, a comprehensive early warning score is constructed. ,in , , For coefficients; S703: Early warning level is generated based on comprehensive score: ； S704: Based on the supply chain topology map, reverse trace the upstream and downstream paths of abnormal nodes to form a set of risk impact paths: ； S705: Retrieve corresponding handling rules from the strategy knowledge base based on the warning level and the type of impact path: ; S706: Execute the matching handling strategy, including freezing orders, adjusting routes, initiating manual review or pushing early warning notifications, and generating a standardized compliance recommendation report to push to the business system; S707: Write the handling results and subsequent execution effects into the historical database to update the risk weight and strategy matching model parameters, forming a self-learning closed loop.

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