Cross-border e-commerce integrated management platform

Abstract

The invention discloses a cross-border e-commerce integrated management platform, and relates to the technical field of cross-border e-commerce management, and the platform comprises a multi-modal semantic reconstruction module which is used for collecting multi-modal policy data of a trade country related to cross-border e-commerce business, and generating cross-modal policy features through cross-modal alignment and fusion; the compliance knowledge graph construction module is used for constructing a policy knowledge graph based on the cross-modal policy characteristics, calculating compliance risk values for fuzzy terms in the cross-modal policy characteristics, marking high-risk commodities and adding risk attributes to the policy knowledge graph; the compliance decision driving module is used for constructing a multi-dimensional state vector, generating a sub-node action set through random sampling, constraining a strategy update amplitude and selecting an action corresponding to an optimal sub-node; and the strategy execution and feedback module is used for executing the selected action, synchronously adjusting declaration parameters, optimizing transportation distribution of the split parcels, monitoring a customs clearance result in real time and feeding back a punishment value, so that the accuracy of fuzzy policy understanding is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cross - border e - commerce management, and particularly to a comprehensive cross - border e - commerce management platform. Background Art

[0002] With the rapid development of Internet technology and the deepening of globalization, cross - border e - commerce has become an important part of international trade. Cross - border e - commerce enterprises conduct commodity transactions across national boundaries, which not only greatly enriches consumers' shopping choices but also provides a convenient way for small and medium - sized enterprises to enter the international market. However, cross - border e - commerce business involves multiple countries and regions, and there are significant differences in policies and regulations, tax policies, customs supervision requirements, etc. among different countries, which brings huge challenges to the operation of cross - border e - commerce enterprises.

[0003] Traditionally, when cross - border e - commerce enterprises handle policy data of different countries, they often rely on manual collection and interpretation. This method is not only inefficient but also error - prone. Since policy data has multi - modal characteristics, including various forms such as text, images, and videos, manual processing is difficult to cover comprehensively and the accuracy is difficult to guarantee. In addition, policy data is updated frequently, and enterprises are difficult to obtain and adapt to these changes in real time, resulting in an increased compliance risk.

[0004] In terms of commodity compliance, one of the biggest challenges faced by cross - border e - commerce enterprises is the uncertain expressions in policies. For example, some policy terms may use ambiguous language to describe prohibited or restricted commodity categories, quantities, or conditions, which makes it difficult for enterprises to accurately judge the compliance of their commodities. At the same time, there are also differences in the regulatory strictness of the same commodity in different countries, further increasing the compliance risk. Summary of the Invention

[0005] (I) Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides a comprehensive cross - border e - commerce management platform, which helps enterprises achieve efficient and compliant operation in the complex and changeable international trade environment by integrating multi - modal policy data, dynamically quantifying compliance risks, and combining real - time feedback to optimize decisions.

[0006] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: A comprehensive cross - border e - commerce management platform, comprising: A multi - modal semantic reconstruction module, which is used to collect multi - modal policy data of trading countries involved in cross - border e - commerce business, extract features of each modality, and generate cross - modal policy features through cross - modal alignment and fusion; A compliance knowledge graph construction module, which is used to construct a policy knowledge graph based on cross - modal policy features, calculate compliance risk values for ambiguous clauses in cross - modal policy features, mark high - risk commodities, and attach risk attributes to the policy knowledge graph; The compliance decision-making driving module is used to construct a multi-dimensional state vector, generate a set of sub-node actions through random sampling, and calculate each sub-node Q value, Q The value represents the expected utility value, which is calculated based on the declared value, tax rate, and fine ceiling, and is corrected according to the enterprise's historical violation records Q value, while maximizing the Q value, update the amplitude of the policy through the information divergence constraint, and select the action corresponding to the optimal sub-node; The policy execution and feedback module is used to execute the selected action, synchronously adjust the declaration parameters, optimize the transportation allocation of split packages, and monitor the customs clearance results in real time and feedback the penalty value.

[0007] Furthermore, the steps to generate cross-modal policy features are as follows: Deploy multiple lightweight crawler nodes through Docker containerization technology, differentially collect multi-modal policy data of each trading country, including at least text, image, PDF, and video data, and extract features of each modality; Based on the generative adversarial network, verify the authenticity of the policy data, and retain the data with a confidence level higher than the confidence threshold; Adopt a cross-modal attention mechanism to fuse text features, image features, and video features. Among them, the text features are used as query vectors, and the image features are used as key-value pairs to generate the fused cross-modal policy features.

[0008] Furthermore, in the compliance knowledge graph construction module: For the same kind of goods in each trading country, a probability distribution model based on historical customs clearance data is constructed for the fuzzy clauses in the cross-modal policy features: , where represents the compliance probability, k represents the regulatory strictness coefficient, x represents the feature value, μ represents the mean of the feature values; for the new declaration data, calculate the compliance risk value according to the commodity category and country R : , , when the compliance risk value is greater than the marking threshold, it is marked as a high-risk commodity.

[0009] Furthermore, extract the entity relationship quadruple from the cross-modal policy features, including the subject, relationship, object, and time limit, and attach the risk attribute compliance risk value to construct a policy knowledge graph. Match the cosine similarity between the new policy features and the existing nodes. If the similarity is greater than the similarity threshold, enhance the weight of the associated edge. Otherwise, create a new node; Scan the expired nodes daily and perform weight decay, and automatically archive the nodes with weights lower than the preset weight threshold.

[0010] Further, in the compliance decision-making driving module: The multi-dimensional state vector includes the commodity code, declared value, trading country code, policy version number, and violation weight. The violation weight is calculated by exponential decay based on the historical number of violations. Taking the current multi-dimensional state vector as the root node, generate a set of child node actions through random sampling. Based on the declared value, tax rate, and fine ceiling of each child node, generate a preliminary Q value: , where Q represents the preliminary Q value, B represents the basic benefit of successful customs clearance, V represents the declared value, represents the tax rate, represents the fine ceiling.

[0011] Further, if the preliminary Q value of the current child node is positive, retain the child node; if the preliminary Q value of the current child node is negative or zero, directly eliminate the child node; According to the enterprise's historical violation records, impose additional penalties on high-risk actions, and correct the Q value through the violation weight: , where represents the corrected Q value, represents the violation weight, r represents the penalty value; while maximizing the Q value, update the amplitude through the information divergence constraint strategy, and select the action corresponding to the optimal child node.

[0012] Further, in the strategy execution and feedback module: If the action is to modify the declaration parameters, synchronize the corrected commodity code and commodity description to the customs declaration form and verify the validity of the code; if the action is to adjust the declaration time, recalculate the latest declaration time based on the customs clearance time limit requirements of the trading country. If the action is to split the package, select a set of low-risk ports according to the port compliance risk value, and solve the multi-package allocation problem with the goal of minimizing the transportation cost: where represents the transportation cost of the j th port, M represents the total number of ports, represents the number of packages allocated to the j th port, represents the total splitting quantity, represents the jThe compliance risk value of each port, represents the low-risk threshold.

[0013] Furthermore, monitor the customs clearance status in real time. If the result is passed, subtract one from the penalty value; If the result is tax supplement, increase the penalty value by the ratio of the tax amount to be supplemented to the original tax amount payable; If the result is seizure, increase the penalty value by the ratio of the fine amount to the declared value.

[0014] An electronic device includes a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, the steps performed by the cross-border e-commerce integrated management platform described in any one of the above are implemented.

[0015] A computer-readable storage medium stores a computer program thereon. When the computer program is executed, the steps performed by the cross-border e-commerce integrated management platform described in any one of the above are implemented.

[0016] (III) Beneficial effects The present invention provides a cross-border e-commerce integrated management platform, which has the following beneficial effects: (1) By using a distributed crawler cluster to capture and process multi-modal policy data in real time, the comprehensiveness and timeliness of data collection are improved, the data features of each modality are extracted and cross-modal fusion is performed, enhancing the accuracy of policy understanding. At the same time, by using an adversarial generative network to identify the authenticity of data, the quality and reliability of the data are ensured.

[0017] (2) By constructing a probability distribution model to accurately calculate the compliance risk value and quantify fuzzy terms, the policy terms are made more explicit, thereby improving the accuracy of compliance decisions. By constructing and updating the policy knowledge graph, the accuracy and timeliness of policy understanding are improved. By using a weight decay mechanism to automatically archive expired nodes, the cleanliness and effectiveness of the graph are maintained, thereby optimizing the compliance decision-making process, reducing the error rate, and enhancing the management efficiency and risk prevention and control capabilities.

[0018] (3) By constructing a multi-dimensional state vector and generating a sub-node action set based on random sampling, the potential benefits and risks of different actions can be evaluated in real time. By calculating the Q value of each sub-node, the path with an expected benefit greater than the cost can be preferentially selected. At the same time, special processing is performed on high-risk goods (such as forced parcel splitting) to reduce compliance risks. Using KL divergence as a constraint to limit the magnitude of policy updates helps enterprises to smoothly adjust their strategies when facing complex and changing customs policies and market environments, avoiding operational risks caused by excessive policy update amplitudes, and thus helping enterprises to make optimal choices.

[0019] (4) By listening in real time to the customs clearance status returned by the customs systems of trading countries, the customs clearance results of goods can be obtained immediately, including key information such as whether the customs clearance is successful, whether additional duties need to be paid, and whether the goods are seized. Based on this information, the penalty value can be dynamically calculated to respond promptly and appropriately to violations, achieving a closed-loop management of compliance operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic structural diagram of the cross-border e-commerce integrated management platform of the present invention; Figure 2 It is a schematic diagram of the steps of the cross-border e-commerce integrated management platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figure 1 - Figure 2 , the present invention provides a cross-border e-commerce integrated management platform, including: a multi-modal semantic reconstruction module, a compliance knowledge graph construction module, a compliance decision-making driving module, and a policy execution and feedback module; wherein: The multi-modal semantic reconstruction module is used to collect multi-modal policy data of trading countries involved in cross-border e-commerce business, extract policy features of each modality, and generate cross-modal policy features through cross-modal alignment and fusion; Specifically, for trading countries involved in cross-border e-commerce business, a distributed crawler cluster is deployed to comprehensively capture policy data. The customs official websites, government affairs API interfaces, social media accounts, news media ports, etc. of each trading country are used as the sources of policy data. For each data source, according to the access method it provides (such as web page, API, RSS subscription, etc.), the corresponding access method is configured. Lightweight crawler nodes are deployed in each trading country, and these nodes are implemented based on Docker containerization technology for rapid deployment and flexible expansion. At the same time, a differentiated collection strategy is configured for each crawler node to adapt to the characteristics and update frequencies of different data sources, ensuring the comprehensiveness and timeliness of data collection; For text - type data collection and parsing, a crawler based on the Scrapy - Redis architecture is used to regularly capture announcements in HTML / XML format to ensure the timely acquisition of text - type policy data; for image / PDF - type data collection and parsing, OpenCV - PDFMiner is used to jointly parse images and PDF files, identify table areas and handwritten annotations in scanned documents, and pay special attention to the detection of red seals to extract key policy information from images and PDFs; for video - stream - type data collection and processing, key frames of live videos are intercepted through FFmpeg - NVIDIA Video SDK, the frame rate is set to 25fps, and one frame is extracted every 5 seconds. At the same time, non - policy - related images, such as opening speeches at meetings, are filtered out, and only video content related to policy updates is retained for subsequent analysis and processing; Build an adversarial generative network (GAN) for data authenticity identification. Among them, the generator is used to forge policy texts and simulate tampered announcements. For example, according to a given template (such as "Import Restriction Order"), country code (such as "JP"), and a flag indicating whether it is maliciously modified, a forged policy text is generated; the discriminator, based on a multi - level attention mechanism, discriminates the text and its metadata (such as source IP address, timestamp), gives a authenticity confidence score, and only data with a confidence greater than the confidence threshold (such as 97%) is retained for downstream processing. The confidence threshold is adjusted based on requirements to ensure that the false - detection rate is less than 0.3%, improving the quality and reliability of the data; For multi - modal data (text, image, video) of the same policy theme, the geographical coordinates (longitude, latitude) are parsed through the source IP address of the publisher, and the global clock is synchronized in combination with the NTP protocol to ensure the consistency of time information, and an index matrix is constructed: In the index matrix, each data item's ID, modal type, longitude, latitude, start time, and end time are recorded correspondingly for subsequent rapid retrieval and analysis; For text - type policy data, the DeBERTa - V3 model is used to extract semantic vectors, focusing on key legal elements such as "Prohibited Imports" and "Duty - Free Quota", and syntactic enhancement encoding is performed on negative sentence patterns (such as "not earlier than...") to improve the accuracy of text understanding; for image and PDF - type policy data, LayoutLMv3 is used to parse multi - column tables in scanned documents, identify structured triples of "Commodity Code - Tax Rate - Validity Period", and perform attention weighting on the red annotation area (such as weight α = 0.7) to extract key information from images and PDFs; for video - type policy data: key - frame features are extracted through the CLIP - ViT - L / 14 model, and concurrent - voice text is extracted in combination with Whisper speech recognition to achieve joint audiovisual feature encoding and improve the utilization rate of video data; Adopt a cross-modal attention mechanism to align and fuse heterogeneous data such as text, images, and videos: , where H represents the fused cross-modal features, Softmax represents the normalized exponential function, Q_text represents the text modality query vector, K_image represents the image modality key vector, V_image represents the image modality value vector, d represents the vector dimension (such as the dimension of Q_text ); Real-time capture and processing of multi-modal policy data through a distributed crawler cluster improve the comprehensiveness and timeliness of data collection, extract the features of each modality data and perform cross-modal fusion, enhance the accuracy of policy understanding. At the same time, the authenticity of the data is identified through an adversarial generation network, ensuring the quality and reliability of the data.

[0023] Compliance knowledge graph construction module, used to construct a policy knowledge graph based on cross-modal policy features, calculate the compliance risk value for the fuzzy clauses in the cross-modal policy features, mark high-risk commodities, and attach risk attributes to the policy knowledge graph; Specifically, obtain the historical customs clearance records of each country (including declaration volume, commodity category, review results, etc.), policy features, conduct a preliminary screening of the historical customs clearance records, filter out invalid records such as missing declaration volume and fuzzy commodity categories, retain the entries with complete data and accurate information, group them by country-commodity category, and count the mean and standard deviation of the historical declaration volume of the same type of commodities; extract key fields, including country code, commodity code, declaration volume, review result (compliance / non-compliance), mark the fuzzy clauses in the policy features (such as "reasonable quantity", "similar commodities"), and associate relevant fields; For the same type of commodities in each trading country, construct a probability distribution model based on historical customs clearance data for the fuzzy clauses in the policy features: , where represents the compliance probability, k represents the regulatory strictness coefficient, , obtained through logistic regression fitting, x represents the feature value (for example, the commodity declaration volume corresponding to "reasonable quantity"), μ represents the mean of the feature values (the mean of the historical declaration volume of commodities corresponding to "reasonable quantity"); For newly declared data, calculate the compliance risk value according to the commodity category and country: , R represents the compliance risk value, and , when the compliance risk value is greater than the marking threshold, mark it as a high-risk commodity. The marking threshold for high-risk commodities (such as 0.7) can be adjusted according to actual business needs and risk tolerance; Extract entity relationship quadruples <subject, relationship, object, time limit> from policy features, such as ("medical device", "needs to provide", "FDA certification", "2024 - 2025"), and attach the compliance risk value of the risk attribute. Based on the extracted entity relationship quadruples, construct an initial policy knowledge graph. The nodes in the graph represent entities (such as goods, countries, certification bodies, etc.), and the edges represent the relationships between entities (such as needs to provide, prohibited from importing, etc.). Add necessary attributes to the nodes and edges, such as time validity period, country code, commodity category, etc.; Match the new policy features with the existing nodes using cosine similarity. If the similarity is greater than the similarity threshold, enhance the weight of the associated edge: , weight denotes the weight, denotes the enhancement coefficient (such as 1.2), , merge duplicate clauses; if the similarity is less than or equal to the similarity threshold, create a new node and connect it to the relevant commodity category node; scan for expired nodes daily (current time is greater than the validity period), and the weight decays to: , denotes the decay coefficient (such as 0.95), ; when the weight is less than the weight threshold (such as 0.1), the node is automatically archived to the historical database. The similarity threshold is adjusted according to the actual business needs and the frequency of policy changes. The weight threshold, enhancement coefficient, and decay coefficient are adjusted according to the actual business needs and operation effects; For high-risk goods (compliance risk value is greater than the marking threshold), automatically select ports with a low review probability, with the objective function of minimizing transportation costs, and at the same time satisfying the constraint condition that the compliance risk value is less than or equal to the marking threshold. Dynamically correct according to the compliance risk value to avoid manual review, and the correction value is: , denotes the correction value. For example, when the compliance risk value is greater than the marking threshold, force the splitting of packages, such as the declared quantity per single package does not exceed the average of the historical declared quantity of the goods μ ; Accurately calculate the compliance risk value by constructing a probability distribution model, quantify fuzzy clauses, make policy clauses clearer, thereby improving the accuracy of compliance decisions, construct and update the policy knowledge graph, improve the accuracy and timeliness of policy understanding, automatically archive expired nodes through the weight decay mechanism, keep the graph clean and effective, thereby optimizing the compliance decision-making process, reducing the error rate, and enhancing management efficiency and risk prevention and control capabilities.

[0024] The compliance decision-making driving module is used to construct a multi-dimensional state vector, generate a set of sub-node actions through random sampling, and calculate the Q value, Q The value represents the expected utility value, which is calculated based on the declared value, tax rate, and fine ceiling, and is corrected according to the enterprise's historical violation recordsQ value, while maximizing the Q value, update the amplitude through the information divergence constraint strategy, and select the action corresponding to the optimal child node; Specifically, real-time extract the commodity code (HS Code), declared value (converted to the standard currency unit according to the exchange rate of the trading country), trading country code (ISO standard), current effective policy version number (such as "2024-EU-Customs-v3"), and query the enterprise's historical violation records to construct a multi-dimensional state vector: S = (commodity code, declared value, trading country code, policy version number, violation weight). Among them, the declared value is converted by the exchange rate according to the currency of the trading country, and the violation weight is obtained by calculating the historical number of violations: , where represents the violation weight, represents the current time, represents the i time of the n th violation, represents the historical number of violations; Take the current multi-dimensional state vector as the root node, generate a set of child node actions through random sampling (such as splitting packages, adjusting HS codes, delaying declarations, etc.), and generate a preliminary Q value through the declared value, tax rate, and fine ceiling of each child node: , where Q represents the preliminary Q value, B represents the basic income for successful customs clearance, V represents the declared value, represents the tax rate, represents the fine ceiling; If the preliminary Q value of the current child node is positive, it means the expected income is greater than the cost, and keep this child node; If the preliminary Q value of the current child node is negative or zero, it means the cost exceeds the expected income or there is no income, and directly eliminate this child node to avoid the expansion of high-risk paths; According to the enterprise's historical violation records, impose additional penalties on high-risk actions, and correct the Q value through the violation weight: , where represents the corrected Q value, represents the violation weight, r represents the penalty value; Use information divergence as the constraint limit strategy to update the amplitude: , where represents the information divergence between the new strategy and the old strategy, Represents the action distribution of the new policy, based on the current multi-dimensional state vector and the action set of the child nodes generated by random sampling. Represents the action distribution of the old policy, that is, the action distribution followed by the system before adopting the new policy. β Represents the constraint amplitude (such as 10%, that is, the difference between the new policy and the old policy does not exceed 10%, which is adjusted based on actual needs to avoid attracting audit attention due to excessive adjustment amplitude between the new policy and the old policy). While maximizing Q the value, the information divergence is used to constrain the policy update amplitude, and the action corresponding to the optimal child node is selected. Information divergence ( KL divergence) is an asymmetric measure used to measure the difference between the action distribution of the new policy and the action distribution of the old policy. By using the policy gradient method, the state-action pair value preference is converted into an action probability distribution, associating the probability of each action with its corresponding value preference. The higher the value preference, the greater the probability of the action being selected. The action probability distribution is the probability of taking an action s in the state a ; By constructing a multi-dimensional state vector and generating the action set of child nodes based on random sampling, the potential benefits and risks of different actions can be evaluated in real time. By calculating the Q value of each child node, the path with an expected benefit greater than the cost can be preferentially selected. At the same time, special handling is performed on high-risk goods (such as forced package splitting) to reduce compliance risks. Using KL divergence as a constraint to limit the policy update amplitude helps enterprises to smoothly adjust their policies when facing complex and changing customs policies and market environments, avoiding operational risks caused by excessive policy update amplitude, and thus helping enterprises to make optimal choices.

[0025] The policy execution and feedback module is used to execute the selected action, synchronously adjust the declaration parameters, optimize the transportation allocation of split packages, and monitor the customs clearance results in real time and feedback the penalty value.

[0026] Specifically, according to the selected action (such as package splitting, HS code adjustment, delayed declaration, etc.), the action parameters are extracted (such as the number of split packages, the adjusted HS code value, the declaration time window); If the action involves modifying the declaration parameters (such as HS code adjustment), the enterprise declaration system is called through the REST API to synchronize the corrected HS code and commodity description to the customs declaration form, and the code validity is verified (such as calling the customs HS code library verification interface); If the action triggers an adjustment of the declaration time (such as delayed declaration), based on the customs clearance time limit requirements of the trading country, the latest declaration time is recalculated to ensure that the logistics time limit constraints are not violated. If the action is to split the package, select the set of low-risk ports according to the port compliance risk value, and solve the multi-package allocation problem with the goal of minimizing the transportation cost: Among them, represents the transportation cost of the j -th port, M represents the total number of ports, represents the number of packages allocated to the j -th port, represents the total number of splits, represents the compliance risk value of the j -th port, represents the low-risk threshold, which is adjusted based on the acceptable risk level; Call the logistics TMS (Transportation Management System) interface, automatically generate split waybills and allocate them to the selected ports, and monitor the customs clearance status returned by the customs system of the trading country in real time (through Webhook or message queue). The classification results include: Pass: Customs clearance is successful without additional fees; Tax supplement: Tax needs to be paid; Seizure: The goods are seized and a fine is triggered; Monitor the customs clearance status in real time. If the result is Pass, subtract one from the penalty value; If the result is Tax supplement, increase the penalty value by twice the ratio of the tax to be paid to the original tax payable; If the result is Seizure, increase the penalty value by five times the ratio of the fine amount to the declared value.

[0027] By monitoring the customs clearance status returned by the customs system of the trading country in real time, the customs clearance result of the goods can be obtained immediately, including key information such as whether the customs clearance is successful, whether tax needs to be paid, and whether the goods are seized. Based on this information, the penalty value can be dynamically calculated to respond to violations in a timely and appropriate manner, realizing the closed-loop management of compliance operations.

[0028] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, the steps performed by the cross-border e-commerce integrated management platform described in any one of the above are implemented.

[0029] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the steps performed by the cross-border e-commerce integrated management platform described in any one of the above are implemented.

[0030] In the application, several formulas involved are calculated by taking their numerical values after dimensionless treatment. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation, and the coefficients in the formula are set by those skilled in the art according to the actual situation.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0032] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0033] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0034] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application.

Claims

1. A cross-border e-commerce integrated management platform, characterized by: include: The multimodal semantic reconstruction module collects multimodal policy data of trading countries involved in cross-border e-commerce business, extracts the features of each modality, and generates cross-modal policy features through cross-modal alignment and fusion; The compliance knowledge graph construction module builds a policy knowledge graph based on cross-modal policy features. It calculates compliance risk values ​​and marks high-risk products for ambiguous clauses in cross-modal policy features, thus adding risk attributes to the policy knowledge graph. The compliance decision-making driving module constructs a multi-dimensional state vector, generates a sub-node action set through random sampling, and calculates each sub-node Q value, Q The value represents the expected utility value, which is calculated based on the declared value, tax rate and fine limit, and is corrected according to the company's historical violation record Q value, in maximizing Q While calculating the value, the information divergence constraint strategy is used to update the amplitude and select the action corresponding to the optimal child node; The strategy execution and feedback module executes the selected actions and adjusts the declaration parameters synchronously, optimizes the transportation allocation of split packages, monitors the customs clearance results in real time and feedbacks the penalty value.

2. A cross-border e-commerce integrated management platform according to claim 1, characterized in that: The steps to generate cross-modal policy features are as follows: Through Docker containerization technology, multiple lightweight crawler nodes are deployed to collect multimodal policy data of various trading countries in a differentiated manner, including at least text, image, PDF and video data, and extract features of each modality; Verify the authenticity of policy data based on the adversarial generative network and retain data with confidence levels higher than the confidence threshold; A cross-modal attention mechanism is used to fuse text features, image features and video features. Text features are used as query vectors and image features are used as key-value pairs to generate fused cross-modal policy features.

3. A cross-border e-commerce integrated management platform according to claim 1, characterized in that: In the compliance knowledge graph construction module: For the same commodities in different trading countries, a probability distribution model based on historical customs clearance data is constructed for the fuzzy clauses in the cross-modal policy characteristics: ,in, represents the compliance probability, k represents the regulatory stringency coefficient, x represents the eigenvalue, μ Represents the mean of the characteristic values; for new declaration data, the compliance risk value is calculated based on the commodity category and country R : , ,When the compliance risk value is greater than the marking threshold, it is marked as a high-risk product.

4. A cross-border e-commerce integrated management platform according to claim 3, characterized in that: Entity relationship quadruples are extracted from cross-modal policy features, including subject, relationship, object and timeliness, and risk attribute compliance risk values ​​are attached to build a policy knowledge graph. New policy features are matched with existing nodes by cosine similarity. If the similarity is greater than the similarity threshold, the weight of the associated edge is enhanced, otherwise, a new node is created. Expired nodes are scanned daily and weight decay is performed, and nodes with weights lower than the preset weight threshold are automatically archived.

5. The cross-border e-commerce integrated management platform according to claim 1, characterized in that: In the compliance decision-driving module: The multidimensional state vector includes commodity code, declared value, trade country code, policy version number and violation weight. The violation weight is calculated by exponential decay based on the number of historical violations. The current multidimensional state vector is used as the root node, and the child node action set is generated by random sampling. According to the declared value, tax rate and fine limit of each child node, a preliminary Q value: ,in, Q Indicates preliminary Q value, B Indicates the basic income of successfully clearing a level. V Indicates the declared value, Indicates the tax rate, Indicates the upper limit of fine.

6. A cross-border e-commerce integrated management platform according to claim 5, characterized in that: If the current child node is initially Q If the value is positive, the child node is retained; If the current child node is initially Q If the value is negative or zero, the child node is directly removed; According to the company's historical violation records, additional penalties are imposed on high-risk actions, and the violation weights are used to Q Correct the value: ,in, Indicates the corrected Q value, represents the violation weight, r Represents the penalty value; in maximizing Q While updating the value, the information divergence constraint strategy is used to update the amplitude and select the action corresponding to the optimal child node.

7. A cross-border e-commerce integrated management platform according to claim 1, characterized in that: In the strategy execution and feedback module: If the action is to modify the declaration parameters, the revised commodity code and commodity description will be synchronized to the customs declaration form, and the validity of the code will be verified; if the action is to adjust the declaration time, the latest declaration time will be recalculated based on the customs clearance time requirements of the trading country; If the action is to split the package, select a low-risk port set based on the port compliance risk value, and solve the multi-package allocation with the goal of minimizing transportation costs: in, Indicates j The transportation cost of each port, M Indicates the total number of ports, Indicates that the j Number of packages at each port, Indicates the total number of splits, Indicates j The compliance risk value of each port, Indicates a low risk threshold.

8. A cross-border e-commerce integrated management platform according to claim 7, characterized in that: Monitor the customs clearance status in real time. If the result is passed, reduce the penalty value by one; If the result is to pay back taxes, the penalty value will be increased by twice the ratio of the amount of back taxes to be paid to the original amount of taxes payable; If the result is seizure, the penalty value will be increased by five times the ratio of the fine amount to the declared value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps performed by the cross-border e-commerce comprehensive management platform described in any one of claims 1-8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed, implements the steps performed by the cross-border e-commerce comprehensive management platform described in any one of claims 1 to 8.

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