An intelligent operation management system and method based on cross-border commodities

Abstract

The application discloses a kind of based on cross-border commodity intelligent operation management system and method, it is related to cross-border e-commerce intelligent supply chain technical field, including the real-time inventory information of overseas warehouse, the state node information of in-transit logistics, the demand prediction information of different national markets and the rule parameter information of multi-country customs constitute whole-link data, by association cleaning and structure conversion form whole life cycle standardization data, build and include commodity, supply chain, market, policy entity and dynamic association relationship Operation knowledge graph, rely on knowledge graph and carry out multi-objective collaborative decision calculation, generate purchase order distribution, inventory dynamic allocation, logistics path optimization, the comprehensive operation strategy of tariff minimization declaration.The application realizes the whole domain fusion linkage of multi-source data, eliminates data island and decision fragmentation problem, makes each operation link collaborative adaptation, improves the overall coordination and cost control precision of cross-border operation.

Description

Technical Field

[0001] This invention belongs to the field of intelligent supply chain technology for cross-border e-commerce, specifically a cross-border commodity intelligent operation management system and method. Background Technology

[0002] Existing cross-border commodity operation and management systems independently collect data such as real-time inventory in overseas warehouses, logistics status in transit, overseas market demand, and customs rules of multiple countries. The data is stored through a decentralized system, and a single-stage analysis model is used to handle procurement, inventory, logistics, and customs declaration-related work separately. Each business stage relies on independent software or manual statistics to complete the operation.

[0003] Multi-source data suffers from format differences and inconsistent definitions, lacks mechanisms for data integration and correlation, and fails to create a linked mapping between data from different nodes. Supply chain, market, and policy data are used independently, making it impossible to build a comprehensive interconnected relationship. Under a single-stage independent decision-making model, procurement allocation, inventory transfer, logistics planning, and tariff declaration are calculated separately, with decision parameters not being shared. This easily leads to mismatches between inventory and demand, redundant logistics routes, poor adaptability of tariff declarations, and high overall operating costs, highlighting the problems of data silos and fragmented decision-making.

[0004] It is necessary to achieve standardized integration of cross-border end-to-end data and dynamic association of multiple entities, and rely on the associated data system to complete multi-objective synchronous collaborative calculation of procurement, inventory, logistics and tariffs, thereby changing the state of independent operation of each link. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a method for intelligent operation management of cross-border commodities, comprising:

[0007] Obtain a complete data set of cross-border goods, including real-time inventory information of overseas warehouses, status node information of logistics in transit, demand forecast information of different national markets, and rule parameter information of customs of multiple countries.

[0008] The entire data set is then subjected to association cleaning and structural transformation to generate a standardized data set covering the entire lifecycle of cross-border goods.

[0009] A cross-border commodity operation knowledge graph is constructed based on the standardized data set. The knowledge graph includes entities such as commodity nodes, supply chain nodes, market nodes, and policy nodes, as well as their dynamic relationships.

[0010] By utilizing the aforementioned cross-border commodity operation knowledge graph, multi-objective collaborative decision-making calculations are performed to generate a comprehensive set of operational strategies, including purchase order allocation schemes, dynamic inventory transfer schemes, logistics route optimization schemes, and tariff minimization declaration schemes.

[0011] Furthermore, the entire data set is subjected to association cleaning and structural transformation to generate a standardized data set for the entire lifecycle of cross-border goods, including:

[0012] Extract the unique product code, warehouse code, inventory quantity, and batch expiration date data from the real-time inventory information of the overseas warehouse, match and verify them against the pre-set global product master data table, and generate a standardized inventory status table.

[0013] The status node information of the logistics in transit is parsed to identify the logistics node type, timestamp, geolocation code and transport vehicle identifier, and the unstructured logistics events are converted into a logistics status record table with a unified field structure.

[0014] The demand forecast information from the different national markets is normalized in format, and the forecast time granularity, geographical dimension and commodity category dimension are unified to generate a market demand forecast matrix with confidence level labels.

[0015] By integrating the rule parameter information of customs from multiple countries, the text rules on commodity classification, rules of origin, tax rates and declaration requirements are transformed into a structured rule clause knowledge base, with each rule clause associated with a specific country, commodity tariff code and effective date;

[0016] The standardized inventory status table, logistics status record table, market demand forecast matrix, and rule and clause knowledge base are aligned in time and space, and linked by shared product unique codes, warehouse codes, timestamps, and country codes to form a standardized data set for the entire lifecycle of the cross-border goods.

[0017] Furthermore, a cross-border commodity operation knowledge graph is constructed based on the standardized data set, including:

[0018] Taking each product batch in the standardized inventory status table as an example, a product entity is created in the knowledge graph, and the product entity is linked to its warehouse entity, supplier entity, and corresponding batch expiration attribute.

[0019] Taking each status change in the logistics status record table as an example, a logistics event entity is created, and the logistics event entity is associated with related commodity entities, transport vehicle entities, origin and destination geographical entities, while recording the event time sequence.

[0020] Taking each forecast entry in the market demand forecast matrix as an example, a market demand entity is created, and the market demand entity is associated with the corresponding target market geographic entity, product category entity and forecast period attribute, and its confidence attribute is stored.

[0021] Using each rule in the rule knowledge base as an example, create a policy rule entity and bind the policy rule entity with the attributes of the issuing country entity, the applicable commodity tariff code entity, and the effective period.

[0022] Based on the supply and demand links of commodity entities, the transportation links of logistics entities, the demand transmission links of market entities, and the influence links of policy entities, the association weights between entities are dynamically calculated and updated to form a cross-border commodity operation knowledge graph containing multi-dimensional dynamic relationships.

[0023] Furthermore, by utilizing the aforementioned cross-border commodity operation knowledge graph for multi-objective collaborative decision-making calculations, a comprehensive set of operational strategies is generated, including purchase order allocation schemes, dynamic inventory transfer schemes, logistics route optimization schemes, and tariff minimization declaration schemes, comprising:

[0024] From the cross-border commodity operation knowledge graph, extract the inventory location, quantity, and expiration date attributes of all current commodity entities, as well as the predicted demand, time, and confidence level attributes of the associated market demand entities;

[0025] Under the premise of meeting the predicted demand and time constraints indicated by the market demand entity, with the objective function of minimizing global inventory holding cost, inventory turnover efficiency and expiration risk, inventory is simulated and allocated between the commodity entities and warehouse entities in the cross-border commodity operation knowledge graph to generate a preliminary purchase order allocation scheme.

[0026] By combining the real-time transportation capacity and cost reflected by the logistics event entities in the cross-border commodity operation knowledge graph, and the customs clearance policies and timeliness of different logistics paths indicated by the policy and rule entities, the logistics accessibility verification and cost assessment of the preliminary purchase order allocation plan are carried out.

[0027] When a logistics bottleneck or excessively high cost is detected, the dynamic inventory allocation calculation is initiated. The alternative inventory source warehouse entities and logistics route entities are searched in the cross-border commodity operation knowledge graph. The allocation relationship of purchase orders is adjusted to generate a dynamic inventory allocation plan.

[0028] After the purchase order allocation scheme and inventory dynamic transfer scheme are determined, the optimal logistics route from the source warehouse to the target market is calculated based on the geographical entity network and logistics event entity history in the cross-border commodity operation knowledge graph. The optimal logistics route must simultaneously meet the requirements of the lowest timeliness, the lowest transportation cost, and the highest policy compliance, and a logistics route optimization scheme is generated.

[0029] Based on the logistics route optimization scheme, the policy and rule entities in the cross-border commodity operation knowledge graph are called to calculate and select the optimal declaration category, certificate of origin combination and applicable tariff terms for all cross-border commodities involved in the scheme, and generate a tariff minimization declaration scheme.

[0030] By integrating the aforementioned purchase order allocation scheme, dynamic inventory transfer scheme, logistics route optimization scheme, and tariff minimization declaration scheme, a comprehensive set of operational strategies that are coordinated in terms of time, space, and cost is formed.

[0031] Furthermore, inventory is simulated and allocated between the commodity entities and warehouse entities in the cross-border commodity operation knowledge graph to generate a preliminary purchase order allocation scheme, including:

[0032] In the cross-border commodity operation knowledge graph, market demand entities whose predicted demand is greater than the current available inventory and whose confidence level is higher than a preset threshold are identified and marked as having replenishment needs.

[0033] For each replenishment requirement, the associated product entities are traversed in reverse in the cross-border commodity operation knowledge graph to find all product batch entities that are in stock and meet the expiration date requirements, as well as the warehouse entities where they are located.

[0034] Calculate the estimated logistics time and unit logistics cost from each eligible warehouse entity to the target market geographic entity associated with the replenishment demand;

[0035] Construct an allocation model whose decision variables are the quantity allocated from each eligible warehouse entity to each replenishment demand. Its objective is to minimize the total logistics cost and the weighted sum of inventory holding costs. Its constraints include not exceeding the upper limit of inventory in each warehouse, meeting the lower limit of market demand, and ensuring that logistics time does not exceed the latest demand time.

[0036] Solving the allocation model yields a detailed allocation plan for each batch of goods, specifying which warehouse to allocate how much quantity to which market demand, and summarizing all allocation plans to form the preliminary purchase order allocation scheme.

[0037] Furthermore, the step of initiating dynamic inventory allocation calculation involves searching for alternative inventory source warehouse entities and logistics route entities in the cross-border commodity operation knowledge graph, adjusting the allocation relationship of purchase orders, and generating a dynamic inventory allocation plan, including:

[0038] In logistics accessibility verification, when it is identified that the original logistics path from the candidate warehouse to the target market does not meet the timeliness or cost constraints, the allocation relationship is marked as an allocation to be adjusted.

[0039] For each allocation to be adjusted, in the cross-border commodity operation knowledge graph, with the commodity entity as the center, other warehouse entities with the same commodity and qualified expiration dates are searched as alternative inventory sources.

[0040] For each alternative inventory source, based on the logistics path optimization results in the cross-border commodity operation knowledge graph, obtain the alternative logistics path from it to the target market and its attributes, including transportation time, cost and policy compliance score.

[0041] From the set of alternative inventory sources and alternative logistics routes, with the goal of replenishing the original allocated quantity and guided by minimizing the increase in overall cost, the allocation is recalculated to form a partial redistribution plan;

[0042] For alternative inventory sources whose inventory is reduced due to the execution of the aforementioned partial redistribution plan, check whether it affects the satisfaction of other market demands. If it does, recursively trigger the calculation of dynamic inventory allocation to market demands.

[0043] Summarize all adjusted allocation relationships to form the final dynamic inventory transfer plan, clearly defining the logistics routes for transferring out warehouses, into markets, product batches, quantities, and the basis for the transfer.

[0044] Furthermore, the calculation of the optimal logistics route from the source warehouse to the target market, wherein the optimal logistics route must simultaneously meet the requirements of lowest timeliness, lowest transportation cost, and highest policy compliance, generates a logistics route optimization scheme, including:

[0045] In the cross-border commodity operation knowledge graph, extract all logistics node entities and logistics connection edges between the source warehouse geographical entity and the target market geographical entity;

[0046] Each logistics connection edge is assigned attribute values ​​in three dimensions: timeliness, cost, and policy compliance. The policy compliance attribute value is derived from the evaluation of a specific mode of transportation by the policy rule entities associated in the cross-border commodity operation knowledge graph.

[0047] Define a comprehensive utility function for a logistics route, which is a weighted sum of three indicators: the reciprocal of transportation timeliness, the reciprocal of transportation cost, and policy compliance score.

[0048] Using the path search algorithm based on the cross-border commodity operation knowledge graph, the path with the highest comprehensive utility function value is found in the network graph composed of logistics nodes and connecting edges, and is used as the candidate optimal logistics path;

[0049] The candidate optimal logistics path is simulated for concurrent transportation of multiple goods. The capacity constraints of key nodes of the path are checked within a specific time period. If the capacity exceeds the limit, the comprehensive utility function is penalized and the path search is performed again.

[0050] Determine the final path that satisfies the capacity constraint and has the highest overall utility. Record the sequence of all logistics nodes traversed by the final path, the transportation methods used, and the attributes of each segment to generate the logistics path optimization scheme.

[0051] Furthermore, the optimal combination of declared product categories, certificates of origin, and applicable tariff terms is calculated and selected for all cross-border goods involved in the scheme to generate a tariff-minimizing declaration scheme, including:

[0052] For each batch of cross-border goods involved in the logistics route optimization scheme, its complete product description, ingredients, and processing information are obtained from the cross-border goods operation knowledge graph;

[0053] In the policy and rule entity of the cross-border commodity operation knowledge graph, query the target market country for multiple commodity tariff classifications corresponding to the corresponding commodity description, as well as the import tariff rate, value-added tax rate, and whether special permission is required under each classification;

[0054] In the knowledge graph of cross-border commodity operations, the supply chain links of the corresponding batch of goods are traced, and the origin certificates of each production and processing stage are collected to form a certificate of origin combination.

[0055] For each commodity tariff classification, combined with the available certificate of origin combinations, the applicable preferential tariff agreement provisions are queried in the policy rules entity to calculate the estimated total tax burden under each "classification-certificate of origin combination-tariff provisions" combination.

[0056] From all the estimated total tax burdens, select the combination that is legal and has the lowest total tax burden as the optimal declaration strategy for the corresponding batch of cross-border goods;

[0057] The optimal declaration strategies for all batches of cross-border goods are summarized to form the aforementioned tariff minimization declaration scheme, which includes the declared tariff code, applicable certificate of origin, and applied tariff preference clause code for each batch of goods.

[0058] Furthermore, the method also includes the step of real-time monitoring and dynamic adjustment of the execution process of the integrated operation strategy set:

[0059] During the execution of the purchase order allocation scheme, inventory dynamic transfer scheme, logistics route optimization scheme and tariff minimization declaration scheme, the latest inventory changes, logistics status updates, market demand fluctuations and customs rule changes are continuously obtained from the full-link data set;

[0060] The latest information obtained will be updated in real time to the cross-border commodity operation knowledge graph, and the attributes and relationships of the commodity entities, logistics event entities, market demand entities and policy rule entities will be dynamically adjusted.

[0061] Based on the updated cross-border commodity operation knowledge graph, the multi-objective collaborative decision calculation is triggered periodically, and the newly calculated comprehensive operation strategy set is compared with the original execution strategy to identify deviations that exceed the preset tolerance range.

[0062] When a risk of strategy failure due to logistics delays, surges in demand, or sudden rule changes is identified, a local recalculation of the strategy is immediately initiated to generate adjustment instructions for the affected parts.

[0063] The adjustment instructions are sent to the corresponding execution systems, including the warehouse management system, transportation management system and customs declaration system, to drive the actual operation to be executed according to the adjusted strategy;

[0064] When a risk of strategy failure due to logistics delays, a surge in demand, or sudden rule changes is identified, a local recalculation of the strategy is immediately initiated to generate adjustment instructions for the affected portion, including:

[0065] When the logistics status update information shows that a critical logistics node has been severely delayed, all in-transit goods entities and related market demand entities affected by this delay are located in the cross-border commodity operation knowledge graph.

[0066] The availability rate of the affected market demand entities within the original time window is recalculated. If it is lower than a preset threshold, the delayed goods entities are removed from the original logistics path, and a local inventory dynamic allocation calculation is triggered for the market demand entities to find alternative sources and paths.

[0067] When market demand forecast information fluctuates sharply upwards, the commodity category entities and market geographic entities involved in the fluctuation are located in the cross-border commodity operation knowledge graph.

[0068] Compare the total existing inventory with the total inventory in transit to see if it meets the increased demand. If there is a shortfall, immediately trigger the calculation of local purchase order allocation for the product categories and corresponding markets affected by the fluctuation, and generate an emergency replenishment order.

[0069] When customs rule change information is captured, the corresponding policy rule entity and its associations are updated in the cross-border commodity operation knowledge graph.

[0070] Check whether all currently executing tariff minimization declaration schemes have become invalid or suboptimal due to rule changes. For invalid or suboptimal schemes, immediately trigger local tariff minimization declaration calculation and generate new declaration strategy adjustment instructions.

[0071] Furthermore, the present invention also includes a cross-border commodity intelligent operation management system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the cross-border commodity intelligent operation management method described above.

[0072] Compared with the prior art, the beneficial effects of the present invention are:

[0073] The system performs correlation cleaning and structural transformation on multi-source heterogeneous data, including real-time inventory in overseas warehouses, logistics status nodes in transit, overseas market demand forecasts, and customs rules parameters from multiple countries, to form standardized data for the entire lifecycle of cross-border goods. Based on the standardized data, an operational knowledge graph is constructed that includes four types of entities: goods, supply chain, market, and policy. Dynamic relationships between entities are established, and the format differences and conflicting definitions of heterogeneous data are unified and regulated. Dispersed data forms a data system that is interconnected across the entire domain. The relationships between entities can be updated and adjusted in real time with the data source. Changes in data at a single node can be synchronously transmitted to the corresponding related entities. The isolated state between data is eliminated, and the data across the entire chain is integrated and presented in a structured manner.

[0074] Based on a knowledge graph of cross-border commodity operations, multi-objective collaborative decision-making calculations are carried out. Purchase order allocation, dynamic inventory transfer, logistics route optimization, and tariff minimization declaration are incorporated into a unified calculation system. The parameters of each decision objective are coupled and iterated synchronously. The decision logic of different operational links is no longer independent and separate. The decision results form a state of mutual constraint and mutual adaptation. Inventory transfer is matched with market demand and logistics status. Logistics routes are collaboratively adapted with supply chain nodes and tariff rules. Purchase allocation is linked and aligned with inventory levels, market demand, and tariff costs. Tariff declaration parameters are accurately aligned with the customs rules of multiple countries. The entire process of operational decision-making achieves synchronous collaboration and full-domain adaptation. Attached Figure Description

[0075] Figure 1 This is a state diagram of the intelligent operation management method for cross-border commodities described in this invention;

[0076] Figure 2 A flowchart for generating a standardized dataset covering the entire lifecycle of cross-border goods;

[0077] Figure 3 A flowchart for generating a preliminary purchase order allocation plan. Detailed Implementation

[0078] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] See Figure 1 The present invention provides a method for intelligent operation management of cross-border commodities, the overall implementation scheme of which includes the following steps.

[0080] This process involves acquiring a complete data set of cross-border goods, including real-time inventory information from overseas warehouses, status nodes of logistics in transit, demand forecasts for different national markets, and rule parameters from customs authorities in multiple countries. The data set is then processed through association cleaning and structural transformation to generate a standardized data set representing the entire lifecycle of cross-border goods. Based on this standardized data set, a cross-border goods operation knowledge graph is constructed, containing entities and their dynamic relationships among product nodes, supply chain nodes, market nodes, and policy nodes. Finally, the established cross-border goods operation knowledge graph is used for multi-objective collaborative decision-making calculations, generating a comprehensive set of operational strategies including purchase order allocation schemes, dynamic inventory transfer schemes, logistics route optimization schemes, and tariff minimization declaration schemes.

[0081] In one embodiment of the present invention, see [reference] Figure 2 The system extracts unique product codes, warehouse codes, inventory quantities, and batch expiration dates from real-time inventory information in overseas warehouses. This data is then matched and verified against a pre-built global product master data table to generate a standardized inventory status table. The system analyzes the status node information of in-transit logistics, identifying logistics node types, timestamps, geolocation codes, and transport vehicle identifiers, converting unstructured logistics events into a logistics status record table with a unified field structure. Demand forecast information from different national markets is formatted and standardized in terms of forecast time granularity, geographical dimension, and product category dimension, generating a market demand forecast matrix with confidence level markers. The system integrates rule parameters from customs authorities of multiple countries, transforming textual rules regarding product classification, rules of origin, tax rates, and declaration requirements into a structured rule clause knowledge base, with each rule clause associated with a specific country, product tariff code, and effective date. Finally, the standardized inventory status table, logistics status record table, market demand forecast matrix, and rule clause knowledge base are time- and spatially aligned and linked through shared unique product codes, warehouse codes, timestamps, and country codes to form a standardized data set covering the entire lifecycle of cross-border goods.

[0082] Taking each product batch in the standardized inventory status table as an example, a product entity is created in the knowledge graph, and the product entity is linked to its warehouse entity, supplier entity, and corresponding batch expiration date attribute. Taking each status change in the logistics status record table as an example, a logistics event entity is created, and the logistics event entity is associated with related product entities, transport vehicle entities, and origin and destination geographical entities, while recording the event time series. Taking each forecast entry in the market demand forecast matrix as an example, a market demand entity is created, and the market demand entity is associated with the corresponding target market geographical entity, product category entity, and forecast period attribute, and its confidence attribute is stored. Taking each rule in the rule clause knowledge base as an example, a policy rule entity is created, and the policy rule entity is bound with the issuing country entity, applicable commodity tariff code entity, and effective time period attribute. Based on the supply and demand links of product entities, the transportation links of logistics entities, the demand transmission links of market entities, and the influence links of policy entities, the association weights between entities are dynamically calculated and updated to form a cross-border commodity operation knowledge graph containing multi-dimensional dynamic association relationships.

[0083] In practical implementation, a method for intelligent operation management of cross-border commodities involves the standardized processing of multi-source heterogeneous data and the construction of a knowledge graph for cross-border commodity operations. Taking a specific example of a cross-border commodity, its unique commodity code is "SKU-001," the overseas warehouse codes involved include "US-WH01" and "DE-WH02," the target market is the country "FR," and the tracking number is "TRK-20230420001."

[0084] In practice, the acquired end-to-end data set contains specific information fragments. The real-time inventory information for the overseas warehouse includes a record: unique product code "SKU-001", warehouse code "US-WH01", inventory quantity "1500", batch number "LOT-A", and expiration date "2023-12-31". The status node information for in-transit logistics includes an unstructured message: "2023-04-20T10:30:00Z,TRK-20230420001 departed from port of Los Angelesn Route to Rotterdam". Demand forecast information for different national markets includes a record: country code "FR", product category code "CAT-10", predicted demand for the next 15 days as "850" units, and confidence level "0.85". Customs regulations from multiple countries include a clause regarding imports from France: "HS code 6302.10, textile articles, general tariff rate 12%, preferential rate under EU-UK TCA is 0% with valid origin declaration."

[0085] In some embodiments, the entire data set is subjected to association cleaning and structural transformation. Fields are extracted from the real-time inventory information of overseas warehouses, and the validity of the “SKU-001” code is verified based on the pre-set global commodity master data table to generate a record in the standardized inventory status table: {Product unique code: “SKU-001”, warehouse code: “US-WH01”, inventory quantity: 1500, batch number: “LOT-A”, expiration date: “2023-12-31”}. The status node information of the logistics in transit is analyzed, and the logistics node type is identified as "departure", the timestamp is "2023-04-20T10:30:00Z", the geolocation code is "USLAX" and "NLRTM", and the transport vehicle identifier is "VSL-SEA-005". A record is generated in the logistics status record table: {Waybill number: "TRK-20230420001", event type: "departure", timestamp: "2023-04-20T10:30:00Z", origin code: "USLAX", destination code: "NLRTM", vehicle identifier: "VSL-SEA-005"}. The demand forecast information is processed, unifying the forecast time granularity to "day", the geographical dimension to "country", and the commodity category dimension to "standard category code", generating an entry in the market demand forecast matrix: {Country Code: "FR", Standard Category Code: "CAT-10", Forecast Date: "2023-05-05", Forecast Demand Quantity: 850, Confidence Level: 0.85}. Customs rule parameter information is integrated, transforming the text rule into a record in the rule clause knowledge base: {Country: "FR", Commodity Tariff Code: "6302.10", Rule Type: "Tariff Rate", Ordinary Tariff Rate: 0.12, Preferential Tariff Rate: 0.0, Preferential Agreement: "EU-UKTCA", Effective Date: "2021-01-01", Expiration Date: "NULL"}. By aligning the aforementioned standardized inventory status table, logistics status record table, market demand forecast matrix, and rule and clause knowledge base, and linking them through the shared "SKU-001" unique product code, "US-WH01" warehouse code, "2023-04-20T10:30:00Z" timestamp, and "FR" country code, a standardized data set for the entire lifecycle of cross-border goods is formed.

[0086] In some embodiments, a cross-border commodity operation knowledge graph is constructed based on a standardized dataset. Taking the product batch "SKU-001-LOT-A" in the standardized inventory status table as an example, a product entity "Product_SKU-001-LOT-A" is created in the cross-border commodity operation knowledge graph, and the warehouse entity "Warehouse_US-WH01", the supplier entity "Supplier_Alpha", and the attribute "Expiration date: 2023-12-31" are linked to this product entity. Taking the status change "departure" in the logistics status record table as an example, create a logistics event entity "LogisticsEvent_001", associate the logistics event entity "LogisticsEvent_001" with the product entity "Product_SKU-001-LOT-A", the transport vehicle entity "Vessel_VSL-SEA-005", the origin geographical entity "Location_USLAX", and the destination geographical entity "Location_NLRTM", and record the event time series attribute "timestamp: 2023-04-20T10:30:00Z". Taking an entry in the market demand forecast matrix as an example, create a market demand entity “Demand_FR_CAT-10_20230505”, and associate it with the target market geographic entity “Location_FR”, the commodity category entity “Category_CAT-10”, and the forecast period attribute “Date: 2023-05-05”, storing the confidence level attribute “Confidence Level: 0.85”. Taking an entry in the rule clause knowledge base as an example, create a policy rule entity “Policy_FR_630210_Tariff”, and bind it with the issuing country entity “Location_FR”, the applicable commodity tariff code entity “HS_630210”, and the effective period attribute “Effective Date: 2021-01-01, Expiry Date: NULL”. Based on the supply and demand links of commodity entities, the transportation links of logistics entities, the demand transmission links of market entities, and the influence links of policy entities, the correlation weights between entities are dynamically calculated and updated. Correlation Weights The calculation can be performed based on the interaction frequency between entities and the time decay factor, where the interaction frequency... The time decay factor refers to the number of times events are associated within a specific time window. The initial weight is calculated based on the difference between the most recent interaction time and the current time. The preset value is calculated using the following formula: This forms a knowledge graph of cross-border commodity operations that includes multi-dimensional and dynamic relationships.

[0087] Optionally, when creating a product entity, the batch expiration date attribute is mandatory, and supplier entities can be linked to upstream raw material entities. When creating a logistics event entity, the transport vehicle entity can be further linked to its corresponding transport service provider entity. The confidence attribute of the market demand entity is used for priority ranking in subsequent decision-making calculations. The effective period attribute of the policy rule entity ensures the timeliness of rule queries. Dynamic updates of association weights enable the cross-border commodity operation knowledge graph to reflect the hotspots and changing trends of the supply chain network. It can be understood that standardized datasets are the foundation for building a high-quality cross-border commodity operation knowledge graph. It can also be understood that dynamic association weights enable the cross-border commodity operation knowledge graph to evolve from a static topology into a semantic network that reflects operational status.

[0088] In one embodiment of the present invention, the inventory location, quantity, and expiration date attributes of all current commodity entities, as well as the predicted demand, time, and confidence level attributes of associated market demand entities, are extracted from the cross-border commodity operation knowledge graph. Under the premise of satisfying the predicted demand and time constraints indicated by the market demand entities, and with the objective function of minimizing global inventory holding costs, inventory turnover efficiency, and expiration date risk, inventory is simulated and allocated between commodity entities and warehouse entities in the cross-border commodity operation knowledge graph to generate a preliminary purchase order allocation scheme. Combining the real-time transportation capacity and costs reflected by logistics event entities in the cross-border commodity operation knowledge graph, and the customs clearance policies and timelines of different logistics paths indicated by policy rule entities, the preliminary purchase order allocation scheme is verified for logistics accessibility and cost assessment. When logistics bottlenecks or excessively high costs are detected, dynamic inventory allocation calculation is initiated. Alternative inventory source warehouse entities and logistics path entities are searched in the cross-border commodity operation knowledge graph to adjust the allocation relationship of purchase orders and generate a dynamic inventory allocation scheme. After the purchase order allocation plan and inventory dynamic transfer plan are determined, the optimal logistics route from the source warehouse to the target market is calculated based on the geographical entity network and logistics event entity history in the cross-border commodity operation knowledge graph. This optimal logistics route must simultaneously meet the requirements of lowest timeliness, lowest transportation cost, and highest policy compliance, generating a logistics route optimization plan. Based on the logistics route optimization plan, the regulations on commodity classification, country of origin, and tax rates from the policy rule entities in the cross-border commodity operation knowledge graph are invoked to calculate and select the optimal declaration category, certificate of origin combination, and applicable tariff terms for all cross-border commodities involved in the plan, generating a tariff minimization declaration plan. The purchase order allocation plan, inventory dynamic transfer plan, logistics route optimization plan, and tariff minimization declaration plan are integrated to form a comprehensive set of operational strategies that are consistent in time, space, and cost.

[0089] In specific implementation, the implementation of a cross-border commodity intelligent operation management method involves using a constructed cross-border commodity operation knowledge graph to perform multi-objective collaborative decision-making calculations. Based on the implementation case, the cross-border commodity operation knowledge graph already contains commodity entities “Product_SKU-001-LOT-A” and “Product_SKU-001-LOT-B”, corresponding warehouse entities “Warehouse_US-WH01” and “Warehouse_DE-WH02”, market demand entity “Demand_FR_CAT-10_20230505”, and target market geographic entity “Location_FR”.

[0090] In practice, the inventory location, quantity, and expiration date attributes of all current product entities, as well as the predicted demand, time, and confidence level attributes of associated market demand entities, are extracted from the cross-border commodity operation knowledge graph. The extracted attribute value set is as follows: the inventory location attribute of product entity "Product_SKU-001-LOT-A" is "Warehouse_US-WH01", the quantity attribute is "1500", and the expiration date attribute is "2023-12-31"; the inventory location attribute of product entity "Product_SKU-001-LOT-B" is "Warehouse_DE-WH02", the quantity attribute is "800", and the expiration date attribute is "2024-01-15"; the predicted demand attribute of market demand entity "Demand_FR_CAT-10_20230505" is "850", the demand time attribute is "2023-05-05", and the confidence level attribute is "0.85". Under the premise of satisfying the predicted demand of "850" indicated by the market demand entity "Demand_FR_CAT-10_20230505" and the time constraint "2023-05-05", the inventory is simulated and allocated between the commodity entities and warehouse entities in the cross-border commodity operation knowledge graph with the objective function of minimizing the total global operating cost. The expression is:

[0091]

[0092] in: This indicates the holding cost of the selected batch of goods in its warehouse. This represents the logistics cost of transporting goods from the warehouse to the target market. This represents the costs of stockouts and expiration due to the risk of product shelf life or inability to meet demand. All cost items are measured in monetary units. The solution process is then used to... The minimum allocation scheme generates a preliminary purchase order allocation scheme, which includes allocating 850 items from the "Product_SKU-001-LOT-A" batch in the "Warehouse_US-WH01" warehouse to meet the demand of "Demand_FR_CAT-10_20230505".

[0093] In some embodiments, by combining the real-time transportation capacity and cost reflected by logistics event entities in the cross-border commodity operation knowledge graph, and the customs clearance policies and timeliness of different logistics routes indicated by policy and rule entities, the logistics accessibility verification and cost assessment of the preliminary purchase order allocation plan are carried out. In the cross-border commodity operation knowledge graph, there is a logistics route entity "Route_A" from "Location_US" to "Location_FR", with a transportation time attribute of "20 days", a unit cost attribute of "5 currency units", and a policy compliance score attribute of "0.9". The verification shows that the transportation time from "Warehouse_US-WH01" to "Location_FR" using the "Route_A" route exceeds the remaining days of the demand time window, and the logistics accessibility verification fails. When a logistics bottleneck is detected, dynamic inventory allocation calculation is initiated. The cross-border commodity operation knowledge graph searches for alternative inventory source warehouse entities and logistics route entities. The cross-border commodity operation knowledge graph search shows that the warehouse entity "Warehouse_DE-WH02" has the same product batch "Product_SKU-001-LOT-B", and there is a logistics route entity "Route_B" connecting "Location_DE" and "Location_FR". Its transportation time attribute is "3 days", unit cost attribute is "2 currency units", and policy compliance score attribute is "0.95". Based on this search result, the allocation relationship of purchase orders is adjusted, generating a dynamic inventory allocation plan. The plan includes canceling the allocation from "Warehouse_US-WH01" and instead allocating "850" items from the "Product_SKU-001-LOT-B" batch in the "Warehouse_DE-WH02" warehouse. After the purchase order allocation plan and inventory dynamic transfer plan are determined, based on the geographic entity network and logistics event entity history in the cross-border commodity operation knowledge graph, the optimal logistics route from the geographic entity "Location_DE" associated with the source warehouse "Warehouse_DE-WH02" to the geographic entity "Location_FR" in the target market is calculated. The optimal logistics route must simultaneously meet the requirements of the lowest timeliness, the lowest transportation cost, and the highest policy compliance. The generated logistics route optimization plan selects the "Route_B" route and records the detailed node sequence: land transportation from the warehouse to the port "Port_HAM", sea transportation from "Port_HAM" to "Port_LEH", and finally land transportation to the destination.Based on the logistics route optimization plan, the policy and rule entities in the cross-border commodity operation knowledge graph are invoked to determine the regulations regarding commodity classification, country of origin, and tariff rates. For the cross-border commodity "Product_SKU-001-LOT-B" involved in the plan, the optimal declaration category, certificate of origin combination, and applicable tariff terms are calculated and selected. The generated tariff minimization declaration plan determines the declaration tariff code as "6302.10," uses the "EUR.1" certificate of origin, and applies for the zero-tariff clause under the "EU-UKTCA" agreement. The purchase order allocation plan, inventory dynamic transfer plan, logistics route optimization plan, and tariff minimization declaration plan are integrated to form a comprehensive operational strategy set that is consistent in time, space, and cost.

[0094] Optionally, logistics accessibility verification not only checks whether timeliness is met, but also assesses whether the policy and rule entities corresponding to the route contain trade restrictions or embargoes. When searching for alternative inventory sources, the expiration date risk cost is the objective function in dynamic inventory allocation calculations. middle This is a crucial component. When calculating the optimal logistics route, policy compliance can be assessed by querying the policy rule entities associated with each segment of the route to determine if there are any prohibitions or additional conditions. It can be understood that multi-objective collaborative decision-making calculations utilize a unified cost function. Multiple optimization objectives are transformed into a single quantifiable objective for solution. It can be understood that the output of the comprehensive operational strategy set serves as the direct basis for the subsequent operations of the warehouse management system, transportation management system, and customs declaration system.

[0095] In one embodiment of the present invention, see [reference] Figure 3 In the cross-border commodity operation knowledge graph, market demand entities with predicted demand exceeding current available inventory and confidence levels above a preset threshold are identified and marked as replenishment needs. For each replenishment need, the knowledge graph is used to traverse its associated commodity entities in reverse order, searching for all in-stock commodity batches that meet expiration requirements and their respective warehouse entities. The estimated logistics time and unit commodity logistics cost from each eligible warehouse entity to the target market geographic entity associated with the replenishment need are calculated. An allocation model is constructed, with the decision variable being the quantity allocated from each eligible warehouse entity to each replenishment need. The objective is to minimize the total logistics cost and the weighted sum of inventory holding costs, with constraints including not exceeding the upper limit of inventory in each warehouse, meeting the lower limit of market demand, and ensuring that logistics time does not exceed the latest demand time. Solving the allocation model yields a detailed allocation plan for each commodity batch, specifying which warehouse to allocate how much to which market demand, and all allocation plans are summarized to form a preliminary purchase order allocation scheme.

[0096] In logistics accessibility verification, when the original logistics path from a candidate warehouse to the target market is identified as not meeting timeliness or cost constraints, the allocation relationship is marked as an allocation to be adjusted. For each allocation to be adjusted, in the cross-border commodity operation knowledge graph, other warehouse entities with the same goods and valid expiration dates are searched as alternative inventory sources, centered on the commodity entity. For each alternative inventory source, combined with the logistics path optimization results in the cross-border commodity operation knowledge graph, alternative logistics paths from it to the target market and their attributes, including transportation time, cost, and policy compliance score, are obtained. In the set of alternative inventory sources and alternative logistics paths, the allocation is recalculated with the goal of replenishing the original allocation quantity and the aim of minimizing the increase in overall cost, forming a partial reallocation plan. For alternative inventory sources whose inventory decreases due to the implementation of the partial reallocation plan, it is checked whether it affects the satisfaction of demand in other markets. If it does, the dynamic inventory transfer calculation for market demand is recursively triggered. All adjusted allocation relationships are summarized to form the final dynamic inventory transfer plan, which clarifies the logistics path of the warehouse to be transferred out, the market to be transferred in, the commodity batch, the quantity, and the basis for the transfer.

[0097] In practice, the process of generating preliminary purchase order allocation schemes through inventory simulation involves specific calculation models and decisions. The cross-border commodity operation knowledge graph contains three market demand entities: “Demand_Market_A_20230510”, “Demand_Market_B_20230515”, and “Demand_Market_C_20230520”, with predicted demand quantities of 1000, 800, and 1200 units respectively, confidence levels of 0.9, 0.8, and 0.95, and latest fulfillment times of 2023-05-10, 2023-05-15, and 2023-05-20 respectively. The available inventory to meet demand is distributed across two warehouses. Warehouse "Warehouse_EU1" has 1,500 units of product "SKU-002" in batch "LOT-EU1", with an expiration date of 2023-11-01. Warehouse "Warehouse_EU2" has 1,000 units of the same product in batch "LOT-EU2", with an expiration date of 2023-12-01.

[0098] In practice, market demand entities with predicted demand exceeding current available inventory and a confidence level higher than a preset threshold of 0.7 are identified in the cross-border commodity operation knowledge graph. For example, the total demand of "Demand_Market_A_20230510" is calculated to be 1000 units, while its total available inventory in "Warehouse_EU1" and "Warehouse_EU2" is 2500 units. Since the demand can be met and the confidence level is above the threshold (0.9), it is marked as a replenishment demand. Similarly, "Demand_Market_B_20230515" and "Demand_Market_C_20230520" are also marked as replenishment demands. For each replenishment demand, the associated commodity entity "SKU-002" is traversed backwards in the cross-border commodity operation knowledge graph to find all in-stock commodity batches that meet the expiration date requirements. The batch entities “Product_SKU-002-LOT-EU1” and “Product_SKU-002-LOT-EU2” were located, along with their respective warehouse entities “Warehouse_EU1” and “Warehouse_EU2”. Geographical entities and logistics relationships were obtained from the cross-border commodity operation knowledge graph. The estimated logistics time and unit commodity logistics cost from each eligible warehouse entity to the target market geographic entity associated with the replenishment demand were calculated. An allocation model was constructed, with its decision variables... Let represent the quantity allocated from warehouse i to demand j, where i belongs to the warehouse set {1,2} and j belongs to the market demand set {1,2,3}. Objective function To minimize the weighted sum of total logistics costs and inventory holding costs, the expression is:

[0099]

[0100] in: This represents the unit logistics cost from warehouse i to market j. This represents the unit inventory holding cost coefficient for warehouse i. This is the holding cost weighting factor. Constraints include: for each warehouse i, the total amount allocated cannot exceed its inventory limit. For each market demand j, the total allocation obtained is no less than its lower demand limit. And the logistics time from warehouse i to market j. No more than the latest time of demand j Solving this allocation model yields a detailed allocation plan for each product batch, specifying which warehouse, quantity, and market demand it will be allocated from. All allocation plans are then compiled to form a preliminary purchase order allocation scheme. The inventory-demand matching allocation results are shown in Table 1.

[0101] Table 1: Preliminary Allocation Plan for Inventory and Market Demand

[0102] MarketA 2023-05-10 1000 Warehouse_EU1 LOT-EU1 1000 5 1.5 MarketB 2023-05-15 800 Warehouse_EU2 LOT-EU2 800 4 1.8 MarketC 2023-05-20 1200 Warehouse_EU1 LOT-EU1 500 8 2.0 MarketC 2023-05-20 1200 Warehouse_EU2 LOT-EU2 200 7 2.2

[0103] In some embodiments, during logistics accessibility verification, when it is identified that the original logistics route from the candidate warehouse to the target market does not meet timeliness or cost constraints, the allocation relationship is marked as an allocation to be adjusted. For example, when verifying the allocation plan in the table above, it was found that the original route from "Warehouse_EU1" to "MarketC" experienced a sudden port congestion, increasing the logistics time from 8 days to 15 days, exceeding the latest demand deadline of "2023-05-20". Therefore, the plan to allocate 500 items from "Warehouse_EU1" to "MarketC" was marked as an allocation to be adjusted. For this allocation to be adjusted, in the cross-border commodity operation knowledge graph, centered on the commodity entity "SKU-002", other warehouse entities with the same product and valid expiration dates were searched as alternative inventory sources. The search revealed that warehouse "Warehouse_EU3" had 800 units of batch "LOT-EU3" in stock, with valid expiration dates, and could be used as an alternative inventory source. For the alternative inventory source "Warehouse_EU3", based on the logistics route optimization results in the cross-border commodity operation knowledge graph, alternative logistics routes and their attributes from it to the target market "MarketC" were obtained. The transportation time is 6 days, the unit cost is 2.5 currency units, and the policy compliance score is 0.92. Within the set of alternative inventory sources "Warehouse_EU3" and alternative logistics routes, a re-allocation calculation was performed with the goal of replenishing the originally allocated quantity of 500 units and minimizing the increase in overall cost. The calculation shows that allocating 500 units from "Warehouse_EU3" to "MarketC" results in the smallest increase in overall cost, forming a partial reallocation plan. For the alternative inventory source "Warehouse_EU3", whose inventory decreased due to the implementation of the partial reallocation plan, it was examined whether this affected the fulfillment of demand in other markets. The examination revealed that a portion of the original planned inventory of "Warehouse_EU3" was used to meet a low-priority demand "Demand_Market_D", and a reduction of 300 units in its inventory would prevent "Demand_Market_D" from being fulfilled. At this point, the dynamic inventory allocation calculation for market demand "Demand_Market_D" is recursively triggered to find alternative sources of goods. All adjusted allocation relationships are summarized to form the final dynamic inventory allocation plan, which specifies: 500 units of "SKU-002LOT-EU3" are allocated from "Warehouse_EU3" to "MarketC". Simultaneously, to compensate for the inventory shortfall in "Warehouse_EU3", 300 units of the same product batch are allocated from "Warehouse_EU4" to "MarketD".

[0104] Optionally, when constructing the allocation model, the inventory holding cost coefficient can be correlated with the inventory value and warehousing fee rate. The logistics time constraint can be expressed as... ,in This refers to the current time. When recursively triggering dynamic inventory allocation calculations, a maximum recursion depth is set to prevent infinite loops. It can be understood that the solution to the allocation model is a set of optimal allocation variables. The value of can be understood as follows: dynamic inventory allocation calculation is a real-time adjustment process to cope with logistics disruptions or changes in constraints.

[0105] In one embodiment of the present invention, all logistics node entities and logistics connection edges between the source warehouse geographical entity and the target market geographical entity are extracted from the cross-border commodity operation knowledge graph. Each logistics connection edge is assigned attribute values ​​in three dimensions: timeliness, cost, and policy compliance. The policy compliance attribute value originates from the evaluation of a specific transportation mode by the policy rule entities associated in the cross-border commodity operation knowledge graph. A comprehensive utility function for the logistics path is defined, which is a weighted sum of the reciprocal of transportation timeliness, the reciprocal of transportation cost, and the policy compliance score. Using a path search algorithm based on the cross-border commodity operation knowledge graph, the path with the highest comprehensive utility function value is found in the network graph composed of logistics nodes and connection edges, and this path is selected as the candidate optimal logistics path. Multi-commodity concurrent transportation simulations are performed on the candidate optimal logistics path to check the capacity constraints of key nodes within a specific time period. If the capacity exceeds the limit, the comprehensive utility function is penalized, and the path search is repeated. Determine the final path that satisfies the capacity constraint and has the highest overall utility. Record the sequence of all logistics nodes along the final path, the transportation methods used, and the attributes of each segment to generate a logistics path optimization scheme.

[0106] For each batch of cross-border goods involved in the logistics route optimization plan, complete product descriptions, ingredients, and processing information are obtained from the cross-border commodity operation knowledge graph. Within the policy and rule entity of the cross-border commodity operation knowledge graph, the target market country is queried for multiple tariff classifications corresponding to the product description, as well as the import tariff rate, VAT rate, and whether special permits are required under each classification. The supply chain of the corresponding batch of goods is traced within the cross-border commodity operation knowledge graph, collecting origin certificates at each production and processing stage to form an origin certificate combination. For each tariff classification, combined with available origin certificate combinations, the applicable preferential tariff agreement clauses are queried in the policy and rule entity to calculate the estimated total tax burden under each "classification-origin certificate combination-tariff clause" combination. From all estimated total tax burdens, the combination with the lowest legal total tax burden is selected as the optimal declaration strategy for the corresponding batch of cross-border goods. The optimal declaration strategies for all batches of cross-border goods are summarized to form a tariff minimization declaration scheme, including the declared tariff code, applicable origin certificates, and applied tariff preference clause codes for each batch of goods.

[0107] In practice, the calculation of the logistics route optimization scheme is based on the geographical entity network and logistics connection relationship in the cross-border commodity operation knowledge graph. It is assumed that there is a cross-border transportation demand from the source warehouse geographical entity "Location_CN_SHA" to the target market geographical entity "Location_FR_PAR". The goods are a batch of cotton T-shirts, totaling 5,000 pieces with a total volume of 50 cubic meters, which need to be delivered within 30 days.

[0108] In practice, the knowledge graph of cross-border commodity operations extracts all logistics node entities and logistics connection edges between the source warehouse geographical entity "Location_CN_SHA" and the target market geographical entity "Location_FR_PAR". The extracted logistics node entities include "Port_Shanghai", "Port_Rotterdam", "Port_Le_Havre", and "Rail_Hub_Duisburg". The logistics connection edges include "CN_SHA_to_RTM_Sea", "RTM_to_DUI_Rail", "DUI_to_PAR_Truck", and "CN_SHA_to_LEH_Sea" and "LEH_to_PAR_Truck". Each logistics connection edge is assigned attribute values ​​in three dimensions: timeliness, cost, and policy compliance. The policy compliance attribute value is derived from the evaluation of specific transportation modes by the policy rule entities associated in the cross-border commodity operations knowledge graph. For example, the policy rule entity "Policy_EU_CarbonTax_Sea" associated with the connecting edge "CN_SHA_to_RTM_Sea" indicates that the current shipping method has a lower carbon emission tax risk, thus assigning a higher policy compliance score of 0.85. Conversely, the policy rule entity "Policy_GER_Rail_Strike" associated with the connecting edge "RTM_to_DUI_Rail" indicates a potential strike risk, assigning a lower policy compliance score of 0.70. See Table 2 for the attributes of each alternative route.

[0109] Table 2: Attributes of Optional Logistics Routes from Shanghai to Paris

[0110] Path 1 Shanghai Port - Rotterdam Port (sea transport) -> Duisburg Railway Hub (rail transport) -> Paris (truck transport) 28 18000 0.80 Path 2 Shanghai Port - Le Havre Port (sea freight) -> Paris (truck freight) 32 19500 0.90 Path 3 Shanghai Port - Rotterdam Port (sea freight) -> Paris (truck freight) 30 19000 0.85

[0111] Define the comprehensive utility function of the logistics route. Comprehensive utility function It is the sum of three indicators—transportation time, transportation cost, and policy compliance score—after being dimensionless and weighted. Its expression is:

[0112]

[0113] in: Indicates the total transportation time of the route. This represents the total transportation cost of the route. The average policy compliance score of the path, and These are preset baseline transportation time and baseline transportation cost, used to normalize the time and cost. , , These are preset weighting coefficients, reflecting the degree of preference for timeliness, cost, and compliance, respectively. A path search algorithm based on a cross-border commodity operation knowledge graph is used to find the comprehensive utility function within the network graph composed of logistics nodes and connecting edges. The path with the highest value. (Setting) sky, Currency unit The path 1 was calculated. The value is 0.4*(30 / 28)+0.4*(20000 / 18000)+0.2*0.80≈1.057, for path 2. The value is approximately 1.025, for path 3. The value is approximately 1.050. Path 1 has the highest comprehensive utility function value and is therefore considered a candidate optimal logistics path.

[0114] In some embodiments, a multi-commodity concurrent transportation simulation is performed on the candidate optimal logistics route to check the capacity constraints of key nodes in the route within a specific time period. The simulation shows that within the next 15 days, planned shipments will occupy 85% of the rail capacity in the "Port_Rotterdam" to "Rail_Hub_Duisburg" segment. The shipment of 5000 T-shirts will increase the capacity demand in this segment by 10%, resulting in a total demand of 95%, which does not exceed the 100% capacity limit. Simultaneously, the transshipment capacity of "Rail_Hub_Duisburg" is checked; the planned throughput for the next 15 days has reached 98% of its maximum capacity. The arrival of this shipment will bring its load to 103%, exceeding the capacity constraint. When the capacity exceeds the limit, a penalty is applied to the comprehensive utility function. The comprehensive utility function value of route 1 is... The value was reduced by 10% to 0.951. A new path search was performed. After excluding path 1, path 3 had the highest overall utility function value among the remaining paths, and no exceedances were found in the port throughput check of its key node "Port_Rotterdam". Path 3 was determined to be the final path with the highest overall utility that satisfies the capacity constraint. The sequence of all logistics nodes traversed by the final path was recorded as "Port_Shanghai->Port_Rotterdam->Location_FR_PAR", using "sea freight + truck" as the mode of transport. A logistics path optimization scheme was then generated.

[0115] In its implementation, the tariff minimization declaration scheme, targeting the cross-border cotton T-shirts involved in the logistics route optimization plan, retrieves the complete product description of this batch of T-shirts from the cross-border commodity operation knowledge graph: "100% cotton men's T-shirt," with the composition being "100% cotton," and processing information including "spinning-weaving-cutting-sewing-ironing." Within the policy and rule entity of the cross-border commodity operation knowledge graph, the target market country "France" is queried for multiple tariff classifications corresponding to "100% cotton men's T-shirts." The query results show two possible classifications: tariff code "6109.10" (cotton knitted T-shirt) and "6110.20" (cotton knitted pullover). Further querying the policy and rule entity reveals that the import tariff rate under tariff code "6109.10" is 12%, and the VAT rate is 20%, requiring no special permits; the import tariff rate under tariff code "6110.20" is 8%, and the VAT rate is 20%, but a "textile import license" is required. In the cross-border commodity operation knowledge graph, the supply chain of the corresponding batch of T-shirts is traced, and the certificate of origin materials at each production and processing stage are collected. The materials show that the cotton raw material originated from China, the spinning and weaving process was completed in China, and the cutting and sewing process was completed in Vietnam, forming a certificate of origin combination of "Chinese cotton certificate of origin + Vietnamese processing certificate". For each commodity tariff classification, combined with the available certificate of origin combination, the applicable preferential tariff agreement clauses are searched in the policy rule entity. For tariff code "6109.10", combined with "Vietnamese processing certificate", the "EU-Vietnam Free Trade Agreement" (EVFTA) is found to be applicable, and the preferential tariff rate is reduced to 0%; for tariff code "6110.20", combined with the same certificate of origin combination, the EVFTA is also applicable, and the preferential tariff rate is reduced to 0%, but a "textile import license" is still required. The estimated total tax burden under each "classification-certificate of origin combination-tariff clause" combination was calculated. For tariff code "6109.10", the total tax burden under the EVFTA is (0% tariff on goods value) + (20% VAT on goods value). The total tax burden under tariff code "6110.20" is the same under the EVFTA, but it increases the administrative costs and uncertainty risks associated with obtaining a "textile import license," thus considered an increase in tax burden. From all estimated total tax burdens, the combination with the lowest legal and total tax burden was selected. Tariff code "6109.10" and the use of a "Vietnam processing certificate" to apply for a preferential EVFTA tariff rate were chosen as the optimal declaration strategy for this batch of cross-border goods. The optimal declaration strategies for this batch of goods were summarized to form a tariff minimization declaration plan, which includes declaring tariff code "6109.10", using the applicable certificate of origin "Vietnam processing certificate VN2023ABC123", and applying for the tariff preference clause code "EVFTAPSR:WO".

[0116] Optional, policy compliance score This could be the geometric mean of the scores for each segment of the path, reflecting how a serious violation in any segment would significantly lower the overall score. Path capacity constraint checks can be performed on specific resources such as port berths, airport runways, and railway trains. When calculating the estimated total tax burden, administrative costs can be quantified as capital occupation costs by converting historical average processing times. This can be understood as a comprehensive utility function. The maximization solution ensures the additivity and comparability of various heterogeneous indicators through normalization. It can be understood that the computational depth of the tariff minimization declaration scheme depends on the precise correlation between policy rule entities and commodity supply chain entities in the knowledge graph.

[0117] In one embodiment of the present invention, during the execution of the purchase order allocation scheme, inventory dynamic transfer scheme, logistics route optimization scheme, and tariff minimization declaration scheme, the latest inventory changes, logistics status updates, market demand fluctuations, and customs rule changes are continuously obtained from the full-link data set. The latest information is updated in real time to the cross-border commodity operation knowledge graph, dynamically adjusting the attributes and relationships of commodity entities, logistics event entities, market demand entities, and policy rule entities. Based on the updated cross-border commodity operation knowledge graph, multi-objective collaborative decision-making calculations are periodically triggered. The newly calculated comprehensive operation strategy set is compared with the original execution strategy to identify deviations exceeding the preset tolerance range. When a strategy failure risk due to logistics delays, surges in demand, or sudden rule changes is identified, a partial recalculation of the strategy is immediately initiated, generating adjustment instructions for the affected parts. The adjustment instructions are issued to the corresponding execution systems, including the warehouse management system, transportation management system, and customs declaration system, to drive actual operations to execute according to the adjusted strategy. When the logistics status update information shows a severe delay at a key logistics node, all in-transit commodity entities and related market demand entities affected by this delay are located in the cross-border commodity operation knowledge graph. The system recalculates the availability rate of affected market demand entities within the original time window. If it falls below a preset threshold, the delayed goods entities are removed from the original logistics path, triggering a local dynamic inventory reallocation calculation for these market demand entities to find alternative sources and routes. When market demand forecasts fluctuate sharply upwards, the system locates the affected product category entities and market geographic entities in the cross-border commodity operation knowledge graph. It compares the total existing inventory with the total inventory in transit to see if it meets the revised demand. If a gap exists, it immediately triggers a local purchase order allocation calculation for the affected product categories and corresponding markets, generating emergency replenishment orders. When customs rule change information is captured, the system updates the corresponding policy rule entities and their associations in the cross-border commodity operation knowledge graph. It checks whether all currently implemented tariff minimization declaration schemes have become invalid or suboptimal due to rule changes. For invalid or suboptimal schemes, it immediately triggers a local tariff minimization declaration calculation, generating new declaration strategy adjustment instructions.

[0118] In practice, the implementation of a cross-border commodity intelligent operation management method involves real-time monitoring and dynamic adjustment of the execution process of a comprehensive set of operation strategies. Taking the operation of commodity "SKU-003" from Shanghai, China to Munich, Germany as an example, its initial comprehensive set of operation strategies includes a logistics route optimization scheme of shipping from "Warehouse_CN_SH" and transiting through "Port_Rotterdam", as well as a tariff minimization declaration scheme of applying for 0% tariff using a "EUR.1" certificate of origin.

[0119] In practice, during the execution of purchase order allocation, dynamic inventory transfer, logistics route optimization, and tariff minimization declaration schemes, the latest inventory changes, logistics status updates, market demand fluctuations, and customs rule changes are continuously obtained from the end-to-end data set. One logistics status update shows that "Waybill TRK-EU-005" at the "Port_Rotterdam" node has been updated to "Port congestion, expected delay of 5 days." Another market demand forecast update shows that the German market "Market_DE_Munich" has increased its predicted demand for product "SKU-003" for the next 7 days from "1000" to "1500." A customs rule change update shows that the European Commission has issued new regulations, temporarily adjusting import tariff rates for certain categories of textiles. The latest information is updated in real-time to the cross-border commodity operation knowledge graph, dynamically adjusting the attributes and relationships of commodity entities, logistics event entities, market demand entities, and policy rule entities within the knowledge graph. The transit status attribute of the product entity "Product_SKU-003-Batch05" was updated to "Delayed"; the predicted demand attribute of the market demand entity "Demand_DE_Munich_D7" was updated from "1000" to "1500"; and the tariff rate attribute of the policy rule entity "Policy_EU_Textile_Tariff_610910" was updated from "0%" to "10%". Based on the updated cross-border commodity operation knowledge graph, multi-objective collaborative decision-making calculations are periodically triggered. The newly calculated comprehensive operation strategy set is compared with the original execution strategy to identify deviations exceeding the preset tolerance range. The preset tolerance range is that the logistics timeliness deviation does not exceed 2 days, the demand fulfillment rate deviation does not exceed 5%, and the tariff cost deviation does not exceed 1%. The comparison found that the logistics delay caused the original route plan to have a timeliness deviation of 5 days, exceeding the tolerance; the demand increase caused the original inventory allocation plan's fulfillment rate to drop from 100% to 73%, exceeding the tolerance; and the tariff rate adjustment caused the original declaration plan's cost to increase by 10%, exceeding the tolerance.

[0120] When a risk of strategy failure due to logistics delays, surges in demand, or sudden rule changes is identified, a partial recalculation of the strategy is immediately initiated to generate adjustment instructions for the affected portion. When the logistics status update information shows a severe delay at the critical logistics node "Port_Rotterdam," all in-transit goods entities and related market demand entities affected by this delay are located in the cross-border commodity operation knowledge graph. The affected in-transit goods entity is "Product_SKU-003-Batch05," and the related market demand entity is "Demand_DE_Munich_D7." The fulfillment rate of the affected market demand entity "Demand_DE_Munich_D7" within the original time window is recalculated. It is calculated that of the originally planned 400 in-transit goods, 300 will not arrive on time. Combined with the existing inventory of 800 items, the fulfillment rate is (800+100) / 1500=0.6, which is lower than the preset threshold of 0.9. The delayed product entity "Product_SKU-003-Batch05" is removed from the original logistics route, and a local dynamic inventory allocation calculation is triggered for the market demand entity "Demand_DE_Munich_D7" to find alternative sources and routes. When the market demand forecast information fluctuates sharply upwards, the product category entity "Category_SKU-003" and the market geographic entity "Location_DE_Munich" involved in the fluctuation are located in the cross-border commodity operation knowledge graph. The total amount of existing inventory and the inventory in transit for transfer is compared to see if it meets the increased demand. The existing inventory is 800 units, the planned inventory in transit is 300 units, the total inventory is 1100 units, and the increased demand is 1500 units, resulting in a shortage of 400 units. A local purchase order allocation calculation is immediately triggered for the product category "Category_SKU-003" and the corresponding market "Location_DE_Munich" involved in the fluctuation, generating an emergency replenishment order. When customs rule change information is captured, the corresponding policy rule entity "Policy_EU_Textile_Tariff_610910" and its associated tax rate attribute are updated in the cross-border commodity operation knowledge graph. All currently implemented tariff minimization declaration schemes are checked to see if they have become invalid or suboptimal due to the rule change. The check reveals that the original scheme, based on a 0% tax rate, is now invalid. For invalid schemes, a local tariff minimization declaration calculation is immediately triggered, recalculated based on the new tax rate rule, and a new declaration strategy adjustment instruction is generated, instructing the declaration tariff code to be changed from "6109.10" to "6110.12," and applying for the preferential tax rate of 5% under the EU-Korea Free Trade Agreement.

[0121] In some embodiments, adjustment instructions are issued to the corresponding execution system to drive actual operational operations to execute according to the adjusted strategy. An adjustment instruction for logistics delays is issued to the transportation management system, changing the subsequent transportation route of product "SKU-003Batch05" from "Rotterdam" to "Hamburg". An emergency replenishment order for a surge in demand is issued to the warehouse management system, urgently transferring 400 units of "SKU-003" from "Warehouse_PL_WAW" to the target market "DE_Munich". A declaration strategy adjustment instruction for changes in tariff rules is issued to the customs declaration system, updating the customs declaration, changing the tariff code to "6110.12", and attaching a "EUR-KORFTA" certificate of origin. Deviation identification function. The expression used to quantify the degree of deviation between the new strategy and the original strategy is:

[0122]

[0123] in: These represent key performance indicators that are being monitored, such as transit time, demand fulfillment rate, and unit tariff cost. This represents the indicator value under the new strategy calculated based on the updated cross-border commodity operation knowledge graph; This represents the indicator value under the original execution strategy. When... The value exceeds the preset threshold. When the deviation exceeds the preset tolerance range, an alarm and local recalculation process are triggered.

[0124] Optionally, the data acquisition frequency for real-time monitoring can be dynamically adjusted according to the operational phase, increasing the monitoring frequency during peak sales seasons or periods of policy instability. Local recalculation can be triggered based on event-driven events, such as changes in specific entity attributes. When adjustment instructions are issued to the execution system, they can include execution priority and effective timestamps. It can be understood that continuous information updates ensure the cross-border commodity operation knowledge graph remains synchronized with the real-world state. It can also be understood that deviation identification and local recalculation mechanisms constitute a dynamic feedback and correction loop for static plans.

[0125] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent operation management of cross-border commodities, characterized in that, The method includes: Obtain a complete data set of cross-border goods, including real-time inventory information of overseas warehouses, status node information of logistics in transit, demand forecast information of different national markets, and rule parameter information of customs of multiple countries. The entire data set is then subjected to association cleaning and structural transformation to generate a standardized data set covering the entire lifecycle of cross-border goods. A cross-border commodity operation knowledge graph is constructed based on the standardized data set. The knowledge graph includes entities such as commodity nodes, supply chain nodes, market nodes, and policy nodes, as well as their dynamic relationships. By utilizing the aforementioned cross-border commodity operation knowledge graph, multi-objective collaborative decision-making calculations are performed to generate a comprehensive set of operational strategies, including purchase order allocation schemes, dynamic inventory transfer schemes, logistics route optimization schemes, and tariff minimization declaration schemes, among others. From the cross-border commodity operation knowledge graph, extract the inventory location, quantity, and expiration date attributes of all current commodity entities, as well as the predicted demand, time, and confidence level attributes of the associated market demand entities; Under the premise of meeting the predicted demand and time constraints indicated by the market demand entity, with the objective function of minimizing global inventory holding cost, inventory turnover efficiency and expiration risk, inventory is simulated and allocated between the commodity entities and warehouse entities in the cross-border commodity operation knowledge graph to generate a preliminary purchase order allocation scheme. By combining the real-time transportation capacity and cost reflected by the logistics event entities in the cross-border commodity operation knowledge graph, and the customs clearance policies and timeliness of different logistics paths indicated by the policy and rule entities, the logistics accessibility verification and cost assessment of the preliminary purchase order allocation plan are carried out. When a logistics bottleneck or excessively high cost is detected, the dynamic inventory allocation calculation is initiated. The alternative inventory source warehouse entities and logistics route entities are searched in the cross-border commodity operation knowledge graph. The allocation relationship of purchase orders is adjusted to generate a dynamic inventory allocation plan. After the purchase order allocation scheme and inventory dynamic transfer scheme are determined, the optimal logistics route from the source warehouse to the target market is calculated based on the geographical entity network and logistics event entity history in the cross-border commodity operation knowledge graph. The optimal logistics route must simultaneously meet the requirements of the lowest timeliness, the lowest transportation cost, and the highest policy compliance, and a logistics route optimization scheme is generated. Based on the logistics route optimization scheme, the policy and rule entities in the cross-border commodity operation knowledge graph are called to calculate and select the optimal declaration category, certificate of origin combination and applicable tariff terms for all cross-border commodities involved in the scheme, and generate a tariff minimization declaration scheme. By integrating the aforementioned purchase order allocation scheme, dynamic inventory transfer scheme, logistics route optimization scheme, and tariff minimization declaration scheme, a comprehensive set of operational strategies that are coordinated in terms of time, space, and cost is formed.

2. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, The entire data set is subjected to association cleaning and structural transformation to generate a standardized data set covering the entire lifecycle of cross-border goods, including: Extract the unique product code, warehouse code, inventory quantity, and batch expiration date data from the real-time inventory information of the overseas warehouse, match and verify them against the pre-set global product master data table, and generate a standardized inventory status table. The status node information of the logistics in transit is parsed to identify the logistics node type, timestamp, geolocation code and transport vehicle identifier, and the unstructured logistics events are converted into a logistics status record table with a unified field structure. The demand forecast information of the different national markets is normalized in format, and the forecast time granularity, geographical dimension and commodity category dimension are unified to generate a market demand forecast matrix with confidence level labels. By integrating the rule parameter information of customs from multiple countries, the text rules on commodity classification, rules of origin, tax rates and declaration requirements are transformed into a structured rule clause knowledge base, with each rule clause associated with a specific country, commodity tariff code and effective date; The standardized inventory status table, logistics status record table, market demand forecast matrix, and rule and clause knowledge base are aligned in time and space, and linked by shared unique product codes, warehouse codes, timestamps, and country codes to form a standardized data set for the entire lifecycle of the cross-border goods.

3. The method for intelligent operation management of cross-border commodities according to claim 2, characterized in that, A knowledge graph for cross-border commodity operations is constructed based on the standardized dataset, including: Taking each product batch in the standardized inventory status table as an example, a product entity is created in the knowledge graph, and the product entity is linked to its warehouse entity, supplier entity, and corresponding batch expiration date attribute. Taking each status change in the logistics status record table as an example, a logistics event entity is created, and the logistics event entity is associated with related commodity entities, transport vehicle entities, origin and destination geographical entities, while recording the event time sequence. Taking each forecast entry in the market demand forecast matrix as an example, a market demand entity is created, and the market demand entity is associated with the corresponding target market geographic entity, product category entity and forecast period attribute, and its confidence attribute is stored. Using each rule in the rule and clause knowledge base as an example, create a policy rule entity and bind the policy rule entity with the issuing country entity, the applicable commodity tariff code entity, and the effective period attribute; Based on the supply and demand links of commodity entities, the transportation links of logistics entities, the demand transmission links of market entities, and the influence links of policy entities, the association weights between entities are dynamically calculated and updated to form a cross-border commodity operation knowledge graph containing multi-dimensional dynamic relationships.

4. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, Inventory is simulated and allocated between commodity entities and warehouse entities in the cross-border commodity operation knowledge graph to generate a preliminary purchase order allocation plan, including: In the cross-border commodity operation knowledge graph, market demand entities whose predicted demand is greater than the current available inventory and whose confidence level is higher than a preset threshold are identified and marked as having replenishment needs. For each replenishment requirement, the associated product entities are traversed in reverse in the cross-border commodity operation knowledge graph to find all product batch entities that are in stock and meet the expiration date requirements, as well as the warehouse entities where they are located. Calculate the estimated logistics time and unit logistics cost from each eligible warehouse entity to the target market geographic entity associated with the replenishment demand; Construct an allocation model whose decision variables are the quantity allocated from each eligible warehouse entity to each replenishment demand. Its objective is to minimize the total logistics cost and the weighted sum of inventory holding costs. Its constraints include not exceeding the upper limit of inventory in each warehouse, meeting the lower limit of market demand, and ensuring that logistics time does not exceed the latest demand time. Solving the allocation model yields a detailed allocation plan for each batch of goods, specifying which warehouse to allocate how much quantity to which market demand, and summarizing all allocation plans to form the preliminary purchase order allocation scheme.

5. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, The process of initiating dynamic inventory allocation calculation involves searching for alternative inventory source warehouse entities and logistics route entities in the cross-border commodity operation knowledge graph, adjusting the allocation relationship of purchase orders, and generating a dynamic inventory allocation plan, including: In logistics accessibility verification, when it is identified that the original logistics path from the candidate warehouse to the target market does not meet the timeliness or cost constraints, the allocation relationship is marked as an allocation to be adjusted. For each allocation to be adjusted, in the cross-border commodity operation knowledge graph, with the commodity entity as the center, other warehouse entities with the same commodity and qualified expiration dates are searched as alternative inventory sources. For each alternative inventory source, based on the logistics path optimization results in the cross-border commodity operation knowledge graph, obtain the alternative logistics path from it to the target market and its attributes, including transportation time, cost and policy compliance score. From the set of alternative inventory sources and alternative logistics routes, with the goal of replenishing the original allocated quantity and guided by minimizing the increase in overall cost, the allocation is recalculated to form a partial redistribution plan; For alternative inventory sources whose inventory is reduced due to the execution of the aforementioned partial redistribution plan, check whether it affects the satisfaction of other market demands. If it does, recursively trigger the calculation of dynamic inventory allocation to market demands. Summarize all adjusted allocation relationships to form the final dynamic inventory transfer plan, clearly defining the logistics routes for transferring out warehouses, into markets, product batches, quantities, and the basis for the transfer.

6. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, The calculation of the optimal logistics route from the source warehouse to the target market requires that the optimal logistics route simultaneously meet the following criteria: lowest timeliness, lowest transportation cost, and highest policy compliance. This generates a logistics route optimization plan, including: In the cross-border commodity operation knowledge graph, extract all logistics node entities and logistics connection edges between the source warehouse geographical entity and the target market geographical entity; Each logistics connection edge is assigned attribute values ​​in three dimensions: timeliness, cost, and policy compliance. The policy compliance attribute value is derived from the evaluation of a specific mode of transportation by the policy rule entities associated in the cross-border commodity operation knowledge graph. Define a comprehensive utility function for a logistics route, which is a weighted sum of three indicators: the reciprocal of transportation timeliness, the reciprocal of transportation cost, and policy compliance score. Using the path search algorithm based on the cross-border commodity operation knowledge graph, the path with the highest comprehensive utility function value is found in the network graph composed of logistics nodes and connecting edges, and is used as the candidate optimal logistics path; The candidate optimal logistics path is simulated for concurrent transportation of multiple goods. The capacity constraints of key nodes of the path are checked within a specific time period. If the capacity exceeds the limit, the comprehensive utility function is penalized and the path search is performed again. Determine the final path that satisfies the capacity constraint and has the highest overall utility. Record the sequence of all logistics nodes traversed by the final path, the transportation methods used, and the attributes of each segment to generate the logistics path optimization scheme.

7. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, The above steps calculate and select the optimal combination of declared product categories, certificates of origin, and applicable tariff terms for all cross-border goods involved in the scheme, generating a tariff-minimizing declaration scheme, including: For each batch of cross-border goods involved in the logistics route optimization scheme, its complete product description, ingredients, and processing information are obtained from the cross-border goods operation knowledge graph; In the policy and rule entity of the cross-border commodity operation knowledge graph, query the target market country for multiple commodity tariff classifications corresponding to the corresponding commodity description, as well as the import tariff rate, value-added tax rate, and whether special permission is required under each classification; In the knowledge graph of cross-border commodity operations, the supply chain links of the corresponding batch of goods are traced, and the origin certificates of each production and processing stage are collected to form a certificate of origin combination. For each commodity tariff classification, combined with the available certificate of origin combinations, the applicable preferential tariff agreement provisions are queried in the policy rules entity to calculate the estimated total tax burden under each "classification-certificate of origin combination-tariff provisions" combination. From all the estimated total tax burdens, select the combination that is legal and has the lowest total tax burden as the optimal declaration strategy for the corresponding batch of cross-border goods; The optimal declaration strategies for all batches of cross-border goods are summarized to form the aforementioned tariff minimization declaration scheme, which includes the declared tariff code, applicable certificate of origin, and applied tariff preference clause code for each batch of goods.

8. The method for intelligent operation management of cross-border commodities according to claim 1, characterized in that, The method also includes the step of real-time monitoring and dynamic adjustment of the execution process of the integrated operation strategy set: During the execution of the purchase order allocation scheme, inventory dynamic transfer scheme, logistics route optimization scheme and tariff minimization declaration scheme, the latest inventory changes, logistics status updates, market demand fluctuations and customs rule changes are continuously obtained from the full-link data set; The latest information obtained will be updated in real time to the cross-border commodity operation knowledge graph, and the attributes and relationships of the commodity entities, logistics event entities, market demand entities and policy rule entities will be dynamically adjusted. Based on the updated cross-border commodity operation knowledge graph, the multi-objective collaborative decision calculation is triggered periodically, and the newly calculated comprehensive operation strategy set is compared with the original execution strategy to identify deviations that exceed the preset tolerance range. When a risk of strategy failure due to logistics delays, surges in demand, or sudden rule changes is identified, a local recalculation of the strategy is immediately initiated to generate adjustment instructions for the affected parts. The adjustment instructions are sent to the corresponding execution systems, including the warehouse management system, transportation management system and customs declaration system, to drive actual operations to be executed according to the adjusted strategy; When a risk of strategy failure due to logistics delays, a surge in demand, or sudden rule changes is identified, a local recalculation of the strategy is immediately initiated to generate adjustment instructions for the affected portion, including: When the logistics status update information shows that a critical logistics node has been severely delayed, all in-transit goods entities and related market demand entities affected by this delay are located in the cross-border commodity operation knowledge graph. The availability rate of the affected market demand entities within the original time window is recalculated. If it is lower than a preset threshold, the delayed goods entities are removed from the original logistics path, and a local inventory dynamic allocation calculation is triggered for the market demand entities to find alternative sources and paths. When market demand forecast information fluctuates sharply upwards, the commodity category entities and market geographic entities involved in the fluctuation are located in the cross-border commodity operation knowledge graph. Compare the total existing inventory with the total inventory in transit to see if it meets the increased demand. If there is a shortfall, immediately trigger the calculation of local purchase order allocation for the product categories and corresponding markets affected by the fluctuation, and generate an emergency replenishment order. When customs rule change information is captured, the corresponding policy rule entity and its associations are updated in the cross-border commodity operation knowledge graph. Check whether all currently executing tariff minimization declaration schemes have become invalid or suboptimal due to rule changes. For invalid or suboptimal schemes, immediately trigger local tariff minimization declaration calculation and generate new declaration strategy adjustment instructions.

9. A cross-border commodity intelligent operation management system, 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, it implements the steps of the intelligent operation management method for cross-border commodities as described in any one of claims 1 to 8.

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