Intelligent cross-border supply chain management optimal distribution method and system

Abstract

The invention provides an intelligent cross-border supply chain management optimization distribution method and system, and belongs to the technical field of data processing, and the method comprises the steps: dividing a supply chain coverage area into a plurality of geographic grids containing warehouses or transfer nodes according to the distribution of logistics hubs and trunk lines; and constructing a dynamic transportation network graph by taking the nodes as vertexes. Based on the network graph and the supply relationship, a corresponding candidate supply warehouse set and an optimal transportation path set to each warehouse are pre-calculated for each geographic grid. And when an inventory allocation request of a target geographic grid is received, according to the emergency degree label of the inventory allocation request, matching a path with corresponding transportation time efficiency from the pre-calculated path set to form a to-be-selected scheme. And finally, based on the real-time inventory data of the candidate warehouses associated with the to-be-selected schemes, generating an inventory allocation instruction. According to the invention, space management of supply chain resources, real-time perception of a logistics network and collaborative optimization of inventory and logistics are realized, and the overall efficiency of a cross-border supply chain is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a smart cross-border supply chain management optimization allocation method and system. Background Technology

[0002] Traditional cross-border supply chain management methods typically rely on static management models divided by administrative regions or fixed warehouse service radii. When making inventory allocation decisions, they lack refined spatial management units for vast and complex cross-border geographical areas, making it difficult to achieve accurate geographical mapping of resources and demand.

[0003] Secondly, the network status information upon which logistics route planning relies is updated laggingly, failing to reflect real-time dynamic changes in transportation costs, time, and availability, leading to decisions based on outdated data. Furthermore, the inventory allocation decision-making process is slow to respond, typically requiring temporary calculations of supply sources and logistics routes only after receiving a allocation request, failing to meet the high timeliness requirements of cross-border operations. In addition, the decision-making process often separates inventory queries from logistics routing planning, failing to achieve coordinated optimization of inventory status and logistics capabilities at the real-time data level, potentially leading to execution dilemmas such as having goods but no route or having a route but no goods. Summary of the Invention

[0004] This application provides a smart cross-border supply chain management optimization allocation method and system to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, embodiments of this application propose a smart cross-border supply chain management optimization allocation method, the method comprising: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, it is divided into multiple connected geographical grids, in which each geographical grid contains at least one warehouse or one transportation transit node. A dynamic transportation network graph is constructed with each warehouse and transportation transfer node as a vertex. The edges of the dynamic transportation network graph are the transportation routes connecting the vertices. Each edge is associated with the available transportation modes, estimated transportation time and unit transportation cost based on real-time data updates. Based on the dynamic transportation network map and the preset supply relationship, the candidate supply warehouse set corresponding to each geographic grid is determined, and the optimal transportation route set from the specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with the total transportation time and the total transportation cost. In response to an inventory allocation request from a target geographic grid, determine the demand details and urgency level label corresponding to the request. The target geographic grid can be any geographic grid. Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions; Based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, the target supply warehouse and target transportation route are determined and inventory transfer instructions are generated.

[0006] In conjunction with the first aspect, optionally, based on the dynamic transportation network map and preset supply relationships, a set of candidate supply warehouses corresponding to each geographic grid is determined, and a set of optimal transportation routes from a specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with total transportation time and total transportation cost, including: Obtain the reliability coefficient for each path, where the reliability coefficient is related to the stability of the path's historical customs clearance time and the current congestion warning information generated by the customs declaration system; In the optimal transportation route set, at least one route with a reliability coefficient higher than the first threshold is reserved for each candidate supply warehouse.

[0007] In conjunction with the first aspect, optionally, a reliability coefficient corresponding to each path can be obtained. This reliability coefficient is related to the stability of the path's historical customs clearance time and the generation of congestion warning information in the current customs declaration system, including: Obtain a dynamic customs clearance risk assessment model, wherein the input feature vector of the dynamic customs clearance risk assessment model includes at least: The variance of the on-time clearance rate of the key cross-border checkpoints corresponding to the route in the past preset time period; Based on the current queue length of pending declarations for each category of goods obtained from the customs system.

[0008] In conjunction with the first aspect, optionally, after determining the target supply warehouse and target transportation route and generating inventory transfer instructions based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, the process includes: Obtain the reliability coefficient of the path corresponding to the target transportation route; If the reliability coefficient of the route drops below the second preset threshold and the transportation has not entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient.

[0009] In conjunction with the first aspect, optionally, if the reliability coefficient of the route drops below a second preset threshold and the transportation has not yet entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient, including: The feasibility of implementing the alternative transportation route is assessed based on the updated route reliability coefficient, whether the estimated transport time meets the original promised timeliness of the dispatch request, and the additional costs incurred in switching to the new route. If there is an alternative transportation route that meets the preset feasibility conditions, a route switching instruction is generated and executed, and the transportation route information in the inventory transfer instruction is updated.

[0010] In conjunction with the first aspect, optionally, based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions, including: When the urgency level label indicates deterministic priority, matching is performed from paths with high reliability coefficients; When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes, with the matching criteria including total transportation cost and route reliability coefficient.

[0011] In conjunction with the first aspect, optionally, when the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes, wherein the matching criteria include the total transportation cost and the route's reliability coefficient, including: A multi-dimensional route scoring model is constructed. Based on preset dynamic weights, the multi-dimensional route scoring model calculates a comprehensive score by weighting the total transportation cost and the reliability coefficient of each route. All paths are ranked based on a comprehensive score, and the top-ranked paths are selected as candidate options. The dynamic weights are determined based on the current market supply and demand tension in the target geographic grid area. When the supply and demand tension is high, the weight of the path reliability coefficient is increased, and when the supply and demand tension is low, the weight of the total transportation cost is increased.

[0012] Secondly, this application proposes an intelligent cross-border supply chain management and optimization allocation system, which is configured as follows: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, it is divided into multiple connected geographical grids, in which each geographical grid contains at least one warehouse or one transportation transit node. A dynamic transportation network graph is constructed with each warehouse and transportation transfer node as a vertex. The edges of the dynamic transportation network graph are the transportation routes connecting the vertices. Each edge is associated with the available transportation modes, estimated transportation time and unit transportation cost based on real-time data updates. Based on the dynamic transportation network map and the preset supply relationship, the candidate supply warehouse set corresponding to each geographic grid is determined, and the optimal transportation route set from the specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with the total transportation time and the total transportation cost. In response to an inventory allocation request from a target geographic grid, determine the demand details and urgency level label corresponding to the request. The target geographic grid can be any geographic grid. Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions; Based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, the target supply warehouse and target transportation route are determined and inventory transfer instructions are generated.

[0013] In conjunction with the second aspect, optionally, the system is configured as follows: Based on the dynamic transportation network map and preset supply relationships, a set of candidate supply warehouses is determined for each geographic grid, and an optimal set of transportation routes from a specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with total transportation time and total transportation cost, including: Obtain the reliability coefficient for each path, where the reliability coefficient is related to the stability of the path's historical customs clearance time and the current congestion warning information generated by the customs declaration system; In the optimal transportation route set, at least one route with a reliability coefficient higher than the first threshold is reserved for each candidate supply warehouse.

[0014] In conjunction with the second aspect, optionally, the system is configured as follows: Obtain the reliability coefficient for each path. This reliability coefficient is related to the stability of the path's historical customs clearance time and the current congestion warning information generated by the customs declaration system, including: Obtain a dynamic customs clearance risk assessment model, wherein the input feature vector of the dynamic customs clearance risk assessment model includes at least: The variance of the on-time clearance rate of the key cross-border checkpoints corresponding to the route in the past preset time period; Based on the current queue length of pending declarations for each category of goods obtained from the customs system.

[0015] In conjunction with the second aspect, optionally, the system is configured as follows: Based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, after determining the target supply warehouse and target transportation route and generating inventory transfer instructions, the process includes: Obtain the reliability coefficient of the path corresponding to the target transportation route; If the reliability coefficient of the route drops below the second preset threshold and the transportation has not entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient.

[0016] In conjunction with the second aspect, optionally, the system is configured as follows: If the reliability coefficient of a route drops below a second preset threshold and the transportation has not yet entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient, including: The feasibility of implementing the alternative transportation route is assessed based on the updated route reliability coefficient, whether the estimated transport time meets the original promised timeliness of the dispatch request, and the additional costs incurred in switching to the new route. If there is an alternative transportation route that meets the preset feasibility conditions, a route switching instruction is generated and executed, and the transportation route information in the inventory transfer instruction is updated.

[0017] In conjunction with the second aspect, optionally, the system is configured as follows: Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions, including: When the urgency level label indicates deterministic priority, matching is performed from paths with high reliability coefficients; When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes, with the matching criteria including total transportation cost and route reliability coefficient.

[0018] In conjunction with the second aspect, optionally, the system is configured as follows: When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes. The matching criteria include the total transportation cost and the route's reliability coefficient, including: A multi-dimensional route scoring model is constructed. Based on preset dynamic weights, the multi-dimensional route scoring model calculates a comprehensive score by weighting the total transportation cost and the reliability coefficient of each route. All paths are ranked based on a comprehensive score, and the top-ranked paths are selected as candidate options. The dynamic weights are determined based on the current market supply and demand tension in the target geographic grid area. When the supply and demand tension is high, the weight of the path reliability coefficient is increased, and when the supply and demand tension is low, the weight of the total transportation cost is increased.

[0019] A third aspect of this invention provides an electronic device, which includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0021] In summary, the above method and apparatus have the following technical effects: This application proposes a smart cross-border supply chain management optimization allocation method and system. First, based on the distribution of logistics hubs and trunk lines, the supply chain coverage area is divided into multiple geographical grids containing warehouses or transit nodes. A dynamic transportation network diagram is constructed using these nodes as vertices. Based on this network diagram and supply relationships, a set of candidate supply warehouses and the optimal transportation route set to each warehouse are pre-calculated for each geographical grid. When an inventory allocation request is received from a target geographical grid, routes with corresponding transportation timeliness are matched from the pre-calculated route set according to the request's urgency label to form candidate solutions. Finally, based on the real-time inventory data of the candidate warehouses associated with the candidate solutions, an inventory allocation instruction is generated. This invention realizes spatial management of supply chain resources, real-time perception of the logistics network, and collaborative optimization of inventory and logistics, improving the overall efficiency of the cross-border supply chain. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an intelligent cross-border supply chain management optimization allocation method proposed in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0024] This application proposes an intelligent cross-border supply chain management optimization allocation method. Please refer to [link / reference]. Figure 1 The method includes the following steps: S101: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, the area is divided into multiple connected geographical grids, wherein each geographical grid contains at least one warehouse or one transportation transit node.

[0025] Understandably, each geographic grid is a logical management unit, and its boundaries are not entirely determined by administrative divisions, but rather by the scope of logistics efficiency. Each grid must contain at least one substantial logistics operation node, i.e., a warehouse or a transportation transit node, such as a port terminal, airport cargo terminal, or railway marshalling yard. For each geographic grid, the core logistics hubs within the region and the main transportation channels connecting these hubs can be identified, such as major shipping routes, railway trunk lines, and highway networks. Based on the distribution and connectivity of these physical infrastructures, the entire region is divided into multiple adjacent geographic grids or grids connected by transportation trunk lines.

[0026] For example, when planning a supply chain from China to Europe, the "Rhine-Ruhr region of Germany" might be divided into a geographical grid. This grid includes the Port of Duisburg and a regional distribution center in Düsseldorf. Simultaneously, this grid is closely connected to the Port of Rotterdam in the Netherlands via highways and railway lines. This division allows the system to manage the region's inventory needs, receive inbound goods from the Port of Rotterdam, and plan distribution routes to various cities within the region, all within a unified unit of the "Rhine-Ruhr grid."

[0027] S102: Construct a dynamic transportation network graph with each warehouse and transportation transfer node as vertices. The edges of the dynamic transportation network graph are the transportation routes connecting the vertices. Each edge is associated with the available transportation modes, estimated transportation time and unit transportation cost based on real-time data updates.

[0028] Understandably, if a feasible direct transport service exists between two vertices, an edge is established between them. Each edge represents a specific transport route. Available transport modes specify the specific transport methods currently supported by the route, such as sea freight to full container load (FCL), air freight to general cargo, China-Europe freight train to rail, etc. The estimated transport time is a dynamic value, based on the route's historical benchmark timeliness and adjusted for real-time information such as ship / flight punctuality rates, weather impacts, and port congestion. The unit transport cost is also a dynamic value, typically calculated based on real-time or near-real-time data such as contract freight rates, fuel surcharges, current market freight rates, and seasonal factors. By constructing such a dynamic transport network graph, the system integrates globally scattered, isolated logistics facilities into an interconnected and dynamically updated digital network.

[0029] S103: Based on the dynamic transportation network map and the preset supply relationship, determine the candidate supply warehouse set corresponding to each geographic grid, and determine the optimal transportation path set from the specified point in the geographic grid to each warehouse in the candidate supply warehouse set, wherein each path is associated with the total transportation time and the total transportation cost.

[0030] Understandably, determining the candidate supply warehouse set involves filtering out a list of warehouses that could potentially supply each geographical grid. Then, for each grid-candidate warehouse pair, the optimal one or more transportation routes are pre-calculated. This process is not random selection, but rather based on objective rules.

[0031] Specifically, based on a dynamic transportation network map, it can be determined whether a complete transportation path exists from a given warehouse vertex to reach the target geographic grid. Then, pre-defined supply relationships must also be met. For example, some warehouses may be designated as exclusive suppliers for specific regions or product lines; or, according to contractual agreements, orders for a particular geographic grid must be preferentially fulfilled by warehouses in a specific region. The system applies these rules to further filter warehouses from all network-connected warehouses, selecting those that conform to the business logic, thus forming the final candidate supply warehouse set.

[0032] For each warehouse in the candidate set, a route planning algorithm is run on the dynamic transportation network graph with a specified point within the geographic grid, such as the location of a major customer or the central coordinates within the grid, as the destination. The optimal criterion is comprehensive; the system calculates one or more routes that are optimal in terms of both total transportation time and total transportation cost under the current real-time network conditions. The result is a set of routes, where each route is explicitly associated with its total transportation time and total transportation cost. This set may include the fastest route, the most economical route, and several balanced routes.

[0033] Furthermore, since the customs clearance process is a crucial factor affecting logistics timeliness, in this application, step S103 may include the following steps: S1031: Obtain the reliability coefficient corresponding to each path, where the reliability coefficient is related to the stability of the historical customs clearance time of the path and the generation of congestion warning information in the current customs declaration system.

[0034] The reliability coefficient is a dynamic score that takes into account both long-term stability and short-term immediate risks. It is a normalized value, for example, between 0 and 1 or between 0 and 100. The higher the score, the lower the expected risk and the higher the certainty of using this route to complete cross-border customs clearance.

[0035] For example, a pre-defined dynamic customs clearance risk assessment model can be obtained. The input feature vector of this model includes at least: the variance of the on-time clearance rate of the key cross-border checkpoints corresponding to the path over a pre-defined period, and the current queue length of pending declarations for each category of goods obtained from the customs system. Of course, in other embodiments, the feature vector can also have other dimensions, which are not limited in this application. It is understood that a path with a historically fluctuating speed and drastic oscillations has a lower stability score; while a path that consistently completes customs clearance within the expected timeframe has a higher stability score. This reflects the predictability of the path. Simultaneously, real-time data, such as the pending declaration volume published by customs, abnormal notifications from specific checkpoints, and industry early warning information, can be accessed to assess the immediate pressure on the current customs clearance environment. Severe congestion currently leads to a higher immediate risk score and lower reliability.

[0036] S1032: In the set of optimal transportation routes, retain at least one route with a reliability coefficient higher than the first threshold for each candidate supply warehouse.

[0037] Understandably, for the multiple optimal routes calculated for each candidate supply warehouse, a first threshold can be set. This threshold represents the minimum level of customs clearance certainty acceptable to management, ensuring that the final subset of routes retained for each warehouse includes at least one route with a reliability coefficient higher than this threshold.

[0038] S104: In response to an inventory allocation request from a target geographic grid, determine the demand details and urgency level label corresponding to the request. The target geographic grid is any geographic grid.

[0039] Understandably, the system first receives inventory allocation requests from external sources. Once a request arrives, its target geographic grid can be identified. The request may originate from warehouse inventory alerts, sales order systems, or manual dispatch instructions within that grid. By parsing the geographic location information in the request, it is mapped to the corresponding geographic grid. Request details typically include the product category, specific specifications, and required quantity, and sometimes the expected delivery time window. An urgency level label is a crucial qualitative business information element. A label is automatically assigned or manually assigned based on preset rules to characterize the urgency of the request. Common labels might include urgent, high priority, or routine.

[0040] S105: Based on the urgency level label, match multiple routes with corresponding transportation timeliness from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions.

[0041] Understandably, based on the urgency of the business requirements, the most feasible logistics option that best suits the current business intent is selected from a pre-prepared solution library. Specifically, in this application, when the urgency label indicates certainty priority, matching is performed from paths with high reliability coefficients; when the urgency label indicates cost-time balance, matching is performed from all optimal transportation paths, where the matching criteria include total transportation cost and path reliability coefficients.

[0042] Understandably, when a request is tagged as "urgent" or "high certainty," the core business requirement is certainty in timely delivery, with a relatively high tolerance for cost. In this case, high-certainty paths with reliability coefficients above the first threshold are directly invoked. That is, these high-reliability paths are quickly filtered out from the entire path set of the target grid and directly used as candidate solutions. This ensures that every path matched for the most urgent needs has predictable timeliness guarantees in the customs clearance process, greatly reducing the risk of failing to meet urgent needs due to customs clearance delays.

[0043] When the urgency level of a request is labeled "normal" or indicates cost optimization, the business objective is to find the optimal total cost within an acceptable timeframe. In this case, all paths in the path set can be evaluated. The evaluation no longer relies on a single metric but uses a multi-dimensional scoring model to comprehensively and weight the total transportation cost, total transportation time, and reliability coefficient of each path, generating a comprehensive score.

[0044] Then, all paths are ranked based on the comprehensive score, and the top-ranked paths are selected as candidate options. The dynamic weight is determined according to the current market supply and demand tension in the target geographic grid area. When the supply and demand tension is high, the weight of the path reliability coefficient is increased, and when the supply and demand tension is low, the weight of the total transportation cost is increased.

[0045] For example, the current market supply and demand tension in the region where the target geographic grid is located can be continuously monitored. This can be quantified by analyzing indicators such as real-time sales data, inventory turnover rate, promotional activity intensity, and even social media buzz.

[0046] When a high level of supply and demand tension is detected, such as during a large-scale promotion in the region leading to a surge in demand and the risk of inventory running out, the core risks to the business are lost sales and decreased customer satisfaction due to stockouts. Therefore, the weight of reliability in the overall score can be increased. This means that in route selection, there is a greater preference for routes with reliable customs clearance and stable delivery times, even if they are more expensive, to ensure that inventory reaches the markets where it is urgently needed.

[0047] When the system detects low supply and demand tensions, such as during a stable sales period, the core business objective becomes optimizing operational efficiency and controlling costs. Therefore, the system automatically increases the weight of total transportation costs in the overall score. This means that, while ensuring basic timeliness, cost reduction becomes the primary consideration. In this situation, the system will be more inclined to choose the most economically efficient route to maximize profit margins.

[0048] S106: Based on the real-time inventory data of the candidate supply warehouses associated with the alternative solutions, determine the target supply warehouse and target transportation route and generate inventory transfer instructions.

[0049] Understandably, the first step is to check whether the warehouses associated with each candidate option have sufficient real-time available inventory to meet the quantity requested in this allocation. If the warehouse associated with a particular option has insufficient inventory, that option will be immediately rejected. If the warehouses associated with multiple options have sufficient inventory, then multiple objectives will be comprehensively evaluated.

[0050] For example, requests with the highest urgency can be prioritized, followed by warehouses with higher inventory levels to prevent a single warehouse from being quickly depleted, thus maintaining the resilience of the entire supply network. In some implementations, the impact of depleting a warehouse's inventory on its ability to serve future projected demand in its own region or other regions can also be assessed. This might lead to a preference for retaining some inventory to address more urgent potential demands. Ultimately, the solution that minimizes total cost is selected while meeting the aforementioned business objectives. Once the target supply warehouses and transportation routes are determined, the system automatically generates structured inventory transfer instructions.

[0051] For example, instructions may include: Source: The target supply warehouse's number and location information.

[0052] Receiving party: The specific receiving warehouse or address within the target geographic grid.

[0053] Product and Quantity: Specific SKUs and the quantity allocated.

[0054] Designated transportation route: carrier, mode of transport, and expected pick-up and delivery time window.

[0055] Key information about the route: customs clearance port, reference waybill number, etc.

[0056] The instruction will be automatically sent to the warehouse management system (WMS) to drive picking and packing, and then sent to the transportation management system (TMS) or logistics platform for carrier booking and transportation tracking.

[0057] Of course, the specific instructions are not limited in this application, depending on the actual situation.

[0058] Furthermore, as one implementation method, after generating the inventory transfer instruction, the following may also be included: S107: Obtain the reliability coefficient of the path corresponding to the target transportation route.

[0059] Understandably, the reliability coefficient obtained at this point is recalculated or retrieved based on the latest data. It is a dynamic risk indicator that may change at any time.

[0060] S108: If the reliability coefficient of the route drops below the second preset threshold and the transportation has not entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient.

[0061] For example, the feasibility of executing an alternative transportation route can be assessed based on the updated route reliability coefficient, whether the estimated transportation time meets the original promised timeliness of the allocation request, and the additional cost incurred in switching to the new route; if there is an alternative transportation route that meets the preset feasibility conditions, a route switching instruction is generated and executed, and the transportation route information in the inventory allocation instruction is updated.

[0062] Understandably, the first step is to confirm whether the alternative route truly addresses the risk of triggering a reselection. Switching is only meaningful if the reliability coefficient of the alternative route is significantly and consistently higher than the risk level that would cause the original route to trigger a warning—the second threshold. Simultaneously, it's crucial to assess whether delivery will still be made within the initially promised time window even after switching routes. If the alternative route inevitably leads to default, then even if it's highly reliable and cost-effective, it may not be an acceptable option. The additional costs incurred by switching to the new route must also be considered. These additional costs might include: the freight difference of the new route, potential additional storage or handling fees at transit points, and penalties for canceling the original booking. The assessment doesn't require costs to remain unchanged, but rather to treat them as a key decision factor, weighing them against other dimensions.

[0063] For example, a shipment of urgent goods was originally scheduled to be sent from warehouse A to Germany via air route P (guaranteed delivery within 48 hours, cost $5000). Before takeoff, route P was reselected due to a weather warning at the destination airport, causing its reliability coefficient to drop below the threshold.

[0064] After the system reselects, it proposes two alternative routes: Alternative Route Q (Air to other airports + Land transport): Reliability rating 85, total estimated time 52 hours, additional cost $800.

[0065] Alternate Route R (All Land Transport): Reliability rating 90, total estimated time 96 hours, additional cost -$3000 (cheaper).

[0066] Assessment (assuming the following conditions: reliability > 75, delay not exceeding 6 hours): Route Q: Reliability (85) > 75; Delay (52-48 = 4 hours) < 6 hours; Additional cost $800. All conditions are met, feasible.

[0067] Route R: Reliability (90) > 75; Delay (96-48=48 hours) > 6 hours. The timeliness requirement is not met, so it is not feasible.

[0068] In this way, the system automatically determines that route Q is a feasible option, generates a switching instruction, updates the transportation route to Q, and automatically notifies relevant parties to adjust the pickup and customs clearance arrangements.

[0069] This application proposes an intelligent cross-border supply chain management optimization allocation method. First, based on the distribution of logistics hubs and trunk lines, the supply chain coverage area is divided into multiple geographical grids containing warehouses or transit nodes. A dynamic transportation network graph is constructed using these nodes as vertices. Based on this network graph and supply relationships, a set of candidate supply warehouses and the optimal transportation route set to each warehouse are pre-calculated for each geographical grid. When an inventory allocation request is received from a target geographical grid, routes with corresponding transportation timeliness are matched from the pre-calculated route set according to the request's urgency label to form candidate solutions. Finally, based on the real-time inventory data of the candidate warehouses associated with the candidate solutions, an inventory allocation instruction is generated. This invention realizes spatial management of supply chain resources, real-time perception of the logistics network, and collaborative optimization of inventory and logistics, improving the overall efficiency of the cross-border supply chain.

[0070] Based on the same inventive concept, this application proposes an intelligent cross-border supply chain management optimization and allocation system, which is configured as follows: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, it is divided into multiple connected geographical grids, in which each geographical grid contains at least one warehouse or one transportation transit node. A dynamic transportation network graph is constructed with each warehouse and transportation transfer node as a vertex. The edges of the dynamic transportation network graph are the transportation routes connecting the vertices. Each edge is associated with the available transportation modes, estimated transportation time and unit transportation cost based on real-time data updates. Based on the dynamic transportation network map and the preset supply relationship, the candidate supply warehouse set corresponding to each geographic grid is determined, and the optimal transportation route set from the specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with the total transportation time and the total transportation cost. In response to an inventory allocation request from a target geographic grid, determine the demand details and urgency level label corresponding to the request. The target geographic grid can be any geographic grid. Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions; Based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, the target supply warehouse and target transportation route are determined and inventory transfer instructions are generated.

[0071] Optionally, the system is configured as follows: Based on the dynamic transportation network map and preset supply relationships, a set of candidate supply warehouses is determined for each geographic grid, and an optimal set of transportation routes from a specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with total transportation time and total transportation cost, including: Obtain the reliability coefficient for each path, where the reliability coefficient is related to the stability of the path's historical customs clearance time and the current congestion warning information generated by the customs declaration system; In the optimal transportation route set, at least one route with a reliability coefficient higher than the first threshold is reserved for each candidate supply warehouse.

[0072] Optionally, the system is configured as follows: Obtain the reliability coefficient for each path. This reliability coefficient is related to the stability of the path's historical customs clearance time and the current congestion warning information generated by the customs declaration system, including: Obtain a dynamic customs clearance risk assessment model, wherein the input feature vector of the dynamic customs clearance risk assessment model includes at least: The variance of the on-time clearance rate of the key cross-border checkpoints corresponding to the route in the past preset time period; Based on the current queue length of pending declarations for each category of goods obtained from the customs system.

[0073] Optionally, the system is configured as follows: Based on real-time inventory data of candidate supply warehouses associated with the alternative solutions, after determining the target supply warehouse and target transportation route and generating inventory transfer instructions, the process includes: Obtain the reliability coefficient of the path corresponding to the target transportation route; If the reliability coefficient of the route drops below the second preset threshold and the transportation has not entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient.

[0074] Optionally, the system is configured as follows: If the reliability coefficient of a route drops below a second preset threshold and the transportation has not yet entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient, including: The feasibility of implementing the alternative transportation route is assessed based on the updated route reliability coefficient, whether the estimated transport time meets the original promised timeliness of the dispatch request, and the additional costs incurred in switching to the new route. If there is an alternative transportation route that meets the preset feasibility conditions, a route switching instruction is generated and executed, and the transportation route information in the inventory transfer instruction is updated.

[0075] Optionally, the system is configured as follows: Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions, including: When the urgency level label indicates deterministic priority, matching is performed from paths with high reliability coefficients; When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes, with the matching criteria including total transportation cost and route reliability coefficient.

[0076] Optionally, the system is configured as follows: When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes. The matching criteria include the total transportation cost and the route's reliability coefficient, including: A multi-dimensional route scoring model is constructed. Based on preset dynamic weights, the multi-dimensional route scoring model calculates a comprehensive score by weighting the total transportation cost and the reliability coefficient of each route. All paths are ranked based on a comprehensive score, and the top-ranked paths are selected as candidate options. The dynamic weights are determined based on the current market supply and demand tension in the target geographic grid area. When the supply and demand tension is high, the weight of the path reliability coefficient is increased, and when the supply and demand tension is low, the weight of the total transportation cost is increased.

[0077] This application proposes an intelligent cross-border supply chain management optimization and allocation system. First, based on the distribution of logistics hubs and trunk lines, the supply chain coverage area is divided into multiple geographical grids containing warehouses or transit nodes. A dynamic transportation network map is constructed using these nodes as vertices. Based on this network map and supply relationships, a set of candidate supply warehouses and the optimal transportation route set to each warehouse are pre-calculated for each geographical grid. When an inventory allocation request is received from a target geographical grid, routes with corresponding transportation timeliness are matched from the pre-calculated route set according to the request's urgency label to form candidate solutions. Finally, based on the real-time inventory data of the candidate warehouses associated with the candidate solutions, an inventory allocation instruction is generated. This invention realizes spatial management of supply chain resources, real-time perception of the logistics network, and collaborative optimization of inventory and logistics, improving the overall efficiency of the cross-border supply chain.

[0078] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the intelligent cross-border supply chain management optimization allocation method of the present application embodiments.

[0079] In addition, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent cross-border supply chain management optimization allocation method of embodiments of this application.

[0080] The following is a detailed introduction to the various components of the electronic device: In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0081] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0082] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0083] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through an interface circuit of an electronic device; the embodiments of the present invention do not specifically limit this.

[0084] A transceiver is used to communicate with network devices or with terminal devices.

[0085] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0086] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0087] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0088] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0089] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0091] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0092] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0093] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0094] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A smart cross-border supply chain management optimization allocation method, characterized in that, The method includes: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, it is divided into multiple connected geographical grids, wherein each geographical grid contains at least one warehouse or one transportation transit node. A dynamic transportation network graph is constructed with each of the aforementioned warehouses and transportation transfer nodes as vertices. The edges of the dynamic transportation network graph are transportation routes connecting the vertices, and each edge is associated with available transportation modes, estimated transportation time, and unit transportation cost based on real-time data updates. Based on the dynamic transportation network map and the preset supply relationship, a set of candidate supply warehouses corresponding to each geographic grid is determined, and an optimal set of transportation routes from a specified point within the geographic grid to each warehouse in the set of candidate supply warehouses is determined, wherein each route is associated with total transportation time and total transportation cost. In response to an inventory allocation request from a target geographic grid, the demand details and urgency level tag corresponding to the request are determined, wherein the target geographic grid is any of the geographic grids mentioned above; Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions; Based on the real-time inventory data of the candidate supply warehouses associated with the proposed alternatives, the target supply warehouse and target transportation route are determined, and an inventory transfer instruction is generated.

2. The intelligent cross-border supply chain management optimization allocation method according to claim 1, characterized in that, Based on the dynamic transportation network map and preset supply relationships, a set of candidate supply warehouses corresponding to each geographic grid is determined, and a set of optimal transportation routes from a specified point within the geographic grid to each warehouse in the candidate supply warehouse set is determined. Each route is associated with total transportation time and total transportation cost, including: Obtain the reliability coefficient corresponding to each of the paths, wherein the reliability coefficient is associated with the stability of the historical customs clearance time of the path and the congestion warning information generated by the current customs declaration system; In the set of optimal transportation routes, at least one of the routes with a reliability coefficient higher than the first threshold is reserved for each of the candidate supply warehouses.

3. The intelligent cross-border supply chain management optimization allocation method according to claim 2, characterized in that, Obtain the reliability coefficient corresponding to each of the aforementioned paths, wherein the reliability coefficient is associated with the stability of the historical customs clearance time of the path and the congestion warning information generated by the current customs declaration system, including: Obtain a dynamic customs clearance risk assessment model, wherein the input feature vector of the dynamic customs clearance risk assessment model includes at least: The variance of the on-time clearance rate of the key cross-border checkpoints corresponding to the path in the past preset time period. Based on the current queue length of pending declarations for each category of goods obtained from the customs system.

4. The intelligent cross-border supply chain management optimization allocation method according to claim 3, characterized in that, Based on real-time inventory data of candidate supply warehouses associated with the proposed alternatives, after determining the target supply warehouse and target transportation route and generating inventory transfer instructions, the process includes: Obtain the reliability coefficient of the path corresponding to the target transportation route; If the reliability coefficient of the route drops below the second preset threshold and the transportation has not entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient.

5. The intelligent cross-border supply chain management optimization allocation method according to claim 4, characterized in that, If the reliability coefficient of the route drops below a second preset threshold and the transportation has not yet entered the customs clearance stage, route reselection is triggered, and an alternative transportation route is determined based on the new reliability coefficient, including: The feasibility of implementing the backup transportation route is assessed based on the updated route reliability coefficient, whether the estimated transportation time meets the original promised timeliness of the dispatch request, and the additional costs incurred in switching to the new route. If there is an alternative transportation route that meets the preset feasibility conditions, a route switching instruction is generated and executed to update the transportation route information in the inventory transfer instruction.

6. The intelligent cross-border supply chain management optimization allocation method according to claim 1, characterized in that, Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions, including: When the urgency level label indicates deterministic priority, matching is performed from the paths with the highest reliability coefficient; When the urgency level label indicates a balance between cost and timeliness, a match is made from all optimal transportation routes, wherein the matching criteria include the total transportation cost and the reliability coefficient of the route.

7. The intelligent cross-border supply chain management optimization allocation method according to claim 6, characterized in that, When the urgency level label indicates a cost-time balance, a match is made from all optimal transportation routes, wherein the matching criteria include the total transportation cost and the reliability coefficient of the route, including: A multi-dimensional route scoring model is constructed. The multi-dimensional route scoring model calculates a comprehensive score by weighting the total transportation cost of each route with the reliability coefficient of the route according to preset dynamic weights. All the paths are ranked based on the comprehensive score, and the top-ranked paths are selected as the candidate solutions. The dynamic weight is determined according to the current market supply and demand tension in the region where the target geographic grid is located. When the supply and demand tension is high, the weight of the reliability coefficient of the path is increased, and when the supply and demand tension is low, the weight of the total transportation cost is increased.

8. A smart cross-border supply chain management and optimization allocation system, characterized in that, The system is configured as follows: Based on the geographical area covered by the supply chain network, and according to the distribution of major logistics hubs and the route of transportation trunk lines, it is divided into multiple connected geographical grids, wherein each geographical grid contains at least one warehouse or one transportation transit node. A dynamic transportation network graph is constructed with each of the aforementioned warehouses and transportation transfer nodes as vertices. The edges of the dynamic transportation network graph are transportation routes connecting the vertices, and each edge is associated with available transportation modes, estimated transportation time, and unit transportation cost based on real-time data updates. Based on the dynamic transportation network map and the preset supply relationship, a set of candidate supply warehouses corresponding to each geographic grid is determined, and an optimal set of transportation routes from a specified point within the geographic grid to each warehouse in the set of candidate supply warehouses is determined, wherein each route is associated with total transportation time and total transportation cost. In response to an inventory allocation request from a target geographic grid, the demand details and urgency level tag corresponding to the request are determined, wherein the target geographic grid is any of the geographic grids mentioned above; Based on the urgency level label, multiple routes with corresponding transportation timeliness are matched from the set of optimal transportation routes corresponding to the target geographic grid to form candidate solutions; Based on the real-time inventory data of the candidate supply warehouses associated with the proposed alternatives, the target supply warehouse and target transportation route are determined, and an inventory transfer instruction is generated.

9. An electronic device, comprising: At least one processor; And, a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, which, when executed by at least one of the processors, enable the at least one of the processors to perform the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as claimed in any one of claims 1-7.

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