CN-122022686-A - Intelligent cross-border supply chain management optimization distribution method and system

CN122022686ACN 122022686 ACN122022686 ACN 122022686ACN-122022686-A

Abstract

The application provides an intelligent cross-border supply chain management optimization distribution method and system, which belong to the technical field of data processing, and firstly divide a supply chain coverage area into a plurality of geographic grids comprising warehouses or transit nodes according to distribution of logistics hubs and trunk lines. And constructing a dynamic transportation network graph by taking the nodes as vertexes. Based on the network map and the supply relationship, a corresponding candidate supply warehouse set and an optimal transportation path set of each warehouse are pre-calculated for each geographic grid. When an inventory allocation request of a target geographic grid is received, paths with corresponding transportation timeliness are matched from a pre-calculated path set to form a candidate scheme according to the emergency label. Finally, an inventory reconciliation instruction is generated based on the real-time inventory data of the candidate warehouse associated with the candidate solution. The application realizes space management of supply chain resources, real-time perception of a logistics network and collaborative optimization of inventory and logistics, and improves the overall efficiency of a cross-border supply chain.

Inventors

ZHANG QI
DAI HUI
GONG YUTONG
GAO HONG
WU HAO

Assignees

南京江北新区鲸跃数字公共服务平台有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. An intelligent cross-border supply chain management optimization allocation method, which is characterized by comprising the following steps: dividing the main logistics hub into a plurality of connected geographic grids according to distribution of the main logistics hub and trend of transportation trunk lines based on a geographic area covered by a supply chain network, wherein each geographic grid at least comprises a warehouse or a transportation transit node; Constructing a dynamic transportation network diagram by taking each warehouse and a transportation transfer node as a vertex, wherein the edges of the dynamic transportation network diagram are transportation routes connected with the vertices, and each edge is associated with an available transportation mode, estimated transportation time and unit transportation cost based on real-time data updating; Determining a candidate supply warehouse set corresponding to each geographic grid based on the dynamic transportation network diagram and a preset supply relation, and determining an optimal transportation path set from a designated point in the geographic grid to each warehouse in the candidate supply warehouse sets, wherein each path is associated with total transportation time and total transportation cost; Responding to an inventory allocation request from a target geographic grid, and determining demand details and emergency labels corresponding to the request, wherein the target geographic grid is any geographic grid; according to the emergency degree label, matching a plurality of paths with corresponding transportation timeliness from the optimal transportation path set corresponding to the target geographic grid to form a to-be-selected scheme; A target supply warehouse and a target transportation route are determined and inventory allocation instructions are generated based on real-time inventory data of candidate supply warehouses associated with the candidate solution.
2. The intelligent cross-border supply chain management optimization allocation method according to claim 1, wherein determining a candidate supply warehouse set corresponding to each geographic grid based on the dynamic transportation network graph and a preset supply relation, and determining an optimal transportation path set from a specified point in the geographic grid to each warehouse in the candidate supply warehouse sets, wherein each path associates a total transportation time and a total transportation cost, comprises: Acquiring a reliability coefficient corresponding to each path, wherein the reliability coefficient is related to the stability of the history clearance time of the path and the generation of congestion early warning information of a current customs declaration system; at least one of the paths with the reliability coefficient above a first threshold value is reserved for each of the candidate supply warehouses in the optimal transportation path set.
3. The intelligent cross-border supply chain management optimization allocation method according to claim 2, wherein the obtaining of the reliability coefficient corresponding to each path, wherein the reliability coefficient relates to the stability of the path history clearance aging and the congestion early warning information generation of the current customs declaration system, comprises: acquiring a dynamic clearance risk assessment model, wherein an input feature vector of the dynamic clearance risk assessment model at least comprises: Clear clearance quasi-time rate variance of the key cross-border gateway corresponding to the path in a preset time period in the past; Based on the length of the declaration pending queue of the current commodity of each category acquired by the customs system.
4. The intelligent cross-border supply chain management optimization allocation method as claimed in claim 3, wherein after determining a target supply warehouse and a target transportation route based on real-time inventory data of candidate supply warehouses associated with the candidate solution, and generating an inventory allocation instruction, comprising: acquiring the reliability coefficient of the path corresponding to the target transportation route; and if the reliability coefficient of the path is reduced to be lower than a second preset threshold value and the transportation does not enter a clearance link, triggering path reselection, and determining a standby transportation route based on the new reliability coefficient.
5. The intelligent cross-border supply chain management optimization allocation method according to claim 4, wherein the reliability coefficient of the path falls below a second preset threshold, and if the transportation does not enter a clear link, a path reselection is triggered, and a standby transportation route is determined based on the new reliability coefficient, comprising: Evaluating the execution feasibility of the standby transportation route based on the updated route reliability coefficient, whether the estimated transportation time meets the original promise timeliness of the allocation request and the additional cost generated by switching to a new route; And if the standby transportation route meeting the preset feasibility condition exists, generating and executing a route switching instruction, and updating transportation route information in the inventory allocation instruction.
6. The optimal distribution method for intelligent cross-border supply chain management according to claim 1, wherein matching a plurality of the paths with corresponding transportation timelines from the optimal transportation path set corresponding to the target geographic grid according to the urgency tag to form a candidate scheme comprises: when the urgency tag indicates deterministic priority, matching from the path of the reliability coefficient that is high; when the urgency tag indicates a cost-to-age balance, then a match is made from all optimal transportation paths, wherein the matching condition includes a total cost of transportation and the reliability coefficient of the path.
7. The intelligent cross-border supply chain management optimization allocation method according to claim 6, wherein when the urgency tag indicates that cost and timeliness are balanced, then matching is performed from all optimal transportation paths, wherein matching conditions include a total transportation cost and the reliability coefficient of the paths, including: Constructing a multi-dimensional path scoring model, and carrying out weighted calculation on the total transportation cost of each path and the reliability coefficient of the path according to a preset dynamic weight by the multi-dimensional path scoring model to generate a comprehensive score; And sorting all paths based on the comprehensive scores, and determining a plurality of paths with top ranking to form the to-be-selected scheme, wherein the dynamic weight is determined according to the current market supply and demand tension of the area where the target geographic grid is located, when the supply and demand tension is high, the weight of the reliability coefficient of the paths is improved, and when the supply and demand tension is low, the weight of the total transportation cost is improved.
8. An intelligent cross-border supply chain management optimization distribution system, the system configured to: dividing the main logistics hub into a plurality of connected geographic grids according to distribution of the main logistics hub and trend of transportation trunk lines based on a geographic area covered by a supply chain network, wherein each geographic grid at least comprises a warehouse or a transportation transit node; Constructing a dynamic transportation network diagram by taking each warehouse and a transportation transfer node as a vertex, wherein the edges of the dynamic transportation network diagram are transportation routes connected with the vertices, and each edge is associated with an available transportation mode, estimated transportation time and unit transportation cost based on real-time data updating; Determining a candidate supply warehouse set corresponding to each geographic grid based on the dynamic transportation network diagram and a preset supply relation, and determining an optimal transportation path set from a designated point in the geographic grid to each warehouse in the candidate supply warehouse sets, wherein each path is associated with total transportation time and total transportation cost; Responding to an inventory allocation request from a target geographic grid, and determining demand details and emergency labels corresponding to the request, wherein the target geographic grid is any geographic grid; according to the emergency degree label, matching a plurality of paths with corresponding transportation timeliness from the optimal transportation path set corresponding to the target geographic grid to form a to-be-selected scheme; A target supply warehouse and a target transportation route are determined and inventory allocation instructions are generated based on real-time inventory data of candidate supply warehouses associated with the candidate solution.
9. An electronic device, the electronic device comprising: At least one processor; And a memory communicatively coupled to at least one of the processors; Wherein the memory stores instructions executable by at least one of the processors, the instructions being executable by at least one of the processors to enable the at least one of the processors to perform the method as set forth in any one of claims 1-7.
10. A computer readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, implements the method as claimed in any of claims 1-7.

Description

Intelligent cross-border supply chain management optimization distribution method and system Technical Field The application relates to the technical field of data processing, in particular to an intelligent cross-border supply chain management optimization distribution method and system. Background Traditional cross-border supply chain management methods typically rely on static management patterns divided by administrative areas or fixed warehouse service radius. When making inventory allocation decisions, a vast and complex cross-border geographic area lacks a refined space management unit, and accurate geographic mapping of resources and demands is difficult to achieve. And secondly, network state information on which logistics path planning depends is updated with hysteresis, so that real-time dynamic changes of transportation cost, time and availability cannot be reflected, and decision is based on outdated data. Moreover, the inventory allocation decision flow has slow response, and the supply source and the logistics path are usually calculated temporarily after the allocation request is received, so that the high requirement of cross-border business on timeliness cannot be met. In addition, the decision process often carries out inventory query and logistics routing planning and splitting treatment, and the cooperative optimization of inventory states and logistics capacity in a real-time data layer is not realized, so that the formulated allocation scheme may face the execution dilemma of whether goods exist or not or whether goods exist or not. Disclosure of Invention The embodiment of the application provides an intelligent cross-border supply chain management optimization distribution method and system for solving the problems. In order to achieve the above purpose, the application adopts the following technical scheme: In a first aspect, an embodiment of the present application provides an intelligent cross-border supply chain management optimization allocation method, where the method includes: Dividing the main logistics hub into a plurality of connected geographic grids according to the distribution of the main logistics hub and the trend of the transportation trunk line based on the geographic area covered by the supply chain network, wherein each geographic grid at least comprises a warehouse or a transportation transit node; Constructing a dynamic transportation network diagram by taking each warehouse and a transportation transfer node as vertexes, wherein the edges of the dynamic transportation network diagram are transportation routes connected with the vertexes, and each edge is associated with an available transportation mode, estimated transportation time and unit transportation cost based on real-time data updating; Determining a candidate supply warehouse set corresponding to each geographic grid based on the dynamic transportation network diagram and a preset supply relation, and determining an optimal transportation path set from a designated point in the geographic grid to each warehouse in the candidate supply warehouse sets, wherein each path is associated with total transportation time and total transportation cost; Responding to an inventory allocation request from a target geographic grid, and determining demand details and an emergency degree label corresponding to the request, wherein the target geographic grid is any geographic grid; According to the emergency degree label, matching a plurality of paths with corresponding transportation timeliness from the optimal transportation path set corresponding to the target geographic grid to form a to-be-selected scheme; A target supply warehouse and a target transportation route are determined and inventory allocation instructions are generated based on real-time inventory data of candidate supply warehouses associated with the candidate solution. With reference to the first aspect, optionally, based on the dynamic transportation network graph and a preset provisioning relationship, determining a candidate provisioning warehouse set corresponding to each geographic grid, and determining an optimal transportation path set from a designated point in the geographic grid to each warehouse in the candidate provisioning warehouse sets, where each path associates a total transportation time with a total transportation cost, including: Acquiring a reliability coefficient corresponding to each path, wherein the reliability coefficient is related to the stability of the history clearance time effect of the path and the generation of congestion early warning information of a current customs declaration system; At least one path with a reliability coefficient above a first threshold value is reserved for each candidate supply warehouse in the optimal transportation path set. With reference to the first aspect, optionally, obtaining a reliability coefficient corresponding to each path, where the reliability coefficient is related to stabi