Multimodal transport end-to-end supply chain collaborative management method based on container logistics

Abstract

The invention relates to a multimodal transport end-to-end supply chain collaborative management method based on container logistics, and belongs to the technical field of supply chain management. The method comprises the following steps: virtually integrating scattered cargo owner demand, carrier transport capacity and transit point operation capability resources through a cloud computing platform to form a shared resource pool, and constructing a dynamic collaborative network; performing intelligent matching and scheduling on the shared resource pool through a contribution degree distribution mechanism to obtain a supply chain full-link state; dynamic routing optimization is carried out according to the full-link state of the supply chain, and optimal path selection is carried out on the transportation mode of container logistics based on the business process of multimodal transportation; a task is automatically decomposed through an intelligent contract and issued to a node, a carrier automatically uploads a voucher through an RFID gate after completing the node task, and the contract verifies the voucher and then triggers a next node task. Seamless connection and efficient collaboration among all nodes of the supply chain are achieved, and the transportation efficiency and reliability of container logistics are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain management, and particularly relates to an end-to-end supply chain collaborative management method for multimodal transport based on container logistics. Background Art

[0002] In the traditional container vehicle logistics management process, the information exchange among shippers, carriers and transfer points is not smooth. Shippers, carriers and transfer points use heterogeneous systems (EDI / email / paper documents), and data needs to be manually transcribed, resulting in delayed order processing, lack of real-time status visualization, and non-cross-platform sharing of IoT data such as vehicle location and container temperature. Under the traditional bidding mode, the selection of carriers takes a long time, and the transport capacity is idle due to information asymmetry. The utilization rate of transfer equipment is low, while the vacancy rate of standby freight yards is relatively high during the same period, and the regional resource coordination is seriously insufficient, leading to low resource matching efficiency, high transportation costs, and difficulty in coping with sudden risk events. In addition, the degree of automation of operations in each link is insufficient, relying on manual coordination, which increases the risk of operation errors and delayed deliveries. Therefore, the market urgently needs an innovative supply chain collaborative management method to achieve efficient integration and optimal allocation of resources, and improve transportation efficiency and reliability. Summary of the Invention

[0003] To solve the above problems existing in the prior art, the present invention provides an end-to-end supply chain collaborative management method for multimodal transport based on container logistics. The object of the present invention can be achieved by the following technical solutions: S1: Virtually integrate the scattered shipper demands, carrier transport capacities, and transfer point operation capabilities through a cloud computing platform to form a shared resource pool, and construct a dynamic collaborative network; S2: Based on the dynamic collaborative network, perform intelligent matching and scheduling on the shipper demands, carrier transport capacities, and port operation capabilities in the shared resource pool through a contribution degree allocation mechanism to obtain the full-link state of the supply chain; S3: Perform dynamic routing optimization according to the full-link state of the supply chain, select the optimal path for the transportation mode of container logistics from the starting point to the destination based on the business process of multimodal transport, and update the transportation plan; S4: Define task assignment logic through an intelligent contract rule library, automatically decompose tasks through an intelligent contract according to the task assignment logic and issue them to nodes. After the carrier completes the node tasks, upload vouchers automatically through an RFID gate, and trigger the next node task after the contract verifies the vouchers.

[0004] Specifically, the shared resource pool in S1 encapsulates shipper demands, carrier transport capacities, and transfer point operation capabilities into standardized microservice modules through containerization technology and supports elastic expansion.

[0005] Specifically, the method for constructing the dynamic collaboration network in S1 is as follows: S101: Based on the graph computing engine, real-time map the multi-dimensional association relationships among shippers, carriers, and transfer points, including geographical location matching degree, historical cooperation credit score, and real-time service capacity threshold; S102: Perform collaborative modeling on the multi-source heterogeneous data of the multi-dimensional association relationships to generate a resource matching weight matrix; S103: Respond to resource status changes through the event-driven architecture, dynamically adjust the collaborative network topology according to the resource matching weight matrix, and automatically trigger capacity reallocation when the operation capacity of the transfer point is overloaded.

[0006] Specifically, the contribution degree allocation mechanism in S2 includes: S201: Construct a multi-objective optimization function, where the objective variables include minimizing transportation costs, optimizing carbon emission intensity, and maximizing delivery on-time rate; S202: Solve the Pareto front through the non-dominated sorting genetic algorithm to generate a candidate solution set for resource matching; S203: Select the matching result with the highest comprehensive score from the candidate solution set based on the fuzzy comprehensive evaluation method and dynamically update the contribution degree weights.

[0007] Specifically, the full-link state of the supply chain in S2 is used to integrate shipper order data, carrier vehicle GPS positioning, and real-time monitoring data of Internet of Things devices at transfer points, store the dynamic data stream through a time-series database, and use a streaming computing platform to calculate key state parameters in real time; the key state parameters include transportation time deviation rate, container detention rate, and equipment failure rate.

[0008] Specifically, the dynamic routing optimization method in S3 is as follows: Build a multimodal transport path decision-making model based on reinforcement learning, with input parameters including real-time transportation costs, transfer node congestion probability, carbon emission intensity threshold, and shipper priority weight, and output the optimal solution set of the combined road-rail-water transport path; By monitoring sudden risk events, automatically trigger path dynamic switching, send adjustment instructions to affected nodes through blockchain smart contracts, and synchronously update the full-link state data.

[0009] Specifically, the triggering conditions for the path dynamic switching include that the operation capacity of the transfer point is overloaded; the transportation time deviation rate continuously exceeds the planned value; the Internet of Things device detects extreme weather leading to the risk of path interruption.

[0010] Specifically, the business process of the multimodal transport in S3 includes: For the order reception, cargo loading, transshipment, transportation tracking, and delivery confirmation in combined rail, road, and water transportation, each business process link realizes automated operation through Internet of Things technology and blockchain smart contracts.

[0011] Specifically, the task allocation logic in S4 adopts a hierarchical rule engine architecture, including a transportation mode recognition layer, a resource constraint verification layer, and a dynamic priority adjustment layer; the rule library builds in a fuzzy logic controller to analyze the shipper's priority, the carrier's service level agreement, and the real-time load status of the transfer point. The rule trigger mechanism adopts an event-driven mode, decomposing the end-to-end transportation into atomic operation units, and encapsulating each atomic task as a smart contract object.

[0012] Specifically, the smart contract in S4 automatically executes the process management after task decomposition, including task progress monitoring, exception handling, and automatic settlement. The smart contract deconstructs the task allocation of multimodal transportation, refining the trigger conditions, execution actions, and constraint rules; the data structure of the smart contract is a data model for on-chain and off-chain collaboration. The on-chain storage layer stores key vouchers, and the off-chain computing layer is used to access external data sources, defining the state migration paths of quotation submission, matching, transaction settlement, and evaluation feedback through a finite state machine diagram.

[0013] The beneficial effects of the present invention are as follows: By integrating advanced technologies such as cloud computing, Internet of Things, blockchain, and reinforcement learning, the end-to-end supply chain efficient collaborative management of container logistics in the multimodal transportation scenario is realized. This method not only improves the utilization rate of logistics resources, reduces transportation costs, but also significantly enhances the flexibility and risk resistance ability of the supply chain. Especially in the face of emergencies such as overloaded operation capacity at the transfer point, transportation time deviation, or extreme weather, the system can automatically trigger dynamic path switching to ensure the continuity and punctuality of cargo transportation. In addition, through the application of smart contracts, the automated management of task allocation, progress monitoring, exception handling, and automatic settlement is realized, further improving the transparency and efficiency of logistics operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 It is a processing timing schematic diagram of the end-to-end supply chain collaborative management method for multimodal transportation based on container logistics of the present invention; Figure 2 It is a schematic diagram of the multi-source data collaborative modeling process of the present invention; Figure 3 It is a schematic diagram of the structure of the on-chain and off-chain collaborative mechanism of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on the specific implementation manners, structures, features and their effects according to the present invention.

[0017] Please refer to Figures 1-3 , a multi-modal end-to-end supply chain collaborative management method based on container logistics, including: S1: Virtually integrate the scattered shipper demands, carrier capacities, and transfer point operation capabilities through a cloud computing platform to form a shared resource pool, and construct a dynamic collaboration network; S2: Based on the dynamic collaboration network, perform intelligent matching and scheduling on the shipper demands, carrier capacities, and port operation capabilities in the shared resource pool through a contribution degree allocation mechanism to obtain the full-link state of the supply chain; S3: Perform dynamic routing optimization according to the full-link state of the supply chain, select the optimal path for the transportation mode of container logistics from the starting point to the destination based on the business process of multi-modal transportation, and update the transportation plan; S4: Define task assignment logic through an intelligent contract rule library, automatically decompose tasks through an intelligent contract according to the task assignment logic and issue them to nodes. After the carrier completes the node tasks, the vouchers are automatically uploaded through an RFID gate, and the next node task is triggered after the contract verifies the vouchers.

[0018] Specifically, the shared resource pool in S1 encapsulates shipper demands, carrier capacities, and transfer point operation capabilities into standardized microservice modules through containerization technology and supports elastic expansion.

[0019] In this embodiment, a "microservice + container orchestration + elastic scheduling" trinity architecture is adopted to realize the dynamic integration of shipper demands, carrier capacities, and transfer point operation capabilities; the microservice modules include a shipper demand parsing module (by parsing shipper demands, outputting structured data and encapsulating it into a Docker container), a capacity registration center (integrating the data stream of in-vehicle IoT devices to construct a dynamic capacity profile); a transfer operation adapter; the elastic expansion system of this embodiment includes a three-level response strategy to meet multi-level expansion requirements from second-level burst demands to seasonal fluctuations. When the regional resource pool is saturated, the load is migrated to other available zones through Kubernetes Federation.

[0020] Specifically, the construction method of the dynamic collaboration network in S1 is: S101: Based on a graph computing engine, real-time map the multi-dimensional association relationships among shippers, carriers, and transfer points, including geographical location matching degree, historical cooperation credit score, and real-time service capacity threshold; S102: Co - model the multi - source heterogeneous data of the multi - dimensional association relationship to generate a resource matching weight matrix; S103: Respond to resource status changes through an event - driven architecture, dynamically adjust the collaborative network topology according to the resource matching weight matrix, and automatically trigger capacity re - allocation when the operation capacity at the transfer point is overloaded.

[0021] In this embodiment, a "graph computing engine + stream processing platform + dynamic weight matrix" trinity architecture is adopted to achieve multi - dimensional resource association modeling and dynamic collaborative optimization. The multi - source data collaborative modeling process is as Figure 2 shown; the dynamic adjustment of the collaborative network topology is based on a decision - making model of reinforcement learning: , where α, β, γ are multi - objective weights, the state space s contains 128 - dimensional features such as the current topology structure and resource utilization rate, and the action space a includes operations such as node addition and deletion, and edge weight adjustment.

[0022] Specifically, the contribution degree allocation mechanism in S2 includes: S201: Construct a multi - objective optimization function, and the objective variables include minimizing transportation costs, optimizing carbon emission intensity, and maximizing delivery on - time rate; S202: Solve the Pareto front through a non - dominated sorting genetic algorithm to generate a candidate solution set for resource matching; S203: Select the matching result with the highest comprehensive score from the candidate solution set based on the fuzzy comprehensive evaluation method, and dynamically update the contribution degree weights.

[0023] In this embodiment, the three - dimensional objective space of the multi - objective optimization function can be expressed as: , where f1 represents the total transportation cost, including the fixed cost C fixed , the variable cost C variable , the transfer operation fee γ i , f2 is the on - time delivery rate; d represents the deviation degree between the actual transportation time and the planned transportation time of the goods from the starting point to the destination point, T transfer represents the time required for transfer operations, t actual represents the actual transportation time, t plan represents the planned transportation time; The constraint conditions are modeled as: , Combined with the generation of the NSGA-II Pareto front, SBX simulated binary crossover (crossover rate 0.8) and polynomial mutation (mutation rate 0.1) are adopted. 200 initial solutions are generated through Latin hypercube sampling to ensure uniform spatial distribution. Real number coding is used to represent the selection of transportation routes, and an example of the gene structure is: , The fuzzy membership function for fuzzy comprehensive evaluation and weight update can be constructed based on the fuzzy optimization method as: , Among them, the ideal value takes the optimal value of each objective in the Pareto solution set; The comprehensive score adopts a fuzzy decision matrix: , Among them, w m is the dynamic weight, λ is the historical performance influence factor, represents the carrier's historical on-time rate.

[0024] Specifically, the full-link state of the supply chain in S2 is used to integrate the shipper's order data, the carrier vehicle GPS positioning, and the real-time monitoring data of the IoT devices at the transfer points, and the dynamic data stream is stored through a time-series database. The key state parameters are calculated in real time using a streaming computing platform; the key state parameters include the transportation time deviation rate, the container detention rate, and the equipment failure rate.

[0025] In this embodiment, it is compatible with the shipper's EDI / API order data (JSON / EDIFACT), the vehicle-mounted GPS positioning message (NMEA0183 standard), and the transfer point IoT device data (Modbus / OPC UA protocol); a Flink+Spark StructuredStreaming hybrid engine is adopted to achieve real-time calculation with sub-second latency.

[0026] Specifically, the dynamic routing optimization method in S3 is as follows: Based on reinforcement learning, a multimodal transport path decision-making model is constructed. The input parameters include the real-time transportation cost, the congestion probability of the transfer nodes, the carbon emission intensity threshold, and the shipper's priority weight, and the optimal solution set of the combined road-rail-water transport path is output; By monitoring sudden risk events, the path is automatically triggered to switch dynamically, and adjustment instructions are sent to the affected nodes through blockchain smart contracts to synchronously update the full-link state data.

[0027] In this embodiment, real-time parameters such as integrated transportation costs (API-connected bank exchange rates), congestion probabilities (real-time traffic conditions on Amap), and shipper weights (EDI order unit data) are integrated; when a risk event is detected, the delay of route switching is triggered; the main chain stores route policy metadata, and the side chain processes regional transportation voucher verification; the multimodal transportation route decision-making model is modeled based on the Markov decision process: , where C t represents the transportation cost, P j represents the congestion probability of transfer node j, and W m represents the priority weight of the shipper.

[0028] Encode discrete transportation events into a continuous vector space. Without sharing the original data, verify the effectiveness of the route optimization plan across enterprises, and realize personalized optimization by adjusting the reward function parameters in real time based on the shipper weight.

[0029] Specifically, the triggering conditions for the dynamic route switching include that the operation capacity of the transfer point is overloaded; the transportation time deviation rate continuously exceeds the planned value; the Internet of Things device detects extreme weather resulting in the risk of route interruption.

[0030] Specifically, the business process of the multimodal transportation in S3 includes: Order reception, cargo loading, transshipment, transportation tracking, and delivery confirmation for road-rail-water multimodal transportation. Each business process link realizes operation automation through Internet of Things technology and blockchain smart contracts.

[0031] Specifically, the task assignment logic in S4 adopts a hierarchical rule engine architecture, including a transportation mode recognition layer, a resource constraint verification layer, and a dynamic priority adjustment layer; the rule library builds in a fuzzy logic controller to analyze the shipper priority, the carrier service level agreement, and the real-time load status of the transfer point. The rule triggering mechanism adopts an event-driven mode, decomposes the end-to-end transportation into atomic operation units, and encapsulates each atomic task as a smart contract object.

[0032] Specifically, the smart contract in S4 automatically executes the process management after task decomposition, including task progress monitoring, exception handling, and automatic settlement. The smart contract deconstructs the task assignment of multimodal transportation, extracts triggering conditions, execution actions, and constraint rules; the data structure of the smart contract is a data model for on-chain and off-chain collaboration. The on-chain storage layer stores key vouchers, and the off-chain calculation layer is used to access external data sources. The state migration path of quotation submission, matching, transaction settlement, and evaluation feedback is defined through a finite state machine diagram.

[0033] In this embodiment, the smart contract is written in Solidity language and supports blockchain platforms such as EVM (Ethereum Virtual Machine) and Hyperledger Fabric; the deployment and upgrade of the smart contract are automatically completed through the CI / CD pipeline to ensure the consistency and security of the code version. The task progress monitoring module integrates the blockchain event listening mechanism to capture task status change events in real time and trigger the corresponding processing logic. The exception handling module automatically diagnoses and fixes common problems during transportation, such as vehicle failures and cargo damages, based on a predefined exception handling policy library. The automatic settlement module automatically calculates the transportation fees according to the settlement rules defined in the smart contract and realizes the instant transfer of funds through the blockchain wallet. The evaluation and feedback module collects service evaluation data from shippers, carriers, and transfer points and provides references for subsequent transportation decisions through data analysis. The on-chain and off-chain collaboration mechanism is as Figure 3 shown, where the on-chain vouchers are stored using Merkle Patricia Tree, and the off-chain accesses the real-time traffic API of Amap.

[0034] The above are only the preferred embodiments of the present invention and do not impose any formal limitations on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A multi-modal end-to-end supply chain collaborative management method based on container logistics, characterized in that Including: S1: Virtually integrate the scattered shipper demands, carrier capacities, and transfer point operation capabilities through a cloud computing platform to form a shared resource pool, and construct a dynamic collaboration network; S2: Based on the dynamic collaboration network, perform intelligent matching and scheduling of the shipper demands, carrier capacities, and port operation capabilities in the shared resource pool through a contribution degree allocation mechanism to obtain the full-link state of the supply chain; S3: Perform dynamic routing optimization according to the full-link state of the supply chain, select the optimal path for the transportation mode of container logistics from the starting point to the destination based on the business process of multimodal transport, and update the transportation plan; S4: Define task allocation logic through an intelligent contract rule library, automatically decompose tasks and distribute them to nodes through an intelligent contract according to the task allocation logic. After the carrier completes the node tasks, the vouchers are automatically uploaded through an RFID gate, and the next node task is triggered after the contract verifies the vouchers.

2. The method according to claim 1, characterized in that In S1, the shared resource pool encapsulates shipper demands, carrier capacities, and transfer point operation capabilities into standardized microservice modules through containerization technology and supports elastic expansion.

3. The method according to claim 1, characterized in that The construction method of the dynamic collaboration network in S1 is as follows: S101: Based on a graph computing engine, map the multi-dimensional association relationships among shippers, carriers, and transfer points in real time, including geographical location matching degree, historical cooperation credit score, and real-time service capacity threshold; S102: Perform collaborative modeling on the multi-source heterogeneous data of the multi-dimensional association relationships to generate a resource matching weight matrix; S103: Respond to resource state changes through an event-driven architecture, dynamically adjust the collaborative network topology according to the resource matching weight matrix, and automatically trigger capacity reallocation when the transfer point operation capacity is overloaded.

4. The method according to claim 1, characterized in that, The contribution degree allocation mechanism in S2 includes: S201: Construct a multi-objective optimization function, and the objective variables include minimizing transportation costs, optimizing carbon emission intensity, and maximizing delivery on-time rate; S202: Solve the Pareto front through a non-dominated sorting genetic algorithm to generate a candidate solution set for resource matching; S203: Select the matching result with the highest comprehensive score from the candidate solution set based on the fuzzy comprehensive evaluation method and dynamically update the contribution degree weights.

5. The method according to claim 1, characterized in that The full-link state of the supply chain in S2 is used to integrate shipper order data, carrier vehicle GPS positioning, and real-time monitoring data of transfer point Internet of Things devices, store the dynamic data stream through a time series database, and use a streaming computing platform to calculate key state parameters in real time; the key state parameters include transportation time deviation rate, container detention rate, and equipment failure rate.

6. The method according to claim 1, wherein The dynamic routing optimization method in S3 is as follows: Construct a multimodal transport path decision model based on reinforcement learning, with input parameters including real-time transportation costs, transfer node congestion probability, carbon emission intensity threshold, and shipper priority weight, and output the optimal solution set of the combined road-rail-water transport path; Automatically trigger path dynamic switching by monitoring sudden risk events, send adjustment instructions to affected nodes through a blockchain intelligent contract, and synchronously update the full-link state data.

7. The method according to claim 6, wherein The triggering conditions for the dynamic path switching include overloading of the transfer point operation capacity; the transportation timeliness deviation rate continuously exceeding the planned value; the Internet of Things devices detecting extreme weather resulting in the risk of path interruption.

8. The method according to claim 1, characterized in that, The business process of the multimodal transport in S3 includes: Order reception, cargo loading, transshipment, transportation tracking and delivery confirmation for road-rail-water multimodal transport. Each business process link realizes operation automation through Internet of Things technology and blockchain smart contracts.

9. The method according to claim 1, characterized in that The task assignment logic in S4 adopts a hierarchical rule engine architecture, including a transportation mode recognition layer, a resource constraint verification layer and a dynamic priority adjustment layer; the rule base incorporates a fuzzy logic controller to analyze the shipper's priority, the carrier's service level agreement and the real-time load status of the transfer point. The rule triggering mechanism adopts an event-driven mode, decomposing the end-to-end transportation into atomic operation units, and encapsulating each atomic task as a smart contract object.

10. The method according to claim 1, characterized in that, The smart contract in S4 automatically executes the process management after task decomposition, including task progress monitoring, exception handling and automatic settlement. The smart contract deconstructs the task assignment of multimodal transport, extracts triggering conditions, execution actions and constraint rules; the data structure of the smart contract is a data model for on-chain and off-chain collaboration. The on-chain storage layer stores key vouchers, and the off-chain calculation layer is used to access external data sources, and defines the state migration paths of quotation submission, matching, transaction settlement and evaluation feedback through a finite state machine diagram.

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