CN-121998536-A - Synchronous multi-mode intermodal dynamic route planning method and system for low-carbon targets

CN121998536ACN 121998536 ACN121998536 ACN 121998536ACN-121998536-A

Abstract

The application relates to the technical field of low-carbon emission of freight logistics and discloses a synchronous multi-modal dynamic route planning method and system for a low-carbon target, wherein the method comprises the steps of constructing a multi-modal digital twin network model; the method comprises the steps of receiving a starting point, a finishing point and a latest delivery time constraint of a freight order, mapping the starting point and the finishing point into a model, constructing a double-target optimization model, generating an initial low-carbon routing scheme through synchronous integrated solving, updating real-time cost and carbon emission intensity in the model based on real-time information in freight execution, triggering rerouting planning when the real-time cost and the carbon emission intensity exceed a threshold value, outputting an adjusted scheme, collecting actual data for an actually experienced road section, calculating the carbon emission intensity, and updating historical average carbon emission intensity in the model through fusion calculation. The system corresponds to the method. The application realizes synchronous decision and dynamic optimization of the transportation mode, the path and the transfer node, and effectively improves the low-carbon economy and the path reliability of the multi-type intermodal transportation.

Inventors

Ke Maoguo
LIAO JUN

Assignees

广州智卡物流科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260116

Claims (10)

1. A synchronous multi-mode intermodal dynamic route planning method facing a low-carbon target is characterized by comprising the following steps: The method comprises the steps of establishing a model, namely establishing a multi-modal digital twin network model based on a topological structure of the multi-modal physical network, node attributes and directed edge attributes, wherein the node attributes comprise the reloading capability of a transfer node, and the directed edge attributes comprise a transportation mode, a distance, a time cost and historical average carbon emission intensity serving as a reference parameter; A constraint input step, namely receiving the starting point and the ending point of a freight order and the latest arrival time constraint; Mapping the starting point and the ending point into the multi-modal digital twin network model, constructing a double-target optimization model which aims at minimizing the total transportation cost and the estimated total carbon emission, and generating an initial low-carbon routing scheme through synchronous integrated solution by taking the latest delivery time constraint and the node replacement capacity as constraint conditions; The dynamic adjustment step is that the real-time cost and the real-time carbon emission intensity in the multi-modal digital twin network model are updated based on the real-time traffic information in the freight transportation execution process, when the real-time carbon emission intensity of the current path or the delay estimated based on the real-time cost exceeds a preset threshold value, the re-route planning based on the updated multi-modal digital twin network model is triggered, and the low-carbon route scheme after dynamic adjustment is output; The method comprises the steps of verifying feedback, namely collecting actual transportation mode, actual energy consumption data and actual running time of each road section actually experienced in a low-carbon routing scheme after dynamic adjustment, calculating actual carbon emission intensity of the road section based on the actual energy consumption data, carrying out fusion calculation on the actual carbon emission intensity and historical average carbon emission intensity corresponding to the road section in the multi-mode intermodal digital twin network model to obtain updated historical average carbon emission intensity, and storing the updated historical average carbon emission intensity in the multi-mode intermodal digital twin network model.
2. The method for planning the synchronous multi-modal dynamic route to the low-carbon target according to claim 1, wherein the process of synchronous integrated solution comprises the following steps: constructing unified decision variables, wherein the decision variables simultaneously encode the selection of a transportation mode, the sequence of path nodes and the connection relation between the path nodes; And solving the double-target optimization model based on the decision variables, and outputting a final routing scheme obtained by solving as the initial low-carbon routing scheme.
3. The method for planning a synchronous multi-modal dynamic route towards a low-carbon target according to claim 2, wherein the solving the dual-target optimization model and outputting a final routing scheme obtained by solving comprises: Solving the double-target optimization model by adopting a multi-target evolutionary algorithm based on decomposition to obtain a group of pareto optimal solutions; selecting a solution from the group of pareto optimal solutions according to a preset decision rule; and outputting the complete transportation mode sequence, the path sequence and the transfer node sequence corresponding to the selected solution as the initial low-carbon routing scheme.
4. The method for dynamic route planning for synchronous multi-modal transportation for low-carbon targets according to claim 1, wherein the optimization targets of the dual-target optimization model include a total cost of transportation objective function and a total estimated carbon emission objective function, and the step of synchronous optimization and the step of dynamic adjustment use the same total cost of transportation objective function and total estimated carbon emission objective function for scheme evaluation.
5. The method for planning a synchronous multi-modal dynamic route to a low-carbon target according to claim 1, wherein the determining means for determining whether the real-time carbon emission intensity of the front path or the delay estimated based on the real-time cost exceeds a preset threshold value comprises: calculating real-time carbon emission intensity of forward planned road section And carbon emission intensity predictions employed in generating the initial low-carbon routing scheme The proportion relation between them if it meets Determining that the preset threshold is exceeded, wherein Is a preset proportional threshold; re-estimating arrival time of goods based on real-time cost And with the planned arrival time in the initial low-carbon routing scheme Comparing if it meets Determining that the preset threshold is exceeded, wherein Is a preset time threshold.
6. The low-carbon goal-oriented synchronous multi-modal dynamic routing method of claim 5, wherein the rerouting plan includes: Reconstructing a double-objective optimization model for the remaining non-transported paths based on the real-time cost and the real-time carbon emission intensity; Taking the actual carbon emission and time consumption of the current transported path as fixed parameters, and inputting the fixed parameters into a reconstructed double-target optimization model; and solving the reconstructed double-target optimization model, and planning a new transportation mode, a new transportation path and new transportation nodes for the residual non-transportation path.
7. The method for planning the synchronous multi-modal dynamic route to the low-carbon target according to claim 1, wherein the process for acquiring the real-time carbon emission intensity comprises the following steps: Acquiring real-time vehicle speed, road congestion index and carrier load rate; and inputting the real-time vehicle speed, the road congestion index and the carrier load rate into a carbon emission model, and dynamically calculating to obtain the real-time carbon emission intensity.
8. The low-carbon object oriented synchronous multi-modal dynamic routing method of claim 7, wherein the carbon emission model performs the following calculation to obtain the real-time carbon emission intensity: According to real-time vehicle speed Querying or calculating corresponding reference emission factors ; Determining correction coefficients according to load rates of carriers ; According to the formula Calculating to obtain the real-time carbon emission intensity 。
9. The low-carbon object oriented synchronous multi-modal dynamic route planning method of claim 1, wherein the updated historical average carbon emission intensity is calculated by the following formula: Wherein, the The historical average carbon emission intensity stored for the pre-fusion model, For the actual carbon emission intensity calculated at present, Is a preset learning rate, and 。
10. A synchronous multi-modal dynamic route planning system for low-carbon targets, the system comprising: The model construction module is configured to construct a multi-modal digital twin network model based on a topological structure, node attributes and directed edge attributes of the multi-modal physical network, wherein the node attributes comprise the reloading capability of transfer nodes, and the directed edge attributes comprise a transportation mode, a distance, a time cost and historical average carbon emission intensity serving as a reference parameter; the constraint input module is configured to receive a start point, an end point and a latest delivery time constraint of a freight order; The synchronous optimization module is configured to map the starting point and the ending point into the multi-modal digital twin network model, construct a double-target optimization model which aims at minimizing the total transportation cost and the estimated total carbon emission, and generate an initial low-carbon routing scheme through synchronous integrated solution by taking the latest delivery time constraint and the node replacement capacity as constraint conditions; The dynamic adjustment module is configured to update the real-time cost and the real-time carbon emission intensity in the multi-modal digital twin network model based on the real-time traffic information in the freight transportation execution process, trigger the rerouting planning based on the updated multi-modal digital twin network model when the real-time carbon emission intensity of the current path or the estimated delay based on the real-time cost exceeds a preset threshold, and output a dynamically adjusted low-carbon routing scheme; The verification feedback module is configured to collect actual transportation mode, actual energy consumption data and actual running time of each road section actually experienced in the low-carbon routing scheme after dynamic adjustment, calculate actual carbon emission intensity of the road section based on the actual energy consumption data, and conduct fusion calculation on the actual carbon emission intensity and historical average carbon emission intensity corresponding to the road section in the multi-mode intermodal digital twin network model to obtain updated historical average carbon emission intensity, and store the updated historical average carbon emission intensity in the multi-mode intermodal digital twin network model.

Description

Synchronous multi-mode intermodal dynamic route planning method and system for low-carbon targets Technical Field The application relates to the technical field of low-carbon emission of freight logistics, in particular to a synchronous multi-mode intermodal dynamic route planning method and system for a low-carbon target. Background In the field of freight logistics, multi-mode intermodal transportation is a key mode for improving efficiency and reducing cost. Existing route planning methods typically focus on static optimization of a single objective (e.g., lowest cost or shortest time), or on carbon emissions alone as a fixed constraint rather than a core optimization objective at the time of planning. The method has obvious limitations that firstly, the traditional segmentation or serial planning is difficult to synchronously decide the transportation mode, the path and the transfer node in a unified frame, so that local optimal rather than global optimal solutions are easily caused, and the collaborative optimization of the cost and the carbon row cannot be truly realized. Secondly, most schemes belong to static planning, once the schemes are generated, the schemes are fixedly executed, and dynamic changes such as traffic conditions and weather cannot be effectively responded in the actual transportation process of days or even weeks, so that obvious deviation between actual carbon emission and a planning target can be caused, and a low-carbon target falls down. Therefore, an intelligent planning method capable of synchronously deciding dynamic response is needed to reliably achieve the goal of low-carbon transportation in a complex physical logistics network. Disclosure of Invention The application aims to provide a synchronous multi-mode intermodal dynamic route planning method and system for a low-carbon target, which are used for solving the technical problems in the background technology. In order to achieve the above purpose, the present application discloses the following technical solutions: in a first aspect, the application discloses a synchronous multi-mode intermodal dynamic route planning method facing to a low-carbon target, which comprises the following steps: The method comprises the steps of establishing a model, namely establishing a multi-modal digital twin network model based on a topological structure of the multi-modal physical network, node attributes and directed edge attributes, wherein the node attributes comprise the reloading capability of a transfer node, and the directed edge attributes comprise a transportation mode, a distance, a time cost and historical average carbon emission intensity serving as a reference parameter; A constraint input step, namely receiving the starting point and the ending point of a freight order and the latest arrival time constraint; Mapping the starting point and the ending point into the multi-modal digital twin network model, constructing a double-target optimization model which aims at minimizing the total transportation cost and the estimated total carbon emission, and generating an initial low-carbon routing scheme through synchronous integrated solution by taking the latest delivery time constraint and the node replacement capacity as constraint conditions; The dynamic adjustment step is that the real-time cost and the real-time carbon emission intensity in the multi-modal digital twin network model are updated based on the real-time traffic information in the freight transportation execution process, when the real-time carbon emission intensity of the current path or the delay estimated based on the real-time cost exceeds a preset threshold value, the re-route planning based on the updated multi-modal digital twin network model is triggered, and the low-carbon route scheme after dynamic adjustment is output; The method comprises the steps of verifying feedback, namely collecting actual transportation mode, actual energy consumption data and actual running time of each road section actually experienced in a low-carbon routing scheme after dynamic adjustment, calculating actual carbon emission intensity of the road section based on the actual energy consumption data, carrying out fusion calculation on the actual carbon emission intensity and historical average carbon emission intensity corresponding to the road section in the multi-mode intermodal digital twin network model to obtain updated historical average carbon emission intensity, and storing the updated historical average carbon emission intensity in the multi-mode intermodal digital twin network model. Optionally, the process of synchronous integrated solution includes: constructing unified decision variables, wherein the decision variables simultaneously encode the selection of a transportation mode, the sequence of path nodes and the connection relation between the path nodes; And solving the double-target optimization model based on the decision variables, and outputting a final