Transportation strategy optimization method and device under branch logistics network, and electronic equipment

Abstract

The invention discloses a method and a device for optimizing a transportation strategy under a branch logistics network and electronic equipment. The method comprises the following steps: acquiring logistics information under the branch logistics network; the logistics information comprises information of each logistics node; inputting the logistics information into a digital twinborn model, performing wiring through the digital twinborn model by taking a transportation rule of a branch logistics network as a constraint condition and taking transportation cost minimization as a target, generating a to-be-optimized transportation strategy, and simulating a transportation process of goods among the logistics nodes according to the to-be-optimized transportation strategy, a simulation result is obtained; and optimizing the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy. By means of the computing power of the electronic equipment, the transportation strategy in the logistics transportation process can be quickly obtained; meanwhile, by simulating the logistics transportation process, the reliability of the transportation strategy can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of logistics transportation. Specifically, it relates to artificial intelligence technology in the technical field of logistics transportation. More specifically, it relates to an optimization method, device, and electronic device for transportation strategies under a feeder logistics network. Background Art

[0002] Logistics networks can generally be divided into two main parts: the trunk logistics network and the feeder logistics network. These two network modes play very important roles in the logistics distribution system. Among them, the feeder logistics network is the logistics transportation link between the express collection and distribution center and the final retailer or consumer. Given the complexity of the feeder logistics network, it usually requires highly experienced professionals to formulate transportation strategies to ensure the timeliness of the logistics transportation process as much as possible.

[0003] However, the above-mentioned method of manually formulating transportation strategies has high requirements for relevant personnel, not only takes a long time, but also has poor reliability of the formulated transportation strategies. Summary of the Invention

[0004] To solve the above technical problems, the present application provides an optimization method, device, and electronic device for transportation strategies under a feeder logistics network to achieve the purpose of quickly generating transportation strategies under the feeder logistics network.

[0005] To achieve the above technical objectives, the embodiments of the present application provide the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides an optimization method for transportation strategies under a feeder logistics network, and the method includes:

[0007] Obtain the logistics information under the feeder logistics network; wherein, the logistics information includes: information of each logistics node; input the logistics information into a digital twin model, and use the transportation rules of the feeder logistics network as constraints and minimize the transportation cost as the goal to arrange the lines, generate a transportation strategy to be optimized, and simulate the transportation process of the goods between each logistics node according to the transportation strategy to be optimized to obtain a simulation result; optimize the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy.

[0008] Optionally, the logistics information further includes at least one of the predicted volume of goods at the cargo consolidation logistics node and / or the cargo deconsolidation logistics node, vehicle resource reserve information, and personnel reserve information.

[0009] Optionally, the transportation cost includes at least one of the transportation route change cost, the transportation vehicle change cost, and the personnel change cost.

[0010] Optionally, the transportation rules of the branch logistics network include: the transportation duration determined based on the association information of the temporary transfer yard is less than or equal to the promised delivery time of the goods; wherein, the association information includes at least one of the processing efficiency of the temporary transfer yard, the shift information, and the distance between the temporary transfer yard and the associated logistics nodes.

[0011] Optionally, after optimizing the to-be-optimized transportation strategy based on the simulation result to obtain the optimized transportation strategy, the method further includes:

[0012] Repeatedly execute the following steps until the simulation result of the digital twin model meets the target conditions: Simulate the transportation process of the goods between each logistics node again according to the optimized transportation strategy through the digital twin model to obtain a new simulation result; Optimize the target transportation strategy based on the new simulation result and update the optimized transportation strategy, where the target transportation strategy is the transportation strategy currently relied on during the simulation process of the digital twin model.

[0013] Optionally, the digital twin model includes: a data mining model, a strategy planning model, and a simulation model; Input the logistics information into the digital twin model, and use the transportation rules of the branch logistics network as the constraint condition through the digital twin model to perform line arrangement with the goal of minimizing the transportation cost, generate the to-be-optimized transportation strategy, and simulate the transportation process of the goods between each logistics node according to the to-be-optimized transportation strategy to obtain the simulation result, including: Mining the target parameter items in the logistics information through the data mining model to determine the final parameter values of the target parameter items; Based on the final parameter values, use the strategy planning model to perform line arrangement with the transportation rules of the branch logistics network as the constraint condition and the goal of minimizing the transportation cost to generate the to-be-optimized transportation strategy; Based on the final parameter values, use the simulation model to simulate the transportation process of the goods between each logistics node according to the to-be-optimized transportation strategy to obtain the simulation result; wherein, the target parameter items include multiple parameter values from different data sources.

[0014] Optionally, using the digital twin model to perform line arrangement with the transportation rules of the branch logistics network as the constraint condition and the goal of minimizing the transportation cost to generate the to-be-optimized transportation strategy includes: Using the digital twin model to perform line arrangement with the transportation rules of the branch logistics network as the constraint condition and the goal of minimizing the transportation cost by using the operations research optimization algorithm or the reinforcement learning algorithm to generate the to-be-optimized transportation strategy.

[0015] Optionally, after optimizing the to-be-optimized transportation strategy based on the simulation result to obtain the optimized transportation strategy, the method further includes: Outputting at least one of the optimized transportation strategy, the simulation result, and the current information of each logistics node.

[0016] Second aspect, an embodiment of the present application provides an optimization device for transportation strategies under a feeder logistics network. The device includes:

[0017] An information acquisition module, configured to acquire logistics information under the feeder logistics network; wherein, the logistics information includes: information of each logistics node; A simulation module, configured to input the logistics information into a digital twin model, and generate a transportation strategy to be optimized by using the digital twin model with the transportation rules of the feeder logistics network as constraint conditions and minimizing the transportation cost as the goal, and simulate the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain a simulation result; An optimization module, configured to optimize the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy.

[0018] Third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory; wherein, the memory is connected to the processor, and the memory is used to store a computer program; the processor is configured to implement the optimization method for transportation strategies under the feeder logistics network as described in the first aspect by running the computer program stored in the memory.

[0019] Fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the optimization method for transportation strategies under the feeder logistics network as described in the first aspect above.

[0020] Fifth aspect, an embodiment of the present application provides a computer program product or a computer program. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the steps of the optimization method for transportation strategies under the feeder logistics network as described in the first aspect.

[0021] For the optimization method for transportation strategies under the feeder logistics network provided by the present application, after acquiring the logistics information under the feeder logistics network, the logistics information is input into a digital twin model, and a transportation strategy to be optimized is generated by means of the digital twin model, and the simulation is carried out according to the transportation strategy to be optimized. The transportation status of goods under the transportation strategy to be optimized is reflected by the simulation result. Furthermore, the transportation strategy to be optimized is optimized by using the simulation result to obtain an optimized transportation strategy. With the computing power of the electronic device, the present application can quickly obtain the transportation strategy in the logistics transportation process; at the same time, by simulating the logistics transportation process, the reliability of the transportation strategy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0023] Figure 1 Flow chart of an optimization method for transportation strategies under a feeder logistics network provided by an embodiment of the present application;

[0024] Figure 2 Practical application flow chart of an optimization method for transportation strategies under a feeder logistics network provided by an embodiment of the present application;

[0025] Figure 3 Structural block diagram of an optimization device for transportation strategies under a feeder logistics network provided by an embodiment of the present application;

[0026] Figure 4 Structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0027] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meaning understood by those of ordinary skill in the art to which the present application belongs. The "first", "second" and similar terms used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to avoid confusion of components.

[0028] Unless otherwise required by the context, throughout the specification, "a plurality of" means "at least two", and "including" is interpreted as open and inclusive, that is, "including, but not limited to". In the description of the specification, the terms "an embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" etc. are intended to indicate that the specific features, structures, materials or characteristics related to the embodiment or example are included in at least one embodiment or example of the present application. The schematic representations of the above terms are not necessarily referring to the same embodiment or example.

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0030] Exemplary method

[0031] An embodiment of the present application provides an optimization method for transportation strategies under a feeder logistics network, as Figure 1 shown. The optimization method for transportation strategies under the feeder logistics network includes:

[0032] Step S101: Obtain the logistics information under the feeder logistics network.

[0033] In this step, the logistics information includes: information of each logistics node; where a logistics node is a place used to store, transfer, or process goods during the logistics transportation process. For example, the logistics node can be a warehouse, transfer yard, distribution station, logistics network point, etc., but is not limited thereto. It can be understood that the logistics information provides a data basis for formulating transportation strategies between the feeder logistics network / logistics network points and for simulating the transportation of goods between each logistics node. In some embodiments, the logistics information includes all data required for formulating transportation strategies between the feeder logistics network / logistics network points and for simulating the transportation of goods between each logistics node. For example, the logistics information includes but is not limited to the positions, states, and related data of transportation vehicles, warehouses, and goods, order quantities, goods information, and customer demands, etc.

[0034] Step S102: Input the logistics information into the digital twin model, and use the transportation rules of the feeder logistics network as the constraint conditions in the digital twin model, and minimize the transportation cost as the goal to arrange the routes, generate the transportation strategy to be optimized, and simulate the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain the simulation result.

[0035] In this step, the digital twin model is a data model constructed corresponding to the feeder logistics network by using digital twin technology. Among them, digital twin is a simulation process that fully utilizes data such as physical models, sensor updates, and operation histories, integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, and completes the mapping in the virtual space, so as to reflect the entire life cycle process of the corresponding physical equipment. It can be understood that the digital twin model includes components with different functions, and the generation of the transportation strategy to be optimized and the simulation process are respectively realized through the components with different functions.

[0036] In the process of generating the transportation strategy to be optimized, the principle of solving the utilization rate mathematical model is used. With logistics information as input, the initial transportation strategy, i.e., the transportation strategy to be optimized, can be obtained by setting constraints and goals. Among them, the transportation rules of the branch logistics network are the conditions that need to be met for the transportation of goods in the branch logistics network, and their specific contents are not limited here. The transportation cost is the cost incurred during the transportation of goods. In the simulation process, the characteristics of the digital twin model are used to simulate the transportation process of goods between various logistics nodes. Among them, the logistics information and the transportation strategy to be optimized are both input data, so that the process of transporting goods in the branch logistics network according to the logistics information under the transportation strategy to be optimized can be accurately simulated. The simulation results include various problems that occur in the process of transporting goods in the simulation environment and / or relevant information of each logistics node.

[0037] Step S103: Optimize the transportation strategy to be optimized based on the simulation results to obtain an optimized transportation strategy.

[0038] It should be noted that when optimizing the transportation strategy, the transportation strategy can be optimized in a targeted manner based on the cargo transportation status under the corresponding transportation strategy. The simulation results can reflect the cargo transportation status under the transportation strategy to be optimized. Therefore, there is a certain correlation between the simulation results and the transportation strategy, and the corresponding optimization strategy can be set based on the correlation to optimize the transportation strategy to be optimized through the optimization strategy. Of course, the simulation results can also be displayed directly so that relevant personnel can see the simulation results and optimize the transportation strategy to be optimized based on the simulation results.

[0039] In an embodiment of the present application, after obtaining the logistics information under the branch logistics network, the logistics information is input into the digital twin model, and the transportation strategy to be optimized is generated with the help of the digital twin model, and the transportation strategy to be optimized is simulated. The simulation results reflect the cargo transportation status under the transportation strategy to be optimized. The simulation results are then used to optimize the transportation strategy to be optimized to obtain the optimized transportation strategy. With the help of the computing power of electronic equipment, the present application can quickly obtain the transportation strategy in the logistics transportation process; at the same time, by simulating the logistics transportation process, the reliability of the transportation strategy can be improved.

[0040] In some embodiments, the logistics information also includes: at least one of the predicted quantity of goods at the consolidation logistics node and / or the bulk logistics node, vehicle resource reserve information, and personnel reserve information.

[0041] It should be noted that in some logistics transportation scenarios, the volume of express deliveries to be transported varies greatly in a short period of time. For example, in the peak logistics scenario, the volume of express deliveries has a significant increase or decrease in a very short time. At this time, in order to ensure the accuracy of the transportation strategy to be optimized and the simulation results, it is necessary to predict the volume of express deliveries. Therefore, the logistics information obtained may include: the predicted volume of express deliveries at the consolidation logistics node and / or the break-bulk logistics node. Among them, the consolidation logistics node is a logistics node that centrally processes the express deliveries collected from customers. Therefore, the predicted volume of express deliveries at the consolidation logistics node can also be referred to as the predicted volume of received express deliveries. The prediction process for the predicted volume of express deliveries is not limited here. In some embodiments, the received express delivery type and the volume of received express deliveries can be predicted for each network point, distribution station, and transfer yard, and the prediction results can be used as logistics information. The break-bulk logistics node is a logistics node that centrally processes the express deliveries to be sent to customers. Therefore, the predicted volume of express deliveries at the break-bulk logistics node can also be referred to as the predicted volume of dispatched express deliveries. The prediction process for the predicted volume of express deliveries is not limited here. In some embodiments, the dispatched express delivery type and the volume of dispatched express deliveries can be predicted for each network point, distribution station, and transfer yard, and the prediction results can be used as logistics information.

[0042] It can be understood that during the peak logistics period, the timeliness of goods is usually given priority to ensure, so as to avoid the deterioration of the customer experience as much as possible and prevent production accidents caused by the explosion of network points in the transfer yard. At the same time, it is not easy to obtain social vehicle resources during the peak logistics period, and a long waiting time is required. Therefore, during the peak logistics period, vehicle resources are closely arranged to ensure sufficient transport capacity resources as much as possible and avoid calling for vehicles temporarily. Specifically, in the peak logistics scenario, in order to better complete the transportation task, vehicle resources can be additionally reserved. Therefore, the logistics information obtained may include: vehicle resource reserve information. Among them, the vehicle resource reserve information can be the vehicle resources that need to be additionally reserved to meet the prediction requirements of the peak logistics scenario. In some embodiments, data such as vehicle type, vehicle quantity, vehicle operation area, available vehicle time period, and vehicle traffic restrictions can be predicted, and the prediction results can be used as logistics information. Similarly, in the peak logistics scenario, in order to better complete the transportation task, additional staff can be reserved. Therefore, the logistics information obtained may include: personnel reserve information. Among them, the personnel reserve information can be the human resources that need to be additionally reserved to meet the prediction requirements of the peak logistics scenario. In some embodiments, data such as personnel type, working time period, and personnel quantity can be predicted, and the prediction results can be used as logistics information.

[0043] In addition, in the logistics peak scenario, it is also possible to additionally predict the parcel collection and delivery capacity during the peak period. For example, predict the express handling capacity of transfer stations and logistics outlets, and use the prediction results as logistics information. At the same time, the shifts during the peak period are frequently adjusted. If they are not included in the consideration of formulating transportation strategies, the shift information of transfer stations and logistics outlets can also be entered and used as logistics information. Similarly, it is also possible to collect data such as the line execution status, traffic jams, waiting time, and changes in operation time of each line and each logistics node, and use them as logistics information.

[0044] In the embodiments of the present application, relevant data in the logistics peak scenario are used as logistics information and jointly input into the digital twin model, which can cope with the logistics peak scenario and improve the accuracy of the transportation strategy to be optimized and the simulation results.

[0045] In some embodiments, the transportation cost includes at least one of: transportation line change cost, transportation vehicle change cost, and personnel change cost.

[0046] It should be noted that during the logistics off-peak period, the frequencies of transportation line changes, transportation vehicle changes, and personnel changes are relatively low and can usually be ignored. However, during the logistics peak period, the frequencies of these three increase significantly, and it will cause problems such as low communication efficiency at the execution level and failure to achieve the expected effect. Therefore, when considering transportation costs, these three need to be taken into account. Among them, the transportation line change cost is the change in cost brought about by the transportation line change, the transportation vehicle change cost is the change in cost brought about by the transportation vehicle change, and the personnel change cost is the change in cost brought about by the personnel change. Here, personnel changes include the increase or decrease in the number of personnel, personnel shift changes, etc., but are not limited thereto.

[0047] In the embodiments of the present application, setting the transportation cost based on the characteristics of logistics transportation in the logistics peak scenario can cope with the logistics peak scenario and improve the accuracy of the transportation strategy to be optimized and the simulation results.

[0048] In some embodiments, the transportation rules of the branch logistics network include:

[0049] The transportation duration determined based on the association information of the temporary transfer station is less than or equal to the committed delivery time of the goods; where the association information includes at least one of: the processing efficiency of the temporary transfer station, shift information, and the distance between the temporary transfer station and the associated logistics node.

[0050] It should be noted that each piece of goods has a promised delivery time limit, that is, the promised delivery time limit of the goods. According to the requirements of the logistics transportation business, the transportation needs to be completed within the promised delivery time limit of the goods. Therefore, when generating the transportation strategy to be optimized, it is necessary to ensure that the generated operation strategy meets the time limit requirements. At the same time, during the peak logistics period, additional temporary transfer stations are usually added to relieve the peak pressure. Different from the original transfer stations in the feeder logistics network, this temporary transfer station is only put into use within the specified time. However, during the use period, the temporary transfer station has the same functions as the original transfer station. Therefore, the aforementioned time limit requirements can be regarded as the transportation duration determined based on the associated information of the temporary transfer station being less than or equal to the promised delivery time limit of the goods.

[0051] The functions of the temporary transfer station in the logistics transportation process will not be elaborated here. In this embodiment, the transportation duration will be determined based on the data required for its implementation functions. These data include but are not limited to the processing efficiency of the temporary transfer station, the shift information, and the distances between the temporary transfer station and the associated logistics nodes. Among them, the processing efficiency of the temporary transfer station is the efficiency of the temporary transfer station in processing express deliveries. The shift information is the shift information of the transportation vehicles in the temporary transfer station.

[0052] It can be understood that the transportation rules of the feeder logistics network also include: the transportation rules associated with the original transfer stations. For example, the transportation duration determined based on the associated information of the original transfer station is less than or equal to the promised delivery time limit of the goods. Of course, other constraint conditions can also be set in the digital twin model. For example, the constraint condition regarding the load capacity, that is, the load capacity of all transportation vehicles is greater than the weight of all goods. The constraint condition regarding vehicle scheduling, that is, all vehicles need to drive in their respective corresponding logistics transportation areas.

[0053] In the embodiment of the present application, corresponding constraint conditions are set based on the temporary transfer station in the peak logistics scenario, which can cope with the peak logistics scenario and improve the accuracy of the digital twin model.

[0054] In order to further improve the logistics transportation effect of the operation strategy, after optimizing the transportation strategy to be optimized based on the simulation results and obtaining the optimized transportation strategy, the method further includes:

[0055] Repeat the following steps until the simulation results of the digital twin model meet the target conditions: Use the digital twin model to simulate the transportation process of the goods between each logistics node again according to the optimized transportation strategy to obtain new simulation results; Optimize the target transportation strategy based on the new simulation results and update the optimized transportation strategy, where the target transportation strategy is the transportation strategy currently relied on during the simulation process of the digital twin model.

[0056] It should be noted that the target condition is the expected result / expected transportation effect of the transportation strategy. In some embodiments, the target condition can be set in advance based on the expectation of the operation strategy. Thus, after determining that the simulation result of a certain simulation meets the target condition, the optimization of the transportation strategy is stopped, and the current transportation strategy is used as the finally optimized transportation strategy. Specifically, the simulation result can reflect the goods transportation status under the transportation strategy to be optimized. Therefore, the target condition can be set based on the expected goods transportation status. For example, the target condition can be set as: more than a certain threshold percentage of goods are transported within the promised time limit, but it is not limited thereto.

[0057] It can be understood that the process of repeatedly simulating and optimizing the transportation strategy can be regarded as an iteration of the transportation strategy. Each iteration includes a simulation process and a process of optimizing the transportation strategy. And the transportation strategy used in the simulation process of each iteration is the transportation strategy generated in the step of optimizing the transportation strategy in the previous iteration.

[0058] In the embodiments of the present application, by continuously iterating and updating the transportation strategy, a transportation strategy with better logistics transportation effect can be obtained.

[0059] In some embodiments, the digital twin model includes: a data mining model, a strategy planning model, and a simulation model; the logistics information is input into the digital twin model, and the digital twin model takes the transportation rules of the branch logistics network as the constraint conditions and the minimization of transportation cost as the goal to arrange the lines, generates the transportation strategy to be optimized, and simulates the transportation process of the goods between each logistics node according to the transportation strategy to be optimized to obtain the simulation result, including:

[0060] The data mining model is used to mine the target parameter items in the logistics information to determine the final parameter values of the target parameter items; the strategy planning model is used to arrange the lines based on the final parameter values with the transportation rules of the branch logistics network as the constraint conditions and the minimization of transportation cost as the goal to generate the transportation strategy to be optimized; the simulation model is used to simulate the transportation process of the goods between each logistics node according to the transportation strategy to be optimized based on the final parameter values to obtain the simulation result; among them, the target parameter items include multiple parameter values from different data sources.

[0061] It should be noted that the data mining model is used to mine data. Specifically, the data mining model is used to process the parameter values under the same parameter item and from multiple data sources to obtain the accurate parameter value under this parameter item. For example, for the parameter item of the location of a transport vehicle, its parameter values include the first location information reported by the positioning system of the transport vehicle, the second location information reported by the logistics information scanning device carried by the operator of the transport vehicle, and the third location information reported by the sealing strip at the door of the transport vehicle. The data mining model judges the three location information to determine the accurate location of the transport vehicle, and uses this accurate location as the final parameter value for other parts of the digital twin model (including but not limited to the strategy planning model and the simulation model).

[0062] The strategy planning model is used to generate transport strategies. This strategy planning model can be regarded as a mathematical model, which is solved by setting various constraint conditions and optimization objectives to obtain transport strategies. The simulation model is used to simulate the cargo transportation process under the branch line logistics network. The simulation environment constructed by this simulation model can be regarded as the real environment composed of each logistics node under the branch line logistics network in the real world.

[0063] In the embodiments of the present application, when the same parameter item of logistics information includes parameter values from multiple data sources, accurate parameter values can be obtained through data mining for subsequent use, improving the accuracy of the digital twin model.

[0064] In some embodiments, the digital twin model is used to arrange lines with the transportation rules of the branch line logistics network as the constraint conditions and the minimization of transportation costs as the objective to generate a transportation strategy to be optimized, including: the digital twin model uses the operation research optimization algorithm or the reinforcement learning algorithm to arrange lines with the transportation rules of the branch line logistics network as the constraint conditions and the minimization of transportation costs as the objective to generate a transportation strategy to be optimized.

[0065] It should be noted that the operation research optimization algorithm is usually applied to situations where resources are limited or a choice needs to be made among many feasible options. For example, problems such as scheduling, path planning, and inventory management. The operation research optimization algorithms in this embodiment include: heuristic algorithms, meta-heuristic algorithms, exact algorithms, approximation algorithms, genetic algorithms, ant colony algorithms, and simulated annealing algorithms, etc., but are not limited thereto.

[0066] The reinforcement learning (RL) algorithm is an algorithm implemented based on reinforcement learning. Among them, reinforcement learning enables the intelligent agent to learn the long-term rewards of performing various possible actions in a given environment through continuous interaction and learning with the environment, and on this basis, formulate an optimal strategy.

[0067] In the embodiments of the present application, an operations research optimization algorithm or a reinforcement learning algorithm is used in the digital twin model to solve the transportation strategy.

[0068] To facilitate the user to view the situation in the logistics transportation process, based on the simulation results, the transportation strategy to be optimized is optimized. After obtaining the optimized transportation strategy, the method further includes: outputting at least one of the optimized transportation strategy, the simulation results, and the current information of each logistics node.

[0069] It should be noted that the current information of the logistics node includes the information of the logistics node at different times during the simulation process. Among them, the information of the logistics node is related to its role in the branch logistics network. For example, if the role of the logistics node is a warehouse, the information of the logistics node includes the information related to the warehouse situation, which will not be listed one by one here.

[0070] It can be understood that when outputting the optimized transportation strategy, the simulation results, and the current information of each logistics node, the three can be displayed on the display device of the electronic device by means of display, but not limited to this. For example, a report recording the information of the three can also be generated and printed out.

[0071] In the embodiments of the present application, by outputting the optimized transportation strategy, the simulation results, and the current information of each logistics node, it is convenient for the user to view the relevant logistics information.

[0072] As Figure 2 shown, the embodiments of the present application provide a practical application flowchart of an optimization method for transportation strategies under a branch logistics network, including:

[0073] Step S201: Construct a digital twin model. Based on the structure of the branch logistics network, logistics nodes, transport vehicles, warehouses, and goods, etc., use digital twin technology to build a digital twin model to realize the simulation of the goods transportation process under the branch logistics network. Among them, in addition to the part that realizes the simulation function, the digital twin model also includes a mathematical model for solving the transportation strategy. When constructing the mathematical model, based on various rules of logistics transportation under the branch logistics network, set constraint conditions and optimization objectives. For example, use transportation cost, transportation time, and cargo loading rate, etc. as parameters of the constraint conditions and / or optimization objectives to optimize the planning of the branch logistics network and solve the transportation strategy.

[0074] Step S202: Train the model through historical data and real-time data under the branch logistics network to improve the accuracy of the digital twin model.

[0075] Step S203: Real-time monitor and receive data from sensors and Internet of Things devices, and continuously update the digital twin model and the real-time state through the received data.

[0076] Among them, the data from sensors and Internet of Things devices include: the locations, statuses, and related data of transportation vehicles, warehouses, and goods, the number of orders, goods information, customer demands, and other data. Optionally, an interface of a supply chain system and a logistics management system is used in the electronic device that executes the method provided in this embodiment, so that real-time data synchronization and transmission can be achieved, ensuring the timeliness and accuracy of planning. Optionally, after receiving the data, the data can be cleaned, integrated, and transformed, and the processed data is input into the digital twin model. It can be understood that when a new order is generated or a problem occurs in one or some links in the feeder logistics network, this data will be reflected in the received data, and then the digital twin model will automatically analyze and adjust solutions such as transportation routes, warehouse allocations, and transportation vehicles, and output corresponding simulation results.

[0077] It should be noted that during the logistics off-peak period, the volume of goods and shifts at each logistics outlet and transfer yard will not change frequently. However, during the logistics peak period, the volume of goods, the transfer yard of the outlet, the shift, and the personnel will change every day, every shift, and even every hour. Since the received data is not completely accurate, this part of the data needs to be updated in real time and corrected according to the data that has occurred. That is to say, according to the real-time peak dynamic data, the entire feeder logistics network is adjusted frequently.

[0078] Step S204: Display data such as the real-time logistics network status, the cargo transportation track, the warehouse situation, and the supply chain data in the user interface.

[0079] Among them, the user interface can be a customized visualization interface for different users. The data displayed in the user interface can also be customized data for different users. For the data displayed in the user interface, it can include the data directly obtained from the digital twin model, or it can include: statistical indicators and reports based on the former.

[0080] In the embodiment of the present application, through real-time data collection and digital twin model processing, the demand changes during the logistics peak period can be quickly responded to, the transportation strategy of the logistics network can be optimized, the logistics transportation efficiency is improved, the transportation cost is reduced, and the goods are ensured to be delivered on time. The status of the feeder logistics network and the relevant information of the transportation vehicles can be monitored in real time, and emergencies can be discovered and processed in time; at the same time, the transportation strategy can be automatically adjusted to reduce delays and misallocations and ensure the on-time delivery of goods. A visual interface is provided to display the relevant information during the logistics transportation process for the user to view. At the same time, the user can evaluate the advantages and disadvantages of different transportation strategies based on the displayed data and make intelligent decisions. Through model training with historical data and real-time data, the bottlenecks and problems of the feeder logistics network can be predicted in advance, potential problems can be avoided, and the stable operation of the feeder logistics network can be ensured.

[0081] Exemplary apparatus

[0082] Some embodiments of the present application also provide an optimization device for transportation strategies under a feeder logistics network, as Figure 3 shown. The optimization device for transportation strategies under the feeder logistics network includes:

[0083] An information acquisition module 31, configured to acquire logistics information under the feeder logistics network; wherein, the logistics information includes: information of each logistics node;

[0084] A simulation module 32, configured to input the logistics information into a digital twin model, and perform route arrangement with the transportation rules of the feeder logistics network as the constraint conditions and the minimization of transportation costs as the goal, generate a transportation strategy to be optimized, and simulate the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain a simulation result;

[0085] An optimization module 33, configured to optimize the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy.

[0086] In some embodiments, the logistics information further includes at least one of the predicted volume of goods at the consolidation logistics node and / or the break-bulk logistics node, vehicle resource reserve information, and personnel reserve information.

[0087] In some embodiments, the transportation costs include at least one of transportation route change costs, transportation vehicle change costs, and personnel change costs.

[0088] In some embodiments, the transportation rules of the feeder logistics network include: the transportation duration determined based on the association information of the temporary transfer yard is less than or equal to the goods commitment time limit; wherein, the association information includes at least one of the processing efficiency of the temporary transfer yard, shift information, and the distance between the temporary transfer yard and the associated logistics node.

[0089] In some embodiments, the device further includes: an iteration module, configured to repeatedly execute the following steps until the simulation result of the digital twin model meets the target conditions: simulate the transportation process of goods between each logistics node again through the digital twin model according to the optimized transportation strategy to obtain a new simulation result; optimize the target transportation strategy based on the new simulation result, and update the optimized transportation strategy, wherein the target transportation strategy is the transportation strategy currently relied on during the simulation process of the digital twin model.

[0090] In some embodiments, the digital twin model includes: a data mining model, a strategy planning model, and a simulation model; the simulation module 32 is specifically configured to: perform data mining on target parameter items in the logistics information through the data mining model to determine the final parameter values of the target parameter items; perform line arrangement based on the final parameter values through the strategy planning model with the transportation rules of the feeder logistics network as the constraint conditions and the minimization of transportation costs as the goal to generate a transportation strategy to be optimized; perform simulation on the transportation process of goods between each logistics node according to the transportation strategy to be optimized based on the final parameter values through the simulation model to obtain a simulation result; wherein, the target parameter items include parameter values from different data sources.

[0091] In some embodiments, the simulation module 32 is specifically configured to perform line arrangement with the transportation rules of the feeder logistics network as the constraint conditions and the minimization of transportation costs as the goal through the digital twin model by using an operations research optimization algorithm or a reinforcement learning algorithm to generate a transportation strategy to be optimized.

[0092] In some embodiments, the device further includes: an output module, configured to output at least one of the optimized transportation strategy, the simulation result, and the current information of each logistics node.

[0093] The optimization device for the transportation strategy under the feeder logistics network provided by the embodiments of the present application belongs to the same inventive concept as the optimization method for the transportation strategy under the feeder logistics network provided by the above embodiments of the present application. For technical details not described in detail in this embodiment, reference may be made to the specific processing content of the optimization method for the transportation strategy under the feeder logistics network provided by the above embodiments of the present application, which will not be elaborated herein.

[0094] Exemplary electronic device

[0095] Another embodiment of the present application further proposes an electronic device. Refer to Figure 4 As shown, an exemplary embodiment of the present application further provides an electronic device, including: a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it executes the steps in the optimization method for the transportation strategy under the feeder logistics network according to various embodiments of the present application described above.

[0096] The internal structure of the electronic device can be as Figure 4As shown in the figure, the electronic device includes a processor, a memory, a network interface, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it performs the steps in the optimization method of the transportation strategy under the feeder logistics network according to various embodiments of the present application described in the above embodiments of the present application.

[0097] The processor may include a main processor, and may also include a baseband chip, a modem, etc.

[0098] The memory stores a program for implementing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include program code, and the program code includes computer operation instructions. More specifically, the memory may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash memory, etc.

[0099] The processor may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0100] The input device may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0101] The output device may include a device for allowing information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0102] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0103] The processor executes the program stored in the memory and calls other devices, which can be used to implement each step of the optimization method for the transportation strategy under any one of the branch logistics networks provided in the above embodiments of the present application.

[0104] The electronic device may further include a display component and a voice component. The display component may be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device may be a touch layer covered on the display component, or a button, a trackball or a touchpad provided on the outer shell of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0105] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0106] Exemplary computer program product and storage medium

[0107] In addition to the above methods and devices, the optimization method for the transportation strategy under the branch logistics network provided by the embodiments of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor executes the steps in the optimization method for the transportation strategy under the branch logistics network according to various embodiments of the present application described in the above "Exemplary Method" section of the present application.

[0108] The computer program product can be written in any combination of one or more programming languages for the program code to execute the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed completely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or completely on a remote computing device or server.

[0109] In addition, the embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps in the optimization method for the transportation strategy under the branch logistics network according to various embodiments of the present application described in the above "Exemplary Method" section of the present application.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0111] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0112] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the solutions provided by the embodiments of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. An optimization method for transportation strategies under a feeder logistics network, characterized in that The method includes: Obtaining logistics information under the branch logistics network; wherein, the logistics information includes information of each logistics node; Inputting the logistics information into a digital twin model, and using the transportation rules of the branch logistics network as constraints in the digital twin model, and taking the minimization of transportation cost as the goal to arrange the lines, generating a transportation strategy to be optimized, and simulating the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain a simulation result; Optimizing the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy.

2. The method according to claim 1, characterized in that The logistics information further includes at least one of the predicted volume of goods at the cargo consolidation logistics node and / or the bulk cargo logistics node, vehicle resource reserve information, and personnel reserve information.

3. The method according to claim 1, wherein The transportation cost includes at least one of transportation route change cost, transportation vehicle change cost, and personnel change cost.

4. The method according to claim 1, wherein The transportation rules of the branch logistics network include: The transportation duration determined based on the association information of the temporary transfer yard is less than or equal to the committed delivery time of the goods; wherein, the association information includes at least one of the processing efficiency of the temporary transfer yard, shift information, and the distance between the temporary transfer yard and the associated logistics node.

5. The method according to claim 1, characterized in that After optimizing the transportation strategy to be optimized based on the simulation result to obtain an optimized transportation strategy, the method further includes: Repeating the following steps until the simulation result of the digital twin model meets the target conditions: Simulating the transportation process of goods between each logistics node again according to the optimized transportation strategy through the digital twin model to obtain a new simulation result; Optimizing the target transportation strategy based on the new simulation result and updating the optimized transportation strategy, wherein the target transportation strategy is the transportation strategy currently relied on during the simulation process of the digital twin model.

6. The method according to claim 1, characterized in that, The digital twin model includes: a data mining model, a strategy planning model, and a simulation model; Inputting the logistics information into a digital twin model, and using the transportation rules of the branch logistics network as constraints in the digital twin model, and taking the minimization of transportation cost as the goal to arrange the lines, generating a transportation strategy to be optimized, and simulating the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain a simulation result, including: Performing data mining on the target parameter items in the logistics information through the data mining model to determine the final parameter values of the target parameter items; Using the strategy planning model to arrange the lines based on the final parameter values with the transportation rules of the branch logistics network as constraints and taking the minimization of transportation cost as the goal to generate a transportation strategy to be optimized; Using the simulation model to simulate the transportation process of goods between each logistics node according to the transportation strategy to be optimized based on the final parameter values to obtain a simulation result; Wherein, the target parameter items include parameter values from multiple different data sources.

7. The method according to claim 1, wherein Using the transportation rules of the branch logistics network as constraints in the digital twin model, and taking the minimization of transportation cost as the goal to arrange the lines, generating a transportation strategy to be optimized, including: Using an operational research optimization algorithm or a reinforcement learning algorithm through a digital twin model, with the transportation rules of the feeder logistics network as the constraint conditions and the minimization of transportation costs as the goal, route planning is carried out to generate a transportation strategy to be optimized.

8. The method according to claim 1, wherein After optimizing the transportation strategy to be optimized based on the simulation results to obtain an optimized transportation strategy, the method further includes: Outputting at least one of the optimized transportation strategy, the simulation results, and the current information of each logistics node.

9. An optimization device for transportation strategies under a feeder logistics network, characterized in that, The device includes: An information acquisition module for acquiring logistics information under the feeder logistics network; wherein, the logistics information includes: information of each logistics node; A simulation module for inputting the logistics information into a digital twin model, using the digital twin model with the transportation rules of the feeder logistics network as the constraint conditions and the minimization of transportation costs as the goal to carry out route planning, generating a transportation strategy to be optimized, and simulating the transportation process of goods between each logistics node according to the transportation strategy to be optimized to obtain simulation results; An optimization module for optimizing the transportation strategy to be optimized based on the simulation results to obtain an optimized transportation strategy.

10. An electronic device, characterized in that, Includes: A processor and a memory; Wherein, the memory is connected to the processor, and the memory is used to store a computer program; The processor is used to implement the optimization method of the transportation strategy under the feeder logistics network as described in any one of claims 1 to 8 by running the computer program stored in the memory.

11. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the optimization method of the transportation strategy under the feeder logistics network as described in any one of claims 1 to 8 is implemented.

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