Logistics route planning method and device based on digital twin system

Abstract

The invention relates to a logistics route planning method and device based on a digital twin system, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring geographical traffic change data and logistics network change data, and accessing the data to a pre-constructed digital twin system; performing simulation testing on the logistics route planning information to be tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used for predicting the logistics transportation state of the logistics line planning information under real-time scheduling; according to a logistics transportation prediction result, adjusting line arrangement in the logistics line planning information to obtain target logistics line planning information; the target logistics route planning information comprises the optimized logistics transportation route. The method can achieve the dynamic and effective verification of the logistics line planning, facilitates the timely optimization and adjustment, and effectively improves the efficiency and accuracy of the logistics line planning.

Description

Technical Field

[0001] The present application relates to the field of intelligent logistics technology, and in particular, to a logistics route planning method, device, computer device, storage medium, and computer program product based on a digital twin system. Background Art

[0002] In the logistics field, it is very important to effectively plan and manage vehicle transportation routes. However, traditional logistics route planning is usually determined based on fixed and static geographical information, and it cannot effectively cope with dynamic changes such as traffic jams and emergencies, and does not have real-time and dynamic characteristics; it is also difficult to obtain detailed and real road environments during route planning, resulting in poor accuracy of logistics route planning.

[0003] For example, in traditional technologies, logistics routes usually operate according to pre-planned fixed routes and scheduling times. Since the pre-planning process is based on fixed map navigation, when actually executed, geographical information may change, such as temporary traffic restrictions and traffic jams, resulting in that traditional technologies can only be used for the verification of pre-planning and cannot dynamically and effectively verify the logistics route arrangement plan. Summary of the Invention

[0004] Based on this, it is necessary to provide a logistics route planning method, device, computer device, storage medium, and computer program product based on a digital twin system that can improve the effectiveness of verifying the logistics route arrangement plan for the above technical problems.

[0005] In a first aspect, the present application provides a logistics route planning method based on a digital twin system, including:

[0006] Obtain geographical traffic change data and logistics network change data, and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0007] Perform a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation state of the logistics route planning information under real-time scheduling;

[0008] According to the logistics transportation prediction result, adjust the route arrangement in the logistics route planning information to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

[0009] In one embodiment, before the step of obtaining geographical traffic change data and logistics network change data and accessing them to a pre-constructed digital twin system, the method further includes:

[0010] Based on a three-dimensional geographic information system and the Unreal Engine, using geographic traffic information and logistics network information, construct a logistics network map in a virtual environment, simulate the vehicle operation status in the logistics network map, and build the digital twin system;

[0011] Among them, the geographic traffic information includes road information, traffic information, and geographic environment information; the logistics network information includes the historical logistics transportation information corresponding to each node in the logistics network.

[0012] In one embodiment, the step of using geographic traffic information and logistics network information to construct a logistics network map in a virtual environment and simulate the vehicle operation status in the logistics network map includes:

[0013] Using the geographic traffic information and the logistics network information to construct a logistics network model; the logistics network model is used to represent each node in the logistics network and the transportation routes between nodes;

[0014] Configure the logistics network model according to the preset logistics operation information to generate the logistics network map in the virtual environment and the vehicle operation status in the logistics network map.

[0015] In one embodiment, the method further includes:

[0016] Display the logistics network map simulated by the digital twin system and the vehicle operation status in the logistics network map.

[0017] In one embodiment, the step of adjusting the line arrangement in the logistics line planning information according to the logistics transportation prediction result to obtain the target logistics line planning information includes:

[0018] Obtain the line update information input into the digital twin system; the line update information is determined based on the logistics transportation prediction result;

[0019] Use the line update information to adjust the line arrangement in the logistics line planning information to obtain the target logistics line planning information.

[0020] In one embodiment, the step of adjusting the line arrangement in the logistics line planning information according to the logistics transportation prediction result to obtain the target logistics line planning information includes:

[0021] Obtain the line recommendation information generated based on the logistics transportation prediction result; the line recommendation information carries the verification result of the predicted logistics transportation status;

[0022] In the case where the verification result of the logistics transportation status passes, the line arrangement in the logistics line planning information is adjusted by using the line recommendation information to obtain the target logistics line planning information.

[0023] In a second aspect, the present application further provides a logistics line planning device based on a digital twin system, including:

[0024] A real-time change data access module, configured to obtain geographical traffic change data and logistics network change data and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0025] A logistics line planning test module, configured to perform a simulation test on the logistics line planning information to be tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics line planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics line planning information under real-time scheduling;

[0026] A logistics line planning optimization module, configured to adjust the line arrangement in the logistics line planning information according to the logistics transportation prediction result to obtain a target logistics line planning information; the target logistics line planning information includes an optimized logistics transportation route.

[0027] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Obtain geographical traffic change data and logistics network change data and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0029] Perform a simulation test on the logistics line planning information to be tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics line planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics line planning information under real-time scheduling;

[0030] Adjust the line arrangement in the logistics line planning information according to the logistics transportation prediction result to obtain a target logistics line planning information; the target logistics line planning information includes an optimized logistics transportation route.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0032] Obtain geographical traffic change data and logistics network change data, and connect them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0033] Conduct a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain the logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling;

[0034] According to the logistics transportation prediction result, adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information; the target logistics route planning information includes the optimized logistics transportation route.

[0035] In a fifth aspect, the present application also provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0036] Obtain geographical traffic change data and logistics network change data, and connect them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0037] Conduct a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain the logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling;

[0038] According to the logistics transportation prediction result, adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information; the target logistics route planning information includes the optimized logistics transportation route.

[0039] The above logistics route planning method, device, computer device, storage medium, and computer program product based on a digital twin system obtain geographical traffic change data and logistics network change data and connect them to a pre-constructed digital twin system. This digital twin system is used to simulate the traffic environment in which the logistics network is located in the real world. Then, the logistics route planning information to be tested is simulated and tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics route planning information. This logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling. Furthermore, based on the logistics transportation prediction result, the route arrangement in the logistics route planning information is adjusted to obtain the target logistics route planning information. The target logistics route planning information includes the optimized logistics transportation route, realizing the dynamic and effective verification of the logistics route planning. Based on the simulation test to predict the logistics transportation status under real-time scheduling, the optimized logistics transportation route can be adjusted according to the logistics transportation prediction result, which helps to optimize and adjust in a timely manner and effectively improves the efficiency and accuracy of the logistics route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0041] Figure 1 It is a schematic flowchart of a logistics route planning method based on a digital twin system in an embodiment;

[0042] Figure 2 It is a schematic flowchart of the steps for constructing a digital twin system in an embodiment;

[0043] Figure 3 It is a schematic flowchart of a logistics route planning method based on a digital twin system in another embodiment;

[0044] Figure 4 It is a structural block diagram of a logistics route planning device based on a digital twin system in an embodiment;

[0045] Figure 5 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] In one embodiment, as Figure 1 shown, a logistics route planning method based on a digital twin system is provided. In this embodiment, an example is given where this method is applied to a terminal. It can be understood that this method can also be applied to a server, or to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 101 to 103. Among them:

[0048] Step 101, obtain geographical traffic change data and logistics network change data, and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0049] In practical applications, a digital twin system for logistics route planning and management can be pre-constructed based on three-dimensional geographic information system (3D GIS) technology and Unreal Engine (UE). It can be used to effectively collect, analyze, and process geographical information, as well as efficiently simulate and optimize logistics transportation routes.

[0050] Exemplarily, a digital twin system can be built using a three-dimensional geographic information system (3D GIS) combined with Unreal Engine (UE). Then, by accessing real-time data (i.e., geographical traffic change data and logistics network change data), dynamic and real traffic data display can be achieved. Thus, based on the digital twin virtual environment, intuitive visualization can be achieved, greatly improving interactivity, and the logistics route can be presented in a three-dimensional form, making the logistics route planning more intuitive and easy to understand.

[0051] Step 102, perform a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation state of the logistics route planning information under real-time scheduling;

[0052] In one example, by building a digital twin (i.e., a digital twin system) that simulates the real world, a pre-determined logistics route plan (i.e., the logistics route planning information to be tested) can be imported into this digital twin to quickly obtain a prediction result through simulation (i.e., the logistics transportation prediction result) by simulation. Since this digital twin can be connected to historical data and can also be connected to the real world in real time, it can then sense the real-time operation changes of the real world through the digital twin to predict the logistics transportation state of the logistics route plan under real-time scheduling.

[0053] In yet another example, by accessing real-time data, the virtual world can be associated with the real world in real time, enabling the digital twin system to not only reflect the operating laws of the real world's history. After connecting to the real world, the virtual world can be regarded as a mapping of the real world, generating the same real-time changes as the real world. Furthermore, in the mapped environment, it is more conducive to predicting the results generated by real-time line scheduling, so as to achieve dynamic and effective verification of the logistics line arrangement plan.

[0054] In specific implementation, through the combination of 3D GIS and the Unreal Engine, the logistics line planning and management can be made more scientific and accurate, which helps to improve the efficiency of logistics transportation. The digital twin technology adopted can simulate and predict the logistics operation status according to real-time data and historical data, which is beneficial to optimizing and adjusting the plan in a timely manner. Furthermore, based on the use of the digital twin system, the verification of the line arrangement plan can be accelerated, effectively improving the efficiency of logistics line planning.

[0055] Step 103: According to the logistics transportation prediction result, adjust the line arrangement in the logistics line planning information to obtain the target logistics line planning information; the target logistics line planning information includes the optimized logistics transportation route.

[0056] After obtaining the logistics transportation prediction result through simulation, the line can be adjusted and optimized according to this result, so as to better complete the logistics transportation task. Thus, through scientific and reasonable line planning, unnecessary transportation and warehousing consumption can be reduced, effectively saving the logistics transportation cost.

[0057] Compared with the traditional method, the technical solution of this embodiment, through the digital twin system constructed based on the integration of the three-dimensional geographic information system (3D GIS) and the Unreal Engine (UE), can generate and update a virtual operation environment in real time, enabling users to roam in the virtual environment and intuitively observe the operation conditions of each node in the real-world logistics network, facilitating timely adjustment. Based on this digital twin system, the automatic generation and verification of the transportation line arrangement plan can also be realized. For example, a reliable experimental environment can be provided to verify the actual application effect of the logistics line arrangement plan, and then it can help to optimize and modify in a timely manner to reduce the mileage and the number of lines, effectively improving the efficiency and accuracy of logistics line planning.

[0058] In the above logistics route planning method based on the digital twin system, by obtaining geographical traffic change data and logistics network change data, and accessing them to the pre-constructed digital twin system, then in the digital twin system, the logistics route planning information to be tested is simulated and tested to obtain the logistics transportation prediction result corresponding to the logistics route planning information. Furthermore, according to the logistics transportation prediction result, the route arrangement in the logistics route planning information is adjusted to obtain the target logistics route planning information, realizing the dynamic and effective verification of the logistics route planning. Based on the simulation test to predict the logistics transportation state under real-time scheduling, the optimized logistics transportation route can be adjusted according to the logistics transportation prediction result, which helps to optimize and adjust in a timely manner and effectively improves the efficiency and accuracy of the logistics route planning.

[0059] In an exemplary embodiment, as Figure 2 shown, before the step of obtaining geographical traffic change data and logistics network change data and accessing them to the pre-constructed digital twin system, the following steps may further be included:

[0060] Step 201, based on the three-dimensional geographic information system and the Unreal Engine, using geographical traffic information and logistics network information, construct a logistics network map in a virtual environment, and simulate the vehicle running state in the logistics network map to construct the digital twin system.

[0061] Among them, the geographical traffic information may include road information, traffic information, and geographical environment information; the logistics network information may include the historical logistics transportation information corresponding to each node in the logistics network.

[0062] In practical applications, the digital twin system background may include multiple basic modules, such as the GIS geographical information processing module, the data center module, and the digital twin simulation module. The main functional modules of the system may include the visual large screen vehicle and road real-time monitoring and scheduling function module and the wiring plan acceleration simulation verification function module.

[0063] In one example, for the GIS geographical information processing module, it can effectively construct the real geographical environment of logistics transportation based on 3D GIS technology by obtaining effective geographical traffic information. For example, various types of sensing devices can be used to collect information materials related to logistics transportation, which may include but are not limited to road information (such as road width, slope, curvature, etc.), traffic information (such as traffic flow data, traffic rules, road construction, traffic accidents, etc. dynamic information), environmental information (such as terrain, buildings, obstacles, etc.). Thus, based on the use of 3D GIS technology, the system can realize the three-dimensional visualization of geographical information, such as real-time tracking and updating of various dynamic data including roads, traffic flow, weather, road conditions, etc.

[0064] In an alternative embodiment, the digital twin system can simulate and predict the logistics operation status based on real-time data and historical data, where the historical data may include historical route information (such as vehicle mileage, duration, speed, whether restricted, etc.), historical network point information (such as the location of the network point, the volume of parcels, the duration of loading and unloading operations, etc.), historical vehicle information (such as vehicle length, volume, load, etc.), as well as historical vehicle task execution status, map and other information. Furthermore, through the above information, a map highly similar to the logistics network-related scenario in the real world (i.e., the logistics network map) can be constructed and fitted in the digital world.

[0065] In another example, for the data center module, it can be used to store the basic information of each node in the logistics network (i.e., historical logistics transportation information), which may include but is not limited to transfer yards, network points, shifts, collection and delivery volumes, vehicle loading rates, information related to vehicle transportation nodes, line arrangement plan information, vehicle parcel information, and vehicle task execution status, etc. Furthermore, the above information can be used for real-time monitoring, scheduling, and optimization simulation input based on the digital twin system.

[0066] In this embodiment, by using the three-dimensional geographic information system and the Unreal Engine, adopting geographic traffic information and logistics network information, constructing a logistics network map in a virtual environment, and simulating the vehicle operation status in the logistics network map, a digital twin system can be constructed, which can provide an intuitive display of the operation status of each node in the logistics network in the real world and can dynamically and effectively verify the line arrangement plan of the logistics transportation route.

[0067] In an exemplary embodiment, the step of adopting geographic traffic information and logistics network information, constructing a logistics network map in a virtual environment, and simulating the vehicle operation status in the logistics network map may include the following steps:

[0068] Adopt the geographic traffic information and the logistics network information to construct a logistics network model; the logistics network model is used to represent each node in the logistics network and the transportation routes between the nodes; configure the logistics network model according to the preset logistics operation information to generate the logistics network map in the virtual environment and the vehicle operation status in the logistics network map.

[0069] In an example, for the digital twin simulation module, it can combine 3D GIS technology with the Unreal Engine (UE), establish a digital model of the logistics network (i.e., the logistics network model), which may include a logistics center, a warehouse, a distribution point, a transportation route, etc., and can define the quantity, performance, and usage rules of various logistics-related equipment, facilities, operations, etc. resources, that is, configure the logistics network model according to the preset logistics operation information, and then can simulate the traffic environment in the real world and realize route planning and simulation testing in the virtual environment.

[0070] Exemplarily, data obtained from the GIS geographic information processing module can be utilized to generate a dynamic three-dimensional virtual environment that can be used to simulate vehicle operation. Since the rendering function of the Unreal Engine can vividly display geographic information and simulate the physical behaviors of the real world, such as the driving states of vehicles under different road conditions, the simulation test of logistics route planning can be realized. In the simulation environment, various complex situations can also be simulated, such as different traffic conditions, weather conditions, vehicle performance, etc., which helps to comprehensively analyze and optimize the logistics operation routes.

[0071] In another example, for the function module of accelerating the simulation verification of the cable laying plan, it can predict and simulate the cable laying plan by using technologies such as machine learning, prediction algorithms, and operations research optimization algorithms based on the collected data. Furthermore, it can predict and evaluate the possible results and impacts of various logistics situations and transportation strategies, and can also be used to verify the problems and effects of the manual cable laying plan. For example, for the vehicle scheduling time and task routes, the running conditions of the vehicles can be simulated and predicted, and the result of whether the route is reasonable can be obtained in advance during the planning.

[0072] In the specific implementation, a digital twin body that simulates the real world can be built through digital twin technology. Based on the changes to the digital twin body, the change results of the real world can be deduced. For example, an unused logistics cable laying plan can be verified and optimized in a highly realistic and accurate verification environment, so as to achieve the effect of accelerating the simulation verification of the cable laying plan.

[0073] In this embodiment, by adopting geographic traffic information and logistics network information, a logistics network model is constructed, and then the logistics network model is configured according to the preset logistics operation information to generate the logistics network map in the virtual environment and the vehicle running states in the logistics network map, providing data support for the further dynamic verification of the logistics route planning.

[0074] In an exemplary embodiment, the following steps may further be included:

[0075] Display the logistics network map simulated by the digital twin system and the vehicle running states in the logistics network map.

[0076] In practical applications, for the real-time monitoring and dispatching function module of the visualization large screen for vehicles and roads, the system can interact with the actual operating logistics network to monitor the operating status of the network in real time, analyze data, and provide feedback and suggestions in a timely manner; based on the digital twin system built using 3D GIS and UE engines, by accessing real-time GIS data and vehicle information, the real situation can be rendered in real time onto the visualization large screen for vehicle management in the logistics network for users to view the overall operating status of the network. It can also track the operating status of a certain logistics transport vehicle or the arrival situation of a certain transfer yard, etc., so as to facilitate real-time logistics transport dispatching.

[0077] In this embodiment, by displaying the logistics network map simulated by the digital twin system and the vehicle operating status in the logistics network map, users can intuitively observe the operating conditions of each node in the real-world logistics network.

[0078] In an exemplary embodiment, the step of adjusting the line arrangement in the logistics line planning information according to the logistics transport prediction result to obtain the target logistics line planning information may include the following steps:

[0079] Obtain the line update information input into the digital twin system; the line update information is determined based on the logistics transport prediction result; use the line update information to adjust the line arrangement in the logistics line planning information to obtain the target logistics line planning information.

[0080] In one example, during the process of line planning and simulation testing in a virtual environment, for the logistics line planning information to be tested, according to the obtained logistics transport prediction result, the initial line can be rearranged in the digital twin system (i.e., obtain the line update information), and then the target logistics line planning information can be adjusted by optimizing the logistics transport route.

[0081] In an alternative embodiment, if it is necessary to determine the impact caused by changing a certain line, the certain line can be rearranged in the digital twin system, and then modified according to the result of the simulation prediction.

[0082] In this embodiment, by obtaining the line update information input into the digital twin system, and then using the line update information to adjust the line arrangement in the logistics line planning information to obtain the target logistics line planning information, it is possible to perform line planning and simulation testing in a virtual environment.

[0083] In an exemplary embodiment, the step of adjusting the line arrangement in the logistics line planning information according to the logistics transport prediction result to obtain the target logistics line planning information may include the following steps:

[0084] Obtain the route recommendation information generated based on the logistics transportation prediction result; the route recommendation information carries the predicted logistics transportation status verification result; in the case where the logistics transportation status verification result passes, use the route recommendation information to adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information.

[0085] In one example, during the process of route planning and simulation testing in a virtual environment, based on the logistics transportation prediction result corresponding to the logistics route planning information to be tested, the digital twin system can automatically generate the routes that need to be modified for recommendation and can provide the corresponding prediction results to prove (that is, the route recommendation information carries the predicted logistics transportation status verification result), which can indicate that the modified logistics route will also have good results if used in the real scenario.

[0086] In this embodiment, by obtaining the route recommendation information generated based on the logistics transportation prediction result, and then in the case where the logistics transportation status verification result passes, using the route recommendation information to adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information, it can realize the automatic generation and verification of the transportation route arrangement plan, and improve the efficiency of logistics route planning.

[0087] In one embodiment, as Figure 3 shown, a flowchart of another logistics route planning method based on a digital twin system is provided. In this embodiment, the method includes the following steps:

[0088] In step 301, based on the three-dimensional geographic information system and the Unreal Engine, use the geographic traffic information and the logistics network information to construct a logistics network map in a virtual environment, and simulate the vehicle operation status in the logistics network map to construct a digital twin system. In step 302, display the logistics network map simulated by the digital twin system and the vehicle operation status in the logistics network map. In step 303, obtain the geographic traffic change data and the logistics network change data and access them to the pre-constructed digital twin system. In step 304, perform a simulation test on the logistics route planning information to be tested in the digital twin system to obtain the logistics transportation prediction result corresponding to the logistics route planning information. In step 305, obtain the route recommendation information generated based on the logistics transportation prediction result; the route recommendation information carries the predicted logistics transportation status verification result. In step 306, in the case where the logistics transportation status verification result passes, use the route recommendation information to adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information. It should be noted that the specific limitations of the above steps can refer to the specific limitations of a logistics route planning method based on a digital twin system described above, and will not be elaborated here.

[0089] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least some of the steps or stages in other steps or other steps.

[0090] Based on the same inventive concept, an embodiment of the present application further provides a digital twin system-based logistics route planning device for implementing the above-mentioned digital twin system-based logistics route planning method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the digital twin system-based logistics route planning device provided below can refer to the limitations on the digital twin system-based logistics route planning method in the above text, and will not be repeated here.

[0091] In an exemplary embodiment, as Figure 4 shown, a digital twin system-based logistics route planning device is provided, including:

[0092] A real-time change data access module 401, configured to obtain geographical traffic change data and logistics network change data and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0093] A logistics route planning test module 402, configured to perform a simulation test on the logistics route planning information to be tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling;

[0094] A logistics route planning optimization module 403, configured to adjust the route arrangement in the logistics route planning information according to the logistics transportation prediction result to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

[0095] In an embodiment, the device further includes:

[0096] A digital twin system construction module, which is used to construct a logistics network map in a virtual environment based on a three-dimensional geographic information system and Unreal Engine, adopt geographic traffic information and logistics network information, simulate the vehicle operation status in the logistics network map, and construct the digital twin system;

[0097] Among them, the geographic traffic information includes road information, traffic information, and geographic environment information; the logistics network information includes the historical logistics transportation information corresponding to each node in the logistics network.

[0098] In one embodiment, the digital twin system construction module includes:

[0099] A logistics network model construction sub-module, which is used to construct a logistics network model by adopting the geographic traffic information and the logistics network information; the logistics network model is used to represent each node in the logistics network and the transportation routes between the nodes;

[0100] A model configuration sub-module, which is used to configure the logistics network model according to the preset logistics operation information, and generate the logistics network map in the virtual environment and the vehicle operation status in the logistics network map.

[0101] In one embodiment, the device further includes:

[0102] A digital twin display module, which is used to display the logistics network map simulated by the digital twin system and the vehicle operation status in the logistics network map.

[0103] In one embodiment, the logistics route planning and optimization module 403 includes:

[0104] A route update information acquisition sub-module, which is used to acquire the route update information input into the digital twin system; the route update information is determined based on the logistics transportation prediction result;

[0105] A first adjustment sub-module, which is used to adjust the route arrangement in the logistics route planning information by adopting the route update information to obtain the target logistics route planning information.

[0106] In one embodiment, the logistics route planning and optimization module 403 includes:

[0107] A route recommendation information acquisition sub-module, which is used to acquire the route recommendation information generated based on the logistics transportation prediction result; the route recommendation information carries the verification result of the predicted logistics transportation status;

[0108] The second adjustment sub-module is configured to, when the logistics transportation status verification result passes, adjust the route arrangement in the logistics route planning information by using the route recommendation information to obtain the target logistics route planning information.

[0109] Each module in the above-mentioned logistics route planning device based on the digital twin system can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0110] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for the processor to exchange information with external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a logistics route planning method based on the digital twin system. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0111] Those skilled in the art can understand that Figure 5 the structure shown in

[0112] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0113] Obtain geographical traffic change data and logistics network change data, and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment in which the logistics network is located in the real world;

[0114] Conduct a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling;

[0115] According to the logistics transportation prediction result, adjust the route arrangement in the logistics route planning information to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

[0116] In one embodiment, when the processor executes the computer program, the steps of the logistics route planning method based on the digital twin system in the above-mentioned other embodiments are also implemented.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0118] Obtain geographical traffic change data and logistics network change data, and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment in which the logistics network is located in the real world;

[0119] Conduct a simulation test on the logistics route planning information to be tested in the digital twin system, and obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling;

[0120] According to the logistics transportation prediction result, adjust the route arrangement in the logistics route planning information to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

[0121] In one embodiment, when the computer program is executed by a processor, the steps of the logistics route planning method based on the digital twin system in the above-mentioned other embodiments are also implemented.

[0122] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0123] Obtain geographical traffic change data and logistics network change data, and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world;

[0124] Conduct a simulation test on the logistics line planning information to be tested in the digital twin system, and obtain a logistics transportation prediction result corresponding to the logistics line planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics line planning information under real-time scheduling;

[0125] According to the logistics transportation prediction result, adjust the line arrangement in the logistics line planning information to obtain target logistics line planning information; the target logistics line planning information includes an optimized logistics transportation route.

[0126] In one embodiment, when the computer program is executed by a processor, it also implements the steps of the logistics line planning method based on the digital twin system in the above-mentioned other embodiments.

[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0128] 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, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0129] 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 described in this specification.

[0130] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A logistics route planning method based on a digital twin system, characterized in that, The method includes: Obtaining geographical traffic change data and logistics network change data, and accessing them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world; Conducting a simulation test on the logistics route planning information to be tested in the digital twin system, and obtaining a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation status of the logistics route planning information under real-time scheduling; According to the logistics transportation prediction result, adjusting the route arrangement in the logistics route planning information to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

2. The method according to claim 1, characterized in that, Before the step of obtaining geographical traffic change data and logistics network change data and accessing them to a pre-constructed digital twin system, the method further includes: Based on a three-dimensional geographic information system and the Unreal Engine, using geographical traffic information and logistics network information, constructing a logistics network map in a virtual environment, and simulating the vehicle operation state in the logistics network map to construct the digital twin system; Among them, the geographical traffic information includes road information, traffic information, and geographical environment information; the logistics network information includes the historical logistics transportation information corresponding to each node in the logistics network.

3. The method according to claim 2, wherein The using geographical traffic information and logistics network information to construct a logistics network map in a virtual environment and simulate the vehicle operation state in the logistics network map includes: Using the geographical traffic information and the logistics network information to construct a logistics network model; the logistics network model is used to represent each of the nodes in the logistics network and the transportation routes between the nodes; Configuring the logistics network model according to preset logistics operation information to generate the logistics network map in the virtual environment and the vehicle operation state in the logistics network map.

4. The method according to claim 2, wherein The method further includes: Displaying the logistics network map simulated by the digital twin system and the vehicle operation state in the logistics network map.

5. The method according to claim 1, characterized in that The according to the logistics transportation prediction result, adjusting the route arrangement in the logistics route planning information to obtain target logistics route planning information includes: Obtaining route update information input into the digital twin system; the route update information is determined based on the logistics transportation prediction result; Using the route update information to adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information.

6. The method according to claim 1, wherein The according to the logistics transportation prediction result, adjusting the route arrangement in the logistics route planning information to obtain target logistics route planning information includes: Obtaining route recommendation information generated based on the logistics transportation prediction result; the route recommendation information carries a verified result of the predicted logistics transportation status; In the case where the verified result of the logistics transportation status passes, using the route recommendation information to adjust the route arrangement in the logistics route planning information to obtain the target logistics route planning information.

7. A logistics route planning device based on a digital twin system, characterized in that, The device includes: A real-time change data access module, which is used to obtain geographical traffic change data and logistics network change data and access them to a pre-constructed digital twin system; the digital twin system is used to simulate the traffic environment where the logistics network is located in the real world; A logistics route planning test module, which is used to conduct a simulation test on the logistics route planning information to be tested in the digital twin system to obtain a logistics transportation prediction result corresponding to the logistics route planning information; the logistics transportation prediction result is used to predict the logistics transportation state of the logistics route planning information under real-time scheduling; A logistics route planning optimization module, which is used to adjust the route arrangement in the logistics route planning information according to the logistics transportation prediction result to obtain target logistics route planning information; the target logistics route planning information includes an optimized logistics transportation route.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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