Logistics flat cable optimization method, device and equipment based on digital twinning and medium

Abstract

The invention relates to a logistics wiring optimization method, device and equipment based on digital twinning and a medium, and relates to the technical field of digital twinning. The method comprises the following steps: in response to a logistics wire arrangement optimization instruction, obtaining a logistics wire arrangement plan corresponding to the logistics wire arrangement optimization instruction; inputting the logistics wiring plan into a pre-established digital twin platform, and performing simulation operation to obtain a simulation operation result; the digital twinborn platform is a virtual simulation platform built in advance based on a real logistics wiring service; based on a pre-trained path optimization model and the simulation operation result, carrying out wire arrangement path optimization on the logistics wire arrangement plan to obtain a target optimization path corresponding to the logistics wire arrangement plan; the path optimization model is a model constructed based on an adaptive large neighborhood search algorithm; and executing a logistics wiring plan according to the target optimization path. By adopting the method, the efficiency of logistics wiring optimization based on digital twinning can be improved.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a logistics routing optimization method, device, equipment and medium based on digital twin. Background Art

[0002] In the logistics industry, logistics routing refers to the transportation plan of vehicles on a certain route within a certain time. A reasonable logistics routing plan is very important for improving logistics efficiency.

[0003] In order to ensure the implementation effect of the logistics routing plan, it is necessary to continuously improve and optimize the logistics routing plan, that is, to optimize the vehicle transportation path in the logistics routing plan. At present, manual optimization is usually adopted, that is, manual verification of the logistics routing plan and then targeted path optimization strategy is formulated. However, this method is time-consuming and labor-intensive, resulting in low efficiency of logistics routing optimization. Summary of the invention

[0004] Based on this, it is necessary to provide a logistics line optimization method, device, computer equipment, computer-readable storage medium and computer program product based on digital twins that can improve the optimization efficiency of logistics lines in response to the above-mentioned technical problems.

[0005] In the first aspect, the present application provides a logistics routing optimization method based on digital twins. The method includes: in response to a logistics routing optimization instruction, obtaining a logistics routing plan corresponding to the logistics routing optimization instruction; inputting the logistics routing plan into a pre-built digital twin platform, performing simulation operation, and obtaining simulation operation results; the digital twin platform refers to a virtual simulation platform pre-built based on the actual logistics routing business; based on the pre-trained path optimization model and simulation operation results, the routing path of the logistics routing plan is optimized to obtain the target optimization path corresponding to the logistics routing plan; the path optimization model is a model constructed based on an adaptive large neighborhood search algorithm; according to the target optimization path, the logistics routing plan is executed.

[0006] On the second aspect, the present application also provides a logistics routing optimization device based on digital twins. The device includes: a routing plan acquisition module, which is used to respond to the logistics routing optimization instruction and obtain the logistics routing plan corresponding to the logistics routing optimization instruction; a simulation operation module, which is used to input the logistics routing plan into the pre-built digital twin platform, perform simulation operation, and obtain the simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on the actual logistics routing business; a path optimization module, which is used to optimize the routing path of the logistics routing plan based on the pre-trained path optimization model and the simulation operation results, and obtain the target optimization path corresponding to the logistics routing plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; a plan execution module, which is used to execute the logistics routing plan according to the target optimization path.

[0007] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: in response to a logistics cable layout optimization instruction, obtain a logistics cable layout plan corresponding to the logistics cable layout optimization instruction; input the logistics cable layout plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable layout operations; based on a pre-trained path optimization model and the simulation operation result, optimize the cable layout path of the logistics cable layout plan to obtain a target optimization path corresponding to the logistics cable layout plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; execute the logistics cable layout plan according to the target optimization path.

[0008] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: in response to a logistics cable layout optimization instruction, obtain a logistics cable layout plan corresponding to the logistics cable layout optimization instruction; input the logistics cable layout plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable layout operations; based on a pre-trained path optimization model and the simulation operation result, optimize the cable layout path of the logistics cable layout plan to obtain a target optimization path corresponding to the logistics cable layout plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; execute the logistics cable layout plan according to the target optimization path.

[0009] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented: in response to a logistics cable layout optimization instruction, obtain a logistics cable layout plan corresponding to the logistics cable layout optimization instruction; input the logistics cable layout plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable layout operations; based on a pre-trained path optimization model and the simulation operation result, optimize the cable layout path of the logistics cable layout plan to obtain a target optimization path corresponding to the logistics cable layout plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; execute the logistics cable layout plan according to the target optimization path.

[0010] The above-mentioned logistics cable laying optimization method, device, computer equipment, computer-readable storage medium, and computer program product first respond to a logistics cable laying optimization instruction, obtain the logistics cable laying plan corresponding to the logistics cable laying optimization instruction, and input the logistics cable laying plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result. The digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable laying operations, which can realistically simulate the relevant business processes of logistics cable laying in a real environment, facilitating subsequent optimization of logistics cable laying. Then, based on a pre-trained path optimization model and the simulation operation result, the cable laying path of the logistics cable laying plan is optimized to obtain the target optimization path corresponding to the logistics cable laying plan. This target optimization path is the optimal cable laying path. The path optimization model is a model constructed based on the adaptive large neighborhood search algorithm. The adaptive large neighborhood search algorithm can continuously search in multiple neighborhoods of the current solution, greatly increasing the search range of the algorithm in the solution space, thereby finding the optimal solution, that is, the target optimization path, more quickly. Finally, according to the target optimization path, the logistics cable laying plan is executed to complete the optimization of logistics cable laying. Conducting the simulation operation of the logistics cable laying plan on the digital twin platform can simulate the actual operation situation of the logistics cable laying plan, eliminating the need for manual on-site verification before optimization, improving the efficiency of logistics cable laying optimization, and using the model constructed based on the adaptive large neighborhood search algorithm to search for the optimal path, further improving the accuracy of logistics cable laying optimization and reducing logistics transportation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 FIG. is a schematic diagram of an application scenario of a logistics cable laying optimization method based on digital twins in an embodiment;

[0012] Figure 2 FIG. is a schematic flowchart of a logistics cable laying optimization method based on digital twins in an embodiment;

[0013] Figure 3 FIG. is a schematic flowchart of neighborhood search in an embodiment;

[0014] Figure 4 FIG. is a schematic flowchart of determining the target optimization path in an embodiment;

[0015] Figure 5 FIG. is a schematic flowchart of establishing a digital twin platform in an embodiment;

[0016] Figure 6 FIG. is a schematic flowchart of fidelity optimization in an embodiment;

[0017] Figure 7 FIG. is a schematic flowchart of establishing a path optimization model in an embodiment;

[0018] Figure 8It is a structural block diagram of a logistics routing optimization device based on digital twin in one embodiment;

[0019] Figure 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying 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.

[0021] In the logistics industry, logistics routing refers to the transportation plan of vehicles on a certain route at a certain time. A reasonable logistics routing plan is very important for improving logistics efficiency. In order to ensure the implementation effect of the logistics routing plan, it is necessary to continuously improve and optimize the logistics routing plan, that is, to optimize the vehicle transportation path in the logistics routing plan. At present, manual optimization is usually adopted, that is, the logistics routing plan is manually verified and then a targeted path optimization strategy is formulated. However, this method is time-consuming and labor-intensive, which not only leads to low efficiency of logistics routing optimization, but also fails to accurately analyze the optimal routing path, resulting in low accuracy of the logistics routing plan, thereby increasing logistics transportation costs. Therefore, the present application proposes a logistics routing optimization method based on digital twins, which simulates and runs the logistics routing plan on the digital twin platform without manual field verification, and optimizes the routing path according to the simulation operation results to obtain the target optimized path, and then executes the flow routing plan based on the target optimized path.

[0022] The logistics routing optimization method based on digital twin provided in the embodiment of the present disclosure can be applied to Figure 1In the application environment shown, it includes a server 102, a terminal 104, and a digital twin platform 106. The server 102 communicates with the terminal 104, and the server 102 is connected to the digital twin platform 106. Specifically, in response to a logistics line arrangement optimization instruction initiated by the terminal 104, the server 102 obtains a logistics line arrangement plan corresponding to the logistics line arrangement optimization instruction, and inputs the logistics line arrangement plan into the pre-built digital twin platform 106 for simulation operation to obtain a simulation operation result. The digital twin platform 106 refers to a virtual simulation platform pre-built based on real logistics line arrangement operations. Then, based on a pre-trained path optimization model and the simulation operation result, the logistics line arrangement plan is optimized for the line arrangement path to obtain a target optimization path corresponding to the logistics line arrangement plan. The path optimization model is a model constructed based on the adaptive large neighborhood search algorithm. Finally, according to the target optimization path, the logistics line arrangement plan is executed. Among them, the server 102 can be implemented by an independent server or a server cluster composed of multiple servers. The terminal 104 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, and Internet of Things devices.

[0023] In one embodiment, as Figure 2 shown, a method is provided. Taking the server 102 in Figure 1 as an example for illustration, it includes the following steps:

[0024] Step S202, in response to a logistics line arrangement optimization instruction, obtain a logistics line arrangement plan corresponding to the logistics line arrangement optimization instruction.

[0025] Among them, the logistics line arrangement optimization instruction can be an instruction to optimize the logistics line arrangement plan. The logistics line arrangement plan can refer to the line arrangement plan to be optimized. The logistics line arrangement plan may include transportation vehicles, transportation routes, transportation sites, transportation times, transportation express information, etc. It should be noted that the logistics line arrangement plan contains a large amount of information such as the transportation routes, transportation times, and scheduling arrangements of vehicles. Therefore, the essence of optimizing the logistics line arrangement plan is to optimize the line arrangement path, that is, to formulate the best transportation path for each logistics line arrangement plan.

[0026] Specifically, the server responds to the logistics line arrangement optimization instruction sent by the terminal and obtains the logistics line arrangement plan to be optimized carried by the logistics line arrangement optimization instruction, so as to facilitate subsequent simulation operation of the logistics line arrangement plan in the digital twin platform.

[0027] Step S204, input the logistics line arrangement plan into the pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics line arrangement operations.

[0028] Among them, the simulation operation result can refer to the result obtained by simulating the operation of the logistics line arrangement plan on the digital twin platform, including but not limited to the number of temporary car calls, vehicle loading rate, operating cost, timeliness statistics and other results. The real logistics line arrangement business can refer to the business process when the logistics line arrangement plan is implemented in the real environment.

[0029] Specifically, after the server obtains the logistics line arrangement plan, it inputs the logistics line arrangement plan into the pre-built digital twin platform. The digital twin platform is a virtual simulation platform built one-to-one based on the business process when the logistics line arrangement plan is implemented in the real environment. It contains virtual business processes that are exactly the same as the real business process, and can realistically simulate the implementation process of the logistics line arrangement plan, so as to quickly discover the design problems of the logistics line arrangement plan and optimize it, thereby avoiding the inability to implement or affecting other logistics line arrangement plans when it is put into use. While improving the optimization efficiency of the logistics line arrangement plan, it can also effectively avoid the occurrence of subsequent logistics accidents. After the digital twin platform finishes the simulation operation, it can generate the simulation operation results corresponding to the logistics line arrangement plan, such as the number of temporary car calls, vehicle loading rate, operating cost, timeliness statistics and other results.

[0030] Step S206, based on the pre-trained path optimization model and the simulation operation result, optimize the routing path of the logistics line arrangement plan to obtain the target optimization path corresponding to the logistics line arrangement plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm.

[0031] Among them, the Adaptive Large Neighborhood Search (ALNS) is a heuristic algorithm that adds a measure of the effect of operators on the basis of neighborhood search, enabling the algorithm to automatically select good operators to destroy and repair the solution, thereby obtaining a better solution. The target optimization path is the optimal path obtained by the path optimization model continuously performing neighborhood search.

[0032] Specifically, input the simulation operation result into the pre-trained path optimization model constructed based on the adaptive large neighborhood search algorithm. The model obtains the optimal solution, that is, the optimal path, through continuous neighborhood search. This optimal path can meet the optimization objectives of the model, such as the shortest transportation time, the lowest cost, the shortest total transportation distance and other optimization objectives.

[0033] Step S208, execute the logistics line arrangement plan according to the target optimization path.

[0034] Specifically, after calculating the target optimization path, update the original logistics line arrangement plan to obtain the latest logistics line arrangement plan, and implement the latest logistics line arrangement plan.

[0035] In this embodiment, the server responds to the logistics cable routing optimization instruction, obtains the logistics cable routing plan corresponding to the logistics cable routing optimization instruction, and inputs the logistics cable routing plan into the pre-built digital twin platform for simulation operation to obtain the simulation operation result. The digital twin platform refers to a virtual simulation platform pre-built based on the real logistics cable routing business, which can realistically simulate the relevant business processes of logistics cable routing in the real environment, facilitating subsequent logistics cable routing optimization. Then, based on the pre-trained path optimization model and the simulation operation result, the cable routing of the logistics cable routing plan is optimized to obtain the target optimization path corresponding to the logistics cable routing plan. This target optimization path is the optimal cable routing path. The path optimization model is a model constructed based on the adaptive large neighborhood search algorithm. The adaptive large neighborhood search algorithm can continuously search in multiple neighborhoods of the current solution, greatly improving the search range of the algorithm in the solution space, and thus finding the optimal solution, that is, the target optimization path, more quickly. Finally, according to the target optimization path, the logistics cable routing plan is executed to complete the optimization of the logistics cable routing. Conducting the simulation operation of the logistics cable routing plan on the digital twin platform can simulate the actual operation of the logistics cable routing plan, eliminating the need for manual on-site verification before optimization, improving the efficiency of logistics cable routing optimization, and using the model constructed based on the adaptive large neighborhood search algorithm to search for the optimal path, further improving the accuracy of logistics cable routing optimization and reducing logistics transportation costs.

[0036] In one embodiment, as Figure 3 shown, based on the pre-trained path optimization model and the simulation operation result, the cable routing of the logistics cable routing plan is optimized to obtain the target optimization path corresponding to the logistics cable routing plan, including:

[0037] Step S302, input the simulation operation result into the path optimization model, and perform initial path optimization on the simulation operation result through the path optimization model to obtain the initial optimization path.

[0038] Among them, the initial optimization path may refer to an initial solution randomly generated by the path optimization model.

[0039] Specifically, when the simulation operation result is output into the path optimization model, the model can randomly generate an initial solution, that is, the initial optimization path. In one example, the greedy algorithm can be used to generate this initial solution. The greedy algorithm (also known as the greedy algorithm) refers to always making the best choice currently when solving a problem. That is to say, this algorithm makes an optimal choice at each step, trying to find the overall optimal method to solve the entire problem. It can be understood that the initial optimization path is the current optimal path.

[0040] Step S304, based on the initial optimization path, perform neighborhood search to obtain the target optimization path corresponding to the logistics cable routing plan.

[0041] Specifically, after obtaining the initial optimization path, the model can continuously search in the neighborhood of the initial optimization path to find a better path, thereby replacing the initial optimization path. Neighborhood search can be understood as an act of destroying and reconstructing the initial optimization path.

[0042] In this embodiment, by performing neighborhood search on the initial optimization path to find a target optimization path better than it, the accuracy of path optimization is improved, thereby improving the accuracy of logistics cable routing optimization.

[0043] In one embodiment, as Figure 4 shown, based on the initial optimization path, local search is performed to obtain the target optimization path corresponding to the logistics cable routing plan, including:

[0044] Step S402, randomly select a first target path point from multiple candidate path points, and remove the first target path point from the initial optimization path to obtain the first optimization path corresponding to the initial optimization path.

[0045] Among them, the candidate path points can refer to each starting point and passing point included in the initial optimization path, such as network points and transfer stations. The first target path point can refer to the path point removed from the initial optimization path. The first optimization path can refer to the path obtained after removing the first target path point from the initial optimization path. It should be noted that in the process of neighborhood search, the adaptive large neighborhood search algorithm will destroy and reconstruct each current solution. Destruction means destroying a part of the current solution, and then reconstructing the destroyed solution. The destruction is random, that is, each destruction randomly destroys different parts of the solution, thereby obtaining a new solution, and evaluating whether the new solution is better than the current solution, so as to judge whether to use the new solution as the optimal solution.

[0046] In one embodiment, corresponding weights can also be assigned to destruction and reconstruction. Through this weight, the frequency of destroying the solution and the frequency of reconstructing the solution during the search process can be controlled, so as to obtain a better solution.

[0047] Specifically, randomly select the first target path point to be removed from multiple candidate path points. Removing the first target path point from the initial optimization path is equivalent to the destruction process, and the first optimization path is obtained.

[0048] Step S404, randomly select a second target path point from multiple candidate path points, and insert the second target path point into the first optimization path to obtain the second optimization path corresponding to the first optimization path.

[0049] Among them, the second target path point can refer to the path point inserted into the first optimization path. The second optimization path can refer to the path obtained after inserting the second target path point into the first optimization path.

[0050] Specifically, the second target path point to be inserted is also randomly selected from multiple candidate path points, and the second target path point is inserted into the vacant position in the first optimized path, that is, the blank position after removing the path point, so as to obtain a new optimized path, namely the second optimized path.

[0051] Step S406: Determine the target optimized path based on the second optimized path.

[0052] Specifically, the second optimized path can be compared with the initial optimized path to detect whether the second optimized path is better than the initial optimized path. If the second optimized path is better than the initial optimized path, the transportation indicators corresponding to the second optimized path can be further evaluated to determine whether to use the second optimized path as the target optimized path.

[0053] In one embodiment, determining the target optimized path based on the second optimized path includes: obtaining the transportation indicators corresponding to the second optimized path; if the transportation indicators are in a converged state, using the second optimized path as the target optimized path.

[0054] Among them, the transportation indicators may include temporary task indicators, loading rate indicators, operating cost indicators, timeliness indicators, etc. The temporary task indicators can be evaluated by the number of temporary car calls corresponding to the second optimized path, the loading rate indicators can be evaluated by the vehicle loading rate corresponding to the second optimized path, the operating cost indicators can be evaluated by the transportation cost corresponding to the second optimized path, and the timeliness indicators can be evaluated by the timeliness statistics corresponding to the second optimized path. The converged state refers to the state where the transportation indicators gradually tend to be stable, and it can be determined according to the actual business which specific indicators to detect for the converged state.

[0055] Specifically, according to the actual business situation, the transportation indicators of the second optimized path can be obtained. If the transportation indicators are in a converged state, it means that the transportation indicators tend to be stable, and the second optimized path can be used as the target optimized path.

[0056] In this embodiment, by destroying and reconstructing the initial optimized path, the second optimized path is obtained, and by evaluating whether the transportation indicators of the second optimized path converge, the target optimized path is determined, which improves the accuracy of the target optimized path, thereby improving the accuracy of logistics route arrangement optimization.

[0057] In one embodiment, as Figure 5 shown, before the step of responding to the logistics route arrangement optimization instruction, the logistics route arrangement optimization method based on digital twin further includes:

[0058] Step S502: Respond to the instruction for building the digital twin platform and obtain the actual business data corresponding to the real logistics route arrangement business.

[0059] Among them, the building instruction refers to the instruction for building the digital twin platform. The real logistics wiring business can refer to the actual business process during the implementation of the logistics wiring plan in the real environment. The actual business data can refer to the data involved in the real logistics wiring business, which can include express data, vehicle data, site data, line data, historical vehicle routing data, and historical wiring plan data. The actual business data can be used for building the business model.

[0060] In one embodiment, the express data can include data such as express type, sending network point, transportation destination, express specification, etc., the vehicle data can include data such as vehicle type, license plate number, vehicle age, driver, etc., the site data can include the geographical location of the transfer yard, the geographical location of the network point, the shift information corresponding to the transfer yard and the network point respectively, etc., the line data can include the time taken by the vehicle during transportation between each network point and each transfer yard, the historical vehicle routing data can include the historical data of the actual transportation of the vehicle, which can include the vehicle speed, the driving habits of the vehicle driver, etc., and the historical wiring plan data can include the historical logistics wiring plan.

[0061] Specifically, when the server receives the building instruction of the digital twin platform, it can first obtain the real logistics wiring business, that is, the actual business process during the implementation of the logistics wiring plan in the real environment, such as vehicle scheduling, the process of the vehicle transporting express between the network point and the transfer yard. Then it obtains the business data involved in these actual business processes, including but not limited to express data, vehicle data, site data, line data, historical vehicle routing data, and historical wiring plan data.

[0062] Step S504, based on the actual business data, build a model to obtain a business model corresponding to the actual business data.

[0063] Among them, in order to ensure that the digital twin platform is as realistic as the real logistics environment, it is necessary to build models for each module in the actual business process, including but not limited to express models, vehicle scheduling models, site models, line models, and these models can be used to implement the corresponding business processes.

[0064] Specifically, after the server obtains the business data in the actual business process, it can carry out the next step of model building, so as to obtain express models, vehicle scheduling models, site models, line models, so as to implement the corresponding business processes on the digital twin platform.

[0065] Step S506, fuse the business model with the virtual scene corresponding to the real logistics wiring business to obtain the digital twin platform.

[0066] Among them, the virtual scene can refer to the virtual environment established according to the real logistics wiring scene, including but not limited to virtual vehicles, virtual lines, virtual sites, etc.

[0067] Specifically, after the server establishes each business model, it embeds each business model into the corresponding virtual scenario. For example, the vehicle scheduling model can be embedded into the virtual vehicle. It should be noted that when the vehicle scheduling model is related to the venue or route, the vehicle scheduling model can also be embedded into the virtual venue or virtual route. The specific fusion method of the business model and the virtual scenario can be flexibly set according to the actual situation and is not limited here. After the fusion of the business model and the virtual scenario is completed, the real wiring plan can be further used for optimization to obtain the digital twin platform.

[0068] In this embodiment, by obtaining the actual business data corresponding to the real logistics wiring business, a business model corresponding to the actual business data is established, and then the digital twin platform is built. In this way, the fidelity of the digital twin platform is ensured, providing a reliable and accurate verification environment for the subsequent logistics wiring plan, thereby improving the accuracy and efficiency of the logistics wiring plan optimization.

[0069] In one embodiment, as Figure 6 shown, fusing the business model with the virtual scenario corresponding to the real logistics wiring business to obtain the digital twin platform, including:

[0070] Step S602, fusing the business model with the virtual scenario corresponding to the real logistics wiring business to obtain a temporary virtual platform.

[0071] Step S604, obtaining the real wiring plan, and optimizing the fidelity of the temporary virtual platform based on the real wiring plan to obtain the optimized digital twin platform.

[0072] Among them, the temporary virtual platform can refer to the temporary digital twin platform generated after the fusion of the business model and the virtual scenario. The real wiring plan can refer to the historical logistics wiring plan in the real environment, that is, the logistics wiring plan that has been implemented, and can be used to optimize the temporary virtual platform.

[0073] Specifically, in order to further improve the fidelity of the digital twin platform, after the fusion of the business model and the virtual scenario to generate the temporary virtual platform, the historical logistics wiring plan of the real environment can be obtained for platform optimization. The historical logistics wiring plan can be obtained from the real logistics wiring system, that is, the historical logistics wiring plan is input into the temporary virtual platform, and the simulation result output by the temporary virtual platform is compared with the actual operation situation of the historical logistics wiring plan. If the simulation result is inconsistent with the actual operation situation, the fidelity parameters of the temporary virtual platform, such as vehicle fidelity, route fidelity, etc., can be adjusted to optimize the fidelity of the temporary virtual platform to obtain the optimized digital twin platform.

[0074] In this embodiment, by optimizing the fidelity of the temporary virtual platform, the fidelity of the digital twin platform is improved, and further the accuracy of subsequent logistics cable routing plan optimization is improved.

[0075] In one embodiment, as Figure 7 shown, after the step of obtaining the digital twin platform, it further includes:

[0076] Step S702, obtain the sample cable routing plan in the digital twin platform, as well as the preset path optimization objective and path constraint conditions.

[0077] Among them, the sample cable routing plan refers to the sample logistics cable routing plan for simulation operation on the digital twin platform, which can be used as a data set for model training. The path optimization objective can include optimization objectives such as the shortest transportation time, the lowest cost, and the shortest total transportation distance, and can be specifically set according to the actual situation. The path constraint conditions refer to the limiting conditions in the process of cable routing path optimization, such as vehicle capacity constraint, driving mileage constraint, time constraint, cargo demand, etc. The target optimization path needs to meet these constraint conditions to achieve the optimization objective.

[0078] In one embodiment, the path constraint conditions can be set based on Mixed Integer Linear Programming (MILP). MILP involves linear constraints and integer constraints of variables, and is used to determine the optimal solution of decision variables under a set of linear inequality constraints. The main feature of integer programming is that the decision variables are integers, and the value range of integer variables and the specific values of constraint conditions need to be determined.

[0079] Step S704, based on the sample cable routing plan, path optimization objective and path constraint conditions, perform model training to obtain a path optimization model.

[0080] Among them, according to the sample cable routing plan, path optimization objective and path constraint conditions, the initial model constructed based on the adaptive large neighborhood search algorithm is trained. That is, according to the transportation indicators of the sample cable routing plan output by the model each time, the parameters of the model are continuously adjusted until the transportation indicators reach a convergence state, or the training times are reached, or the accuracy value of the model reaches the preset accuracy threshold, and then the model training is stopped to obtain the path optimization model.

[0081] In this embodiment, by setting the path optimization objective and path constraint conditions, and using the sample cable routing plan as a data set for model training, the accuracy of model training is improved, which is beneficial for the subsequent model to find the optimal target optimization path, thereby improving the accuracy of logistics cable routing optimization.

[0082] In a specific embodiment, the actual business data corresponding to the real logistics cable routing business, such as express data, vehicle data, site data, route data, historical vehicle routing data, and historical cable routing plan data, can be obtained in advance. Based on these data, a model is built to obtain business models corresponding to the actual business data, such as an express model, a vehicle scheduling model, a site model, a route model, etc. These business models are integrated with the virtual scenario corresponding to the real logistics cable routing business to obtain a temporary virtual platform. Then, the real cable routing plan is obtained, and the temporary virtual platform is optimized for fidelity based on the real cable routing plan to obtain an optimized digital twin platform. Then, optimization objectives such as the shortest transportation time, the lowest cost, and the shortest total transportation distance are preset in advance, as well as constraint conditions such as vehicle capacity constraints, driving mileage constraints, time constraints, and cargo demand, and the sample cable routing plan on the digital twin platform is obtained as a data set for model training to obtain a path optimization model constructed based on the adaptive large neighborhood search algorithm. When a logistics cable routing optimization instruction is received, the logistics cable routing plan corresponding to the instruction is obtained, and the logistics cable routing plan is input into the digital twin platform for simulation operation to obtain a simulation operation result. Then, the simulation operation result is input into the path optimization model, and the initial path of the simulation operation result is optimized through the path optimization model to obtain an initial optimized path. And a first target path point is randomly selected from multiple candidate path points, the first target path point is removed from the initial optimized path to obtain a first optimized path corresponding to the initial optimized path, a second target path point is randomly selected from multiple candidate path points, and the second target path point is inserted into the first optimized path to obtain a second optimized path corresponding to the first optimized path. The transportation index corresponding to the second optimized path is obtained. If the transportation index is in a convergent state, the second optimized path is used as the target optimized path. Finally, according to the target optimized path, the logistics cable routing plan is executed.

[0083] In this embodiment, in response to the logistics cable layout optimization instruction, obtain the logistics cable layout plan corresponding to the logistics cable layout optimization instruction, and input the logistics cable layout plan into the pre-built digital twin platform for simulation operation to obtain the simulation operation result. The digital twin platform refers to a virtual simulation platform pre-built based on the real logistics cable layout business, which can realistically simulate the relevant business processes of logistics cable layout in the real environment, facilitating subsequent optimization of the logistics cable layout. Then, based on the pre-trained path optimization model and the simulation operation result, optimize the cable layout path of the logistics cable layout plan to obtain the target optimization path corresponding to the logistics cable layout plan. This target optimization path is the optimal cable layout path. The path optimization model is a model constructed based on the adaptive large neighborhood search algorithm. The adaptive large neighborhood search algorithm can continuously search in multiple neighborhoods of the current solution, greatly improving the search range of the algorithm in the solution space, and thus finding the optimal solution, that is, the target optimization path, more quickly. Finally, execute the logistics cable layout plan according to the target optimization path, thereby completing the optimization of the logistics cable layout. Conducting the simulation operation of the logistics cable layout plan on the digital twin platform can simulate the actual operation situation of the logistics cable layout plan, eliminating the need for manual on-site verification before optimization, improving the efficiency of logistics cable layout optimization, and using the model constructed based on the adaptive large neighborhood search algorithm to search for the optimal path, further improving the accuracy of logistics cable layout optimization and reducing the logistics transportation cost.

[0084] It should be understood that although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, 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 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 turn with at least a part of other steps or steps or stages in other steps.

[0085] Based on the same inventive concept, the embodiment of the present application also provides a digital twin-based logistics cable layout optimization device for implementing the above-mentioned digital twin-based logistics cable layout optimization method. The implementation solution provided by this device to solve problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the digital twin-based logistics cable layout optimization device provided below can refer to the limitations on the digital twin-based logistics cable layout optimization method in the above text, and will not be repeated here.

[0086] In one embodiment, as Figure 8As shown, a logistics cable routing optimization device based on digital twin is provided, including: a cable routing plan acquisition module 802, configured to acquire a logistics cable routing plan corresponding to a logistics cable routing optimization instruction in response to the logistics cable routing optimization instruction; a simulation operation module 804, configured to input the logistics cable routing plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable routing operations; a path optimization module 806, configured to optimize the cable routing path of the logistics cable routing plan based on a pre-trained path optimization model and the simulation operation result to obtain a target optimization path corresponding to the logistics cable routing plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; a plan execution module 808, configured to execute the logistics cable routing plan according to the target optimization path.

[0087] In one embodiment, the path optimization module 806 further includes: an initial optimization unit, configured to input the simulation operation result into the path optimization model to perform initial path optimization on the simulation operation result through the path optimization model to obtain an initial optimization path; a neighborhood search unit, configured to perform neighborhood search based on the initial optimization path to obtain a target optimization path corresponding to the logistics cable routing plan.

[0088] In one embodiment, the neighborhood search unit further includes: a removal subunit, configured to randomly select a first target path point from multiple candidate path points and remove the first target path point from the initial optimization path to obtain a first optimization path corresponding to the initial optimization path; an insertion subunit, configured to randomly select a second target path point from multiple candidate path points and insert the second target path point into the first optimization path to obtain a second optimization path corresponding to the first optimization path; a path determination subunit, configured to determine the target optimization path based on the second optimization path.

[0089] In one embodiment, the path determination subunit is further configured to: acquire a transportation index corresponding to the second optimization path; if the transportation index is in a converged state, use the second optimization path as the target optimization path.

[0090] In one embodiment, the logistics cable routing optimization device based on digital twin is further configured to: in response to a building instruction for the digital twin platform, acquire actual business data corresponding to real logistics cable routing operations; perform model building based on the actual business data to obtain a business model corresponding to the actual business data; fuse the business model with a virtual scenario corresponding to real logistics cable routing operations to obtain the digital twin platform.

[0091] In one embodiment, the logistics cable layout optimization device based on digital twin is further configured to: integrate the business model with the virtual scenario corresponding to the real logistics cable layout business to obtain a temporary virtual platform; obtain the real cable layout plan, and optimize the fidelity of the temporary virtual platform based on the real cable layout plan to obtain the optimized digital twin platform.

[0092] In one embodiment, the logistics cable layout optimization device based on digital twin is further configured to: obtain the sample cable layout plan in the digital twin platform, as well as the preset path optimization target and path constraint conditions; perform model training based on the sample cable layout plan, the path optimization target and the path constraint conditions to obtain a path optimization model.

[0093] Each module in the above-mentioned logistics cable layout optimization device based on digital twin can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0094] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory and a network interface connected through a system bus. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store item recommendation data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a logistics cable layout optimization method based on digital twin.

[0095] Those skilled in the art can understand that Figure 9 the structure shown in

[0096] In one 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: in response to a logistics cable routing optimization instruction, obtain a logistics cable routing plan corresponding to the logistics cable routing optimization instruction; input the logistics cable routing plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable routing operations; based on a pre-trained path optimization model and the simulation operation result, optimize the cable routing path of the logistics cable routing plan to obtain a target optimization path corresponding to the logistics cable routing plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; execute the logistics cable routing plan according to the target optimization path.

[0097] In one embodiment, when the processor executes the computer program, the following steps are further implemented: input the simulation operation result into the path optimization model, and perform initial path optimization on the simulation operation result through the path optimization model to obtain an initial optimization path; based on the initial optimization path, perform neighborhood search to obtain a target optimization path corresponding to the logistics cable routing plan.

[0098] In one embodiment, when the processor executes the computer program, the following steps are further implemented: randomly select a first target path point from multiple candidate path points, and remove the first target path point from the initial optimization path to obtain a first optimization path corresponding to the initial optimization path; randomly select a second target path point from multiple candidate path points, and insert the second target path point into the first optimization path to obtain a second optimization path corresponding to the first optimization path; based on the second optimization path, determine the target optimization path.

[0099] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtain a transportation index corresponding to the second optimization path; if the transportation index is in a converged state, use the second optimization path as the target optimization path.

[0100] In one embodiment, when the processor executes the computer program, the following steps are further implemented: in response to a platform building instruction for the digital twin platform, obtain actual business data corresponding to real logistics cable routing operations; based on the actual business data, perform model building to obtain a business model corresponding to the actual business data; fuse the business model with a virtual scene corresponding to real logistics cable routing operations to obtain a digital twin platform.

[0101] In one embodiment, when the processor executes the computer program, the following steps are further implemented: fuse the business model with a virtual scene corresponding to real logistics cable routing operations to obtain a temporary virtual platform; obtain a real cable routing plan, and perform fidelity optimization on the temporary virtual platform based on the real cable routing plan to obtain an optimized digital twin platform.

[0102] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining a sample wire routing plan in the digital twin platform, as well as a preset path optimization target and path constraint conditions; based on the sample wire routing plan, path optimization target, and path constraint conditions, performing model training to obtain a path optimization model.

[0103] 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: in response to a logistics wire routing optimization instruction, obtaining a logistics wire routing plan corresponding to the logistics wire routing optimization instruction; inputting the logistics wire routing plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics wire routing operations; based on a pre-trained path optimization model and the simulation operation result, optimizing the wire routing path of the logistics wire routing plan to obtain a target optimization path corresponding to the logistics wire routing plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; according to the target optimization path, executing the logistics wire routing plan.

[0104] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: inputting the simulation operation result into the path optimization model, and performing initial path optimization on the simulation operation result through the path optimization model to obtain an initial optimization path; based on the initial optimization path, performing neighborhood search to obtain a target optimization path corresponding to the logistics wire routing plan.

[0105] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: randomly selecting a first target path point from multiple candidate path points, and removing the first target path point from the initial optimization path to obtain a first optimization path corresponding to the initial optimization path; randomly selecting a second target path point from multiple candidate path points, and inserting the second target path point into the first optimization path to obtain a second optimization path corresponding to the first optimization path; based on the second optimization path, determining the target optimization path.

[0106] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining a transportation index corresponding to the second optimization path; if the transportation index is in a converged state, using the second optimization path as the target optimization path.

[0107] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: in response to a digital twin platform construction instruction, obtaining actual business data corresponding to real logistics wire routing operations; based on the actual business data, performing model construction to obtain a business model corresponding to the actual business data; fusing the business model with a virtual scenario corresponding to real logistics wire routing operations to obtain a digital twin platform.

[0108] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: integrating the business model with a virtual scenario corresponding to the real logistics wiring business to obtain a temporary virtual platform; obtaining a real wiring plan, and optimizing the fidelity of the temporary virtual platform based on the real wiring plan to obtain an optimized digital twin platform.

[0109] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining a sample wiring plan in the digital twin platform, as well as a preset path optimization objective and path constraint conditions; performing model training based on the sample wiring plan, the path optimization objective, and the path constraint conditions to obtain a path optimization model.

[0110] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the following steps: in response to a logistics wiring optimization instruction, obtaining a logistics wiring plan corresponding to the logistics wiring optimization instruction; inputting the logistics wiring plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on the real logistics wiring business; optimizing the wiring path of the logistics wiring plan based on a pre-trained path optimization model and the simulation operation result to obtain a target optimization path corresponding to the logistics wiring plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; and executing the logistics wiring plan according to the target optimization path.

[0111] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: inputting the simulation operation result into the path optimization model, and performing initial path optimization on the simulation operation result through the path optimization model to obtain an initial optimization path; performing neighborhood search based on the initial optimization path to obtain a target optimization path corresponding to the logistics wiring plan.

[0112] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: randomly selecting a first target path point from multiple candidate path points, and removing the first target path point from the initial optimization path to obtain a first optimization path corresponding to the initial optimization path; randomly selecting a second target path point from multiple candidate path points, and inserting the second target path point into the first optimization path to obtain a second optimization path corresponding to the first optimization path; and determining the target optimization path based on the second optimization path.

[0113] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining a transportation index corresponding to the second optimization path; if the transportation index is in a converged state, then using the second optimization path as the target optimization path.

[0114] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: in response to a setup instruction for the digital twin platform, obtain the actual business data corresponding to the real logistics cable laying business; based on the actual business data, perform model setup to obtain a business model corresponding to the actual business data; fuse the business model with the virtual scenario corresponding to the real logistics cable laying business to obtain the digital twin platform.

[0115] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: fuse the business model with the virtual scenario corresponding to the real logistics cable laying business to obtain a temporary virtual platform; obtain the real cable laying plan, and optimize the fidelity of the temporary virtual platform based on the real cable laying plan to obtain the optimized digital twin platform.

[0116] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtain the sample cable laying plan in the digital twin platform, as well as the preset path optimization objective and path constraint conditions; based on the sample cable laying plan, path optimization objective and path constraint conditions, perform model training to obtain a path optimization model.

[0117] 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 that have been authorized by the user or fully authorized by all parties.

[0118] 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 memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, 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.

[0119] 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.

[0120] The above embodiments only 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 cable layout optimization method based on digital twin, characterized in that, The method includes: In response to a logistics cable layout optimization instruction, obtaining a logistics cable layout plan corresponding to the logistics cable layout optimization instruction; Inputting the logistics cable layout plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on real logistics cable layout operations; Based on a pre-trained path optimization model and the simulation operation result, optimizing the cable layout path of the logistics cable layout plan to obtain a target optimization path corresponding to the logistics cable layout plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; Executing the logistics cable layout plan according to the target optimization path.

2. The method according to claim 1, characterized in that The step of optimizing the cable layout path of the logistics cable layout plan based on a pre-trained path optimization model and the simulation operation result to obtain a target optimization path corresponding to the logistics cable layout plan includes: Inputting the simulation operation result into the path optimization model, and performing initial path optimization on the simulation operation result through the path optimization model to obtain an initial optimization path; Based on the initial optimization path, performing neighborhood search to obtain a target optimization path corresponding to the logistics cable layout plan.

3. The method according to claim 2, wherein The initial optimization path includes multiple candidate path points. The step of performing neighborhood search based on the initial optimization path to obtain a target optimization path corresponding to the logistics cable layout plan includes: Randomly selecting a first target path point from the multiple candidate path points, and removing the first target path point from the initial optimization path to obtain a first optimization path corresponding to the initial optimization path; Randomly selecting a second target path point from the multiple candidate path points, and inserting the second target path point into the first optimization path to obtain a second optimization path corresponding to the first optimization path; Based on the second optimization path, determining the target optimization path.

4. The method according to claim 3, characterized in that, The step of determining the target optimization path based on the second optimization path includes: Obtaining a transportation index corresponding to the second optimization path; If the transportation index is in a converged state, taking the second optimization path as the target optimization path.

5. The method according to claim 1, wherein Before the step of responding to the logistics cable layout optimization instruction, the method further includes: In response to a construction instruction for the digital twin platform, obtaining actual business data corresponding to real logistics cable layout operations; Based on the actual business data, performing model construction to obtain a business model corresponding to the actual business data; Fusing the business model with a virtual scene corresponding to the real logistics cable layout operation to obtain the digital twin platform.

6. The method according to claim 5, wherein The step of fusing the business model with a virtual scene corresponding to the real logistics cable layout operation to obtain the digital twin platform includes: Fusing the business model with a virtual scene corresponding to the real logistics cable layout operation to obtain a temporary virtual platform; Obtaining a real cable layout plan, and optimizing the fidelity of the temporary virtual platform based on the real cable layout plan to obtain the optimized digital twin platform.

7. The method according to claim 5, characterized in that After the step of obtaining the digital twin platform, it further includes: Obtain the sample wire routing plan in the digital twin platform, as well as the preset path optimization objective and path constraint conditions; Based on the sample wire routing plan, the path optimization objective, and the path constraint conditions, perform model training to obtain the path optimization model.

8. A logistics wire arrangement optimization device based on digital twin, characterized in that, The device includes: A wire routing plan acquisition module, configured to obtain the wire routing plan corresponding to the logistics wire routing optimization instruction in response to the logistics wire routing optimization instruction; A simulation operation module, configured to input the wire routing plan into a pre-built digital twin platform for simulation operation to obtain a simulation operation result; the digital twin platform refers to a virtual simulation platform pre-built based on the actual logistics wire routing business; A path optimization module, configured to optimize the wire routing path of the wire routing plan based on the pre-trained path optimization model and the simulation operation result to obtain the target optimization path corresponding to the wire routing plan; the path optimization model is a model constructed based on the adaptive large neighborhood search algorithm; A plan execution module, configured to execute the wire routing plan according to the target optimization path.

9. 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 7.

10. 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 7.

11. A computer program product, comprising a computer program, characterized in that, When this computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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