CN114676911A

CN114676911A - Method and device for determining driving route of transport vehicle

Info

Publication number: CN114676911A
Application number: CN202210309451.8A
Authority: CN
Inventors: 唐杰聪; 蔡为彬; 周远侠; 杜姗
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-03-28
Filing date: 2022-03-28
Publication date: 2022-06-28

Abstract

The application provides a method and a device for determining a driving route of a transport vehicle, which relate to the technical field of big data and can also be used in the financial field, wherein the method comprises the following steps: determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed; performing iterative optimization on the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix; and graphically displaying the driving route optimization matrix to obtain the driving route of the vehicle. The method and the device can plan and show the driving route of the transport vehicle.

Description

Method and device for determining driving route of transport vehicle

Technical Field

The application relates to the technical field of big data, can be used in the financial field, and particularly relates to a method and a device for determining a driving route of a transport vehicle.

Background

With the economic development, social material communication is increasingly tight, and the financial service coverage field is increasingly expanded. Under a certain scene, cash transportation requirements exist among financial service network points, and cash trucks are born by transportation. How to promote the transport efficiency of the cash truck, the transportation safety of the cash truck becomes the problem that needs to be considered by financial service network points. In order to achieve the above purpose, the driving path of the securicar needs to be reasonably planned.

The reasonable planning of the driving path refers to that when a plurality of different cargo transportation demands exist at the same time, the cargo transportation dispatching center assigns vehicles to provide cargo transportation services, and a reasonable driving path is determined through algorithm analysis, so that each demand is met under a certain constraint condition, and the purposes of shortest transportation path, lowest cost, least time consumption and the like are achieved.

With the rise of Machine Learning (ML) algorithms, various fields have begun to use neural network models to solve business scenario problems. For example, the prior art adopts a Pointer Network (Pointer Network) to solve the problem of driving path planning. However, such an analysis algorithm based on a neural network model often needs to have a certain scale of historical data to train a usable model with a better effect. This causes a certain difficulty in actual application deployment, because in a real service scene, problems such as incomplete history service data record and few history service data are often encountered, which makes it difficult to establish a high-quality neural network model.

Further, the actual route planning scene of the securicar often causes the change of the delivery time consumption of the securicar between financial service network points due to the fact that urban traffic has a rush hour in the morning and evening. For such time-varying factors, even if a good-quality neural network model is established, it is difficult to deal with the influence thereof. Therefore, the scheme for planning the route of the securicar during driving, which is not required to be based on long-time historical data and can eliminate the influence of time-varying factors, has important significance and value.

Disclosure of Invention

The method and the device for determining the driving route of the transport vehicle can plan and display the driving route of the transport vehicle.

In order to solve the technical problem, the application provides the following technical scheme:

in a first aspect, the present application provides a method for determining a driving route of a transportation vehicle, including:

determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed;

performing iterative optimization on the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix;

and graphically displaying the driving route optimization matrix to obtain the driving route of the vehicle.

Further, the step of constructing the distribution time cost analysis model includes:

determining congestion time cost of each vehicle according to a preset driving route of each vehicle, the geographic position of each network point to be distributed and historical congestion data of a road section corresponding to the geographic position;

determining the early-arrival time cost of each vehicle according to the preset earliest delivery time and the actual time of each vehicle arriving at each to-be-delivered network point;

determining the late arrival time cost of each vehicle according to the preset latest delivery time and the actual time;

determining the driving time cost consumed by each vehicle when the vehicle is delivered according to the preset driving route according to the historical driving data of the preset driving route;

and determining the distribution time cost of each vehicle according to the congestion time cost, the early arrival time cost, the late arrival time cost and the driving time cost to obtain the distribution time cost analysis model.

Further, the initial matrix of driving routes includes: a random initial matrix; the method for determining the initial matrix of the driving route according to the number of the vehicles and the number of the network points to be distributed comprises the following steps:

carrying out random clustering on the network points to be distributed according to the number of the network points to be distributed;

and determining the random initial matrix according to the random clustering result and the number of the vehicles.

Further, the iterative optimization of the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix includes:

performing iterative operation on the random initial matrix until an iterative operation result meets a first preset stop condition, and stopping iteration to obtain a first process matrix; the first preset stopping condition is set according to the distribution time cost analysis model;

determining whether the first process matrix meets a second preset stop condition;

if so, determining the first process matrix as the driving route optimization matrix.

Further, the initial matrix of driving routes includes: a geographical initial matrix; the method for determining the initial matrix of the driving route according to the number of the vehicles and the number of the network points to be distributed comprises the following steps:

clustering the network points to be distributed according to the number of vehicles, the number of the network points to be distributed and the geographic position to obtain a network point cluster set corresponding to each cluster;

sorting the network points in the network point cluster set according to a preset bypassing strategy to obtain a driving route corresponding to each cluster;

and generating the geographical initial matrix according to the driving routes and the number of the vehicles.

performing iterative operation on the initial geographic matrix until the initial geographic matrix meets the first preset stop condition, and stopping iteration to obtain a second process matrix;

determining whether the second process matrix satisfies the second preset stop condition;

if so, determining the second process matrix as the driving route optimization matrix.

Further, the method for determining the driving route of the transport vehicle further comprises the following steps:

and comparing the first process matrix with the second process matrix according to the distribution time cost analysis model, and selecting the matrix with lower distribution time cost as the driving route optimization matrix.

Further, after obtaining the driving route optimization matrix, the method further includes:

and adjusting the driving route optimization matrix by using an insert operator and a delete operator in a heuristic algorithm.

Further, the adjusting the driving route optimization matrix by using an insert operator and a delete operator in a heuristic algorithm includes:

deleting the network points to be distributed with far geographic positions according to the deleting operator and the first execution weight thereof;

and executing an inserting operation on the network points to be distributed with the closer geographic positions according to the inserting operator and the second executing weight thereof to obtain an adjusted driving route optimization matrix.

calculating the distribution time cost of the adjusted driving route optimization matrix according to the distribution time cost analysis model;

and if the distribution time cost of the driving route optimization matrix after adjustment is lower than that of the driving route optimization matrix before adjustment, adjusting the first execution weight and the second execution weight.

In a second aspect, the present application provides a transportation vehicle driving route determining device, comprising:

the initial matrix generating unit is used for determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed;

the optimization matrix generation unit is used for performing iterative optimization on the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix;

and the driving route display unit is used for graphically displaying the driving route optimization matrix to obtain a vehicle driving route.

Further, the device for determining the driving route of the transport vehicle further comprises:

the congestion time cost determining unit is used for determining congestion time cost of each vehicle according to a preset driving route of each vehicle, the geographic position of each network point to be distributed and historical congestion data of a road section corresponding to the geographic position;

the early-arrival time cost determining unit is used for determining the early-arrival time cost of each vehicle according to the preset earliest delivery time and the actual time of each vehicle arriving at each to-be-delivered network point;

the late arrival time cost determining unit is used for determining the late arrival time cost of each vehicle according to the preset latest delivery time and the actual time;

the driving time cost determining unit is used for determining the driving time cost consumed by each vehicle when the delivery is carried out according to the preset driving route according to the historical driving data of the preset driving route;

and the cost analysis model generation unit is used for determining the distribution time cost of each vehicle according to the congestion time cost, the early arrival time cost, the late arrival time cost and the driving time cost so as to obtain the distribution time cost analysis model.

Further, the initial matrix of driving routes includes: a random initial matrix; the initial matrix generation unit includes:

the random clustering module is used for carrying out random clustering on the network points to be distributed according to the number of the network points to be distributed;

and the random initial matrix generation module is used for determining the random initial matrix according to a random clustering result and the number of the vehicles.

Further, the optimization matrix generation unit includes:

the first process matrix generation module is used for carrying out iterative operation on the random initial matrix, and stopping iteration until an iterative operation result meets a first preset stop condition to obtain a first process matrix; the first preset stopping condition is set according to the distribution time cost analysis model;

the first stopping judgment module is used for determining whether the first process matrix meets a second preset stopping condition;

and the first optimization matrix generation module is used for determining the first process matrix as the driving route optimization matrix.

Further, the initial matrix of driving routes includes: a geographical initial matrix; the initial matrix generation unit includes:

the network point clustering module is used for clustering the network points to be distributed according to the number of vehicles, the number of the network points to be distributed and the geographic position to obtain a network point clustering set corresponding to each cluster;

the driving route setting module is used for sequencing the network points in the network point cluster set according to a preset detour strategy to obtain a driving route corresponding to each cluster;

and the geographical initial matrix generating module is used for generating the geographical initial matrix according to the driving route and the number of the vehicles.

Further, the optimization matrix generation unit includes:

the second process matrix generation module is used for carrying out iterative operation on the geographical initial matrix until the first preset stop condition is met and then stopping iteration to obtain a second process matrix;

a second stop judgment module, configured to determine whether the second process matrix meets the second preset stop condition;

and the second optimization matrix generation module is used for determining the second process matrix as the driving route optimization matrix.

and the process matrix selection unit is used for comparing the first process matrix with the second process matrix according to the distribution time cost analysis model, and selecting the first process matrix with lower distribution time cost as the driving route optimization matrix.

Further, the device for determining the driving route of the transportation vehicle further comprises:

and the operator adjusting unit is used for adjusting the driving route optimization matrix by utilizing an insert operator and a delete operator in a heuristic algorithm.

Further, the operator adjusting unit of the transportation vehicle driving route determining apparatus includes:

the deleting module is used for executing deleting operation on the network points to be distributed with far geographic positions according to the deleting operator and the first executing weight thereof;

and the inserting module is used for executing inserting operation on the network points to be distributed with the closer geographic positions according to the inserting operator and the second executing weight thereof to obtain the adjusted driving route optimization matrix.

a time cost resetting unit, configured to calculate a delivery time cost of the adjusted driving route optimization matrix according to the delivery time cost analysis model;

and the execution weight adjusting unit is used for adjusting the first execution weight and the second execution weight if the distribution time cost of the adjusted driving route optimization matrix is lower than the distribution time cost of the driving route optimization matrix before adjustment.

In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the transportation vehicle driving route determination method when executing the program.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for determining a driving route of a transportation vehicle.

In a fifth aspect, the present application provides a computer program product comprising computer program/instructions which, when executed by a processor, implement the steps of the transportation vehicle route determination method.

Aiming at the problems in the prior art, the method and the device for determining the driving route of the transport vehicle solve the problems that time-varying factors are not considered sufficiently and the planning effect is not ideal enough in a neural network model and a traditional heuristic algorithm by improving the heuristic algorithm to establish a mathematical model and adding time punishment cost into the mathematical model, so that the route of the transport vehicle is planned under the condition of lacking historical data, and the transport efficiency of the transport vehicle is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of a method for determining a driving route of a transportation vehicle according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating steps for constructing a distribution time cost analysis model according to an embodiment of the present application;

FIG. 3 is a flowchart illustrating an exemplary method for determining an initial matrix of driving routes according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of generating a driving route optimization matrix according to an embodiment of the present disclosure;

FIG. 5 is a second flowchart of the method for determining an initial matrix of driving routes in the embodiment of the present application;

FIG. 6 is a second flowchart of the method for generating the driving route optimization matrix in the embodiment of the present application;

FIG. 7 is a flowchart of adjusting a vehicle route optimization matrix according to an embodiment of the present application;

FIG. 8 is a second flowchart of a method for determining a driving route of a transportation vehicle according to an embodiment of the present application;

FIG. 9 is a view showing one of the structures of a vehicle route determining apparatus according to the embodiment of the present application;

FIG. 10 is a second block diagram of the driving route determining apparatus of the vehicle according to the embodiment of the present application;

fig. 11 is one of structural diagrams of an initial matrix generation unit in the embodiment of the present application;

fig. 12 is one of structural diagrams of an optimization matrix generation unit in the embodiment of the present application;

FIG. 13 is a second block diagram of an initial matrix generation unit according to an embodiment of the present invention;

FIG. 14 is a second block diagram of an optimization matrix generation unit according to an embodiment of the present invention;

FIG. 15 is a diagram illustrating a structure of an operator adjusting unit according to an embodiment of the present application;

FIG. 16 is a third block diagram of a vehicle route determination device according to an embodiment of the present invention;

fig. 17 is a schematic structural diagram of an electronic device in an embodiment of the present application;

fig. 18 is a schematic diagram of a graphical display of a driving route optimization matrix in the embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the method and the device for determining the driving route of the transportation vehicle provided by the present application can be used in the financial field and can also be used in any field except the financial field, and the application fields of the method and the device for determining the driving route of the transportation vehicle provided by the present application are not limited.

As the economy evolves, there is a need for cash transport between financial services sites. In order to safely deliver cash to a financial service network as soon as possible, the driving path of the securicar needs to be reasonably planned. In the prior art, a neural network model for planning a driving path is difficult to establish due to the problems of lack of historical service data and the like in a method for planning the driving path of the securicar; and even if the model is built, various time-varying factors in the actual service scene can greatly reduce the model prediction effect. In order to solve the problems in the prior art, the application provides a method and a device for determining a driving route of a transport vehicle.

In one embodiment, referring to fig. 1, in order to plan and show a driving route of a transportation vehicle, a driving route determination method of a transportation vehicle provided by the present application includes:

s101: determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed;

s102: performing iterative optimization on the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix;

s103: and carrying out graphical display on the driving route optimization matrix to obtain the driving route of the vehicle.

It should be understood that the method for determining the driving route of a transportation vehicle provided by the present application can be applied to various transportation vehicles in various industry fields, and the following description is only given by taking an armored car in the financial field as an example, but the present application is not limited thereto.

In a planning scene of a securicar driving route, the method provided by the application can establish a distribution time cost analysis model according to the number of securicars and a plurality of factors such as the number of financial service nodes (also called to-be-distributed nodes), geographic positions, distribution demands and the like; and then optimizing the initial driving route of each securicar by using the model. The initial driving route of each securicar is stored in the driving route initial matrix. And performing iterative optimization on the initial matrix of the driving route to obtain an optimized matrix of the driving route. The driving route optimization matrix is used for storing the driving route of the optimized securicar. And finally, graphically displaying the driving route optimization matrix of each securicar to enable the driving route of the securicar to be visualized.

When the distribution time cost analysis model is established, the method provided by the application fully considers various factors such as time punishment cost, service unqualified punishment cost and the like, and solves the problems of insufficient consideration of time-varying factors and unsatisfactory calculation effect when the existing neural network model and the traditional heuristic algorithm are used for calculating the driving route.

In one embodiment, referring to FIG. 2, the step of constructing a delivery time cost analysis model includes:

s201: determining congestion time cost of each vehicle according to a preset driving route of each vehicle, the geographic position of each network point to be distributed and historical congestion data of a road section corresponding to the geographic position;

s202: determining the early arrival time cost of each vehicle according to the preset early delivery time and the actual time of each vehicle arriving at each network point to be delivered;

s203: determining the late arrival time cost of each vehicle according to the preset latest delivery time and the actual time;

s204: determining the driving time cost consumed by each vehicle when the vehicle is delivered according to the preset driving route according to the historical driving data of the preset driving route;

s205: and determining the distribution time cost of each vehicle according to the congestion time cost, the early arrival time cost, the late arrival time cost and the driving time cost to obtain a distribution time cost analysis model.

Specifically, the distribution time cost analysis model in the embodiment of the present application is as follows:

the delivery time cost is the vehicle travel time cost (corresponding to step S204) + the early arrival time cost (corresponding to step S202) + the late arrival time cost (corresponding to step S203) + the congestion penalty cost (corresponding to step S201).

The mathematical language is expressed as:

wherein, alpha, beta and delta are weight coefficients;

v represents the set of all the network points to be distributed;

k represents the set of all transport vehicles;

x_ijk1 represents the k-th vehicle from the i-site to the j-site;

x_ijk0 means that the k-th vehicle does not go from point i to point j;

Q_i，P_irespectively representing the upper bound (also called the earliest arrival time) and the lower bound (also called the latest arrival time) of the service time window of the ith mesh point;

t_kiis the real time when the kth vehicle reaches the point i;

C_kis the congestion penalty cost for the kth vehicle;

d_ij(t) represents the distance (time) between the two nodes i, j, the value of which is related to the time when the vehicle runs between the two nodes i, j, the specific configuration method is that the position (or i->The departure time of the j-link).

Example 1:

d_ij(t) may be the function described above. During the time period from 0 minute to 120 minutes, traffic is congested. The vehicle travel time period from 0 minute to 30 minute is 1.5 times the time period from 120 minute to 180 minute, the vehicle travel time period from 30 minute to 90 minute is 2 times the time period from 120 minute to 180 minute, and the vehicle travel time period from 90 minute to 120 minute is 1.5 times the time period from 120 minute to 180 minute.

Example 2:

d_ij(t) may be the function described above. The vehicle running elapsed time in the 0 th to 30 th minute period is W_ijThe elapsed time for the vehicle traveling in the 30 th to 90 th minute time periods is X_ijThe elapsed time for the vehicle traveling in the 90 th to 120 th minute time periods is Y_ijThe elapsed time for the vehicle traveling from the 120 th minute to the 180 th minute is Z_ij。

It should be noted that the congestion time cost of each vehicle is closely related to the preset driving route of each vehicle, the geographic position of each network point to be delivered, and the road section corresponding to the geographic position; the preset driving routes can be stored in a driving route initial matrix and/or a driving route optimization matrix, and each element of each row (also called a row vector) from left to right in the matrix represents a network point to be distributed, which is to be reached by one cash carrier in sequence. Therefore, according to each row vector of the driving route initial matrix and/or the driving route optimization matrix, the network points to be distributed, which are responsible for the cash trucks and correspond to the row vector, can be determined; these points to be allocated correspond to the preset driving routes. On the basis, the geographic position and the road section corresponding to the geographic position of the network point to be distributed can be determined by combining a digital map, and the congestion time cost of the securicar is further determined through historical congestion Data (such as the acquisition by using Big Data technology).

The following examples of the driving route initial matrix/driving route optimization matrix are given:

in the route planning of the securicar of the N cars and M to-be-distributed network points, the securicar route will be represented as an N × M matrix. Taking 4 trolleys and 50 nodes to be distributed as an example, the specific form of the matrix is as follows:

cars_list＝[

[1,3,5,7,8,9,13,16,18,23,29,47,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],

[2,4,6,10,14,15,19,40,41,42,45,46,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],

[11,12,17,20,21,25,30,31,32,34,35,36,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]，

[22,24,26,27,28,33,37,38,39,43,44,48,49,50,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]

]

each row (also called row vector) corresponds to a driving route of an armored car, and the armored car needs to finish the route from left to right and sequentially sends cash to all to-be-distributed outlets on the route. The serial number of the cash center is set to be 0, and other serial numbers respectively represent all the to-be-distributed network points.

For example:

the location number of the driving route corresponding to [1,3,5,7,8,9,13,16,18,23,29,47, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1 is:

0->1->3->5->7->8->9->13->16->18->23->29->49->0

the-1 is added later to ensure that the number of elements in each row is the same.

Similarly, when the distribution is performed according to the preset driving route, the driving time cost consumed by each vehicle is closely related to the preset driving route; likewise, the cost of travel time consumed by each vehicle may be determined by historical travel Data, such as acquired using Big Data (Big Data) technology.

From the above description, the method for determining the driving route of the transport vehicle provided by the application can establish the mathematical model by improving the heuristic algorithm, and solves the problems that the time-varying factors are not considered sufficiently and the planning effect is not ideal enough in the neural network model and the traditional heuristic algorithm by adding the time punishment cost into the mathematical model, so that the route of the transport vehicle is planned under the condition of lacking historical data, and the transport efficiency of the transport vehicle is improved.

In one embodiment, referring to fig. 3, the initial matrix of driving routes includes: a random initial matrix; determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed, comprising the following steps:

s301: carrying out random clustering on the network points to be distributed according to the number of the network points to be distributed;

s302: and determining a random initial matrix according to the random clustering result and the number of the vehicles.

It can be understood that, in this embodiment, the driving route initial matrix is a random initial matrix, that is, each network point to be allocated is clustered randomly, and the specific allocation method may use a random clustering algorithm, including but not limited to using a Partition-based clustering method (Partition-based Methods), a Density-based clustering method (Density-based Methods), a Hierarchical clustering method (Hierarchical Methods), and the like.

For example, in an actual business scenario, the required time for cash delivery may be clustered according to the number of the network points to be delivered, but the present application is not limited thereto. Assuming that the transportation task is borne by R securicars, the network points to be distributed are aggregated into R types, and then each securicar is randomly assigned with one type (corresponding to one driving route).

From the above description, the method for determining the driving route of the transport vehicle can determine the initial matrix of the driving route according to the number of vehicles and the number of network points to be distributed.

In an embodiment, referring to fig. 4, the performing iterative optimization on the initial matrix of the driving route according to a pre-constructed distribution time cost analysis model to obtain an optimized matrix of the driving route, includes:

s401: performing iterative operation on the random initial matrix, and stopping iteration until an iterative operation result meets a first preset stop condition to obtain a first process matrix; setting a first preset stop condition according to the distribution time cost analysis model;

s402: determining whether the first process matrix meets a second preset stop condition;

s403: and if so, determining the first process matrix as a driving route optimization matrix.

It can be understood that the purpose of the iterative operation method in the embodiment of the present application is to optimize the initial matrix of the driving route. The method can be realized by using a simulated annealing algorithm, and comprises the following steps:

the first step is as follows: selecting an initial solution (also called a driving route initial matrix) cars _ list _ 0; let the current solution cars _ list _ i be cars _ list _ 0; the current iteration step number k is 0; current temperature t_k＝t_max。

The second step is that: if the internal circulation stop condition is reached at the temperature, the method is switched to the first stepThree steps; otherwise, randomly selecting a neighbor cars _ list _ j from the neighborhood N (cars _ list _ i), calculating the delta f as f (cars _ list _ i) -f (cars _ list _ j), and if the delta f is not equal to f<0, cars _ list _ i is cars _ list _ j, otherwise if exp (- Δ f/t)_k) > random (0,1), cars _ list _ i ═ cars _ list _ j, the second step is repeated.

The third step: k is k +1, t_k+i＝d(t_k) (function for showing temperature drop), if the end condition is satisfied, go to the fourth step; otherwise, go to the second step.

The fourth step: and outputting a calculation result and stopping.

The simulated annealing algorithm includes an inner loop and an outer loop. The inner loop is the second step, which means that a random search is performed in some states at the same temperature t. The external circulation mainly comprises a temperature drop variation t of the third step_k+i＝d(t_k) The number of iteration steps is increased by k +1, a stop condition, and the like.

Here, Δ f (cars _ list _ i) -f (cars _ list _ j) is the "first preset stop condition" described in step S401, and is also referred to as an "internal cycle stop condition". The f-function is a distribution time cost analysis model, described with reference to steps S201 to S205. Inputting the cars _ list _ i matrix into a distribution time cost analysis model to obtain f (cars _ list _ i); inputting the cars _ list _ j matrix into the delivery time cost analysis model may result in f (cars _ list _ j).

In addition, how to select neighbors and how to iterate can be seen in existing simulated annealing algorithms.

The driving route optimization matrix can be obtained through the method.

According to the description, the method for determining the driving route of the transport vehicle can be used for carrying out iterative optimization on the initial matrix of the driving route according to the pre-constructed distribution time cost analysis model to obtain the optimized matrix of the driving route.

In one embodiment, referring to fig. 5, the initial matrix of driving routes includes: a geographical initial matrix; determining a driving route initial matrix according to the number of vehicles and the number of network points to be distributed, comprising the following steps:

s501: clustering the network points to be distributed according to the number of vehicles, the number of the network points to be distributed and the geographic position to obtain a network point cluster set corresponding to each cluster;

s502: the method comprises the steps that the network points in a network point cluster set are sequenced according to a preset bypassing strategy, and a driving route corresponding to each cluster is obtained;

s503: and generating a geographical initial matrix according to the driving routes and the number of the vehicles.

It can be understood that, in this embodiment, the driving route initial matrix is a geographical initial matrix, that is, the driving routes of the securicars need to be distributed according to the geographical positions of the nodes to be distributed, and the nodes to be distributed in the geographical positions are arranged to one securicar as much as possible, so as to save the transportation cost. As described above, the present application does not limit a specific clustering method. For example, in one embodiment, a k-means clustering algorithm (k-means clustering) may be selected to cluster each to-be-delivered mesh point according to its geographic location.

After the clustering is completed, the detour sequence among the same-class mesh points needs to be considered. In this embodiment, the mesh points in the same category (also referred to as a same mesh point cluster set) may be sorted according to a preset detour strategy, so as to obtain a driving route corresponding to each cluster. For example, the bypass policy may be a wrap-around policy from far to near.

Finally, continuing the above example, for a transport fleet of R securicars, the number of net points to be allocated is aggregated into R classes, and then each securicar is assigned a class (corresponding to a driving route).

The difference from the embodiment of the "random initial matrix" is mainly in the principle of clustering. The "random initial matrix" is a random clustering, and the clustering is performed according to the geographic location in this embodiment.

From the above description, the method for determining the driving route of the transport vehicle provided by the application can determine the initial matrix of the driving route according to the number of vehicles and the number of network points to be distributed.

In an embodiment, referring to fig. 6, the performing iterative optimization on the initial matrix of the driving route according to a pre-constructed distribution time cost analysis model to obtain an optimized matrix of the driving route, includes:

s601: performing iterative operation on the geographical initial matrix until a first preset stop condition is met, and stopping iteration to obtain a second process matrix;

s602: determining whether the second process matrix meets a second preset stop condition;

s603: and if so, determining the second process matrix as a driving route optimization matrix.

It is understood that the present embodiment names this iterative operation method as the SA-Adapt algorithm. Considering that, starting from the cash center, as each network point to be distributed has a certain service time, in order to fully utilize the low peak period of traffic and avoid the high peak period of traffic, the driving route of the embodiment is to skip a plurality of network points close to the cash center, take the network point with a moderate distance (not close) to the cash center as the first distribution point, then start turning back through the farthest point according to the classic round-robin strategy from near to far, and finally visit the point close to the cash center.

In order to achieve the above effect, the driving route initial matrix used in the present embodiment is a geographical initial matrix. The iterative operation method is the same as the method of steps S401 to S403 described above, and the difference is only "first step: an initial solution cars _ list _0 of one initial solution cars _ list _0 "is selected as the geographical initial matrix. The subsequent steps are not described in detail.

From the above description, the method for determining the driving route of the transport vehicle provided by the application can perform iterative optimization on the initial matrix of the driving route according to the pre-constructed distribution time cost analysis model to obtain the optimal matrix of the driving route.

In one embodiment, the method for determining the driving route of the transport vehicle further comprises the following steps:

and comparing the first process matrix with the second process matrix according to the distribution time cost analysis model, and selecting the first process matrix with lower distribution time cost as a driving route optimization matrix.

In an embodiment, after obtaining the driving route optimization matrix, the method further includes:

In an embodiment, referring to fig. 7, the adjusting the driving route optimization matrix by using an insert operator and a delete operator in a heuristic algorithm includes:

s701: executing deletion operation on the network points to be distributed with far geographic positions according to the deletion operator and the first execution weight thereof;

s702: and executing an inserting operation on the network points to be distributed with the closer geographic positions according to the inserting operator and the second executing weight thereof to obtain the adjusted driving route optimization matrix.

It will be appreciated that this embodiment is implemented by modifying the aln algorithm. In this embodiment, there are two kinds of operators, namely a destroy operator and a repair operator, which are the "node deletion operator" and the "node insertion operator" in the route planning scene. Wherein the node deletion operator comprises: and deleting operators and ordinary deletion operators according to the geographic positions. The node insertion operator includes: and inserting operators and ordinary operators according to the geographic positions. The operator according to the geographic position considers the longitude and latitude distance or the traveling distance between the distribution points.

For example:

in one embodiment, there are three node deletion operators, D_General、D_{Latitude and longitude distance}、D_{Distance of vehicle}。D_GeneralCorresponding to unrestricted node deletion, D_{Latitude and longitude distance}The corresponding limit longitude and latitude distance must be less than the threshold value Y_{Latitude and longitude distance}Node deletion of D_{Distance to drive a vehicle}The corresponding limit driving distance must be less than the threshold value Y_{Distance to drive a vehicle}The node of (2) is deleted. The node deleting operator has the function of randomly selecting a route or a distribution point to delete the route or the distribution point.

Similarly, there are three node insertion operators, I_General、I_{Latitude and longitude distance}、I_{Distance to drive a vehicle}。I_GeneralCorresponding to unrestricted node insertion, I_{Latitude and longitude distance}The corresponding limited longitude and latitude distance must be less than the threshold value Y_{Latitude and longitude distance}Corresponding node socketIn, I_{Distance of vehicle}The corresponding limit driving distance must be less than the threshold value Y_{Distance to drive a vehicle}Node insertion of (2). The node insertion operator is used for randomly selecting a route and inserting the distribution points deleted by the node deletion operator at the random point sequence position.

The algorithm operation step:

1. a copy of the initialization plan car _ list is provided and the weights p are initialized. Wherein the weight p can be seen as two types, namely the weight p of the node deletion operator_D(comprising p)_{General of D}、p_{Distance between D and latitude}、p_{Distance between two adjacent vehicles}) Weight p of sum node insertion operator_I. All p will be initialized to 1.

2. Improved ALNS algorithm loop

a) According to weight p respectively_DSelecting a node deletion operator, according to the weight p_iA node insertion operator is selected. Specifically, one [0,1] is randomly selected]A random number X in between.

When X is not more than

Then select D_General，

When X does not satisfy a condition and is not more than

Then select D_{Longitude and latitude}Distance of degree

When X does not satisfy a condition and is not more than

Then select D_{Distance to drive a vehicle}

b) And executing the node deletion operator and the node insertion operator to obtain a new plan new _ car _ list.

c) Step C is consistent with the method for evaluating the plan in the second step of the SA-Adapt algorithm. If the new _ car _ list is better than the car _ list, then the car _ list is new _ car _ list, otherwise, the car _ list is new _ car _ list if the random number X between [0,1] is random and X is higher than the threshold value.

d) The above operations a, b and c are repeated until a certain termination condition is reached (specifically, the number of iterations reaches 1000 rounds or car _ list is not optimized in 20 iterations).

The method for determining the driving route of the transport vehicle can adjust the driving route optimization matrix by using an insert operator and a delete operator in a heuristic algorithm.

In an embodiment, referring to fig. 8, the method for determining the driving route of the transportation vehicle further includes:

s801: calculating the distribution time cost of the adjusted driving route optimization matrix according to the distribution time cost analysis model;

s802: and if the distribution time cost of the driving route optimization matrix after adjustment is lower than that of the driving route optimization matrix before adjustment, adjusting the first execution weight and the second execution weight.

Finally, referring to fig. 18, after the driving route optimization matrix is established, the driving route optimization matrix can be graphically displayed to obtain the driving route of the vehicle.

1. And inquiring the geographical position of the network point to be distributed. And calling a map service according to the address of the network points to be distributed, inquiring the longitude and latitude of each network point to be distributed, and drawing the position of each point position on the way according to the position relation information of the network points to be distributed.

2. And (6) planning a route display. And analyzing the driving route optimization matrix, and respectively drawing the route sequence of each vehicle by using solid lines with different colors.

The route display graph omits the actual travel route between every two mesh points and directly connects the routes between every two mesh points. The complexity of route display is simplified, and readability is improved.

From the above description, the method and the device for determining the driving route of the transport vehicle provided by the application establish the mathematical model by improving the heuristic algorithm, and solve the problems of insufficient consideration of time-varying factors and unsatisfactory planning effect in the neural network model and the traditional heuristic algorithm by adding the time penalty cost into the mathematical model, so that the route of the transport vehicle is planned under the condition of lacking historical data, and the transportation efficiency of the transport vehicle is improved.

Based on the same inventive concept, the embodiment of the present application further provides a vehicle driving route determining apparatus, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Because the principle of solving the problems of the vehicle driving route determining device is similar to the method for determining the driving route of the transport vehicle, the implementation of the vehicle driving route determining device can refer to the implementation of the method for determining the performance reference based on software, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

In one embodiment, referring to fig. 9, in order to plan and show the driving route of the transportation vehicle, the present application provides a transportation vehicle driving route determining apparatus, including: an initial matrix generating unit 901, an optimization matrix generating unit 902 and a driving route displaying unit 903.

An initial matrix generating unit 901, configured to determine a driving route initial matrix according to the number of vehicles and the number of network points to be distributed;

an optimization matrix generation unit 902, configured to perform iterative optimization on the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix;

and the driving route display unit 903 is used for graphically displaying the driving route optimization matrix to obtain a vehicle driving route.

In an embodiment, referring to fig. 10, the transportation vehicle driving route determining apparatus further includes: congestion time cost determination section 1001, early arrival time cost determination section 1002, late arrival time cost determination section 1003, travel time cost determination section 1004, and cost analysis model generation section 1005.

A congestion time cost determination unit 1001, configured to determine a congestion time cost of each vehicle according to a preset driving route of each vehicle, a geographic position of each to-be-distributed network point, and historical congestion data of a road segment corresponding to the geographic position;

an early-arrival time cost determining unit 1002, configured to determine an early-arrival time cost of each vehicle according to a preset earliest delivery time and an actual time when each vehicle arrives at each to-be-delivered network point;

a late arrival time cost determination unit 1003, configured to determine late arrival time costs of each vehicle according to a preset latest delivery time and the actual time;

a driving time cost determination unit 1004, configured to determine, according to the historical driving data of the preset driving route, a driving time cost consumed by each vehicle when delivery is performed according to the preset driving route;

a cost analysis model generation unit 1005, configured to determine a delivery time cost of each vehicle according to the congestion time cost, the early arrival time cost, the late arrival time cost, and the travel time cost, so as to obtain the delivery time cost analysis model.

In one embodiment, referring to fig. 11, the transportation vehicle driving route determining apparatus includes: a random initial matrix; the initial matrix generating unit 901 includes: a random clustering module 1101 and a random initial matrix generation module 1102.

A random clustering module 1101, configured to perform random clustering on the mesh points to be distributed according to the mesh points to be distributed;

and a random initial matrix generation module 1102, configured to determine the random initial matrix according to a random clustering result and the number of vehicles.

In an embodiment, referring to fig. 12, the transportation vehicle driving route determining apparatus, the optimization matrix generating unit 902, includes: a first process matrix generation module 1201, a first stop judgment module 1202, and a first optimization matrix generation module 1203.

A first process matrix generation module 1201, configured to perform iterative operation on the random initial matrix, and stop iteration until an iterative operation result satisfies a first preset stop condition, to obtain a first process matrix; the first preset stopping condition is set according to the distribution time cost analysis model;

a first stop determination module 1202, configured to determine whether the first process matrix meets a second preset stop condition;

a first optimization matrix generation module 1203, configured to determine the first process matrix as the driving route optimization matrix.

In one embodiment, referring to fig. 13, the initial matrix of driving routes includes: a geographical initial matrix; the initial matrix generating unit 901 includes: the system comprises a website clustering module 1301, a driving route setting module 1302 and a geographical initial matrix generating module 1303.

The website clustering module 1301 is configured to cluster the websites to be distributed according to the number of vehicles, the number of the websites to be distributed, and the geographic position to obtain a website cluster set corresponding to each cluster;

a driving route setting module 1302, configured to sort the mesh points in the mesh point cluster set according to a preset detour strategy, so as to obtain a driving route corresponding to each cluster;

and a geographic initial matrix generating module 1303, configured to generate the geographic initial matrix according to the driving route and the number of vehicles.

In an embodiment, referring to fig. 14, the optimization matrix generating unit 902 includes: a second process matrix generation module 1401, a second stop determination module 1402 and a second optimization matrix generation module 1403.

A second process matrix generation module 1401, configured to perform iterative operation on the initial geographic matrix, and stop iteration until the first preset stop condition is met, to obtain a second process matrix;

a second stop determining module 1402, configured to determine whether the second process matrix meets the second preset stop condition;

a second optimization matrix generation module 1403, configured to determine the second process matrix as the driving route optimization matrix.

In one embodiment, the device for determining the driving route of a transportation vehicle further includes:

In an embodiment, referring to fig. 15, the operator adjusting unit includes: a delete module 1501 and an insert module 1502.

The deleting module 1501 is configured to execute a deleting operation on a to-be-distributed network point in a farther geographic location according to the deleting operator and the first execution weight thereof;

and the inserting module 1502 is configured to execute an inserting operation on a network point to be distributed with a closer geographic position according to the inserting operator and the second execution weight thereof, so as to obtain an adjusted driving route optimization matrix.

In one embodiment, referring to fig. 16, the device for determining the driving route of a transportation vehicle further includes: a temporal cost resetting unit 1601 and an execution weight adjusting unit 1602.

A time cost resetting unit 1601, configured to calculate a delivery time cost of the adjusted driving route optimization matrix according to the delivery time cost analysis model;

an execution weight adjusting unit 1602, configured to adjust the first execution weight and the second execution weight if the distribution time cost of the adjusted driving route optimization matrix is lower than the distribution time cost of the driving route optimization matrix before adjustment.

In order to plan and show the driving route of the transportation vehicle from a hardware level, the application provides an embodiment of an electronic device for implementing all or part of the contents of the method for determining the driving route of the transportation vehicle, and the electronic device specifically includes the following contents:

a Processor (Processor), a Memory (Memory), a communication Interface (Communications Interface) and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the vehicle driving route determining device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented by referring to the embodiment of the method for determining a driving route of a transportation vehicle and the embodiment of the device for determining a driving route of a vehicle in the embodiments, which are incorporated herein, and repeated descriptions thereof are omitted.

It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical applications, part of the transportation vehicle driving route determining method may be performed on the electronic device side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be in communication connection with a remote server to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

Fig. 17 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 17, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 17 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications or other functions.

In one embodiment, the functions of the transportation vehicle routing method may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:

s103: and graphically displaying the driving route optimization matrix to obtain the driving route of the vehicle.

In another embodiment, the vehicle driving route determining device may be configured separately from the central processor 9100, for example, the data composite transmission device may be configured as a chip connected to the central processor 9100, and the function of the transportation vehicle driving route determining method is realized by the control of the central processor.

As shown in fig. 17, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 17; in addition, the electronic device 9600 may further include components not shown in fig. 17, which can be referred to in the related art.

As shown in fig. 17, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.

The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless lan module, may be disposed in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps of the transportation vehicle route determination method in which the execution subject is the server or the client in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the transportation vehicle route determination method in which the execution subject is the server or the client, for example, the processor implements the following steps when executing the computer program:

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method for determining a route of a transportation vehicle, comprising:

2. The transportation vehicle driving route determination method according to claim 1, wherein the step of constructing the delivery time cost analysis model includes:

3. A transportation vehicle route determination method as claimed in claim 2, wherein the route initial matrix comprises: a random initial matrix; the method for determining the initial matrix of the driving route according to the number of the vehicles and the number of the network points to be distributed comprises the following steps:

4. The transportation vehicle driving route determination method according to claim 3, wherein the iterative optimization of the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix comprises:

5. A transportation vehicle routing method as set forth in claim 4, wherein the routing initial matrix comprises: a geographical initial matrix; the method for determining the initial matrix of the driving route according to the number of the vehicles and the number of the network points to be distributed comprises the following steps:

6. The transportation vehicle driving route determination method according to claim 5, wherein the iterative optimization of the driving route initial matrix according to a pre-constructed distribution time cost analysis model to obtain a driving route optimization matrix comprises:

if yes, the second process matrix is determined to be the driving route optimization matrix.

7. The transportation vehicle driving route determination method according to claim 4 or 6, characterized by further comprising:

8. The transportation vehicle routing method of claim 1, further comprising, after obtaining the routing optimization matrix:

9. The transportation vehicle routing method of claim 8, wherein the adjusting the routing optimization matrix using an insert operator and a delete operator in a heuristic algorithm comprises:

executing deletion operation on the network points to be distributed with far geographic positions according to the deletion operator and the first execution weight thereof;

10. The transportation vehicle driving route determination method according to claim 9, further comprising:

11. A transit vehicle driving route determination apparatus, comprising:

12. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the transportation vehicle route determination method according to any one of claims 1 to 10.

13. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for determining a route for a transport vehicle according to any one of claims 1 to 10.

14. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method for determining a route for a transportation vehicle according to any of claims 1 to 10.