CN115660244A

CN115660244A - Route information generation method, apparatus, device and medium

Info

Publication number: CN115660244A
Application number: CN202211680153.6A
Authority: CN
Inventors: 张华鲁; 庄晓天; 吴盛楠
Original assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2022-12-27
Filing date: 2022-12-27
Publication date: 2023-01-31
Anticipated expiration: 2042-12-27
Also published as: CN115660244B

Abstract

The embodiment of the disclosure discloses a route information generation method, a route information generation device, a route information generation apparatus and a route information generation medium. One embodiment of the method comprises: acquiring candidate transportation route information sets and historical shipment volume data corresponding to each candidate transportation route information; for each candidate transportation route information, executing a vehicle model scheme information generating step: determining whether the transportation of the corresponding goods amount of the target transportation route is stable; in response to the fact that the cargo transportation is unstable and the corresponding historical shipment data meet the target distribution, generating candidate vehicle type selection scheme information by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information; and screening candidate transportation route information meeting preset transportation conditions as actual transportation route information. This embodiment is relevant with wisdom commodity circulation, can more accurately select the transportation route information that is applicable to the corresponding freight of target transportation task.

Description

Route information generation method, apparatus, device and medium

Technical Field

The embodiment of the disclosure relates to the technical field of computers, in particular to a route information generation method, a route information generation device, route information generation equipment and a route information generation medium.

Background

Currently, for the execution of transportation tasks, there are usually more alternative transportation routes for the transportation of corresponding goods. For the selection of the transport route, the following is generally used: and screening a route with stable cargo quantity from the plurality of transportation routes to obtain at least one transportation route. And then, determining the recommended vehicle type combination and transportation value information corresponding to at least one transportation route through a planning algorithm. And finally, screening out the actual transportation route of the goods from the at least one transportation route through the recommended vehicle type combination and the transportation value information.

However, the inventors have found that when the transportation route is selected in the above manner, there are often technical problems as follows:

selecting only routes with stable cargo quantities for transportation of subsequent cargo results in a small number of optimizable routes when the number of routes with stable cargo quantities is small in practice, resulting in a small optimizable space and a less accurate route to be screened for actual transportation of cargo.

The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art in this country.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose a route information generation method, apparatus, device and medium to solve the technical problems mentioned in the above background section.

In a first aspect, some embodiments of the present disclosure provide a route information generating method, including: acquiring a candidate transportation route information set aiming at a target transportation task and historical shipment quantity data corresponding to each candidate transportation route information; for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type scheme information generating step: determining whether the transportation of the goods amount corresponding to a target transportation route is stable or not according to the historical shipment data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; in response to the fact that the transportation of the goods amount is determined to be unstable and historical shipment data corresponding to the candidate transportation route information meet target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information; and according to the obtained vehicle type optimization scheme information set, screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set to serve as actual transportation route information of the target transportation task.

Optionally, the determining whether the transportation of the cargo volume corresponding to the target transportation route is stable according to the historical shipment volume data corresponding to the candidate transportation route information includes: determining the ratio of the number of delivery days of the route with the cargo volume according to the historical delivery cargo volume data corresponding to the candidate transportation route information; in response to determining that the ratio of the number of delivery days of the route to the number of delivery days is greater than a first threshold, determining a route cargo quantity stability factor corresponding to the candidate transportation route information; and in response to determining that the route cargo capacity stability factor is larger than a second threshold value, determining that the target transportation route is unstable in transportation corresponding to the cargo capacity.

Optionally, after determining whether transportation of the target transportation route by the corresponding cargo volume is stable according to the historical shipment volume data corresponding to the candidate transportation route information, the method further includes: and generating vehicle type selection scheme information for the target transportation route as vehicle type optimization scheme information for the target transportation route by using the first-stage optimization model in response to the determination that the cargo transportation is stable.

Optionally, the generating candidate vehicle type selection scheme information for the target transportation route by using the first-stage optimization model includes: acquiring a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function, wherein the first-stage optimization objective function represents the sum of value information of a whole vehicle type and value information of a part vehicle type; and substituting the acquired vehicle type information set and the cargo transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the first-stage constraint conditions, and taking the sum of the value information of the whole vehicle type and the value information of the part vehicle type meeting the objective value condition as an optimization objective to obtain the candidate vehicle type selection scheme information.

Optionally, the performing, by using the second-stage optimization model, vehicle type optimization on the candidate vehicle type selection scheme information to obtain vehicle type optimization scheme information includes: acquiring a second-stage optimization objective function and a plurality of second-stage constraint conditions of the second-stage optimization objective function, wherein the second-stage optimization objective function represents value transformation information brought by vehicle type transformation based on the random transportation quantity of goods to be transported; and substituting the candidate vehicle information corresponding to the candidate vehicle type selection scheme information and the average transportation cargo quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and outputting vehicle type optimization scheme information by taking the value transformation information meeting the target value condition as an optimization target.

Optionally, the screening, according to the obtained vehicle type optimization scheme information set, candidate transportation route information that meets a preset transportation condition from the candidate transportation route information set as actual transportation route information of the target transportation task includes: determining value information corresponding to each vehicle type optimization scheme information in the vehicle type optimization scheme information set to obtain a value information set; and screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the vehicle type optimization scheme information set and the value information set, and taking the candidate transportation route information as the actual transportation route information of the target transportation task.

In a second aspect, some embodiments of the present disclosure provide a route information generating apparatus, including: the acquisition unit is configured to acquire a candidate transportation route information set aiming at the target transportation task and historical shipment volume data corresponding to each candidate transportation route information; an execution unit configured to execute the vehicle type plan information generating step for each of the candidate transportation route information in the candidate transportation route information set: determining whether the transportation of the goods amount corresponding to a target transportation route is stable or not according to the historical shipment data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; in response to the fact that the transportation of the goods amount is determined to be unstable and historical shipment data corresponding to the candidate transportation route information meet target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information; and the screening unit is configured to screen candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the obtained vehicle type optimization scheme information set, and the candidate transportation route information is used as actual transportation route information of the target transportation task.

Optionally, the execution unit may be configured to: determining the ratio of the number of freight shipment days of the route according to historical shipment volume data corresponding to the candidate transportation route information; in response to determining that the ratio of the number of delivery days of the route to the number of delivery days is greater than a first threshold, determining a route cargo quantity stability factor corresponding to the candidate transportation route information; and in response to determining that the route cargo capacity stability factor is larger than a second threshold value, determining that the target transportation route is unstable in transportation corresponding to the cargo capacity.

Optionally, the apparatus further comprises: and generating vehicle type selection scheme information for the target transportation route as vehicle type optimization scheme information for the target transportation route by using the first-stage optimization model in response to the determination that the cargo transportation is stable.

Optionally, the execution unit may be configured to: acquiring a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function, wherein the first-stage optimization objective function represents the sum of value information of a whole vehicle type and value information of a part vehicle type; and substituting the acquired vehicle type information set and the cargo transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the first-stage constraint conditions, and taking the sum of the value information of the whole vehicle type and the value information of the part vehicle type meeting the objective value condition as an optimization objective to obtain the candidate vehicle type selection scheme information.

Optionally, the execution unit may be configured to: acquiring a second-stage optimization objective function and a plurality of second-stage constraint conditions of the second-stage optimization objective function, wherein the second-stage optimization objective function represents value transformation information brought by vehicle type transformation based on the random transportation quantity of goods to be transported; and substituting the candidate vehicle information corresponding to the candidate vehicle type selection scheme information and the average transportation cargo quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and outputting vehicle type optimization scheme information by taking the value transformation information meeting the target value condition as an optimization target.

Optionally, the screening unit may be configured to: determining value information corresponding to each vehicle type optimization scheme information in the vehicle type optimization scheme information set to obtain a value information set; and screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the vehicle type optimization scheme information set and the value information set, and taking the candidate transportation route information as the actual transportation route information of the target transportation task.

In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.

In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program when executed by a processor implements a method as described in any of the implementations of the first aspect.

The above embodiments of the present disclosure have the following beneficial effects: the transportation route information suitable for the corresponding cargo transportation of the target transportation task can be screened out more accurately through the route information generation method of some embodiments of the disclosure. Specifically, the reason for the inaccurate screening of the related transportation route information is that: selecting only routes with stable cargo quantities for transportation of subsequent cargo results in a small number of optimizable routes when the number of routes with stable cargo quantities is small in practice, resulting in a small optimizable space and a less accurate route to be screened for actual transportation of cargo. Based on this, the route information generation method of some embodiments of the present disclosure first obtains a candidate transportation route information set for a target transportation task and historical shipment volume data corresponding to each candidate transportation route information for screening of subsequent actual transportation route information and determination of the transportation stability of the volume of each candidate transportation route information. Then, for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type plan information generating step: and step one, according to the historical shipment volume data corresponding to the candidate transportation route information, whether the transportation of the volume corresponding to the target transportation route is stable or not can be accurately determined. Wherein the target transportation route is a route corresponding to the candidate transportation route information. And secondly, in response to the fact that the transportation of the goods amount is determined to be unstable and the historical shipment data corresponding to the candidate transportation route information meets the target distribution, the candidate vehicle type selection scheme information aiming at the target transportation route can be generated more accurately by using the first-stage optimization model. And thirdly, vehicle type optimization is carried out on the candidate vehicle type selection scheme information by using a second-stage optimization model to obtain vehicle type optimization scheme information. Herein, for a transportation route in which the transportation of the cargo amount is unstable and the historical shipment data corresponding to the candidate transportation route information satisfies the target distribution, the vehicle type optimization scheme information can be accurately generated, so that the problems that only the route with stable cargo amount is selected for the transportation of subsequent cargos, and the number of the optimizable routes is small and the optimizable space is small due to the fact that the number of the routes with stable cargo amount is small in practice are solved. And finally, according to the obtained vehicle type optimization scheme information set, candidate transportation route information which meets preset transportation conditions and is more suitable for goods transportation corresponding to the target transportation task can be screened out from more candidate transportation route information and used as actual transportation route information of the target transportation task. Therefore, according to the route information generation method, the number of the routes which can be optimized can be increased through the first-stage optimization model and the second-stage optimization model, the space which can be optimized is increased, and the selected routes which are used for actually transporting goods are more accurate.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.

1-2 are schematic diagrams of one application scenario of a route information generation method according to some embodiments of the present disclosure;

FIG. 3 is a flow diagram of some embodiments of a route information generation method according to the present disclosure;

FIG. 4 is a flow diagram of further embodiments of a route information generation method according to the present disclosure;

FIG. 5 is a schematic block diagram of some embodiments of a route information generation apparatus according to the present disclosure;

FIG. 6 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The operations of collecting, storing, using, etc. the historical shipment volume data referred to in the present disclosure are performed before the corresponding operations are performed, and the relevant organization or individual has to perform the obligations including the performance of the evaluation of the security impact of the vehicle information, the fulfillment of the notification obligation to the vehicle information body, the acquisition of the authorization approval of the vehicle information body in advance, etc.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1-2 are schematic diagrams of an application scenario of a route information generation method according to some embodiments of the present disclosure.

In the application scenarios of fig. 1-2, the electronic device 101 may obtain a set of candidate transportation route information 103 for the target transportation task 102 and historical shipment volume data corresponding to each candidate transportation route information. In the present application scenario, the set of candidate haul route information 103 may include: candidate haul route information 1031, candidate haul route information 1032, and candidate haul route information 1033. The candidate haul route information 1031 may be an a-B haul route. Candidate haul route information 1032 may be an A-C-B haul route. The candidate haul route information 1033 may be a D-B haul route. The historical shipment amount data corresponding to the candidate shipment route information 1031 is historical shipment amount data 104. The historical shipment data corresponding to the candidate transportation route information 1032 is the historical shipment data 105. The historical shipment volume data corresponding to the candidate transportation route information 1033 is historical shipment volume data 106. Then, for each candidate transportation route information in the above candidate transportation route information set 103, a vehicle type plan information generating step is performed: in the first step, the electronic device 101 may determine whether the transportation of the goods amount corresponding to the target transportation route is stable according to the historical shipment amount data corresponding to the candidate transportation route information. Wherein the target transportation route is a route corresponding to the candidate transportation route information. In response to determining that the transportation of the cargo volume is unstable and the historical shipment volume data corresponding to the candidate transportation route information satisfies the target distribution, the electronic device 101 may generate candidate vehicle type selection scheme information for the target transportation route by using the first-stage optimization model. Thirdly, the electronic device 101 may perform vehicle type optimization on the candidate vehicle type selection scheme information by using the second-stage optimization model to obtain vehicle type optimization scheme information. In the present application scenario, in a first step, for the candidate transportation route information 1031, the electronic device 101 may determine whether the transportation of the amount of goods corresponding to the target transportation route is stable according to the historical shipment amount data 104 corresponding to the candidate transportation route information 1031. In a second step, in response to determining that the transportation of the cargo volume is unstable and the historical shipment volume data 104 corresponding to the candidate transportation route information 1031 meets the target distribution, the electronic device 101 may generate candidate vehicle type selection scenario information 108 for the target transportation route by using the first stage optimization model. The candidate vehicle type selection scenario information 108 may be "vehicle type 1:11, model 2:5, model 3:1 vehicle ". Thirdly, the electronic device 101 may perform vehicle type optimization on the candidate vehicle type selection scheme information 108 by using the second-stage optimization model to obtain vehicle type optimization scheme information 1071. The vehicle type optimization scheme information 1071 may be "vehicle type 1:12, vehicle type 2:4, model 3:1 vehicle ". Finally, the electronic device 101 may screen candidate transportation route information satisfying preset transportation conditions from the candidate transportation route information set 103 according to the obtained vehicle type optimization scheme information set 107, and use the candidate transportation route information as the actual transportation route information of the target transportation task 102. In the application scenario, the vehicle model optimization scheme information set 107 includes: vehicle type optimization plan information 1071 corresponding to the candidate transportation route information 1031, vehicle type optimization plan information 1072 corresponding to the candidate transportation route information 1032, and vehicle type optimization plan information 1073 corresponding to the candidate transportation route information 1033. The vehicle type optimization scheme information 1071 may be "vehicle type 1:12, vehicle type 2:4, model 3:1 vehicle ". The vehicle type optimization scheme information 1072 may be "vehicle type 1:8, vehicle type 2:6, vehicle type 3:2 pieces of the raw materials. The vehicle type optimization scenario information 1073 may be "vehicle type 1:10 vehicles, vehicle type 2:7, model 4:1 vehicle ". The actual transportation route information may be vehicle type optimization scenario information 1071.

The electronic device 101 may be hardware or software. When the electronic device is hardware, the electronic device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it may be installed in the above-listed hardware devices. It may be implemented, for example, as multiple software or software modules for providing distributed services, or as a single software or software module. And is not particularly limited herein.

It should be understood that the number of electronic devices in fig. 1-2 is merely illustrative. There may be any number of electronic devices, as desired for an implementation.

With continued reference to fig. 3, a flow 300 of some embodiments of a route information generation method according to the present disclosure is shown. The route information generation method comprises the following steps:

step 301, obtaining candidate transportation route information set for the target transportation task and historical shipment volume data corresponding to each candidate transportation route information.

In some embodiments, an executing subject (e.g., the electronic device 101 shown in fig. 1) of the route information generating method may acquire the candidate transportation route information set for the target transportation task and the historical shipment volume data corresponding to each candidate transportation route information through a wired connection manner or a wireless connection manner. Wherein the target transportation task may be a task for which a transportation route is to be determined. The target transportation task may be a task of transporting goods. For example, the target transportation task may be a task of transporting stone. In practice, a destination transportation task exists as a transportation starting point and a transportation ending point. The shipping origin may be a shipping location. The transport termination point may be a cargo transport destination. The candidate transportation route information set for the target transportation task may be a plurality of route information corresponding to each transportation route in which a route start point is a transportation start point and a route end point is a transportation end point. In practice, the historical shipment data corresponding to the candidate transportation route information may be the transportation amount of the goods of the transportation route on each historical day. For example, the historical shipment volume data may be {1 month, 1 day: volume of 15, 1 month and 2 days: stock size 18 square, 1 month and 3 days: volume of goods 20 square }.

Step 302, for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type scheme information generating step:

step 3021, determining whether the transportation of the cargo volume corresponding to the target transportation route is stable according to the historical shipment volume data corresponding to the candidate transportation route information.

In some embodiments, the execution subject may determine whether the transportation of the cargo volume corresponding to the target transportation route is stable according to the historical shipment volume data corresponding to the candidate transportation route information. Wherein, the target transportation route is a route corresponding to the candidate transportation route information.

As an example, the execution subject may first determine a standard deviation of the freight transportation amount corresponding to the historical shipment amount data. Then, in response to the standard deviation of the freight transportation amount being greater than the target value, it is determined that the target transportation route is unstable in freight transportation corresponding to the freight amount.

In some optional implementations of some embodiments, after step 3021, the steps further include:

in response to determining that the cargo transportation is stable, the execution main body may generate vehicle type selection plan information for the target transportation route as vehicle type optimization plan information for the target transportation route using the first-stage optimization model.

And the vehicle type optimization scheme information obtained by the first-stage optimization model is utilized to dynamically adjust the vehicle type scheme assigned to the current candidate transportation line aiming at the candidate transportation line with stable cargo volume transportation, so that the cost of transporting cargos by using the vehicle type optimization scheme information corresponding to the vehicle set is less than the cost of transporting cargos by using the vehicle type scheme corresponding to the current candidate transportation line corresponding to the vehicle set, and the efficiency of transporting cargos by using the vehicle type optimization scheme information corresponding to the vehicle set is not lower than the cost and the efficiency of transporting cargos by using the vehicle type scheme corresponding to the current candidate transportation line corresponding to the vehicle set.

Step 3022, in response to determining that the transportation of the cargo volume is unstable and the historical shipment volume data corresponding to the candidate transportation route information meets the target distribution, generating candidate vehicle type selection scheme information for the target transportation route by using the first-stage optimization model.

In some embodiments, in response to determining that the transportation of the cargo volume is unstable and the historical shipment volume data corresponding to the candidate transportation route information satisfies the target distribution, the executing body may generate candidate vehicle type selection scheme information for the target transportation route by using a first-stage optimization model. For example, the target distribution may be a predetermined distribution. For example, the target distribution may be a gaussian distribution. The first-stage optimization model may be a model that determines vehicle type selection scenario information. For example, the first stage optimization model may be a neural network model. In practice, the first-stage optimization model may be a Convolutional Neural Networks (CNN) model. The candidate vehicle type selection plan information may be vehicle type matching plan information for executing the target transportation task with respect to the candidate transportation route information. In practice, the type of the vehicle type matching scheme can be vehicle matching plus tail cargo capacity, or vehicle matching plus piece goods vehicle. In addition, the scheme type corresponding to the vehicle type matching scheme can be vehicle matching. The vehicle type matching plan information may include vehicle selection types and selection numbers corresponding to each vehicle type. For example, for candidate haul route information for a- > B, the selected vehicle model mix plan may be { heavy truck: 4, medium truck: 3, the piece goods vehicle: 1 vehicle }. As another example, for candidate haul route information for a- > B, the selected vehicle type collocation plan may be { heavy truck: 4, medium truck: 2, quantity of end goods: 100 pieces }.

It should be noted that, the vehicle type selection scheme information obtained by the first-stage optimization model is used to dynamically adjust the vehicle type scheme of the current candidate transportation route, so that the cost of transporting the goods by using the vehicle type selection scheme information corresponding to the vehicle set is less than the cost of transporting the goods by using the vehicle type scheme of the current candidate transportation route corresponding to the vehicle set, and the efficiency of transporting the goods by using the vehicle type selection scheme information corresponding to the vehicle set is not lower than the cost and efficiency of transporting the goods by using the vehicle type scheme of the current candidate transportation route corresponding to the vehicle set.

As an example, the execution subject may determine whether the historical shipment volume data corresponding to the candidate transportation route information satisfies the target distribution using a chi-square verification method.

In some optional implementations of some embodiments, the generating candidate vehicle type selection scenario information for the target transportation route by using the first-stage optimization model may include the following steps:

the method comprises the following steps of firstly, obtaining a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function. The first-stage optimization objective function represents the sum of the value information of the whole vehicle type and the value information of the part vehicle type. The value information of the entire vehicle type may be transportation cost generated by the entire vehicle type. The value information of the piece goods model may be transportation cost generated by the piece goods model.

It should be noted that the transportation cost generated by the piece-size vehicle model may be one of the following: the space cost used by the selected piece vehicle type in the current batch transportation and the space cost used by the selected piece vehicle type in the next batch transportation. Here, the piece-size vehicle transports the end-load amount corresponding goods. And when the tail cargo volume is more than or equal to a certain numerical value, adopting the selected part load vehicle type vehicle in the current batch transportation to transport the cargos corresponding to the tail cargo volume. And when the tail cargo quantity is smaller than a certain numerical value, adopting the selected part vehicle type vehicle in the next batch of transportation to transport the cargo with the corresponding tail cargo quantity.

Wherein the first stage optimization objective function is generated by:

step 1, acquiring a vehicle type set for cargo transportation, the number of vehicles of various vehicle types and the vehicle type cost of various vehicle types.

Wherein, the motorcycle type set includes: each whole vehicle type and the part vehicle type.

And 2, generating a transportation cost expression of the vehicle type combination for combining all the vehicle types in the vehicle type set according to the combination of the number of the vehicles of all the vehicle types and the transportation cost of all the vehicle types, and taking the transportation cost expression as a first-stage optimization objective function.

And substituting the acquired vehicle type information set and the freight volume transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and taking the sum of the value information of the whole vehicle type and the value information of the part load vehicle type meeting the objective value condition as an optimization objective to obtain the candidate vehicle type selection scheme information.

The target value condition may be value information in which the sum of the value information of the entire vehicle type and the value information of the part vehicle type is the lowest. Wherein, the vehicle type information may include: vehicle type set, vehicle type cost of various vehicle types. The cargo transportation information corresponding to the vehicle type information may include: the lower limit of the vehicle type transportation cargo quantity, the upper limit of the vehicle type transportation cargo quantity and the total cargo quantity limit of line transportation. The plurality of first stage constraints include: the method comprises the steps of representing constraint conditions of at most two vehicle types in each route, representing constraint conditions of upper and lower limits of the using quantity of the whole vehicle types, representing constraint conditions of upper and lower limits of the transporting goods quantity of the vehicle types corresponding to the part load vehicle types, representing constraint conditions of tail goods value range and representing constraint conditions of vehicles using the whole vehicle types preferentially.

And step 3023, performing vehicle type optimization on the candidate vehicle type selection scheme information by using the second-stage optimization model to obtain vehicle type optimization scheme information.

In some embodiments, the execution subject may perform vehicle type optimization on the candidate vehicle type selection scheme information by using a second-stage optimization model to obtain vehicle type optimization scheme information. The second-stage optimization model can be a model for optimizing the vehicle type of the vehicle type selection scheme information, so that the vehicle optimization scheme corresponding to the vehicle type optimization scheme information can meet the cargo volume transportation change condition of the target proportion. For example, the second stage optimization model may be a convolutional neural network model. The cargo volume transportation change situation may be a situation in which the cargo volume is temporarily changed. For example, the predetermined freight transportation volume before the target transportation task is 19 parties. The freight transportation volume is then temporarily adjusted to 25 square. The target ratio may be set in advance. For example, the target proportion may be 95%.

It should be noted that, the vehicle type optimization scheme information obtained by the second-stage optimization model is used to dynamically adjust the vehicle type scheme to which the current candidate transportation route belongs, so that the cost of transporting the goods by using the vehicle type optimization scheme information corresponding to the vehicle set is less than the cost of transporting the goods by using the vehicle type scheme to which the current candidate transportation route belongs corresponding to the vehicle set, and the efficiency of transporting the goods by using the vehicle type optimization scheme information corresponding to the vehicle set is not lower than the efficiency of transporting the goods by using the vehicle type scheme to which the current candidate transportation route belongs corresponding to the vehicle set.

As an example, the vehicle type optimization scheme information is obtained by optimizing the vehicle type and/or the corresponding number of each vehicle type for the candidate vehicle type selection scheme information by using a second-stage optimization model.

In some optional implementation manners of some embodiments, the performing, by using the second-stage optimization model, vehicle type optimization on the candidate vehicle type selection scheme information to obtain vehicle type optimization scheme information may include:

the method comprises the following steps of firstly, obtaining a second-stage optimization objective function and a plurality of second-stage constraint conditions of the second-stage optimization objective function. And the second-stage optimization objective function representation is based on value transformation information brought by random transportation of the goods to be transported and vehicle type transformation. The random traffic may be randomly varied cargo traffic.

Wherein, the second stage optimization objective function is generated by the following steps:

step 1, determining the selection number of each vehicle type corresponding to the candidate vehicle type selection scheme information.

And 2, generating a cost expression with the number changing according to the selection number of each vehicle type as a second-stage optimization model.

As an example, generating a cost expression in which the number of choices for each vehicle type varies includes the steps of:

and substep 1, generating at least one vehicle model parameter for each vehicle model.

Substep 2, for each vehicle type parameter in the at least one vehicle type parameter, executing the following transportation cost determination step:

a first substep of determining a first transportation cost for the vehicle type parameter in response to determining that the vehicle type parameter characterization corresponds to a reduced number of vehicle type selections.

A second substep of multiplying the first transportation cost by a first weight. Wherein the first weight may be a preset weight. For example, the first weight may be 0.8.

And a third substep of determining a second transportation cost for the vehicle type parameter in response to determining that the vehicle type parameter characterization corresponds to an increased number of vehicle type selections.

A fourth substep of multiplying the second transportation cost by a second weight. Wherein the second weight may be a preset weight. For example, the second weight may be 1.5.

And substep 3, determining an expression of subtracting the first transportation cost and the second transportation cost from the transportation cost corresponding to the candidate vehicle type selection scheme information as a second-stage optimization model.

And step two, substituting the candidate vehicle information corresponding to the candidate vehicle type selection scheme information and the average transportation cargo quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and outputting vehicle type optimization scheme information by taking the value transformation information meeting the target value condition as an optimization target. Wherein the value transformation information is transportation cost transformation information. The candidate vehicle information corresponding to the candidate vehicle type selection scheme information may be vehicle selection condition information corresponding to the candidate vehicle type selection scheme. The transportation cost conversion information may be the cost obtained by subtracting or/and adding the vehicle conversion cost from the transportation cost corresponding to the candidate vehicle type selection scheme information. The vehicle conversion cost may be a cost of adding a vehicle or reducing a vehicle conversion. The target haul route corresponding to the average haul cargo may be an average haul cargo per day for the haul route. The plurality of second stage constraints may include: the method comprises the steps of representing a first volume constraint condition that vehicle volume corresponding to vehicle optimization scheme information is larger than that corresponding to candidate vehicle type selection scheme information, representing a second volume constraint condition that vehicle volume corresponding to vehicle optimization scheme information is larger than minimum volume for opening vehicle transportation, representing a constraint condition that the number of conversion aiming at vehicle type selection number is smaller than that of corresponding vehicle type selection number, representing a constraint condition that the number of conversion aiming at vehicle type selection number can only be one of a reduction condition and a variation condition, representing a constraint condition that the probability that the first volume constraint condition is met under the influence of random transportation volume is larger than 95%, and representing a constraint condition that the probability that the second volume constraint condition is met under the influence of random transportation volume is larger than 95%.

Here, the first volume constraint condition and the second volume constraint condition are subjected to deterministic equivalent form conversion to obtain a constraint condition whose input includes the average transportation quantity corresponding to the target transportation route and the variance of the transportation quantity corresponding to the target transportation route. Therefore, further, the output result (namely, the vehicle model optimization scheme information) of the second-stage optimization objective function is obtained through an optimization solver such as SCIP (security context protocol) or Gurobi.

And 203, screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the obtained vehicle type optimization scheme information set, and taking the candidate transportation route information as the actual transportation route information of the target transportation task.

In some embodiments, the execution subject may select, according to the obtained vehicle type optimization plan information set, candidate transportation route information that satisfies a preset transportation condition from the candidate transportation route information set as actual transportation route information of the target transportation task. Wherein the preset transportation condition may be a transportation condition preset by a related art person. For example, the preset transportation condition may be that the candidate transportation route information is the lowest transportation cost information in the candidate transportation route information set.

As an example, first, the execution subject may determine a transportation cost corresponding to each vehicle model optimization scenario information. Then, the execution subject may screen out the candidate transportation route information having the lowest corresponding transportation cost from the set of candidate transportation route information as actual transportation route information of the target transportation task.

In some optional implementation manners of some embodiments, the screening, according to the obtained vehicle type optimization scheme information set, candidate transportation route information that satisfies a preset transportation condition from the candidate transportation route information set as actual transportation route information of the target transportation task may include the following steps:

the method comprises the steps of firstly, determining value information corresponding to each vehicle type optimization scheme information in the vehicle type optimization scheme information set to obtain a value information set.

The value information may be transportation cost information consumed by the vehicle model optimization scheme information.

As an example, first, the execution subject may collect vehicle use costs of respective types of vehicles to which respective vehicle type optimization scenario information relates in the vehicle type optimization scenario information. Then, the execution subject may determine transportation cost information for completing the target transportation task using the vehicle combination corresponding to each vehicle type optimization plan information according to the vehicle use cost of each type of vehicle.

And secondly, screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the vehicle type optimization scheme information set and the value information set, and taking the candidate transportation route information as actual transportation route information of the target transportation task.

Wherein the value information may be transportation cost information. For example, the preset transportation condition may be that the candidate transportation route information is information that the vehicle type optimization scheme information in the candidate transportation route information set corresponds to a small number of vehicles and the use cost of the vehicles is low.

The above embodiments of the present disclosure have the following advantages: the transportation route information suitable for the corresponding cargo transportation of the target transportation task can be screened out more accurately through the route information generation method of some embodiments of the disclosure. Specifically, the reason for the inaccurate screening of the related transportation route information is that: selecting only routes with stable cargo quantities for transportation of subsequent cargo results in a small number of optimizable routes when the number of routes with stable cargo quantities is small in practice, resulting in a small optimizable space and a less accurate route to be screened for actual transportation of cargo. Based on this, the route information generation method of some embodiments of the present disclosure first obtains a candidate transportation route information set for a target transportation task and historical shipment volume data corresponding to each candidate transportation route information for screening of subsequent actual transportation route information and determination of the transportation stability of the volume of each candidate transportation route information. Then, for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type plan information generating step: and step one, according to historical shipment quantity data corresponding to the candidate transportation route information, whether the transportation of the quantity of the goods corresponding to the target transportation route is stable or not can be accurately determined. Wherein the target transportation route is a route corresponding to the candidate transportation route information. And secondly, in response to the fact that the transportation of the goods amount is determined to be unstable and the historical shipment data corresponding to the candidate transportation route information meets the target distribution, the candidate vehicle type selection scheme information aiming at the target transportation route can be generated more accurately by using the first-stage optimization model. And thirdly, vehicle type optimization is carried out on the candidate vehicle type selection scheme information by using a second-stage optimization model to obtain vehicle type optimization scheme information. Herein, for a transportation route in which the transportation of the quantity of goods is unstable and the historical shipment quantity data corresponding to the candidate transportation route information meets the target distribution, the vehicle type optimization scheme information can be accurately generated, so that the problems that only the route with stable quantity of goods is selected for the transportation of subsequent goods, and the number of the routes which can be optimized is small and the space for optimization is small due to the fact that the number of the routes with stable quantity of goods is small in practice are solved. And finally, according to the obtained vehicle type optimization scheme information set, candidate transportation route information which meets preset transportation conditions and is more suitable for goods transportation corresponding to the target transportation task can be screened out from more candidate transportation route information and used as actual transportation route information of the target transportation task. Therefore, the route information generation method can increase the number of the routes which can be optimized and the space which can be optimized through the first-stage optimization model and the second-stage optimization model, so that the screened routes for actually transporting goods are more accurate.

With further reference to fig. 4, a flow 400 of further embodiments of a route information generation method according to the present disclosure is shown. The route information generation method comprises the following steps:

step 401, obtaining candidate transportation route information sets for the target transportation task and historical shipment volume data corresponding to each candidate transportation route information.

Step 402, for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type scheme information generating step:

step 4021, determining the ratio of the number of delivery days of the route with the cargo volume according to the historical delivery cargo volume data corresponding to the candidate transportation route information.

In some embodiments, the executing entity (e.g., the electronic device 101 shown in fig. 1) may determine the ratio of the number of days of shipment of the route to the number of days of shipment based on the historical shipment data corresponding to the candidate transportation route information. Wherein the number of days of shipment of the quantity of cargo on the route is greater than the number of days of shipment of the quantity of cargo characterizing the candidate transportation route.

As an example, first, the execution subject described above may determine the respective calendar shipment volumes to which the historical shipment volume data relates. Next, the execution body may determine, as the number of days without capacity, days in which the amount of the shipment is 0 in each calendar shipment amount. Then, the execution body may determine the number of days corresponding to each calendar shipment amount as the total number of days. Finally, the executive body can divide the days without goods by the total days to obtain the ratio of the days of the goods delivery of the route.

Step 4022, in response to determining that the ratio of the number of days of shipment of the route to the number of days of shipment of the candidate transportation route is greater than a first threshold, determining a route cargo quantity stability factor corresponding to the candidate transportation route information.

In some embodiments, in response to determining that the ratio of the number of days of shipment of the route is greater than the first threshold, the execution body may determine a route cargo amount stability factor corresponding to the candidate transportation route information. Wherein, the route cargo quantity stability coefficient can represent the cargo quantity transportation stability of the candidate transportation route. The larger the route cargo quantity stability coefficient is, the more stable the cargo quantity transportation representing the corresponding candidate transportation route is. The smaller the route cargo volume stability factor, the less stable the cargo volume transportation characterizing the corresponding candidate transportation route. The first threshold may be a preset value. For example, the first threshold may be 0.8.

As an example, first, the execution body described above may determine the freight transportation amount of each day of the history referred to by the history shipment amount data. The enforcement agent may then determine a daily shipment volume mean and a volume standard deviation for the shipment volume of the goods over the historical days. Finally, the execution subject may divide the standard deviation of the shipment quantity by the average value of the daily shipment quantity to obtain a division value as a route shipment quantity stability factor.

Step 4023, in response to determining that the route cargo quantity stability factor is larger than a second threshold value, determining that the cargo quantity transportation corresponding to the target transportation route is unstable.

In some embodiments, in response to determining that the route cargo capacity stability factor is greater than a second threshold, the execution subject may determine that the target transportation route is unstable for cargo transportation. The second threshold may be a preset value. For example, the second threshold may be 0.5.

And 4024, generating candidate vehicle type selection scheme information for the target transportation route by using a first-stage optimization model in response to the situation that the transportation of the cargo volume is unstable and the historical shipment volume data corresponding to the candidate transportation route information meets the target distribution.

In some embodiments, in response to determining that the transportation of the cargo volume is unstable and the historical shipment volume data corresponding to the candidate transportation route information satisfies the target distribution, the executing entity may generate candidate vehicle type selection scenario information for the target transportation route by using a first-stage optimization model.

And 4025, optimizing the vehicle type of the candidate vehicle type selection scheme information by using the second-stage optimization model to obtain vehicle type optimization scheme information.

In some embodiments, the execution subject may perform vehicle type optimization on the candidate vehicle type selection scheme information by using a second-stage optimization model to obtain vehicle type optimization scheme information.

And step 403, according to the obtained vehicle type optimization scheme information set, screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set to serve as actual transportation route information of the target transportation task.

In some embodiments, specific implementation of

steps

401, 4024, 4025, and 403 and technical effects brought by the implementation may refer to

steps

301, 3022, 3023, and 303 in the embodiment corresponding to fig. 3, and are not described herein again.

As can be seen from fig. 4, compared with the description of some embodiments corresponding to fig. 3, in the flow 400 of the route information generation method in some embodiments corresponding to fig. 4, first, a preliminary stability determination is performed on the historical shipment volume data by the ratio of the shipment volume shipment days in the route, and then, the stability of the historical shipment volume data is further determined by the route shipment volume stability coefficient. Therefore, whether the target transportation route is stable or not when the cargo transportation is carried out can be determined more accurately.

With further reference to fig. 5, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of a route information generation apparatus, which correspond to those illustrated in fig. 3, and which may be particularly applicable in various electronic devices.

As shown in fig. 5, a route information generating apparatus 500 includes: an acquisition unit 501, an execution unit 502, and a screening unit 503. The acquiring unit 501 is configured to acquire a candidate transportation route information set for a target transportation task and historical shipment volume data corresponding to each candidate transportation route information; an execution unit 502 configured to execute, for each candidate transportation route information in the candidate transportation route information set, a vehicle type plan information generation step of: determining whether the transportation of the goods amount corresponding to a target transportation route is stable or not according to the historical shipment data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; in response to the fact that the cargo transportation is unstable and the historical shipment data corresponding to the candidate transportation route information meet the target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information; a screening unit 503, configured to screen candidate transportation route information satisfying preset transportation conditions from the candidate transportation route information set according to the obtained vehicle type optimization scheme information set, as actual transportation route information of the target transportation task.

In some optional implementations of some embodiments, the execution unit 502 in the route information generation apparatus 500 may be further configured to: determining the ratio of the number of delivery days of the route with the cargo volume according to the historical delivery cargo volume data corresponding to the candidate transportation route information; in response to determining that the ratio of the number of delivery days of the route to the number of delivery days is greater than a first threshold, determining a route cargo quantity stability factor corresponding to the candidate transportation route information; and in response to determining that the route cargo capacity stability factor is larger than a second threshold value, determining that the target transportation route is unstable in transportation corresponding to the cargo capacity.

In some optional implementations of some embodiments, the route information generating apparatus 500 further includes: a generating unit (not shown in the figure). Wherein the generating unit may be configured to: and generating vehicle type selection scheme information for the target transportation route as vehicle type optimization scheme information for the target transportation route by using the first-stage optimization model in response to the determination that the cargo transportation is stable.

In some optional implementations of some embodiments, the execution unit 502 in the route information generation apparatus 500 may be further configured to: acquiring a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function, wherein the first-stage optimization objective function represents the sum of value information of a whole vehicle type and value information of a part vehicle type; and substituting the acquired vehicle type information set and the cargo transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the first-stage constraint conditions, and taking the sum of the value information of the whole vehicle type and the value information of the part vehicle type meeting the objective value condition as an optimization objective to obtain the candidate vehicle type selection scheme information.

In some optional implementations of some embodiments, the filtering unit 503 in the route information generating apparatus 500 may be further configured to: determining value information corresponding to each vehicle type optimization scheme information in the vehicle type optimization scheme information set to obtain a value information set; and screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the vehicle type optimization scheme information set and the value information set, and taking the candidate transportation route information as the actual transportation route information of the target transportation task.

It is understood that the units described in the route information generation apparatus 500 correspond to the respective steps in the method described with reference to fig. 3. Thus, the operations, features and beneficial effects of the methods described above are also applicable to the route information generating apparatus 500 and the units included therein, and are not repeated herein.

Referring now to FIG. 6, a block diagram of an electronic device (e.g., electronic device 101 of FIG. 1) 600 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 6, the electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring candidate transportation route information sets for the target transportation task and historical shipment volume data corresponding to each candidate transportation route information; for each candidate transportation route information in the candidate transportation route information set, executing a vehicle type scheme information generating step: determining whether the transportation of the goods amount corresponding to a target transportation route is stable or not according to the historical shipment data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; in response to the fact that the transportation of the goods amount is determined to be unstable and historical shipment data corresponding to the candidate transportation route information meet target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information; and according to the obtained vehicle type optimization scheme information set, screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set to serve as actual transportation route information of the target transportation task.

Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an execution unit, and a screening unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, the acquisition unit may also be described as a "unit that acquires candidate transportation route information sets for a target transportation task and historical shipment volume data corresponding to each candidate transportation route information".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.

Some embodiments of the present disclosure also provide a computer program product comprising a computer program which, when executed by a processor, implements any of the route information generation methods described above.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and other embodiments in which the above-mentioned features or their equivalents are combined arbitrarily without departing from the spirit of the invention are also encompassed. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. A route information generation method, comprising:

acquiring candidate transportation route information sets for the target transportation task and historical shipment volume data corresponding to each candidate transportation route information;

for each candidate haul route information in the set of candidate haul route information, performing a vehicle model scenario information generation step:

determining whether the freight volume transportation corresponding to a target transportation route is stable or not according to the historical shipment volume data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information;

in response to the fact that the transportation of the goods amount is unstable and the historical shipment data corresponding to the candidate transportation route information meet the target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model;

vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information;

and screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the obtained vehicle type optimization scheme information set, and taking the candidate transportation route information as actual transportation route information of the target transportation task.

2. The method of claim 1, wherein the determining whether the transportation of the goods corresponding to the target transportation route is stable according to the historical shipment data corresponding to the candidate transportation route information comprises:

determining the ratio of the number of delivery days of the route with the cargo volume according to the historical delivery cargo volume data corresponding to the candidate transportation route information;

in response to determining that the ratio of the number of days of shipment of the route to the number of days of shipment is greater than a first threshold, determining a route freight volume stability factor corresponding to the candidate transportation route information;

in response to determining that the route cargo capacity stability factor is greater than a second threshold, determining that the target transportation route corresponds to cargo capacity transportation instability.

3. The method of claim 1, wherein after determining whether the transportation of the cargo volume corresponding to the target transportation route is stable according to the historical shipment volume data corresponding to the candidate transportation route information, the method further comprises:

in response to determining that the cargo transportation is stable, generating, using the first-stage optimization model, vehicle type selection plan information for the target transportation route as vehicle type optimization plan information for the target transportation route.

4. The method of claim 1, wherein said generating candidate vehicle type selection scenario information for the target haul route using a first stage optimization model comprises:

acquiring a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function, wherein the first-stage optimization objective function represents the sum of value information of a whole vehicle type and value information of a part vehicle type;

and substituting the acquired vehicle type information set and the freight volume transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and taking the sum of the value information of the whole vehicle type and the value information of the part vehicle type meeting the objective value condition as an optimization objective to obtain the candidate vehicle type selection scheme information.

5. The method of claim 1, wherein the performing vehicle type optimization on the candidate vehicle type selection scheme information by using a second-stage optimization model to obtain vehicle type optimization scheme information comprises:

acquiring a second-stage optimization objective function and a plurality of second-stage constraint conditions of the second-stage optimization objective function, wherein the second-stage optimization objective function represents value transformation information brought by vehicle type transformation based on the random transportation quantity of goods to be transported;

and substituting the candidate vehicle information corresponding to the candidate vehicle type selection scheme information and the average transportation cargo quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, taking the condition that the value transformation information meets the target value condition as an optimization objective, and outputting vehicle type optimization scheme information.

6. The method of claim 1, wherein the screening, according to the obtained vehicle type optimization plan information set, candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set as actual transportation route information of the target transportation task comprises:

determining value information corresponding to each vehicle type optimization scheme information in the vehicle type optimization scheme information set to obtain a value information set;

and screening candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the vehicle type optimization scheme information set and the value information set, and taking the candidate transportation route information as the actual transportation route information of the target transportation task.

7. A route information generation apparatus comprising:

the acquisition unit is configured to acquire a candidate transportation route information set aiming at the target transportation task and historical shipment volume data corresponding to each candidate transportation route information;

an execution unit configured to execute, for each candidate transportation route information in the candidate transportation route information set, a vehicle type plan information generation step of: determining whether the freight volume transportation corresponding to a target transportation route is stable or not according to the historical shipment volume data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; in response to the fact that the transportation of the goods amount is unstable and the historical shipment data corresponding to the candidate transportation route information meet the target distribution, generating candidate vehicle type selection scheme information aiming at the target transportation route by using a first-stage optimization model; vehicle type optimization is carried out on the candidate vehicle type selection scheme information by utilizing a second-stage optimization model to obtain vehicle type optimization scheme information;

and the screening unit is configured to screen candidate transportation route information meeting preset transportation conditions from the candidate transportation route information set according to the obtained vehicle type optimization scheme information set, and the candidate transportation route information is used as actual transportation route information of the target transportation task.

8. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.

9. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.