CN115660244B

CN115660244B - Route information generation method, device, equipment and medium

Info

Publication number: CN115660244B
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: 2023-09-01
Anticipated expiration: 2042-12-27
Also published as: CN115660244A

Abstract

The embodiment of the disclosure discloses a route information generation method, a route information generation device, route information generation equipment and a route information generation medium. One embodiment of the method comprises the following steps: acquiring a candidate transportation route information set and historical shipping volume data corresponding to each candidate transportation route information set; for each candidate transportation route information, performing a model scheme information generation step of: determining whether the target transportation route is stable in corresponding cargo quantity transportation; generating candidate vehicle type selection scheme information by using a first-stage optimization model in response to determining that the cargo volume transportation is unstable and the corresponding historical shipping cargo volume data meets the target distribution; performing model optimization on the candidate model selection scheme information by using the second-stage optimization model to obtain model optimization scheme information; and screening out candidate transportation route information meeting preset transportation conditions, and taking the candidate transportation route information as actual transportation route information. This embodiment is related with wisdom commodity circulation, can more accurately screen out the transportation route information that is applicable to the corresponding goods transportation of target transportation task.

Description

Route information generation method, device, equipment and medium

Technical Field

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

Background

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

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

the selection of only a route with a stable cargo amount for the transportation of the subsequent cargo results in a small number of optimizable routes when the number of routes with a stable cargo amount is small in practice, so that the optimizable space is small, resulting in insufficient accuracy in the route screened for the actual transportation of the cargo.

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

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure 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 background section above.

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 shipping quantity data corresponding to each candidate transportation route information; for each candidate transportation route information in the candidate transportation route information set, performing a model scheme information generation step: determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; generating candidate vehicle model selection scheme information for the target transportation route by using a first-stage optimization model in response to determining that the cargo volume transportation is unstable and that historical shipping cargo volume data corresponding to the candidate transportation route information meets a target distribution; performing model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information; and screening out 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.

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

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

Optionally, the generating candidate vehicle model 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 spare wheel type; substituting the obtained vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining the candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition.

Optionally, the performing, by using the second-stage optimization model, the 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 fact that the quantity of goods to be transported is random traffic; substituting the candidate vehicle type selection scheme information corresponding to the candidate vehicle information and the average transportation quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and taking the value transformation information meeting the target value condition as an optimization target to output vehicle type optimization scheme information.

Optionally, the selecting candidate transportation route information satisfying the preset transportation condition 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 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 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: an acquisition unit configured to acquire a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information; an execution unit configured to execute the vehicle model scenario information generation step for each candidate transportation route information in the candidate transportation route information set: determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; generating candidate vehicle model selection scheme information for the target transportation route by using a first-stage optimization model in response to determining that the cargo volume transportation is unstable and that historical shipping cargo volume data corresponding to the candidate transportation route information meets a target distribution; performing model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information; and a screening unit configured to screen candidate transportation route information satisfying a preset transportation condition from the candidate transportation route information set as actual transportation route information of the target transportation task according to the obtained vehicle type optimization scheme information set.

Alternatively, the execution unit may be configured to: determining the proportion of the shipping days with the shipping quantity of the route according to the historical shipping quantity data corresponding to the candidate shipping route information; determining a route cargo quantity stability factor corresponding to the candidate transportation route information in response to determining that the route cargo quantity shipping day ratio is greater than a first threshold; and determining that the target transportation route corresponds to the unstable cargo volume transportation in response to determining that the route cargo volume stability factor is greater than a second threshold.

Optionally, the apparatus further includes: and generating model selection scheme information for the target transportation route by using the first-stage optimization model in response to determining that the cargo quantity transportation is stable, wherein the model selection scheme information is used as model optimization scheme information for the target transportation route.

Alternatively, 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 spare wheel type; substituting the obtained vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining the candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition.

Alternatively, 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 fact that the quantity of goods to be transported is random traffic; substituting the candidate vehicle type selection scheme information corresponding to the candidate vehicle information and the average transportation quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and taking the value transformation information meeting the target value condition as an optimization target to output vehicle type optimization scheme information.

Alternatively, 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 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 present disclosure provide a computer readable medium having a computer program stored thereon, wherein 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 advantageous effects: according to the route information generation method, the transportation route information suitable for the transportation of the goods corresponding to the target transportation task can be screened out more accurately. Specifically, the reason for the insufficient accuracy of the screening of the relevant transportation route information is that: the selection of only a route with a stable cargo amount for the transportation of the subsequent cargo results in a small number of optimizable routes when the number of routes with a stable cargo amount is small in practice, so that the optimizable space is small, resulting in insufficient accuracy in the route screened for the actual transportation of the cargo. Based on this, route information generating methods of some embodiments of the present disclosure first acquire a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information for use in screening of subsequent actual transportation route information and determination of the shipping volume transportation stability of each piece of candidate transportation route information. Then, for each candidate transportation route information in the above candidate transportation route information set, a model scheme information generation step is performed: the first step, according to the historical shipping volume data corresponding to the candidate transportation route information, whether the shipping 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 a second step of generating candidate vehicle model selection scheme information for the target transportation route more accurately by using the first-stage optimization model in response to determining that the cargo amount transportation is unstable and that the historical shipping cargo amount data corresponding to the candidate transportation route information satisfies the target distribution. And thirdly, performing model optimization on the candidate model selection scheme information by using a second-stage optimization model to obtain model optimization scheme information. Here, for the transport route that the volume of goods transport is unstable and the historical shipping volume data that candidate transport route information corresponds to satisfies the target distribution, the vehicle type optimization scheme information can be accurately generated, so that the problem that only the route with stable volume of goods is selected for the transportation of subsequent goods, and the number of optimizable routes is small and the optimizable space is small due to the fact that the number of routes with stable volume of goods in reality is small is solved. And finally, according to the obtained vehicle type optimization scheme information set, screening out candidate transportation route information which meets preset transportation conditions and is more suitable for the transportation of the goods corresponding to the target transportation task from more candidate transportation route information, and taking the candidate transportation route information as the actual transportation route information of the target transportation task. Therefore, the route information generation method can increase the number of optimizable routes and increase the optimizable space through the first-stage optimization model and the second-stage optimization model, so that the screened routes for actually transporting the goods are more accurate.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures 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 chart of some embodiments of a route information generation method according to the present disclosure;

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

fig. 5 is a schematic structural view of some embodiments of a route information generating device 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 should be understood that the present 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 so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

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

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

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

The operations such as collection, storage, and use of the historical shipping volume data referred to in the present disclosure involve the relevant organizations or individuals being up to the end of the obligations including developing the assessment of the safety impact of the vehicle information, fulfilling the notification obligations to the vehicle information body, and obtaining the authorized consent of the vehicle information body in advance, before performing the corresponding operations.

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 one application scenario of a route information generation method according to some embodiments of the present disclosure.

In the application scenario of fig. 1-2, the electronic device 101 may obtain a set of candidate transportation route information 103 for the target transportation mission 102 and historical shipping volume data corresponding to each candidate transportation route information. In the present application scenario, the candidate transportation route information set 103 may include: candidate transportation route information 1031, candidate transportation route information 1032, and candidate transportation route information 1033. The candidate transportation route information 1031 may be an a-B transportation route. The candidate transportation route information 1032 may be an a-C-B transportation route. The candidate transportation route information 1033 may be a D-B transportation route. The historical shipping volume data corresponding to the candidate transportation route information 1031 is the historical shipping volume data 104. The historical shipping volume data corresponding to the candidate transportation route information 1032 is the historical shipping volume data 105. The historical shipping volume data corresponding to the candidate transportation route information 1033 is the historical shipping volume data 106. Then, for each candidate transportation route information in the above candidate transportation route information set 103, a vehicle model scenario information generation step is performed: in the first step, the electronic device 101 may determine whether the shipment volume corresponding to the target shipment route is stable according to the historical shipment volume data corresponding to the candidate shipment route information. Wherein the target transportation route is a route corresponding to the candidate transportation route information. In response to determining that the amount of cargo is unstable in transportation and that the historical shipping amount data corresponding to the candidate transportation route information satisfies the target distribution, the electronic device 101 may generate candidate vehicle model selection scheme information for the target transportation route using the first-stage optimization model. Thirdly, the electronic device 101 may perform the vehicle type optimization on the candidate vehicle type selection scheme information by using the second stage optimization model, so as to obtain the vehicle type optimization scheme information. In the present application scenario, in the first step, with respect to the candidate transportation route information 1031, the electronic device 101 may determine whether the transportation of the target transportation route corresponding to the amount of goods is stable according to the historical shipping amount data 104 corresponding to the candidate transportation route information 1031. In response to determining that the amount of cargo is unstable in transportation and the historical shipping amount data 104 corresponding to the candidate transportation route information 1031 meets the target profile, the electronic device 101 may generate candidate vehicle model selection scheme information 108 for the target transportation route using the first-stage optimization model. The candidate model selection scheme information 108 may be "model 1:11, model 2:5, motorcycle type 3:1 vehicle). Third, the electronic device 101 may perform the vehicle type optimization on the candidate vehicle type selection scheme information 108 by using the second-stage optimization model, to obtain the vehicle type optimization scheme information 1071. The model optimization scheme information 1071 may be "model 1:12, model 2:4, model 3:1 vehicle). Finally, the electronic device 101 may screen out candidate transportation route information satisfying a preset transportation condition from the candidate transportation route information set 103 according to the obtained vehicle type optimization scheme information set 107, as actual transportation route information of the target transportation task 102. In the present application scenario, the vehicle model optimization scheme information set 107 includes: the vehicle type optimization scheme information 1071 corresponding to the candidate transportation route information 1031, the vehicle type optimization scheme information 1072 corresponding to the candidate transportation route information 1032, and the vehicle type optimization scheme information 1073 corresponding to the candidate transportation route information 1033. The model optimization scheme information 1071 may be "model 1:12, model 2:4, model 3:1 vehicle). The model optimization scheme information 1072 may be "model 1:8, model 2:6, motorcycle type 3:2 vehicles). The model optimization scheme information 1073 may be "model 1:10, model 2:7, vehicle model 4:1 vehicle). The actual transportation route information may be vehicle model optimization scheme 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 device. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention 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 generating method comprises the following steps:

step 301, acquiring a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information.

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

Step 302, for each candidate transportation route information in the candidate transportation route information set, a model scheme information generation step is performed:

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

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

As an example, the execution body described above may first determine a standard deviation of the cargo traffic corresponding to the historical shipping traffic data. Then, in response to the standard deviation of the cargo traffic being greater than the target value, it is determined that the target transport route is unstable with respect to the cargo traffic.

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

in response to determining that the cargo amount transportation is stable, the execution subject may generate model selection scheme information for the target transportation route as model optimization scheme information for the target transportation route using the first-stage optimization model.

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

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

In some embodiments, in response to determining that the volume of cargo is unstable and the historical shipping volume data corresponding to the candidate shipping route information meets a target profile, the execution entity may generate candidate vehicle model selection scheme information for the target shipping route 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 for determining model selection scheme 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 network (Convolutional Neural Networks, CNN) model. The candidate vehicle type selection scheme information may be vehicle type collocation scheme information for performing a target transportation task with respect to the candidate transportation route information. In practice, the scheme type corresponding to the vehicle type collocation scheme can be complete vehicle collocation+tail cargo quantity, and also can be complete vehicle collocation+spare part vehicle. In addition, the scheme type corresponding to the vehicle type collocation scheme can be the whole vehicle collocation. The vehicle model collocation scheme information may include a vehicle selection type and a selection number corresponding to each vehicle type. For example, for candidate transportation route information of a- > B, the selected model collocation scheme may be { heavy goods vehicle: 4 medium-sized trucks: 3, the spare part vehicle: 1 }. For another example, for candidate transportation route information of a- > B, the selected model collocation scheme may be { heavy goods vehicle: 4 medium-sized trucks: 2, tailstock number: 100 pieces }.

It should be noted that, the vehicle type selection scheme information obtained by the first-stage optimization model is utilized 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 smaller than the cost of transporting the goods by using the vehicle type scheme corresponding to the vehicle set of the current candidate transportation route, 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 the efficiency of transporting the goods by using the vehicle type scheme corresponding to the vehicle set of the current candidate transportation route.

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

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

the first step, a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function are obtained. The first-stage optimization objective function represents the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type. The value information of the whole vehicle model can be transportation cost generated by the whole vehicle model. The value information of the spare-wheel model may be a transportation cost generated by the spare-wheel model.

It should be noted that, the transportation cost generated by the spare-wheel type may be one of the following: space costs for the selected spare wheel type vehicle in the current batch of transportation and space costs for the selected spare wheel type vehicle in the next batch of transportation. Here, the spare wheel type vehicle is transported with the amount of the tail cargo corresponding to the cargo. And when the tail cargo quantity is greater than or equal to a certain value, adopting the selected spare wheel type vehicle in the current batch transportation to transport the cargo corresponding to the tail cargo quantity. And when the tail cargo quantity is smaller than a certain value, adopting the selected spare wheel type vehicle in the next batch of transportation to transport the cargo corresponding to the tail cargo quantity.

Wherein the first-stage optimization objective function is generated by:

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

Wherein, the motorcycle type collection includes: each whole car model and the part car model.

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

Substituting the acquired vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining the candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition.

The target value condition may be value information in which the sum of value information of the whole vehicle type and value information of the spare part vehicle type is the lowest. The vehicle type information may include: vehicle model collection, vehicle model cost of various vehicle models. The cargo quantity transportation information corresponding to the vehicle type information may include: lower limit of the vehicle type transportation cargo amount, upper limit of the vehicle type transportation cargo amount and limit of the total cargo amount of line transportation. The plurality of first stage constraints includes: the method comprises the steps of representing constraint conditions of two types of vehicles at most in each route, representing constraint conditions of upper and lower limits of the number of used vehicle types, representing constraint conditions of upper and lower limits of the quantity of transported goods of the corresponding vehicle types of the spare-vehicle types, representing constraint conditions of the range of the value of the tail goods, and representing constraint conditions of vehicles which use the vehicle types of the whole vehicle preferentially.

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

In some embodiments, the executing body may perform model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model 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 quantity transportation transformation condition of the target proportion. For example, the second-stage optimization model may be a convolutional neural network model. The cargo quantity transportation change condition may be a condition in which cargo quantity is temporarily changed. For example, the predetermined cargo traffic before the target transportation task is 19 parties. The cargo traffic is then temporarily adjusted to 25 parties. The target ratio may be preset. 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 utilized 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 optimization scheme information corresponding to the vehicle set is smaller than the cost of transporting the goods by using the vehicle type scheme corresponding to the vehicle set of the current candidate transportation route, 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 corresponding to the vehicle set of the current candidate transportation route.

As an example, the second-stage optimization model is utilized to optimize the traffic volume type and/or the corresponding number of each vehicle type of the candidate vehicle type selection scheme information, so as to obtain the vehicle type optimization scheme information.

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

the first step, a second-stage optimization objective function and a plurality of second-stage constraint conditions of the second-stage optimization objective function are obtained. The second-stage optimization objective function representation is based on value transformation information brought by the fact that the quantity of goods to be transported is random transportation quantity and the vehicle type is transformed. The random traffic may be a randomly transformed cargo traffic.

Wherein the second-stage optimization objective function is generated by:

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

And 2, generating a cost expression with the number of the selected numbers of the vehicle models changed as a second-stage optimization model.

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

And (2) generating at least one model parameter for each model.

Sub-step 2, for each of the at least one model parameters, performing the following transportation cost determination steps:

a first sub-step of determining a first transportation cost for the model parameters in response to determining that the model parameters characterize the corresponding model selection number is reduced.

A second sub-step of multiplying the first transportation cost by the first weight. The first weight may be a preset weight. For example, the first weight may be 0.8.

And a third sub-step of determining a second transportation cost for the vehicle model parameter in response to determining that the vehicle model parameter characterizes the increase in the number of vehicle model selections corresponding to the vehicle model parameter.

And a fourth sub-step of multiplying the second transportation cost by the second weight. The second weight may be a preset weight. For example, the second weight may be 1.5.

And 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 substituting the candidate vehicle type selection scheme information corresponding to the candidate vehicle information and the average transportation quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and taking the value transformation information meeting the target value condition as an optimization target to output vehicle type optimization scheme information. Wherein the value conversion information is transportation cost conversion information. The candidate vehicle type selection scheme information may be vehicle selection condition information corresponding to a candidate vehicle type selection scheme. The transportation cost conversion information may be a 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 cost of the vehicle change may be the cost of adding a vehicle or reducing the change in the vehicle. The target route corresponds to the average shipment amount and may be an average shipment amount per day of the 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 type 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 type optimization scheme information is larger than that of the minimum volume for opening vehicle transportation, representing a constraint condition that the number of transformation for the vehicle type selection number is smaller than that of the corresponding vehicle type selection number, representing a constraint condition that the transformation condition for the vehicle type selection number can only be one of a reduced condition and a changed condition, representing a constraint condition that the probability of the first volume constraint condition being met under the influence of random transportation is larger than 95%, and representing a constraint condition that the probability of the second volume constraint condition being met under the influence of random transportation is larger than 95%.

Here, the first volume constraint and the second volume constraint are subjected to deterministic equivalent form conversion to obtain constraint conditions of which the input includes the average transport amount corresponding to the above-mentioned target transport route and the variance of the transport amount corresponding to the above-mentioned target transport route. Thus, further, the second-stage optimization objective function output result (i.e., the model optimization scheme information) is obtained through an optimization solver such as SCIP or Gurobi.

And 203, screening out 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.

In some embodiments, the executing body may screen candidate transportation route information satisfying a preset transportation condition 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. The preset transportation condition may be a transportation condition preset by a related technician. For example, the preset transportation condition may be that the candidate transportation route information is the lowest transportation cost information corresponding to the candidate transportation route information set.

As an example, first, the above-described execution subject may determine a transportation cost corresponding to each vehicle model optimization scheme information. Then, the executing body may select the candidate transportation route information having the lowest corresponding transportation cost from the candidate transportation route information set as the actual transportation route information of the target transportation task.

In some optional implementations of some embodiments, the selecting candidate transportation route information satisfying the preset transportation condition 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 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 type optimization scheme information.

As an example, first, the above-described execution subject may set the vehicle use costs of the various types of vehicles referred to by the respective vehicle type optimization scheme information in the vehicle type optimization scheme information. Then, the executing body may determine transportation cost information for completing the target transportation task using the vehicle combination corresponding to each model optimization scheme information according to the vehicle use costs of the respective types of vehicles.

And a second step of 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 information that the candidate transportation route information is a vehicle type optimization scheme information in the candidate transportation route information set, the number of vehicles corresponding to the information is small, and the vehicle use cost is low.

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

step 401, acquiring a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information.

Step 402, for each candidate transportation route information in the candidate transportation route information set, performing a model scheme information generation step:

step 4021, determining the number of days of route shipment with the shipment amount according to the historical shipment amount data corresponding to the candidate transportation route information.

In some embodiments, an executing entity (e.g., the electronic device 101 shown in fig. 1) may determine a route volume shipping day ratio based on historical shipping volume data corresponding to the candidate shipping route information. Wherein the route has a volume delivery number of days to rate a volume delivery number of days characterizing the candidate delivery route.

As an example, first, the execution subject may determine the daily historical shipping volume to which the historical shipping volume data relates. Then, the execution subject may determine the number of days in which the amount of the daily historic shipping amount is 0 amount of the daily historic shipping amount as the no-amount number of days. Then, the execution subject may determine the number of days corresponding to the daily historic shipment amount as the total number of days. Finally, the executing body may divide the number of days without the amount of goods by the total number of days to obtain the number of days with the amount of goods for the route.

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

In some embodiments, in response to determining that the route volume shipping day ratio is greater than a first threshold, the executing entity may determine a route volume stability factor corresponding to the candidate shipping route information. Wherein the route cargo quantity stability factor may characterize cargo quantity transportation stability of the candidate transportation route. The larger the route volume stability factor, the more stable the volume transportation characterizing the corresponding candidate transportation route. The smaller the route volume stability factor, the more unstable the volume transport characterizing the corresponding candidate transport route. The first threshold may be a preset value. For example, the first threshold may be 0.8.

As an example, first, the execution subject may determine the cargo traffic of the history days to which the history shipping cargo traffic data relates. Then, the execution subject may determine a daily shipping volume mean and a volume standard deviation for the cargo volume of each day of history. Finally, the execution body may divide the standard deviation of the amount of goods by the average value of the daily shipping amount to obtain a division value as a route amount stability factor.

In step 4023, in response to determining that the route volume stability factor is greater than a second threshold, determining that the target shipping route is unstable with respect to volume transportation.

In some embodiments, in response to determining that the route volume stability factor is greater than a second threshold, the execution body may determine that the target shipping route corresponds to volume shipping instability. The second threshold may be a preset value. For example, the second threshold may be 0.5.

In step 4024, in response to determining that the cargo volume transportation is unstable and the historical shipping cargo volume data corresponding to the candidate transportation route information meets the target distribution, candidate vehicle model selection scheme information for the target transportation route is generated using the first-stage optimization model.

In some embodiments, in response to determining that the volume of cargo is unstable and the historical shipping volume data corresponding to the candidate shipping route information meets a target profile, the execution entity may generate candidate vehicle model selection scheme information for the target shipping route using a first stage optimization model.

And step 4025, performing model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information.

In some embodiments, the executing body may perform model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information.

And step 403, screening out 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.

In some embodiments, the specific implementation of steps 401, 4024, 4025 and 403 and the technical effects thereof may refer to steps 301, 3022, 3023 and 303 in the corresponding embodiment of fig. 3, which are not described herein.

As can be seen from fig. 4, compared with the description of some embodiments corresponding to fig. 3, the flow 400 of the route information generating method in some embodiments corresponding to fig. 4 first makes a preliminary stability determination for the historical shipping volume data by the route shipment number of days, and then further determines the stability of the historical shipping volume data by the route shipment stability factor. Therefore, whether the target transportation route is stable in the process of carrying out the cargo quantity transportation can be accurately determined.

With further reference to fig. 5, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a route information generating apparatus, which correspond to those method embodiments shown in fig. 3, and which are particularly applicable to 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. Wherein the obtaining unit 501 is configured to obtain a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information; an execution unit 502 configured to execute a vehicle model scenario information generation step for each candidate transportation route information in the candidate transportation route information set described above: determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; generating candidate vehicle model selection scheme information for the target transportation route by using a first-stage optimization model in response to determining that the cargo volume transportation is unstable and that historical shipping cargo volume data corresponding to the candidate transportation route information meets a target distribution; performing model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information; and a screening unit 503 configured to screen out candidate transportation route information satisfying a preset transportation condition from the candidate transportation route information set as actual transportation route information of the target transportation task according to the obtained vehicle type optimization scheme information set.

In some optional implementations of some embodiments, the execution unit 502 in the route information generating device 500 may be further configured to: determining the proportion of the shipping days with the shipping quantity of the route according to the historical shipping quantity data corresponding to the candidate shipping route information; determining a route cargo quantity stability factor corresponding to the candidate transportation route information in response to determining that the route cargo quantity shipping day ratio is greater than a first threshold; and determining that the target transportation route corresponds to the unstable cargo volume transportation in response to determining that the route cargo volume stability factor is greater than a second threshold.

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

In some optional implementations of some embodiments, the execution unit 502 in the route information generating device 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 spare wheel type; substituting the obtained vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining the candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition.

In some optional implementations of some embodiments, the filtering unit 503 in the route information generating device 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 actual transportation route information of the target transportation task.

It will be appreciated that the elements described in the route information generation device 500 correspond to the individual steps in the method described with reference to fig. 3. Thus, the operations, features and advantages described above for the method are equally applicable to the route information generating device 500 and the units contained therein, and are not described here again.

Referring now to fig. 6, a schematic diagram of an electronic device 600 (e.g., electronic device 101 of fig. 1) suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 6 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to 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 required 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 through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic 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 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts 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 shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 609, or from storage device 608, or from ROM 602. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.

It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 present 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, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication 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 networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being 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 a candidate transportation route information set aiming at a target transportation task and historical shipping quantity data corresponding to each candidate transportation route information; for each candidate transportation route information in the candidate transportation route information set, performing a model scheme information generation step: determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity data corresponding to the candidate transportation route information, wherein the target transportation route is a route corresponding to the candidate transportation route information; generating candidate vehicle model selection scheme information for the target transportation route by using a first-stage optimization model in response to determining that the cargo volume transportation is unstable and that historical shipping cargo volume data corresponding to the candidate transportation route information meets a target distribution; performing model optimization on the candidate model selection scheme information by using a second stage optimization model to obtain model optimization scheme information; and screening out 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.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts 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 means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, an execution unit, and a screening unit. The names of these units do not constitute a limitation of the unit itself in some cases, and for example, the acquisition unit may also be described as "a unit that acquires a candidate transportation route information set for a target transportation task and historical shipping amount data corresponding to each candidate transportation route information".

The functions described above herein 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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), 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 of the preferred embodiments of the present disclosure and description of the principles of the technology being 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 combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A route information generation method, comprising:

acquiring a candidate transportation route information set aiming at a target transportation task and historical shipping quantity data corresponding to each candidate transportation route information;

for each candidate transportation route information in the candidate transportation route information set, performing a model scheme information generation step:

determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity 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 volume transportation is stable or the volume transportation is unstable and the historical shipping volume data corresponding to the candidate transportation route information meets a target distribution, determining the candidate transportation route information as candidate transportation route information to be optimized;

in response to determining that the candidate transportation route information to be optimized is unstable in corresponding cargo quantity transportation and the corresponding historical shipping cargo quantity data meets target distribution, 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; substituting the obtained vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition; 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 fact that the quantity of goods to be transported is random traffic;

Substituting the candidate vehicle type selection scheme information corresponding to the candidate vehicle information and the average transportation quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and taking the value transformation information meeting the target value condition as an optimization target to output vehicle type optimization scheme information;

and screening out 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 target transportation route corresponds to stable transportation of the cargo amount according to the historical shipping cargo amount data corresponding to the candidate transportation route information comprises:

determining the proportion of the shipping days with the shipping quantity of the route according to the historical shipping quantity data corresponding to the candidate shipping route information;

responsive to determining that the route shipment days account for a number of days greater than a first threshold, determining a route shipment stability factor corresponding to the candidate shipping route information;

responsive to determining that the route volume stability factor is greater than a second threshold, determining that the target shipping route corresponds to volume shipping instability.

3. The method of claim 1, wherein after the determining whether the target transportation route corresponds to the amount of shipment being stable based on the historical shipping amount data corresponding to the candidate transportation route information, the method further comprises:

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

4. The method according to claim 1, wherein the screening candidate transportation route information satisfying a preset transportation condition 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 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 out 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.

5. A route information generating apparatus, comprising:

an acquisition unit configured to acquire a candidate transportation route information set for a target transportation task and historical shipping volume data corresponding to each candidate transportation route information;

an execution unit configured to execute a vehicle model scenario information generation step for each candidate transportation route information in the candidate transportation route information set: determining whether the cargo quantity transportation corresponding to a target transportation route is stable or not according to the historical shipping cargo quantity 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 volume transportation is stable or the volume transportation is unstable and the historical shipping volume data corresponding to the candidate transportation route information meets a target distribution, determining the candidate transportation route information as candidate transportation route information to be optimized; acquiring a first-stage optimization objective function and a plurality of first-stage constraint conditions of the first-stage optimization objective function in response to the fact that the cargo volume transportation is unstable and the historical shipping cargo volume data corresponding to the candidate transportation route information meets target distribution, wherein the first-stage optimization objective function represents the sum of value information of a whole vehicle type and value information of a spare-vehicle type; substituting the obtained vehicle type information set and the cargo quantity transportation information corresponding to each vehicle type information into the first-stage optimization objective function and the plurality of first-stage constraint conditions, and obtaining candidate vehicle type selection scheme information by taking the sum of the value information of the whole vehicle type and the value information of the spare part vehicle type as an optimization objective and meeting the objective value condition; 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 fact that the quantity of goods to be transported is random traffic; substituting the candidate vehicle type selection scheme information corresponding to the candidate vehicle information and the average transportation quantity corresponding to the target transportation route into the second-stage optimization objective function and the plurality of second-stage constraint conditions, and taking the value transformation information meeting the target value condition as an optimization target to output 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.

6. 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, causes the one or more processors to implement the method of any of claims 1-4.

7. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-4.