阅读说明：本技术 一种车辆调度方法 (Vehicle dispatching method ) 是由 *** 于 2019-10-14 设计创作，主要内容包括：本发明公开的车辆调度方法,涉及车辆调度技术领域,利用训练过的机器学习模型进行车辆调度,能够满足非固定网点的车辆调度需求及即时响应用户的需求,提高了调度的灵活性及效率。(The invention discloses a vehicle scheduling method, which relates to the technical field of vehicle scheduling, and can meet the vehicle scheduling requirements of non-fixed network points and the requirements of instant response users by utilizing a trained machine learning model to schedule vehicles, thereby improving the scheduling flexibility and efficiency.)

1. A vehicle scheduling method, comprising:

Acquiring the position and the car taking time of a user who opens the APP currently to obtain a current position set and a current car taking time set of the user;

Acquiring the scheduling start time of each website vehicle from an operation and maintenance management system to obtain a scheduling start time set;

Inputting the current position set, the current vehicle taking time set and the scheduling starting time set into a trained machine learning model to obtain a first network point needing to call in a vehicle, a second network point needing to call out the vehicle and the scheduling starting time;

And carrying out vehicle scheduling according to the scheduling starting time, the first network point and the second network point.

2. The vehicle scheduling method according to claim 1, wherein the generating process of the machine learning model comprises:

In a set time period, obtaining time, times, positions and car taking time of historical opening of an APP by a user to obtain a historical position set, a historical opening APP time set and a historical opening APP time set of the user;

in a set time period, obtaining historical vehicle taking time of a website and a user which are historically called in vehicles of each website from an operation and maintenance management system to obtain a historical calling website set and a historical vehicle taking time set;

and inputting the historical position set, the historical opening APP time set, the historical opening APP frequency set, the historical vehicle taking time set, the historical call-in site set and the scheduling starting time set as training data into a machine learning model to train the machine learning model, and generating a trained machine learning model.

3. The vehicle scheduling method of claim 1, wherein after obtaining the network points that need to be called into the vehicle and the corresponding network points that need to be called out of the vehicle, the method further comprises:

And pushing the website to a user.

4. The vehicle scheduling method of claim 1, wherein the machine learning model is an SVM model.

5. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of claims 1-3 are implemented when the computer program is executed by the processor.

Technical Field

The invention belongs to the technical field of vehicle scheduling, and particularly relates to a vehicle scheduling method.

background

With the trend of the company operation mode towards the platformization, the market and the scale are further expanded, and most of the current offline operations are based on fixed network points, so that the key for improving the yield and reducing the energy is to accurately schedule vehicles for a large number of network points.

most of the existing vehicle scheduling schemes sort orders of parking points by checking orders of the parking points rented by companies and schedule vehicles of each point according to a sorting result. This solution has the following drawbacks:

(1) The vehicle scheduling requirements of non-fixed network points cannot be met, and the scheduling flexibility is poor;

(2) The demand of the user can not be responded immediately, and the scheduling efficiency is low.

Disclosure of Invention

Aiming at the defects in the prior art, the embodiment of the invention provides a vehicle dispatching method.

The vehicle scheduling method provided by the embodiment of the invention comprises the following steps:

Acquiring the position and the car taking time of a user who opens the APP currently to obtain a current position set and a current car taking time set of the user;

Acquiring the scheduling start time of each website vehicle from an operation and maintenance management system to obtain a scheduling start time set;

Inputting the current position set, the current vehicle taking time set and the scheduling starting time set into a trained machine learning model to obtain a first network point needing to call in a vehicle, a second network point needing to call out the vehicle and the scheduling starting time;

And carrying out vehicle scheduling according to the scheduling starting time, the first network point and the second network point.

Preferably, the generation process of the machine learning model includes:

in a set time period, obtaining time, times, positions and car taking time of historical opening of an APP by a user to obtain a historical position set, a historical opening APP time set and a historical opening APP time set of the user;

In a set time period, obtaining historical vehicle taking time of a website and a user which are historically called in vehicles of each website from an operation and maintenance management system to obtain a historical calling website set and a historical vehicle taking time set;

And inputting the historical position set, the historical opening APP time set, the historical opening APP frequency set, the historical vehicle taking time set, the historical call-in site set and the scheduling starting time set as training data into a machine learning model to train the machine learning model, and generating a trained machine learning model.

Preferably, after obtaining the network point needing to call in the vehicle and the corresponding network point needing to call out the vehicle, the method further comprises the following steps:

And pushing the website to a user.

preferably, the machine learning model is an SVM model.

the vehicle scheduling method provided by the embodiment of the invention has the following beneficial effects:

The trained machine learning model is used for vehicle scheduling, the vehicle scheduling requirements of non-fixed network points and the requirements of instant response users can be met, and the scheduling flexibility and efficiency are improved.

Detailed Description

the present invention will be described in detail with reference to the following embodiments.

the vehicle scheduling method provided by the embodiment of the invention comprises the following steps:

S101, obtaining the position and the car taking time of a user who opens the APP at present, and obtaining the current position set and the current car taking time set of the user.

As a specific example, the APP is an official client program launched by a car rental company, such as GoFun travel.

S102, obtaining the scheduling start time of each website vehicle from the operation and maintenance management system to obtain a scheduling start time set.

And S103, inputting the current position set, the current vehicle taking time set and the scheduling start time set into the trained machine learning model to obtain a first network point needing to call in the vehicle, a second network point needing to call out the vehicle and the scheduling start time.

and S104, carrying out vehicle scheduling according to the scheduling start time, the first network point and the second network point.

Optionally, the generating process of the machine learning model includes:

and in a set time period, obtaining the time, the times, the position and the car taking time of the historical opening APP of the user, and obtaining a historical position set, a historical opening APP time set and a historical opening APP time set of the user.

As a specific embodiment, the times and positions of opening the APP by each user in a one-month period are obtained in real time through a buried point log.

and in a set time period, obtaining historical vehicle taking time of the website and the user which are historically called in the vehicle of each website from the operation and maintenance management system to obtain a historical calling-in website set and a historical vehicle taking time set.

And inputting the historical position set, the historical opening APP time set, the historical opening APP frequency set, the historical vehicle taking time set, the historical calling network point set and the scheduling start time set as training data into a machine learning model to train the machine learning model, and generating the trained machine learning model.

As a specific example, the process of training the machine learning model is as follows:

Clustering the historical position set and the historical opening APP frequency set respectively by using a k-means algorithm according to the frequency of 15 minutes/time, and normalizing each data in the clustered historical position set;

and taking the historical opening APP time set, the historical opening APP frequency set, the historical vehicle taking time set and the scheduling start time set as characteristics, and taking the historical call-in website set as a label to train the machine learning model.

Optionally, after obtaining the network point that needs to be called into the vehicle and the corresponding network point that needs to be called out of the vehicle, the method further includes:

and pushing the website to the user.

Optionally, the machine learning model is an SVM model.

According to the vehicle scheduling method provided by the embodiment of the invention, the trained machine learning model is used for vehicle scheduling, so that the vehicle scheduling requirements of non-fixed network points and the requirements of instant response users can be met, and the scheduling flexibility and efficiency are improved.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

It will be appreciated that the relevant features of the method and apparatus described above are referred to one another.

it is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

in addition, the memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

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

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

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

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

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

it should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

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

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

it should be noted that the above-mentioned embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the protection scope of the present invention.