CN111445052A

CN111445052A - Vehicle information processing method and device and electronic equipment

Info

Publication number: CN111445052A
Application number: CN201910044427.4A
Authority: CN
Inventors: 裴成
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2019-01-17
Filing date: 2019-01-17
Publication date: 2020-07-24
Anticipated expiration: 2039-01-17
Also published as: CN111445052B

Abstract

The invention discloses a vehicle information processing method, a vehicle information processing device and electronic equipment, wherein the processing method comprises the following steps: acquiring vehicle information and a vehicle number task distributed aiming at the vehicle; obtaining a selected feature vector, wherein the feature vector comprises a plurality of features influencing mileage of the vehicle to execute the vehicle number task, and the plurality of features comprise vehicle features and vehicle number features; acquiring a mapping function between the feature vector and the driving mileage; obtaining the predicted mileage of the vehicle for executing the vehicle task according to the mapping function and the vector value of the feature vector; and determining whether the vehicle executes the processing result of the vehicle-level task according to the predicted mileage.

Description

Vehicle information processing method and device and electronic equipment

Technical Field

The present invention relates to the technical field of vehicle information processing, and more particularly, to a method for processing vehicle information, an apparatus for processing vehicle information, an electronic device, and a computer-readable storage medium.

Background

With the rapid development of urban transportation, public transportation plays an increasingly important role in passenger flow transportation, and higher requirements are certainly put forward for the application of the public transportation. For example, for a public vehicle, after the public vehicle finishes the operation plan of the day and returns to the yard, the returning public vehicle needs to be compiled to meet the operation plan of the next day.

Generally, after the train number list of the second day is selected, a vehicle with good state is selected to serve as a corresponding train number task in the list, and the process is called vehicle operation daily plan compiling. In the prior art, a manual configuration mode is still adopted for planning, namely, maintenance scheduling is matched according to the vehicle state, parking position and other subjectives, and as the mileage corresponding to different vehicle number tasks is often different, the traveling mileage in the vehicle operation process has great difference, the reliability of the vehicle with overlarge traveling mileage is lower than a threshold value when the scheduled maintenance date is reached, so that the driving unsafe coefficient is increased, and for the vehicle with smaller traveling mileage, maintenance resource waste can be caused by frequent scheduled maintenance.

Therefore, it is necessary to provide a new method for operating and compiling a vehicle and a vehicle number task assigned to the vehicle so that the driving mileage of the vehicle is performed in the direction of the scheduled inspection date.

Disclosure of Invention

It is an object of embodiments of the present invention to provide a new solution for processing vehicle information.

According to a first aspect of the present invention, there is provided a vehicle information processing method including:

acquiring vehicle information and a vehicle number task distributed aiming at the vehicle;

obtaining a selected feature vector, wherein the feature vector comprises a plurality of features influencing mileage of a vehicle executing the vehicle number task, and the plurality of features comprise vehicle features and vehicle number features;

acquiring a mapping function between the feature vector and the driving mileage;

obtaining the predicted mileage of the vehicle for executing the vehicle number task according to the mapping function and the vector value of the feature vector;

and obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage.

Optionally, the vehicle characteristic includes at least one of a vehicle number and a vehicle type; and/or the train number characteristics include at least one of a departure time, an arrival time, a departure location, and an arrival location.

Optionally, the plurality of features further comprises a cross-over feature, wherein the cross-over feature is a feature that cross-correlates vehicles with the train number assignment, the cross-over feature comprising a service assignment; the service tasks include at least one of: inspection tasks, repair tasks, maintenance tasks, cleaning tasks.

Optionally, the step of obtaining a mapping function between the feature vector and the mileage includes:

obtaining training samples according to historical operating data, wherein each training sample comprises matched vehicles and actually executed vehicle number tasks;

and training to obtain the mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

Optionally, the step of obtaining the mapping function by training includes:

determining a mileage prediction expression of each training sample by taking undetermined coefficients of the mapping function as variables and respectively according to vector values of the feature vectors of each training sample;

constructing a loss function according to the mileage prediction expression of each training sample and the actual mileage of each training sample;

and determining the undetermined coefficient according to the loss function, and finishing the training of the mapping function.

Optionally, the step of constructing a loss function includes:

for each training sample, determining a corresponding loss expression according to the mileage prediction expression and the actual mileage;

and summing the loss expressions of each training sample to obtain the loss function.

Optionally, the processing method further includes:

after the vehicle executes the train number task, taking the vehicle and the train number task as new training samples;

and correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

Optionally, the processing method further includes:

and executing the step of training the mapping function according to a preset training period.

Optionally, the step of obtaining a processing result of whether the vehicle executes the vehicle number task according to the predicted mileage includes:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

and obtaining a processing result of the vehicle executing the vehicle number task under the condition that the difference value is smaller than or equal to a preset mileage threshold value.

Optionally, the step of obtaining the target mileage of the vehicle includes:

acquiring the planned mileage of the vehicle and the current actual mileage of the vehicle;

acquiring the number of running days of the current date from the overhaul date of the vehicle; and the number of the first and second groups,

and obtaining the target mileage of the vehicle according to the overhaul plan mileage, the current actual mileage and the operation days.

Optionally, the vehicle information processing method further includes:

detecting whether an event for processing vehicle information occurs;

and when the event occurs, executing the steps of acquiring the vehicle information and the vehicle number task distributed to the vehicle.

Optionally, the event includes at least any one or more of the following:

reaching the preset processing time;

processing operation of receiving externally triggered vehicle information;

and receiving a processing instruction of the vehicle information sent by the terminal equipment.

Optionally, the vehicle information processing method further includes:

and displaying the processing result to carry out vehicle configuration.

According to a second aspect of the present invention, there is provided a vehicle information processing apparatus comprising:

the vehicle task acquisition module is used for acquiring vehicle information and a train number task distributed aiming at the vehicle;

the characteristic vector acquisition module is used for acquiring a selected characteristic vector, wherein the characteristic vector comprises a plurality of characteristics influencing the mileage of the vehicle to execute the vehicle-level task, and the plurality of characteristics comprise vehicle characteristics and vehicle-level characteristics;

the mapping function acquisition module is used for acquiring a mapping function between the feature vector and the driving mileage;

the mileage prediction module is used for obtaining the predicted mileage of the vehicle for executing the vehicle number task according to the mapping function and the vector value of the feature vector; and the number of the first and second groups,

and the execution determining module is used for obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage.

Optionally, the mapping function obtaining module is further configured to:

Optionally, the training to obtain the mapping function includes:

Optionally, the constructing a loss function includes:

Optionally, the processing apparatus further includes:

the new sample determining module is used for taking the vehicle and the train number task as new training samples after the vehicle executes the train number task;

and the function correction module is used for correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample in the corresponding operation day.

Optionally, the mapping function obtaining module is further configured to:

and training the mapping function according to a preset training period.

Optionally, the execution determination module is further configured to:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

Optionally, the obtaining the target mileage of the vehicle includes:

acquiring the number of running days of the current date from the overhaul date of the vehicle;

Optionally, the vehicle information processing apparatus further includes:

the event detection module is used for detecting whether an event for processing the vehicle information occurs or not;

the vehicle task acquisition module is used for acquiring vehicle information and vehicle number tasks distributed for the vehicle under the condition that the event detection module detects that the event occurs.

Optionally, the event includes at least any one or more of the following:

reaching the preset processing time;

processing operation of receiving externally triggered vehicle information;

Optionally, the vehicle information processing apparatus further includes:

and the display module is used for displaying the processing result so as to carry out vehicle configuration.

According to a third aspect of the present invention, there is also provided an electronic device, comprising the processing apparatus of the second aspect of the present invention; or, a memory for storing executable instructions and a processor; the processor is configured to execute the processing method according to any one of the first aspect of the present invention according to the control of the instruction.

According to a fourth aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the processing method according to any one of the first aspects of the present invention.

The method, the device and the electronic equipment have the advantages that after the vehicle information and the vehicle number task allocated by the vehicle are obtained, the mapping function between the feature vector and the driving mileage can be obtained by obtaining the selected feature vector, the predicted mileage of the vehicle for executing the vehicle number task is obtained according to the mapping function and the vector value of the feature vector, and therefore the processing result of whether the vehicle executes the vehicle number task is obtained according to the predicted mileage. And furthermore, the driving mileage of the vehicle can be executed according to the planned maintenance direction, and the mileage of the vehicle is controllable.

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a block diagram of one example of a hardware configuration of an electronic device that may be used to implement an embodiment of the invention;

FIG. 2 is a block diagram of another example of a hardware configuration of an electronic device that may be used to implement an embodiment of the invention;

fig. 3 is a flowchart illustrating a processing method of vehicle information according to a first embodiment of the present invention;

fig. 4 is a flowchart illustrating a processing method of vehicle information according to a second embodiment of the present invention;

fig. 5 is a flowchart illustrating a processing method of vehicle information according to a third embodiment of the present invention;

fig. 6 is a flowchart illustrating a processing method of vehicle information according to a fourth embodiment of the present invention;

fig. 7 is a flowchart illustrating a processing method of vehicle information according to a fifth embodiment of the present invention;

fig. 8 is a flowchart illustrating an example of a vehicle information processing method according to an embodiment of the invention;

fig. 9 is a functional block diagram of a vehicle information processing apparatus according to an embodiment of the invention;

FIG. 10 is a functional block diagram of an electronic device provided in accordance with a first embodiment of the invention;

fig. 11 is a schematic diagram of a hardware structure of an electronic device according to a second embodiment of the present invention.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< hardware configuration >

Fig. 1 and 2 are block diagrams of hardware configurations of an electronic apparatus 1000 that can be used to implement a processing method of any embodiment of the present invention.

In one embodiment, as shown in FIG. 1, the electronic device 1000 may be a server 1100.

The server 1100 provides a service point for processes, databases, and communications facilities. The server 1100 can be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.

In this embodiment, the server 1100 may include a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, and an input device 1160, as shown in fig. 1.

In this embodiment, the server 1100 may also include a speaker, a microphone, and the like, which are not limited herein.

The processor 1110 may be a dedicated server processor, or may be a desktop processor, a mobile processor, etc. that meets performance requirements, without limitation, the memory 1120 includes, for example, ROM (read only memory), RAM (random access memory), a non-volatile memory such as a hard disk, etc., the interface device 1130 includes, for example, various bus interfaces such as a serial bus interface (including a USB interface), a parallel bus interface, etc., the communication device 1140 is capable of wired or wireless communication, the display device 1150 is, for example, a liquid crystal display, L ED display touch screen, etc., the input device 1160 may include, for example, a touch screen, a keyboard, etc.

In this embodiment, the memory 1120 of the server 1100 is configured to store instructions for controlling the processor 1110 to operate to perform at least a processing method of vehicle information according to any of the embodiments of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

Although shown as multiple devices in fig. 1, the present invention may relate to only some of the devices, e.g., server 1100 may relate to only memory 1120 and processor 1110.

In one embodiment, the electronic device 1000 may be a terminal device 1200 such as a PC, a notebook computer, or the like used by an operator, which is not limited herein.

In this embodiment, referring to fig. 2, the terminal apparatus 1200 may include a processor 1210, a memory 1220, an interface device 1230, a communication device 1240, a display device 1250, an input device 1260, a speaker 1270, a microphone 1280, and the like.

The processor 1210 may be a mobile version processor, the memory 1220 may include, for example, a ROM (read only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc., the interface device 1230 may include, for example, a USB interface, a headset interface, etc., the communication device 1240 may, for example, be capable of wired or wireless communication, the communication device 1240 may include a short-range communication device, for example, any device capable of short-range wireless communication based on a short-range wireless communication protocol such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, L iFi, etc., the communication device 1240 may also include a remote communication device, for example, any device capable of W L AN, GPRS, 2G/3G/4G/5G remote communication, the display device 1250, for example, a liquid crystal display, touch display, etc., the input device 1260 may include, for example, a touch screen, keyboard, etc., the user may input/output voice information through the speaker 1270 and the microphone 1280.

In this embodiment, the memory 1220 of the terminal device 1200 is used to store instructions for controlling the processor 1210 to operate to perform at least a processing method of vehicle information according to any of the embodiments of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

Although a plurality of devices of the terminal apparatus 1200 are shown in fig. 2, the present invention may relate only to some of the devices, for example, the terminal apparatus 1200 relates only to the memory 1220 and the processor 1210 and the display device 1250.

< method examples >

Fig. 3 is a flowchart illustrating a method of processing vehicle information, which may be implemented by an electronic device, according to an embodiment of the present invention. The electronic device may be the server 1100 shown in fig. 1 or the terminal device 1200 shown in fig. 2.

As shown in fig. 3, the method for processing vehicle information according to the present embodiment may include steps S3100 to S2400:

in step S3100, vehicle information and a vehicle number task assigned to the vehicle are acquired.

The vehicle in the embodiment may be a bus, a subway, a light rail, a train, or the like that travels along a predetermined route. Specifically, the vehicle may be any vehicle that performs a task back to the yard in the evening before the operation day to be compiled. For example, the day of operation to be orchestrated is 2018.12.14, and the set of vehicles may include any vehicle that performed a task back to the yard 2018.12.13 evening.

The train number task may be one of the tasks assigned to the vehicle in a train number list of the operating day schedule to be compiled.

In step S3100, vehicle information and a vehicle number task assigned to the vehicle may be acquired from a terminal device in an operation site. Specifically, the operator may enter the vehicle information and the vehicle number task assigned to the vehicle into the terminal device. After the operator enters the train number list and the yard route list into the terminal device, the electronic device selects one of the vehicles, acquires the information of the vehicle and the train number task allocated to the vehicle, and executes the processing method of the embodiment.

In this embodiment, the terminal device in the operation site and the electronic device executing the processing method of this embodiment may be the same or different.

In one embodiment, the processing method may further include: it is detected whether an event for processing the vehicle information has occurred, and if the event has occurred, the step of step S3100 is executed.

Specifically, the event may include any one or more of the following:

reaching the preset processing time;

processing operation of receiving externally triggered vehicle information;

In the embodiment where the event includes reaching the preset processing time, the preset processing time may be set according to an application scenario or a specific requirement. For example, the preset treatment time may be 5 am per day. Then, the processing method of the present invention may be performed at 5 am per day.

In an embodiment where the event includes a processing operation of receiving externally triggered vehicle information, the processing operation of the vehicle information may be triggered by an operator directly on the electronic device executing the processing method of the embodiment. The electronic device may execute the processing method of the present invention upon receiving the processing operation of the vehicle information.

In the embodiment where the event includes receiving a processing instruction of the vehicle information sent by the terminal device, the processing instruction may be that the operator performs a processing operation on the terminal device, and triggers the terminal device to send the processing instruction of the vehicle information to the electronic device executing the processing method of the embodiment. The electronic device may be configured to execute the processing method of the present invention when receiving the processing execution.

Step S3200, obtaining a selected feature vector, where the feature vector includes a plurality of features that affect the mileage of the vehicle executing the vehicle number task, and the plurality of features include vehicle features and vehicle number features.

The feature vector X includes a plurality of features X that affect the mileage of the vehicle performing the vehicle number task_jJ takes a natural number from 1 to n, and n represents the total number of features of the feature vector X.

The plurality of features x_jMay include vehicle featuresAnd a train number characteristic.

The vehicle characteristic may be determined from vehicle information. The vehicle characteristic may be at least one of a vehicle number and a vehicle type. The vehicle number may be composed of numbers, letters, and/or words, etc. For example, the vehicle number may be a license plate number.

The train number characteristic may include at least one of a departure time, an arrival time, a departure location, and an arrival location.

In this example, x_jThe feature may be a vehicle feature, a vehicle number feature, a vehicle type feature, or the like that can affect the mileage of the vehicle performing the vehicle-number task, for example, the vehicle feature may be a vehicle number and a vehicle type, and the vehicle-number feature may be a departure time, an arrival time, a departure point, and an arrival point, where the feature vector X may have 6 features, that is, n is 6, and in this case, the feature vector X may be represented as X (X is 6)₁,x₂,x₃,x₄,x₅,x₆). Of course, other features related to vehicle configuration may also be included in the feature vector X.

The above other feature may be a cross-feature, which is a feature that cross-correlates the vehicle with the assigned vehicle-number task. The cross-over feature includes a service task that may include at least one of an inspection task, a repair task, a maintenance task, a cleaning task. For example, the vehicle may be subjected to minor repair operation on the operation day to be compiled, or may be subjected to water washing operation on the operation day to be compiled, or may be subjected to wheel grinding operation on the operation day to be compiled, and the like, and the operation is not limited herein.

And step S3300, obtaining a mapping function between the feature vector and the driving range.

The independent variable of the mapping function F (X) is the feature vector X, and the dependent variable F (X) is the predicted mileage determined by the feature vector X.

In this embodiment, the step S3300 of obtaining the mapping function between the feature vector and the mileage may further include steps S3310 to S3320 shown in fig. 4:

step S3310, obtain the training sample according to the historical operating data.

Each training sample includes the paired vehicle and the number of vehicle tasks actually performed.

Step S3320, training to obtain a mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

In one embodiment, the steps S3310 to S3320 of training the mapping function may be performed according to a preset training period. The training period may be set according to a specific application scenario or application requirements, and may be set to 1 day, for example.

In this embodiment, the mapping function f (x) may be obtained by various fitting means based on the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample, for example, the mapping function f (x) may be obtained by using an arbitrary multiple linear regression model, which is not limited herein.

In one example, the multiple linear regression model may be a simple polynomial function reflecting the mapping function f (x), wherein each order coefficient of the polynomial function is unknown, and each order coefficient of the polynomial function may be determined by substituting the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample into the polynomial function, thereby obtaining the mapping function f (x).

In another example, various regression models, such as an additive model, may be used to perform multiple rounds of training with the vector values of the feature vectors of the training samples and the actual mileage corresponding to the training samples as accurate samples, each round learns the residual after the last round of fitting, and the residual is controlled to a very low value by iterating T rounds, so that the resulting mapping function f (x) has very high accuracy.

In one embodiment, as shown in fig. 5, the mapping function trained in the above step S3320 may further include the following steps S3321 to S3323:

step S3321, determining the mileage prediction expression of each training sample according to the vector value of the feature vector of each training sample by taking the undetermined coefficient of the mapping function as a variable.

Assume that the feature vector X in the mapping function includes n features X₁,x₂,......,x_nDetermining the value of the k training sample for n features

Then, the undetermined coefficient set comprises a constant weight b and n characteristic weights a₁,a₂,......,a_nAs a variable, the prediction expression of the mileage of the kth training sample can be obtained as Y_k:

Step S3322, a loss function is constructed according to the mileage prediction expression of each training sample and the actual mileage of each training sample.

In this embodiment, the constructing the loss function in step S3322 may further include steps S3322-1 to S3322-2 as shown in fig. 6:

and step S3322-1, determining a corresponding loss expression according to the mileage prediction expression and the actual mileage for each training sample.

Assuming that the number of the collected training samples is m, the obtained actual mileage is y for the k training sample_kThe mileage prediction expression is Y_kThe corresponding loss expression is (y)_k-Y_k)²(k ═ 1.., m); wherein the content of the first and second substances,

and step S3322-2, summing the loss expressions of each training sample to obtain a loss function.

In this embodiment, the loss function is:

wherein the content of the first and second substances,

and step S3323, determining the undetermined coefficient according to the loss function, and finishing the training of the mapping function.

In this embodiment, in the step S3323, the undetermined coefficient is determined according to the loss function, and the completion of the training of the mapping function may further include steps S3323-1 to S3323-3:

step S3323-1, setting constant weights in the undetermined coefficient set and initial values of each characteristic weight as random numbers in a preset numerical range.

Assume a set of pending coefficients b, a₁,a₂,......,a_nComprises a constant weight b and n characteristic weights a₁,a₂,......,a_nThe initial value may be set to a random number of a preset numerical range. The preset value range may be set according to an application scenario or an application requirement, for example, the preset value range is set to 0-1, such that the constant weight b and the n feature weights a₁,a₂,......,a_nAre all random numbers between 0-1.

Step S3323-2, substituting the constant weight and each characteristic weight after the initial value is set into the loss function, and performing iterative processing.

In this embodiment, the step S3323-2 of substituting the constant weight and each feature weight after setting the initial value into the loss function may further include the following steps S3323-21 to S3323-22:

and S3323-21, respectively obtaining the value of the constant weight or the characteristic weight after the corresponding iteration according to the constant weight or the value and the convergence parameter of the characteristic weight before the current iteration and the loss function substituted into the undetermined coefficient set before the current iteration for the constant weight and each characteristic weight.

The convergence parameter is a relevant parameter for controlling the convergence speed of the iterative process, and may be set according to an application scenario or an application requirement, for example, to 0.01.

And S3323-22, obtaining a undetermined coefficient set after the iteration according to the constant weight and the value after the iteration of each characteristic weight.

Assuming that the iteration is the (k + 1) th iteration (the initial value of k is 0, and 1 is added along with each iteration), the undetermined coefficient set after the iteration is the { b, a₁,a₂,...,a_n}^(k+1)。

And S3323-3, when the undetermined coefficient set obtained by the iterative processing meets the convergence condition, terminating the iterative processing, and determining the constant weight of the undetermined coefficient set and the value of each characteristic weight, otherwise, continuing the iterative processing.

The convergence condition may be set according to a specific application scenario or application requirements.

For example, the convergence condition is that the number of iterative processes is greater than a preset number threshold. The preset time threshold may be set according to engineering experience or experimental simulation results, and may be set to 300, for example. Correspondingly, assuming that the number of iterative processes is k +1, the number threshold is itemnams, and the corresponding convergence condition is: k is not less than itemNums.

For another example, the convergence condition is that an iteration result value of the undetermined coefficient set obtained by the iteration processing is smaller than a preset result threshold. The iteration result value is determined according to the result of partial derivation of the loss function substituted by the undetermined coefficient set obtained by iteration processing and the corresponding constant weight or each characteristic weight.

In an example, the convergence condition is that any one of the convergence conditions in the two examples is satisfied, and the specific convergence condition has been described in the two examples and is not described herein again.

Suppose that the undetermined coefficient set { b, a obtained by the (k + 1) th iteration processing₁,a₂,...,a_n}^(k+1)When the convergence condition is met, stopping the iterative processing to obtain all the corresponding a_i ^(k+1)(i ═ 1.., n) and b^(k+1)And taking values, otherwise, continuing the iterative processing until the undetermined coefficient set meets the convergence condition.

According to the embodiment of the invention, the mapping function can be obtained according to a large number of training samples, so that when the mapping function is used for determining the predicted mileage, the accuracy of the obtained predicted mileage can be improved.

And step S3400, obtaining the predicted mileage of the vehicle for executing the vehicle task according to the mapping function and the vector value of the feature vector.

The vector value may specifically be a value of a feature vector.

In this embodiment, a mapping function between the feature vector and the driving range is obtained according to step S3300, and the vector value is substituted into the mapping function f (x) according to the vector value of the feature vector, so as to obtain the predicted mileage of the vehicle for executing the vehicle task.

According to the embodiment of the invention, the predicted mileage of the vehicle for executing the vehicle number task can be obtained according to the feature vector and the mapping function, and the mapping function is obtained according to a large number of training samples, so that when the predicted mileage is determined by using the mapping function, the accuracy of the obtained predicted mileage can be improved.

And step S3500, obtaining the processing result of whether the vehicle executes the train number task or not according to the predicted mileage.

In one embodiment, the step S3500 may further include steps S3510-S3530 as shown in fig. 7:

and S3510, acquiring the target mileage of the vehicle.

The target mileage of the vehicle may be the daily average mileage traveled by the vehicle plan, and when the vehicle travels in accordance with the target mileage, the travel of the vehicle can be performed in the planned inspection direction, i.e., when the specified inspection date is reached, the vehicle has substantially traveled the planned inspection mileage.

The planned mileage for inspection of the vehicle is the mileage specified for travel of the vehicle when the inspection date of the vehicle is reached. For example, the specified inspection date is 2018.12.30, the planned mileage of the vehicle is 10000 km, and when the vehicle travels, if the vehicle travels in accordance with the target mileage, the vehicle can be made to travel almost 10000 km by the time the vehicle arrives at the specified inspection date 2018.12.30.

In one embodiment, obtaining the target mileage of the vehicle may further include the following steps S3511 to S3513:

step S3511, the planned mileage of the vehicle for maintenance and the current actual mileage of the vehicle are obtained.

The planned mileage for inspection of the vehicle is the mileage specified for travel of the vehicle when the inspection date of the vehicle is reached. For example, the specified inspection date is 2018.12.30, and the planned mileage of the vehicle is 10000 km, that is, when the specified inspection date 2018.12.30 is reached, it is necessary to ensure that the vehicle has traveled substantially 10000 km.

The current actual mileage of the vehicle is the total traveled mileage of the vehicle before the operating day to be compiled. Taking the operation day to be compiled as 2018.12.14 as an example, the current actual mileage of the vehicle obtained according to the step S4100 may be 2000 km, i.e. the total mileage traveled by the vehicle before 2018.12.14 (excluding 2018.12.14) is 2000 km.

In one example, the present actual mileage of the vehicle may be actively transmitted to the electronic device executing the method of the present invention by the terminal device on site after the vehicle has performed the task back to the yard.

In one example, after responding to the processing request for the vehicle and the vehicle number task, the electronic device may request the terminal device on site to obtain the current actual mileage of the vehicle.

Step S3512, the number of running days of the current date from the inspection date of the vehicle is acquired.

Still taking the specified inspection date of 2018.12.30 and the operation date of 2018.12.14 to be compiled as an example, the planned mileage of the vehicle obtained according to step S3511 is 10000 km, and the current actual mileage of the vehicle is 2000 km, and the number of days of operation of the inspection date of the vehicle from the current date obtained according to step S3512 is 16 days.

And S3513, obtaining the target mileage of the vehicle according to the mileage of the maintenance plan, the current actual mileage and the running days.

In this embodiment, the calculation formula of the target mileage of the vehicle is as follows:

wherein M represents the target mileage of the vehicle, b₂Representing the scheduled mileage of the vehicle, b₁Indicating the current actual mileage of the vehicle and day indicating the number of days of operation of the current date from the inspection date of the vehicle.

Still taking the specified inspection date of 2018.12.30 and the operation date of 2018.12.14 to be compiled as an example, the planned mileage of the vehicle is 10000 km according to the inspection schedule obtained in step S3513, the current actual mileage of the vehicle is 2000 km, the number of operation days from the current date to the inspection date of the vehicle is 16 days according to step S3512, and the target mileage of the vehicle is obtained according to step S3513

That is, the target mileage of the vehicle is 500 kilometers, it can also be understood that the planned travel mileage of the vehicle at 2018.12.14 is 500 kilometers, and after the vehicle has executed the vehicle number task at 2018.12.14 according to the configuration result, when 2018.12.15 is configured, the mileage of the vehicle actually running at 2018.12.14 for executing the vehicle number task is added, and the target mileage of the vehicle at 2018.12.15 days is recalculated to improve the accuracy of the target mileage of the vehicle at each operating day, so that the current actual mileage gradually converges to the maintenance planned mileage during the period when the vehicle reaches the set maintenance date.

Step S3520, calculating a difference between the predicted mileage and the target mileage of the vehicle.

And S3530, obtaining a processing result of the vehicle executing the assigned vehicle number task under the condition that the difference value is smaller than or equal to the preset mileage threshold value.

According to the embodiment of the invention, the overhaul plan mileage of the vehicle and the current actual mileage of the vehicle are obtained, the running days of the current date from the overhaul date of the vehicle are obtained, the target mileage of the vehicle on the running day to be compiled is obtained according to the overhaul plan mileage, the current actual mileage of the vehicle and the running days, and the difference value between the target mileage and the predicted mileage is calculated. In the case that the difference value is smaller than or equal to the preset mileage threshold value, a processing result that the vehicle can execute the assigned vehicle number task can be obtained. When the difference is greater than the mileage threshold, a processing result that the vehicle cannot execute the assigned vehicle number task may be obtained, and other vehicle number tasks need to be assigned to the vehicle again, and the processing method of the embodiment is executed again until it is determined that the vehicle can execute the reassigned vehicle number task.

The mileage threshold may be set according to a specific application scenario or application requirements, and may be set to 1 km, for example.

In one embodiment, after step S3500 is executed, the processing method may further include: and displaying the processing result so as to carry out vehicle configuration.

Specifically, the electronic device executing the processing method of the present embodiment may have a display screen, and after step S3500 is executed, the processing result may be displayed on the electronic device. The electronic device executing the processing method of the present embodiment may also send the processing result to other display devices (for example, a terminal device used by an operator) for presentation.

The operator can compile the vehicle according to the processing result. For example, in a case where the processing result is that the vehicle executes the assigned vehicle-number task, the vehicle-number task may be issued to the vehicle so that the vehicle executes the vehicle-number task. In the case where the processing result is that the vehicle does not execute the assigned vehicle number task, the vehicle number task may be newly assigned to the vehicle until the processing result that the vehicle executes the assigned vehicle number task is obtained.

In another embodiment of the present invention, steps S3200-S3400 may be performed on the vehicle and each of the vehicle-class tasks corresponding to the vehicle in the vehicle-class set, so as to obtain the predicted mileage of each of the vehicle-class tasks. Then, step S3500 may further be: and obtaining a processing result of whether the vehicle executes each train number task in the train number set or not according to the deviation of each predicted mileage and the target mileage. Specifically, an optimal vehicle-number task may be selected from the vehicle-number set according to a deviation between each predicted mileage and the target mileage, and then the processing result may be that the vehicle executes the optimal vehicle-number task without executing other vehicle-number tasks.

The train number set comprises all train number tasks in a train number list arranged for the operation days to be compiled, and here, the train number list of the operation days to be compiled can be obtained, so that all train number tasks of the operation days to be compiled are obtained from the train number list to form the train number set.

In one embodiment, the set of vehicle numbers may be obtained from a terminal device at the operation site. Here, the operator can input the train number list into the terminal device, and the electronic device can obtain the train number set according to the train number list.

In this embodiment, the vehicle-number task that meets the execution condition of the vehicle may be screened from the vehicle-number set as the vehicle-number task corresponding to the vehicle. Further, the vehicle-number task meeting the execution condition of the vehicle can be screened according to the state information of the vehicle and the set constraint condition.

The state information of the vehicle may be determined based on the vehicle information. In the case where the vehicle of the present embodiment is a train such as a subway, a train, a light rail, or the like, the above state information may include at least one of a parked station track position, a parked station track type, or the like.

The parked track location is used to indicate the parking location of the train within the yard track. For example, in the case of a subway, each track has a track number, and the track number may be a number or a letter, as long as different tracks can be distinguished, and is not limited herein. At most two trains can be parked on each track, which can be respectively distinguished by letters "S" and "N" in the north and south directions on the same track, for example, the track number of the parked train is 23, and at this time, the position of the parked track can be 23N, which indicates that the train is parked in the north direction of the track with the track number of 23.

According to the method, the vehicle-number tasks which can be distributed to the vehicle, namely the vehicle-number tasks matched with the vehicle, are screened according to the state information of the vehicle, so that the vehicle-number tasks which are basically impossible to be matched with the vehicle in the vehicle-number set can be filtered, and the efficiency and the accuracy of obtaining the vehicle-number tasks corresponding to the vehicle are improved.

Obtaining an optimal vehicle number task corresponding to the vehicle according to the deviation between each predicted mileage and the target mileage, and specifically, the step of executing the optimal vehicle number task by the vehicle may be: and taking the train number task corresponding to the predicted mileage with the minimum deviation as the optimal train number task.

For example, after the train number tasks corresponding to the vehicle include the first train number task, the second train number task, … …, L th train number task, and the aforementioned steps S3200-S3400 are performed for the vehicle and each train number task, the predicted mileage for the vehicle to perform the first train number task is S1, the predicted mileage for the vehicle to perform the second train number task is S2, … …, the predicted mileage for the vehicle to perform the L th train number task is S L, the difference between each predicted mileage and the mileage threshold S is calculated, the difference between the predicted mileage S1 and the mileage threshold S is D1, the difference between the predicted mileage S2 and the mileage threshold S is D2, … …, the difference between the predicted mileage S L and the mileage threshold S is D L, and the difference between the predicted mileage S1 and D L is D L, if the difference is the minimum, the predicted mileage is D4, the predicted mileage is the fourth train number task may be performed, and the fourth train number task is determined as the predicted mileage task S4.

According to the method of the embodiment, after the vehicle information and the vehicle-class task allocated by the vehicle are obtained, the mapping function between the feature vector and the driving mileage is obtained by obtaining the selected feature vector, the predicted mileage of the vehicle for executing the vehicle-class task is obtained according to the mapping function and the vector value of the feature vector, and therefore the processing result of whether the vehicle executes the vehicle-class task is obtained according to the predicted mileage. And furthermore, the driving mileage of the vehicle can be executed according to the planned maintenance direction, and the mileage of the vehicle is controllable.

In one embodiment, after step S3500 is executed, the method for processing vehicle information may further include the following steps S3600-S3700:

step S3600, after the vehicle executes the train number task, taking the vehicle and the train number task as new training samples.

Step S3700, revise the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

According to the embodiment of the invention, after the vehicle executes the train number task, the actual mileage of the vehicle executing the train number task can be obtained, the vehicle and the train number task are used as new training samples, the mapping function is corrected, namely the new training samples are added, and the mapping function is retrained, so that the mileage prediction is more and more accurate.

< example >

FIG. 8 is an exemplary method of processing vehicle information that may include the steps of:

and step S8100, vehicle information and the vehicle number task distributed to the vehicle are obtained.

Step S8200, the selected feature vector is obtained.

Step S8310, training samples are obtained according to the historical operating data.

Step S8320, the undetermined coefficient of the mapping function is taken as a variable, and the mileage prediction expression of each training sample is determined according to the vector value of the feature vector of each training sample.

Step S8330, for each training sample, determining a corresponding loss expression according to the mileage prediction expression and the actual mileage.

Step S8340, the loss expressions of each training sample are summed to obtain a loss function.

And S8350, determining the undetermined coefficient according to the loss function, and finishing the training of the mapping function.

And step S8400, obtaining the predicted mileage of the vehicle for executing the vehicle task according to the mapping function and the vector value of the feature vector.

And step S8510, acquiring the planned mileage of the vehicle and the current actual mileage of the vehicle.

Step S8520, the running days between the current date and the inspection date of the vehicle are obtained.

And step S8530, obtaining the target mileage of the vehicle according to the overhaul plan mileage, the current actual mileage and the operation days.

Step S8610, a difference between the predicted mileage and the target mileage of the vehicle is calculated.

And step S8620, obtaining a processing result of the vehicle executing the distributed vehicle number task under the condition that the difference value is less than or equal to the preset mileage threshold value.

< apparatus embodiment >

Fig. 9 is a functional block diagram of a vehicle information processing device 9000 according to an embodiment of the present invention.

As shown in fig. 9, the vehicle information processing apparatus 9000 of the present embodiment may include a vehicle task obtaining module 9100, a feature vector obtaining module 9200, a mapping function obtaining module 9300, a mileage predicting module 9400, and an execution determining module 9500. The vehicle task obtaining module 9100 is used for obtaining vehicle information and vehicle number tasks allocated for vehicles; the feature vector obtaining module 9200 is configured to obtain a selected feature vector, where the feature vector includes a plurality of features that affect the mileage of a vehicle to execute a vehicle-level task, and the plurality of features include vehicle features and vehicle-level features; the mapping function obtaining module 9300 is configured to obtain a mapping function between the feature vector and the mileage; the mileage prediction module 9400 is configured to obtain a predicted mileage of a vehicle to execute a vehicle number task according to the mapping function and the vector value of the feature vector; the execution determination module 9500 is configured to obtain a processing result of whether the vehicle executes the vehicle number task according to the predicted mileage.

In one embodiment, the vehicle characteristics may include at least one of a vehicle number and a vehicle type; and/or, the train number characteristic includes at least one of a departure time, an arrival time, a departure location, and an arrival location.

In one embodiment, the plurality of features further includes a cross-over feature, wherein the cross-over feature is a feature that cross-correlates the vehicle with a vehicle number task, the cross-over feature including a service task.

In one embodiment, the mapping function obtaining module 9300 is further configured to:

and training to obtain a mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

In this embodiment, training the mapping function may include:

determining a mileage prediction expression of each training sample by taking undetermined coefficients of the mapping function as variables and respectively according to the vector value of the feature vector of each training sample;

In this embodiment, the step of constructing the loss function may include:

and summing the loss expressions of each training sample to obtain a loss function.

In this embodiment, the operation orchestration device 9000 may further include a new sample determination module and a function modification module (not shown in the figure). The new sample determining module is used for taking the vehicle and the train number task as new training samples after the vehicle executes the train number task; the function correction module is used for correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample in the corresponding operation day.

In this embodiment, the mapping function obtaining module 9300 can be further configured to:

In one embodiment, the execution determination module 9500 may also be configured to:

acquiring a target mileage of a vehicle;

calculating a difference between the predicted mileage and the target mileage;

and obtaining a processing result of the vehicle executing the vehicle number task under the condition that the difference value is less than or equal to the preset mileage threshold value.

In this embodiment, the step of obtaining the target mileage of the vehicle includes:

acquiring the number of running days between the current date and the overhaul date of the vehicle;

and obtaining the target mileage of the vehicle according to the mileage of the maintenance plan, the current actual mileage and the operation days.

In one embodiment, the vehicle information processing apparatus further includes an event detection module (not shown in the figure) for detecting whether an event for processing the vehicle information occurs. The vehicle task obtaining module 9100 is configured to obtain vehicle information and a vehicle-number task assigned to the vehicle when the event detecting module detects that the event occurs.

Further, the event includes at least any one or more of:

reaching the preset processing time;

processing operation of receiving externally triggered vehicle information;

In one embodiment, the vehicle information processing apparatus may further include: and the display module (not shown in the figure) is used for displaying the processing result so as to carry out vehicle assembly.

< electronic device embodiment >

In this embodiment, an electronic device 1000 is also provided, where the electronic device 1000 may be the server 1100 shown in fig. 1, or may be the terminal device 1200 shown in fig. 2.

As shown in fig. 10, the electronic device 1000 may include a processing device 9000 of the vehicle information according to any embodiment of the present invention for implementing a processing method of the vehicle information according to any embodiment of the present invention.

In another embodiment, as shown in fig. 11, the electronic device 1000 may further comprise a processor 1300 and a memory 1400, the memory 1400 for storing executable instructions; the processor 1300 is configured to operate the electronic device 1000 to perform a processing method of vehicle information according to any embodiment of the present invention according to the control of the instruction.

< computer-readable storage Medium >

In the present embodiment, there is also provided a computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, realizing the processing method of the vehicle information according to any of the embodiments of the present invention.

The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

Computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including AN object oriented programming language such as Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method of processing vehicle information, comprising:

obtaining a selected feature vector, wherein the feature vector comprises a plurality of features affecting mileage of the vehicle performing the vehicle number task, and the plurality of features comprise vehicle features and vehicle number features;

2. The processing method of claim 1, wherein the vehicle characteristic comprises at least one of a vehicle number and a vehicle type; and/or the train number characteristics include at least one of a departure time, an arrival time, a departure location, and an arrival location.

3. The process of claim 1, wherein the plurality of features further comprises a cross-over feature, wherein the cross-over feature is a feature that cross-correlates vehicles with the train number assignment, the cross-over feature comprising a service assignment; the service tasks include at least one of: inspection tasks, repair tasks, maintenance tasks, cleaning tasks.

4. The processing method according to claim 1, wherein the step of obtaining a mapping function between the feature vector and a mileage includes:

5. The process of claim 4, wherein said step of training to derive said mapping function comprises:

6. The process of claim 5, wherein the step of constructing a loss function comprises:

7. The processing method of claim 4, wherein the processing method further comprises:

8. The processing method of claim 4, wherein the processing method further comprises:

9. The processing method according to claim 1, wherein the step of obtaining a processing result of whether the vehicle executes the vehicle number task according to the predicted mileage includes:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

10. The process of claim 9, wherein the step of obtaining the target mileage of the vehicle comprises:

11. The processing method according to claim 1, wherein the vehicle information processing method further includes:

detecting whether an event for processing vehicle information occurs;

12. The processing method of claim 11, wherein the event comprises at least any one or more of:

reaching the preset processing time;

processing operation of receiving externally triggered vehicle information;

13. The processing method according to claim 1, wherein the vehicle information processing method further includes:

and displaying the processing result to carry out vehicle configuration.

14. A processing apparatus of vehicle information, comprising:

the feature vector acquisition module is used for acquiring a selected feature vector, wherein the feature vector comprises a plurality of features influencing mileage of the vehicle to execute the vehicle-level task, and the plurality of features comprise vehicle features and vehicle-level features;

15. An electronic device comprising the processing apparatus of claim 14; or, a memory for storing executable instructions and a processor; the processor is configured to execute the processing method according to any one of claims 1 to 13 in accordance with control of the instruction.

16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a processing method according to any one of claims 1 to 13.