CN115730004A

CN115730004A - Method, device, equipment and storage medium for constructing automobile driving condition curve

Info

Publication number: CN115730004A
Application number: CN202211505452.6A
Authority: CN
Inventors: 李琦
Original assignee: Chongqing Changan Automobile Co Ltd
Current assignee: Chongqing Changan Automobile Co Ltd
Priority date: 2022-11-28
Filing date: 2022-11-28
Publication date: 2023-03-03

Abstract

The invention provides a method, a device, equipment and a storage medium for constructing a vehicle running condition curve, wherein a speed change curve is divided according to a preset speed reference value to obtain a plurality of kinematic segments; clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions; inputting a vehicle running data group under various running conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions; and splicing the speed change prediction curves under various working conditions to obtain an automobile running condition curve. According to the invention, the vehicle running data families under various running conditions are obtained by clustering the plurality of kinematic segments based on the running condition data, and then the vehicle running condition curve is obtained based on the vehicle running data families under various running conditions. The road running condition of the vehicle under various working conditions can be comprehensively reflected by considering various working conditions of the vehicle in running.

Description

Method, device, equipment and storage medium for constructing automobile driving condition curve

Technical Field

The application relates to the field of vehicle driving data processing, in particular to a method, a device, equipment and a storage medium for constructing a vehicle driving condition curve.

Background

The automobile running condition is a curve describing the relation between the automobile running speed and time, and the real road running condition of the automobile can be reflected by using the kinematic characteristics of the average automobile running speed, the average automobile running acceleration and the like in the curve. The method can well represent the motion rule of a certain vehicle type, a certain driving area and a certain driving condition. The method is an important and common basic technology in the automobile industry, is the basis of a vehicle energy consumption/emission test method and a limit value standard, and is also a main reference for calibrating and optimizing various performance indexes of an automobile.

The existing method for constructing the running condition data of the automobile is mainly a working condition construction mode with mathematical scientific derivation. The main construction idea is as follows: a large amount of real road vehicle driving data are collected in advance, and a working condition with statistical significance is constructed through a series of mathematical processing. However, when the existing method for constructing the automobile driving condition data is used for clustering the original data, the driving condition characteristics of the vehicle are not fully considered, so that the finally obtained working condition data cannot comprehensively reflect the road driving conditions of the vehicle under various working conditions.

Disclosure of Invention

In view of the above drawbacks of the prior art, the present invention provides a method, an apparatus, a device and a storage medium for constructing a driving condition curve of an automobile, so as to solve the above technical problems.

The invention provides a method for constructing a running condition curve of an automobile, which comprises the following steps:

acquiring a speed change curve and running condition data of a vehicle in a running process, wherein the speed change curve is positioned in a two-dimensional coordinate system, one dimension of the two-dimensional coordinate system is time, and the other dimension of the two-dimensional coordinate system is speed;

dividing the speed change curve to obtain a plurality of kinematic segments;

clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions;

inputting the vehicle running data groups under various running conditions into a preset prediction algorithm to obtain speed change prediction curves under various working conditions;

and splicing the speed change prediction curves under various working conditions to obtain an automobile running working condition curve.

In one embodiment of the present invention, obtaining a speed variation curve of a vehicle during driving includes:

the method comprises the steps of obtaining a plurality of moving nodes of a vehicle in the driving process, wherein the moving nodes comprise time data and speed data;

calculating the time data difference value of two adjacent moving nodes; when the time data difference value is smaller than a preset time threshold value, performing interpolation between the two adjacent motion nodes;

filtering the plurality of interpolated motion nodes;

and mapping the plurality of filtered motion nodes to a two-dimensional coordinate system, and fitting the plurality of filtered motion nodes in the two-dimensional coordinate system to obtain a speed change curve of the vehicle in the running process.

In an embodiment of the present invention, the dividing the speed variation curve according to a preset speed reference value to obtain a plurality of kinematic segments includes:

taking the motion node with the speed value equal to the speed reference value as an idle node;

and extracting a speed change curve between two adjacent idle speed nodes to obtain a plurality of kinematic segments.

In an embodiment of the present invention, clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data population under a plurality of driving conditions includes:

clustering the plurality of kinematic segments according to a preset speed range to obtain a plurality of segment sets;

dividing the kinematic segments in the segment set according to the driving condition data to obtain a plurality of working condition segments;

clustering the same working condition fragments to obtain a model event;

extracting the kinematic characteristics in the model event, and dividing the working condition segments in the model event according to the kinematic characteristics to obtain various kinematic characteristic nodes;

and clustering the motion characteristic nodes with the same kinematic characteristics to obtain a vehicle driving data group under various driving conditions.

In an embodiment of the present invention, the dividing the kinematic segment in the segment set according to the driving condition data to obtain multiple working condition segments includes:

carrying out principal component analysis on the driving condition data of the kinematic segments in the segment set to obtain the characteristic of the maximum speed contribution degree of the kinematic segments in the segment set;

obtaining the working condition type of the vehicle according to the characteristic with the maximum speed contribution degree and a preset working condition type mapping table, wherein the working condition mapping table comprises the mapping relation between the characteristic and the working condition type, and the working condition type comprises an acceleration working condition, a deceleration working condition, an idle working condition, a constant speed working condition and a staggered acceleration and deceleration working condition;

and dividing the kinematic segments in the segment set according to the working condition types to obtain a plurality of working condition segments.

In an embodiment of the present invention, extracting the kinematic features in the model event includes:

and extracting the kinematic characteristics of the working condition segments in the model events, wherein the kinematic characteristics comprise one or more of average speed, average acceleration, average deceleration, acceleration time ratio and deceleration time ratio.

In an embodiment of the present invention, the step of inputting the vehicle driving data groups under the multiple driving conditions into a preset prediction algorithm to obtain the speed change prediction curves under the multiple driving conditions includes:

constructing an observable sequence by using vehicle driving data groups under various working conditions;

and inputting the observable sequence and the preset observation probability matrix into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions.

In an embodiment of the present invention, the splicing the speed change prediction curves under the multiple working conditions to obtain an automobile driving condition curve includes:

calculating the ratio of the duration time of the kinematic segment corresponding to the working condition segment to the duration time of the working condition segment to obtain a working condition proportion;

and splicing the speed change prediction curves under the various working conditions based on the working condition proportion to obtain an automobile running working condition curve.

In an embodiment of the present invention, after the speed change prediction curves under the multiple working conditions are spliced to obtain an automobile driving condition curve, the method further includes:

acquiring a plurality of automobile running condition curves;

and carrying out normality inspection on the plurality of automobile running condition curves to obtain a target working condition curve.

The invention also provides a device for constructing the running condition curve of the automobile, which is characterized by comprising the following components:

the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a speed change curve and running condition data of a vehicle in a running process, the speed change curve is positioned in a two-dimensional coordinate system, one dimension of the two-dimensional coordinate system is time, and the other dimension of the two-dimensional coordinate system is speed;

the segmentation module is used for dividing the speed change curve to obtain a plurality of kinematic segments;

the clustering module is used for clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions;

the prediction module is used for inputting the vehicle running data groups under the various running working conditions into a preset prediction algorithm to obtain speed change prediction curves under the various working conditions;

and the splicing module is used for splicing the speed change prediction curves under various working conditions to obtain an automobile driving condition curve.

The present invention also provides an electronic device, including:

one or more processors;

the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic equipment is enabled to realize the automobile driving condition curve construction method.

The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute a method of constructing a driving condition curve of an automobile as described above.

The invention has the beneficial effects that: according to the method, the device, the equipment and the storage medium for constructing the automobile driving condition curve, the speed change curve is divided according to the preset speed reference value to obtain a plurality of kinematic segments; clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions; inputting a vehicle running data group under various running conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions; and splicing the speed change prediction curves under various working conditions to obtain an automobile running condition curve. According to the invention, the vehicle running data families under various running conditions are obtained by clustering the plurality of kinematic segments based on the running condition data, and then the vehicle running condition curve is obtained based on the vehicle running data families under various running conditions. The road running condition of the vehicle under various working conditions can be comprehensively reflected by considering various working conditions of the vehicle in running.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 is an application scenario diagram of a working condition curve construction method according to an exemplary embodiment of the present application

FIG. 2 is a flow chart illustrating a method of operating condition curve construction according to an exemplary embodiment of the present application;

FIG. 3 is a flow chart of step S210 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 4 is a flow chart of step S220 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 5 is a flow chart of step S230 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 6 is a flow chart of step S520 in the embodiment shown in FIG. 5 in an exemplary embodiment;

FIG. 7 is a flowchart of step S540 in the embodiment shown in FIG. 5 in an exemplary embodiment;

FIG. 8 is a flowchart of step S240 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 9 is a flow chart of step S250 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 10 is a flowchart in an exemplary embodiment after step S250 in the embodiment shown in FIG. 2;

FIG. 11 is a block diagram of a behavior curve construction system shown in an exemplary embodiment of the present application;

FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

Detailed Description

Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure herein, wherein the embodiments of the present invention are described in detail with reference to the accompanying drawings and preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, and are not intended to limit the scope of the present invention.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.

In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, to avoid obscuring embodiments of the present invention.

Fig. 1 is an application scene diagram of a method for constructing a running condition curve of an automobile according to an exemplary embodiment of the present disclosure, as shown in fig. 1, in this embodiment, speed data and working condition data of the automobile during running are collected by a vehicle-mounted device system, and specifically, the working condition data at least includes characteristic parameters such as positioning speed, acceleration, engine speed, torque percentage, instantaneous oil consumption, accelerator pedal opening, air-fuel ratio, engine load percentage, and intake air flow. The vehicle-mounted system uploads the acquired speed data and the acquired working condition data to a server through a vehicle-mounted internet for unified storage. And related workers acquire the speed data and the working condition data from the server through an internal local area network or the Internet, and perform operation and processing in the terminal equipment to obtain a vehicle running working condition curve.

The terminal device 110 shown in fig. 1 is any device that supports network connection and data processing, such as a computer host, a mobile phone, a tablet, a wearable device, and the like, and the server 120 is an automobile navigation server, and may be, for example, an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network), and a big data and artificial intelligence platform, which is not limited herein. The terminal device 110 may communicate with the server 120 through a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, which is not limited herein.

Fig. 2 is a flowchart illustrating a method for constructing a driving condition curve of an automobile according to an exemplary embodiment of the present application, and as shown in fig. 2, in an exemplary embodiment, a method for constructing a driving condition curve of an automobile at least includes steps S210 to S250, which are described in detail as follows:

s210, acquiring a speed change curve and running condition data of a vehicle in a running process, wherein the speed change curve is located in a two-dimensional coordinate system, one dimension of the two-dimensional coordinate system is time, and the other dimension of the two-dimensional coordinate system is speed;

in this embodiment, the speed variation curve may be a speed variation curve of one vehicle during running, may be a speed variation curve of a plurality of vehicles during running, may be a speed variation curve during continuous running, or may be a speed variation curve during a plurality of continuous running processes.

In addition, when the vehicle running speed is actually acquired, the vehicle running speed is acquired at a time point, so that the speed change curve is originally a plurality of discrete motion nodes, and the speed change curve is obtained through interpolation, filtering and fitting.

S220, dividing a speed change curve according to a preset speed reference value to obtain a plurality of kinematic segments;

in step S220, the speed reference value is a speed value of the vehicle at idle, that is, when the speed is zero, a speed variation curve, that is, a kinematic segment, is divided according to a motion stroke from one idle to the next idle. In addition, since the speed variation curve may be a speed variation curve of a plurality of continuous driving processes, a situation that data is missing at a certain time may occur, and therefore, in the division, as long as the idle node is used as an end point of the kinematics segment, it can be ensured that the obtained kinematics segment is continuous and contains complete acceleration and deceleration processes.

S230, clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions;

the process of dividing a collection of physical or abstract objects into classes composed of similar objects is called clustering; in step S230, the driving condition data includes characteristic parameters such as positioning speed, acceleration, engine speed, torque percentage, instantaneous fuel consumption, accelerator pedal opening, air-fuel ratio, engine load percentage, intake air flow, etc., so that the result of the clustering, i.e., the vehicle driving data population, is related to the driving condition data.

S240, inputting a vehicle driving data group under various driving conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions;

in step S240, since the vehicle travel data group is associated with the travel pattern data, the prediction results obtained based on the vehicle travel data group under the plurality of travel patterns correspond to the plurality of patterns. In addition, the prediction algorithm used in this embodiment may be a viterbi algorithm. The viterbi algorithm is a dynamic programming algorithm used to find the sequence of viterbi paths-hidden states that are most likely to produce a sequence of observed events, particularly in markov source contexts and hidden markov models.

And S250, splicing the speed change prediction curves under various working conditions to obtain an automobile driving condition curve.

In step S250, the speed change prediction curve calculated by the prediction algorithm is in a discrete state, and therefore, the vehicle driving condition curves of various conditions are obtained by splicing, thereby comprehensively reflecting the form state of the vehicle.

Fig. 3 is a flowchart of step S210 in the embodiment shown in fig. 2 in an exemplary embodiment, and as shown in fig. 3, the process of acquiring the speed variation curve of the vehicle during driving may include steps S310 to S340, which are described in detail as follows:

s310, acquiring a plurality of moving nodes of a vehicle in a driving process, wherein the moving nodes comprise time data and speed data;

in step S310, the motion node includes time data and speed data, and the motion speed of the vehicle is collected at regular time during the collection, and the time data corresponds to the speed data, so that the motion node can be obtained.

S320, calculating a time data difference value of two adjacent motion nodes; when the time data difference value is smaller than a preset time threshold value, interpolation is carried out between two adjacent motion nodes;

in step S320, since the acquisition process of the velocity nodes is not necessarily continuous, there may be a large time difference between multiple sets of velocity nodes, and in this case, a large error may be caused when the velocity nodes are subsequently fitted, and therefore when the difference between the time data of adjacent motion nodes is greater than the time threshold, the data is directly discarded; when the difference value of the time data of the adjacent motion nodes is smaller than a preset time threshold value, performing interpolation between the adjacent motion nodes so as to facilitate subsequent fitting; specifically, the time threshold is 10S, and the difference algorithm adopts a newton interpolation method.

S330, filtering the plurality of interpolated motion nodes;

in step S330, kalman filtering is used, and the operation of the kalman filter includes two steps: and (4) predicting and updating. And in the prediction step, forward iteration is performed once on the estimation value at the previous moment, and the prediction value is obtained by inputting observation noise and a motion equation. The updating step uses the most recent measured value of the true state to calculate the estimated value after correction.

And S340, mapping the plurality of filtered motion nodes to a two-dimensional coordinate system, and fitting the plurality of filtered motion nodes in the two-dimensional coordinate system to obtain a speed change curve of the vehicle in the running process.

In step S340, the two-dimensional coordinate system is a corresponding coordinate of time-speed, and the speed variation curve obtained by fitting may reflect the motion state of the vehicle to a certain extent.

Fig. 4 is a flowchart of step S220 in the embodiment shown in fig. 2 in an exemplary embodiment, and as shown in fig. 4, the process of dividing the speed variation curve according to the preset speed reference value to obtain a plurality of kinematic segments may include steps S410 to S420, which are described in detail as follows:

step S410, taking the motion node with the speed value equal to the speed reference value as an idle node;

in step S410, when the speed reference value is 0, that is, the vehicle stops, the idling time of the vehicle is recorded.

And S420, extracting a speed change curve between two adjacent idle speed nodes to obtain a plurality of kinematic segments.

In step S420, the speed variation curve between any two adjacent idle nodes includes a complete acceleration, uniform speed, and deceleration process, or an acceleration and acceleration process; in this embodiment, such a completed motion process is taken as a kinematic segment.

Fig. 5 is a flowchart of step S230 in the embodiment shown in fig. 2 in an exemplary embodiment, and as shown in fig. 5, the process of clustering the plurality of kinematic segments based on the driving condition data to obtain the vehicle driving data population under the plurality of driving conditions may include steps S510 to S550, which are described in detail as follows:

s510, clustering a plurality of kinematic segments according to a preset speed range to obtain a plurality of segment sets;

in step S510, the clustered kinematic segments are classified into four segment sets (or four classes), which are a low-speed set, a medium-speed set, a high-speed set, and an idle set.

S520, dividing the kinematics segments in the segment set according to the running condition data to obtain multiple working condition segments;

in step S520, the driving condition data includes characteristic parameters such as GPS speed, acceleration, engine speed, torque percentage, instantaneous oil consumption, accelerator pedal opening, air-fuel ratio, engine load percentage, intake air flow, etc., and features with large contribution are selected from the driving condition data by a principal component analysis method, and the kinematic segments are further divided according to the features to obtain working condition segments, such as an acceleration segment, a deceleration segment, an idle segment, a constant velocity segment, and a staggered acceleration and deceleration segment. Specifically, each motion node is taken as observation data of a gaussian mixture model, so that the motion nodes in each type of kinematic segment are divided, and the feature with larger contribution is selected.

S530, clustering the same working condition segments to obtain a model event;

in step S530, an acceleration model event, a deceleration model event, an idle model event, a constant speed model event, and a staggered acceleration/deceleration model event are obtained by clustering according to the operating condition segments, such as an acceleration segment, a deceleration segment, an idle segment, a constant speed segment, and a staggered acceleration/deceleration segment.

S540, extracting the kinematic characteristics in the model event, and dividing the working condition segments in the model event according to the kinematic characteristics to obtain various kinematic characteristic nodes;

in step S540, the kinematic characteristics include average speed, average acceleration, average deceleration, acceleration time ratio, deceleration time ratio, and the like, and the kinematic nodes in the working condition segments are divided according to the characteristics, so as to obtain kinematic nodes with various kinematic characteristics;

and S550, clustering the motion characteristic nodes with the same kinematic characteristics to obtain a vehicle driving data group under various driving conditions.

In step S550, the vehicle driving data groups are the visible states of the kinematic segment.

The running condition data comprises positioning speed, acceleration, engine rotating speed, torque percentage, instantaneous oil consumption, accelerator pedal opening, air-fuel ratio, engine load percentage and air intake flow;

fig. 6 is a flowchart of step S520 in the embodiment shown in fig. 5 in an exemplary embodiment, and as shown in fig. 6, the process of dividing the kinematic segment in the segment set according to the driving condition data to obtain multiple working condition segments may include steps S610 to S620, which are described in detail as follows:

s610, carrying out principal component analysis on the driving condition data of the kinematics segments in the segment set to obtain the characteristic of the maximum speed contribution degree of the kinematics segments in the segment set;

in step S610, the contribution degree of each feature is analyzed by Principal Component Analysis (PCA), which is a statistical method. A group of variables which may have correlation are converted into a group of linear uncorrelated transformations through orthogonal transformation, and the group of the converted variables are called principal components.

S620, obtaining the working condition type of the vehicle according to the characteristic with the largest speed contribution degree and a preset working condition type mapping table, wherein the working condition mapping table comprises the mapping relation between the characteristic and the working condition type, and the working condition type comprises an acceleration working condition, a deceleration working condition, an idle working condition, a constant speed working condition and a staggered acceleration and deceleration working condition;

in step S620, by analyzing characteristics of the positioning speed, the acceleration, the engine speed, the torque percentage, the instantaneous oil consumption, the accelerator pedal opening, the air-fuel ratio, the engine load percentage, the intake air flow, and the like, it can be obtained that the vehicle is in states of acceleration, deceleration, uniform speed, staggered acceleration and deceleration, and the like; and if the positioning speed is increased, the acceleration is positive, the rotating speed of the engine is increased, the instantaneous oil consumption is increased and other characteristics correspond to the acceleration working conditions, the characteristics and the working condition types are correspondingly generated to generate a mapping table, and the subsequent obtaining of the working condition types is facilitated.

And S630, dividing the kinematic segments in the segment set according to the working condition types to obtain multiple working condition segments.

In step S630, the kinematic segment is divided according to the duration of the operating condition type to obtain an operating condition segment corresponding to the operating condition type.

Fig. 7 is a flow chart of step S540 in the embodiment shown in fig. 5 in an exemplary embodiment, and as shown in fig. 7, the process of extracting the kinematic feature in the model event may include step S710, which is described in detail as follows:

and S710, extracting the kinematic characteristics of the working condition segments in the model events, wherein the kinematic characteristics comprise one or more of average speed, average acceleration, average deceleration, acceleration time ratio and deceleration time ratio.

In step S710, the specific composition of the kinematic features is selected according to actual needs.

Fig. 8 is a flowchart of step S240 in the embodiment shown in fig. 2, in an exemplary embodiment, and as shown in fig. 8, the process of inputting the vehicle driving data groups under various driving conditions into the preset prediction algorithm to obtain the speed variation prediction curves under various conditions may include steps S810 to S820, which are described in detail as follows:

s810, constructing an observable sequence by using vehicle running data groups under various working conditions;

in step S810, a vehicle driving data group under various operating conditions includes speed change speeds of the vehicle under various operating conditions, an observable sequence is constructed by the vehicle driving data group under various operating conditions, and vehicle form data is constructed in a sequence form.

And S820, inputting the observable sequence and the preset observation probability matrix into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions.

In step S820, if the process of the vehicle driving speed is regarded as a kind of hidden markov process, the process of constructing the representative working condition is converted into a process of searching for the visible observation sequence with the maximum probability. And (3) estimating hidden Markov model parameters (namely an observation probability matrix) by using a Baum-Welch algorithm (Bomb-Welch algorithm), and predicting an optimal path by using a Viterbi algorithm, so that an automobile driving visible state sequence with the maximum probability can be obtained, and further a representative automobile working condition is obtained. Specifically, the observable sequence and the observation probability matrix are used as the input of the viterbi algorithm, and the sequence time length condition is set, so as to obtain the corresponding vehicle driving visible state sequence (i.e. the vehicle driving data group) with the maximum probability.

Fig. 9 is a flowchart of step S250 in the embodiment shown in fig. 2 in an exemplary embodiment, and as shown in fig. 9, the process of splicing the speed variation prediction curves under various conditions to obtain the vehicle driving condition curve may include steps S910 to S920, which are described in detail as follows:

step S910, calculating the ratio of the duration time of the kinematic segment corresponding to the working condition segment to the duration time of the working condition segment to obtain a working condition proportion;

in step S910, the working condition ratio is the ratio of the model event duration in the kinematics segment to the whole working condition duration. For example, the ratio of the model event duration to the total duration of the vehicle corresponding to the acceleration condition.

And S920, splicing the speed change prediction curves under various working conditions based on the working condition proportion to obtain an automobile driving working condition curve.

In step S920, the speed change prediction curve calculated by using the prediction algorithm is the visible automobile driving state sequence with the highest probability, and the inside of the speed change prediction curve includes a plurality of moving nodes, so that the visible driving state sequences corresponding to various working conditions are spliced based on the working condition ratios, and the automobile driving condition curve can be obtained. For example, the multiple working conditions in this embodiment include acceleration, constant speed, acceleration and deceleration staggering, and deceleration, and the starting acceleration working condition percentage of the automobile is 5%, the constant speed working condition percentage is 35%, the acceleration and deceleration staggering percentage is 55%, and the deceleration working condition percentage is 5%, and the 5% acceleration working condition prediction curve, the 35% constant speed working condition prediction curve, the 55% acceleration and deceleration staggering working condition prediction curve, and the 5% deceleration working condition prediction curve are spliced to obtain the last complete automobile driving working condition curve.

Fig. 10 is a flowchart of an exemplary embodiment after step S250 in the embodiment shown in fig. 2, and as shown in fig. 10, the process after the speed change prediction curves under various conditions are spliced to obtain the driving condition curve of the vehicle may further include steps S1010 to S1020, which are described in detail as follows:

s1010, obtaining a plurality of automobile running condition curves;

in step S1010, since the working condition and the working condition ratio are different in each prediction, a working condition curve closest to the actual condition is obtained by analyzing a plurality of vehicle driving condition curves.

And S1020, performing normality inspection on the multiple automobile running condition curves to obtain a target working condition curve.

In step S1020, a K-S test (Kolmogorov-sminovest, kolmogorov-sminov test) method is adopted to perform a normal distribution test on a plurality of vehicle driving condition curves, a candidate working condition with an average double-tail similarity level of 0.9 or more in the K-S test is selected as a final candidate working condition, and the vehicle driving condition curve corresponding to the candidate working condition is taken as a target working condition curve.

According to the method for constructing the automobile driving condition curve, the speed change curve is divided according to the preset speed reference value to obtain a plurality of kinematic segments; clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions; inputting a vehicle running data group under various running conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions; and splicing the speed change prediction curves under various working conditions to obtain an automobile running condition curve. According to the invention, the vehicle running data families under various running conditions are obtained by clustering the plurality of kinematic segments based on the running condition data, and then the vehicle running condition curve is obtained based on the vehicle running data families under various running conditions. The road running condition of the vehicle under various working conditions can be comprehensively reflected by considering various working conditions of the vehicle in running.

As shown in fig. 11, the present invention further provides a vehicle driving condition curve constructing apparatus, which includes:

the system comprises an acquisition module, a speed change module and a control module, wherein the acquisition module is used for acquiring a speed change curve and running condition data of a vehicle in the running process, the speed change curve is positioned in a two-dimensional coordinate system, one dimension of the two-dimensional coordinate system is time, and the other dimension of the two-dimensional coordinate system is speed;

the prediction module is used for inputting the vehicle running data group under various running conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions;

According to the automobile driving condition curve construction device, a speed change curve is divided according to a preset speed reference value to obtain a plurality of kinematic segments; clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data group under various driving conditions; inputting a vehicle running data group under various running conditions into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions; and splicing the speed change prediction curves under various working conditions to obtain an automobile running condition curve. According to the invention, a plurality of kinematic segments are clustered based on the driving condition data to obtain vehicle driving data groups under various driving conditions, and then an automobile driving condition curve is obtained based on the vehicle driving data groups under various driving conditions. The road running condition of the vehicle under various working conditions can be comprehensively reflected by considering various working conditions of the vehicle in running.

It should be noted that the device for constructing the vehicle driving condition curve provided by the above embodiment and the method for constructing the vehicle driving condition curve provided by the above embodiment belong to the same concept, wherein the specific manner in which each module and unit execute operations has been described in detail in the method embodiment, and is not described herein again. In practical applications, the function distribution can be completed by different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above, which is not limited herein.

An embodiment of the present application further provides an electronic device, including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic equipment is enabled to realize the automobile driving condition curve construction method provided in the above embodiments.

FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. It should be noted that the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.

As shown in fig. 12, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes, such as executing the method in the above-described embodiment, according to a program stored in a Read-only memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM1203, various programs and data necessary for system operation are also stored. The CPU1201, ROM1202, and RAM1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.

The following components are connected to the I/O interface 12012: an input portion 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a network interface card such as a LAN (local area network) card, a modem, and the like. The communication section 1209 performs communication processing via a network such as the internet. The driver 1210 is also connected to the I/O interface 12012 as necessary. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication portion 1209 and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.

It should be noted that the computer readable media shown in the embodiments of the present application may be computer readable signal media or computer readable storage media or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a 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 the present application, a computer-readable signal medium may comprise a propagated data signal with a computer-readable computer program embodied therein, either in baseband or as part of a carrier wave. 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 thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

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 application. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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 the embodiments of the present application may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

Another aspect of the present application also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor of a computer, causes the computer to execute a method for constructing a driving condition curve of an automobile as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist alone without being assembled into the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for constructing the driving condition curve of the automobile provided in the above embodiments.

The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Those skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims

1. A method for constructing a running condition curve of an automobile is characterized by comprising the following steps:

dividing the speed change curve to obtain a plurality of kinematic segments;

2. The method for constructing the automobile driving condition curve according to claim 1, wherein the step of obtaining the speed change curve of the automobile in the driving process comprises the following steps:

filtering the plurality of interpolated motion nodes;

3. The method for constructing the automobile driving condition curve according to claim 2, wherein the step of dividing the speed change curve according to a preset speed reference value to obtain a plurality of kinematic segments comprises the following steps:

4. The method for constructing the vehicle driving condition curve according to claim 1 or 3, wherein clustering the plurality of kinematic segments based on the driving condition data to obtain a vehicle driving data population under a plurality of driving conditions comprises:

clustering the same working condition segments to obtain model events;

5. The method for constructing the automobile driving condition curve according to claim 4, wherein the step of dividing the kinematic segments in the segment set according to the driving condition data to obtain a plurality of working condition segments comprises the following steps:

6. The method for constructing the automobile driving condition curve according to claim 4, wherein the step of extracting the kinematic features in the model event comprises the following steps:

7. The method for constructing the curve of the driving condition of the automobile according to claim 4, wherein the step of inputting the group of the driving data of the automobile under the multiple driving conditions into a preset prediction algorithm to obtain the prediction curve of the speed change under the multiple driving conditions comprises the following steps:

and inputting the observable sequence and a preset observation probability matrix into a preset prediction algorithm to obtain a speed change prediction curve under various working conditions.

8. The method for constructing the automobile driving condition curve according to claim 4, wherein the step of splicing the speed change prediction curves under the multiple working conditions to obtain the automobile driving condition curve comprises the following steps:

and splicing the speed change prediction curves under various working conditions based on the working condition proportions to obtain an automobile running working condition curve.

9. The method for constructing the automobile driving condition curve according to claim 1 or 8, wherein after the speed change prediction curves under the multiple conditions are spliced to obtain the automobile driving condition curve, the method further comprises the following steps:

acquiring a plurality of automobile driving condition curves;

10. An automobile driving condition curve constructing device, which is characterized by comprising:

the system comprises an acquisition module, a speed change module and a driving condition data acquisition module, wherein the acquisition module is used for acquiring a speed change curve and driving condition data of a vehicle in the driving process, the speed change curve is positioned in a two-dimensional coordinate system, one dimension of the two-dimensional coordinate system is time, and the other dimension of the two-dimensional coordinate system is speed;

the prediction module is used for inputting the vehicle running data group under the multiple running working conditions into a preset prediction algorithm to obtain a speed change prediction curve under the multiple working conditions;

11. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a storage device for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement a vehicle driving condition curve construction method according to any one of claims 1 to 9.

12. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute a method of constructing a curve of a driving condition of an automobile according to any one of claims 1 to 9.