CN115641160A - Method, device and storage medium for improving new energy automobile - Google Patents
Method, device and storage medium for improving new energy automobile Download PDFInfo
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Abstract
The invention relates to the field of data processing, and discloses an improvement method, equipment and a storage medium of a new energy automobile. The method comprises the following steps: acquiring behavior characteristic data of a new energy automobile driver; classifying the behavior characteristic data to respectively obtain first characteristic data used for representing the subjective idea of the driver and second characteristic data used for representing the objective attribute of the driver; processing the first characteristic data by a factor analysis method to obtain first processing result data; and processing the second characteristic data by a principal component analysis method to obtain second processing result data. The method and the device achieve the purpose of scientifically, objectively and accurately determining the performance and functional requirements of the user on the new energy automobile, and provide effective data support for the engineering development of new energy automobile products.
Description
Technical Field
The invention relates to the field of data processing, in particular to an improved method, equipment and a storage medium for a new energy automobile.
Background
With the popularization of new energy automobiles, the consumer population of the new energy automobiles is continuously increased. However, the complaint rate of the use of new energy vehicles is far higher than that of fuel vehicles, and there is an increasing situation year by year. This means that the use driver of present new energy automobile can not effectively be changed into the loyalty driver, and new energy automobile still need promote to consumer's appeal, and on the other hand, the driver basis is weak and driver operation use behavior characteristic is not yet obvious, also forms certain resistance to new energy product research. Therefore, from the perspective of a driver, detailed data analysis is needed to be performed on the driver demand and the driver behavior, a driver data label is described, and further the behavior characteristics, the preference, the demand and the like of the driver are deeply mined, so that effective data support is provided for the engineering development and the definition of new energy products.
Therefore, a method capable of accurately determining the performance and functional requirements of a user on a new energy automobile is needed, so as to provide effective data support for new energy product engineering development definition.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In order to solve the technical problems, the invention provides an improved method, equipment and a storage medium of a new energy automobile, which achieve the aim of scientifically, objectively and accurately determining the performance and functional requirements of a user on the new energy automobile and provide effective data support for the engineering development of new energy automobile products.
The embodiment of the invention provides an improvement method of a new energy automobile, which comprises the following steps:
acquiring behavior characteristic data of a new energy automobile driver;
classifying the behavior characteristic data to respectively obtain first characteristic data used for representing the subjective idea of the driver and second characteristic data used for representing the objective attribute of the driver;
processing the first characteristic data by a factor analysis method to obtain first processing result data;
processing the second characteristic data by a principal component analysis method to obtain second processing result data;
clustering the first processing result data through a k-means algorithm to obtain a first clustering result;
clustering the second processing result data through a k-means algorithm to obtain a second clustering result;
clustering the first clustering result and the second clustering result through a k-means algorithm to obtain a target clustering result, wherein the target clustering result comprises vehicle demand data corresponding to drivers with different labels;
and improving the related products of the new energy automobile based on the target clustering result.
An embodiment of the present invention provides an electronic device, including:
a processor and a memory;
the processor is used for executing the steps of the method for improving the new energy automobile according to any embodiment by calling the program or the instructions stored in the memory.
The embodiment of the invention provides a computer-readable storage medium, which stores a program or instructions for causing a computer to execute the steps of the method for improving a new energy automobile according to any one of the embodiments.
The embodiment of the invention has the following technical effects:
the behavior characteristic data of a driver of the new energy automobile is obtained; classifying the behavior characteristic data to respectively obtain first characteristic data used for representing the subjective idea of the driver and second characteristic data used for representing the objective attribute of the driver; processing the first characteristic data by a factor analysis method to obtain first processing result data; processing the second characteristic data by a principal component analysis method to obtain second processing result data; clustering the first processing result data through a k-means algorithm to obtain a first clustering result; clustering the second processing result data through a k-means algorithm to obtain a second clustering result; clustering the first clustering result and the second clustering result through a k-means algorithm to obtain a target clustering result, wherein the target clustering result comprises vehicle using demand data corresponding to drivers with different labels; the technical means for improving the related products of the new energy automobile based on the target clustering result realizes the purpose of scientifically, objectively and accurately determining the performance and functional requirements of the user on the new energy automobile, and provides effective data support for the engineering development of new energy automobile products.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an improved method of a new energy vehicle according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of processing the feature data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for improving the new energy automobile provided by the embodiment of the invention can be executed by electronic equipment. Fig. 1 is a flowchart of an improved method for a new energy vehicle according to an embodiment of the present invention. Referring to fig. 1, the method for improving the new energy automobile specifically includes the following steps:
and S110, acquiring behavior characteristic data of a driver of the new energy automobile.
Optionally, the behavior characteristic data of the driver may include characteristic data of basic information of the driver (such as industry engaged, unit, job, personal income, family income, sex, age, height, city of residence, family, etc.), vehicle using habits, living habits, vehicle using value view, living value view, vehicle purchasing preference, vehicle using purpose, etc.
After the behavior characteristic data are obtained, preprocessing is carried out on the behavior characteristic data to remove invalid data and/or null value data.
And S120, classifying the behavior characteristic data to respectively obtain first characteristic data for representing the subjective idea of the driver and second characteristic data for representing the objective attribute of the driver.
The step of classifying the behavior feature data to obtain first feature data used for representing subjective ideas of a driver and second feature data used for representing objective attributes of the driver respectively comprises the following steps:
and classifying the behavior characteristic data based on key fields, wherein the key fields corresponding to the first characteristic data are different from the key fields corresponding to the second characteristic data.
S130, processing the first characteristic data through a factor analysis method to obtain first processing result data.
And S140, processing the second characteristic data by a principal component analysis method to obtain second processing result data.
S150, clustering the first processing result data through a k-means algorithm to obtain a first clustering result.
And S160, clustering the second processing result data through a k-means algorithm to obtain a second clustering result.
S170, clustering the first clustering result and the second clustering result through a k-means algorithm to obtain a target clustering result, wherein the target clustering result comprises vehicle demand data corresponding to drivers with different labels.
Exemplarily, referring to a schematic flow chart of processing the feature data as shown in fig. 2, first, the first feature data is processed by a factor analysis method to obtain first processing result data, the second feature data is processed by a principal component analysis method to obtain second processing result data, the first processing result data is clustered by a k-means algorithm to obtain a first clustering result, the second processing result data is clustered by a k-means algorithm to obtain a second clustering result, and the first clustering result and the second clustering result are clustered by a k-means algorithm to obtain a target clustering result.
And S180, improving the related products of the new energy automobile based on the target clustering result.
The target clustering result may specifically include distinctive behavior features differentiated among each type of driver, for example, the driver with higher personal income and family income in the economic attribute may pay more attention to the comfort level of the energy vehicle, the driver with lower personal income and family income in the economic attribute may pay more attention to the durability of the energy vehicle, and the like. And improving related components and functions of the new energy automobile according to the target clustering result.
Optionally, the improving the related products of the new energy vehicle based on the target clustering result includes:
and if the target clustering result comprises data related to the air-conditioning outlet of the new energy automobile, improving the air-conditioning outlet of the new energy automobile according to the target clustering result.
The performance of the new energy automobile can be improved in a targeted manner by analyzing the automobile using requirements of a driver.
The embodiment of the invention is based on the angle of a driver, carries out data analysis on the demand and behavior of the driver, and describes the data label of the driver, so as to deeply mine the behavior characteristics, the preference, the demand and the like of the driver, thereby providing effective data support for the engineering development and definition of new energy products. By analyzing the differentiated outstanding behavior characteristics of each type of drivers, and the performance, functional requirements and common vehicle using scenes of the new energy vehicle, effective data support is provided for the engineering development and definition of new energy products. Specifically, the method is used for clustering the behavior characteristic data of the driver of the new energy automobile based on the machine learning algorithm, so that the index requirements of the driver on the performance and the function of the new energy automobile are mined, the objectivity and the scientificity of analysis are improved, and powerful support is provided for the subsequent product engineering development.
Further, the first feature data includes value view data, which can be subdivided into life value view data and vehicle use value view data, and step S130 includes the following sub-steps:
131. constructing a first feature matrix according to the first feature data:
wherein the content of the first and second substances,uthe number of categories representing the value view, for example, the value view of Zhang III is ' Xiaojia is everybody ', the value view of Li IV is ' nothing but high-rise, the value views of the two people can be respectively regarded as two different value views, and the number of categories of the value view is 2;vindicating the number of said drivers and the number of said drivers,x vu denotes the firstvThe individual driver is rightuThe degree of acceptance of the seed value view,x vu the larger the value of (A) is, the morevThe individual driver is rightuSpecies values were observed and appreciated.
132. Normalizing each element in the first feature matrix according to a first formula as follows:
133. Calculating the first correlation matrix according to the following second formular 1 =（r ij ） v u× ：
（r ij ） v u× Representing a first correlation matrixr 1 Is composed ofvLine ofuA matrix of the columns is formed,r ij are elements in a matrix.
134. And calculating eigenvalues and eigenvectors of the first correlation matrix.
135. Calculating an elementary load matrix A according to the eigenvalues and the eigenvectors based on a third equation as follows:
wherein λ is 1 、λ 2 ……λ u Representing the eigenvalues in descending order,e 1 、e 2 ……e u representing the feature vector; i.e. lambda 1 ＞λ 2 ……＞λ u ，e 1 Is λ 1 The corresponding feature vector is set to be,e 2 is λ 2 The corresponding feature vector is set to be,e u is λ u The corresponding feature vector.
136. Calculating the cumulative contribution rate according to the characteristic value by a fourth formula as follows:
137. the selected factor is determined under the condition that the cumulative contribution rate is greater than or equal to 70%.
It can be understood that λ 1 、λ 2 ……λ u Is arranged from big to small, firstly, the lambda is arranged 1 Substituting the above fourth equation to obtain an accumulated contribution rate, determining whether the accumulated contribution rate is greater than or equal to 70%, and if the accumulated contribution rate is less than 70%, continuing to apply λ 2 And substituting the fourth equation until the cumulative contribution rate of more than or equal to 70% is obtained, determining the number of the substituted eigenvalues as the number of factors, and selecting the factors from the substituted eigenvalues.
138. Determining a factor load matrix according to the selected factor; rotating the factor load matrix to obtain a rotated factor load matrix B = A 'T, wherein the matrix A' is the first g columns of the matrix A, T is an orthogonal matrix, and g is the number of the factors;
wherein, the elements in the factor load matrix B after rotation represent the correlation coefficient between the value view and the factor, and the larger the correlation coefficient is, the more the factor can represent the value view; and determining the factor with the maximum relation number as a category label of the value view, wherein the category label forms the first processing result data. Wherein, the determining of the factor load matrix according to the selected factor is to use the selected factor as an element in the factor load matrix.
For each value view, the correlation coefficients between the value view and the factors are compared, the factor with the maximum correlation coefficient is selected to represent the value view, and then the classification of the value views can be realized, and the number of the factors is the number of the classifications.
Further, the second characteristic data comprises industry characteristic data, unit characteristic data, job characteristic data, personal income characteristic data and family income characteristic data;
the processing the second feature data by a principal component analysis method to obtain second processing result data includes:
and constructing a second feature matrix according to the second feature data:
and constructing a third feature matrix according to the second feature data:
wherein the content of the first and second substances,b v the corresponding line data representsvThe characteristic data of the individual driver is stored,h 1 the corresponding column data represents industry characterization data,h 2 the corresponding column data represents unit feature data,h 3 the corresponding column data represents job feature data,y v1 is shown asvThe industry in which the individual driver is engaged,y v2 is shown asvThe unit in which the individual driver is located,y v3 is shown asvThe job the individual driver is undertaking in the work unit,d 1 the corresponding column data represents personal income feature data,d 2 the corresponding column data represents household revenue signature data,l v1 is shown asvCharacteristic data of the individual income of the individual driver,l v2 denotes the firstvAnd respectively performing principal component analysis on the second feature matrix and the third feature matrix according to the feature data of the family income of the individual driver, wherein the performing of the principal component analysis on the second feature matrix comprises:
normalizing each element in the second feature matrix to obtain normalized elements; the specific formula used for the normalization process may be replaced with the first formula described above, instead of the elements.
Calculating a second correlation matrix based on the elements after the normalization process; the formula for calculating the second correlation matrix may refer to the second formula.
Calculating the eigenvalue of the second correlation matrix, and arranging the eigenvalue according to the sequence from large to small;
the contribution ratio of each principal component is calculated based on the eigenvalues arranged in descending order by the following equation (5)α i ：
n represents the number of characteristic values; lambda [ alpha ] i Denotes the ith characteristic value, λ k Representing the kth characteristic value;
and selecting the principal component corresponding to the maximum contribution rate as a representative characteristic, wherein the representative characteristic forms the second processing result data.
The process of clustering by adopting a k-means algorithm is as follows: first, select initialized k samples as initializationCluster center c = { c = { (c) } 1 ,c 2 ,……，c k And secondly, calculating the distance from each sample to each cluster center and dividing the distance into the class corresponding to the cluster center with the minimum distance, wherein the distance calculation mode is as follows:
wherein the content of the first and second substances,b i representing the ith sample (i.e. the driver sample),c j is shown asjThe center of each cluster is determined by the center of each cluster,b it a t-th feature representing an ith driver,c jt is shown asjFirst of the cluster centertA feature; third, for each categoryC j Recalculating its cluster centers
Wherein
Is shown asjThe calculation of the new clustering center is essentially to average all samples in each class in each feature dimension. And repeatedly executing the steps based on the updated clustering center until the change degree of the clustering center is smaller than a set value, and ending the process.
The embodiment has the following technical effects: behavior characteristic data of a driver of the new energy automobile is obtained; classifying the behavior characteristic data to respectively obtain first characteristic data used for representing the subjective idea of the driver and second characteristic data used for representing the objective attribute of the driver; processing the first characteristic data by a factor analysis method to obtain first processing result data; processing the second characteristic data by a principal component analysis method to obtain second processing result data; clustering the first processing result data through a k-means algorithm to obtain a first clustering result; clustering the second processing result data through a k-means algorithm to obtain a second clustering result; clustering the first clustering result and the second clustering result through a k-means algorithm to obtain a target clustering result, wherein the target clustering result comprises vehicle using demand data corresponding to drivers with different labels; the technical means for improving the related products of the new energy automobile based on the target clustering result realizes the purpose of scientifically, objectively and accurately determining the performance and functional requirements of the user on the new energy automobile, and provides effective data support for the engineering development of new energy automobile products.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic device 400 includes one or more processors 401 and memory 402.
The processor 401 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities and may control other components in the electronic device 400 to perform desired functions.
In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 403 may include, for example, a keyboard, a mouse, and the like. The output device 404 can output various information to the outside, including warning prompt information, braking force, etc. The output devices 404 may include, for example, a display, speakers, printer, and the like, as well as a communication network and its connected remote output devices.
Of course, for simplicity, only some of the components of the electronic device 400 relevant to the present invention are shown in fig. 3, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 400 may include any other suitable components depending on the particular application.
In addition to the above method and apparatus, an embodiment of the present invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the method for improving a new energy automobile provided by any embodiment of the present invention.
The computer program product may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, for carrying out operations according to embodiments of the present invention. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, an embodiment of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method for improving a new energy vehicle provided by any embodiment of the present invention.
The computer readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present application. As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not to be taken in a singular sense, but rather are intended to include a plural sense unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, or apparatus that comprises the element.
It is also noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used herein to denote an orientation or positional relationship, as illustrated in the accompanying drawings, for convenience in describing the present invention and to simplify the description, but are not intended to denote or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be operated in a particular orientation, and thus should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," "coupled," and the like are to be construed broadly and encompass, for example, both fixed and removable coupling as well as integral coupling; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (8)
1. An improvement method of a new energy automobile is characterized by comprising the following steps:
acquiring behavior characteristic data of a new energy automobile driver;
classifying the behavior characteristic data to respectively obtain first characteristic data used for representing the subjective idea of the driver and second characteristic data used for representing the objective attribute of the driver;
processing the first characteristic data by a factor analysis method to obtain first processing result data;
processing the second characteristic data by a principal component analysis method to obtain second processing result data;
clustering the first processing result data through a k-means algorithm to obtain a first clustering result;
clustering the second processing result data through a k-means algorithm to obtain a second clustering result;
clustering the first clustering result and the second clustering result through a k-means algorithm to obtain a target clustering result, wherein the target clustering result comprises vehicle using demand data corresponding to drivers with different labels;
and improving the related products of the new energy automobile based on the target clustering result.
2. The method of claim 1, wherein the first feature data comprises an objective data, and the processing the first feature data by a factor analysis method to obtain a first processing result data comprises:
constructing a first feature matrix according to the first feature data:
wherein, the first and the second end of the pipe are connected with each other,uthe number of categories representing the view of value,vindicating the number of said drivers and the number of said drivers,x vu is shown asvThe individual driver is rightuThe degree of acceptance of the seed value view,x vu the larger the value of (A) is, the morevThe individual driver is rightuSpecies value view and recognition;
normalizing each element in the first feature matrix according to a first formula as follows:
calculating the first correlation matrix according to the following second formular 1 =（r ij ） v u× ：
Calculating eigenvalues and eigenvectors of the first correlation matrix;
calculating an elementary load matrix A according to the eigenvalues and the eigenvectors based on a third formula as follows:
wherein λ is 1 、λ 2 ……λ u Representing said eigenvalues in order of magnitude,e 1 、e 2 ……e u representing the feature vector;
calculating the cumulative contribution rate according to the characteristic value by the following fourth formula:
determining the selected factor under the condition that the accumulated contribution rate is more than or equal to 70%;
determining a factor load matrix according to the selected factor;
rotating the factor load matrix to obtain a rotated factor load matrix B = A 'T, wherein the matrix A' is the first g columns of the matrix A, T is an orthogonal matrix, and g is the number of the factors;
the elements in the rotated factor load matrix B represent correlation coefficients between the value views and the factors, and the larger the correlation coefficient is, the more the factor can represent the value views;
and determining the factor with the maximum relation number as a category label of the value view, wherein the category label forms the first processing result data.
3. The method of claim 1, wherein the second characteristic data comprises industry characteristic data, unit characteristic data, job characteristic data, personal income characteristic data, and household income characteristic data;
the processing the second feature data by a principal component analysis method to obtain second processing result data includes:
and constructing a second feature matrix according to the second feature data:
constructing a third feature matrix according to the second feature data:
wherein the content of the first and second substances,b v the corresponding line data representsvThe characteristic data of the individual driver is stored,h 1 the corresponding column data represents industry characterization data,h 2 the corresponding column data represents unit feature data,h 3 the corresponding column data represents job feature data,y v1 is shown asvThe industry in which the individual driver is engaged,y v2 denotes the firstvThe unit in which the individual driver is located,y v3 is shown asvThe job the individual driver is undertaking in the work unit,d 1 the corresponding column data represents personal income feature data,d 2 the corresponding column data represents household revenue signature data,l v1 is shown asvCharacteristic data of the individual income of the individual driver,l v2 denotes the firstvCharacteristic data of family income of an individual driver;
performing principal component analysis on the second feature matrix and the third feature matrix, respectively, wherein performing principal component analysis on the second feature matrix includes:
normalizing each element in the second feature matrix to obtain normalized elements;
calculating a second correlation matrix based on the elements after the normalization process;
calculating the eigenvalue of the second correlation matrix, and arranging the eigenvalues in a descending order;
the contribution ratio of each principal component is calculated based on the eigenvalues arranged in descending order by the following equation (5)α i ：
n denotes the number of characteristic values, λ i Denotes the ith characteristic value, λ k Representing the kth characteristic value;
and selecting the principal component corresponding to the maximum contribution rate as a representative characteristic, wherein the representative characteristic forms the second processing result data.
4. The method according to claim 1, wherein the classifying the behavior feature data to obtain a first feature data for characterizing subjective thoughts of the driver and a second feature data for characterizing objective attributes of the driver respectively comprises:
and classifying the behavior characteristic data based on key fields, wherein the key fields corresponding to the first characteristic data are different from the key fields corresponding to the second characteristic data.
5. The method according to claim 1, wherein the improving related products of new energy vehicles based on the target clustering result comprises:
and if the target clustering result comprises data related to the air-conditioning outlet of the new energy automobile, improving the air-conditioning outlet of the new energy automobile according to the target clustering result.
6. The method of claim 1, wherein before the classifying the behavior feature data, the method further comprises:
and preprocessing the behavior characteristic data to remove invalid data and/or null data.
7. An electronic device, characterized in that the electronic device comprises:
a processor and a memory;
the processor is used for executing the steps of the new energy automobile improvement method according to any one of claims 1 to 6 by calling the program or the instructions stored in the memory.
8. A computer-readable storage medium storing a program or instructions for causing a computer to execute the steps of the method for improving a new energy vehicle according to any one of claims 1 to 6.
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| CN116906561A (en) * | 2023-09-14 | 2023-10-20 | 北京理工大学 | Vehicle gear shifting point optimal control method and system based on short-time driving style identification |
| CN116906561B (en) * | 2023-09-14 | 2023-12-08 | 北京理工大学 | Vehicle gear shifting point optimal control method and system based on short-time driving style identification |
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