CN113642600B - Driving behavior feature extraction method based on mRMR algorithm and principal component analysis - Google Patents
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Abstract
The invention discloses a driving behavior feature extraction method based on an mRMR algorithm and principal component analysis, which comprises the steps of acquiring internet of vehicles data of an operation vehicle on line based on a vehicle-mounted data acquisition terminal data management system; preprocessing the Internet of vehicles data, and performing data cleaning work and index data calculation; calculating each index data by using the mutual information, and calculating the mutual information by using the index edge probability density and the index joint probability density; sequentially calculating the correlation and redundancy among the index data by using a forward ordering method to finish the ordering and selection of the mRMR characteristic importance; and extracting data information in the index by combining a principal component analysis method, and analyzing to obtain driving behavior information in the Internet of vehicles data. The invention reduces the redundancy among data indexes in the driving behavior analysis and reduces the data dimension, thereby improving the data use efficiency and providing an effective tool for better utilizing the internet of vehicles data and extracting the driving behavior characteristics.
Description
Technical Field
The invention relates to the technical field of internet of vehicles data analysis, in particular to a driving behavior feature extraction method based on an mRMR algorithm and principal component analysis.
Background
With the progress of internet technology, more and more vehicles are connected into the internet of vehicles system, and the internet connection and intelligent degree of the vehicles are improved. How to more effectively use these internet of vehicles data, however, contributes to society and economy is the focus of current research. In particular, when driving behavior application analysis is performed by using related data, the index data often has the characteristics of unclear information and high redundancy in the studied problems.
Therefore, the method takes the problem of high dimension and redundancy when the internet of vehicles data is used as a starting point, and selects the feature optimization method to reduce the dimension of the internet of vehicles data and remove the redundancy of the data.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the technical problems solved by the invention are as follows: the problem of redundancy in the use process of the internet of vehicles data is solved.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of acquiring internet of vehicles data of an operation vehicle on line based on an on-vehicle data acquisition terminal and a data management system; preprocessing the Internet of vehicles data, and performing data cleaning work and index data calculation; calculating each index data by using the mutual information, and calculating the mutual information by using the index edge probability density and the index joint probability density; sequentially calculating the correlation and redundancy among the index data by using a forward ordering method to finish the ordering and selection of the mRMR characteristic importance; and extracting data information in the index by combining a principal component analysis method, and analyzing to obtain driving behavior information in the Internet of vehicles data.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: the internet of vehicles data comprises a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the position of the vehicle, GPS altitude of the position of the vehicle, ECU total oil consumption of the vehicle, accumulated total oil consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine speed of the vehicle, acceleration of the vehicle, engine torque load rate of the vehicle and engine load rate of the vehicle.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: the data cleaning work comprises time jump checking, data outlier processing and data missing processing; the index data calculation comprises index calculation of relevant parameters of the engine and the altitude, wherein the index comprises an engine load rate average value, an engine torque load rate average value, an engine rotating speed average value, an altitude average value, an engine load rate standard deviation, an engine torque load rate standard deviation, an engine rotating speed standard deviation and an altitude standard deviation; and calculating indexes of related parameters of driving behaviors, wherein the indexes comprise a speed average value, an acceleration average value, an accelerator opening average value, a gearbox rotating speed average value, a speed standard deviation, an acceleration standard deviation, an accelerator opening standard deviation and a gearbox rotating speed standard deviation.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: calculating the mutual information includes the steps of,
wherein I (X, Y) is the mutual information quantity between the feature variables X and Y, X, Y are data variables, p (X) and p (Y) are the edge probability distribution functions of X and Y, respectively, and p (X, Y) represents the joint probability density function of X and Y.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: the mRMR feature importance ranking and selection is performed by calculating an average information correlation of the index data and the target data, including,
wherein S represents a subset of the feature variables, c represents the target variable, f i For the ith index variable, D (S, c) is the average of mutual information.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: the correlation and redundancy between the index data are sequentially calculated using a forward ordering method, including,
where R (S) is the minimum redundancy measure for the feature subset.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: calculating the mRMR characteristic values and ordering of the index data and the target data includes,
wherein mRMR is a measure of combining maximum mutual information with minimum redundancy.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: extracting data information in the index comprises KMO ball type test and significance test on the index data; when the test data meets the requirements, extracting the characteristics of the main components, including calculating a correlation coefficient matrix and corresponding characteristic vectors and characteristic values thereof, and representing the correlation coefficient matrix and the corresponding characteristic vectors and characteristic values as the main components;
where k, n denote the dimension of the sample matrix, x ij For the elements in the sample matrix, ρ is the standard covariance matrix, z i For i principal components extracted, l ij Is the element contained in the feature vector of the data matrix.
As a preferable scheme of the driving behavior feature extraction method based on the mRMR algorithm and the principal component analysis, the driving behavior feature extraction method based on the mRMR algorithm comprises the following steps: the driving behavior information comprises features extracted by integrating the mRMR algorithm with features extracted by principal component analysis.
The invention has the beneficial effects that: the invention reduces the redundancy among data indexes in the driving behavior analysis and reduces the data dimension, thereby improving the data use efficiency and providing an effective tool for better utilizing the internet of vehicles data and extracting the driving behavior characteristics.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a schematic flow chart of a driving behavior feature extraction method based on an mRMR algorithm and principal component analysis according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of an mRMR algorithm of a driving behavior feature extraction method based on an mRMR algorithm and principal component analysis according to an embodiment of the invention;
fig. 3 is a schematic diagram of a driving behavior feature extraction method based on mRMR algorithm and principal component analysis according to an embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, fig. 2 and fig. 3, for a first embodiment of the present invention, a driving behavior feature extraction method based on an mRMR algorithm and principal component analysis is provided, which specifically includes:
s1: and acquiring the Internet of vehicles data of the operation vehicle on line based on the vehicle-mounted data acquisition terminal data management system. The internet of vehicles data includes:
the vehicle comprises a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the position of the vehicle, GPS altitude of the position of the vehicle, ECU total fuel consumption of the vehicle, accumulated total fuel consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine speed of the vehicle, acceleration of the vehicle, engine torque load rate of the vehicle and engine load rate of the vehicle.
S2: preprocessing the Internet of vehicles data, and performing data cleaning work and index data calculation. The step of performing the data cleaning operation includes:
time jump checking, data outlier processing and data missing processing;
the index data calculation comprises the step of calculating indexes of relevant parameters of the engine and the altitude, wherein the indexes comprise an engine load rate average value, an engine torque load rate average value, an engine rotating speed average value, an altitude average value, an engine load rate standard deviation, an engine torque load rate standard deviation, an engine rotating speed standard deviation and an altitude standard deviation;
and calculating indexes of the driving behavior related parameters, wherein the indexes comprise a speed average value, an acceleration average value, an accelerator opening average value, a gearbox rotating speed average value, a speed standard deviation, an acceleration standard deviation, an accelerator opening standard deviation and a gearbox rotating speed standard deviation.
S3: and calculating each index data by using the mutual information, and calculating the mutual information by using the index edge probability density and the index joint probability density. It should be further noted that, calculating mutual information includes:
wherein I (X, Y) is the mutual information quantity between the feature variables X and Y, X, Y are data variables, p (X) and p (Y) are the edge probability distribution functions of X and Y, respectively, and p (X, Y) represents the joint probability density function of X and Y.
S4: and sequentially calculating the correlation and redundancy among the index data by using a forward ordering method, and finishing the ordering and selection of the mRMR characteristic importance. The step further needs to be described, performing mRMR feature importance ranking and selecting the average information correlation of the index data to be calculated and the target data, including:
wherein S represents a subset of the feature variables, c represents the target variable, f i For the ith index variable, D (S, c) is the average of mutual information.
Sequentially calculating the correlation and redundancy between the index data by using a forward sorting method, wherein the method comprises the following steps:
where R (S) is the minimum redundancy measure for the feature subset.
Calculating the mRMR characteristic values and the sequences of the index data and the target data comprises the following steps:
wherein mRMR is a measure of combining maximum mutual information with minimum redundancy.
S5: and extracting data information in the indexes by combining a principal component analysis method, and analyzing to obtain driving behavior information in the vehicle networking data. It should be noted that, the data information in the extraction index includes:
performing KMO ball type test and significance test on the index data;
when the inspection data meets the requirements, main city inverse characteristic extraction is carried out, wherein the main city inverse characteristic extraction comprises the steps of calculating a correlation coefficient matrix and corresponding characteristic vectors and characteristic values thereof, and representing the correlation coefficient matrix and the corresponding characteristic vectors and characteristic values as main components;
where k, n denote the dimension of the sample matrix, x ij For the elements in the sample matrix, ρ is the standard covariance matrix, z i For i principal components extracted, l ij Elements contained in the feature vector of the data matrix;
the driving behavior information includes features extracted by integrating mRMR algorithm with features extracted by principal component analysis.
Example 2
Preferably, this embodiment is different from the first embodiment in that:
(1) And acquiring the Internet of vehicles data of the operation vehicle on line based on the vehicle-mounted data acquisition terminal data management system.
The focus is on whether the following categories of data items are acquired: the vehicle comprises a vehicle identification number, driving time, GPS longitude of the vehicle, GPS latitude of the position of the vehicle, GPS altitude of the position of the vehicle, ECU total fuel consumption of the vehicle, accumulated total fuel consumption of the vehicle, meter mileage of the vehicle, ECU speed of the vehicle, engine speed of the vehicle, acceleration of the vehicle, engine torque load rate of the vehicle and engine load rate of the vehicle.
(2) And performing data cleaning work and index data calculation on the acquired internet of vehicles data.
The method comprises time jump checking, data abnormal value processing and data missing processing, and index calculation is carried out on relevant parameters of the engine and the altitude, wherein the indexes mainly comprise an engine load rate average value, an engine torque load rate average value, an engine rotating speed average value, an altitude average value, an engine load rate standard deviation, an engine torque load rate standard deviation, an engine rotating speed standard deviation and an altitude standard deviation.
And carrying out index calculation on related parameters of driving behaviors, wherein the indexes mainly comprise a speed average value, an acceleration average value, an accelerator opening average value, a gearbox rotating speed average value, a speed standard deviation, an acceleration standard deviation, an accelerator opening standard deviation and a gearbox rotating speed standard deviation.
The index edge probability density, the index joint probability density, and the mutual information are calculated for each of the calculated index data using the following formula.
And sequentially calculating the correlation and redundancy among indexes by using a forward ordering method, so as to order and select the importance of the mRMR characteristics.
The calculated correlation redundancy and mRMR importance ranking results are shown in the table below.
Table 1: mRMR importance ranking results
And (3) selecting 10 further extracted features, wherein the mRMR importance ranking result has the minimum redundancy between the first six index sets and other indexes.
And further extracting data information in the index by using a principal component analysis method, firstly carrying out KMO ball type test and significance test on the data, and extracting principal component characteristics when the test data meets the requirements. And is represented as a main component. The results are shown in Table 2.
Table 2: KMO ball type test and significance test
Kmo=0.853 >0.6, and a significance test F <0.005, indicating that the data meets the principal component analysis requirements.
The correlation matrix is given in the calculation as shown in table 3.
Table 3: and (5) correlation analysis results after feature extraction.
Further, the contribution rate of the principal components in the data and the cumulative contribution rate thereof are shown in table 4 below, and when the number of the principal components is four, the extraction information rate reaches 88.02% (the principal components can be extracted by default more than 80%), and the extraction information rate can be used as the extraction characteristic of the original data index.
TABLE 4 contribution rate of extracted principal components and cumulative contribution rate
And extracting the first four main components to replace driving behavior information in the original data by combining the results.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (2)
1. A driving behavior feature extraction method based on an mRMR algorithm and principal component analysis is characterized by comprising the following steps: comprising the steps of (a) a step of,
acquiring the internet of vehicles data of the operation vehicle on line based on a vehicle-mounted data acquisition terminal data management system;
the data of the internet of vehicles comprises,
the vehicle identification number, the driving time, the GPS longitude of the vehicle, the GPS latitude of the position of the vehicle, the GPS altitude of the position of the vehicle, the ECU total fuel consumption of the vehicle, the accumulated total fuel consumption of the vehicle, the meter mileage of the vehicle, the ECU speed of the vehicle, the engine speed of the vehicle, the acceleration of the vehicle, the engine torque load rate of the vehicle and the engine load rate of the vehicle;
preprocessing the Internet of vehicles data, and performing data cleaning work and index data calculation;
calculating each index data by using the mutual information, and calculating the mutual information by using the index edge probability density and the index joint probability density;
calculating the mutual information includes the steps of,
wherein I (X, Y) is the mutual information quantity between the characteristic variables X and Y, X and Y are data variables, p (X) and p (Y) are the edge probability distribution functions of X and Y respectively, and p (X, Y) represents the joint probability density function of X and Y;
sequentially calculating the correlation and redundancy among the index data by using a forward ordering method to finish the ordering and selection of the mRMR characteristic importance;
the mRMR feature importance ranking and selection is performed by calculating an average information correlation of the index data and the target data, including,
wherein S represents a subset of the feature variables, c represents the target variable, f i D (S, c) is the average value of mutual information for the i-th index variable;
the correlation and redundancy between the index data are sequentially calculated using a forward ordering method, including,
wherein R (S) is the minimum redundancy measure of the feature subset;
calculating the mRMR characteristic values and ordering of the index data and the target data includes,
wherein mRMR is a measure of combining maximum mutual information and minimum redundancy;
extracting data information in the indexes by combining a principal component analysis method, and analyzing to obtain driving behavior information in the Internet of vehicles data;
extracting the data information in the index includes,
performing KMO ball type test and significance test on the index data;
when the test data meets the requirements, extracting the characteristics of the main components, including calculating a correlation coefficient matrix and corresponding characteristic vectors and characteristic values thereof, and representing the correlation coefficient matrix and the corresponding characteristic vectors and characteristic values as the main components;
wherein k and n represent dimensions of the sample matrix, ρ is a standard covariance matrix, z i For the extracted i principal components, l ij Elements contained in the feature vector of the data matrix;
the driving behavior information comprises features extracted by integrating the mRMR algorithm with features extracted by principal component analysis.
2. The driving behavior feature extraction method based on mRMR algorithm and principal component analysis according to claim 1, characterized in that: the data cleaning work comprises time jump checking, data outlier processing and data missing processing;
the index data calculation comprises index calculation of relevant parameters of the engine and the altitude, wherein the index comprises an engine load rate average value, an engine torque load rate average value, an engine rotating speed average value, an altitude average value, an engine load rate standard deviation, an engine torque load rate standard deviation, an engine rotating speed standard deviation and an altitude standard deviation;
and calculating indexes of related parameters of driving behaviors, wherein the indexes comprise a speed average value, an acceleration average value, an accelerator opening average value, a gearbox rotating speed average value, a speed standard deviation, an acceleration standard deviation, an accelerator opening standard deviation and a gearbox rotating speed standard deviation.
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