Vehicle typical road spectrum extraction method, system and device and storage medium

Abstract

The invention discloses a vehicle typical road spectrum extraction method, system and device, and a storage medium, belongs to the technical field of vehicle performance analysis, and solves the technical problems of high time consumption and low efficiency due to the fact that current vehicle typical road spectrum extraction generally depends on manual analysis and selection. The method comprises the following steps: acquiring original road spectrum data; preprocessing the original road spectrum data to obtain preprocessed road spectrum data; slicing and segmenting the preprocessed road spectrum data to obtain slice data; classifying the slice data, and calculating an intersection proportion between the slice data to determine whether the slice data are the same path; counting the total slice data and the vehicle speed characteristic value, the slope characteristic value, the rotating speed characteristic value and the torque characteristic value of each slice data; selecting data with the minimum overall characteristic deviation from the slice data as typical cyclic data; and outputting the typical cyclic data. Through the automatic data slicing and classification algorithm, the efficiency and accuracy of data processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle performance analysis, and more specifically, to a method, system, device and storage medium for extracting typical road spectra of a vehicle. Background Art

[0002] At present, the main method for extracting road spectrum is the chi-square test method, which is also called the chi-square test. It is a statistical method used to test whether there is a significant difference between the observed frequency and the expected frequency. It is widely used in social sciences, market research, medical research and other fields, especially when dealing with nominal scale data.

[0003] In existing technologies, the extraction of typical vehicle road spectra usually relies on manual analysis and selection, which is time-consuming and inefficient. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art. One purpose of the present invention is to provide a method for extracting typical road spectra of vehicles that can improve the efficiency and accuracy of data processing.

[0005] A second objective of the present invention is to provide a vehicle typical road spectrum extraction system.

[0006] A third object of the present invention is to provide a computer device.

[0007] A fourth objective of the present invention is to provide a computer storage medium.

[0008] In order to achieve the above-mentioned objective 1, the present invention provides a method for extracting a typical road spectrum of a vehicle, comprising the following steps:

[0009] Step 1. Obtain original road spectrum data;

[0010] Step 2. Preprocessing the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data;

[0011] Step 3. Slice and segment the pre-processed road spectrum data according to the slicing method selected by the user to obtain slice data;

[0012] Step 4. Classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road;

[0013] Step 5. Count the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of each slice data; select the data with the smallest deviation from the overall characteristic from each slice data as the typical cycle data;

[0014] Step 6: Output the typical cycle data.

[0015] As a further improvement, the method of obtaining the original road spectrum data includes reading the vehicle driving data recorded by the on-board computer and receiving the vehicle driving data input or imported by the user.

[0016] Furthermore, the user-set parameters include at least one of a mileage limit and a sampling frequency.

[0017] Furthermore, the slicing method selected by the user is any one of vehicle speed and rotation speed.

[0018] Further, in step 5, a joint distribution of vehicle speed and slope is calculated based on the vehicle speed characteristic value and the slope characteristic value, and a joint distribution of speed and torque is calculated based on the speed characteristic value and the torque characteristic value;

[0019] Calculate the chi-square value corresponding to the slice data as a whole and the chi-square value corresponding to each slice data;

[0020] Compare the chi-square value corresponding to the slice data with the chi-square value corresponding to the overall slice data, and take the fragment with the smallest deviation as the typical cycle data.

[0021] Furthermore, step 5 also includes special vehicle data extraction, by clustering the slice data and performing cyclic extraction according to the special vehicle indicators to obtain the special vehicle data.

[0022] Furthermore, step 6 includes:

[0023] Output the typical cycle data to a designated folder and provide a statistical characteristic parameter table;

[0024] Take out the typical cycle data file with the largest amount of data and draw the trajectory on the map.

[0025] In order to achieve the above-mentioned second objective, the present invention provides a vehicle typical road spectrum extraction system, comprising:

[0026] An acquisition module is used to acquire original road spectrum data;

[0027] A preprocessing module, used for preprocessing the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data;

[0028] A data slicing calculation module, used for slicing and segmenting the pre-processed road spectrum data according to a slicing method selected by a user to obtain slice data;

[0029] A road trajectory classification module, used to classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road;

[0030] a typical cycle extraction module, for counting the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of each slice data; and selecting the data with the smallest deviation from the overall characteristic from each slice data as the typical cycle data;

[0031] The data output module is used to output the typical cycle data.

[0032] In order to achieve the third objective above, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for extracting typical road spectra of a vehicle when executing the computer program.

[0033] In order to achieve the fourth objective above, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for extracting a typical road spectrum of a vehicle is implemented.

[0034] Beneficial Effects

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] 1. The present invention improves the efficiency and accuracy of data processing through automated data slicing and classification algorithms.

[0037] 2. The present invention can extract special vehicle data for the special vehicle market segment.

[0038] 3. The typical cycle selection algorithm of the present invention is based on multiple features to ensure the representativeness of the extraction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of the present invention;

[0040] Figure 2 The figure shows the typical cycle extraction results;

[0041] Figure 3 The figure is a flow chart for slicing segments according to vehicle speed and rotation speed. DETAILED DESCRIPTION

[0042] The present invention will be further described below with reference to the specific embodiments in the accompanying drawings.

[0043] See also Figure 1 to Figure 3 A typical vehicle road spectrum extraction method includes the following steps 1 to 6:

[0044] Step 1. Obtain the original road data and save it in a selected folder as a CSV file. The specific method includes reading the vehicle driving data recorded by the on-board computer, receiving the vehicle driving data input or imported by the user.

[0045] Step 2. Preprocess the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data. The parameters set by the user include at least one of the mileage limit and the sampling frequency. That is, only the data that meets the user's requirements is retained to reduce the amount of data for subsequent processing and ensure data accuracy.

[0046] Step 3: Slice the pre-processed road spectrum data according to the slicing method selected by the user to obtain sliced ​​data. The slicing method selected by the user is either by vehicle speed or by rotation speed. Figure 3 The flowchart of slicing and segmenting according to vehicle speed and rotation speed is shown. The processed data is saved in the specified folder.

[0047] Step 4. Classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road. According to the calculation results, the data is classified and saved in different folders.

[0048] Step 5. Count the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, torque characteristic value of each slice data; select the data with the smallest deviation from the overall characteristic from each slice data as the typical cycle data.

[0049] Specifically, the joint distribution of vehicle speed and slope is calculated based on the vehicle speed characteristic value and the slope characteristic value, and the joint distribution of speed and torque is calculated based on the speed characteristic value and the torque characteristic value;

[0050] Calculate the chi-square value corresponding to the overall slice data and the chi-square value corresponding to each slice data;

[0051] Compare the chi-square value corresponding to the slice data with the chi-square value corresponding to the overall slice data, and take the fragment with the smallest deviation as the typical cycle data.

[0052] Step 5 also includes special vehicle data extraction, which is specifically performed by clustering analysis to slice data and cyclically extract special vehicle data according to the special vehicle index. Special vehicle data extraction can be performed for special vehicle market segments.

[0053] Step 6. Output typical cycle data. Specifically include:

[0054] Output typical cycle data to a specified folder and provide a statistical characteristic parameter table, such as Figure 2 As shown;

[0055] Take out the typical cycle data file with the largest amount of data and draw the trajectory on the map.

[0056] In addition, this method will check for potential errors during execution, such as file non-existence, data format error, etc., and report errors to the user through a message box or log.

[0057] A vehicle typical road spectrum extraction system, comprising:

[0058] An acquisition module is used to acquire original road spectrum data;

[0059] A preprocessing module, used to preprocess the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data;

[0060] A data slicing calculation module is used to slice and segment the pre-processed road spectrum data according to the slicing method selected by the user to obtain slice data;

[0061] The road trajectory classification module is used to classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road;

[0062] The typical cycle extraction module is used to count the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, torque characteristic value of each slice data; and select the data with the smallest deviation from the overall characteristic from each slice data as the typical cycle data;

[0063] The data output module is used to output typical cycle data.

[0064] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for extracting typical road spectra of a vehicle when executing the computer program.

[0065] A computer-readable storage medium stores a computer program, which implements the above-mentioned method for extracting typical road spectra of a vehicle when executed by a processor.

[0066] The present invention improves the efficiency and accuracy of data processing through automated data slicing and classification algorithms, and ensures the representativeness of extraction results based on a typical cycle selection algorithm based on multiple features.

[0067] The present invention is applicable to vehicle application scenarios in major market segments of commercial vehicles such as tractors, trucks, dump trucks, special-purpose vehicles, and buses, and a corresponding processing method is developed based on the usage characteristics of special-purpose vehicles to improve the efficiency and accuracy of the extraction process.

[0068] The above are only preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for extracting typical vehicle road spectra, characterized in that: The following steps are involved: Step 1. Obtain original road spectrum data; Step 2. Preprocessing the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data; Step 3. Slice and segment the pre-processed road spectrum data according to the slicing method selected by the user to obtain slice data; Step 4. Classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road; Step 5. Count the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of each slice data; Select the data with the smallest deviation from the overall characteristics from each slice data as the typical cycle data; Step 6: Output the typical cycle data.

2. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: Ways to obtain original road data include reading vehicle driving data recorded by an onboard computer and receiving vehicle driving data input or imported by a user.

3. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: The user-set parameters include at least one of a mileage limit and a sampling frequency.

4. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: The slicing method selected by the user is any one of vehicle speed and rotation speed.

5. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: In step 5, a joint distribution of vehicle speed and slope is calculated based on the vehicle speed characteristic value and the slope characteristic value, and a joint distribution of speed and torque is calculated based on the speed characteristic value and the torque characteristic value; Calculate the chi-square value corresponding to the slice data as a whole and the chi-square value corresponding to each slice data; Compare the chi-square value corresponding to the slice data with the chi-square value corresponding to the overall slice data, and take the fragment with the smallest deviation as the typical cycle data.

6. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: Step 5 also includes special vehicle data extraction, which is performed by clustering analysis on the slice data and circularly extracting the special vehicle data according to the special vehicle's indicators.

7. The method for extracting typical vehicle road spectra according to claim 1, characterized in that: In step 6 include: Output the typical cycle data to a designated folder and provide a statistical characteristic parameter table; Take out the typical cycle data file with the largest amount of data and draw the trajectory on the map.

8. A vehicle typical road spectrum extraction system, characterized in that: include: An acquisition module is used to acquire original road spectrum data; A preprocessing module, used for preprocessing the original road spectrum data according to the parameters set by the user to obtain preprocessed road spectrum data; A data slicing calculation module, used for slicing and segmenting the pre-processed road spectrum data according to a slicing method selected by a user to obtain slice data; A road trajectory classification module, used to classify the slice data and calculate the intersection ratio between the slice data to determine whether they are the same road; A typical cycle extraction module is used to count the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of the overall slice data, as well as the vehicle speed characteristic value, slope characteristic value, rotation speed characteristic value, and torque characteristic value of each slice data; Select the data with the smallest deviation from the overall characteristics from each slice data as the typical cycle data; The data output module is used to output the typical cycle data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for extracting typical road spectra of a vehicle as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for extracting typical road spectra of a vehicle as described in any one of claims 1 to 7 is implemented.

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