A vehicle comfort real-time evaluation method and system based on local vibration information

Abstract

The application discloses a kind of based on local vibration information's vehicle comfort real-time evaluation method and system;Pack through the sensor arranged on vehicle collects the vibration data received by vehicle in driving process;Add the vibration data after pre-processing to the historical vibration information stored, form a continuous vehicle vibration data stream;Establish the mapping relationship between vibration intensity and local vibration information extraction window, determine the length of window;According to variable time window, vibration information extraction is carried out to the historical vibration, to obtain local vibration information, using specific time decay function in time domain to analyze and calculate comfort, to consider the instantaneous change and sustained influence of vibration;Integrate the vibration data of current time and the extracted historical vibration data, to obtain the final real-time comfort evaluation index;The application provides real-time comfort feedback for different road surface and specific time vehicle motion state, so as to make necessary adjustment, thereby improving the comfort experience of passenger.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, specifically to a method and system for real-time evaluation of vehicle comfort based on local vibration information. Background Technology

[0002] With the rapid development of the automotive industry, ride comfort has become an important indicator for measuring vehicle quality. Vibrations generated by a vehicle during actual driving not only affect the passenger experience but also impact their physical and mental health.

[0003] Currently, although there is considerable research on the discomfort caused to the human body by vehicle travel, existing research results often fail to accurately reflect human perception when vehicle vibration information has specific spatiotemporal distribution characteristics. This is because most existing evaluation methods use averaging operators to integrate vibration information or simply integrate over the entire journey time. These methods are overly simplistic and have many problems. First, the comfort index obtained by integrating vibration information only reflects vibration information within an interval, without considering the interval length. It often increases with time and cannot indicate passenger comfort at a specific moment. Furthermore, most existing real-time comfort evaluation methods use fixed time windows to process data. This method often fails to accurately reflect the persistent effects of continuous vibration in the time domain. Summary of the Invention

[0004] To address the aforementioned issues, this patent proposes a method and system for real-time vehicle comfort evaluation based on local vibration information. This method sets a variable-length time window according to the current impact intensity. Next, historical impact information within the generated perception window is processed to extract the interactions between local impacts. Through local vibration information, comfort indices related to the vibration intensity at the current moment are obtained. This method can not only assess passenger comfort accurately and in real-time but also consider the impact of continuous impacts on passenger comfort, thus achieving a more accurate and practical comfort assessment.

[0005] This patented method avoids the lack of real-time consideration in existing comfort evaluation methods, enabling it to output comfort information in real time and taking into account continuous shocks in the recent period, thus improving the accuracy of ride comfort evaluation. This method can serve as a novel vehicle comfort evaluation approach, used to assess and optimize the comfort of autonomous driving systems.

[0006] A method for real-time evaluation of vehicle comfort based on local vibration information includes the following steps:

[0007] Step 1: Collect vibration data of the vehicle during driving by using sensors installed on the vehicle;

[0008] Step 2: Preprocess the received vibration data;

[0009] Step 3: Add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream;

[0010] Step 4: Establish the mapping relationship between vibration intensity and local vibration information extraction window, and determine the length of the window based on the vibration information intensity at the current moment;

[0011] Step 5: Extract local vibration information from historical vibrations using a variable time window, and use a specific time decay function to analyze and calculate comfort in the time domain to consider the instantaneous changes and continuous effects of vibration.

[0012] Step Six: Integrate the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index.

[0013] Furthermore, the sensor mentioned in step one collects vibration data received by the vehicle during operation, as detailed below:

[0014] Through the sensor at frequency f senso Collect information on the jerk's speed. raw ={jerk x_raw jerk y_raw jerk z_raw}

[0015] The subscripts x, y, and z represent the directions in the vehicle's coordinate system, and the jerk is defined as the derivative of the acceleration. jerk x_raw jerk y_raw jerk z_raw These are the jerkiness of the vehicle in the x-direction, y-direction, and z-direction, respectively.

[0016] Furthermore, the preprocessing of the received vibration data described in step two is as follows:

[0017] First, a sixth-order Butterworth filter is used to filter the collected vibration data, and the cutoff frequency f of the filter is set. filter ;

[0018] The transfer function of the sixth-order filter used is determined as follows:

[0019]

[0020] jerk raw The jerkiness obtained after filtering with a sixth-order Butterworth filter is:

[0021] jerk filter ={jerk x_filter jerk y_filter jerk z_filter}

[0022] Then, the filtered vibration data was frequency-weighted according to the ISO 2631-1 standard; the ISO 2631-1 standard provides a method for assessing human perception and response to vibration. In this standard, the weights of the x, y, and z axes are set to W, respectively. x W y W z These weights are frequency-related. The specific weighting function is expressed as:

[0023] jerk wx =W x jerk x_filter jerk wy =W y jerk y_filter jerk wz =W z jerk z_filter ; among them jerk wx jerk wy jerk wz These are the weighted jerk values ​​on the x, y, and z axes, respectively. wx jerk wy jerk wz After frequency weighting, the resulting data is the preprocessed vibration data.

[0024] jerk filter This represents a set of vectors, including the jerk values ​​after filtering in the x, y, and z directions.

[0025] Furthermore, in step three, the preprocessed vibration data is added to the stored historical vibration information to form a continuous vehicle vibration data stream; specifically, this includes the following:

[0026] Add the jerkiness processed in step two to the historical sequence of a fixed duration t;

[0027] The vehicle sensor in step one uses a frequency f sensor The sampling was performed, and in this historical sequence, there are a total of N = f sensor ×t data points; represent this historical sequence as a vector.

[0028] Where h iThis is the value of the i-th vibration data point, containing the jerk data along the three axes after preprocessing: h i ={jerk wx jerk wy jerk wz}

[0029] When the new data point h N+1 When adding data after preprocessing, the historical sequence needs to be updated, i.e., h. N+1 Add to the end of the sequence while removing the data point h1 from the beginning of the sequence; this process is represented by the formula:

[0030] in For the updated historical sequence, after the last data stream is updated, the data indices are reset to obtain... Maintain a continuous vehicle vibration data stream This data stream contains vibration data within a recent duration of t, and this data stream is used for subsequent comfort evaluation.

[0031] Furthermore, step four establishes a mapping relationship between vibration intensity and the local vibration information extraction window, determining the window length based on the current vibration intensity. The main objective of this process is to ensure that both the instantaneous changes and continuous effects of vibration are fully considered when calculating real-time comfort evaluation indicators. Specifically, if the current vibration intensity is high, the corresponding time window will be larger to better capture the continuous effects of the vibration; conversely, if the current vibration intensity is low, a shorter variable time window will be selected. The specific content includes:

[0032] Swiftness data for each axis (x, y, z) in the vehicle coordinate system Extract the sampling points within the most recent 1 second;

[0033] For the sampling points in the x, y, and z directions respectively, calculate the maximum value of the sampling points in each direction and the average value of the sampling points within one second;

[0034] When the result of multiplying the maximum value and the average value by the corresponding weight is less than the set threshold T, the time window is set to one second. When it is greater than the set threshold T, the length of the dynamic time window is designed according to the corresponding exponential function.

[0035] Furthermore, when the result of multiplying the maximum and average values ​​by their corresponding weights is less than a set threshold T, the time window is set to one second. When the result is greater than the set threshold T, the length of the dynamic time window is designed according to the corresponding exponential function. This process consists of the following steps:

[0036] Define the vibration intensity I for each axial jerk.x I y I z for:

[0037]

[0038]

[0039]

[0040] Where ω1 and ω2 are the weights of the maximum and average values, respectively, f sensor It is the sensor's sampling frequency; j x (t), j y (t), j z (t) represents the values ​​of the sampling points in the x, y, and z directions at time t; t x_max , t y_max , t z_max These represent the times corresponding to the maximum values ​​of the sampling points in the x, y, and z directions within one second, respectively.

[0041] The process of determining the length of the dynamic time window is divided into two cases based on the relationship between vibration intensity and the threshold, and a relevant threshold T is defined. Functions are defined for these two cases as follows:

[0042] Case 1: If the intensity in a certain direction is less than the threshold, then set the duration of the variable time window to 1 second; define the function f. small f small (I) = 1, indicating that the active window time is fixed at 1 second;

[0043] The second scenario: If the vibration intensity in a certain direction is greater than the corresponding threshold, define the function f. large for:

[0044] f large =a·I b , where a and b are parameters determined experimentally; the length of the variable time window is determined based on the vibration intensity.

[0045] Then, the function f() that determines the length of the time window is obtained, which includes the above two cases:

[0046]

[0047] I represents the vibration intensity of the axial jerk. x I y I z These represent the jerk intensity in the x, y, and z directions, respectively.

[0048] Finally, the variable time window length corresponding to each axial jerk is obtained:

[0049] L x =f(I x ), L y =f(I y ), L z =f(I z ).

[0050] Furthermore, in step five, information is extracted from historical vibrations using a variable time window to obtain local vibration information. The specific details are as follows:

[0051] First, the jerkiness over the last 1 second is integrated over time; assuming the jerkiness j in each axial direction is j x (t), j y (t), j z (t), then index 1 is calculated using the following formulas:

[0052]

[0053]

[0054]

[0055] Next, in the historical sequence Extract the data h from the time window length L determined in the previous step. L Iterate through the retrieved historical data and define the maximum value as jerk. peak Defined in the retrieved historical data h L Medium greater than d·jerk peak And the data point whose time is closest to the current time, where d is the set threshold factor (0 < d < 1);

[0056] Then, at data point jerk peak The position is used as the center of the normal distribution for weighting, μ and σ are the mean and standard deviation of the Gaussian distribution, e is the natural constant, τ is the time offset, and L x L y L z This represents the length of the time window calculated in the corresponding direction above. Integrating the weighted jerky value over time yields index 2, which is calculated using the following formula:

[0057]

[0058]

[0059]

[0060] α and β are the weights of Indicator 1 and Indicator 2 in the final real-time comfort index;

[0061] Finally, the real-time comfort evaluation index C along the x, y, and z axes was obtained. x C y C z :

[0062] C x =α·J αx +β·J βx

[0063] C y =α·J αy +β·J βy

[0064] C z =α·J αz +β·J βz

[0065] ω1, ω2, and ω3 represent the impact of vibrations along the x, y, and z axes on the final real-time comfort index. A real-time vehicle comfort evaluation index C based on local vibration information is defined. comfort :

[0066] C comfort =ω1·C x +ω2·C y +ω3·C z .

[0067] A real-time vehicle comfort evaluation system based on local vibration information includes:

[0068] The data acquisition module is used by sensors to collect vibration data experienced by the vehicle during operation.

[0069] The data processing module is used to preprocess the received vibration data;

[0070] The data stream forming module is used to add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream.

[0071] The window length determination module is used to establish a mapping relationship between vibration intensity and local vibration information extraction window, and to determine the length of the window based on the vibration information intensity at the current moment.

[0072] The analysis and calculation module is used to extract local vibration information from historical vibrations based on a variable time window, and to perform comfort analysis and calculation in the time domain using a specific time decay function, taking into account the instantaneous changes and continuous effects of vibration.

[0073] The evaluation module integrates the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index.

[0074] An apparatus comprising one or more processors;

[0075] Memory, used to store one or more programs;

[0076] When the one or more programs are executed by the one or more processors, the one or more processors implement the real-time vehicle comfort evaluation method based on local vibration information as described above.

[0077] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the real-time vehicle comfort evaluation method based on local vibration information as described above.

[0078] The advantages of this invention compared to the prior art are as follows:

[0079] This invention proposes a real-time vehicle comfort evaluation method based on local vibration information, incorporating the vehicle's dynamic vibration state into the comfort evaluation system. By comprehensively analyzing the vehicle's instantaneous jerk and historical vibration data, it provides a new dimension for real-time assessment of vehicle comfort. This method can provide real-time comfort feedback for different road surfaces and specific moments of vehicle motion, allowing for necessary adjustments and thus improving passenger comfort.

[0080] This invention introduces a time decay function, incorporating the importance distribution of historical vibration data into comfort evaluation. This method considers the interaction between different vibrations over a period of time, as well as the importance distribution of historical data, thus better aligning with human perception of vibration. This approach can more accurately assess vehicle comfort and provide more realistic comfort feedback.

[0081] This invention presents a comfort evaluation method based on local vibration information, enabling vehicles to acquire and process vibration data over a period of time, overcoming the limitations of traditional comfort evaluation methods that assess the entire path. This method can more comprehensively consider the instantaneous changes and continuous effects of vibration, improving the accuracy and real-time performance of comfort evaluation and providing data support for enhancing passenger comfort. Attached Figure Description

[0082] Figure 1 This is a flowchart of a real-time vehicle comfort evaluation method based on local vibration information as described in this invention. Detailed Implementation

[0083] The process of the present invention will be further described in detail below with reference to the accompanying drawings.

[0084] See appendix Figure 1 The present invention proposes a real-time vehicle comfort evaluation method based on local vibration information, which includes the following explanatory process:

[0085] like Figure 1 As shown, this invention provides an embodiment of a real-time vehicle comfort evaluation method based on local vibration information. This method incorporates the dynamic vibration state of the vehicle into the comfort evaluation system, providing a new perspective for measuring vehicle comfort. This invention measures the comfort level of a vehicle through comprehensive analysis of its instantaneous acceleration jerk and historical vibration data. Using a variable time window, this invention can acquire and process vibration data over a period of time to obtain local vibration information, and fuse instantaneous and historical vibration information through a time decay function. If the weighted result of the instantaneous acceleration jerk or historical vibration data exceeds a threshold, the vehicle is considered to have poor comfort, thereby achieving effective comfort assessment and ensuring an improved passenger comfort experience.

[0086] Specifically, it includes:

[0087] Step 1: Collect vibration data received by the vehicle during driving using sensors:

[0088] Through the sensor at frequency f sensor Collect information on the jerk's speed. raw ={jerk x_raw jerk y_raw jerk z_raw}

[0089] The subscripts x, y, and z represent the directions in the vehicle's coordinate system, and the jerk is defined as the derivative of the acceleration. jerk x_raw jerk y_raw jerk z_raw These are the jerkiness of the vehicle in the x-direction, y-direction, and z-direction, respectively.

[0090] Step 2: Filter and weight the received data:

[0091] The collected vibration data were filtered using a sixth-order Butterworth filter, with a cutoff frequency f set. filter The transfer function of the sixth-order filter used is determined as follows:

[0092]

[0093] jerk raw The jerkiness obtained after filtering with a sixth-order Butterworth filter is:

[0094] jerkfilter ={jerk x_filter jerk y_filter jerk z_filter}

[0095] According to the ISO 2631-1 standard, frequency-weighted analysis is applied to the filtered vibration data. The ISO 2631-1 standard provides a method for assessing human perception and response to vibration. In this standard, the weights for the x, y, and z axes are set to W, respectively. x W y W z The specific weighting function can be expressed as:

[0096] jerk wx =W x jerk x_filter jerk wy =W y jerk y_filter jerk wz =W z jerk z_filter Among them, jerk wx jerk wy jerk wz These are the weighted jerk values ​​on the x, y, and z axes, respectively. filter After frequency weighting, the final vibration jerk after preprocessing is obtained. w jerk filter This represents a set of vectors, including the jerk values ​​after filtering in the x, y, and z directions.

[0097] In step three: the jerkiness data from step two, after preprocessing, is added to the historical sequence, thus updating the historical sequence.

[0098] In step three, the jerkiness data after preprocessing in step two is added to the historical sequence.

[0099] In this step, the filtered and weighted jerkiness from step two is added to a historical sequence of fixed duration t. This process can be viewed as a sliding window operation, where the window size is equal to the duration of the historical sequence.

[0100] The vehicle sensor in step one uses a frequency f sensor If sampling is performed, then in this fixed-length historical sequence, there are a total of N = f sensor There are ×t data points. This historical sequence can be represented as a vector. Where h iThis is the value of the i-th vibration data point, containing the jerk data along the three axes after preprocessing: h i ={jerk wx jerk wy jerk wz}

[0101] When the new data point h N+1 When adding data after preprocessing, the historical sequence needs to be updated, i.e., h. N+1 Add to the end of the sequence, while removing the data point h1 from the beginning of the sequence. This process can be represented by the formula: in For the updated historical sequence, after the last data stream is updated, the data indices are reset to obtain...

[0102] In this way, a continuous stream of vehicle vibration data can be maintained. This data stream contains vibration data within a recent duration of t, and this data stream can be used for subsequent comfort assessments.

[0103] Step 4: Using vehicle vibration data stream Based on this, the length of the variable time window is determined according to the vibration intensity at the current moment. The main goal of this process is to ensure that both the instantaneous changes and the continuous effects of vibration are fully considered when calculating real-time comfort evaluation indicators. Specifically, if the current vibration intensity is high, the corresponding time window will be larger to better capture the continuous effects of the vibration; conversely, if the current vibration intensity is low, a shorter variable time window will be selected.

[0104] First, in the historical data stream Extract the sampling points within the most recent 1 second, and define the vibration intensity at the current moment based on this data stream, thereby determining the intensity of the dynamic time window.

[0105] Swiftness data for each axis (x, y, z) in the vehicle coordinate system Extract sampling points from the most recent 1 second, and then calculate the maximum jerkiness and the average jerkiness for each direction from multiple sampling points within that 1 second. Multiply the maximum and average values ​​by their respective weights. If the result is less than a set threshold T, the time window is set to one second. If it exceeds the threshold T, the length of the dynamic time window is designed according to a corresponding exponential function. This process can be divided into the following steps:

[0106] Define the vibration intensity I for each axial jerk. x I y I z for:

[0107]

[0108]

[0109]

[0110] Where ω1 and ω2 are the weights of the maximum and average values, respectively, f sensor It is the sensor's sampling frequency; j x (t), j y (t), j z (t) represents the values ​​of the sampling points in the x, y, and z directions at time t; t x_max , t y_max , t z_max These represent the times corresponding to the maximum values ​​of the sampling points in the x, y, and z directions within one second.

[0111] The process of determining the length of the dynamic time window can be divided into two cases based on the relationship between vibration intensity and the threshold: defining the relevant threshold T. Functions are defined for these two cases as follows:

[0112] Case 1: If the intensity in a certain direction is less than the threshold, then the duration of the variable time window is set to 1 second. Define the function f. small f small (I) = 1, indicating that the activity window time is fixed at 1 second. Second case: If the vibration intensity in a certain direction is greater than the corresponding threshold, define the function f. large f large =a·I b , where a and b are parameters determined experimentally. The length of the variable time window is then determined based on the vibration intensity.

[0113] The function f() that determines the length of the time window includes the two cases mentioned above:

[0114]

[0115] I 表 The vibration intensity indicating axial sag, I x I y I z These represent the jerk intensity in the x, y, and z directions, respectively.

[0116] Finally, the variable time window length corresponding to each axial jerk is obtained:

[0117] L x =f(I x ), L y =f(I y ), Lz =f(I z ).

[0118] Step 5: Integrate the vibration information within the variable time window.

[0119] First, the jerkiness over the last 1 second is integrated over time. Let the jerkiness j in each axial direction be j... x (t), j y (t), j z If (t), then index 1 can be calculated using the following formulas:

[0120]

[0121]

[0122]

[0123] Then in the historical sequence Extract the data h from the time window length L determined in the previous step. L Iterate through the retrieved historical data and define the maximum value as jerk. peak Defined in the retrieved historical data h L Medium greater than d·jerk peak The data point whose time is closest to the current time is selected, and d is the set threshold factor (0 < d < 1).

[0124] Then, at data point jerk peak The position is used as the center of the normal distribution for weighting, μ and σ are the mean and standard deviation of the Gaussian distribution, e is the natural constant, τ is the time offset, and L x L y L z This represents the length of the time window calculated in the corresponding direction above. Integrating the weighted jerky value over time yields index 2, which is calculated using the following formula:

[0125]

[0126]

[0127]

[0128] α and β represent the weights of index 1 and index 2 in the final real-time comfort index; finally, the real-time comfort evaluation index C along the x, y, and z axes is obtained. x C y C z :

[0129] C x =α·J αx +β·J βx

[0130] C y =α·J αy +β·J βy

[0131] C z =α·J αz +β·J βz

[0132] ω1, ω2, and ω3 represent the impact of vibrations along the x, y, and z axes on the final real-time comfort index. A real-time vehicle comfort evaluation index C based on local vibration information is defined. comfort :

[0133] C comfort =ω1·C x +ω2·C y +ω3·C z

[0134] This invention provides another embodiment of a real-time vehicle comfort evaluation system based on local vibration information, comprising:

[0135] The data acquisition module is used by sensors to collect vibration data experienced by the vehicle during operation.

[0136] The data processing module is used to preprocess the received vibration data;

[0137] The data stream forming module is used to add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream.

[0138] The window length determination module is used to establish a mapping relationship between vibration intensity and local vibration information extraction window, and to determine the length of the window based on the vibration information intensity at the current moment.

[0139] The analysis and calculation module is used to extract local vibration information from historical vibrations based on a variable time window, and to perform comfort analysis and calculation in the time domain using a specific time decay function, taking into account the instantaneous changes and continuous effects of vibration.

[0140] The evaluation module integrates the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index.

[0141] The present invention provides another embodiment of an apparatus comprising one or more processors;

[0142] Memory, used to store one or more programs;

[0143] When the one or more programs are executed by the one or more processors, the one or more processors implement the real-time vehicle comfort evaluation method based on local vibration information as described above.

[0144] The present invention provides another embodiment of a computer-readable storage medium having a computer program stored thereon, characterized in that: when the program is executed by a processor, it implements the above-described method for real-time evaluation of vehicle comfort based on local vibration information.

[0145] Based on the above-described method for real-time vehicle comfort evaluation based on local vibration information, this invention provides another device. The device includes, but is not limited to, one or more processors and a memory.

[0146] Memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions corresponding to the vehicle comfort real-time evaluation method based on local vibration information in this embodiment of the invention. The processor executes the software programs, instructions, and modules stored in the memory to perform various vehicle functions and data processing, thereby realizing the aforementioned vehicle comfort real-time evaluation method based on local vibration information.

[0147] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the terminal. Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0148] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for real-time evaluation of vehicle comfort based on local vibration information. This method includes the following steps:

[0149] Step 1: Collect vibration data of the vehicle during driving by using sensors installed on the vehicle;

[0150] Step 2: Preprocess the received vibration data;

[0151] Step 3: Add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream;

[0152] Step 4: Establish the mapping relationship between vibration intensity and local vibration information extraction window, and determine the length of the window based on the vibration information intensity at the current moment;

[0153] Step 5: Extract local vibration information from historical vibrations using a variable time window, and use a specific time decay function to analyze and calculate comfort in the time domain to consider the instantaneous changes and continuous effects of vibration.

[0154] Step Six: Integrate the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index.

[0155] The computer-readable storage medium provided by the present invention has computer-executable instructions that are not limited to the method operations described above, but can also execute related operations in the real-time vehicle comfort evaluation method based on local vibration information provided in any embodiment of the present invention.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0157] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line DSL) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk, SSD), etc.

[0158] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be included within the scope of protection of the present invention. Furthermore, all content not described in detail in this specification is prior art known to those skilled in the art.

Claims

1. A method for real-time evaluation of vehicle comfort based on local vibration information, characterized in that, Includes the following steps: Step 1: Collect vibration data of the vehicle during driving by using sensors installed on the vehicle; Step 2: Preprocess the received vibration data; Step 3: Add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream; Step 4: Establish the mapping relationship between vibration intensity and local vibration information extraction window, and determine the length of the window based on the vibration information intensity at the current moment; Step 5: Extract local vibration information from historical vibrations based on a variable time window, and use a time decay function to analyze and calculate comfort in the time domain to consider the instantaneous changes and continuous effects of vibration. Step Six: Integrate the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index; Step four establishes a mapping relationship between vibration intensity and the local vibration information extraction window, determining the window length based on the current vibration intensity; specifically, it includes: In the vehicle coordinate system Swiftness data for each axis Extract the sampling points within the most recent 1 second; For the sampling points in the x, y, and z directions respectively, calculate the maximum value of the sampling points in each direction and the average value of the sampling points within one second; When the result of multiplying the maximum value and the average value by the corresponding weight is less than the set threshold T, the time window is set to one second. When it is greater than the set threshold T, the length of the dynamic time window is designed according to the corresponding exponential function. When the result of multiplying the maximum value and average value by their corresponding weights is less than a set threshold T, the time window is set to one second. When the result is greater than the set threshold T, the length of the dynamic time window is designed according to the corresponding exponential function. This process consists of the following steps: Define the vibration intensity for each axial jerk. for: in, It is the weight of the maximum value and the average value. It is the sampling frequency of the sensor; This represents the value of the sampling point in the x, y, z directions at time t; , , respectively This represents the time corresponding to the maximum value of the sampled points in the x, y, and z directions within one second; N represents the index of the sampled data at the latest moment. The process of determining the length of the dynamic time window is divided into two cases based on the relationship between vibration intensity and threshold, and relevant thresholds are defined. Define functions for these two cases as follows: Case 1: If the intensity in a certain direction is less than the threshold, then set the duration of the variable time window to 1 second; define the function. for: This indicates that the active window time is fixed at 1 second. The second scenario: If the vibration intensity in a certain direction exceeds the corresponding threshold, define a function. for: ,in These are parameters, determined through experiments; the length of the variable time window is then determined based on the vibration intensity. Then, a function is obtained to determine the length of the time window. (), which includes both of the above situations: Vibration intensity indicating axial jerk. These represent the jerk intensity in the x, y, and z directions, respectively. Finally, the variable time window length corresponding to each axial jerk is obtained: ； Step five involves extracting local vibration information from historical vibrations using a variable time window. The specific details are as follows: First, the abruptness over the past 1 second is integrated over time; assuming that in each axial direction... The degrees of urgency are respectively Then, index 1 is calculated using the following formulas: Next, in the historical sequence Extract the data from the time window of length L determined in the previous step. Iterate through the retrieved historical data and define the maximum value among them as... Defined in the retrieved historical data Medium to large And the data point whose time is closest to the current time, For setting the threshold factor ( ); Then, at the data points The position is used as the center of the normal distribution for weighting, and μ and σ are the mean and standard deviation of the Gaussian distribution. It is a natural constant. This is the time offset. , , This represents the length of the time window calculated in the corresponding direction above. Integrating the weighted jerky value over time yields index 2, which is calculated using the following formula: and The weights of Indicator 1 and Indicator 2 in the final real-time comfort index; Finally, we get Real-time comfort evaluation index of axial direction : for The impact of axle vibration on the final real-time comfort index is investigated, and a real-time vehicle comfort evaluation index based on local vibration information is defined. : 。 2. The method for real-time evaluation of vehicle comfort based on local vibration information according to claim 1, characterized in that: The sensor described in step one collects vibration data received by the vehicle during operation, as detailed below: via sensor at frequency Collect the vehicle's speed Information ; Subscript Represents the direction in the vehicle coordinate system, defining the jerk. The derivative of acceleration , Each of the vehicles is in Swiftness in direction, The speed of movement in the direction and The degree of abrupt change in direction.

3. The method for real-time evaluation of vehicle comfort based on local vibration information according to claim 2, characterized in that: The preprocessing of the received vibration data described in step two is as follows: First, a sixth-order Butterworth filter is used to filter the collected vibration data, and the cutoff frequency of the filter is set. ; The transfer function of the sixth-order filter used is determined as follows: The jerkiness obtained after filtering with a sixth-order Butterworth filter is: ; Then, the filtered vibration data were frequency-weighted according to the ISO2631-1 standard; The weights of the axes are set as follows: The specific weighting function is expressed as follows: ;in They are respectively Weighted jerk on the axis; After frequency weighting, the resulting data is the preprocessed vibration data.

4. The method for real-time evaluation of vehicle comfort based on local vibration information according to claim 3, characterized in that: Step three involves adding the preprocessed vibration data to the stored historical vibration information, forming a continuous vehicle vibration data stream; specifically, this includes the following: Add the jerkiness processed in step two to the historical sequence of a fixed duration t; The vehicle sensor in step one is based on frequency. The sampling was conducted, and in this historical sequence, there were a total of 10 data points; represent this historical sequence as a vector. , in It is the first The vibration data values ​​include the jerk data for the three axes after preprocessing: ; When new data points When adding data after preprocessing is complete, the historical sequence needs to be updated, that is... Add to the end of the sequence, and remove the data point at the beginning of the sequence. This process can be represented by the following formula: ,in For the updated historical sequence, after the last data stream is updated, the data indices are reset to obtain... Maintain a continuous stream of vehicle vibration data. This data stream contains vibration data within a recent duration of t, and this data stream is used for subsequent comfort evaluation.

5. The evaluation system for a real-time vehicle comfort evaluation method based on local vibration information according to claim 1, characterized in that, include: The data acquisition module is used by sensors to collect vibration data experienced by the vehicle during operation. The data processing module is used to preprocess the received vibration data; The data stream forming module is used to add the preprocessed vibration data to the stored historical vibration information to form a continuous vehicle vibration data stream. The window length determination module is used to establish a mapping relationship between vibration intensity and local vibration information extraction window, and to determine the length of the window based on the vibration information intensity at the current moment. The analysis and calculation module is used to extract local vibration information from historical vibrations based on a variable time window, and to perform comfort analysis and calculation in the time domain using a time decay function to take into account the instantaneous changes and continuous effects of vibration. The evaluation module integrates the current vibration data with the extracted historical vibration data to obtain the final real-time comfort evaluation index.

6. An apparatus, characterized in that: Includes one or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the method as described in any one of claims 1-4.

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