CN117763290A - Automobile seat dynamic comfort evaluation method based on seat vibration - Google Patents
Automobile seat dynamic comfort evaluation method based on seat vibration Download PDFInfo
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Abstract
The invention relates to the technical field of automobile data processing, in particular to an automobile seat dynamic comfort evaluation method based on seat vibration. The method obtains the final time segment of the path spectrum data of all dimensions; obtaining correlation characteristics between the road spectrum data of each dimension and road spectrum data of other dimensions, and further obtaining all target dimension combinations of each dimension; obtaining a data discrete degree through a correlation relation of a target dimension combination, and obtaining a first cluster; carrying out wavelet denoising on the first cluster to obtain a second cluster; obtaining an objective function of a wavelet threshold through intra-cluster change degree and inter-cluster change degree of the first cluster and the second cluster and respectively giving weights, and screening out an optimal wavelet threshold; and obtaining the optimal denoising result, and further evaluating the dynamic comfort of the automobile seat. According to the invention, an ideal denoising result is obtained, so that a subsequently constructed dynamic comfort evaluation model of the automobile seat is more accurate, and accurate evaluation of comfort is realized.
Description
Technical Field
The invention relates to the technical field of automobile data processing, in particular to an automobile seat dynamic comfort evaluation method based on seat vibration.
Background
In modern life, automobiles have become an important tool for people to travel. The time for drivers and passengers to stay in the car is longer and longer, and thus the comfort requirements for car seats are also increasing. However, vibration is a dynamic factor, greatly affecting the feel of the driver and passengers. The vibration characteristics of the seat may change when the vehicle travels under different road conditions. For example, a bumpy road surface or a seat vibration generated when turning may cause discomfort to the driver and passengers. Therefore, research is particularly important by adopting an automobile seat dynamic comfort evaluation method based on seat vibration.
The vehicle seat dynamic comfort evaluation model is constructed by collecting road spectrum data of different dimensions describing the running condition of the vehicle. However, the accuracy of the comfort evaluation result is affected by the acquired road spectrum data, but the multi-dimensional road spectrum data contains a lot of noise due to the complexity of road conditions and the motion randomness in the running process of the automobile, so that the multi-dimensional road spectrum data is often required to be subjected to denoising treatment. The traditional denoising method of the road spectrum data is a wavelet threshold denoising method, but the wavelet threshold denoising result is related to wavelet threshold selection, if the wavelet threshold is set smaller, the denoising effect is not ideal, larger noise can be reserved, and the response of the seat data to the multidimensional road spectrum data can be deviated; if the wavelet threshold is set to be larger, excessive denoising is caused, so that the original change information of the multidimensional road spectrum data is lost, and the evaluation score obtained by constructing the seat comfort model is larger.
Disclosure of Invention
In order to solve the technical problem that in the traditional wavelet threshold denoising process of road spectrum data, the wavelet threshold is unreasonably selected to cause the non-ideal denoising effect of the road spectrum data, so that the dynamic comfort level of an automobile seat cannot be accurately evaluated, the invention aims to provide an automobile seat dynamic comfort level evaluation method based on seat vibration, and the adopted technical scheme is as follows:
a method for evaluating dynamic comfort of an automobile seat based on seat vibration, the method comprising:
acquiring road spectrum data of all dimensions affecting seat vibration;
acquiring all final time segments of all dimension road spectrum data according to the acquisition time of all dimension road spectrum data; obtaining correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions; obtaining all target dimension combinations of each dimension road spectrum data according to the correlation characteristics;
obtaining the data discrete degree of each dimension at each moment according to the correlation between the road spectrum data in each target dimension combination; taking all final time segments as sample points, and clustering all sample points according to the data discrete degree to obtain all first clustering clusters;
Carrying out wavelet threshold denoising on each first cluster by utilizing different preset wavelet thresholds in sequence to obtain a corresponding second cluster; obtaining the intra-cluster variation degree according to the density variation degree of the sample points in the first cluster and the corresponding second cluster; obtaining the inter-cluster variation degree according to the direction vector of the first cluster pointing to the second cluster; obtaining intra-cluster variation weights according to the sample point distribution characteristics in the first cluster; obtaining an objective function corresponding to a wavelet threshold according to the intra-cluster variation degree, the inter-cluster variation degree and the intra-cluster variation weight; screening out an optimal wavelet threshold according to the objective function corresponding to each wavelet threshold;
denoising the path spectrum data of all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result;
and evaluating the dynamic comfort level of the automobile seat according to the optimal denoising result.
Further, the method for acquiring the final time segment includes:
acquiring all maximum value points and all minimum value points of road spectrum data of all dimensions; taking the time points corresponding to all the maximum value points and the minimum value points as dividing time points;
Dividing the road spectrum data through adjacent dividing time points of the road spectrum data of each dimension to obtain an initial time segment; obtaining initial merging degree between adjacent initial time segments according to the time length of the adjacent initial time segments and the change correlation between the adjacent initial time segments; averaging the initial merging degree between the same two adjacent initial time segments in each dimension to obtain the final merging degree; and obtaining all final time segments of the path spectrum data of all dimensions according to the final merging degree.
Further, the method for acquiring the initial merging degree comprises the following steps:
the merging degree is obtained according to an initial merging degree calculation formula, wherein the initial merging degree calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Indicate->Initial time segment and->Initial degree of merging between initial time segments; />Indicate->The time length of the initial time segment; />Representing the maximum value of the time length of all the initial time segments; />Indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment;indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment; / >Indicate->Initial time segment and->A varying correlation between the initial time segments; />An exponential function based on a natural constant is represented.
Further, obtaining all final time segments of the road spectrum data of each dimension according to the final merging degree comprises:
presetting a final merging degree threshold, and merging adjacent initial time segments with the final merging degree larger than the final merging degree threshold to obtain a first merging segment; calculating the final merging degree of the first merging segment and the next adjacent initial time segment, if the final merging degree is larger than the final merging degree threshold, merging the first merging segment with the next adjacent initial time segment, repeating the operation until the final merging degree is smaller than the final merging degree threshold, stopping merging, and obtaining a final time segment;
all final time segments are obtained by traversing each initial time segment.
Further, the method for acquiring the correlation characteristic comprises the following steps:
the correlation characteristic is obtained according to a correlation characteristic calculation formula, wherein the correlation characteristic calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Indicate->All road spectrum data of each dimension and +. >Correlation features between all path spectrum data of the individual dimensions; />Representing the number of final time segments; />A sequence number representing a final time segment; />Indicate->The time length of the final time segment; />Representing the maximum time length of all final time segments; />Indicate->Dimension and->The individual dimension is at%>Pearson correlation coefficients between all the path spectrum data of the final time segment; />Representing the maximum function.
Further, the method for acquiring the data discrete degree comprises the following steps:
acquiring the data discrete degree according to a data discrete degree calculation formula, wherein the data discrete degree calculation formula is as follows:
in the method, in the process of the invention,representing the>The degree of data dispersion of road spectrum data at each moment; />Indicate->The>Mapping functions at each moment; />Indicate->The>Removal of the final time segment at which the respective moment is located +.>Pearson correlation values after each instant; />Indicate->The>The pearson correlation coefficient value of the final time segment where the moments are located; />A target dimension combination number representing road spectrum data of each dimension; / >A target dimension combination sequence number representing the road spectrum data of each dimension; />Representing the road spectrum data of each dimension and +.>Correlation features between road spectrum data of another dimension in the target dimension combination.
Further, the method for acquiring the intra-cluster variation degree comprises the following steps:
the intra-cluster change degree is obtained according to an intra-cluster change degree calculation formula, and the intra-cluster change degree calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Representing the intra-cluster variation degree of the first cluster and the second cluster; />Representing the number of sample points of the first cluster and the second cluster; />Sample point sequence numbers representing the first cluster and the second cluster; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the first cluster; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the second cluster; />An exponential function based on a natural constant is represented.
Further, the method for obtaining the inter-cluster variation degree comprises the following steps:
establishing a Cartesian coordinate system by taking the mean value of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an abscissa and taking the variance of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an ordinate; the Cartesian coordinate system comprises all the first cluster groups and corresponding second cluster groups;
Obtaining a unit vector of a straight line of y=x constructed in a Cartesian coordinate system from a maximum value to a coordinate origin; acquiring a direction vector of the cluster center of the first cluster to the cluster center of the second cluster;
and calculating a cosine similarity function between the direction vector and the unit vector as the inter-cluster variation degree between the first cluster and the second cluster.
Further, the method for acquiring the intra-cluster variation weight comprises the following steps:
the intra-cluster change weight is obtained according to an intra-cluster change weight calculation formula, and the intra-cluster change weight calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Representing intra-cluster variation weights;representing the average Euclidean distance between all sample points in the first cluster; />Representing the maximum value of Euclidean distances among all sample points in the first cluster; />An abscissa representing a cluster center of the first cluster; />An ordinate representing a cluster center of the first cluster; />Representing the abscissa maximum value of all sample points in the first cluster; />Representing the maximum value of the ordinate of all sample points in the first cluster; />An exponential function based on a natural constant is represented.
Further, the method for acquiring the objective function comprises the following steps:
Performing negative correlation mapping on the intra-cluster variation weights to obtain inter-cluster variation weights;
taking the product of the intra-cluster variation weight and the intra-cluster variation degree as a first product and taking the product of the inter-cluster variation weight and the inter-cluster variation degree as a second product;
the sum of the first product and the second product is taken as an objective function between the first cluster and the second cluster.
The invention has the following beneficial effects:
the road spectrum data of all dimensions affecting the seat vibration are obtained, and a data basis is provided for the subsequent evaluation of the dynamic comfort level of the automobile seat; obtaining final time segments of the road spectrum data of all dimensions, wherein each final time segment reflects different change characteristics of the road spectrum data; in order to denoise the road spectrum data of each dimension subsequently, the correlation relationship between the road spectrum data of other dimensions and the road spectrum data of the dimension needs to be analyzed, so that the correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions are obtained; obtaining all target dimension combinations of the road spectrum data of each dimension according to the correlation characteristics, wherein the target dimension combinations comprise road spectrum data of other dimensions with higher correlation with the road spectrum data of each dimension; since the road spectrum data of all dimensions need to be denoised subsequently, the distribution characteristics among the road spectrum data of all dimensions at each moment should be analyzed first, so that the degree of data dispersion at each moment is obtained; judging the denoising effect through the change difference of the clustering clusters before and after denoising; the intra-cluster variation degree can represent the variation condition in the clusters from the first cluster to the second cluster; according to the direction vector of the first cluster pointing to the second cluster, obtaining the inter-cluster change degree, and reflecting the change condition of the second cluster through the inter-cluster direction change degree; obtaining intra-cluster variation weights according to sample point distribution characteristics in the first cluster, wherein the intra-cluster variation weights reflect the importance degree of the intra-cluster variation degree; obtaining an objective function between a first cluster and a second cluster according to the intra-cluster change degree, the inter-cluster change degree and the intra-cluster change weight, and screening out an optimal wavelet threshold according to the objective function corresponding to each wavelet threshold; denoising the path spectrum data of all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result; and evaluating the dynamic comfort level of the automobile seat according to the optimal denoising result. The method and the device solve the problem of non-ideal denoising effect caused by unreasonable wavelet threshold selection in the traditional wavelet threshold denoising process, so that a subsequently constructed dynamic comfort evaluation model of the automobile seat is more accurate, and accurate evaluation of comfort is realized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for evaluating dynamic comfort of an automobile seat based on seat vibration according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of the method for evaluating the dynamic comfort of the automobile seat based on seat vibration according to the invention, which are provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the automobile seat dynamic comfort evaluation method based on seat vibration provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for evaluating dynamic comfort of an automobile seat based on seat vibration according to an embodiment of the invention is shown, where the method includes:
step S1: road spectrum data is acquired for all dimensions affecting seat vibration.
The embodiment of the invention mainly provides an automobile seat dynamic comfort evaluation method based on seat vibration, which aims at evaluating the automobile seat dynamic comfort and firstly provides a data base for the construction of an automobile seat dynamic comfort evaluation model, namely, road spectrum data of all dimensions affecting the seat vibration is obtained.
In one embodiment of the invention, road spectrum data with different dimensions are acquired through sensors of different types such as a wheel hexad force sensor, a vehicle body acceleration sensor, a spindle head acceleration sensor and the like. The sensor types can be set by the specific implementation personnel according to the implementation scene, and are not limited and described in detail herein.
In one embodiment of the invention, the frequency of the road spectrum data is set to be 5s for one time, and the road spectrum data of all dimensions within 30 minutes is acquired. It should be noted that, the road spectrum data of all dimensions is normalized to eliminate the influence of the dimensions.
Step S2: acquiring all final time segments of all dimension road spectrum data according to the acquisition time of all dimension road spectrum data; obtaining correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions; and obtaining all target dimension combinations of the road spectrum data of each dimension according to the correlation characteristics.
Because the acquired multidimensional road spectrum data changes along with time, road spectrum data of all dimensions are different in road change conditions at different times, the road spectrum data are segmented to obtain time segments, and the road spectrum data of all dimensions of each time segment are in the same road change condition. Since there may be noise in the actual situation, which causes the segments that would otherwise belong to the same road change situation to be divided into multiple segments, all final time segments of all dimension road spectrum data need to be obtained.
Preferably, in one embodiment of the present invention, the method for acquiring the final time segment includes:
Acquiring all maximum value points and all minimum value points of road spectrum data of all dimensions; taking the time points corresponding to all the maximum value points and the minimum value points as dividing time points; if no external environment factors influence, the initial time segments obtained by dividing adjacent dividing time points reflect the condition that the road spectrum data of all dimensions of the initial time segments are in the same road change;
dividing the road spectrum data through adjacent dividing time points of the road spectrum data of each dimension to obtain an initial time segment; obtaining initial merging degree between adjacent initial time segments according to the time length of the adjacent initial time segments and the change correlation between the adjacent initial time segments; averaging the initial merging degree between the same two adjacent initial time segments in each dimension to obtain the final merging degree; and obtaining all final time segments of the road spectrum data of all dimensions according to the final merging degree, wherein the road spectrum data of all dimensions of each final time segment are in the same road change condition.
Preferably, in one embodiment of the present invention, the method for obtaining the initial combining degree includes:
obtaining the merging degree according to an initial merging degree calculation formula, wherein the initial merging degree calculation formula is as follows:
In the method, in the process of the invention,indicate->Initial time segment and->Initial degree of merging between initial time segments; />Indicate->The time length of the initial time segment; />Representing the maximum value of the time length of all the initial time segments; />Indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment; />Indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment; />Indicate->Initial time segment and->A varying correlation between the initial time segments; />Indicate->The time length of the initial time segment; />Representing the maximum function;>an exponential function based on a natural constant is represented.
In the initial combining degree calculation formula,indicate will be->Time length normalization of the initial time segments, < >>Smaller, no->The smaller the time length of the initial time segment, the reflection of the +.>The greater the likelihood that the initial time segment is a segment due to noise factors, at this point +.>Initial time segment and->The greater the degree of initial merging between the initial time segments; / >Indicate->Initial time segment and->A varying correlation between the initial time segments; the smaller the change correlation is, the +.>Initial time segment and->The smaller the corresponding slope differences of the initial time segments, the description of +.>Initial time segment and->The more likely that the initial time segments are in the same road change situation, at this point +.>Initial time segment and->The greater the degree of initial merging between the initial time segments.
And averaging the initial merging degree between the same two adjacent initial time segments in each dimension to obtain a final merging degree, and merging the initial time segments influenced by noise according to the final merging degree to obtain the final time segments. Preferably, in one embodiment of the present invention, obtaining all final time segments of the road spectrum data of each dimension according to the final merging degree includes:
presetting a final merging degree threshold, and merging adjacent initial time segments with the final merging degree larger than the final merging degree threshold to obtain a first merging segment; calculating the final merging degree of the first merging segment and the next adjacent initial time segment, if the final merging degree is larger than the final merging degree threshold, merging the first merging segment with the next adjacent initial time segment, repeating the operation until the final merging degree is smaller than the final merging degree threshold, stopping merging, and obtaining a final time segment; each initial time segment is traversed to obtain all final time segments.
In one embodiment of the present invention, a step of obtaining a final time segment is provided, which specifically includes:
setting a merging degree threshold to be 0.75, and if the final merging degree between the first initial time segment and the second initial time segment is greater than 0.75, merging the first initial time segment with the second initial time segment to obtain a first merging segment; if the degree of merging between the first merging segment and the third initial time segment is greater than 0.75, merging the first merging segment with the third initial time segment to obtain a second merging segment, continuously comparing the final degree of merging between the second merging segment and the fourth initial time segment, and if the final degree of merging between the second merging segment and the fourth initial time segment is less than or equal to 0.75, taking the second merging segment as a final time segment.
It should be noted that, the setting of the merging degree threshold may be set by an implementer according to a specific implementation scenario, which is not limited herein.
So far, all final time segments are obtained.
Because the road spectrum data of each dimension are often correlated, in order to denoise the road spectrum data of each dimension later, correlation relations between the road spectrum data of other dimensions and the road spectrum data of the dimension need to be analyzed. Therefore, in the embodiment of the invention, the correlation characteristic between the road spectrum data of each dimension and the road spectrum data of other dimensions is required to be acquired.
Preferably, in one embodiment of the present invention, the method for acquiring the correlation feature includes:
obtaining a correlation characteristic according to a correlation characteristic calculation formula, wherein the correlation characteristic calculation formula is as follows:
in the method, in the process of the invention,indicate->All final time segments of the dimension with +.>Correlation features between all final time segments of the individual dimensions; />Representing the number of final time segments; />A sequence number representing a final time segment; />Represent the firstThe time length of the final time segment; />Representing the time length of all final time segments; />Indicate->Dimension and->The individual dimension is at%>Pearson correlation coefficients between all the path spectrum data of the final time segment;representing the maximum function.
In the related characteristic calculation formula, the method aims at the firstEach final time segment of the dimension is associated with +.>The correlation of the corresponding final time segments of the individual dimensions is studied. First->Dimension and->No. 5 of the individual dimension>The longer the time length of the final time segment is, < +.>The larger, the description of->Dimension and->The longer the individual dimensions pass the same road change, the +.>Dimension and->The individual dimension is at%>The greater the correlation of the final time segment, and +. >Dimension and->No. 5 of the individual dimension>The greater the pearson correlation coefficient between all the road spectrum data of the final time segment, the description +.>Dimension and the firstThe individual dimension is at%>The greater the correlation of the individual final time segments; calculate->Dimension and->Average value of correlation of all final time segments of each dimension, the larger the average value of correlation, the description +.>Road spectrum data of individual dimensions and +.>The larger the correlation between the road spectrum data of the individual dimensions as a whole, the +.>Road spectrum data of individual dimensions and +.>The larger the correlation feature between the path spectrum data of each dimension.
And obtaining all target dimension combinations of the path spectrum data of each dimension by utilizing the correlation characteristics.
In one embodiment of the invention, a correlation characteristic threshold value is set to be 0.68, road spectrum data of each dimension is used as a target dimension, road spectrum data of other dimensions are used as dimensions to be calculated, correlation characteristics between the target dimension and each dimension to be calculated are calculated, each dimension to be calculated with the correlation characteristics larger than 0.68 and the target dimension form a target dimension combination, and all target dimension combinations of the target dimension are obtained; traversing each dimension to obtain all target dimension combinations of the road spectrum data of each dimension, wherein each target dimension combination comprises road spectrum data of other single dimension with higher correlation with the road spectrum data of each dimension.
It should be noted that, the setting of the correlation characteristic threshold may be set by an implementer according to a specific implementation scenario, which is not limited herein.
Step S3: obtaining the data discrete degree of each dimension at each moment according to the correlation between the road spectrum data in each target dimension combination; and taking all final time segments as sample points, and clustering all sample points according to the degree of data dispersion to obtain all first clustering clusters.
Since the road spectrum data of all dimensions need to be denoised subsequently, the distribution characteristics among the road spectrum data of all dimensions of each moment should be analyzed first, and the degree of data dispersion reflects the degree of distribution dispersion among the road spectrum data of each dimension between each moment and the road spectrum data of other dimensions in all target dimension combinations.
Preferably, in one embodiment of the present invention, the method for acquiring the degree of data dispersion includes:
obtaining the data discrete degree according to a data discrete degree calculation formula, wherein the data discrete degree calculation formula is as follows:
In the method, in the process of the invention,representing the>The degree of data dispersion of road spectrum data at each moment; />Indicate->The>Mapping functions at each moment; />Indicate->The>Removal of the final time segment at which the respective moment is located +.>Pearson correlation values after each instant; />Indicate->The>Pearson correlation coefficient values for time periods in which the respective moments are located; />A target dimension combination number representing road spectrum data of each dimension; />A target dimension combination sequence number representing the road spectrum data of each dimension; />Representing the road spectrum data of each dimension and +.>Correlation features between road spectrum data of another dimension in the target dimension combination.
In the data discrete degree calculation formula, in each target dimension combination, the larger the correlation characteristic is, the larger the correlation relationship between the road spectrum data of two dimensions in the target dimension combination is, and at the momentThe smaller the degree of data dispersion, the smaller; and->The>The mapping function at each instant represents the first by pearson correlation coefficientWithin the individual target dimension combinations +.>Within the final time segment where the moments are located, there is a +. >Pearson correlation coefficient at each instant and remove +.>The difference between the pearson correlation coefficients at the respective instants as +.>Distribution characteristics of road spectrum data at each momentSign, wherein the larger the difference between pearson correlation coefficients, the instruction +.>The more discrete the distribution of road spectrum data at each time, at that timeThe greater the degree of data dispersion of the road spectrum data at each time.
So far, the degree of data dispersion at each moment in each dimension is obtained.
And taking all road spectrum data of all final time segments of each dimension as sample points, clustering all sample points according to the degree of data dispersion, wherein the integral degree of dispersion in all final time segments in each cluster is similar.
In one embodiment of the invention, a Cartesian coordinate system is established by taking the mean value of the data discrete degree of the final time segment corresponding to the sample point as an abscissa and taking the variance of the data discrete degree of the final time segment corresponding to the sample point as an ordinate; and performing DBSCAN clustering on all sample points to obtain a plurality of clustering clusters, wherein all sample points in each clustering cluster reflect all final time segments with similar discrete degrees.
It should be noted that, the DBSCAN clustering algorithm is a technical means well known to those skilled in the art, and is not described herein in detail, and in other embodiments of the present invention, other clustering algorithms may be used for clustering, which is not limited herein.
Step S4: carrying out wavelet threshold denoising on each first cluster by utilizing different preset wavelet thresholds in sequence to obtain a corresponding second cluster; obtaining the intra-cluster variation degree according to the density variation degree of the sample points in the first cluster and the corresponding second cluster; obtaining the inter-cluster variation degree according to the direction vector of the first cluster pointing to the corresponding second cluster; obtaining intra-cluster variation weights according to sample point distribution characteristics in the first cluster; obtaining an objective function corresponding to the wavelet threshold according to the intra-cluster variation degree, the inter-cluster variation degree and the intra-cluster variation weight; and screening out the optimal wavelet threshold according to the objective function corresponding to each wavelet threshold.
Because the traditional denoising method of the road spectrum data is a wavelet threshold denoising method, the wavelet threshold denoising result is related to wavelet threshold selection, and if the wavelet threshold is set smaller, the denoising effect is not ideal, and larger noise can be reserved; if the wavelet threshold is set to be larger, excessive denoising is caused, so that the original change information of the multidimensional road spectrum data is lost. In order to obtain the optimal wavelet denoising threshold value of the final time segment corresponding to each sample point in each cluster, analysis is required to be performed on each final time segment in the first cluster and the corresponding denoised second cluster, and the effect of obtaining the optimal wavelet threshold value is achieved by constructing an objective function. The optimal wavelet threshold expected to be obtained by the embodiment of the invention can keep the change condition of the final time segment corresponding to all sample points in the second cluster consistent after the wavelet threshold is denoised. In the embodiment of the present invention, it is required to first acquire the corresponding second cluster of each first cluster.
In one embodiment of the invention, the wavelet threshold traversal range [0.1,0.3] is set, and the step length is set to be 0.02, so that 11 wavelet thresholds are obtained. And denoising each first cluster by using 11 wavelet thresholds, firstly marking all sample points in the first cluster to obtain a denoising result of a final time segment corresponding to each sample point in the first cluster, and taking the denoising result of the marked sample points as a second cluster. It should be noted that, the selection of the wavelet threshold may be set by an implementer according to a specific implementation scenario, which is not limited herein.
The change condition of the final time segment corresponding to all the sample points in the second cluster needs to be analyzed, so that the density change degree of the sample points in the first cluster and the second cluster needs to be obtained when the first cluster is changed to the second cluster, the change degree in the clusters is reflected, and the change degree in the clusters can represent the change condition when the first cluster is changed to the second cluster.
Preferably, in one embodiment of the present invention, the method for acquiring the intra-cluster variation degree includes:
the intra-cluster change degree is obtained according to an intra-cluster change degree calculation formula, and the intra-cluster change degree calculation formula is as follows:
In the method, in the process of the invention,representing the intra-cluster variation degree of the first cluster and the second cluster; />Representing the number of sample points of the first cluster and the second cluster; />Sample point sequence numbers representing the first cluster and the second cluster; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the first cluster; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the second cluster;representing the first cluster and the second clusterFirst->The degree of density variation of the individual sample points;an exponential function based on a natural constant is represented. In one embodiment of the present invention, the preset range is set to a circle with a radius of 4. It should be noted that the preset range may be set by an implementation person according to an implementation scenario, which is not limited herein.
In the intra-cluster variation degree calculation formula, respectively carrying out normalization processing on the density of each sample point in a preset range of each corresponding sample point in the first cluster and the second cluster, and comparing the difference, wherein the larger the difference is, the larger the density variation between each sample point of the second cluster relative to each sample point of the first cluster is; calculating a sample point density difference mean value, and reflecting the sample point density change of the whole of the first cluster and the second cluster, wherein the greater the sample point density change is, the greater the intra-cluster change degree is.
In addition to reflecting the change condition from the first cluster to the second cluster by analyzing the change degree in the clusters, the change vector can also be represented by the direction change vector between the first cluster and the second cluster. Because the sample points in the second cluster are obtained by denoising the sample points in the first cluster, the integral discrete degree of the final time segment corresponding to the sample points in the second cluster can be reduced, the second cluster changes towards the direction with smaller discrete degree relative to the first cluster, and the change condition of the second cluster can be reflected according to the change degree of the direction. Therefore, in the embodiment of the invention, the inter-cluster variation degree is obtained according to the direction variation vector between the first cluster and the second cluster.
Preferably, in one embodiment of the present invention, the method for obtaining the inter-cluster variation degree includes:
establishing a Cartesian coordinate system by taking the mean value of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an abscissa and taking the variance of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an ordinate; the Cartesian coordinate system comprises all first cluster groups and corresponding second cluster groups; obtaining a unit vector of a straight line of y=x constructed in a Cartesian coordinate system from a maximum value to a coordinate origin, wherein the unit vector reflects a change direction with the minimum degree of dispersion of the second cluster; acquiring a direction vector of a cluster center of a first cluster to a cluster center of a second cluster; and calculating a cosine similarity function between the direction vector and the unit vector to serve as the inter-cluster change degree between the first cluster and the second cluster, wherein if the cosine similarity function is larger, the direction change between the direction change vector between the first cluster and the second cluster and the unit vector is more similar, and the second cluster is changed towards the direction with smaller discrete degree relative to the first cluster, and the inter-cluster change degree is larger.
If the first cluster is discrete and the horizontal coordinate mean value and the vertical coordinate mean value of the final time segment corresponding to all sample points in the first cluster are larger, the distribution characteristics of the sample points in the first cluster are more disordered, and the inter-cluster change degree between the first cluster and the second cluster should be more cared; if the first cluster is more aggregated and the abscissa mean value and the ordinate mean value of the final time segment corresponding to all the sample points in the first cluster are smaller, the distribution characteristics of the sample points in the first cluster are more stable and the degree of abnormality is smaller, and at the moment, the intra-cluster change degree in the first cluster and the second cluster should be more careful. Therefore, in the embodiment of the invention, the intra-cluster variation weight is obtained according to the sample point distribution characteristics in the first cluster, and the importance degree of the intra-cluster variation degree is reflected by the intra-cluster variation weight.
Preferably, in one embodiment of the present invention, the method for acquiring the intra-cluster variation weight includes:
the intra-cluster change weight is obtained according to an intra-cluster change weight calculation formula, and the intra-cluster change weight calculation formula is as follows:
in the method, in the process of the invention,representing intra-cluster variation weights; />Representing the average Euclidean distance between all sample points in the first cluster; / >Representing the maximum value of Euclidean distances among all sample points in the first cluster; />An abscissa representing a cluster center of the first cluster; />An ordinate representing a cluster center of the first cluster; />Representing the abscissa maximum value of all sample points in the first cluster; />Representing the maximum value of the ordinate of all sample points in the first cluster;an exponential function that is based on a natural constant; />Representing sample point distribution characteristics in a first cluster. />
In the intra-cluster change weight calculation formula, the larger the average Euclidean distance among all sample points in the first cluster is, the more discrete the sample point distribution in the first cluster is, and at the moment, the more chaotic the sample point distribution characteristics in the first cluster are; the larger the values of the abscissa and the ordinate of the cluster center in the first cluster, the larger the discrete degree of the first cluster, the more chaotic the distribution characteristics of the sample points in the first cluster, the smaller the importance degree of the change degree in the cluster, and the smaller the change weight in the cluster.
And constructing an objective function according to the intra-cluster change degree, the inter-cluster change degree and the intra-cluster change weight to analyze the change condition between the first cluster and the second cluster.
Preferably, in one embodiment of the present invention, the method for acquiring an objective function includes:
because the intra-cluster variation weight and the inter-cluster variation weight are associated, the intra-cluster variation weight is subjected to negative correlation mapping to obtain the inter-cluster variation weight; taking the product of the intra-cluster change weight and the intra-cluster change degree as a first product, and taking the product of the inter-cluster change weight and the inter-cluster change degree as a second product; the sum of the first product and the second product is taken as an objective function between the first cluster and the second cluster. In one embodiment of the invention, the calculation formula of the objective function is as follows:
in the method, in the process of the invention,representing an objective function between the first cluster and the second cluster; />Representing intra-cluster variation weights; />Indicating the degree of change within the cluster; />Indicating the degree of inter-cluster variation; />Representing the inter-cluster variation weights.
In the objective function calculation formula, the greater the intra-cluster variation degree is, the greater the intra-cluster variation weight is, at this time, the degree of attention to the inter-cluster variation degree is reduced, and the smaller the inter-cluster variation weight is, at this time, the intra-cluster variation isThe inverse ratio between the change weight and the cluster change weight is used in the embodiment of the inventionAnd expressing the inter-cluster change weight, respectively giving the intra-cluster change weight and the inter-cluster change weight to the intra-cluster change degree and the inter-cluster change degree, and obtaining the objective function between the first cluster and the second cluster by utilizing the intra-cluster change degree and the inter-cluster change degree.
Thus, an objective function between each first cluster and the corresponding second cluster is obtained.
And acquiring a wavelet threshold corresponding to the maximum objective function value as an optimal wavelet threshold of a final time segment corresponding to all sample points in each first cluster.
Step S5: and denoising the path spectrum data in all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result.
Step S4 obtains the optimal wavelet threshold of the final time segment corresponding to all sample points in each first cluster, and further obtains the optimal wavelet threshold of each final time segment. In one embodiment of the invention, all the road spectrum data corresponding to each final time segment can be denoised by utilizing the optimal wavelet threshold value, and finally the optimal denoising result of the road spectrum data of each dimension is obtained.
So far, the optimal denoising result of the road spectrum data of each dimension is obtained.
Step S6: and evaluating the dynamic comfort level of the automobile seat according to the optimal denoising result.
And (5) according to the step (S5), obtaining the optimal denoising result of the road spectrum data of each dimension, and constructing an automobile seat dynamic comfort evaluation model.
The embodiment of the invention provides a method for constructing an automobile seat dynamic comfort evaluation model, which specifically comprises the following steps:
The comfort level is evaluated by constructing an evaluation neural network, wherein the neural network adopts a DNN network, the input data in the adopted training set is the optimal denoising result of the road spectrum data of each dimension, the output result is the score of the professional on the comfort level of the seat, and the loss function is a root mean square error function, so that a trained seat dynamic comfort level evaluation model is obtained. It should be noted that, the construction of the neural network is a technical means well known to those skilled in the art, and is not limited and described herein.
And evaluating the dynamic comfort level of the automobile seat through a seat dynamic comfort level evaluation model.
In summary, the invention obtains road spectrum data of all dimensions affecting the seat vibration; obtaining final time segments of the road spectrum data of all dimensions, wherein each final time segment reflects different change characteristics of the road spectrum data; obtaining correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions; obtaining all target dimension combinations of the path spectrum data of each dimension according to the correlation characteristics; obtaining the degree of data dispersion at each moment; the intra-cluster variation degree can represent the variation condition in the clusters from the first cluster to the second cluster; obtaining the inter-cluster variation degree according to the direction vector of the first cluster pointing to the corresponding second cluster; obtaining intra-cluster variation weights according to sample point distribution characteristics in the first cluster, wherein the intra-cluster variation weights reflect the importance degree of the intra-cluster variation degree; obtaining an objective function between the first cluster and the second cluster according to the intra-cluster change degree, the inter-cluster change degree and the intra-cluster change weight, wherein the objective function reflects the change condition between the first cluster and the second cluster, and an optimal wavelet threshold is obtained according to the objective function; denoising the path spectrum data of all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result; and evaluating the dynamic comfort level of the automobile seat according to the optimal denoising result. The method and the device solve the problem of non-ideal denoising effect caused by unreasonable wavelet threshold selection in the traditional wavelet threshold denoising process, so that a subsequently constructed dynamic comfort evaluation model of the automobile seat is more accurate, and accurate evaluation of comfort is realized.
An embodiment of a denoising method for multidimensional road spectrum data comprises the following steps:
because of the complexity of road conditions and the motion randomness in the running process of automobiles, the multidimensional road spectrum data contains a lot of noise, so that the multidimensional road spectrum data is often required to be subjected to denoising treatment. In the prior art, the traditional denoising method of the road spectrum data is a wavelet threshold denoising method, but the wavelet threshold denoising result is related to wavelet threshold selection, if the wavelet threshold is set smaller, the denoising effect is not ideal, larger noise can be reserved, and the response of the seat data to the multidimensional road spectrum data can be deviated; if the wavelet threshold is set to be larger, excessive denoising is caused, so that the original change information of the multidimensional road spectrum data is lost, and the technical problem that an ideal denoising result cannot be obtained is caused. In order to solve the technical problem, a denoising method embodiment of multi-dimensional road spectrum data is provided.
Step S1: road spectrum data is acquired for all dimensions affecting seat vibration.
Step S2: obtaining all final time segments of all dimension road spectrum data according to the change characteristics of all dimension road spectrum data; obtaining correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions; and obtaining all target dimension combinations of the road spectrum data of each dimension according to the correlation characteristics.
Step S3: obtaining the data discrete degree of each dimension at each moment according to the correlation between the road spectrum data in each target dimension combination; and taking all final time segments as sample points, and clustering all sample points according to the degree of data dispersion to obtain all first clustering clusters.
Step S4: carrying out wavelet threshold denoising on each first cluster by utilizing different preset wavelet thresholds in sequence to obtain a corresponding second cluster; obtaining the intra-cluster variation degree according to the density variation degree of the sample points in the first cluster and the corresponding second cluster; obtaining the inter-cluster variation degree according to the direction vector of the first cluster pointing to the corresponding second cluster; obtaining intra-cluster variation weights according to sample point distribution characteristics in the first cluster; obtaining an objective function corresponding to the wavelet threshold according to the intra-cluster variation degree, the inter-cluster variation degree and the intra-cluster variation weight; and screening out the optimal wavelet threshold according to the objective function corresponding to each wavelet threshold.
Step S5: and denoising the path spectrum data in all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result.
Since the specific embodiments of step S1 to step S5 are already mentioned in the above-mentioned method for evaluating the dynamic comfort level of the car seat based on the vibration of the seat, the description thereof will be omitted.
The technical effect of this embodiment is: the method comprises the steps that road spectrum data of all dimensions affecting seat vibration are obtained, and a data base is provided for subsequent evaluation of dynamic comfort of an automobile seat; obtaining final time segments of the road spectrum data of all dimensions, wherein each final time segment reflects different change characteristics of the road spectrum data; in order to denoise the road spectrum data of each dimension subsequently, the correlation relationship between the road spectrum data of other dimensions and the road spectrum data of the dimension needs to be analyzed, so that the correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions are obtained; obtaining all target dimension combinations of the road spectrum data of each dimension according to the correlation characteristics, wherein the target dimension combinations comprise road spectrum data of other dimensions with higher correlation with the road spectrum data of each dimension; since the road spectrum data of all dimensions need to be denoised subsequently, the distribution characteristics among the road spectrum data of all dimensions at each moment should be analyzed first, so that the degree of data dispersion at each moment is obtained; judging the denoising effect through the change difference of the clustering clusters before and after denoising; the intra-cluster variation degree can represent the variation condition in the clusters from the first cluster to the second cluster; according to the direction vector of the first cluster pointing to the second cluster, obtaining the inter-cluster change degree, and reflecting the change condition of the second cluster through the inter-cluster direction change degree; obtaining intra-cluster variation weights according to sample point distribution characteristics in the first cluster, wherein the intra-cluster variation weights reflect the importance degree of the intra-cluster variation degree; obtaining an objective function between the first cluster and the second cluster according to the intra-cluster change degree, the inter-cluster change degree and the intra-cluster change weight, wherein the objective function reflects the change condition between the first cluster and the second cluster, and an optimal wavelet threshold is obtained according to the objective function; and denoising the path spectrum data in all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result. The embodiment avoids the problem of non-ideal denoising effect caused by unreasonable wavelet threshold selection in the traditional wavelet threshold denoising process, and obtains ideal denoising result of multidimensional road spectrum data.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. A method for evaluating dynamic comfort of an automobile seat based on seat vibration, the method comprising:
acquiring road spectrum data of all dimensions affecting seat vibration;
acquiring all final time segments of all dimension road spectrum data according to the acquisition time of all dimension road spectrum data; obtaining correlation characteristics between the road spectrum data of each dimension and the road spectrum data of other dimensions; obtaining all target dimension combinations of each dimension road spectrum data according to the correlation characteristics;
obtaining the data discrete degree of each dimension at each moment according to the correlation between the road spectrum data in each target dimension combination; taking all final time segments as sample points, and clustering all sample points according to the data discrete degree to obtain all first clustering clusters;
Carrying out wavelet threshold denoising on each first cluster by utilizing different preset wavelet thresholds in sequence to obtain a corresponding second cluster; obtaining the intra-cluster variation degree according to the density variation degree of the sample points in the first cluster and the corresponding second cluster; obtaining the inter-cluster variation degree according to the direction vector of the first cluster pointing to the second cluster; obtaining intra-cluster variation weights according to the sample point distribution characteristics in the first cluster; obtaining an objective function corresponding to a wavelet threshold according to the intra-cluster variation degree, the inter-cluster variation degree and the intra-cluster variation weight; screening out an optimal wavelet threshold according to the objective function corresponding to each wavelet threshold;
denoising the path spectrum data of all dimensions by utilizing the optimal wavelet threshold value to obtain an optimal denoising result;
and evaluating the dynamic comfort level of the automobile seat according to the optimal denoising result.
2. The method for evaluating the dynamic comfort of the automobile seat based on the seat vibration according to claim 1, wherein the method for acquiring the final time segment comprises the following steps:
acquiring all maximum value points and all minimum value points of road spectrum data of all dimensions; taking the time points corresponding to all the maximum value points and the minimum value points as dividing time points;
Dividing the road spectrum data through adjacent dividing time points of the road spectrum data of each dimension to obtain an initial time segment; obtaining initial merging degree between adjacent initial time segments according to the time length of the adjacent initial time segments and the change correlation between the adjacent initial time segments; averaging the initial merging degree between the same two adjacent initial time segments in each dimension to obtain the final merging degree; and obtaining all final time segments of the path spectrum data of all dimensions according to the final merging degree.
3. The method for evaluating the dynamic comfort of the automobile seat based on the seat vibration according to claim 2, wherein the method for acquiring the initial merging degree comprises the following steps:
the merging degree is obtained according to an initial merging degree calculation formula, wherein the initial merging degree calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Indicate->Initial time segment and->Initial degree of merging between initial time segments; />Indicate->The time length of the initial time segment; />Representing the maximum value of the time length of all the initial time segments; />Indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment; / >Indicate->The line slope of the connection between the path spectrum data corresponding to the initial end point and the path spectrum data corresponding to the termination end point of each initial time segment; />Indicate->Initial time segment and->A varying correlation between the initial time segments;an exponential function based on a natural constant is represented.
4. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 2, wherein obtaining all final time segments of road spectrum data of each dimension according to the final merging degree comprises:
presetting a final merging degree threshold, and merging adjacent initial time segments with the final merging degree larger than the final merging degree threshold to obtain a first merging segment; calculating the final merging degree of the first merging segment and the next adjacent initial time segment, if the final merging degree is larger than the final merging degree threshold, merging the first merging segment with the next adjacent initial time segment, repeating the operation until the final merging degree is smaller than the final merging degree threshold, stopping merging, and obtaining a final time segment;
all final time segments are obtained by traversing each initial time segment.
5. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 1, wherein the method for acquiring the correlation characteristics comprises:
the correlation characteristic is obtained according to a correlation characteristic calculation formula, wherein the correlation characteristic calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Indicate->All road spectrum data of each dimension and +.>Correlation features between all path spectrum data of the individual dimensions; />Representing the number of final time segments; />A sequence number representing a final time segment; />Indicate->The time length of the final time segment; />Representing the maximum time length of all final time segments; />Indicate->Dimension and->The individual dimension is at%>Pearson correlation coefficients between all the path spectrum data of the final time segment; />Representing the maximum function.
6. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 1, wherein the method for acquiring the degree of data dispersion comprises:
acquiring the data discrete degree according to a data discrete degree calculation formula, wherein the data discrete degree calculation formula is as follows:
in the method, in the process of the invention, Representing the>The degree of data dispersion of road spectrum data at each moment; />Indicate->The>Mapping functions at each moment; />Indicate->The>Removal of the final time segment at which the respective moment is located +.>Pearson correlation values after each instant; />Indicate->The>The pearson correlation coefficient value of the final time segment where the moments are located; />A target dimension combination number representing road spectrum data of each dimension; />A target dimension combination sequence number representing the road spectrum data of each dimension; />Representing path spectrum data of each dimensionCorrelation features between road spectrum data of another dimension in the target dimension combination.
7. The method for evaluating the dynamic comfort level of an automobile seat based on seat vibration according to claim 1, wherein the method for acquiring the intra-cluster variation degree comprises the steps of:
the intra-cluster change degree is obtained according to an intra-cluster change degree calculation formula, and the intra-cluster change degree calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Representing the intra-cluster variation degree of the first cluster and the second cluster; />Representing the number of sample points of the first cluster and the second cluster; / >Sample point sequence numbers representing the first cluster and the second cluster; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the +.>Sample point densities within a predetermined range of sample points; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the first cluster; />Representing the maximum value of the density of the sample points in a preset range of all the sample points in the second cluster; />An exponential function based on a natural constant is represented.
8. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 1, wherein the method for acquiring the degree of inter-cluster variation comprises:
establishing a Cartesian coordinate system by taking the mean value of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an abscissa and taking the variance of the data discrete degrees at all the moments in the final time segment corresponding to the sample point as an ordinate; the Cartesian coordinate system comprises all the first cluster groups and corresponding second cluster groups;
obtaining a unit vector of a straight line of y=x constructed in a Cartesian coordinate system from a maximum value to a coordinate origin; acquiring a direction vector of the cluster center of the first cluster to the cluster center of the second cluster;
And calculating a cosine similarity function between the direction vector and the unit vector as the inter-cluster variation degree between the first cluster and the second cluster.
9. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 8, wherein the method for acquiring the intra-cluster variation weight comprises the steps of:
the intra-cluster change weight is obtained according to an intra-cluster change weight calculation formula, and the intra-cluster change weight calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Representing intra-cluster variation weights; />Representing the average Euclidean distance between all sample points in the first cluster; />Representing the maximum value of Euclidean distances among all sample points in the first cluster; />An abscissa representing a cluster center of the first cluster; />An ordinate representing a cluster center of the first cluster; />Representing the abscissa maximum value of all sample points in the first cluster; />Representing the maximum value of the ordinate of all sample points in the first cluster; />An exponential function based on a natural constant is represented.
10. The method for evaluating the dynamic comfort of an automobile seat based on seat vibration according to claim 1, wherein the method for acquiring the objective function comprises:
Performing negative correlation mapping on the intra-cluster variation weights to obtain inter-cluster variation weights;
taking the product of the intra-cluster variation weight and the intra-cluster variation degree as a first product and taking the product of the inter-cluster variation weight and the inter-cluster variation degree as a second product;
the sum of the first product and the second product is taken as an objective function between the first cluster and the second cluster.
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