An intelligent network connected vehicle test bench road resistance time sequence data analysis method

Abstract

The present application relates to the technical field of data acquisition, in particular to a kind of intelligent network connection automobile test bench road resistance time series data analysis method.The method comprises: obtaining the oscillation stationary characteristic value and response decay time characteristic value of road resistance data;According to the difference between different road resistance data sequences, the oscillation stationary characteristic value, the response decay time characteristic value of the marked road resistance data, the target metric distance between the test bench road resistance data in the test bench road resistance data sequence and the road test road resistance data in the road test road resistance data sequence is obtained, and the difference between the test bench road resistance data sequence and the road test road resistance data sequence is obtained according to the target metric distance difference evaluation result.The present application can improve the accuracy and reliability of the difference or similarity evaluation between the test bench test road resistance data and the real road test road resistance data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data acquisition, in particular to a kind of intelligent network connection automobile test bench road resistance time series data analysis method. BACKGROUND

[0002] In the research and development and verification process of intelligent network connection automobile, there is a process of similarity or difference analysis between the road resistance data collected by bench test and the road resistance data collected by real road test, and this process is mainly to verify the equivalence and accuracy of test method, optimize vehicle design and performance calibration etc.;In the prior art, the traditional dynamic time warping algorithm is usually used to calculate and obtain the difference or similarity between the road resistance data collected by bench test and the road resistance data collected by real road test. The traditional dynamic time warping algorithm generally takes the form of absolute value of difference to measure the distance between data and data. However, at present, when the vehicle longitudinal driving force or braking force changes in the initial stage, a high-frequency damping oscillation caused by tire wall additional deformation and rebound is superimposed in the road resistance signal of bench test. The existence of high-frequency damping oscillation will cause the road resistance data in the process of bench test to deviate from the true value, and thus the reliability or reliability of the difference or similarity result between the road resistance data of bench test and the road resistance data of real road test obtained by traditional dynamic time warping algorithm is low. Therefore, how to improve the accuracy and reliability of the difference or similarity analysis between the road resistance data of bench test and the road resistance data of real road test becomes a problem to be solved. SUMMARY

[0003] In order to solve the above problems, the present application provides a kind of intelligent network connection automobile test bench road resistance time series data analysis method, the technical scheme adopted is as follows:

[0004] An embodiment of the present application provides a kind of intelligent network connection automobile test bench road resistance time series data analysis method, comprising the following steps:

[0005] Obtain the road resistance data sequence of target automobile and the power data of each road resistance data in the road resistance data sequence, the road resistance data sequence includes bench test road resistance data sequence and road test road resistance data sequence;

[0006] According to the difference value of the power data of all road resistance data in the local time window of the collection time of each road resistance data, the oscillation stationary characteristic value of each road resistance data is obtained;

[0007] According to the oscillation stationary characteristic value of the road resistance data, the road resistance data in the road resistance data sequence is marked, and the marked road resistance data is obtained;

[0008] According to the extreme value closest to the power data of the marked road resistance data in the power data sequence of the road resistance data sequence, a response decay time characteristic value of the marked road resistance data is obtained;

[0009] According to the road resistance data difference between different road resistance data sequences, the oscillation stability characteristic value, and the response decay time characteristic value of the marked road resistance data, a target metric distance between the test track road resistance data in the test track road resistance data sequence and the road test road resistance data in the road test road resistance data sequence is obtained.

[0010] According to the target metric distance, a difference evaluation result between the test track road resistance data sequence and the road test road resistance data sequence is obtained.

[0011] Beneficial effects: The present application first obtains the road resistance data sequence of the target automobile and the power data of each road resistance data in the road resistance data sequence; then, according to the difference value of the power data of all road resistance data in the local time window corresponding to the collection time of each road resistance data, the oscillation stability characteristic value of each road resistance data is obtained; then, according to the oscillation stability characteristic value of the road resistance data, the road resistance data in the road resistance data sequence is marked to obtain the marked road resistance data, and according to the extreme value closest to the power data of the marked road resistance data in the power data sequence of the road resistance data sequence, the response decay time characteristic value of the marked road resistance data is obtained; then, according to the road resistance data difference between different road resistance data sequences, the oscillation stability characteristic value, and the response decay time characteristic value of the marked road resistance data, the target metric distance between the test track road resistance data in the test track road resistance data sequence and the road test road resistance data in the road test road resistance data sequence is obtained; finally, according to the target metric distance, the difference evaluation result between the test track road resistance data sequence and the road test road resistance data sequence is obtained. And according to the oscillation stability characteristic value and the response decay time characteristic value, the reliability and accuracy of the distance metric of the test track road resistance data in the test track road resistance data sequence and the road test road resistance data in the road test road resistance data sequence can be improved, so that the accuracy and reliability of the difference or similarity evaluation between the test bench road resistance data and the real road test road resistance data can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0013] Figure 1 The flowchart of the intelligent networked vehicle test bench road resistance time sequence data analysis method of the present application. Detailed Implementation

[0014] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the protection scope of the embodiments of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0016] This embodiment provides a method for analyzing road resistance time-series data on an intelligent connected vehicle test bench, detailed as follows:

[0017] like Figure 1 As shown, the method for analyzing road resistance time-series data on the intelligent connected vehicle test bench includes the following steps:

[0018] Step S001: Obtain the road resistance data sequence of the target vehicle and the power data of each road resistance data in the road resistance data sequence. The road resistance data sequence includes the bench test road resistance data sequence and the road test road resistance data sequence.

[0019] In the process of bench test, due to the difference between the curved surface contact characteristics between the test bench roller and the tire and the real flat road, a high-frequency damping oscillation caused by the tire wall additional deformation and rebound is superimposed in the initial stage of the step change of the vehicle longitudinal driving force or braking force. The existence of the high-frequency damping oscillation will cause the numerical accumulation of the road resistance signal reflecting the real performance of the vehicle, so that when the response of the power or braking system of the measured vehicle is faster, the transient impact on the test bench roller tire system is more intense, and the amplitude of the generated high-frequency damping oscillation is larger, which leads to a large numerical value of the dynamic time warping algorithm in the cost accumulation due to the high-frequency damping oscillation, and further leads to a larger DTW distance obtained finally, that is, the existence of the high-frequency damping oscillation will cause the road resistance data in the process of bench test to deviate from the real value, so that the accuracy and reliability of the distance measurement result determined based on the absolute value of the difference between the road resistance data are low, and then the accuracy and reliability of the DTW distance between the bench test road resistance data and the real road test road resistance data determined based on the above distance measurement are low, so that the reliability or reliability of the difference or similarity between the bench test road resistance data and the real road test road resistance data obtained by the traditional dynamic time warping algorithm is low, which affects the reliability of subsequent research and development and verification analysis of the automobile. The DTW distance between sequences can reflect the similarity or difference between sequences. In order to ensure the reliability of subsequent research and development and verification analysis of the automobile, the embodiment needs to reduce the negative influence of the deviation of the bench test road resistance data from the real value on the similarity or difference evaluation as much as possible, that is, to improve the accuracy and reliability of the difference or similarity analysis between the bench test road resistance data and the real road test road resistance data, so as to improve or ensure the reliability of the research and development and verification of the automobile. In order to facilitate understanding, the process of difference or similarity analysis between the bench test road resistance data and the real road test road resistance data of any automobile will be described in the following embodiment, and is recorded as the target automobile, that is, the road resistance data appearing in the following embodiment belongs to the same automobile.

[0020] The embodiment obtains all bench test road resistance data collected in the bench test process of the target vehicle and all road test road resistance data collected in the real road test process of the target vehicle, and records the time sequence formed by all bench test road resistance data collected in the bench test process of the target vehicle as the bench test road resistance data sequence of the target vehicle, and records the time sequence formed by all road test road resistance data collected in the real road test process of the target vehicle as the road test road resistance data sequence of the target vehicle. The bench test road resistance data sequence and the road test road resistance data sequence both belong to the road resistance data sequence of the target vehicle. Road resistance data refers to the resistance received by the vehicle during driving or the equivalent longitudinal force on the driving shaft of the vehicle to overcome all driving resistance, that is, road resistance is a resultant force, which includes rolling resistance, wind resistance, etc.

[0021] Since road resistance data cannot be directly measured in general cases, but can be indirectly obtained through other collected data, the specific acquisition process of road resistance data is as follows: first, the vehicle-mounted sensor network, the data acquisition system, etc. are used to collect the vehicle speed and power data of the target vehicle in the bench test and real road test processes, to obtain all bench test vehicle speed data and bench test power data collected in the bench test process of the target vehicle and all road test vehicle speed data and road test power data collected in the real road test process of the target vehicle. The bench test vehicle speed data and the bench test power data are synchronously collected, and the road test vehicle speed data and the road test power data are synchronously collected, that is, at any collection time point in the bench test process of the target vehicle, one bench test power data and one bench test vehicle speed data of the target vehicle can be collected and obtained, and at any collection time point in the real road test process of the target vehicle, one road test power data and one road test vehicle speed data of the target vehicle can be collected and obtained. In addition, the data collection time reference and sampling frequency in the bench test and real road test processes are the same. Since the product of road resistance and vehicle speed is power, the road resistance data can be obtained under the condition that the vehicle speed and the power are known, that is, for any collection time point in the bench test process of the target vehicle, the ratio of the bench test power data collected at the collection time point to the bench test vehicle speed data is the bench test road resistance data at the collection time point, and the collection time point is also the collection time point corresponding to the bench test road resistance data. For any collection time point in the real road test process of the target vehicle, the ratio of the road test power data collected at the collection time point to the road test vehicle speed data is the road test road resistance data at the collection time point, and the collection time point is also the collection time point corresponding to the road test road resistance data. In specific applications, the implementer needs to set the data collection frequency in the bench test and real road test processes according to the actual situation, etc. For example, the collection frequency can be set to 100 Hz in the embodiment.

[0022] Since the subsequent analysis of the negative impact of the deviation of the bench test road resistance data from the true value on the similarity or difference analysis is based on the oscillation characteristics and the degree of influence of the road resistance data on the high-frequency damping oscillation, the oscillation characteristics and the degree of influence of the road resistance data on the high-frequency damping oscillation refer to the response decay time characteristic value and the oscillation smoothness characteristic value, respectively. Since the power data can more obviously reflect the various characteristics of the oscillation compared with the road resistance data, in order to improve the accuracy and reliability of the analysis of the oscillation characteristics and the degree of influence of the road resistance data on the high-frequency damping oscillation, the subsequent embodiment does not directly analyze the response decay time characteristic value and the oscillation smoothness characteristic value based on the road resistance data, but analyzes and obtains them based on the power data. Therefore, the embodiment needs to obtain the power data of each road resistance data in the road resistance data sequence of the target vehicle. That is, for any bench test road resistance data in the bench test road resistance data sequence of the target vehicle, the bench test power data collected at the collection time corresponding to the bench test road resistance data is the power data of the bench test road resistance data. For any road test road resistance data in the road test road resistance data sequence of the target vehicle, the road test power data collected at the collection time corresponding to the road test road resistance data is the power data of the road test road resistance data.

[0023] In step S002, the oscillation smoothness characteristic value of each road resistance data is obtained according to the difference value of the power data of all road resistance data in the local time window of the collection time corresponding to the road resistance data.

[0024] The embodiment will subsequently obtain the oscillation smoothness characteristic value of each road resistance data based on the above-mentioned obtained power data. The oscillation smoothness characteristic value can reflect whether the corresponding road resistance data belongs to the high-frequency oscillation region or the signal sharp bending region, and is an important parameter for subsequent improvement of difference or similarity evaluation. Since the oscillation smoothness characteristic value of each bench test road resistance data in the bench test road resistance data sequence is obtained in the same way as the oscillation smoothness characteristic value of each road test road resistance data in the road test road resistance data sequence, the subsequent description will take the oscillation smoothness characteristic value of each bench test road resistance data in any bench test road resistance data sequence as an example for description. The specific process of obtaining the oscillation smoothness characteristic value of each bench test road resistance data in the bench test road resistance data sequence is as follows:

[0025] For any test road resistance data c in the test road resistance data sequence, first, a time window is constructed with the collection time of the test road resistance data c as the center, and is recorded as the local time window of the test road resistance data c. A time sequence composed of the power data of all test road resistance data collected within the local time window of the test road resistance data c and belonging to the test road resistance data sequence is recorded as the local power sequence of the test road resistance data c. Then, first-order difference and second-order difference are performed on the local power sequence of the test road resistance data c to obtain a first-order difference sequence and a second-order difference sequence. The fth first-order difference value in the first-order difference sequence is the result of the subtraction of the fth power data from the f+1th power data in the local power sequence. The gth second-order difference value in the second-order difference sequence is the result of the subtraction of the gth first-order difference value from the g+1th first-order difference value in the local power sequence. The process of first-order difference and second-order difference is a known technology. Then, according to the first-order difference sequence and the second-order difference sequence of the local power sequence of the test road resistance data c and the length of the local power sequence, the oscillation stationary characteristic value of the test road resistance data c is obtained. In specific applications, the implementer sets the length of the local time window or the length of the local power sequence according to the typical period of the oscillation signal, that is, considering that the oscillation frequency introduced by the tire drum system is usually in a specific physical range, the preset window length can be set to the time period length corresponding to the number of data points that can cover approximately one complete oscillation period in the frequency range. If the oscillation frequency introduced by the tire drum system is approximately 15 Hz, then under a sampling frequency of 100 Hz, in order to effectively capture the oscillation, the length of the local time window can be set to the length of the time period composed of the collection time of 21 consecutive data, or the length of the local power sequence can be set to 21. Since the local time window of a road resistance data is constructed with the collection time of the road resistance data as the center in this embodiment, but the road resistance data corresponding to the power data in the local power sequence of the road resistance data is required to belong to the road resistance data sequence in which the road resistance data is located, the length of the local power sequence of the road resistance data may be less than the set length. When this situation occurs, the length of the local power sequence of all road resistance data can be made consistent by lengthening. For example, if the test road resistance data c is the 5th data in the test road resistance data sequence, then the local power sequence of the test road resistance data c is composed of the 1st data to the 21st data in the test road resistance data sequence.

[0026] In the embodiment, according to the first-order difference sequence and the second-order difference sequence of the local power sequence of the obtained bench test road resistance data c and the length of the local power sequence, the specific process of obtaining the oscillation stationary characteristic value of the bench test road resistance data c is as follows: the absolute values of all difference values in the second difference sequence are accumulated, and the accumulation result is recorded as the second-order difference accumulation value; the absolute values of all difference values in the first difference sequence are accumulated, and the accumulation result is recorded as the first-order difference accumulation value; the ratio of the second-order difference accumulation value to the first-order difference accumulation value is calculated, and is recorded as the difference ratio; the product of the difference ratio and the length of the local power sequence of the bench test road resistance data c is calculated, and the result of the negative correlation mapping of the obtained product is recorded as the oscillation stationary characteristic value of the bench test road resistance data c, wherein the negative correlation mapping process is that the obtained product is first mapped by using the hyperbolic tangent function, and then the mapping result is subtracted by 1; and the specific calculation expression of the oscillation stationary characteristic value of the bench test road resistance data c is as follows:

[0027]

[0028] wherein, is the oscillation stationary characteristic value of the bench test road resistance data c, M2 is the second-order difference accumulation value, M1 is the first-order difference accumulation value, and tanh() is the hyperbolic tangent function, is the length of the local power sequence of the bench test road resistance data c, and can also be the length of the local time window of the bench test road resistance data c; when is larger, that is, is smaller, it indicates that the bending degree of the local power sequence of the bench test road resistance data c is higher, and the probability that the collection time corresponding to the bench test road resistance data c is in the high-frequency oscillation stage or the probability that the power data of the bench test road resistance data c is in the high-frequency oscillation stage is larger; when is smaller, that is, is larger, it indicates that the bending degree of the local power sequence of the bench test road resistance data c is lower, and the probability that the collection time corresponding to the bench test road resistance data c is in the high-frequency oscillation stage or the probability that the power data of the bench test road resistance data c is in the high-frequency oscillation stage is smaller. For the bench test road resistance data, the smaller the oscillation stationary characteristic value of the bench test road resistance data is, the larger the probability that the bench test time corresponding to the bench test road resistance data is in the high-frequency oscillation stage or the probability that the power data of the bench test road resistance data is in the high-frequency oscillation stage is, or the bench test road resistance data is more affected by the high-frequency damping oscillation; for the road test road resistance data, the smaller the oscillation stationary characteristic value of the road test road resistance data is, the larger the probability that the road test time corresponding to the road test road resistance data is in the high-frequency oscillation stage or the probability that the power data of the road test road resistance data is in the high-frequency oscillation stage is, but the high-frequency oscillation stage in the road test process is not the high-frequency damping oscillation, but usually caused by the change of the test instruction or the dynamic change of the automobile, such as acceleration and deceleration in the test process.

[0029] Furthermore, the first-order differential accumulation value is placed in the denominator as a normalization benchmark. The first-order differential accumulation value represents the total amplitude of power change within a local time window. Dividing by this term allows the final characteristic value to focus on the shape of the signal change rather than its magnitude, eliminating the interference of power amplitude differences under different operating conditions on oscillation identification. The second-order differential accumulation value is a discrete approximation of the signal's local curvature, reflecting the rate of change of the signal direction. In a high-frequency oscillating signal, the power curve rapidly transitions between convex and concave sections, resulting in a large second-order differential value. However, in a unidirectional response signal with a basically unchanged direction, whether it's a rapid step or a slow linear ramp, the direction of the power curve remains essentially unchanged, the power curvature is close to zero, and the second-order differential value is very small. Therefore, the second-order differential accumulation value is key to reflecting the probability that the acquisition time corresponding to the test resistance data c is in the high-frequency oscillation stage. Multiplying by... Its purpose is to enhance the calculated normalized curvature metric, specifically for weak oscillations whose values ​​are very small, by multiplying them by a factor greater than 1. integers This can amplify the difference ratio, allowing the subsequent hyperbolic tangent function to respond more sensitively. It is dimensionless; the hyperbolic tangent function in the above equation can map a large difference ratio to a value approaching... The output of , thus making the final Approaching Conversely, one tends to be The difference ratio will be mapped to approximate The output of makes Approaching This facilitates subsequent efforts to reduce the negative impact of high-frequency damped oscillations on similarity or difference analysis. The participation of the original absolute value of the difference when measuring the final distance between road resistance data is determined, and it is also the criterion for subsequent calculations to determine whether to perform response decay time characteristic value calculation on the road resistance data.

[0030] Furthermore, the method for calculating and obtaining the oscillating stationary characteristic value of the road test road resistance data is the same as the method for calculating and obtaining the oscillating stationary characteristic value of the bench test road resistance data. Therefore, this embodiment will not describe in detail the process of obtaining the oscillating stationary characteristic value of the road test road resistance data.

[0031] Therefore, this embodiment can obtain the oscillation stationary characteristic values ​​of each road resistance data in the bench test road resistance data sequence and the road test road resistance data sequence through the above process.

[0032] Step S003, according to the oscillation stationary eigenvalue of the road resistance data, the road resistance data in the road resistance data sequence is marked, and the marked road resistance data is obtained; according to the extreme value located behind the power data of the marked road resistance data and closest in the power data sequence of the road resistance data sequence, the response decay time eigenvalue of the marked road resistance data is obtained.

[0033] Because when the test road resistance data corresponds to the test time or the power data of the test road resistance data is in the high-frequency damping oscillation stage, the test road resistance data deviates from the true value, that is, the test road resistance data at this time is composed of the true road resistance and the high-frequency damping oscillation, therefore, the confidence or credibility of the metric distance obtained only based on the absolute value of the difference between the instantaneous test road resistance data and the instantaneous road test road resistance data is low, and the accuracy and credibility of the difference or similarity result between the finally obtained test road resistance data sequence and the road test road resistance data is also low, and because the energy decay speed or change speed of different oscillations in the test process is different, and the oscillation energy change can reflect the real state of the vehicle in the test process, therefore, in order to improve the accuracy and credibility of the difference or similarity evaluation result, the embodiment will also introduce other similarity measurement indexes, that is, the response decay time eigenvalue representing the energy decay or change, but not all road resistance data need to calculate the response decay time eigenvalue, for example, the data in the non-oscillation stage or the data in the stationary stage cannot calculate the response decay time eigenvalue, therefore, the embodiment only calculates the response decay time eigenvalue for the data in the oscillation stage, that is, before calculating the response decay time eigenvalue, the road resistance data that can calculate the response decay time eigenvalue, that is, the marked road resistance data, needs to be obtained, and then the specific acquisition process of the marked road resistance data is as follows:

[0034] For any test road resistance data c in the test road resistance data sequence, a time sequence composed of the oscillation stationary eigenvalues of all road resistance data in the test road resistance data sequence is denoted as an oscillation stationary eigenvalue sequence of the test road resistance data sequence, the hth oscillation stationary eigenvalue in the oscillation stationary eigenvalue sequence of the test road resistance data sequence is the oscillation stationary eigenvalue of the hth test road resistance data in the test road resistance data sequence, all maxima in the oscillation stationary eigenvalue sequence of the test road resistance data sequence are obtained, and it is determined whether the test road resistance data c belongs to the maxima in the oscillation stationary eigenvalue sequence of the test road resistance data sequence. If yes, it indicates that the time corresponding to the test road resistance data c is in the oscillation phase, the test road resistance data c meets the condition for calculating the response decay time eigenvalue, and then the response decay time eigenvalue of the test road resistance data c needs to be calculated. Therefore, the test road resistance data c needs to be marked at this time, and the test road resistance data c is denoted as a marked road resistance data. Otherwise, when the test road resistance data c does not belong to the maxima in the oscillation stationary eigenvalue sequence of the test road resistance data sequence, it indicates that the time corresponding to the test road resistance data c is not in the oscillation phase, and the test road resistance data c does not meet the condition for calculating the response decay time eigenvalue, and it does not need to be marked. In addition, the process of marking the road resistance data in the road test resistance data sequence to obtain the marked road resistance data is the same as the process of marking the road resistance data in the test road resistance data sequence to obtain the marked road resistance data. Therefore, the process of marking the road resistance data in the road test resistance data sequence to obtain the marked road resistance data is not described in this embodiment.

[0035] Therefore, the marked road resistance data is obtained through the above process in this embodiment. After obtaining the marked road resistance data, the response decay time eigenvalue corresponding to the marked road resistance data is obtained according to the extreme value located behind the power data of the marked road resistance data and closest to the marked road resistance data in the power data sequence of the road resistance data sequence. The response decay time eigenvalue of the marked road resistance data mainly reflects the change of the power data, that is, the energy, corresponding to the marked road resistance data, and also reflects the time or speed of recovery to stability after being impacted by the external impact. The greater the response decay time eigenvalue, the slower and more persistent the change. The response decay time eigenvalue difference between the data will be involved in the distance measurement in the subsequent process, which can avoid the influence of the instantaneous test road resistance deviating from the true value on the similarity or difference analysis.

[0036] In addition, the response decay time eigenvalue of all the marked road resistance data in this embodiment is obtained in the same way. In order to facilitate the understanding of this embodiment, the response decay time eigenvalue of any marked road resistance data A in the test road resistance data sequence will be taken as an example to describe the process of obtaining the response decay time eigenvalue of the marked road resistance data A. The specific process of obtaining the response decay time eigenvalue of the marked road resistance data A is as follows:

[0037] Firstly, the power data of the marked road resistance data A is recorded as power data a, the power data sequence of the test road resistance data sequence is recorded as sequence B, the power data of the kth test road resistance data in the test road resistance data sequence is the kth power data in the power data sequence of the test road resistance data sequence, among all the power data behind the power data a and belonging to sequence B, the extreme value closest to the power data a in position is obtained and recorded as the starting power data corresponding to the marked road resistance data A, the extreme value includes the maximum value and the minimum value, for example, if the power data a is the zth power data in sequence B, if the extreme value first appearing behind the zth power data is the z+3th power data in sequence B, then the z+3th power data is the starting power data corresponding to the marked road resistance data A, the collection time of the road resistance data corresponding to the starting power data of the marked road resistance data A is recorded as the starting time of the marked road resistance data A, for example, if a certain power data is the vth power data in sequence B, then the collection time of the road resistance data corresponding to the power data is the time when the vth test road resistance data in the test road resistance data sequence is collected, a preset local time period after the starting time of the marked road resistance data A is obtained and recorded as the preset local time period corresponding to the marked road resistance data A, and the arithmetic mean of all the test road resistance data in the test road resistance data sequence whose collection time is in the preset local time period is recorded as the local steady-state power corresponding to the marked road resistance data A; then, according to the local steady-state power corresponding to the marked road resistance data A, the time period to be analyzed corresponding to the marked road resistance data A and the initial change amplitude of the starting time of the marked road resistance data A are obtained; in addition, in specific application, the implementer needs to set the length of the preset local time period according to the actual situation such as the collection frequency, for example, the length of the preset local time period can be set to 1 second in this embodiment; and if there is no extreme value closest to the power data a among all the power data behind the power data a and belonging to sequence B, the response decay time characteristic value of the marked road resistance data A is not calculated, or if there is no extreme value closest to the power data a among all the power data behind the power data a and belonging to sequence B, then the extreme value closest to the power data a in the entire sequence B is selected as the starting power data corresponding to the marked road resistance data A.

[0038] The specific acquisition process of the initial variation amplitude of the starting moment corresponding to the marked road resistance data A and the time period to be analyzed corresponding to the marked road resistance data A is as follows: the absolute value of the difference between the starting power data corresponding to the marked road resistance data A and the local steady-state power corresponding to the marked road resistance data A is recorded as the initial variation amplitude of the starting moment corresponding to the marked road resistance data A; a sequence formed by all the power data in sequence B after the starting power data corresponding to the marked road resistance data A is recorded as the to-be-analyzed sequence of the marked road resistance data A, for example, if the z1th power data in sequence B is the starting power data corresponding to the marked road resistance data A, then the data sequence formed by the z1+1th power data in sequence B to the last data in sequence B is the to-be-analyzed sequence of the marked road resistance data A; the variation amplitudes of the power data in the to-be-analyzed sequence are obtained, and the variation amplitude of any power data in the to-be-analyzed sequence is the absolute value of the difference between the power data and the local steady-state power corresponding to the marked road resistance data A; the variation amplitude difference values of the power data in the to-be-analyzed sequence are obtained according to the differences between the variation amplitudes of the power data in the to-be-analyzed sequence and the initial variation amplitude of the starting moment corresponding to the marked road resistance data A, and the variation amplitude difference value of any power data in the to-be-analyzed sequence is the absolute value of the difference between the variation amplitude of the power data and the initial variation amplitude of the starting moment corresponding to the marked road resistance data A; the ratios of the variation amplitude difference values of the power data in the to-be-analyzed sequence to the initial variation amplitude of the starting moment corresponding to the marked road resistance data A are obtained and recorded as the amplitude variation degree values of the power data in the to-be-analyzed sequence, and the amplitude variation degree value of any power data in the to-be-analyzed sequence is the ratio of the variation amplitude difference value of the power data to the initial variation amplitude of the starting moment corresponding to the marked road resistance data A; it is judged whether the amplitude variation degree value of the uth power data in the to-be-analyzed sequence of the marked road resistance data A is greater than the preset variation threshold value, if not, it is continued to be judged whether the amplitude variation degree value of the u+1th power data in the to-be-analyzed sequence is greater than the preset variation threshold value, and so on, until the amplitude variation degree value of the corresponding power data is greater than the preset variation threshold value, and the acquisition moment of the road resistance data corresponding to the corresponding power data is recorded as the ending moment of the marked road resistance data A, the time period from the starting moment of the marked road resistance data A to the ending moment of the marked road resistance data A is taken as the to-be-analyzed time period corresponding to the marked road resistance data A, for example, if the data whose amplitude variation degree value is greater than the preset variation threshold value appears for the first time in the to-be-analyzed sequence is the u+4th power data in the to-be-analyzed sequence, then the acquisition moment of the road resistance data corresponding to the u+4th power data is the ending moment of the marked road resistance data A.In addition, in specific applications, implementers need to set a preset change threshold according to the actual situation. For example, the preset change threshold can be set to five percent, that is, the time when the power data corresponding to the first change amplitude in the analysis sequence of the marked road resistance data A is collected when the difference between the first change amplitude and the initial change amplitude exceeds five percent of the initial change amplitude is the end time of the marked road resistance data A.

[0039] Then, based on the power data of the test road resistance data collected during the analysis period corresponding to the marked road resistance data A, the local steady-state power corresponding to the marked road resistance data A, and the initial change amplitude at the starting time corresponding to the marked road resistance data A, the response decay time characteristic value of the marked road resistance data A is obtained; and the specific expression for the response decay time characteristic value of the marked road resistance data A is as follows:

[0040]

[0041] in, To label the response decay time characteristic value of the road resistance data A, To mark the start time of the time period to be analyzed corresponding to the road resistance data A, To mark the end time of the time period to be analyzed corresponding to the road resistance data A, This refers to the power data of the test road resistance data collected at time t within the analysis time period corresponding to road resistance data A. To label the local steady-state power corresponding to the road resistance data A, exp() is an exponential function with a base of constant e, and τ is the unknown quantity to be optimized. To mark the acquisition time t in the time period to be analyzed corresponding to the road resistance data A, and The time interval between To mark the initial change magnitude of the road resistance data A at the starting time, argmin represents the set of independent variable values ​​that minimize the objective function. When the objective function involves integration (such as functionals in variational calculus), argmin is used to find the function or parameter that maximizes the integral expression. Therefore, in this embodiment... This optimization process aims to attenuate the power deviation amplitude. Fit to exponential decay function The purpose of the above is not to assert that the decay process strictly follows an exponential law, but to use this function as an analytical template to extract features from the actual decay process. In other words, it is used to analyze and obtain the characteristic time of the system's recovery to stability after being impacted. This embodiment treats the brief transient response process as the behavioral manifestation of a micro-dynamic system. The change of the oscillation and a series of conditions that can reflect the speed of the system returning to the stable state from the excited state or the speed of the power recovering to the stable state from the oscillation state have a direct relationship with the vehicle state, so the calculated τ value can reflect the vehicle state, and thus the response decay time characteristic value can be used to measure the difference or distance between data; The greater the value, the slower the energy change or the slower the decay at the collection time corresponding to the marked road resistance data A, The smaller the value, the faster the energy change or the faster the decay at the collection time corresponding to the marked road resistance data A.

[0042] In addition, in the above formula, is a theoretical decay model, and the actual observed signal decay process is approximated by using the decay function, and the model parameter τ that can make the simulation result closest to the actual situation should be obtained, so as to find the best parameter τ, that is, the response decay time characteristic value. The purpose is not to assert that the actual physical process strictly follows the exponential law, but to abstract a complex decay process into a characteristic parameter that can represent the overall decay trend of the decay process. In the above formula, is the actual observed object, which represents the actual power deviation from the local steady-state power The amplitude of the power data of the road resistance data collected at the collection time t in the analysis period deviates from the local steady-state power The deviation amplitude sequence formed by the amplitude of the power data of the road resistance data collected at the collection time t in the analysis period deviates from the local steady-state power characterizes the initial amplitude of the decay at the collection time position corresponding to the marked road resistance data A, is an exponential decay function, which is used to describe the decay process over time. The square of the difference value represents the difference between the actual observed value and the theoretical model value. This is the idea of the least squares method. A small difference will be calculated as a small penalty, and a huge difference will be imposed a huge penalty of exponential growth. In the above formula, represents the integral in the analysis period corresponding to the marked road resistance data A, which adds up all the square differences from the extreme value time to the end time of the event. The result of this integral represents the overall fitting error of the theoretical model to the actual process under the given τ value, is .

[0043] Therefore, the response decay time characteristic value of the marked road resistance data can be obtained through the above process.

[0044] Step S004, obtaining a target metric distance between the test road resistance data in the test road resistance data sequence and the road test road resistance data in the road test road resistance data sequence according to the road resistance data difference between different road resistance data sequences, the oscillation stabilization characteristic value, and the response decay time characteristic value of the marked road resistance data; and obtaining a difference evaluation result between the test road resistance data sequence and the road test road resistance data sequence according to the target metric distance.

[0045] After the response decay time characteristic value is obtained, a target metric distance between the test road resistance data in the test road resistance data sequence and the road test road resistance data in the road test road resistance data sequence is obtained according to the road resistance data difference between different road resistance data sequences, the oscillation stabilization characteristic value, and the response decay time characteristic value of the marked road resistance data. The specific obtaining process is as follows:

[0046] For the ith test road resistance data in the test road resistance data sequence and the jth road test road resistance data in the road test road resistance data sequence, it is determined whether the ith test road resistance data and the jth road test road resistance data both belong to the marked road resistance data. If not, the absolute value of the difference between the ith test road resistance data and the jth road test road resistance data is directly taken as the target metric distance between the ith test road resistance data and the jth road test road resistance data. If both belong to the marked road resistance data, the target metric distance between the ith test road resistance data and the jth road test road resistance data is obtained according to the numerical difference and the response decay time characteristic value difference between the ith test road resistance data and the jth road test road resistance data, the mean value of the road test road resistance data sequence, and the oscillation stabilization characteristic value of the ith test road resistance data.

[0047] In this embodiment, the specific process of obtaining the target metric distance between the ith test road resistance data and the jth road test road resistance data according to the numerical difference and the response decay time characteristic value difference between the ith test road resistance data and the jth road test road resistance data, the mean value of the road test road resistance data sequence, and the oscillation stabilization characteristic value of the ith test road resistance data is as follows:

[0048] According to the numerical difference between the i-th bench test road resistance data and the j-th road test road resistance data, the response decay time characteristic value difference, the mean of the road test road resistance data sequence, and the oscillation stationary characteristic value of the i-th bench test road resistance data, the first distance representation value and the second distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data are obtained, and the sum of the first distance representation value and the second distance representation value is taken as the target metric distance between the i-th bench test road resistance data and the j-th road test road resistance data. The specific obtaining process of the first distance representation value and the second distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data is that the result of multiplying the absolute value of the difference between the i-th bench test road resistance data and the j-th road test road resistance data by the oscillation stationary characteristic value of the i-th bench test road resistance data is recorded as the first distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data; the normalized result of the absolute value of the difference between the response decay time characteristic value of the i-th bench test road resistance data and the response decay time characteristic value of the j-th road test road resistance data is recorded as the time characteristic difference value, the mean of the road test road resistance data sequence is recorded as the road test road resistance mean, and the product of the oscillation stationary characteristic value of the i-th bench test road resistance data, the time characteristic difference value, and the road test road resistance mean is taken as the second distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data.

[0049] In addition, according to the numerical difference between the i-th bench test road resistance data and the j-th road test road resistance data, the response decay time characteristic value difference, the mean of the road test road resistance data sequence, and the oscillation stationary characteristic value of the i-th bench test road resistance data, the specific expression of the target metric distance between the i-th bench test road resistance data and the j-th road test road resistance data is obtained.

[0050]

[0051] wherein, is the target metric distance between the i-th bench test road resistance data and the j-th road test road resistance data, is the oscillation stationary characteristic value of the i-th bench test road resistance data, is the i-th bench test road resistance data, is the j-th road test road resistance data, is the response decay time characteristic value of the j-th road test road resistance data, is the response decay time characteristic value of the i-th bench test road resistance data, is the mean of the road test road resistance data sequence; and since the smaller the oscillation stationary characteristic value of the i-th bench test road resistance data is, the greater the probability that the i-th bench test road resistance data is affected by high-frequency damping oscillation is, at this time, the difference in instantaneous value is less referenced, that is, the difference in the road test road resistance data sequence is less referenced. is compared in order to normalize and eliminate the dimension of is compared in order to normalize and eliminate the dimension of is the mean of the road test road resistance data sequence; and since the smaller the oscillation stationary characteristic value of the i-th bench test road resistance data is, the greater the probability that the i-th bench test road resistance data is affected by high-frequency damping oscillation is, at this time, the difference in instantaneous value is less referenced, that is, the difference in the road test road resistance data sequence is less referenced. That is, the smaller the the smaller the the more dominant the result of the target metric distance, the more dominant the result of the target metric distance; the more dominant the result of the target metric distance; the more similar the dynamic characteristics, such as the vehicle speeds, of the test vehicle at the time corresponding to the ith test track road resistance data and the time corresponding to the jth road test road resistance data, are, and the dynamic characteristics of the vehicle are directly related to the road resistance data, so the similarity of the dynamic characteristics can indicate the similarity of the road resistance; is present as an evaluation benchmark, and the order of magnitude and scale of the entire cost calculation should be determined by the benchmark data to ensure the objectivity and consistency of the evaluation, i.e., the multiplication by has the effect of achieving dimensional unification and giving the cost term a physical meaning, can convert the time difference in response characteristics into an equivalent force cost, ensuring that the physical meanings of the terms of the final cost function are consistent; in the DTW algorithm, the cost usually refers to the distance between corresponding points of two time series. In addition, the greater the the greater the the greater the the greater the difference between the ith test track road resistance data and the jth road test road resistance data.

[0052] After obtaining the target metric distance between the test track road resistance data in the test track road resistance data sequence and the road test road resistance data in the road test road resistance data sequence, matrix construction, path backtracking, etc. are performed based on the obtained target metric distance, so as to calculate the DTW distance between the test track road resistance data sequence and the road test road resistance data sequence. The DTW distance between the test track road resistance data sequence and the road test road resistance data sequence is the difference evaluation result between the test track road resistance data sequence and the road test road resistance data sequence, and the greater the DTW distance between the test track road resistance data sequence and the road test road resistance data sequence, the less similar the test track road resistance data sequence and the road test road resistance data sequence are. In this embodiment, only the way of obtaining the metric distance is changed when obtaining the DTW distance between the test track road resistance data sequence and the road test road resistance data sequence, and the matrix construction, path backtracking, etc. after obtaining the metric distance are consistent with the traditional dynamic time warping, so they will not be described in detail.

[0053] So far, the embodiment completes the analysis of the difference or similarity between the bench test road resistance data sequence and the road test road resistance data sequence, and outputs the analysis result to the user terminal or the test report generation system. The embodiment can improve the accuracy and reliability of the corresponding distance metric as much as possible by referring to the difference between the instantaneous road resistance less and the difference between the response decay time characteristic value more when the bench test road resistance data is more likely to be affected by high-frequency damping oscillation, thereby improving the accuracy and reliability of the difference or similarity analysis between the bench test road resistance data and the real road test road resistance data. In addition, the difference or similarity analysis result between the bench test road resistance data and the real road test road resistance data can be used in various engineering application scenarios, such as quantitative evaluation and calibration of the simulation accuracy of the vehicle test bench, comparison of the real influence of different control strategies or hardware changes on the transient response performance of the vehicle, automatic screening of abnormal test results with a difference exceeding a preset threshold from the benchmark data in a large amount of test data, thereby realizing automatic monitoring and quality control of the test process.

[0054] In summary, the embodiment first acquires the road resistance data sequence of the target vehicle and the power data of each road resistance data in the road resistance data sequence. Then, the oscillation stationary characteristic value of each road resistance data is obtained according to the difference value of the power data of all road resistance data in the local time window corresponding to the collection time of each road resistance data. After that, the road resistance data in the road resistance data sequence is marked according to the oscillation stationary characteristic value of the road resistance data, to obtain the marked road resistance data, and the response decay time characteristic value of the marked road resistance data is obtained according to the extreme value closest to the power data of the marked road resistance data in the power data sequence of the road resistance data sequence. Then, the target metric distance between the bench test road resistance data in the bench test road resistance data sequence and the road test road resistance data in the road test road resistance data sequence is obtained according to the road resistance data difference, the oscillation stationary characteristic value, and the response decay time characteristic value of the marked road resistance data between different road resistance data sequences. Finally, the difference evaluation result between the bench test road resistance data sequence and the road test road resistance data sequence is obtained according to the target metric distance. The embodiment can improve the reliability and accuracy of the distance metric between the bench test road resistance data in the bench test road resistance data sequence and the road test road resistance data in the road test road resistance data sequence according to the oscillation stationary characteristic value and the response decay time characteristic value, thereby improving the accuracy and reliability of the difference or similarity evaluation between the bench test road resistance data and the real road test road resistance data.

[0055] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A road resistance time sequence data analysis method for a smart connected vehicle test bench, characterized in that, The method comprises the following steps: Obtaining a road resistance data sequence of a target automobile and power data of each road resistance data in the road resistance data sequence, wherein the road resistance data sequence comprises a bench test road resistance data sequence and a road test road resistance data sequence; Obtaining an oscillation stationary characteristic value of each road resistance data according to a difference value of power data of all road resistance data in a local time window corresponding to a collection time of the road resistance data; Marking the road resistance data in the road resistance data sequence according to the oscillation stationary characteristic value of the road resistance data to obtain marked road resistance data; Obtaining a response decay time characteristic value of the marked road resistance data according to an extreme value located behind the power data of the marked road resistance data in a power data sequence of the road resistance data sequence and closest to the marked road resistance data; Obtaining a target metric distance between the bench test road resistance data in the bench test road resistance data sequence and the road test road resistance data in the road test road resistance data sequence according to a road resistance data difference between different road resistance data sequences, the oscillation stationary characteristic value and the response decay time characteristic value of the marked road resistance data; Obtaining a difference evaluation result between the bench test road resistance data sequence and the road test road resistance data sequence according to the target metric distance. 2.The intelligent connected vehicle test bench road resistance time sequence data analysis method of claim 1, wherein, The method for obtaining the oscillation stationary characteristic value comprises: For any road resistance data in the road resistance data sequence, a time sequence formed by power data of all road resistance data in a local time window located at a collection time corresponding to the road resistance data is recorded as a local power sequence of the road resistance data, and the oscillation stationary characteristic value of the road resistance data is obtained according to results of first-order difference and second-order difference of the local power sequence. 3.The intelligent connected vehicle test bench road resistance time sequence data analysis method of claim 2, wherein, The method for obtaining the oscillation stationary characteristic value of the road resistance data according to results of first-order difference and second-order difference of the local power sequence comprises: The results of first-order difference and second-order difference of the local power sequence are recorded as a first difference sequence and a second difference sequence respectively, a ratio of an accumulated result of all difference values in the second difference sequence to an accumulated result of all difference values in the first difference sequence is recorded as a difference ratio, and a result of negative correlation mapping of a product of the difference ratio and a length of the local power sequence is recorded as the oscillation stationary characteristic value of the road resistance data. 4.The intelligent connected vehicle test bench road resistance time sequence data analysis method of claim 1, wherein, The method for obtaining the marked road resistance data comprises: For any road resistance data in the road resistance data sequence, if the road resistance data is a maximum value in an oscillation stationary characteristic value sequence of the road resistance data sequence, the road resistance data is marked and recorded as marked road resistance data, and the oscillation stationary characteristic value sequence of the road resistance data sequence is a time sequence formed by oscillation stationary characteristic values of all road resistance data in the road resistance data sequence. 5.The intelligent vehicle test bench road resistance time sequence data analysis method of claim 1, wherein, The method for obtaining the response decay time characteristic value of the marked road resistance data comprises: For any marked road resistance data A in the road resistance data sequence, Power data of the marked road resistance data A is denoted as power data a, and a power data sequence of the road resistance data sequence is denoted as sequence B; an extreme value located behind the power data a and closest to the power data a in position in the sequence B is denoted as starting power data, a collection time of road resistance data corresponding to the starting power data is denoted as a starting time of the marked road resistance data A, and a mean value of power data of all road resistance data in a preset local time period after the starting time in the road resistance data sequence is denoted as local steady-state power; sequence B is a time sequence constructed by power data of all road resistance data in the road resistance data sequence; According to the local steady-state power, an initial change amplitude of the starting time corresponding to the marked road resistance data A is obtained; According to the power data of the road resistance data in the to-be-analyzed time period, the local steady-state power and the initial change amplitude, a response decay time characteristic value of the marked road resistance data A is obtained.

6. The intelligent vehicle test bench road resistance time sequence data analysis method of claim 5, wherein, The method for obtaining the to-be-analyzed time period corresponding to the marked road resistance data A comprises: A sequence formed by all power data located behind the starting power data in the sequence B is denoted as a to-be-analyzed sequence; whether a ratio of a change amplitude difference value of the u-th power data in the to-be-analyzed sequence to the initial change amplitude is greater than a preset change threshold value is judged, if not, whether a ratio of a change amplitude difference value of the u+1-th power data in the to-be-analyzed sequence to the initial change amplitude is greater than the preset change threshold value is judged, and so on, until the ratio of the change amplitude difference value of the corresponding power data to the initial change amplitude is greater than the preset change threshold value, and the judgment is stopped, and a collection time of road resistance data corresponding to the corresponding power data is denoted as an end time of the marked road resistance data A, and a time period formed from the starting time to the end time is denoted as the to-be-analyzed time period corresponding to the marked road resistance data A.

7. The intelligent connected vehicle test bench road resistance time sequence data analysis method of claim 6, wherein, The initial change amplitude is an absolute value of a difference between the starting power data and the local steady-state power, the change amplitude difference value of any power data in the to-be-analyzed sequence is an absolute value of a difference between a change amplitude of the power data and the initial change amplitude, and the change amplitude of any power data in the to-be-analyzed sequence is an absolute value of a difference between the power data and the local steady-state power. 8.The intelligent vehicle test bench road resistance time sequence data analysis method of claim 7, wherein, A specific calculation expression for obtaining the response decay time characteristic value of the marked road resistance data A is: ； wherein, is a response decay time characteristic value of the marked road impedance data A, is a starting time in a time period to be analyzed corresponding to the marked road impedance data A, is an ending time in the time period to be analyzed corresponding to the marked road impedance data A, is power data of the bench test road impedance data collected at a collection time t in the time period to be analyzed, is the local steady-state power, exp() is an exponential function with constant e as the base, and τ is an unknown to be optimized, is a time interval between the collection time t and , is the initial change amplitude. 9.The intelligent vehicle test bench road resistance time sequence data analysis method of claim 1, wherein, The method for obtaining the target metric distance comprises: For the i-th test road resistance data in the test road resistance data sequence and the j-th road test resistance data in the road test resistance data sequence: If the i-th bench test road resistance data and the j-th road test road resistance data do not belong to the marked road resistance data, an absolute value of a difference between the i-th bench test road resistance data and the j-th road test road resistance data is directly taken as a target metric distance between the i-th bench test road resistance data and the j-th road test road resistance data; if the i-th bench test road resistance data and the j-th road test road resistance data belong to the marked road resistance data, a first distance representation value and a second distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data are obtained according to a numerical difference between the i-th bench test road resistance data and the j-th road test road resistance data, a response decay time characteristic value difference, a mean value of the road test road resistance data sequence and an oscillation stationary characteristic value of the i-th bench test road resistance data, and a sum of the first distance representation value and the second distance representation value is taken as the target metric distance between the i-th bench test road resistance data and the j-th road test road resistance data. 10.The intelligent vehicle test bench road resistance time sequence data analysis method of claim 9, wherein, The first distance representation value and the second distance representation value are obtained by the following method: an absolute value of a difference between the i-th bench test road resistance data and the j-th road test road resistance data is multiplied by an oscillation stationary characteristic value of the i-th bench test road resistance data, and a result is taken as the first distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data; a response decay time characteristic value of the i-th bench test road resistance data is subtracted from a response decay time characteristic value of the j-th road test road resistance data, an absolute value of a difference is normalized, and a result is taken as a time characteristic difference value; a mean value of the road test road resistance data sequence is taken as a road test road resistance mean value; and a product of a supplement of the oscillation stationary characteristic value of the i-th bench test road resistance data, the time characteristic difference value and the road test road resistance mean value is taken as the second distance representation value between the i-th bench test road resistance data and the j-th road test road resistance data.

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