Vehicle test data processing method, computing device and readable storage medium

Abstract

The invention discloses a vehicle test data processing method, computing equipment and a computer readable storage medium. The vehicle test data processing method comprises the steps of obtaining to-be-processed test data; determining a target test type corresponding to target data in the test data; the target data is at least one group of data in the test data; judging whether the target data is valid or not according to the target test type; and if the target data is valid, fitting the target data to generate a change curve, and processing and analyzing the target data based on the change curve. Thus, by processing and analyzing the target data, the labor cost is reduced, and meanwhile, by detecting whether the target data is valid or not, the processing efficiency and accuracy of the to-be-processed test data are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for processing vehicle test data, a computing device, and a readable storage medium. Background Art

[0002] The pace of vehicle replacement is getting faster and faster. In order to seize the market opportunity, vehicle companies are shortening the cycle of new vehicle development. Generally, a large amount of testing is required during the development of new models to verify the performance of new models, but the test data processing steps after the test are completed are cumbersome and the result display is not intuitive enough.

[0003] Currently, when conducting vehicle tests, since one test needs to complete multiple test conditions, the test data processing steps after the test are completed are cumbersome, so the subsequent analysis and processing of the test data takes a long time. At the same time, when processing test data, the existing analysis and processing is generally based on manual analysis and processing, which is prone to errors and requires a certain amount of time and cost. Summary of the invention

[0004] The purpose of this application is to provide a vehicle test data processing method, computing device and computer-readable storage medium, which reduces labor costs by processing and analyzing target data, and improves the processing efficiency and accuracy of the test data to be processed by detecting whether the target data is valid.

[0005] To achieve the above objectives: In a first aspect, an embodiment of the present application provides a method for processing vehicle test data, comprising: Obtaining test data to be processed; Determine a target test type corresponding to target data in the test data; the target data is at least one group of data in the test data; According to the target test type, determining whether the target data is valid; If it is valid, the target data is fitted to generate a change curve, and the target data is processed and analyzed based on the change curve.

[0006] In one embodiment, when the target data includes a steering wheel angle, a longitudinal acceleration, and a vehicle speed, determining the target test type of the target data in the test data includes: Comparing the longitudinal acceleration with a preset acceleration interval to determine a first test type corresponding to the target data; the first test type includes an acceleration test, a deceleration test, and a uniform speed test; Based on the first test type, a target test type of the target data is determined according to the steering wheel angle or the vehicle speed.

[0007] In one embodiment, when the first test type is a constant speed test, the target data further includes lateral acceleration, and the target test type of determining the target data according to the vehicle speed further includes: Compare the vehicle speed with at least one preset vehicle speed interval to determine a second test type of the target data; the second test type includes a low speed test, a medium-low speed test, a medium speed test, a medium-high speed test, and a high speed test; Based on the second test type, the lateral acceleration is compared with a preset second acceleration interval to determine the target test type of the target data.

[0008] In one embodiment, judging whether the target data is valid according to the target test type includes: Determine at least one characteristic data in the target data; the characteristic data includes longitudinal acceleration, vehicle speed, steering wheel angular velocity, and lateral acceleration; Determining characteristic conditions corresponding to the characteristic data and the target test type; At least one of the characteristic data is compared with the characteristic condition, and when it is determined that the target data meets the characteristic condition, the target data is determined to be valid.

[0009] In one embodiment, the step of fitting the target data to generate a change curve includes: Calculating and processing the target data by vehicle dynamics theory to determine a first calculation result; The first settlement results are averaged and the calculation results are fitted in a coordinate system to generate a change curve.

[0010] In one embodiment, the processing and analyzing the target data based on the change curve includes: Determine at least one performance indicator according to the change curve; Determining a theoretical indicator interval corresponding to the at least one performance indicator; The at least one performance indicator is compared with the theoretical indicator range to determine the performance difference of the vehicle and analyze the direction of the vehicle to be optimized.

[0011] In one implementation, determining the theoretical indicator interval corresponding to the at least one performance indicator includes: Acquire historical test data, and divide the historical test data into first historical test data and second historical test data; the vehicle corresponding to the historical test data and the target data is the same model; Fitting a Gaussian curve distribution to the first historical test data; Calculating the probability distribution interval of the Gaussian curve distribution to obtain an initial theoretical index interval; The initial theoretical indicator range is corrected based on the second historical test data to determine the theoretical indicator range.

[0012] In one embodiment, the method further comprises: Classify and summarize the target data, the target test type, the characteristic data, the theoretical index interval, the change curve and the direction to be optimized of the vehicle, and generate a test report; The test report is output.

[0013] In a second aspect, an embodiment of the present application provides a computing device, specifically comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to execute the vehicle test data processing method as described in the first aspect.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the instructions in the computer-readable storage medium are executed by a processor of a computing device, the computing device is enabled to implement the vehicle test data processing method as described in the first aspect.

[0015] The embodiment of the present application provides a method for processing vehicle test data, a computing device, and a computer-readable storage medium, including: obtaining test data to be processed; determining the target test type corresponding to the target data in the test data; the target data is at least one group of data in the test data; judging whether the target data is valid according to the target test type; if valid, fitting the target data to generate a change curve, and processing and analyzing the target data based on the change curve. In this way, by processing and analyzing the target data, the labor cost is reduced, and by detecting whether the target data is valid, the processing efficiency and accuracy of the test data to be processed are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a process flow of a method for processing vehicle test data provided by an embodiment of the present invention; Figure 2 A schematic diagram of a framework of target data types provided by an embodiment of the present invention; Figure 3 A specific schematic diagram of the validity determination of target data provided by an embodiment of the present invention; Figure 4A schematic diagram of model training based on historical data provided by an embodiment of the present invention; Figure 5 A schematic diagram of model training for determining theoretical index intervals provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention; Processor 210 , memory 211 , network interface 212 , and bus system 213 . DETAILED DESCRIPTION

[0017] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0018] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0019] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0020] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0021] It should be noted that, in this article, step codes such as S101, S102, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the scope of protection of this application.

[0022] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0024] See also Figure 1 The embodiment of the present application provides a method for processing vehicle test data. The method for processing vehicle test data can be implemented in software and / or hardware. In the present embodiment, the method for processing vehicle test data is applied to a server as an example. The method for processing vehicle test data provided by the present embodiment includes: Step S101: Obtain test data to be processed.

[0025] Optionally, when testing a vehicle, corresponding test data can be obtained based on sensors installed on the vehicle, such as accelerometers, speedometers, pressure sensors, etc. In this way, the changes in various physical quantities of the vehicle during the test are monitored in real time through the installed sensors, and recorded in real time through the test software Dewesoft, and determined as test data to be processed.

[0026] In one embodiment, after obtaining the test data to be processed, the data needs to be preprocessed, including data detection, data screening and format conversion. Specifically, when performing data detection, it is first necessary to detect the integrity of the data, and to detect the integrity of the test data to be processed by comparing the expected amount of data with the actual amount of data collected, to ensure that all necessary data are collected and not missed. Secondly, considering that abnormal values ​​may be generated due to various reasons (such as sensor failure, electromagnetic interference, etc.) during the data collection process, it is necessary to process the abnormal values, that is, the abnormal values ​​can be corrected or removed from the test data. At the same time, for the missing values ​​in the detected test data, you can also choose to fill (such as using the mean, median or mode filling), delete or interpolate methods for processing.

[0027] In one embodiment, when performing data screening, feature selection may be performed on the test data to retain features that contribute to the task and remove irrelevant or redundant features.

[0028] In one embodiment, when performing format conversion, a special tool or script can be used to perform format conversion. For example, the readtable function of MATLAB can be used to convert a .txt file into a MATLAB table format.

[0029] In this way, through effective data preprocessing, the accuracy of processing test data can be improved, making it better suitable for various application scenarios.

[0030] In one embodiment, when obtaining the test data to be processed, the test data from 3S before the start of the test to the end of the test is generally intercepted. Here, the data before 3S is in the initial state of the test and is directly cleared.

[0031] Step S102, determining a target test type corresponding to target data in the test data; the target data is at least one group of data in the test data.

[0032] Optionally, when determining the target data, the test data needs to be grouped according to a certain logic or rule, and can be grouped based on multiple factors such as time, working conditions, sensor type, etc. to determine the target data. In this way, multiple groups of data in the acquired data to be tested can be processed at the same time, thereby improving the processing efficiency of the test data.

[0033] In one embodiment, when the target data includes a steering wheel angle, a longitudinal acceleration, and a vehicle speed, determining the target test type of the target data in the test data includes: Compare the longitudinal acceleration with a preset acceleration range to determine the first test type corresponding to the target data; the first test type includes an acceleration test, a deceleration test, and a uniform speed test; Based on the first test type, the target test type of the target data is determined according to the steering wheel angle or the vehicle speed.

[0034] Optionally, the longitudinal acceleration in the target data is determined, and the longitudinal acceleration is compared with a preset first acceleration interval. If it is determined that the longitudinal acceleration is not within the first acceleration interval and is less than the minimum value of the first acceleration interval, then the first test type corresponding to the target data is determined to be an acceleration test; if it is determined that the longitudinal acceleration is not within the first acceleration interval and is greater than the maximum value of the first acceleration interval, then the first test type corresponding to the target data is determined to be a deceleration test; if it is determined that the longitudinal acceleration is within the first acceleration interval, then the first test type corresponding to the target data is determined to be a uniform speed test.

[0035] like Figure 2 As shown, the first acceleration interval is set to (-0.05, 0.05). In this way, the longitudinal acceleration ax in the target data is obtained. If 0.05g≤ax, the first test type is determined to be an acceleration test; if ax≤-0.05g, the first test type is determined to be a deceleration test; if -0.05g≤ax≤0.05g, the first test type is determined to be a uniform speed test.

[0036] Optionally, when determining that the first test type is an acceleration test, the steering wheel angle is compared with a preset steering wheel angle threshold. If the steering wheel angle is less than the steering wheel angle threshold, the target test type corresponding to the target data is determined to be a straight-line acceleration test; if the steering wheel angle is greater than or equal to the steering wheel angle threshold, the target test type corresponding to the target data is determined to be a curve acceleration test.

[0037] Optionally, when determining that the first test type is a deceleration test, the steering wheel angle is compared with a preset steering wheel angle threshold. If the steering wheel angle is less than the steering wheel angle threshold, the target test type corresponding to the target data is determined to be a straight-line deceleration test; if the steering wheel angle is greater than or equal to the steering wheel angle threshold, the target test type corresponding to the target data is determined to be a curve deceleration test.

[0038] like Figure 2 As shown, the steering wheel angle threshold is set to 5. When the first test type is an acceleration test, the steering wheel angle is compared with the steering wheel angle threshold. If the steering wheel angle <5, the target test type is determined to be a straight-line acceleration test; if the steering wheel angle ≥5, the target test type is determined to be a curve acceleration test. When the first test type is a deceleration test, if the steering wheel angle <5, the target test type is determined to be a straight-line deceleration test; if the steering wheel angle ≥5, the target test type is determined to be a curve deceleration test.

[0039] Optionally, when determining that the first test type is a uniform speed test, the vehicle speed is compared with at least one preset speed interval, including: if the vehicle speed is within the preset first speed interval, determining that the target test type corresponding to the target data is a parking test; if the vehicle speed is within the preset second speed interval, determining that the target test type corresponding to the target data is an idle test; if the vehicle speed is within the preset third speed interval, determining that the target test type corresponding to the target data is a low speed test; if the vehicle speed is within the preset fourth speed interval, determining that the target test type corresponding to the target data is a medium-low speed test; if the vehicle speed is within the preset fifth speed interval, determining that the target test type corresponding to the target data is a medium-speed test; if the vehicle speed is within the preset sixth speed interval, determining that the target test type corresponding to the target data is a medium-high speed test; if the vehicle speed is within the preset seventh speed interval, determining that the target test type corresponding to the target data is a high-speed test. Here, the setting values ​​of the first speed interval, the second speed interval, the third speed interval, the fourth speed interval, the fifth speed interval, the sixth speed interval, and the seventh speed interval should be set to increase in sequence. At the same time, the speed here generally refers to the average speed of the vehicle detected during the test.

[0040] like Figure 2As shown, the first speed interval can be set to (-∞, 1], the second speed interval can be set to [6, 10], the third speed interval can be set to [12, 18], the fourth speed interval can be set to [48, 52], the fifth speed interval can be set to [78, 82], the sixth speed interval can be set to [98, 102], and the seventh speed interval can be set to [118, 122]. In this way, if the vehicle speed is within (-∞, 1], the target test type corresponding to the target data is determined to be the parking test; if the vehicle speed is within [6, 10], the target test type corresponding to the target data is determined to be the idling test. speed test; if the vehicle speed is within [12, 18], the target test type corresponding to the target data is determined to be a low-speed test; if the vehicle speed is within [48, 52], the target test type corresponding to the target data is determined to be a medium-low speed test; if the vehicle speed is within [78, 82], the target test type corresponding to the target data is determined to be a medium-speed test; if the vehicle speed is within [98, 102], the target test type corresponding to the target data is determined to be a medium-high speed test; if the vehicle speed is within [118, 122], the target test type corresponding to the target data is determined to be a high-speed test.

[0041] In one embodiment, when the first test type is a uniform speed test, the target data further includes lateral acceleration, and the target test type of the target data is determined according to the vehicle speed, further including: Compare the vehicle speed with at least one preset vehicle speed interval to determine the second test type of the target data; the second test type includes a low speed test, a medium-low speed test, a medium speed test, a medium-high speed test, and a high speed test; Based on the second test type, the lateral acceleration is compared with a preset second acceleration interval to determine the target test type of the target data.

[0042] Optionally, when the vehicle speed is compared with at least one preset vehicle speed interval to determine that the target data is one of a low-speed test, a medium-low speed test, a medium-speed test, a medium-high speed test, and a high-speed test, the vehicle lateral acceleration can also be compared with a preset second acceleration interval to further determine a more specific target test type of the target data, including: if the lateral acceleration is not in the second acceleration interval, and the lateral acceleration is less than or equal to the minimum value of the second acceleration interval, then the target test type corresponding to the target data is further determined to be a uniform speed test in a small lateral direction; if the lateral acceleration is not in the second acceleration interval, and the lateral acceleration is greater than the maximum value of the second acceleration interval, then the target test type corresponding to the target data is further determined to be a uniform speed test in a large lateral direction; if the lateral acceleration is in the second acceleration interval, then the target test type corresponding to the target data is further determined to be a uniform speed test in a medium lateral direction.

[0043] For example, Figure 2 As shown, the second acceleration interval can be set to (0.2, 0.5]. In this way, the lateral acceleration ay in the target data is obtained. If ay ≤ 0.2g, it is determined that the target test type is a uniform test with a small lateral direction; if 0.5g < ay, it is determined that the target test type is a uniform test with a large lateral direction; if 0.2g < ay ≤ 0.5g, it is determined that the target test type is a uniform test with a medium lateral direction.

[0044] Step S103: Determine whether the target data is valid according to the target test type.

[0045] Optionally, when a vehicle test needs to complete multiple test conditions in one test and there are many human factors in the consistency of test data, the number of data for each test condition is at least five groups. Since each group of data is detected and processed during the test, it is necessary to determine whether the consistency of each index meets the requirements to determine the validity of the target data.

[0046] In one embodiment, determining whether the target data is valid according to the target test type includes: Determine at least one characteristic data in the target data; the characteristic data includes longitudinal acceleration, vehicle speed, steering wheel angular velocity, and lateral acceleration; Determine the characteristic conditions corresponding to the characteristic data and the target test type; Compare and judge at least one characteristic data with the characteristic conditions, and determine that the target data is valid when it is determined that the target data meets the characteristic conditions.

[0047] Optionally, in order to determine whether the target data meets the regulations of the test standard, according to the target test type of the target data, some judgments on the change rate of characteristic signals of the target data are made to ensure that the target data meets the regulations in the test standard.

[0048] As Figure 3 shown, when determining the validity of the longitudinal acceleration in the target data, if the target test type is a straight-line acceleration test, it is detected whether the longitudinal acceleration meets the setting of the acceleration point. For example, it is detected whether the longitudinal acceleration meets the preset acceleration points (such as 0.05, 0.1, 0.15, 0.2, 0.25, 0.3) to ensure that the longitudinal acceleration level reached or not exceeded by the vehicle under different working conditions. If the target test type is a curve acceleration test, it is judged whether the longitudinal acceleration meets the preset acceleration gradient requirement. If the target test type is a straight-line deceleration test, it is detected whether the longitudinal acceleration meets the setting of the deceleration point. For example, when it is detected whether the longitudinal acceleration meets the preset (such as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6), to ensure that the longitudinal deceleration level that the vehicle needs to reach or not exceed under different working conditions. If the target test type is a curve deceleration test, it is detected whether the longitudinal acceleration meets the acceleration gradient requirement.

[0049] Optionally, when judging the validity of the vehicle speed in the target data, it is detected whether the vehicle speed during the test satisfies the vehicle speed threshold. For example, the vehicle speed threshold may be set to V+2 km / h.

[0050] Optionally, when judging the validity of the steering wheel angular velocity in the target data, if the target test type is a parking test or an idle test, the steering wheel angular velocity is detected to see if it meets the first angular velocity threshold. For example, the first angular velocity threshold can be set to 90±45deg / s. If the target test type is other tests in the uniform speed test, it can be detected whether the input frequency of the steering wheel angle meets the frequency threshold, for example, the frequency threshold can be set to 0.2HZ, or it can be detected whether the steering wheel angular velocity meets the second angular velocity threshold, for example, the second angular velocity threshold can be set to 10~14deg / s.

[0051] Optionally, when judging the validity of the lateral acceleration in the target data, if the target test type is a small lateral test, judge whether the lateral acceleration meets the first lateral acceleration threshold; if the target test type is a medium lateral test, judge whether the lateral acceleration meets the second lateral acceleration threshold; if the target test type is a large lateral test, judge whether the lateral acceleration meets the third lateral acceleration threshold.

[0052] Step S104: If it is valid, fit the target data to generate a change curve, and process and analyze the target data based on the change curve.

[0053] In one embodiment, fitting the target data to generate a change curve includes: Calculating and processing the target data by using vehicle dynamics theory to determine a first calculation result; The first settlement result is averaged and the calculation result is fitted in a coordinate system to generate a change curve.

[0054] Optionally, the target data is processed using basic equations and models of vehicle dynamics theory, such as a single-track model, a double-track model, or a more complex multi-body dynamics model, including solving differential equations, matrix operations, etc., to obtain a first calculation result reflecting the vehicle's motion characteristics. The first calculation result here is used to characterize a certain performance data of the vehicle test.

[0055] Optionally, a logistic regression model is used to perform Gaussian curve distribution on the same performance index data of all vehicle models, that is, the mean of the first calculation result is calculated, and the mean is fitted in the coordinate system to generate one or more change curves, which can intuitively show the motion characteristics of the vehicle under different working conditions. Here, the mean of the first calculation result is used to characterize the possibility of a specific category (such as "high performance" or "low performance").

[0056] In one embodiment, processing and analyzing the target data based on the change curve includes: Determining at least one performance indicator according to the change curve; Determining a theoretical index interval corresponding to at least one performance index; Compare at least one performance indicator with a theoretical indicator range to determine the performance difference of the vehicle and analyze the direction of the vehicle to be optimized.

[0057] Optionally, at least one performance indicator in the vehicle test data is determined based on the change curve generated by fitting, and the theoretical indicator interval corresponding to the performance indicator is determined. Here, the theoretical indicator interval can be preset or obtained through continuous training. In one embodiment, based on the comparison between different performance indicators and the corresponding theoretical indicator intervals, it is determined whether the performance indicator is within the theoretical indicator interval to analyze the performance of the vehicle test. Specifically, if the performance indicator is within the theoretical indicator interval, it is determined that the performance of the vehicle is good and meets the test standards; if the performance indicator is not within the theoretical indicator interval, based on the difference between the performance indicator and the theoretical indicator interval, the direction of the vehicle to be optimized is specifically analyzed in combination with theoretical knowledge. Here, the theoretical indicator intervals corresponding to different performance indicators may be different.

[0058] In one implementation, determining a theoretical index interval corresponding to at least one performance index includes: Acquire historical test data, and divide the historical test data into first historical test data and second historical test data; the vehicle corresponding to the historical test data and the target data is the same model; Fitting a Gaussian curve distribution to the first historical test data; Calculate the probability distribution interval of the Gaussian curve distribution to obtain the initial theoretical index interval; The initial theoretical index range is revised based on the second historical test data to determine the theoretical index range.

[0059] like Figure 4As shown, by collecting historical test data, the first historical test data of the vehicle model with the same suspension form is screened out, and the same performance index data of all vehicle models is distributed by Gaussian curve using the logistic regression model, and the probability distribution interval in the Gaussian curve distribution is calculated. Here, the model is trained by the first historical test data to determine the initial theoretical index interval. In one embodiment, according to the definition of the performance style (comfort, standard, sports) of the actual vehicle model, the probability of the actual performance value in the index distribution interval is set. For example, the probability interval corresponding to the comfort style is 20%~50%; the probability interval corresponding to the standard style is 35%~65%; the probability interval corresponding to the sports style is 50%~80%, etc. Further, the initial theoretical index intervals A, B, C... are calculated by comparing the algorithm model.

[0060] Optionally, the current actual test data or the historical test data of the actual vehicle can be used as the second historical test data, and the model can be tested by the second historical test data to correct the initial theoretical index range. In this way, the proportion of the number of samples in the theoretical value range interval to the total number of samples is continuously corrected based on the second historical test data, and finally the optimal theoretical index range is obtained.

[0061] In summary, in the vehicle test data processing method provided in the above embodiment, the labor cost is reduced by processing and analyzing the target data, and at the same time, the processing efficiency and accuracy of the test data to be processed are improved by detecting whether the target data is valid.

[0062] In one embodiment, the method further comprises: Classify and summarize target data, target test type, characteristic data, theoretical index range, change curve and vehicle optimization direction, and generate a test report; Output test report.

[0063] Optionally, the target data, target test type, characteristic data, theoretical index interval, change curve and vehicle optimization direction are integrated, and key data and trends are intuitively displayed in the form of charts, pictures, etc., which can improve the readability and comprehensibility of the report. In one embodiment, the final report can also be output in electronic or paper form for review and reference by relevant personnel.

[0064] Based on the same inventive concept as the above embodiments, refer to Figure 5The present application also proposes a specific example to explain in detail a method for processing vehicle test data provided by the present application. Taking the application of the method for processing vehicle test data to a preset AI algorithm model as an example, the test data to be processed collected by the test equipment is directly imported into the AI ​​algorithm model, the test data is preprocessed, and the neural network algorithm is run to perform parallel processing on the target data in the test data to generate respective characteristic indicators, which are then automatically summarized to form test results and test reports.

[0065] Specifically, this AI algorithm model is designed to solve three data processing problems. The entire model mainly includes three core contents: data preprocessing, feature indicator processing, and result analysis and display.

[0066] In one embodiment, data preprocessing includes data format conversion, data classification, and data screening.

[0067] 1. Data format conversion Convert the test data to be processed recorded by the test software Dewesoft into a data format with one click, and convert the .dxd data file into a .mat file to facilitate subsequent indicator processing.

[0068] 2. Data Classification According to the judgment of some channel signals in the data, the target test type of the target data in the test data is determined, including: According to the longitudinal acceleration ax, the target test type includes: acceleration test, deceleration test, and uniform speed test; According to the steering wheel angle swa, the target test type is determined to include: straight line test and curve test; In the uniform speed test, the target test types are determined according to the test vehicle speed, including: parking test, idling test, low speed test, medium-low speed test, medium speed test, medium-high speed test, and high speed test; According to the lateral acceleration ay, the target test types are further determined to include: small lateral test, medium lateral test, and large lateral test.

[0069] 3. Data Screening In order to determine whether the test data meets the requirements of the test standard, the change rate of the characteristic signal is judged according to the target data and the corresponding target test type, and the test data must be guaranteed to meet the requirements of the test standard.

[0070] In one embodiment, when processing characteristic indicators of target data, the target data is input into the corresponding test condition processing subroutine according to the target data type, and the calculation processing of each group of data is completed through the vehicle dynamics theory calculation formula, and the average value of the calculation result is fitted into the corresponding change curve in the coordinate system. Finally, all the characteristic indicators of the corresponding test condition are extracted from the fitted change curve and classified and summarized by the test name.

[0071] In one embodiment, the result analysis display includes an experimental report, a curve display, and a comparative analysis. Specifically, it includes: 1. Test Report According to the prepared test report Word template, the completed test results and curves are exported to the test report Word template in sequence according to the test conditions to complete the preparation of the test report.

[0072] 2. Curve Display The corresponding generated change curves and characteristic indicators of the test results are displayed together in a chart Figure 1 Displayed for easy viewing.

[0073] 3. Comparative Analysis Comparing the previously completed processing results and curves under the same test conditions together makes it easier to discover the changing patterns and differences, which is conducive to analyzing the causes.

[0074] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides a computing device, such as Figure 6 As shown, the computing device includes: a processor 210 and a memory 211 storing a computer program; wherein, Figure 6 The processor 210 shown in the figure is not used to indicate that the number of the processor 210 is one, but is only used to indicate the positional relationship of the processor 210 relative to other devices. In actual applications, the number of the processor 210 may be one or more; similarly, Figure 6 The memory 211 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 211 relative to other devices. In practical applications, the number of memories 211 can be one or more. When the processor 210 runs the computer program, the above-mentioned vehicle test data processing method is implemented.

[0075] The computing device may also include: at least one network interface 212. The various components in the computing device are coupled together via a bus system 213. It is understood that the bus system 213 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 213 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 213 is not used in the example above. Figure 6 Various buses are labeled as bus system 213.

[0076] The memory 211 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 211 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0077] The memory 211 in the embodiment of the present invention is used to store various types of data to support the operation of the computing device. Examples of these data include: any computer program used to operate on the computing device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player, a browser, etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0078] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the vehicle test data processing method applied to the above-mentioned computing device is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0079] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0081] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for processing vehicle test data, characterized in that: include: Obtaining test data to be processed; Determining a target test type corresponding to the target data in the test data; The target data is at least one set of data in the test data; According to the target test type, determining whether the target data is valid; If it is valid, the target data is fitted to generate a change curve, and the target data is processed and analyzed based on the change curve.

2. The method according to claim 1, characterized in that When the target data includes a steering wheel angle, a longitudinal acceleration, and a vehicle speed, the determining of the target test type of the target data in the test data includes: Comparing the longitudinal acceleration with a preset acceleration interval to determine a first test type corresponding to the target data; the first test type includes an acceleration test, a deceleration test, and a uniform speed test; Based on the first test type, a target test type of the target data is determined according to the steering wheel angle or the vehicle speed.

3. The method according to claim 2, characterized in that When the first test type is a uniform speed test, the target data further includes lateral acceleration, and the target test type of determining the target data according to the vehicle speed further includes: Compare the vehicle speed with at least one preset vehicle speed interval to determine a second test type of the target data; the second test type includes a low speed test, a medium-low speed test, a medium speed test, a medium-high speed test, and a high speed test; Based on the second test type, the lateral acceleration is compared with a preset second acceleration interval to determine the target test type of the target data.

4. The method according to claim 1, characterized in that: The step of judging whether the target data is valid according to the target test type includes: Determine at least one characteristic data in the target data; the characteristic data includes longitudinal acceleration, vehicle speed, steering wheel angular velocity, and lateral acceleration; Determining characteristic conditions corresponding to the characteristic data and the target test type; At least one of the characteristic data is compared with the characteristic condition, and when it is determined that the target data meets the characteristic condition, the target data is determined to be valid.

5. The method according to claim 1, characterized in that The step of fitting the target data to generate a change curve includes: Calculating and processing the target data by vehicle dynamics theory to determine a first calculation result; The first settlement results are averaged and the calculation results are fitted in a coordinate system to generate a change curve.

6. The method according to claim 1, characterized in that The processing and analyzing of the target data based on the change curve includes: Determine at least one performance indicator according to the change curve; Determining a theoretical indicator interval corresponding to the at least one performance indicator; The at least one performance indicator is compared with the theoretical indicator range to determine the performance difference of the vehicle and analyze the direction of the vehicle to be optimized.

7. The method according to claim 6, characterized in that The determining of the theoretical indicator range corresponding to the at least one performance indicator includes: Acquire historical test data, and divide the historical test data into first historical test data and second historical test data; the vehicle corresponding to the historical test data and the target data is the same model; Fitting a Gaussian curve distribution to the first historical test data; Calculating the probability distribution interval of the Gaussian curve distribution to obtain an initial theoretical index interval; The initial theoretical indicator range is corrected based on the second historical test data to determine the theoretical indicator range.

8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: Classify and summarize the target data, the target test type, the characteristic data, the theoretical index interval, the change curve and the direction to be optimized of the vehicle, and generate a test report; The test report is output.

9. A computing device, characterized in that include: A processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions to implement the vehicle test data processing method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor, the vehicle test data processing method according to any one of claims 1 to 8 is implemented.

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