Snow vehicle ice skate blade test data processing method and system, electronic equipment and storage medium

Abstract

The invention discloses a snow vehicle ice blade test data processing method and system, electronic equipment and a storage medium, and the method comprises the steps: traversing a detection file exported by a preset sensor, and obtaining a target file; reading according to the target file to obtain original detection data, and performing data preprocessing on the original detection data to obtain a first data set; smoothing the first data set through a preset data processing algorithm to obtain a second data set; and determining a stationary data segment of the second data set through a preset stationary threshold, and constructing a target data set according to the stationary data segment. According to the embodiment of the invention, the processing efficiency of the snow vehicle ice skate blade test data can be effectively improved, and the accuracy of data analysis is improved. The method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, system, electronic device and storage medium for processing bobsleigh and ice skate test data. Background Art

[0002] The bobsleigh blade test is a series of test processes to verify the various performance of the blades, such as physical properties, structural strength, sliding efficiency and safety. Among them, a large amount of raw data is often generated during the bobsleigh blade test process. In related technologies, the raw data generated by the bobsleigh blade test bench usually needs to be analyzed and interpreted through complex manual processing procedures, which is prone to human errors and is time-consuming and labor-intensive.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method, system, electronic device and storage medium for processing bobsleigh and ice skate test data, which can effectively improve the processing efficiency of bobsleigh and ice skate test data and improve the accuracy of data analysis.

[0005] To achieve the above-mentioned purpose, an embodiment of the present application provides a method for processing snowmobile ice skate test data, the method comprising the following steps:

[0006] Traverse the detection files exported by the preset sensor to obtain the target file;

[0007] Reading the target file to obtain original detection data, and performing data preprocessing on the original detection data to obtain a first data set;

[0008] Smoothing the first data set using a preset data processing algorithm to obtain a second data set;

[0009] The stationary data segment of the second data set is determined by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment.

[0010] In some embodiments, traversing the detection files exported by the preset sensor to obtain the target file includes:

[0011] Traversing the file name information of the detection files to obtain the experimental test parameters corresponding to each of the detection files according to the file name information; wherein the file name information is constructed according to the experimental test parameters, and the experimental test parameters include skate side slip angle data, speed data and load value;

[0012] The target file is extracted from the detection file according to the experimental test parameters.

[0013] In some embodiments, the step of obtaining the original detection data by reading the target file and performing data preprocessing on the original detection data to obtain a first data set includes:

[0014] Read the target file line by line through a preset file reading function to obtain a preset cell array;

[0015] Converting the preset cell array into a first data matrix;

[0016] The first data set is obtained by extracting data from the first data matrix using a regular expression.

[0017] In some embodiments, the step of smoothing the first data set by using a preset data processing algorithm to obtain a second data set includes:

[0018] Converting the preset input parameters in the first data set into radians to obtain preset radian data; wherein the preset input parameters include a skate side slip angle parameter and a sensor angle parameter;

[0019] Performing coordinate transformation on the preset radian data through a coordinate transformation matrix to obtain preset transformation data;

[0020] Constructing a second data matrix according to the preset conversion data and the preset radian data;

[0021] The preset data columns in the second data matrix are smoothed to obtain the second data set.

[0022] In some embodiments, determining the stationary data segment of the second data set by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment, comprises:

[0023] Calculate the average value of each column of data according to the second data set to obtain preset column average data;

[0024] Standardizing the preset column average data and the second data set to calculate moving standard deviation data;

[0025] Searching for an index data point that satisfies a preset stability condition according to the moving standard deviation data; wherein the preset stability condition includes that the moving standard deviation data corresponding to the index data point is less than a preset standard deviation threshold, and there are a preset number of preset data points after the index data point;

[0026] Determine the stationary data segment according to the index data point and the preset number, so as to extract a stationary data set according to the stationary data segment;

[0027] The preset stationary average value of each column of data in the stationary data set is calculated, so as to construct the target data set according to the preset stationary average value and the stationary data set.

[0028] In some embodiments, before executing the step of traversing the detection files exported by the preset sensor to obtain the target file, the method further includes:

[0029] Set the blade calibration angle and gravity compensation;

[0030] The preset sensor is zero-point calibrated by preset non-interference parameters; wherein the preset non-interference parameters include the detection data of the preset sensor after the skates are lifted, the preload data of the bobsleigh skate test bench, and the bolt preload data.

[0031] In some embodiments, after performing the step of determining the stationary data segment of the second data set by presetting the stationary threshold to construct a target data set according to the stationary data segment, the method further includes:

[0032] Constructing a target array to store the target data set into the target array; wherein the first column of the target array stores the skate side slip angle data and the speed data;

[0033] Data visualization is performed according to the target array to generate a preset visualization image.

[0034] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a snowmobile ice skate test data processing system, the system comprising:

[0035] The first module is used to traverse the detection files exported by the preset sensor to obtain the target file;

[0036] A second module is used to read and obtain original detection data according to the target file, so as to perform data preprocessing on the original detection data to obtain a first data set;

[0037] A third module is used to perform smoothing processing on the first data set by using a preset data processing algorithm to obtain a second data set;

[0038] The fourth module is used to determine the stable data segment of the second data set by presetting a stable threshold, so as to construct a target data set according to the stable data segment.

[0039] To achieve the above object, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising:

[0040] at least one processor;

[0041] at least one memory for storing at least one program;

[0042] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0043] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0044] The embodiments of the present application include at least the following beneficial effects: The present application provides a method, system, electronic device and storage medium for processing snowmobile and ice skate test data. The scheme obtains a target file by traversing the detection file exported by the preset sensor, and reads the original detection data according to the target file to perform data preprocessing on the original detection data to obtain a first data set. Then, the embodiment of the present invention smoothes the first data set by a preset data processing algorithm to obtain a second data set, and then determines the stable data segment of the second data set by a preset stable threshold, so as to calculate the target data set according to the stable data segment, realize the automatic processing of the original data of the snowmobile and ice skate test, and effectively improve the processing efficiency of the snowmobile and ice skate test data. Accordingly, the embodiment of the present invention can effectively improve the stability and accuracy of the data by smoothing the first data set. At the same time, the embodiment of the present invention can effectively improve the reliability and stability of data analysis and processing by determining the stable data segment by a preset stable threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of the steps of the method for processing snowmobile ice skate test data provided by an embodiment of the present invention;

[0046] Figure 2 It is a flowchart of the steps of traversing the detection files exported by the preset sensor to obtain the target file provided by the embodiment of the present invention;

[0047] Figure 3 It is a flowchart of the steps of obtaining original detection data by reading the target file according to an embodiment of the present invention, performing data preprocessing on the original detection data, and obtaining a first data set;

[0048] Figure 4 is a flowchart of the steps of obtaining a second data set by smoothing a first data set using a preset data processing algorithm provided by an embodiment of the present invention;

[0049] Figure 5 It is a flow chart of determining a stable data segment of a second data set by presetting a stable threshold value, so as to calculate a target data set according to the stable data segment, provided by an embodiment of the present invention;

[0050] Figure 6is a flowchart of steps for initializing parameters related to a snowmobile and ice skate test bench provided by an embodiment of the present invention;

[0051] Figure 7 is a flowchart of the steps of data storage and visualization provided by an embodiment of the present invention;

[0052] Figure 8 It is a schematic diagram of the overall process of the method for processing snowmobile ice skate test data provided by an embodiment of the present invention;

[0053] Fig. 9 It is a structural schematic diagram of a snowmobile ice skate test data processing system provided by an embodiment of the present invention;

[0054] Fig.10 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0056] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, 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 words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0057] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0060] Six-dimensional force sensor: It is a kind of sensor that can simultaneously measure force and torque in three-dimensional space, that is, force and torque information of six degrees of freedom. It can accurately measure the force in six directions and the torque in three directions through precisely designed elastic bodies and signal conditioning circuits.

[0061] Bobsleigh and ice skate test bench: It is a special equipment for conducting various performance tests on bobsleigh and ice skates. It simulates the usage scenarios of bobsleigh in actual competitions and training, and is used to evaluate the performance of ice skates under different conditions, such as sliding speed, stability and durability.

[0062] The bobsleigh blade test is a series of test processes to verify the various performance of the blades, such as physical properties, structural strength, sliding efficiency and safety. Among them, a large amount of raw data is often generated during the bobsleigh blade test process. In related technologies, the raw data generated by the bobsleigh blade test bench usually needs to be analyzed and interpreted through complex manual processing procedures, which is prone to human errors and is time-consuming and labor-intensive.

[0063] In view of this, a method, system, electronic device and storage medium for processing snowmobile and ice skate test data are provided in the embodiments of the present application. The scheme obtains a target file by traversing the detection file exported by the preset sensor, and reads the original detection data according to the target file to perform data preprocessing on the original detection data to obtain a first data set. Then, the embodiment of the present invention smoothes the first data set through a preset data processing algorithm to obtain a second data set, and then determines the stable data segment of the second data set through a preset stable threshold, so as to calculate the target data set based on the stable data segment, thereby realizing the automatic processing of the raw data of the snowmobile and ice skate test, effectively improving the processing efficiency of the snowmobile and ice skate test data, and improving the accuracy of data analysis.

[0064] The method for processing snowmobile and ice skate test data provided in the embodiment of the present application relates to the field of data processing technology. The method for processing snowmobile and ice skate test data provided in the embodiment of the present application can be applied to a terminal, can be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the method for processing snowmobile and ice skate test data, etc., but is not limited to the above forms.

[0065] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] Figure 1 is an optional flow chart of the method for processing snowmobile ice skate test data provided in an embodiment of the present application, Figure 1 The method may include but is not limited to steps S110 to S140.

[0067] Step S110: traverse the detection files exported by the preset sensor to obtain the target file.

[0068] Step S120: obtaining original detection data by reading the target file, and performing data preprocessing on the original detection data to obtain a first data set.

[0069] Step S130: Smoothing the first data set using a preset data processing algorithm to obtain a second data set.

[0070] Step S140: determining a stationary data segment of the second data set by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment.

[0071] During the working process of this specific embodiment, the embodiment of the present invention first traverses the detection files exported by the preset sensor to obtain the target file. Specifically, the preset sensor in the embodiment of the present invention refers to a sensor for performing a snowmobile blade test, such as a six-dimensional force sensor. Accordingly, the detection file refers to a file containing raw data of the snowmobile blade test exported by the preset sensor. At the same time, the target file refers to a detection file that needs to be processed. Accordingly, the embodiment of the present invention first traverses each detection file to extract the target file that needs to be processed for raw data. Then, the embodiment of the present invention reads the raw detection data according to the target file to perform data preprocessing on the raw detection data to obtain a first data set. Specifically, after extracting the target file, the embodiment of the present invention reads the target file through the corresponding file reading function to obtain the raw detection data, that is, the unprocessed data detected by the preset sensor. Then, the embodiment of the present invention traverses the extracted raw detection data to perform data preprocessing, and preliminarily screens the raw detection data to obtain the first data set. Further, the embodiment of the present invention smoothes the first data set through a preset data processing algorithm to obtain a second data set. Specifically, the data originally collected in the embodiment of the present invention may have data fluctuations and noise, which may easily lead to data instability. Therefore, the embodiment of the present invention performs data smoothing processing on the first data set through a preset data processing algorithm, thereby constructing a second data set. Among them, the preset data processing algorithm in the embodiment of the present invention refers to a related smoothing algorithm. Further, the embodiment of the present invention determines the stable data segment of the second data set through a preset stable threshold value, so as to construct a target data set based on the stable data segment. Specifically, the stable data segment in the embodiment of the present invention refers to a data segment in the second data that meets the stability condition. Correspondingly, the preset stable threshold value refers to a data threshold used to judge the stability of the data. Among them, the embodiment of the present invention first analyzes the second data set through a preset stable threshold value to determine whether the stability of the data in the second data set meets the stability condition, and then determines the corresponding stable data segment. Then, the embodiment of the present invention constructs the target data set by extracting the obtained stable data segment, completes the processing of the original data of the bobsleigh and ice skates test, effectively improves the processing efficiency of the bobsleigh and ice skates test data, and improves the accuracy of data analysis.

[0072] Reference Figure 2 In some embodiments of the present invention, traversing the detection files exported by the preset sensor to obtain the target file includes but is not limited to the following steps:

[0073] Step S210: traverse the file name information of the detection file to obtain the experimental test parameters corresponding to each of the detection files according to the file name information; wherein the file name information is constructed according to the experimental test parameters, and the experimental test parameters include skate side slip angle data, speed data and load value.

[0074] Step S220: extracting the target file from the detection file according to the experimental test parameters.

[0075] In this specific embodiment, the embodiment of the present invention first traverses the file name information of the detection file to obtain the experimental test parameters corresponding to each detection file according to the file name information, and then extracts the target file from the detection file according to the experimental test parameters. Specifically, the embodiment of the present invention constructs the file name information of each detection file according to the experimental test parameters. Among them, the experimental test parameters include ice skate side slip angle data, speed data and load value. For example, the file naming format of the detection file in the embodiment of the present invention is such as 0000-000-0000, which respectively represents the angle item, speed item and load item. Among them, the first 0 of the angle item represents a negative number, and 1 represents a positive number. When naming, the angle is multiplied by 10, and it is stipulated that the side slip angle is negative to the left and positive to the right. Correspondingly, the embodiment of the present invention traverses the file names of each detection file to convert the side slip angle, speed and load in the file name information into numerical values ​​through string conversion and mathematical operations, and is used for subsequent data processing, such as determining the target file from each detection file. Exemplarily, the embodiment of the present invention searches for all .txt files, i.e., detection files, under the specified path through a corresponding search function, such as a dir function, and stores the search results in a variable. Next, the embodiment of the present invention pre-allocates space to increase processing speed and initializes an array whose size matches the number of files to store processed data. Furthermore, the embodiment of the present invention reads the file names of the detection files one by one through a while loop to parse out key information such as slip angle, speed, and load, and then determines the corresponding target file.

[0076] Reference Figure 3 In some embodiments of the present invention, the original detection data is obtained by reading the target file, and the original detection data is preprocessed to obtain a first data set, including but not limited to the following steps:

[0077] Step S310: Read the target file line by line through a preset file reading function to obtain a preset cell array.

[0078] Step S320: converting the preset cell array into a first data matrix.

[0079] Step S330: extracting data from the first data matrix using a regular expression to obtain a first data set.

[0080] In this specific embodiment, the embodiment of the present invention first reads the target file line by line through the preset file reading function to obtain a preset cell array, and then converts the preset cell array into a first data matrix, so as to extract data from the first data matrix through a regular expression to obtain a first data set. Specifically, the preset file reading function of the embodiment of the present invention refers to a function for reading file content, such as a textscan function. For example, the embodiment of the present invention first opens the target file, such as opening the target file in read-only mode through the fopen function. At the same time, the embodiment of the present invention skips the title line to avoid reading the title or metadata. Then, the embodiment of the present invention reads the target file content line by line through the textscan function, and after reading all the lines, closes the target file, such as closing the target file by making the fclose function to release the corresponding resources, and alleviate the problem of the target file being accidentally modified or locked in subsequent operations. Further, since there may be different data types and formats in the read data, the embodiment of the present invention converts the read preset cell array to obtain a first data matrix. For example, the embodiment of the present invention converts the cell array returned by the textscan function into a first data matrix through the cell2mat function. Finally, the embodiment of the present invention traverses the converted first data matrix through a loop to extract data through a regular expression, such as a regexp function, such as extracting the integer before the equal sign and the floating-point number after the equal sign in each row as an array. Accordingly, when the regular expression is successfully matched, that is, tokens is not empty, the embodiment of the present invention stores the extracted array into the corresponding matrix to construct a first data set.

[0081] Reference Figure 4 In some embodiments of the present invention, the first data set is smoothed by a preset data processing algorithm to obtain a second data set, including but not limited to the following steps:

[0082] Step S410: converting the preset input parameters in the first data set into radians to obtain preset radian data, wherein the preset input parameters include a skate side slip angle parameter and a sensor angle parameter.

[0083] Step S420: performing coordinate transformation on the preset radian data through a coordinate transformation matrix to obtain preset transformation data.

[0084] Step S430: constructing a second data matrix according to the preset conversion data and the preset radian data.

[0085] Step S440: Smoothing the preset data columns in the second data matrix to obtain a second data set.

[0086] In this specific embodiment, the embodiment of the present invention first converts the preset input parameters in the first data set into radians to obtain preset radian data. Specifically, the preset input parameters in the embodiment of the present invention include the skate side slip angle parameter and the sensor angle parameter, and the first data set includes the corresponding preset input parameters, namely the skate side slip angle parameter and the sensor angle parameter. Accordingly, the embodiment of the present invention first initializes the preset input parameters, that is, converts the skate side slip angle, the sensor angle and other parameters into radians to obtain the preset radian data. Among them, the preset radian data includes the radians corresponding to the skate side slip angle and the sensor angle. Then, the embodiment of the present invention performs coordinate conversion on the preset radian data through a coordinate transformation matrix to obtain preset conversion data. Specifically, the embodiment of the present invention traverses the preset radian data to perform data conversion through a coordinate transformation matrix to obtain the preset conversion data. For example, the embodiment of the present invention traverses each row or each group of data in the preset radian data to perform data conversion on each group of data through a coordinate transformation matrix to convert the data from one coordinate system to another coordinate system, so as to facilitate subsequent data analysis and processing. Next, the embodiment of the present invention constructs a second data matrix based on the preset conversion data and the preset radian data. Specifically, the embodiment of the present invention updates the preset conversion data obtained after the coordinate transformation and the untransformed data (preset radian data) into a new matrix to construct the second data matrix. Finally, the embodiment of the present invention smoothes the preset data columns in the second data matrix to obtain a second data set. Specifically, in order to reduce noise and fluctuations in the data, the embodiment of the present invention smoothes the preset data columns in the second data matrix through a smoothing algorithm. Among them, the preset data columns refer to all data columns except the first column in the second data matrix. For example, the embodiment of the present invention smoothes all columns except the first column in the second data matrix through the smoothdata function and the moving average method, and creates a matrix of the same size as the original data to store the smoothed data, and constructs the second data set. Among them, the embodiment of the present invention sets the window size to determine the granularity of the smoothing process. In addition, the embodiment of the present invention also plots the smoothed data through the plot function to enhance the readability and practicality of the data.

[0087] Reference Figure 5 In some examples of the present invention, the stationary data segment of the second data set is determined by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment, including but not limited to the following steps:

[0088] Step S510: Calculate the average value of each column of data according to the second data set to obtain preset column average data.

[0089] Step S520: performing standardization according to the preset column average data and the second data set to calculate moving standard deviation data.

[0090] Step S530: Find an index data point that meets a preset stability condition based on the moving standard deviation data, wherein the preset stability condition includes that the moving standard deviation data corresponding to the index data point is less than a preset standard deviation threshold, and there are a preset number of preset data points after the index data point.

[0091] Step S540: determining a stationary data segment according to the index data point and a preset number, so as to extract a stationary data set according to the stationary data segment.

[0092] Step S550: Calculate the preset stationary average value of each column of data in the stationary data set, so as to construct the target data set according to the preset stationary average value and the stationary data set.

[0093] In this specific embodiment, the embodiment of the present invention first calculates the average value of each column of data according to the second data set to obtain the preset column average data. Specifically, the embodiment of the present invention first calculates the column average value of the entire second data to obtain the average value of each characteristic parameter and obtain the preset column average data. At the same time, a new data set is created to facilitate subsequent data processing. Then, the embodiment of the present invention performs standardization according to the preset column average data and the second data to calculate the moving standard deviation data. Specifically, the embodiment of the present invention divides the second data set obtained by smoothing by the average value of the corresponding column, that is, the preset column average data, so as to calculate the moving standard deviation data of the standardized data to measure the change of the data. Among them, the embodiment of the present invention sets the window size and the threshold value to evaluate the stability of the data. For example, the example of the present invention sets the window size to control the number of data points considered when calculating the moving standard deviation, and the threshold value is used to determine whether the data is sufficiently stable within the window. Further, the embodiment of the present invention searches for index data points that meet the preset stability conditions according to the moving standard deviation data, and determines the stable data segment according to the index data point and the preset number, so as to extract the stable data set according to the stable data segment. Specifically, the preset stability condition in the embodiment of the present invention includes that the moving standard deviation data corresponding to the index data point is less than the preset standard deviation threshold, and there are a preset number of preset data points behind the index data point. Among them, the embodiment of the present invention uses a corresponding search function, such as a find function, to find the first data index that meets the stability condition, that is, the first data point whose moving standard deviation is less than the preset standard deviation threshold, and uses the data point as the index data point. Correspondingly, after finding the index data point, it is determined whether there are enough data points behind the index data point, that is, whether there are a preset number of preset data points behind the index data point, such as whether there are 8000 data points behind the index data point. When it is determined that there are a preset number of preset data points behind the index data point, the embodiment of the present invention starts from the index data point and extracts the preset number of preset data points behind as the stable data segment. On the contrary, when the index data point is not found or there are not enough data points behind the index data point, the prompt message "No stable segment that meets the conditions is found" is output and the function ends. Finally, the embodiment of the present invention calculates the preset stable average value of each column of the stable data, so as to obtain the target data set according to the preset stable average value and the stable data set. Specifically, after extracting the data set of the stationary segment (stationary data set), the embodiment of the present invention first adds a row at the end of the stationary data set, and stores the average values ​​of all columns of the stationary data set except the first column in the row to construct the target data set. The row also includes a new sequence number.

[0094] Reference Figure 6In some embodiments of the present invention, before traversing the detection files exported by the preset sensor to obtain the target file, the snowmobile ice skate test data processing method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0095] Step S610: Setting the skate calibration angle and gravity compensation.

[0096] Step S620: zero-point calibration is performed on the preset sensor by using preset non-interference parameters, wherein the preset non-interference parameters include detection data of the preset sensor after the skate is lifted, preload data of the bobsleigh skate test bench, and bolt preload data.

[0097] In this specific embodiment, before processing the detection file exported by the preset sensor, the embodiment of the present invention first initializes the relevant parameters of the snowmobile ice blade test bench to correct the measurement error caused by the error in the safety loop process of the snowmobile ice blade test bench. Accordingly, the embodiment of the present invention first sets the ice blade calibration angle and gravity compensation. Specifically, in the process of installing the snowmobile ice blade test bench, due to installation errors or environmental reasons, it is easy to cause a certain angle deviation between the bench and the ice blade, which in turn leads to the accuracy of the test results. Therefore, the embodiment of the present invention improves the accuracy of the test results by setting the ice blade calibration angle to compensate for the angle deviation during the installation of the bench. At the same time, when measuring forces and moments, gravity may introduce additional errors. Therefore, the embodiment of the present invention compensates for the effects caused by gravity by setting the gravity compensation method, thereby improving the accuracy of the measurement results. Next, the embodiment of the present invention performs zero point correction on the preset sensor by presetting the interference-free parameters. Specifically, the preset non-interference parameters in the embodiment of the present invention refer to the internal stress parameters of the test bench in a state without external force interference, including the detection data of the preset sensor after the ice skates are lifted, the preload data of the bobsleigh ice skate test bench itself, and the bolt preload data, etc. Accordingly, the embodiment of the present invention performs a data zeroing operation on the test system by detecting the data detected by the preset sensor after the ice skates are lifted, the preload of the bench itself, and the bolt preload, so as to alleviate the initial offset caused by non-test factors and improve the accuracy of subsequent data detection.

[0098] Reference Figure 7 In some embodiments of the present invention, after determining the stationary data segment of the second data set by a preset stationary threshold value to construct a target data set according to the stationary data segment, the snowmobile skate test data processing method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0099] Step S710: constructing a target array to store the target data in the target array, wherein the first column of the target array stores the skate slip angle data and the speed data.

[0100] Step S720: Perform data visualization according to the target array to generate a preset visualization image.

[0101] In this specific embodiment, the embodiment of the present invention first constructs a target array to store the target data set in the target array, and performs data visualization based on the target array to generate a preset visualization image. Specifically, after the embodiment of the present invention calls the data processing function and executes a series of data processing steps to obtain the processed target data set, the processing result (target data set) is stored in the constructed target array. At the same time, the first column of the target array in the embodiment of the present invention stores the skate side slip angle data and speed data, and the first column of the target array is updated to include the angle and speed information obtained by parsing the file name. Furthermore, the embodiment of the present invention visualizes the data in the target array and generates a preset visualization image, such as importing it into an Excel file to draw a corresponding data scatter plot. The embodiment of the present invention effectively enhances the readability and practicality of the data by visually displaying the data in the target array.

[0102] Next, in conjunction with a specific snowmobile ice skate test data processing scenario, the solution of the embodiment of the present invention is described in detail and explained:

[0103] For example, Figure 8 As shown, Figure 8The overall flow diagram of the method for processing the test data of the snowmobile ice blade provided by the embodiment of the present invention. Specifically, the embodiment of the present invention first sets the calibration angle of the ice blade to compensate for the angle deviation during the installation process of the snowmobile ice blade test bench, and sets the gravity compensation to eliminate the influence of gravity on the measurement results. At the same time, the embodiment of the present invention clears the test system data by measuring the six-dimensional force sensor data after lifting the ice blade, as well as the preload of the bench itself and the size of the bolt preload. Then, after the test experiment is carried out by the snowmobile ice blade test bench, the files exported by the six-dimensional force sensor are traversed. For example, the embodiment of the present invention searches for all .txt files under the specified path through the dir function, and stores the search results in a variable. Accordingly, the processing speed is increased by pre-allocating space, and an array whose size matches the number of files is initialized to store the processed data. Then, the embodiment of the present invention reads the file names one by one through a while loop, and parses out key information such as the slip angle, speed and load therefrom. Among them, the file exported by the six-dimensional force sensor in the embodiment of the present invention is constructed with the slip angle information, speed information and load information of the ice blade. Further, the embodiment of the present invention performs data preprocessing on the file data. For example, an embodiment of the present invention opens a file in read-only mode through the fopen function, skips the header row, and reads the file content line by line through the textscan function. Accordingly, after reading all the rows, the file is closed through the fclose function. Then, an embodiment of the present invention converts the cell array returned by the textscan function into a matrix through the cell2mat function. Next, an embodiment of the present invention traverses the converted matrix through a loop, and uses the regular expression regexp function to extract the integer before the equal sign and the floating-point number after the equal sign in each row as an array. Among them, when the regular expression successfully matches (that is, tokens is not empty), the extracted array is stored in the corresponding matrix.

[0104] Further, the embodiment of the present invention initializes the data parameters extracted by matching the regular expression, and converts the skate side slip angle and sensor angle parameters in the data parameters into radians. Further, by traversing the data after radian conversion, the data is converted through the coordinate transformation matrix, and the original data without coordinate conversion and the transformed data are updated to a new matrix. Further, the embodiment of the present invention smoothes all columns except the first column in the new matrix through the smoothdata function and the moving average method, and creates a matrix of the same size as the original data to store the smoothed data. At the same time, the embodiment of the present invention sets the window size, which determines the granularity of the smoothing process. In addition, the embodiment of the present invention plots the smoothed data through the plot function. Next, the embodiment of the present invention extracts the stationary segment data of the smoothed data and calculates the corresponding average value. For example, the embodiment of the present invention first calculates the column average of the entire data set (the data obtained by the smoothing process), and creates a new data set, which includes all columns of the entire data set except the first column (the sequence number column). Accordingly, the embodiment of the present invention sets the window size and threshold value, which are used to evaluate the stability of the data. Furthermore, the embodiment of the present invention measures the change of data by dividing the smoothed data by the average value of its corresponding column and calculating the moving standard deviation of the standardized data. Next, the embodiment of the present invention uses the find function to find the first data index that meets the stability condition, that is, the first point whose moving standard deviation is less than the set threshold. When it is determined that the first data index that meets the stability condition is found, and it is ensured that there are enough data points behind the index of the first stable data, such as 8000, then starting from this index, the following 8000 data points are extracted as a stable segment. On the contrary, when it is determined that the corresponding data index is not found or there are not enough data points behind, the prompt message "No stable segment that meets the conditions is found" is output and the function ends. Accordingly, a row is added to the end of the extracted stable segment data set, which contains a new sequence number and the average value of all columns of the stable segment data except the first column. Finally, the embodiment of the present invention stores the processed results in a new array and updates the first column of the array to contain the angle and speed information parsed from the file name. At the same time, the embodiment of the present invention writes the processed data into an Excel file and draws a scatter plot of the data.

[0105] It is easy to understand that the embodiment of the present invention simplifies the complexity of traditional data processing by encapsulating data processing into modular tasks, realizes the automation of the process, and significantly improves the speed and efficiency of data processing. At the same time, the embodiment of the present invention takes into account the human errors in the actual installation process of the bobsleigh and ice skate test bench, calibrates and compensates it by presetting the initial value, and uses the coordinate transformation matrix and smoothing algorithm to effectively reduce data fluctuations and improve the stability and reliability of the data. Accordingly, the embodiment of the present invention can accurately identify and extract the stable segment in the data and calculate its average value by setting the threshold and moving standard deviation calculation, providing more robust results for data analysis. In addition, the embodiment of the present invention not only stores the processed data in an easily accessible Excel file, but also visualizes the data in the form of scatter plots, etc., which enhances the readability and practicality of the data.

[0106] See also Fig. 9 The embodiment of the present application also provides a snowmobile ice skate test data processing system, which can implement the above-mentioned snowmobile ice skate test data processing method, and the system includes:

[0107] The first module 810 is used to traverse the detection files exported by the preset sensor to obtain the target file.

[0108] The second module 820 is used to obtain original detection data by reading the target file, so as to perform data preprocessing on the original detection data to obtain a first data set.

[0109] The third module 830 is used to perform smoothing processing on the first data set by using a preset data processing algorithm to obtain a second data set.

[0110] The fourth module 840 is used to determine the stationary data segment of the second data set by presetting the stationary threshold, so as to construct a target data set according to the stationary data segment.

[0111] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0112] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned snowmobile ice skate test data processing method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0113] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0115] The processor 910 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0116] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the snowmobile ice skate test data processing method of the embodiment of this application;

[0117] Input / output interface 930, used to implement information input and output;

[0118] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0119] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0120] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0121] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned bobsleigh and ice skate test data processing method is implemented.

[0122] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0125] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0126] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0128] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0129] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0130] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0134] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for processing snowmobile skate test data, characterized in that: The method comprises the following steps: Traverse the detection files exported by the preset sensor to obtain the target file; Reading the target file to obtain original detection data, and performing data preprocessing on the original detection data to obtain a first data set; Smoothing the first data set using a preset data processing algorithm to obtain a second data set; The stationary data segment of the second data set is determined by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment.

2. The method according to claim 1, characterized in that The traversing of the detection files exported by the preset sensor to obtain the target file includes: Traversing the file name information of the detection files to obtain the experimental test parameters corresponding to each of the detection files according to the file name information; wherein the file name information is constructed according to the experimental test parameters, and the experimental test parameters include skate side slip angle data, speed data and load value; The target file is extracted from the detection file according to the experimental test parameters.

3. The method according to claim 1, characterized in that The step of obtaining the original detection data by reading the target file and performing data preprocessing on the original detection data to obtain a first data set includes: Read the target file line by line through a preset file reading function to obtain a preset cell array; Converting the preset cell array into a first data matrix; The first data set is obtained by extracting data from the first data matrix using a regular expression.

4. The method according to claim 1, characterized in that: The step of smoothing the first data set by using a preset data processing algorithm to obtain a second data set includes: Converting the preset input parameters in the first data set into radians to obtain preset radian data; wherein the preset input parameters include a skate side slip angle parameter and a sensor angle parameter; Performing coordinate transformation on the preset radian data through a coordinate transformation matrix to obtain preset transformation data; Constructing a second data matrix according to the preset conversion data and the preset radian data; The preset data columns in the second data matrix are smoothed to obtain the second data set.

5. The method according to claim 1, characterized in that The step of determining the stationary data segment of the second data set by presetting a stationary threshold, so as to construct a target data set according to the stationary data segment, includes: Calculate the average value of each column of data according to the second data set to obtain preset column average data; Standardizing the preset column average data and the second data set to calculate moving standard deviation data; Searching for an index data point that satisfies a preset stability condition according to the moving standard deviation data; wherein the preset stability condition includes that the moving standard deviation data corresponding to the index data point is less than a preset standard deviation threshold, and there are a preset number of preset data points after the index data point; Determine the stationary data segment according to the index data point and the preset number, so as to extract a stationary data set according to the stationary data segment; The preset stationary average value of each column of data in the stationary data set is calculated, so as to construct the target data set according to the preset stationary average value and the stationary data set.

6. The method according to claim 1, characterized in that Before executing the step of traversing the detection files exported by the preset sensor to obtain the target file, the method further includes: Set the blade calibration angle and gravity compensation; The preset sensor is zero-point calibrated by preset non-interference parameters; wherein the preset non-interference parameters include the detection data of the preset sensor after the skates are lifted, the preload data of the bobsleigh skate test bench, and the bolt preload data.

7. The method according to claim 2, characterized in that After performing the step of determining the stationary data segment of the second data set by presetting the stationary threshold to construct a target data set according to the stationary data segment, the method further includes: Constructing a target array to store the target data set into the target array; wherein the first column of the target array stores the skate side slip angle data and the speed data; Data visualization is performed according to the target array to generate a preset visualization image.

8. A snowmobile ice skate test data processing system, characterized in that: The system comprises: The first module is used to traverse the detection files exported by the preset sensor to obtain the target file; A second module is used to read and obtain original detection data according to the target file, so as to perform data preprocessing on the original detection data to obtain a first data set; A third module is used to perform smoothing processing on the first data set by using a preset data processing algorithm to obtain a second data set; The fourth module is used to determine the stable data segment of the second data set by presetting a stable threshold, so as to construct a target data set according to the stable data segment.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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