Test data analysis method and device for vehicle-mounted telescopic tent

Abstract

The invention discloses a test data analysis method and device for a vehicle-mounted telescopic tent. The method comprises the steps that a performance index test data set and a corresponding test site set of the vehicle-mounted telescopic tent are obtained through measurement; the test site set comprises position information of test sites; the performance index test data set comprises a rainproof performance test data sequence, a snow load performance test data sequence, an expansion time test data sequence, a failure rate test data sequence, an energy consumption test data sequence and a storage time test data sequence; each piece of data in the test data sequence is obtained by testing at a test site in a test site set; data with the same serial number in all the test data sequences correspond to the same test site; preprocessing the performance index test data set to obtain a preprocessed data set; and performing evaluation calculation processing on the preprocessed data set to obtain a performance comprehensive evaluation result value of the field tent.

Description

Technical Field

[0001] The present invention relates to the fields of industrial data processing and data modeling, and particularly to a method and device for analyzing test data of a vehicle-mounted telescopic tent. Background Art

[0002] With the increasing popularity of outdoor travel and camping activities, vehicle-mounted telescopic tents, as a camping equipment with both convenience and practicality, have been favored by more and more users. However, there are still some problems to be solved urgently in the analysis of test data on the performance of vehicle-mounted telescopic tents.

[0003] On the one hand, traditional performance test methods usually only focus on a single performance index, such as rainproof performance or deployment time, lacking comprehensive consideration of multiple performance indexes, and it is difficult to fully reflect the overall performance of the tent in actual use.

[0004] On the other hand, in the process of data processing of existing methods, the integrity and consistency of test data are often ignored, such as data differences at different test sites and the interference of data noise, which may lead to deviations in evaluation results.

[0005] In addition, the influence of test sites on the performance of the tent has not been fully considered in the existing technology, and the actual performance of the tent varies significantly under different environmental conditions (such as climate, terrain, etc.) at different sites. These problems limit the accuracy and reliability of the performance evaluation of vehicle-mounted telescopic tents, cannot provide strong technical support for product improvement and optimization, and are also difficult to meet the needs of users for high-quality tents. Summary of the Invention

[0006] The present invention mainly solves the problems of the accuracy and reliability of the performance evaluation of vehicle-mounted telescopic tents, and discloses a method and device for analyzing test data of a vehicle-mounted telescopic tent.

[0007] In the first aspect of the embodiments of the present invention, a method for analyzing test data of a vehicle-mounted telescopic tent is disclosed, including:

[0008] S1, measuring a set of test data of performance indexes of a vehicle-mounted telescopic tent and a corresponding set of test sites; the set of test sites includes the location information of the test sites; the set of test data of performance indexes includes a test data sequence of rainproof performance, a test data sequence of snow load performance, a test data sequence of deployment time, a test data sequence of failure rate, a test data sequence of energy consumption, and a test data sequence of storage time; each data in the test data sequence is obtained by testing at the test sites in the set of test sites; the data with the same serial number in all test data sequences corresponds to the same test site;

[0009] S2. Preprocess the performance index test data set to obtain a preprocessed data set;

[0010] S3. Perform evaluation and calculation processing on the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent.

[0011] The preprocessing of the performance index test data set to obtain a preprocessed data set includes:

[0012] S21. Perform data cleaning processing on the performance index test data set to obtain a first data set;

[0013] S22. Perform time registration processing on the first data set to obtain a second data set;

[0014] S23. Perform data reduction processing on the second data set to obtain a preprocessed data set.

[0015] The evaluation and calculation processing of the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent includes:

[0016] S31. Obtain a rainproof performance pass value, a snow load performance pass value, an unfolding time pass value, a failure rate pass value, an energy consumption pass value, and a packing time pass value;

[0017] S32. Subtract each data sequence in the preprocessed data set from the corresponding pass value to obtain a corresponding difference sequence;

[0018] S33. Use the difference sequences of the rainproof performance and the snow load performance to construct a first difference matrix;

[0019] S34. Use the difference sequences of the unfolding time and the packing time to construct a second difference matrix;

[0020] S35. Use the difference sequences of the failure rate and the energy consumption to construct a third difference matrix;

[0021] S36. Perform fusion evaluation processing on each difference matrix to obtain a corresponding evaluation sequence;

[0022] S37. Perform performance evaluation processing on all the evaluation sequences and the test location set to obtain the comprehensive performance evaluation result value of the field tent.

[0023] The fusion evaluation processing includes:

[0024] Calculate the weight value for each row vector of the difference matrix to obtain the corresponding weight value;

[0025] Using the weight values of each row vector, perform weighted summation on the row vectors of the difference matrix to obtain the corresponding evaluation sequence.

[0026] Performing performance evaluation processing on all the evaluation sequences and the test location set to obtain the comprehensive performance evaluation result value of the field tent, including:

[0027] S371, performing image modeling processing on all the evaluation sequences and the test location set to obtain the image information to be evaluated;

[0028] S372, performing corner detection processing on the image information to be evaluated to obtain a set of corner information;

[0029] S373, performing edge point detection processing on the image information to be evaluated to obtain a set of edge point information;

[0030] S374, performing fusion feature calculation processing on the set of corner information and the set of edge point information to obtain the comprehensive performance evaluation result value of the field tent.

[0031] The performing image modeling processing on all the evaluation sequences and the test location set to obtain the image information to be evaluated includes:

[0032] Performing normalization processing on each evaluation sequence respectively to obtain the corresponding normalized evaluation sequence;

[0033] Taking the normalized evaluation sequences corresponding to the first difference matrix, the second difference matrix, and the third difference matrix as the R-channel value, G-channel value, and B-channel value of the image pixel points respectively, and taking the position information of the test location corresponding to each element of the normalized evaluation sequence as the two-dimensional image plane position coordinates of the image pixel points, to construct the image information to be evaluated.

[0034] The performing fusion feature calculation processing on the set of corner information and the set of edge point information to obtain the comprehensive performance evaluation result value of the field tent includes:

[0035] Performing statistical processing on the pixel values of all the pixel points in the set of corner information to obtain a first set of statistical information; the first set of statistical information includes the variance and mean value of the pixel values of all the pixel points in the set of corner information;

[0036] Performing statistical processing on the pixel values of all the pixel points in the set of edge point information to obtain a second set of statistical information; the second set of statistical information includes the median value and the mode value of the pixel values of all the pixel points in the set of edge point information;

[0037] Performing statistical calculation processing on the first set of statistical information and the second set of statistical information to obtain the comprehensive performance evaluation result value of the field tent;

[0038] The expression for the statistical calculation and processing is as follows:

[0039]

[0040] Among them, zp is the comprehensive performance evaluation result value of the field tent, T2() is the second-order polynomial of the first kind of Chebyshev polynomial, L2() is the second-order Legendre polynomial, μ and σ are the variance and mean value in the first statistical information set respectively, and ν and η are the median value and mode value in the second statistical information set respectively.

[0041] In the second aspect of the present invention, a test data analysis device for a vehicle-mounted telescopic tent is disclosed. The device includes:

[0042] A memory storing executable program code;

[0043] A processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the test data analysis method for the vehicle-mounted telescopic tent as described above.

[0045] In the third aspect of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the test data analysis method for the vehicle-mounted telescopic tent as described above.

[0046] In the fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the test data analysis method for the vehicle-mounted telescopic tent as described above.

[0047] The beneficial effects of the present invention are as follows:

[0048] The present invention provides a test data analysis method for a vehicle-mounted telescopic tent. By comprehensively considering various performance indicators and the influence of the test location, and combining systematic data preprocessing and evaluation calculation means, a comprehensive and accurate analysis of the performance of the vehicle-mounted telescopic tent is achieved. Specifically, the present invention has the following beneficial effects:

[0049] 1. Comprehensive evaluation of multiple indicators: The present invention covers various key performance indicators such as rainproof performance, snow load performance, deployment time, failure rate, energy consumption, and storage time, which can comprehensively reflect the comprehensive performance of the vehicle-mounted telescopic tent in different usage scenarios, overcome the limitation of traditional methods that only focus on a single indicator, and provide a comprehensive performance evaluation perspective for users.

[0050] 2. Test location correlation analysis: The present invention combines the location information of the test location with the performance index test data, fully considering the influence of environmental conditions at different test locations on the tent performance, making the evaluation results more targeted and practical. This correlation analysis can help users understand the adaptability of the tent in different environments, providing a more scientific basis for product optimization and user selection.

[0051] 3. Efficient data preprocessing: Through preprocessing steps such as data cleaning, time registration, and data reduction, the present invention can effectively solve problems such as noise, missing values, time inconsistency, and data redundancy in the test data, ensuring the quality and consistency of the data, thereby providing a reliable data basis for subsequent evaluation calculations and improving the accuracy of evaluation results.

[0052] 4. Scientific evaluation calculation method: The present invention constructs a difference matrix and performs fusion evaluation processing, which can scientifically quantify the differences in various performance indicators and conduct comprehensive evaluation in combination with the test location information. This evaluation method can not only highlight the influence of key performance indicators but also effectively reflect the actual influence of different test locations on the tent performance, making the evaluation results more scientific and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the implementation of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] To better understand the content of the present invention, an embodiment is given here.

[0055] Figure 1 It is a flowchart of the implementation of the method of the present invention.

[0056] In the first aspect of the embodiment of the present invention, a method for analyzing test data of a vehicle-mounted telescopic tent is disclosed, including:

[0057] S1, measuring a set of performance index test data of the vehicle-mounted telescopic tent and a corresponding set of test locations; the set of test locations includes the location information of the test location; the set of performance index test data includes a rainproof performance test data sequence, a snow load performance test data sequence, an unfolding time test data sequence, a failure rate test data sequence, an energy consumption test data sequence, a folding time test data sequence; each data in the test data sequence is tested at the test location in the set of test locations; the data with the same serial number in all test data sequences corresponds to the same test location;

[0058] S2, preprocessing the set of performance index test data to obtain a set of preprocessed data;

[0059] S3. Perform evaluation and calculation processing on the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent;

[0060] The preprocessing of the performance index test data set to obtain the preprocessed data set includes:

[0061] S21. Perform data cleaning processing on the performance index test data set to obtain the first data set;

[0062] S22. Perform time registration processing on the first data set to obtain the second data set;

[0063] S23. Perform data reduction processing on the second data set to obtain the preprocessed data set;

[0064] The evaluation and calculation processing of the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent includes:

[0065] S31. Obtain the qualified value of rainproof performance, snow load performance, deployment time qualified value, failure rate qualified value, energy consumption qualified value, and storage time qualified value;

[0066] S32. Subtract each data sequence in the preprocessed data set from the corresponding qualified value to obtain the corresponding difference sequence;

[0067] S33. Use the difference sequences of rainproof performance and snow load performance to construct the first difference matrix;

[0068] S34. Use the difference sequences of deployment time and storage time to construct the second difference matrix;

[0069] S35. Use the difference sequences of failure rate and energy consumption to construct the third difference matrix;

[0070] S36. Perform fusion evaluation processing on each difference matrix to obtain the corresponding evaluation sequence;

[0071] S37. Perform performance evaluation processing on all evaluation sequences and the test location set to obtain the comprehensive performance evaluation result value of the field tent.

[0072] The row vectors of each difference matrix are the corresponding difference sequences;

[0073] The fusion evaluation processing includes:

[0074] Calculate the weight value for each row vector of the difference matrix to obtain the corresponding weight value;

[0075] Using the weight values of each row vector, perform a weighted summation on the row vectors of the difference matrix to obtain the corresponding evaluation sequence;

[0076] Each element of the evaluation sequence corresponds to a test location in the test location set;

[0077] Perform performance evaluation processing on all the evaluation sequences and the test location set to obtain the performance comprehensive evaluation result value of the field tent, including:

[0078] S371, perform image modeling processing on all the evaluation sequences and the test location set to obtain the image information to be evaluated;

[0079] S372, perform corner detection processing on the image information to be evaluated to obtain a set of corner information;

[0080] S373, perform edge point detection processing on the image information to be evaluated to obtain a set of edge point information;

[0081] S374, perform fusion feature calculation processing on the set of corner information and the set of edge point information to obtain the performance comprehensive evaluation result value of the field tent;

[0082] The performing image modeling processing on all the evaluation sequences and the test location set to obtain the image information to be evaluated includes:

[0083] Perform normalization processing on each evaluation sequence respectively to obtain the corresponding normalized evaluation sequence;

[0084] Use the normalized evaluation sequences corresponding to the first difference matrix, the second difference matrix, and the third difference matrix as the R-channel value, G-channel value, and B-channel value of the image pixel points respectively, and use the position information of the test location corresponding to each element of the normalized evaluation sequence as the two-dimensional image plane position coordinates of the image pixel points to construct the image information to be evaluated;

[0085] The performing fusion feature calculation processing on the set of corner information and the set of edge point information to obtain the performance comprehensive evaluation result value of the field tent includes:

[0086] Perform statistical processing on the pixel values of all pixel points in the set of corner information to obtain a first set of statistical information; the first set of statistical information includes the variance and mean of the pixel values of all pixel points in the set of corner information;

[0087] Perform statistical processing on the pixel values of all pixel points in the set of edge point information to obtain a second set of statistical information; the second set of statistical information includes the median value and the mode value of the pixel values of all pixel points in the set of edge point information;

[0088] Perform statistical calculation processing on the first set of statistical information and the second set of statistical information to obtain the comprehensive performance evaluation result value of the field tent;

[0089] The expression of the statistical calculation processing is:

[0090]

[0091] Among them, zp is the comprehensive performance evaluation result value of the field tent, T2() is the second-order polynomial of the first kind of Chebyshev polynomial, L2() is the second-order Legendre polynomial, μ and σ are the variance and mean in the first set of statistical information respectively, and ν and η are the median value and mode value in the second set of statistical information respectively;

[0092] The row vector in the difference matrix is the difference sequence;

[0093] Performing weight value calculation on each row vector of the difference matrix to obtain the corresponding weight value includes:

[0094]

[0095] Among them, p ij is the element in the i-th row and j-th column of the difference matrix, ε i is the weight value corresponding to the i-th row of the difference matrix, α i and β i are the mean and variance of the i-th row of the difference matrix respectively, and M is the column dimension of the difference matrix;

[0096] Performing fusion evaluation processing on each difference matrix to obtain the corresponding evaluation sequence further includes:

[0097] Performing cross-correlation calculation processing on all row vectors of the difference matrix to obtain a cross-correlation matrix; the element in the i-th row and j-th column of the cross-correlation matrix is the cross-correlation value of the i-th row vector and the j-th row vector of the difference matrix;

[0098] Performing evaluation calculation processing on the cross-correlation matrix and the difference matrix to obtain an evaluation sequence;

[0099] The expression of the evaluation calculation processing is:

[0100]

[0101] Among them, h i is the i-th element in the evaluation sequence, is the mean of the i-th row of the difference matrix A, A ij is the element in the i-th row and j-th column of the difference matrix A, B ijis the element at the i-th row and j-th column of the cross-correlation matrix B, and N is the column dimension of the difference matrix;

[0102] The rain-proof performance test data can be obtained through a tent rain-proof test device;

[0103] The snow load performance test data sequence can be obtained through a tent snow load detection device;

[0104] The time test data sequence can be obtained by measuring the deployment time and storage time of the tent;

[0105] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outlier points;

[0106] The time registration process can adopt interpolation / extrapolation method, Lagrange three-point interpolation method, etc.;

[0107] The data reduction process includes:

[0108] For the data of each data attribute in the second data set, taking the data acquisition information of the data as the independent variable and the data value of the data as the dependent variable, perform autoregressive-moving average modeling to obtain the regression model of the class data attribute respectively;

[0109] Using the regression model, perform calculation processing on the independent variable to obtain the regression data value; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than the set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second data set, and if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0110] Perform fusion processing on all the data after the execution mode discrimination processing in the second data set to obtain a preprocessed data set;

[0111] The data acquisition information can be acquisition time information;

[0112] In the second aspect of the present invention, a test data analysis device for a vehicle-mounted telescopic tent is disclosed, and the device includes:

[0113] A memory storing executable program code;

[0114] A processor coupled to the memory;

[0115] The processor calls the executable program code stored in the memory to execute the test data analysis method of the vehicle-mounted telescopic tent.

[0116] In the third aspect of the implementation of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, which are used to execute the test data analysis method of the vehicle-mounted telescopic tent when called by a computer.

[0117] In the fourth aspect of the implementation of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the test data analysis method of the vehicle-mounted telescopic tent.

[0118] The above are only the embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for analyzing test data of a vehicle-mounted telescopic tent, characterized in that, Including: S1, measuring a set of test data of the performance indicators of the vehicle-mounted telescopic tent and the corresponding set of test locations; The set of test locations includes the location information of the test locations; The set of test data of the performance indicators includes a rainproof performance test data sequence, a snow load performance test data sequence, an unfolding time test data sequence, a failure rate test data sequence, an energy consumption test data sequence, and a storage time test data sequence; each data in the test data sequence is tested at the test locations in the set of test locations; The data with the same serial number in all test data sequences corresponds to the same test location; S2, preprocessing the set of test data of the performance indicators to obtain a set of preprocessed data; S3, performing evaluation calculation processing on the set of preprocessed data to obtain the comprehensive performance evaluation result value of the field tent.

2. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 1, characterized in that, The preprocessing of the set of test data of the performance indicators to obtain a set of preprocessed data includes: S21, performing data cleaning processing on the set of test data of the performance indicators to obtain a first data set; S22, performing time registration processing on the first data set to obtain a second data set; S23, performing data reduction processing on the second data set to obtain a set of preprocessed data.

3. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 1, characterized in that, The performing evaluation calculation processing on the set of preprocessed data to obtain the comprehensive performance evaluation result value of the field tent includes: S31, obtaining a rainproof performance pass value, a snow load performance pass value, an unfolding time pass value, a failure rate pass value, an energy consumption pass value, and a storage time pass value; S32, subtracting each data sequence in the set of preprocessed data from the corresponding pass value to obtain a corresponding difference sequence; S33, using the difference sequence of the rainproof performance and the difference sequence of the snow load performance to construct a first difference matrix; S34, using the difference sequence of the unfolding time and the difference sequence of the storage time to construct a second difference matrix; S35, using the difference sequence of the failure rate and the difference sequence of the energy consumption to construct a third difference matrix; S36, performing fusion evaluation processing on each difference matrix respectively to obtain a corresponding evaluation sequence; S37, performing performance evaluation processing on all the evaluation sequences and the set of test locations to obtain the comprehensive performance evaluation result value of the field tent.

4. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 3, characterized in that, The fusion evaluation processing includes: Calculating the weight value for each row vector of the difference matrix to obtain the corresponding weight value; Using the weight value of each row vector to perform weighted summation on the row vectors of the difference matrix to obtain the corresponding evaluation sequence.

5. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 3, characterized in that, The performing performance evaluation processing on all the evaluation sequences and the set of test locations to obtain the comprehensive performance evaluation result value of the field tent includes: S371, performing image modeling processing on all the evaluation sequences and the set of test locations to obtain the image information to be evaluated; S372, performing corner detection processing on the image information to be evaluated to obtain a set of corner information; S373, performing edge point detection processing on the image information to be evaluated to obtain a set of edge point information; S374. Perform a fusion feature calculation process on the corner point information set and the edge point information set to obtain the performance comprehensive evaluation result value of the field tent.

6. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 5, characterized in that, The image modeling process on all the evaluation sequences and the test location set to obtain the image information to be evaluated includes: Perform a normalization process on each evaluation sequence respectively to obtain the corresponding normalized evaluation sequence; Use the normalized evaluation sequences corresponding to the first difference matrix, the second difference matrix, and the third difference matrix as the R-channel value, G-channel value, and B-channel value of the image pixel points respectively, and use the position information of the test location corresponding to each element of the normalized evaluation sequence as the two-dimensional image plane position coordinates of the image pixel points to construct the image information to be evaluated.

7. The method for analyzing test data of a vehicle-mounted telescopic tent according to claim 5, characterized in that, The process of performing a fusion feature calculation process on the corner point information set and the edge point information set to obtain the performance comprehensive evaluation result value of the field tent includes: Perform a statistical process on the pixel values of all the pixel points in the corner point information set to obtain the first statistical information set; the first statistical information set includes the variance and mean of the pixel values of all the pixel points in the corner point information set; Perform a statistical process on the pixel values of all the pixel points in the edge point information set to obtain the second statistical information set; the second statistical information set includes the median value and the mode value of the pixel values of all the pixel points in the edge point information set; Perform a statistical calculation process on the first statistical information set and the second statistical information set to obtain the performance comprehensive evaluation result value of the field tent; The expression of the statistical calculation process is: where zp is the performance comprehensive evaluation result value of the field tent, T2() is the second-order polynomial of the first kind of Chebyshev polynomial, L2() is the second-order Legendre polynomial, μ and σ are the variance and mean in the first statistical information set respectively, and v and η are the median value and the mode value in the second statistical information set respectively.

8. A test data analysis device for a vehicle-mounted telescopic tent, characterized in that, The device includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the test data analysis method of the vehicle-mounted telescopic tent according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which are used to execute the test data analysis method of the vehicle-mounted telescopic tent according to any one of claims 1 to 7 when called by a computer.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the test data analysis method of the vehicle-mounted telescopic tent according to any one of claims 1 to 7.

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