A method and device for analyzing performance test data of a field tent

Abstract

The present invention discloses a method and device for analyzing performance test data of a field tent. The method comprises: obtaining a performance index test data set of the field tent by measuring; the performance index test data set comprises a rainproof performance test data sequence, a snow load performance test data sequence, a deployment time test data sequence, and a wind resistance performance test data sequence; preprocessing the performance index test data set to obtain a preprocessed data set; and performing evaluation and calculation processing on the preprocessed data set to obtain a comprehensive performance evaluation result value of the field tent.

Description

Technical Field

[0001] The present invention relates to the fields of industrial data analysis and performance testing, and in particular to a method and device for analyzing performance test data of a field tent. Background Art

[0002] With the continuous development of outdoor adventure, wilderness survival training, and emergency rescue, outdoor tents, as important wilderness survival equipment, have a performance directly related to the safety and comfort of users. However, there are still some shortcomings in the current test data analysis of outdoor tent performance. Traditional analysis methods usually only test and evaluate a single performance indicator, such as rainproofness or wind resistance, and lack comprehensive consideration of multiple performance indicators, making it difficult to fully reflect the overall performance of the tent in complex outdoor environments. In addition, during the data processing process, data integrity and consistency are often overlooked, such as time synchronization issues of test data for different performance indicators and interference from data noise, which can lead to deviations in the evaluation results.

[0003] At the same time, the existing methods lack scientific weight distribution and effective fusion calculation means in comprehensive evaluation, and cannot accurately reflect the contribution of each performance indicator to the overall performance, thereby limiting the accuracy and reliability of field tent performance evaluation and failing to provide strong technical support for product improvement and optimization. Summary of the Invention

[0004] The present invention mainly solves the problem that the existing methods lack scientific weight distribution and effective fusion calculation means in comprehensive evaluation, and cannot accurately reflect the contribution of each performance indicator to the overall performance, thereby limiting the accuracy and reliability of field tent performance evaluation. The present invention discloses a performance test data analysis method and device for field tents.

[0005] In a first aspect of an embodiment of the present invention, a method for analyzing performance test data of a field tent is disclosed, comprising:

[0006] S1, measuring and obtaining a performance index test data set of a field tent; the performance index test data set includes a rainproof performance test data sequence, a snow load performance test data sequence, a deployment time test data sequence, and a wind resistance performance test data sequence;

[0007] S2, preprocessing the performance index test data set to obtain a preprocessed data set;

[0008] S3, performing evaluation and calculation processing on the pre-processed data set to obtain a comprehensive performance evaluation result value of the field tent.

[0009] The preprocessing of the performance indicator test data set to obtain a preprocessed data set includes:

[0010] S21, performing data cleaning processing on the performance indicator test data set to obtain a first data set;

[0011] S22, performing temporal registration processing on the first data set to obtain a second data set;

[0012] S23: Perform data reduction processing on the second data set to obtain a preprocessed data set.

[0013] The evaluating and calculating the pre-processed data set to obtain a comprehensive performance evaluation result value of the outdoor tent includes:

[0014] S31, constructing a test data matrix using the preprocessed data set; the row vectors of the test data matrix are test data sequences corresponding to each type of performance in the preprocessed data set;

[0015] S32, performing principal component analysis on the observation data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​a sample value of the extracted principal component index at each moment;

[0016] S33, performing fusion evaluation calculation processing on the coefficient matrix and the principal component matrix to obtain a comprehensive performance evaluation result value of the field tent.

[0017] The fusion evaluation calculation processing of the coefficient matrix and the principal component matrix to obtain the comprehensive performance evaluation result value of the field tent includes:

[0018] S331, performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain an evaluation vector;

[0019] S332, performing weight vector calculation processing on the principal component matrix to obtain a weight vector;

[0020] S333 , performing weighted sum processing on the evaluation vector and the weight vector to obtain a comprehensive performance evaluation result value of the outdoor tent.

[0021] The performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain the evaluation vector includes:

[0022] Performing cross-correlation calculation on the principal component matrix to obtain a cross-correlation coefficient matrix;

[0023] Performing eigenvalue calculation on the mutual correlation coefficient matrix to obtain an eigenvalue vector;

[0024] The characteristic matrix is ​​constructed by taking the eigenvalue vector as the diagonal element of the matrix and setting the off-diagonal elements of the matrix to 0;

[0025] Multiplying the feature matrix by the coefficient matrix to obtain a factor matrix;

[0026] Multiplying the factor matrix by a preset orthogonal matrix to obtain a rotation factor matrix;

[0027] An evaluation vector calculation process is performed on the rotation factor matrix, the eigenvalue vector, and the orthogonal matrix to obtain an evaluation vector.

[0028] The step of performing weight vector calculation processing on the principal component matrix to obtain a weight vector includes:

[0029] Performing dimensionality increase processing on the principal component matrix to obtain a three-dimensional component matrix;

[0030] Perform feature extraction processing on the three-dimensional component matrix to obtain a weight vector.

[0031] The expression for the evaluation vector calculation process is:

[0032] k ij =b ij / ρ i ,

[0033]

[0034] Among them, b ij is the element of the i-th row and j-th column of the rotation factor matrix, ρ i is the i-th element of the eigenvalue vector, k ij for b ij The corresponding transformation value, d j is the common factor value of the jth column of the rotation factor matrix, β ii is the element of the i-th row and i-th column of the orthogonal matrix, P is the column dimension of the rotation factor matrix, V j is the jth element of the evaluation vector.

[0035] According to a second aspect of the present invention, a device for analyzing performance test data of a field tent is disclosed, the device comprising:

[0036] a memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the field tent performance test data analysis method.

[0039] According to a third aspect of the present invention, a computer-storable medium is disclosed. The computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the field tent performance test data analysis method.

[0040] According to a fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the performance test data analysis method of the field tent.

[0041] The beneficial effects of the present invention are:

[0042] Multi-index comprehensive evaluation: This invention covers the test data of multiple key performance indicators such as rain protection performance, snow load performance, deployment time and wind resistance, which can fully reflect the comprehensive performance of field tents in complex environments. It overcomes the limitation of traditional methods that only focus on a single indicator and provides a comprehensive perspective for the performance evaluation of field tents.

[0043] Efficient data preprocessing: Through preprocessing steps such as data cleaning, time alignment, 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 foundation for subsequent evaluation calculations and improving the accuracy of the evaluation results.

[0044] Scientific evaluation calculation method: The present invention uses principal component analysis to extract the principal components of key performance indicators, and combines weight vectors to perform weighted summation calculations. It can scientifically allocate the weights of various performance indicators, highlight the influence of major performance indicators, and at the same time reduce data dimensions, improve evaluation efficiency, and make the evaluation results more scientific and reliable.

[0045] Improve product optimization capabilities: The comprehensive evaluation results of this invention can provide a clear direction and basis for the design and improvement of field tents, helping R&D personnel to quickly locate performance bottlenecks and optimize product design, thereby improving the overall performance and market competitiveness of field tents and better meeting users' usage needs in different field environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] In order to better understand the content of the present invention, an embodiment is given here.

[0048] Figure 1 4 is an implementation flow chart of the method of the present invention.

[0049] In a first aspect, embodiments of the present invention disclose a method and apparatus for analyzing performance test data of a field tent, comprising:

[0050] S1, measuring and obtaining a performance index test data set of a field tent; the performance index test data set includes a rainproof performance test data sequence, a snow load performance test data sequence, a deployment time test data sequence, and a wind resistance performance test data sequence;

[0051] S2, preprocessing the performance index test data set to obtain a preprocessed data set;

[0052] S3, performing evaluation and calculation processing on the pre-processed data set to obtain a comprehensive performance evaluation result value of the field tent;

[0053] The preprocessing of the performance indicator test data set to obtain a preprocessed data set includes:

[0054] S21, performing data cleaning processing on the performance indicator test data set to obtain a first data set;

[0055] S22, performing temporal registration processing on the first data set to obtain a second data set;

[0056] S23, performing data reduction processing on the second data set to obtain a preprocessed data set;

[0057] The evaluating and calculating the pre-processed data set to obtain a comprehensive performance evaluation result value of the outdoor tent includes:

[0058] S31, constructing a test data matrix using the preprocessed data set; the row vectors of the test data matrix are test data sequences corresponding to each type of performance in the preprocessed data set;

[0059] S32, performing principal component analysis on the observation data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​a sample value of the extracted principal component index at each moment;

[0060] S33, performing fusion evaluation calculation processing on the coefficient matrix and the principal component matrix to obtain a comprehensive performance evaluation result value of the field tent;

[0061] The fusion evaluation calculation processing of the coefficient matrix and the principal component matrix to obtain the comprehensive performance evaluation result value of the field tent includes:

[0062] S331, performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain an evaluation vector;

[0063] S332, performing weight vector calculation processing on the principal component matrix to obtain a weight vector;

[0064] S333, performing weighted summation processing on the evaluation vector and the weight vector to obtain a comprehensive performance evaluation result value of the outdoor tent;

[0065] The performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain the evaluation vector includes:

[0066] Performing cross-correlation calculation on the principal component matrix to obtain a cross-correlation coefficient matrix;

[0067] Performing eigenvalue calculation on the mutual correlation coefficient matrix to obtain an eigenvalue vector;

[0068] The characteristic matrix is ​​constructed by taking the eigenvalue vector as the diagonal element of the matrix and setting the off-diagonal elements of the matrix to 0;

[0069] Multiplying the feature matrix by the coefficient matrix to obtain a factor matrix;

[0070] Multiplying the factor matrix by a preset orthogonal matrix to obtain a rotation factor matrix;

[0071] Performing evaluation vector calculation processing on the rotation factor matrix, the eigenvalue vector, and the orthogonal matrix to obtain an evaluation vector;

[0072] The step of performing weight vector calculation processing on the principal component matrix to obtain a weight vector includes:

[0073] Performing dimensionality increase processing on the principal component matrix to obtain a three-dimensional component matrix;

[0074] Performing feature extraction processing on the three-dimensional component matrix to obtain a weight vector;

[0075] The cross-correlation calculation processing is performed on the principal component matrix to obtain the cross-correlation coefficient matrix, which is performed on the row vectors in the principal component matrix to obtain the corresponding cross-correlation coefficients; the elements in the i-th row and j-th column of the cross-correlation coefficient matrix are the cross-correlation values ​​between the i-th row vector and the j-th row vector of the principal component matrix;

[0076] The expression of the principal component analysis process is:

[0077] Y=CX,

[0078] Among them, Y is the principal component matrix, C is the coefficient matrix, and X is the test data matrix. The principal component analysis process can be implemented by the PCA algorithm. The principal component matrix and the coefficient matrix are both determined by the principal component analysis process;

[0079] The expression for the evaluation vector calculation process is:

[0080] k ij=b ij / ρ i ,

[0081]

[0082] Among them, b ij is the element of the i-th row and j-th column of the rotation factor matrix, ρ i is the i-th element of the eigenvalue vector, k ij for b ij The corresponding transformation value, d j is the common factor value of the jth column of the rotation factor matrix, β ii is the element of the i-th row and i-th column of the orthogonal matrix, P is the column dimension of the rotation factor matrix, V j is the jth element of the evaluation vector;

[0083] The orthogonal matrix may be a Hermitian matrix.

[0084] The dimension of the evaluation vector is the same as the row dimension of the test data matrix;

[0085] In the above matrix multiplication process, it is necessary to perform corresponding transposition processing on the two matrices involved in the multiplication according to the dimension matching requirements of the matrix multiplication.

[0086] The weighted summation of the two vectors can be achieved by vector dot multiplication;

[0087] The data of the performance test data sequence is the difference between the test value of each performance indicator and the corresponding standard value;

[0088] The expression of the dimension increase process is:

[0089] R(i,j,:)=dot(X(i,:),X(j,:)),

[0090] Wherein, X(i,:) and X(j,:) represent the i-th row vector and the j-th row vector of the principal component matrix, respectively, R(i,j,:) represents the vector of the i-th row and j-th column of the three-dimensional component matrix, and dot represents a vector cross product operation;

[0091] The expression of the feature extraction process is:

[0092]

[0093] Where R(i,j,k) represents the kth element of the vector in the i-th row and j-th column of the three-dimensional component matrix, P and K are the first and third dimensions of the three-dimensional component matrix respectively, and p j It is the comprehensive performance evaluation result of the field tent.

[0094] The rainproof performance test data can be obtained by testing with a tent rainproof test device;

[0095] The snow load performance test data sequence can be obtained by a tent snow load detection device;

[0096] The wind resistance performance test data sequence can be obtained through a wind resistance test device;

[0097] The deployment time test data sequence can be obtained by measuring the deployment time of a field tent;

[0098] The data cleaning process includes filling missing values, smoothing noisy data, and smoothing or deleting outliers;

[0099] The time registration process may adopt an interpolation / extrapolation method, a Lagrange three-point interpolation method, etc.

[0100] The data specification processing includes:

[0101] For each type of data attribute of the second data set, autoregressive-sliding average modeling is performed with the data collection information of the data as the independent variable and the data value of the data as the dependent variable to obtain a regression model for the data attribute of the type;

[0102] Using the regression model, calculating and processing the independent variable to obtain a regression data value; determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if the absolute value is greater than the first regression discrimination threshold, deleting the data from the second data set; if the absolute value is less than or equal to the first regression discrimination threshold, not processing the data;

[0103] performing fusion processing on all data of the second data set after the execution mode discrimination processing to obtain a preprocessed data set;

[0104] The data collection information may be collection time information;

[0105] According to a second aspect of the present invention, a device for analyzing performance test data of a field tent is disclosed, the device comprising:

[0106] a memory storing executable program code;

[0107] a processor coupled to the memory;

[0108] The processor calls the executable program code stored in the memory to execute the field tent performance test data analysis method.

[0109] According to a third aspect of the present invention, a computer-storable medium is disclosed. The computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the field tent performance test data analysis method.

[0110] According to a fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the performance test data analysis method of the field tent.

[0111] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for analyzing performance test data of a field tent, characterized in that: include: S1, obtain the performance index test data set of the outdoor tent by measurement; The performance index test data set includes a rainproof performance test data sequence, a snow load performance test data sequence, a deployment time test data sequence, and a wind resistance performance test data sequence; S2, preprocessing the performance index test data set to obtain a preprocessed data set; S3, performing evaluation and calculation processing on the pre-processed data set to obtain a comprehensive performance evaluation result value of the field tent; The evaluating and calculating the pre-processed data set to obtain a comprehensive performance evaluation result value of the outdoor tent includes: S31, constructing a test data matrix using the preprocessed data set; the row vectors of the test data matrix are test data sequences corresponding to each type of performance in the preprocessed data set; S32, performing principal component analysis on the test data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​a sample value of the extracted principal component index at each moment; S33, performing fusion evaluation calculation processing on the coefficient matrix and the principal component matrix to obtain a comprehensive performance evaluation result value of the field tent; The fusion evaluation calculation processing of the coefficient matrix and the principal component matrix to obtain the comprehensive performance evaluation result value of the field tent includes: S331, performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain an evaluation vector; S332, performing weight vector calculation processing on the principal component matrix to obtain a weight vector; S333, performing weighted sum processing on the evaluation vector and the weight vector to obtain a comprehensive performance evaluation result value of the outdoor tent; The performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain the evaluation vector includes: Performing cross-correlation calculation on the principal component matrix to obtain a cross-correlation coefficient matrix; Performing eigenvalue calculation on the mutual correlation coefficient matrix to obtain an eigenvalue vector; The characteristic matrix is ​​constructed by taking the eigenvalue vector as the diagonal element of the matrix and setting the off-diagonal elements of the matrix to 0; Multiplying the feature matrix by the coefficient matrix to obtain a factor matrix; Multiplying the factor matrix by a preset orthogonal matrix to obtain a rotation factor matrix; An evaluation vector calculation process is performed on the rotation factor matrix, the eigenvalue vector, and the orthogonal matrix to obtain an evaluation vector.

2. The method for analyzing performance test data of a field tent according to claim 1, wherein: The preprocessing of the performance indicator test data set to obtain a preprocessed data set includes: S21, performing data cleaning processing on the performance indicator test data set to obtain a first data set; S22, performing temporal registration processing on the first data set to obtain a second data set; S23: Perform data reduction processing on the second data set to obtain a preprocessed data set.

3. The method for analyzing performance test data of a field tent according to claim 1, wherein: The step of performing weight vector calculation processing on the principal component matrix to obtain a weight vector includes: Performing dimensionality increase processing on the principal component matrix to obtain a three-dimensional component matrix; Perform feature extraction processing on the three-dimensional component matrix to obtain a weight vector.

4. The method for analyzing performance test data of a field tent according to claim 1, wherein: The expression for the evaluation vector calculation process is: k ij =b ij / r i , Among them, b ij is the element of the i-th row and j-th column of the rotation factor matrix, ρ i is the i-th element of the eigenvalue vector, k ij for b ij The corresponding transformation value, d j is the common factor value of the jth column of the rotation factor matrix, β ii is the element of the i-th row and i-th column of the orthogonal matrix, P is the column dimension of the rotation factor matrix, V j is the jth element of the evaluation vector.

5. A performance test data analysis device for a field tent, characterized in that: The device comprises: 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 performance test data analysis method for a field tent according to any one of claims 1 to 4.

6. A computer storable medium, characterized in that The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the performance test data analysis method for a field tent according to any one of claims 1 to 4.

7. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the performance test data analysis method of a field tent according to any one of claims 1 to 4.

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