Performance test data analysis method and device for field tent

Abstract

The invention discloses a method and a device for analyzing performance test data of a field tent. The method comprises the following steps: measuring to obtain a performance index test data set of the field tent; 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 test data sequence; 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 analysis and performance testing, and particularly to a method and device for analyzing performance test data of a field tent. Background Art

[0002] With the continuous development of fields such as outdoor exploration, field survival training, and emergency rescue, as an important field survival equipment, the performance of a field tent is directly related to the safety and comfort of users. However, there are still some deficiencies in the current analysis of performance test data of field tents. Traditional analysis methods usually only test and evaluate a single performance index, such as rainproof performance or wind resistance performance, lacking comprehensive consideration of multiple performance indexes and being difficult to comprehensively reflect the overall performance of the tent in a complex field environment. In addition, during the data processing process, the integrity and consistency of the data are often ignored, such as the time synchronization problem of test data for different performance indexes and the interference of data noise, which may lead to deviations in the evaluation results.

[0003] At the same time, existing methods lack scientific weight assignment and effective fusion calculation means in comprehensive evaluation, and cannot accurately reflect the contribution degree of each performance index to the overall performance, thus restricting the accuracy and reliability of the performance evaluation of field tents and being unable to provide strong technical support for product improvement and optimization. Summary of the Invention

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

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

[0006] S1, measuring a set of performance index test data of the field tent; the set of performance index test data 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 set of performance index test data to obtain a set of preprocessed data;

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

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

[0010] S21, perform data cleaning on the performance index test data set to obtain a first data set;

[0011] S22, perform time registration on the first data set to obtain a second data set;

[0012] S23, perform data reduction on the second data set to obtain a preprocessed data set.

[0013] Performing evaluation and calculation on the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent includes:

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

[0015] S32, perform 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 the sampling value of the extracted principal component index at each moment;

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

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

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

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

[0020] S333, perform weighted summation on the evaluation vector and the weight vector to obtain the comprehensive performance evaluation result value of the field tent.

[0021] Performing evaluation vector calculation on the coefficient matrix and the principal component matrix to obtain an evaluation vector includes:

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

[0023] Perform eigenvalue calculation on the cross-correlation coefficient matrix to obtain an eigenvalue vector;

[0024] Using the eigenvalue vector as the diagonal elements of the matrix and setting the non-diagonal elements of the matrix to 0, construct a characteristic matrix;

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

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

[0027] Perform evaluation vector calculation processing on the rotation factor matrix, eigenvalue vector, and orthogonal matrix to obtain an evaluation vector.

[0028] The weight vector calculation processing for the principal component matrix to obtain a weight vector includes:

[0029] Perform dimensionality augmentation 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 processing is:

[0032] k ij = b ij / ρ i ,

[0033]

[0034] where b ij is the element in 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 is the transformation value corresponding to b ij , d j is the common factor value of the j-th column of the rotation factor matrix, β ii is the element in the i-th row and i-th column of the orthogonal matrix, P is the column dimension of the rotation factor matrix, and V j is the j-th element of the evaluation vector.

[0035] In the second aspect of the implementation of the present invention, a device for analyzing the performance test data of a field tent is disclosed. The device includes:

[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 and executes the method for analyzing the performance test data of the field tent.

[0039] 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 performance test data analysis method of the field tent when called by a computer.

[0040] 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 performance test data analysis method of the field tent.

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

[0042] Comprehensive multi-index evaluation: The present invention covers test data of various key performance indicators such as rainproof performance, snow load performance, deployment time, and wind resistance performance, and can comprehensively reflect the comprehensive performance of the field tent in a complex environment, overcoming the limitation of the traditional method that only focuses on a single index, and providing a comprehensive perspective for the performance evaluation of the field tent.

[0043] 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, ensure the quality and consistency of the data, and thus provide a reliable data basis for subsequent evaluation calculations and improve 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 them with a weight vector for weighted summation calculation, which can scientifically allocate the weights of each performance indicator, highlight the influence of the main performance indicators, reduce the data dimension at the same time, improve the evaluation efficiency, and make the evaluation results more scientific and reliable.

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

[0046] Figure 1 It is a flowchart of the implementation of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

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

[0048] Figure 1 It is a flowchart of the implementation of the method of the present invention.

[0049] In the first aspect of the embodiment of the present invention, a performance test data analysis method and device for a field tent are disclosed, including:

[0050] S1. Measure the test data set of the performance indicators of the field tent; the test data set of the performance indicators 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. Preprocess the test data set of the performance indicators to obtain a preprocessed data set;

[0052] S3. Perform evaluation and calculation processing on the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent;

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

[0054] S21. Perform data cleaning processing on the test data set of the performance indicators to obtain a first data set;

[0055] S22. Perform time registration processing on the first data set to obtain a second data set;

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

[0057] The evaluation and calculation processing of the preprocessed data set to obtain the comprehensive performance evaluation result value of the field tent includes:

[0058] S31. Use the preprocessed data set to construct a test data matrix; the row vectors of the test data matrix are the test data sequences corresponding to each type of performance in the preprocessed data set;

[0059] S32. Perform principal component analysis processing on the observed data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is the sampling value of the extracted principal component index at each moment;

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

[0061] The fusion evaluation and 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. Perform evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain an evaluation vector;

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

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

[0065] The calculation of the evaluation vector by processing the coefficient matrix and the principal component matrix includes:

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

[0067] Perform eigenvalue calculation on the cross - correlation coefficient matrix to obtain an eigenvalue vector;

[0068] Using the eigenvalue vector as the diagonal elements of the matrix and setting the non - diagonal elements of the matrix to 0, construct a characteristic matrix;

[0069] Multiply the characteristic matrix by the coefficient matrix to obtain a factor matrix;

[0070] Multiply the factor matrix by a preset orthogonal matrix to obtain a rotated factor matrix;

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

[0072] The calculation of the weight vector by processing the principal component matrix includes:

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

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

[0075] The calculation of the cross - correlation coefficient matrix by performing cross - correlation calculation processing on the principal component matrix is to perform cross - correlation calculation processing on the row vectors in the principal component matrix to obtain corresponding cross - correlation coefficients; the element in the i - th row and j - th column of the cross - correlation coefficient matrix is the cross - correlation value 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 processing is:

[0077] Y = CX,

[0078] where Y is the principal component matrix, C is the coefficient matrix, X is the test data matrix, the principal component analysis processing can be implemented by the PCA algorithm, and the principal component matrix and the coefficient matrix are both determined by the principal component analysis processing;

[0079] The expression of the evaluation vector calculation processing is:

[0080] k ij= b ij / ρ i ,

[0081]

[0082] where b ij is the element in 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 is the transformation value corresponding to b ij d j is the common factor value of the j-th column of the rotation factor matrix, β ii is the element in 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 j-th element of the evaluation vector;

[0083] The orthogonal matrix can 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 multiplication process of the above matrices, corresponding transpose processing needs to be performed on the two matrices participating in the multiplication according to the dimension matching requirements of matrix multiplication.

[0086] The weighted summation process of the two vectors can be implemented by vector dot product;

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

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

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

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

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

[0092]

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

[0094] The rainproof performance test data can be obtained through the rainproof test device for tents;

[0095] The snow load performance test data sequence can be obtained through the snow load detection device for tents;

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

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

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

[0099] The time registration process can adopt interpolation / extrapolation methods, Lagrange three-point interpolation method, etc.;

[0100] The data reduction process includes:

[0101] 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, autoregressive-moving average modeling is performed to obtain the regression model of the data attribute of the class respectively;

[0102] Using the regression model, calculate and process 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;

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

[0104] The data acquisition information can be acquisition time information;

[0105] In the second aspect of the implementation of the present invention, a performance test data analysis device for field tents is disclosed. The device includes:

[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 performance test data analysis method for the field tent described above.

[0109] In the third aspect of the embodiments 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 method for analyzing the performance test data of the field tent when called by a computer.

[0110] In the fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the method for analyzing the performance test data of the field tent.

[0111] 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, various modifications and changes can be made to the present invention. 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 performance test data of a field tent, characterized in that: include: S1, measuring and obtaining a set of test data on performance indicators of outdoor tents; The performance index test data set includes a rain protection 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 indicator 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 outdoor tent.

2. The method for analyzing performance test data of a field tent according to claim 1, characterized in that: 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 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 preprocessed data set.

3. The method for analyzing performance test data of a field tent according to claim 2, characterized in that: The evaluating and calculating the preprocessing 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 vector of the test data matrix is ​​a test data sequence corresponding to each type of performance in the preprocessed data set; 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 sampling 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 outdoor tent.

4. The method for analyzing performance test data of a field tent according to claim 3, characterized in that: The fusion evaluation calculation process of the coefficient matrix and the principal component matrix is ​​performed to obtain the comprehensive performance evaluation result value of the outdoor tent, including: 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.

5. The method for analyzing performance test data of a field tent according to claim 4, characterized in that: The step of performing evaluation vector calculation processing on the coefficient matrix and the principal component matrix to obtain the evaluation vector comprises: Performing cross-correlation calculation processing 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 eigenvalue vector is used as the diagonal element of the matrix, and the off-diagonal elements of the matrix are set to 0 to construct the characteristic matrix; Multiplying the feature matrix by the coefficient matrix to obtain a factor matrix; Using a preset orthogonal matrix to multiply the factor 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.

6. The method for analyzing performance test data of a field tent according to claim 4, characterized in that: The step of performing weight vector calculation processing on the principal component matrix to obtain a weight vector comprises: 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.

7. The method for analyzing performance test data of a field tent according to claim 5, characterized in that: 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 j-th element of the evaluation vector.

8. 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 7.

9. 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 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the performance test data analysis method of the outdoor tent as described in any one of claims 1 to 7.

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