A performance evaluation method and device for automatically and quickly deploying and retracting a tent

Abstract

The present invention discloses a performance evaluation method and device for an automatic and rapid deployment and retraction tent. The method comprises: collecting a performance index test data set for the automatic and rapid deployment and retraction tent; the performance index test data set comprises an deployment and retraction index test data set, an efficiency index test data set, and an environmental adaptability index test data set; each type of index test data set comprises several types of index test data sequences; preprocessing the performance index test data set to obtain a preprocessed data set; performing classification evaluation processing on each type of index test data set in the preprocessed data set to obtain a sub-index test value set; and performing fusion evaluation processing on the sub-index test value set to obtain a performance evaluation result value for the automatic and rapid deployment and retraction tent.

Description

Technical Field

[0001] The present invention relates to the fields of performance evaluation and artificial intelligence, and in particular to a performance evaluation method and device for automatically and quickly deploying and retracting a tent. Background Art

[0002] With the increasing demand for tents in areas such as outdoor activities and emergency rescue, automatic rapid deployment and retraction tents have attracted widespread attention due to their high efficiency and convenience. However, there are still many problems in the performance evaluation of automatic rapid deployment and retraction tents. Traditional evaluation methods mainly focus on testing a single performance indicator, such as deployment and retraction time or wind resistance, and lack comprehensive consideration of multiple performance indicators. In addition, existing methods have deficiencies in data processing and do not fully consider the integrity and time consistency of the data, resulting in inaccurate and unreliable evaluation results. At the same time, there is a lack of effective technical means for the classification and comprehensive evaluation of different categories of performance indicators, making it difficult to fully reflect the comprehensive performance of the tent in actual use. These problems limit the scientificity and practicality of the performance evaluation of automatic rapid deployment and retraction tents, cannot provide strong technical support for product improvement and optimization, and cannot meet user demand for high-quality tent products. Summary of the Invention

[0003] The present invention mainly solves the problem that the existing technology lacks effective technical means for the classification evaluation and comprehensive evaluation of different categories of performance indicators, making it difficult to fully reflect the comprehensive performance of the automatic rapid deployment and retraction tent in actual use. The present invention discloses a performance evaluation method and device for the automatic rapid deployment and retraction tent.

[0004] In a first aspect, an embodiment of the present invention discloses a performance evaluation method for automatically and quickly deploying and retracting a tent, comprising:

[0005] S1, collecting a performance index test data set for an automatic rapid deployment and folding tent; the performance index test data set includes a deployment index test data set, an efficiency index test data set, and an environmental adaptability index test data set; each index test data set includes several types of index test data sequences;

[0006] S2, preprocessing the performance index test data set to obtain a preprocessed data set;

[0007] S3, performing classification evaluation processing on each type of indicator test data set in the preprocessed data set to obtain a sub-indicator test value set;

[0008] S4, performing fusion evaluation processing on the sub-index test value set to obtain a performance evaluation result value of automatically and quickly deploying and retracting the 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 time alignment processing on the first data set to obtain a second data set;

[0012] S23: Perform pattern discrimination processing on the second data set to obtain a third data set.

[0013] The classification evaluation processing is performed on each type of indicator test data set in the preprocessed data set to obtain a sub-indicator test value set, including:

[0014] For each type of indicator test data set in the preprocessed data set, a corresponding standard test data set is obtained; the standard test data set includes a standard value of each type of indicator test data;

[0015] For each type of indicator test data sequence in each type of indicator test data set, subtract the standard value of the corresponding type of indicator test data to obtain the corresponding type of difference data sequence;

[0016] For each indicator test data set, all types of difference data sequences are used to construct a difference data matrix;

[0017] Performing principal component analysis on the difference data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​the difference value of the extracted principal component index at each test time;

[0018] Performing sub-index calculation processing on the principal component matrix to obtain sub-index vectors corresponding to the index test data set of the category;

[0019] The sub-indicator test value set is constructed by using the sub-indicator vectors corresponding to all indicator test data sets.

[0020] The sub-index calculation process is expressed as follows:

[0021] t=(P T V+aI) -1 P T y,

[0022] V=QR -1 ,

[0023] Where t is the calculated index vector, Q and R are the Q matrix and R matrix obtained by QR decomposition of the principal component matrix P, V is the intermediate matrix, a is the maximum eigenvalue of the principal component matrix P, y is the eigenvector of the principal component matrix P, and I is the identity matrix.

[0024] The fusion evaluation process of the sub-index test value set is performed to obtain the performance evaluation result value of the automatic rapid tent deployment, including:

[0025] A test matrix is ​​constructed using the sub-indicator test value set; the row vectors of the test matrix are the sub-indicator vectors corresponding to the indicator test data set of each category;

[0026] Constructing a pattern matching model for the test matrix and the intermediate matrix;

[0027] Solving the pattern matching model to obtain a coefficient matrix;

[0028] Decompose and transform the coefficient matrix to obtain the evaluation factor vector;

[0029] The evaluation factor vector is subjected to fusion evaluation calculation processing to obtain a performance evaluation result value of automatically and quickly deploying and retracting the tent.

[0030] The decomposition and transformation of the coefficient matrix to obtain the evaluation factor vector includes:

[0031] Decomposing the coefficient matrix to obtain a left decomposition matrix, a characteristic matrix, and a right decomposition matrix of the coefficient matrix;

[0032] The calculation expression of the decomposition process is:

[0033] Y=UAV1,

[0034] Among them, U is the left decomposition matrix, Y is the coefficient matrix, A is the characteristic matrix, V1 is the right decomposition matrix, U and V1 are both orthogonal matrices, and A is a diagonal matrix;

[0035] Extracting the diagonal elements of the characteristic matrix to obtain a characteristic vector;

[0036] Performing linear fitting processing on the elements and element sequence numbers of the eigenvector to obtain a factor polynomial;

[0037] Calculating the mean of each row vector of the coefficient matrix using a factor polynomial to obtain a corresponding calculated value;

[0038] An evaluation factor vector is constructed using the calculated values ​​of all row vectors of the coefficient matrix.

[0039] The pattern matching model is expressed as follows:

[0040] min|VY-R|,

[0041] subject to YY T =I Y ,

[0042] Among them, I Y represents the identity matrix with the row dimension of the coefficient matrix Y as its dimension, the coefficient matrix Y represents the matrix to be solved, V represents the intermediate matrix, and R represents the test matrix.

[0043] According to a second aspect of the present invention, a performance evaluation device for automatically and quickly deploying and retracting a tent is disclosed, the device comprising:

[0044] a memory storing executable program code;

[0045] a processor coupled to the memory;

[0046] The processor calls the executable program code stored in the memory to execute the performance evaluation method for automatically and quickly deploying and retracting a tent.

[0047] 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 performance evaluation method of the automatic rapid deployment and retraction of the tent.

[0048] 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 evaluation method for automatically and quickly deploying and retracting a tent.

[0049] The beneficial effects of the present invention are:

[0050] The present invention provides a performance evaluation method for an automatic and rapid deployment and retraction tent. By collecting multiple types of performance indicator test data and combining technical means such as preprocessing, classification evaluation, and fusion evaluation, a comprehensive and accurate evaluation of the performance of an automatic and rapid deployment and retraction tent is achieved.

[0051] The present invention comprehensively considers multiple performance indicators such as deployment, efficiency and environmental adaptability, and can fully reflect the performance of the automatic rapid deployment tent in different usage scenarios, overcoming the limitation of traditional methods that only focus on a single indicator.

[0052] The present invention classifies and evaluates different categories of indicator test data sets and uses principal component analysis to extract key features, which can highlight the impact of major performance indicators while reducing data dimensions and improving evaluation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 14 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

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

[0055] Figure 1 4 is an implementation flow chart of the method of the present invention.

[0056] In a first aspect, an embodiment of the present invention discloses a performance evaluation method for automatically and quickly deploying and retracting a tent, comprising:

[0057] S1, collecting a performance index test data set for an automatic rapid deployment and folding tent; the performance index test data set includes a deployment index test data set, an efficiency index test data set, and an environmental adaptability index test data set; each index test data set includes several types of index test data sequences;

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

[0059] S3, performing classification evaluation processing on each type of indicator test data set in the preprocessed data set to obtain a sub-indicator test value set;

[0060] S4, performing fusion evaluation processing on the sub-index test value set to obtain a performance evaluation result value of automatically and quickly deploying and retracting the tent.

[0061] The preprocessing of the performance indicator test data set to obtain a preprocessed data set includes:

[0062] S21, performing data cleaning processing on the performance indicator test data set to obtain a first data set;

[0063] S22, performing time alignment processing on the first data set to obtain a second data set;

[0064] S23, performing pattern discrimination processing on the second data set to obtain a third data set;

[0065] The data cleaning process includes filling missing values, smoothing noisy data, and smoothing or deleting outliers;

[0066] The time alignment process may adopt an interpolation / extrapolation method, a Lagrange three-point interpolation method, etc.

[0067] The mode discrimination process includes:

[0068] 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;

[0069] 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;

[0070] performing fusion processing on all data of the second data set after the execution mode discrimination processing to obtain a third data set;

[0071] Through preprocessing steps such as data cleaning, time alignment and pattern discrimination, the present invention can effectively solve problems such as noise, missing values ​​and time deviation in test data, ensure the integrity and consistency of data, and thus improve the reliability of evaluation results.

[0072] The classification evaluation processing is performed on each type of indicator test data set in the preprocessed data set to obtain a sub-indicator test value set, including:

[0073] For each type of indicator test data set in the preprocessed data set, a corresponding standard test data set is obtained; the standard test data set includes a standard value of each type of indicator test data;

[0074] For each type of indicator test data sequence in each type of indicator test data set, subtract the standard value of the corresponding type of indicator test data to obtain the corresponding type of difference data sequence;

[0075] For each indicator test data set, all types of difference data sequences are used to construct a difference data matrix;

[0076] Performing principal component analysis on the difference data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​the difference value of the extracted principal component index at each test time;

[0077] The principal component matrix is ​​subjected to sub-index calculation processing to obtain a sub-index vector corresponding to the index test data set of the category.

[0078] Using the sub-indicator vectors corresponding to all indicator test data sets, a sub-indicator test value set is constructed;

[0079] The sub-index calculation process is expressed as follows:

[0080] t=(P T V+aI) -1 P T y,

[0081] V=QR -1 ,

[0082] Where t is the calculated index vector, Q and R are the Q matrix and R matrix obtained by QR decomposition of the principal component matrix P, V is the intermediate matrix, a is the maximum eigenvalue of the principal component matrix P, and y is the eigenvector of the principal component matrix P.

[0083] The data collection information may be collection time information;

[0084] The fusion evaluation process of the sub-index test value set is performed to obtain the performance evaluation result value of the automatic rapid tent deployment, including:

[0085] A test matrix is ​​constructed using the sub-indicator test value set; the row vectors of the test matrix are the sub-indicator vectors corresponding to the indicator test data set of each category;

[0086] Constructing a pattern matching model for the test matrix and the intermediate matrix;

[0087] Solving the pattern matching model to obtain a coefficient matrix;

[0088] Decompose and transform the coefficient matrix to obtain the evaluation factor vector;

[0089] Performing fusion evaluation calculation processing on the evaluation factor vector to obtain a performance evaluation result value of the automatic rapid deployment and folding of the tent;

[0090] The decomposition and transformation of the coefficient matrix to obtain the evaluation factor vector includes:

[0091] Decomposing the coefficient matrix to obtain a left decomposition matrix, a characteristic matrix, and a right decomposition matrix of the coefficient matrix;

[0092] The calculation expression of the decomposition process is:

[0093] Y=UAV1,

[0094] Among them, U is the left decomposition matrix, Y is the coefficient matrix, A is the characteristic matrix, V1 is the right decomposition matrix, U and V1 are both orthogonal matrices, and A is a diagonal matrix;

[0095] Extracting the diagonal elements of the characteristic matrix to obtain a characteristic vector;

[0096] Performing linear fitting processing on the elements and element sequence numbers of the eigenvector to obtain a factor polynomial;

[0097] The decomposition process can be implemented by using a matrix singular value decomposition algorithm.

[0098] The linear fitting process is to use the characteristic vector element serial number value Ix as a known independent variable and the characteristic vector element value as a known dependent variable, and to construct a curve to be approximated using the known independent variable and the known dependent variable, and to perform curve fitting on the curve to be approximated using the function approximation method to obtain a factor polynomial.

[0099] The feature vector is represented as I a , I a =[λ1,λ2,…,λ N1 ], N1 is the number of elements contained in the characteristic vector; the curve fitting of the curve to be approximated by using the function approximation method can adopt the best consistent linear approximation method. The factor polynomial f(Ix) is expressed as:

[0100] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0101] Where P1 is the order of the factor polynomial f(Ix), α0, α1, α2, ..., α P1 are the coefficients of the factor polynomial f(Ix);

[0102] Calculating the mean of each row vector of the coefficient matrix using a factor polynomial to obtain a corresponding calculated value;

[0103] An evaluation factor vector is constructed using the calculated values ​​of all row vectors of the coefficient matrix.

[0104] The fusion evaluation calculation process includes:

[0105]

[0106] Among them, F 1-α (m-1,i) represents the 1-α quantile of the F distribution according to m-1 and i, α is the preset scaling factor, which can be 0.6, and m is the length of the evaluation factor vector. is the i-th element of the evaluation factor vector, rs is the performance evaluation result value of the automatic rapid tent deployment;

[0107] The value distribution of the evaluation factor vector conforms to the F distribution, so the quantile of the F distribution is used to weight it to compensate for the difference in the values ​​of each evaluation factor vector and obtain an accurate performance evaluation result value.

[0108] The pattern matching model is expressed as follows:

[0109] min|VY-R|,

[0110] subject to YY T =I Y ,

[0111] Among them, I Y represents the identity matrix with the row dimension of the coefficient matrix Y as the dimension, the coefficient matrix Y represents the matrix to be solved, V represents the intermediate matrix, and R represents the test matrix;

[0112] The pattern matching model may adopt a numerical optimization algorithm, such as a genetic algorithm.

[0113] The expression of the principal component analysis process is:

[0114] P=CX,

[0115] Wherein, P is the principal component matrix, C is the coefficient matrix, and X is the difference data matrix. The principal component analysis process can be implemented by the PCA algorithm, and both the principal component matrix and the coefficient matrix are determined by the principal component analysis process;

[0116] The principal component matrix P is a square matrix;

[0117] According to a second aspect of the present invention, a performance evaluation device for automatically and quickly deploying and retracting a tent is disclosed, the device comprising:

[0118] a memory storing executable program code;

[0119] a processor coupled to the memory;

[0120] The processor calls the executable program code stored in the memory to execute the performance evaluation method for automatically and quickly deploying and retracting a tent.

[0121] 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 performance evaluation method of the automatic rapid deployment and retraction of the tent.

[0122] 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 evaluation method for automatically and quickly deploying and retracting a tent.

[0123] By constructing a pattern matching model and fusion evaluation calculation, the present invention can effectively integrate the classification evaluation results to obtain a comprehensive performance evaluation result value. This comprehensive evaluation method can comprehensively reflect the overall performance level of the tent, providing a scientific basis for product optimization design and user selection.

[0124] 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 performance evaluation method for automatically and quickly deploying and retracting a tent, characterized in that: include: S1, collecting a set of performance index test data for automatic rapid deployment and folding of tents; The performance index test data set includes a collection index test data set, an efficiency index test data set, and an environmental adaptability index test data set; each type of index test data set includes several types of index test data sequences; S2, preprocessing the performance index test data set to obtain a preprocessed data set; S3, performing classification evaluation processing on each type of indicator test data set in the pre-processed data set to obtain a sub-indicator test value set, specifically including: For each type of indicator test data set in the preprocessed data set, a corresponding standard test data set is obtained; the standard test data set includes a standard value of each type of indicator test data; For each type of indicator test data sequence in each type of indicator test data set, subtract the standard value of the corresponding type of indicator test data to obtain the corresponding type of difference data sequence; For each indicator test data set, all types of difference data sequences are used to construct a difference data matrix; Performing principal component analysis on the difference data matrix to obtain a coefficient matrix and a principal component matrix; each row vector of the principal component matrix is ​​the difference value of the extracted principal component index at each test time; Performing sub-index calculation processing on the principal component matrix to obtain sub-index vectors corresponding to the index test data set of the type; Using the sub-indicator vectors corresponding to all indicator test data sets, a sub-indicator test value set is constructed; The sub-index calculation process is expressed as follows: t=(P T V+aI) -1 P T yes, V=QR -1 , Where t is the calculated index vector, Q and R are the Q matrix and R matrix obtained by QR decomposition of the principal component matrix P, V is the intermediate matrix, a is the maximum eigenvalue of the principal component matrix P, y is the eigenvector of the principal component matrix P, and I is the identity matrix; S4, performing a fusion evaluation process on the sub-index test value set to obtain a performance evaluation result value of the automatic rapid tent deployment, specifically including: A test matrix is ​​constructed using the sub-indicator test value set; the row vectors of the test matrix are the sub-indicator vectors corresponding to the indicator test data set of each category; Constructing a pattern matching model for the test matrix and the intermediate matrix; Solving the pattern matching model to obtain a coefficient matrix; Decompose and transform the coefficient matrix to obtain the evaluation factor vector; The evaluation factor vector is subjected to fusion evaluation calculation processing to obtain a performance evaluation result value of automatically and quickly deploying and retracting the tent.

2. The performance evaluation method for automatically and quickly deploying and retracting a tent according to claim 1, wherein: include: 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 alignment processing on the first data set to obtain a second data set; S23: Perform pattern discrimination processing on the second data set to obtain a third data set.

3. The performance evaluation method for automatically and quickly deploying and retracting a tent according to claim 1, wherein: The decomposition and transformation of the coefficient matrix to obtain the evaluation factor vector includes: Decomposing the coefficient matrix to obtain a left decomposition matrix, a characteristic matrix, and a right decomposition matrix of the coefficient matrix; The calculation expression of the decomposition process is: Y=UAV1, Among them, U is the left decomposition matrix, Y is the coefficient matrix, A is the characteristic matrix, V1 is the right decomposition matrix, U and V1 are both orthogonal matrices, and A is a diagonal matrix; Extracting the diagonal elements of the characteristic matrix to obtain a characteristic vector; Performing linear fitting processing on the elements and element sequence numbers of the eigenvector to obtain a factor polynomial; Calculating the mean of each row vector of the coefficient matrix using a factor polynomial to obtain a corresponding calculated value; An evaluation factor vector is constructed using the calculated values ​​of all row vectors of the coefficient matrix.

4. The performance evaluation method for automatically and quickly deploying and retracting a tent according to claim 1, wherein: The pattern matching model is expressed as follows: min|VY-R|, subject to YY T =I Y , Among them, I Y represents the identity matrix with the row dimension of the coefficient matrix Y as its dimension, the coefficient matrix Y represents the matrix to be solved, V represents the intermediate matrix, and R represents the test matrix.

5. A performance evaluation device for automatically and quickly deploying and retracting a 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 evaluation method for automatically and quickly deploying and retracting a 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, the computer storable medium is used to execute the performance evaluation method of the automatic rapid deployment and folding 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 evaluation method for automatically and quickly deploying and retracting a tent as claimed in any one of claims 1 to 4.

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