A motor control method and device for automatically and quickly deploying and retracting a tent

Abstract

The present invention discloses a motor control method and device for automatically and quickly deploying and retracting a tent. The method comprises: acquiring a set of motor technical indicator information during the operation of the automatically and quickly deploying and retracting tent; the set of motor technical indicator information comprises a sequence of acquired information of each type of technical indicator of the motor; obtaining a standard value of the motor's working current; preprocessing the set of motor technical indicator information to obtain a preprocessed set of motor technical indicator information; and performing control feedback processing on the preprocessed set of motor technical indicator information and the standard value of the working current to obtain an input current adjustment value of the motor.

Description

Technical Field

[0001] The present invention relates to the fields of industrial data processing, intelligent control and strategy optimization technology, and in particular to a motor control method and device for automatically and quickly deploying and retracting a tent. Background Art

[0002] In existing automatic and rapid tent deployment and folding technologies, the motor is the core power component, and its control accuracy directly affects the tent's deployment and folding efficiency and the system's reliability. However, traditional motor control methods have the following problems: First, the motor will be interfered with by various factors during operation, such as load changes, ambient temperature fluctuations, mechanical vibrations, etc., resulting in noise and outliers in the collected technical indicator data, affecting control accuracy. Secondly, the existing methods are relatively simple in processing motor technical indicators, lack in-depth analysis and preprocessing of the data, and cannot effectively extract key information. In addition, traditional control methods usually only focus on a single indicator of current or speed, and it is difficult to comprehensively consider the inherent correlation between multiple technical indicators, resulting in inaccurate control feedback and the inability to achieve the optimal operating state of the motor. These problems not only reduce the tent's deployment and folding efficiency, but may also cause motor overload and damage, increasing the system's maintenance costs. Summary of the Invention

[0003] The present invention mainly solves the problem of how to achieve precise control of a motor for automatically and quickly deploying and retracting a tent and realize the optimal operating state of the motor. The present invention discloses a motor control method and device for automatically and quickly deploying and retracting a tent.

[0004] In a first aspect of an embodiment of the present invention, a motor control method for automatically and quickly deploying and retracting a tent is disclosed, comprising:

[0005] S1, collecting a set of motor technical indicator information during the automatic rapid tent expansion and contraction process; the motor technical indicator information set includes a collection information sequence of each type of motor technical indicator; obtaining a standard value of the motor's operating current;

[0006] S2, preprocessing the motor technical indicator information set to obtain a preprocessed motor technical indicator information set;

[0007] S3, performing control feedback processing on the pre-processed motor technical indicator information set and the working current standard value to obtain an input current adjustment value of the motor.

[0008] The preprocessing of the motor technical indicator information set to obtain the preprocessed motor technical indicator information set includes:

[0009] S21, performing data cleaning processing on the motor technical indicator information set to obtain a first information set;

[0010] S22, performing category discrimination processing on the first information set to obtain a second information set;

[0011] S23: performing pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set.

[0012] The performing of pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set includes:

[0013] For each type of technical indicator collection information sequence of the second information set, using the data collection information of the collection information sequence as a known independent variable and the collection information sequence as a known dependent variable, constructing a curve to be approximated using the known independent variables and the known dependent variables;

[0014] Performing curve fitting on the curve to be approximated using a function approximation method to obtain an optimal consistent approximation polynomial for the class technical indicator;

[0015] Using the optimal consistent approximation polynomial, calculating and processing the known independent variables to obtain approximate dependent variables;

[0016] Determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second information set; if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0017] All the data after execution mode discrimination of the second information set are fused to obtain a pre-processed motor technical indicator information set.

[0018] The controlling feedback processing is performed on the pre-processed motor technical indicator information set and the working current standard value to obtain the input current adjustment value of the motor, including:

[0019] S31, performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value;

[0020] S32, performing fusion difference calculation processing on the collected information sequence of the working current in the pre-processed motor technical indicator information set and the working current standard value to obtain a current difference value;

[0021] S33, performing feedback calculation on the current difference value, the statistical evaluation value and the pre-processed motor technical indicator information set to obtain an input current adjustment value of the motor.

[0022] The performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value includes:

[0023] The pre-processed motor technical indicator information set is represented as a technical indicator acquisition matrix; the row vector of the technical indicator acquisition matrix is ​​a collection information sequence of a type of technical indicator;

[0024] Perform principal component analysis on the technical indicator acquisition matrix to obtain the coefficient matrix and principal component matrix;

[0025] Performing cross-correlation calculation on the principal component matrix to obtain a cross-correlation coefficient matrix;

[0026] Performing eigenvalue calculation on the mutual correlation coefficient matrix to obtain an eigenvalue vector;

[0027] Assuming that the eigenvalues ​​in the eigenvalue vector obey a standard normal distribution, the second-order central moment and the third-order origin moment of the eigenvalues ​​are calculated;

[0028] Performing feature calculation on the mutual correlation coefficient matrix to obtain a statistical evaluation value;

[0029] The expression for the feature calculation is:

[0030]

[0031] Among them, η is the second-order central moment, ε is the third-order origin moment, z ij is the element in the i-th row and j-th column of the cross-correlation coefficient matrix, d i is the i-th element of the eigenvalue vector, K and P are the column and row dimensions of the cross-correlation coefficient matrix, and T is the statistical evaluation value.

[0032] The step of performing fusion difference calculation processing on the collected information sequence of the working current in the preprocessed motor technical indicator information set and the working current standard value to obtain a current difference value includes:

[0033] Representing the collected information sequence of the working current in the pre-processed motor technical indicator information set as a current vector;

[0034] A fusion difference calculation is performed on the current vector and the working current standard value to obtain a current difference value.

[0035] The expression for calculating the fusion difference is:

[0036]

[0037] Where cy is the current difference value, l i is the i-th element of the current vector, l0 is the standard value of the working current, l max is the maximum value of all elements of the current vector, and N is the number of elements contained in the current vector.

[0038] According to a second aspect of the present invention, a motor control device for automatically and quickly deploying and retracting a tent is disclosed, the device comprising:

[0039] a memory storing executable program code;

[0040] a processor coupled to the memory;

[0041] The processor calls the executable program code stored in the memory to execute the motor control method for automatically and quickly deploying and retracting the tent.

[0042] 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 motor control method for automatically and quickly deploying and retracting a tent.

[0043] 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 motor control method for automatically and quickly deploying and retracting a tent.

[0044] The beneficial effects of the present invention are:

[0045] The present invention's motor control method for automatically deploying and retracting a tent achieves efficient and stable motor control by collecting a set of motor technical indicators and combining them with standard operating current values. This data is then subjected to in-depth preprocessing and precise control feedback. Through data cleaning, category discrimination, and pattern recognition, the present invention effectively removes noise and outliers from the collected data, extracts representative key information, and provides a high-quality data foundation for subsequent control.

[0046] The present invention utilizes function approximation and regression discrimination thresholds to fit and screen data, further optimizing data quality and ensuring data accuracy and consistency. In addition, through statistical methods such as principal component analysis, cross-correlation calculation, and eigenvalue analysis, the inherent correlations between various technical indicators are comprehensively considered, and comprehensive and accurate statistical evaluation values ​​are obtained. Finally, feedback calculations are performed in combination with the current difference value and the statistical evaluation value to obtain the input current adjustment value of the motor, thereby achieving dynamic and precise control of the motor. The present invention can significantly improve the control accuracy and reliability of the motor for automatic and rapid tent deployment, enhance the efficiency of tent deployment, extend the service life of the motor, and reduce system maintenance costs, with significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] In order to better understand the content of the present invention, an embodiment is given here.

[0049] Figure 1 4 is an implementation flow chart of the method of the present invention.

[0050] In a first aspect of an embodiment of the present invention, a motor control method for automatically and quickly deploying and retracting a tent is disclosed, comprising:

[0051] S1, collecting a set of motor technical indicator information during the automatic rapid tent expansion and contraction process; the motor technical indicator information set includes a collection information sequence of each type of motor technical indicator; obtaining a standard value of the motor's operating current;

[0052] S2, preprocessing the motor technical indicator information set to obtain a preprocessed motor technical indicator information set;

[0053] S3, performing control feedback processing on the pre-processed motor technical indicator information set and the working current standard value to obtain an input current adjustment value of the motor;

[0054] S4, inputting the input current adjustment value to the input interface of the motor to complete the motor control of the automatic and rapid tent expansion and contraction.

[0055] The preprocessing of the motor technical indicator information set to obtain the preprocessed motor technical indicator information set includes:

[0056] S21, performing data cleaning processing on the motor technical indicator information set to obtain a first information set;

[0057] S22, performing category discrimination processing on the first information set to obtain a second information set;

[0058] S23, performing pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set;

[0059] The performing of pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set includes:

[0060] For each type of technical indicator collection information sequence of the second information set, using the data collection information of the collection information sequence as a known independent variable and the collection information sequence as a known dependent variable, constructing a curve to be approximated using the known independent variables and the known dependent variables;

[0061] Performing curve fitting on the curve to be approximated using a function approximation method to obtain an optimal consistent approximation polynomial for the class technical indicator;

[0062] Using the optimal consistent approximation polynomial, calculating and processing the known independent variables to obtain approximate dependent variables;

[0063] Determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second information set; if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0064] Performing fusion processing on all the data after the execution mode discrimination of the second information set to obtain a pre-processed motor technical indicator information set;

[0065] The curve fitting of the curve to be approximated by the function approximation method can be performed using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0066]

[0067] Wherein, P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, ..., α P1 are the coefficients of the best consistent approximation polynomial f(Ix);

[0068] The category determination processing performed on the first information set is to determine whether the category of each data in the first information set is consistent with a preset data category, and to delete inconsistent data from the first information set.

[0069] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers. Smoothing noise data involves first identifying noise data and then smoothing it based on the preceding and following data. Noise data is defined as values ​​that are less than the sensor's sensitivity or greater than the sensor's upper limit. Kalman filtering can be used to identify outliers. The value to fill missing values ​​can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0070] The data collection information may be collection time information;

[0071] The controlling feedback processing is performed on the pre-processed motor technical indicator information set and the working current standard value to obtain the input current adjustment value of the motor, including:

[0072] S31, performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value;

[0073] S32, performing fusion difference calculation processing on the collected information sequence of the working current in the pre-processed motor technical indicator information set and the working current standard value to obtain a current difference value;

[0074] S33, performing feedback calculation on the current difference value, the statistical evaluation value and the pre-processed motor technical indicator information set to obtain an input current adjustment value of the motor.

[0075] The performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value includes:

[0076] The pre-processed motor technical indicator information set is represented as a technical indicator acquisition matrix; the row vector of the technical indicator acquisition matrix is ​​a collection information sequence of a type of technical indicator;

[0077] Performing principal component analysis on the technical indicator acquisition 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 indicator at each moment;

[0078] Performing cross-correlation calculation on the principal component matrix to obtain a cross-correlation coefficient matrix;

[0079] Performing eigenvalue calculation on the mutual correlation coefficient matrix to obtain an eigenvalue vector;

[0080] Assuming that the eigenvalues ​​in the eigenvalue vector obey a standard normal distribution, the second-order central moment and the third-order origin moment of the eigenvalues ​​are calculated;

[0081] Performing feature calculation on the mutual correlation coefficient matrix to obtain a statistical evaluation value;

[0082] The cross-correlation calculation process is to perform cross-correlation calculation on every two row vectors of the principal component matrix to obtain corresponding cross-correlation values. 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.

[0083] The expression for the feature calculation is:

[0084]

[0085] Among them, η is the second-order central moment, ε is the third-order origin moment, z ij is the element in the i-th row and j-th column of the cross-correlation coefficient matrix, d i is the i-th element of the eigenvalue vector, K and P are the column and row dimensions of the cross-correlation coefficient matrix, and T is the statistical evaluation value.

[0086] The eigenvalue calculation may adopt a matrix eigenvalue solving algorithm.

[0087] The expression of the principal component analysis process is:

[0088] Y=CX,

[0089] Among them, Y is the principal component matrix, C is the coefficient matrix, and X is the technical indicator collection 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;

[0090] The step of performing fusion difference calculation processing on the collected information sequence of the working current in the preprocessed motor technical indicator information set and the working current standard value to obtain a current difference value includes:

[0091] Representing the collected information sequence of the working current in the pre-processed motor technical indicator information set as a current vector;

[0092] Performing a fusion difference calculation on the current vector and the working current standard value to obtain a current difference value;

[0093] The expression for calculating the fusion difference is:

[0094]

[0095] Where cy is the current difference value, l i is the i-th element of the current vector, l0 is the standard value of the working current, l max is the maximum value of all elements of the current vector, and N is the number of elements contained in the current vector;

[0096] The performing feedback calculation on the current difference value, the statistical evaluation value, and the pre-processed motor technical indicator information set to obtain the input current adjustment value of the motor includes:

[0097] Get the standard value of each technical indicator;

[0098] Subtracting the collected information sequence of each type of technical indicator in the preprocessed motor technical indicator information set from the standard value of the corresponding technical indicator and taking the absolute value to obtain a difference value sequence of each type of technical indicator;

[0099] Using the difference value sequences of all technical indicators as row vectors, a difference matrix is ​​constructed;

[0100] Performing fusion feedback calculation on the difference matrix to obtain an input current adjustment value of the motor;

[0101] The expression of the fusion feedback calculation is:

[0102]

[0103] Among them, p is the input current adjustment value of the motor, A ij is the element of the i-th row and j-th column of the difference matrix, A i is the mean of the i-th row of the difference matrix, A max is the maximum value of all elements of the difference matrix, m and n are the row and column dimensions of the difference matrix respectively.

[0104] The technical indicators of the motor, including speed, output power, operating current, operating temperature, and inverter output voltage;

[0105] According to a second aspect of the present invention, a motor control device for automatically and quickly deploying and retracting a 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 motor control method for automatically and quickly deploying and retracting the tent.

[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 motor control method for automatically and quickly deploying and retracting a tent.

[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 motor control method for automatically and quickly deploying and retracting a 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 motor control method for automatically and quickly deploying and retracting a tent, characterized in that: include: S1, collecting a set of motor technical indicator information during the automatic rapid tent expansion and contraction process; the motor technical indicator information set includes a collection information sequence of each type of motor technical indicator; obtaining a standard value of the motor's operating current; S2, preprocessing the motor technical indicator information set to obtain a preprocessed motor technical indicator information set; S3, performing control feedback processing on the pre-processed motor technical indicator information set and the working current standard value to obtain an input current adjustment value of the motor, specifically including: S31, performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value; S32, performing a fusion difference calculation process on the collected information sequence of the working current in the pre-processed motor technical indicator information set and the working current standard value to obtain a current difference value, specifically including: Representing the collected information sequence of the working current in the pre-processed motor technical indicator information set as a current vector; Performing a fusion difference calculation on the current vector and the working current standard value to obtain a current difference value; The expression for calculating the fusion difference is: Where cy is the current difference value, l i is the i-th element of the current vector, l0 is the standard value of the working current, l max is the maximum value of all elements of the current vector, and N is the number of elements contained in the current vector; S33, performing feedback calculation on the current difference value, the statistical evaluation value and the pre-processed motor technical indicator information set to obtain an input current adjustment value of the motor.

2. The motor control method for automatically and quickly deploying and retracting a tent according to claim 1, wherein: The preprocessing of the motor technical indicator information set to obtain the preprocessed motor technical indicator information set includes: S21, performing data cleaning processing on the motor technical indicator information set to obtain a first information set; S22, performing category discrimination processing on the first information set to obtain a second information set; S23: performing pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set.

3. The motor control method for automatically and quickly deploying and retracting a tent according to claim 2, wherein: The performing of pattern discrimination processing on the second information set to obtain a preprocessed motor technical indicator information set includes: For each type of technical indicator collection information sequence of the second information set, using the data collection information of the collection information sequence as a known independent variable and the collection information sequence as a known dependent variable, constructing a curve to be approximated using the known independent variables and the known dependent variables; Performing curve fitting on the curve to be approximated using a function approximation method to obtain an optimal consistent approximation polynomial for the class technical indicator; Using the optimal consistent approximation polynomial, calculating and processing the known independent variables to obtain approximate dependent variables; Determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second information set; if it is less than or equal to the first regression discrimination threshold, do not process the data; All the data after execution mode discrimination of the second information set are fused to obtain a pre-processed motor technical indicator information set.

4. The motor control method for automatically and quickly deploying and retracting a tent according to claim 1, wherein: The performing statistical discrimination processing on the pre-processed motor technical indicator information set to obtain a statistical evaluation value includes: The pre-processed motor technical indicator information set is represented as a technical indicator acquisition matrix; the row vector of the technical indicator acquisition matrix is ​​a collection information sequence of a type of technical indicator; Perform principal component analysis on the technical indicator acquisition matrix to obtain the coefficient matrix and principal component matrix; 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; Assuming that the eigenvalues ​​in the eigenvalue vector obey a standard normal distribution, the second-order central moment and the third-order origin moment of the eigenvalues ​​are calculated; Performing feature calculation on the mutual correlation coefficient matrix to obtain a statistical evaluation value; The expression for the feature calculation is: Among them, η is the second-order central moment, ε is the third-order origin moment, z ij is the element in the i-th row and j-th column of the cross-correlation coefficient matrix, d i is the i-th element of the eigenvalue vector, K and P are the column and row dimensions of the cross-correlation coefficient matrix, and T is the statistical evaluation value.

5. A motor control 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 motor control 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, they are used to execute the motor control method for automatically and quickly deploying and retracting a tent as claimed in 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 motor control method for automatically and quickly deploying and retracting a tent as claimed in any one of claims 1 to 4.

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