Motor state monitoring method and device for automatically and rapidly unfolding and folding tent

Abstract

The invention discloses a motor state monitoring method and device for an automatic and rapid unfolding and folding tent. The method comprises the steps that a driving motor operation parameter value set of the automatic and rapid unfolding and folding tent is acquired; the driving motor operation parameter value set comprises an operation value sequence of each parameter; the parameters comprise the rotating speed of a motor rotor and the voltage of an inverter; the operation value sequence is discrete values acquired at a plurality of moments; preprocessing the driving motor operation parameter value set to obtain a preprocessed parameter set; and performing state health monitoring processing on the pre-processing parameter set to obtain a motor state monitoring result value of the automatic and rapid unfolding and folding tent.

Description

Technical Field

[0001] The present invention relates to the fields of industrial data processing, automatic control evaluation and strategy optimization processing, and particularly relates to a method and device for monitoring the motor state of an automatically quick-opening and -closing tent. Background Art

[0002] With the increasing demand for quickly setting up and taking down tents in fields such as outdoor activities and emergency rescue, automatically quick-opening and -closing tents have emerged. However, in the prior art, there are many problems in monitoring the operating state of the motors of automatically quick-opening and -closing tents. On the one hand, traditional monitoring methods mostly rely on manual inspections or simple sensor alarms, and cannot obtain the operating parameters of the motors in real time and accurately, making it difficult to accurately evaluate the motor state. On the other hand, in the process of data processing of existing monitoring technologies, the discreteness and dynamic change characteristics of operating parameters are often ignored, resulting in insufficient accuracy and reliability of the monitoring results. In addition, there is no effective screening and processing mechanism for abnormal data of motor operating parameters, and it is easy to misjudge the motor state due to interference from noise data, affecting the normal use and safety of the tent. Therefore, there is an urgent need for a method and device that can efficiently and accurately monitor the motor state of an automatically quick-opening and -closing tent to meet the requirements of actual application scenarios. Summary of the Invention

[0003] The present invention mainly solves the problem of how to quickly and effectively monitor the motor state of an automatically quick-opening and -closing tent, and discloses a method and device for monitoring the motor state of an automatically quick-opening and -closing tent.

[0004] In the first aspect of the embodiments of the present invention, a method for monitoring the motor state of an automatically quick-opening and -closing tent is disclosed, including:

[0005] S1, collecting a set of operating parameter values of the drive motor of the automatically quick-opening and -closing tent; the set of operating parameter values of the drive motor includes an operating value sequence for each parameter; the parameters include motor rotor speed, inverter voltage, torque, temperature, and power; the operating value sequence is a discrete value of the parameter collected at several moments;

[0006] S2, preprocessing the set of operating parameter values of the drive motor to obtain a set of preprocessed parameters;

[0007] S3, performing state health monitoring processing on the set of preprocessed parameters to obtain a monitoring result value of the motor state of the automatically quick-opening and -closing tent.

[0008] The preprocessing of the set of operating parameter values of the drive motor to obtain a set of preprocessed parameters includes:

[0009] S21, performing data filtering processing on the set of operating parameter values of the drive motor to obtain a first data set;

[0010] S22. Perform a category check process on the first data set to obtain a second data set;

[0011] S23. Perform a pattern discrimination process on the second data set to obtain a set of preprocessing parameters.

[0012] The performing a pattern discrimination process on the second data set to obtain a set of preprocessing parameters includes:

[0013] S231. Use each running value sequence in the second data set as a row vector to construct an approximation matrix; use the data acquisition information of each running value sequence as a row vector to construct a collection matrix; use the approximation matrix as the dependent variable and the collection matrix as the independent variable to construct a matching optimization model;

[0014] S232. Solve the matching optimization model to obtain a matching discrimination model; the expression of the matching discrimination model is f(x) = xA, where x is the collection matrix input to the matching discrimination model, and the matrix A is obtained by solving the matching optimization model;

[0015] S233. Use the matching discrimination model to perform a calculation process on the data acquisition information of each type of running value sequence to obtain a solution matrix;

[0016] S234. Subtract each data in the solution matrix from the data in the corresponding same row and column of the approximation matrix, and take the absolute value to obtain the difference value of the data;

[0017] S235. Delete the data in the second data set whose difference value is greater than the set difference threshold;

[0018] S236. Perform S234 to S235 on each data in the solution matrix to obtain a set of preprocessing parameters.

[0019] The difference threshold takes a value of 11.

[0020] The performing a state health monitoring process on the set of preprocessing parameters to obtain the motor state monitoring result value of the automatic quick-deploying and retracting tent includes:

[0021] Calculate the distribution stability value for the running value sequence of the motor rotor speed in the set of preprocessing parameters;

[0022] Calculate the distribution concentration value for the running value sequence of the inverter voltage in the set of preprocessing parameters;

[0023] Judge whether the distribution stability value and the distribution concentration value simultaneously satisfy that the distribution stability value is greater than a preset first threshold and the distribution concentration value is less than a preset second threshold, and obtain a first discrimination result;

[0024] When the first discrimination result is yes, use the distribution discrimination model to process the preprocessing parameter set to obtain the motor status monitoring result value of the automatic quick-deployable tent;

[0025] When the first discrimination result is no, use the difference discrimination model to process the preprocessing parameter set to obtain the motor status monitoring result value of the automatic quick-deployable tent.

[0026] The calculation of the distribution concentration value includes:

[0027] Assume that the sequence of operating values of the inverter voltage follows a chi-square distribution with n degrees of freedom; 2 Distribution;

[0028] Calculate the quantile boundary value α according to the sequence of operating values of the inverter voltage;

[0029] Obtain the (1 - α)-quantile value of the chi-square distribution with n degrees of freedom according to the quantile boundary value α; 2 Distribution;

[0030] Determine the (1 - α)-quantile value of the chi-square distribution with n degrees of freedom as the distribution concentration value. 2 Distribution;

[0031] The calculation of the quantile boundary value α according to the sequence of operating values of the inverter voltage includes:

[0032] Calculate the mean and variance of the sequence of operating values of the inverter voltage;

[0033] Perform calculation processing on the mean and variance of the operating value sequence to obtain the quantile boundary value α;

[0034] The calculation expression of the quantile boundary value α is:

[0035]

[0036] where μ and δ are the mean and variance of the operating value sequence respectively.

[0037] The calculation of the distribution stability includes:

[0038] Assume that the sequence of operating values of the motor rotor speed follows a standard normal distribution;

[0039] Calculate the second central moment and the third origin moment of the sequence of operating values of the motor rotor speed;

[0040] Perform joint calculation and processing on the second-order central moment and the third-order origin moment to obtain a distribution stability value;

[0041] The expression for the joint calculation and processing is:

[0042]

[0043] where p is the distribution stability value, li i is the i-th element of the sequence of operating values of the motor rotor speed, l0 is the second-order central moment, ε is the third-order origin moment, N is the total number of elements in the sequence of operating values of the motor rotor speed, and lmax is the maximum element in the sequence of operating values of the motor rotor speed.

[0044] In the second aspect of the present invention, a motor status monitoring device for an automatically quickly unfolding and retracting tent is disclosed. The device includes:

[0045] A memory storing executable program code;

[0046] A processor coupled to the memory;

[0047] The processor calls the executable program code stored in the memory to execute the motor status monitoring method for the automatically quickly unfolding and retracting tent.

[0048] In the third aspect 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 motor status monitoring method for the automatically quickly unfolding and retracting tent when called by a computer.

[0049] In the fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the motor status monitoring method for the automatically quickly unfolding and retracting tent.

[0050] The beneficial effects of the present invention are:

[0051] By collecting discrete value sequences of key operating parameters such as motor rotor speed and inverter voltage, and performing preprocessing operations such as data filtering, category checking, and pattern discrimination on them, noise data and abnormal data are effectively removed, improving the accuracy and reliability of monitoring data, and laying a solid foundation for subsequent status health monitoring.

[0052] In the status health monitoring stage, the distribution stability and distribution concentration are calculated for the sequences of operating values of the motor rotor speed and the inverter voltage respectively, and comprehensive judgment is carried out in combination with the distribution discrimination model and the difference discrimination model, realizing a comprehensive and accurate assessment of the motor status, being able to timely discover potential fault hazards of the motor, and ensuring the reliable operation of the automatically quickly unfolding and retracting tent.

[0053] The method and device of the present invention have a high degree of automation, can monitor the motor status in real time and dynamically, without manual intervention, greatly improving the monitoring efficiency, reducing the labor cost, and at the same time improving the use safety and service life of the automatic rapid unfolding and retracting tent, and have broad application prospects and important practical significance. Description of the Drawings

[0054] Figure 1 It is the implementation flowchart of the method of the present invention. Detailed Embodiment

[0055] To better understand the content of the present invention, an embodiment is given here.

[0056] Figure 1 It is the implementation flowchart of the method of the present invention.

[0057] In the first aspect of the embodiment of the present invention, a method for monitoring the motor status of an automatic rapid unfolding and retracting tent is disclosed, including:

[0058] S1, collecting a set of operation parameter values of the drive motor of the automatic rapid unfolding and retracting tent; the set of operation parameter values of the drive motor includes an operation value sequence for each parameter; the parameters include motor rotor speed, inverter voltage, torque, temperature, power; the operation value sequence is a discrete value collected at several moments;

[0059] S2, preprocessing the set of operation parameter values of the drive motor to obtain a set of preprocessing parameters;

[0060] S3, performing state health monitoring processing on the set of preprocessing parameters to obtain a motor status monitoring result value of the automatic rapid unfolding and retracting tent;

[0061] The preprocessing of the set of operation parameter values of the drive motor to obtain a set of preprocessing parameters includes:

[0062] S21, performing data filtering processing on the set of operation parameter values of the drive motor to obtain a first data set;

[0063] S22, performing category check 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 set of preprocessing parameters;

[0065] The pattern discrimination processing of the second data set to obtain a set of preprocessing parameters includes:

[0066] S231. Using each running value sequence in the second dataset as a row vector, construct an approximation matrix; using the data acquisition information of each running value sequence as a row vector, construct an acquisition matrix; using the approximation matrix as the dependent variable and the acquisition matrix as the independent variable, construct a matching optimization model;

[0067] S232. Solve the matching optimization model to obtain a matching discrimination model; the expression of the matching discrimination model is f(x) = xA, where x is the acquisition matrix input to the matching discrimination model, and the matrix A is obtained by solving the matching optimization model;

[0068] S233. Using the matching discrimination model, perform calculation processing on the data acquisition information of each type of running value sequence to obtain a solution matrix;

[0069] S234. For each data in the solution matrix, subtract and take the absolute value of the corresponding data in the same row and column of the approximation matrix as the data in the solution matrix to obtain the difference value of the data;

[0070] S235. Delete the data with the difference value greater than the set difference threshold from the second dataset;

[0071] S236. For each data in the solution matrix, execute S234 to S235 to obtain a preprocessing parameter set.

[0072] The present invention constructs a matching optimization model using an approximation matrix and an acquisition matrix, and obtains a matching discrimination model through solution, further optimizing the data acquisition information processing process of the running value sequence, being able to accurately identify and eliminate data with a difference value greater than the set threshold, ensuring that the preprocessed parameter set is more representative and improving the accuracy of the monitoring result.

[0073] Performing state health monitoring processing on the preprocessing parameter set to obtain the motor state monitoring result value of the automatic rapid unfolding and retracting tent, including:

[0074] Calculating the distribution stability value for the running value sequence of the motor rotor speed in the preprocessing parameter set;

[0075] Calculating the distribution concentration value for the running value sequence of the inverter voltage in the preprocessing parameter set;

[0076] Judging whether the distribution stability value and the distribution concentration value simultaneously satisfy that the distribution stability value is greater than a preset first threshold and the distribution concentration value is less than a preset second threshold to obtain a first discrimination result;

[0077] When the first discrimination result is yes, use the distribution discrimination model to process the preprocessing parameter set to obtain the motor status monitoring result value of the automatic quick-deployable tent;

[0078] When the first discrimination result is no, use the difference discrimination model to process the preprocessing parameter set to obtain the motor status monitoring result value of the automatic quick-deployable tent;

[0079] The calculation of the distribution concentration value includes:

[0080] Assume that the sequence of operating values of the inverter voltage follows a chi-square distribution with n degrees of freedom; 2 distribution;

[0081] According to the sequence of operating values of the inverter voltage, calculate the quantile boundary value α;

[0082] According to the quantile boundary value α, obtain the (1 - α)-quantile value of the chi-square distribution with n degrees of freedom; 2 distribution;

[0083] Determine the (1 - α)-quantile value of the chi-square distribution with n degrees of freedom as the distribution concentration value. 2

[0084] The value of the degrees of freedom n can be 3; For the (1 - α)-quantile of the chi-square distribution with n degrees of freedom 2 distribution

[0085] According to the quantile boundary value α, obtain the (1 - α)-quantile value of the chi-square distribution with n degrees of freedom, which can be obtained by looking up the probability density function value table of the chi-square distribution; 2 distribution; 2 table;

[0086] The first threshold can be 0.8, and the second threshold can be 0.6.

[0087] The calculation of the quantile boundary value α according to the sequence of operating values of the inverter voltage includes:

[0088] Calculate the mean and variance of the sequence of operating values of the inverter voltage;

[0089] Perform calculation processing on the mean and variance of the sequence of operating values to obtain the quantile boundary value α;

[0090] The calculation expression of the quantile boundary value α is:

[0091]

[0092] where μ and ε are the mean and variance of the sequence of operating values respectively; ​

[0093] The calculation of the distribution stability includes:

[0094] Assume that the sequence of operating values of the motor rotor speed follows a standard normal distribution;

[0095] Calculate the second central moment and the third origin moment of the sequence of operating values of the motor rotor speed;

[0096] Perform a joint calculation process on the second central moment and the third origin moment to obtain the distribution stability value;

[0097] The expression of the joint calculation process is:

[0098]

[0099] where p is the distribution stability value, li i is the i-th element of the sequence of operating values of the motor rotor speed, l0 is the second central moment, ε is the third origin moment, N is the total number of elements of the sequence of operating values of the motor rotor speed, and lmax is the maximum element of the sequence of operating values of the motor rotor speed.

[0100] The distribution discrimination model includes:

[0101] Obtain the standard value of each parameter of the drive motor of the automatic quick-opening and closing tent;

[0102] Subtract the standard value corresponding to each parameter from the sequence of operating values of each parameter in the preprocessing parameter set to obtain the first difference sequence of each parameter;

[0103] Use all the first difference sequences to construct a first difference matrix;

[0104] Perform a cross-correlation calculation on the first difference matrix to obtain a cross-correlation matrix; the element in the i-th row and j-th column of the cross-correlation matrix is the cross-correlation value between the i-th row vector and the j-th row vector of the first difference matrix;

[0105] Perform an eigenvalue calculation process on the cross-correlation matrix to obtain an eigenvalue set; sort all the elements of the eigenvalue set in descending order of value to obtain an eigenvalue vector;

[0106] Perform a fusion calculation process on the eigenvalue vector to obtain the motor status monitoring result value of the automatic quick-opening and closing tent;

[0107] The expression of the first feature calculation process is:

[0108]

[0109] where DJZ is the motor status monitoring result value of the automatic quick-opening and closing tent, ri is the i-th element of the eigenvalue vector, ω i represents the mean of the i-th row of the first difference matrix, τ represents the mean of all elements of the first difference sequence, and N0 represents the number of rows of the first difference sequence;

[0110] The difference discrimination model includes:

[0111] Obtain the standard value of each parameter of the drive motor of the automatic quick-opening and closing tent;

[0112] Divide the running value sequence of each parameter in the preprocessing parameter set by the corresponding standard value to obtain the second difference sequence of each parameter;

[0113] Use all the second difference sequences to construct a second difference matrix;

[0114] Perform singular value solution processing on the second difference matrix to obtain a singular value set;

[0115] Sort all elements of the singular value set in ascending order of value to obtain a singular value sequence;

[0116] Perform evaluation calculation processing on the singular value sequence to obtain the motor status monitoring result value of the automatic quick-opening and closing tent;

[0117] The expression of the evaluation calculation processing is:

[0118]

[0119] where T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i represents the i-th element of the singular value sequence, J is the length of the singular value sequence, and DJZ is the motor status monitoring result value of the automatic quick-opening and closing tent;

[0120] Dividing the running value sequence of each parameter in the preprocessing parameter set by the corresponding standard value means dividing each element of the running value sequence by the standard value;

[0121] The data filtering process includes filling missing values, smoothing noise data, and smoothing or deleting outlier points;

[0122] The category check process includes discriminating whether the data attribute of each data in the first data set is consistent with the preset data attribute, and deleting the inconsistent data from the first data set to obtain a second data set;

[0123] The data acquisition information is acquisition time information;

[0124] The matching optimization model has the following expression:

[0125] min vA - R,

[0126] subject to AA T = I A ,

[0127] where I A represents the identity matrix with the row dimension of matrix A. Matrix A represents the matrix to be solved, v represents the acquisition matrix, and R represents the approximation matrix;

[0128] The solution algorithm for the matching optimization model can adopt the genetic algorithm and the particle filter algorithm;

[0129] The smaller the value of the monitored result, the healthier the operating state of the motor.

[0130] The motor of the automatic rapid unfolding and retracting tent is used to control the unfolding or retraction of the bracket of the automatic rapid unfolding and retracting tent.

[0131] In the second aspect of the present invention, a device for monitoring the state of the motor of an automatic rapid unfolding and retracting tent is disclosed. The device includes:

[0132] A memory storing executable program code;

[0133] A processor coupled to the memory;

[0134] The processor calls the executable program code stored in the memory to execute the method for monitoring the state of the motor of the automatic rapid unfolding and retracting tent.

[0135] In the third aspect of the present invention, a computer - storable medium is disclosed. The computer - storable medium stores computer instructions, and when the computer instructions are called by the computer, they are used to execute the method for monitoring the state of the motor of the automatic rapid unfolding and retracting tent.

[0136] In the fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the method for monitoring the state of the motor of the automatic rapid unfolding and retracting tent.

[0137] The above are only embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for monitoring the motor status of a tent that is automatically and quickly deployed, characterized in that: include: S1, collecting and obtaining a set of operating parameter values ​​of a driving motor for automatically and quickly unfolding and retracting a tent; the set of operating parameter values ​​of the driving motor includes an operating value sequence of each parameter; the parameters include motor rotor speed, inverter voltage, torque, temperature, and power; the operating value sequence is a discrete value of a parameter collected at a number of moments; S2, preprocessing the drive motor operating parameter value set to obtain a preprocessing parameter set; S3, performing health monitoring processing on the preprocessing parameter set to obtain a motor health monitoring result value for automatically and quickly extending and retracting the tent.

2. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 1, characterized in that: The preprocessing of the drive motor operating parameter value set to obtain a preprocessing parameter set includes: S21, performing data filtering processing on the set of operating parameter values ​​of the drive motor to obtain a first data set; S22, performing category checking processing on the first data set to obtain a second data set; S23, performing pattern discrimination processing on the second data set to obtain a preprocessing parameter set.

3. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 2, characterized in that: The performing pattern discrimination processing on the second data set to obtain a preprocessing parameter set includes: S231, using each operating value sequence in the second data set as a row vector to construct an approximation matrix; using the data collection information of each operating value sequence as a row vector to construct a collection matrix; using the approximation matrix as a dependent variable and the collection matrix as an independent variable to construct a matching optimization model; S232, solving the matching optimization model to obtain a matching discrimination model; the expression of the matching discrimination model is f(x)=xA, x is the acquisition matrix of the input of the matching discrimination model, and the matrix A is obtained by solving the matching optimization model; S233, using the matching discrimination model, calculating and processing the data collection information of each type of operation value sequence to obtain a solution matrix; S234, subtracting each data in the solution matrix from the data in the approximation matrix corresponding to the same row and column as the data in the solution matrix, and calculating the absolute value to obtain a difference value of the data; S235, deleting the data whose difference value is greater than a set difference threshold from the second data set; S236, executing S234 to S235 for each data in the solution matrix to obtain a preprocessing parameter set.

4. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 1, characterized in that: The performing of state health monitoring processing on the preprocessing parameter set to obtain a motor state monitoring result value for automatically and quickly unfolding and retracting the tent includes: Performing distribution stability calculation on the running value sequence of the motor rotor speed in the preprocessing parameter set to obtain a distribution stability value; Calculating the distribution concentration value of the operating value sequence of the inverter voltage in the preprocessing parameter set to obtain the distribution concentration value; Determine whether the distribution stability value and the distribution concentration value simultaneously satisfy that the distribution stability value is greater than a preset first threshold value, and the distribution concentration value is less than a preset second threshold value, and obtain a first determination result; When the first discrimination result is yes, the preprocessing parameter set is processed by using a distribution discrimination model to obtain a motor state monitoring result value for automatically and quickly unfolding and retracting the tent; When the first discrimination result is no, the preprocessing parameter set is processed using a difference discrimination model to obtain a motor state monitoring result value for automatically and quickly deploying and retracting the tent.

5. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 4, characterized in that: The distribution concentration value calculation includes: Assume that the operating value sequence of the inverter voltage follows the χ2 equation with n degrees of freedom. 2 distributed; According to the operating value sequence of the inverter voltage, a quantile boundary value α is calculated; According to the quantile boundary value α, obtain the χ with n degrees of freedom 2 The 1-α quantile value of the distribution; Determine the χ with n degrees of freedom 2 The 1-α percentile value of the distribution is the distribution concentration value.

6. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 5, characterized in that: The step of calculating the quantile boundary value α according to the operating value sequence of the inverter voltage includes: Calculating and obtaining the mean and variance of the operating value sequence of the inverter voltage; Calculate the mean and variance of the running value sequence to obtain a quantile boundary value α; The calculation expression of the quantile boundary value α is: Wherein, μ and ε are the mean and variance of the running value sequence respectively.

7. The motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in claim 4, characterized in that: The distribution stability calculation includes: Assume that the running value sequence of the motor rotor speed obeys the standard normal distribution; Calculate the second-order central moment and the third-order origin moment of the running value sequence of the motor rotor speed; The second-order central moment and the third-order origin moment are jointly calculated to obtain a distribution stability value; The expression of the joint calculation process is: Among them, p is the distribution stability value, l i is the i-th element of the running value sequence of the motor rotor speed, l0 is the second-order central moment, ε is the third-order origin moment, N is the total number of elements in the running value sequence of the motor rotor speed, and lmax is the maximum element in the running value sequence of the motor rotor speed.

8. A motor status monitoring 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 status monitoring method for automatically and quickly deploying and retracting a tent as described in 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 motor status monitoring method for automatically and quickly deploying and retracting a tent as claimed in 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 motor status monitoring method for automatically and quickly deploying and retracting a tent as described in any one of claims 1 to 7.

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