Electromechanical intelligent control method and system combined with Internet of Things technology

Abstract

The invention discloses an electromechanical intelligent control method and system combined with the Internet of Things technology, and relates to the technical field of electromechanical equipment control, and the method comprises the steps: obtaining the amplitude data and frequency data of operation parameters in real time through a force sensor, calculating the abnormal coefficient of the operation parameters, and evaluating the stability of the operation process of electromechanical equipment; a temperature sensor is used for monitoring the temperature change of the operation area, the influence of operation parameter fluctuation on the temperature is analyzed in combination with the operation parameter abnormal coefficient, and the temperature abnormal coefficient is calculated; comprehensively analyzing the temperature abnormal coefficient and the operation parameter abnormal coefficient to obtain an influence coefficient, and judging the influence degree of the operation parameter fluctuation on the temperature change; and dynamically adjusting operation parameters including operation speed and operation power according to an analysis result, keeping the temperature of an operation area in a normal range, and optimizing the stability of the operation parameters, so that the operation precision and stability are improved, and equipment faults and operation errors are reduced.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical equipment control technology, and in particular to an electromechanical intelligent control method and system combined with Internet of Things technology. Background Art

[0002] As industrial manufacturing develops towards precision and intelligence, the application of electromechanical equipment is becoming more and more extensive, especially in the field of high-precision operation. As core equipment, electromechanical equipment plays a vital role. However, with the continuous improvement of operating accuracy requirements, electromechanical equipment often faces problems such as fluctuations in operating parameters and increased temperature during actual operation. These factors will not only affect the stability of the operation process, but may also lead to loss of operating accuracy, increased equipment wear, and even failure. Abnormal fluctuations in operating parameters usually lead to operating errors, and drastic changes in temperature will cause thermal deformation, affecting the final size and surface quality of the workpiece. Therefore, how to monitor and analyze the operating parameters and temperature changes during the operation process in real time, and optimize the operating parameters through dynamic control, has become an important issue to ensure operating accuracy and stability.

[0003] Existing technologies lack the ability to dynamically optimize. Once operating parameters or temperature anomalies occur, they often rely on manual intervention or static adjustments, and cannot achieve automated real-time optimization and adjustment. Therefore, existing technologies still have certain limitations in improving operating accuracy, ensuring equipment stability, and improving production efficiency. Summary of the invention

[0004] In order to solve the above technical problems, a mechatronic intelligent control method and system combined with Internet of Things technology is provided. This technical solution solves the problem that the above-mentioned existing technology lacks the ability of dynamic optimization. Once abnormal operating parameters or temperature occur, it often relies on manual intervention or static adjustment, and cannot achieve automatic real-time optimization and adjustment.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] The electromechanical intelligent control method combined with the Internet of Things technology includes the following steps:

[0007] Through monitoring sensors connected to the Internet of Things, the amplitude data and frequency data of operating parameters of electromechanical equipment during operation are monitored in real time;

[0008] Analyze the amplitude data and frequency data of the operating parameters of the electromechanical equipment, and calculate the operating parameter abnormality coefficient according to the operating parameter values ​​of the electromechanical equipment during operation and the periodic characteristics of the operating parameter fluctuations, so as to evaluate the stability of the operation process of the electromechanical equipment;

[0009] Use temperature sensors to monitor the temperature changes in the operating area in real time, and combine them with the operating parameter abnormality index to analyze the impact of operating parameter fluctuations on temperature changes based on the real-time temperature changes in the operating area;

[0010] According to the analysis results of the operating parameter abnormality index and temperature change, the operating parameters are adjusted so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during operation.

[0011] Preferably, the process of obtaining the abnormal coefficient of the operating parameter is as follows:

[0012] During a monitoring period;

[0013] Obtain the amplitude data of the electromechanical equipment during operation, process the amplitude data, and calculate the amplitude fluctuation index according to the fluctuation of the amplitude data;

[0014] Obtain the frequency data of electromechanical equipment during operation, process the frequency data, and calculate the frequency fluctuation index based on the periodic characteristics of the operating parameter fluctuations;

[0015] The amplitude fluctuation index and frequency fluctuation index of the electromechanical equipment during operation are comprehensively analyzed, and the amplitude fluctuation index and frequency fluctuation index are normalized and calculated to obtain the abnormal coefficient of the operating parameters.

[0016] Preferably, the process of obtaining the amplitude fluctuation index is as follows:

[0017] The amplitude data is obtained according to the time series, which is expressed as a time signal within a period of time, denoted as F ( t ) ;

[0018] Where t represents time, F ( t ) Amplitude signal representing operating parameters;

[0019] Use continuous wavelet transform to transform the running parameter amplitude signal F ( t ) Decompose into detailed components at different frequencies and time scales;

[0020] The calculation expression of the wavelet transform is:

[0021]

[0022] Among them, W ψ ( a,b ) It is expressed as a wavelet transform system, where a is the scale factor, b is the translation factor, and ψ * is its conjugate complex number;

[0023] For each scale a, the corresponding energy density is calculated, and the calculation expression is:

[0024]

[0025] Among them, E ( a ) represents the energy density at scale a;

[0026] Calculate the standard deviation σ of the energy density at all scales ( E ) and mean μ ( E ) ;

[0027] According to the standard deviation of energy density at all scales σ ( E ) and mean μ ( E ) , calculate the amplitude fluctuation index, the calculation expression is:

[0028]

[0029] Where, ΔF index Represents the amplitude fluctuation index.

[0030] Preferably, the process of obtaining the frequency fluctuation index is as follows:

[0031] Obtain frequency data in time series and express it as a time domain signal S(t), where t represents time;

[0032] Use the fast Fourier transform to transform the time domain signal of the frequency data into the frequency domain. The calculation expression is:

[0033]

[0034] In the formula, f k represents the kth frequency, k represents the number of frequencies, X(f k ) represents the frequency f k The Fourier transform result under the condition, n represents the acquisition point of the time series, N represents the total number of acquisition points of the time series, S(t n ) represents the time domain signal value at each acquisition point, e represents the logarithm of the natural number base, and j represents the imaginary unit;

[0035] The Fourier transform result X(f k ) to calculate the amplitude and obtain the amplitude spectrum of the frequency component. The calculation expression is:

[0036]

[0037] In the formula, Re(X(fk )) represents the real part of the Fourier transform result, Im(X(f k )) represents the imaginary part of the Fourier transform result, |X(f k )| represents the frequency f k The amplitude spectrum under ;

[0038] Calculate the energy power spectrum of each frequency component. The calculation expression is:

[0039] P(f k )=|X(f k )| 2 ;

[0040] In the formula, P(f k ) represents the energy power spectrum;

[0041] Get the standard deviation of the frequency amplitude spectrum σ(|X(f k )|) and the standard deviation of the energy power spectrum σP(f k );

[0042] The standard deviation of the frequency-amplitude spectrum σ(|X(f k )|) and the standard deviation of the energy power spectrum σ(|P(f k )|)Calculate the frequency fluctuation index, the calculation expression is:

[0043]

[0044] In the formula, ΔS index Represents the frequency fluctuation index.

[0045] Preferably, the step of evaluating the stability of the operation process of the electromechanical equipment specifically includes:

[0046] It is determined whether the abnormal coefficient of the operating parameter is greater than or equal to a preset threshold value. If so, the corresponding electromechanical equipment is unstable during operation; if not, the corresponding electromechanical equipment is stable during operation.

[0047] Preferably: according to the time series, the temperature data of the operating area is obtained in real time, and the temperature anomaly coefficient is calculated according to the real-time abnormal temperature fluctuation of the operating area;

[0048] The influence relationship between the temperature anomaly coefficient and the operating parameter anomaly coefficient is analyzed, and the temperature anomaly coefficient and the operating parameter anomaly coefficient are fused and calculated to obtain the influence coefficient. According to the obtained influence coefficient, the influence of the operating parameter fluctuation on the temperature change is evaluated, and the influence signal and the non-influence signal are generated.

[0049] Preferably, the process of obtaining the temperature anomaly coefficient is as follows:

[0050] Obtain the temperature data of the operating area within the monitoring period, recorded as T(t), where t represents time;

[0051] For each temperature data collection point, calculate the temperature difference between each two adjacent temperature points, recorded as ΔT i , i represents the temperature data collection point;

[0052] According to the temperature difference of the temperature data, the reachable density of each data point is calculated. The calculation expression is:

[0053] reach-dist(T i )=max(ΔT i ,k-dist(T i ))

[0054]

[0055] In the formula, ΔT i represents the temperature difference, k-dist(T i ) represents the k-distance of the temperature data point Ti, lrd(T i ) represents the reachable density, N k (T i ) represents the k nearest neighbors of the temperature data point, reach-dist(T i ) represents the reachable distance;

[0056] According to the accessible density and temperature difference of temperature data, the temperature anomaly coefficient is calculated, and the calculation expression is:

[0057]

[0058] In the formula, L(T i ) represents the temperature anomaly coefficient of the i-th temperature data collection point.

[0059] Preferably: the process of obtaining the influence coefficient is:

[0060] Obtaining a temperature anomaly coefficient for each temperature data collection point, and comparing the temperature anomaly coefficient with a preset threshold, calculating the ratio of the number of temperature data points whose temperature anomaly coefficient is greater than or equal to the preset threshold to the total number of temperature data points, to obtain a temperature anomaly ratio;

[0061] The temperature anomaly ratio and the operating parameter anomaly coefficient are calculated comprehensively in combination with the preset proportional factor to obtain the influence coefficient.

[0062] Preferably, the generation criteria for dividing the impact signal and the non-impact signal include:

[0063] It is determined whether the influence coefficient is greater than or equal to a preset threshold value. If so, an influence signal is generated; if not, a non-influence signal is generated.

[0064] Furthermore, a mechatronic intelligent control system combining Internet of Things technology is proposed, including:

[0065] A data acquisition module, which monitors the amplitude data and frequency data of the operating parameters of the electromechanical equipment in real time during operation by means of monitoring sensors connected to the Internet of Things;

[0066] Use temperature sensors to collect temperature data of the operating area in real time;

[0067] An operation process stability evaluation module, which analyzes the amplitude data and frequency data of the operation parameters of the electromechanical equipment, and calculates the operation parameter abnormality coefficient according to the operation parameter value of the electromechanical equipment during operation and the periodic characteristics of the operation parameter fluctuation, so as to evaluate the stability of the operation process of the electromechanical equipment;

[0068] An impact degree analysis module, which analyzes the impact degree of operating parameter fluctuations on temperature changes based on real-time temperature changes in the operating area;

[0069] The operating parameter adjustment module adjusts the operating parameters according to the operating parameter abnormality index and the temperature change analysis results so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during the operation process.

[0070] Beneficial effects of the present invention:

[0071] (1) The present invention combines the abnormal coefficient of operating parameters and temperature change analysis, and uses real-time monitoring data to dynamically adjust the operating parameters of CNC electromechanical equipment, thereby achieving precise control of the operating process. Specifically, the amplitude and frequency data of the operating parameters obtain the fluctuation characteristics of the operating parameters through precise analysis methods, while the temperature data of the operating area monitored by the temperature sensor reveals the changes in thermal deformation. By combining the abnormal fluctuation of the operating parameters with the temperature fluctuation, it is possible to comprehensively evaluate the potential stability risks in the operating process, and adjust the operating parameters such as operating speed and operating power according to actual conditions, thereby reducing the impact of abnormal fluctuations in operating parameters and temperature increases, and ensuring that the operating area is always in a preset stable state. This process can not only prevent the loss of accuracy caused by operating parameters and temperature fluctuations, but also reduce the risk of equipment wear and equipment damage, extend the service life of electromechanical equipment, and improve operating efficiency. Through refined process control, the present invention can effectively improve the stability of production and the consistency of products while ensuring the operating accuracy, and ensure the high efficiency, low consumption and high quality operation of the operating process.

[0072] (2) The calculation and application of the operating parameter abnormality coefficient in the present invention improves the stability and accuracy of the operation process of CNC electromechanical equipment through real-time monitoring and precise analysis of the operating parameter amplitude and frequency data. The operating parameter abnormality coefficient can accurately reflect the mechanical fluctuations during the operation process and provide a quantitative basis for whether the electromechanical equipment is in a stable operating state. Through a comprehensive evaluation of the operating parameters, abnormal fluctuations caused by factors such as equipment wear, improper operating conditions or mechanical failure of electromechanical equipment can be identified in a timely manner, and potential operating problems can be warned in advance. The operating parameter abnormality coefficient is not only an important reference indicator for the monitoring system, but also provides a real-time adjustment basis for the automatic control system. When the operating parameter fluctuation exceeds the preset threshold, the system will actively adjust the operating parameters, such as operating speed, feed rate, etc., so as to balance the operating parameters and thermal deformation and maintain operating stability. Through this process, not only the operating error caused by excessive fluctuations is effectively prevented, but also the operating accuracy can be significantly improved, the risk of equipment wear and electromechanical equipment damage can be reduced, and the downtime and maintenance costs caused by unstable production can be reduced. Ultimately, this optimization measure can improve the operating efficiency and quality on the basis of ensuring the operating accuracy, and promote the efficient and safe operation of intelligent manufacturing.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention proposes. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of the electromechanical intelligent control method combined with Internet of Things technology proposed by the present invention;

[0076] Figure 2 It is a flow chart of the method for real-time monitoring of the operation process of electromechanical equipment in the present invention;

[0077] Figure 3 It is a flow chart of the method for calculating the abnormal coefficient of operating parameters in the present invention;

[0078] Figure 4 A flow chart of the method for analyzing the influence of operating parameter fluctuations on temperature changes in the present invention;

[0079] Figure 5 The present invention is a flow chart of the method for optimizing the accuracy and stability during operation. DETAILED DESCRIPTION

[0080] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0081] Please refer to Figure 1As shown, the present invention is a mechatronic intelligent control method combined with Internet of Things technology, comprising the following steps:

[0082] Through monitoring sensors connected to the Internet of Things, the amplitude data and frequency data of operating parameters of electromechanical equipment during operation are monitored in real time;

[0083] Analyze the amplitude data and frequency data of the operating parameters of the electromechanical equipment, and calculate the operating parameter abnormality coefficient according to the operating parameter values ​​of the electromechanical equipment during operation and the periodic characteristics of the operating parameter fluctuations, so as to evaluate the stability of the operation process of the electromechanical equipment;

[0084] Use temperature sensors to monitor the temperature changes in the operating area in real time, and combine them with the operating parameter abnormality index to analyze the impact of operating parameter fluctuations on temperature changes based on the real-time temperature changes in the operating area;

[0085] According to the analysis results of the operating parameter abnormality index and temperature change, the operating parameters are adjusted so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during operation.

[0086] Reference Figure 2 As shown, the amplitude data and frequency data of the operating parameters of the electromechanical equipment are monitored in real time through the monitoring sensors connected to the Internet of Things, including:

[0087] Monitoring sensors connected to the Internet of Things measure operating parameters by installing strain sensors on electromechanical equipment;

[0088] The collected amplitude data is usually transmitted in the form of real-time signal stream, and after filtering and sampling, amplitude data with time series is obtained;

[0089] The specific collection steps are: installing monitoring sensors connected to the Internet of Things on electromechanical equipment and in the operating area;

[0090] Monitoring sensors connected to the Internet of Things collect real-time data on operating parameters;

[0091] Set the frequency of data collection to ensure that instantaneous changes can be captured;

[0092] Collect real-time amplitude data;

[0093] Use monitoring sensors connected to the Internet of Things to monitor operating parameters in real time, and use Fast Fourier Transform (FFT) to convert time domain signals into frequency domain signals.

[0094] Reference Figure 3As shown, the amplitude data and frequency data of the operating parameters of the electromechanical equipment are analyzed, and the operating parameter abnormality coefficient is calculated according to the operating parameter values ​​of the electromechanical equipment during operation and the periodic characteristics of the operating parameter fluctuations, which is used to evaluate the stability of the operation process of the electromechanical equipment, including:

[0095] During a monitoring period;

[0096] Obtain the amplitude data of the electromechanical equipment during operation, process the amplitude data, and calculate the amplitude fluctuation index according to the fluctuation of the amplitude data;

[0097] Obtain the frequency data of electromechanical equipment during operation, process the frequency data, and calculate the frequency fluctuation index based on the periodic characteristics of the operating parameter fluctuations;

[0098] The amplitude fluctuation index and frequency fluctuation index of electromechanical equipment during operation are comprehensively analyzed, and the amplitude fluctuation index and frequency fluctuation index are normalized and calculated to obtain the abnormal coefficient of operating parameters. The calculation expression is:

[0099]

[0100] In the formula, Z F Indicates the abnormal coefficient of operating parameters, ΔF index Indicates the amplitude fluctuation index, ΔS index represents the frequency fluctuation index, a1 and a2 represent the preset proportional coefficients, and both a1 and a2 are greater than 0;

[0101] The process of obtaining the amplitude fluctuation index is as follows:

[0102] The amplitude data is obtained according to the time series, which is expressed as a time signal within a period of time, denoted as F(t);

[0103] Where t represents time, and F(t) represents the amplitude signal of the operating parameter;

[0104] The continuous wavelet transform is used to decompose the operating parameter amplitude signal F(t) into detail components at different frequencies and time scales;

[0105] The calculation expression of wavelet transform is:

[0106]

[0107] Among them, W ψ (a,b) represents the wavelet transform system, a is the scale factor, b is the translation factor, ψ * is its conjugate complex number;

[0108] For each scale a, the corresponding energy density is calculated, and the calculation expression is:

[0109]

[0110] Where E(a) represents the energy density at scale a;

[0111] Calculate the standard deviation σ(E) and mean μ(E) of energy density at all scales;

[0112] According to the standard deviation σ(E) and mean μ(E) of energy density at all scales, the amplitude fluctuation index is calculated, and the calculation expression is:

[0113]

[0114] Where, ΔF index Represents the amplitude fluctuation index;

[0115] The process of obtaining the frequency fluctuation index is as follows:

[0116] Obtain frequency data in time series and express it as a time domain signal S(t), where t represents time;

[0117] Use the fast Fourier transform to transform the time domain signal of the frequency data into the frequency domain. The calculation expression is:

[0118]

[0119] In the formula, f k represents the kth frequency, k represents the number of frequencies, X(f k ) represents the frequency f k The Fourier transform result under the condition, n represents the acquisition point of the time series, N represents the total number of acquisition points of the time series, S(t n ) represents the time domain signal value at each acquisition point, e represents the logarithm of the natural number base, and j represents the imaginary unit;

[0120] The Fourier transform result X(f k ) to calculate the amplitude and obtain the amplitude spectrum of the frequency component. The calculation expression is:

[0121]

[0122] In the formula, Re(X(f k )) represents the real part of the Fourier transform result, Im(X(f k )) represents the imaginary part of the Fourier transform result, |X(f k )| represents the frequency f k The amplitude spectrum under ;

[0123] Calculate the energy power spectrum of each frequency component. The calculation expression is:

[0124] P(fk )=|X(f k )| 2

[0125] In the formula, P(f k ) represents the energy power spectrum;

[0126] Get the standard deviation of the frequency amplitude spectrum σ(|X(f k )|) and the standard deviation of the energy power spectrum σP(f k );

[0127] The standard deviation of the frequency-amplitude spectrum σ(|X(f k )|) and the standard deviation of the energy power spectrum σ(|P(f k )|)Calculate the frequency fluctuation index, the calculation expression is:

[0128]

[0129] In the formula, ΔS index represents the frequency fluctuation index;

[0130] Evaluate the stability of the operation process of electromechanical equipment, including:

[0131] Determine whether the abnormal coefficient of the operating parameter is greater than or equal to a preset threshold value. If so, the corresponding electromechanical equipment is unstable during operation; if not, the corresponding electromechanical equipment is stable during operation;

[0132] It should be noted that the operating parameter abnormality coefficient reflects the stability of the electromechanical equipment during the operation process, and the larger the value of the operating parameter abnormality coefficient is, the more unstable the corresponding operation process is.

[0133] Reference Figure 4 As shown, a temperature sensor is used to monitor the temperature changes in the operating area in real time, and combined with the operating parameter abnormality index, the influence of operating parameter fluctuations on temperature changes is analyzed according to the real-time temperature changes in the operating area, including:

[0134] Acquire the temperature data of the operating area in real time according to the time series, and calculate the temperature anomaly coefficient according to the real-time abnormal temperature fluctuation in the operating area;

[0135] The influence relationship between the temperature anomaly coefficient and the operating parameter anomaly coefficient is analyzed, and the temperature anomaly coefficient and the operating parameter anomaly coefficient are fused and calculated to obtain the influence coefficient. According to the obtained influence coefficient, the influence degree of the operating parameter fluctuation on the temperature change is evaluated, and the influence signal and the non-influence signal are generated;

[0136] The process of obtaining the temperature anomaly coefficient is as follows:

[0137] Obtain the temperature data of the operating area within the monitoring period, recorded as T(t), where t represents time;

[0138] For each temperature data collection point, calculate the temperature difference between each two adjacent temperature points, recorded as ΔT i , i represents the temperature data collection point;

[0139] According to the temperature difference of the temperature data, the reachable density of each data point is calculated. The calculation expression is:

[0140] reach-dist(T i )=max(ΔT i ,k-dist(T i ))

[0141]

[0142] In the formula, ΔT i represents the temperature difference, k-dist(T i ) represents the temperature data point T i k-distance, lrd(T i ) represents the reachable density, N k (T i ) represents the k nearest neighbors of the temperature data point, reach-dist(T i ) represents the reachable distance;

[0143] According to the accessible density and temperature difference of temperature data, the temperature anomaly coefficient is calculated, and the calculation expression is:

[0144]

[0145] In the formula, L(T i ) represents the temperature anomaly coefficient of the i-th temperature data collection point;

[0146] The process of obtaining the influence coefficient is as follows:

[0147] Obtaining a temperature anomaly coefficient for each temperature data collection point, and comparing the temperature anomaly coefficient with a preset threshold, calculating the ratio of the number of temperature data points whose temperature anomaly coefficient is greater than or equal to the preset threshold to the total number of temperature data points, to obtain a temperature anomaly ratio;

[0148] The temperature anomaly ratio and the operating parameter anomaly coefficient are calculated in combination with the preset proportional factor to obtain the influence coefficient. The calculation expression is:

[0149]

[0150] In the formula, H S Indicates the influence coefficient, C Drepresents the temperature anomaly ratio, Z F Indicates the abnormal coefficient of operating parameters, b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0;

[0151] comparing the impact coefficient with a preset threshold;

[0152] If the influence coefficient is greater than or equal to the preset threshold, it means that the corresponding operating parameter abnormality affects the temperature of the operating area, and an influence signal is generated;

[0153] If the influence coefficient is less than the preset threshold, it means that the corresponding operating parameter abnormality has no effect on the temperature of the operating area, and a non-influence signal is generated.

[0154] Reference Figure 5 As shown in the figure, according to the abnormal index of operating parameters and the analysis results of temperature changes, the operating parameters are adjusted so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during the operation, including:

[0155] Based on the generated impact signal, the operating parameters, such as operating speed and operating power, are adjusted according to different operating parameter fluctuations and temperature abnormalities to reduce the fluctuation range of the operating parameters and ensure that the temperature is maintained within the preset normal range during operation;

[0156] These include: when high operating parameter fluctuations cause temperature increases, reducing the operating speed, thereby reducing operating parameter fluctuations and lowering temperatures;

[0157] After adjusting the operating parameters, continue to track the abnormal index and temperature changes of the operating parameters through the real-time monitoring system to ensure that both are in a stable state;

[0158] If the operating parameter abnormality index still exceeds the preset range, adjust the parameters again until the operating parameters and temperature during operation return to the normal range;

[0159] Real-time adjustment is achieved through automated control systems to ensure dynamic optimization of the operation process;

[0160] Finally, the accuracy and stability during operation are evaluated in combination with the stability of operating parameters and temperature.

[0161] The electromechanical intelligent control system combined with the Internet of Things technology includes:

[0162] Data acquisition module: The data acquisition module monitors the amplitude data and frequency data of the operating parameters of the electromechanical equipment in real time during operation by means of monitoring sensors connected to the Internet of Things;

[0163] Use temperature sensors to collect temperature data of the operating area in real time;

[0164] An operation process stability evaluation module analyzes the amplitude data and frequency data of the operation parameters of the electromechanical equipment, and calculates the abnormal coefficient of the operation parameters according to the operation parameter values ​​of the electromechanical equipment during operation and the periodic characteristics of the fluctuation of the operation parameters, so as to evaluate the stability of the operation process of the electromechanical equipment;

[0165] Impact degree analysis module: The impact degree analysis module analyzes the impact degree of operating parameter fluctuations on temperature changes based on the real-time temperature changes in the operating area;

[0166] The operating parameter adjustment module adjusts the operating parameters according to the operating parameter abnormality index and temperature change analysis results so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during the operation process.

[0167] The working principle of the present invention is as follows: the amplitude and frequency data of the operating parameters of the electromechanical equipment are monitored in real time by the monitoring sensors connected to the Internet of Things, and the frequency domain analysis is performed using wavelet transform and fast Fourier transform to calculate the abnormal coefficient of the operating parameters, which is used to evaluate the stability of the operation process. The temperature data of the operating area is collected in real time by the temperature sensor, and the temperature abnormality coefficient is calculated according to the fluctuation of the temperature data, and the temperature abnormality coefficient is fused and analyzed with the operating parameter abnormality coefficient to evaluate the impact of the fluctuation of the operating parameter on the temperature change. According to the analysis results of the operating parameter abnormality index and the temperature change, the operating parameters including the operating speed and the operating power are adjusted so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and the temperature, and optimize the accuracy and stability during the operation.

[0168] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0169] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0170] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0171] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0172] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The electromechanical intelligent control method combined with Internet of Things technology is characterized by: The following steps are involved: Through monitoring sensors connected to the Internet of Things, the amplitude data and frequency data of operating parameters of electromechanical equipment during operation are monitored in real time; Analyze the amplitude data and frequency data of the operating parameters of the electromechanical equipment, and calculate the operating parameter abnormality coefficient according to the operating parameter values ​​of the electromechanical equipment during operation and the periodic characteristics of the operating parameter fluctuations, so as to evaluate the stability of the operation process of the electromechanical equipment; Use temperature sensors to monitor the temperature changes in the operating area in real time, and combine them with the operating parameter abnormality index to analyze the impact of operating parameter fluctuations on temperature changes based on the real-time temperature changes in the operating area; According to the analysis results of the operating parameter abnormality index and temperature change, the operating parameters are adjusted so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during operation.

2. The electromechanical intelligent control method combined with Internet of Things technology according to claim 1 is characterized in that: The process of obtaining the abnormal coefficient of the operating parameter is as follows: During a monitoring period; Obtain the amplitude data of the electromechanical equipment during operation, process the amplitude data, and calculate the amplitude fluctuation index according to the fluctuation of the amplitude data; Obtain the frequency data of electromechanical equipment during operation, process the frequency data, and calculate the frequency fluctuation index based on the periodic characteristics of the operating parameter fluctuations; The amplitude fluctuation index and frequency fluctuation index of the electromechanical equipment during operation are comprehensively analyzed, and the amplitude fluctuation index and frequency fluctuation index are normalized and calculated to obtain the abnormal coefficient of the operating parameters.

3. The electromechanical intelligent control method combined with Internet of Things technology according to claim 2 is characterized in that: The process of obtaining the amplitude fluctuation index is as follows: The amplitude data is obtained according to the time series, which is expressed as a time signal within a period of time, denoted as F(t); Where t represents time, and F(t) represents the amplitude signal of the operating parameter; The continuous wavelet transform is used to decompose the operating parameter amplitude signal F(t) into detail components at different frequencies and time scales; The calculation expression of the wavelet transform is: Among them, W ψ (a,b) represents the wavelet transform system, a is the scale factor, b is the translation factor, ψ * is its conjugate complex number; For each scale a, the corresponding energy density is calculated, and the calculation expression is: Where E(a) represents the energy density at scale a; Calculate the standard deviation σ(E) and mean μ(E) of energy density at all scales; According to the standard deviation σ(E) and mean μ(E) of energy density at all scales, the amplitude fluctuation index is calculated, and the calculation expression is: Where, ΔF index Represents the amplitude fluctuation index.

4. The electromechanical intelligent control method combined with Internet of Things technology according to claim 2 is characterized in that: The process of obtaining the frequency fluctuation index is as follows: Obtain frequency data in time series and express it as a time domain signal S(t), where t represents time; Use the fast Fourier transform to transform the time domain signal of the frequency data into the frequency domain. The calculation expression is: In the formula, f k represents the kth frequency, k represents the number of frequencies, X(f k ) represents the frequency f k The Fourier transform result under the condition, n represents the acquisition point of the time series, N represents the total number of acquisition points of the time series, S(t n ) represents the time domain signal value at each acquisition point, e represents the logarithm of the natural number base, and j represents the imaginary unit; The Fourier transform result X(f k ) to calculate the amplitude and obtain the amplitude spectrum of the frequency component. The calculation expression is: In the formula, Re(X(f k )) represents the real part of the Fourier transform result, Im(X(f k )) represents the imaginary part of the Fourier transform result, |X(f k )| represents the frequency f k The amplitude spectrum under ; Calculate the energy power spectrum of each frequency component. The calculation expression is: P(f k )=|X(f k )| 2 ; In the formula, P(f k ) represents the energy power spectrum; Get the standard deviation of the frequency amplitude spectrum σ(|X(f k )| and the standard deviation of the energy power spectrum σP(f k ); The standard deviation of the frequency-amplitude spectrum σ(|X(f k )|) and the standard deviation of the energy power spectrum σ(P|(f k )|)Calculate the frequency fluctuation index, the calculation expression is: In the formula, ΔS index Represents the frequency fluctuation index.

5. The electromechanical intelligent control method combined with Internet of Things technology according to claim 1 is characterized in that: The evaluation of the stability of the operation process of the electromechanical equipment specifically includes: It is determined whether the abnormal coefficient of the operating parameter is greater than or equal to a preset threshold value. If so, the corresponding electromechanical equipment is unstable during operation; if not, the corresponding electromechanical equipment is stable during operation.

6. The electromechanical intelligent control method combined with Internet of Things technology according to claim 1 is characterized in that: The degree of influence of the fluctuation of the analysis operating parameters on the temperature change specifically includes: Acquire the temperature data of the operating area in real time according to the time series, and calculate the temperature anomaly coefficient according to the real-time abnormal temperature fluctuation in the operating area; The influence relationship between the temperature anomaly coefficient and the operating parameter anomaly coefficient is analyzed, and the temperature anomaly coefficient and the operating parameter anomaly coefficient are fused and calculated to obtain the influence coefficient. According to the obtained influence coefficient, the influence of the operating parameter fluctuation on the temperature change is evaluated, and the influence signal and the non-influence signal are generated.

7. The electromechanical intelligent control method combined with Internet of Things technology according to claim 6 is characterized in that: The process of obtaining the temperature anomaly coefficient is as follows: Obtain the temperature data of the operating area within the monitoring period, recorded as T(t), where t represents time; For each temperature data collection point, calculate the temperature difference between each two adjacent temperature points, recorded as ΔT i , i represents the temperature data collection point; According to the temperature difference of the temperature data, the reachable density of each data point is calculated. The calculation expression is: reach-dist(T i )=max(ΔT i ,k-dist(T i )) In the formula, ΔT i represents the temperature difference, k-dist(T i ) represents the temperature data point T i k-distance, lrd(T i ) represents the reachable density, N k (T i ) represents the k nearest neighbors of the temperature data point, reach-dist(T i ) represents the reachable distance; According to the accessible density and temperature difference of temperature data, the temperature anomaly coefficient is calculated, and the calculation expression is: In the formula, L(T i ) represents the temperature anomaly coefficient of the i-th temperature data collection point.

8. The electromechanical intelligent control method combined with Internet of Things technology according to claim 6 is characterized in that: The process of obtaining the influence coefficient is as follows: Obtaining a temperature anomaly coefficient for each temperature data collection point, and comparing the temperature anomaly coefficient with a preset threshold, calculating the ratio of the number of temperature data points whose temperature anomaly coefficient is greater than or equal to the preset threshold to the total number of temperature data points, to obtain a temperature anomaly ratio; The temperature anomaly ratio and the operating parameter anomaly coefficient are calculated comprehensively in combination with the preset proportional factor to obtain the influence coefficient.

9. The electromechanical intelligent control method combined with Internet of Things technology according to claim 6 is characterized in that: The generation criteria for dividing the impact signal and the non-impact signal include: It is determined whether the influence coefficient is greater than or equal to a preset threshold value. If so, an influence signal is generated; if not, a non-influence signal is generated.

10. The electromechanical intelligent control system combined with Internet of Things technology is characterized by: The electromechanical intelligent control method combined with the Internet of Things technology as claimed in any one of claims 1 to 9 comprises: A data acquisition module, which monitors the amplitude data and frequency data of the operating parameters of the electromechanical equipment in real time during operation by means of monitoring sensors connected to the Internet of Things; Use temperature sensors to collect temperature data of the operating area in real time; An operation process stability evaluation module, which analyzes the amplitude data and frequency data of the operation parameters of the electromechanical equipment, and calculates the operation parameter abnormality coefficient according to the operation parameter value of the electromechanical equipment during operation and the periodic characteristics of the operation parameter fluctuation, so as to evaluate the stability of the operation process of the electromechanical equipment; An impact degree analysis module, which analyzes the impact degree of operating parameter fluctuations on temperature changes based on real-time temperature changes in the operating area; The operating parameter adjustment module adjusts the operating parameters according to the operating parameter abnormality index and the temperature change analysis results so that the operating area is within the normal temperature range to ensure the stability of the operating parameters and temperature, thereby optimizing the accuracy and stability during the operation process.

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