A remote intelligent control method and system for a coal mine tunneling device

Abstract

This invention relates to the field of intelligent control technology, and discloses a remote intelligent control method and system for coal mine tunneling equipment. The method includes: performing analog-to-digital conversion on multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor; performing empirical mode decomposition on the vibration acceleration signal to analyze the mutual information data between its intrinsic mode function components and the torque components of the current signal; reconstructing high-frequency components based on the mutual information data to obtain a lithology-sensitive vibration waveform; performing synchronous squeezing wavelet transform on the current signal to obtain a time-frequency matrix, and coupling analysis of the deviation between the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state; performing frequency mismatch inverse compensation on the cutting head speed and the cutting arm swing speed to obtain cutting control parameters and sending them to the equipment's onboard controller to drive the equipment to complete the cutting action. This invention can improve the efficiency of remote intelligent control of coal mine tunneling equipment.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a remote intelligent control method and system for coal mine tunneling equipment. Background Technology

[0002] The existing multi-source sensor data processing methods for remote control of coal mine tunneling equipment are relatively simple. They do not perform in-depth coupling analysis of the cutting arm vibration signal and the cutting motor current signal, lack specificity in the extraction of lithological sensitive features, and have insufficient reconstruction accuracy of high-frequency components. As a result, the identification of the cutting contact state lacks effective feature support, and the accuracy and real-time performance of the working condition judgment are both lacking.

[0003] The cutting parameters of existing coal mine tunneling equipment are mostly controlled by static adjustment, without frequency mismatch compensation based on the dynamic changes in the cutting contact state. The adjustment of the cutting head speed and cutting arm swing speed is poorly adapted to the actual cutting conditions. The matching degree between remote control commands and onboard equipment execution is insufficient, resulting in low cutting efficiency and poor overall intelligent control effect. Therefore, how to improve the accuracy and adaptability of remote control of coal mine tunneling equipment and realize dynamic intelligent control of cutting parameters has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a remote intelligent control method and system for coal mine tunneling equipment to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a remote intelligent control method for coal mine tunneling equipment, comprising: S1. Perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. S2. Perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and analyze the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor. S3. Based on the mutual information data, the intrinsic mode function components are reconstructed using high-frequency components, and based on the reconstructed lithology-sensitive vibration waveform, the three-phase current signal of the cutting motor is subjected to synchronous extrusion wavelet transform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. S4. Perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment; S5. Based on the cutting contact state results, perform frequency mismatch reverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment to obtain the cutting control parameters of the coal mine tunneling equipment. S6. The cutting control parameters are sent to the onboard controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform cutting action.

[0006] In a preferred embodiment, the step of performing analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment includes: The analog vibration voltage signal output by the accelerometer installed on the cutting arm of the coal mine tunneling equipment and the analog current signal output by the current sensor installed on the power supply line of the cutting motor of the coal mine tunneling equipment are obtained. The simulated vibration voltage signal and the simulated current signal are subjected to anti-aliasing filtering to obtain the filtered simulated vibration voltage signal and the filtered simulated current signal of the coal mine tunneling equipment. The filtered analog vibration voltage signal and the filtered analog current signal are synchronously sampled and sorted at a preset sampling frequency to obtain the digital vibration voltage sequence and digital current sequence of the coal mine tunneling equipment. Based on the sensitivity coefficient of the accelerometer and the transformation ratio of the current sensor, a multi-channel scaling transformation is performed on the digital vibration voltage sequence and the digital current sequence to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment.

[0007] In a preferred embodiment, the step of performing empirical mode decomposition on the vibration acceleration signal of the cutting arm and resolving the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor includes: Extreme point detection is performed on the vibration acceleration signal of the cutting arm to construct the upper and lower envelopes of the vibration acceleration signal of the cutting arm. Based on the upper envelope and the lower envelope, the vibration acceleration signal of the cutting arm is screened and iterated to obtain the candidate intrinsic mode function components of the vibration acceleration signal of the cutting arm. Based on the termination iteration condition of the candidate intrinsic mode function components, the candidate intrinsic mode function components are decomposed by convergence judgment to obtain the intrinsic mode function components of the cutting arm vibration acceleration signal. The three-phase current signal of the cutting motor is subjected to Clarke transform to obtain the torque component of the three-phase current signal of the cutting motor; Correlation analysis is performed on the intrinsic mode function components and the torque components to obtain mutual information data between the intrinsic mode function components and the torque components.

[0008] In a preferred embodiment, the step of reconstructing high-frequency components of the intrinsic mode function based on the mutual information data includes: Based on the mutual information data, the mutual information values ​​corresponding to the intrinsic mode function components are arranged in descending order to form an intrinsic mode function component sequence; Based on the statistical mean of the intrinsic mode function component sequence, candidate reconstructed components in the intrinsic mode function component sequence are selected; Perform a Hilbert transform on the candidate reconstructed components to obtain the instantaneous frequency curves of the candidate reconstructed components; Based on the instantaneous frequency curve, reconstructed components with frequency values ​​higher than the natural frequency of the cutting arm structure in the coal mine tunneling equipment are identified from the candidate reconstructed components, and the reconstructed components are marked as high-frequency sensitive components of the intrinsic mode function components. The high-frequency sensitive components are linearly superimposed and bandpass filtered to obtain the lithology-sensitive vibration waveform of the coal mine tunneling equipment.

[0009] In a preferred embodiment, the step of performing synchronous squeezing wavelet transform on the three-phase current signal of the cutting motor based on the reconstructed lithology-sensitive vibration waveform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor includes: The lithology-sensitive vibration waveform is subjected to Fourier transform to obtain the spectral distribution of the lithology-sensitive vibration waveform; Based on the main peak frequency in the spectral distribution, the dominant frequency band of the lithology-sensitive vibration waveform is determined; Based on the advantageous frequency band, frequency-guided wavelet analysis is performed on the three-phase current signal of the cutting motor to obtain the initial wavelet coefficient matrix of the three-phase current signal of the cutting motor. The initial wavelet coefficient matrix is ​​synchronously squeezed and rearranged to obtain the time-frequency matrix of the three-phase current signal of the cut motor.

[0010] In a preferred embodiment, the step of performing coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment includes: By performing time-frequency peak tracking on the time-frequency matrix, the first frequency curve of the three-phase current signal of the cutting motor is obtained; The instantaneous frequency of the lithology-sensitive vibration waveform is filtered by moving average to obtain the second frequency curve of the lithology-sensitive vibration waveform; Based on the frequency deviation between the first frequency curve and the second frequency curve, the cutting condition of the coal mine tunneling equipment is identified, and the cutting contact state result of the coal mine tunneling equipment is obtained.

[0011] In a preferred embodiment, the step of identifying the cutting condition of the coal mine tunneling equipment based on the frequency deviation between the first frequency curve and the second frequency curve, and obtaining the cutting contact state result of the coal mine tunneling equipment, includes: The differences between the first frequency curve and the second frequency curve are arranged into a frequency deviation sequence; Sliding window statistics are performed on the frequency deviation sequence to obtain the mean and variance characteristics of the frequency deviation sequence; When the mean characteristic exceeds a preset load threshold and the variance characteristic is less than a preset stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be a stable cutting state. When the mean characteristic exceeds the load threshold and the variance characteristic is greater than the stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an impact cutting state. When the mean characteristic is less than the load threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an unloaded state.

[0012] In a preferred embodiment, the step of performing frequency mismatch inverse compensation on the cutting head rotation speed and cutting arm swing speed of the coal mine tunneling equipment based on the cutting contact state result to obtain the cutting control parameters of the coal mine tunneling equipment includes: Based on the cutting contact state results, determine the compensation triggering conditions corresponding to the current cutting state of the coal mine tunneling equipment; When the compensation triggering condition is met, the real-time frequency deviation between the first frequency curve and the second frequency curve at the current moment is obtained; Based on the real-time frequency deviation, calculate the compensation amount for the cutting head rotation speed and the compensation amount for the cutting arm swing speed of the coal mine tunneling equipment; Based on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment under the current cutting state, the compensation amount of the cutting head speed and the compensation amount of the cutting arm swing speed are corrected and fused to obtain the compensated cutting head speed and the compensated cutting arm swing speed. The compensated cutting head rotation speed and the compensated cutting arm swing speed are used as the cutting control parameters of the coal mine tunneling equipment.

[0013] In a preferred embodiment, the formula for calculating the cutting head rotation speed compensation is as follows: ; In the formula, For the current moment The cutting head speed compensation amount. For the first frequency curve at the current time clock frequency value, For the second frequency curve at the current time The instantaneous frequency value, The cutting frequency of the coal mine tunneling equipment is given. The rated speed of the cutting head of the coal mine tunneling equipment. The preset speed compensation coefficient, The preset dynamic adjustment factor, The fundamental angular frequency of the lithology-sensitive vibration waveform is given.

[0014] To address the above problems, the present invention also provides a remote intelligent control system for coal mine tunneling equipment, the system comprising: The multi-source signal acquisition and conversion module is used to perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. The vibration and current coupling analysis module is used to perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and to analyze the mutual information data between the decomposed intrinsic mode function components and the torque components in the three-phase current signal of the cutting motor. The lithology-sensitive feature extraction module is used to reconstruct the high-frequency components of the intrinsic mode function based on the mutual information data, and to perform synchronous squeezing wavelet transform on the three-phase current signal of the cutting motor based on the reconstructed lithology-sensitive vibration waveform, so as to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. The cutting state determination module is used to perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state result of the coal mine tunneling equipment. The mismatch compensation and parameter generation module is used to perform frequency mismatch inverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment based on the cutting contact state results, so as to obtain the cutting control parameters of the coal mine tunneling equipment. The control command execution module is used to send the cutting control parameters to the onboard controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform cutting actions.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention realizes the coupled analysis and precise feature extraction of multi-source sensor signals of coal mine tunneling equipment. Through empirical mode decomposition and synchronous extrusion wavelet transform, it effectively reconstructs the lithology-sensitive vibration waveform and obtains the time-frequency matrix of the current signal, improving the accuracy of cutting contact state determination. It can quickly and accurately identify different cutting conditions, providing a reliable state basis for remote intelligent control and ensuring the real-time and effectiveness of state perception.

[0016] 2. This invention performs reverse compensation for frequency mismatch based on the cutting contact state results, realizing dynamic and precise control of the cutting head speed and cutting arm swing speed. The generated cutting control parameters can accurately adapt to the current cutting working conditions, improving the response efficiency and control accuracy of remote control of coal mine tunneling equipment, optimizing the execution effect of equipment cutting actions, effectively improving the overall efficiency of remote intelligent control of coal mine tunneling equipment, and enhancing the stability and intelligence level of remote control. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a remote intelligent control method for coal mine tunneling equipment according to an embodiment of the present invention. Figure 2 A functional block diagram of a remote intelligent control system for coal mine tunneling equipment provided in one embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a remote intelligent control method for coal mine tunneling equipment. The executing entity of this remote intelligent control method for coal mine tunneling equipment includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the remote intelligent control method for coal mine tunneling equipment can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a remote intelligent control method for coal mine tunneling equipment according to an embodiment of the present invention. In this embodiment, the remote intelligent control method for coal mine tunneling equipment includes: S1. Perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. In this embodiment of the invention, the step of performing analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment includes: The analog vibration voltage signal output by the accelerometer installed on the cutting arm of the coal mine tunneling equipment and the analog current signal output by the current sensor installed on the power supply line of the cutting motor of the coal mine tunneling equipment are obtained. The simulated vibration voltage signal and the simulated current signal are subjected to anti-aliasing filtering to obtain the filtered simulated vibration voltage signal and the filtered simulated current signal of the coal mine tunneling equipment. The filtered analog vibration voltage signal and the filtered analog current signal are synchronously sampled and sorted at a preset sampling frequency to obtain the digital vibration voltage sequence and digital current sequence of the coal mine tunneling equipment. Based on the sensitivity coefficient of the accelerometer and the transformation ratio of the current sensor, a multi-channel scaling transformation is performed on the digital vibration voltage sequence and the digital current sequence to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment.

[0021] The system collects analog vibration voltage signals from an acceleration sensor installed on the cutting arm of a coal mine tunneling equipment, and analog current signals from a current sensor installed on the power supply line of the cutting motor of the coal mine tunneling equipment. During the acquisition process, the signal acquisition actions of the two sensors are kept completely synchronized in the time dimension. The two types of signals acquired are real-time raw analog electrical signals generated during the operation of the coal mine tunneling equipment.

[0022] Anti-aliasing filtering was performed on the acquired simulated vibration voltage signal and simulated current signal. The processing operation was carried out using a finite-length unit impulse response low-pass filter. The cutoff frequency of the filtering operation was set to half of the preset sampling frequency of the subsequent sampling operation. The two types of simulated signals were filtered point by point according to the order of signal generation. Interference components with frequencies higher than the cutoff frequency were removed from the signals. Finally, the filtered simulated vibration voltage signal and filtered simulated current signal of the coal mine tunneling equipment were obtained.

[0023] Synchronous sampling is performed on the filtered analog vibration voltage signal and the filtered analog current signal at a preset fixed sampling frequency. According to the fixed time interval corresponding to the preset sampling frequency, the two types of filtered analog signals are continuously sampled at the same time node. All sampled values ​​of the filtered analog vibration voltage signal are arranged in the order of sampling time to form a digital vibration voltage sequence of the coal mine tunneling equipment. All sampled values ​​of the filtered analog current signal are arranged in the order of sampling time to form a digital current sequence of the coal mine tunneling equipment. The number of sampling points of the digital vibration voltage sequence and the digital current sequence are exactly the same, and the timestamp of each sampling point has a one-to-one correspondence.

[0024] The sensitivity coefficient of the accelerometer and the transformation ratio of the current sensor, which are pre-calibrated and stored, are retrieved. Both coefficients are fixed parameters calibrated at the factory. Multi-channel scaling transformation is performed on the digital vibration voltage sequence and the digital current sequence. Each value in the digital vibration voltage sequence is converted with the sensitivity coefficient of the accelerometer to obtain the cutting arm vibration acceleration value corresponding to each value in the digital vibration voltage sequence. These cutting arm vibration acceleration values ​​are arranged in the original time sequence to form the cutting arm vibration acceleration signal of the coal mine tunneling equipment. Each value in the digital current sequence is converted with the transformation ratio of the current sensor to obtain the three-phase current value of the cutting motor corresponding to each value in the digital current sequence. These three-phase current values ​​of the cutting motor are arranged in the original time sequence to form the three-phase current signal of the cutting motor of the coal mine tunneling equipment. The time dimension of the cutting arm vibration acceleration signal and the three-phase current signal of the cutting motor are completely consistent with the timestamp of the sampling operation, and the values ​​of the two types of signals form a one-to-one correspondence.

[0025] The beneficial effects of this implementation process are that it establishes a reproducible, standardized operating procedure for the analog-to-digital conversion of multi-source sensor data from coal mine tunneling equipment. The entire process, from the acquisition of the original analog signal to the generation of the final target electrical signal, maintains the time synchronization of the signals from the accelerometer and current sensor. Anti-aliasing filtering, through setting a clear cutoff frequency and a fixed filtering method, effectively eliminates high-frequency interference components in the original analog signal, ensuring the signal purity of the filtered analog signal. Synchronous sampling operations complete numerical acquisition and sorting at a fixed frequency and time interval, allowing the digital vibration voltage sequence and digital current sequence to form a precise time correspondence. Multi-channel scaling transformation is based on the fixed calibration of the sensors at the factory. The coefficients are converted numerically, avoiding numerical distortion during signal conversion. The resulting cutting arm vibration acceleration signal and cutting motor three-phase current signal can accurately reflect the actual operating status of the cutting arm and cutting motor of the coal mine tunneling equipment. Each value of the two types of signals can form a clear correspondence with the original sensor data, providing accurate, reliable and synchronized basic data support for subsequent technical steps such as signal coupling analysis, feature extraction, and cutting status determination. This ensures that subsequent steps of the entire remote intelligent control method for coal mine tunneling equipment can be carried out in an orderly manner based on the actual operating status of the equipment, effectively improving the implementation stability and reproducibility of the entire technical solution.

[0026] S2. Perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and analyze the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor. In this embodiment of the invention, the step of performing empirical mode decomposition on the vibration acceleration signal of the cutting arm and resolving the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor includes: Extreme point detection is performed on the vibration acceleration signal of the cutting arm to construct the upper and lower envelopes of the vibration acceleration signal of the cutting arm. Based on the upper envelope and the lower envelope, the vibration acceleration signal of the cutting arm is screened and iterated to obtain the candidate intrinsic mode function components of the vibration acceleration signal of the cutting arm. Based on the termination iteration condition of the candidate intrinsic mode function components, the candidate intrinsic mode function components are decomposed by convergence judgment to obtain the intrinsic mode function components of the cutting arm vibration acceleration signal. The three-phase current signal of the cutting motor is subjected to Clarke transform to obtain the torque component of the three-phase current signal of the cutting motor; Correlation analysis is performed on the intrinsic mode function components and the torque components to obtain mutual information data between the intrinsic mode function components and the torque components.

[0027] The vibration acceleration signal of the cutting arm is traversed point by point along the time dimension to identify all local maxima and local minima of the signal within the entire time range. The cubic spline interpolation method is used to fit curves to all local maxima to form an upper envelope that can completely cover the time dimension of the cutting arm vibration acceleration signal. At the same time, the cubic spline interpolation method is used to fit curves to all local minima to form a lower envelope that can completely cover the time dimension of the cutting arm vibration acceleration signal. The upper and lower envelopes maintain the same timestamp dimension as the cutting arm vibration acceleration signal.

[0028] Calculate the average values ​​of the upper and lower envelopes of the cutting arm vibration acceleration signal at the corresponding timestamp. Subtract the average value of the envelope at the corresponding timestamp from the value of the cutting arm vibration acceleration signal at each timestamp to obtain the initial screening component. Repeat the operations of extreme point detection, envelope construction, and envelope mean subtraction on the initial screening component, and continue to carry out multiple rounds of screening processing until the obtained screening component meets the basic judgment condition of the intrinsic mode function. The screening component that meets the basic judgment condition is determined as the candidate intrinsic mode function component of the cutting arm vibration acceleration signal.

[0029] The termination iteration condition for candidate intrinsic mode function components is set as follows: the absolute value of the difference between the corresponding timestamp values ​​of the screening components obtained in two adjacent iterations is within a fixed threshold range, and the screening components simultaneously satisfy the two inherent conditions of intrinsic mode functions, namely, the difference between the number of local extrema and the number of zero crossings of the screening components does not exceed one, and the average value of the upper and lower envelope values ​​of the screening components at any timestamp is zero. The convergence of candidate intrinsic mode function components is verified and judged. When the candidate intrinsic mode function components fully satisfy the termination iteration condition, all screening iteration operations are stopped, and the final candidate intrinsic mode function component is determined as the intrinsic mode function component of the cutting arm vibration acceleration signal.

[0030] The three-phase current signal of the cutting motor is subjected to Clarke transformation. The current values ​​of phases A, B, and C of the cutting motor in the three-phase stationary coordinate system are converted into α-axis and β-axis current values ​​in the two-phase stationary coordinate system according to a fixed coordinate transformation relationship. Then, based on the electromagnetic characteristics of the cutting motor, the α-axis and β-axis current values ​​are integrated to obtain the torque component of the three-phase current signal of the cutting motor that can accurately reflect the torque output state of the cutting motor. The torque component maintains the same time dimension as the original three-phase current signal of the cutting motor.

[0031] The intrinsic mode function components of the cutting arm vibration acceleration signal and the torque components of the three-phase current signal of the cutting motor are precisely aligned according to the timestamp to ensure that there is a one-to-one correspondence between the two data points in the entire time dimension. Based on this precise correspondence in the time dimension, correlation analysis is carried out on the intrinsic mode function components and the torque components to quantify the degree of information correlation and mutual transmission between the two data. The set of results obtained after the quantification calculation is determined as the mutual information data between the intrinsic mode function components and the torque components in the three-phase current signal of the cutting motor.

[0032] The beneficial effects of this implementation process are that it achieves accurate empirical mode decomposition of the cutting arm vibration acceleration signal and effective transformation of the three-phase current signal of the cutting motor through standardized signal processing operations. The accuracy of intrinsic mode function component extraction is ensured by a clear extreme point detection and envelope construction method. Clear termination iteration conditions ensure the reproducibility of the decomposition process. The standardized execution of the Clarke transform allows the torque component to truly reflect the operating state of the cutting motor. Correlation analysis based on precise timestamp alignment allows mutual information data to effectively characterize the intrinsic relationship between vibration and current signals. The obtained intrinsic mode function components and mutual information data provide reliable basic data for subsequent lithology-sensitive feature extraction. Simultaneously, this signal processing process can accurately capture the state characteristics of the equipment's cutting operation, providing data support for subsequent intelligent control of cutting parameters. It can effectively optimize the cutting action of coal mine tunneling equipment, reduce energy consumption caused by ineffective equipment operation, improve the efficiency of coal tunneling operations, and meet the technical requirements of reducing carbon emissions from fossil energy and the clean and efficient utilization of coal. Furthermore, it improves the signal analysis link of intelligent control of coal mine tunneling equipment, enhancing the accuracy of the control system.

[0033] S3. Based on the mutual information data, the intrinsic mode function components are reconstructed using high-frequency components, and based on the reconstructed lithology-sensitive vibration waveform, the three-phase current signal of the cutting motor is subjected to synchronous extrusion wavelet transform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. In this embodiment of the invention, the high-frequency component reconstruction of the intrinsic mode function components based on the mutual information data includes: Based on the mutual information data, the mutual information values ​​corresponding to the intrinsic mode function components are arranged in descending order to form an intrinsic mode function component sequence; Based on the statistical mean of the intrinsic mode function component sequence, candidate reconstructed components in the intrinsic mode function component sequence are selected; Perform a Hilbert transform on the candidate reconstructed components to obtain the instantaneous frequency curves of the candidate reconstructed components; Based on the instantaneous frequency curve, reconstructed components with frequency values ​​higher than the natural frequency of the cutting arm structure in the coal mine tunneling equipment are identified from the candidate reconstructed components, and the reconstructed components are marked as high-frequency sensitive components of the intrinsic mode function components. The high-frequency sensitive components are linearly superimposed and bandpass filtered to obtain the lithology-sensitive vibration waveform of the coal mine tunneling equipment.

[0034] The synchronous squeezing wavelet transform of the three-phase current signal of the cutting motor is performed based on the reconstructed lithology-sensitive vibration waveform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor, including: The lithology-sensitive vibration waveform is subjected to Fourier transform to obtain the spectral distribution of the lithology-sensitive vibration waveform; Based on the main peak frequency in the spectral distribution, the dominant frequency band of the lithology-sensitive vibration waveform is determined; Based on the advantageous frequency band, frequency-guided wavelet analysis is performed on the three-phase current signal of the cutting motor to obtain the initial wavelet coefficient matrix of the three-phase current signal of the cutting motor. The initial wavelet coefficient matrix is ​​synchronously squeezed and rearranged to obtain the time-frequency matrix of the three-phase current signal of the cut motor.

[0035] Based on the mutual information data between the intrinsic mode function components and the torque components, the mutual information value corresponding to each intrinsic mode function component is extracted. All intrinsic mode function components are arranged in descending order of mutual information value to form an ordered sequence of intrinsic mode function components. Each intrinsic mode function component in the sequence retains the same time dimension and numerical characteristics as the original signal.

[0036] Calculate the statistical mean of all mutual information values ​​in the intrinsic mode function component sequence, use this statistical mean as a screening threshold, and extract all intrinsic mode function components in the intrinsic mode function component sequence whose mutual information value is greater than the screening threshold. These extracted components are determined as candidate reconstructed components of the cutter arm vibration acceleration signal. The candidate reconstructed components retain the time dimension and numerical characteristics of the original sequence.

[0037] For each candidate reconstructed component, a Hilbert transform is performed. The values ​​of the candidate reconstructed components are transformed point by point according to the time dimension of the signal to obtain the instantaneous frequency value corresponding to each candidate reconstructed component. The instantaneous frequency values ​​are arranged in sequence according to their corresponding timestamps to form an independent instantaneous frequency curve for each candidate reconstructed component. The time dimension of the instantaneous frequency curve is completely consistent with that of the candidate reconstructed component.

[0038] The natural frequency of the cutting arm structure, which is the factory-calibrated frequency parameter of the cutting arm structure, is retrieved. All frequency values ​​in the instantaneous frequency curves of each candidate reconstruction component are compared with the natural frequency of the cutting arm structure one by one. Candidate reconstruction components with frequency values ​​higher than the natural frequency in the instantaneous frequency curves are extracted. These components are uniformly marked as high-frequency sensitive components of the intrinsic mode function components. The high-frequency sensitive components retain the original time dimension and numerical characteristics.

[0039] All marked high-frequency sensitive components are precisely linearly superimposed according to timestamps, and the values ​​of each high-frequency sensitive component at the same timestamp are accumulated to obtain the superimposed composite vibration signal. Bandpass filtering is performed on the composite vibration signal to remove irrelevant frequency interference components in the signal, and finally the rock-sensitive vibration waveform of the coal mine tunneling equipment is obtained. This waveform maintains the same time dimension as the original cutting arm vibration acceleration signal.

[0040] Perform a Fourier transform on the lithology-sensitive vibration waveform to convert it from a time-domain signal to a frequency-domain signal, obtaining the spectral distribution of the lithology-sensitive vibration waveform across the entire frequency range. This spectral distribution contains all frequency values ​​and their corresponding signal amplitudes, and can fully reflect the frequency composition characteristics of the lithology-sensitive vibration waveform.

[0041] The frequency value with the largest amplitude is extracted from the spectral distribution of the lithology-sensitive vibration waveform. This frequency value is determined as the main peak frequency of the spectral distribution. A fixed frequency range is extended to both sides of this main peak frequency to form a continuous frequency range. This frequency range is determined as the dominant frequency band of the lithology-sensitive vibration waveform. This frequency band is the core frequency range of the lithology-sensitive characteristics.

[0042] Using the dominant frequency band of the lithology-sensitive vibration waveform as the core frequency reference, frequency-guided wavelet analysis is performed on the three-phase current signal of the cutting motor. The scale and frequency parameters of the wavelet analysis are set according to the frequency range of the dominant frequency band. Wavelet coefficients are calculated for the three-phase current signal of the cutting motor at each time point and frequency scale. All the calculated wavelet coefficients are arranged in a matrix according to the time and frequency dimensions to form the initial wavelet coefficient matrix of the three-phase current signal of the cutting motor.

[0043] A synchronous squeezing and rearrangement operation is performed on the initial wavelet coefficient matrix of the three-phase current signal of the cutting motor. The wavelet coefficients at each position in the initial wavelet coefficient matrix are re-aggregated and rearranged according to their true frequency positions to correct the frequency offset problem generated during wavelet analysis. This ensures that the rearranged coefficients accurately correspond to the matching relationship between time and frequency, and finally, the time-frequency matrix of the three-phase current signal of the cutting motor is obtained. The row dimension of this matrix corresponds to time, the column dimension corresponds to frequency, and the matrix value is the wavelet coefficient value at the corresponding time and frequency point.

[0044] The beneficial effects of this implementation process are that it enables high-frequency reconstruction of intrinsic mode function components based on mutual information data and time-frequency analysis of current signals based on lithology-sensitive characteristics. Through clear screening and transformation criteria, lithology-sensitive vibration waveforms are accurately extracted. These waveforms can truly reflect the vibration characteristics of the cutting arm in contact with different lithologies during coal mine tunneling. At the same time, relying on the guiding effect of the dominant frequency band, synchronous extrusion wavelet transform is completed. The resulting time-frequency matrix can accurately characterize the time-frequency variation law of the three-phase current signal of the cutting motor, providing high-precision feature data support for the subsequent coupling determination of the cutting contact state. This feature extraction process improves the signal analysis link of the G05B control and regulation system and enhances the feature recognition accuracy of the control system. The accurate capture of lithology features allows the subsequent cutting parameter adjustment to be highly compatible with the actual tunneling lithology, reducing invalid cutting actions and energy consumption caused by lithology mismatch, and improving the operating efficiency of coal mine tunneling equipment.

[0045] S4. Perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment; In this embodiment of the invention, the step of performing coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment includes: By performing time-frequency peak tracking on the time-frequency matrix, the first frequency curve of the three-phase current signal of the cutting motor is obtained; The instantaneous frequency of the lithology-sensitive vibration waveform is filtered by moving average to obtain the second frequency curve of the lithology-sensitive vibration waveform; Based on the frequency deviation between the first frequency curve and the second frequency curve, the cutting condition of the coal mine tunneling equipment is identified, and the cutting contact state result of the coal mine tunneling equipment is obtained.

[0046] The step of identifying the cutting condition of the coal mine tunneling equipment based on the frequency deviation between the first frequency curve and the second frequency curve, and obtaining the cutting contact state result of the coal mine tunneling equipment, includes: The differences between the first frequency curve and the second frequency curve are arranged into a frequency deviation sequence; Sliding window statistics are performed on the frequency deviation sequence to obtain the mean and variance characteristics of the frequency deviation sequence; When the mean characteristic exceeds a preset load threshold and the variance characteristic is less than a preset stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be a stable cutting state. When the mean characteristic exceeds the load threshold and the variance characteristic is greater than the stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an impact cutting state. When the mean characteristic is less than the load threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an unloaded state.

[0047] The time-frequency matrix of the three-phase current signal of the cutting motor is traversed point by point along the time dimension. Within the frequency dimension interval corresponding to each time point, the frequency value corresponding to the maximum wavelet coefficient value is extracted as the main frequency value of that time point. The main frequency values ​​of all time points are arranged in chronological order according to the timestamps to form the first frequency curve of the three-phase current signal of the cutting motor. The time dimension of this curve is completely consistent with the time dimension of the original time-frequency matrix.

[0048] A moving average filtering process is performed on the instantaneous frequency data of the lithology-sensitive vibration waveform. A sliding window with a fixed data length is set, and the window is continuously traversed from front to back along the time dimension. The arithmetic mean of all instantaneous frequency values ​​within each sliding window is calculated, and this mean is used as the filtered frequency value at the center time point of the sliding window. The edge time points of the frequency data are filled in using the same window calculation rules. All filtered frequency values ​​are arranged in chronological order according to the timestamps to form the second frequency curve of the lithology-sensitive vibration waveform. The time dimension of this curve is completely aligned with the first frequency curve and the number of data points is the same.

[0049] The first and second frequency curves are precisely matched according to their timestamps. The difference between the main frequency value of the first frequency curve and the filtered frequency value of the second frequency curve at the same timestamp is calculated to obtain the frequency deviation value corresponding to each timestamp. The frequency deviation values ​​of all timestamps are arranged in chronological order to form the frequency deviation sequence of the coal mine tunneling equipment. The time dimension of this sequence is consistent with the two frequency curves.

[0050] A sliding window statistical analysis is performed on the frequency deviation sequence. A statistical sliding window of fixed duration is set, and the window is continuously slid along the time dimension to traverse the entire frequency deviation sequence. The arithmetic mean and variance of all frequency deviation values ​​within each statistical sliding window are calculated separately. The arithmetic mean calculated from all statistical sliding windows is integrated into the mean feature of the frequency deviation sequence, and the variance calculated from all statistical sliding windows is integrated into the variance feature of the frequency deviation sequence. Both the mean feature and the variance feature retain the time dimension feature corresponding to the frequency deviation sequence.

[0051] The system retrieves pre-set load and stability thresholds. The load threshold is the critical value of the mean characteristic of the frequency deviation sequence when the coal mine tunneling equipment is carrying out effective cutting operations. The stability threshold is the critical value of the variance characteristic of the frequency deviation sequence when the cutting operation of the coal mine tunneling equipment is stable. The mean characteristic of the frequency deviation sequence is compared with the load threshold, and the variance characteristic of the frequency deviation sequence is compared with the stability threshold. When the mean characteristic exceeds the load threshold and the variance characteristic is less than the stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be a stable cutting state. When the mean characteristic exceeds the load threshold and the variance characteristic is greater than the stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an impact cutting state. When the mean characteristic is less than the load threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an unloaded state. All the determination results together constitute the cutting contact state result of the coal mine tunneling equipment.

[0052] The beneficial effects of this implementation process are that it obtains two precisely aligned frequency curves through standardized time-frequency analysis and filtering operations. Based on fixed-rule numerical calculations and statistical analysis, it forms the mean and variance characteristics of the frequency deviation sequence. Combined with clearly preset thresholds, it accurately determines the cutting contact state, providing clear criteria and reproducible operating procedures for identifying stable cutting, impact cutting, and no-load states. This effectively improves the accuracy and real-time performance of cutting contact state determination, accurately capturing the actual cutting conditions of coal mine tunneling equipment. This state determination process improves the state perception link of remote intelligent control of equipment, allowing subsequent cutting parameter adjustments to be based entirely on the actual operating conditions of the equipment. It avoids ineffective control and unreasonable cutting actions caused by state determination errors, reduces energy consumption and component wear caused by equipment idling and impact cutting, and improves the operating efficiency and energy utilization efficiency of coal mine tunneling equipment. At the same time, accurate operating condition identification makes the response of the control and regulation system more in line with actual operational needs, improving the overall accuracy and stability of the remote intelligent control system for coal mine tunneling equipment, aligning with the technical direction of clean and efficient coal utilization and equipment energy-saving transformation.

[0053] S5. Based on the cutting contact state results, perform frequency mismatch reverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment to obtain the cutting control parameters of the coal mine tunneling equipment. In this embodiment of the invention, the step of performing frequency mismatch inverse compensation on the cutting head rotation speed and cutting arm swing speed of the coal mine tunneling equipment based on the cutting contact state result to obtain the cutting control parameters of the coal mine tunneling equipment includes: Based on the cutting contact state results, determine the compensation triggering conditions corresponding to the current cutting state of the coal mine tunneling equipment; When the compensation triggering condition is met, the real-time frequency deviation between the first frequency curve and the second frequency curve at the current moment is obtained; Based on the real-time frequency deviation, calculate the compensation amount for the cutting head rotation speed and the compensation amount for the cutting arm swing speed of the coal mine tunneling equipment; Based on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment under the current cutting state, the compensation amount of the cutting head speed and the compensation amount of the cutting arm swing speed are corrected and fused to obtain the compensated cutting head speed and the compensated cutting arm swing speed. The compensated cutting head rotation speed and the compensated cutting arm swing speed are used as the cutting control parameters of the coal mine tunneling equipment.

[0054] The formula for calculating the cutting head rotation speed compensation is as follows: ; In the formula, For the current moment The cutting head speed compensation amount. For the first frequency curve at the current time clock frequency value, For the second frequency curve at the current time The instantaneous frequency value, The cutting frequency of the coal mine tunneling equipment is given. The rated speed of the cutting head of the coal mine tunneling equipment. The preset speed compensation coefficient, The preset dynamic adjustment factor, The fundamental angular frequency of the lithology-sensitive vibration waveform is given.

[0055] Based on the cutting contact state results of the coal mine tunneling equipment, unique compensation trigger conditions are set for stable cutting state, impact cutting state, and no-load state. The compensation trigger condition for no-load state is that the mean characteristic of the frequency deviation sequence is continuously lower than the load threshold for ten seconds. The compensation trigger condition for stable cutting state is that the variance characteristic of the frequency deviation sequence exceeds the stability threshold. The compensation trigger condition for impact cutting state is that the mean characteristic of the frequency deviation sequence exceeds the load threshold and the variance characteristic exceeds the stability threshold. The load threshold and the stability threshold are fixed values ​​used in the cutting contact state determination. Each compensation trigger condition has clear numerical and time determination standards to ensure that the determination process of compensation trigger is reproducible.

[0056] At the same time point when the compensation trigger condition corresponding to the current cutting state is met, the main frequency value of the first frequency curve at that time point is extracted, and the instantaneous frequency value of the second frequency curve at that time point is also extracted. The difference between the two frequency values ​​is then calculated, and the result is the real-time frequency deviation of the coal mine tunneling equipment at the current moment. The calculation rule for this value is completely consistent with the calculation rule for each timestamp value in the frequency deviation sequence, ensuring that the real-time frequency deviation can accurately reflect the actual frequency difference between the two frequency curves at the current moment.

[0057] When calculating the cutting head speed compensation of coal mine tunneling equipment, the real-time frequency deviation at the current moment is used as the core calculation basis. This is combined with the cutting natural frequency and the rated speed of the cutting head, as specified by the equipment manufacturer, to complete the conversion of the basic compensation value. Next, the rate of change of the absolute value of the real-time frequency deviation over time is calculated. This is then combined with the fundamental angular frequency of the lithology-sensitive vibration waveform and a pre-set dynamic adjustment factor to obtain the dynamic compensation adjustment value. The basic compensation value and the dynamic compensation adjustment value are then fused and multiplied by a pre-set speed compensation coefficient to finally obtain the cutting head speed compensation at the current moment. All values ​​involved in the calculation are either the real-time measured values ​​at the current moment or the equipment's factory-calibrated natural frequency. The parameters, or the control parameters preset according to the equipment performance, ensure that the calculation results are highly adapted to the current cutting conditions. When calculating the cutting arm swing speed compensation of the coal mine tunneling equipment, the numerical calculation logic is completely consistent with the cutting head speed compensation. Taking the real-time frequency deviation at the current moment as the core, the basic compensation value is converted by combining the inherent swing frequency and rated swing speed of the cutting arm calibrated by the factory of the coal mine tunneling equipment. Then, the rate of change of the absolute value of the real-time frequency deviation over time and the preset swing speed compensation coefficient and swing speed dynamic adjustment factor are incorporated to complete the calculation and fusion of the dynamic compensation adjustment value. Finally, the cutting arm swing speed compensation at the current moment is obtained, ensuring that the adjustment rhythm of the swing speed compensation and the speed compensation is matched.

[0058] The actual operating speed of the cutting head and the swing speed of the cutting arm of the coal mine tunneling equipment under the current cutting contact state are extracted. These two values ​​are operating parameters collected in real time by the equipment's onboard sensor module. For the cutting head speed, the actual operating speed is algebraically calculated and then compared with the calculated compensation amount. The direction of the compensation is determined by the sign of the real-time frequency deviation: addition is performed when the deviation is positive, and subtraction is performed when the deviation is negative, resulting in a pre-compensated cutting head speed. This pre-compensated speed is then compared with the preset operating range of the cutting head speed of the coal mine tunneling equipment. This operating range is a fixed range of values ​​pre-set based on the equipment's mechanical performance and the requirements of coal mine cutting operations. If the value after preliminary compensation is within the specified range, it is directly determined as the compensated cutting head speed. If the value exceeds the range, the critical value of the range is taken as the compensated cutting head speed. For the cutting arm swing speed, a correction and fusion logic that is completely consistent with the cutting head speed is adopted. The current actual operating cutting arm swing speed and the cutting arm swing speed compensation amount are algebraically calculated according to the positive and negative values ​​of the real-time frequency deviation to obtain the preliminary compensated cutting arm swing speed. Then, it is compared with the preset cutting arm swing speed operating range. If it exceeds the range, the critical value of the range is taken; if it does not exceed the range, it is directly determined. Finally, the compensated cutting arm swing speed is obtained. The preset cutting arm swing speed operating range and the cutting head speed operating range are set based on the same criteria, both conforming to the actual operating performance of the equipment and the on-site requirements of coal mine tunneling.

[0059] The compensated cutting head speed and compensated cutting arm swing speed obtained after correction and fusion are numerically integrated to form a parameter set containing two core operating parameters. Each value in this parameter set is a precise control value that fits the current cutting contact state, and all values ​​are within the equipment's preset safe operating range. This parameter set is determined as the cutting control parameter of the coal mine tunneling equipment. The numerical format of this cutting control parameter is completely consistent with the parameter receiving format of the onboard controller of the coal mine tunneling equipment, ensuring that subsequent control parameters can be directly transmitted to the onboard controller and executed.

[0060] The beneficial effects of this implementation process are that it establishes standardized and reproducible operating procedures for reverse compensation of frequency mismatch between the cutting head speed and the cutting arm swing speed of coal mine tunneling equipment. It sets clear and unique compensation trigger conditions for three different cutting contact states: stable cutting, impact cutting, and no-load cutting. The judgment criteria for the trigger conditions are consistent with the numerical thresholds used in the previous cutting contact state judgment, ensuring that the triggering of the compensation operation accurately matches the actual cutting conditions of the equipment, thus fundamentally avoiding invalid compensation. The extraction rules for real-time frequency deviation are consistent with the calculation rules for the previous frequency deviation sequence, ensuring the accuracy and consistency of the compensation calculation data source. The calculation process for the cutting head speed compensation integrates the inherent parameters of the equipment, real-time operating parameters, and the dynamic changes in frequency deviation, allowing the compensation amount to accurately adapt to the frequency mismatch of the current cutting operation. The cutting arm swing speed compensation adopts a consistent calculation logic, achieving synergy between speed and swing speed compensation adjustments, avoiding the problem of uncoordinated cutting actions caused by single parameter adjustments. The correction and integration process is combined with the equipment's preset operating range. Numerical calibration effectively avoids situations where compensated parameters exceed the equipment's performance range, ensuring the safety and stability of coal mine tunneling equipment operation. The final determined cutting control parameters are perfectly matched with the receiving format of the onboard controller, laying a solid foundation for the efficient execution of subsequent control commands. This process improves the parameter control link of remote intelligent control for coal mine tunneling equipment, achieving dynamic and precise compensation for the cutting head speed and cutting arm swing. It effectively solves the problem of incompatibility between cutting parameters and actual working conditions caused by frequency mismatch, reducing ineffective operation and impact cutting caused by parameter mismatch, lowering energy consumption and wear of mechanical parts, and improving the operating efficiency and energy utilization efficiency of coal mine tunneling equipment. At the same time, precise parameter control makes the response of the control and regulation system more in line with the actual operational needs of coal mine tunneling, improving the overall accuracy and operational stability of the remote intelligent control system for coal mine tunneling equipment. This aligns with the technical direction of clean and efficient coal utilization and energy-saving equipment transformation, making the remote intelligent control of coal mine tunneling equipment more practical and operable on-site.

[0061] In this embodiment of the invention, S6, the cutting control parameters are sent to the airborne controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform a cutting action.

[0062] The format verification of the cutting control parameters of the coal mine tunneling equipment is performed. The verification is based on the parameter communication protocol preset by the onboard controller of the coal mine tunneling equipment. This protocol clearly specifies the numerical encoding format, data transmission frame structure, field arrangement order and check bit setting rules of the cutting control parameters. According to the protocol requirements, the values ​​of the compensated cutting head speed and the compensated cutting arm swing speed are converted into transmission data frames that meet the frame structure requirements. The numerical encoding, field length and check bit value in the data frame are checked field by field to see if they are completely consistent with the protocol requirements. If all the checked items meet the protocol standard, the format verification is deemed to have passed. If it fails, the data frame structure is readjusted according to the protocol requirements until the verification passes.

[0063] The data frames for the truncated control parameters that have passed format verification are transmitted via industrial Ethernet. A fixed communication baud rate is set during transmission, which is completely consistent with the receiving baud rate of the airborne controller. The transmission link adopts an anti-interference communication link specifically designed for coal mine tunneling operations. Real-time link data verification is performed on the transmitted data frames throughout the entire data transmission process. Each frame is checked for data packet loss, numerical distortion, or frame structure damage during transmission. When an abnormality is detected, a data retransmission mechanism is immediately triggered to resend the transmitted data frame until the data is transmitted to the receiving end of the airborne controller without any abnormalities.

[0064] The onboard controller of the coal mine tunneling equipment receives and parses the transmitted data frames from the receiving end. The onboard controller first verifies the check bit of the data frame. After the verification is successful, it parses the data frame field by field according to the preset parameter communication protocol, accurately extracting the values ​​of the compensated cutting head speed and the compensated cutting arm swing speed. The two types of values ​​are compared with the preset parameter valid range of the onboard controller. This valid range is completely consistent with the equipment operating range used when correcting and integrating the cutting control parameters. If the value is within the valid range, the parsing is deemed valid and the parameter reception is completed. If it is not within the valid range, a data reception error prompt is triggered and a request to retransmit the data is sent to the sending end.

[0065] The onboard controller converts the compensated cutting head speed and compensated cutting arm swing speed values ​​into corresponding motor drive electrical signal commands. The conversion is based on a fixed correspondence table between parameters and electrical signal commands pre-stored inside the onboard controller. Each cutting control parameter value in this correspondence table corresponds to a unique electrical signal amplitude and frequency. According to this correspondence, a precise one-to-one conversion from numerical values ​​to electrical signal commands is completed. The converted electrical signal commands are continuous analog electrical signals, and their amplitude and frequency are within the rated operating range of the cutting head drive motor and the cutting arm drive motor.

[0066] The onboard controller synchronously transmits the converted electrical signal commands for the cutting head and cutting arm motors to the control terminals of the cutting head drive motor and the cutting arm drive motor, respectively. After receiving the electrical signal commands, the drive motor control terminals precisely adjust the input power and operating speed of the motors according to the amplitude and frequency of the commands. This controls the cutting head to rotate at the compensated cutting head speed and the cutting arm to swing at the compensated cutting arm swing speed. The movement adjustments of the cutting head and cutting arm respond in real time according to the requirements of the electrical signal commands until the operating parameters reach the corresponding control parameter values ​​and maintain a stable operating state.

[0067] Throughout the entire process of the coal mine tunneling equipment executing the cutting action according to the cutting control parameters, the onboard controller continuously collects the actual speed of the cutting head and the actual swing speed of the cutting arm in real time through the speed sensor module and swing speed sensor module on the equipment. The collected actual operating values ​​are continuously compared with the control values ​​of the compensated cutting head speed and the compensated cutting arm swing speed in real time. When the difference between the actual value and the control value is within the preset action execution error threshold, it is determined that the cutting action meets the control requirements. This error threshold is a fixed value range preset according to the accuracy requirements of coal mine tunneling operations. If the difference exceeds the threshold, the onboard controller immediately fine-tunes the amplitude and frequency of the motor drive electrical signal command according to a fixed ratio until the difference between the actual value and the control value returns to within the error threshold.

[0068] The beneficial effects of this implementation process are that it establishes standardized, reproducible, and fully verifiable operational specifications for the conversion of cutting control parameters into cutting actions of coal mine tunneling equipment. From the format verification of control parameters to the link verification of data transmission, and then to the parsing verification of the onboard controller, the multi-stage verification operations and fixed judgment standards ensure the accuracy of the transmission and parsing of cutting control parameters, effectively avoiding control failures caused by data distortion, packet loss, or format inconsistencies. The pre-stored parameter-electrical signal command correspondence table inside the onboard controller allows the control parameters to be accurately converted into electrical signal commands adapted to the drive motor, ensuring the uniqueness and accuracy of parameter-to-command conversion. The synchronous transmission of motor drive electrical signals ensures that the adjustment of the cutting head speed and the cutting arm swing speed remains coordinated, avoiding the problem of uncoordinated cutting operations caused by deviations in the action of a single component. The real-time acquisition, comparison, and dynamic fine-tuning during the execution of the cutting action ensure that the actual operating parameters of the cutting head and cutting arm are always aligned with the actual operating parameters of the cutting head and cutting arm. By meeting the requirements of the control parameters, the precision of the cutting action was ensured. The entire process improved the command execution link of the remote intelligent control of coal mine tunneling equipment, realizing the precise implementation of the control parameters from remote transmission to equipment action execution. This allows the remote intelligent control commands to be efficiently and accurately converted into the actual cutting actions of the coal mine tunneling equipment, improving the execution efficiency and accuracy of remote intelligent control. At the same time, the use of anti-interference communication links allows data transmission to adapt to the complex operating scenarios of coal mines, ensuring the stability of the control process. Real-time dynamic fine-tuning reduces the invalid operation caused by cutting action deviations, reduces equipment energy consumption and mechanical wear, and improves the operating efficiency and energy utilization efficiency of coal mine tunneling equipment. This aligns with the technical direction of clean and efficient coal utilization and energy-saving equipment transformation, and also forms a complete closed loop from state perception and parameter generation to command execution in the remote intelligent control system of coal mine tunneling equipment, greatly improving the overall reliability and practicality of the control system.

[0069] like Figure 2 The diagram shown is a functional block diagram of a remote intelligent control system for coal mine tunneling equipment provided in an embodiment of the present invention.

[0070] The remote intelligent control system 100 for coal mine tunneling equipment described in this invention can be installed in an electronic device. Depending on the functions implemented, the remote intelligent control system 100 may include a multi-source signal acquisition and conversion module 101, a vibration and current coupling analysis module 102, a lithology-sensitive feature extraction module 103, a cutting state determination module 104, a mismatch compensation and parameter generation module 105, and a control command execution module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0071] In this embodiment, the functions of each module / unit are as follows: The multi-source signal acquisition and conversion module 101 is used to perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. The vibration and current coupling analysis module 102 is used to perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and to analyze the mutual information data between the decomposed intrinsic mode function components and the torque components in the three-phase current signal of the cutting motor. The lithology-sensitive feature extraction module 103 is used to reconstruct the high-frequency components of the intrinsic mode function based on the mutual information data, and to perform synchronous squeezing wavelet transform on the three-phase current signal of the cutting motor based on the reconstructed lithology-sensitive vibration waveform, so as to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. The cutting state determination module 104 is used to perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state result of the coal mine tunneling equipment. The mismatch compensation and parameter generation module 105 is used to perform frequency mismatch reverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment based on the cutting contact state result, so as to obtain the cutting control parameters of the coal mine tunneling equipment. The control command execution module 106 is used to send the cutting control parameters to the airborne controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform cutting actions.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0076] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A remote intelligent control method for coal mine tunneling equipment, characterized in that, The method includes: S1. Perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. S2. Perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and analyze the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor. S3. Based on the mutual information data, the intrinsic mode function components are reconstructed using high-frequency components, and based on the reconstructed lithology-sensitive vibration waveform, the three-phase current signal of the cutting motor is subjected to synchronous extrusion wavelet transform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. S4. Perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment; S5. Based on the cutting contact state results, perform frequency mismatch reverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment to obtain the cutting control parameters of the coal mine tunneling equipment. S6. The cutting control parameters are sent to the onboard controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform cutting action.

2. The remote intelligent control method for coal mine tunneling equipment as described in claim 1, characterized in that, The analog-to-digital conversion of multi-source sensor data from the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor includes: The analog vibration voltage signal output by the accelerometer installed on the cutting arm of the coal mine tunneling equipment and the analog current signal output by the current sensor installed on the power supply line of the cutting motor of the coal mine tunneling equipment are obtained. The simulated vibration voltage signal and the simulated current signal are subjected to anti-aliasing filtering to obtain the filtered simulated vibration voltage signal and the filtered simulated current signal of the coal mine tunneling equipment. The filtered analog vibration voltage signal and the filtered analog current signal are synchronously sampled and sorted at a preset sampling frequency to obtain the digital vibration voltage sequence and digital current sequence of the coal mine tunneling equipment. Based on the sensitivity coefficient of the accelerometer and the transformation ratio of the current sensor, a multi-channel scaling transformation is performed on the digital vibration voltage sequence and the digital current sequence to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment.

3. The remote intelligent control method for coal mine tunneling equipment as described in claim 1, characterized in that, The step of performing empirical mode decomposition on the vibration acceleration signal of the cutting arm and analyzing the mutual information data between the decomposed intrinsic mode function components and the torque component in the three-phase current signal of the cutting motor includes: Extreme point detection is performed on the vibration acceleration signal of the cutting arm to construct the upper and lower envelopes of the vibration acceleration signal of the cutting arm. Based on the upper envelope and the lower envelope, the vibration acceleration signal of the cutting arm is screened and iterated to obtain the candidate intrinsic mode function components of the vibration acceleration signal of the cutting arm. Based on the termination iteration condition of the candidate intrinsic mode function components, the candidate intrinsic mode function components are decomposed by convergence judgment to obtain the intrinsic mode function components of the cutting arm vibration acceleration signal. The three-phase current signal of the cutting motor is subjected to Clarke transform to obtain the torque component of the three-phase current signal of the cutting motor; Correlation analysis is performed on the intrinsic mode function components and the torque components to obtain mutual information data between the intrinsic mode function components and the torque components.

4. The remote intelligent control method for coal mine tunneling equipment as described in claim 1, characterized in that, The high-frequency component reconstruction of the intrinsic mode function components based on the mutual information data includes: Based on the mutual information data, the mutual information values ​​corresponding to the intrinsic mode function components are arranged in descending order to form an intrinsic mode function component sequence; Based on the statistical mean of the intrinsic mode function component sequence, candidate reconstructed components are selected from the intrinsic mode function component sequence; Perform a Hilbert transform on the candidate reconstructed components to obtain the instantaneous frequency curves of the candidate reconstructed components; Based on the instantaneous frequency curve, reconstructed components with frequency values ​​higher than the natural frequency of the cutting arm structure in the coal mine tunneling equipment are identified from the candidate reconstructed components, and the reconstructed components are marked as high-frequency sensitive components of the intrinsic mode function components. The high-frequency sensitive components are linearly superimposed and bandpass filtered to obtain the lithology-sensitive vibration waveform of the coal mine tunneling equipment.

5. The remote intelligent control method for coal mine tunneling equipment as described in claim 4, characterized in that, The synchronous squeezing wavelet transform of the three-phase current signal of the cutting motor is performed based on the reconstructed lithology-sensitive vibration waveform to obtain the time-frequency matrix of the three-phase current signal of the cutting motor, including: The lithology-sensitive vibration waveform is subjected to Fourier transform to obtain the spectral distribution of the lithology-sensitive vibration waveform; Based on the main peak frequency in the spectrum distribution, the dominant frequency band of the lithology-sensitive vibration waveform is determined; Based on the aforementioned advantageous frequency band, frequency-guided wavelet analysis is performed on the three-phase current signal of the cutting motor to obtain the initial wavelet coefficient matrix of the three-phase current signal of the cutting motor. The initial wavelet coefficient matrix is ​​synchronously squeezed and rearranged to obtain the time-frequency matrix of the three-phase current signal of the cut motor.

6. The remote intelligent control method for coal mine tunneling equipment as described in claim 1, characterized in that, The coupling deviation analysis of the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state of the coal mine tunneling equipment includes: By performing time-frequency peak tracking on the time-frequency matrix, the first frequency curve of the three-phase current signal of the cutting motor is obtained; The instantaneous frequency of the lithology-sensitive vibration waveform is filtered by moving average to obtain the second frequency curve of the lithology-sensitive vibration waveform; Based on the frequency deviation between the first frequency curve and the second frequency curve, the cutting condition of the coal mine tunneling equipment is identified, and the cutting contact state result of the coal mine tunneling equipment is obtained.

7. The remote intelligent control method for coal mine tunneling equipment as described in claim 6, characterized in that, The step of identifying the cutting condition of the coal mine tunneling equipment based on the frequency deviation between the first frequency curve and the second frequency curve, and obtaining the cutting contact state result of the coal mine tunneling equipment, includes: The differences between the first frequency curve and the second frequency curve are arranged into a frequency deviation sequence; Sliding window statistics are performed on the frequency deviation sequence to obtain the mean and variance characteristics of the frequency deviation sequence; When the mean characteristic exceeds a preset load threshold and the variance characteristic is less than a preset stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be a stable cutting state. When the mean characteristic exceeds the load threshold and the variance characteristic is greater than the stability threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an impact cutting state. When the mean characteristic is less than the load threshold, the cutting contact state of the coal mine tunneling equipment is determined to be an unloaded state.

8. The remote intelligent control method for coal mine tunneling equipment as described in claim 7, characterized in that, Based on the cutting contact state results, frequency mismatch inverse compensation is performed on the cutting head rotation speed and cutting arm swing speed of the coal mine tunneling equipment to obtain the cutting control parameters of the coal mine tunneling equipment, including: Based on the cutting contact state results, determine the compensation triggering conditions corresponding to the current cutting state of the coal mine tunneling equipment; When the compensation triggering condition is met, the real-time frequency deviation between the first frequency curve and the second frequency curve at the current moment is obtained; Based on the real-time frequency deviation, calculate the compensation amount for the cutting head rotation speed and the compensation amount for the cutting arm swing speed of the coal mine tunneling equipment; Based on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment under the current cutting state, the compensation amount of the cutting head speed and the compensation amount of the cutting arm swing speed are corrected and fused to obtain the compensated cutting head speed and the compensated cutting arm swing speed. The compensated cutting head rotation speed and the compensated cutting arm swing speed are used as the cutting control parameters of the coal mine tunneling equipment.

9. The remote intelligent control method for coal mine tunneling equipment as described in claim 8, characterized in that, The formula for calculating the cutting head rotation speed compensation is as follows: ； In the formula, For the current moment The cutting head speed compensation amount. For the first frequency curve at the current time clock frequency value, For the second frequency curve at the current time The instantaneous frequency value, The cutting frequency of the coal mine tunneling equipment is given. The rated speed of the cutting head of the coal mine tunneling equipment. The preset speed compensation coefficient, The preset dynamic adjustment factor, The fundamental angular frequency of the lithology-sensitive vibration waveform is given.

10. A remote intelligent control system for coal mine tunneling equipment, characterized in that, The system is used to implement the remote intelligent control method for coal mine tunneling equipment as described in claim 1, the system comprising: The multi-source signal acquisition and conversion module is used to perform analog-to-digital conversion on the multi-source sensor data of the coal mine tunneling equipment to obtain the vibration acceleration signal of the cutting arm and the three-phase current signal of the cutting motor of the coal mine tunneling equipment. The vibration and current coupling analysis module is used to perform empirical mode decomposition on the vibration acceleration signal of the cutting arm, and to analyze the mutual information data between the decomposed intrinsic mode function components and the torque components in the three-phase current signal of the cutting motor. The lithology-sensitive feature extraction module is used to reconstruct the high-frequency components of the intrinsic mode function based on the mutual information data, and to perform synchronous squeezing wavelet transform on the three-phase current signal of the cutting motor based on the reconstructed lithology-sensitive vibration waveform, so as to obtain the time-frequency matrix of the three-phase current signal of the cutting motor. The cutting state determination module is used to perform coupling deviation analysis on the time-frequency matrix and the instantaneous frequency of the lithology-sensitive vibration waveform to determine the cutting contact state result of the coal mine tunneling equipment. The mismatch compensation and parameter generation module is used to perform frequency mismatch inverse compensation on the cutting head speed and cutting arm swing speed of the coal mine tunneling equipment based on the cutting contact state results, so as to obtain the cutting control parameters of the coal mine tunneling equipment. The control command execution module is used to send the cutting control parameters to the airborne controller of the coal mine tunneling equipment to drive the coal mine tunneling equipment to perform cutting actions.

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