Intelligent control method and system for coal mining tunneling

Abstract

The invention relates to the technical field of coal mining tunneling, and discloses an intelligent control method and system for coal mining tunneling, and the method comprises the steps: carrying out the multi-axis vibration synchronous collection of the operation state of coal mining tunneling equipment in a target coal mining environment, and obtaining phase alignment vibration data; lithology abrupt change instantaneous inversion is carried out on the target coal mining environment to obtain an impedance transient distribution field, and change point analysis is carried out on the impedance transient distribution field to obtain abrupt change transient characteristics of the target coal mining environment; performing low-consumption penetration vector reconstruction on the tunneling track of the coal mining tunneling equipment to obtain a dynamic compensation vector; performing impact momentum coding on the tunneling track and the dynamic compensation vector to obtain a variable-frequency tunneling instruction; based on the variable-frequency tunneling instruction, torque ripple suppression is carried out on the coal mining tunneling equipment, and steady-flow energy-saving operation parameters are obtained; performing global efficiency optimization adaptation on the tunneling process of the coal mining tunneling equipment to obtain an optimal tunneling scheme of the coal mining tunneling equipment; according to the invention, the efficiency of coal mining tunneling intelligent control can be improved.

Description

Technical Field

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

[0002] In the field of intelligent control technology for coal mining and tunneling, existing technologies lack the ability to synchronously acquire multi-axis vibrations to perceive the operating status of coal mining and tunneling equipment. They are unable to acquire phase-aligned vibration data, and there is a significant lag in the inversion of lithological changes in the coal mining environment. It is difficult to accurately construct the impedance transient distribution field and extract the transient features of the abrupt changes. The recognition accuracy and real-time performance of lithological changes in the coal mining environment are insufficient, making it impossible for the planning of the tunneling trajectory to keep up with the dynamic changes in the site environment, which brings deviations in the basic data level to the subsequent tunneling control.

[0003] In existing coal mining and tunneling control methods, the adjustment of the tunneling trajectory lacks low-consumption and penetrating vector reconstruction logic, the matching degree between impact momentum coding and actual tunneling needs is low, the accuracy of frequency conversion tunneling commands is insufficient, and the means to suppress torque pulsation of coal mining and tunneling equipment are imperfect, failing to effectively avoid the impact of sudden load changes in the cutting motor. Furthermore, global efficiency optimization and adaptation based on equipment operating parameters are not carried out, and the ability to coordinate and regulate cutting energy consumption and tunneling efficiency is lacking. This results in poor energy-saving effect of coal mining and tunneling equipment operation, and the overall tunneling control efficiency and intelligence level are difficult to meet the needs of actual coal mining operations. Therefore, how to improve the intelligent control efficiency of coal mining and tunneling has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an intelligent control method and system for coal mining and tunneling to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an intelligent control method for coal mining and tunneling, comprising: S01. Multi-axis vibration synchronous acquisition is performed on the operating status of the coal mining and tunneling equipment in the target coal mining environment to obtain the phase-aligned vibration data of the coal mining and tunneling equipment; S02. Based on the phase-aligned vibration data, perform lithological abrupt change instantaneous inversion on the target coal mining environment to obtain the impedance transient distribution field of the target coal mining environment, and perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. S03. Based on the sudden transient characteristics, the tunneling trajectory of the coal mining tunneling equipment is reconstructed using a low-consumption penetration vector to obtain the dynamic compensation vector of the tunneling trajectory; S04. The tunneling trajectory and the dynamic compensation vector are encoded with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment; S05. Based on the variable frequency tunneling command, torque pulsation suppression is performed on the coal mining tunneling equipment to obtain the stable flow and energy-saving operation parameters of the coal mining tunneling equipment; S06. Based on the stable flow and energy-saving operating parameters, perform global efficiency optimization and adaptation on the tunneling process of the coal mining tunneling equipment to obtain the optimal tunneling scheme of the coal mining tunneling equipment.

[0006] In a preferred embodiment, the step of synchronously acquiring multi-axis vibration data of the coal mining and tunneling equipment in the target coal mining environment to obtain phase-aligned vibration data of the coal mining and tunneling equipment includes: The vibration measuring points of the coal mining and tunneling equipment in the target coal mining environment are synchronously triggered and collected to obtain multiple original vibration waveforms of the vibration measuring points. The offset of the multiple original vibration waveforms is analyzed to obtain the time delay of the multiple original vibration waveforms. Based on the time delay, waveform time registration is performed on the multiple original vibration waveforms to obtain the synchronous vibration sequence of the multiple original vibration waveforms; The amplitude of the synchronous vibration sequence is normalized to obtain the phase-aligned vibration data of the coal mining and tunneling equipment.

[0007] In a preferred embodiment, the step of performing abrupt lithological change inversion on the target coal mining environment based on the phase-aligned vibration data to obtain the impedance transient distribution field of the target coal mining environment, and performing change point analysis on the impedance transient distribution field to obtain the abrupt transient characteristics of the target coal mining environment, includes: By tracing the wave energy transfer path of the phase-aligned vibration data, the mining disturbance transmission trajectory of the phase-aligned vibration data can be obtained; Based on the aforementioned mining disturbance transmission trajectory, the impedance interface of the target coal mining environment is calibrated point by point to obtain the lithological boundary point of the target coal mining environment; Kriging space extrapolation was performed on the lithological boundary points to obtain the transient impedance distribution field at the lithological boundary points. By tracing back the wavefront propagation direction of the transient impedance distribution field, the arrival time sequence of the wavefront of the transient impedance distribution field is obtained. Based on the arrival time of the wavefront, the wave velocity difference of the impedance transient distribution field is located to obtain the lithological abrupt boundary of the impedance transient distribution field; Singularity distribution analysis was performed on the lithological abrupt change boundary to obtain the transient characteristics of the abrupt change in the target coal mining environment.

[0008] In a preferred embodiment, the step of tracing back the wavefront propagation direction of the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field includes: The second-order directional gradient of the impedance transient distribution field is extracted to construct the Hessian matrix of the impedance transient distribution field; The Hessian matrix is ​​subjected to eigenvalue separation to obtain the maximum and minimum eigenvalues ​​of the Hessian matrix. The interface sharpness coefficient of the impedance transient distribution field is obtained by merging the interface transition band thickness of the impedance gradient mode of the impedance transient distribution field. Based on the maximum eigenvalue, the minimum eigenvalue, and the interface sharpness coefficient, anisotropic attenuation compensation is performed on the transient impedance distribution field to obtain the compensated wave velocity field of the transient impedance distribution field. The calculation formula for the compensated wave velocity field is as follows: ; In the formula, To compensate for the wave velocity field at position wave velocity at that location Let V be the initial wave velocity of the transient impedance distribution field. Spatial location coordinates, It is an exponential function. The preset wave velocity attenuation coefficient, The largest eigenvalue, The minimum eigenvalue, This is a preset regularization constant. The spatial gradient of the transient impedance distribution field. The preset characteristic thickness of the lithological interface transition zone. The mean impedance of the transient impedance distribution field is the global impedance value. It is the Euclidean norm; Based on the compensated wave velocity field, the wavefront time is recursively calculated for the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field.

[0009] In a preferred embodiment, the step of reconstructing the tunneling trajectory of the coal mining tunneling equipment using a low-consumption penetration vector based on the abrupt transient characteristics to obtain the dynamic compensation vector of the tunneling trajectory includes: The cutting vibration waveform of the tunneling trajectory of the coal mining tunneling equipment is decoupled to obtain the cutting tooth impact response component and the cutting friction response component of the tunneling trajectory. Based on the impact response component of the cutting tooth, the cutting friction response component, and the sudden transient characteristics, the energy dissipation of the tunneling trajectory is traced to obtain the ineffective energy consumption air-blow section and the effective breaking section of the tunneling trajectory. The impact frequency of the cutting teeth in the ineffective energy loss air-impact segment is increased to obtain the high-frequency impact sequence of the cutting teeth in the ineffective energy loss air-impact segment; The cutting force of the cutting teeth is unloaded on the effective crushing section to obtain the cutting tooth holding pressure and deceleration timing of the effective crushing section; Based on the high-frequency impact sequence of the cutting teeth and the timing of the cutting teeth pressure holding and cutting tool deflection, the amplitude of the tunneling trajectory is jointly reconstructed to obtain the dynamic compensation vector of the tunneling trajectory.

[0010] In a preferred embodiment, the step of tracing the energy dissipation of the tunneling trajectory based on the impact response component of the cutting teeth, the cutting friction response component, and the abrupt transient characteristics to obtain the ineffective energy-consuming air-blast section and the effective breaking section of the tunneling trajectory includes: The transient energy of the impact response component of the cutting tooth is extracted to obtain the time-series impact energy of the impact response component of the cutting tooth; The instantaneous amplitude demodulation of the cutting friction response component is performed to obtain the time-series friction energy of the cutting friction response component; Based on the aforementioned abrupt transient characteristics, the energy dissipation coefficient of the tunneling trajectory is calculated, wherein the formula for calculating the energy dissipation coefficient is: ; In the formula, The energy dissipation coefficient is... For the current moment, The preset time window length, The time-series impact energy at time... The value, It is an exponential function. The preset energy decay factor, For integration variables, The time-series frictional energy at time... The value, It is a natural exponential function. The preset lithological influence coefficient, For the tunneling trajectory at time... Instantaneous lithological characteristic values, The preset benchmark lithological characteristic values, The preset nonlinear index for lithological abrupt change. It is the absolute value symbol; Based on the energy dissipation coefficient and the preset critical value judgment rule, the tunneling trajectory is segmented by a threshold to obtain the ineffective energy-consuming air-blow section and the effective breaking section of the tunneling trajectory.

[0011] In a preferred embodiment, the step of encoding the tunneling trajectory and the dynamic compensation vector using impact momentum to obtain the variable frequency tunneling command for the coal mining tunneling equipment includes: The spatial curvature abrupt change point of the tunneling trajectory is identified to obtain the impact trigger point of the cutting tooth of the tunneling trajectory; Based on the dynamic compensation vector, the impact momentum amplitude of the impact trigger point of the cutting tooth is matched to obtain the impact energy allocation of the impact trigger point of the cutting tooth. The impact energy allocation is mapped to the coal mining and tunneling equipment using a rotational phase mapping method to obtain the impact timing phase of the cutting teeth of the coal mining and tunneling equipment. Based on the impact timing phase of the cutting tooth and the impact trigger point of the cutting tooth, the impact density of the coal mining tunneling equipment is calibrated to obtain the impact frequency of the cutting tooth of the coal mining tunneling equipment. The impact frequency of the cutting teeth is encoded by the rotational speed of the cutting tooth drum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment.

[0012] In a preferred embodiment, the step of suppressing torque pulsation in the coal mining tunneling equipment based on the variable frequency tunneling command to obtain the stable current and energy-saving operating parameters of the coal mining tunneling equipment includes: The variable frequency tunneling command is adaptively decomposed to obtain the intrinsic mode components of the variable frequency tunneling command; The transient load abrupt change point of the intrinsic mode component is located to obtain the truncation load jump time of the intrinsic mode component; Based on the moment of the cutting load increase, the timing of rotor kinetic energy release of the cutting motor of the coal mining and tunneling equipment is predicted to obtain the advance release phase of the cutting motor. Based on the early release phase, transient enhancement injection is performed on the cutting motor to obtain the instantaneous torque compensation waveform of the cutting motor; The instantaneous torque compensation waveform is corrected by back electromotive force waveform alignment, and the suppression coefficient is extracted from the corrected waveform to obtain the steady flow and energy-saving operation parameters of the coal mining and tunneling equipment.

[0013] In a preferred embodiment, the step of performing global performance optimization and adaptation on the tunneling process of the coal mining tunneling equipment based on the stable flow energy-saving operating parameters to obtain the optimal tunneling scheme for the coal mining tunneling equipment includes: The energy consumption topology mapping of the tunneling process of the coal mining tunneling equipment is performed to obtain the energy consumption distribution map of the tunneling process; Based on the energy consumption distribution map, the traction speed in the high-energy-consumption section of the tunneling process is dynamically released with tolerance to obtain the allowable fluctuation range of the traction speed. Based on the allowable fluctuation range, the cutting depth and drum speed of the tunneling process are jointly reconstructed to obtain the collaborative control parameters of the tunneling process; Based on the collaborative control parameters, the tunneling process is iteratively updated to obtain the optimal tunneling scheme for the coal mining tunneling equipment.

[0014] To address the above problems, the present invention also provides an intelligent control system for coal mining and tunneling, the system comprising: The vibration synchronization acquisition module is used to perform multi-axis vibration synchronization acquisition on the operating status of coal mining and tunneling equipment in the target coal mining environment, and obtain the phase-aligned vibration data of the coal mining and tunneling equipment; The lithological abrupt change transient inversion module is used to perform lithological abrupt change transient inversion on the target coal mining environment based on the phase-aligned vibration data, obtain the impedance transient distribution field of the target coal mining environment, and perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. The low-power penetration vector reconstruction module is used to reconstruct the tunneling trajectory of the coal mining tunneling equipment based on the sudden transient characteristics, so as to obtain the dynamic compensation vector of the tunneling trajectory. The impact momentum encoding module is used to encode the tunneling trajectory and the dynamic compensation vector with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment. The torque pulsation suppression module is used to suppress torque pulsation in the coal mining tunneling equipment based on the frequency conversion tunneling command, and obtain the stable current energy-saving operation parameters of the coal mining tunneling equipment; The global performance optimization and adaptation module is used to perform global performance optimization and adaptation on the tunneling process of the coal mining tunneling equipment based on the stable flow energy-saving operation parameters, so as to obtain the optimal tunneling scheme of the coal mining tunneling equipment.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves accurate acquisition of phase-aligned vibration data of coal mining and tunneling equipment through multi-axis vibration synchronous acquisition. Relying on lithological abrupt change instantaneous inversion technology, it can quickly construct the impedance transient distribution field and extract abrupt change transient features. Combining these features, it completes low-consumption penetration vector reconstruction of the tunneling trajectory, accurately generates dynamic compensation vectors, and dynamically matches the tunneling trajectory with the coal mining environment. At the same time, it generates high-precision variable frequency tunneling commands through impact momentum encoding. The entire process from data acquisition to command generation achieves a precise technical upgrade, effectively reducing ineffective energy consumption during tunneling and improving the accuracy and adaptability of coal mining and tunneling trajectory control.

[0016] 2. This invention uses variable frequency tunneling commands to suppress torque pulsation, accurately obtaining stable and energy-saving operating parameters. This effectively improves the stability and energy efficiency of coal mining tunneling equipment, preventing operational fluctuations caused by sudden load changes. Simultaneously, through global efficiency optimization and adaptation, it performs multi-parameter coordinated control of the tunneling process, generating the optimal tunneling scheme. This achieves dynamic matching of cutting energy consumption, traction speed, and cutting depth, significantly improving the overall efficiency of intelligent control in coal mining tunneling. It achieves dual optimization of tunneling efficiency and energy utilization, enhancing the intelligence and efficiency of coal mining tunneling operations. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an intelligent control method for coal mining and tunneling according to an embodiment of the present invention. Figure 2 This is a functional block diagram of an intelligent control system for coal mining and tunneling provided in an 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 an intelligent control method for coal mining and tunneling. The executing entity of this intelligent control method 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 intelligent control method for coal mining and tunneling 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 (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent control method for coal mining and tunneling according to an embodiment of the present invention. In this embodiment, the intelligent control method for coal mining and tunneling includes: S01. Multi-axis vibration synchronous acquisition is performed on the operating status of the coal mining and tunneling equipment in the target coal mining environment to obtain the phase-aligned vibration data of the coal mining and tunneling equipment; In this embodiment of the invention, the step of synchronously acquiring multi-axis vibration data of the coal mining and tunneling equipment in the target coal mining environment to obtain phase-aligned vibration data of the coal mining and tunneling equipment includes: The vibration measuring points of the coal mining and tunneling equipment in the target coal mining environment are synchronously triggered and collected to obtain multiple original vibration waveforms of the vibration measuring points. The offset of the multiple original vibration waveforms is analyzed to obtain the time delay of the multiple original vibration waveforms. Based on the time delay, waveform time registration is performed on the multiple original vibration waveforms to obtain the synchronous vibration sequence of the multiple original vibration waveforms; The amplitude of the synchronous vibration sequence is normalized to obtain the phase-aligned vibration data of the coal mining and tunneling equipment.

[0021] Vibration acquisition points are set up at key vibration monitoring locations of the coal mining and tunneling equipment. All acquisition points are connected to the same trigger control link. The trigger control link sends a synchronous trigger command to the acquisition components of all acquisition points. At the same time that all acquisition components receive the command, they start to continuously acquire vibration signals during the operation of the coal mining and tunneling equipment. The continuous vibration signals acquired by each acquisition point are converted into vibration waveforms according to the time dimension. The vibration waveforms corresponding to each acquisition point together form the multi-channel original vibration waveforms of the vibration acquisition points of the coal mining and tunneling equipment in the target coal mining environment.

[0022] The original vibration waveforms of multiple channels are initially arranged according to the time axis. Vibration segments with consistent vibration characteristics are selected as common reference segments. The positional deviation of the reference segment of each original vibration waveform on the time axis is compared with the preset standard reference segment. The offset value of each original vibration waveform relative to the standard reference segment on the time axis is recorded. This offset value is the time delay of the corresponding single original vibration waveform. The offset values ​​of all single original vibration waveforms are integrated to form the time delay of the multiple original vibration waveforms.

[0023] Based on the time delay corresponding to each of the multiple original vibration waveforms, the single original vibration waveform is shifted and adjusted on the time axis. For single original vibration waveforms with time offset, they are shifted in the corresponding direction on the time axis according to their corresponding time delay value until the reference segment and the standard reference segment completely coincide. After the time axis shift adjustment of all single original vibration waveforms is completed, all the adjusted vibration waveforms are arranged in the order of the measurement points. The resulting ordered waveform set is the synchronous vibration sequence of the multiple original vibration waveforms.

[0024] The maximum and minimum amplitude values ​​of each vibration waveform in the synchronous vibration sequence are extracted, and a unified amplitude conversion interval is determined. The amplitude values ​​corresponding to each time point on each vibration waveform are converted according to the unified amplitude conversion interval. During the conversion process, the amplitude change trend of each vibration waveform is kept unchanged. After the amplitude conversion of all vibration waveforms in the synchronous vibration sequence is completed, all the adjusted vibration waveforms are integrated according to the original arrangement order. The integrated waveform data is the phase-aligned vibration data of the coal mining and tunneling equipment.

[0025] The beneficial effects are as follows: Synchronous acquisition of vibration signals from various vibration monitoring points of the coal mining and tunneling equipment is achieved through a unified trigger control link. This ensures the complete capture of vibration signals from each monitoring point and their conversion into corresponding vibration waveforms, guaranteeing the synchronization and integrity of multiple original vibration waveform acquisitions. By selecting a reference segment for comparison to determine the time delay and completing waveform time registration, the time-dimensional offset deviation of each original vibration waveform can be eliminated, ensuring a high degree of temporal fit between the synchronized vibration sequences and restoring the true temporal relationship of vibrations at each monitoring point. Amplitude normalization is achieved by extracting amplitude extrema to determine a unified conversion interval. This unifies the amplitude standard while preserving the amplitude variation trend of each vibration waveform. The resulting phase-aligned vibration data accurately, realistically, and consistently reflects the actual operating vibration state of each monitoring location of the coal mining and tunneling equipment, providing reliable and accurate basic data support for subsequent related analysis and control work.

[0026] S02. Based on the phase-aligned vibration data, perform lithological abrupt change instantaneous inversion on the target coal mining environment to obtain the impedance transient distribution field of the target coal mining environment, and perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. In this embodiment of the invention, the step of performing abrupt lithological change inversion on the target coal mining environment based on the phase-aligned vibration data to obtain the impedance transient distribution field of the target coal mining environment, and performing change point analysis on the impedance transient distribution field to obtain the abrupt transient characteristics of the target coal mining environment, includes: By tracing the wave energy transfer path of the phase-aligned vibration data, the mining disturbance transmission trajectory of the phase-aligned vibration data can be obtained; Based on the aforementioned mining disturbance transmission trajectory, the impedance interface of the target coal mining environment is calibrated point by point to obtain the lithological boundary point of the target coal mining environment; Kriging space extrapolation was performed on the lithological boundary points to obtain the transient impedance distribution field at the lithological boundary points. By tracing back the wavefront propagation direction of the transient impedance distribution field, the arrival time sequence of the wavefront of the transient impedance distribution field is obtained. Based on the arrival time of the wavefront, the wave velocity difference of the impedance transient distribution field is located to obtain the lithological abrupt boundary of the impedance transient distribution field; Singularity distribution analysis was performed on the lithological abrupt change boundary to obtain the transient characteristics of the abrupt change in the target coal mining environment.

[0027] The step of tracing back the wavefront propagation direction of the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field includes: The second-order directional gradient of the impedance transient distribution field is extracted to construct the Hessian matrix of the impedance transient distribution field; The Hessian matrix is ​​subjected to eigenvalue separation to obtain the maximum and minimum eigenvalues ​​of the Hessian matrix. The interface sharpness coefficient of the impedance transient distribution field is obtained by merging the interface transition band thickness of the impedance gradient mode of the impedance transient distribution field. Based on the maximum eigenvalue, the minimum eigenvalue, and the interface sharpness coefficient, anisotropic attenuation compensation is performed on the transient impedance distribution field to obtain the compensated wave velocity field of the transient impedance distribution field. The calculation formula for the compensated wave velocity field is as follows: ; In the formula, To compensate for the wave velocity field at position wave velocity at that location Let V be the initial wave velocity of the transient impedance distribution field. Spatial location coordinates, It is an exponential function. The preset wave velocity attenuation coefficient, The largest eigenvalue, The minimum eigenvalue, This is a preset regularization constant. The spatial gradient of the transient impedance distribution field. The preset characteristic thickness of the lithological interface transition zone. The mean impedance of the transient impedance distribution field is the global impedance value. It is the Euclidean norm; Based on the compensated wave velocity field, the wavefront time is recursively calculated for the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field.

[0028] By analyzing the energy change trends of each vibration signal in the phase-aligned vibration data, tracing the propagation path of each segment of vibration energy from the point of action of the coal mining equipment to the interior of the target coal mining environment, and integrating all the propagation path information of vibration energy, a complete mining disturbance transmission trajectory of the phase-aligned vibration data is formed.

[0029] Based on the energy propagation direction and range presented by the mining disturbance transmission trajectory, the medium impedance characteristics at different locations are detected point by point along the energy propagation path within the spatial range of the target coal mining environment. The impedance characteristic changes of adjacent detection points are compared, and the points where the impedance characteristics change are marked as impedance interface points. All marked impedance interface points together constitute the lithological boundary points of the target coal mining environment.

[0030] Based on the impedance characteristic data of lithological boundary points, spatial interpolation is performed to complete the undetected areas between adjacent lithological boundary points within the overall spatial range of the target coal mining environment. During the completion process, the impedance characteristic change law of the surrounding detected points is followed, and the instantaneous state of impedance characteristics of all spatial locations in the target coal mining environment is completely restored, forming the transient impedance distribution field of lithological boundary points.

[0031] The Hessian matrix of the impedance transient distribution field is constructed by extracting the secondary variation trend of impedance characteristics in different directions at each spatial location from the impedance transient distribution field, integrating the secondary variation trend data in all directions and arranging them according to the correspondence of spatial dimensions.

[0032] The feature attributes of all data in the Hessian matrix are decomposed and separated. All values ​​that can represent the matrix features after decomposition are selected. The feature value with the largest value is selected as the largest eigenvalue of the Hessian matrix, and the feature value with the smallest value is selected as the smallest eigenvalue of the Hessian matrix.

[0033] By analyzing the numerical changes of impedance gradient magnitudes at each impedance interface in the transient impedance distribution field, and combining them with the actual thickness of the transition band at each impedance interface, the impedance gradient magnitudes at different locations within the same impedance interface are integrated and merged according to the transition band thickness. The merged comprehensive characteristic value is then calculated, which is the interface sharpness coefficient of the transient impedance distribution field.

[0034] Based on the characteristics of the Hessian matrix reflected by the maximum and minimum eigenvalues ​​and the impedance interface characteristics characterized by the interface sharpness coefficient, targeted attenuation correction and compensation are performed on the wave velocity propagation characteristics in different spatial directions in the impedance transient distribution field. During the correction and compensation process, the differences in impedance characteristics in each direction are matched to form the wave velocity propagation characteristic distribution state after correction and compensation. This state is the compensated wave velocity field of the impedance transient distribution field.

[0035] Based on the wave velocity propagation characteristics of each spatial location presented by the compensated wave velocity field, the time for the wave front to propagate to each spatial location in the target coal mining environment is calculated point by point starting from the wave source location of the impedance transient distribution field. The wave front arrival times of all spatial locations are sorted out in chronological order to form a complete wave front arrival time sequence of the impedance transient distribution field.

[0036] Based on the difference in wavefront arrival time at each location presented by the wavefront arrival time sequence, the wave velocity propagation characteristics at each location in the impedance transient distribution field are matched, and the spatial boundaries where the wave velocity propagation characteristics change and the corresponding lithological characteristics change are located. All such spatial boundaries located together constitute the lithological abrupt boundary of the impedance transient distribution field.

[0037] The characteristics of all spatial locations on the lithological abrupt boundary are analyzed one by one, and singularities with special changes in impedance and wave velocity characteristics on the lithological abrupt boundary are identified. The spatial distribution, characteristic change type and instantaneous change state of all singularities are sorted out. The distribution information and characteristic change information of all singularities obtained from the analysis are integrated to form the transient characteristics of the abrupt change of the target coal mining environment.

[0038] The initial wave velocity is obtained by directly extracting the basic wave velocity data corresponding to each spatial location from the impedance transient distribution field. The spatial location coordinates are the spatial positioning information of each detection point in the impedance transient distribution field. The wave velocity attenuation coefficient, regularization constant, and characteristic thickness of the lithological interface transition zone are all preset fixed values. The maximum and minimum eigenvalues ​​are obtained after separating the eigenvalues ​​of the Hessian matrix constructed from the impedance transient distribution field. The spatial gradient of the impedance transient distribution field is the gradient data of each spatial location obtained after performing spatial gradient analysis on the impedance characteristics of the impedance transient distribution field. The global impedance mean is the result obtained by calculating the overall average of the impedance values ​​at all spatial locations in the impedance transient distribution field. The Euclidean norm is a scalar value obtained by performing norm calculation on the spatial gradient data of the impedance transient distribution field. The exponential function is a general mathematical function used for related calculations in the formula. The wave velocity value of the compensation wave velocity field at the location is the result obtained by integrating the above data through the formula.

[0039] This formula is the core calculation basis for anisotropic attenuation compensation of the impedance transient distribution field. By integrating multiple types of data such as the eigenvalues ​​of the Hessian matrix, the spatial gradient of the impedance transient distribution field, and the characteristic thickness of the lithological interface transition zone, and combining them with pre-set coefficients and constants, it performs targeted attenuation correction on the initial wave velocity at each spatial location of the impedance transient distribution field. Finally, it obtains a compensated wave velocity field that fits the actual characteristics of the impedance transient distribution field. This provides accurate wave velocity data support for subsequent wavefront time recursion of the impedance transient distribution field to obtain the wavefront arrival time sequence, and is a key link in realizing the backtracking of the wavefront propagation direction of the impedance transient distribution field.

[0040] When the maximum eigenvalue of the Hessian matrix increases, the value of the corresponding calculation term in the formula increases synchronously. After the exponential function is applied, the attenuation effect on the initial wave velocity is enhanced, and the wave velocity value of the compensated wave velocity field at the corresponding spatial location decreases accordingly. When the Euclidean norm of the spatial gradient of the impedance transient distribution field or the characteristic thickness of the lithological interface transition zone increases, the value of the corresponding calculation term in the formula increases synchronously. After the exponential function is applied, the attenuation effect on the initial wave velocity is enhanced, and the wave velocity value of the compensated wave velocity field at the corresponding spatial location decreases accordingly. When the minimum eigenvalue of the Hessian matrix or the global impedance mean of the impedance transient distribution field increases, the value of the corresponding calculation term in the formula decreases synchronously. After the exponential function is applied, the attenuation effect on the initial wave velocity is weakened, and the wave velocity value of the compensated wave velocity field at the corresponding spatial location increases accordingly. The wave velocity attenuation coefficient and the regularization constant are fixed values ​​and do not change with the actual characteristics of the impedance transient distribution field, playing a fixed adjustment role in the trend formed by the formula calculation.

[0041] The beneficial effects include: by tracing the transmission trajectory of mining disturbances, the energy propagation law of phase-aligned vibration data can be accurately grasped, providing clear directional guidance for subsequent lithological testing. Point-by-point calibration of lithological boundary locations can accurately identify the changes in impedance characteristics in the coal mining environment. Combined with the impedance transient distribution field formed by spatial interpolation, the transient impedance state of all spatial locations within the target coal mining environment can be completely reconstructed. Through a series of wavefront propagation-related analyses and corrections, the wave velocity propagation characteristics and temporal laws of the impedance transient distribution field can be accurately captured, thereby accurately locating the lithological abrupt change boundary. Combined with singularity distribution analysis, the spatial distribution and characteristic change state of lithological abrupt changes can be comprehensively analyzed. The resulting transient abrupt change characteristics can truly and completely reflect the transient lithological changes in the target coal mining environment, providing accurate and comprehensive environmental characteristic basis for subsequent coal mining and tunneling control.

[0042] Clearly defining the sources and acquisition methods of all data in the formula ensures that the initial wave velocity, eigenvalues, spatial gradients, and other data relied upon for the compensation wave velocity field calculation all have clear and standardized acquisition paths, guaranteeing the authenticity and relevance of the calculated data. The formula integrates multiple types of core data to specifically correct the initial wave velocity, accurately completing the anisotropic attenuation compensation of the transient impedance distribution field, generating a realistic compensation wave velocity field, and providing reliable data for wavefront time recursion. Clearly analyzing the formula's changing trends allows for precise understanding of the impact of changes in each data point on the compensation wave velocity value, ensuring the calculation process of the compensation wave velocity field is controllable and the results are accurate, laying a solid foundation for subsequent acquisition of wavefront arrival time sequences.

[0043] S03. Based on the sudden transient characteristics, the tunneling trajectory of the coal mining tunneling equipment is reconstructed using a low-consumption penetration vector to obtain the dynamic compensation vector of the tunneling trajectory; In this embodiment of the invention, the step of reconstructing the tunneling trajectory of the coal mining equipment using a low-consumption penetration vector based on the transient characteristics of the sudden change, to obtain the dynamic compensation vector of the tunneling trajectory, includes: The cutting vibration waveform of the tunneling trajectory of the coal mining tunneling equipment is decoupled to obtain the cutting tooth impact response component and the cutting friction response component of the tunneling trajectory. Based on the impact response component of the cutting tooth, the cutting friction response component, and the sudden transient characteristics, the energy dissipation of the tunneling trajectory is traced to obtain the ineffective energy consumption air-blow section and the effective breaking section of the tunneling trajectory. The impact frequency of the cutting teeth in the ineffective energy loss air-impact segment is increased to obtain the high-frequency impact sequence of the cutting teeth in the ineffective energy loss air-impact segment; The cutting force of the cutting teeth is unloaded on the effective crushing section to obtain the cutting tooth holding pressure and deceleration timing of the effective crushing section; Based on the high-frequency impact sequence of the cutting teeth and the timing of the cutting teeth pressure holding and cutting tool deflection, the amplitude of the tunneling trajectory is jointly reconstructed to obtain the dynamic compensation vector of the tunneling trajectory.

[0044] The energy dissipation source tracing of the tunneling trajectory is performed based on the impact response component of the cutting teeth, the cutting friction response component, and the abrupt transient characteristics to obtain the ineffective energy-consuming air-blast section and the effective breaking section of the tunneling trajectory, including: The transient energy of the impact response component of the cutting tooth is extracted to obtain the time-series impact energy of the impact response component of the cutting tooth; The instantaneous amplitude demodulation of the cutting friction response component is performed to obtain the time-series friction energy of the cutting friction response component; Based on the aforementioned abrupt transient characteristics, the energy dissipation coefficient of the tunneling trajectory is calculated, wherein the formula for calculating the energy dissipation coefficient is: ; In the formula, The energy dissipation coefficient is... For the current moment, The preset time window length, The time-series impact energy at time... The value, It is an exponential function. The preset energy decay factor, For integration variables, The time-series frictional energy at time... The value, It is a natural exponential function. The preset lithological influence coefficient, For the tunneling trajectory at time... Instantaneous lithological characteristic values, The preset benchmark lithological characteristic values, The preset nonlinear index for lithological abrupt change. It is the absolute value symbol; Based on the energy dissipation coefficient and the preset critical value judgment rule, the tunneling trajectory is segmented by a threshold to obtain the ineffective energy-consuming air-blow section and the effective breaking section of the tunneling trajectory.

[0045] The complete cutting vibration waveform corresponding to the tunneling trajectory of the coal mining tunneling equipment is extracted. According to the actual cause of the vibration signal, the extracted waveform is subjected to signal separation operation to accurately distinguish the vibration signal generated by the impact between the cutting teeth and the coal mining environment medium and the vibration signal generated by the friction between the cutting teeth and the coal mining environment medium. The vibration signals generated by all impact are integrated into a complete signal set, which is the cutting tooth impact response component of the tunneling trajectory. The vibration signals generated by all friction are integrated into a complete signal set, which is the cutting friction response component of the tunneling trajectory.

[0046] The impact response component of the cutting tooth is uniformly divided into continuous and non-overlapping time segments according to the time dimension. The transient energy of the vibration signal in each time segment is extracted one by one to obtain the transient energy value corresponding to each time segment. The transient energy values ​​of all time segments are arranged in the natural order of time, and the resulting continuous energy value sequence is the time-series impact energy of the impact response component of the cutting tooth.

[0047] Instantaneous amplitude extraction is performed on the vibration signal of the truncated friction response component. The instantaneous amplitude value of the vibration signal at each time step is obtained according to the time dimension. The instantaneous amplitude value at each time step is converted into the friction energy value at that time step according to a fixed conversion method. The friction energy values ​​at all times step are integrated in the natural order of time. The resulting continuous energy value sequence is the time-series friction energy of the truncated friction response component.

[0048] By combining the transient characteristics of abrupt changes with the instantaneous lithological changes at various locations in the target coal mining environment, this characteristic is matched with time-series impact energy and time-series friction energy in a full-dimensional manner. This matches the actual impact characteristics of lithological changes on energy dissipation. The impact energy and friction energy values ​​at each moment are comprehensively integrated and analyzed. The characteristic values ​​that can accurately characterize the energy dissipation state at the corresponding moment of the tunneling trajectory are determined at each moment. These characteristic values ​​are the energy dissipation coefficients at the corresponding moment of the tunneling trajectory. The energy dissipation coefficients at each moment are integrated to form an energy dissipation coefficient sequence covering the entire tunneling trajectory.

[0049] The preset critical value judgment rule is used as the sole standard for dividing the tunneling trajectory into sections. This rule includes the critical value definition of the energy dissipation coefficient corresponding to different energy consumption states. The energy dissipation coefficient of each moment of the tunneling trajectory is compared with the critical value in the judgment rule. All moments when the energy dissipation coefficient meets the characteristics of air-blast energy consumption are integrated into a continuous time segment. This time segment is the ineffective energy consumption air-blast segment of the tunneling trajectory. All moments when the energy dissipation coefficient meets the characteristics of effective breaking energy consumption are integrated into a continuous time segment. This time segment is the effective breaking segment of the tunneling trajectory.

[0050] Extract the fundamental frequency value of the cutting tooth impact within the ineffective energy loss air-impact segment, and raise this fundamental frequency value to a set high-frequency value according to a fixed adjustment method. Using the raised high-frequency value as a unified standard, match the corresponding cutting tooth impact frequency parameter for each time node of the ineffective energy loss air-impact segment. Integrate the impact frequency parameters of all time nodes in the natural order of time, and the resulting continuous parameter sequence is the cutting tooth high-frequency impact sequence of the ineffective energy loss air-impact segment.

[0051] Based on the transient characteristics of the abrupt change corresponding to the effective crushing section, the fixed unloading amplitude of the cutting force of the cutting tooth is determined. According to this amplitude, the cutting force of the cutting tooth at each moment in the effective crushing section is adjusted in a targeted manner. During the adjustment process, the basic pressure holding value of the cutting force of the cutting tooth is retained. The triggering time and duration of the cutting tooth deflection action are determined moment by moment according to the time dimension. The relevant information of the deflection action at all moments is integrated in the natural order of time. The continuous time sequence information formed is the cutting tooth pressure holding and deflection time sequence of the effective crushing section.

[0052] The frequency parameters in the high-frequency impact sequence of the cutting teeth and the action parameters in the cutting teeth pressure holding and cutting tool deflection sequence are precisely matched to each time node and spatial position of the tunneling trajectory. The original amplitude of the tunneling trajectory is adjusted and reconstructed according to the parameters matched at each node. The amplitude parameters of the tunneling trajectory in all spatial and temporal dimensions are integrated and adjusted. These parameters are transformed into vector data that can comprehensively characterize the compensation features of the tunneling trajectory. This vector data is the dynamic compensation vector of the tunneling trajectory.

[0053] The current moment is the point in time during the tunneling process. The time window length, energy decay factor, lithological influence coefficient, benchmark lithological characteristic value, and lithological abrupt change nonlinear index are all preset fixed values. The time-series impact energy and time-series friction energy are continuous energy value sequences extracted from the impact response component and cutting friction response component of the cutting teeth. The integral variable is the time variable used in the calculation process. The natural exponential function and absolute value symbol are common mathematical operation forms. The instantaneous lithological characteristic value is the lithological characteristic value corresponding to the current moment extracted from the abrupt transient characteristics. The energy dissipation coefficient is the result obtained by integrating the above data and operation forms.

[0054] This formula is the core calculation basis for tracing the energy dissipation of the tunneling trajectory. By integrating time-series impact energy, time-series friction energy, and abrupt transient characteristics, and combining pre-set coefficients and constants, it quantifies the energy dissipation state at each moment of the tunneling trajectory. The resulting energy dissipation coefficient can accurately reflect the energy consumption type and efficiency at each time period during the tunneling process, providing a clear basis for the segment threshold division of the subsequent tunneling trajectory. It is a key link in achieving accurate division between ineffective energy consumption air-blow sections and effective fracture sections.

[0055] When the proportion of time-series impact energy increases within the time window, the value of the corresponding calculation term in the formula will increase synchronously, and the energy dissipation coefficient will increase accordingly. When the proportion of time-series friction energy increases within the time window, the value of the corresponding calculation term in the formula will decrease synchronously, and the energy dissipation coefficient will decrease accordingly. When the difference between the instantaneous lithological characteristic value and the benchmark lithological characteristic value increases, after the action of the absolute value and the exponential function, the value of the corresponding calculation term in the formula will decrease, and the energy dissipation coefficient will decrease accordingly. The pre-set fixed value will not change with the actual state of the tunneling process, and plays a fixed regulating role in the trend formed by the formula calculation.

[0056] The beneficial effects include: by accurately decoupling the vibration waveform of the tunneling trajectory, the vibration signals corresponding to the impact and friction of the cutting teeth can be clearly distinguished, providing a precise signal basis for subsequent energy dissipation analysis. Extracting time-series energy from the two types of response components allows for real-time monitoring of the impact and friction energy consumption during tunneling. Combined with the energy dissipation coefficient determined by the transient characteristics of abrupt changes, the energy consumption type at each stage of tunneling can be accurately determined, enabling precise division between ineffective energy consumption air-blow sections and effective fracture sections. Adopting differentiated parameter adjustment strategies for different sections allows for targeted optimization of tunneling actions. Finally, the dynamic compensation vector obtained through amplitude joint reconstruction allows the tunneling trajectory to accurately adapt to the lithological abrupt changes in the target coal mining environment, achieving low-consumption penetration vector reconstruction of the tunneling trajectory and providing a realistic trajectory compensation basis for the precise control of coal mining tunneling equipment.

[0057] The source and acquisition method of each data item in the formula are clearly defined to ensure that data such as time-series impact energy, time-series friction energy, and instantaneous lithological characteristic values ​​all have clear acquisition paths, guaranteeing the authenticity and relevance of the calculated data. The formula integrates multiple core data to accurately quantify the energy dissipation state at each moment of the tunneling trajectory. The resulting energy dissipation coefficient accurately reflects the energy consumption type and efficiency at each time period during tunneling, providing a reliable basis for segment threshold division of the tunneling trajectory. Clearly analyzing the changing trends of the formula allows for precise understanding of the influence of changes in time-series impact energy, time-series friction energy, and lithological characteristics on the energy dissipation coefficient, ensuring that the energy dissipation analysis process is controllable and the results are accurate, laying a solid foundation for subsequent optimization and adjustment of the tunneling trajectory.

[0058] S04. The tunneling trajectory and the dynamic compensation vector are encoded with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment; In this embodiment of the invention, the step of encoding the tunneling trajectory and the dynamic compensation vector using impact momentum to obtain the variable frequency tunneling command of the coal mining tunneling equipment includes: The spatial curvature abrupt change point of the tunneling trajectory is identified to obtain the impact trigger point of the cutting tooth of the tunneling trajectory; Based on the dynamic compensation vector, the impact momentum amplitude of the impact trigger point of the cutting tooth is matched to obtain the impact energy allocation of the impact trigger point of the cutting tooth. The impact energy allocation is mapped to the coal mining and tunneling equipment using a rotational phase mapping method to obtain the impact timing phase of the cutting teeth of the coal mining and tunneling equipment. Based on the impact timing phase of the cutting tooth and the impact trigger point of the cutting tooth, the impact density of the coal mining tunneling equipment is calibrated to obtain the impact frequency of the cutting tooth of the coal mining tunneling equipment. The impact frequency of the cutting teeth is encoded by the rotational speed of the cutting tooth drum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment.

[0059] Extract the spatial coordinate information of the entire tunneling trajectory, calculate the curvature value of the trajectory at each spatial point, compare the curvature value changes of adjacent points, mark the spatial points where the curvature value changes abruptly, integrate all marked spatial points, and the resulting set of points is the cutter impact trigger point of the tunneling trajectory.

[0060] Extract the vector amplitude data corresponding to each impact trigger point of the cutting tooth from the dynamic compensation vector, and accurately match the amplitude data with the momentum amplitude required for the impact of the cutting tooth. Based on the matching result, determine the corresponding impact energy supply value for each impact trigger point of the cutting tooth, and integrate the impact energy supply values ​​of all impact trigger points of the cutting tooth. The resulting set of values ​​is the impact energy allocation quota for the impact trigger points of the cutting tooth.

[0061] Extract the full-process rotation phase information of the cutting tooth drum of the coal mining tunneling equipment, accurately match the impact energy allocation corresponding to the impact trigger point of each cutting tooth with the specific phase during the drum rotation process, match a unique drum rotation phase for each impact energy allocation, and integrate all the matched drum rotation phase information to obtain the phase set as the impact timing phase of the cutting tooth of the coal mining tunneling equipment.

[0062] By combining the drum rotation time information corresponding to the impact timing phase of the cutting teeth, and the spatial distribution information of the impact trigger points of the cutting teeth, the number of cutting teeth impacts per unit time is counted at each point. Based on the statistical results, a unified impact density standard is determined for the coal mining tunneling equipment. The corresponding cutting tooth impact frequency value is obtained based on the impact density standard. This value is the cutting tooth impact frequency of the coal mining tunneling equipment.

[0063] The numerical features corresponding to the impact frequency of the cutting teeth are extracted, and these numerical features are encoded and converted one-to-one with the speed adjustment parameters of the cutting teeth drum of the coal mining tunneling equipment. According to a fixed encoding rule, the impact frequency of the cutting teeth is converted into drum speed adjustment information that can be directly executed by the equipment. All speed adjustment-related execution information is integrated to form a complete set of instructions, which is the frequency conversion tunneling instruction of the coal mining tunneling equipment.

[0064] The beneficial effects are as follows: By accurately identifying the impact trigger points of the cutting teeth along the tunneling trajectory, the precise spatial action position of the impact control of coal mining tunneling equipment is clarified, laying the point-based foundation for impact momentum coding. Based on the impact energy allocation quota of dynamic compensation vector matching, the impact energy supply is highly aligned with the compensation requirements of the tunneling trajectory, ensuring the accuracy of energy allocation. Mapping the energy allocation quota to the drum rotation phase achieves a precise correspondence between impact energy and equipment rotation phase, ensuring that the timing of impact actions matches the equipment's operating rhythm. Combining the cutting tooth impact frequency determined by phase and point calibration unifies the equipment's impact density standard, allowing the impact frequency to adapt to actual tunneling needs. Finally, the variable frequency tunneling command obtained through speed coding transforms all impact control parameters into information that the equipment can directly execute, providing a reliable and complete execution basis for the precise variable frequency control of coal mining tunneling equipment, and improving the adaptability of tunneling commands to actual tunneling conditions.

[0065] S05. Based on the variable frequency tunneling command, torque pulsation suppression is performed on the coal mining tunneling equipment to obtain the stable flow and energy-saving operation parameters of the coal mining tunneling equipment; In this embodiment of the invention, the step of suppressing torque pulsation in the coal mining tunneling equipment based on the variable frequency tunneling command to obtain the stable current energy-saving operation parameters of the coal mining tunneling equipment includes: The variable frequency tunneling command is adaptively decomposed to obtain the intrinsic mode components of the variable frequency tunneling command; The transient load abrupt change point of the intrinsic mode component is located to obtain the truncation load jump time of the intrinsic mode component; Based on the moment of the cutting load increase, the timing of rotor kinetic energy release of the cutting motor of the coal mining and tunneling equipment is predicted to obtain the advance release phase of the cutting motor. Based on the early release phase, transient enhancement injection is performed on the cutting motor to obtain the instantaneous torque compensation waveform of the cutting motor; The instantaneous torque compensation waveform is corrected by back electromotive force waveform alignment, and the suppression coefficient is extracted from the corrected waveform to obtain the steady flow and energy-saving operation parameters of the coal mining and tunneling equipment.

[0066] The overall signal characteristics of the frequency conversion tunneling command are fully extracted. According to the inherent modal characteristics of the signal, the signal of the frequency conversion tunneling command is separated layer by layer. The overall command signal is decomposed into multiple signal components with independent modal characteristics and no modal overlap. Each signal component retains some core features of the original frequency conversion tunneling command. The set of signal components formed by integrating all the decomposed independent signal components is the intrinsic modal component of the frequency conversion tunneling command.

[0067] The amplitude variation characteristics of each independent signal component in the intrinsic modal components are extracted throughout the entire process. The amplitude variation status of each signal component is monitored step by step along the time dimension. The amplitude value changes of adjacent time points are compared one by one. The time points where the amplitude shows a step increase are marked one by one. The marked time points in all intrinsic modal components are integrated and feature filtering is performed. The marked time points related to the increase of the cutting load are retained. This time point is the cutting load jump time of the intrinsic modal component.

[0068] Extract the full-range rotation phase information of the rotor of the cutting motor of the coal mining and tunneling equipment, accurately match the timing of the cutting load jump with the phase time axis of the rotor rotation, and calculate a fixed phase interval based on the rotor rotation phase corresponding to the cutting load jump to determine the specific rotation phase at which the rotor kinetic energy begins to be released. This phase is the advance release phase of the cutting motor.

[0069] The start and end times of the transient enhancement injection of the cutting motor are determined by the early release phase. The appropriate energy signal is injected into the drive system of the cutting motor according to the timing. The injected energy signal is superimposed and fused with the original torque signal of the motor. The torque signal value after superposition and fusion is collected moment by moment in time. The torque signal values ​​at all moments are integrated into continuous waveform data in chronological order. This waveform data is the instantaneous torque compensation waveform of the cutting motor.

[0070] The back electromotive force (EMF) waveform generated during the operation of the cutting motor is extracted, and the time axis of the back EMF waveform is precisely aligned with that of the instantaneous torque compensation waveform. The amplitude of the instantaneous torque compensation waveform at each moment is specifically corrected and adjusted according to the waveform change characteristics of the back EMF, so that the corrected compensation waveform matches the change trend of the back EMF waveform. After completing the waveform correction, the characteristic coefficients that can characterize the torque pulsation suppression effect in the corrected waveform are extracted. The parameter set formed by integrating these characteristic coefficients with the relevant parameters of the corrected waveform is the steady-flow energy-saving operation parameters of the coal mining and tunneling equipment.

[0071] The beneficial effects include obtaining intrinsic mode components (IMCs) through adaptive decomposition of variable frequency tunneling commands, which accurately decomposes the inherent signal characteristics of the commands, providing a clear signal foundation for subsequent load change monitoring. Locating the moment of load surge by the IMCs allows for precise capture of the sudden change node in the load, making torque control more targeted. Combining the predicted release phase of the load surge moment with precise matching of rotor kinetic energy release timing with load changes lays the timing foundation for torque compensation. The instantaneous torque compensation waveform obtained from transient enhancement injection based on this phase enables precise transient compensation of motor torque. After back EMF waveform alignment and correction, the compensation waveform is more suitable for the actual operating state of the motor. The finally extracted steady-current energy-saving operating parameters can effectively suppress equipment torque pulsation, ensuring the stability of coal mining and tunneling equipment operation, while simultaneously achieving energy-saving effects during operation, providing precise parameter support for efficient equipment operation.

[0072] S06. Based on the stable flow energy-saving operation parameters, perform global efficiency optimization and adaptation on the tunneling process of the coal mining tunneling equipment to obtain the optimal tunneling scheme of the coal mining tunneling equipment; In this embodiment of the invention, the step of performing global performance optimization and adaptation on the tunneling process of the coal mining tunneling equipment based on the stable flow energy-saving operating parameters to obtain the optimal tunneling scheme for the coal mining tunneling equipment includes: The energy consumption topology mapping of the tunneling process of the coal mining tunneling equipment is performed to obtain the energy consumption distribution map of the tunneling process; Based on the energy consumption distribution map, the traction speed in the high-energy-consumption section of the tunneling process is dynamically released with tolerance to obtain the allowable fluctuation range of the traction speed. Based on the allowable fluctuation range, the cutting depth and drum speed of the tunneling process are jointly reconstructed to obtain the collaborative control parameters of the tunneling process; Based on the collaborative control parameters, the tunneling process is iteratively updated to obtain the optimal tunneling scheme for the coal mining tunneling equipment.

[0073] Extract spatial segment division information of the entire tunneling process of coal mining equipment, simultaneously retrieve the cutting energy consumption data corresponding to each tunneling segment from the stable flow energy-saving operation parameters, and accurately match the cutting energy consumption data of each segment with the corresponding spatial location according to the actual spatial topology relationship of the tunneling process. Mark different cutting energy consumption values ​​in a fixed representation form on the corresponding spatial topology location, and integrate all the spatial topology information with completed energy consumption data marking to form a complete energy consumption distribution map of the tunneling process.

[0074] High-energy-consuming sections with higher cutting energy consumption than conventional tunneling sections are accurately identified from the energy consumption distribution map. The basic traction speed values ​​currently implemented in each high-energy-consuming section are extracted. Combined with the correlation characteristics between cutting energy consumption and traction speed in the stable flow energy-saving operation parameters, clear upper and lower limits are defined for the traction speed of each high-energy-consuming section. The interval between these upper and lower limits is the allowable fluctuation range of the traction speed for the corresponding high-energy-consuming section. The allowable fluctuation ranges of the traction speed of all high-energy-consuming sections are integrated to form the allowable fluctuation range of the traction speed for the entire tunneling process.

[0075] The basic values ​​of cutting depth and drum speed in all sections of the tunneling process are extracted. The allowable fluctuation range of traction speed is used as the core constraint. The basic values ​​of cutting depth in high-energy-consuming sections are adjusted for adaptation. Simultaneously, the basic values ​​of drum speed in the corresponding sections are adjusted in a linkage manner according to the adjusted values ​​of cutting depth. The adjusted cutting depth values ​​and drum speed values ​​are matched with each other and conform to the fluctuation constraints of traction speed. The set of values ​​formed by integrating the adjusted cutting depth values ​​and drum speed values ​​of all sections of the tunneling process is the collaborative control parameter of the tunneling process.

[0076] The collaborative control parameters are directly applied to the actual tunneling process of the coal mining tunneling equipment. The adjusted cutting depth and drum speed parameters are executed sequentially according to the spatial segment sequence of the tunneling process. At the same time, the equipment traction speed is adjusted within the allowable fluctuation range of the traction speed. The actual cutting energy consumption data and actual tunneling efficiency data of each segment are collected in real time during the parameter execution process. The collected actual operating data is comprehensively compared with the preset tunneling efficiency standard. If the actual operating data does not conform to the preset efficiency standard, the collaborative control parameters are fine-tuned according to the actual data. The operation of parameter and data collection comparison and parameter fine-tuning is repeated until the actual operating data completely conforms to the preset efficiency standard. The collaborative control parameters that finally conform to the efficiency standard are fully integrated with the spatial segment division and time execution sequence of the tunneling process. The resulting complete execution plan is the optimal tunneling plan for the coal mining tunneling equipment.

[0077] The beneficial effects include an energy consumption distribution map formed by the energy consumption topology mapping of the cutting process, which clearly and accurately presents the energy consumption distribution status of each spatial segment of the tunneling process, providing specific targeted adjustment segments for subsequent global efficiency optimization. Dynamic tolerance release and allowable fluctuation ranges are defined for the traction speed of high-energy-consumption segments, setting reasonable and clear constraints for adjusting the cutting depth and drum speed. Based on this range, the collaborative control parameters obtained by jointly reconstructing the cutting depth and drum speed achieve precise adaptation and coordinated control of multiple tunneling parameters. Through iterative updates of the tunneling process, the parameters are verified and fine-tuned to meet efficiency standards. The resulting optimal tunneling scheme achieves global efficiency optimization of the tunneling process, enabling coal mining tunneling equipment to achieve dual optimization of cutting energy consumption and tunneling efficiency while maintaining stable and energy-saving operation, thus improving the overall adaptability and efficiency of the tunneling operation.

[0078] like Figure 2 The diagram shown is a functional block diagram of an intelligent control system for coal mining and tunneling provided in an embodiment of the present invention.

[0079] The intelligent control system 10 for coal mining and tunneling described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent control system 10 may include a vibration synchronous acquisition module 11, a lithological abrupt change instantaneous inversion module 12, a low-power penetration vector reconstruction module 13, an impact momentum encoding module 14, a torque pulsation suppression module 15, and a global performance optimization and adaptation module 16. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0080] In this embodiment, the functions of each module / unit are as follows: The vibration synchronization acquisition module 11 is used to perform multi-axis vibration synchronization acquisition on the operating status of coal mining and tunneling equipment in the target coal mining environment, and obtain phase-aligned vibration data of the coal mining and tunneling equipment. The lithological abrupt change instantaneous inversion module 12 is used to perform lithological abrupt change instantaneous inversion on the target coal mining environment based on the phase-aligned vibration data, to obtain the impedance transient distribution field of the target coal mining environment, and to perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. The low-power penetration vector reconstruction module 13 is used to reconstruct the tunneling trajectory of the coal mining tunneling equipment using low-power penetration vector based on the sudden transient characteristics, so as to obtain the dynamic compensation vector of the tunneling trajectory. The impact momentum encoding module 14 is used to encode the tunneling trajectory and the dynamic compensation vector with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment. The torque pulsation suppression module 15 is used to suppress torque pulsation of the coal mining tunneling equipment based on the frequency conversion tunneling command, and obtain the stable current energy-saving operation parameters of the coal mining tunneling equipment. The global performance optimization and adaptation module 16 is used to perform global performance optimization and adaptation on the tunneling process of the coal mining tunneling equipment based on the stable flow energy-saving operation parameters, so as to obtain the optimal tunneling scheme of the coal mining tunneling equipment.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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 method for intelligent control of coal mining and tunneling, characterized in that, The method includes: S01. Multi-axis vibration synchronous acquisition is performed on the operating status of the coal mining and tunneling equipment in the target coal mining environment to obtain the phase-aligned vibration data of the coal mining and tunneling equipment; S02. Based on the phase-aligned vibration data, perform lithological abrupt change instantaneous inversion on the target coal mining environment to obtain the impedance transient distribution field of the target coal mining environment, and perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. S03. Based on the sudden transient characteristics, the tunneling trajectory of the coal mining tunneling equipment is reconstructed using a low-consumption penetration vector to obtain the dynamic compensation vector of the tunneling trajectory; S04. The tunneling trajectory and the dynamic compensation vector are encoded with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment; S05. Based on the variable frequency tunneling command, torque pulsation suppression is performed on the coal mining tunneling equipment to obtain the stable flow and energy-saving operation parameters of the coal mining tunneling equipment; S06. Based on the stable flow and energy-saving operating parameters, perform global efficiency optimization and adaptation on the tunneling process of the coal mining tunneling equipment to obtain the optimal tunneling scheme of the coal mining tunneling equipment.

2. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, The process involves multi-axis vibration synchronous acquisition of the operating status of the coal mining and tunneling equipment in the target coal mining environment to obtain phase-aligned vibration data of the coal mining and tunneling equipment, including: The vibration measuring points of the coal mining and tunneling equipment in the target coal mining environment are synchronously triggered and collected to obtain multiple original vibration waveforms of the vibration measuring points. The offset of the multiple original vibration waveforms is analyzed to obtain the time delay of the multiple original vibration waveforms. Based on the time delay, waveform time registration is performed on the multiple original vibration waveforms to obtain the synchronous vibration sequence of the multiple original vibration waveforms; The amplitude of the synchronous vibration sequence is normalized to obtain the phase-aligned vibration data of the coal mining and tunneling equipment.

3. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, Based on the phase-aligned vibration data, a lithological abrupt change transient inversion is performed on the target coal mining environment to obtain the impedance transient distribution field of the target coal mining environment. Then, a change point analysis is performed on the impedance transient distribution field to obtain the abrupt transient characteristics of the target coal mining environment, including: By tracing the wave energy transfer path of the phase-aligned vibration data, the mining disturbance transmission trajectory of the phase-aligned vibration data can be obtained; Based on the aforementioned mining disturbance transmission trajectory, the impedance interface of the target coal mining environment is calibrated point by point to obtain the lithological boundary point of the target coal mining environment; Kriging space extrapolation was performed on the lithological boundary points to obtain the transient impedance distribution field at the lithological boundary points. By tracing back the wavefront propagation direction of the transient impedance distribution field, the arrival time sequence of the wavefront of the transient impedance distribution field is obtained. Based on the arrival time of the wavefront, the wave velocity difference of the impedance transient distribution field is located to obtain the lithological abrupt boundary of the impedance transient distribution field; Singularity distribution analysis was performed on the lithological abrupt change boundary to obtain the transient characteristics of the abrupt change in the target coal mining environment.

4. The intelligent control method for coal mining and tunneling as described in claim 3, characterized in that, The step of tracing back the wavefront propagation direction of the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field includes: The second-order directional gradient of the impedance transient distribution field is extracted to construct the Hessian matrix of the impedance transient distribution field; The Hessian matrix is ​​subjected to eigenvalue separation to obtain the maximum and minimum eigenvalues ​​of the Hessian matrix. The interface sharpness coefficient of the impedance transient distribution field is obtained by merging the interface transition band thickness of the impedance gradient mode of the impedance transient distribution field. Based on the maximum eigenvalue, the minimum eigenvalue, and the interface sharpness coefficient, anisotropic attenuation compensation is performed on the transient impedance distribution field to obtain the compensated wave velocity field of the transient impedance distribution field. The calculation formula for the compensated wave velocity field is as follows: ； In the formula, To compensate for the wave velocity field at position wave velocity at that location Let V be the initial wave velocity of the transient impedance distribution field. Spatial location coordinates, It is an exponential function. The preset wave velocity attenuation coefficient, The largest eigenvalue, The minimum eigenvalue, This is a preset regularization constant. The spatial gradient of the transient impedance distribution field. The preset characteristic thickness of the lithological interface transition zone. The mean impedance of the transient impedance distribution field is the global impedance value. It is the Euclidean norm; Based on the compensated wave velocity field, the wavefront time is recursively calculated for the transient impedance distribution field to obtain the wavefront arrival time sequence of the transient impedance distribution field.

5. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, Based on the aforementioned transient characteristics, the low-consumption penetration vector reconstruction of the tunneling trajectory of the coal mining equipment is performed to obtain the dynamic compensation vector of the tunneling trajectory, including: The cutting vibration waveform of the tunneling trajectory of the coal mining tunneling equipment is decoupled to obtain the cutting tooth impact response component and the cutting friction response component of the tunneling trajectory. Based on the impact response component of the cutting tooth, the cutting friction response component, and the sudden transient characteristics, the energy dissipation of the tunneling trajectory is traced to obtain the ineffective energy consumption air-blow section and the effective breaking section of the tunneling trajectory. The impact frequency of the cutting teeth in the ineffective energy loss air-impact segment is increased to obtain the high-frequency impact sequence of the cutting teeth in the ineffective energy loss air-impact segment; The cutting force of the cutting teeth is unloaded on the effective crushing section to obtain the cutting tooth holding pressure and deceleration timing of the effective crushing section; Based on the high-frequency impact sequence of the cutting teeth and the timing of the cutting teeth pressure holding and cutting tool deflection, the amplitude of the tunneling trajectory is jointly reconstructed to obtain the dynamic compensation vector of the tunneling trajectory.

6. The intelligent control method for coal mining and tunneling as described in claim 5, characterized in that, The energy dissipation source tracing of the tunneling trajectory is performed based on the impact response component of the cutting teeth, the cutting friction response component, and the abrupt transient characteristics to obtain the ineffective energy-consuming air-blast section and the effective breaking section of the tunneling trajectory, including: The transient energy of the impact response component of the cutting tooth is extracted to obtain the time-series impact energy of the impact response component of the cutting tooth; The instantaneous amplitude demodulation of the cutting friction response component is performed to obtain the time-series friction energy of the cutting friction response component; Based on the aforementioned abrupt transient characteristics, the energy dissipation coefficient of the tunneling trajectory is calculated, wherein the formula for calculating the energy dissipation coefficient is: ； In the formula, The energy dissipation coefficient is... For the current moment, The preset time window length, The time-series impact energy at time... The value, It is an exponential function. The preset energy decay factor, For integration variables, The time-series frictional energy at time... The value, It is a natural exponential function. The preset lithological influence coefficient, For the tunneling trajectory at time... Instantaneous lithological characteristic values, The preset benchmark lithological characteristic values, The preset nonlinear index for lithological abrupt change. It is the absolute value symbol; Based on the energy dissipation coefficient and the preset critical value judgment rule, the tunneling trajectory is segmented by a threshold to obtain the ineffective energy-consuming air-blow section and the effective breaking section of the tunneling trajectory.

7. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, The step of encoding the tunneling trajectory and the dynamic compensation vector using impact momentum to obtain the variable frequency tunneling command for the coal mining tunneling equipment includes: The spatial curvature abrupt change point of the tunneling trajectory is identified to obtain the impact trigger point of the cutting tooth of the tunneling trajectory; Based on the dynamic compensation vector, the impact momentum amplitude of the impact trigger point of the cutting tooth is matched to obtain the impact energy allocation of the impact trigger point of the cutting tooth. The impact energy allocation is mapped to the coal mining and tunneling equipment using a rotational phase mapping method to obtain the impact timing phase of the cutting teeth of the coal mining and tunneling equipment. Based on the impact timing phase of the cutting tooth and the impact trigger point of the cutting tooth, the impact density of the coal mining tunneling equipment is calibrated to obtain the impact frequency of the cutting tooth of the coal mining tunneling equipment. The impact frequency of the cutting teeth is encoded by the rotational speed of the cutting tooth drum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment.

8. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, The process of suppressing torque pulsation in the coal mining tunneling equipment based on the variable frequency tunneling command, and obtaining the stable current and energy-saving operating parameters of the coal mining tunneling equipment, includes: The variable frequency tunneling command is adaptively decomposed to obtain the intrinsic mode components of the variable frequency tunneling command; The transient load abrupt change point of the intrinsic mode component is located to obtain the truncation load jump time of the intrinsic mode component; Based on the moment of the cutting load increase, the timing of rotor kinetic energy release of the cutting motor of the coal mining and tunneling equipment is predicted to obtain the advance release phase of the cutting motor. Based on the early release phase, transient enhancement injection is performed on the cutting motor to obtain the instantaneous torque compensation waveform of the cutting motor; The instantaneous torque compensation waveform is corrected by back electromotive force waveform alignment, and the suppression coefficient is extracted from the corrected waveform to obtain the steady flow and energy-saving operation parameters of the coal mining and tunneling equipment.

9. The intelligent control method for coal mining and tunneling as described in claim 1, characterized in that, The process of optimizing the tunneling process of the coal mining equipment based on the stable flow and energy-saving operating parameters to obtain the optimal tunneling scheme for the coal mining equipment includes: The energy consumption topology mapping of the tunneling process of the coal mining tunneling equipment is performed to obtain the energy consumption distribution map of the tunneling process; Based on the energy consumption distribution map, the traction speed in the high-energy-consumption section of the tunneling process is dynamically released with tolerance to obtain the allowable fluctuation range of the traction speed. Based on the allowable fluctuation range, the cutting depth and drum speed of the tunneling process are jointly reconstructed to obtain the collaborative control parameters of the tunneling process; Based on the collaborative control parameters, the tunneling process is iteratively updated to obtain the optimal tunneling scheme for the coal mining tunneling equipment.

10. An intelligent control system for coal mining and tunneling, characterized in that, The system for implementing the intelligent control method for coal mining and tunneling as described in claim 1 includes: The vibration synchronization acquisition module is used to perform multi-axis vibration synchronization acquisition on the operating status of coal mining and tunneling equipment in the target coal mining environment, and obtain the phase-aligned vibration data of the coal mining and tunneling equipment; The lithological abrupt change transient inversion module is used to perform lithological abrupt change transient inversion on the target coal mining environment based on the phase-aligned vibration data, obtain the impedance transient distribution field of the target coal mining environment, and perform change point analysis on the impedance transient distribution field to obtain the abrupt change transient characteristics of the target coal mining environment. The low-power penetration vector reconstruction module is used to reconstruct the tunneling trajectory of the coal mining tunneling equipment based on the sudden transient characteristics, so as to obtain the dynamic compensation vector of the tunneling trajectory. The impact momentum encoding module is used to encode the tunneling trajectory and the dynamic compensation vector with impact momentum to obtain the frequency conversion tunneling command of the coal mining tunneling equipment. The torque pulsation suppression module is used to suppress torque pulsation in the coal mining tunneling equipment based on the frequency conversion tunneling command, and obtain the stable current energy-saving operation parameters of the coal mining tunneling equipment; The global performance optimization and adaptation module is used to perform global performance optimization and adaptation on the tunneling process of the coal mining tunneling equipment based on the stable flow energy-saving operation parameters, so as to obtain the optimal tunneling scheme of the coal mining tunneling equipment.

Images