Mine electromechanical equipment operation safety situation assessment method and system

Abstract

The application discloses a mine electromechanical equipment operation safety situation evaluation method and system, relates to the technical field of mine electromechanical equipment safety monitoring and evaluation, and comprises the following steps: establishing an electromechanical coupling time-frequency observation layer, continuously acquiring dynamic characteristics of tension data, displacement data, current data and torque data, and generating a modal fingerprint baseline based on joint analysis; under the support of the modal fingerprint baseline, coherent decomposition is carried out, the modal drift speed is calculated, and a risk window in which the driving frequency gradually approaches is identified, so that the frequency evolution trend is revealed in advance. The application constructs an electromechanical coupling time-frequency observation system, extracts a modal fingerprint baseline, combines coherent decomposition and counterfactual playback to identify resonance risks in advance, realizes energy dissipation through staggered driving and structural damping, finally utilizes phase traction and phonon band gap structure to quickly dampen vibration, and forms a complete dynamic regulation and control closed-loop system.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring and assessment technology for mining electromechanical equipment, specifically to a method and system for assessing the operational safety status of mining electromechanical equipment. Background Technology

[0002] Safety status assessment of mine electromechanical equipment operation refers to the multi-source sensing and data analysis of the operating status of electromechanical equipment such as motors, conveyors, hoists, and ventilators in the mine production environment. By monitoring electrical parameters, mechanical vibration, temperature, pressure, load changes, and other information, combined with environmental conditions and operating conditions, potential risks and hazards are dynamically identified, and the equipment health, safety margin, and failure trends are comprehensively determined. Focusing on the collaborative management needs of "safety-efficiency-environment" in Sichuan mines, a three-level indicator system is constructed, with equipment health status, environmental impact, and management compliance as its core. The analytic hierarchy process (AHP) is used to determine weights, and fuzzy comprehensive evaluation is combined to quantify the safety status level. The system displays real-time trends in safety status changes, risk warning points, and improvement suggestions on a visualization platform. This method has been applied in scenarios such as the Guangyuan phosphate mine and the Dazhou coal mine in Sichuan, achieving minute-level dynamic status updates and high-coverage risk identification, providing scientific and quantitative support for preventive maintenance, emergency response, and safety production decision-making for mine electromechanical equipment.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, under high-speed operation, the tension distribution of wire ropes in mine hoists dynamically changes due to factors such as load fluctuations, running inertia, and changes in winding position, leading to modal frequency drift in the entire structure. When this modal frequency coincides with the motor drive frequency instantaneously, it can easily trigger a coupled resonance effect, causing a sharp amplification of the vibration amplitude of the entire machine. Existing technologies struggle to monitor and effectively suppress this modal frequency drift in real time. If resonance persists, it will significantly exacerbate fatigue accumulation in the wire rope and drum, potentially leading to rope breakage and posing a major safety hazard in severe cases.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for assessing the operational safety status of mining electromechanical equipment, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the operational safety status of mining electromechanical equipment, comprising the following steps:

[0008] S1. Establish an electromechanical coupling time-frequency observation layer to continuously acquire dynamic characteristics of tension data, displacement data, current data and torque data, and generate a modal fingerprint baseline based on joint analysis to provide a basic reference for subsequent frequency evolution.

[0009] S2, with the support of the modal fingerprint baseline, performs coherent decomposition, calculates modal drift velocity and identifies risk windows where the driving frequency gradually approaches, so that the frequency evolution trend can be revealed in advance;

[0010] S3 runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, and quantifies the risk prediction into an executable indicator.

[0011] S4. Based on the phase correction budget, a time-domain dual-mirror anchor point is established at the boundary of the risk window. The unified time scale is redefined using this anchor point to form a continuous staggered driving trajectory, thereby achieving synchronous alignment of frequency offset.

[0012] S5, relying on the staggered driving trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, realizes the graded compression of resonant energy, and gradually suppresses the vibration amplification effect.

[0013] S6 performs time-reversal phase traction under stable conditions formed by the dynamic energy dissipation channel. By injecting inverse micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure, a nonlinear frequency band migration mechanism is triggered to achieve rapid extinguishing of resonance, thereby completing the closed-loop dynamic control process.

[0014] Preferably, step S1 includes:

[0015] Tension sensors, displacement measuring devices, current transformers, and torque sensors are installed around the wire rope, lifting container, motor stator winding, and motor output shaft to acquire dynamic data on tension, displacement, current, and torque.

[0016] The slope of change, acceleration, harmonic amplitude and location of abrupt response changes were extracted from tension, displacement, current and torque data, and multi-source cross-comparison was performed to identify the correlation between physical quantities.

[0017] By aligning various feature data on a unified time axis and constructing modal response trajectories through time series and amplitude synchronization, a modal fingerprint baseline is formed.

[0018] Using the modal fingerprint baseline as an operational reference, incremental comparisons are performed on real-time data to identify deviations in tension, displacement, current, and torque in time, amplitude, frequency, and phase, thereby achieving modal frequency drift monitoring.

[0019] Preferably, step S2 includes:

[0020] Based on the modal fingerprint baseline, the tension, displacement, current and torque data acquired in the current operating cycle are compared point by point to extract offset information in time, frequency, amplitude and phase.

[0021] By combining the comparison results of multiple operating cycles, a modal response evolution time series is constructed, the drift velocity of the modal frequencies is calculated, and the trend correlation between multiple physical quantities is identified.

[0022] By conducting an intersection analysis of the drift trend of the modal frequency and the change path of the motor drive frequency, the convergent relationship between the two in terms of frequency value and response behavior can be identified.

[0023] Based on the time interval during which the modal frequency enters the driving frequency range, and combined with the characteristics of synchronous enhancement of multiple physical quantities, the time range in which resonance risk exists is determined and marked as the risk window.

[0024] Preferably, the risk window is determined based on the initial moment when the modal frequency enters the range of fluctuation within one Hertz above or below the driving frequency, and is combined with the synchronous enhancement of tension wave peak, current harmonics, displacement acceleration and torque slope as the judgment criteria to confirm the existence of modal coupling risk.

[0025] Preferably, step S3 includes:

[0026] High-response data segments of tension, displacement, current and torque are collected within the risk window, and structural response playback paths are constructed in chronological order.

[0027] Key feature points are selected along the playback path, and virtual substitution processing is performed to generate multiple physically consistent substitution expression paths, forming a counterfactual playback set.

[0028] Frequency proximity analysis is performed on the counterfactual replay set to calculate the time proportion of the modal frequency and driving frequency of each path falling into the proximity interval, and the probability of the same frequency is evaluated accordingly.

[0029] Based on the same frequency probability in the playback set, determine the early intervention time, intervention duration period and phase change amplitude, and output a phase correction budget that includes the time point and phase requirements.

[0030] Preferably, step S4 includes:

[0031] Based on the phase correction budget, the intervention start time and termination time are set, and four types of parameters, namely tension, current, torque and displacement, are extracted at the risk window boundary. Two sets of anchor points are established as time domain references.

[0032] Using the time span between the two sets of anchor points as a unified reference time scale, a time mirror mapping relationship is constructed, and symmetrical constraints on response behavior are established on both sides of the mirror axis.

[0033] Under a unified time scale, the main trajectory, secondary trajectory and phase trajectory of the driving frequency are set so that the excitation signal avoids the modal response path in the frequency, amplitude and phase dimensions at the same time.

[0034] The set drive trajectory is superimposed on the real playback path and the virtual alternative path to monitor the dynamic changes of tension, current, torque and displacement, and verify the effectiveness of the trajectory peak-shifting control.

[0035] Preferably, in the process of constructing the main frequency trajectory, the secondary frequency trajectory, and the phase trajectory, the main frequency trajectory avoids the modal frequency by setting a segmented linearly adjusted frequency value, the secondary frequency trajectory suppresses harmonic energy by limiting the harmonic amplitude to no more than 20% of the main frequency, and the phase trajectory ensures that the excitation phase and the modal response are staggered by increasing and decreasing phase offsets, thereby achieving synchronous coordination of three-dimensional peak-shifting control.

[0036] Preferably, step S5 includes:

[0037] After the off-peak driving trajectory is set, the virtual impedance value is dynamically set according to the trend of structural response amplitude change within the risk window, and the growth rate and decline amplitude of the virtual impedance over time are adjusted synchronously.

[0038] Viscoelastic damping devices with physical response adjustment capabilities are installed in key parts of the structure. By controlling the thermal expansion structure to adjust the contact area, the energy dissipation capacity in the high coupling range is enhanced.

[0039] Under the synergistic effect of virtual impedance and viscoelastic damping, a three-layer energy dissipation channel is constructed, which includes main driving buffer delay, energy transfer within the structure, and harmonic frequency band isolation, and the energy flow evolution process is continuously tracked.

[0040] Under stable conditions of the energy dissipation channel, the structural response intensity is divided into three levels of events, which correspond to increasing the damping parameter, jointly adjusting the impedance amplitude and driving frequency, and lowering the execution frequency and phase lag, respectively, to achieve graded compression of resonant energy.

[0041] Preferably, step S5 includes:

[0042] After the dynamic energy dissipation channel reaches a stable diffusion state, the time-reversal phase traction start-up interval is determined based on the section where the energy dissipation rate decreases and the vibration response delay falls back. A phase traction path is established within the start-up interval, and the phase of the driving signal is reduced in reverse time to form a time backtracking process.

[0043] While performing time-reversal phase traction, the excitation residual energy captured and stored in the previous stage of energy dissipation is injected into the input terminal of the drive source in the form of reverse phase micropulses through the piezoelectric energy storage array, forming an interference wave opposite to the main drive signal to interrupt the resonant chain.

[0044] During the phase traction and reverse phase micropulse injection process, the tunable frequency phonon bandgap device deployed at key nodes in the structural propagation path is activated. By adjusting the cavity geometry and material stiffness, the frequency band shift is achieved, pushing the resonant excitation frequency band away from the modal frequency response region.

[0045] Under the combined effect of time-reversal phase traction, inverse micropulse injection, and phonon bandgap structure frequency shift, the modal frequency and driving frequency are rapidly decoupled, and the vibration response amplitude decreases abruptly, thus completing the rapid extinguishing control of the resonance energy and forming a closed-loop regulation.

[0046] The mine electromechanical equipment operation safety status assessment system includes an electromechanical coupling time-frequency observation module, a modal frequency evolution analysis module, a risk quantification and prediction module, a time-domain anchor point correction module, an energy dissipation control module, and a rapid vibration extinguishing control module.

[0047] The electromechanical coupling time-frequency observation module establishes an electromechanical coupling time-frequency observation layer, continuously acquires the dynamic characteristics of tension data, displacement data, current data, and torque data, and generates a modal fingerprint baseline based on joint analysis to provide a basic reference for subsequent frequency evolution.

[0048] The modal frequency evolution analysis module performs coherent decomposition based on the modal fingerprint baseline, calculates modal drift velocity, and identifies risk windows where driving frequencies gradually approach each other, thus revealing frequency evolution trends in advance.

[0049] The risk quantification and prediction module runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, quantifying risk prediction into an executable indicator.

[0050] The time-domain anchor point correction module establishes a time-domain double mirror anchor point at the boundary of the risk window based on the phase correction budget. It uses this anchor point to redefine the unified time scale, forming a continuous staggered drive trajectory to achieve synchronous alignment of frequency offset.

[0051] The energy dissipation control module, relying on the staggered drive trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, thereby realizing the graded compression of resonant energy and gradually suppressing the vibration amplification effect.

[0052] The rapid oscillation extinguishing control module performs time-reversal phase traction under stable conditions formed by the dynamic energy dissipation channel. By injecting inverse phase micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure, it triggers a nonlinear frequency band migration mechanism to achieve rapid oscillation extinguishing of resonance, thereby completing the closed-loop dynamic control process.

[0053] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0054] This invention constructs an electromechanical coupled time-frequency observation layer to comprehensively perceive multi-dimensional operational characteristics such as tension, displacement, current, and torque, achieving baseline extraction of modal fingerprints for the first time. Furthermore, by combining coherent decomposition and counterfactual playback mechanisms, it reveals the coupling risk window where modal frequencies and driving frequencies converge in advance, accurately depicting the evolution path of potential resonance hazards. Then, through the collaborative design of time-domain dual-mirror anchor points, staggered driving trajectories, and adjustable structural damping, it constructs an energy dissipation channel with time response capabilities, compressing potential resonance energy in stages. Finally, under the action of inverted phase traction, inverse-phase micropulse injection, and phonon bandgap modulation, it rapidly triggers nonlinear frequency band migration, achieving active vibration extinguishing control of the structure. This method breaks through the limitations of real-time performance and predictability in traditional vibration control technology, forming a complete closed-loop dynamic control system from "risk identification—trend evolution—path intervention—energy reduction—vibration extinguishing," significantly improving the safety assurance capability of mine electromechanical equipment operation and providing solid support for intelligent, proactive fault prevention and safe production decision-making. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0056] Figure 1 This is a flowchart of the method for assessing the operational safety status of mining electromechanical equipment according to the present invention.

[0057] Figure 2 This is a schematic diagram of the modules of the mine electromechanical equipment operation safety status assessment system of the present invention. Detailed Implementation

[0058] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0059] This invention provides, for example Figure 1 The method for assessing the operational safety status of mining electromechanical equipment, as shown, includes the following steps:

[0060] S1. Establish an electromechanical coupling time-frequency observation layer to continuously acquire dynamic characteristics of tension data, displacement data, current data and torque data, and generate a modal fingerprint baseline based on joint analysis to provide a basic reference for subsequent frequency evolution.

[0061] During the operation of a mine hoist, in order to dynamically grasp the modal frequency evolution behavior of the entire structure, it is necessary to establish a high-precision data observation mechanism for key mechanical parameters, thereby extracting multi-source features and constructing a stable modal recognition reference structure. The specific steps are as follows:

[0062] In the electromechanical structure of a mine hoist, a real-time data acquisition foundation is established around the wire rope, hoisting container, motor stator winding, and motor output shaft. For the wire rope, high-sensitivity fiber optic tension sensors are used, with multiple mounting points along the winding path on the drum, located at the initial, intermediate, and outermost winding layers. This arrangement can identify tension differences caused by changes in winding layers, as well as the non-uniform distribution of force on the wire rope during rapid hoisting. Each sensing point is sealed with a stainless steel protective shell to ensure long-term operation without corrosion or interference in dusty, high-humidity, and high-pressure environments. To record the real-time positional changes of the hoisting container, three sets of reflective targets are installed equidistantly inside the shaft, and a laser interferometer is installed at the top of the shaft. The change in distance of the reflective targets is measured using optical interference principles, thereby accurately calculating the instantaneous displacement of the hoisting container. On the motor side, wideband current transformers with excellent electromagnetic interference resistance are installed on the three-phase input cable to capture the real-time current variation curve. A signal shaping circuit is installed at the data output end to eliminate high-frequency noise interference, ensuring accurate reconstruction of the current waveform for each cycle. Simultaneously, a non-contact rotary torque sensor is installed at the motor output shaft, utilizing inductive coupling to measure minute changes in force at the shaft end and record the continuous change in torque over time. All sensors are synchronized at the millisecond level via a unified time controller, ensuring consistency in timestamps across various physical quantities. Unlike traditional methods using a single tension meter or current sampling point, this approach covers the most structurally responsive key locations in the electromechanical link, ensuring that the collected data comprehensively reflects the dynamic changes in the operating state.

[0063] After the sensing structure completes data acquisition, dynamic feature extraction and preprocessing are performed on the acquired tension, displacement, current, and torque data. In tension data processing, a sliding window method is first used to identify the rising and falling edges of the tension signal, marking short-term abrupt changes and recording the peak duration and slope to identify instantaneous load abrupt changes. For displacement data, high-resolution differential calculations are used to extract the velocity curve, and acceleration is further calculated in steep velocity change regions to identify braking points, starting points, and limit speed points during the lifting process. In current data processing, the complete power frequency cycle waveform is preserved, and the peak current, current phase difference, and harmonic amplitude of each cycle are calculated to identify changes in the motor's electromagnetic response under load. In torque data, the hysteresis relationship between motor output power and load response is determined by detecting slope change points and torque peak intervals. To ensure the accuracy and reliability of the feature data, a multi-source cross-validation method is used, such as comparing the tension change rate with the torque change slope to verify whether the two have a synchronous relationship. This feature extraction process not only focuses on the change pattern of each physical quantity itself, but also pays more attention to the dynamic response relationship between physical quantities, thereby ensuring that the data can truly and accurately reflect the operating status of the equipment.

[0064] After extracting various dynamic features, these features are jointly aligned on a unified time axis and multi-source fusion analysis is performed to construct a modal fingerprint baseline. During data alignment, based on the unified clock marking mechanism established in the previous steps, tension peaks, displacement acceleration abrupt changes, current phase shifts, and torque fluctuation slopes are mapped to the same time nodes, forming a unified multi-measurement sequence. Subsequently, the sequences are cross-compared, for example, observing whether drastic tension changes are always accompanied by sudden torque increases, and whether current harmonics exhibit specific trends before and after displacement abrupt changes. If significant temporal correlations exist, these responses are determined to be different manifestations of the same physical modal behavior. By repeating the above extraction, alignment, and comparison process under different operating conditions, a set of stable response trajectories containing multiple physical quantities and spanning time periods can be established. These trajectories constitute the modal fingerprint baseline, representing the typical modal response structure exhibited by the equipment under normal operating conditions. Compared with existing methods that rely solely on single-point displacement or single-frequency response for modal identification, this method not only achieves multi-dimensional characterization of modal response but also significantly improves the accuracy and anti-interference capability of modal identification through cross-physical domain joint construction, making it more practical and adaptable.

[0065] After the modal fingerprint baseline is constructed, it serves as a standard response reference for the entire device's operating status, used for continuous comparison and evolution monitoring of dynamic behavior in subsequent operating cycles. In practical applications, the collected real-time data is imported into the comparison process in batches, and tension fluctuation slope, displacement transition amplitude, current harmonic variation, and torque response abrupt change are re-extracted according to the previous alignment and feature extraction methods. Each type of feature is precisely compared with the corresponding feature trajectory in the baseline to identify anomalies such as time delay, peak increase, frequency drift, or phase shift. In particular, bidirectional verification is performed on the response matching between tension and torque, i.e., determining whether the torque abrupt change occurs before or after the tension peak. This timing shift can provide early warning of possible modal drift signs. Similarly, if the periodic enhancement or waveform distortion of current harmonics continuously deviates from the modal fingerprint trajectory, it will also serve as an early signal of the gradual evolution of modal frequencies. By incrementally comparing real-time data with the modal fingerprint baseline point by point, the degree of deviation of each type of physical quantity in the four dimensions of time, amplitude, frequency and phase can be quantified, thereby realizing dynamic monitoring of the modal frequency drift trend of the whole structure.

[0066] S2, with the support of the modal fingerprint baseline, performs coherent decomposition, calculates modal drift velocity and identifies risk windows where the driving frequency gradually approaches, so that the frequency evolution trend can be revealed in advance;

[0067] After establishing the modal fingerprint baseline, it is necessary to identify the evolution trend of modal frequencies during device operation, quantify modal response offset behavior, and reveal risk periods close to the driving frequency in advance, thereby achieving proactive identification of potential hazards. The specific steps are as follows:

[0068] Using the established modal fingerprint baseline as a standard reference, the tension change curves, displacement transition trajectories, current waveform responses, and torque dynamic profiles collected during the current operating cycle are compared point by point to clarify their correspondence with the modal fingerprint in four dimensions: time, frequency, amplitude, and phase. To ensure the accuracy of the comparison, each physical response needs to be synchronously calibrated on the time axis. For example, for the wire rope tension curve, it is determined whether the peak value corresponds to a shift in the loading stage position in the fingerprint, whether the peak amplitude rises or falls, and whether the slope change accelerates or slows down; for the displacement response of the lifting container, it is analyzed whether the acceleration rise segment appears earlier or whether the amplitude increases; for the motor current response, it is necessary to compare whether the main frequency distribution area shifts to higher or lower frequencies overall and whether the harmonic content increases; for the torque response, it is necessary to check whether the abrupt change point occurs earlier and whether the rise rate increases. The above comparison results need to establish a complete data structure to ensure that each type of response behavior has a corresponding offset to its standard fingerprint, so that the subsequent trend evolution has a quantitative basis. Unlike existing technologies that rely on manual observation of frequency spectra, this step establishes a complete comparison dimension based on standard fingerprints, significantly improving the automation and accuracy of frequency evolution identification.

[0069] Based on the results of multidimensional comparison, the offset changes within multiple consecutive operating cycles are extracted to establish a time series of modal response evolution, and the modal drift velocity is calculated accordingly. Modal drift velocity does not refer to the rate of change of a single physical quantity, but rather the time-derived behavior of each physical quantity during its deviation from its standard trajectory. For example, in the change of tension in a wire rope, if its peak value advances from 38 seconds to 36 seconds and then to 34 seconds in three consecutive cycles, it indicates that the loading point is continuously moving forward, and its time drift rate is calculated to be 2 seconds per cycle. If the peak tension increases from the original 800 Newtons to 840 Newtons and then to 890 Newtons, the trend of tension enhancement is inferred. Similarly, if the current dominant frequency shifts from the original 48 Hz to 49.5 Hz and then to 51 Hz, the rate of increase of the dominant frequency can be calculated to further analyze whether it approximates the modal frequency. To ensure the accuracy of the analysis, the changes in tension and current need to be correlated and verified, i.e., whether the increase in the tension slope is synchronized with the enhancement of current harmonics; and whether the advance in displacement transition is synchronized with the increase in the torque slope. If two types of physical quantities exhibit the same trend of change in the same direction and their rates steadily increase over multiple operating cycles, it indicates that the modal response has indeed undergone an overall evolution. Compared to methods that only monitor frequency fluctuations, this method emphasizes the integrity of the structural response behavior and verification of cross-trends at multiple points, thereby ensuring that the assessment results of modal frequency drift velocity are more reliable.

[0070] After calculating the rate of change of the modal frequency, the trend of the modal response frequency change needs to be compared with the current drive frequency of the motor to identify any potential convergence risk. This assessment is based on two time series: one is the evolution path of the modal response frequency, and the other is the time change path of the motor drive frequency. The convergence of these two time series requires that their frequency values ​​gradually approach each other and that there is a temporal convergence trend. For example, if the modal response frequency drifts upward at a rate of 0.8 Hz per minute, while the drive frequency remains at a fixed value of 49 Hz, it is necessary to calculate how far the current modal frequency is from 49 Hz, and how long it will take to enter the ±1 Hz approach range at the current drift rate. If this time is less than a certain threshold (e.g., 10 minutes), a preliminary assessment of convergence risk can be made. However, it should be noted that merely having similar frequency values ​​is insufficient to constitute a criterion for determining resonance risk; therefore, the synchronicity of fluctuations in the physical responses during the frequency convergence period must also be verified. If tension peaks, enhanced current harmonics, a surge in displacement acceleration, and abrupt changes in torque slope are found to occur simultaneously near the frequency value, then the frequency approach can be further identified as a high-risk state. Through this dual-condition identification—the synergistic verification of the frequency value approach trend and the synchronous enhancement of multiple responses—potential modal coupling risks can be identified with high reliability.

[0071] Based on the frequency convergence identification results, a time window where the driving frequency and modal frequency may overlap is constructed and marked as a risk window. The boundary of the risk window is calculated from the time point when the modal frequency enters the range of ±1 Hz of the driving frequency, until the modal frequency drift trend reverses or the driving frequency change direction changes. For example, if the modal frequency enters 48 Hz at the 50th minute, the driving frequency is fixed at 48 Hz, and the modal frequency continues to drift to 50 Hz and then falls back, then the risk window can be defined as the period from the 50th minute to the 65th minute. Within this risk window, it is necessary to continuously monitor whether key physical quantities exhibit nonlinear surge phenomena, such as tension fluctuations increasing from a period of 0.3 seconds to 0.1 seconds, current harmonic amplitude amplifying by more than 2 times, torque peaks lasting for more than 10 seconds, and displacement surges doubling in number. If the above abnormal responses persist within the risk window without any load changes or braking intervention, it can be confirmed that the equipment has entered the coupling critical region. At this time, the risk window must be the priority target for subsequent phase correction, peak shaving control, and energy suppression interventions.

[0072] S3 runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, and quantifies the risk prediction into an executable indicator.

[0073] To prevent the equipment from entering a critical resonance state when the modal frequency is close to the driving frequency, it is necessary to reconstruct the potential response evolution path within the identified risk window, assess the probability of resonance, and set executable intervention parameters accordingly to achieve a quantitative output of risk prediction. The specific steps are as follows:

[0074] Key physical response data within the risk window defined in the preceding steps are selected as the basis for analysis. These include the time fluctuation curve of wire rope tension, the dynamic acceleration curve of lifting container displacement, the real-time three-phase waveform curve of motor stator current, and the dynamic torque response curve of motor output shaft. These data must meet the same timestamp accuracy as the modal fingerprint baseline and maintain a sampling frequency of no less than 100 times per second. Based on this, segments showing significant changes in structural response are extracted from these continuous data and used as input segments for counterfactual playback. Specifically, segments in the tension curve where the continuous fluctuation amplitude exceeds 10% of the rated tension are designated as high-response segments; periods in the current waveform where harmonic energy increases by more than 30% are marked as excitation enhancement segments; and points where the torque change rate increases by more than 50% within 2 seconds are designated as critical turning points. Subsequently, these segments are organized chronologically to form a dynamic path sequence of structural response evolution. By sequentially combining these segments on the time axis, the playback and reconstruction process of the true response can be achieved, thereby accurately reproducing the evolution trajectory of the structural modal frequency drift before and after.

[0075] After constructing the realistic playback path, replacement operations are performed around the key feature points of the aforementioned segments to construct a set of physically plausible virtual alternative paths. The construction of the alternative paths must adhere to three principles: keeping the data of the non-replaced segments unchanged, limiting each replacement to only one parameter type, and not introducing abrupt changes that violate dynamic constraints after replacement. Taking wire rope tension as an example, if there is a segment in the playback path where the tension jumps to 950 Newtons and fluctuates continuously for 3 seconds, a simulated curve is constructed to replace this segment with a maximum amplitude of 750 Newtons, a fluctuation period shortened to 2 seconds, and a slope that changes from steep to a gradual increase. For the current response, the period in the original waveform where the third harmonic amplitude is 50% higher than the dominant frequency is replaced with a waveform where the harmonic amplitude is only 30% of the dominant frequency, and the waveform rise time is doubled to reflect low-intensity excitation. On the torque curve, if there is an event in the original path where a sudden 150 N·m occurs within 0.5 seconds, an alternative path is constructed that smoothly transitions to the same peak value within 1 second. Using the above method, a complete set of virtual operating paths can be generated. Each path retains the basic form of the structural response while eliminating extreme factors that trigger modal excitation. The virtual paths and real paths together constitute a replay set for subsequent quantitative analysis of synchronous risks.

[0076] Frequency proximity analysis and dynamic probability estimation are performed on all paths in the playback set to determine the probability of modal and drive frequencies coinciding under actual and hypothetical operating conditions. The analysis steps include: first, calculating the modal frequency value for each path at each time point, which can be obtained by jointly fitting the tension fluctuation period, current harmonic dominant frequency, torque mutation period, and displacement acceleration mutation interval from the previous modal response characteristics; then, calculating the point-by-point difference between the modal frequency value and the motor drive frequency, and setting a frequency proximity threshold of ±1 Hz; the cumulative time of all time periods falling within this range is the same-frequency exposure time for that path. This process is repeated to traverse all playback paths, calculating the total exposure time for each path, and calculating its same-frequency ratio within the entire risk window. For example, if the risk window is 30 seconds, and a path is in the same-frequency range for 15 seconds within that window, its same-frequency ratio is 50%. By summing the same-frequency ratios of all paths and performing statistical distribution analysis, the time-weighted average of the same-frequency probability can be obtained. If the same frequency ratio exceeds 40% for more than 80% of the paths, and the physical response amplitude significantly increases within the frequency range, the current risk window is determined to be a high-coupling-risk state, requiring proactive intervention. This method effectively overcomes the limitations of traditional resonance prediction methods that rely solely on historical response experience or static mode superposition estimation, possessing dynamism, multi-path coverage, and physical consistency.

[0077] Based on the same-frequency probability assessment results of the path set, a phase correction budget for structural intervention is formulated, clarifying the optimal timing, duration, and required amplitude of future intervention. The budget setting process is as follows: First, identify the earliest time point in the playback path where the modal frequency first enters the driving frequency threshold range, as the initiation time for early intervention; then, locate the path with the longest frequency overlap duration, and use its time span as the minimum intervention duration; finally, assess the required phase correction response time based on the maximum rate of response change to avoid intervention failure in the steepest response slope segment. For example, if the analysis results indicate that the tension modal frequency first enters 47.5 Hz at 42 seconds, and the motor driving frequency is 48 Hz, then the early intervention time is 40 seconds; if the longest path frequency overlap time is 18 seconds, then the intervention should last at least 20 seconds; if the current harmonics increase from 20% to 60% in this segment, then the intervention phase adjustment is required to achieve an offset of at least 0.5π. The final budget document should clearly define: the start time, control duration, phase change range, and response amplitude requirements, serving as the input parameters for subsequent anchor point insertion and drive trajectory staggered control. This structured phase budget output achieves precise alignment between risk identification and control implementation.

[0078] S4. Based on the phase correction budget, a time-domain dual-mirror anchor point is established at the boundary of the risk window. The unified time scale is redefined using this anchor point to form a continuous staggered driving trajectory, thereby achieving synchronous alignment of frequency offset.

[0079] To effectively execute the intervention parameters set in the phase correction budget, two sets of time-domain anchor points need to be set at the boundary moments of the risk window. A unified time reference scale is established based on the anchor points, and a continuous drive trajectory capable of frequency peak-shifting control is constructed accordingly to ensure that the modal frequency and the drive frequency maintain a synchronous avoidance relationship. The steps are as follows:

[0080] Based on the three parameters specified in the generated phase correction budget—intervention start time, control duration, and phase adjustment amplitude—the boundary information of the risk window is extracted, and two sets of anchor points are set at the start and end time points respectively. Anchor points are not merely time coordinate markers, but dynamic reference points set in conjunction with the key physical state parameters corresponding to that time point. For example, if the budget results show that the modal frequency will enter the ±1 Hz range of the motor drive frequency at the 60th second, and the budget duration is 18 seconds, then the 57th second is set as the start anchor point and the 75th second as the end anchor point. At these two time points, four types of structural response parameters are extracted: at the 57th second, the local peak value of the wire rope tension curve is 810 Newtons, the main frequency of the current waveform is 47.8 Hz, the torque rise slope is 45 N·m / s, and the displacement acceleration is 3.6 m / s²; the corresponding data at the 75th second are 785 Newtons, 48.2 Hz, 42 N·m / s, and 3.2 m / s². These data, together with the time coordinates, form a dual-anchor structure, used to bind the time axis and the response status as a dual standard.

[0081] Using the time span between the starting and ending anchor points as the original length of a unified reference time scale, all sub-time segments within this period are re-encoded, and a mirror mapping relationship is established. Specifically, the period from 57 seconds to 75 seconds is divided into 36 isochronous segments, each 0.5 seconds long. Using the midpoint 66 seconds as the mirror axis, a symmetrical mapping is constructed: segment 1 mirrors segment 36, segment 2 mirrors segment 35, and so on. This mirror structure is not only symmetrical in time but also requires constructing symmetrical mapping scales for all response behavior parameters in the first half of the segment in the corresponding second half. For example, if the maximum fluctuation amplitude of the tension peak at 59 seconds is 22 Newtons, after mirroring, the expected fluctuation at 73 seconds should not exceed 18 Newtons; if the current harmonic rise rate at 60 seconds is 0.4 Hz per second, after mirroring, the fall rate at 72 seconds should be controlled within 0.35 Hz per second. This method achieves temporal symmetry modeling of response behavior, ensuring that within the risk window, the response behavior can achieve controllable symmetry and energy balance on both sides of the time axis. This method is more flexible than the existing technology that only sets a fixed control section, and has the ability to dynamically respond and provide symmetrical compensation.

[0082] Based on a unified time scale, a continuously controllable drive trajectory curve is constructed to ensure that the drive frequency stays as far away from the modal frequency trajectory as possible throughout the risk window, achieving off-peak operation. This drive trajectory consists of three levels: the main frequency trajectory, the secondary frequency trajectory, and the phase trajectory. The main frequency trajectory starts at 57 seconds, slightly adjusting downwards from the original constant value of 48 Hz to 47.6 Hz, and then recovers at a rate of 0.025 Hz per second, returning to 48 Hz at 66 seconds, and stabilizing at 48.2 Hz at 75 seconds. This prevents the main frequency from overlapping with the modal frequency of 47.9 Hz throughout the window. The secondary frequency trajectory refers to controlling the harmonic generator in the inverter during the main frequency adjustment process to suppress the amplitude of the third and fifth harmonics to no more than 20% of the main frequency, preventing the harmonic frequencies from falling into the modal response excitation range. Regarding the phase trajectory, the initial phase is set to 0.2π starting from the 57th second, increasing by 0.05π per second until reaching 0.65π at the 66th second, where it remains stable. Then, after the 70th second, it gradually decreases to 0.3π at the termination point, achieving phase transition balance. This triple trajectory together forms the drive control path, ensuring that the motor excitation signal is simultaneously offset from the modal response in the three dimensions of frequency, amplitude, and phase, thereby avoiding entering the excitation region of the structure's natural frequency.

[0083] To verify the effectiveness of the aforementioned driving trajectory, it needs to be re-overlaid onto the previously constructed playback path and virtual alternative path for simulation verification. During the simulation, the constructed main frequency, harmonic amplitude, and phase trajectory are input cycle by cycle, and the corresponding tension fluctuation peak, current harmonic intensity, torque transition response, and displacement abrupt change time points are recorded in real time. In actual execution, if the modal frequency is still within ±0.5 Hz of the driving frequency during the period from 63 to 67 seconds, but the response amplitude is reduced by more than 30% compared to the original predicted value, it indicates that the driving trajectory has an effective buffering effect; if the torque response does not show continuous abrupt changes or vibration superposition during the high-frequency excitation period, it indicates that the phase modulation trajectory has a response decoupling function; if the current harmonic peak value is stable below 15% of the main frequency during the high-risk period, it indicates that the harmonic suppression trajectory can significantly weaken the excitation source energy. If all three dimensions meet the set error range, the driving trajectory can be determined as an effective trajectory, and the next step, dynamic energy suppression strategy stage, can be entered.

[0084] S5, relying on the staggered driving trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, realizes the graded compression of resonant energy, and gradually suppresses the vibration amplification effect.

[0085] To effectively respond to the time-controlled path constructed by the staggered driving trajectory, it is necessary to introduce a virtual impedance with time evolution characteristics and a structural damping device with variable physical response. This forms a dynamic energy dissipation channel between the drive and the structure, realizing the multi-level distribution and gradual dissipation of resonant excitation energy, thereby suppressing the amplitude superposition trend of the modal response. The specific steps are as follows:

[0086] Based on the established staggered drive trajectory, key sections within the risk window where frequency changes and structural response fluctuate significantly are identified, and virtual impedance parameters are dynamically set according to the time progression. Taking the control section between the 57th and 75th seconds as an example, as the drive frequency increases from 47.6 Hz to 48.2 Hz, the peak structural tension increases from 815 N to 865 N, and the peak motor output torque jumps from 260 N·m to 315 N·m. Based on this response trend, the initial virtual impedance value is set to 3.8 N·s / m, and then increased at a rate of 0.1 N·s / m, reaching a peak of 5.6 N·s / m at the 66th second, before decreasing back at the same rate. This virtual impedance value actively buffers the drive signal before it reaches the high coupling probability stage by controlling the transient component response in the motor excitation output. During this process, the energy density change in the harmonic diffusion region also needs to be calculated in real time and compared synchronously with the impedance adjustment process to ensure that each virtual impedance adjustment is directly related to the energy characteristic change of the actual response.

[0087] During the virtual impedance adjustment process, damping components with responsive adjustment capabilities are selected and deployed at key structural nodes to enable the energy conduction path to have physical dissipation capabilities. Specifically, a set of bidirectional compressible viscoelastic dampers is installed between the guide wheel axle of the lifting container and the main wire rope drum support. This damper consists of an outer ceramic friction plate, a middle polymer adhesive layer, and an inner metal expansion core. The damper thickness is adjusted by controlling the thermal expansion of the core, thereby indirectly controlling the contact area and friction path length. During the period from the 63rd to the 70th second, when the modal frequency enters the range of ±0.5 Hz from the motor drive frequency, the damper is expanded by increasing the control current, increasing the contact area by 12%. This raises the overall damping coefficient from the initial value of 350 N·s / m to 425 N·s / m, effectively suppressing the continued increase in structural amplitude during this period. The tension waveform is acquired in every second and the rate of change of the peak value is determined. If the amplitude increase rate is found to be greater than 30% of the previous second, the damping enhancement program is immediately triggered to ensure that the structural response is always kept within the controllable amplitude range.

[0088] Within the control period of the combined effect of virtual impedance and structural damping, a dynamic energy dissipation path is constructed that can automatically switch according to the response energy level. This energy dissipation channel includes three layers of control mechanisms: the first layer is the energy buffer delay between the main drive and the structure, that is, by adjusting the rate of change of virtual impedance, the rise time of the excitation signal is extended, so that the structural response is in a phase of gradual growth; the second layer is the energy conversion channel inside the structural vibration path, that is, the high-frequency energy carried by the displacement abrupt segment is converted into local strain dissipation, forming local heating and deformation accumulation in the structural region where the damping device is located, replacing the load of the whole structural response; the third layer is the construction of a harmonic energy isolation zone, that is, by inserting a phase difference fine-tuning operation into the drive segment corresponding to the harmonic frequency, the harmonic concentrated energy segment is moved out of the modal excitation frequency band, interrupting the excitation path. Taking the 67th second as an example, when the tension peak reaches 870 Newtons and the harmonics are enhanced to 0.45 times the amplitude of the main frequency, energy transfer occurs in the structural displacement fluctuation segment. The frequency energy originally concentrated in the drum bearing seat is moved to the lifting container guide rail, causing the main excitation point to shift, thereby disrupting the energy closed chain of the resonance path. Compared to the traditional method of relying on a single damper to absorb impact energy, this multi-layer energy dissipation construction method has path control capability and dynamic response adjustment capability, which is more in line with the dynamic coupling behavior control requirements of complex structures under variable load conditions.

[0089] After the energy dissipation channel is operating stably, all excitation behaviors within the channel are classified and managed, and a graded compression response strategy is set according to their vibration energy levels to achieve layer-by-layer suppression of resonant energy. In this embodiment, response events are divided into three levels: Level 1 events are local disturbances, characterized by tension wave peaks not exceeding 10% of the rated value, harmonic enhancement not exceeding 20% ​​of the main frequency, and torque changes not exceeding 10% of the average value within 5 seconds; Level 2 events are periodic transition disturbances, characterized by structural vibration duration greater than 3 seconds, frequency fluctuation range within ±0.8 Hz, and displacement change rate exceeding 1.5 m / s; Level 3 events are coupled excitation critical responses, characterized by modal frequencies continuously coinciding with the driving frequency for more than 6 seconds, accompanied by a sudden increase in current harmonic amplitude, high-frequency jumps in torque response, and amplification of tension slope. For Level 1 events, the damping coefficient is increased by only 10%. For Level 2 events, the virtual impedance and damping value are increased by 20%, and the driving frequency offset is increased by 0.2 Hz. For Level 3 events, phase lag control is immediately implemented, reducing the driving frequency to below 47 Hz, and the intervention is maintained for more than 12 seconds until all vibration indicators return to the Level 1 response level. Taking the 70th second as an example, after the detection of the harmonic amplitude reaching 0.6 times the dominant frequency and the torque response slope jumping to 90 N·m per second, the system determines that it has entered the Level 3 response level, the driving frequency is forcibly reduced to 46.8 Hz, and returns to normal after 72 seconds, with the peak response decreasing by 37%. Compared with conventional fixed damping control, this graded compression strategy has the ability to classify responses in a refined manner, which can minimize the risk propagation speed in the energy excitation and conduction path, and truly realize closed-loop control of the entire process from frequency peak shifting to energy management.

[0090] S6, under the stable conditions formed by the dynamic energy dissipation channel, performs time-reversal phase traction. By injecting inverse micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure, a nonlinear frequency band migration mechanism is triggered to achieve rapid extinguishing of resonance, thereby completing the closed-loop dynamic control process.

[0091] After the constructed dynamic energy dissipation path reaches a stable energy diffusion state, to prevent residual excitation energy from forming continuous resonance in the structure, a time-reversal phase traction operation needs to be further performed. This is achieved by injecting a reverse micro-pulse sequence with specific phase characteristics, which synergistically activates the pre-deployed shadow energy storage device and the spatial acoustic structural unit. This constructs a frequency band migration channel within the structural propagation path, enabling rapid decoupling and vibration extinguishing of the modal response frequency. The specific steps are as follows:

[0092] Based on the time control window formed during energy dissipation, the section where the energy dissipation rate decreases significantly and the vibration response slows down is identified as the starting interval for inversion traction. Taking the period from 72 seconds to 76 seconds as an example, monitoring data shows that the tension wave peak slowly decreases from 840 Newtons to 810 Newtons, with the rate of decrease decreasing from 10 Newtons per second to 3 Newtons per second. The current harmonic energy decreases from 40% of the main frequency peak to 28% and then tends to stabilize, indicating that there is still residual excitation energy in the structure. At this time, the inversion starting point is set at 74 seconds, and a phase traction path is established. The phase of the original driving signal is reduced in reverse by 0.05π per second, starting from the current 0.4π, forming a time-shifting path of continuous reverse displacement. This path reverses the phase change by controlling the excitation sequence input in reverse order within the driving signal generator. The signal output no longer evolves forward with time, but simulates the "time backtracking" process of the signal source, thus creating a directional deviation from the excitation response already formed in the structure, creating an instantaneous decoupling state between the main frequency excitation and the response frequency.

[0093] To enhance the penetration of the inverted phase traction along the structural path, the residual excitation energy captured and stored in the previous stage of energy dissipation needs to be released simultaneously to the input of the drive source via a piezoelectric energy storage array in the form of inverse micropulses. This energy storage array employs a double-layer inductor-piezoelectric coupling structure, capturing mid-to-low frequency vibration energy concentrated between 46.5-47.2 Hz during the high-excitation phase from 60 to 70 seconds. At 74 seconds, the energy release program is initiated, injecting five inverse excitation signals per second, each 0.15 seconds long, with a phase equal to the current drive phase plus π. The peak voltage of these signals is 70% of the main drive signal, and their energy direction is 180° opposite to the main drive signal, forming a stable superimposed interference wave. When this interference wave propagates along the structural path, it completely phase-counters the original vibration wave, suppressing the vibration response in space and forming an energy "cavity" region. The appearance of this cavity region causes an instantaneous interruption of the original resonant chain of the structure and actively reduces the modal frequency response speed.

[0094] During the phase traction and reverse-phase pulse injection processes, to suppress high-frequency reflections and residual energy loops within the structural path, a phonon bandgap device with a tunable frequency is activated at key nodes in the structural propagation path. This device consists of multiple sets of unconnected resonant cavities, each whose external micro-drive actuator can control in real time regarding its dimensions, internal pressure, and material hardness. For example, three sets of third-order resonant cavities are arranged at the interface between the wire rope and the drive drum, with cavity lengths set to 3.2 mm, 2.7 mm, and 2.4 mm, corresponding to blocking frequencies of 47.6 Hz, 47.1 Hz, and 46.8 Hz, respectively. At the 75th second, a bandgap adjustment program is initiated, causing thermal expansion of the cavities through micro-thermal excitation, shortening the cavity lengths to 2.9 mm, 2.5 mm, and 2.2 mm, respectively. The overall bandgap frequency shifts downward by 0.6 Hz, pushing the resonant excitation frequency band away from the modal frequency response region. Within 0.5 seconds after the phonon bandgap is triggered, the frequency energy distribution in the structural vibration path changes from a single frequency band concentration to a multi-frequency band dispersion, the maximum vibration energy density decreases by 21%, and the structural vibration response waveform tends to be flat, indicating that the phonon band successfully blocks the energy echo and the high-frequency flyback path, breaking the feedback resonance loop of the structure.

[0095] After the triple intervention measures were implemented, the structure entered a rapid response weakening process, characterized by a sudden decrease in the amplitude of vibration response fluctuations, a rapid drop in the frequency tracking curve, and a gradual widening of the phase difference between the motor drive signal and the structural response. Between the 77th and 80th seconds, the wire rope tension peak dropped below 790 Newtons, the structural acceleration decreased from 3.2 m / s² to 2.1 m / s², and the torque increase returned to a linear growth state after a high-frequency jump. Frequency monitoring data showed that the modal frequency rapidly decreased from 48.0 Hz to 46.5 Hz, while the drive frequency stabilized at 47.0 Hz, indicating that the drive and response decoupling was complete. Simultaneously, the frequency shift band of the phonon bandgap covered the original path of the modal excitation, the stored energy inverse micropulse automatically depleted, the excitation channel closed, and the structure no longer received any interference signals that could trigger re-excitation. At this point, the overall modal response exhibited typical "vibration extinguishing" characteristics—that is, the response energy dropped directly from the concentrated peak to a steady state without any secondary rise.

[0096] This invention constructs an electromechanical coupled time-frequency observation layer to comprehensively perceive multi-dimensional operational characteristics such as tension, displacement, current, and torque, achieving baseline extraction of modal fingerprints for the first time. Furthermore, by combining coherent decomposition and counterfactual playback mechanisms, it reveals the coupling risk window where modal frequencies and driving frequencies converge in advance, accurately depicting the evolution path of potential resonance hazards. Then, through the collaborative design of time-domain dual-mirror anchor points, staggered driving trajectories, and adjustable structural damping, it constructs an energy dissipation channel with time response capabilities, compressing potential resonance energy in stages. Finally, under the action of inverted phase traction, inverse-phase micropulse injection, and phonon bandgap modulation, it rapidly triggers nonlinear frequency band migration, achieving active vibration extinguishing control of the structure. This method breaks through the limitations of real-time performance and predictability in traditional vibration control technology, forming a complete closed-loop dynamic control system from "risk identification—trend evolution—path intervention—energy reduction—vibration extinguishing," significantly improving the safety assurance capability of mine electromechanical equipment operation and providing solid support for intelligent, proactive fault prevention and safe production decision-making.

[0097] The present invention relates to a method and system for assessing the operational safety status of mining electromechanical equipment. This system organically integrates an electromechanical coupling dynamic monitoring mechanism with an intelligent safety assessment model, constructing a closed-loop safety assessment system that combines real-time perception, risk identification, and proactive control capabilities. Based on multi-source sensing data, this system establishes an electromechanical coupling time-frequency observation layer through joint observation of key operating parameters such as tension, displacement, current, and torque. This forms a modal fingerprint baseline with multi-physical quantity correlation, achieving a high-precision characterization of equipment operating status and providing reliable time-series data support for safety assessment. Compared to traditional static analysis methods relying on single-point parameters, this scheme achieves dynamic quantification of structural response and global understanding of modal behavior, providing a realistic, continuous, and computable operational baseline for safety assessment.

[0098] Building upon this foundation, the system employs modal frequency evolution analysis and counterfactual playback mechanisms to proactively identify and quantitatively predict potential resonance risks, transforming traditional "post-event assessment" into "pre-event prediction." By calculating the convergence of modal drift velocity and driving frequency, the system dynamically defines risk windows and establishes a quantitative safety assessment model using indicators such as the probability of same frequency, phase offset, and energy dissipation rate, enabling multi-dimensional assessment of equipment health and risk levels. Simultaneously, driven by the assessment results, the system generates staggered driving trajectories based on phase correction budgets and introduces virtual impedance and adjustable structural damping to proactively construct dynamic energy dissipation channels, forming a real-time response chain from risk detection to energy regulation. This transforms safety assessment from passive analysis into an executable, traceable, and interventionist dynamic process.

[0099] Furthermore, after energy dissipation stabilizes, the system achieves rapid vibration extinguishing through time-reversal phase traction and phonon bandgap structure, forming a self-healing safety closed loop, enabling the assessment results to have real-time correction and feedback functions. Combining the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to construct a safety index system, this method can quantify equipment health, safety margin, and situational level at multiple levels, achieving intelligent, dynamic, and visualized safety assessment. This technology breaks through the limitations of "static monitoring and post-event correction" in mine electromechanical equipment safety assessment, establishing a new full-process assessment model from multi-source perception to active protection, providing systematic support for the inherent safety and intelligent decision-making of mine equipment.

[0100] Building upon this foundation, to further improve the dynamic prediction accuracy and state transition interpretability of safety assessments, a Markov model can be introduced to probabilistically model and predict the safety status of equipment. Specifically, the operating states of mining electromechanical equipment are divided into four safety levels: safe state (S1), slightly abnormal state (S2), high-risk state (S3), and failure state (S4). Based on the multi-source monitoring characteristics collected by the system, such as modal drift rate, frequency co-current probability, energy dissipation rate, and phase shift, a state transition matrix P = [p_ij] is constructed. Each p_ij in the matrix represents the probability that the system will transition from state Si to state Sj in the next assessment cycle. For example, when the modal frequency drift rate exceeds the threshold and the energy dissipation rate decreases, the probability of the system transitioning from S2 to S3 increases significantly; if peak shifting and virtual impedance linkage are effective, the probability of reversing from S3 to S2 or S1 increases. Through continuous time series state transition analysis, the steady-state distribution π = πP can be calculated, thereby obtaining the probability distribution of the equipment in each safe state under long-term operating conditions.

[0101] Furthermore, by combining the multi-path frequency probability data generated by the counterfactual replay chain, the Markov transition matrix can be dynamically updated to achieve real-time correction of the risk window. For example, after the system identifies a high-frequency resonance risk and initiates phase traction, the model can decrease the value of p(S3→S4) in real time while increasing p(S3→S2), reflecting the restorative effect of proactive intervention on the safety situation. Finally, the Markov steady-state distribution results are fused with the safety level output by the fuzzy comprehensive evaluation method to form a dynamic safety situation index (DSAI) based on state transition probability, thereby achieving the quantification, trend analysis, and adaptive prediction of the safety situation. This Markov model-based evaluation framework not only enables the system to quantify the evolutionary relationships between different risk events but also provides a dynamic probabilistic decision-making basis for safety management, further enhancing the foresight and scientific nature of this invention in the field of intelligent mine safety assessment.

[0102] This invention provides, for example Figure 2The mine electromechanical equipment operation safety status assessment system shown includes an electromechanical coupling time-frequency observation module, a modal frequency evolution analysis module, a risk quantification and prediction module, a time-domain anchor point correction module, an energy dissipation control module, and a rapid vibration extinguishing control module.

[0103] The electromechanical coupling time-frequency observation module establishes an electromechanical coupling time-frequency observation layer, continuously acquires the dynamic characteristics of tension data, displacement data, current data, and torque data, and generates a modal fingerprint baseline based on joint analysis to provide a basic reference for subsequent frequency evolution.

[0104] The modal frequency evolution analysis module performs coherent decomposition based on the modal fingerprint baseline, calculates modal drift velocity, and identifies risk windows where driving frequencies gradually approach each other, thus revealing frequency evolution trends in advance.

[0105] The risk quantification and prediction module runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, quantifying risk prediction into an executable indicator.

[0106] The time-domain anchor point correction module establishes a time-domain double mirror anchor point at the boundary of the risk window based on the phase correction budget. It uses this anchor point to redefine the unified time scale, forming a continuous staggered drive trajectory to achieve synchronous alignment of frequency offset.

[0107] The energy dissipation control module, relying on the staggered drive trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, thereby realizing the graded compression of resonant energy and gradually suppressing the vibration amplification effect.

[0108] The rapid oscillation extinguishing control module performs time-reversal phase traction under stable conditions formed by the dynamic energy dissipation channel. By injecting inverse phase micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure, it triggers a nonlinear frequency band migration mechanism to achieve rapid oscillation extinguishing of resonance, thereby completing the closed-loop dynamic control process.

[0109] The method for assessing the operational safety status of mining machinery and equipment provided in this embodiment of the invention is implemented through the aforementioned operational safety status assessment system for mining machinery and equipment. For details of the specific methods and processes of the operational safety status assessment system for mining machinery and equipment, please refer to the embodiments of the above-mentioned method for assessing the operational safety status of mining machinery and equipment, which will not be repeated here.

[0110] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the operational safety status of mining electromechanical equipment, characterized in that, Includes the following steps: S1, establish an electromechanical coupling time-frequency observation layer to continuously acquire dynamic characteristics of tension data, displacement data, current data and torque data, and generate a modal fingerprint baseline based on joint analysis; S2, with the support of the modal fingerprint baseline, performs coherent decomposition, calculates modal drift velocity and identifies risk windows where driving frequencies gradually overlap, so that frequency evolution trends can be revealed in advance; S3 runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, and quantifies the risk prediction into an executable indicator. S4. Based on the phase correction budget, establish a time-domain double mirror anchor point at the boundary of the risk window, and use this anchor point to redefine the unified time scale to form a continuous peak-shifting drive trajectory. S5, relying on the staggered driving trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, thereby achieving graded compression of resonant energy; S6 performs time-reversal phase traction under stable conditions formed by the dynamic energy dissipation channel. By injecting inverse micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure, a nonlinear frequency band migration mechanism is triggered.

2. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S1 includes: Tension sensors, displacement measuring devices, current transformers, and torque sensors are installed around the wire rope, lifting container, motor stator winding, and motor output shaft to acquire dynamic data on tension, displacement, current, and torque. The slope of change, acceleration, harmonic amplitude and location of abrupt response changes were extracted from tension, displacement, current and torque data, and multi-source cross-comparison was performed to identify the correlation between physical quantities. By aligning various feature data on a unified time axis and constructing modal response trajectories through time series and amplitude synchronization, a modal fingerprint baseline is formed. Using the modal fingerprint baseline as a reference, incremental comparisons are performed on real-time data to identify the shifts in tension, displacement, current, and torque in time, amplitude, frequency, and phase.

3. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S2 includes: Based on the modal fingerprint baseline, the tension, displacement, current and torque data acquired in the current operating cycle are compared point by point to extract offset information in time, frequency, amplitude and phase. By combining the comparison results of multiple operating cycles, a modal response evolution time series is constructed, the drift velocity of the modal frequencies is calculated, and the trend correlation between multiple physical quantities is identified. By conducting an intersection analysis of the drift trend of the modal frequency and the change path of the motor drive frequency, the convergent relationship between the two in terms of frequency value and response behavior can be identified. Based on the time interval during which the modal frequency enters the driving frequency range, and combined with the characteristics of synchronous enhancement of multiple physical quantities, the time range in which resonance risk exists is determined and marked as the risk window.

4. The method for assessing the operational safety status of mining electromechanical equipment according to claim 3, characterized in that, The risk window is determined based on the initial moment when the modal frequency enters the range of fluctuation within one hertz above or below the driving frequency, and is combined with the synchronous enhancement of tension wave peak, current harmonics, displacement acceleration and torque slope as the judgment criteria to confirm the existence of modal coupling risk.

5. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S3 includes: High-response data segments of tension, displacement, current and torque are collected within the risk window, and structural response playback paths are constructed in chronological order. Key feature points are selected along the playback path, and virtual substitution processing is performed to generate multiple physically consistent substitution expression paths, forming a counterfactual playback set. Frequency proximity analysis is performed on the counterfactual replay set to calculate the time proportion of the modal frequency and driving frequency of each path falling into the proximity interval, and the probability of the same frequency is evaluated accordingly. Based on the same frequency probability in the playback set, determine the early intervention time, intervention duration period and phase change amplitude, and output the phase correction budget.

6. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S4 includes: Based on the phase correction budget, the intervention start time and termination time are set, and four types of parameters, namely tension, current, torque and displacement, are extracted at the risk window boundary. Two sets of anchor points are established as time domain references. Using the time span between the two sets of anchor points as a unified reference time scale, a time mirror mapping relationship is constructed, and symmetrical constraints on response behavior are established on both sides of the mirror axis. Under a unified time scale, the main trajectory, secondary trajectory and phase trajectory of the driving frequency are set so that the excitation signal avoids the modal response path in the frequency, amplitude and phase dimensions at the same time. The set drive trajectory is superimposed on the real playback path and the virtual alternative path to monitor the dynamic changes of tension, current, torque and displacement, and verify the effectiveness of the trajectory peak-shifting control.

7. The method for assessing the operational safety status of mining electromechanical equipment according to claim 6, characterized in that, In the process of constructing the main frequency trajectory, secondary frequency trajectory and phase trajectory, the main frequency trajectory avoids the modal frequency by setting a segmented linearly adjusted frequency value, the secondary frequency trajectory suppresses harmonic energy by limiting the harmonic amplitude to no more than 20% of the main frequency, and the phase trajectory ensures that the excitation phase and the modal response are staggered by increasing and decreasing phase offset.

8. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S5 includes: After the off-peak driving trajectory is set, the virtual impedance value is dynamically set according to the trend of structural response amplitude change within the risk window, and the growth rate and decline amplitude of the virtual impedance over time are adjusted synchronously. Viscoelastic damping devices with physical response adjustment capabilities are installed in key parts of the structure. By controlling the thermal expansion structure to adjust the contact area, the energy dissipation capacity in the high coupling range is enhanced. Under the synergistic effect of virtual impedance and viscoelastic damping, a three-layer energy dissipation channel is constructed, which includes main driving buffer delay, energy transfer within the structure, and harmonic frequency band isolation, and the energy flow evolution process is continuously tracked. Under stable conditions of the energy dissipation channel, the events are divided into three levels according to the structural response intensity, which correspond to increasing the damping parameter, jointly adjusting the impedance amplitude and driving frequency, and reducing the execution frequency and phase lag, respectively.

9. The method for assessing the operational safety status of mining electromechanical equipment according to claim 1, characterized in that, Step S5 includes: After the dynamic energy dissipation channel reaches a stable diffusion state, the time-reversal phase traction start-up interval is determined based on the section where the energy dissipation rate decreases and the vibration response delay falls back. A phase traction path is established within the start-up interval, and the phase of the driving signal is reduced in reverse time to form a time backtracking process. While performing time-reversal phase traction, the excitation residual energy captured and stored in the previous stage of energy dissipation is injected into the input terminal of the drive source in the form of reverse phase micropulses through the piezoelectric energy storage array, forming an interference wave opposite to the main drive signal to interrupt the resonant chain. During the phase traction and reverse phase micropulse injection process, the tunable frequency phonon bandgap device deployed at key nodes in the structural propagation path is activated. By adjusting the cavity geometry and material stiffness, the frequency band shift is achieved, pushing the resonant excitation frequency band away from the modal frequency response region. The combined effects of time-reversal phase traction, inverse micropulse injection, and phonon bandgap structure frequency shift enable rapid decoupling of modal frequencies from driving frequencies and a sudden decrease in vibration response amplitude.

10. A mine electromechanical equipment operation safety status assessment system, used to implement the mine electromechanical equipment operation safety status assessment method according to any one of claims 1-9, characterized in that, It includes an electromechanical coupling time-frequency observation module, a modal frequency evolution analysis module, a risk quantification and prediction module, a time-domain anchor point correction module, an energy dissipation control module, and a rapid vibration extinguishing control module. The electromechanical coupling time-frequency observation module establishes an electromechanical coupling time-frequency observation layer, continuously acquires the dynamic characteristics of tension data, displacement data, current data, and torque data, and generates a modal fingerprint baseline based on joint analysis; The modal frequency evolution analysis module performs coherent decomposition with the support of the modal fingerprint baseline, calculates the modal drift velocity, and identifies the risk window where the driving frequencies gradually overlap, thus revealing the frequency evolution trend in advance. The risk quantification and prediction module runs a counterfactual replay chain based on the risk window, reconstructs the load transition segment using a virtual replacement method, estimates the instantaneous same-frequency probability and generates a phase correction budget, quantifying risk prediction into an executable indicator. The time-domain anchor point correction module establishes a time-domain double mirror anchor point at the boundary of the risk window based on the phase correction budget. It then uses this anchor point to redefine the unified time scale and form a continuous peak-shifting drive trajectory. The energy dissipation control module, relying on the staggered drive trajectory, synthesizes time-varying virtual impedance and introduces adjustable structural damping to construct a dynamic energy dissipation channel, thereby realizing the graded compression of resonant energy and gradually suppressing the vibration amplification effect. The rapid vibration extinguishing control module performs time-reversal phase traction under stable conditions formed by the dynamic energy dissipation channel. It triggers a nonlinear frequency band migration mechanism by injecting inverse micropulses and linking the shadow energy storage unit and the programmable phonon bandgap structure.

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