Multivariate monitoring data fusion method and system for slope tunnel model test

Abstract

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geotechnical engineering physical model test, more particularly, the present application relates to a kind of slope tunnel model test multi-element monitoring data fusion method and system. BACKGROUND

[0002] In the indoor physical model test of slope tunnel engineering, to comprehensively evaluate the mechanical response and stability state of the model under complex load conditions, multiple monitoring means are usually used. The existing technology generally uses geotechnical instruments such as soil pressure cell and strain gauge to monitor static mechanical parameters; at the same time, in order to obtain soil dynamic characteristic parameters, a resonance column test system is integrated and applied to apply controllable dynamic excitation to the model. This test mode combining static monitoring and dynamic testing aims to more deeply reveal the mechanical behavior of slope tunnel structure by comprehensively analyzing multi-source monitoring data.

[0003] However, in the test process of such integrated static and dynamic measurement, there is an inherent technical problem: the dynamic stress wave generated by the resonance column system propagates in the model medium and directly mechanically acts on the sensitive elements of the static parameter monitoring sensor buried therein. This dynamic excitation environment causes the output signal of the static sensor to be coupled with significant vibration interference components unrelated to the overall mechanical state change of the model, resulting in distortion of the original monitoring data. Due to the differences in the physical response characteristics of different types of sensors to dynamic excitation, this signal distortion further increases the difficulty of reliably extracting effective information directly related to the model stability from multi-element data, and restricts the accuracy and reliability of the model state evaluation results based on multi-source data fusion. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a kind of slope tunnel model test multi-element monitoring data fusion method and system to solve the problems raised in the above background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A kind of slope tunnel model test multi-element monitoring data fusion method, comprising the following steps: S1, collecting multi-element monitoring data in slope tunnel model test, including dynamic excitation signal generated by resonance column system and static response signal collected by static parameter monitoring sensor; S2, judging whether there is a coupled interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupled interference component, after detecting a preset model state mutation event, respectively collecting static response signal segments before and after the event as comparison samples; S4, analyzing the frequency domain characteristics of the contrast samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; S6, fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

[0006] Further, the multi-element monitoring data in the slope tunnel model test is collected, including the dynamic excitation signal generated by the resonance column system and the static response signal collected by the static parameter monitoring sensor, including: Synchronously collecting the dynamic excitation signal generated by the resonance column system and the static response signal collected by the static parameter monitoring sensor, and giving the synchronously collected dynamic excitation signal and static response signal a unified time stamp.

[0007] Further, based on the frequency characteristics of the dynamic excitation signal, it is judged whether there is a coupling interference component caused by dynamic stress wave in the static response signal, including: Analyzing the dynamic excitation signal to determine its dominant frequency component; Converting the static response signal to the frequency domain to obtain the static response spectrum; Judging whether there is a significant frequency amplitude in the frequency band corresponding to the dominant frequency component in the static response spectrum; If so, it is judged that there is a coupling interference component caused by dynamic stress wave in the static response signal.

[0008] Further, whether there is a significant frequency amplitude in the frequency band corresponding to the dominant frequency component in the static response spectrum is realized by the following way: calculating the average amplitude of the static response spectrum in the frequency band corresponding to the dominant frequency component, and calculating the full-band average amplitude of the entire static response spectrum; if the average amplitude in the frequency band is higher than the full-band average amplitude by a preset multiple, it is determined that there is a significant frequency amplitude.

[0009] Further, when it is judged that there is a coupling interference component, after monitoring a preset model state mutation event, the static response signal segments before and after the event are collected as contrast samples, including: Monitoring the acoustic emission signal of the slope tunnel model based on the acoustic emission monitoring device, and when the energy rate of the acoustic emission signal exceeds a preset threshold, it is determined that a preset model state mutation event has occurred; According to the unified time stamp given to the static response signal, the same length of static response signal segments before and after the moment of the model state mutation event are intercepted respectively as the static response signal segments before and after the event.

[0010] Further, the frequency domain characteristics of the comparative sample are analyzed to identify the characteristic frequency components related to the mutation, and the non-characteristic frequency components overlapping with the dynamic excitation frequency band are evaluated as the dominant frequency band of the coupling interference, including: The static response signal segments before and after the event are analyzed in the frequency domain respectively to obtain the pre-event spectrum and the post-event spectrum; The amplitude difference between the post-event spectrum and the pre-event spectrum is calculated; The frequency points with an amplitude difference exceeding a preset change threshold are identified, and the frequency components corresponding to the frequency points are determined as the characteristic frequency components related to the mutation; The frequency range within the dynamic excitation frequency band and not belonging to the characteristic frequency components is evaluated as the dominant frequency band of the coupling interference.

[0011] Further, the static response signal segments before and after the event are analyzed in the frequency domain respectively to obtain the pre-event spectrum and the post-event spectrum, which is achieved by: applying a windowing function to the static response signal segments before and after the event respectively to reduce spectral leakage, and then transforming the windowed signal segments to obtain the amplitude distribution of their frequency components, thereby obtaining the pre-event spectrum and the post-event spectrum respectively.

[0012] Further, based on the dominant frequency band of the coupling interference, the effective static response component related to the mechanical state change of the slope tunnel model is separated from the static response signal, including: According to the evaluated dominant frequency band of the coupling interference, a band-stop filter is designed; The band-stop filter is applied to the complete static response signal to filter out the frequency components within the dominant frequency band of the coupling interference; The remaining frequency components in the filtered signal are reconstructed as the effective static response component related to the mechanical state change of the slope tunnel model.

[0013] Further, the effective static response component is fused with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data, including: The shear modulus and damping ratio of the soil are calculated from the dynamic excitation signal as soil dynamic parameters; The time history curve characteristic values of the effective static response component and the soil dynamic parameters are input into a preset stability evaluation criterion for weighted fusion calculation to generate a comprehensive slope tunnel model stability evaluation index as the slope tunnel model stability evaluation data.

[0014] In another aspect, the present application provides a multi-element monitoring data fusion system for a slope tunnel model test, comprising the following modules: a data acquisition module for acquiring multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; an interference judgment module for judging whether there is a coupling interference component caused by dynamic stress waves in the static response signal based on the frequency characteristics of the dynamic excitation signal; a sample collection module for collecting static response signal segments before and after a preset model state mutation event as comparison samples when it is judged that there is a coupling interference component; a frequency band evaluation module for analyzing the frequency domain characteristics of the comparison samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; a component separation module for separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; a data fusion module for fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

[0015] Compared with the prior art, the present application has the following beneficial effects: 1. Through the systematic signal processing and data fusion strategy, the problem of distortion of static monitoring signals in the dynamic excitation environment is effectively solved. Firstly, the existence of the coupling interference component introduced by the dynamic stress waves in the static signal is accurately identified through the synchronous acquisition and correlation analysis of the dynamic excitation signal and the static response signal. Then, the model state mutation event is used as a time reference to intercept the static response signal segments before and after the event for comparative analysis, which converts the model internal damage evolution process into a favorable condition for signal analysis. Through frequency domain difference processing, the characteristic frequency components related to the real mechanical state mutation of the model can be clearly identified and separated from the wide dynamic excitation frequency band. Finally, by filtering out the frequency band components dominated by the coupling interference, the effective signal component reflecting the static state change of the model is obtained, which fundamentally suppresses the adverse effects of dynamic environmental noise on static parameter monitoring and significantly improves the reliability and authenticity of the static response signal.

[0016] 2. By organically fusing the purified effective static response component with the dynamic parameters of the soil body derived directly from the dynamic excitation signal, multi-dimensional and deep evaluation of the stability state of the slope tunnel model is realized. Based on deep understanding of the physical causes of the signal, signal purification is performed first, and then feature fusion is performed. The effective static response component reflects the deformation and stress redistribution trend of the model under static load, while the dynamic parameters such as the shear modulus and damping ratio of the soil body reveal the stiffness and energy dissipation characteristics of the soil material under dynamic action. The comprehensive analysis and judgment of these two types of parameters representing the macro mechanical response of the model and the micro dynamic characteristics of the material respectively enables the final generated stability evaluation data to contain both static evolution information and dynamic characteristic information, overcoming the limitations of single type data evaluation. This evaluation method based on feature extraction and fusion of signal source characteristics enables the conclusion to more comprehensively and accurately reflect the comprehensive stability state of the slope tunnel model under complex load conditions, providing a more scientific and reliable basis for model safety evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the multi-element monitoring data fusion method of the slope tunnel model test of the present application; Figure 2 A structural schematic diagram of the multi-element monitoring data fusion system of the slope tunnel model test of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] Embodiment 1: Figure 1 A multi-element monitoring data fusion method for a slope tunnel model test of the present application is given, which comprises the following steps: S1, collecting multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; S2, judging whether there is a coupling interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupling interference component, after monitoring a preset model state mutation event, collecting the static response signal segments before and after the event respectively as comparison samples; S4, analyze the frequency domain characteristics of the contrast sample, identify the characteristic frequency components related to mutation, and evaluate the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, based on the coupling interference dominant frequency band, separate the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal; S6, fuse the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

[0020] To realize the synchronous acquisition and time alignment of multi-element monitoring data in the slope tunnel model test, the data acquisition steps are described in detail. The slope tunnel model test is carried out in a model box, and similar materials are filled in the model box to simulate the actual slope rock-soil mass. The front of the model box is made of high-strength transparent material to observe the deformation and failure process of the model. Static parameter monitoring sensors are buried at specific positions inside the model, including micro soil pressure cells and strain gauges. The micro soil pressure cells are used to measure the soil pressure distribution inside the model, and the strain gauges are used to measure the strain response of the supporting structure. The resonant column system is composed of an exciter and a control system. The exciter is in close contact with the center position of the top of the model through a force transmission rod, and the control system is used to generate a sine wave electrical signal of a specific frequency and amplitude to drive the exciter, thereby applying dynamic excitation to the slope tunnel model.

[0021] The acquisition of dynamic excitation signal is completed by the data acquisition unit built in the resonant column system. The data acquisition unit is directly connected to the control circuit and feedback sensor of the exciter, and can continuously record the voltage value sequence of the driving electrical signal output by the control system at a first sampling frequency. The voltage value sequence directly reflects the time change of the dynamic excitation signal, and the first sampling frequency is set to 1000 Hz. The acquisition of static response signal is completed by a set of independent multi-channel static data acquisition instrument. The multi-channel static data acquisition instrument is connected to all micro soil pressure cells and strain gauges buried in the model interior through a shielded cable. The micro soil pressure cells output a voltage signal proportional to the soil pressure, and the strain gauges output a resistance change signal proportional to the strain value. The multi-channel static data acquisition instrument synchronously scans all channels at a second sampling frequency, converts the received analog signals to digital quantities, and forms a discrete time sequence of the static response signal. The second sampling frequency is set to 500 Hz. The first sampling frequency and the second sampling frequency are not the same.

[0022] To realize synchronous acquisition, a scheme of hardware synchronous trigger combined with software timestamp alignment is adopted. Specifically, a synchronization controller is used, which generates a high-precision master clock signal, whose time reference is derived from the built-in temperature-compensated crystal oscillator. The synchronization controller is connected to the control system of the resonance column system through the first trigger signal line and to the external trigger port of the multi-channel static data acquisition instrument through the second trigger signal line. At the moment of the formal start of the test, the synchronization controller is manually started, and the synchronization controller sends a rising trigger pulse signal to the control system of the resonance column system and the multi-channel static data acquisition instrument at the same time. After receiving the trigger pulse signal, the control system of the resonance column system immediately starts recording the dynamic excitation signal, and records the absolute time when the trigger pulse is received as the starting reference point of dynamic signal acquisition. After receiving the same trigger pulse signal, the multi-channel static data acquisition instrument also immediately starts synchronous sampling of all channels, and also records the time when the trigger pulse arrives as the starting reference point of static signal acquisition.

[0023] Since the data acquisition unit of the resonance column system and the multi-channel static data acquisition instrument use their own independent internal clocks, although the acquisition start time is synchronized, the slight frequency drift of the two device clocks during the long acquisition process will cause cumulative time errors. To assign a unified timestamp, time alignment post-processing is performed after data acquisition. First, the dynamic excitation signal data is derived from the resonance column system, which contains a time sequence with the trigger time as the starting point and the first sampling interval as the time step and the corresponding signal amplitude sequence. Then, the static response signal data is derived from the multi-channel static data acquisition instrument, which contains a time sequence for each channel with its own trigger time as the starting point and the second sampling interval as the time step and the corresponding signal amplitude sequence. Since the trigger signal is derived from the same synchronization controller, the starting reference points of the two systems are the same moment in physical time.

[0024] Next, a unified time axis is constructed. The time zero of the unified time axis corresponds to the absolute time of the trigger pulse sent by the synchronization controller. The sampling interval of the unified time axis is set to the period corresponding to the least common multiple of the first sampling frequency and the second sampling frequency, or is set to a suitable common sampling interval, such as 1 millisecond, according to the needs of subsequent analysis. Then, the dynamic excitation signal data is processed. Since the time sequence thereof is constructed based on the starting point of the unified time axis, it only needs to check the clock drift. If the drift is within the allowable range, it is directly adopted. For the static response signal data, since the second sampling frequency and the sampling frequency of the unified time axis can be different, resampling processing is needed. According to the original discrete time points and their amplitudes of the static response signal, the estimated amplitudes at each time point of the unified time axis are calculated by using a linear interpolation algorithm, so as to generate the static response signal data aligned with the unified time axis. The specific process of the linear interpolation algorithm is as follows: for any target time point on the unified time axis, find the two time points and their corresponding signal amplitudes in the original time sequence of the static response signal which are closest to the target time point before and after the target time point, and then assume that the signal is linearly changed between the two original time points. According to the position of the target time point relative to the two original time points, the signal amplitude thereof is calculated in a linear proportion.

[0025] After the above synchronization triggering and resampling alignment processing, the dynamic excitation signal and the static response signal of each channel have discrete time sequences based on the same time zero and having the same time interval, that is, the unified time stamp is given to the synchronously collected dynamic excitation signal and static response signal. These data with unified time stamp are stored as data files in a specific format, for example, each row of data contains a time stamp, a dynamic excitation signal amplitude, and signal amplitudes of various static parameter monitoring sensor channels, so as to facilitate direct calling and analysis in subsequent steps. The unified time stamp is the basis for all subsequent time-related analysis, especially for accurately intercepting signal segments before and after the event, and ensures the accuracy of the time correlation between data from different sources.

[0026] The specific process of determining whether the coupling interference component caused by dynamic stress wave exists in the static response signal based on the frequency characteristics of dynamic excitation signal is described in detail. The input data of the process is the dynamic excitation signal and the static response signal collected according to the method of the foregoing embodiment and has been given a uniform timestamp. First, the analysis of the dynamic excitation signal is performed to determine its dominant frequency component. From the dynamic excitation signal data with a uniform timestamp, a piece of dynamic excitation signal segment is selected, which is in the stable loading stage of the slope tunnel model test in time and has a stable signal amplitude. The duration of the segment should contain a sufficient number of excitation cycles to ensure the accuracy of the frequency analysis, for example, a signal segment of 10 seconds in length is intercepted. The selected dynamic excitation signal segment is processed by frequency domain transformation, and the fast Fourier transform algorithm is used to convert it from the time domain to the frequency domain to obtain the frequency-amplitude spectrum of the dynamic excitation signal, which is called the dynamic excitation spectrum. The specific process of fast Fourier transform is: grouping the discrete time signal sequence, recursively calculating, and finally obtaining the complex representation of the signal at the discrete frequency point, and its amplitude represents the strength of the frequency component. Observe the dynamic excitation spectrum to find the frequency component with the largest amplitude, which is the dominant frequency component of the dynamic excitation signal. For example, if the resonant column system is set to apply a sinusoidal excitation with a frequency of 5 Hz, there will be a significant peak at a frequency of 5 Hz in the dynamic excitation spectrum, and this 5 Hz is determined as the dominant frequency component. Record the specific value of the dominant frequency component, for example, 5 Hz.

[0027] Next, the static response signal is converted to the frequency domain to obtain the static response spectrum. A static response signal segment that is completely synchronized in time with the above dynamic excitation signal segment is selected, that is, according to the uniform timestamp, the static response signal data from the same static parameter monitoring sensor (for example, a certain specific soil pressure cell) in the same time period is intercepted. The same fast Fourier transform is applied to this static response signal segment to convert it into a frequency-amplitude spectrum, that is, the static response spectrum of this channel in this time period is obtained. The static response spectrum shows various frequency components contained in the signal of the monitoring point and their respective strengths.

[0028] Subsequently, it is determined whether there is a significant frequency amplitude in the frequency band corresponding to the dominant frequency component in the static response spectrum. This determination is achieved by a quantitative comparison. First, the frequency band corresponding to the dominant frequency component is defined. The frequency band is centered on the determined dominant frequency component (e.g. 5 Hz) and extends to both sides by a narrow frequency range, for example set to [dominant frequency component - 0.5 Hz, dominant frequency component + 0.5 Hz], i.e. [4.5 Hz, 5.5 Hz]. The average amplitude of the static response spectrum in this specific frequency band is calculated, denoted as the band average amplitude. The calculation method is to add the amplitudes corresponding to all discrete frequency points in the frequency band, and then divide by the total number of frequency points contained in the frequency band. Next, the full-band average amplitude of the entire static response spectrum is calculated. The full band refers to the effective frequency range that can be represented after the fast Fourier transform, i.e. from 0 Hz to the Nyquist frequency (equal to half of the sampling frequency, for a sampling frequency of 500 Hz, the Nyquist frequency is 250 Hz). The calculation method of the full-band average amplitude is to add the amplitudes corresponding to all discrete frequency points in the full band, and then divide by the total number of frequency points in the full band.

[0029] Then, the significance determination is made. The calculated band average amplitude is compared with the full-band average amplitude. A preset multiple is set as the determination threshold, for example, the preset multiple is 3. The setting of the preset multiple is based on the common requirement for signal-to-noise ratio in engineering experience, i.e. when the energy of a specific frequency component is significantly higher than the average level of background noise (approximated by the full-band average amplitude) by more than 3 times, it is considered that the component is significant and non-random. If the band average amplitude is higher than the full-band average amplitude by the preset multiple or more (i.e. the band average amplitude divided by the full-band average amplitude is greater than or equal to 3), it is determined that there is a significant frequency amplitude in the frequency band corresponding to the dominant frequency component in the static response spectrum. Otherwise, if the ratio is lower than the preset multiple, it is determined that there is no significant frequency amplitude.

[0030] Finally, a final determination is made according to the result of the significance determination. If it is determined that there is a significant frequency amplitude, it means that in the static response signal, a strong frequency component highly consistent with the dominant frequency component of the dynamic excitation signal is detected. This strongly indicates that the dynamic stress waves generated by the resonance column system have propagated to the location of the static parameter monitoring sensor and have produced a non-negligible modulation effect on the sensor output, i.e. there is a coupling interference component caused by the dynamic stress waves in the static response signal. If it is determined that there is no significant frequency amplitude, it can be concluded that the dynamic excitation has a weak and negligible effect on the specific static monitoring point in the current monitoring period. This determination will directly determine whether the subsequent step S3 is started.

[0031] After the existence of the coupling interference component caused by dynamic stress wave in the static response signal is determined, how to capture the model state mutation event and obtain the contrast sample for subsequent analysis is described in detail. The starting condition of this process is that step S2 has confirmed that there is a significant coupling interference component in the signal of at least one static parameter monitoring sensor channel. In order to achieve sensitive capture of the initiation and expansion of internal damage of the model, an acoustic emission monitoring device is used as the main identification means of the model state mutation event. The core component of the acoustic emission monitoring device is a piezoelectric acoustic emission sensor, whose working principle is to convert the elastic stress wave released by material deformation or cracking into an electrical signal based on the piezoelectric effect. Multiple acoustic emission sensors are arranged at key positions inside and on the surface of the slope tunnel model, such as near the potential sliding surface, the tunnel vault and the side wall. The sensors are in close contact with the model surface through coupling agent to ensure good acoustic conduction. The output signal of the acoustic emission sensor is amplified by a preamplifier and then continuously recorded by a dedicated acoustic emission acquisition system. The acoustic emission acquisition system is also connected to the synchronization controller, so that the data stream recorded by it has a unified time reference with the dynamic excitation signal and the static response signal.

[0032] The continuously recorded acoustic emission signal is a collection of a large number of discrete events, each event corresponding to a small damage activity. In order to identify the critical point representing the macro-structure state mutation from these continuous streams, the energy rate parameter of the acoustic emission signal is monitored. The energy rate of the acoustic emission signal is defined as the cumulative sum of the energy of all acoustic emission events recorded in unit time, and its calculation is completed inside the acoustic emission acquisition system. Specifically, the system calculates the energy rate with a fixed time window (e.g. 1 second) sliding, i.e. every 1 second, the total energy of all acoustic emission events collected in the past 1 second is calculated as the energy rate value at the current time. The energy of the acoustic emission event is obtained by squaring the event waveform signal and then integrating over time. In this way, an acoustic emission energy rate time history curve aligned with the unified time stamp can be obtained.

[0033] The preset model state mutation event is determined by judging whether the energy rate time history curve exceeds a preset threshold. The preset threshold is set according to the initial background noise level of the model test and the model material properties. Before the formal loading starts, a background signal of acoustic emission of the model in a stable state is collected, and the average value and the standard deviation of the acoustic emission energy rate during the background are calculated. The preset threshold is set to be the average value of the background energy rate plus three times the standard deviation. For example, the average value of the background energy rate is 10 millivolt square millisecond, and the standard deviation is 2 millivolt square millisecond, and the preset threshold is set to 16 millivolt square millisecond. This setting is based on the three-sigma criterion in statistics, which considers that an event exceeding three times the average value of the standard deviation is an abnormal event with a small probability, which is likely to correspond to a significant damage mutation inside the model, such as the generation or expansion of macroscopic cracks. Therefore, when the real-time monitored acoustic emission energy rate value first exceeds the preset threshold (16 millivolt square millisecond), it is determined that a preset model state mutation event has occurred. The exact time of the event, that is, the time point when the energy rate curve first breaks through the threshold, is recorded, and the time is determined based on the unified timestamp.

[0034] After determining that the model state mutation event has occurred, immediately collect the static response signal segments before and after the event as comparison samples. This operation is strictly based on the unified timestamp assigned to the static response signal. From the stored complete static response signal data with unified timestamp, the signal segments are intercepted for the specific sensor channel to be analyzed. First, the length of the intercepted time is determined, which should be long enough to include the characteristic changes of the signal and not too long to introduce unnecessary information. The length is usually set according to experience, for example, 10 seconds. Then, the recorded time of the model state mutation event is taken as the reference point. The static response signal segment before the event is intercepted from the time interval starting from the reference point and ending at the reference point. The static response signal segment after the event is intercepted from the time interval starting from the reference point and ending at the reference point. In this way, two static response signal segments with the same length (both 10 seconds) and closely connected before and after the mutation event are obtained. The two signal segments are marked as the static response signal segment before the event and the static response signal segment after the event, respectively, which together constitute the comparison sample for the next frequency domain feature analysis.

[0035] During the whole process, acoustic emission monitoring provides accurate positioning of the internal damage of the model, while the unified timestamp ensures the accurate correlation in time between the acoustic emission event and the static response signal, so that the key time point of the mutation of the model state can be accurately captured, and the comparable signal samples before and after the event can be obtained, laying a solid foundation for subsequent identification and signal features related to the real mechanical state change of the model. This method effectively converts the destructive evolution process of the model itself into an effective time marker that can be used for signal tracing.

[0036] The frequency domain characteristics of the contrast samples are analyzed in detail to identify the signal components related to the state mutation of the model and accurately evaluate the dominant frequency band of the coupled interference. The input of this process is the two key samples obtained in step S3: the static response signal segment before the event and the static response signal segment after the event. First, fine frequency domain analysis is needed for the two signal segments to obtain their frequency spectra. Direct transformation of a finite length signal will produce spectral leakage, i.e. energy dispersion to non-actual frequency components. To reduce spectral leakage, a windowing function is applied to the static response signal segment before the event and the static response signal segment after the event before transformation. The windowing function is a weight function that gradually decays to zero at both ends of the signal segment. The Hanning window is selected as the windowing function, and its window function coefficient sequence is generated by the cosine function, with the specific weight values decreasing smoothly from the center of the window function to both ends. Each data point in the static response signal segment before the event is multiplied by the corresponding point in the Hanning window function sequence to obtain the windowed pre-event signal segment. Similarly, the static response signal segment after the event is multiplied by the same Hanning window function sequence to obtain the windowed post-event signal segment. This multiplication operation effectively suppresses the discontinuity at the beginning and end of the signal segment.

[0037] Next, the windowed pre-event signal segment and the windowed post-event signal segment are transformed to obtain the amplitude distribution of their frequency components. This transformation is implemented using the Fast Fourier Transform algorithm. Fast Fourier Transform decomposes the time domain signal into a series of sine and cosine waves of different frequencies and calculates the complex result corresponding to each frequency component. The amplitude of the frequency component is taken as the modulus (i.e. square root of the square sum) of the complex result. Fast Fourier Transform is performed on the windowed pre-event signal segment to obtain its frequency-amplitude relationship, called the pre-event spectrum. Similarly, Fast Fourier Transform is performed on the windowed post-event signal segment to obtain the post-event spectrum. Both the pre-event spectrum and the post-event spectrum are sequences composed of discrete frequency points and their corresponding amplitudes, which reflect the frequency components and their strengths in the respective signal segments.

[0038] After obtaining the pre-event spectrum and the post-event spectrum, the amplitude difference between the post-event spectrum and the pre-event spectrum is calculated. This calculation is performed for each identical discrete frequency point. For each frequency point in the spectrum, the amplitude of the post-event spectrum at that frequency point is subtracted from the amplitude of the pre-event spectrum at the same frequency point. The resulting value is the amplitude difference for that frequency point. If the post-event amplitude is greater than the pre-event amplitude, the amplitude difference is positive; otherwise, it is negative. The amplitude differences for all frequency points are arranged in order of frequency, which forms an amplitude difference spectrum. This amplitude difference spectrum clearly shows the changes in the different frequency components of the static response signal after the occurrence of the model state mutation event.

[0039] Next, the frequency points in the amplitude difference spectrum whose amplitude differences exceed a pre-set change threshold are identified. The pre-set change threshold is set to distinguish significant frequency changes that are likely to be related to the mutation event from small random fluctuations. The threshold is set based on the background fluctuation level of the static response signal segment before the event in the steady state. Specifically, before calculating the amplitude differences, the pre-event spectrum is analyzed separately to calculate its amplitude standard deviation over the entire frequency band. The standard deviation is multiplied by an empirical coefficient (e.g., 3 times) to serve as the pre-set change threshold. This means that only when the change in a frequency component before and after the event exceeds three times its background fluctuation level, is the change considered significant. Each frequency point in the amplitude difference spectrum is traversed to determine whether the absolute value of its amplitude difference is greater than the pre-set change threshold. All frequency points that meet the condition are marked.

[0040] The frequency components corresponding to these marked frequency points are determined as the mutation-related characteristic frequency components. For example, if the amplitude differences at frequency points f1, f2, and f3 exceed the pre-set change threshold, then the frequencies f1, f2, and f3 are identified as characteristic frequency components. These characteristic frequency components reflect the specific frequency positions where the static response signal is significantly enhanced or weakened due to the internal state mutation of the model (such as crack generation, stress redistribution), and they are considered to be closely related to the real mechanical state change of the model.

[0041] Finally, the frequency range within the dynamic excitation band and not belonging to the characteristic frequency components is evaluated as the coupling interference dominant frequency band. The dynamic excitation band has been determined in step S2, for example a range centered on a dominant frequency component (e.g. 5 Hz), for example [4.5 Hz, 5.5 Hz]. All frequency points contained in this dynamic excitation band are checked. Those among them which do not belong to the identified characteristic frequency components are filtered out. The continuous or discrete frequency intervals formed by these frequency points are the coupling interference dominant frequency bands. For example, if the dynamic excitation band is [4.5 Hz, 5.5 Hz] and the identified characteristic frequency components are only 5.2 Hz, then the coupling interference dominant frequency bands are the two intervals [4.5 Hz, 5.2 Hz) and (5.2 Hz, 5.5 Hz]. The evaluation of this frequency band as the coupling interference dominant means that in this frequency band, in addition to that characteristic point (5.2 Hz) associated with the mutation, the energy of the remaining frequency components mainly comes from the dynamic stress wave interference of the resonance column system, rather than the mechanical state change of the model itself. This precise frequency band evaluation provides a direct basis for the next step of effectively separating the interference signal. The entire process successfully separates and identifies the real changes associated with the model damage from the inherent dynamic coupling interference background in the response signal through precise frequency domain difference comparison.

[0042] The specific process of separating the effective static response component from the disturbed static response signal based on the evaluated coupling interference dominant frequency band is described in detail. The input of this process includes the coupling interference dominant frequency band (for example, the intervals composed of frequency points fa to fb and fc to fd) accurately evaluated in step S4 and the complete original static response signal collected according to the S1 method with uniform timestamps. The goal of separation is to maximize the suppression of energy within the coupling interference dominant frequency band while preserving the frequency components outside the frequency band and the characteristic frequency components within the frequency band that have been identified as being associated with the mutation (see S4), thereby obtaining a pure signal component related to the mechanical state change of the slope tunnel model.

[0043] First, a digital band-stop filter is designed according to the dominant frequency band of the coupling interference. The function of the band-stop filter is to prevent signals in a specific frequency range (stopband) from passing through, while allowing signals in the frequency range on both sides of the stopband (passband) to pass through. A finite-length unit-impulse-response filter is designed because it has a strict linear phase characteristic, which can avoid introducing signal phase distortion in the filtering process. The core of the filter design is to determine its filter coefficients. The design process begins with setting the key performance parameters of the filter. The order of the filter is an important parameter that affects the transition band steepness and computational complexity of the filter. According to experience, it is usually set to between 100 and 200, for example, 128 orders are selected. The sampling frequency is set to the sampling frequency of the original static response signal, which is 500 Hz. The stopband range is directly specified as the dominant frequency band of the coupling interference evaluated, for example, [fa, fb] and [fc, fd]. The passband range is set to the frequency interval on both sides of the stopband, for example, [0, fa - Δf], [fb + Δf, fc - Δf] and [fd + Δf, Nyquist frequency], where Δf is the transition bandwidth, which is set according to the filter order and the desired degree of attenuation, for example, 5 Hz.

[0044] Next, the window function method is used to design the filter coefficients. The ideal band-stop filter has a rectangular frequency response in the frequency domain, but its corresponding time-domain impulse response is infinite and non-causal. In order to approximate the ideal response with a finite-length coefficient sequence, the ideal infinite-length impulse response needs to be truncated and a window function needs to be applied to improve the Gibbs phenomenon caused by truncation. First, according to the set stopband and passband, the unit impulse response sequence of an ideal band-stop filter is calculated. This ideal sequence is obtained by inverse Fourier transform of the ideal frequency response, and it is a symmetric sequence centered at zero. Since the ideal sequence is infinite, it needs to be truncated to a finite length, which is equal to the set filter order plus one (for example, 129 points). The truncation operation itself is equivalent to applying a rectangular window, but this will cause fluctuations in the passband and stopband. In order to suppress these fluctuations, a window function with better performance is used, such as the Hamming window. The coefficient sequence of the Hamming window is also symmetric, and its value decreases smoothly from the center to both ends. The truncated ideal unit impulse response sequence is multiplied point by point with the Hamming window sequence to obtain the final actual filter coefficients. These coefficients form a linear phase filter, and the center coefficient corresponds to the time zero point.

[0045] Then, the designed band-stop filter is applied to the complete static response signal. This is a discrete convolution operation in time domain. The complete static response signal is a discrete time series with length N. The filtering operation is to convolve the input series with the filter coefficient series obtained above, which has length M (M = filter order + 1). The specific steps of the convolution are: reverse the filter coefficient series; then slide the reversed coefficient series along the input signal series; at each sliding position, multiply the values of the overlapping part of the input signal with the corresponding filter coefficient values, and sum all the products to get the value of the output series at that position. This operation effectively realizes frequency selection, and the frequency components in the dominant frequency band of the coupling interference are significantly attenuated in the convolution summation process. In order to ensure that the length of the filtered signal series is consistent with the input signal, and to avoid transient effects at the beginning and end of the stage, usually the edge extension is performed before the convolution of the input signal, for example, symmetric extension is used to add M / 2 mirror data at the beginning and end of the signal, and the extended part is removed after the convolution is completed.

[0046] After the band-stop filter processing, the frequency components in the output signal located in the dominant frequency band of the coupling interference have been greatly filtered out. Finally, the remaining frequency components in the filtered signal are reconstructed into the effective static response component related to the mechanical state change of the slope tunnel model. It should be noted that "reconstruction" here does not refer to an independent signal generation step, but a clear definition of the physical meaning of the filtered output signal. The signal after the above filtering process is the effective static response component obtained finally. It retains all frequency components in the passband of the original signal, as well as the energy in the dominant frequency band of the coupling interference that is identified as a characteristic frequency component (because its energy changes significantly before and after the mutation event, it is considered to be related to the true response of the model). Since the band-stop filter is a linear system, the filtering process maintains the linear characteristics of the signal, so this effective static response component can directly reflect the trend of the mechanical state (such as soil pressure, structural strain) of the slope tunnel model changing with time in the time domain, while the periodic oscillation interference caused by dynamic stress waves has been effectively suppressed. This effective static response component, as the purified key data, will be used for the final comprehensive stability evaluation. The entire separation process is based on accurate frequency band evaluation and customized digital filtering technology, achieving the purpose of extracting effective signals from strong background noise.

[0047] The complete process of fusing the isolated effective static response component with the soil dynamic parameters derived from the dynamic excitation signal, and finally generating the slope-tunnel model stability evaluation data, is elaborated. The inputs of the process include the effective static response component obtained in step S5, which is related to the mechanical state change of the slope-tunnel model, and the dynamic excitation signal segment corresponding to the effective static response component in time collected in step S1. The goal of fusion is to combine the two types of information reflecting the static state evolution of the model and the dynamic characteristics of the soil to form a comprehensive stability criterion.

[0048] First, the shear modulus and damping ratio of the soil are calculated from the dynamic excitation signal as soil dynamic parameters. This calculation is based on the basic principle of the resonant column test. A segment of the dynamic excitation signal synchronized with the time period of the effective static response component is selected, which is usually a sine wave with known amplitude and frequency. By analyzing this dynamic excitation signal segment and the response of the model to the excitation (usually measured by the sensors built into the resonant column system), the dynamic characteristics of the soil can be determined. The specific calculation process is as follows: the shear modulus is obtained by calculating the ratio of dynamic shear stress to dynamic shear strain. The dynamic shear stress is calculated by dividing the dynamic excitation force applied to the top of the model by the equivalent shear area of the model, and the excitation force can be converted by the drive current of the exciter and the calibration coefficient. The dynamic shear strain is calculated by measuring the torsional deformation amplitude of the model under dynamic action and combining the geometric dimensions of the model. The determination of the damping ratio is usually through the measurement of the bandwidth of the resonance curve (the curve of the response amplitude changing with frequency) or by using the free vibration decay method. For example, in the free vibration decay method, after reaching a stable resonance, the excitation is suddenly stopped, and the time history curve of the free vibration decay of the model is recorded, and the damping ratio can be obtained by calculating the natural logarithm of the amplitude ratio of two adjacent vibration periods divided by two. Through these calculations, the shear modulus (for example, in kilopascals) representing the dynamic stiffness characteristics of the model soil in this time period and the damping ratio (dimensionless) representing the energy dissipation characteristics of the soil are obtained. These two parameters are the soil dynamic parameters derived from the dynamic excitation signal.

[0049] Next, the time history curve characteristic values of the effective static response component are extracted. The effective static response component is a signal that changes over time, such as the time history curve of the cleaned pressure reading of a certain soil pressure cell. Key mathematical features that can represent the stability state of the model are extracted from this time history curve. These characteristic values include but are not limited to: the trend item slope, which is obtained by linear fitting of the time history curve to obtain the slope of the straight line, which reflects the average rate of change of the physical quantity; the fluctuation amplitude, which calculates the standard deviation or root mean square value of the time history curve within a certain time window, which reflects the degree of signal fluctuation; and the amplitude at a specific time, such as the signal reading at a specific time point after a model state mutation event occurs. These characteristic values quantify the behavior pattern of the effective static response component from different angles.

[0050] Then, the time history curve eigenvalues of the effective static response components obtained above are inputted into a preset stability evaluation criterion together with the soil dynamic parameters (shear modulus and damping ratio) for weighted fusion calculation. The preset stability evaluation criterion is a multi-parameter comprehensive judgment rule, which can be a weighted summation model. The criterion is established based on statistical analysis of a large number of slope tunnel model test historical data or numerical simulation results to determine the contribution weight of each parameter to stability. For example, the stability evaluation criterion can be expressed as: stability index = trend item slope × weight coefficient A + inverse of fluctuation amplitude × weight coefficient B + shear modulus × weight coefficient C - damping ratio × weight coefficient D. The numerical values of the weight coefficients A, B, C and D are determined by regression analysis to make the calculated stability index have the best corresponding relationship with the known model stability state (stable, critical, unstable). In the weighted fusion calculation, first, the normalized processing of each eigenvalue and dynamic parameter is needed to eliminate the dimension influence and make them in the same numerical range (for example, between 0 and 1). The normalization method can adopt the minimum-maximum value scaling, that is, the value of each parameter is subtracted by the minimum value of its possible value range, and then divided by the length of its value range. Then, the normalized parameter values are multiplied by the corresponding weight coefficients, and all the weighted values are added to obtain a preliminary comprehensive index.

[0051] Finally, the preliminary comprehensive index is appropriately converted (for example, mapped to a 0-100 score interval) to generate a comprehensive slope tunnel model stability evaluation index as the final slope tunnel model stability evaluation data. The index is a single quantitative value, and its size directly reflects the relative stability degree of the slope tunnel model at the evaluation time. The higher the value, the more stable it is, and the lower the value, the worse the stability. The comprehensive index integrates the static response trend and soil dynamic characteristics, and compared with single type data, can more comprehensively reflect the overall mechanical state of the model, and provides intuitive and quantitative basis for judging the model safety condition. The whole fusion process converts the monitoring data with different physical meanings and different dimensions into a unified evaluation scale through the preset criterion based on experience or theory, and realizes the deep comprehensive utilization of multi-element monitoring information.

[0052] Embodiment 2: Figure 2 A structural schematic diagram of a multi-element monitoring data fusion system for a slope tunnel model test is given, and the multi-element monitoring data fusion system for the slope tunnel model test comprises the following modules: A data acquisition module is used for acquiring multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; The interference judgment module is configured to judge whether a coupling interference component caused by the dynamic stress wave exists in the static response signal based on a frequency characteristic of the dynamic excitation signal. The sample collection module is configured to, when it is judged that the coupling interference component exists, collect static response signal segments before and after a preset model state mutation event is monitored as comparison samples. The frequency band evaluation module is configured to analyze frequency domain characteristics of the comparison samples, identify a characteristic frequency component related to the mutation, and evaluate a non-characteristic frequency component overlapping with the dynamic excitation frequency band as a coupling interference dominant frequency band. The component separation module is configured to separate an effective static response component related to a mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band. The data fusion module is configured to fuse the effective static response component with a soil dynamic parameter derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

[0053] The calculations involved in the embodiments are all de-dimensioned numerical calculations, and preset parameters and threshold values in the calculations are set by a person skilled in the art according to actual conditions.

[0054] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially.

[0055] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0056] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0057] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is only a logical function division, and there can be another division manner for actual implementation, for example, multiple devices or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different parts can be indirect couplings or communication connections through some interfaces, devices or modules, and can be in electrical, mechanical or other forms.

[0058] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any modification or replacement within the technical range disclosed by the present application can be easily thought by any person skilled in the art, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0059] Finally: the above described is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A multi-element monitoring data fusion method for a side slope tunnel model test, characterized in that, The method comprises the following steps: S1, collecting multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; S2, judging whether there is a coupling interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupling interference component, after a preset model state mutation event is monitored, static response signal segments before and after the event occur are collected respectively as comparison samples; S4, analyzing the frequency domain characteristics of the comparison samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; S6, fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data. 2.The method according to claim 1, characterized in that, The method comprises the following steps: S1, collecting multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; 3.The method according to claim 1, characterized in that, S2, judging whether there is a coupling interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupling interference component, after a preset model state mutation event is monitored, static response signal segments before and after the event occur are collected respectively as comparison samples; S4, analyzing the frequency domain characteristics of the comparison samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; S6, fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

4. The multi-element monitoring data fusion method for a slope tunnel model test according to claim 3, characterized in that, The method comprises the following steps:

5. The multi-element monitoring data fusion method for a slope tunnel model test according to claim 1, characterized in that, S1, collecting multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; S2, judging whether there is a coupling interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupling interference component, after a preset model state mutation event is monitored, static response signal segments before and after the event occur are collected respectively as comparison samples; S4, analyzing the frequency domain characteristics of the comparison samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; S6, fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data. The method comprises the following steps: S1, collecting multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by the static parameter monitoring sensor; S2, judging whether there is a coupling interference component caused by dynamic stress wave in the static response signal based on the frequency characteristics of the dynamic excitation signal; S3, when it is judged that there is a coupling interference component, after a preset model state mutation event is monitored, static response signal segments before and after the event occur are collected respectively as comparison samples; S4, analyzing the frequency domain characteristics of the comparison samples, identifying the characteristic frequency components related to the mutation, and evaluating the non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; S5, separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; S6, fusing the effective static response component with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data.

6. The multi-element monitoring data fusion method for a slope tunnel model test according to claim 1, characterized in that, The frequency domain characteristics of the comparison samples are analyzed, the characteristic frequency components related to the mutation are identified, and the non-characteristic frequency components overlapping with the dynamic excitation frequency band are evaluated as the coupling interference dominant frequency band, including: The static response signal segments before and after the event occur are respectively analyzed in the frequency domain to obtain the pre-event spectrum and the post-event spectrum; The amplitude difference between the post-event spectrum and the pre-event spectrum is calculated; The frequency points with amplitude difference exceeding the preset change threshold are identified, and the frequency components corresponding to the frequency points are determined as the characteristic frequency components related to the mutation; The frequency range within the dynamic excitation frequency band and not belonging to the characteristic frequency components is evaluated as the coupling interference dominant frequency band.

7. The multi-element monitoring data fusion method for a slope tunnel model test according to claim 6, characterized in that, The static response signal segments before and after the event occur are respectively analyzed in the frequency domain to obtain the pre-event spectrum and the post-event spectrum, which is realized by: applying a window function to the static response signal segments before and after the event occurs respectively to reduce spectral leakage, and then transforming the windowed signal segments to obtain the amplitude distribution of their frequency components, thereby obtaining the pre-event spectrum and the post-event spectrum respectively. 8.The method according to claim 1, wherein, The effective static response component related to the mechanical state change of the slope tunnel model is separated from the static response signal based on the coupling interference dominant frequency band, including: A band-stop filter is designed according to the evaluated coupling interference dominant frequency band; The band-stop filter is applied to the complete static response signal to filter out the frequency components within the coupling interference dominant frequency band; The remaining frequency components in the filtered signal are reconstructed as the effective static response component related to the mechanical state change of the slope tunnel model. 9.The method of claim 1, wherein, The effective static response component is fused with the soil dynamic parameters derived from the dynamic excitation signal to generate slope tunnel model stability evaluation data, including: The shear modulus and damping ratio of the soil are calculated from the dynamic excitation signal as soil dynamic parameters; The time history curve characteristic values of the effective static response component and the soil dynamic parameters are input into the preset stability evaluation criterion for weighted fusion calculation to generate a comprehensive slope tunnel model stability evaluation index as the slope tunnel model stability evaluation data.

10. A multi-element monitoring data fusion system for a slope tunnel model test, for implementing the multi-element monitoring data fusion method of any one of claims 1-9, characterized in that, It includes the following modules: A data acquisition module for acquiring multi-element monitoring data in the slope tunnel model test, including dynamic excitation signals generated by the resonance column system and static response signals collected by static parameter monitoring sensors; An interference judgment module for judging whether there is a coupling interference component caused by dynamic stress waves in the static response signal based on the frequency characteristics of the dynamic excitation signal; A sample collection module for collecting static response signal segments before and after the occurrence of a preset model state mutation event as comparison samples when it is judged that there is a coupling interference component; A frequency band evaluation module for analyzing the frequency domain characteristics of the comparison samples, identifying characteristic frequency components related to the mutation, and evaluating non-characteristic frequency components overlapping with the dynamic excitation frequency band as the coupling interference dominant frequency band; A component separation module for separating the effective static response component related to the mechanical state change of the slope tunnel model from the static response signal based on the coupling interference dominant frequency band; A data fusion module is configured to fuse the effective static response component with the dynamic parameters of the soil derived from the dynamic excitation signal to generate the slope tunnel model stability evaluation data.

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