A dangerous rock collapse monitoring and early warning method and device based on MEMS sensing

Abstract

The present invention discloses a dangerous rock collapse monitoring and early warning method and device based on MEMS sensing, and relates to the dangerous rock collapse monitoring and early warning technical field. The method comprises: fixing the MEMS sensing system to the dangerous rock body and the bedrock body respectively, and obtaining the vibration signals of the dangerous rock body and the bedrock body respectively; determining the bedrock vibration time history curve and the dangerous rock vibration time history curve, determining the frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determining the natural frequency of the dangerous rock body based on the fitting of the frequency response function by the theoretical amplitude amplification factor; performing integral calculation on the two vibration time history curves to obtain the root mean square velocity amplitude ratio; constructing a dual dynamic index stability judgment model of the dangerous rock body based on the natural frequency of the dangerous rock body and the root mean square velocity amplitude ratio, judging the stability of the dangerous rock body, and performing graded early warning according to the judgment result. The present invention can improve the accuracy of collapse early warning and the timeliness of dangerous rock collapse early warning due to displacement monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of dangerous rock collapse monitoring and early warning, and in particular to a dangerous rock collapse monitoring and early warning method and device based on MEMS sensing. Background Art

[0002] Due to complex geological conditions, frequent earthquakes, and construction disturbances, collapse accidents caused by internal stress imbalance in rock masses often occur. The collapse of dangerous rocks is the focus and difficulty of engineering geological disaster prevention and control because of its sudden and spatial dispersion characteristics. Therefore, real-time stability monitoring of dangerous rock masses and early warning of collapse damage are practical problems that geotechnical engineering needs to solve.

[0003] As a rock mass or block that may become unstable on a natural slope, the deterioration and damage of its main control structure surface is the key to controlling its stability. The monitoring method based on the vibration characteristics of the dangerous rock mass to characterize the degree of structural surface deterioration has been experimentally verified and has been well applied in actual engineering. The dynamic indicators obtained through the vibration response of the dangerous rock mass provide a new idea for the monitoring of dangerous rock collapse. However, traditional seismic equipment and a single displacement indicator method are difficult to guarantee the accuracy and timeliness of dangerous rock monitoring and early warning. Compared with traditional mechanical vibration sensors, MEMS sensors have the characteristics of small size, low energy consumption and wide frequency response, which provide objective conditions for dangerous rock monitoring in complex engineering scenarios. The integration of MEMS sensors with wireless transmission, big data and other Internet of Things technologies has become a new research field for geotechnical disaster monitoring, but there are few studies on the application of MEMS sensors to dangerous rock collapse monitoring. The occurrence of dangerous rock collapse is unpredictable and is affected by many factors. Most of the locations where collapses occur are located in harsh environments, with disturbances such as construction projects and blasting, as well as poor network signals and difficulty in power supply, resulting in low efficiency and accuracy in dangerous rock collapse monitoring and early warning. Summary of the invention

[0004] In order to solve the technical problems of low efficiency and accuracy of dangerous rock collapse monitoring and early warning in the prior art, the embodiment of the present invention provides a dangerous rock collapse monitoring and early warning method and device based on MEMS sensing. The technical solution is as follows:

[0005] On the one hand, a dangerous rock collapse monitoring and early warning method based on MEMS sensing is provided, and the method is implemented by a dangerous rock collapse monitoring and early warning device based on MEMS sensing, and the method comprises:

[0006] S1. Fix the MEMS sensing system to the dangerous rock mass and the bedrock mass respectively, and obtain the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass respectively;

[0007] S2. Determine a bedrock vibration time history curve based on the vibration signal of the bedrock mass, determine a dangerous rock vibration time history curve based on the vibration signal of the dangerous rock mass, determine a frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine the natural frequency of the dangerous rock mass based on fitting the frequency response function with a theoretical amplitude amplification factor of a spring-proton model system;

[0008] S3, integrating the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain the root mean square velocity amplitude ratio;

[0009] S4. Based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, a dual dynamic index stability judgment model of the dangerous rock mass is constructed to judge the stability of the dangerous rock mass, and a graded warning is issued according to the judgment result.

[0010] On the other hand, a dangerous rock collapse monitoring and early warning device based on MEMS sensing is provided, and the device is applied to a dangerous rock collapse monitoring and early warning method based on MEMS sensing, and the device comprises:

[0011] An acquisition unit is used to fix the MEMS sensing system to the dangerous rock mass and the bedrock mass respectively, and acquire vibration signals of the dangerous rock mass and the bedrock mass respectively;

[0012] a determination unit, configured to determine a bedrock vibration time history curve based on the vibration signal of the bedrock body, determine a dangerous rock vibration time history curve based on the vibration signal of the dangerous rock body, determine a frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine a natural frequency of the dangerous rock body based on fitting the frequency response function with a theoretical amplitude amplification factor of a spring-proton model system;

[0013] A calculation unit, used for performing integral calculation on the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio;

[0014] The early warning unit is used to construct a dual dynamic index stability judgment model of the dangerous rock body based on the natural frequency of the dangerous rock body and the root mean square velocity amplitude ratio, judge the stability of the dangerous rock body, and issue a graded early warning according to the judgment result.

[0015] On the other hand, a dangerous rock collapse monitoring and early warning device based on MEMS sensing is provided, and the dangerous rock collapse monitoring and early warning device based on MEMS sensing comprises: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the dangerous rock collapse monitoring and early warning methods based on MEMS sensing is implemented.

[0016] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned dangerous rock collapse monitoring and early warning methods based on MEMS sensing.

[0017] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0018] The MEMS sensing system is fixed on the dangerous rock mass and the bedrock mass respectively to obtain the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass respectively; the bedrock vibration time history curve is determined based on the vibration signal of the bedrock mass, the dangerous rock vibration time history curve is determined based on the vibration signal of the dangerous rock mass, the frequency response function is determined based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and the natural frequency of the dangerous rock mass is determined by fitting the frequency response function based on the theoretical amplitude amplification factor of the spring-proton model system; the dangerous rock vibration time history curve and the bedrock vibration time history curve are integrated and calculated to obtain the root mean square velocity amplitude ratio; based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, a dual dynamic index stability judgment model of the dangerous rock mass is constructed to judge the stability of the dangerous rock mass, and a graded warning is given according to the judgment result. The present invention overcomes the unpredictability of instability under the sudden characteristics of dangerous rock collapse, improves the accuracy of collapse warning, improves the timeliness of dangerous rock collapse warning due to displacement monitoring, and improves the efficiency of warning feedback. It has the characteristics of simple operation, low cost, and reliable warning judgment, solves the practical problem of early warning of collapse damage in geotechnical engineering, and can be widely used in the monitoring and early warning of various sudden rock and soil disasters such as cascade power station development and slope dangerous rock collapse during mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a dangerous rock collapse monitoring and early warning method based on MEMS sensing provided by an embodiment of the present invention;

[0021] Figure 2 It is a schematic diagram of the arrangement and position of a MEMS sensor system on dangerous rocks and bedrock provided by an embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of components of a MEMS sensing system provided by an embodiment of the present invention;

[0023] Figure 4It is a schematic diagram of a data collection and transmission mechanism of a MEMS sensing system provided by an embodiment of the present invention;

[0024] Figure 5 It is a schematic diagram of acceleration, velocity or displacement in three-axis directions including gravity obtained by a MEMS sensing system provided by an embodiment of the present invention;

[0025] Figure 6 It is a schematic diagram of a method for fitting a dangerous rock frequency response function and a theoretical amplitude amplification factor provided by an embodiment of the present invention;

[0026] Figure 7 It is a curve diagram of a spectrum response function collected by a MEMS sensing system under different cutting lengths provided by an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of the relationship between the natural frequency, safety factor and relative bonding length of a dangerous rock mass provided by an embodiment of the present invention;

[0028] Fig. 9 This is a schematic diagram of the relationship between the root mean square velocity amplitude ratio, the inclination angle and the relative bonding length of a dangerous rock mass provided by an embodiment of the present invention;

[0029] Fig.10 A dual dynamic index stability judgment model for dangerous rock mass and a multi-level early warning schematic diagram provided by an embodiment of the present invention;

[0030] Fig.11 This is a block diagram of a dangerous rock collapse monitoring and early warning device based on MEMS sensing provided by an embodiment of the present invention;

[0031] Fig.12 It is a structural schematic diagram of a dangerous rock collapse monitoring and early warning device based on MEMS sensing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0033] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0034] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0035] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0036] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0037] The embodiment of the present invention provides a dangerous rock collapse monitoring and early warning method based on MEMS sensing, which can be implemented by a dangerous rock collapse monitoring and early warning device based on MEMS sensing, and the dangerous rock collapse monitoring and early warning device based on MEMS sensing can be a terminal or a server. Figure 1 The flowchart of the dangerous rock collapse monitoring and early warning method based on MEMS sensing is shown. The processing flow of the method may include the following steps:

[0038] S1. Fix the MEMS sensing system to the dangerous rock mass and the bedrock mass respectively, and obtain the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass respectively.

[0039] In a feasible implementation, the MEMS sensing system is fixed to the dangerous rock mass and the bedrock mass using plaster or bolts, and the vibration signal of the rock mass is obtained by wired or wireless transmission. The acquisition frequency and transmission mechanism of each MEMS sensing system are configured.

[0040] At least one monitoring point should be set up at each location of dangerous rock and bedrock, and a MEMS sensor system should be set up at each monitoring point. The monitoring points should be as close as possible, such as Figure 2 shown.

[0041] Among them, the MEMS (Micro-Electro-Mechanical System) sensing system is an independent perception system at the micron or even nanometer level based on semiconductor manufacturing technology. It collects the original vibration signals of dangerous rocks and bedrock at a preset collection frequency of 100Hz~1000Hz, removes invalid information of the original indicators locally, and activates the encrypted collection mechanism when necessary to obtain vibration information for a longer period of time.

[0042] Optionally, Figure 2A case is given in which two MEMS sensing systems are included, which are respectively called the first MEMS sensing system and the second MEMS sensing system. Figure 2 As shown, the first MEMS sensor system is marked as 21, the second MEMS sensor system is marked as 22, the bedrock is marked as 23, the dangerous rock is marked as 24, the I-shaped bracket is marked as 25, and the nut is marked as 26. The installation steps of the two MEMS sensor systems are basically the same, and the connection method between the second MEMS sensor system 22 and the dangerous rock 24 is used to illustrate, which may specifically include: connecting and fixing the second MEMS sensor system 22 on the dangerous rock 24 using the nut 26 of the I-shaped bracket 25.

[0043] Alternatively, if Figure 3 As shown, field protection of the exterior of the MEMS sensor system may include: using a bonding material to fix a cylindrical polycarbonate (PC) transparent protective cover 31 to the exterior of the sensor system, the protective cover does not interfere with signal collection and does not block the charging system of the solar panel, and effectively reduces the erosion and interference effects of external environments such as rain and dust on the MEMS sensor system.

[0044] Alternatively, if Figure 3 As shown, the MEMS sensing system also includes a rechargeable battery 34, a solar charging panel 32 and a wireless communication antenna 33. The MEMS sensing system integrates Beidou communication, GPS positioning, dual power supply and external conventional sensing interface, providing conditions for high-frequency uninterrupted multi-level, multi-dimensional and multi-indicator integrated linkage collection.

[0045] Optionally, the collection and transmission mechanism of the MEMS sensor system may include: the vibration signal collection frequency in daily mode is not higher than 500Hz, the timing transmission frequency is not lower than 1 time / hour, and the warning level is judged every 1s. However, after reaching the warning level, the vibration signal collection frequency is increased to 1000Hz, the warning level is judged every 1s, and the timing transmission frequency is increased to 1 time / s. The specific data collection and transmission flow chart is as follows: Figure 4 shown.

[0046] Optionally, the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass are both three-axis accelerations including gravity, and the three-axis accelerations including gravity are converted from the acquired original electrical signals, and the acquired original electrical signals are electrical signals representing different pressure magnitudes.

[0047] In a feasible implementation, Figure 5 As shown, the original vibration signal obtained by the MEMS sensing system is an electrical signal representing different pressure magnitudes, which is converted into output as acceleration, velocity or displacement in three-axis directions including gravity.

[0048] S2. Determine the bedrock vibration time history curve based on the vibration signal of the bedrock mass, determine the dangerous rock vibration time history curve based on the vibration signal of the dangerous rock mass, determine the frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine the natural frequency of the dangerous rock mass by fitting the frequency response function based on the theoretical amplitude amplification factor of the spring-proton model system.

[0049] In a feasible implementation, the method for determining the vibration time history curve based on the vibration signal is to intercept several segments of vibration signals of the same length, align the ends and take the average as the vibration time history curve, reduce the interference of unstable electrical signals, and obtain a stable vibration time history curve.

[0050] Optionally, S2 determines a frequency response function based on a bedrock vibration time history curve and a dangerous rock vibration time history curve, and determines a natural frequency of the dangerous rock mass based on the frequency response function, including:

[0051] S21. Preprocessing is performed on both the bedrock vibration time history curve and the dangerous rock vibration time history curve. The preprocessing includes high-pass filtering and outlier removal.

[0052] In a feasible implementation, high-pass filtering is used to eliminate the interference of the vibration time-history curve of the dangerous rock mass caused by the constant micro-vibration low-frequency noise, and generally retains the signal components higher than the cut-off frequency of 4 Hz.

[0053] Optionally, when the preprocessing in S21 is outlier removal, S21 preprocesses both the bedrock vibration time history curve and the dangerous rock vibration time history curve, including:

[0054] When the electrical signal of the bedrock vibration time history curve or the electrical signal of the dangerous rock vibration time history curve does not satisfy the following formula (1), the unsatisfactory electrical signal will be eliminated:

[0055] (1)

[0056] in, , is the acceleration of the rock mass at time t, represents the x-axis acceleration in the three-axis direction, Represents the y-axis acceleration in the three-axis direction, Represents the z-axis acceleration in the three-axis direction; is the acceleration due to gravity; Indicates the absolute value operation.

[0057] S22. Perform Fourier transform on the vibration time history curve of the dangerous rock to determine the cross-correlation power spectrum density function between the dangerous rock and the bedrock.

[0058] Optionally, S22 performs Fourier transformation on the dangerous rock vibration time history curve to determine the cross-correlation power spectrum density function between the dangerous rock and the bedrock, including:

[0059] According to the following formula (2), the vibration time history curve of the dangerous rock is transformed by Fourier transform to obtain the cross-correlation power spectrum density function between the dangerous rock and the bedrock: :

[0060] (2)

[0061] in, is the cross-correlation power spectrum density function between dangerous rock and bedrock, is the vibration time history curve of dangerous rock, Represents the bedrock vibration time history curve The conjugate curve of represents the time shift, represents the cross-correlation function between dangerous rock and bedrock, represents the base of natural logarithm, j represents the imaginary unit, and w represents the angular frequency.

[0062] S23. Perform Fourier transform on the bedrock vibration time history curve to determine the bedrock autocorrelation power spectrum density function.

[0063] Optionally, S23 performs Fourier transformation on the bedrock vibration time history curve to determine the bedrock autocorrelation power spectrum density function, including:

[0064] According to the following formula (3), the bedrock vibration time history curve is Fourier transformed to obtain the bedrock autocorrelation power spectrum density function:

[0065] (3)

[0066] in, is the bedrock autocorrelation power spectral density function; is the bedrock vibration time history curve, is the bedrock vibration time history curve The conjugate curve of Represents the autocorrelation function of the bedrock.

[0067] S24. Determine the frequency response function of the dangerous rock under constant micro-motion based on the cross-correlation power spectral density function between the dangerous rock and the bedrock and the autocorrelation power spectral density function of the bedrock.

[0068] Optionally, S24 determines the frequency response function of the dangerous rock under constant micro-motion based on the cross-correlation power spectrum density function between the dangerous rock and the bedrock and the autocorrelation power spectrum density function of the bedrock, including:

[0069] S241. Based on the following formula (4), the angular frequency and the vibration frequency are converted:

[0070] (4)

[0071] S242, based on the following formula (5), the conversion of angular frequency and vibration frequency, the cross-correlation power spectrum density function between the dangerous rock and the bedrock, and the autocorrelation power spectrum density function of the bedrock, determine the frequency response function of the dangerous rock under constant micro-motion:

[0072] (5)

[0073] in, It is the frequency response function of dangerous rock under constant micro-motion.

[0074] S25. Determine the abscissa frequency corresponding to the peak value of the dangerous rock frequency response function as the natural frequency of the dangerous rock mass.

[0075] In a feasible implementation, the theoretical amplitude amplification factor of the spring-proton model system is fitted to the frequency response function of the dangerous rock mass by the least square method, and the abscissa corresponding to the peak value of the amplitude amplification factor after fitting is taken as the natural frequency of the dangerous rock mass. The fitting process is as follows:

[0076] The theoretical amplitude amplification factor of the spring-proton model system is shown in equation (6):

[0077] (6)

[0078] Where: is the theoretical amplitude amplification factor; frequency ratio , is the frequency of dangerous rocks under constant micromotion, is the natural frequency of the dangerous rock mass, Represents the damping ratio.

[0079] like Figure 6 As shown, when hour, , The value is the largest, which shows that the frequency response function of the dangerous rock under the actual constant micro-motion is The peak value in is the response amplitude amplification factor caused by the resonance of the dangerous rock mass, and the abscissa frequency corresponding to the peak value is the natural frequency of the dangerous rock mass.

[0080] S3. Integrate the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain the root mean square velocity amplitude ratio.

[0081] Optionally, S3 integrates and calculates the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio, including:

[0082] The vibration time history curve of the dangerous rock and the vibration time history curve of the bedrock are integrated and calculated according to the following formula (6) to obtain the root mean square velocity amplitude ratio:

[0083] (6)

[0084] In the formula, is the RMS velocity amplitude ratio; is the recording start time of the vibration time history curve; is the recording cut-off time of the vibration time history curve, is the vibration time history curve of dangerous rock, It is the bedrock vibration time history curve.

[0085] In a feasible implementation, the amplitude amplification factor As the ratio of the vibration amplitude of the dangerous rock mass to the vibration amplitude of the bedrock under different excitation frequency conditions, with the natural frequency of the dangerous rock mass The amplitude amplification factor of the system at the same excitation frequency increases with the decrease of . Therefore, the ratio of the vibration amplitude of the dangerous rock mass to the vibration amplitude of the bedrock also increases.

[0086] S4. Based on the ratio of the natural frequency of the dangerous rock mass to the root mean square velocity amplitude, a dual dynamic index stability judgment model of the dangerous rock mass is constructed to judge the stability of the dangerous rock mass, and a graded warning is issued according to the judgment results.

[0087] In a feasible implementation, the dual dynamic index stability judgment model of dangerous rock mass refers to an evaluation model for inverting the degree of adhesion between dangerous rock and bedrock based on real-time measurement of natural frequency and root mean square velocity amplitude ratio using MEMS sensors.

[0088] As the relative bonding length of the dangerous rock mass structure surface decreases, the natural frequency of the dangerous rock mass collected by the MEMS sensing system decreases, and has the same downward trend as the limit equilibrium safety factor. The root mean square velocity amplitude ratio collected by the MEMS sensor gradually increases as the stability of the dangerous rock mass decreases.

[0089] Optionally, S4 constructs a dual dynamic index stability judgment model for dangerous rock masses based on the ratio of the natural frequency of the dangerous rock masses to the root mean square velocity amplitude, judges the stability of the dangerous rock masses, and issues graded warnings based on the judgment results, including:

[0090] According to the characteristics that the natural frequency of dangerous rock mass decreases and the root mean square velocity amplitude ratio increases when the stability of dangerous rock mass decreases, a dual dynamic index stability judgment model of dangerous rock mass is constructed;

[0091] According to the dual dynamic index stability judgment model, when the natural frequency of the dangerous rock mass is greater than or equal to 300 Hz and less than 400 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 1.2 and less than 2, a yellow warning is issued, which indicates that the dangerous rock mass is currently in a basically stable state and there is no risk of collapse;

[0092] When the natural frequency of the dangerous rock mass is greater than or equal to 200 Hz and less than 300 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 2 and less than 3.5, an orange warning is issued, which indicates that the dangerous rock mass is currently in a state of poor stability and has a certain risk of collapse;

[0093] When the natural frequency of the dangerous rock mass is less than 200 Hz and the root mean square velocity amplitude ratio is greater than or equal to 3.5, a red warning is issued, which indicates that the dangerous rock mass is currently in an unstable state and may collapse at any time.

[0094] In a feasible implementation, the influence of different cutting lengths on the natural frequency and root mean square velocity amplitude ratio of the dangerous rock mass is described below:

[0095] Alternatively, if Figure 7 As shown in Figure 7, as the relative bonding length decreases, the resonance peak of the frequency response curve of the dangerous rock moves to the left, that is, the natural frequency gradually decreases. The relative bonding length is calculated as shown in formula (7):

[0096] (7)

[0097] Where L is the cumulative length of the artificially cut structural surface; is the height of dangerous rock mass, take 10cm.

[0098] Alternatively, if Figure 8 As shown in the figure, as the relative bonding length decreases, the natural frequency decreases in the same way as the safety factor. When the relative bonding length is about 0.8, the natural frequency change trend is slightly different from the safety factor change trend. The reason for this may be that there is a certain error in the cutting depth during the manual cutting process and the actual rock mass is a non-homogeneous isotropic elastic material.

[0099] Alternatively, if Fig. 9 As shown in the figure, as the relative length of the bond decreases, the root mean square velocity amplitude ratio shows an increasing trend, and the change amplitude is significantly larger than the tilt angle. When the relative length of the bond decreases from 1 to 0.8, the root mean square velocity amplitude ratio increases from 1.02 to 1.46, and the tilt angle does not change significantly. When the relative length of the bond decreases to 0.5, the tilt angle suddenly increases, and the root mean square velocity amplitude ratio has reached more than 3 times the initial amplitude. The spatial geometric relationship of the three-axis acceleration of the dangerous rock collected by MEMS sensing is used to calculate the angle change of the dangerous rock in the direction of collapse. The calculation formula is shown in the following formula (8):

[0100] (8)

[0101] In the formula, It is the inclination angle of the MEMS sensing device in the X direction (tilting direction) relative to the horizontal plane of the ground.

[0102] In a feasible implementation, a dynamic stability judgment model for dangerous rock masses is established based on the characteristics of decreased stability of dangerous rock masses, decreased natural frequency, and increased root mean square velocity amplitude ratio. Fig.10 As shown in the figure, when the natural frequency and root mean square velocity amplitude ratio obtained by MEMS sensing are in the upper left corner, the dangerous rock mass is in a stable state. When the dangerous rock mass moves to the lower right corner, the stability of the dangerous rock mass decreases. When the natural frequency of the dangerous rock mass is greater than or equal to 300Hz and less than 400Hz, and the root mean square velocity amplitude ratio is greater than or equal to 1.2 and less than 2, a yellow warning is issued, indicating that the stability of the dangerous rock mass is slightly reduced, but there is no collapse risk at present; when the natural frequency of the dangerous rock mass is greater than or equal to 200Hz and less than 300Hz, and the root mean square velocity amplitude ratio is greater than or equal to 2 and less than 3.5, an orange warning is issued, indicating that the stability of the dangerous rock mass is poor and there is a certain collapse risk. When the natural frequency of the dangerous rock mass is less than the critical frequency of 200Hz, and the root mean square velocity amplitude ratio is greater than or equal to the critical value of 3.5, a red warning is issued, indicating that the dangerous rock mass may collapse at any time.

[0103] Optionally, in addition to the above dual dynamic index stability judgment model constructed by the ratio of the natural frequency of the dangerous rock mass to the root mean square velocity amplitude, other methods can also be used to judge the stability of the dangerous rock mass and provide graded warnings:

[0104] S4B, the thresholds of different warning levels are mainly determined based on the relationship between the natural frequency and the safety factor, mainly including:

[0105] S4B1. Simplify the single proton swing model of the dangerous rock mass, establish the motion equation based on Newton's second law, and obtain the mathematical expression of the natural frequency.

[0106] S4B2. Correlate the mathematical expression of the safety factor of dangerous rock mass under the limit equilibrium theory with the mathematical expression of the natural frequency to obtain the quantitative relationship between the natural frequency and the safety factor.

[0107] S4B3. By obtaining the corresponding natural frequency value through the value of the safety factor under different stable states of the dangerous rock mass, the threshold values ​​of different warning levels can be obtained.

[0108] Optionally, the motion equation of the single proton swing model of the dangerous rock mass established in S4B1 is as shown in formula (9), and the mathematical expression of the natural frequency is as shown in formula (10):

[0109] (9)

[0110] (10)

[0111] In the formula is the vibration time history curve of dangerous rock mass; The quality of dangerous rock mass; is the natural frequency of dangerous rock mass. is the bonding length of the structural surface; is the gravity of dangerous rock mass; is the thickness of the bonding layer; is the distance between the center of gravity of the dangerous rock mass and the structural surface; is the thickness of the dangerous rock mass; is the elastic modulus of dangerous rock mass.

[0112] Optionally, the quantitative relationship between the natural frequency and safety factor of the S4B2 medium-risk rock mass is as shown in formula (11):

[0113] (11)

[0114] In the formula, is the safety factor of dangerous rock mass; is the tensile strength of the dangerous rock mass; except for the natural frequency of the dangerous rock mass, the rest can be obtained through on-site testing, and the one-to-one correspondence between the safety factor and the natural frequency can be obtained.

[0115] Optionally, the stability state of the dangerous rock mass in S4B3 can be divided into a stable state, a basically stable state, a state of poor stability and an unstable state, and the corresponding safety factor dividing values ​​are 1.15, 1.05 and 1. That is to say, when the safety factor is greater than or equal to 1.15, it indicates that the dangerous rock mass is currently in a stable state and no warning is required; when the safety factor is less than 1.15 and greater than or equal to 1.05, a yellow warning is issued, indicating that the dangerous rock mass is currently in a basically stable state and there is no risk of collapse; when the safety factor is less than 1.05 and greater than or equal to 1, an orange warning is issued, indicating that the dangerous rock mass is currently in a state of poor stability and has a certain risk of collapse; when the safety factor is less than 1, a red warning is issued, indicating that the dangerous rock mass is currently in an unstable state and may collapse at any time.

[0116] In an embodiment of the present invention, a MEMS sensing system is fixed to a dangerous rock mass and a bedrock mass, respectively, to obtain a vibration signal of the dangerous rock mass and a vibration signal of the bedrock mass, respectively; a bedrock vibration time history curve is determined based on the vibration signal of the bedrock mass, a dangerous rock vibration time history curve is determined based on the vibration signal of the dangerous rock mass, a frequency response function is determined based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and the natural frequency of the dangerous rock mass is determined by fitting the frequency response function based on a theoretical amplitude amplification factor of a spring-proton model system; an integral calculation is performed on the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio; a dual dynamic index stability judgment model of the dangerous rock mass is constructed based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, the stability of the dangerous rock mass is judged, and a graded warning is performed according to the judgment result. The present invention overcomes the unpredictability of instability under the sudden characteristics of dangerous rock collapse, improves the accuracy of collapse warning, improves the timeliness of dangerous rock collapse warning due to displacement monitoring, and improves the efficiency of warning feedback. It has the characteristics of simple operation, low cost, and reliable warning judgment, solves the practical problem of early warning of collapse damage in geotechnical engineering, and can be widely used in the monitoring and early warning of various sudden rock and soil disasters such as cascade power station development and slope dangerous rock collapse during mining.

[0117] Fig.11 1 is a block diagram of a dangerous rock collapse monitoring and early warning device based on MEMS sensing according to an exemplary embodiment. The device is used in a dangerous rock collapse monitoring and early warning method based on MEMS sensing. Fig.11 The device includes an acquisition unit 1110, a determination unit 1120, a calculation unit 1130 and an early warning unit 1140. Among them:

[0118] An acquisition unit 1110 is used to fix the MEMS sensing system to the dangerous rock mass and the bedrock mass respectively, and acquire a vibration signal of the dangerous rock mass and a vibration signal of the bedrock mass respectively;

[0119] A determination unit 1120 is used to determine a bedrock vibration time history curve based on the vibration signal of the bedrock body, determine a dangerous rock vibration time history curve based on the vibration signal of the dangerous rock body, determine a frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine the natural frequency of the dangerous rock body based on fitting the frequency response function with a theoretical amplitude amplification factor of a spring-proton model system;

[0120] A calculation unit 1130 is used to perform integral calculation on the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio;

[0121] The early warning unit 1140 is used to construct a dual dynamic index stability judgment model of the dangerous rock body based on the natural frequency of the dangerous rock body and the root mean square velocity amplitude ratio, judge the stability of the dangerous rock body, and issue a graded early warning according to the judgment result.

[0122] Optionally, the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass are both three-axis accelerations including gravity, and the three-axis accelerations including gravity are converted from the acquired original electrical signals, and the acquired original electrical signals are electrical signals representing different pressure magnitudes.

[0123] Optionally, the determining unit 1120 is configured to:

[0124] S21, preprocessing the bedrock vibration time history curve and the dangerous rock vibration time history curve, wherein the preprocessing includes high-pass filtering and outlier removal;

[0125] S22, performing Fourier transform on the vibration time history curve of the dangerous rock to determine the cross-correlation power spectrum density function between the dangerous rock and the bedrock;

[0126] S23, performing Fourier transform on the bedrock vibration time history curve to determine the bedrock autocorrelation power spectrum density function;

[0127] S24, determining a frequency response function of dangerous rock under constant micromotion based on the cross-correlation power spectrum density function between the dangerous rock and the bedrock and the autocorrelation power spectrum density function of the bedrock;

[0128] S25. Determine the abscissa frequency corresponding to the peak value of the dangerous rock frequency response function as the natural frequency of the dangerous rock mass.

[0129] Optionally, when the preprocessing is outlier removal, the determining unit 1120 is configured to:

[0130] When the electrical signal of the bedrock vibration time history curve or the electrical signal of the dangerous rock vibration time history curve does not satisfy the following formula (1), the unsatisfactory electrical signal will be eliminated:

[0131] (1)

[0132] in, , is the acceleration of the rock mass at time t, represents the x-axis acceleration in the three-axis direction, Represents the y-axis acceleration in the three-axis direction, Represents the z-axis acceleration in the three-axis direction; is the acceleration due to gravity; Indicates the absolute value operation.

[0133] Optionally, the determining unit 1120 is configured to:

[0134] According to the following formula (2), the vibration time history curve of the dangerous rock is transformed by Fourier transform to obtain the cross-correlation power spectrum density function between the dangerous rock and the bedrock: :

[0135] (2)

[0136] in, is the cross-correlation power spectrum density function between dangerous rock and bedrock, is the vibration time history curve of dangerous rock, Represents the bedrock vibration time history curve The conjugate curve of represents the time shift, represents the cross-correlation function between dangerous rock and bedrock, represents the base of natural logarithm, j represents the imaginary unit, and w represents the angular frequency.

[0137] Optionally, the determining unit 1120 is configured to:

[0138] According to the following formula (3), the bedrock vibration time history curve is Fourier transformed to obtain the bedrock autocorrelation power spectrum density function:

[0139] (3)

[0140] in, is the bedrock autocorrelation power spectral density function; is the bedrock vibration time history curve, is the bedrock vibration time history curve The conjugate curve of Represents the autocorrelation function of the bedrock.

[0141] Optionally, the determining unit 1120 is configured to:

[0142] S241. Based on the following formula (4), the angular frequency and the vibration frequency are converted:

[0143] (4)

[0144] S242, based on the following formula (5), the conversion of angular frequency and vibration frequency, the cross-correlation power spectrum density function between the dangerous rock and the bedrock, and the autocorrelation power spectrum density function of the bedrock, determine the frequency response function of the dangerous rock under constant micro-motion:

[0145] (5)

[0146] in, It is the frequency response function of dangerous rock under constant micro-motion.

[0147] Optionally, the computing unit 1130 is configured to:

[0148] The vibration time history curve of the dangerous rock and the vibration time history curve of the bedrock are integrated and calculated according to the following formula (6) to obtain the root mean square velocity amplitude ratio:

[0149] (6)

[0150] in, is the RMS velocity amplitude ratio; is the recording start time of the vibration time history curve; is the recording cut-off time of the vibration time history curve, is the vibration time history curve of dangerous rock, It is the bedrock vibration time history curve.

[0151] Optionally, the early warning unit 1140 is used to:

[0152] According to the characteristics that the natural frequency of dangerous rock mass decreases and the root mean square velocity amplitude ratio increases when the stability of dangerous rock mass decreases, a dual dynamic index stability judgment model of dangerous rock mass is constructed;

[0153] According to the dual dynamic index stability judgment model, when the natural frequency of the dangerous rock mass is greater than or equal to 300 Hz and less than 400 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 1.2 and less than 2, a yellow warning is issued, which indicates that the dangerous rock mass is currently in a basically stable state and there is no risk of collapse;

[0154] When the natural frequency of the dangerous rock mass is greater than or equal to 200 Hz and less than 300 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 2 and less than 3.5, an orange warning is issued, which indicates that the dangerous rock mass is currently in a state of poor stability and has a certain risk of collapse;

[0155] When the natural frequency of the dangerous rock mass is less than 200 Hz and the root mean square velocity amplitude ratio is greater than or equal to 3.5, a red warning is issued, which indicates that the dangerous rock mass is currently in an unstable state and may collapse at any time.

[0156] In an embodiment of the present invention, a MEMS sensing system is fixed to a dangerous rock mass and a bedrock mass, respectively, to obtain a vibration signal of the dangerous rock mass and a vibration signal of the bedrock mass, respectively; a bedrock vibration time history curve is determined based on the vibration signal of the bedrock mass, a dangerous rock vibration time history curve is determined based on the vibration signal of the dangerous rock mass, a frequency response function is determined based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and the natural frequency of the dangerous rock mass is determined by fitting the frequency response function based on a theoretical amplitude amplification factor of a spring-proton model system; an integral calculation is performed on the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio; a dual dynamic index stability judgment model of the dangerous rock mass is constructed based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, the stability of the dangerous rock mass is judged, and a graded warning is performed according to the judgment result. The present invention overcomes the unpredictability of instability under the sudden characteristics of dangerous rock collapse, improves the accuracy of collapse warning, improves the timeliness of dangerous rock collapse warning due to displacement monitoring, and improves the efficiency of warning feedback. It has the characteristics of simple operation, low cost, and reliable warning judgment, solves the practical problem of early warning of collapse damage in geotechnical engineering, and can be widely used in the monitoring and early warning of various sudden rock and soil disasters such as cascade power station development and slope dangerous rock collapse during mining.

[0157] Fig.12 FIG. 1 is a schematic diagram of a structure of a dangerous rock collapse monitoring and early warning device based on MEMS sensing provided by an embodiment of the present invention. Fig.12 As shown, the dangerous rock collapse monitoring and early warning equipment based on MEMS sensing may include the above Fig.11 The dangerous rock collapse monitoring and early warning device based on MEMS sensing is shown. Optionally, the dangerous rock collapse monitoring and early warning device based on MEMS sensing 1210 may include a first processor 2001 .

[0158] Optionally, the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing may also include a memory 2002 and a transceiver 2003 .

[0159] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0160] Combine the following Fig.12 The various components of the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing are introduced in detail:

[0161] The first processor 2001 is the control center of the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0162] Optionally, the first processor 2001 can perform various functions of the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0163] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Fig.12 CPU0 and CPU1 are shown in FIG.

[0164] In a specific implementation, as an embodiment, the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing may also include multiple processors, such as Fig.12 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0165] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0166] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be accessed through the interface circuit ( Fig.12 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0167] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0168] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Fig.12 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0169] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Fig.12 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0170] It should be noted that Fig.12 The structure of the MEMS-based dangerous rock collapse monitoring and early warning device 1210 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0171] In addition, the technical effects of the dangerous rock collapse monitoring and early warning device 1210 based on MEMS sensing can refer to the technical effects of the dangerous rock collapse monitoring and early warning method based on MEMS sensing described in the above method embodiment, which will not be repeated here.

[0172] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0173] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0174] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0175] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0176] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0177] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0178] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0179] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0180] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0183] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0184] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A dangerous rock collapse monitoring and early warning method based on MEMS sensing, characterized in that: The method comprises: S1. Fix the MEMS sensing system to the dangerous rock mass and the bedrock mass respectively, and obtain the vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass respectively; S2. Determine a bedrock vibration time history curve based on the vibration signal of the bedrock mass, determine a dangerous rock vibration time history curve based on the vibration signal of the dangerous rock mass, determine a frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine the natural frequency of the dangerous rock mass based on fitting the frequency response function with a theoretical amplitude amplification factor of a spring-proton model system; Wherein, the S2 determines the frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determines the natural frequency of the dangerous rock mass based on the frequency response function, including: S21, preprocessing the bedrock vibration time history curve and the dangerous rock vibration time history curve, wherein the preprocessing includes high-pass filtering and outlier removal; S22, performing Fourier transform on the vibration time history curve of the dangerous rock to determine the cross-correlation power spectrum density function between the dangerous rock and the bedrock; S23, performing Fourier transform on the bedrock vibration time history curve to determine the bedrock autocorrelation power spectrum density function; S24, determining a frequency response function of dangerous rock under constant micromotion based on the cross-correlation power spectrum density function between the dangerous rock and the bedrock and the autocorrelation power spectrum density function of the bedrock; S25, determining the abscissa frequency corresponding to the peak value of the dangerous rock frequency response function as the natural frequency of the dangerous rock mass; S3, integrating the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain the root mean square velocity amplitude ratio; S4. Based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, a dual dynamic index stability judgment model of the dangerous rock mass is constructed to judge the stability of the dangerous rock mass, and a graded warning is issued according to the judgment result; Among them, S4 also includes: S4B, the thresholds for different warning levels are determined based on the relationship between the natural frequency and the safety factor, including: S4B1, simplify the single proton swing model of the dangerous rock mass, establish the motion equation based on Newton's second law, and obtain the mathematical expression of the natural frequency; S4B2. Correlate the mathematical expression of the safety factor of dangerous rock mass under the limit equilibrium theory with the mathematical expression of the natural frequency to obtain the quantitative relationship between the natural frequency and the safety factor; S4B3. The corresponding natural frequency value is obtained by the value of the safety factor under different stable states of the dangerous rock mass, and the threshold value of different warning levels can be obtained; The motion equation of the single proton swing model of the dangerous rock mass established in S4B1 is shown in formula (9), and the mathematical expression of the natural frequency is shown in formula (10): （9） （10） In the formula is the vibration time history curve of dangerous rock mass; The quality of dangerous rock mass; is the natural frequency of dangerous rock mass; is the bonding length of the structural surface; is the gravity of dangerous rock mass; is the thickness of the bonding layer; is the distance between the center of gravity of the dangerous rock mass and the structural surface; is the thickness of the dangerous rock mass; is the elastic modulus of dangerous rock mass.

2. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 1 is characterized in that: The vibration signal of the dangerous rock mass and the vibration signal of the bedrock mass are both three-axis accelerations including gravity, and the three-axis accelerations including gravity are obtained by converting the acquired original electrical signals, and the acquired original electrical signals are electrical signals representing different pressure magnitudes.

3. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 1 is characterized in that: When the preprocessing in S21 is outlier removal, the preprocessing of the bedrock vibration time history curve and the dangerous rock vibration time history curve in S21 includes: When the electrical signal of the bedrock vibration time history curve or the electrical signal of the dangerous rock vibration time history curve does not satisfy the following formula (1), the unsatisfactory electrical signal will be eliminated: （1） in, , is the acceleration of the rock mass at time t, represents the x-axis acceleration in the three-axis direction, Represents the y-axis acceleration in the three-axis direction, Represents the z-axis acceleration in the three-axis direction; is the acceleration due to gravity; Indicates the absolute value operation.

4. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 1 is characterized in that: The step S22 performs Fourier transformation on the dangerous rock vibration time history curve to determine the cross-correlation power spectrum density function between the dangerous rock and the bedrock, including: According to the following formula (2), the vibration time history curve of the dangerous rock is transformed by Fourier transform to obtain the cross-correlation power spectrum density function between the dangerous rock and the bedrock: : （2） in, is the cross-correlation power spectrum density function between dangerous rock and bedrock, is the vibration time history curve of dangerous rock, Represents the bedrock vibration time history curve The conjugate curve of represents the time shift, represents the cross-correlation function between dangerous rock and bedrock, represents the base of natural logarithm, j represents the imaginary unit, and w represents the angular frequency.

5. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 4 is characterized in that: The step S23 performs Fourier transformation on the bedrock vibration time history curve to determine the bedrock autocorrelation power spectrum density function, including: According to the following formula (3), the bedrock vibration time history curve is Fourier transformed to obtain the bedrock autocorrelation power spectrum density function: （3） in, is the bedrock autocorrelation power spectral density function; is the bedrock vibration time history curve, is the bedrock vibration time history curve The conjugate curve of Represents the autocorrelation function of the bedrock.

6. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 5 is characterized in that: The step S24 of determining the frequency response function of the dangerous rock under constant micro-motion based on the cross-correlation power spectrum density function between the dangerous rock and the bedrock and the autocorrelation power spectrum density function of the bedrock includes: S241. Based on the following formula (4), the angular frequency and the vibration frequency are converted: （4） S242, based on the following formula (5), the conversion of angular frequency and vibration frequency, the cross-correlation power spectrum density function between the dangerous rock and the bedrock, and the autocorrelation power spectrum density function of the bedrock, determine the frequency response function of the dangerous rock under constant micro-motion: （5） in, It is the frequency response function of dangerous rock under constant micro-motion.

7. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 1 is characterized in that: The step S3 integrates and calculates the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain the root mean square velocity amplitude ratio, including: The vibration time history curve of the dangerous rock and the vibration time history curve of the bedrock are integrated and calculated according to the following formula (6) to obtain the root mean square velocity amplitude ratio: （6） in, is the RMS velocity amplitude ratio; is the recording start time of the vibration time history curve; is the recording cut-off time of the vibration time history curve, is the vibration time history curve of dangerous rock, It is the bedrock vibration time history curve.

8. The dangerous rock collapse monitoring and early warning method based on MEMS sensing according to claim 1 is characterized in that: The step S4 constructs a dual dynamic index stability judgment model for the dangerous rock mass based on the natural frequency of the dangerous rock mass and the root mean square velocity amplitude ratio, judges the stability of the dangerous rock mass, and issues graded warnings according to the judgment results, including: According to the characteristics that the natural frequency of dangerous rock mass decreases and the root mean square velocity amplitude ratio increases when the stability of dangerous rock mass decreases, a dual dynamic index stability judgment model of dangerous rock mass is constructed; According to the dual dynamic index stability judgment model, when the natural frequency of the dangerous rock mass is greater than or equal to 300 Hz and less than 400 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 1.2 and less than 2, a yellow warning is issued, which indicates that the dangerous rock mass is currently in a basically stable state and there is no risk of collapse; When the natural frequency of the dangerous rock mass is greater than or equal to 200 Hz and less than 300 Hz, and the root mean square velocity amplitude ratio is greater than or equal to 2 and less than 3.5, an orange warning is issued, which indicates that the dangerous rock mass is currently in a state of poor stability and has a certain risk of collapse; When the natural frequency of the dangerous rock mass is less than 200 Hz and the root mean square velocity amplitude ratio is greater than or equal to 3.5, a red warning is issued, which indicates that the dangerous rock mass is currently in an unstable state and may collapse at any time.

9. A dangerous rock collapse monitoring and early warning device based on MEMS sensing, the dangerous rock collapse monitoring and early warning device based on MEMS sensing is used to implement the dangerous rock collapse monitoring and early warning method based on MEMS sensing as claimed in any one of claims 1 to 8, characterized in that: The device comprises: An acquisition unit is used to fix the MEMS sensor system to the dangerous rock mass and the bedrock mass respectively, and acquire vibration signals of the dangerous rock mass and the bedrock mass respectively; a determination unit, configured to determine a bedrock vibration time history curve based on the vibration signal of the bedrock body, determine a dangerous rock vibration time history curve based on the vibration signal of the dangerous rock body, determine a frequency response function based on the bedrock vibration time history curve and the dangerous rock vibration time history curve, and determine a natural frequency of the dangerous rock body based on fitting the frequency response function with a theoretical amplitude amplification factor of a spring-proton model system; A calculation unit, used for performing integral calculation on the dangerous rock vibration time history curve and the bedrock vibration time history curve to obtain a root mean square velocity amplitude ratio; The early warning unit is used to construct a dual dynamic index stability judgment model of the dangerous rock body based on the natural frequency of the dangerous rock body and the root mean square velocity amplitude ratio, judge the stability of the dangerous rock body, and issue a graded early warning according to the judgment result.

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