Method for measuring stability change of dangerous rock mass based on excellent frequency
Through the Higuchi fractal dimension and Volterra prediction model combined with principal component analysis and planar polarization spectroscopy, the problem of obtaining excellent frequency of dangerous rock mass under normal micro-movement conditions is solved, real-time monitoring of the stability changes of dangerous rock mass is achieved, and the safety and accuracy of monitoring are improved.
Patent Information
- Application Number
- CN202510059757.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-14
- Publication Date
- 2025-07-11
AI Technical Summary
The prior art is difficult to effectively obtain the excellent frequency of dangerous rock mass under normal micro-movement conditions, which makes it difficult to judge the stability changes of dangerous rock mass.
The abnormal vibration sequence was identified by Higuchi fractal dimension calculation, and the Volterra prediction model was used for data prediction. The excellent frequency of dangerous rock mass was obtained by combining principal component analysis and planar polarization spectroscopy, and the stability change was judged by analyzing the characteristics of excellent frequency change.
It realizes the excellent frequency of dangerous rock mass in real time under normal micro-movement conditions, provides effective identification support for changes in the stability of dangerous rock mass, and avoids the danger and uncertainty of artificial incentives.
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Figure CN120296333A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of monitoring the stability of dangerous rock masses, and more particularly to a method for measuring the stability change of dangerous rock masses based on dominant frequency. Background Art
[0002] At present, the threats of unstable collapse of dangerous rock masses to water conservancy projects are mainly reflected in aspects such as structural damage, blocking of water flow, sedimentation problems, and water quality deterioration. The collapse may cause cracks or damage to structures such as dams and levees, and even lead to their failure, directly endangering the stability and safety of the project. In addition, the fallen debris will block the water flow, causing the water level to rise upstream, increasing the flood risk, and sediment deposition will also reduce the water storage and flood discharge capabilities of reservoirs and river channels. The sediment and pollutants brought by the collapse may also make the water body turbid, affecting the water quality and posing a threat to the ecology and human water use. More seriously, the collapse may trigger secondary disasters such as debris flows and barrier lakes, further exacerbating the risks. Therefore, it is necessary to ensure the safety and function of water conservancy projects through the method of safety monitoring of the stability of dangerous rock masses.
[0003] The stability of dangerous rock masses is controlled by the cracks at their trailing edges. The dominant frequency of dangerous rock masses is relatively sensitive to the change in the depth of the cracks at their trailing edges and can be used as a characteristic index for identifying the damage of dangerous rock masses. Currently, the method for obtaining the vibration data of dangerous rock masses in most indoor experiments is artificial percussion excitation. In actual engineering, dangerous rock masses are often located on high and steep slopes. The method of climbing manually to the location of the dangerous rock mass for excitation is highly dangerous, and the volume of dangerous rock masses is often large, making it difficult to determine the intensity of artificial excitation. In addition, it is not easy to determine the three-dimensional vibration orientation of dangerous rock masses, and the wrong measurement orientation cannot reflect the main vibration characteristics of dangerous rock masses. In fact, micro-vibrations that are imperceptible to humans always exist at any time and any location on the earth's surface, that is, ambient vibration.
[0004] Therefore, how to obtain the dominant frequency of dangerous rock masses under the condition of ambient vibration and then judge the stability change of dangerous rock masses is an urgent problem to be solved by those skilled in the art. Summary of the Invention
[0005] In view of this, the present invention provides a method for measuring the stability change of dangerous rock masses based on dominant frequency to solve the problems in the background art.
[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A method for measuring the stability change of dangerous rock masses based on dominant frequency, comprising: Calculating and identifying abnormal vibration sequences by using the Higuchi fractal dimension; After removing the abnormal vibration sequences, using the Volterra prediction model to predict the data in the vacant time period to obtain the data sequence segments before and after the vacant time period; The data sequence segments before and after the predicted vacant time period are spliced using the fade-in / fade-out technique to obtain a stable vibration sequence; Analyze the stable vibration sequence based on the principal component analysis method to obtain the main vibration direction of the dangerous rock mass under the condition of ambient micro-vibration; Analyze the main vibration direction of the dangerous rock mass using the plane polarization spectrum method to obtain the dominant frequency of the dangerous rock mass under the condition of ambient micro-vibration; Analyze the stability change of the dangerous rock mass according to the change characteristics of the dominant frequency.
[0007] Optionally, the identification of the abnormal vibration sequence includes: Collect the three-dimensional vibration acceleration data of the dangerous rock mass under the condition of ambient micro-vibration using a vibration acceleration acquisition system, and record them as the X direction, Y direction, and Z direction respectively; Adopt the segmented moving window method to calculate the Higuchi fractal dimension of the vibration acceleration sequences in the X direction, Y direction, and Z direction respectively; Superimpose the fractal dimension sequences in the three directions to obtain the overall fractal dimension sequence of the three-axis vibration; Based on Principle, set the outlier threshold, and regard the data with the overall fractal dimension greater than Or less than As outliers, and then obtain the abnormal data segment; where Is the average value of the overall fractal dimension sequence, Is the standard deviation of the overall fractal dimension sequence.
[0008] Optionally, the specific calculation method of the Higuchi fractal dimension is as follows: For the vibration sequence , select the maximum interval scale ; for each interval k And offset m Construct a subsequence: ; In the formula, , , the number of subsequences is rounded , Is the rounding operator; Calculate the curve length of each subsequence : ; Then the average curve length of all subsequences : ; Fit Curve, and its slope is the fractal dimension D.
[0009] Optionally, the data sequence segments before and after the obtained vacant time period are specifically: After determining the abnormal vibration sequence segment, the three-axis vibration data sequences are processed respectively: The vibration sequence in a certain direction is , with a length of n the abnormal vibration data segment; Forward predict to obtain ; Backward predict to obtain ; Among them, the Volterra model is used for prediction. The Volterra series represents the output of the system as a polynomial series of the input, and the output is expressed as the sum of multiple integral terms of the input, and each term is weighted by the Volterra kernel function.
[0010] Optionally, for the input signal and the output signal , the Volterra series is expressed as: ; In the formula: is the system constant bias term; n represents the order of the series; is n the nth-order Volterra kernel, which is used to describe the nonlinear characteristics of the system at this order.
[0011] Optionally, the specific method of splicing the data sequence segments before and after the predicted vacant time period by using the fade-in / fade-out technique is: Based on the fade-in / fade-out technique for signal splicing, denote ; ; The connection method between and In the formula: , are the fade-out and fade-in functions; After the above processing, the original vibration data sequence changes from to .
[0012] Optionally, the specific method of analyzing the stationary vibration sequence based on the principal component analysis method to obtain the main vibration direction of the dangerous rock mass under the condition of constant micro-vibration is: Obtain the processed three-axis vibration acceleration sequence; Perform principal component analysis on the stationary sequence after processing the outliers in the three-direction vibration sequence, and calculate the eigenvalues and eigenvectors of the data covariance matrix; Sort the eigenvalues in descending order and select the eigenvectors corresponding to the three largest eigenvalues which respectively correspond to the three principal axis directions.
[0013] Optionally, the specific steps for calculating the eigenvalues and eigenvectors of the data covariance matrix are as follows: Suppose the sequences of the vibration signals measured in the X, Y, and Z directions after processing are respectively Calculate the covariance between the sequences, and then obtain the covariance matrix C: ; ; In the formula, is the covariance between the sequence and the sequence ; represents the mean or expected value of the calculated sequence.
[0014] Optionally, use the plane polarization spectrum method to analyze the main vibration direction of the dangerous rock mass, and the specific calculation formula for obtaining the dominant frequency of the dangerous rock mass under the condition of ambient vibration is as follows: ; In the formula, , and are the spectra of the dangerous rock mass , and directions; Analyze the PSR spectrum and find the frequency corresponding to the peak, which is the dominant frequency of the dangerous rock mass.
[0015] Optionally, analyzing the stability change of the dangerous rock mass according to the variation characteristics of the dominant frequency is specifically as follows: When the dominant frequency of the dangerous rock mass shows an upward trend, it indicates that the dangerous rock mass is in the initial stage of detaching from the bedrock matrix; when the dominant frequency of the dangerous rock mass shows a downward trend, it indicates that the depth of the trailing edge crack of the dangerous rock mass has reached the preset degree, and the depth of the trailing edge crack is continuously increasing, and the stability is continuously decreasing.
[0016] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method for measuring the stability change of dangerous rock masses based on dominant frequency, including: using the Higuchi fractal dimension calculation to identify abnormal vibration sequences; after removing the abnormal vibration data sequences, using the Volterra prediction model to predict the data in the vacant time period to obtain the data sequence segments before and after the vacant time period; using the fade-in / fade-out technique to splice the data sequence segments before and after the vacant time period obtained by prediction to obtain a stable vibration sequence; analyzing the stable vibration sequence based on the principal component analysis method to obtain the main vibration direction of the dangerous rock mass under the condition of ambient vibration; using the plane polarization spectrum method to obtain the dominant frequency of the dangerous rock mass under the condition of ambient vibration; analyzing the stability change of the dangerous rock mass according to the change characteristics of the dominant frequency. The present invention comprehensively considers the three-dimensional vibration characteristics of the dangerous rock mass, can obtain the dominant frequency of the dangerous rock mass in real time under the condition of ambient vibration, and thus provides strong support for identifying the stability change of the dangerous rock mass. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.
[0018] Figure 1 Schematic diagram of the method flow provided by the present invention; Figure 2a Schematic diagram of the model size provided by the present invention, Figure 2b Schematic diagram of the test model layout provided by the present invention; Figure 3 Acceleration time domain curve provided by the present invention; Figure 4 Overall fractal dimension change curve and threshold provided by the present invention; Figure 5 Plane polarization ratio spectrum of the dangerous rock mass provided by the present invention; Figure 6 Curve of the dominant frequency of the dangerous rock mass changing with the crack depth provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
[0020] The embodiment of the present invention discloses a method for measuring the stability change of dangerous rock mass based on the dominant frequency, such as Figure 1 As shown, including: S1: Identification of abnormal vibration sequences using Higuchi fractal dimension calculation; S1-1: Use a high-sensitivity vibration acceleration acquisition system to collect three-dimensional vibration acceleration data of the dangerous rock mass under normal micro-motion conditions, which are recorded in the X direction, Y direction and Z direction respectively; the high-sensitivity vibration acceleration acquisition system is a prior art and is not limited here.
[0021] S1-2: Use the segmented moving window method to calculate the Higuchi fractal dimension of the vibration acceleration sequence in the X direction, Y direction and Z direction respectively; the specific calculation method of the Higuchi fractal dimension is as follows: For the vibration sequence , select the maximum interval scale ; For each interval k and offset m Construct a subsequence: ; In the formula, , , the number of subsequences is rounded , is the rounding operator; Calculate each subsequence The length of the curve : ; Then the average curve length of all subsequences is : ; Fitting The slope of the curve is the fractal dimension D.
[0022] S1-3: Superimpose the fractal dimension sequences of the three directions to obtain the overall fractal dimension sequence of the three-axis vibration; S1-4: Based on The outlier threshold is set according to the principle, and the overall fractal dimension is greater than or less than The data is regarded as abnormal values, and then the abnormal data segment is obtained; is the average value of the overall fractal dimension sequence, is the standard deviation of the overall fractal dimension sequence.
[0023] S2: After removing the abnormal vibration data sequence, the Volterra prediction model is used to predict the data in the vacant time period to obtain the data sequence segments before and after the vacant time period; After determining the abnormal vibration sequence segment, the three-axis vibration data sequences are processed separately: S2-1: Assume that the vibration sequence in a certain direction is , is the length of n abnormal vibration data segment; S2-2: Forward predict to get ; Backward predict to get ; Among them, the Volterra model is used for prediction. The Volterra series represents the output of the system as a polynomial series of the input, and the output is expressed as the sum of multiple integral terms of the input, and each term is weighted by the Volterra kernel function. For the input signal and the output signal , the Volterra series is expressed as: ; In the formula: is the system constant bias term; n represents the order of the series; is n order Volterra kernel, which is used to describe the nonlinear characteristics of the system at this order.
[0024] Among them, the estimation of the Volterra kernel can adopt the least squares method, the orthogonal Volterra series method, the gradient descent method, and machine learning methods, etc.
[0025] In addition to using the Volterra prediction model for data prediction, algorithms such as support vector machines and neural networks can also be used.
[0026] S3: Use the fade-in / fade-out technology to splice the data sequence segments before and after the predicted vacant time period to obtain a smooth vibration sequence; Based on the fade-in / fade-out technology for signal splicing, denote ; ; The connection method between is: ; In the formula: , are the fade-out and fade-in functions; The fade-out and fade-in functions can be linear functions, exponential functions, logarithmic functions, trigonometric functions, etc. Here, the linear function is used for calculation: ; After the above processing, the original vibration data sequence is from become 。
[0027] S4: Analyze the stationary vibration sequence based on the Principal Component Analysis (PCA) method to obtain the main vibration direction of the dangerous rock mass under the condition of ambient micro - tremors; S4 - 1: After steps S1 - S3, obtain the processed three - dimensional vibration acceleration sequence; S4 - 2: Conduct principal component analysis on the stationary sequence after processing the outliers of the three - dimensional vibration sequence, and calculate the eigenvalues and eigenvectors of the data covariance matrix; The specific steps for calculating the eigenvalues and eigenvectors of the data covariance matrix are as follows: Suppose the sequences of the vibration signals measured in the X, Y, and Z directions after processing are respectively , calculate the covariance between the sequences, and then obtain the covariance matrix C: ; ; In the formula, is the covariance between the sequence and the sequence ; represents calculating the mean or expected value of the sequence.
[0028] S4 - 3: Sort the eigenvalues in descending order , and select the eigenvectors corresponding to the three largest eigenvalues , which respectively correspond to the three main axis directions.
[0029] S5: Analyze the main vibration direction of the dangerous rock mass using the Plane polarization SpectnumRatio (PSR) method to obtain the dominant frequency of the dangerous rock mass under the condition of ambient micro - tremors; The specific calculation formula is as follows: ; In the formula, , and are the frequency spectra of the dangerous rock mass , and azimuths; Analyze the PSR spectrum and find the frequency corresponding to the peak. This frequency is the dominant frequency of the dangerous rock mass.
[0030] S6: Analyze the stability change of the dangerous rock mass according to the change characteristics of the dominant frequency.
[0031] Identification of the stability of dangerous rock masses: The change trend of the stability of dangerous rock masses can be effectively identified according to the change trend of the dominant frequency of dangerous rock masses. When the dominant frequency of the dangerous rock mass shows an upward trend, it indicates that the dangerous rock mass is in the initial stage of detaching from the bedrock matrix. When the dominant frequency of the dangerous rock mass shows a downward trend, it indicates that the depth of the trailing edge crack of the dangerous rock mass has reached the preset degree, and the depth of the trailing edge crack is continuously increasing, and the stability is continuously decreasing.
[0032] In a specific embodiment, the effectiveness and advantages of the present invention are verified through indoor tests. By pouring a dangerous rock mass - bedrock model indoors, the damage is simulated by cutting the trailing edge crack of the dangerous rock model during the test. Each time the crack is cut to a depth of 2 cm, and after each cut, the vibration signal of the dangerous rock mass model is collected under the condition of no artificial excitation (constant micro - vibration condition). The total depth of the cut crack in the whole test process is 18 cm, and the sampling frequency is 5000 Hz. The schematic diagram of the model size is as Figure 2a shown, and the schematic diagram of the test model layout is as Figure 2b shown.
[0033] Due to the large amount of data, only the three - dimensional vibration acceleration when the crack depth is 2 cm is shown, as Figure 3 shown.
[0034] Calculate the overall Higuchi fractal dimension and threshold according to the three - dimensional acceleration data, as Figure 4 shown.
[0035] After identifying and processing the abnormal vibration sequence based on the Higuchi fractal dimension, perform PSR calculation on the three - dimensional vibration data. From the PSR spectrum, the dominant frequency of the dangerous rock mass when the crack depth is 2 cm is 1197 Hz, as Figure 5 shown.
[0036] Use the same method to calculate the dominant frequency of the dangerous rock mass under other crack depth conditions. The change curve of the dominant frequency of the dangerous rock mass with the crack depth is as Figure 6 shown. When the crack depth increases from 0 cm to 2 cm, the natural frequency shows an upward trend. When the crack depth exceeds 2 cm, the dominant frequency of the dangerous rock mass continuously decreases. Generally speaking, the change trend of the dominant frequency can effectively capture the change of the stability of the rock mass. When the dominant frequency of the dangerous rock mass shows an upward trend, it indicates that the dangerous rock mass is in the initial stage of detaching from the bedrock matrix. When the dominant frequency of the dangerous rock mass shows a downward trend, it indicates that the depth of the trailing edge crack of the dangerous rock mass has reached a certain degree, and the depth of the trailing edge crack is continuously increasing, and the stability is continuously decreasing.
[0037] The algorithm adopted by the present invention can be automatically implemented based on computer programming, without any artificially set prior parameters, and can be fully automated.
[0038] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For related parts, reference can be made to the description in the method section.
[0039] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for measuring the stability change of dangerous rock masses based on excellent frequency, characterized in that, Including: Calculating the Higuchi fractal dimension to identify abnormal vibration sequences; After removing the abnormal vibration sequences, using the Volterra prediction model to predict the data in the vacant time period, and obtaining the data sequence segments before and after the vacant time period; Using the fade-in / fade-out technique to splice the data sequence segments before and after the predicted vacant time period to obtain a stable vibration sequence; Analyzing the stable vibration sequence based on the principal component analysis method to obtain the main vibration direction of the dangerous rock mass under the condition of ambient micro-vibration; Analyzing the main vibration direction of the dangerous rock mass using the plane polarization spectrum method to obtain the dominant frequency of the dangerous rock mass under the condition of ambient micro-vibration; Analyzing the stability change of the dangerous rock mass according to the change characteristics of the dominant frequency.
2. The method for measuring the stability change of dangerous rock mass based on excellent frequency according to claim 1, characterized in that, The identification of abnormal vibration sequences includes: Using a vibration acceleration acquisition system to collect the three-dimensional vibration acceleration data of the dangerous rock mass under the condition of ambient micro-vibration, denoted as the X direction, Y direction, and Z direction respectively; Adopting the segmented moving window method to calculate the Higuchi fractal dimensions of the vibration acceleration sequences in the X direction, Y direction, and Z direction respectively; Superposing the fractal dimension sequences in the three directions to obtain the overall fractal dimension sequence of the three-axis vibration; Based on the principle, set the outlier threshold, and regard the data whose overall fractal dimension is greater than or less than as outliers, and then obtain the abnormal data segment; where is the average value of the overall fractal dimension sequence, and is the standard deviation of the overall fractal dimension sequence.
3. The method for measuring the stability change of dangerous rock mass based on excellent frequency according to claim 2, wherein The specific calculation method of the Higuchi fractal dimension is as follows: For the vibration sequence , select the maximum interval scale ; for each interval k and offset m construct a subsequence: ； In the formula, , , the number of subsequences is rounded , is the rounding operator; Calculate the length of the curve for each subsequence : ； The average curve length of all subsequences : ； Fitting The slope of the curve is the fractal dimension D.
4. A method for measuring the change in the stability of dangerous rock masses based on excellent frequencies according to claim 1, characterized in that, The specific method for obtaining the data sequence segments before and after the vacant time period is as follows: After determining the abnormal vibration sequence segments, processing the three-direction vibration data sequences respectively: The vibration sequence in a certain direction is , is an abnormal vibration data segment with a length of n Forward prediction is performed on to obtain ; backward prediction is performed on to obtain ; Among them, the Volterra model is used for prediction. The Volterra series represents the output of the system as a polynomial series of the input, and the output is expressed as the sum of multiple integral terms of the input, and each term is weighted by the Volterra kernel function.
5. A method for measuring the stability change of dangerous rock masses based on excellent frequency according to claim 4, characterized in that, For the input signal and the output signal , the Volterra series is expressed as: ； In the formula: is the system constant bias term; n represents the order of the series; is n the \(n\)th-order Volterra kernel, which is used to describe the nonlinear characteristics of the system at this order.
6. A method for measuring the change in the stability of dangerous rock masses based on dominant frequencies according to claim 4, characterized in that, The specific method of using the fade-in / fade-out technique to splice the data sequence segments before and after the predicted vacant time period is as follows: Signal splicing is performed based on the fade-in / fade-out technique, denoted as ; ; And The connection method is as follows: In the formula: , are fade-out and fade-in functions; After the above processing, the original vibration data sequence changes from to .
7. A method for measuring the change in the stability of dangerous rock masses based on excellent frequencies according to claim 1, characterized in that, The specific method of analyzing the stable vibration sequence based on the principal component analysis method to obtain the main vibration direction of the dangerous rock mass under the condition of ambient micro-vibration is as follows: Obtain the processed three-axis vibration acceleration sequence; Performing principal component analysis on the stable sequence after processing the abnormal values of the three-direction vibration sequences, and calculating the eigenvalues and eigenvectors of the data covariance matrix; Sort the eigenvalues in descending order , and select the eigenvectors corresponding to the three largest eigenvalues , which respectively correspond to the three principal axis directions 8. A method for measuring the change in the stability of dangerous rock masses based on the dominant frequency according to claim 7, characterized in that, The specific steps for calculating the eigenvalues and eigenvectors of the data covariance matrix are as follows: Suppose the sequences after processing the measured vibration signals in the X, Y, and Z directions are respectively , calculate the covariance between the sequences, and then obtain the covariance matrix C: ； ； In the formula, is the covariance of sequence and sequence ; represents the mean or expected value of the calculated sequence.
9. A method for measuring the change in the stability of dangerous rock masses based on excellent frequencies according to claim 1, characterized in that, The specific calculation formula for analyzing the main vibration direction of the dangerous rock mass using the plane polarization spectrum method to obtain the dominant frequency of the dangerous rock mass under the condition of ambient micro-vibration is as follows: ； In the formula, , and are the spectra of the dangerous rock mass , and in the azimuth; Analyzing the PSR spectrum and finding the frequency corresponding to the peak, and this frequency is the dominant frequency of the dangerous rock mass.
10. A method for measuring the change in the stability of dangerous rock masses based on excellent frequencies according to claim 1, characterized in that, The specific method of analyzing the stability change of the dangerous rock mass according to the change characteristics of the dominant frequency is as follows: When the dominant frequency of the dangerous rock mass shows an upward trend, it indicates that the dangerous rock mass is in the initial stage of detaching from the bedrock matrix; when the dominant frequency of the dangerous rock mass shows a downward trend, it indicates that the depth of the trailing edge crack of the dangerous rock mass has reached the preset degree, and the depth of the trailing edge crack is continuously increasing, and the stability is continuously decreasing.