Intelligent anchor rod system early warning method and threshold determination method

Abstract

The application discloses a kind of intelligent anchor rod system early warning method and threshold determination method, belong to anchor rod intelligent early warning field, including S1: monitoring data preprocessing, S2: data trend regression analysis and S3: early warning method and index;Step S1 includes substep, S11: detection and correction of singular point, S12: obtain equidistant data and S13: data denoising smoothing;Step S2 includes substep S21: regression model establishment and S22: determine regression coefficient;Step S3 includes substep, S31: the determination method of limit stress static threshold of each monitoring point, S32: the determination method of stress change rate static threshold, S33: sharp end mutation model, the determination method of section stability threshold and S34: multi-factor joint early warning method.The application is by monitoring data preprocessing, data trend regression analysis and division early warning method and index. According to this, the overall trend and law of stress change of roadway surrounding rock can be accurately analyzed, and the anchor rod early warning system early warning threshold is accurately determined.

Description

Technical Field

[0001] This invention relates to the field of intelligent early warning technology for anchor bolts, and more specifically, to an early warning method and threshold determination method for an intelligent anchor bolt system. Background Technology

[0002] The intelligent anchor bolt system mainly collects time-related stress values, while the stress and strain changes of the surrounding rock in the roadway are a complex nonlinear dynamic system. Currently, the stress values ​​monitored by the intelligent anchor bolt are the main indicators for analyzing the overall trend and law of stress changes in the surrounding rock of the roadway. The data curves obtained are highly individualized, and due to the influence of prestress, the magnitude and trend of the collected data are not the same. This is mainly reflected in the following aspects: (1) The early data collection is unstable and is significantly affected by interference factors. It can only be used as a reference in the later stage and is not included in the early warning statistics. (2) Due to the inability to control the prestress applied at the tail end of the anchor bolt, the magnitude of the monitored data for each anchor bolt is different, which makes it very difficult to study the fixed threshold. (3) The early data collection intervals are inconsistent, which makes it difficult to uniformize the data. It should be unified in the later stage. (4) The data trends are different, and the overall changes show a nonlinear and complex trend. Some data show obvious periodic characteristics, some data show violent oscillations, and some data show multiple peaks and troughs. Summary of the Invention

[0003] To address the technical problems of unstable data acquisition, significant interference, difficulty in fixing thresholds, and large data fluctuations in the prior art, this invention provides an early warning method and a threshold determination method for an intelligent anchor bolt system.

[0004] According to one aspect of the present invention, an early warning method for an intelligent anchor bolt system is provided, comprising the steps of:

[0005] S1: Monitoring data preprocessing, the step S1 includes sub-steps, S11: detection and correction of singular points, S12: acquisition of equally spaced data and S13: data noise reduction and smoothing;

[0006] S2: Data trend regression analysis, step S2 includes sub-steps, S21: regression model establishment and S22: determination of regression coefficients;

[0007] S3: Early warning methods and indicators. Step S3 includes sub-steps: S31: Method for determining the static threshold of limit stress at each monitoring point; S32: Method for determining the static threshold of stress change rate; S33: Tip mutation model, method for determining the segment stability threshold; and S34: Multi-factor joint early warning method.

[0008] Furthermore, in step S11, the collected data is set as a sequence. Calculate the average of two terms within a certain distance from each other in the collected data sequence. :

[0009]

[0010] Calculate the allowable value of deformation fluctuation range :

[0011]

[0012] calculate fluctuation value :

[0013]

[0014] when At that time, it was considered The formula for adjusting outliers is as follows:

[0015]

[0016] Furthermore, in step S12, it is set as a sequence. It is a data sequence that has been collected. It is the stress value of the (i-1)th term in the sequence. This is the (i+1)th stress value. The formula for calculating the ith stress value x using interpolation is as follows:

[0017]

[0018] Let the (i-2)th term and the (i+1)th deformation be the actual observed values. We need to interpolate the stress value of the (i-1)th term and the deformation value of the ith term. The calculation formula is:

[0019]

[0020]

[0021] Furthermore, in step S13, the data is subjected to Fourier transform processing and noise reduction processing to make the data smoother and avoid complex fluctuations from affecting the trend analysis.

[0022] Further, in step S2,

[0023] S21: Regression Model Establishment: Perform polynomial regression simulation on the monitored time-stress curve:

[0024]

[0025] S22: Determining Regression Coefficients: When determining a simple trend regression equation, m is taken as 3; when constructing a catastrophe model, m is taken as 5. The regression coefficients β0, β1, β2, β3, ... β are obtained using the least squares method. m .

[0026] Further, step S31 includes,

[0027] Historical data extreme value statistics: use the max and min functions to find the maximum and minimum values ​​in the monitored historical data, and use the interval between these two values ​​as the safe interval;

[0028] The newly collected data is compared with the threshold. If the newly collected data exceeds 1.2 times the extreme value, a single-factor alarm is triggered. If the alarm is canceled and no problem is found, the new maximum value is used as the historical extreme value data, and the warning range is expanded.

[0029] Furthermore, in step S32, four consecutive points are set with a time interval of 30 minutes, lasting for 2 hours. When the slope of these points continuously exceeds the stress change rate threshold tan60, a single-factor alarm is triggered.

[0030] Step 1: Fit the data from the day before the current point in time; short-term predictions can be performed using a third-order multinomial fitting to determine the rate of stress change.

[0031] Fitted curve:

[0032]

[0033] Stress change rate:

[0034]

[0035] Step 2: Statistically analyze the stress change rate over the past 2 hours. If the stress change rate is too fast for 2 consecutive hours and the slope is greater than tan60 or less than -tan60, it indicates that the stress activity distribution inside the surrounding rock is obvious and the surrounding rock may be unstable. In this case, a single-factor alarm will be triggered.

[0036] Furthermore, in step S33, the early warning threshold of the potential function is determined using the mutation amount at the mutation point of the potential function in the cusp catastrophe model, eliminating the influence of subjective factors in the calculation process and making up for the deficiencies of steps S31 and S32.

[0037] Step 1: Construct a catastrophe mathematical model of the time-stress function, treating the collected stress values ​​as a function of time t; perform regression analysis on the data from the two days prior to the current time point, and approximate the result using a 5th-order polynomial, obtaining the following formula:

[0038]

[0039] The coefficients are determined using multiple regression analysis, and the derivative of the above equation is then taken:

[0040]

[0041] Let b0 = β1, b1 = 2β2, b2 = 3β3, b3 = 4β4, b4 = 5β5, then we get

[0042]

[0043] make Substituting it into the above equation and eliminating the cubic term, we get:

[0044]

[0045] In the formula:

[0046]

[0047] make Substituting this into the above equation, we obtain the potential function expression for the cusp catastrophe model of the stress-time curve:

[0048]

[0049] Taking the derivative of the potential function, the system is in equilibrium when the first derivative of the potential function is zero. At this point, we have:

[0050]

[0051] The sum of the broken lines or cusps of this surface is called an odd number of points. The equation for the set of abrupt change points, which consists of all points with vertical tangents on the equilibrium surface, is:

[0052]

[0053] Solving the above four equations simultaneously and eliminating Z, we obtain the equation for the bifurcation set formed by the projections of the singularity set onto the plane of the control variables:

[0054]

[0055] Step 2: Combine the latest 3-day data from each monitoring point and use the cusp catastrophe model to make a judgment: When Δ > 0, the system is in a stable equilibrium state, the deformation of the system is continuous and there will be no sudden change; when Δ = 0, and u and v are not both 0, the system is in a critical stable state, and the system state variables will change suddenly if there is any disturbance; when Δ < 0, the surrounding rock system is in an unstable state and a single-factor warning should be issued.

[0056] Furthermore, in step S34, combining steps S31, S32, and S33, and considering the relationships between different data collection points, a multi-parameter joint early warning is performed to predict the risk level of roadway surrounding rock instability.

[0057] Furthermore, methods for placing sensors at the construction site and analyzing and processing their data include:

[0058] S41: Data preprocessing, compare the original collected data with the preprocessed data, and remove data with obvious deviations;

[0059] S42: Stress extreme value threshold, using the maximum and minimum values ​​of historical sensor data as the upper and lower limits of the threshold, respectively;

[0060] S43: Stress change rate threshold; monitoring data can be expressed using a stress-time relationship equation.

[0061]

[0062] Therefore, the rate of stress change is expressed as:

[0063]

[0064] Calculations of the stress change rate over the past two hours show that the stress change rate is relatively small over the two consecutive hours, with the slope between tan60 and -tan60, indicating that the stress activity inside the surrounding rock is not significant and has not triggered the warning value, and the surrounding rock of the surface roadway is stable.

[0065] S44: Mutation model early warning, performs 5th polynomial regression analysis on the data, determines the undetermined coefficients, and obtains the fitted data relationship curve;

[0066] The fitting equation based on two days of data is as follows:

[0067]

[0068] The mathematical equation for the mutation, derived using the method described above, is as follows:

[0069]

[0070] The threshold for discrimination in the advanced mutation model:

[0071]

[0072] Judgment result: Δ>0, the system is in a stable state and no warning value was triggered, indicating that the surrounding rock of the roadway is stable.

[0073] Compared with the prior art, the beneficial effects of the present invention are:

[0074] This invention provides an early warning method and threshold determination method for an intelligent rock bolt system. The intelligent rock bolt system collected primarily time-dependent stress values. However, the changes in stress and strain of the surrounding rock in a roadway constitute a complex, nonlinear, dynamic system. To address the cumbersome nature of measuring these changes, this invention uses the stress values ​​monitored by the intelligent rock bolt as the main indicator. It preprocesses the monitoring data, performs regression analysis on data trends, and classifies the data into early warning methods and indicators. Based on this, the overall trend and pattern of stress changes in the surrounding rock in the roadway can be accurately analyzed, and the early warning threshold of the rock bolt system can be precisely determined. Attached Figure Description

[0075] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0076] Figure 1 Finding graphs for historical extreme values;

[0077] Figure 2 Diagram showing the installation location of sensor 20080;

[0078] Figure 3 A graph showing data collected by the 20080 sensor;

[0079] Figure 4 This is a preprocessed image of sensor data from 20080.

[0080] Figure 5 Find the stress threshold for sensor 20080;

[0081] Figure 6 For monitoring data and fitting curve plots;

[0082] Figure 7 A fitted plot of data from the last two days;

[0083] Figure 8 A fitted plot of data from the last 3 days;

[0084] Figure 9 The flowchart illustrates an early warning method and threshold determination method for an intelligent anchor bolt system provided by this invention. Detailed Implementation

[0085] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0086] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0087] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0088] See Figures 1 to 9 As shown, this invention provides an early warning method and threshold determination method for an intelligent anchor bolt system, including the following steps: S1: monitoring data preprocessing; S2: data trend regression analysis; and S3: early warning method and indicators.

[0089] Step S1 includes sub-steps: S11: detection and correction of singular points, S12: acquisition of equally spaced data, and S13: data denoising and smoothing.

[0090] Step S2 includes sub-steps S21: establishing the regression model and S22: determining the regression coefficients;

[0091] Step S3 includes sub-steps: S31: method for determining the static threshold of ultimate stress at each monitoring point; S32: method for determining the static threshold of stress change rate; S33: tip mutation model, method for determining the segment stability threshold; and S34: multi-factor joint early warning method.

[0092] S1: Monitoring Data Preprocessing

[0093] The system-collected data has many problems. Before data analysis, the collected data must first be preprocessed. Based on the typical characteristics of the monitored data, preprocessing should be carried out on the following three main issues:

[0094] (1) During the monitoring process, there may be interference from abnormal events or errors, which may cause the collected data to show obvious abnormal trends and data.

[0095] (2) The curves at each monitoring point are highly individualized. Due to the influence of prestress, the magnitude and trend of the collected data are also different.

[0096] (3) During the monitoring process, the data collected by each system is not at the same frequency, resulting in the monitoring data not being at equal intervals;

[0097] Based on the typical problems found in the monitoring data, the raw data must be preprocessed before it can be used for subsequent analysis. To address these problems, a three-step preprocessing process is implemented.

[0098] S11: Singularity Detection and Correction

[0099] There are many reasons for the occurrence of outlier data in monitoring data, such as electromagnetic interference, human disturbance, and systematic errors. Based on the analysis of typical waveforms above, obvious interference data mainly falls into three categories: data that suddenly jumps or drops at a certain moment; high-frequency, violently fluctuating data; and data with values ​​that are particularly large or small. These outliers will have a significant impact on the subsequent analysis process, affecting the accuracy of the prediction model and the final prediction results. Deleting outlier data is crucial for early warning. The first step is to identify such data and correct or delete it.

[0100] Singularity detection or correction can be performed using the moving average detection method. The basic process is as follows:

[0101] The collected data is set as a sequence. Calculate the average of two terms within a certain distance from each other in the collected data sequence. :

[0102]

[0103] Calculate the allowable value of deformation fluctuation range :

[0104]

[0105] calculate fluctuation value :

[0106]

[0107] when At that time, it was considered These are outliers and need to be adjusted or deleted. The adjustment formula is as follows:

[0108]

[0109] S12: Obtain equally spaced data

[0110] The system collects data, but outliers are removed, and the collection intervals are inconsistent. Even after the first step of data processing, the data is still not evenly spaced. Predictive models typically require equally spaced monitoring data; non-equally spaced data causes significant inconvenience in predictive analysis. Therefore, before building the predictive model, the original collected data needs to be homogenized to obtain an evenly spaced data sequence.

[0111] Mathematical methods are used to interpolate based on the changing trends of the collected data sequence, and then the interpolated data is calculated.

[0112] Set as a sequence It is a data sequence that has been collected. It is the stress value of the (i-1)th term in the sequence. This is the (i+1)th stress value. The formula for calculating the ith stress value x using interpolation is as follows:

[0113]

[0114] Two observed values ​​are interpolated between two measured values.

[0115] Let the (i-2)th term and the (i+1)th deformation be the actual observed values. We need to interpolate the stress value of the (i-1)th term and the deformation value of the ith term. The calculation formula is:

[0116]

[0117]

[0118] In other cases, the linear interpolation method can be used to calculate the data in turn, and the data can be organized into evenly distributed and equally spaced data.

[0119] S13: Data Denoising and Smoothing

[0120] After the first two steps of data processing, the data still contains some complex and volatile inflection points and spikes. Therefore, it is necessary to perform Fourier transform processing and noise reduction on the data to smooth it out and avoid the impact of complex fluctuations on trend analysis. The goal is to output the noise-reduced data.

[0121] S2: Data Trend Regression Analysis

[0122] The preprocessed data still represents the relationship between stress and time. The monitored time-stress curve is represented by a mathematical equation, and a multiple linear regression model is used for fitting. Different polynomial equations are fitted according to different data lengths, time periods, and early warning indicator requirements.

[0123] The general method for establishing a polynomial regression model consists of two steps:

[0124] (1) Establishing a regression model: Let the monitored stress value be the variable y, and the time independent variable be the sampling period number t at equal intervals, t=1,2,…,n. Perform a polynomial regression simulation on the monitored time-stress curve.

[0125]

[0126] In the formula, β0, β1, ... β m ε is the regression coefficient; ε is the random error.

[0127] (2) Determine the regression coefficients. First, preliminarily determine the polynomial exponent m. According to relevant literature, the value of m can be preliminarily determined to be 3 for simple trend regression equations. When constructing a catastrophe model, m is generally taken as 5. Use the least squares method to calculate the regression coefficients β0, β1, β2, β3, ... β m .

[0128] S3: Early Warning Methods and Indicators

[0129] After the data is processed, it is analyzed based on statistical and regression analysis to determine the early warning methods and indicators.

[0130] Traditional early warning thresholds are mostly determined based on historical data and experience. However, smart anchor bolts have a relatively short application history, and there is a lack of research by scholars and experts on their implementation, resulting in a lack of established theories. Common early warning methods include setting fixed thresholds based on experience, triggering an alert when the threshold is exceeded—a simple and convenient method; or analyzing data trends—for relatively simple trends, inflection points and rates of change can be used for prediction and early warning, but this method is only suitable for cases with simple change patterns; for highly nonlinear prediction and early warning technologies, methods such as grey prediction and neural networks can be used based on large amounts of data, but this method requires a sufficiently large test database and a large amount of successful data; another highly nonlinear prediction technology utilizes mathematical methods, such as catastrophe theory, fractal theory, and random chaos theory, to find the intrinsic relationships between data factors through mathematical calculations, establish a suitable mathematical early warning model, and issue an early warning by inputting variable parameters and outputting a fixed result. This method has too high a mathematical requirement, and the construction of multi-factor mathematical models is too complex, making it unsuitable for application.

[0131] The internal stress of the surrounding rock in a roadway is a physical quantity with a special subjective meaning. Due to the influence of various factors such as the original rock stress, roadway shape, structural effects, surrounding rock conditions, loosened zone thickness, and monitoring location, the pressure of the surrounding rock in a roadway is difficult to measure accurately. Therefore, a medium must be used to transmit this special physical quantity—that is, a medium that can interact with the roadway and is easy to monitor—to transmit changes in the internal stress of the roadway. The stress changes monitored by intelligent anchor bolts are independent of the order of magnitude of the monitored stress, but they can reflect changes in the surrounding rock stress.

[0132] First, a single early warning parameter is set to determine the safety, critical, and disaster states that a single factor indicator may trigger. Then, based on the set early warning method, multiple parameters are used for joint early warning. The early warning parameters for a single factor are as follows:

[0133] (1) Warning parameter 1: is the static threshold of the ultimate stress at each monitoring point;

[0134] (2) Warning parameter 2: Static threshold for stress change rate;

[0135] (3) Warning parameter 3: Tip mutation model, segment stability threshold.

[0136] The specific application methods for the three early warning parameters are introduced.

[0137] S31: Method for determining warning parameter 1

[0138] In the initial stage of anchor bolt installation, the anchoring force acts on the surrounding rock, initially in a balancing process. Generally, stress monitoring data fluctuates significantly and cannot be used for early warning. After 7 days, the anchor bolt and surrounding rock begin to change in tandem, and stress values ​​are collected. The most recent collected data is compared with the historical maximum or minimum values ​​to analyze the stress trend. Based on experience, combined with the stress distribution of the surrounding rock and the anchor bolt strength, a single-factor alarm is triggered when the stress value reaches 1.2 times the stress threshold (based on previous data, the historical maximum or minimum value). The basic process consists of two steps:

[0139] Step 1: As Figure 1 As shown, historical data extreme value statistics are first performed using the max and min functions (please refer to the relevant functions when implementing the program) to conduct a simple analysis of the historical data, find the maximum and minimum values ​​in the monitored historical data, and use the interval between these two values ​​as the safe interval.

[0140] Step 2: Compare the newly collected data with the threshold (historical extreme value). If the newly collected data exceeds 1.2 times the extreme value, a single-factor alarm is triggered. If the alarm is canceled and no problem is found, the new maximum value is used as the historical extreme value data, and the warning range is expanded.

[0141] S32: Method for determining warning parameter 2

[0142] Trend analysis and early warning: Based on the rate of change and acceleration of the measurement point data and the correlation analysis between data from different substations, roadway instability is predicted.

[0143] The time-stress curve reflects the stress distribution and adjustment within the surrounding rock. A high rate of stress change indicates a gradual increase in the scale of stress redistribution within the surrounding rock, with an accelerating rate of change. This often signifies intensified stress changes and potential danger. A single-factor alarm is triggered when the slope of four consecutive points (30-minute time intervals, lasting 2 hours) consistently exceeds a stress change rate threshold (tan60). The basic steps are also divided into two parts:

[0144] Step 1: At the current time point, the data from the previous day is fitted. According to relevant literature, short-term predictions can be made using a tertiary multinomial fitting to obtain the stress change rate.

[0145] Fitted curve:

[0146]

[0147] Stress change rate:

[0148]

[0149] Step 2: Statistical analysis of stress change rate over the past 2 hours. If the stress change rate is too fast for 2 consecutive hours, and the slope is greater than tan60 or less than -tan60, it indicates that the stress activity distribution inside the surrounding rock is obvious, and the surrounding rock may be unstable. In this case, a single-factor alarm will be triggered.

[0150] S33: Method for determining warning parameter 3

[0151] Cusp mutation, also known as Riena Hugonioc point mutation, is the most commonly used mutation model. Cusp mutations are characterized by abrupt changes, hysteresis, divergence, uncontrollability, and bimodality. The cusp mutation model is suitable for data sequences exhibiting a certain trend. Data collected by intelligent anchor bolt monitoring systems possesses characteristics described by cusp mutations, such as hysteresis, divergence, and abrupt changes. Therefore, applying cusp mutation theory to the study of early warning thresholds for intelligent anchor bolts aligns with objective laws and the system's own requirements. The critical value of the potential function, i.e., the early warning threshold, is determined by the mutation amount at the mutation point of the potential function in the cusp mutation model, eliminating the influence of subjective factors in the calculation process and compensating for the shortcomings of the previous two early warning parameter methods. The basic steps consist of three steps:

[0152] Step 1: Construct a catastrophe mathematical model of the time-stress function. The collected data has significant time sensitivity, meaning the collected stress values ​​can be considered a function of time t. Regression analysis is performed on the data from the previous two days at the current time point. Based on relevant literature, a fifth-order polynomial can be used for approximate fitting, resulting in the following formula:

[0153]

[0154] The coefficients are determined using multiple regression analysis, and the derivative of the above equation is then calculated.

[0155]

[0156] Let b0 = β1, b1 = 2β2, b2 = 3β3, b3 = 4β4, b4 = 5β5, then we get

[0157]

[0158] make Substituting it into the above equation and eliminating the cubic term, we get:

[0159]

[0160] In the formula:

[0161]

[0162] Again

[0163] Substituting into the above equation, we obtain the potential function expression for the cusp catastrophe model of the stress-time curve:

[0164]

[0165] In the formula:

[0166] ,

[0167] Taking the derivative of the potential function, the system is in equilibrium when the first derivative of the potential function is zero. At this point, we have:

[0168]

[0169] The sum of the broken lines or cusps of this surface is called an odd number of points. The equation for the set of abrupt change points, which consists of all points with vertical tangents on the equilibrium surface, is:

[0170]

[0171] Solving the above four equations simultaneously and eliminating Z, we obtain the equation for the bifurcation set formed by the projections of the singularity set onto the plane of the control variables:

[0172]

[0173] Step 2: Based on the latest data from each monitoring point within the last 3 days, use the cusp catastrophe model to make a judgment: When Δ > 0, the system is in a stable equilibrium state, the deformation of the system is continuous, and there will be no sudden changes; when Δ = 0, and u and v are not both 0, the system is in a critical stable state, and the system state variables will change abruptly if there is any disturbance; when Δ < 0, the surrounding rock system is in an unstable state, and a single-factor early warning should be issued.

[0174] S34: Multi-factor joint early warning method

[0175] The three single-factor early warning indicators mentioned above each have their own advantages: the maximum stress value can reflect the degree of energy accumulation from a side perspective; the stress change rate can reflect the degree of stress activity within the surrounding rock mass; and the cusp catastrophe model can determine the stability of the surrounding rock system over a period of time from nonlinear data. However, if each parameter is used for early warning independently, there are unavoidable drawbacks: the maximum stress value and stress-strain rate value are based on a large amount of empirical data, which is greatly affected by subjective factors, and can only determine the state at a certain point in the trend, and cannot strictly reflect the state of the catastrophe evolution; while catastrophe theory can determine the state of the system based on the trend of change over a period of time, it is slightly lagging behind the first two indicators to some extent.

[0176] Therefore, by combining three separate early warning parameters with the correlation between different data collection points, a multi-parameter joint early warning system can be implemented to predict the risk level of roadway surrounding rock instability.

[0177] Warning level classification

[0178]

[0179] Early warning methods and standard settings

[0180]

[0181] Based on the aforementioned early warning method for intelligent anchor bolt systems, a threshold determination method for intelligent anchor bolt systems can be further obtained. This threshold determination method for intelligent anchor bolt systems includes the following steps:

[0182] 1. Verification of single-parameter early warning threshold;

[0183] like Figure 2As shown, the set early warning threshold is verified by combining the specific data of the 20080 sensor; the 20080 sensor is located at the center of the arch of the monitoring section of the No. 2 auxiliary well.

[0184] 2. Collect data;

[0185] like Figure 3 As shown, the data was read and statistically analyzed according to the stress-time relationship. This part of the data is from sensor 20080, collected before June 12, 2014, with a time interval of 30 minutes.

[0186] 3. Data preprocessing;

[0187] like Figure 4 As shown, based on the data preprocessing methods introduced above, the data is processed by removing singular points, supplementing data at equal intervals, and performing data noise reduction and smoothing. The result is that data with obvious deviations is removed and the curves are smoothed.

[0188] 4. Determination of single-parameter early warning threshold

[0189] (1) Threshold 1: Stress extreme value threshold

[0190] like Figure 5 As shown, based on the previous introduction: find the maximum and minimum values ​​of the historical data of the 20080 sensor, and use them as the upper and lower limits of the threshold respectively.

[0191] according to Figure 5 It is known that the upper and lower limits of the stress threshold for 20080 are 44KN and 16KN, respectively. When the stress exceeds 1.2 times the stress amplitude, i.e., exceeding 47KN or falling below 13KN, a single-parameter alarm is triggered. Subsequent data collected were all within the stress threshold range, indicating that the surrounding rock of the roadway was stable and no alarm was triggered.

[0192] (2) Threshold 2: Stress change rate

[0193] Data collected on June 12, 2014, the last day of the 20080 sensor's lifecycle, was selected:

[0194] Data collected by sensor 20080 in the last 24 hours

[0195]

[0196] A third-order polynomial regression analysis was performed on the data to determine the undetermined coefficients, resulting in the following: Figure 6 The relationship curve is shown below. The points represent preprocessed data, and the curve is the fitted curve, which can be expressed by the stress-time relationship equation:

[0197]

[0198] Therefore, the rate of stress change is expressed as:

[0199]

[0200] Calculations of the stress change rate over the past two hours show that the stress change rate is relatively small, with a slope between tan60 and -tan60, indicating that the internal stress activity of the surrounding rock is not significant, the warning value was not triggered, and the surrounding rock of the surface roadway is stable. The statistical results are shown in the table.

[0201] Statistical results of stress change rate

[0202]

[0203] (3) Threshold 3: Early warning of mutation model

[0204] Data collected from the last two days of June 12, 2014, using sensor 20080, was analyzed using five-fold polynomial regression to determine the undetermined coefficients, resulting in the following... Figure 7 , Figure 8 The relationship curves shown are fitted curves with a time interval of 30 minutes.

[0205] Data from 3 days generally shows a poor fit, while data from 2 days exhibits a simpler trend and a higher fit. Furthermore, the data from the most recent two days meets the requirements for stability analysis. Therefore, the fitting equation for 2 days of data is:

[0206]

[0207] The mathematical equation for the mutation, derived using the method described above, is as follows:

[0208]

[0209] The threshold for discrimination in the advanced mutation model:

[0210]

[0211] Judgment result: Δ>0, the system is in a stable state and no warning value was triggered, indicating that the surrounding rock of the roadway is stable.

[0212] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for early warning of an intelligent anchor bolt system, characterized in that, Including the following steps: S1: Monitoring data preprocessing. Step S1 includes sub-steps: S11: Singularity detection and correction, S12: Acquiring equally spaced data, and S13: Data noise reduction and smoothing. In step S11, the collected data is set as a sequence. Calculate the average of two terms within a certain distance from each other in the collected data sequence. : ； In the formula: x i It is the stress value of the i-th term in the collected data sequence; x i-1 It is the stress value of the (i-1)th term of the collected data sequence; x i+1 It is the stress value of the (i+1)th term in the collected data sequence; Calculate the allowable value of deformation fluctuation range : ； In the formula: x i+m It is the stress value of the i+m term in the collected data sequence; calculate fluctuation value : ； when At that time, it was considered The formula for adjusting outliers is as follows: ； In step S12, it is set as a sequence. It is a sequence of collected data, and the stress value x of the i-th term is calculated based on interpolation. i The calculation formula is: ； Let the (i-2)th stress value and the (i+1)th stress value be the actual observed values. If we need to interpolate the (i-1)th stress value and the ith stress value, the calculation formula is: ； ； In the formula: x i-2 It is the stress value of the i-2th term of the collected data sequence; S2: Data trend regression analysis. Step S2 includes sub-steps: S21: Regression model establishment and S22: Determining regression coefficients. S3: Early warning methods and indicators. Step S3 includes sub-steps: S31: Method for determining the static threshold of limit stress at each monitoring point; S32: Method for determining the static threshold of stress change rate; S33: Tip mutation model, method for determining the segment stability threshold; and S34: Multi-factor joint early warning method.

2. The early warning method for the intelligent anchor bolt system according to claim 1, characterized in that, In step S13, Fourier transform and noise reduction are performed on the data to smooth it out and avoid complex fluctuations from affecting trend analysis.

3. The early warning method for the intelligent anchor bolt system according to claim 2, characterized in that, In step S2, S21: Regression Model Establishment: Let the monitored stress value be the variable y. t , To account for random error, a polynomial regression simulation was performed on the monitored time-stress curve: ； In the formula: t represents time; S22: Determining Regression Coefficients: When determining a simple trend regression equation, m is taken as 3; when constructing a catastrophe model, m is taken as 5. The regression coefficients β0, β1, β2, β3, ... β are obtained using the least squares method. m .

4. The early warning method for the intelligent anchor bolt system according to claim 3, characterized in that, Step S31 includes: Historical data extreme value statistics: use the max and min functions to find the maximum and minimum values ​​in the monitored historical data, and use the interval between these two values ​​as the safe interval; The newly collected data is compared with the threshold. If the newly collected data exceeds 1.2 times the extreme value, a single-factor alarm is triggered. If the alarm is canceled and no problem is found, the new maximum value is used as the historical extreme value data, and the warning range is expanded.

5. The early warning method for the intelligent anchor bolt system according to claim 4, characterized in that, In step S32, four consecutive points with a time interval of 30 minutes and a duration of 2 hours are set. When the slope of these points continuously exceeds the stress change rate threshold tan60, a single-factor alarm is triggered. Step 1: Fit the data from the day before the current time point; for short-term predictions, use a third-order multinomial stress fitting to determine the rate of stress change. Fitted curve: ； Stress change rate : ； Step 2: Statistically analyze the stress change rate over the past 2 hours. If the stress change rate is too fast for 2 consecutive hours, and the slope is greater than tan60 or less than -tan60, it indicates that the stress activity distribution inside the surrounding rock is obvious and the surrounding rock is unstable. In this case, a single-factor alarm will be triggered.

6. The early warning method for the intelligent anchor bolt system according to claim 5, characterized in that, In step S33, the early warning threshold of the potential function is determined by the mutation amount at the mutation point of the potential function in the cusp catastrophe model, eliminating the influence of subjective factors in the calculation process and making up for the deficiencies of steps S31 and S32. Step 1: Construct a catastrophe mathematical model of the time-stress function, treating the collected stress values ​​as a function of time t; perform regression analysis on the data from the two days prior to the current time point, and fit it with a 5th-order polynomial stress to obtain the following formula: ； The coefficients are determined using multiple regression analysis, and the derivative of the above equation is then taken: ； Let b0 = β1, b1 = 2β2, b2 = 3β3, b3 = 4β4, b4 = 5β5, then we have: ； make Substituting it into the above equation and eliminating the cubic term, we get: ； In the formula: ； Let the monitored stress value be the variable. Substituting this into the above equation, we obtain the potential function expression for the cusp catastrophe model of the stress-time curve: ； Taking the derivative of the potential function, when the first derivative of the potential function... When the value is zero, the system is in equilibrium, at which point: ； The sum of the broken lines or cusps of this surface is called the odd number of points, and the equation of the set of abrupt change points is formed by all points with vertical tangents on the equilibrium surface. for: ； Solving the above four equations simultaneously and eliminating Z, we obtain the equation for the bifurcation set formed by the projections of the singularity set onto the plane of the control variables: ； Step 2: Combining the latest 3-day data from each monitoring point, use the cusp catastrophe model to make a judgment: When Δ > 0, the system is in a stable equilibrium state, the deformation of the system is continuous, and there will be no sudden changes; when Δ = 0, and u, When Δ is not simultaneously zero, the system is in a critically stable state. Any slight disturbance will cause a sudden change in the system's state variables. When Δ < 0, the surrounding rock system is in an unstable state, and a single-factor early warning should be issued.

7. The early warning method for the intelligent anchor bolt system according to claim 6, characterized in that, In step S34, combining steps S31, S32, and S33, and considering the connections between different data collection points, a multi-parameter joint early warning is performed to predict the risk level of roadway surrounding rock instability.

8. A method for determining a smart anchor bolt threshold, comprising the smart anchor bolt system early warning method as described in any one of claims 6 or 7, characterized in that, Methods for placing sensors at the construction site and analyzing and processing their data include: S41: Data preprocessing, compare the original collected data with the preprocessed data, and remove data with obvious deviations; S42: Stress extreme value threshold, using the maximum and minimum values ​​of historical sensor data as the upper and lower limits of the threshold, respectively; S43: Stress rate of change threshold; monitoring data is expressed using a stress-time relationship equation. ； Therefore, the rate of stress change is expressed as: ； Calculations of the stress change rate over the past two hours show that the stress change rate is relatively small over the two consecutive hours, with the slope between tan60 and -tan60, indicating that the stress activity inside the surrounding rock is not significant and has not triggered the warning value, and the surrounding rock of the surface roadway is stable. S44: Mutation model early warning, performs 5th polynomial regression analysis on the data, determines the undetermined coefficients, and obtains the fitted data relationship curve; The fitting equation based on two days of data is as follows: ； The mathematical equation for the mutation, derived using the method described above, is as follows: ； The threshold for discrimination in the advanced mutation model: ； Judgment result: Δ>0, the system is in a stable state and no warning value was triggered, indicating that the surrounding rock of the roadway is stable.

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