A dangerous rock mass instability risk evaluation method and system based on a probability model

Abstract

This invention discloses a method and system for assessing the instability risk of unstable rock masses based on a probabilistic model, relating to the field of large-scale unstable rock mass instability risk assessment technology. The method includes: acquiring time-domain and frequency-domain monitoring data of the target rock mass during vibration; constructing multiple instability risk assessment indicators for the target rock mass based on the time-domain and frequency-domain monitoring data; calculating the comprehensive weight of each instability risk assessment indicator based on order relation analysis and entropy methods; calculating the maximum likelihood value of multiple instability risk assessment indicators with respect to each risk level based on the correlation function value and comprehensive weight of each instability risk assessment indicator corresponding to each risk level; calculating the instability probability of the target rock mass based on the maximum likelihood value; and determining the instability risk assessment result of the target rock mass based on the instability probability and a preset instability risk level table. This invention alleviates the technical problem of low accuracy in assessing the instability risk of unstable rock masses in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology for large unstable rock masses, and in particular to a method and system for risk assessment of unstable rock masses based on a probabilistic model. Background Technology

[0002] As a global geological hazard in mountainous areas, large-scale rockfalls are characterized by complex causes, wide distribution, suddenness, and severe damage. Early warning technology for these rocks has always been a key issue that urgently needs breakthroughs in the field of geological disaster prevention and control. In my country, such disasters often cause serious casualties and economic losses. Therefore, conducting research on the early identification and warning of rockfall instability risks is of significant theoretical and practical importance for improving geological disaster risk prevention and control capabilities and ensuring public safety.

[0003] Currently, internationally adopted intelligent prediction algorithms, such as Monte Carlo methods, neural network models, and support vector machines, largely rely on foreign technical and theoretical systems. However, these methods generally suffer from limitations such as excessive sensitivity to input data, highly subjective weight setting, and a lack of support from fundamental physical and mechanical models. Their applicability and accuracy are significantly constrained, especially when dealing with the brittle failure of large unstable rock masses. Furthermore, traditional evaluation models often rely on single mechanical indicators, making it difficult to capture the dynamic risk evolution of unstable rock masses during creep in real time, thus limiting the accuracy and timeliness of instability risk probability assessment. Summary of the Invention

[0004] To address the aforementioned technical problems in existing technologies, this invention provides a method and system for assessing the instability risk of unstable rock masses based on a probabilistic model. The technical solution is as follows: On the one hand, a method for assessing the instability risk of a rock mass based on a probabilistic model is provided. The method includes: acquiring time-domain and frequency-domain monitoring data of the target rock mass during vibration; constructing multiple instability risk assessment indicators for the target rock mass based on the time-domain and frequency-domain monitoring data; calculating the comprehensive weight of each instability risk assessment indicator based on the order relation analysis method and the entropy method; calculating the maximum likelihood value of the multiple instability risk assessment indicators with respect to each risk level based on the correlation function value of each instability risk assessment indicator corresponding to each risk level and the comprehensive weight; calculating the instability probability of the target rock mass based on the maximum likelihood value; and determining the instability risk assessment result of the target rock mass based on the instability probability and a preset instability risk level table.

[0005] Optionally, acquiring time-domain and frequency-domain monitoring data of the target rock mass during the vibration process includes: monitoring the time-domain index of the vibration velocity of the target rock mass during the process using a non-contact remote sensing monitoring device to obtain time-domain monitoring data; and performing a fast Fourier transform on the time-domain monitoring data to obtain frequency-domain monitoring data.

[0006] Optionally, the plurality of instability risk assessment indicators include root mean square value, waveform value, root mean square amplitude of spectrum, spectral skewness value, impact energy, peak kinetic energy, natural frequency, and damping.

[0007] Optionally, based on the order relation analysis method and the entropy method, the comprehensive weight of each instability risk assessment indicator is calculated, including: calculating the expert weight of each instability risk assessment indicator based on the order relation analysis method; and calculating the objective weight of each instability risk assessment indicator based on the entropy method; wherein the formula for calculating the objective weight includes:

[0008] In the formula, H i Let be the index entropy value of the i-th instability risk assessment indicator; The expert weights and objective weights are combined and normalized to obtain the comprehensive weight for each instability risk assessment indicator; wherein, the formula for calculating the comprehensive weight includes:

[0009] In the formula, ω' i Unnormalized composite weights:

[0010] η i and β i These are the expert weights and the objective weights, respectively. i Indicates the first i One instability risk assessment indicator.

[0011] Optionally, the formula for calculating the maximum likelihood value of the multiple instability risk assessment indicators with respect to each risk level includes:

[0012] In the formula, K j ( R k ) represents the maximum likelihood value of the plurality of instability risk assessment indicators with respect to the j-th risk level. ω i The comprehensive weight of the i-th instability risk assessment indicator is...n The total number of instability risk assessment indicators. K j ( X ki ) represents the correlation function value of the i-th instability risk assessment index corresponding to the j-th risk level.

[0013] Optionally, calculating the instability probability of the target rock mass based on the maximum likelihood value includes: normalizing the maximum likelihood value to obtain a normalized maximum likelihood value; and calculating the instability probability of the target rock mass based on the normalized maximum likelihood value; the calculation formula for the instability probability includes:

[0014] In the formula, RLE The instability probability is... Let j be the normalized maximum likelihood value, and j be the risk level determined according to the principle of maximum correlation.

[0015] Optionally, the preset instability risk level table includes multiple preset instability risk levels and corresponding multiple preset instability probability ranges; determining the instability risk assessment result of the target rock mass based on the instability probability and the preset instability risk level table includes: determining the preset instability risk level corresponding to the preset instability probability range in which the instability probability is located as the instability risk assessment result of the target rock mass.

[0016] On the other hand, a probabilistic model-based risk assessment system for unstable rock masses is also provided to implement the probabilistic model-based risk assessment method for unstable rock masses provided in this embodiment of the invention. The system includes: an acquisition module, a construction module, a first calculation module, a second calculation module, and an evaluation module. The acquisition module is used to acquire time-domain and frequency-domain monitoring data of the target rock mass during vibration. The construction module is used to construct multiple instability risk assessment indicators for the target rock mass based on the time-domain and frequency-domain monitoring data. The first calculation module is used to calculate the comprehensive weight of each instability risk assessment indicator based on the order relation analysis method and the entropy method. The second calculation module is used to calculate the maximum likelihood value of the multiple instability risk assessment indicators with respect to each risk level based on the correlation function value of each risk level corresponding to each instability risk assessment indicator and the comprehensive weight. The evaluation module is used to calculate the instability probability of the target rock mass based on the maximum likelihood value and determine the instability risk assessment result of the target rock mass based on the instability probability and a preset instability risk level table.

[0017] On the other hand, an electronic device is also provided, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0018] On the other hand, a computer-readable storage medium is also provided, which stores computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0019] This invention provides a method and system for assessing the instability risk of unstable rock masses based on a probabilistic model. By introducing the probability of instability risk as the core evaluation index, it realizes dynamic probabilistic assessment and scientific early warning of the instability risk of unstable rock masses, significantly improving the accuracy and foresight of early warning identification. It provides a new technical approach for the early prevention and control of large-scale rock mass collapse disasters and alleviates the technical problem of low accuracy in assessing the instability risk of unstable rock masses in existing technologies. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for assessing the instability risk of unstable rock masses based on a probabilistic model, provided in an embodiment of the present invention. Figure 2 This is a location map of the Diaozui dangerous rock mass in Qutang Gorge, Fengjie County, provided in an embodiment of the present invention; Figure 3 This is a diagram showing the location of the lifting nozzle on the unstable rock mass B1 and the micro-core pile installation points provided in this embodiment of the invention. Figure 4 This is a graph showing the variation of tangent angle values ​​for various evaluation indicators of the dangerous rock mass B1 at the tipping point, provided in an embodiment of the present invention. Figure 5 This is a quantitative evaluation result and trend chart of the instability risk probability of the B1 unstable rock mass at the tipping point, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of a risk assessment system for unstable rock mass based on a probabilistic model, provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0024] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0025] Figure 1 This is a flowchart of a method for assessing the instability risk of unstable rock masses based on a probabilistic model, provided by an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps: Step S102: Obtain time-domain monitoring data and frequency-domain monitoring data of the target rock mass during the vibration process.

[0026] Step S104: Based on time-domain detection data and frequency-domain monitoring data, construct multiple instability risk assessment indicators for the target rock mass.

[0027] Step S106: Based on the order relation analysis method and the entropy method, calculate the comprehensive weight of each instability risk assessment indicator.

[0028] Step S108: Based on the correlation function value and comprehensive weight of each instability risk assessment indicator corresponding to each risk level, calculate the maximum likelihood value of multiple instability risk assessment indicators with respect to each risk level.

[0029] Step S110: Calculate the instability probability of the target rock mass based on the maximum likelihood value, and determine the instability risk assessment result of the target rock mass based on the instability probability and the preset instability risk level table.

[0030] Specifically, step S102 further includes the following steps: Step S1021: Monitor the time-domain index of vibration velocity of the target rock mass during the process using non-contact remote sensing monitoring equipment to obtain time-domain monitoring data. For example, the non-contact remote sensing monitoring device includes a laser Doppler vibrometer.

[0031] Step S1022: Perform a fast Fourier transform on the time-domain monitoring data to obtain the frequency-domain monitoring data.

[0032] For example, using x ( n ) represents the time-domain signal. n One signal value, n =1,2,…, NA fast Fourier transform is performed on the time-domain signal to obtain the frequency-domain monitoring data. s ( k (This is a musical score) k =1,…, K , K The total number of spectral lines. f k For the first k The frequency corresponding to each spectral line.

[0033] Preferably, the multiple instability risk assessment indicators include root mean square value, waveform value, root mean square amplitude of spectrum, spectral skewness value, impact energy, peak kinetic energy, natural frequency, and damping.

[0034] Specifically, root mean square value p The calculation formula is as follows: (1) Waveform value p 2. The calculation formula is: (2) Spectral root amplitude p 3. The calculation formula is: (3) Spectral skewness p 4. The calculation formula is: (4) Impact energy p 5. The calculation formula is: (5) Peak kinetic energy p The calculation formula for 6 is: (6) Natural frequency p 7. The calculation formula is: (7) in, K x The stiffness of the rock mass system is expressed in N / m. M The mass of the rock mass system is expressed in kg.

[0035] Damping p The calculation formula for 8 is: (8) in, D The energy dissipation of a rock mass structure system during one vibration cycle, kg m 2 / s 2 ; E The total energy of the rock mass structure system, kg m 2 / s 2 .

[0036] Specifically, step S106 further includes the following steps: Step S1061: Based on the order relation analysis method, calculate the expert weight of each instability risk assessment indicator.

[0037] Specifically, the main steps of calculating expert weights using the G1 method (Order Relationship Analysis) are as follows: 1) Experts evaluate the importance of each instability risk assessment indicator; 2) Determine the order of importance based on the various instability risk assessment indicators; 3) Determine the assessment indicators for adjacent instability risks Y i-1 and Y i The relative importance between r i = Y i-1 / Y i The most important metric can be set as Y 1. The least important indicator is Y i ; 4) Calculate the expert weights.

[0038] Specifically, the weight of the least important indicator η i The calculation formula is: (9) Other indicator weights η i-1 The calculation formula is: (10) Step S1062: Based on the entropy method, calculate the objective weight of each instability risk assessment indicator.

[0039] This invention introduces entropy values ​​to measure the variability and information utility of evaluation indicators, thereby calculating the objective weights of each instability risk evaluation indicator. Historical data from the previous ten days is used as the initial judgment matrix. R n×m The values ​​are then normalized. The objective weights of each instability risk assessment indicator can be determined using the entropy method. The formulas for calculating the objective weights include:

[0040] In the formula, H iLet be the index entropy value of the i-th instability risk assessment indicator; β i is the objective weight of the i-th instability risk assessment indicator.

[0041] Specifically, the index entropy value H i The calculation formula is: (12) P ij The formula for calculating the weight of the indicator is as follows: (13) a ij These are the elements of the normalized judgment matrix.

[0042] Step S1063 involves combining and normalizing the expert weights and objective weights to obtain the comprehensive weight for each instability risk assessment indicator. The formula for calculating the comprehensive weight includes:

[0043] In the formula, ω' i Unnormalized composite weights:

[0044] η i and β i These are expert weights and objective weights, respectively. i Indicates the first i One instability risk assessment indicator.

[0045] Specifically, the formulas for calculating the maximum likelihood values ​​of multiple instability risk assessment indicators for each risk level include:

[0046] In the formula, K j ( R k Let be the maximum likelihood value of multiple instability risk assessment indicators with respect to the j-th risk level. ω i The comprehensive weight of the i-th instability risk assessment indicator is... n The total number of instability risk assessment indicators. K j ( X ki ) represents the correlation function value of the i-th instability risk assessment index corresponding to the j-th risk level.

[0047] Specifically, in step S110, calculating the instability probability of the target rock mass based on the maximum likelihood value includes: The maximum likelihood value is normalized to obtain the normalized maximum likelihood value.

[0048] Specifically, the formula for calculating the normalized maximum likelihood value is: (17) The instability probability of the target rock mass is calculated based on the normalized maximum likelihood value; the formula for calculating the instability probability includes:

[0049] In the formula, RLE The probability of instability. is the normalized maximum likelihood value, and j is the risk level determined according to the principle of maximum correlation.

[0050] Preferably, the preset instability risk level table includes multiple preset instability risk levels and corresponding multiple preset instability probability ranges.

[0051] In an optional embodiment of this invention, the risk level of rock mass instability is classified according to relevant national standards and actual site requirements. Based on my country's current rock mass safety risk classification, the preset instability risk levels are divided into: low risk (Level I), low-to-medium risk (Level II), medium-to-high risk (Level III), and high risk (Level IV), with corresponding preset instability probability ranges shown in Table 1. Table 1 Preset Instability Risk Level Table

[0052] Specifically, in step S110, the instability risk assessment result of the target rock mass is determined based on the instability probability and a preset instability risk level table, including: The preset instability risk level corresponding to the preset instability probability range where the instability probability is located is determined as the instability risk assessment result of the target rock mass.

[0053] For example, according to Table 1, when RLE When the risk is in the range of 0-33.3%, the instability risk assessment result is low risk (Level I); when RLE When the instability risk is within the range of 33.3%-55.6%, the assessment result is low to medium risk (Level II); when RLE When the instability risk is in the range of 55.6%-66.7%, the assessment result is medium-high risk (Level III); when RLE When the risk level is between 66.7% and 100%, the risk assessment result for instability is high (Level IV).

[0054] The effectiveness and practicality of the method provided in this embodiment will be illustrated below with reference to actual embodiments.

[0055] To verify the applicability and accuracy of the method provided in this invention, a practical application case was selected. This embodiment uses the Diaozui dangerous rock mass in the Qutang Gorge of the Yangtze River in Fengjie County, my country, as the research object. The Diaozui dangerous rock mass is located in the Qutang Gorge of the Yangtze River in Fengjie County, China. Figure 2 As shown, the slope is nearly vertical, with some sections forming inverted cliffs. The relative elevation difference of the riverbank is 258m. The steep cliff zone where the dangerous rocks are located protrudes in a near-V shape on the plane, with the left side dipping approximately 320° northwest and about 120m wide, and the right side dipping approximately 40° northeast and about 110m wide. The width of the river surface at the foot of the Diaozui dangerous rocks is 365m (at a water level of 145m) and 399m (at a water level of 175m). The bedrock is mainly composed of dolomite, dolomitic limestone, and microcrystalline limestone, with thin layers of argillaceous limestone in some areas. The dip of each rock layer ranges from 350° to 1°∠9° to 12°. Under the influence of unloading fissures and geological structures, the nearly vertically distributed rock mass of the Diaozui dangerous rocks has been divided into dangerous rock blocks B1 to B5. However, due to fissure cutting, collapse and rockfall, and local differential weathering, several relatively small dangerous rock units have developed in the local areas of each slab rock mass and steep cliff zone.

[0056] Under the long-term deterioration effects of rainfall and the cyclical rise and fall of water levels in the Three Gorges Reservoir area, the Diaozui rock mass is susceptible to collapse, and given the unclear degree of internal cohesion, large-scale rock mass instability due to collapse is possible. Vibration data monitored using B1 micro-core piles in the Diaozui rock mass, such as... Figure 3 As shown, a quantitative assessment of the instability risk of the Diaozui unstable rock mass B1 was conducted. Field investigation revealed that the Diaozui unstable rock mass B1 is generally stable; therefore, the assessment period was selected from January to March 2024, starting on January 1st, with one-week intervals, ending on March 25th, 26th, 27th, and 28th. The calculation results of relevant indicators are shown in Table 2 and... Figure 4 As shown.

[0057] Table 2 Diaozui dangerous rock mass B1 p 1- p 8. Noise Reduction Indicator Tangent Angle Value

[0058] Table 3 Weighting Table for Evaluation Indicators of Diaozui Rock Mass B1

[0059] Table 4. Comprehensive correlation and risk level of Diaozui dangerous rock mass B1 after noise reduction.

[0060] Table 4 shows that, based on the maximum comprehensive correlation, the risk level of the Diaozui unstable rock mass B1 is very low and it is in a stable state overall, but the change in risk cannot be seen.

[0061] Based on the eight instability risk assessment indicators, the instability risk of the Diaozui unstable rock mass is in a state of significant fluctuation. Figure 5 This data shows the trend of instability risk probability for the Diaozui rock mass from January 1st to March 28th, 2024. The overall instability risk of the Diaozui rock mass showed a slow upward trend. Although the probability of instability risk increased slightly on February 5th and February 12th, it eventually decreased to a fluctuation range of 53.35% to 40.55%. Based on the instability risk probability, the risk level of the Diaozui rock mass can be determined to be at level two (low to medium risk), developing towards level three (medium to high risk) at a relatively slow pace, consistent with the actual survey results. On March 28th, the instability risk probability reached 52.65%, but it cannot be determined whether the instability risk of the Diaozui rock mass will continue to increase; continued monitoring of dynamic monitoring indicators is necessary.

[0062] This invention presents an engineering case study of the Diaozui unstable rock mass B1 in the Qutang Gorge of the Yangtze River in Fengjie County. A quantitative evaluation model for rock mass instability risk was used to determine the trend of instability risk from January 1st to March 28th, 2024. The overall instability risk of the Diaozui unstable rock mass showed a slow upward trend. Although the probability of instability risk increased slightly on February 5th and February 12th, it eventually decreased to a fluctuation range of 53.35% to 40.55%. Based on the probability of instability risk, the risk level was determined to be at a relatively low to medium level (Level II), developing towards a medium to high level (Level III) at a relatively slow pace, consistent with actual survey results.

[0063] Figure 6 This is a schematic diagram of a risk assessment system for unstable rock mass based on a probabilistic model, provided by an embodiment of the present invention. Figure 6 As shown, the system includes: an acquisition module 10, a construction module 20, a first calculation module 30, a second calculation module 40, and an evaluation module 50.

[0064] Specifically, the acquisition module 10 is used to acquire time-domain monitoring data and frequency-domain monitoring data of the target rock mass during the vibration process; Module 20 is used to construct multiple instability risk assessment indicators for the target rock mass based on time-domain detection data and frequency-domain monitoring data; The first calculation module 30 is used to calculate the comprehensive weight of each instability risk assessment index based on the order relation analysis method and the entropy method. The second calculation module 40 is used to calculate the maximum likelihood value of multiple instability risk assessment indicators with respect to each risk level based on the correlation function value and comprehensive weight of each risk level corresponding to each instability risk assessment indicator. Evaluation module 50 is used to calculate the instability probability of the target rock mass based on the maximum likelihood value, and to determine the instability risk evaluation result of the target rock mass based on the instability probability and the preset instability risk level table.

[0065] Specifically, the first calculation module 30 is also used for: Based on the order relation analysis method, the expert weights of each instability risk assessment indicator are calculated. Based on the entropy method, the objective weight of each instability risk assessment indicator is calculated; the formula for calculating the objective weight includes:

[0066] In the formula, H i Let be the index entropy value of the i-th instability risk assessment indicator; The expert weights and objective weights are combined and normalized to obtain the comprehensive weight for each instability risk assessment indicator; the formula for calculating the comprehensive weight includes:

[0067] In the formula, ω' i Unnormalized composite weights:

[0068] η i and β i These are expert weights and objective weights, respectively. i Indicates the first i One instability risk assessment indicator.

[0069] Specifically, the preset instability risk level table includes multiple preset instability risk levels and corresponding multiple preset instability probability ranges; the evaluation module 50 is also used for: The instability probability of the target rock mass is calculated based on the maximum likelihood value, including: The maximum likelihood value is normalized to obtain the normalized maximum likelihood value. The instability probability of the target rock mass is calculated based on the normalized maximum likelihood value; the formula for calculating the instability probability includes:

[0070] In the formula, RLE The probability of instability. is the normalized maximum likelihood value, and j is the risk level determined according to the principle of maximum correlation.

[0071] The preset instability risk level corresponding to the preset instability probability range where the instability probability is located is determined as the instability risk assessment result of the target rock mass.

[0072] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0073] The present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0076] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0079] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the instability risk of unstable rock masses based on a probabilistic model, characterized in that, The method includes: Acquire time-domain and frequency-domain monitoring data of the target rock mass during the vibration process; Based on the time-domain detection data and the frequency-domain monitoring data, multiple instability risk assessment indicators for the target rock mass are constructed. Based on the order relation analysis method and the entropy method, the comprehensive weight of each instability risk assessment indicator is calculated respectively; Based on the correlation function value of each risk level corresponding to each instability risk assessment indicator and the comprehensive weight, the maximum likelihood value of the multiple instability risk assessment indicators with respect to each risk level is calculated; The instability probability of the target rock mass is calculated based on the maximum likelihood value, and the instability risk assessment result of the target rock mass is determined based on the instability probability and the preset instability risk level table.

2. The method according to claim 1, characterized in that, Acquire time-domain and frequency-domain monitoring data of the target rock mass during the vibration process, including: The vibration velocity time-domain index of the target rock mass during the process is monitored using non-contact remote sensing monitoring equipment to obtain time-domain monitoring data; The time-domain monitoring data is subjected to a fast Fourier transform to obtain the frequency-domain monitoring data.

3. The method according to claim 1, characterized in that, The multiple instability risk assessment indicators include root mean square value, waveform value, root mean square amplitude of spectrum, spectral skewness value, impact energy, peak kinetic energy, natural frequency, and damping.

4. The method according to claim 1, characterized in that, Based on the order relation analysis method and the entropy method, the comprehensive weight of each instability risk assessment indicator is calculated, including: Based on the order relation analysis method, the expert weights of each instability risk assessment indicator are calculated. Based on the entropy method, the objective weight of each instability risk assessment indicator is calculated; wherein, the formula for calculating the objective weight includes: In the formula, H i Let be the index entropy value of the i-th instability risk assessment indicator; The expert weights and objective weights are combined and normalized to obtain the comprehensive weight for each instability risk assessment indicator; wherein, the formula for calculating the comprehensive weight includes: In the formula, ω' i Unnormalized composite weights: η i and β i These are the expert weights and the objective weights, respectively. i Indicates the first i One instability risk assessment indicator.

5. The method according to claim 1, characterized in that, The formulas for calculating the maximum likelihood values ​​of the multiple instability risk assessment indicators for each risk level include: In the formula, K j ( R k ) represents the maximum likelihood value of the multiple instability risk assessment indicators with respect to the j-th risk level. ω i The comprehensive weight of the i-th instability risk assessment indicator is... n The total number of instability risk assessment indicators. K j ( X ki ) represents the correlation function value of the i-th instability risk assessment index corresponding to the j-th risk level.

6. The method according to claim 1, characterized in that, Calculating the instability probability of the target rock mass based on the maximum likelihood value includes: The maximum likelihood value is normalized to obtain the normalized maximum likelihood value; Based on the normalized maximum likelihood value, the instability probability of the target rock mass is calculated; the formula for calculating the instability probability includes: In the formula, RLE The instability probability is... Let j be the normalized maximum likelihood value, and j be the risk level determined according to the principle of maximum correlation.

7. The method according to claim 1, characterized in that, The preset instability risk level table includes multiple preset instability risk levels and corresponding multiple preset instability probability ranges; The instability risk assessment result of the target rock mass is determined based on the instability probability and the preset instability risk level table, including: The preset instability risk level corresponding to the preset instability probability range in which the instability probability falls is determined as the instability risk assessment result of the target rock mass.

8. A risk assessment system for unstable rock mass based on a probabilistic model, characterized in that, This system is used to implement the probabilistic model-based risk assessment method for unstable rock masses as described in any one of claims 1-7; the system comprises: an acquisition module, a construction module, a first calculation module, a second calculation module, and an evaluation module; wherein, The acquisition module is used to acquire time-domain monitoring data and frequency-domain monitoring data of the target rock mass during the vibration process; The construction module is used to construct multiple instability risk assessment indicators for the target rock mass based on the time-domain detection data and the frequency-domain monitoring data. The first calculation module is used to calculate the comprehensive weight of each instability risk assessment indicator based on the order relation analysis method and the entropy method, respectively. The second calculation module is used to calculate the maximum likelihood value of the multiple instability risk assessment indicators with respect to each risk level based on the correlation function value of each risk level corresponding to each instability risk assessment indicator and the comprehensive weight; The evaluation module is used to calculate the instability probability of the target rock mass based on the maximum likelihood value, and to determine the instability risk evaluation result of the target rock mass based on the instability probability and a preset instability risk level table.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-7.

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