A landslide geological disaster vulnerability model, risk assessment method and system

Abstract

The application discloses a kind of collapse geological disaster vulnerability model and risk assessment method and system, it is related to geological disaster risk assessment technical field, including: collection target rock mass information and pre-processing;Triaxial acceleration sensor monitoring data and long-term rainfall data are collected;Acceleration fluctuation characteristics are analyzed, and risk of rock mass instability is identified in combination with rainfall data;Quantify the instability risk of rock mass based on Monte Carlo method;The motion path and accumulation range of rockfall are predicted using numerical simulation, and the threat to surrounding power facilities is evaluated;The surrounding area of the substation is managed by risk level model.The application uses real-time monitoring, probability calculation and numerical simulation technology, which overcomes the problem of lack of dynamic monitoring, accurate assessment and hierarchical protection of power facilities in the prior art.The overall scheme realizes real-time, automatic monitoring and risk classification of collapse geological disasters, effectively improving the safety and reliability of power facilities under disaster conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster risk assessment, in particular to a collapse geological disaster vulnerability model and a risk assessment method and system. BACKGROUND

[0002] In mountainous or complex geological structure areas, power facilities (such as substations, transmission towers, distribution lines, etc.) often face the threat of collapse, landslide, debris flow and other geological disasters, especially under continuous rainfall or extreme weather conditions. These facilities are more likely to be damaged by geological disasters. Collapse disasters can cause instability of rock mass and rockfall, directly affecting the safety and normal operation of power facilities, and even causing equipment damage, power outages, and significant losses of property and personnel.

[0003] Currently, traditional geological disaster assessment methods mainly rely on manual patrol, regular detection and post-maintenance, and cannot realize real-time monitoring and dynamic risk assessment of collapse disasters. These methods have a slow response to environmental and rainfall triggers, cannot accurately predict potentially unstable rock masses, and lack risk classification and disaster threat models for specific power facility surrounding areas. Therefore, there is an urgent need for a systematic method that integrates real-time monitoring, data correlation analysis, probability calculation and numerical simulation to dynamically assess collapse disasters around power facilities and provide early warning and protection support for different risk levels. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is to realize real-time monitoring, dynamic risk assessment and regional risk level classification of collapse geological disasters around power facilities, thereby effectively warning and protecting power facilities from geological disasters.

[0006] To solve the above technical problems, the present application provides the following technical solution: a collapse geological disaster vulnerability model and a risk assessment method, comprising the following steps,

[0007] Collecting relevant information of the target rock mass and preprocessing;

[0008] Collecting monitoring data of the triaxial acceleration sensor and obtaining long-term rainfall data of the target area;

[0009] Analyzing the acceleration fluctuation characteristics and correlating them with the rainfall data to identify the influence of rainfall on rock mass instability;

[0010] Based on the Monte Carlo method, the instability probability of the rock mass under different environmental conditions is calculated to quantify the instability risk of the rock mass;

[0011] The motion and accumulation range of the collapse rockfall are predicted by using a numerical simulation method, and the threat to the surrounding power facilities is evaluated;

[0012] The surrounding area of the substation and other facilities is divided into different risk levels by a risk level division model, and a risk distribution map is generated.

[0013] As a preferred scheme of the collapse geological disaster vulnerability model and risk assessment method, the collection of the relevant information of the target rock mass includes,

[0014] The basic geological and environmental information related to the stability of the rock mass is obtained using sensors, including geological structure characteristics, rock layer properties, and topographic and geomorphic data;

[0015] The environmental conditions of the target area are collected, including meteorological and hydrological data such as long-term rainfall, air temperature, and groundwater level, and historical collapse event records of the target area are collected;

[0016] The preprocessing is to format, normalize and clean the collected information, and save it to a database;

[0017] Real-time collection of environmental data related to geological disaster risk includes rainfall, soil moisture, and ground displacement;

[0018] The preprocessing is completed by removing noise, filling missing data, and format conversion.

[0019] As a preferred scheme of the collapse geological disaster vulnerability model and risk assessment method, the collection of the three-axis component data of the triaxial acceleration sensor and the acquisition of the rainfall data of the target area includes,

[0020] During or after rainfall, the triaxial acceleration data and rainfall data are correlated and analyzed to observe the fluctuation trend of acceleration to determine whether rainfall has an impact on the displacement or deformation of the rock mass;

[0021] If abnormal fluctuations in acceleration are monitored in a certain direction, it is determined that the rock mass may have signs of instability due to rainfall;

[0022] By comparing the time series of rainfall and acceleration fluctuation, the time lag effect between the acceleration peak value and the rainfall change is identified to determine the potential impact of rainfall penetration on the stability of the rock mass;

[0023] If abnormal fluctuations in acceleration components occur in a certain direction, it is determined that the rock mass has signs of instability due to rainfall;

[0024] By comparing the time series of rainfall and the time series of acceleration fluctuation, it is observed whether there is a time lag phenomenon between the acceleration peak value and the rainfall change.

[0025] As a preferred embodiment of the vulnerability model and risk assessment method for landslide geological hazards described in this invention, the step of analyzing acceleration fluctuation characteristics and correlating them with rainfall data to identify the impact of rainfall on rock mass instability includes:

[0026] Key feature parameters are extracted from triaxial acceleration component data to identify the movement trend of rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate.

[0027] Acceleration characteristic parameters are quantified into instability risk indicators and monitored in real time. When acceleration fluctuations reach a specific abnormal threshold, it is determined that the rock mass has potential signs of instability.

[0028] As a preferred embodiment of the vulnerability model and risk assessment method for landslide geological hazards described in this invention, the step of calculating the instability probability of rock mass under different environmental conditions based on the Monte Carlo method, and quantifying the instability risk of the rock mass, includes...

[0029] Based on the calculation model of unstable rock mass, a stability coefficient formula is established. Random variables are selected to consider the main influences, and the random variables are processed by standard normality. Finally, the instability probability is calculated by substituting them into the formula.

[0030] Based on the morphological characteristics, crack development, and main controlling structural planes of the unstable rock mass, a simplified calculation model for sliding-type collapse unstable rock masses is established, with the following expression:

[0031] T = Wsinβ - Qsinαcosβ

[0032] α=N / L τ=T / L

[0033] N=Wcosβ-U-Qsinαcosβ

[0034]

[0035] Where U is the buoyancy force of the water, Q is the hydrostatic pressure, and c is the cohesion of the structural surface. denoted as the internal friction angle of the structural plane, W as the number of unstable rock masses, T as the tangential force at the bottom of the unstable rock mass, N as the normal force, and K as the stability coefficient.

[0036] For random variable c, After standard normal processing, the expression is:

[0037] X=μ+σx'

[0038] Where μ is a random number;

[0039] Using MATLAB software, based on the multiplicative linear congruence method, we can process random variable c. It generates random numbers and outputs random numbers that conform to a normal distribution through calculation;

[0040] In the process of calculating the stability of unstable rock masses, c, Simultaneously assign values, and calculate c under natural working conditions and extreme rainstorm conditions using random numbers conforming to a normal distribution. Random values, and with relatively independent results, are used to calculate the instability probability of a collapsing rock mass. The expression is as follows:

[0041]

[0042] Where P is the instability probability of the collapsed rock mass.

[0043] As a preferred embodiment of the vulnerability model and risk assessment method for landslide geological hazards described in this invention, the step of using numerical simulation methods to predict the movement and accumulation range of landslide rocks and assess their threat to surrounding power facilities includes analyzing the distribution characteristics of landslide rocks.

[0044] The instability probability of collapsed rock masses is classified, and the arrival probability is calculated and classified in combination with the accumulation characteristics of collapsed rocks, so as to establish a collapse hazard risk assessment model.

[0045] Based on the instability probability P f The size is used to classify the degree of collapse susceptibility. When P f When the percentage is greater than 15%, it is considered highly susceptible; when 15% ≥ P f When the percentage is >5%, it is considered moderately prone to occur; when 5% ≥ P f At that time, it is low-risk and prone to occur;

[0046] Using ArcGIS, the substation was divided into 0.5m grids for statistical analysis, and the arrival probability was calculated. The expression is as follows:

[0047] p a =N i / N T

[0048] Where, N i N represents the number of collapsed rocks remaining at a certain location. T This represents the total number of collapsed rocks.

[0049] As a preferred embodiment of the vulnerability model and risk assessment method for landslide geological hazards described in this invention, the method of using numerical simulation to predict the movement and accumulation range of landslide rocks and assess their threat to surrounding power facilities also includes...

[0050] The intensity of landslide disasters is divided into zones based on the probability of arrival. When P a When P > 30%, it is considered a high-intensity zone; when 30% ≥ P aWhen P > 10%, it is in the medium intensity zone; when 10% ≥ P a At that time, it was a low-intensity area;

[0051] Combining the instability probability classification and the arrival probability classification, the hazard of the substation collapse rock mass is calculated using the following expression:

[0052] H = P f ×P a

[0053] Among them, P a Let P be the probability of a landslide or falling rock. f H represents the probability of rock mass instability and the risk of rock mass collapse.

[0054] Another objective of this invention is to provide a vulnerability model and risk assessment system for landslide geological hazards. This system, through the construction of a data acquisition module, a real-time monitoring and analysis module, an instability probability calculation module, and a risk assessment and simulation module, can automatically classify the risk level of areas surrounding power facilities. This enables efficient and scientific early warning and protection of power facilities from the impact of landslide geological hazards, overcoming the problems of existing technologies that rely on manual detection, lack dynamic analysis, and are difficult to implement regional risk classification.

[0055] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a vulnerability model and risk assessment system for landslide geological hazards, comprising: a data acquisition and preprocessing module, a real-time monitoring and analysis module, an instability probability calculation module, and a risk assessment and simulation module;

[0056] The data acquisition and preprocessing module collects geological structural features of the rock mass, rock strata properties, topographic and geomorphological information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, noise reduction and missing data filling on the data.

[0057] The real-time monitoring and analysis module performs real-time monitoring and correlation analysis on the collected triaxial acceleration and rainfall data, extracts key characteristic parameters of acceleration, and combines rainfall data to determine whether there are signs of instability in the rock mass.

[0058] The instability probability calculation module uses the Monte Carlo method to simulate the stability of the unstable rock mass under different environmental conditions, and calculates the instability probability of the rock mass under natural conditions and extreme rainstorm conditions.

[0059] The risk assessment and simulation module predicts the movement path and accumulation range of falling rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities; at the same time, it establishes a risk level model by combining the instability probability and the arrival probability, and divides the area around the facilities into different risk levels.

[0060] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the landslide geological hazard vulnerability model and risk assessment method as described above.

[0061] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the collapse geological hazard vulnerability model and risk assessment method as described above.

[0062] The beneficial effects of this invention are as follows: This invention employs real-time monitoring, probability calculation, and numerical simulation techniques to solve the problems of lack of dynamic monitoring, accurate risk assessment, and regionalized graded protection for power facilities in existing technologies. Specifically, this invention collects real-time displacement and environmental data of the rock mass by installing a triaxial accelerometer and a rainfall monitoring module, and calculates the instability probability of the rock mass under different working conditions using the Monte Carlo method, thereby quantifying the instability risk.

[0063] Numerical simulation technology was used to predict the movement path and accumulation range of landslide rocks, analyze the potential impact of the rocks on surrounding power facilities, and implement graded management and protection of the area surrounding the facilities through a risk level classification model. This approach achieves real-time, automated monitoring and risk classification of landslide geological hazards, overcoming the shortcomings of traditional methods that rely on manual inspections, suffer from delayed assessments, and lack regionalized protection measures. This effectively ensures the safety and reliability of power facilities under disaster conditions. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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. Wherein:

[0065] Figure 1 This is an overall flowchart of a vulnerability model and risk assessment method for landslide geological hazards provided in the first embodiment of the present invention;

[0066] Figure 2 The geographical location and transportation map of the study area are provided in the vulnerability model and risk assessment method for landslide geological hazards in the third embodiment of the present invention;

[0067] Figure 3 The monthly average precipitation distribution map of Duyun City is provided as part of the third embodiment of the present invention for a vulnerability model and risk assessment method for landslide geological hazards.

[0068] Figure 4A simplified geological map of the study area is provided in the vulnerability model and risk assessment method for landslide geological hazards in the third embodiment of the present invention.

[0069] Figure 5 A close-up view of the WY1 unstable rock mass in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0070] Figure 6 A close-up view of the WY2 unstable rock mass in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0071] Figure 7 Engineering geological profile of the Qixingshan landslide dangerous rock mass provided in the vulnerability model and risk assessment method for landslide geological hazards in the third embodiment of the present invention;

[0072] Figure 8 The WY1 acceleration component and rainfall monitoring data are provided in the vulnerability model and risk assessment method for landslide geological hazards in the third embodiment of the present invention;

[0073] Figure 9 The WY2 acceleration component and rainfall monitoring data are provided in the vulnerability model and risk assessment method for landslide geological hazards in the third embodiment of the present invention;

[0074] Figure 10 This is a diagram illustrating the instability and failure evolution pattern in a vulnerability model and risk assessment method for landslide geological hazards, provided in the third embodiment of the present invention.

[0075] Figure 11 A simplified calculation model diagram of sliding collapse rock mass in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0076] Figure 12 Typical particle velocity diagram in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0077] Figure 13 Typical particle displacement diagram in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0078] Figure 14 A percentage diagram of parallel bond fracture in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention;

[0079] Figure 15 This is a schematic diagram illustrating the variation of parallel bonding quantity in a vulnerability model and risk assessment method for landslide geological hazards provided in the third embodiment of the present invention.

[0080] Figure 16 This is a landslide hazard zoning map provided in a landslide geological hazard vulnerability model and risk assessment method according to the third embodiment of the present invention. Detailed Implementation

[0081] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0082] Example 1, referring to Figure 1 As an embodiment of the present invention, a vulnerability model and risk assessment method for landslide geological hazards are provided, including:

[0083] Collect relevant information about the target rock mass and perform preprocessing;

[0084] Collect monitoring data from a triaxial accelerometer and obtain long-term rainfall data for the target area;

[0085] Analyze the characteristics of acceleration fluctuations and correlate them with rainfall data to identify the impact of rainfall on rock mass instability;

[0086] The Monte Carlo method is used to calculate the instability probability of rock masses under different environmental conditions, thereby quantifying the instability risk of rock masses.

[0087] Numerical simulation methods were used to predict the movement and accumulation range of landslide rocks and assess their threat to surrounding power facilities.

[0088] The risk level classification model is used to divide the area around facilities such as substations into different risk levels and generate a risk distribution map.

[0089] Sensors are used to acquire basic geological and environmental information related to rock mass stability, including geological structural features, rock strata properties, and topographic data; environmental conditions of the target area are collected, including long-term rainfall, temperature, and groundwater level meteorological and hydrological data, as well as historical landslide event records of the target area; the collected information is formatted, normalized, and cleaned, and saved to a database; environmental data related to geological hazard risk, including rainfall, soil moisture, and surface displacement, are collected in real time.

[0090] Preprocessing is completed by removing noise, filling in missing data, and performing format conversion.

[0091] It should be further explained that:

[0092] Collecting and preprocessing relevant information about the target rock mass refers to comprehensively acquiring basic geological and environmental information related to rock mass stability, including but not limited to geological structural features (such as faults, joints, and bedding planes), rock strata properties (including physical parameters such as lithology, compressive strength, and elastic modulus), and topographic data (such as slope, aspect, and digital elevation models). This information can be obtained through various means such as field surveys, borehole sampling, UAV remote sensing, and ground-penetrating radar. Simultaneously, environmental conditions of the target area should be collected, such as long-term rainfall, temperature, and groundwater level data, along with meteorological and hydrological data, and combined with records of historical landslide events to assess the instability trend within the area.

[0093] The process involves collecting triaxial component data from a triaxial accelerometer and acquiring rainfall data for the target area. During or after rainfall, the triaxial acceleration data is correlated with rainfall data to observe acceleration fluctuation trends and determine whether rainfall affects rock mass displacement or deformation. If abnormal acceleration fluctuations are detected in a certain direction, it is determined that the rock mass may show signs of instability due to rainfall. By comparing the time series of rainfall and acceleration fluctuations, the time lag effect between acceleration peaks and rainfall changes is identified to determine the potential impact of rainfall infiltration on rock mass stability.

[0094] If the acceleration component shows abnormal fluctuations in a certain direction, it is determined that the rock mass has shown signs of instability due to rainfall. By comparing the time series of rainfall with the time series of acceleration fluctuations, it is observed whether there is a time lag between the peak acceleration and the change in rainfall.

[0095] It should be further explained that:

[0096] By comparing the time series of rainfall and acceleration fluctuations, the main purpose is to observe the dynamic response of acceleration data before and after changes in rainfall. Specific technical solutions may include the following aspects:

[0097] The system synchronously compares the time series of rainfall (including the start of rainfall, changes in rainfall intensity, and the time when rainfall stops) with the time series of acceleration fluctuations to observe whether there are significant changes in acceleration data during and after rainfall.

[0098] If the acceleration data shows a trend of increased fluctuation or frequent fluctuations at the beginning of rainfall or during heavy rainfall, the system can preliminarily determine that the rainfall has had a direct impact on the rock mass.

[0099] The system will focus on changes in acceleration data after the rainfall ends, especially whether there is a delay in the appearance of the acceleration peak.

[0100] For example, if the amplitude of the acceleration component gradually increases and reaches a peak within hours or days after the rainfall stops, this hysteresis response may be due to the gradual softening of the internal structure of the rock mass by rainfall infiltration, leading to a gradual decrease in the stability of the rock mass.

[0101] The system can simulate and evaluate the process of rock softening caused by rainfall infiltration by inputting parameters such as rainfall, soil moisture, and peak lag time of acceleration into the model.

[0102] By combining the geological and mechanical properties of the rock mass, the system can infer the relationship between the intensity of the hysteresis effect and acceleration fluctuations, thereby quantifying the potential impact of rainfall infiltration on rock mass stability.

[0103] In summary, by comparing the time series of rainfall and acceleration fluctuations, the system identifies the lag phenomenon of acceleration peak after rainfall ends. Combined with the geological characteristics of the rock mass, the system can preliminarily determine the potential impact of rainfall infiltration on rock mass stability and provide a basis for instability early warning.

[0104] Key feature parameters are extracted from triaxial acceleration component data to identify the movement trend of rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate.

[0105] Acceleration characteristic parameters are quantified into instability risk indicators and monitored in real time. When acceleration fluctuations reach a specific abnormal threshold, it is determined that the rock mass has potential signs of instability.

[0106] Based on the calculation model of unstable rock mass, a stability coefficient formula is established. Random variables are selected to consider the main influences, and the random variables are processed by standard normality. Finally, the instability probability is calculated by substituting them into the formula.

[0107] Based on the morphological characteristics, crack development, and main controlling structural planes of the unstable rock mass, a simplified calculation model for sliding-type collapse unstable rock masses is established, with the following expression:

[0108] T = Wsinβ - Qsinαcosβ

[0109] α=N / L τ=T / L

[0110] N=Wcosβ-U-Qsinαcosβ

[0111]

[0112] Where U is the buoyancy force of the water, Q is the hydrostatic pressure, and c is the cohesion of the structural surface. denoted as the internal friction angle of the structural plane, W as the number of unstable rock masses, T as the tangential force at the bottom of the unstable rock mass, N as the normal force, and K as the stability coefficient.

[0113] For random variable c, After standard normal processing, the expression is:

[0114] X=μ+σx'

[0115] Where μ is a random number;

[0116] Using MATLAB software, based on the multiplicative linear congruence method, we can process random variable c. Random numbers are generated by calculating and outputting random numbers that conform to a normal distribution.

[0117] In the process of calculating the stability of unstable rock masses, c, Simultaneously assign values, and calculate c under natural working conditions and extreme rainstorm conditions using random numbers conforming to a normal distribution. Random values, and with relatively independent results, are used to calculate the instability probability of a collapsing rock mass. The expression is as follows:

[0118]

[0119] Where P is the instability probability of the collapsed rock mass.

[0120] The instability probability of collapsed rock masses is classified, and the arrival probability is calculated and classified in combination with the accumulation characteristics of collapsed rocks, so as to establish a collapse hazard risk assessment model.

[0121] Based on the instability probability P f The size is used to classify the degree of collapse susceptibility. When P f When the percentage is greater than 15%, it is considered highly susceptible; when 15% ≥ P f When the percentage is >5%, it is considered moderately prone to occur; when 5% ≥ P f At that time, it is low-risk and prone to occur;

[0122] Using ArcGIS, the substation was divided into 0.5m grids for statistical analysis, and the arrival probability was calculated. The expression is as follows:

[0123] p a =N i / N T

[0124] Where, N i N represents the number of collapsed rocks remaining at a certain location. T This represents the total number of collapsed rocks.

[0125] The intensity of landslide disasters is divided into zones based on the probability of arrival. When P a When P > 30%, it is considered a high-intensity zone; when 30% ≥ P a When P > 10%, it is in the medium intensity zone; when 10% ≥ P a At that time, it was a low-intensity area;

[0126] Combining the instability probability classification and the arrival probability classification, the hazard of the substation collapse rock mass is calculated using the following expression:

[0127] H = P f ×P a

[0128] Among them, P a Let P be the probability of a landslide or falling rock. f H represents the probability of rock mass instability and the risk of rock mass collapse.

[0129] Example 2, an embodiment of the present invention, provides a system for a vulnerability model and risk assessment method for landslide geological hazards, including: a data acquisition and preprocessing module, a real-time monitoring and analysis module, an instability probability calculation module, and a risk assessment and simulation module;

[0130] The data acquisition and preprocessing module collects geological structural features of the rock mass, rock strata properties, topographic and geomorphological information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, noise reduction and missing data filling on the data.

[0131] The real-time monitoring and analysis module performs real-time monitoring and correlation analysis on the collected triaxial acceleration and rainfall data, extracts key characteristic parameters of acceleration, and combines rainfall data to determine whether there are signs of instability in the rock mass.

[0132] The instability probability calculation module uses the Monte Carlo method to simulate the stability of the unstable rock mass under different environmental conditions, and calculates the instability probability of the rock mass under natural conditions and extreme rainstorm conditions.

[0133] The risk assessment and simulation module predicts the movement path and accumulation range of falling rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities; at the same time, it establishes a risk level model by combining the instability probability and the arrival probability, and divides the area around the facilities into different risk levels.

[0134] 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, essentially, or the part that contributes to the prior art, or a portion 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.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0137] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0138] Example 3: In this example, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. This example compares the existing conventional methods with the method of this example.

[0139] Reference Figure 2 The 110kV Shabaobao Substation is located in Group 5, Ying'en Village, Shabaobao Subdistrict, Duyun City, Qiannan Buyi and Miao Autonomous Prefecture, Guizhou Province. It is approximately 1km from the Shabaobao Subdistrict Office, 1.5km from the Duyun North Exit of the Lanhai Expressway, and 8.5km from the Duyun Station of the Qian-Gui Railway. The central geographical coordinates of the study area are: 107°31′01″E, 26°18′48″N.

[0140] Reference Figure 3 Duyun City is located in the southern part of the Guizhou Plateau and has a mid-subtropical monsoon climate. The temperature is pleasant and the climate is humid. According to meteorological data, the average annual temperature is 16.1℃, the average daily temperature in January is 5.6℃, and the extreme minimum temperature is -6.9℃ (January 30, 1977); the average daily temperature in July is 24.8℃, and the extreme maximum temperature is 36.3℃ (August 17, 1966). The frost-free period is approximately 300 days per year.

[0141] Duyun City is located in the rainstorm area and rainy center of Guizhou Province, with abundant rainfall and rain and heat occurring simultaneously. The average annual rainfall is 1431.1 mm, with the highest being 1968 mm in 1977 and the lowest being 868 mm in 1962. Rainfall is concentrated from April to September each year, with the rainy season starting in mid-April. The rainfall in June reaches 251.1 mm, accounting for 17.4% of the annual total, reaching its peak. After that, the rainfall begins to decline, recovering slightly in October, until January when the rainfall is only 27.7 mm, accounting for only 1.9% of the annual total. The annual rainfall duration in Duyun City is as long as 180 days. The period of maximum rainfall intensity is generally from May to July. Heavy rain (daily rainfall of 50-100 mm) occurs from April to November, 5 days / year; torrential rain (daily rainfall of 100-250 mm) occurs from May to September, 0.4 days / year. The maximum daily rainfall was 170.5 mm (May 10, 1967).

[0142] Duyun City is located in the middle section of the Miaoling Mountains, on a slope transitioning from the Yunnan-Guizhou Plateau to the Guangxi Hills. The general terrain trend is high in the northwest and low in the southeast. The main landform is karst topography, with some areas featuring erosion landforms. Influenced and controlled by tectonic structures, the mountain ranges generally follow the tectonic patterns. Doupeng Mountain in the northwest of Duyun City, at 1961m, is the highest point within the city, while Wenglezhai in Guilan Shui Ethnic Township in the southeast, at 540m, is the lowest point, with a maximum elevation difference of 1421m.

[0143] The 110kV Shabaobao Substation area is characterized by low-to-medium mountainous terrain formed by karst and erosion, with the surface featuring peak clusters and peak forests, and localized depressions and valleys. To the east of the substation lies Qixing Mountain, named for its numerous peaks distributed like stars. The area's slopes are open on three sides, with an overall terrain that slopes from northeast to southwest. The highest elevation is 1592m, the lowest is 795m, and the maximum relative elevation difference is 797m. The slopes range from 30° to 80°. Generally, the slopes are gentler at the top and steeper at the bottom, with the steepest section (765m-895m, 50°-80°) gradually decreasing to approximately 30°-40° upwards, resulting in an overall slope angle of 30°. Most of the slopes consist of exposed bedrock, with Quaternary residual soil and colluvial soil covering the rock crevices and gentle slopes, and the vegetation is very lush.

[0144] Reference Figure 4 The exposed strata near the 110kV Shabaobao Substation are relatively simple, consisting of Quaternary (Q) residual slope deposits, colluvial deposits, and alluvial deposits from newest to oldest, composed of gray and yellow clay interbedded with gravel and pebbles; the Lower Permian Maokou Formation (P1m) of gray thick-bedded to massive limestone; the Lower Permian Qixia Formation (P1q) of gray to dark gray medium-thick-bedded limestone; the Lower Permian Liangshan Formation (P1l) of gray thin-bedded to thick-bedded quartz sandstone interbedded with shale; and the Middle Carboniferous Huanglong Formation (C2h) of grayish-white thick-bedded limestone.

[0145] The Duyun City tectonic zone is located within the Guiding north-south trending tectonic deformation area, a fourth-order tectonic unit of the Qiannan Platform Depression of the Yangtze Paraplatform. It is characterized by the development of compact synclines, box-shaped anticlines, and reverse faults, forming a typical trough-type fold system with compact synclines and gentle anticlines. Its structural lines run north-south and are primarily controlled by the Jiangnan axis. Due to the compression caused by the Yanshanian Movement, two north-south trending folds formed in the region: the Guiding syncline and the Duyun syncline. Controlled by these two synclines, numerous east-west trending faults and secondary folds have developed within the region. The study area is situated on the footwall of the Duyun fault, which is a reverse fault, trending nearly north-south, dipping eastward at a 45° angle. The 110kV Shabaobao substation is located on the western side of the fault zone. Influenced by tectonic activity, the rock mass exhibits well-developed joints and fractures, primarily near-parallel fractures trending 335°. A near-north-south trending reverse fault is developed on the west side of the study area, dipping eastward at an angle of 40°, and extending along the valley west of the substation. The fault is exposed for about 1.8 km in the study area.

[0146] Qixing Mountain is located east of the 110kV Shabaobao Substation. The slope is gentle at the top and steep at the bottom, with an angle ranging from 50° to 85°. The terrain is steep and uneven, with some areas in the middle and lower sections nearly vertical. The bedrock is exposed on the slope surface, with dense vegetation between the rock blocks, indicating strong weathering and well-developed joints and fissures. The main slope angle is 275°, and the overall rock strata dip 285°∠65°. The toe of the slope is adjacent to the substation. The unstable rock mass is located in the upper-middle part of Qixing Mountain, with an elevation of 835m at the front edge and 885m at the rear edge, a height difference of 50m relative to the substation, classifying it as a high-altitude rock collapse unstable rock mass. There are two relatively dangerous typical unstable rock masses in this area: WY1 and WY2.

[0147] Reference Figure 5 WY1: Located in the upper-middle part of the western slope of Qixing Mountain, with a slope of 60° to 70°. The unstable rock mass is about 8m wide, 10m high, and 3m thick, with a volume of about 240m3. It is a small-scale rock collapse unstable rock mass.

[0148] Reference Figure 6 WY2: This unstable rock mass is located approximately 10 meters below WY1, with a slope of 55°–65°. The leading edge of the unstable rock mass is a steep cliff, below which are a substation and a residential area. The unstable rock mass is approximately 5 meters wide, 5 meters high, and 3 meters thick, with a volume of approximately 75 cubic meters, classifying it as a small-scale rock collapse.

[0149] The unstable rock mass is mainly composed of gray to dark gray medium-thick layered limestone from the Lower Permian Qixia Formation (P1q). The surface is strongly weathered, while the interior is moderately weathered, exhibiting massive bedding. Two sets of joints are relatively well-developed: J1: 73°∠82°, 2.5m long, 0.3m spacing, opening 2–10cm; J2: 353°, ∠83°, 2m long, 0.2m spacing, opening 1–5cm. All joints and fractures have rough surfaces. Figure 7 .

[0150] The unstable rock mass is divided into blocks of varying sizes by structural planes. Individual rock blocks have relatively good physical properties, while the properties of the structural planes between the blocks are poor, which is the main controlling factor for the stability of the unstable rock mass. The deposits at the toe of the slope are relatively small in volume, mainly composed of limestone and residual slope deposits. The structure is loose, and the blocks are relatively small in volume, generally less than 0.5m in diameter, with occasional boulders between 0.9m and 1.5m in diameter scattered in the middle and lower parts.

[0151] Two sets of triaxial accelerometers were installed on the collapsed body, positioned at the upper middle part of WY1 and WY2 respectively. (Refer to...) Figure 8 and Figure 9The figures show the acceleration components and daily rainfall monitoring data for WY1 and WY2 in 2022. The component of gravity along the slope direction is represented by gX, the vertical component by gY, and the component perpendicular to the slope surface by gZ. The two figures show that rainfall in the study area is concentrated from April to June, and the three-axis components of gravitational acceleration fluctuate continuously. Considering instrument installation and internal errors, gY, the main component of gravitational acceleration, fluctuates around 1g throughout the year, reaching its peak fluctuation from May to July, and its minimum average fluctuation from December to February of the following year. Both gX and gZ show slight fluctuations, similarly reaching their peak fluctuation from May to July, and their minimum average fluctuation from December to February of the following year.

[0152] On-site investigation revealed that the surface rocks of Qixing Mountain have undergone severe erosion, with well-developed erosion fissures. A small cave with an area of ​​90 square meters is located halfway up the mountain. The slope is covered with lush vegetation, with trees primarily growing within rock crevices, indicating significant root weaving. Some rock blocks show obvious signs of deformation, posing a significant threat to the substation. According to records from substation staff and reports from local residents, Qixing Mountain has experienced several instances of localized instability, with falling rocks causing some property damage to the substation.

[0153] Under intense weathering, multiple tensional fissures have developed along the rear edge and middle of the unstable rock mass. These fissures are 1.5–4.5 m long, 10–50 cm wide, and 0.5–2.8 m deep, trending at 320°–350°. The fissures contain residual slope soil and humus. Under the influence of internal and external forces, the fissures are gradually expanding and extending, showing a tendency to connect. Because the Qixingshan rock mass collapse is a high-altitude rockfall, the energy converted after its instability and failure is substantial, which will cause significant damage to the 110kV Shabaobao substation.

[0154] The main factors influencing the instability and failure of the Qixingshan landslide rock mass are: ① The rock mass mainly develops three sets of structural planes: bedding plane: 285°∠65°; joint plane 1: 73°∠82°; joint plane 2: 353°∠83°; the slope structure is a dip slope; ② The surface layer of the rock mass is mainly strongly weathered, extending 1m to 5m inward to moderately weathered. Numerous unloading and weathering fissures are developed in the rock mass, with lengths ranging from 1.5 to 4.5m, widths from 10 to 50cm, depths from 0.5 to 2.8m, strikes from 320° to 350°, and unloading depths from 5m to 10m. Bedding planes are the main controlling structural planes; ③ Dense vegetation grows within the fissures, and karst phenomena are evident on-site. Unfavorable external weathering will accelerate its deformation.

[0155] Taking WY1 as an example, this unstable rock mass is located in the upper-middle part of the western slope of Qixing Mountain, with a slope of 60°–70°. The unstable rock mass is approximately 8m wide, 10m high, and 3m thick, covering an area of ​​approximately 80m², with a volume of approximately 240m³. On-site investigation shows that the unstable rock mass is nearly elliptical in shape, and the rock mass is cut by three sets of structural planes. Furthermore, the Qixing Mountain slope is relatively steep, and under the influence of gravity, the unstable rock mass tends to deform and fail along the main controlling structural plane, i.e., along the bedding plane. Affected by weathering effects such as rainfall and root wedging, the unstable rock mass will deform more rapidly, ultimately leading to a collapse.

[0156] Based on the above analysis, the instability failure mode of the Qixingshan landslide is presumed to be sliding. The main characteristics of this mode are a main controlling structural plane within the rock mass that is slope-sloping, with a moderately steep dip angle, and is either continuous or discontinuously continuous. Instability failure is primarily characterized by overall sliding or rolling collapse. The specific evolution process is as follows: karst weathering and unloading lead to tensile cracks appearing at the rear edge of the slope, which gradually develop deeper into the rock mass until the cracks gradually connect. When the shear strength of the rock mass is less than its gravitational component, sliding instability occurs along the main controlling structural plane under the induction of adverse factors such as rainfall. Figure 10 This type of pattern generally creates a large impact area, posing a significant threat to the Pojiao substation and residents, and therefore requires special attention.

[0157] This paper selects the instability probability of collapsed rock masses and the arrival probability of falling rocks as the main evaluation factors for typical landslide hazard. Secondly, the evaluation area is divided into grids based on the characteristics of the substation. The overall evaluation approach is as follows: First, the instability probability of the rock mass under different working conditions is calculated using the Monte Carlo method; then, the dynamic process and accumulation distribution characteristics of falling rocks are analyzed through numerical simulation to obtain the arrival probability of falling rocks; finally, the landslide hazard is evaluated using matrix analysis.

[0158] The Monte Carlo method is a reliability calculation method whose results are less affected by conditions, are more accurate and reliable, and have strong applicability. With the development of computer technology, this method has been widely used in various engineering fields. In engineering geology, it is commonly used to calculate the instability probability of rock mass collapse. Its main process is as follows: first, establish a stability coefficient formula based on the rock mass calculation model; then, consider the main influencing factors, select random variables, and perform standard normal distribution processing on the random variables; finally, substitute them into the formula to calculate the instability probability. A simplified calculation model is shown below. Figure 11 .

[0159] Based on field investigations and the analysis of instability failure modes described above, a simplified calculation model for sliding-type collapse rock masses is established, taking into account factors such as the morphological characteristics of the unstable rock mass, crack development, and controlling structural planes. Referring to standards and engineering cases, this paper considers two different working conditions and load combinations: Working condition 1: natural working condition, primarily based on self-weight; Working condition 2: extreme rainstorm working condition, primarily based on self-weight and saturated fissure water pressure. The formulas for the stability coefficient under either the natural or extreme rainstorm working condition are derived as follows:

[0160] Tangential force at the base of the unstable rock mass:

[0161] T=Wsinβ-Qsinαcosβ(2.1)

[0162] α=N / L τ=T / L

[0163] Normal force:

[0164] N=Wcosβ-U-Qsinαcosβ(2.2)

[0165] Stability coefficient:

[0166]

[0167] Where U is the buoyancy force of the water, Q is the hydrostatic pressure, and c is the cohesion of the structural surface. denoted as the internal friction angle of the structural plane, W as the number of unstable rock masses, T as the tangential force at the bottom of the unstable rock mass, N as the normal force, and K as the stability coefficient.

[0168] Random variables mainly consider the degree of influence of factors on the stability of unstable rock masses, which are divided into endogenous factors and external factors: endogenous factors refer to the characteristics of the unstable rock mass itself, mainly including topography, rock mass structure, etc.; external factors mainly include natural conditions such as rainfall, as well as some human errors in the research process. The basic characteristics and natural conditions of the Qixingshan landslide unstable rock mass have been integrated into the stability evaluation above. Now, only the effect of human errors needs to be considered, with the values ​​of rock and soil parameters as the main focus. Affected by various factors such as sampling, testing, and post-processing, the shear strength index (c, The uncertainty is relatively large, while the coefficient of variation of the specific gravity (γ) is small, resulting in minimal error. Therefore, we consider treating the specific gravity γ as a constant and treating c, It is selected as a random variable.

[0169] Generally speaking, random variable c, Since they usually follow a non-standard normal distribution, they need to be processed by a formula to convert them into a standard normal distribution, as shown in Formula 2.4.

[0170] X=μ+σx'(2.4)

[0171] Using a bivariate function, a random variable conforming to a standard normal distribution can be obtained, as shown in Formula 2.5:

[0172]

[0173] In the formula: μ1 and μ2 are uniform random numbers between 0 and 1.

[0174] Formulas 2.4 and 2.5 are combined to derive formulas 2.6 and 2.7 as follows:

[0175]

[0176] In the formula: μn is a random number generated by the programming software and has been verified. In actual operation, it can be directly generated by calling the above formula.

[0177] The physical parameters of the structural surfaces of the collapsed rock mass at Qixingshan are shown in Table 1:

[0178] Table 1 Physical parameters of the structural surfaces of the collapsed unstable rock mass

[0179]

[0180] c. The random value generation process is as follows:

[0181] ① Cohesion c

[0182] This paper uses MATLAB software to generate 10,000 random numbers based on the multiplicative linear congruence method. Substituting the generated results into formula 2.7 yields random numbers that conform to a normal distribution.

[0183] ② Angle of internal friction

[0184] In the process of calculating the stability of unstable rock masses, it is necessary to consider c, Simultaneous assignment, so parameters The process of obtaining the value is the same as for parameter c. Substitute 10,000 random numbers into formula 2.7 for calculation.

[0185] Finally, using formula 2.4, the values ​​of c under natural conditions and extreme rainstorm conditions are calculated. 10,000 sets of random values.

[0186] (4) Instability probability calculation

[0187] The c generated by the above method and Substituting the values ​​into formula 2.3, 10,000 sets of stability coefficients can be obtained under both natural and extreme rainstorm conditions, and the results are relatively independent. This paper defines KS≤1 as indicating that the collapsed rock mass has become unstable and failed, and defines the number of times instability occurs as M. The formula for the instability probability of the collapsed rock mass is then derived as follows:

[0188]

[0189] Where P is the instability probability of the collapsed rock mass.

[0190]

[0191] Table 3. Probability of WY2 instability failure

[0192]

[0193]

[0194] The calculated instability probabilities of the two unstable rock masses are shown in Tables 2.2 and 2.3. It can be seen that the instability probability of WY1 under natural conditions is 15.92%, and under extreme rainstorm conditions it is 26.58%; the instability probability of WY2 under natural conditions is 13.98%, and under extreme rainstorm conditions it is 25.76%. The calculation results confirm that rainfall is a major factor in the collapse of unstable rock masses.

[0195] A key factor in instability. Because WY1 and WY2 have similar morphologies, are close together, and have similar degrees of fracture development, their instability failure modes are the same. According to calculations, the instability probabilities of the two unstable rock masses are similar under the same conditions. Furthermore, the focus of subsequent analysis is on exploring the damage modes of electrical facilities structures under the impact of rockfalls. Therefore, this paper will consider both unstable rock masses simultaneously in subsequent simulations and evaluations, analyzing the maximum potential impact of the unstable rock masses on the substation. The impact on a single unstable rock mass will not be elaborated upon. Therefore, the instability probability is taken as the average of the instability probabilities of the two unstable rock masses under extreme rainstorm conditions, which is calculated to be 26.17%.

[0196] Currently, both domestic and international research on the risk assessment of single-unit landslide disasters often employs numerical simulation to scientifically demonstrate the entire process of rock mass instability, failure, and impact. This method is repeatable and beneficial for the comprehensive analysis of landslide disasters. Therefore, this paper uses a coupled method of Discrete Element Method (PFC3D) and Finite Difference Method (FLAC3D) software developed by TASCA to simulate the motion and deposition characteristics of the Qixingshan landslide after instability under extreme conditions. The simulation represents the landslide's motion process, motion parameters, deposition range, and impact on the structure in three dimensions, enabling a risk assessment of the single-unit landslide disaster at the substation.

[0197] High-precision imagery data was obtained through preliminary on-site drone mapping, followed by on-site investigation to determine the characteristics of features such as landslide bodies and substation facilities. Based on these investigation results, this paper uses a ball-ball model to simulate the collapsed rock mass, a ball-wall model to simulate the slope and ground surface using the rigid wall surface, and a wall model to simulate the substation facilities. The numerical model establishment process is shown below:

[0198] (1) Pix4Dmapper was used to process UAV imagery data to obtain point cloud data and generate DSM data for the study area. Based on the field survey data, areas such as the landslide source area and the substation area were identified.

[0199] (2) Import the DSM data into GlobalMapper, extract and classify the point cloud, eliminate the influence of trees and houses on the terrain, and then generate more accurate DEM and contour data.

[0200] (3) In Rhino, a surface model of the study area's terrain was generated using contour lines, and then an initial geological model was obtained through extrusion and cutting. Since the Qixingshan landslide was still in the deformation stage, two unstable rock masses were identified based on a comprehensive analysis of factors such as the extent, volume, and joint and fracture distribution of the unstable rock mass from the on-site investigation. The substation structure and layout were analyzed, and the substation foundation, buildings, and electrical facilities were simplified and modeled using post-evaluation methods to obtain the final analysis model. The griddle plugin was used to generate the mesh, which was then exported as an f3grid file.

[0201] (4) Import the f3grid file into FLAC3D and load it into the PFC3D analysis module. Analyze the dynamic process of the collapse by coupling the command.

[0202] Taking into account both the real-world surface conditions and simulation efficiency, the initial FLAC3D model used in this simulation consisted of 10,818 grid cells, with a model size of 140m × 190m. The unstable rock mass in the model was divided into two parts: WY1 and WY2. Based on the field investigation, WY1 was approximately 240m³, and WY2 was approximately 75m³, with a distance of 10m between them.

[0203] The deformation and failure process in the collapse source area can be summarized as follows: long-term weathering and erosion—development of fissures in the upper part of the rock block—deformation of the rock block towards the free surface—downward propagation of fissures—shearing of the locked section—instability and collapse of the unstable rock mass. Based on the analysis of the field investigation results, the unstable rock mass was simplified and reconstructed in PFC3D, mainly considering its rolling and sliding characteristics during the potential collapse. Considering that the Qixingshan unstable rock mass is limestone with prominent karst phenomena and is cut by two sets of joints, the final unstable rock mass is composed of 11,512 spheres with a porosity of 30% and a radius of 10cm to 20cm, which is close to the actual situation. A parallel bonded contact model was used between the unstable rock mass particles, and a linear contact model was used between the falling rocks and the wall.

[0204] Due to the complexity of the substation facilities and layout, they were simplified into 105 components for ease of subsequent simulation, monitoring, and evaluation. Based on the structure and value of the facilities, the components are divided into three categories: 2 main transformers, 92 outdoor high-voltage electrical equipment, and 11 buildings. Therefore, they were simulated using finite element zone elements.

[0205] In this simulation, the contact model between particles was a parallel bonding model. Since the Qixingshan landslide rock mass is mainly composed of limestone with prominent karst phenomena, the macroscopic mechanical parameters of the rock were confirmed after comprehensive analysis based on previous research results (see Table 4). The parameters required for the PFC3D model are the mesoscopic parameters of its basic units. Based on the macroscopic parameters of the limestone, a PFC3D uniaxial compression experiment was first conducted to assign mesoscopic parameters to the model in the numerical experiment, and these parameters were continuously adjusted. This ensured that the characteristics shown in the numerical experiment were basically consistent with those shown in the actual experiment. Then, an analogy was made with the dynamic characteristics of similar landslides to finally obtain the model's microscopic parameters (see Table 5).

[0206] Table 4 Comparison of Actual Macroscopic Parameters and PFC3D Simulation Parameters

[0207]

[0208]

[0209] To investigate the motion patterns of the falling rock particles, 16 typical particles (8 in each of the four rows from top to bottom) were selected from WY1 and WY2 for detailed monitoring. Within 0–5 seconds, the lower particles of WY2 reached a speed of 16 m / s, then rapidly decreased in velocity after impact with the ground, experiencing several rebounds before finally reaching zero. Within 5–10 seconds, the lower particles of WY1 accelerated to 20 m / s, the maximum velocity of all particles; some particles in the middle and upper parts also became unstable due to stress changes. Within 10–20 seconds, the middle particles of WY1 and WY2 accelerated and slid, with maximum velocities ranging from 14–18 m / s. Within 20–30 seconds, the remaining particles in the upper parts of WY1 and WY2 became unstable, reaching maximum velocities of 15–20 m / s. After 30 seconds, the main particles of the unstable rock mass showed almost no deformation; some particles bounced after impact, with rebound velocities decreasing from 3 m / s to zero. Figures 12-13 .

[0210] Analysis of the particle velocity change process reveals that in the initial stage of unstable rock mass failure, deformation begins in the lower rocks, reaching their maximum velocity, which is also the peak rebound velocity. After a period of time, the middle rocks slide down, and rock particles from WY1 and WY2 collide on the slope, generating energy dissipation, causing a slight decrease in both the maximum velocity and the rebound velocity. Finally, a few remaining rocks in the upper part deform and break down. At this point, there are no other rocks interfering with the slope, and the velocity approaches its maximum level. However, due to the accumulation of lower rocks, the rebound velocity decreases significantly.

[0211] The fracturing tendency of collapsed rocks during movement can be represented by time-history statistics of the number of parallel bonds between particles. Figure 14 and Figure 15The figures show the time history curves for the percentage of parallel bond fracture and the real-time number of parallel bonds during the numerical simulation. It can be seen that there were 12,532 initial parallel bonds between the spherical particles; within 0-12 seconds, the number of parallel bonds decreased to 1,148, with a fracture percentage of 90%; between 12-20 seconds, approximately 5% of the collapsed rocks fractured during the collision; the remaining 5% of the collapsed rocks did not fracture due to their properties and the movement process. The fracture process reflects that in sliding collapses, collisions continuously occur between soil and rock particles, and between soil and rock masses and the slope / ground surface. The forces exerted exceed the bond strength between the soil and rock masses, ultimately leading to shear failure and the formation of falling rock particles.

[0212] The instability probability of the collapsed rock mass has been obtained through stability analysis. This section will analyze the distribution characteristics of the collapsed rockfall based on numerical simulation results and calculate the arrival probability of the rockfall at the substation. Based on the instability probability and arrival probability classification, a substation collapse hazard assessment model is established using matrix analysis, and the hazard zoning results are obtained.

[0213] During its movement, the landslide gradually broke into boulders. Due to the dense vegetation, steep slope, and proximity to the ground in the Qixingshan area, the slope's rebound coefficient was low, resulting in a relatively short jump height for the boulders. Most boulders moved downhill along the slope, with only a small portion bouncing after colliding with the slope. In terms of the movement stages, during the initial initiation phase, a small portion of the landslide boulders had significant kinetic energy, with some particles bouncing directly off the slope and impacting the upper and middle parts of the substation facilities at high speed. The remaining boulders impacted the substation ground at high speed. Due to the relatively flat ground and high rebound coefficient, some of these boulders bounced up and impacted the lower and middle parts of the facilities, while most impacted the bottom of the facilities. In the high-speed destructive phase, the initial potential energy of the landslide boulders decreased, and the bouncing boulders gradually shifted from impacting distant facilities and the top of the facilities to impacting nearby facilities and the upper and middle parts. After reaching the ground, the remaining boulders, influenced by the accumulated mass, had a low rebound coefficient. A small portion bounced up and impacted the lower and middle parts of the facilities, while most impacted the lower part of the facilities. During the accumulation phase, the initial potential energy of the collapsed rocks gradually decreases to its lowest point, and most of the rocks move along the slope to the ground, mainly impacting the bottom of the facilities.

[0214] The typical single-unit landslide hazard assessment model is based on the classification of instability probability, the classification of arrival probability, and the coupled calculation of the two. The previous section calculated the instability probability of the unstable rock mass using the Monte Carlo method. This section will classify the instability probability and, combined with the accumulation characteristics of the landslide rocks, calculate and classify the arrival probability. Based on this, a typical landslide hazard assessment model will be established to divide the Qixingshan landslide unstable rock mass into hazard zones within the 110kV Shabaobao substation area.

[0215] (1) Instability probability classification

[0216] The instability probability characterizes the susceptibility of landslides under different working conditions. A higher instability probability indicates that the unstable rock mass is more prone to instability and failure. For unstable rock masses that have not yet completely failed, calculating the instability probability is an important part of the hazard assessment, reflecting its forward-looking and predictive nature. Landslide susceptibility is classified according to the magnitude of the instability probability Pf. When p... f When the percentage is greater than 15%, it is considered highly susceptible; when 15% ≥ p f When the percentage is >5%, it is considered moderately prone to occur; when 5% ≥ p f At that time, it is low and prone to occur.

[0217] The probability of arrival generally refers to the proportion of falling rocks that reach the affected area out of all falling rocks, and it can characterize the disaster-causing range and relative intensity of the collapse. Numerical simulation analysis results show that the falling rocks exhibit a cone-shaped trajectory during the collapse process. Because the substation is close to the unstable rock mass, the falling rocks primarily land within the substation area, covering a large area. Most falling rocks stop rapidly after impacting the substation facilities, while some rocks do not impact electrical facilities and travel relatively far. The overall impact area is large, with an approximate radial distribution. Based on these simulation results, ArcGIS was used to divide the substation into 0.5m grids for statistical analysis. The probability of arrival Pa is calculated using the following formula:

[0218] pa=Ni / NT(2.9)

[0219] In the formula, Ni is the number of collapsed rocks remaining at a certain location; NT is the total number of collapsed rocks.

[0220] The intensity of landslide disasters is divided into zones based on the probability of arrival: when Pa > 30%, it is a high-intensity zone; when 30% ≥ Pa > 10%, it is a medium-intensity zone; and when 10% ≥ Pa, it is a low-intensity zone.

[0221] Combining the instability probability classification and the arrival probability classification, the hazard calculation formula for substation collapse rock mass is as follows:

[0222] H = p f ×p a (2.10)

[0223] In the formula, H represents the risk of rockfall; Pf represents the probability of rockfall instability; and Pa represents the probability of rockfall reaching the site.

[0224] Reference Figure 16 Based on the simulation results under extreme conditions, raster overlay analysis was performed using ArcGIS software. The total area of ​​the 110kV Shabaobao Substation is 9131m2, of which the high-risk area is 502m2, accounting for 5.5%; the medium-risk area is 2525m2, accounting for 27.7%; and the low-risk area is 6104m2, accounting for 66.8%.

[0225] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A vulnerability model and risk assessment method for landslide geological hazards, characterized in that, include: Collect relevant information about the target rock mass and perform preprocessing; Collect monitoring data from a triaxial accelerometer and obtain long-term rainfall data for the target area; Analyze the characteristics of acceleration fluctuations and correlate them with rainfall data to identify the impact of rainfall on rock mass instability; The Monte Carlo method is used to calculate the instability probability of rock masses under different environmental conditions, thereby quantifying the instability risk of rock masses. Numerical simulation methods were used to predict the movement and accumulation range of landslide rocks and assess their threat to surrounding power facilities. The risk level classification model is used to divide the area surrounding substation facilities into different risk levels and generate a risk distribution map. The method based on Monte Carlo calculations determines the instability probability of rock masses under different environmental conditions, quantifying the instability risk of the rock mass. Based on the calculation model of unstable rock mass, a stability coefficient formula is established. Random variables are selected to consider the main influences, and the random variables are processed by standard normality. Finally, the instability probability is calculated by substituting them into the formula. Based on the morphological characteristics, crack development, and main controlling structural planes of the unstable rock mass, a simplified calculation model for sliding-type collapse unstable rock masses is established, with the following expression: , Wherein, U is the buoyancy force of water, Q is the hydrostatic pressure, c is the cohesion of the structural surface, φ is the internal friction angle of the structural surface, W is the number of unstable rock masses, T is the tangential force at the bottom of the unstable rock mass, N is the normal force, and K is the stability coefficient. The standard normal distribution of random variables c and φ is expressed as follows: , Where μ is a random number; Using MATLAB software, based on the multiplicative linear congruence method, random variables c and φ are processed to generate random numbers that conform to a normal distribution. In the calculation of the stability of the unstable rock mass, values ​​are assigned to c and φ simultaneously. Random numbers conforming to a normal distribution are used to calculate the random values ​​of c and φ under natural working conditions and extreme rainstorm conditions, and the results are relatively independent. The expression for calculating the instability probability of the collapsed unstable rock mass is as follows: , in, This represents the probability of instability of a collapsed rock mass.

2. The vulnerability model and risk assessment method for landslide geological hazards as described in claim 1, characterized in that: The collected relevant information about the target rock mass includes, Sensors are used to acquire basic geological and environmental information related to rock mass stability, including geological structural features, rock strata properties, and topographic data. The environmental conditions of the target area are collected, including long-term rainfall, temperature and groundwater level meteorological and hydrological data, as well as historical landslide event records of the target area. The preprocessing involves formatting, normalizing, and cleaning the collected information, and then saving it to the database. Real-time collection of environmental data related to geological disaster risks, including rainfall, soil moisture, and surface displacement; Preprocessing is completed by removing noise, filling in missing data, and performing format conversion.

3. The vulnerability model and risk assessment method for landslide geological hazards as described in claim 2, characterized in that: The acquisition of triaxial component data from a triaxial accelerometer and the acquisition of rainfall data for the target area include, During or after rainfall, triaxial acceleration data is correlated with rainfall data to observe the fluctuation trend of acceleration and determine whether rainfall affects the displacement or deformation of the rock mass. If abnormal fluctuations in acceleration are detected in a certain direction, it is determined that the rock mass may show signs of instability due to rainfall; By comparing the time series of rainfall and acceleration fluctuations, the time lag effect between the peak acceleration and rainfall changes is identified, and the potential impact of rainfall infiltration on rock mass stability is determined. If the acceleration component shows abnormal fluctuations in a certain direction, it is determined that the rock mass is showing signs of instability due to rainfall; By comparing the time series of rainfall with the time series of acceleration fluctuations, we can observe whether there is a time lag between the peak acceleration and changes in rainfall.

4. The vulnerability model and risk assessment method for landslide geological hazards as described in claim 3, characterized in that: The analysis of acceleration fluctuation characteristics, and their correlation with rainfall data, identifies the impact of rainfall on rock mass instability, including... Key feature parameters are extracted from triaxial acceleration component data to identify the movement trend of rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate. Acceleration characteristic parameters are quantified into instability risk indicators and monitored in real time. When acceleration fluctuations reach a specific abnormal threshold, it is determined that the rock mass has potential signs of instability.

5. The vulnerability model and risk assessment method for landslide geological hazards as described in claim 4, characterized in that: The method of using numerical simulation to predict the movement and accumulation range of landslides and assess their threat to surrounding power facilities includes analyzing the distribution characteristics of landslides and rocks. The instability probability of collapsed rock masses is classified, and the arrival probability is calculated and classified in combination with the accumulation characteristics of collapsed rocks, so as to establish a collapse hazard risk assessment model. The degree of collapse susceptibility is classified according to the magnitude of the instability probability Pf. At that time, it is easy to cause; when At that time, it is easy to cause; when At that time, it is low-risk and prone to occur; Using ArcGIS, the substation was divided into 0.5m grids for statistical analysis, and the arrival probability was calculated. The expression is as follows: , in, This represents the number of collapsed rocks remaining at a certain location. This represents the total number of collapsed rocks.

6. The vulnerability model and risk assessment method for landslide geological hazards as described in claim 5, characterized in that: The method of using numerical simulation to predict the movement and accumulation range of landslides and assess their threat to surrounding power facilities also includes... The intensity of landslide disasters is divided into zones based on the probability of arrival. At that time, it is a high-intensity zone; when At that time, it is in the medium intensity zone; when At that time, it was a low-intensity area; Combining the instability probability classification and the arrival probability classification, the hazard of the substation collapse rock mass is calculated using the following expression: , in, The probability of a landslide or falling rock. This represents the probability of instability of the unstable rock mass. The risk of collapse of the unstable rock mass.

7. A system employing a vulnerability model and risk assessment method for landslide geological hazards as described in any one of claims 1 to 6, characterized in that: It includes a data acquisition and preprocessing module, a real-time monitoring and analysis module, an instability probability calculation module, and a risk assessment and simulation module; The data acquisition and preprocessing module collects geological structural features of the rock mass, rock strata properties, topographic and geomorphological information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, noise reduction and missing data filling on the data. The real-time monitoring and analysis module performs real-time monitoring and correlation analysis on the collected triaxial acceleration and rainfall data, extracts key characteristic parameters of acceleration, and combines rainfall data to determine whether there are signs of instability in the rock mass. The instability probability calculation module uses the Monte Carlo method to simulate the stability of the unstable rock mass under different environmental conditions, and calculates the instability probability of the rock mass under natural conditions and extreme rainstorm conditions. The risk assessment and simulation module predicts the movement path and accumulation range of falling rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities; at the same time, it establishes a risk level model by combining the instability probability and the arrival probability, and divides the area around the facilities into different risk levels.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the vulnerability model and risk assessment method for landslide geological hazards as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vulnerability model and risk assessment method for landslide geological hazards as described in any one of claims 1 to 6.

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