Collapse geological disaster vulnerability model and risk assessment method and system

Abstract

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

Description

Technical Field

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

[0002] In mountainous areas or areas with complex geological structures, power facilities (such as substations, transmission towers, distribution lines, etc.) are often threatened by geological disasters such as collapse, landslides, and mud-rock flows. Especially under continuous rainfall or extreme weather conditions, these facilities are more vulnerable to damage from geological disasters. Collapse disasters can cause rock mass instability and rockfall, which directly affects the safety and normal operation of power facilities, and may even cause equipment damage, power outages, and significant losses of property and personnel.

[0003] At present, traditional geological disaster assessment methods mainly rely on manual inspections, regular testing and post-maintenance, but cannot achieve real-time monitoring and dynamic risk assessment of collapse disasters. These methods are slow to respond to inducing factors such as the environment and rainfall, cannot accurately predict potentially unstable rock masses, and lack risk classification and disaster threat models for areas around specific power facilities. 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 of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to achieve real-time monitoring, dynamic risk assessment and regional risk level classification of collapse geological disasters around power facilities, so as to effectively warn and protect power facilities from damage caused by geological disasters.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a collapse geological disaster vulnerability model and risk assessment method, which comprises the following steps:

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

[0008] Collect monitoring data from the three-axis acceleration sensor and obtain long-term rainfall data in the target area;

[0009] Analyze acceleration fluctuation characteristics and correlate them with rainfall data to identify the impact of rainfall on rock mass instability;

[0010] Calculate the instability probability of rock mass under different environmental conditions based on the Monte Carlo method and quantify the instability risk of rock mass;

[0011] Use numerical simulation methods to predict the movement and accumulation range of rockfall and assess its threat to surrounding power facilities;

[0012] Through the risk level classification model, the areas around facilities such as substations are divided into different risk levels, and a risk distribution map is generated.

[0013] As a preferred solution of the collapse geological disaster vulnerability model and risk assessment method described in the present invention, the relevant information of the target rock mass collected includes:

[0014] Use sensors to obtain basic geological and environmental information related to rock mass stability, including geological structural characteristics, rock formation properties, and topographic data;

[0015] Collecting meteorological and hydrological data on environmental conditions in the target area, including long-term rainfall, temperature, and groundwater level, as well as historical records of collapse events in the target area;

[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 hazard risks including rainfall, soil moisture, and surface displacement;

[0018] Preprocessing is done by removing noise, filling missing data, and performing format conversion.

[0019] As a preferred solution of the collapse geological disaster vulnerability model and risk assessment method described in the present invention, wherein: the acquisition of triaxial component data of a triaxial acceleration sensor and the acquisition of rainfall data in a target area include:

[0020] During or after rainfall, the triaxial acceleration data is correlated with the rainfall data 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 the monitored acceleration fluctuates abnormally in a certain direction, it is determined that the rock mass may be unstable due to rainfall;

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

[0023] If the acceleration component fluctuates abnormally in a certain direction, it is judged that the rock mass shows signs of instability due to rainfall;

[0024] By comparing the time series of rainfall and the time series of acceleration fluctuations, we can observe whether there is a time lag between the acceleration peak and rainfall changes.

[0025] As a preferred solution of a collapse geological disaster vulnerability model and risk assessment method described in the present invention, the analysis of acceleration fluctuation characteristics and associating them with rainfall data to identify the impact of rainfall on rock mass instability includes:

[0026] Extract key characteristic parameters from the triaxial acceleration component data to identify the movement trend of the rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate;

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

[0028] As a preferred solution of the collapse geological disaster vulnerability model and risk assessment method described in the present invention, the Monte Carlo method is used to calculate the instability probability of the rock mass under different environmental conditions and quantify the instability risk of the rock mass, including:

[0029] According to the calculation model of dangerous rock mass, the stability coefficient formula is established, the random variables are selected considering the main influence, and the random variables are subjected to standard normal processing, and finally substituted into the calculation of the instability probability;

[0030] Based on the morphological characteristics of dangerous rock mass, the development of cracks, and the main controlling structural surface factors, a simplified calculation model of sliding collapse dangerous rock mass is established, and the expression is:

[0031] T=Wsinβ-Qsinαcosβ

[0032] α=N / Lτ=T / L

[0033] N=Wcosβ-U-Qsinαcosβ

[0034]

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

[0036] For random variables c, Perform standard normal processing, the expression is:

[0037] X=μ+σx'

[0038] Among them, μ is a random number;

[0039] Using MATLAB software, based on the multiplicative linear congruential method, the random variables c, , generate random numbers by calculating and outputting random numbers that conform to the normal distribution;

[0040] In the calculation process of the stability of dangerous rock mass, c, At the same time, the random numbers that conform to the normal distribution are calculated to obtain c, Random values, and the results are relatively independent, in calculating the instability probability of collapse dangerous rock mass, the expression is:

[0041]

[0042] Among them, P is the instability probability of the collapsed dangerous rock mass.

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

[0044] The instability probability of the collapsed rock mass is classified, and the calculation and classification of the arrival probability are carried out in combination with the accumulation characteristics of the collapsed rocks, so as to establish a collapse disaster risk assessment model;

[0045] According to the instability probability P f The size of the collapse is classified into the level of susceptibility. f When ≥15%, it is highly prone to occur; when 15%≥P f >5%, it is moderately prone; when 5% ≥ P f When, it is low and prone to occur;

[0046] ArcGIS is used to divide the substation into 0.5m grids and calculate the arrival probability. The expression is:

[0047] p a =N i / N T

[0048] Among them, N i is the number of collapsed rocks retained at a certain location, N T is the total number of collapsed rocks.

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

[0050] According to the arrival probability, the collapse disaster intensity is divided into zones. a >30%, it is a high-intensity zone; when 30%≥P a>10%, it is the medium intensity zone; when 10%≥P a When , it is a low intensity area;

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

[0052] H=P f ×P a

[0053] Among them, P a is the probability of rockfall arriving, P f is the probability of instability of dangerous rock mass, and H is the hazard of collapse of dangerous rock mass.

[0054] Another object of the present invention is to provide a collapse geological disaster vulnerability model and risk assessment system, which can automatically classify the risk level of the area around the power facilities by building a data acquisition module, a real-time monitoring and analysis module, an instability probability calculation module, and a risk assessment and simulation module, so as to efficiently and scientifically warn and protect the power facilities from the impact of collapse geological disasters, overcoming the problems of relying on manual detection, lack of dynamic analysis, and difficulty in realizing regional risk classification in the prior art. .

[0055] In order to solve the above technical problems, the present invention provides the following technical solutions: a collapse geological disaster vulnerability model and risk assessment system, 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;

[0056] The data acquisition and preprocessing module collects geological structural characteristics of rock mass, rock layer properties, topographic information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, denoising and filling missing data 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 the acceleration, and determines whether the rock mass has signs of instability in combination with the rainfall data;

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

[0059] The risk assessment and simulation module predicts the movement path and accumulation range of collapsed rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities. At the same time, a risk level model is established by combining the instability probability and arrival probability to divide the area around the facility into different risk levels.

[0060] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the collapse geological disaster vulnerability model and risk assessment method as described above are implemented.

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

[0062] Beneficial effects of the invention: The invention adopts technical means such as real-time monitoring, probability calculation and numerical simulation to solve the problem of lack of dynamic monitoring, accurate risk assessment and regionalized graded protection of power facilities in the prior art. Specifically, the invention collects the displacement and environmental data of the rock mass in real time by installing a three-axis acceleration sensor and a rainfall monitoring module, and calculates the probability of instability of the rock mass under different working conditions by combining the Monte Carlo method, thereby quantifying the instability risk.

[0063] Numerical simulation technology is used to predict the movement path and accumulation range of rockfall, analyze the potential impact of rockfall on surrounding power facilities, and classify and protect the surrounding areas of facilities through risk classification models. The above scheme realizes real-time, automated monitoring and risk classification of geological collapse disasters, overcomes the shortcomings of traditional methods that rely on manual inspections, delayed assessments, and lack of regional protection measures, and effectively ensures the safety and reliability of power facilities under disaster conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0065] Figure 1 An overall flow chart of a collapse geological disaster vulnerability model and risk assessment method provided by the first embodiment of the present invention;

[0066] Figure 2 A geographical location and traffic map of the study area in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

[0067] Figure 3 A monthly average precipitation distribution map of Duyun City, which is a collapse geological disaster vulnerability model and risk assessment method provided by the third embodiment of the present invention;

[0068] Figure 4A geological map of the study area in a collapse geological hazard vulnerability model and risk assessment method provided in the third embodiment of the present invention;

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

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

[0071] Figure 7 An engineering geological profile of the Qixingshan collapsed dangerous rock mass in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

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

[0073] Fig. 9 The WY2 acceleration component and rainfall monitoring data in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

[0074] Fig.10 A diagram of the instability and failure evolution pattern in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

[0075] Fig.11 A simplified calculation model diagram of sliding-type collapse dangerous rock mass in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

[0076] Fig.12 A typical particle movement velocity diagram in a collapse geological disaster vulnerability model and risk assessment method provided in a third embodiment of the present invention;

[0077] Fig.13 A typical particle displacement diagram in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention;

[0078] Fig.14 A parallel bonding fracture percentage diagram in a collapse geological disaster vulnerability model and risk assessment method provided by the third embodiment of the present invention;

[0079] Fig.15 A schematic diagram of changes in the number of parallel bonds in a collapse geological disaster vulnerability model and risk assessment method provided in a third embodiment of the present invention;

[0080] Fig.16 A collapse hazard zoning map in a collapse geological disaster vulnerability model and risk assessment method provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0082] Example 1, reference Figure 1 In one embodiment of the present invention, a collapse geological disaster vulnerability model and risk assessment method are provided, including:

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

[0084] Collect monitoring data from the three-axis acceleration sensor and obtain long-term rainfall data in the target area;

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

[0086] Calculate the instability probability of rock mass under different environmental conditions based on the Monte Carlo method and quantify the instability risk of rock mass;

[0087] Use numerical simulation methods to predict the movement and accumulation range of rockfall and assess its threat to surrounding power facilities;

[0088] Through the risk level classification model, the areas around facilities such as substations are divided into different risk levels, and a risk distribution map is generated.

[0089] Use sensors to obtain basic geological and environmental information related to rock mass stability, including geological structural characteristics, rock layer properties, and topographic data; collect environmental conditions in the target area, including long-term rainfall, temperature, groundwater level and other meteorological and hydrological data, as well as historical records of collapse events in the target area; format, normalize and clean the collected information and save it to a database; collect environmental data related to geological disaster risks in real time, including rainfall, soil moisture, and surface displacement;

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

[0091] It is further explained that:

[0092] Collecting relevant information of the target rock mass and preprocessing it means comprehensively acquiring basic geological and environmental information related to the stability of the rock mass, including but not limited to the characteristics of geological structures (such as faults, joints, bedding planes, etc.), rock formation properties (including physical parameters such as lithology type, compressive strength, elastic modulus, etc.), and topographic data (such as slope, aspect, and digital elevation model, etc.). This information can be obtained through field surveys, borehole sampling, drone remote sensing, geological radar, and other means. At the same time, environmental conditions in the target area should be collected, such as meteorological and hydrological data such as long-term rainfall, temperature, and groundwater level, and combined with records of historical collapse events to assess the instability trend in the area.

[0093] Collecting the triaxial component data of the triaxial acceleration sensor and obtaining the rainfall data of the target area includes correlating the triaxial acceleration data with the rainfall data during or after rainfall, observing the fluctuation trend of the acceleration to determine whether the rainfall has an impact on the displacement or deformation of the rock mass; if abnormal fluctuations in the acceleration in a certain direction are monitored, 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, identifying the time lag effect between the acceleration peak and rainfall changes, and determining the potential impact of rainfall penetration on the stability of the rock mass.

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

[0095] It is 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 rainfall changes. The specific technical solutions may include the following aspects:

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

[0098] If the acceleration data shows a trend of increasing fluctuations 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 the changes in acceleration data after the rainfall ends, especially whether there is a delay in the acceleration peak.

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

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

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

[0103] In summary, by comparing the time series of rainfall and acceleration fluctuations and identifying the hysteresis phenomenon of the acceleration peak after the end of rainfall, combined with the geological characteristics of the rock mass, the system can preliminarily judge the potential impact of rainfall penetration on the stability of the rock mass and provide a basis for instability warning.

[0104] Extract key characteristic parameters from the triaxial acceleration component data to identify the movement trend of the rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate;

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

[0106] According to the calculation model of dangerous rock mass, the stability coefficient formula is established, the random variables are selected considering the main influence, and the random variables are subjected to standard normal processing, and finally substituted into the calculation of the instability probability;

[0107] Based on the morphological characteristics of dangerous rock mass, the development of cracks, and the main controlling structural surface factors, a simplified calculation model of sliding collapse dangerous rock mass is established, and the expression is:

[0108] T=Wsinβ-Qsinαcosβ

[0109] α=N / Lτ=T / L

[0110] N=Wcosβ-U-Qsinαcosβ

[0111]

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

[0113] For random variables c, Perform standard normal processing, the expression is:

[0114] X=μ+σx'

[0115] Among them, μ is a random number;

[0116] Using MATLAB software, based on the multiplicative linear congruential method, the random variables c, Generate random numbers by calculating and outputting random numbers that conform to the normal distribution;

[0117] In the calculation process of the stability of dangerous rock mass, c, At the same time, the random numbers that conform to the normal distribution are calculated to obtain c, Random values, and the results are relatively independent, in calculating the instability probability of collapse dangerous rock mass, the expression is:

[0118]

[0119] Among them, P is the instability probability of the collapsed dangerous rock mass.

[0120] The instability probability of the collapsed rock mass is classified, and the calculation and classification of the arrival probability are carried out in combination with the accumulation characteristics of the collapsed rocks, so as to establish a collapse disaster risk assessment model;

[0121] According to the instability probability P f The size of the collapse is classified into the level of susceptibility. f When ≥15%, it is highly prone to occur; when 15%≥P f >5%, it is moderately prone; when 5% ≥ P f When, it is low and prone to occur;

[0122] ArcGIS is used to divide the substation into 0.5m grids and calculate the arrival probability. The expression is:

[0123] p a =N i / N T

[0124] Among them, N i is the number of collapsed rocks retained at a certain location, N T is the total number of collapsed rocks.

[0125] According to the arrival probability, the collapse disaster intensity is divided into zones. a >30%, it is a high-intensity zone; when 30%≥P a >10%, it is the medium intensity zone; when 10%≥P a When , it is a low intensity area;

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

[0127] H=P f ×P a

[0128] Among them, P a is the probability of rockfall arriving, P f is the probability of instability of dangerous rock mass, and H is the hazard of collapse of dangerous rock mass.

[0129] Embodiment 2 is an embodiment of the present invention, which provides a system for a collapse geological disaster vulnerability model and a risk assessment method, 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 characteristics of rock mass, rock layer properties, topographic information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, denoising and filling missing data 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 the acceleration, and determines whether the rock mass has signs of instability in combination with the rainfall data;

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

[0133] The risk assessment and simulation module predicts the movement path and accumulation range of collapsed rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities. At the same time, a risk level model is established by combining the instability probability and arrival probability to divide the area around the facility into different risk levels.

[0134] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

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

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] Embodiment 3: In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0139] Reference Figure 2 ,The 110kV Shabaobao substation is located in Group 5, Yingen Village, Shabaobao Street, Duyun City, Qiannan Buyi and Miao Autonomous Prefecture, Guizhou Province, about 1km away from the location of Shabaobao Street Office, 1.5km away from Duyun North Gate of Lanhai Expressway, and 8.5km away from Duyun Station of Guizhou-Guangxi Railway. The geographical coordinates of the center 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 Guizhou Plateau and has a mid-subtropical monsoon climate. The temperature is suitable 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 about 300 days throughout the year.

[0141] Duyun City is a rainstorm area and rainy center in Guizhou Province, with abundant precipitation, rainy and hot seasons, and an average annual precipitation of 1431.1 mm. The most abundant year was 1968 mm (1977), and the driest year was 868 mm (1962). Rainfall is concentrated from April to September every year. The rainy season begins in mid-April, with precipitation reaching 251.1 mm in June, accounting for 17.4% of the year, reaching the peak of the year, and then began to decline, rising in October, and until January, the precipitation was 27.7 mm, accounting for only 1.9% of the annual precipitation. The annual rainfall in Duyun City lasts up to 180 days, and the 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, and the maximum daily precipitation is 170.5 mm (May 10, 1967).

[0142] Duyun City is located in the middle section of the Miaoling Mountains, and is a slope zone where the Yunnan-Guizhou Plateau transitions to the hills of Guangxi. The general trend of the terrain is high in the northwest and low in the southeast. The main landforms are karst dissolution landforms, and erosion landforms in some areas. Affected and controlled by the structure, the direction of the mountains is basically consistent with the structural features. Doupeng Mountain in the northwest of Duyun City is 1,961 meters above sea level, the highest point in the territory, and Wengliazhai in Guilan Shuizu Township in the southeast is 540 meters above sea level, the lowest point in the territory, with a maximum height difference of 1,421 meters.

[0143] The 110kV Shabaobao substation area is a low-to-medium mountain landform with karst-erosion features, with peaks and peak forests on the surface, and depressions and troughs in some areas. The Qixing Mountain is located to the east of the 110kV Shabaobao substation, and is named after its numerous peaks and star-like distribution. The slopes in the area are open on three sides, and the overall terrain is high in the northeast and low in the southwest. The highest altitude is 1,592m, the lowest altitude is 795m, the maximum relative height difference is 797m, and the terrain slope is 30° to 80°. The slopes are generally characterized by being gentle at the top and steep at the bottom. The elevations of 765m to 895m are relatively steep, reaching 50° to 80°, and gradually become gentler upwards, about 30° to 40°, with an overall slope angle of 30°. Most of the slopes are exposed bedrock, and the rock crevices and gentle slopes are covered with Quaternary residual soil and colliery soil, with very lush vegetation.

[0144] Reference Figure 4 The exposed strata near the 110kV Shabaobao substation are relatively simple. From new to old, there are Quaternary (Q) residual slope accumulation layers, landslide accumulation layers and alluvial-diluvian alluvial layers, which are composed of gray and yellow clay interbedded with crushed stone and gravel; gray thick to massive limestone of the Maokou Formation (P1m) of the Lower Permian; gray to dark gray medium-thick layered limestone of the Qixia Formation (P1q) of the Lower Permian; gray thin to thick layered quartz sandstone of the Liangshan Formation (P1l) of the Lower Permian; gray thick-layered limestone of the Huanglong Formation (C2h) of the Middle Carboniferous.

[0145] The geotectonic structure of Duyun City is located in the Guiding north-south structural deformation zone of the fourth-level structural unit of the Qiannan Depression of the Yangtze Platform. It mainly develops tight synclines, box anticlines and reverse faults, thus forming typical trough-type folds with tight synclines and wide and gentle anticlines. The direction of its structural line is north-south, mainly controlled by the Jiangnan axis. Due to the compression of the Yanshan Movement, two north-south folds were formed in the region, namely the Guiding syncline and the Duyun syncline. Controlled by the two major synclines, a large number of east-west faults and secondary folds are developed in the region. The study area is located in the footwall of the Duyun Fault as a whole. The fault is a reverse fault with a near north-south trend, dipping to the east and a dip angle of 45°. The 110kV Shabaobao substation is located on the west side of the fault zone. Affected by tectonic action, rock joints and fissures are developed, mainly with nearly parallel fissures with a strike of 335°. A nearly north-south reverse fault develops on the west side of the study area, dipping eastward with a dip angle of 40° and extending along the trough on the west side of the substation. The exposed length of the fault in the study area is about 1.8 km.

[0146] Qixing Mountain is located on the east side of the 110kV Shabaobao substation. The slope is gentle at the top and steep at the bottom, with a slope of 50° to 85°. The mountain has a steep terrain, an uneven slope, and is nearly vertical in some places in the middle and lower parts. The bedrock is exposed on the surface of the slope, with dense vegetation between the rocks, strong weathering, and developed joints and fissures. The main slope of the mountain is 275°, the overall dip of the rock layer is 285°∠65°, and the foot of the slope is close to the substation. The collapsed dangerous rock mass is located in the middle and upper part of Qixing Mountain, with a front edge elevation of 835m and a rear edge elevation of 885m. The height difference relative to the substation is 50m, which is a high-level rock collapse dangerous rock mass. There are two typical dangerous rock masses in this area, WY1 and WY2.

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

[0148] Reference Figure 6 , WY2: The dangerous rock mass is located about 10m below WY1, with a slope of 55°~65°. The front edge of the dangerous rock mass is a steep cliff, and below the steep cliff is a substation and a residential area for villagers. The dangerous rock mass is about 5m wide, 5m high, 3m thick, and has a volume of about 75m3. It is a small rock collapse dangerous rock mass.

[0149] The dangerous rock mass is mainly composed of gray to dark gray medium-thick limestone of the Qixia Formation (P1q) of the Lower Permian System. The surface is strongly weathered, the interior is moderately weathered, and the stratification is massive. The rock mass has two groups of well-developed joints, namely J1: 73°∠82°, 2.5m long, 0.3m apart, and 2-10cm wide; J2: 353°, ∠83°, 2m long, 0.2m apart, and 1-5cm wide. The joints and fissures are all rough, refer to Figure 7 .

[0150] The dangerous rock mass is divided into blocks of different sizes by the structural surface. The physical properties of the individual rock blocks are good, and the properties of the structural surface between the rock blocks are poor, which is the main controlling factor for the stability of the dangerous rock mass. The volume of the accumulation body at the foot of the slope is small, mainly limestone and residual slope accumulation layer, with loose structure and small block volume, generally within 0.5m in diameter, and occasionally there are blocks with diameters between 0.9m and 1.5m scattered in the middle and lower parts.

[0151] Two sets of triaxial accelerometers were installed on the collapse body, respectively in the middle and upper parts of WY1 and WY2. Figure 8 and Fig. 9These are the acceleration components and daily rainfall monitoring data of WY1 and WY2 in 2022. The component of gravity along the slope is expressed as gX, the component in the vertical direction is expressed as gY, and the component on the vertical slope is expressed as gZ. It can be seen from the two figures that rainfall in the study area is concentrated from April to June, and the three-axis components of gravity acceleration are constantly fluctuating. Taking into account the installation and internal errors of the instrument, gY, the main component of gravity acceleration, basically fluctuates around 1g throughout the year, reaching a peak fluctuation from May to July, and the minimum fluctuation mean from December to February of the following year. Both gX and gZ have slight fluctuations, also reaching a peak fluctuation from May to July, and the minimum fluctuation mean from December to February of the following year.

[0152] According to the on-site investigation, the rocks on the slope of Qixing Mountain are strongly corroded, with developed corrosion cracks. There is a small cave on the hillside with an area of ​​90m2. The vegetation on the slope is lush, and the trees basically grow in the cracks of the rocks, with obvious root splitting. Some rocks have obvious signs of deformation, which poses a great threat to the substation. According to the records of the substation staff and the feedback from local residents, Qixing Mountain has experienced several local instability phenomena, and the impact of falling rocks has caused certain property losses to the substation.

[0153] Under the strong weathering action, there are many tensile cracks in the rear edge and middle of the dangerous rock mass, which are 1.5-4.5m long, 10-50cm wide, 0.5-2.8m deep, and strike 320°-350°. There are residual slope soil and humus in the cracks. Under the action of internal and external forces, the cracks gradually expand and extend, and there is a tendency to penetrate. Since the Qixingshan landslide dangerous rock mass is a high-level rock collapse, the energy converted after the instability damage is large, which will cause great damage to the 110kV Shabaobao substation.

[0154] The factors that affect the instability and destruction of the Qixingshan landslide rock mass are mainly as follows: ① The rock mass mainly develops three groups of structural planes, namely, bedding plane: 285°∠65°; joint plane 1: 73°∠82°; joint plane 2: 353°∠83°, and the slope structure is a forward slope; ② The surface of the rock mass is mainly strongly weathered, extending 1m to 5m to medium weathering inward. There are many unloading and weathering cracks in the rock mass, which are statistically 1.5 to 4.5m long, 10 to 50cm wide, 0.5 to 2.8m deep, 320° to 350° in direction, and 5m to 10m in unloading depth. The forward bedding plane is the main controlling structural plane. ③ The vegetation in the cracks is dense, and the karst phenomenon is obvious on site. The external unfavorable weathering will accelerate its deformation.

[0155] Taking WY1 as an example, the dangerous rock mass is located in the middle and upper part of the west slope of Qixing Mountain, with a slope of 60°~70°. The dangerous rock mass is about 8m wide, 10m high, 3m thick, with a distribution area of ​​about 80m2 and a volume of about 240m3. From the on-site investigation, the dangerous rock area is distributed in a nearly elliptical shape. The rock mass is cut by three sets of structural planes, and the slope of Qixing Mountain is relatively steep. Under the action of gravity, the dangerous rock mass has a tendency to deform and damage along the main control structural plane, that is, along the plane. Affected by weathering such as rainfall and root splitting, the collapsed dangerous rock mass will accelerate deformation and eventually lead to collapse.

[0156] Through the analysis of the above phenomena, it can be known that the instability failure mode of the Qixingshan collapsed dangerous rock mass is speculated to be sliding. The main characteristics of this mode are that the dangerous rock mass has a main control structure surface that is along the slope, with a medium-steep inclination, and is connected or intermittently connected. The instability failure is mainly overall sliding or collapse rolling. The specific evolution process is: karst weathering and unloading lead to tensile cracks on the rear edge of the slope, and gradually develop into the deep part of the rock mass until the cracks gradually penetrate. When the shear strength of the rock mass is less than the gravity component of the rock mass, sliding instability occurs along the main control structure surface under the induction of adverse factors such as rainfall, such as Fig.10 This type of mode generally has a large impact range and poses a great threat to the slope foot substation and residents, so it needs special attention.

[0157] This paper selects the instability probability of dangerous rock mass and the arrival probability of rockfall as the main evaluation factors of typical collapse hazards. Secondly, the evaluation area is divided into grids based on the characteristics of the substation. The overall evaluation idea is: first, the instability probability of dangerous rock mass under different working conditions is calculated based on the Monte Carlo method; then, the dynamic process and accumulation distribution characteristics of rockfall are analyzed by numerical simulation analysis to obtain the arrival probability of rockfall; finally, the collapse hazard is evaluated by matrix analysis.

[0158] The Monte Carlo method is a kind of reliability calculation. Its results are less restricted by conditions, 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 the field of engineering geology, it is often used to calculate the instability probability of rock collapse and dangerous rock mass. Its main process is: first, establish a stability coefficient formula based on the dangerous rock mass calculation model, then consider the main influence to select random variables, and perform standard normal processing on the random variables, and finally substitute them into the calculation of the instability probability. The simplified calculation model is shown in Fig.11 .

[0159] Based on the field investigation and the above analysis of the instability failure mode, a simplified calculation model of sliding collapse dangerous rock mass is established by integrating the morphological characteristics of the dangerous rock mass, the development of cracks, the main control structure surface and other factors. Referring to the specifications and engineering cases, this paper considers two different working conditions and load combinations: working condition 1: natural working condition, mainly based on self-weight; working condition 2: extreme rainstorm working condition, mainly based on self-weight and saturated fissure water pressure. The formula for the stability coefficient under natural working conditions or extreme rainstorm working conditions is derived as follows:

[0160] Tangential force at the bottom of dangerous rock mass:

[0161] T=Wsinβ-Qsinαcosβ(2.1)

[0162] α=N / Lτ=T / L

[0163] Normal force:

[0164] N=Wcosβ-U-Qsinαcosβ(2.2)

[0165] Stability coefficient:

[0166]

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

[0168] Random variables mainly consider the degree of influence of influencing factors on the stability of dangerous rock masses, which are divided into endogenous factors and external factors: endogenous factors refer to the characteristics of the dangerous 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 above has integrated the basic characteristics and natural conditions of the Qixingshan collapsed dangerous rock mass into the stability evaluation. Now we only need to consider the role of human errors, mainly based on the values ​​of rock and soil parameters. Affected by various factors such as sampling, testing, and post-processing, the shear strength index of the structural surface (c, ) has a large uncertainty, while the coefficient of variation of severity (γ) is small, and the error is not large. Therefore, consider treating severity γ as a constant and changing c, Select as a random variable.

[0169] In general, the random variables c, It often obeys a non-standard normal distribution, so a formula is needed to convert it into a standard normal distribution, see formula 2.4.

[0170] X=μ+σx'(2.4)

[0171] Using the binary function, we can obtain a random variable that conforms to the standard normal distribution, as shown in Formula 2.5:

[0172]

[0173] Where: μ1 and μ2 are uniform random numbers between 0 and 1.

[0174] Formula 2.4 and Formula 2.5 are combined to form Formula 2.6 and Formula 2.7 as follows:

[0175]

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

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

[0178] Table 1 Physical parameters of the structural surface of the collapsed dangerous rock mass

[0179]

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

[0181] ① Cohesion c

[0182] This article uses MATLAB software to generate random numbers (10,000) based on the multiplicative linear congruential method. Substituting the generated results into formula 2.7, we can get random numbers that conform to the normal distribution.

[0183] ②Internal friction angle

[0184] In the calculation process of the stability of dangerous rock mass, it is necessary to calculate c, Assign values ​​at the same time, so the parameters The value-taking process is the same as parameter c. Substitute 10,000 random numbers into formula 2.7 for calculation.

[0185] Finally, using formula 2.4, we can calculate c, 10,000 groups of random values.

[0186] (4) Calculation of instability probability

[0187] The c and Substituting the value into formula 2.3, we can obtain 10,000 groups of stability coefficients under natural conditions and extreme rainstorm conditions, and the results are relatively independent. This paper defines that when KS≤1, it means that the collapse of the dangerous rock mass has unstable failure, and defines the number of occurrences of unstable failure as M, and the instability probability formula of the collapse of the dangerous rock mass is obtained as:

[0188]

[0189] Among them, P is the instability probability of the collapsed dangerous rock mass.

[0190]

[0191] Table 3 Probability of WY2 instability failure

[0192]

[0193]

[0194] The calculation results of the instability probability of the two dangerous rock masses are shown in Table 2.2 and Table 2.3. It can be seen that the probability of instability of WY1 under natural conditions is 15.92%, and under extreme rainstorm conditions is 26.58%; the probability of instability of WY2 under natural conditions is 13.98%, and under extreme rainstorm conditions is 25.76%. The calculation results can confirm that rainfall is the cause of collapse of dangerous rock masses.

[0195] An important factor for instability. Because WY1 and WY2 have similar shapes, a small distance, and a similar degree of crack development, the instability failure mode is the same. According to the calculation results, the instability probability of the two dangerous rock masses under the same conditions is close, and the subsequent focus is to explore the damage and failure mode of the electrical facility structure under the impact of collapse and falling rocks. Therefore, this article will consider the two dangerous rock masses at the same time in subsequent simulations and evaluations, that is, analyze the maximum impact that the dangerous rock mass may have on the substation, and the impact on a single dangerous rock mass will not be repeated. Therefore, the instability probability is taken as the average of the instability probability of the two dangerous rock masses under extreme rainstorm conditions, which is calculated to be 26.17%.

[0196] At present, the research on the risk assessment of single collapse disasters at home and abroad often uses numerical simulation in order to scientifically show the whole process of instability, destruction and impact of dangerous rock mass. And this method is repeatable, which is conducive to the whole process analysis of collapse disasters. Therefore, this paper uses the coupling method of discrete element software (PFC3D) and finite difference software (FLAC3D) developed by TASCA to simulate the movement and accumulation characteristics of Qixingshan collapse after instability under extreme conditions, and show the movement process, movement parameters, accumulation range and impact process of collapse on the structure in three dimensions, and carry out risk assessment of single collapse disaster of substation.

[0197] In the early stage, high-precision image data was obtained through on-site drone mapping, and the characteristics of the collapse body, substation facilities and other features were obtained through on-site investigation. Based on the above survey results, this paper uses the ball-ball model to simulate the collapse of dangerous rock mass, the wall rigid surface in the ball-wall model to simulate the slope and ground, and the wall model to simulate the substation facilities. The process of establishing the numerical model is as follows:

[0198] (1) Pix4Dmapper was used to process drone image data, obtain point cloud data and generate DSM data of the study area. Areas such as the landslide source area and substation area were distinguished based on the field survey data.

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

[0200] (3) Generate a surface model of the terrain of the study area through contour lines in Rhino, and then obtain the initial geological body model through extrusion and cutting. Since the Qixingshan landslide is still in the deformation stage, two dangerous rock masses are divided according to the scope, volume, and joint and fissure arrangement of the dangerous rock mass in the field investigation. Analyze the structure and layout of the substation, and simplify the modeling of the substation foundation, house, and electrical facilities in combination with the later evaluation method to obtain the final analysis model. Use the Griddle plug-in to perform mesh division and export it as an f3grid file.

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

[0202] Taking into account the real surface and simulation efficiency, the FLAC3D initial model in this simulation has a total of 10,818 grid units, and the model size is 140m×190m. The dangerous rock mass in the model is divided into two parts: WY1 and WY2. According to the on-site investigation, WY1 is about 240m3 and WY2 is about 75m3, and the distance between the two is 10m.

[0203] The deformation and destruction process of the collapse source area can be briefly summarized as: long-term weathering and erosion - development of cracks in the upper part of the rock block - deformation of the rock block toward the free surface - downward development of cracks - shearing of the locking section - instability and collapse of the dangerous rock mass. According to the analysis of the results of the on-site investigation, the dangerous rock mass was simplified and restored in PFC3D, mainly considering the characteristics of its rolling and sliding when potential collapse occurs. Considering that the dangerous rock mass of Qixing Mountain is limestone, the karst phenomenon is prominent, and it is cut by two sets of joints, the final dangerous rock mass consists of 11,512 spheres with a porosity of 30% and a radius of 10 cm to 20 cm, which is close to the actual situation. The parallel bonding contact model is used between the particles of the dangerous rock mass, and the linear contact model is used between the falling rocks and the wall.

[0204] Due to the complexity of the substation facilities and layout, they are simplified into 105 components to facilitate subsequent simulation calculations, monitoring and evaluation. According to 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, so they are simulated by finite element Zone units.

[0205] In this simulation, the contact model between particles is a parallel bonding model. Since the material composition of the Qixingshan collapsed dangerous rock mass is mainly limestone, the karst phenomenon is more prominent. After comprehensive analysis with reference to previous research results, the macroscopic mechanical parameters of the rock were confirmed, as shown in Table 4. The parameters required by the PFC3D model are the microscopic parameters of its basic units. Based on the macroscopic parameters of limestone, the PFC3D uniaxial compression experiment was first carried out to assign microscopic parameters to the model in the numerical experiment, and then continuously adjusted. Ensure that the characteristics shown in the numerical experiment are basically consistent with those shown in the actual experiment, and then compare them with the dynamic characteristics of similar collapses, and finally obtain the microscopic parameters of the model, as shown in Table 5.

[0206] Table 4 Comparison of actual macro parameters and PFC3D simulation parameters

[0207]

[0208]

[0209] In order to explore the movement law of rockfall particles, 4 rows of 8 particles, a total of 16 typical particles, were selected from top to bottom in WY1 and WY2 for detailed monitoring. Within 0 to 5 seconds, the lower particles of WY2 first reached 16 m / s, and the speed dropped rapidly after hitting the ground, and then after several rebounds, the speed finally dropped to 0; within 5 to 10 seconds, the lower particles of WY1 accelerated to 20 m / s, which was the maximum speed of all particles, and some middle and upper particles also became unstable due to stress changes; within 10 to 20 seconds, the middle particles of WY1 and WY2 accelerated sliding, with a maximum speed ranging from 14 to 18 m / s; within 20 to 30 seconds, the remaining particles in the upper part of WY1 and WY2 became unstable, with a maximum speed of 15 to 20 m / s; after 30 seconds, the main particles of the dangerous rock mass were basically not deformed, and some particles bounced after the collision, and the rebound speed dropped from 3 m / s to 0. Reference Figures 12-13 .

[0210] Analysis of the particle velocity change process shows that in the early stage of instability and destruction of the dangerous rock mass, the lower rock deforms first and the velocity reaches the maximum value, at which time the rebound velocity is also the maximum. After a period of time, the middle rock slides down, and the rock particles of WY1 and WY2 collide on the slope, generating energy consumption, resulting in a slight decline in the maximum velocity and rebound velocity. Finally, some of the remaining rocks in the upper part are deformed and destroyed. At this time, there is no other rock interference on the slope, and the velocity is close to the maximum level, but due to the accumulation of rocks in the lower part, the rebound velocity is significantly reduced.

[0211] The fracture trend of collapsed rocks during movement can be expressed by the time history statistics of the number of parallel bonds between particles. Fig.14 and Fig.15They are respectively the time course curve of the percentage of parallel bond fracture and the real-time number diagram of parallel bond during the numerical simulation. It can be seen that there are 12532 initial parallel bonds between the spherical particles; the number of parallel bonds decreases to 1148 within 0-12s, and the fracture percentage reaches 90%; at 12-20s, the collapsed rocks complete about 5% of the parallel bond fractures in the collision; the remaining 5% of the collapsed rocks are not fractured due to the nature and movement process. The fracture process reflects that in the sliding collapse, there are continuous collisions between the rock and soil particles, between the rock and soil and the slope and the ground, and the force exceeds the bonding force between the rock and soil, which eventually leads to shear failure of the rock and soil and forms rock particles.

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

[0213] The collapse body gradually broke into rocks during the movement. Since the Qixing Mountain area has lush vegetation, a large slope, and is close to the ground, the slope has a low rebound coefficient, and the jumping height of the rocks is low. Most of the rocks move downward along the slope, and only a few of them jump up after colliding with the slope. From the perspective of the movement stage, in the initial startup stage, a small number of collapsed rocks have large kinetic energy, and some particles directly bounce off the slope and impact the middle and upper parts of the substation facilities at high speed. The remaining rocks impact the ground of the substation at high speed. Since the ground is relatively flat and the rebound coefficient is high, some of the rocks jump up to impact the middle and lower parts of the facilities, and most of the rocks have a greater impact on the bottom of the facilities. In the high-speed destruction stage, the initial potential energy of the collapsed rocks is reduced, and the jumping rocks gradually change from impacting distant facilities and the top of the facilities to impacting nearby facilities and the middle and upper parts of the facilities. After the remaining rocks reach the ground, they are affected by the accumulation body and have a low rebound coefficient. A small number of them jump up to impact the middle and lower parts of the facilities, and most of the rocks impact the lower part of the facilities. When it reaches the stage of accumulation, the initial potential energy of the collapsed rocks gradually drops to the minimum, and most of the rocks move along the slope to the ground, mainly impacting the bottom of the facilities.

[0214] The typical single-body collapse hazard hazard assessment model is based on the classification of instability probability, the classification of arrival probability, and the coupling calculation of the two. The instability probability of the dangerous rock mass has been calculated based on the Monte Carlo method in the previous article. This section will classify the instability probability and combine the accumulation characteristics of the collapsed rocks to carry out the calculation and classification of the arrival probability. Based on this, a typical collapse hazard hazard assessment model is established to divide the dangerous rock mass of the Qixingshan collapse into dangerous zones in the 110kV Shabaobao substation area.

[0215] (1) Instability probability classification

[0216] The instability probability can characterize the susceptibility of collapse disasters under different working conditions. The higher the instability probability, the more likely the collapse-prone rock mass is to become unstable and damaged. For the collapse-prone rock mass that has not yet completely become unstable and damaged, the calculation of its instability probability is an important part of the hazard assessment, which reflects the foresight and predictive nature of the hazard assessment. The collapse susceptibility level is divided into grades according to the size of the instability probability Pf. When p f >15%, it is highly likely to occur; when 15% ≥ p f >5%, it is moderately prone; when 5% ≥ p f When it is low, it is prone to occur.

[0217] The arrival probability generally refers to the ratio of the number of rocks that reach the hazard-bearing body to all rocks that fall, which can characterize the disaster-causing range and relative intensity of the collapse. The results of numerical simulation analysis show that the movement trajectory of the collapsed rocks is cone-shaped during the collapse process. Since the substation is close to the dangerous rock mass, the collapsed rocks basically fall within the substation area, covering a large area. Most of the rocks stopped quickly after impacting the substation facilities, and some rocks did not impact the electrical facilities and moved relatively far. The overall impact range is large, approximately radially distributed. Based on this simulation result, ArcGIS was used to perform 0.5m grid division statistics on the substation, and the calculation formula for the arrival probability Pa is as follows:

[0218] pa=Ni / NT(2.9)

[0219] Where Ni is the number of collapsed rocks retained at a certain location; NT is the total number of collapsed rocks.

[0220] The collapse disaster intensity zones are divided according to the arrival probability. When Pa>30%, it is a high-intensity zone; when 30%≥Pa>10%, it is a medium-intensity zone; when 10%≥Pa, it is a low-intensity zone.

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

[0222] H=p f ×p a (2.10)

[0223] Where H is the hazard of rock mass collapse; Pf is the probability of instability of the dangerous rock mass; Pa is the probability of rockfall arrival.

[0224] Reference Fig.16 According to the simulation results under extreme conditions, raster overlay analysis was performed through ArcGIS software. The total area of ​​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%; 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A collapse geological disaster vulnerability model and risk assessment method, characterized in that: include: Collect relevant information of the target rock mass and perform preprocessing; Collect monitoring data from the three-axis acceleration sensor and obtain long-term rainfall data in the target area; Analyze acceleration fluctuation characteristics and correlate them with rainfall data to identify the impact of rainfall on rock mass instability; Calculate the instability probability of rock mass under different environmental conditions based on the Monte Carlo method and quantify the instability risk of rock mass; Use numerical simulation methods to predict the movement and accumulation range of rockfall and assess its threat to surrounding power facilities; Through the risk level classification model, the areas around facilities such as substations are divided into different risk levels, and a risk distribution map is generated.

2. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 1, characterized in that: The relevant information collected about the target rock mass includes: Use sensors to obtain basic geological and environmental information related to rock mass stability, including geological structural characteristics, rock formation properties, and topographic data; Collecting meteorological and hydrological data on environmental conditions in the target area, including long-term rainfall, temperature, and groundwater level, as well as historical records of collapse events in the target area; The preprocessing is to format, normalize and clean the collected information and save it to a database; Real-time collection of environmental data related to geological hazard risks including rainfall, soil moisture, and surface displacement; Preprocessing is done by removing noise, filling missing data, and performing format conversion.

3. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 2, characterized in that: The collecting of the three-axis component data of the three-axis acceleration sensor and obtaining the rainfall data of the target area includes: During or after rainfall, the triaxial acceleration data is correlated with the rainfall data to observe the fluctuation trend of acceleration to determine whether rainfall has an impact on the displacement or deformation of the rock mass; If the monitored acceleration fluctuates abnormally in a certain direction, it is determined that the rock mass may be unstable due to rainfall; By comparing the time series of rainfall and acceleration fluctuations, the time lag effect between acceleration peak and rainfall change is identified, and the potential impact of rainfall penetration on rock mass stability is determined; If the acceleration component fluctuates abnormally in a certain direction, it is judged that the rock mass shows signs of instability due to rainfall; By comparing the time series of rainfall and the time series of acceleration fluctuations, we can observe whether there is a time lag between the acceleration peak and rainfall changes.

4. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 3, characterized in that: The analysis of acceleration fluctuation characteristics and associating them with rainfall data to identify the impact of rainfall on rock mass instability includes: Extract key characteristic parameters from the triaxial acceleration component data to identify the movement trend of the rock mass in different directions, including fluctuation amplitude, fluctuation frequency, and acceleration change rate; The acceleration characteristic parameters are quantified as instability risk indicators and monitored in real time. When the acceleration fluctuation reaches a specific abnormal threshold, it is determined that the rock mass has potential signs of instability.

5. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 4, characterized in that: The Monte Carlo method is used to calculate the probability of rock mass instability under different environmental conditions and quantify the risk of rock mass instability, including: According to the calculation model of dangerous rock mass, the stability coefficient formula is established, the random variables are selected considering the main influence, and the random variables are subjected to standard normal processing, and finally substituted into the calculation of the instability probability; Based on the morphological characteristics of dangerous rock mass, the development of cracks, and the main controlling structural surface factors, a simplified calculation model of sliding collapse dangerous rock mass is established, and the expression is: T=Wsinβ-Qsinαcosβ α=N / Lτ=T / L N=Wcosβ-U-Qsinαcosβ Among them, U is the buoyancy of water, Q is the hydrostatic pressure, and c is the cohesion of the structural surface. is the internal friction angle of the structural surface, W is the number of dangerous rock masses; T is the tangential force at the bottom of the dangerous rock mass, N is the normal force, and K is the stability coefficient; For random variables c, Perform standard normal processing, the expression is: X=μ+σx' Among them, μ is a random number; Using MATLAB software, based on the multiplicative linear congruential method, the random variables c, Generate random numbers by calculating and outputting random numbers that conform to the normal distribution; In the calculation process of the stability of dangerous rock mass, c, At the same time, the random numbers that conform to the normal distribution are calculated to obtain c, Random values, and the results are relatively independent, in calculating the instability probability of collapse dangerous rock mass, the expression is: Among them, P is the instability probability of the collapsed dangerous rock mass.

6. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 5, characterized in that: The use of numerical simulation methods to predict the movement and accumulation range of rockfall and assess its threat to surrounding power facilities includes analyzing the distribution characteristics of rockfall, The instability probability of the collapsed rock mass is classified, and the calculation and classification of the arrival probability are carried out in combination with the accumulation characteristics of the collapsed rocks, so as to establish a collapse disaster risk assessment model; According to the instability probability P f The size of the collapse is classified into the level of susceptibility. f When ≥15%, it is highly prone to occur; when 15%≥P f >5%, it is moderately prone; when 5% ≥ P f When, it is low and prone to occur; ArcGIS is used to divide the substation into 0.5m grids and calculate the arrival probability. The expression is: p a =N i / N T Among them, N i is the number of collapsed rocks retained at a certain location, N T is the total number of collapsed rocks.

7. A collapse geological disaster vulnerability model and risk assessment method as claimed in claim 6, characterized in that: The use of numerical simulation methods to predict the movement and accumulation range of rockfall and assess the threat to surrounding power facilities also includes: According to the arrival probability, the collapse disaster intensity is divided into zones. a >30%, it is a high-intensity zone; when 30%≥P a >10%, it is the medium intensity zone; when 10%≥P a When , it is a low intensity area; Combining the instability probability classification and the arrival probability classification, the danger of the substation collapse dangerous rock mass is calculated, and the expression is: H=P f ×P a Among them, P a is the probability of rockfall arriving, P f is the probability of instability of dangerous rock mass, and H is the hazard of collapse of dangerous rock mass.

8. A system using a collapse geological hazard vulnerability model and risk assessment method as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition and preprocessing module, real-time monitoring and analysis module, instability probability calculation module, risk assessment and simulation module; The data acquisition and preprocessing module collects geological structural characteristics of rock mass, rock layer properties, topographic information, historical collapse data, long-term rainfall, real-time rainfall data, soil moisture and surface displacement data, and performs preprocessing operations such as formatting, denoising and filling missing data 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 the acceleration, and determines whether the rock mass has signs of instability in combination with the rainfall data; The instability probability calculation module uses the Monte Carlo method to simulate the stability of dangerous rock mass under different environmental conditions, and calculates the instability probability of rock mass under natural conditions and extreme rainstorm conditions; The risk assessment and simulation module predicts the movement path and accumulation range of collapsed rocks based on numerical simulation, and analyzes the potential threat to surrounding power facilities. At the same time, a risk level model is established by combining the instability probability and arrival probability to divide the area around the facility into different risk levels.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a collapse geological hazard vulnerability model and risk assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a collapse geological hazard vulnerability model and risk assessment method as described in any one of claims 1 to 7 are implemented.

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