Slope disaster prevention and control management method and system for hard mountainous area

Abstract

The invention relates to the technical field of slope disaster prevention and control, in particular to a slope disaster prevention and control management method and system for a hard mountainous area. The method comprises the following steps: acquiring a holographic feature map of a mountain slope; performing stress field analysis on the mountainous area slope based on the mountainous area slope holographic feature map to obtain a mountainous area slope stress field; performing freeze-thaw sensitive area identification on the mountainous area slope based on the mountainous area slope stress field to obtain a slope freeze-thaw risk distribution map; according to the slope freezing and thawing risk distribution map, microenvironment simulation regulation and control are conducted on the mountain slope, and a slope microenvironment regulation and control scheme is obtained; carrying out biological material adaptability analysis on the mountainous area slope to obtain a slope ecological matrix distribution diagram; and according to the slope ecological matrix distribution diagram, laying anchoring points on the mountainous area slope to obtain a mountainous area slope anchoring network. According to the invention, the prevention accuracy of freeze-thaw disasters is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of slope disaster prevention and control, and in particular to a slope disaster prevention and control management method and system for difficult mountainous areas. Background Art

[0002] Slope hazard prevention and control in difficult mountainous areas has always been a complex and challenging field. Due to the complex terrain of mountainous areas, the stability of slopes is affected by many factors, such as geological structure, climatic conditions and human activities. Traditional slope hazard prevention and control methods mainly rely on manual monitoring and simple engineering measures, such as the construction of retaining walls and drainage systems. However, these methods often have many shortcomings when facing the complex and changeable mountainous environment.

[0003] First, in high-altitude and cold areas, the freeze-thaw effect of slopes is an important cause of disasters. Due to the drastic changes in temperature, the moisture inside the slope rock mass will undergo freeze-thaw cycles, resulting in a significant decrease in the strength and stability of the rock mass. Traditional monitoring methods are difficult to accurately capture the slight changes in the freeze-thaw process in this environment, and thus cannot timely warn of potential landslide risks. For example, in some mine slopes, due to the small number of monitoring equipment and low accuracy, the workload and difficulty of manual monitoring are extremely high, making it difficult to achieve real-time and accurate monitoring of the slopes. Secondly, the ecological protection of slopes is also an urgent problem to be solved. In some mountainous areas with fragile ecological environments, traditional slope support methods often cause secondary damage to the ecological environment. For example, a large number of slope cutting and filling operations will destroy the original vegetation and soil structure, leading to soil erosion and ecological imbalance. In addition, some slope support materials are prone to aging and failure during long-term use, and cannot effectively resist erosion and damage from the natural environment. For example, in slopes in expansive soil areas, traditional rigid support structures such as gravity retaining walls and anti-slide piles are easily damaged due to volume changes in expansive soil. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a method and system for slope disaster prevention and control management in dangerous mountainous areas to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a slope disaster prevention and control management method for dangerous mountainous areas includes the following steps:

[0006] Step S1: obtaining a holographic characteristic map of a mountain slope; performing a stress field analysis on the mountain slope based on the holographic characteristic map of the mountain slope to obtain a stress field of the mountain slope;

[0007] Step S2: Based on the mountain slope stress field, the freeze-thaw sensitive area of ​​the mountain slope is identified to obtain the slope freeze-thaw risk distribution map; according to the slope freeze-thaw risk distribution map, the microenvironment simulation and regulation of the mountain slope is carried out to obtain the slope microenvironment regulation plan;

[0008] Step S3: Analyze the adaptability of biomaterials on the mountain slope to obtain a distribution map of the slope ecological matrix; arrange anchor points on the mountain slope according to the distribution map of the slope ecological matrix to obtain an anchor network for the mountain slope; simulate the support structure of the mountain slope based on the anchor network to obtain a configuration plan for the slope reinforcement system;

[0009] Step S4: performing stability assessment on the mountain slope according to the mountain slope stress field to obtain slope stability assessment data; performing time series analysis on the slope stability assessment data to obtain a slope instability pattern library; performing early warning on the mountain slope based on the slope instability pattern library to obtain slope risk early warning data;

[0010] Step S5: Adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0011] The present invention accurately identifies freeze-thaw sensitive areas, transforms prevention and control work from passive response to active prediction, locks high-risk areas in advance, effectively avoids the limitations of traditional monitoring methods, and greatly improves the accuracy of prevention of freeze-thaw disasters. According to the freeze-thaw risk distribution map, a microenvironment control plan is formulated, which can effectively improve the environment in which the rock mass is located, reduce the damage of the freeze-thaw cycle to the rock mass, reduce the risk of disasters such as landslides from the source, and make up for the shortcomings of traditional prevention and control measures in dealing with freeze-thaw disasters in high-altitude and cold areas. Through the analysis of biomaterial adaptability and the generation of ecological matrix distribution maps, the layout of anchor points and the simulation of support structures are guided, the damage to the ecological environment by traditional support methods is avoided, soil erosion is reduced, and the self-repair and balance of the slope ecosystem are promoted, and the coordinated progress of slope disaster prevention and control and ecological environment protection is achieved. Through time series analysis, the dynamic changes of the slope can be monitored in an all-round and high-precision manner, breaking through the limitations of the few traditional manual monitoring points and low precision. Through in-depth analysis of stability assessment data, a slope instability model library is established, and risk warning data is generated based on this, and a systematic warning system is constructed. This can identify the instability mode and risk level of the slope in advance, buy time for taking preventive measures, and make the prevention work more forward-looking and proactive. Dynamically adjust the micro-environment control plan according to the slope risk warning data, which can make the prevention measures flexibly adapt to the changes in slope risks, ensure that the most effective prevention measures can be taken at different risk stages, and avoid the problem that traditional prevention methods cannot cope with complex and changeable mountain environments due to fixed strategies.

[0012] Preferably, the present invention also provides a slope disaster prevention and control management system for dangerous mountainous areas, which is used to execute the above-mentioned slope disaster prevention and control management method for dangerous mountainous areas. The slope disaster prevention and control management system for dangerous mountainous areas includes:

[0013] Stress analysis module, used to analyze the stress field of mountain slopes and obtain the stress field of mountain slopes;

[0014] The freeze-thaw control module is used to identify freeze-thaw sensitive areas of mountain slopes based on the mountain slope stress field and obtain a slope freeze-thaw risk distribution map; perform microenvironment simulation and control on mountain slopes according to the slope freeze-thaw risk distribution map and obtain a slope microenvironment control plan;

[0015] The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain the distribution map of the ecological matrix of the slopes; to arrange anchor points on the mountain slopes according to the distribution map of the ecological matrix of the slopes to obtain the anchor network of the mountain slopes; to simulate the support structure of the mountain slopes based on the anchor network of the mountain slopes to obtain the configuration plan of the slope reinforcement system;

[0016] The early warning module is used to evaluate the stability of mountain slopes according to the mountain slope stress field to obtain slope stability evaluation data; perform time series analysis on the slope stability evaluation data to obtain a slope instability pattern library; and perform early warning on mountain slopes based on the slope instability pattern library to obtain slope risk early warning data;

[0017] The prevention and control decision-making module is used to adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0018] The present invention can accurately analyze the stress field of mountain slopes through the stress analysis module, ensure the scientificity and pertinence of the prevention and control measures, and enable the system to accurately grasp the stress condition and stability change trend of the slope. The freeze-thaw control module can adjust the microenvironment of the slope in a targeted manner, such as temperature control, so as to effectively improve the environment in which the rock mass is located, reduce the impact of the freeze-thaw cycle on the strength and stability of the rock mass, and reduce the risk of disasters such as landslides caused by freeze-thaw from the source. The ecological reinforcement module avoids the damage to the ecological environment by traditional support methods through a reinforcement method that emphasizes both ecology and safety, promotes the recovery and balance of the slope ecosystem, and realizes the coordinated development of slope disaster prevention and control and ecological environment protection. The early warning module can identify the potential risks of the slope in advance and buy time for taking prevention and control measures. The prevention and control decision module can flexibly adapt to the changes in slope risks, ensure that the most effective prevention and control measures can be taken at different risk stages, improve the flexibility and adaptability of slope disaster prevention and control, enhance the timeliness and pertinence of prevention and control work, and also help to respond quickly and accurately when disasters occur and take effective emergency measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0020] Figure 1 A schematic flow chart of the steps of a slope disaster prevention and control management method for dangerous mountainous areas according to an embodiment is shown.

[0021] Figure 2 A detailed flowchart diagram of step S35 of an embodiment is shown.

[0022] Figure 3 A detailed flowchart diagram of step S36 of an embodiment is shown. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for preventing and controlling slope disasters in dangerous mountainous areas, comprising the following steps:

[0027] Step S1: obtaining a holographic characteristic map of a mountain slope; performing a stress field analysis on the mountain slope based on the holographic characteristic map of the mountain slope to obtain a stress field of the mountain slope;

[0028] Step S2: Based on the mountain slope stress field, the freeze-thaw sensitive area of ​​the mountain slope is identified to obtain the slope freeze-thaw risk distribution map; according to the slope freeze-thaw risk distribution map, the microenvironment simulation and regulation of the mountain slope is carried out to obtain the slope microenvironment regulation plan;

[0029] Step S3: Analyze the adaptability of biomaterials on the mountain slope to obtain a distribution map of the slope ecological matrix; arrange anchor points on the mountain slope according to the distribution map of the slope ecological matrix to obtain an anchor network for the mountain slope; simulate the support structure of the mountain slope based on the anchor network to obtain a configuration plan for the slope reinforcement system;

[0030] Step S4: performing stability assessment on the mountain slope according to the mountain slope stress field to obtain slope stability assessment data; performing time series analysis on the slope stability assessment data to obtain a slope instability pattern library; performing early warning on the mountain slope based on the slope instability pattern library to obtain slope risk early warning data;

[0031] Step S5: Adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0032] In this embodiment, a 3D laser scanner (such as Riegl VZ-4000) and a hyperspectral imager (such as Headwall Hyperspec III) are used to obtain a holographic feature map of a mountain slope, including topography, material composition, and vibration modal data. A finite element analysis software (such as ANSYS) is used to perform stress field analysis based on the holographic feature map to identify the stress concentration area of ​​the slope. Combined with meteorological data and ground temperature monitoring (using Sensirion SHT75 sensor), the freeze-thaw sensitive area of ​​the slope is identified to generate a freeze-thaw risk distribution map. Based on this map, a microenvironment regulation scheme is simulated by GIS software (such as ArcGIS), such as setting a drainage system and a thermal insulation layer. At the same time, a biomaterial adaptability analysis is performed, suitable vegetation and soil improvement materials are selected, and an ecological matrix distribution map is generated. According to the ecological matrix distribution map, GIS is used to lay out anchor points to form an anchor network, and a slope reinforcement system configuration scheme is obtained by a support structure simulation software (such as GeoStudio). Furthermore, stability assessment is performed based on the results of stress field analysis, and a slope instability pattern library is obtained using time series analysis tools (such as the forecast package in R language). Based on this library, an early warning system is established, and when the monitoring data (using GNSS and total station) triggers an early warning, an alarm is issued in time. Finally, the microenvironment control plan is adjusted according to the risk warning data to form a prevention and control strategy, and the effect is evaluated through on-site monitoring data to generate a prevention and control effect evaluation report. Based on the report, an emergency plan is prepared using the emergency plan management system to form a slope disaster prevention and control decision-making plan.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: performing three-dimensional scanning on the mountain slope to obtain a mountain slope point cloud data set, and extracting terrain features from the mountain slope point cloud data set to obtain a slope terrain feature parameter set;

[0035] Specifically, a Riegl VZ-4000 3D laser scanner can be used for 3D scanning. Five scanning stations are set at different locations on the slope. The scanning range of each station covers an area of ​​about 100 meters by 100 meters. The resolution of the scanner is set to 0.05 meters. During the scanning process, the device automatically records the 3D coordinates (X, Y, Z) and reflection intensity information of each point. After the scanning is completed, the data of each station is imported into the point cloud processing software CloudCompare. The terrain feature extraction function of the software is used to extract the terrain feature parameters of the slope, such as slope, slope direction, and roughness, so as to obtain the slope terrain feature parameter set.

[0036] Step S12: performing vibration monitoring on the mountain slope to obtain vibration characteristic data of the mountain slope, and performing modal decomposition on the vibration characteristic data of the mountain slope to obtain vibration modal data of the mountain slope;

[0037] Specifically, five accelerometers of the Brüel & 4508B, these accelerometers can measure acceleration values ​​in three directions, the sampling frequency is set to 200Hz, the accelerometer is connected to the computer through a data acquisition device (NI USB-6259), and on the computer, a self-developed vibration monitoring software is used. The software is based on the LabVIEW platform and can display acceleration data in real time and perform modal decomposition analysis. The software uses the fast Fourier transform algorithm to convert the acceleration signal in the time domain into a frequency domain signal, thereby extracting the vibration modal data of the slope, including the frequency, amplitude and phase information of each mode.

[0038] Step S13: monitoring the internal stress of the mountain slope to obtain stress characteristic data of the mountain slope;

[0039] Specifically, a distributed optical fiber strain sensor model FOS-RTS2000 can be selected for internal stress monitoring. Five optical fibers are pre-buried inside the slope, each about 100 meters long, and arranged at different depths and directions along the slope. The buried depth of the optical fiber ranges from 0.5 meters to 10 meters below the surface. The monitoring system emits laser pulses and receives reflected signals, using optical time domain reflection technology to measure the strain changes along the optical fiber in real time. The sampling interval set by the system is 10 minutes, and the monitoring data is processed by data acquisition and analysis software. The software can automatically calibrate the data and extract the stress characteristic data of the mountain slope, such as the maximum strain value, strain gradient and strain change rate.

[0040] Step S14: monitoring the deformation of the mountain slope to obtain original displacement data of the mountain slope, and performing baseline correction on the original displacement data of the mountain slope to obtain standard displacement data of the mountain slope;

[0041] Specifically, eight GNSS monitoring points can be installed on the slope, each equipped with a Trimble R10 GNSS receiver. The receiver receives GPS and GLONASS satellite signals in real time through a satellite signal receiving antenna. The sampling frequency is set to 1Hz. The data of all monitoring points are transmitted to the central data processing center through a wireless network, and the original displacement data is baseline corrected using Trimble BusinessCenter software. During the baseline correction process, the base station data is selected as a reference, and the satellite clock error, ionospheric delay, and tropospheric delay are eliminated through a differential positioning algorithm to obtain standard displacement data. These standard displacement data include the displacement of each monitoring point in the east, north, and sky directions, and the data accuracy reaches the millimeter level.

[0042] Step S15: performing hyperspectral imaging scanning on the mountain slope to obtain a material composition map of the mountain slope, and performing data fusion on the slope terrain characteristic parameter set, the mountain slope vibration modal data and the mountain slope material composition map to obtain a holographic characteristic map of the mountain slope;

[0043] Specifically, a Headwall Hyperspec III hyperspectral imager can be used to set up three scanning stations at different heights of the slope. The scanning range of each station covers an area of ​​about 50 meters by 50 meters. During the scanning process, the device automatically records the spectral information of each pixel to generate a hyperspectral data cube. After the scanning is completed, the hyperspectral data is imported into the ENVI software for processing. In the software, the spectral unmixing technology is used to extract the spectral characteristics of different materials on the slope surface, such as rocks, soil, vegetation and water bodies, and a material composition map is generated. At the same time, the slope terrain feature parameter set and vibration modal data are imported into the same software platform, and the three are spatially aligned and data fused through geographic information system technology. The fused data set contains multi-dimensional information on terrain, vibration and material composition, generating a holographic feature map of the mountain slope.

[0044] Step S16: quantifying the correlation between the standard displacement data of the mountain slope and the stress characteristic data of the mountain slope to obtain a stress-displacement relationship diagram of the mountain slope;

[0045] Specifically, the standard displacement data of mountain slopes and the stress characteristic data of mountain slopes can be imported into MATLAB software. In MATLAB, the Pearson correlation coefficient method is used to perform correlation analysis on the two sets of data. The specific steps include: pairing the displacement data of each monitoring point with the stress data of the corresponding position, calculating the Pearson correlation coefficient between each pair of data, and obtaining a correlation matrix. The value range of the correlation coefficient is between -1 and 1. The closer the value is to 1 or -1, the stronger the linear relationship between stress and displacement. The linear regression model is used to fit the data pairs with significant correlation to obtain the stress-displacement relationship equation. Through these equations, the degree of influence of stress changes on displacement can be quantitatively described, and the stress-displacement relationship diagram of mountain slopes can be generated. These relationship diagrams intuitively show the displacement response of the slope under different stress conditions.

[0046] Step S17: Based on the mountain slope holographic characteristic map and the mountain slope stress-displacement relationship map, the mountain slope stress field modeling is performed to obtain the mountain slope stress field.

[0047] Specifically, the holographic characteristic map of the mountain slope and the stress-displacement relationship diagram of the mountain slope can be imported into the ANSYS finite element analysis software. In the software, the geometric model of the slope is constructed according to the terrain, material composition and vibration modal data in the holographic characteristic map, and the mechanical parameters of the slope material, such as elastic modulus, Poisson's ratio and friction coefficient, are defined according to the quantitative relationship in the stress-displacement relationship diagram. These parameters are obtained through field sampling and laboratory testing. The slope model is divided into multiple small units using the meshing function of ANSYS, and the size of each unit is about 1 meter × 1 meter × 1 meter. In the boundary condition setting of the model, corresponding constraints and loads, such as ground stress, water pressure and external loads, are applied according to the actual geological conditions and monitoring data. Finally, the finite element analysis is run to calculate the stress distribution inside the slope and generate the stress field map of the mountain slope.

[0048] The present invention obtains rich data of mountain slopes from different angles through multiple means, including terrain features, vibration modes, stress characteristics, displacement data and material composition, which ensures a comprehensive grasp of the slope conditions and avoids the data one-sidedness and limitations of a single monitoring method. Through data fusion, it is possible to integrate information from all aspects and eliminate the noise and error existing in a single data source. Through correlation quantitative analysis, the inherent connection between slope stress and displacement is clearly revealed, making the stress field analysis more scientific and reasonable, and being able to more accurately reflect the actual stress state and deformation of the slope. Through stress field modeling, the stress distribution inside the slope can be accurately reflected.

[0049] Preferably, step S2 comprises the following steps:

[0050] Step S21: perform critical stress assessment on the mountain slope stress field to obtain a mountain slope stress sensitive area map;

[0051] Specifically, the critical stress of the slope stress field model established in step S17 can be evaluated by using ANSYS finite element analysis software. In the model, the yield strength and failure criteria of the slope material, such as the Mohr-Coulomb criterion, are defined. By simulating the stress distribution under different working conditions, the stress level of each part of the slope is calculated, and the calculated stress value is compared with the yield strength of the material to identify the area where the stress exceeds the yield strength. These areas are considered to be stress sensitive areas. Five representative locations can also be selected on site, and strain gauges of the Vishay EA-03-062AA-350 model are installed to monitor the stress changes at these locations in real time. The strain gauge is connected to the computer through a data acquisition device (NI USB-6259), the sampling frequency is set to 1Hz, and the monitoring data is analyzed in real time by dedicated software. Through the comparative analysis of numerical simulation and field monitoring data, a stress sensitive area map of the mountain slope is finally generated.

[0052] Step S22: monitoring the surface temperature of the mountain slope to obtain the slope surface temperature monitoring data, and performing deep temperature detection on the mountain slope to obtain the mountain slope ground temperature distribution data;

[0053] Specifically, the FLIR T640 infrared thermal imager can be used to monitor the temperature of the slope surface. Ten monitoring points are set at different heights and directions of the slope. The scanning range of each monitoring point covers an area of ​​about 20 meters × 20 meters. The sampling frequency of the thermal imager is set to 1 time / hour. At the same time, temperature sensors of the Sensirion SHT75 model are used. These sensors are installed at different depths inside the slope, ranging from 0.5 meters to 10 meters below the surface. A total of 15 monitoring points are set. The temperature sensor is connected to the computer through a data acquisition device, and the sampling frequency is set to 1 time / hour. The monitoring data is recorded and analyzed in real time through special software. The surface temperature and ground temperature data obtained by infrared thermal imaging technology and deep temperature detection technology are processed by data fusion and interpolation to generate a ground temperature distribution map of the mountain slope.

[0054] Step S23: reconstructing the temperature field of the mountain slope based on the slope surface temperature monitoring data and the ground temperature distribution data of the mountain slope to obtain a temperature gradient map of the mountain slope;

[0055] Specifically, the slope surface temperature monitoring data and the ground temperature distribution data of the mountain slope can be imported into ArcGIS software. In the software, the Kriging interpolation method is used to perform spatial interpolation on these discrete temperature data to generate continuous temperature field data on the surface and inside of the slope. Kriging interpolation is a geostatistical method that can estimate the temperature value of unknown points based on the temperature data of known points and take into account the spatial autocorrelation of the data. During the interpolation process, the search radius is set to 30 meters to ensure that each interpolation point can refer to enough known data points. Then, the raster calculator function of ArcGIS is used to calculate the temperature gradient of the slope surface and internal temperature field. The temperature gradient is obtained by calculating the temperature difference between adjacent grid points divided by the distance, and the unit is ℃ / meter. The temperature gradient map of the mountain slope generated in the end clearly shows the temperature change trend and gradient distribution inside and on the surface of the slope.

[0056] Step S24: performing time series decomposition on the slope surface temperature monitoring data to obtain slope surface temperature change characteristic data;

[0057] Specifically, the slope surface temperature monitoring data can be imported into MATLAB software. In MATLAB, the wavelet transform method is used to perform time series decomposition on the time series data. The specific steps include: selecting Morlet wavelet as the mother wavelet, performing continuous wavelet transform on the slope surface temperature time series, and decomposing the temperature change characteristics on different time scales. By analyzing the wavelet coefficients, the main cycles and trends of temperature changes can be identified. For example, daily change cycles, seasonal change cycles, and long-term trend changes can be identified. Then, the temperature change amplitude and change rate in each cycle are calculated to obtain the slope surface temperature change characteristic data. These characteristic data include daily temperature difference, seasonal temperature difference, and annual temperature difference.

[0058] Step S25: performing heat conduction simulation on the mountain slope based on the ground temperature distribution data of the mountain slope to obtain a thermal conductivity coefficient map of the mountain slope;

[0059] Specifically, the geothermal distribution data of mountain slopes can be imported into COMSOL Multiphysics software. In the software, a heat conduction model is established based on the geometric model of the slope and the geothermal distribution data. The model defines the initial temperature field and boundary conditions of the slope material, such as surface temperature, groundwater flow and atmospheric temperature, and selects a suitable heat conduction equation, such as the Fourier heat conduction equation, to describe the heat conduction process inside the slope. The thermophysical parameters of the slope material, such as specific heat capacity, thermal conductivity and density, are obtained through field sampling and laboratory testing. These parameters are input into the model as material properties. Then, a numerical simulation is run to calculate the temperature distribution and heat flux density of the slope at different time points. By analyzing the simulation results, a thermal conductivity distribution map inside the slope can be obtained. The thermal conductivity map clearly shows the thermal conductivity capacity of different areas of the slope.

[0060] Step S26: performing freeze-thaw cycle simulation on the mountain slope according to the mountain slope temperature gradient map and the slope surface temperature change characteristic data to obtain mountain slope freeze-thaw prediction data;

[0061] Specifically, the temperature gradient map of the mountain slope and the characteristic data of the temperature change on the slope surface can be imported into the MATLAB software. In MATLAB, the finite difference method is used to numerically simulate the freeze-thaw cycle process. The specific steps include: defining the initial conditions and boundary conditions of the freeze-thaw cycle, such as the initial temperature, freezing temperature and melting temperature, according to the temperature gradient map and the characteristic data of temperature change. Select a suitable freeze-thaw model, such as a thermodynamic model based on phase change, to describe the physical changes of the slope material during freezing and melting. The model considers factors such as the latent heat of phase change, density change and mechanical property change of the material. Then, through numerical iterative calculation, the freeze-thaw state of the slope at different time points is simulated, including the freezing depth, melting depth and number of freeze-thaw cycles. During the simulation process, the time step is set to 1 hour, and finally the freeze-thaw prediction data of the mountain slope is generated, including the freeze-thaw depth map, the freeze-thaw cycle number map and the freeze / thaw rate map.

[0062] Step S27: Based on the thermal conductivity map of the mountain slope and the freeze-thaw prediction data of the mountain slope, a risk coupling assessment is performed on the mountain slope to obtain a slope freeze-thaw risk distribution map, and microenvironment simulation and regulation is performed on the mountain slope according to the slope freeze-thaw risk distribution map to obtain a slope microenvironment regulation plan.

[0063] Specifically, please refer to the sub-steps of step S27 for the detailed implementation process of this embodiment.

[0064] The present invention can accurately identify stress-sensitive areas of the slope through critical stress assessment. By combining surface temperature monitoring and deep temperature detection, not only the temperature changes on the slope surface are obtained, but also the distribution characteristics of the ground temperature are deeply understood. Through temperature field reconstruction, the temperature change trend and gradient distribution inside the slope are clearly displayed, which helps to deeply understand the heat transfer path and rate during the freeze-thaw process. Through freeze-thaw cycle simulation, the freeze-thaw behavior and change trend of the slope under different conditions can be predicted in advance. Through risk coupling assessment, high-risk areas can be accurately located, and the risk level of each area can be quantified. Through microenvironment simulation and regulation, environmental factors such as temperature and humidity of the slope can be adjusted in a targeted manner to create microenvironmental conditions that are conducive to slope stability.

[0065] Preferably, step S27 includes the following steps:

[0066] Step S271: Divide the thermal conductivity map of the mountain slope into regions to obtain a thermal zoning map of the mountain slope;

[0067] Specifically, the thermal conductivity map of the mountain slope can be imported into the MATLAB software. In MATLAB, the K-means clustering algorithm is used to divide the thermal conductivity map into regions. In this example, the slope is divided into three thermal partitions, namely, the high thermal conductivity zone, the medium thermal conductivity zone, and the low thermal conductivity zone. The specific steps include: randomly selecting three initial cluster centers, calculating the distance between each pixel and the cluster center, assigning the pixel to the cluster to which the nearest cluster center belongs, and then updating the cluster center to the mean of all pixels in the cluster, and repeating the above process until the cluster center no longer changes or the preset number of iterations is reached. Finally, a thermal partition map of the mountain slope is generated, and different colors in the map represent different thermal partitions.

[0068] Step S272: Based on the freeze-thaw prediction data of the mountain slopes, risk level zoning assessment is performed on the mountain slopes to obtain an initial risk distribution map of the mountain slopes;

[0069] Specifically, the freeze-thaw prediction data of mountain slopes can be imported into ArcGIS software. In ArcGIS, based on the freeze-thaw prediction data, the risk assessment indicator system is defined, including freeze-thaw depth, freeze-thaw cycle number and freeze / thaw rate. Each indicator is assigned different weights according to its influence on slope stability. For example, the weight of freeze-thaw depth is 0.4, the weight of freeze-thaw cycle number is 0.3, and the weight of freeze / thaw rate is 0.3. Then, the weighted superposition method is used to comprehensively evaluate each indicator and calculate the risk value of each pixel. The risk value ranges from 0 to 1, and the closer the value is to 1, the higher the risk. According to the size of the risk value, the slope is divided into four risk levels, namely low risk area (risk value <0.25), medium risk area (0.25≤risk value <0.5), high risk area (0.5≤risk value <0.75) and extremely high risk area (risk value ≥0.75). Finally, the initial risk distribution map of mountain slopes is generated, and different colors in the map represent different risk levels.

[0070] Step S273: modifying the initial risk distribution map of the mountain slope according to the thermal zoning map of the mountain slope to obtain a freeze-thaw risk distribution map of the slope;

[0071] Specifically, the thermal zoning map and the initial risk distribution map of the mountain slope can be imported into ArcGIS software. In ArcGIS, the thermal zoning map is superimposed on the initial risk distribution map using the spatial overlay analysis function. The specific steps include: recalculating the risk value within each thermal zone, considering the impact of the thermal conductivity of the thermal zone on the freeze-thaw risk. For example, in the high thermal conductivity zone, the freeze-thaw cycle is more intense due to the faster heat transfer, so the risk value is adjusted upward; in the low thermal conductivity zone, the freeze-thaw cycle is milder due to the slower heat transfer. The adjustment range is determined according to the relative size of the thermal conductivity. For example, the risk value of the high thermal conductivity zone is increased by 10%, the risk value of the medium thermal conductivity zone remains unchanged, and the risk value of the low thermal conductivity zone is reduced by 10%. Finally, a revised slope freeze-thaw risk distribution map is generated, and different colors in the map represent different risk levels.

[0072] Step S274: iterating the temperature control strategy for the mountain slope based on the mountain slope temperature gradient map to obtain an initial slope temperature control plan;

[0073] Specifically, the temperature gradient map of the mountain slope can be imported into MATLAB software. In MATLAB, the temperature field of the slope is simulated using the finite element method to establish a temperature field model. The geometric shape, material properties and boundary conditions of the slope, such as surface temperature, groundwater flow and atmospheric temperature, are defined in the model. Next, the target area and target temperature for temperature control are determined based on the temperature gradient map. For example, for areas with large temperature gradients, the goal is to reduce the temperature gradient and reduce the frequency and intensity of freeze-thaw cycles. Select appropriate temperature control measures, such as surface cover, ventilation system or heating system. In this example, surface cover and ventilation system are selected as the main temperature control measures. Genetic algorithms are used to optimize the parameters of temperature control measures, such as the thickness of surface cover and the air volume of the ventilation system. Genetic algorithms search for the optimal parameter combination by simulating natural selection and genetic variation processes. The specific steps include: defining a fitness function, such as minimizing freeze-thaw risk or minimizing energy consumption; initializing a population, where each individual represents a set of parameter combinations; performing selection, crossover and mutation operations to generate a new population; evaluating the fitness of the new population, and selecting the individual with the highest fitness as the optimal solution. Finally, an initial plan for slope temperature control is generated, including a thickness distribution map of the surface cover and an air volume distribution map of the ventilation system.

[0074] Step S275: performing energy consumption evaluation on the initial slope temperature control scheme to obtain energy consumption evaluation data of the temperature control scheme;

[0075] Specifically, the initial temperature control scheme of the slope (including the thickness distribution map of the surface cover and the air volume distribution map of the ventilation system) can be imported into the EnergyPlus energy consumption simulation software. In the software, the energy consumption model of the slope is established according to the geometric shape, material properties and local meteorological data of the slope. The energy consumption parameters of the temperature control measures are defined in the model, such as the thermal resistance of the surface cover material and the power of the ventilation system. Then, the energy consumption simulation is run to calculate the energy consumption of different temperature control measures within one year. The simulation results include total energy consumption, peak energy consumption and time-sharing energy consumption data. Three representative locations can also be selected on site to install an energy monitor to monitor the actual energy consumption of the ventilation system in real time. The energy monitor is connected to the computer through a data acquisition device (NI USB-6259), the sampling frequency is set to 1 time / hour, and the monitoring data is recorded and analyzed in real time through dedicated software. Through the comparative analysis of energy consumption simulation and field test data, the energy consumption evaluation data of the temperature control scheme is finally obtained.

[0076] Step S276: fine-tune the initial slope temperature control scheme according to the energy consumption evaluation data of the temperature control scheme to obtain a set of mountain slope temperature control parameters;

[0077] Specifically, the energy consumption evaluation data of the temperature control scheme can be imported into the MATLAB software. In MATLAB, the particle swarm optimization algorithm is used to fine-tune the parameters of the temperature control measures. The specific steps include: defining the optimization goal, such as minimizing the total energy consumption or balancing the energy consumption and temperature control effect; initializing the particle swarm, each particle represents a set of parameter combinations; calculating the fitness of each particle, such as the total energy consumption in the energy consumption evaluation data; updating the position and velocity of the particles, searching for the optimal parameter combination; evaluating the fitness of the new particle swarm, and selecting the particle with the highest fitness as the optimal solution. During the optimization process, the parameters of the optimization algorithm, such as the learning factor and the inertia weight, are continuously adjusted according to the field verification results. Finally, a set of temperature control parameters for mountain slopes is generated, including an optimized surface cover thickness distribution map and a ventilation system air volume distribution map.

[0078] Step S277: Based on the mountain slope temperature control parameter set and the slope freeze-thaw risk distribution map, the microenvironment response simulation control of the mountain slope is performed to obtain the slope microenvironment control plan.

[0079] Specifically, the temperature control parameter set of the mountain slope and the slope freeze-thaw risk distribution map can be imported into COMSOL Multiphysics software. In COMSOL, a coupled thermal-hydraulic-mechanical multiphysics model is established, which can simultaneously simulate the interaction of temperature field, moisture field and stress field. The geometric shape, material properties, boundary conditions and parameters of temperature control measures of the slope are defined in the model. Then, the numerical simulation is run to calculate the response of the slope microenvironment under different temperature control measures, including temperature change, moisture migration and stress distribution. During the simulation process, the time step is set to 1 day. Three representative locations are selected to install temperature, humidity and stress sensors to monitor the actual response of the slope microenvironment in real time. The sensor is connected to the computer through a data acquisition device (NI USB-6259), the sampling frequency is set to 1 time / hour, and the monitoring data is recorded and analyzed in real time through dedicated software. By comparing the simulation results with the field monitoring data, the temperature control parameters are further adjusted and optimized. Finally, a mountain slope microenvironment control plan is generated, including the specific implementation steps, time and expected effects of the temperature control measures.

[0080] The present invention can understand the thermal conductivity characteristics of different areas of the slope in more detail through regional division. This helps to more accurately consider the physical differences of each area when conducting risk level zoning assessment, so that the initial risk distribution map is closer to reality. The initial risk distribution map is corrected according to the thermal zoning map, and the accuracy of the slope freeze-thaw risk distribution map is further improved. This can ensure that prevention and control resources are reasonably allocated to the areas with the highest risk, improve the pertinence and effectiveness of prevention and control work, and avoid waste of resources and excessive prevention and control. Through the iteration of temperature control strategy, the temperature control scheme can be continuously optimized to make it more in line with the actual thermodynamic characteristics of the slope. This ensures the effectiveness and scientificity of temperature control measures, can more accurately achieve the expected temperature control target, and reduce the slope stability problems caused by temperature changes. Through energy consumption evaluation, the energy consumption of different control measures can be understood in advance. By fine-tuning the initial temperature control plan, the actual temperature control operation can be guided more accurately. Through micro-environment response simulation control, the actual impact of control measures on the slope microenvironment can be predicted in advance, and the effectiveness and adaptability of the scheme can be evaluated.

[0081] Preferably, step S3 comprises the following steps:

[0082] Step S31: obtaining a material composition map of the mountain slope, and characterizing the physical and chemical properties of the soil of the mountain slope based on the material composition map of the mountain slope to obtain soil characteristic data of the mountain slope;

[0083] Specifically, the Headwall Hyperspec III hyperspectral imager can be used to scan the slope and obtain spectral data of the slope surface. The device is capable of high-resolution imaging in the 400-1000nm band, with 270 bands and a spectral resolution of up to 5nm. Five scanning stations are set up at different locations on the slope, and the scanning range of each station covers an area of ​​about 50 meters × 50 meters. After the scanning is completed, the hyperspectral data is imported into the ENVI software for processing, and the spectral characteristics of different substances on the slope surface are extracted by spectral unmixing technology to generate a material composition map. Then, 10 representative points on the slope are selected for on-site soil sampling. Soil samples at a depth of 0-30cm are collected using a soil sampler, and 3 replicates are collected for each sample, for a total of 30 samples. The samples are taken back to the laboratory for soil physical and chemical property analysis. The analysis items include soil texture (sand, silt, clay content), pH value, organic matter content, total nitrogen, total phosphorus and total potassium. The organic matter content was determined using a muffle furnace, the total nitrogen content was determined using the Kjeldahl method, the total phosphorus content was determined using the vanadium-phosphorus-molybdenum yellow colorimetric method, and the total potassium content was determined using the flame photometry method. The analysis results were organized into a soil property data table.

[0084] Step S32: Conducting a biodiversity survey on the mountain slope to obtain biodiversity characteristic data of the slope, and conducting a bioactivity assessment on the mountain slope based on the biodiversity characteristic data of the slope to obtain a bioactivity map of the mountain slope;

[0085] Specifically, 15 representative areas on the slope can be selected, and a 1-square-meter sample plot can be set in each area. In each sample plot, the species, quantity and coverage of the plants are recorded. At the same time, soil samples are collected, and 3 replicates are collected in each sample plot, for a total of 45 soil samples. The soil samples are taken back to the laboratory for biological activity testing. The test items include soil enzyme activity (such as urease, phosphatase, catalase), microbial biomass carbon and microbial community structure. Soil enzyme activity is determined by colorimetry, microbial biomass carbon is determined by chloroform fumigation-extraction, and microbial community structure is determined by phospholipid fatty acid (PLFA) analysis. The analysis results are organized into a biological community characteristic data table and a biological activity map. The biological community characteristic data table includes plant species, quantity, coverage and soil biological activity indicators. The biological activity map is drawn using GIS software to show the biological activity levels in different areas of the slope. Different colors in the figure represent different levels of biological activity.

[0086] Step S33: monitoring the nutrients of the mountain slopes to obtain a nutrient distribution map of the mountain slopes;

[0087] Specifically, 20 representative points on the slope can be selected for soil nutrient monitoring. A portable soil nutrient rapid tester (such as Hanna Instruments HI98199) is used to measure the pH, nitrogen (N), phosphorus (P), and potassium (K) content of the soil on site. The device can provide accurate measurement results within a few minutes and is suitable for rapid on-site monitoring. Three replicate samples are collected at each monitoring point, and a total of 60 samples are collected. The measurement results are recorded in a data table, including the geographical location, pH, nitrogen, phosphorus, and potassium content of each sample. Then, the measurement results are imported into ArcGIS software. In ArcGIS, the Kriging interpolation method is used to spatially interpolate the soil nutrient data to generate a continuous nutrient distribution map. Kriging interpolation is a geostatistical method that can estimate the nutrient value of an unknown point based on the nutrient data of a known point and take into account the spatial autocorrelation of the data. During the interpolation process, the search radius is set to 50 meters to ensure that each interpolation point can refer to enough known data points. The final generated mountain slope nutrient distribution map clearly shows the nutrient levels in different areas of the slope.

[0088] Step S34: screening ecological plants on the mountain slopes according to the soil characteristic data of the mountain slopes to obtain slope plant adaptability data;

[0089] Specifically, the main physical and chemical properties of slope soil, including soil texture, pH value, organic matter content, total nitrogen, total phosphorus and total potassium, can be determined based on the soil characteristic data of mountain slopes. These data are organized into a detailed soil characteristic data table. Next, a series of plant species suitable for the local environment are selected with reference to the local flora and existing successful cases of ecological restoration. These plants include herbaceous plants (such as ryegrass and alfalfa), shrubs (such as sea buckthorn and caragana) and trees (such as poplar and pine). For each plant, the soil condition data required for its growth are collected, including the appropriate pH range, soil texture, and nutrient requirements. For example, the suitable pH range for ryegrass is 6.0-7.5, the soil texture is loam, and the total nitrogen requirement is high; the suitable pH range for sea buckthorn is 7.0-8.5, the soil texture is sandy loam, and it is resistant to barrenness. The suitable growth conditions of each plant are compared and analyzed with the slope soil characteristic data to screen out plant species that adapt to local soil conditions. For example, for areas with a pH value of 7.2, sandy loam soil texture, and medium total nitrogen content, sea buckthorn and ryegrass were selected for planting. The screening results were organized into a slope plant adaptability data table, including the name of each plant, suitable growth conditions, and recommended planting areas.

[0090] Step S35: performing biomaterial adaptability analysis on the mountain slope based on the mountain slope biological activity map, the mountain slope nutrient distribution map and the slope plant adaptability data to obtain the slope ecological matrix distribution map;

[0091] Specifically, please refer to the sub-steps of step S35 for the detailed implementation process of this embodiment.

[0092] Step S36: Arrange anchor points on the mountain slope according to the slope ecological matrix distribution map to obtain the mountain slope anchor network, and simulate the support structure of the mountain slope based on the mountain slope anchor network to obtain the slope reinforcement system configuration plan.

[0093] Specifically, please refer to the sub-steps of step S36 for the detailed implementation process of this embodiment.

[0094] The present invention characterizes the physical and chemical properties of soil, which helps to accurately select plants and biological materials suitable for local soil conditions. Through the biodiversity survey, the biodiversity status and ecological activity of the slope can be fully understood, which helps to fully consider biological factors when designing ecological protection, protect and utilize the original biocommunity, promote the balance and stability of the slope ecosystem, reduce the damage to the ecological environment caused by engineering construction, and achieve the synergistic effect of slope protection and ecological restoration. The nutrient status of different areas of the slope can be clearly displayed through nutrient monitoring. Through ecological plant screening, it is ensured that the selected plants can adapt to local soil and environmental conditions, which helps to build a stable and efficient ecological protection system so that plants can grow well on the slope. Through biomaterial adaptability analysis, the applicability and ecological compatibility of different biomaterials on the slope can be accurately evaluated, which helps to select the most suitable biomaterial for ecological matrix configuration, ensure that the ecological matrix can provide a good environment for plant growth, and integrate with the soil and ecosystem of the slope. By showing the spatial distribution of the ecological matrix on the slope, a basis is provided for the layout of anchor points, so that the anchor points can be reasonably distributed in the key areas of the ecological matrix to form an effective anchor network. Through support structure simulation, the synergistic effect of ecological matrix and engineering support can be fully utilized.

[0095] Preferably, step S35 includes the following steps:

[0096] Step S351: performing functional bacterial community identification on the biological activity map of the mountain slope to obtain a microbial functional map of the mountain slope;

[0097] Specifically, 10 representative areas can be selected from the bioactivity map of the slope to collect soil samples, with 3 replicates collected in each area, for a total of 30 samples. Soil samples at a depth of 0-10 cm were collected using a soil sampler, each sample was about 100 grams, and the soil samples were brought back to the laboratory for DNA extraction and 16S rRNA gene sequencing. Soil DNA was extracted using the MoBioPowerSoil DNA Isolation Kit, and then 16S rRNA gene sequencing was performed using the Illumina MiSeq platform. The sequencing results were analyzed using the QIIME2 software to identify the bacterial and archaeal community structure in the soil. The metabolic functions of functional flora, such as nitrogen cycle, carbon cycle, and phosphorus cycle, were predicted using functional prediction tools (such as PICRUSt2). The analysis results were organized into a functional flora data table, including the types and relative abundance of functional flora of each sample. These data were imported into ArcGIS software, and the functional flora data were spatially interpolated using the Kriging interpolation method to generate a microbial functional map of mountain slopes. During the interpolation process, the search radius was set to 30 meters. The final microbial functional map clearly shows the distribution and metabolic functions of functional bacteria in different areas of the slope.

[0098] Step S352: performing matrix formula proportioning on the mountain slope according to the mountain slope nutrient distribution map to obtain a mountain slope matrix proportioning scheme;

[0099] Specifically, the nutrient distribution map of mountain slopes can be imported into GIS software to analyze the nutrient levels of different areas of the slopes, including nitrogen (N), phosphorus (P), potassium (K) content and organic matter content. According to the nutrient level, the slopes are divided into high nutrient zones, medium nutrient zones and low nutrient zones. With reference to the local flora and existing successful cases of ecological restoration, a series of matrix materials suitable for the local environment are selected, including humus, peat, river sand and organic fertilizer. For each nutrient zone, a matrix formula is formulated according to the nutrient requirements of the plants and the soil improvement goals. For example, for the low nutrient zone, the matrix formula is 50% humus, 30% peat, 15% river sand and 5% organic fertilizer; for the medium nutrient zone, the matrix formula is 40% humus, 30% peat, 20% river sand and 10% organic fertilizer; for the high nutrient zone, the matrix formula is 30% humus, 40% peat, 20% river sand and 10% organic fertilizer. The substrate formula for each nutrient zone is organized into a substrate ratio table, including the type and proportion of substrate materials for each area.

[0100] Step S353: Performing ecological synergy evaluation based on the slope plant adaptability data and the mountain slope microbial function map to obtain the mountain slope ecological adaptability map;

[0101] Specifically, the slope plant adaptability data and the mountain slope microbial function map can be imported into the GIS software. In GIS, the spatial overlay analysis function is used to overlay the plant adaptability data with the microbial function map. The specific steps include: matching and analyzing the suitable growth conditions of each plant species and the microbial function of the corresponding area. For example, for ryegrass, its suitable pH range is 6.0-7.5, the total nitrogen demand is high, and the support of nitrogen cycle functional bacteria is required. In the microbial function map, the areas with rich nitrogen cycle functional bacteria are identified, and these areas are overlaid with the suitable growth areas of ryegrass to evaluate the degree of synergy between the two. Then, the overlay results are quantitatively evaluated using ecological models (such as ecosystem service value assessment models). The model defines synergistic indicators of plant growth and microbial function, such as plant growth index and microbial function index. By calculating the synergy index of each area, an ecological adaptability map is generated. The synergy index ranges from 0 to 1, and the closer the value is to 1, the higher the degree of synergy between plants and microorganisms. The final generated mountain slope ecological adaptability map clearly shows the ecological adaptability level of different areas of the slope.

[0102] Step S354: Performing a matrix stability test on the matrix ratio scheme of the mountain slope to obtain the matrix stability data of the slope;

[0103] Specifically, according to the matrix ratio scheme of mountain slope, matrix samples with different ratios can be prepared. Three replicate samples are prepared for each ratio, and 15 samples are prepared in total. The matrix samples are placed in standard soil culture dishes, and each culture dish is filled with 100 grams of matrix. Then, the physical and mechanical properties of the matrix samples are tested using a soil physical and mechanical tester (such as a ring knife tester). The test items include the bulk density, porosity, permeability and shear strength of the matrix. The specific steps include: using the ring knife method to determine the bulk density of the matrix, determining the porosity by the saturation method, using a permeameter to determine the permeability, and using a direct shear instrument to determine the shear strength. The test results are recorded in a data table, including the bulk density, porosity, permeability and shear strength values ​​of each sample. The test results are organized into a matrix stability data table, including the physical and mechanical performance indicators of the matrix samples of each ratio.

[0104] Step S355: Based on the ecological adaptability map of the mountain slope and the slope matrix stability data, the ecological matrix space of the mountain slope is partitioned to obtain the slope ecological matrix distribution map.

[0105] Specifically, the ecological adaptability map of mountain slopes and the slope matrix stability data can be imported into the GIS software. In GIS, the spatial overlay analysis function is used to overlay the ecological adaptability map with the matrix stability data. The specific steps include: comprehensively evaluating the ecological adaptability index and matrix stability index of each area. For example, the comprehensive index is defined as the weighted sum of the ecological adaptability index and the matrix stability index, with weights of 0.6 and 0.4, respectively. By calculating the comprehensive index of each area, the slope is divided into four ecological matrix partitions, namely, a high adaptability and high stability zone, a high adaptability and low stability zone, a low adaptability and high stability zone, and a low adaptability and low stability zone. Then, the raster calculator function of GIS is used to generate a slope ecological matrix distribution map. Different colors in the figure represent different ecological matrix partitions.

[0106] The present invention can reveal the distribution and activity of different functional flora in slope soil through functional flora identification, which helps to deeply understand the role of soil microorganisms in slope ecosystems, such as decomposing organic matter, fixing nitrogen, and dissolving phosphorus, thereby providing a scientific basis for ecological restoration and soil improvement. Through ecological synergistic evaluation, the interaction and synergistic effect between plants and microorganisms can be fully reflected, which helps to select plant species that match local microbial communities when configuring plants, optimize plant community structure, and improve plant growth quality and ecological protection effects. Through the matrix formula ratio, the nutrients lacking in slope soil can be accurately supplemented, the physical and chemical properties of the soil can be improved, and the fertility and water and fertilizer retention capacity of the soil can be improved, providing a better soil environment for plant growth, promoting the healthy growth of plants, and enhancing the ecological protection function of the slope. Through the matrix stability test, the stability and durability of different matrix formulas in the slope environment can be evaluated, which helps to select a matrix formula with good stability, ensure that the ecological matrix can maintain good performance on the slope for a long time, and avoid the decline of ecological protection effects caused by matrix loss or degradation. Through the spatial zoning of ecological matrix, the types and configuration requirements of ecological matrix in different areas can be clearly displayed, providing precise spatial guidance for the ecological protection measures of the slope. Through the spatial zoning of ecological matrix, the slope can be divided into multiple areas with different ecological functions and protection requirements, forming an organic ecological protection system.

[0107] Preferably, step S36 includes the following steps:

[0108] Step S361: performing a stress assessment on the slope ecological matrix distribution map according to the mountain slope stress field to obtain a mountain slope stress concentration area map;

[0109] Specifically, the stress field data of the mountain slope and the distribution map of the slope ecological matrix can be imported into the ANSYS finite element analysis software. In ANSYS, the mechanical model of the slope is established according to the geometric model and stress field data of the slope. The mechanical parameters of the slope material, such as elastic modulus, Poisson's ratio and friction coefficient, are defined in the model, which are obtained through field sampling and laboratory testing. Next, different areas in the ecological matrix distribution map are defined as different material properties, such as the material parameters of the high adaptability and high stability area are different from those of the low adaptability and low stability area. Run the finite element analysis to calculate the stress distribution of the slope under different working conditions, especially the stress concentration in the ecological matrix area. By analyzing the stress distribution results, the areas where the stress exceeds the yield strength of the material are identified, and these areas are considered to be stress concentration areas. These stress concentration areas are marked on the ecological matrix distribution map of the slope to generate a stress concentration area map of the mountain slope. The stress concentration area map finally generated clearly shows the stress concentration in different areas of the slope.

[0110] Step S362: Arrange the density of anchor points on the mountain slope according to the mountain slope stress concentration area map to obtain a mountain slope anchor point layout map;

[0111] Specifically, the stress concentration area map of the mountain slope can be imported into the GIS software. In GIS, the density of the anchor points is determined according to the distribution of the stress concentration area. The specific steps include: for high stress concentration areas, the anchor point density is set to 1 anchor point per 10 square meters; for medium stress concentration areas, the anchor point density is set to 1 anchor point per 20 square meters; for low stress concentration areas, the anchor point density is set to 1 anchor point per 30 square meters. Next, the location of the anchor point is generated in GIS using a random point generation tool. The generated anchor point location takes into account the topography and ecological matrix distribution of the slope. The generated anchor point location is exported as a coordinate file for on-site layout. At the construction site, a GPS positioning device (such as Trimble R10) is used to accurately layout the anchor points according to the coordinate file. The layout depth and diameter of each anchor point are constructed according to the design requirements. The final generated mountain slope anchor point layout map clearly shows the location and density of the anchor points in different areas of the slope.

[0112] Step S363: planning the connection lines of the mountain slope anchor point layout diagram to obtain the mountain slope anchor network topology diagram;

[0113] Specifically, the layout diagram of the anchor points of the mountain slope can be imported into the ArcGIS software. In ArcGIS, the network analysis tool is used to plan the connection lines of the anchor points. The specific steps include: defining the connection rules between the anchor points, such as each anchor point is connected to a maximum of 4 adjacent anchor points, and the length of the connection line does not exceed 50 meters. Use ArcGIS's "Create Network Dataset" tool to generate connection lines between the anchor points to form an anchor network. Next, perform a topological check on the generated anchor network. The topological check includes checking whether the anchor points are isolated and whether the connection lines cross. Through the topological check, the layout of the anchor network is optimized to ensure that each anchor point can effectively disperse stress. The final generated topological map of the mountain slope anchor network clearly shows the anchor network structure in different areas of the slope.

[0114] Step S364: quantifying the load on the grid structure according to the topological map of the mountain slope anchoring network to obtain a distribution map of the supporting force of the mountain slope;

[0115] Specifically, the topological diagram of the anchor network of the mountain slope can be imported into the ANSYS finite element analysis software. In ANSYS, a mechanical model of the slope is established based on the topological structure of the anchor network. The mechanical parameters of the anchor points and connecting lines are defined in the model, such as the pull-out force of the anchor points and the tensile strength of the connecting lines. These parameters are obtained through field tests and laboratory analysis. Next, the finite element analysis is run to calculate the stress conditions of the anchor network under different working conditions. The specific steps include: applying the deadweight, water pressure and external load of the slope, and calculating the force magnitude of each anchor point and connecting line. By analyzing the force results, a support force distribution diagram of the mountain slope is generated. In the support force distribution diagram, different colors represent different force magnitudes, red represents a larger force, and blue represents a smaller force. The final generated support force distribution diagram of the mountain slope clearly shows the support force distribution in different areas of the slope.

[0116] Step S365: matching the supporting material parameters of the mountain slope according to the mountain slope supporting force distribution map to obtain the mountain slope material configuration plan;

[0117] Specifically, the support force distribution map of the slope in the mountainous area can be imported into the MATLAB software. In MATLAB, according to the force size in the support force distribution map, the appropriate support material is selected. The specific steps include: for areas with greater force (such as the red area), select high-strength anchor cables and steel strands, whose tensile strength is not less than 1500MPa; for areas with medium force (such as the yellow area), select medium-strength anchor cables and steel strands, whose tensile strength is not less than 1000MPa; for areas with less force (such as the blue area), select low-strength anchor cables and steel strands, whose tensile strength is not less than 500MPa. Then, the mechanical properties of the selected support materials are tested. Use a universal material testing machine (such as Instron 5980) to perform tensile tests on anchor cables and steel strands to determine their tensile strength, yield strength and elongation. The test results are recorded in a data table, including the specifications, tensile strength, yield strength and elongation of each material. The test results are matched with the force data in the support force distribution diagram to generate a material configuration plan for mountain slopes. The material configuration plan lists in detail the specifications and quantities of support materials for each area.

[0118] Step S366: Perform stability coupling verification on the mountain slope support force distribution map and the mountain slope material configuration plan to obtain the slope reinforcement system configuration plan.

[0119] Specifically, the support force distribution diagram of the mountain slope and the material configuration scheme of the mountain slope can be imported into the ANSYS finite element analysis software. In ANSYS, the reinforcement system model of the slope is established according to the support force distribution diagram and the material configuration scheme. The mechanical parameters of the anchor points, connecting lines and supporting materials are defined in the model, such as the pull-out force of the anchor points, the tensile strength of the connecting lines and the elastic modulus of the supporting materials, which are obtained through field tests and laboratory analysis. Then, the finite element analysis is run to calculate the stability of the reinforcement system under different working conditions. The specific steps include: applying the dead weight, water pressure and external load of the slope, calculating the force conditions of each anchor point and connecting line, and the displacement and stress distribution of the slope. By analyzing the calculation results, the stability of the reinforcement system is evaluated. If the calculation results show that the displacement or stress of the slope exceeds the design allowable value, adjust the material configuration scheme and re-perform the finite element analysis until the stability of the reinforcement system meets the design requirements. The final slope reinforcement system configuration scheme lists in detail the anchor point location, connection line layout and support material specifications for each area.

[0120] By identifying stress concentration areas, the present invention can ensure that anchoring measures can strengthen these key parts in a targeted manner. By arranging the density of anchor points, it can be ensured that the distribution of anchor points on the slope is both reasonable and efficient. Reasonable anchor point density can ensure that when the slope is subjected to external forces, the anchor system can evenly share the stress, avoid damage caused by excessive local stress, and also save materials and costs. Through connecting line planning, it is helpful to build a systematic and complete anchoring network. Through quantitative analysis of the load on the grid structure, the force borne by each anchor point and grid unit can be accurately understood. This helps to further optimize the design of the anchoring network, ensure that each part can work within a safe stress range, and improve the reliability and durability of the entire anchoring system. Through matching of support material parameters, it can be ensured that the selected support materials fully meet the actual load requirements of the slope in terms of performance and specifications. Through stability coupling verification, the effectiveness and reliability of the designed reinforcement system in practical applications can be ensured.

[0121] Preferably, step S4 comprises the following steps:

[0122] Step S41: interpreting the geological structure of the mountain slope based on the holographic characteristic map of the mountain slope to obtain a geological structure map of the mountain slope;

[0123] Specifically, the holographic feature map of the mountain slope can be imported into the ArcGIS software. The holographic feature map contains multi-dimensional information on the terrain characteristics, material composition, vibration mode and stress-displacement relationship of the slope, and geological experts are invited to interpret the geological structure of the processed image. During the interpretation process, geological experts identify the geological structural elements of the slope, such as faults, joints, folds and fissures, based on information such as terrain characteristics, material composition and stress-displacement relationship. The specific steps include: using ArcGIS's annotation tool to mark the location and direction of the fault; using the polygon tool to draw the distribution area of ​​joints and fissures; using the linear tool to depict the shape and extension direction of the folds, and finally generating a geological structure map of the mountain slope.

[0124] Step S42: performing stress field evolution modeling on the mountain slope stress field to obtain a mountain slope stress evolution model;

[0125] Specifically, the stress field data of mountain slopes and the geological structure map of mountain slopes can be imported into the ANSYS finite element analysis software. In ANSYS, the mechanical model of the slope is established according to the geometric model of the slope, the geological structure elements and the initial stress field data. The mechanical parameters of the slope material, such as elastic modulus, Poisson's ratio and friction coefficient, are defined in the model. These parameters are obtained through field sampling and laboratory testing. Next, considering the influence of geological structure elements on the stress field, such as the sliding of faults, the opening of joints and the deformation of folds, the corresponding boundary conditions and loads are set. For example, for faults, sliding boundary conditions are set to simulate the influence of fault sliding on the slope stress field; for joints, opening boundary conditions are set to simulate the influence of joint opening on the slope stress field, and finite element analysis is run to calculate the stress field distribution of the slope at different time points. The specific steps include: setting the time step to 1 month to simulate the stress field evolution process of the slope within one year; recording the stress field data of each time step, including stress magnitude, stress direction and stress change rate, and finally generating a stress evolution model of the mountain slope.

[0126] Step S43: extracting the structural stability factor of the mountain slope according to the geological structure map of the mountain slope to obtain a slope structural stability factor set;

[0127] Specifically, the geological structure map of the mountain slope can be imported into GeoStudio software. Using the slope stability analysis module of GeoStudio (such as Slope / W), the structure of the slope is defined according to the elements such as faults, joints, folds and cracks in the geological structure map. The specific steps include: identifying potential sliding surfaces in the slope, such as fault zones and weak rock layer interfaces; defining the geometry and size of the structure, such as the thickness and width of the sliding body; extracting the stability factors of the structure, such as friction angle, cohesion, water pressure and seismic force. For example, for a potential sliding surface, through geological drilling and laboratory testing, it is determined that its friction angle is 30 degrees, cohesion is 10kPa, and water pressure coefficient is 0.5. The extracted stability factors are sorted into a slope structure stability factor set, including the name, location, geometry and stability factor value of each structure.

[0128] Step S44: identifying the critical state of the mountain slope stress evolution model according to the slope structure stability factor set, and obtaining a critical state diagram of the mountain slope;

[0129] Specifically, the stress evolution model of the mountain slope and the stability factor set of the slope structure can be imported into the ANSYS finite element analysis software. In ANSYS, the critical state conditions of the slope are defined according to the stress evolution model and the stability factor set. The specific steps include: setting the critical values ​​of stability factors such as friction angle, cohesion, water pressure and seismic force; defining the instability criterion of the slope, such as a safety factor less than 1.0 indicates that the slope is in a critical state. Then, run the finite element analysis to calculate the stress field distribution of the slope at different time points and compare it with the critical state conditions. The specific steps include: calculating the safety factor of the slope at each time step, the safety factor is defined as the ratio of the anti-sliding force to the sliding force; identifying areas with a safety factor less than 1.0, which are considered to be critical state areas of the slope. Mark the identified critical state areas on the stress evolution model of the slope to generate a critical state diagram of the mountain slope.

[0130] Step S45: monitor the displacement of the mountain slope to obtain the displacement time series data of the mountain slope;

[0131] Specifically, 10 representative monitoring points can be selected on the slope, which are distributed at different heights and positions of the slope. A Trimble R10 GNSS receiver is installed at each monitoring point. The receiver receives GPS and GLONASS satellite signals in real time through a satellite signal receiving antenna. The sampling frequency is set to 1 time / hour to obtain the three-dimensional position data of the slope at different time points. At the same time, the Leica TPS1200 total station is used to perform conventional measurements on the slope. The measurement accuracy of the total station is 1mm+1ppm, which can provide high-precision horizontal and vertical displacement data. The measurement frequency of the total station is 1 time / day, and the measurement time is selected at the same time every day. The measurement data is processed by a data collector to generate displacement data for each monitoring point. The GNSS monitoring data and the total station measurement data are imported into the MATLAB software for data fusion and time series analysis. The specific steps include: differential positioning processing of GNSS data to eliminate errors such as satellite clock error, ionospheric delay and tropospheric delay; adjustment processing of total station data; time alignment of the two types of data to generate displacement time series data for each monitoring point, and finally obtain displacement time series data.

[0132] Step S46: performing stability assessment on the mountain slope according to the mountain slope critical state diagram and the mountain slope displacement time series data to obtain slope stability assessment data;

[0133] Specifically, the critical state diagram of the mountain slope and the displacement time series data of the mountain slope can be imported into the ANSYS finite element analysis software. In ANSYS, a slope stability assessment model is established based on the critical state diagram and displacement time series data. The model defines the geometric shape, material properties, boundary conditions and loads of the slope, and these parameters are obtained through field tests and laboratory analysis. Then, the finite element analysis is run to calculate the stress field and displacement field distribution of the slope at different time points. The specific steps include: calculating the safety factor of the slope at each time step, and the safety factor is defined as the ratio of the anti-sliding force to the sliding force; comparing the calculated safety factor with the critical value in the critical state diagram to evaluate the stability state of the slope. If the safety factor is less than 1.0, it means that the slope is in an unstable state; if the safety factor is between 1.0 and 1.5, it means that the slope is in a critical state; if the safety factor is greater than 1.5, it means that the slope is in a stable state. The evaluation results are organized into a slope stability evaluation data table, including the slope stability state, safety factor value and displacement data at each time point.

[0134] Step S47: performing time series decomposition on the slope stability assessment data to obtain a slope stability trend graph, and performing deformation and instability pattern recognition on the mountain slope based on the slope stability trend graph to obtain a slope instability pattern library;

[0135] Specifically, the slope stability assessment data can be imported into the R language environment. The forecast package in the R language is used to perform time series analysis on the stability assessment data. The specific steps include: time series modeling of the stability data (such as safety factor and displacement data) of each monitoring point, fitting the data using the ARIMA model (autoregressive integrated moving average model), and identifying the trend, seasonality, and random fluctuation components in the data. Then, the ggplot2 package in the R language is used to draw a slope stability trend chart to show the stability change trend of each monitoring point. In the trend chart, the horizontal axis represents time, the vertical axis represents the safety factor or displacement value, and lines of different colors represent different monitoring points. By analyzing the trend chart, the deformation and instability modes of the slope are identified, such as accelerated deformation, periodic deformation, and sudden deformation. For example, if the displacement data of a certain monitoring point shows an obvious acceleration trend in a short period of time and the safety factor continues to decrease, this indicates that the slope is in an accelerated deformation and instability mode. The identified deformation and instability modes are organized into a slope instability mode library, including the name, characteristic description, and corresponding monitoring point of each instability mode.

[0136] Step S48: Perform instability risk deduction on the slope instability pattern library to obtain slope risk warning data.

[0137] Specifically, the slope instability pattern library can be imported into MATLAB software. In MATLAB, a risk assessment model is established according to each instability pattern in the instability pattern library. The risk level of the instability pattern is defined in the model, such as low risk (safety factor>1.5), medium risk (1.0<safety factor≤1.5) and high risk (safety factor≤1.0). The specific steps include: for each instability pattern, the risk value is calculated according to its characteristic description and monitoring data. The risk value is defined as the product of the danger and urgency of the instability pattern, the danger indicates the severity of the disaster caused by the instability pattern, and the urgency indicates the time urgency of the occurrence of the instability pattern. Then, the fuzzy toolbox in MATLAB is used to perform fuzzy reasoning on the risk value to generate slope risk warning data. In the fuzzy reasoning process, fuzzy rules are defined, such as "if the danger of the instability pattern is high and the urgency is high, the risk level is high risk". According to the fuzzy rules, the risk value is converted into a risk level, and the risk level is represented by color, green indicates low risk, yellow indicates medium risk, and red indicates high risk, and finally the slope risk warning data is generated.

[0138] The present invention can display the geological structure and potential unstable factors of the slope in detail through geological structural interpretation, which is helpful to identify key geological structures such as sliding surfaces and fracture zones in advance. The established mountain slope stress evolution model can simulate the change trend of the slope stress field over time and predict the stress state of the slope under different conditions. By extracting the slope structure stability factor set, the stability assessment is made more scientific and accurate, and the actual stability of the slope can be more realistically reflected. Through critical state identification, the critical area and state of the slope close to instability can be clearly displayed. The displacement time series data obtained by displacement monitoring can reflect the dynamic deformation of the slope in real time and can capture the slight changes of the slope in time. Through time series decomposition, the changing trend of the slope stability can be intuitively displayed. By identifying the slope instability pattern library, the potential instability patterns of the slope can be classified and summarized. Through instability risk deduction, direct support can be provided for prevention and control decisions.

[0139] Preferably, step S5 comprises the following steps:

[0140] Step S51: performing hierarchical mapping on the slope risk warning data to obtain a mountain slope risk level map;

[0141] Specifically, the slope risk warning data can be imported into ArcGIS software to define the grading standards for risk levels. For example, risk values ​​between 0 and 0.33 are low risk (green), between 0.33 and 0.66 are medium risk (yellow), and between 0.66 and 1.0 are high risk (red). Use ArcGIS's symbology tool to color-code each monitoring point according to the risk value. The specific steps include: selecting the "grading color" symbology, setting the grading field to the risk value, and defining the grading range and corresponding color. In this way, each monitoring point represents its risk level on the map with a different color. Then, use ArcGIS's interpolation tools (such as Kriging or IDW) to spatially interpolate the risk levels of the monitoring points and generate a continuous risk level map. During the interpolation process, set the search radius to 50 meters. The resulting mountain slope risk level map clearly shows the risk levels of different areas of the slope.

[0142] Step S52: extracting parameters of the slope microenvironment regulation scheme to obtain a mountain slope regulation parameter set;

[0143] Specifically, the slope microenvironment control scheme can be imported into MATLAB software to define parameter extraction standards. For example, for temperature control measures, extract the set temperature, start time and duration; for humidity control measures, extract the frequency, amount and duration of water spraying; for vegetation restoration measures, extract plant species, planting density and maintenance cycle. Use MATLAB's data processing function to extract these parameters from the microenvironment control scheme. The specific steps include: writing a script to read the scheme file, using regular expressions to match parameter values, storing the extracted parameters in a data table, and organizing the extracted parameters into a mountain slope control parameter set, including the name, parameter name and parameter value of each measure.

[0144] Step S53: Associating and mapping the mountain slope risk level map with the mountain slope control parameter set to obtain the mountain slope control response matrix;

[0145] Specifically, the risk level map of mountain slopes and the set of mountain slope control parameters can be imported into ArcGIS software, and the spatial analysis tool of ArcGIS can be used to spatially associate the risk level map with the set of control parameters. The specific steps include: extracting the corresponding control parameters for each risk level area. For example, for high-risk areas, extract parameters such as the set temperature for temperature control, the spraying frequency for humidity control, and the plant species for vegetation restoration. Store these parameters together with the risk level in the attribute table to generate an associated data set. Then, export the associated data set to MATLAB software, and use the matrix analysis method to generate a control response matrix. The specific steps include: defining the rows of the matrix to represent the risk level, the columns to represent the control parameters, and the matrix elements to represent the values ​​of a certain control parameter at a specific risk level. For example, the first row of the matrix represents a low-risk area, the first column represents the set temperature for temperature control, and the matrix elements are the set temperature values ​​for the area, and finally a mountain slope control response matrix is ​​generated.

[0146] Step S54: Optimize the parameters of the slope microenvironment control scheme according to the mountain slope control response matrix to obtain the mountain slope control strategy, and evaluate the effect of the mountain slope control strategy to obtain a slope control effect evaluation report;

[0147] Specifically, the control response matrix of the mountain slope can be imported into MATLAB software. The control parameters are tuned using MATLAB's optimization toolbox. The specific steps include: defining the optimization goal, such as minimizing the risk level or maximizing stability; selecting a suitable optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm; and setting algorithm parameters, such as population size, number of iterations, and crossover probability. Running the optimization algorithm, adjusting the control parameters, and generating an optimized control scheme. Next, the optimized control scheme is implemented in the microenvironment control of the slope. For example, according to the optimization results, the set temperature of temperature control is adjusted, the spraying frequency of humidity control is increased, and more suitable plant species for vegetation restoration are selected. During the implementation process, field monitoring equipment (such as temperature sensors, humidity sensors, and displacement sensors) are used to monitor the microenvironment and stability changes of the slope in real time. The monitoring data is transmitted to the computer through a data acquisition device (such as NI USB-6259), and special software is used for data processing and analysis. Finally, the effectiveness of the prevention and control strategy is evaluated based on the monitoring data. The specific steps include: calculating the change in risk level before and after implementation, and evaluating the degree of improvement in stability indicators, such as displacement reduction and safety factor improvement. The evaluation results are compiled into a slope control effectiveness evaluation report, which describes in detail the optimized control plan, implementation process and effect evaluation results.

[0148] Step S55: performing residual risk identification on the slope control effect evaluation report to obtain a residual risk map of the mountain slope;

[0149] Specifically, the data in the slope prevention and control effect evaluation report can be imported into ArcGIS software, and the spatial analysis tools of ArcGIS can be used to perform spatial interpolation on the risk level after implementation to generate a residual risk map. The specific steps include: defining the classification standards for residual risk, such as low residual risk (risk reduction of more than 50%), medium residual risk (risk reduction of 20% to 50%), and high residual risk (risk reduction of less than 20%). Using the Kriging interpolation method, a continuous residual risk map is generated based on the residual risk data of the monitoring points. During the interpolation process, the search radius is set to 50 meters, and the generated residual risk map is compared and analyzed with the original risk level map to identify areas where higher risks are still present. The final generated residual risk map of mountain slopes clearly shows the residual risk levels of different areas of the slopes.

[0150] Step S56: Prepare an emergency plan based on the residual risk map of the mountain slope to obtain a mountain slope emergency plan library, and generate decision rules for the mountain slope based on the mountain slope emergency plan library to obtain a slope disaster prevention and control decision plan.

[0151] Specifically, the residual risk map of mountain slopes can be imported into the emergency plan management system, which is a web-based platform that can store and manage various emergency plans. It supports users to quickly retrieve and generate emergency plans according to risk levels and risk types, and compile emergency plans according to the risk levels and risk types in the residual risk map. The specific steps include: for high residual risk areas, prepare detailed emergency evacuation plans, emergency rescue plans and monitoring encryption measures; for medium residual risk areas, prepare emergency warning mechanisms and strengthen monitoring plans; for low residual risk areas, prepare regular monitoring and maintenance plans. The prepared emergency plans are stored in the emergency plan library. Each plan includes risk description, emergency measures, responsible units and contact information. The decision support system is used to generate decision rules based on the emergency plan library. The specific steps include: defining the trigger conditions of the decision rules, such as the risk level exceeds a certain threshold or the monitoring data is abnormal; according to the trigger conditions, the corresponding emergency plan is automatically called and decision suggestions are generated. For example, if the risk level of a monitoring point suddenly rises to high risk, the decision support system will automatically call the emergency evacuation plan and send warning information and decision suggestions to project managers and emergency departments. Finally, the generated decision-making scheme for slope hazard prevention and control includes decision-making rules, emergency plans and implementation procedures.

[0152] The present invention can intuitively display the risk levels of different areas of the slope through hierarchical mapping. Through associative mapping, the correspondence between areas of different risk levels and corresponding control parameters can be clearly displayed. Through parameter tuning, it can dynamically adapt to changes in slope risks. Through effect evaluation, the actual effects of prevention and control measures can be scientifically and objectively reflected. Through residual risk identification, the risk areas that still exist after prevention and control can be accurately located. Through the compiled emergency plan library, a comprehensive and systematic solution is provided for the emergency management of slope disasters, ensuring that emergency plans can be quickly activated in different risk scenarios.

[0153] Preferably, the present invention also provides a slope disaster prevention and control management system for dangerous mountainous areas, which is used to execute the above-mentioned slope disaster prevention and control management method for dangerous mountainous areas. The slope disaster prevention and control management system for dangerous mountainous areas includes:

[0154] Stress analysis module, used to analyze the stress field of mountain slopes and obtain the stress field of mountain slopes;

[0155] The freeze-thaw control module is used to identify freeze-thaw sensitive areas of mountain slopes based on the mountain slope stress field and obtain a slope freeze-thaw risk distribution map; perform microenvironment simulation and control on mountain slopes according to the slope freeze-thaw risk distribution map and obtain a slope microenvironment control plan;

[0156] The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain the distribution map of the ecological matrix of the slopes; to arrange anchor points on the mountain slopes according to the distribution map of the ecological matrix of the slopes to obtain the anchor network of the mountain slopes; to simulate the support structure of the mountain slopes based on the anchor network of the mountain slopes to obtain the configuration plan of the slope reinforcement system;

[0157] The early warning module is used to evaluate the stability of mountain slopes according to the mountain slope stress field to obtain slope stability evaluation data; perform time series analysis on the slope stability evaluation data to obtain a slope instability pattern library; and perform early warning on mountain slopes based on the slope instability pattern library to obtain slope risk early warning data;

[0158] The prevention and control decision-making module is used to adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0159] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0160] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for preventing and controlling slope disasters in dangerous mountainous areas, characterized in that: The following steps are involved: Step S1: obtaining a holographic characteristic map of a mountain slope; performing a stress field analysis on the mountain slope based on the holographic characteristic map of the mountain slope to obtain a stress field of the mountain slope; Step S2: identifying freeze-thaw sensitive areas of mountain slopes based on the mountain slope stress field to obtain a slope freeze-thaw risk distribution map; According to the slope freeze-thaw risk distribution map, the microenvironmental simulation and regulation of the mountain slopes were carried out to obtain the slope microenvironmental regulation plan; Step S3: Analyze the adaptability of biomaterials on the mountain slope to obtain a distribution map of the slope ecological matrix; arrange anchor points on the mountain slope according to the distribution map of the slope ecological matrix to obtain an anchor network for the mountain slope; simulate the support structure of the mountain slope based on the anchor network to obtain a configuration plan for the slope reinforcement system; Step S4: performing stability assessment on the mountain slope according to the mountain slope stress field to obtain slope stability assessment data; performing time series analysis on the slope stability assessment data to obtain a slope instability pattern library; performing early warning on the mountain slope based on the slope instability pattern library to obtain slope risk early warning data; Step S5: Adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

2. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: performing three-dimensional scanning on the mountain slope to obtain a mountain slope point cloud data set, and extracting terrain features from the mountain slope point cloud data set to obtain a slope terrain feature parameter set; Step S12: performing vibration monitoring on the mountain slope to obtain vibration characteristic data of the mountain slope, and performing modal decomposition on the vibration characteristic data of the mountain slope to obtain vibration modal data of the mountain slope; Step S13: monitoring the internal stress of the mountain slope to obtain stress characteristic data of the mountain slope; Step S14: monitoring the deformation of the mountain slope to obtain original displacement data of the mountain slope, and performing baseline correction on the original displacement data of the mountain slope to obtain standard displacement data of the mountain slope; Step S15: performing hyperspectral imaging scanning on the mountain slope to obtain a material composition map of the mountain slope, and performing data fusion on the slope terrain characteristic parameter set, the mountain slope vibration modal data and the mountain slope material composition map to obtain a holographic characteristic map of the mountain slope; Step S16: quantifying the correlation between the standard displacement data of the mountain slope and the stress characteristic data of the mountain slope to obtain a stress-displacement relationship diagram of the mountain slope; Step S17: Based on the mountain slope holographic characteristic map and the mountain slope stress-displacement relationship map, the mountain slope stress field modeling is performed to obtain the mountain slope stress field.

3. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: perform critical stress assessment on the mountain slope stress field to obtain a mountain slope stress sensitive area map; Step S22: monitoring the surface temperature of the mountain slope to obtain the slope surface temperature monitoring data, and performing deep temperature detection on the mountain slope to obtain the mountain slope ground temperature distribution data; Step S23: reconstructing the temperature field of the mountain slope based on the slope surface temperature monitoring data and the ground temperature distribution data of the mountain slope to obtain a temperature gradient map of the mountain slope; Step S24: performing time series decomposition on the slope surface temperature monitoring data to obtain slope surface temperature change characteristic data; Step S25: performing heat conduction simulation on the mountain slope based on the ground temperature distribution data of the mountain slope to obtain a thermal conductivity coefficient map of the mountain slope; Step S26: performing freeze-thaw cycle simulation on the mountain slope according to the mountain slope temperature gradient map and the slope surface temperature change characteristic data to obtain mountain slope freeze-thaw prediction data; Step S27: Based on the thermal conductivity map of the mountain slope and the freeze-thaw prediction data of the mountain slope, a risk coupling assessment is performed on the mountain slope to obtain a slope freeze-thaw risk distribution map, and microenvironment simulation and regulation is performed on the mountain slope according to the slope freeze-thaw risk distribution map to obtain a slope microenvironment regulation plan.

4. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 3 is characterized in that: Step S27 includes the following steps: Step S271: Divide the thermal conductivity map of the mountain slope into regions to obtain a thermal zoning map of the mountain slope; Step S272: Based on the freeze-thaw prediction data of the mountain slopes, risk level zoning assessment is performed on the mountain slopes to obtain an initial risk distribution map of the mountain slopes; Step S273: correcting the initial risk distribution map of the mountain slope according to the thermal zoning map of the mountain slope to obtain a freeze-thaw risk distribution map of the slope; Step S274: iterating the temperature control strategy for the mountain slope based on the mountain slope temperature gradient map to obtain an initial slope temperature control plan; Step S275: performing energy consumption evaluation on the initial slope temperature control scheme to obtain energy consumption evaluation data of the temperature control scheme; Step S276: fine-tune the initial slope temperature control scheme according to the energy consumption evaluation data of the temperature control scheme to obtain a set of mountain slope temperature control parameters; Step S277: Based on the mountain slope temperature control parameter set and the slope freeze-thaw risk distribution map, the microenvironment response simulation control of the mountain slope is performed to obtain the slope microenvironment control plan.

5. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: obtaining a material composition map of the mountain slope, and characterizing the physical and chemical properties of the soil of the mountain slope based on the material composition map of the mountain slope to obtain soil characteristic data of the mountain slope; Step S32: Conducting a biodiversity survey on the mountain slope to obtain biodiversity characteristic data of the slope, and conducting a bioactivity assessment on the mountain slope based on the biodiversity characteristic data of the slope to obtain a bioactivity map of the mountain slope; Step S33: monitoring the nutrients of the mountain slopes to obtain a nutrient distribution map of the mountain slopes; Step S34: screening ecological plants on the mountain slopes according to the soil characteristic data of the mountain slopes to obtain slope plant adaptability data; Step S35: performing biomaterial adaptability analysis on the mountain slope based on the mountain slope biological activity map, the mountain slope nutrient distribution map and the slope plant adaptability data to obtain the slope ecological matrix distribution map; Step S36: Arrange anchor points on the mountain slope according to the slope ecological matrix distribution map to obtain the mountain slope anchor network, and simulate the support structure of the mountain slope based on the mountain slope anchor network to obtain the slope reinforcement system configuration plan.

6. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 5 is characterized in that: Step S35 includes the following steps: Step S351: performing functional bacterial community identification on the mountain slope biological activity map to obtain a mountain slope microbial function map; Step S352: performing matrix formula proportioning on the mountain slope according to the mountain slope nutrient distribution map to obtain a mountain slope matrix proportioning scheme; Step S353: Performing ecological synergy evaluation based on the slope plant adaptability data and the mountain slope microbial function map to obtain the mountain slope ecological adaptability map; Step S354: Performing a matrix stability test on the matrix ratio scheme of the mountain slope to obtain the matrix stability data of the slope; Step S355: Based on the ecological adaptability map of the mountain slope and the slope matrix stability data, the ecological matrix space of the mountain slope is partitioned to obtain the slope ecological matrix distribution map.

7. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 5 is characterized in that: Step S36 includes the following steps: Step S361: performing a stress assessment on the slope ecological matrix distribution map according to the mountain slope stress field to obtain a mountain slope stress concentration area map; Step S362: Arrange the density of anchor points on the mountain slope according to the mountain slope stress concentration area map to obtain a mountain slope anchor point layout map; Step S363: planning the connection lines of the mountain slope anchor point layout diagram to obtain the mountain slope anchor network topology diagram; Step S364: quantifying the load on the grid structure according to the topological map of the mountain slope anchoring network to obtain a distribution map of the supporting force of the mountain slope; Step S365: matching the supporting material parameters of the mountain slope according to the mountain slope supporting force distribution map to obtain the mountain slope material configuration plan; Step S366: Perform stability coupling verification on the mountain slope support force distribution map and the mountain slope material configuration plan to obtain the slope reinforcement system configuration plan.

8. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: interpreting the geological structure of the mountain slope based on the holographic characteristic map of the mountain slope to obtain a geological structure map of the mountain slope; Step S42: performing stress field evolution modeling on the mountain slope stress field to obtain a mountain slope stress evolution model; Step S43: extracting the structural stability factor of the mountain slope according to the geological structure map of the mountain slope to obtain a slope structural stability factor set; Step S44: identifying the critical state of the mountain slope stress evolution model according to the slope structure stability factor set, and obtaining a critical state diagram of the mountain slope; Step S45: monitor the displacement of the mountain slope to obtain the displacement time series data of the mountain slope; Step S46: performing stability assessment on the mountain slope according to the mountain slope critical state diagram and the mountain slope displacement time series data to obtain slope stability assessment data; Step S47: performing time series decomposition on the slope stability assessment data to obtain a slope stability trend graph, and performing deformation and instability pattern recognition on the mountain slope based on the slope stability trend graph to obtain a slope instability pattern library; Step S48: Perform instability risk deduction on the slope instability pattern library to obtain slope risk warning data.

9. The method for preventing and controlling slope disasters in dangerous mountainous areas according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: performing hierarchical mapping on the slope risk warning data to obtain a mountain slope risk level map; Step S52: extracting parameters of the slope microenvironment regulation scheme to obtain a mountain slope regulation parameter set; Step S53: Associating and mapping the mountain slope risk level map with the mountain slope regulation parameter set to obtain the mountain slope regulation response matrix; Step S54: Optimize the parameters of the slope microenvironment control scheme according to the mountain slope control response matrix to obtain the mountain slope control strategy, and evaluate the effect of the mountain slope control strategy to obtain a slope control effect evaluation report; Step S55: performing residual risk identification on the slope control effect evaluation report to obtain a residual risk map of the mountain slope; Step S56: Prepare an emergency plan based on the residual risk map of the mountain slope to obtain a mountain slope emergency plan library, and generate decision rules for the mountain slope based on the mountain slope emergency plan library to obtain a slope disaster prevention and control decision plan.

10. A slope disaster prevention and control management system for dangerous mountainous areas, characterized in that: Used to execute the slope disaster prevention and control management method for dangerous mountainous areas as claimed in claim 1, the slope disaster prevention and control management system for dangerous mountainous areas comprises: Stress analysis module, used to analyze the stress field of mountain slopes and obtain the stress field of mountain slopes; The freeze-thaw control module is used to identify freeze-thaw sensitive areas of mountain slopes based on the mountain slope stress field and obtain a slope freeze-thaw risk distribution map; perform microenvironment simulation and control on mountain slopes according to the slope freeze-thaw risk distribution map and obtain a slope microenvironment control plan; The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain the distribution map of the ecological matrix of the slopes; to arrange anchor points on the mountain slopes according to the distribution map of the ecological matrix of the slopes to obtain the anchor network of the mountain slopes; to simulate the support structure of the mountain slopes based on the anchor network of the mountain slopes to obtain the configuration plan of the slope reinforcement system; The early warning module is used to evaluate the stability of mountain slopes according to the mountain slope stress field to obtain slope stability evaluation data; perform time series analysis on the slope stability evaluation data to obtain a slope instability pattern library; and perform early warning on mountain slopes based on the slope instability pattern library to obtain slope risk early warning data; The prevention and control decision-making module is used to adjust the slope microenvironment control plan according to the slope risk warning data to obtain the mountain slope prevention and control strategy; evaluate the effectiveness of the mountain slope prevention and control strategy to obtain a slope prevention and control effectiveness evaluation report; generate an emergency plan for the slope prevention and control effectiveness evaluation report to obtain a slope disaster prevention and control decision-making plan.

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