A method and system for preventing and controlling slope disasters in dangerous mountainous areas

Abstract

The present invention relates to the field of slope disaster prevention and control technology, and in particular to a slope disaster prevention and control management method and system for difficult mountainous areas. The method comprises the following steps: obtaining a holographic characteristic map of the mountain slope; performing 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; identifying freeze-thaw sensitive areas of the mountain slope based on the stress field of the mountain slope to obtain a freeze-thaw risk distribution map of the slope; performing microenvironment simulation and control on the mountain slope according to the freeze-thaw risk distribution map of the slope to obtain a slope microenvironment control plan; performing biomaterial adaptability analysis on the mountain slope to obtain a slope ecological matrix distribution map; and arranging anchor points on the mountain slope according to the slope ecological matrix distribution map to obtain a mountain slope anchor network. The present invention improves the accuracy of preventing freeze-thaw disasters.

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, slope stability is affected by multiple factors, including geological structure, climatic conditions, and human activities. Traditional slope hazard prevention and control methods rely primarily on manual monitoring and simple engineering measures, such as retaining walls and drainage systems. However, these methods often have numerous shortcomings when faced with the complex and changing mountainous environment.

[0003] First, in high-altitude, cold regions, freeze-thaw cycles on slopes are a significant cause of disasters. Due to drastic temperature fluctuations, moisture within the rock mass of slopes undergoes freeze-thaw cycles, significantly reducing the rock's strength and stability. Traditional monitoring methods struggle to accurately capture subtle changes during freeze-thaw cycles in these environments, making it impossible to provide timely warnings of potential landslide risks. For example, on some mining slopes, the limited deployment and low accuracy of monitoring equipment make manual monitoring extremely labor-intensive and difficult, making real-time, accurate monitoring difficult. Second, ecological protection of slopes is also a pressing issue. In ecologically fragile mountainous areas, traditional slope support methods often cause secondary damage to the environment. For example, extensive slope cutting and filling operations destroy existing vegetation and soil structure, leading to soil erosion and ecological imbalance. Furthermore, some slope support materials are prone to aging and failure over time, making them ineffective against erosion and damage from the natural environment. For example, on slopes in areas with expansive soils, traditional rigid support structures such as gravity retaining walls and anti-slide piles are easily damaged by the volumetric changes of the 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 objectives, a slope disaster prevention and control management method for difficult 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: Identifying freeze-thaw sensitive areas of the mountain slope based on the mountain slope stress field to obtain a slope freeze-thaw risk distribution map; performing microenvironmental simulation and control on the mountain slope based on the slope freeze-thaw risk distribution map to obtain a slope microenvironment control plan;

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

[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; and 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 based on 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, transforming prevention and control work from passive response to active prediction, locking high-risk areas in advance, effectively avoiding the limitations of traditional monitoring methods, and greatly improving the accuracy of prevention of freeze-thaw disasters. Based on the freeze-thaw risk distribution map, a micro-environment control plan is formulated, which can effectively improve the environment in which the rock mass is located, reduce the damage to the rock mass caused by the freeze-thaw cycle, 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, avoiding the damage to the ecological environment caused by traditional support methods, reducing soil erosion, promoting the self-repair and balance of slope ecosystems, and realizing the coordinated progress of slope disaster prevention and control and ecological environment protection. 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 traditional manual monitoring with few points and low precision. Through in-depth analysis of stability assessment data, a slope instability pattern library is established, and risk warning data is generated based on this, and a systematic early warning system is constructed. This enables early identification of slope instability patterns and risk levels, buying time for implementing preventive measures and making prevention efforts more proactive and proactive. Dynamically adjusting microenvironmental control plans based on slope risk warning data allows prevention measures to flexibly adapt to changing slope risks, ensuring the most effective prevention measures are employed at every risk stage. This avoids the problem of traditional prevention methods, which suffer from fixed strategies and are unable to cope with complex and changing mountain environments.

[0012] Preferably, the present invention further provides a slope disaster prevention and control management system for difficult mountainous areas, which is used to implement the above-mentioned slope disaster prevention and control management method for difficult mountainous areas. The slope disaster prevention and control management system for difficult 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 on mountain slopes based on the slope stress field and obtain a slope freeze-thaw risk distribution map; it also simulates and controls the microenvironment of mountain slopes based on the slope freeze-thaw risk distribution map and obtains a slope microenvironment control plan;

[0015] The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain a distribution map of the slope's ecological matrix. Based on the distribution map, anchor points are arranged on the mountain slopes to obtain a mountain slope anchor network. Based on the mountain slope anchor network, support structure simulations are performed on the mountain slopes to obtain a slope reinforcement system configuration plan.

[0016] The early warning module is used to evaluate the stability of mountain slopes based on their stress fields to obtain slope stability assessment data; perform time series analysis on the slope stability assessment data to obtain a slope instability pattern library; and issue early warnings to mountain slopes based on the slope instability pattern library to obtain slope risk warning data.

[0017] The prevention and control decision-making module is used to adjust the slope microenvironment control plan based on 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 effect evaluation report; generate an emergency plan based on the slope prevention and control effect evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0018] The present invention, through its stress analysis module, can accurately analyze the stress field of mountain slopes, ensuring the scientific and targeted nature of preventive measures and enabling the system to accurately grasp the slope's stress conditions and stability trends. The freeze-thaw control module can specifically adjust the slope's microenvironment, such as through temperature control, thereby effectively improving the rock mass's environment, reducing the impact of freeze-thaw cycles on its strength and stability, and mitigating the risk of landslides and other disasters caused by freeze-thaw at the source. The ecological reinforcement module, through a reinforcement approach that prioritizes both ecology and safety, avoids the damage to the ecological environment caused by traditional support methods, promotes the restoration and balance of slope ecosystems, and achieves the coordinated development of slope disaster prevention and control with ecological and environmental protection. The early warning module can identify potential slope risks in advance, buying time for the implementation of preventive measures. The prevention and control decision-making module can flexibly adapt to changes in slope risk, ensuring the most effective preventive measures are implemented at different risk stages. This improves the flexibility and adaptability of slope disaster prevention and control, enhances the timeliness and targeted nature of prevention and control efforts, and facilitates a quick and accurate response to disasters and the implementation of effective emergency measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from reading 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 one embodiment is shown.

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

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

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as 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: Identifying freeze-thaw sensitive areas of the mountain slope based on the mountain slope stress field to obtain a slope freeze-thaw risk distribution map; performing microenvironmental simulation and control on the mountain slope based on the slope freeze-thaw risk distribution map to obtain a slope microenvironment control plan;

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

[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; and 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 based on 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. 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), freeze-thaw sensitive areas of the slope are identified to generate a freeze-thaw risk distribution map. Based on this map, GIS software (such as ArcGIS) is used to simulate microenvironmental control schemes, such as setting up drainage systems and insulation layers. 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 arrange anchor points to form an anchor network, and a slope reinforcement system configuration scheme is obtained through support structure simulation software (such as GeoStudio). Furthermore, based on the stress field analysis results, a stability assessment is conducted. Time series analysis tools (such as the forecast package in the R language) are used to generate a library of slope instability patterns. Based on this library, an early warning system is established to issue timely alerts when monitoring data (using GNSS and total stations) triggers an early warning. Finally, microenvironmental control plans are adjusted based on the risk warning data to formulate a prevention and control strategy. The effectiveness of the strategy is evaluated using on-site monitoring data, and a prevention and control effectiveness evaluation report is generated. Based on this report, an emergency plan is compiled using the emergency plan management system, forming a decision-making plan for slope disaster prevention and control.

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

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

[0035] Specifically, a Riegl VZ-4000 3D laser scanner was used for 3D scanning. Five scanning stations were set up at different locations on the slope, each covering an area approximately 100 meters by 100 meters. The scanner's resolution was set to 0.05 meters. During the scanning process, the device automatically recorded the 3D coordinates (X, Y, Z) of each point, as well as the reflection intensity information. After the scan was completed, the data from each station was imported into the point cloud processing software CloudCompare. Using the software's terrain feature extraction function, terrain characteristic parameters such as slope, aspect, and roughness were extracted, resulting in a set of slope terrain characteristic parameters.

[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 & These accelerometers, including the NI 4508B, can measure acceleration in three directions, with a sampling frequency set to 200 Hz. The accelerometers are connected to a computer via a data logger (NI USB-6259). On this computer, self-developed vibration monitoring software, based on the LabVIEW platform, displays acceleration data in real time and performs modal decomposition analysis. The software uses a fast Fourier transform algorithm to convert time-domain acceleration signals into frequency-domain signals, thereby extracting the slope's vibration modal data, 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, the FOS-RTS2000 distributed fiber optic strain sensor can be used for internal stress monitoring. Five optical fibers, each approximately 100 meters long, are embedded within the slope, arranged at varying depths and directions along the slope. The fibers are buried at depths ranging from 0.5 to 10 meters below the surface. The monitoring system uses optical time-domain reflectometry (OTDR) to measure strain changes along the fiber lines in real time by emitting laser pulses and receiving reflected signals. The system's sampling interval is set at 10 minutes, and the monitoring data is processed using data acquisition and analysis software. The software automatically calibrates the data and extracts characteristic stress data for mountain slopes, such as maximum strain, 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 via a satellite signal receiving antenna, with a sampling frequency set to 1 Hz. Data from all monitoring points is transmitted via a wireless network to a central data processing center, where Trimble BusinessCenter software is used to perform baseline correction on the raw displacement data. During baseline correction, the base station data is selected as a reference. A differential positioning algorithm is used to eliminate errors such as satellite clock errors, ionospheric delay, and tropospheric delay to obtain standard displacement data. This standard displacement data includes the displacement of each monitoring point in the east, north, and sky directions, with data accuracy reaching millimeter levels.

[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 feature 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 on the slope. The scanning range of each station covers an area of approximately 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 scan is completed, the hyperspectral data is imported into the ENVI software for processing. In the software, 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 characteristic 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 stress-displacement relationship diagrams 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, a stress field model is performed on the mountain slope to obtain the mountain slope stress field.

[0047] Specifically, the holographic characteristic map and 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 based on the topography, material composition, and vibration modal data in the holographic characteristic map. The mechanical parameters of the slope material, such as the elastic modulus, Poisson's ratio, and friction coefficient, are defined based on the quantitative relationship in the stress-displacement relationship diagram. These parameters are obtained through field sampling and laboratory testing. Using the meshing function of ANSYS, the slope model is divided into multiple small units, each with a size of approximately 1 meter by 1 meter by 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 based on the actual geological conditions and monitoring data. Finally, a finite element analysis is run to calculate the stress distribution within the slope and generate a stress field map of the mountain slope.

[0048] The present invention uses a variety of means to obtain rich data on mountain slopes from different angles, including terrain features, vibration modes, stress characteristics, displacement data and material composition. This ensures a comprehensive understanding 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 stress field analysis more scientific and reasonable, and 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 includes the following steps:

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

[0051] Specifically, ANSYS finite element analysis software can be used to assess critical stress based on the slope stress field model established in step S17. Within the model, the yield strength of the slope material and the failure criterion, such as the Mohr-Coulomb criterion, are defined. By simulating stress distribution under different working conditions, the stress levels at various locations on the slope are calculated. The calculated stress values are compared with the material's yield strength to identify areas where stress exceeds the yield strength. These areas are considered stress-sensitive areas. Alternatively, five representative locations can be selected on-site and Vishay EA-03-062AA-350 strain gauges installed to monitor stress changes in real time. The strain gauges are connected to a computer via a data acquisition device (NI USB-6259) with a sampling frequency set to 1 Hz. The monitoring data is analyzed in real time using specialized software. Through comparative analysis of numerical simulation and field monitoring data, a map of stress-sensitive areas on the mountain slope is ultimately generated.

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

[0053] Specifically, a FLIR T640 infrared thermal imager was used to monitor the slope surface temperature. Ten monitoring points were set up at different heights and orientations along the slope, each covering an area approximately 20 meters by 20 meters. The thermal imager's sampling rate was set to once per hour. Furthermore, Sensirion SHT75 temperature sensors were installed at various depths within the slope, ranging from 0.5 to 10 meters below the surface. A total of 15 monitoring points were set up. The temperature sensors were connected to a computer via a data logger, with a sampling rate set to once per hour. The monitoring data was recorded and analyzed in real time using specialized software. Surface and ground temperature data acquired through infrared thermal imaging and deep temperature detection techniques were fused and interpolated to generate a ground temperature distribution map for 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 the 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 slope surface and inside. Kriging interpolation is a geostatistical method that can estimate the temperature value of an unknown point based on the temperature 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 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 and dividing it by the distance, and the unit is ℃ / meter. The final generated temperature gradient map of the mountain slope 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 ground temperature 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 ground temperature 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 an appropriate heat conduction equation, such as the Fourier heat conduction equation, to describe the heat conduction process inside the slope. Through field sampling and laboratory testing, the thermophysical parameters of the slope material, such as specific heat capacity, thermal conductivity, and density, are obtained. 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 heat conduction 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 temperature change characteristic data of the slope surface can be imported into MATLAB software. In MATLAB, the finite difference method is used to perform numerical simulation of 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, based on the temperature gradient map and temperature change characteristic data. 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 the freezing and melting process. The model considers factors such as the material's phase change latent heat, density change and mechanical property change. 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 coefficient 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 of the mountain slope is performed 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 gain a deeper understanding of 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 microenvironmental simulation and control, 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 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 zones, namely high thermal conductivity zone, medium thermal conductivity zone and 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 points 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. The above process is repeated until the cluster center no longer changes or the preset number of iterations is reached. Finally, a thermal zone map of the mountain slope is generated, in which different colors represent different thermal zones.

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

[0069] Specifically, freeze-thaw prediction data for mountain slopes can be imported into ArcGIS software. In ArcGIS, a risk assessment indicator system is defined based on the freeze-thaw prediction data, including freeze-thaw depth, number of freeze-thaw cycles, and freeze / thaw rate. Each indicator is assigned a weight based on its impact on slope stability. For example, freeze-thaw depth is weighted 0.4, number of freeze-thaw cycles is weighted 0.3, and freeze / thaw rate is weighted 0.3. Next, a weighted overlay method is used to comprehensively evaluate each indicator and calculate the risk value for each pixel. The risk value ranges from 0 to 1, with values closer to 1 indicating higher risk. Based on the risk value, the slopes are divided into four risk levels: low risk (risk value < 0.25), medium risk (0.25 ≤ risk value < 0.5), high risk (0.5 ≤ risk value < 0.75), and extremely high risk (risk value ≥ 0.75). This ultimately generates an initial risk distribution map for mountain slopes, with different colors representing different risk levels.

[0070] Step S273: Modifying the initial risk distribution map of the mountainous slopes according to the thermal zoning map of the mountainous slopes to obtain a freeze-thaw risk distribution map of the slopes;

[0071] Specifically, the thermal zoning map and initial risk distribution map of mountain slopes can be imported into ArcGIS software. In ArcGIS, the spatial overlay analysis function is used to overlay the thermal zoning map with the initial risk distribution map. 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 high thermal conductivity areas, due to faster heat transfer, the freeze-thaw cycle is more intense, so the risk value is adjusted upward; in low thermal conductivity areas, due to slower heat transfer, the freeze-thaw cycle is milder, so the risk value is adjusted downward. The adjustment range is determined according to the relative size of the thermal conductivity. For example, the risk value of the high thermal conductivity area is increased by 10%, the risk value of the medium thermal conductivity area remains unchanged, and the risk value of the low thermal conductivity area is reduced by 10%. Finally, a revised slope freeze-thaw risk distribution map is generated, in which different colors 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, a temperature gradient map of a mountain slope can be imported into MATLAB. In MATLAB, the finite element method is used to simulate the slope's temperature field and establish a temperature field model. The model defines the slope's geometry, material properties, and boundary conditions, such as surface temperature, groundwater flow, and atmospheric temperature. Next, based on the temperature gradient map, target areas and target temperatures for temperature control are determined. For example, in areas with large temperature gradients, the goal is to reduce the temperature gradient and thus reduce the frequency and intensity of freeze-thaw cycles. Appropriate temperature control measures are selected, such as ground cover, ventilation systems, or heating systems. In this example, ground cover and ventilation systems are selected as the primary temperature control measures. A genetic algorithm is used to optimize the parameters of these temperature control measures, such as ground cover thickness and ventilation system air volume. Genetic algorithms simulate the processes of natural selection and genetic mutation to search for the optimal parameter combination. The specific steps include: defining a fitness function, such as minimizing freeze-thaw risk or minimizing energy consumption; initializing a population, with each individual representing a set of parameter combinations; performing selection, crossover, and mutation operations to generate a new population; and evaluating the fitness of the new population, 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 slope temperature control plan (including the surface cover thickness distribution map and the ventilation system air volume distribution map) can be imported into EnergyPlus energy consumption simulation software. Within the software, an energy consumption model for the slope is established based on the slope's geometry, material properties, and local meteorological data. The model defines energy consumption parameters for the temperature control measures, such as the thermal resistance of the surface cover material and the power of the ventilation system. Next, an energy consumption simulation is run to calculate the energy consumption of different temperature control measures over a year. Simulation results include total energy consumption, peak energy consumption, and time-based energy consumption data. Three representative locations can also be selected on-site and energy monitors installed to monitor the actual energy consumption of the ventilation system in real time. The energy monitors are connected to a computer via a data logger (NI USB-6259), with a sampling frequency set to once per hour. Monitoring data is recorded and analyzed in real time using specialized software. Comparative analysis of the energy consumption simulation with field test data ultimately yields energy consumption evaluation data for the temperature control plan.

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

[0077] Specifically, the energy consumption evaluation data of the temperature control scheme can be imported into 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, where 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 and 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 inertia weight, are continuously adjusted based on the results of field verification. 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, a microenvironment response simulation control is performed on the mountain slope to obtain a slope microenvironment control plan.

[0079] Specifically, the temperature control parameter set for mountain slopes and the slope freeze-thaw risk distribution map can be imported into COMSOL Multiphysics. A coupled thermal-hydraulic-mechanical multiphysics model is established in COMSOL to simultaneously simulate the interactions among temperature, moisture, and stress fields. The model defines the slope geometry, material properties, boundary conditions, and parameters of the temperature control measures. Next, numerical simulations are run to calculate the responses of the slope microenvironment to different temperature control measures, including temperature changes, moisture migration, and stress distribution. During the simulation, a time step of one day is set. Temperature, humidity, and stress sensors are installed at three representative locations to monitor the actual responses of the slope microenvironment in real time. The sensors are connected to a computer via a data acquisition device (NI USB-6259) with a sampling frequency set to once per hour. The monitoring data is recorded and analyzed in real time using specialized software. By comparing the simulation results with 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, timeframe, and expected results of the temperature control measures.

[0080] Through regional division, the present invention enables a more detailed understanding of the thermal conductivity characteristics of different slope regions. This helps to more accurately consider the physical differences between regions when conducting risk level zoning assessments, making the initial risk distribution map more realistic. By correcting the initial risk distribution map based on the thermal zoning map, the accuracy of the slope freeze-thaw risk distribution map is further improved. This ensures that prevention and control resources are rationally allocated to the highest-risk areas, improving the targetedness and effectiveness of prevention and control work, and avoiding resource waste and excessive prevention and control. Through iteration of the temperature control strategy, the temperature control plan can be continuously optimized to better match the actual thermodynamic characteristics of the slope. This ensures the effectiveness and scientific nature of the temperature control measures, enables more accurate achievement of the desired temperature control targets, and reduces slope stability issues caused by temperature changes. Energy consumption assessment allows for an advance understanding of the energy consumption of different control measures. By fine-tuning the initial temperature control plan, actual temperature control operations can be more accurately guided. Through microenvironment response simulation and control, the actual impact of control measures on the slope microenvironment can be predicted in advance, and the effectiveness and adaptability of the plan can be evaluated.

[0081] Preferably, step S3 includes 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 on the mountain slope based on the material composition map to obtain soil characteristic data of the mountain slope;

[0083] Specifically, a Headwall Hyperspec III hyperspectral imager can be used to scan the slope and acquire spectral data of the slope surface. This device is capable of high-resolution imaging in the 400-1000nm band, encompassing 270 bands and a spectral resolution of up to 5nm. Five scanning stations are set up at different locations on the slope, each covering an area of approximately 50 meters by 50 meters. After scanning, the hyperspectral data is imported into ENVI software for processing. Spectral unmixing techniques are used to extract the spectral signatures of different substances on the slope surface, generating a material composition map. Next, 10 representative points on the slope are selected for on-site soil sampling. Soil samples are collected at a depth of 0-30cm using a soil sampler, with three replicates collected for each sample, for a total of 30 samples. The samples are then brought back to the laboratory for analysis of soil physical and chemical properties. Analysis includes soil texture (sand, silt, and clay content), pH, 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 molybdenum yellow colorimetric method, and the total potassium content was determined using flame photometry. The analysis results were compiled 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 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 up 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 for each sample plot, for a total of 45 soil samples. The soil samples are brought 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 method, 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 on 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 the Hanna Instruments HI98199) is used to measure the soil pH, nitrogen (N), phosphorus (P), and potassium (K) content on-site. This 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, for a total of 60 samples. The measurement results are recorded in a data table, including the geographic location, pH, nitrogen, phosphorus, and potassium content of each sample. Next, 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 values of unknown points based on the nutrient data of known points, taking into account the spatial autocorrelation of the data. During the interpolation process, a search radius of 50 meters is set to ensure that each interpolation point can refer to enough known data points. The resulting 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 compiled into a detailed soil characteristic data table. Next, referring to the local flora and existing successful cases of ecological restoration, a series of plant species suitable for the local environment are selected. 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 suitable 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 relatively 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, in an area with a pH of 7.2, sandy loam soil, and medium total nitrogen content, sea buckthorn and ryegrass are selected for planting. The screening results are compiled into a slope plant adaptability data table, including the name of each plant, suitable growing 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 a 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 biomaterials suitable for local soil conditions. Through the biome diversity 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 biome, promote the balance and stability of the slope ecosystem, reduce the damage to the ecological environment caused by engineering construction, and achieve synergistic efficiency between slope protection and ecological restoration. Through nutrient monitoring, the nutrient status of different areas of the slope can be clearly displayed. 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 and enable plants to 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, ensuring that the ecological matrix can provide a good environment for plant growth and integrate with the soil and ecosystem of the slope. By displaying 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 anchoring 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 mountain slope biological activity map to obtain a mountain slope microbial functional map;

[0097] Specifically, 10 representative areas can be selected from the bioactivity map of the slope, and soil samples can be collected. Three replicates are collected from each area, for a total of 30 samples. Soil samples weighing approximately 100 grams are collected at a depth of 0–10 cm using a soil sampler. The soil samples are then brought back to the laboratory for DNA extraction and 16S rRNA gene sequencing. Soil DNA is extracted using the MoBioPowerSoil DNA Isolation Kit, followed by 16S rRNA gene sequencing using the Illumina MiSeq platform. The sequencing results are analyzed using QIIME2 software to identify the bacterial and archaeal community structure in the soil. Functional prediction tools (such as PICRUSt2) are used to predict the metabolic functions of the functional microbial communities, such as nitrogen cycling, carbon cycling, and phosphorus cycling. The analysis results are compiled into a functional microbial community data table, including the functional microbial species and relative abundance for each sample. This data is then imported into ArcGIS software, and the functional microbial community data is spatially interpolated using the Kriging interpolation method to generate a microbial functional map of the mountain slope. During the interpolation process, a search radius of 30 meters is set. 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 formulation ratio on the mountain slope according to the mountain slope nutrient distribution map to obtain a matrix ratio scheme for the mountain slope;

[0099] Specifically, nutrient distribution maps of mountain slopes can be imported into GIS software to analyze nutrient levels in different slope areas, including nitrogen (N), phosphorus (P), potassium (K), and organic matter content. Based on nutrient levels, the slopes are divided into high-nutrient, medium-nutrient, and low-nutrient zones. By referring to the local flora and existing successful ecological restoration cases, a range of substrate materials suitable for the local environment is selected, including humus, peat, river sand, and organic fertilizer. For each nutrient zone, a substrate formula is developed based on plant nutrient requirements and soil improvement goals. For example, for the low-nutrient zone, the substrate formula is 50% humus, 30% peat, 15% river sand, and 5% organic fertilizer; for the medium-nutrient zone, the substrate formula is 40% humus, 30% peat, 20% river sand, and 10% organic fertilizer; and for the high-nutrient zone, the substrate 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 scheme table, including the type and proportion of substrate materials for each area.

[0100] Step S353: performing an ecological synergistic assessment based on the slope plant adaptability data and the mountain slope microbial function map to obtain an ecological adaptability map of the mountain slope;

[0101] Specifically, slope plant adaptability data and mountain slope microbial function maps can be imported into GIS software. Using the spatial overlay analysis function in GIS, the plant adaptability data and the microbial function map can be overlaid. Specific steps include matching the suitable growth conditions for each plant species with the microbial function of the corresponding region. For example, ryegrass has an optimal pH range of 6.0-7.5, a high total nitrogen requirement, and the support of nitrogen-cycling functional bacteria. Within the microbial function map, areas rich in nitrogen-cycling functional bacteria are identified and overlaid with suitable ryegrass growth areas to assess the degree of synergy between the two. Next, the overlay results are quantitatively evaluated using an ecological model (such as an ecosystem service value assessment model). The model defines synergistic indicators for plant growth and microbial function, such as the plant growth index and the microbial function index. By calculating the synergy index for each region, an ecological adaptability map is generated. The synergy index ranges from 0 to 1, with values closer to 1 indicating a higher degree of synergy between plants and microorganisms. The resulting mountain slope ecological adaptability map clearly demonstrates the ecological adaptability levels of different slope regions.

[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, matrix samples of different proportions can be prepared according to the matrix ratio scheme of mountain slopes. 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 method 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 measure the bulk density of the matrix, measuring the porosity by the saturation method, using a permeameter to measure the permeability, and using a direct shear instrument to measure 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 a slope ecological matrix distribution map.

[0105] Specifically, the ecological adaptability map of mountain slopes and the slope matrix stability data can be imported into 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 zones, 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 zones.

[0106] The present invention can reveal the distribution and activity of different functional bacterial communities in slope soil through functional bacterial community identification, which helps to gain a deeper understanding of the role of soil microorganisms in slope ecosystems, such as decomposing organic matter, fixing nitrogen, and solubilizing phosphorus, thereby providing a scientific basis for ecological restoration and soil improvement. Through ecological synergistic assessment, 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 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 matrix stability testing, 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 in ecological protection effects caused by matrix loss or degradation. Through ecological matrix spatial zoning, the types and configuration requirements of ecological matrix in different areas can be clearly displayed, providing precise spatial guidance for ecological protection measures for slopes. Through ecological matrix spatial zoning, slopes 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 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 mountain slopes and the distribution map of the ecological matrix of the slopes can be imported into the ANSYS finite element analysis software. In ANSYS, a mechanical model of the slope is established based on the geometric model and stress field data of the slope. The mechanical parameters of the slope material, such as the elastic modulus, Poisson's ratio and friction coefficient, are defined in the model. These parameters are obtained through field sampling and laboratory testing. Next, different areas in the ecological matrix distribution map are defined as different material properties. For example, the material parameters of the high adaptability and high stability area are different from those of the low adaptability and low stability area. Finite element analysis is run 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, areas where the stress exceeds the yield strength of the material are identified. 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 resulting stress concentration area map clearly shows the stress concentration in different areas of the slope.

[0110] Step S362: Arranging 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 mountain slopes can be imported into GIS software. In GIS, the density of anchor points is determined based on the distribution of stress concentration areas. 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 random point generation tool is used to generate the location of the anchor points in GIS. The generated anchor point locations take into account the topography and ecological matrix distribution of the slope. The generated anchor point locations are exported as a coordinate file for on-site layout. At the construction site, GPS positioning equipment (such as Trimble R10) is used to accurately layout anchor points according to the coordinate file. The layout depth and diameter of each anchor point are constructed according to design requirements. The final generated mountain slope anchor point layout map clearly shows the location and density of 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 the "Create Network Dataset" tool of ArcGIS to generate connection lines between the anchor points to form an anchor network. Then, perform a topology check on the generated anchor network. The topology check includes checking whether the anchor points are isolated and whether the connection lines cross. Through the topology check, the layout of the anchor network is optimized to ensure that each anchor point can effectively disperse stress. The final generated topology 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 anchorage network to obtain a distribution map of the mountain slope support force;

[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 indicates a larger force, and blue indicates 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 support material parameters for the mountainous slope according to the mountainous slope support force distribution map to obtain a mountainous slope material configuration plan;

[0117] Specifically, the support force distribution map of mountain slopes can be imported into MATLAB software. In MATLAB, appropriate support materials are selected according to the force magnitude in the support force distribution map. The specific steps include: for areas with greater force (such as the red area), select high-strength anchor cables and steel strands with a tensile strength of not less than 1500MPa; for areas with medium force (such as the yellow area), select medium-strength anchor cables and steel strands with a tensile strength of not less than 1000MPa; for areas with less force (such as the blue area), select low-strength anchor cables and steel strands with a tensile strength of not less than 500MPa. Next, 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 were matched with the force data from the support force distribution diagram to generate a material allocation plan for mountain slopes. The material allocation plan detailed 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 map and material configuration plan for mountain slopes can be imported into ANSYS finite element analysis software. In ANSYS, a slope reinforcement system model is constructed based on the support force distribution map and material configuration plan. The model defines the mechanical parameters of anchor points, connecting lines, and support materials, such as the pullout resistance of anchor points, the tensile strength of connecting lines, and the elastic modulus of support materials. These parameters are obtained through field testing and laboratory analysis. Next, a finite element analysis is run to calculate the stability of the reinforcement system under different operating conditions. This involves applying the slope's deadweight, water pressure, and external loads, calculating the forces at each anchor point and connecting line, as well as the displacement and stress distribution of the slope. The stability of the reinforcement system is assessed by analyzing the calculation results. If the calculation results indicate that the slope's displacement or stress exceeds the design allowable values, the material configuration plan is adjusted and the finite element analysis is repeated until the reinforcement system's stability meets the design requirements. The resulting slope reinforcement system configuration plan details the anchor point locations, connecting 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 areas 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 anchoring system can evenly share the stress, avoiding damage caused by excessive local stress, while also saving 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 forces 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 includes 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 slope's topographic features, material composition, vibration mode, and stress-displacement relationship. 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 topographic features, 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, mountain slope stress field data and geological structure maps can be imported into ANSYS finite element analysis software. In ANSYS, a mechanical model of the slope is established based on the slope's geometric model, geological structure elements, and initial stress field data. The model defines the mechanical parameters of the slope material, such as the elastic modulus, Poisson's ratio, and friction coefficient, obtained through field sampling and laboratory testing. Next, the effects of geological structure elements on the stress field, such as fault slip, joint opening, and fold deformation, are considered, and corresponding boundary conditions and loads are set. For example, for faults, sliding boundary conditions are set to simulate the impact of fault slip on the slope stress field; for joints, opening boundary conditions are set to simulate the impact of joint opening on the slope stress field. Finite element analysis is then run to calculate the stress field distribution of the slope at different time points. The specific steps include: setting a time step of one month to simulate the slope stress field evolution over a year; recording the stress field data at each time step, including stress magnitude, stress direction, and stress change rate, to ultimately generate a stress evolution model for 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 GeoStudio's slope stability analysis module (such as Slope / W), the structure of the slope is defined based on 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 organized into a slope structure stability factor set, including the name, location, geometry and stability factor value of each structure.

[0128] Step S44: performing critical state identification on the mountain slope stress evolution model according to the slope structure stability factor set to obtain a mountain slope critical state diagram;

[0129] Specifically, the stress evolution model of mountain slopes and the stability factor set of slope structures can be imported into the ANSYS finite element analysis software. In ANSYS, the critical state conditions of the slope are defined based on 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, where the safety factor is defined as the ratio of the anti-sliding force to the sliding force; identifying areas where the safety factor is 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: monitoring the displacement of the mountain slope to obtain the time series data of the mountain slope displacement;

[0131] Specifically, 10 representative monitoring points can be selected on the slope, distributed at different heights and locations. 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, with a sampling frequency set to once per hour, to obtain the three-dimensional position data of the slope at different time points. Simultaneously, a Leica TPS1200 total station is used for routine slope measurements. The total station has a measurement accuracy of 1mm + 1ppm and can provide high-precision horizontal and vertical displacement data. The total station's measurement frequency is once per 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 total station measurement data are imported into MATLAB software for data fusion and time series analysis. The specific steps include: performing differential positioning processing on GNSS data to eliminate errors such as satellite clock error, ionospheric delay and tropospheric delay; performing adjustment processing on total station data; and time aligning the two types of data to generate displacement time series data for each monitoring point, and finally obtaining 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 and displacement time series data for mountain slopes can be imported into 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 slope's geometry, material properties, boundary conditions, and loads, all of which are obtained through field testing and laboratory analysis. Next, a finite element analysis is run to calculate the stress and displacement field distributions of the slope at different time points. The specific steps include: calculating the slope's safety factor at each time step, 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 assess the slope's stability. If the safety factor is less than 1.0, the slope is unstable; if it is between 1.0 and 1.5, the slope is critical; and if it is greater than 1.5, the slope is stable. The assessment results are compiled into a slope stability assessment data table, including the slope's 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: performing time series modeling on 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, cyclical 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: performing instability risk deduction on the slope instability pattern library to obtain slope risk warning data.

[0137] Specifically, a slope instability pattern library can be imported into MATLAB software. In MATLAB, a risk assessment model is established based on each instability pattern in the library. The model defines risk levels for each instability pattern, 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, a risk value is calculated based on its characteristic description and monitoring data. The risk value is defined as the product of the hazard and urgency of the instability pattern. The hazard represents the severity of the disaster caused by the instability pattern, and the urgency represents the timeliness of the instability pattern's occurrence. Next, fuzzy reasoning is performed on the risk value using the fuzzy toolbox in MATLAB to generate slope risk warning data. During the fuzzy reasoning process, fuzzy rules are defined, such as "If the hazard and urgency of the instability pattern are high, then the risk level is high." Based on the fuzzy rules, the risk value is converted into a risk level, which is represented by a color: green represents low risk, yellow represents medium risk, and red represents high risk. Ultimately, slope risk warning data is generated.

[0138] Through geological structural interpretation, the present invention can display the geological structure and potential instability factors of the slope in detail, which helps to identify key geological structures such as sliding surfaces and fracture zones in advance. The established mountain slope stress evolution model can simulate the changing 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 can more realistically reflect the actual stability status of the slope. Through critical state identification, the critical areas and states of the slope close to instability can be clearly displayed. The displacement time series data obtained through displacement monitoring can reflect the dynamic deformation of the slope in real time and can capture the slight changes in 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 includes 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 the symbology tool of ArcGIS 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 with a different color on the map. Then, use ArcGIS's interpolation tools (such as Kriging or IDW) to spatially interpolate the risk levels of the monitoring points to generate a continuous risk level map. During the interpolation process, set the search radius to 50 meters. The final generated mountain slope risk level map clearly shows the risk levels of different areas of the slope.

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

[0143] Specifically, the slope microenvironment control plan can be imported into MATLAB software to define parameter extraction criteria. For example, for temperature control measures, the set temperature, start time, and duration are extracted; for humidity control measures, the frequency, amount, and duration of water spraying are extracted; and for vegetation restoration measures, the plant species, planting density, and maintenance cycle are extracted. Using MATLAB's data processing capabilities, these parameters are extracted from the microenvironment control plan. The specific steps include: writing a script to read the plan 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 of each measure, parameter name, and parameter value.

[0144] Step S53: performing correlation mapping between the mountainous area slope risk level map and the mountainous area slope control parameter set to obtain a mountainous area slope control response matrix;

[0145] Specifically, the mountain slope risk level map and the mountain slope control parameter set 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 control parameter set. 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 under a specific risk level. For example, the first row of the matrix represents the 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: Optimizing parameters of the slope microenvironment control scheme according to the mountainous area slope control response matrix to obtain a mountainous area slope control strategy, and evaluating the effectiveness of the mountainous area slope control strategy to obtain a slope control effectiveness evaluation report;

[0147] Specifically, the control response matrix for mountain slopes can be imported into MATLAB software. The control parameters can then be tuned using MATLAB's optimization toolbox. Specific steps include: defining the optimization objective, such as minimizing risk or maximizing stability; selecting an appropriate optimization algorithm, such as a genetic algorithm or particle swarm optimization algorithm; and setting algorithm parameters, such as population size, number of iterations, and crossover probability. The optimization algorithm is then run, the control parameters are adjusted, and an optimized control scheme is generated. Next, the optimized control scheme is implemented to control the slope's microenvironment. For example, based on the optimization results, the set temperature for temperature control can be adjusted, the spraying frequency for humidity control can be increased, and more suitable plant species for vegetation restoration can be selected. During implementation, field monitoring equipment (such as temperature sensors, humidity sensors, and displacement sensors) is used to monitor changes in the slope's microenvironment and stability in real time. The monitoring data is transmitted to a computer via a data logger (such as the NI USB-6259) for data processing and analysis using specialized software. Finally, the effectiveness of the control strategy is evaluated based on the monitoring data. 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 effect 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 prevention and control effect assessment report to obtain a residual risk map of the mountain slope;

[0149] Specifically, the data in the slope prevention and control effect assessment 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 with the original risk level map to identify areas where higher risks still exist. The final generated residual risk map for mountain slopes clearly shows the residual risk levels of different areas of the slope.

[0150] Step S56: Prepare an emergency plan based on the mountain slope residual risk map 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 for mountain slopes can be imported into the emergency plan management system, a web-based platform that stores and manages various emergency plans. Users can quickly retrieve and generate emergency plans based on risk levels and risk types. Emergency plans are compiled based on the risk levels and risk types in the residual risk map. Specific steps include: For high residual risk areas, detailed emergency evacuation plans, rescue and rescue plans, and enhanced monitoring measures are compiled; for medium residual risk areas, emergency warning mechanisms and enhanced monitoring plans are compiled; and for low residual risk areas, routine monitoring and maintenance plans are compiled. The compiled emergency plans are stored in an emergency plan library. Each plan includes risk descriptions, emergency measures, responsible units, and contact information. A decision support system (DSS) generates decision rules based on the emergency plan library. Specific steps include defining trigger conditions for decision rules, such as when the risk level exceeds a certain threshold or when monitoring data exhibits an anomaly. Based on these trigger conditions, the corresponding emergency plan is automatically invoked and decision recommendations are generated. For example, if the risk level at a monitoring point suddenly rises to high risk, the DSS will automatically invoke the emergency evacuation plan and send warning information and decision recommendations to project managers and emergency response departments. Finally, the generated decision-making scheme for slope disaster 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, it can scientifically and objectively reflect the actual effects of prevention and control measures. Through residual risk identification, it can accurately locate risk areas that still exist after prevention and control. Through the compilation of emergency plan database, 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 further provides a slope disaster prevention and control management system for difficult mountainous areas, which is used to implement the above-mentioned slope disaster prevention and control management method for difficult mountainous areas. The slope disaster prevention and control management system for difficult 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 on mountain slopes based on the slope stress field and obtain a slope freeze-thaw risk distribution map; it also simulates and controls the microenvironment of mountain slopes based on the slope freeze-thaw risk distribution map and obtains a slope microenvironment control plan;

[0156] The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain a distribution map of the slope's ecological matrix. Based on the distribution map, anchor points are arranged on the mountain slopes to obtain a mountain slope anchor network. Based on the mountain slope anchor network, support structure simulations are performed on the mountain slopes to obtain a slope reinforcement system configuration plan.

[0157] The early warning module is used to evaluate the stability of mountain slopes based on their stress fields to obtain slope stability assessment data; perform time series analysis on the slope stability assessment data to obtain a slope instability pattern library; and issue early warnings to mountain slopes based on the slope instability pattern library to obtain slope risk warning data.

[0158] The prevention and control decision-making module is used to adjust the slope microenvironment control plan based on 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 effect evaluation report; generate an emergency plan based on the slope prevention and control effect evaluation report to obtain a slope disaster prevention and control decision-making plan.

[0159] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0160] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed 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: Identify freeze-thaw sensitive areas of mountain slopes based on the stress field of mountain slopes to obtain a slope freeze-thaw risk distribution map; According to the slope freeze-thaw risk distribution map, the microenvironmental simulation and control of the mountain slopes were carried out to obtain the slope microenvironmental control plan; Step S3: Analyze the biomaterial adaptability of the mountain slope to obtain a slope ecological matrix distribution map; arrange anchor points on the mountain slope based on the slope ecological matrix distribution map to obtain a mountain slope anchor network; simulate the support structure of the mountain slope based on the mountain slope anchor network to obtain a slope reinforcement system configuration plan; 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; and 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 based on 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 a three-dimensional scan on the mountain slope to obtain a mountain slope point cloud dataset, and extracting terrain features from the mountain slope point cloud dataset 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 feature 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, a stress field model is performed on the mountain slope 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: performing critical stress assessment on the mountain slope stress field to obtain a stress sensitive area map of the mountain slope; Step S22: performing surface temperature monitoring on the mountain slope to obtain slope surface temperature monitoring data, and performing deep temperature detection on the mountain slope to obtain ground temperature distribution data on the mountain slope; 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 coefficient 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 of the mountain slope is performed 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: performing risk level zoning assessment on the mountain slopes based on the freeze-thaw prediction data of the mountain slopes to obtain an initial risk distribution map of the mountain slopes; Step S273: Modifying the initial risk distribution map of the mountainous slopes according to the thermal zoning map of the mountainous slopes to obtain a freeze-thaw risk distribution map of the slopes; 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-tuning the initial slope temperature control scheme based on the energy consumption evaluation data of the temperature control scheme to obtain a set of temperature control parameters for the mountain slope; Step S277: Based on the mountain slope temperature control parameter set and the slope freeze-thaw risk distribution map, a microenvironment response simulation control is performed on the mountain slope to obtain a 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 on the mountain slope based on the material composition map 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 to obtain a bioactivity map of the mountain slope; Step S33: monitoring the nutrients on 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 a 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 functional map; Step S352: performing matrix formulation ratio on the mountain slope according to the mountain slope nutrient distribution map to obtain a matrix ratio scheme for the mountain slope; Step S353: performing an ecological synergistic assessment based on the slope plant adaptability data and the mountain slope microbial function map to obtain an ecological adaptability map of the mountain slope; 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 a 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 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: Arranging 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 anchorage network to obtain a distribution map of the mountain slope support force; Step S365: matching support material parameters for the mountainous slope according to the mountainous slope support force distribution map to obtain a mountainous 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: performing critical state identification on the mountain slope stress evolution model according to the slope structure stability factor set to obtain a mountain slope critical state diagram; Step S45: monitoring the displacement of the mountain slope to obtain the time series data of the mountain slope displacement; 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: performing 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, 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 from the slope microenvironment control scheme to obtain a mountain slope control parameter set; Step S53: performing correlation mapping between the mountainous area slope risk level map and the mountainous area slope control parameter set to obtain a mountainous area slope control response matrix; Step S54: Optimizing parameters of the slope microenvironment control scheme according to the mountainous area slope control response matrix to obtain a mountainous area slope control strategy, and evaluating the effectiveness of the mountainous area slope control strategy to obtain a slope control effectiveness evaluation report; Step S55: performing residual risk identification on the slope prevention and control effect assessment report to obtain a residual risk map of the mountain slope; Step S56: Prepare an emergency plan based on the mountain slope residual risk map 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 by: For executing the slope disaster prevention and control management method for difficult mountainous areas as claimed in claim 1, the slope disaster prevention and control management system for difficult 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 on mountain slopes based on the slope stress field and obtain a slope freeze-thaw risk distribution map; it also simulates and controls the microenvironment of mountain slopes based on the slope freeze-thaw risk distribution map and obtains a slope microenvironment control plan; The ecological reinforcement module is used to analyze the adaptability of biomaterials on mountain slopes to obtain a distribution map of the slope's ecological matrix. Based on the distribution map, anchor points are arranged on the mountain slopes to obtain a mountain slope anchor network. Based on the mountain slope anchor network, support structure simulations are performed on the mountain slopes to obtain a slope reinforcement system configuration plan. The early warning module is used to evaluate the stability of mountain slopes based on their stress fields to obtain slope stability assessment data; perform time series analysis on the slope stability assessment data to obtain a slope instability pattern library; and issue early warnings to mountain slopes based on the slope instability pattern library to obtain slope risk warning data. The prevention and control decision-making module is used to adjust the slope microenvironment control plan based on 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 effect evaluation report; generate an emergency plan based on the slope prevention and control effect evaluation report to obtain a slope disaster prevention and control decision-making plan.

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