Geological ecological monitoring system and method thereof

Abstract

The invention relates to the technical field of geological ecological monitoring, in particular to a geological ecological monitoring system and method. The method comprises the following steps: acquiring a topographic map of a monitoring area; performing real-time data acquisition by using a sensor network according to the topographic map of the monitored area, and constructing a rock-soil physical characteristic matrix to obtain the rock-soil physical characteristic matrix; performing rock-soil mechanical response spectrum construction according to the rock-soil physical characteristic matrix to obtain a rock-soil mechanical response spectrum; according to the monitored area topographic map, point cloud data processing is utilized to construct a side slope geometric model, and a side slope three-dimensional geometric model is obtained; performing unit stress analysis according to the three-dimensional geometric model of the side slope to obtain unit stress data; and performing strength reduction calculation according to the unit stress data to obtain a reduction strength parameter. The geological ecological system is comprehensively, dynamically and systematically monitored and evaluated, so that geological disaster prevention and control and ecological environment protection are better served.

Description

Technical Field

[0001] The present invention relates to the technical field of geological ecological monitoring, and in particular to a geological ecological monitoring system and method thereof. Background Art

[0002] The development of geoecological monitoring methods has gone through a process from simple field observation to complex numerical simulation and multi-source data fusion. In the early days, it mainly relied on manual inspections, geological surveying and other means to obtain basic information such as surface deformation and groundwater level. With the advancement of technology, GPS, inclinometers, remote sensing and other technologies have been gradually introduced to improve the accuracy and efficiency of monitoring. However, these methods are isolated analyses and lack systematicity.

[0003] Isolated analysis lacks systematicity: (1) Scattered monitoring indicators: Traditional monitoring often focuses on individual indicators, such as slope stability, foundation settlement, groundwater pollution, etc., and lacks consideration of the interactions between these indicators. For example, rainfall will affect both slope stability and groundwater level, which in turn affects foundation settlement, but traditional monitoring methods are difficult to capture this correlation.

[0004] (2) Fragmented data analysis: Different monitoring indicators are usually managed by different departments or institutions, and the data collection and analysis methods are also different, which makes it difficult to integrate and share data and form a holistic understanding of the geological ecosystem. For example, there is a lack of effective data communication and collaborative analysis between geological disasters and environmental pollution.

[0005] (3) Static assessment, ignoring dynamic evolution: Traditional monitoring methods are usually based on data at a certain point in time, which makes it difficult to reflect the dynamic evolution of geological ecosystems and the interaction between different geological processes. For example, long-term changes in groundwater levels can lead to changes in soil properties, which in turn affect the stability of slopes, but traditional static assessments are difficult to capture this dynamic evolution process.

[0006] Therefore, the present invention provides a geological ecological monitoring method, which solves the problem of isolated analysis and lack of systematicity of traditional methods. Through the technology of multi-source data fusion, multi-factor coupling analysis, dynamic prediction and evaluation, and comprehensive health index, the limitations of isolated analysis of traditional geological ecological monitoring methods are solved, and comprehensive, dynamic and systematic monitoring and evaluation of geological ecosystems are achieved, so as to better serve geological disaster prevention and ecological environment protection. Summary of the invention

[0007] Based on this, it is necessary to provide a geological ecological monitoring system and method thereof to solve at least one of the above technical problems.

[0008] To achieve the above purpose, a geological ecological monitoring method comprises the following steps: Step S1: obtaining a topographic map of the monitoring area; according to the topographic map of the monitoring area, using a sensor network to collect real-time data, and constructing a geotechnical physical characteristic matrix to obtain a geotechnical physical characteristic matrix; Step S2: constructing a geotechnical response spectrum according to the geotechnical physical characteristic matrix to obtain a geotechnical response spectrum; Step S3: Based on the topographic map of the monitoring area, the geometric model of the slope is constructed by processing the point cloud data to obtain a three-dimensional geometric model of the slope; unit force analysis is performed based on the three-dimensional geometric model of the slope to obtain unit force data; strength reduction calculation is performed based on the unit force data to obtain a reduced strength parameter; slope stability assessment is performed based on the reduced strength parameter to obtain a slope safety factor; Step S4: Initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; Step S5: Conduct a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor, and foundation settlement trend curve to obtain a geological disaster risk map; conduct a geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index to implement geological ecological monitoring operations.

[0009] The present invention realizes the effective integration and management of multi-source sensor data in the monitoring area by constructing a geotechnical physical characteristic matrix, provides a complete and accurate data basis for subsequent analysis, and can intuitively reflect the temporal and spatial variation laws of geotechnical physical characteristics in the region. The construction of geotechnical mechanical response spectrum can comprehensively characterize the mechanical behavior of geotechnical materials under different stress conditions, and provide key mechanical parameters and reference basis for subsequent geological disaster risk assessment. Through three-dimensional laser scanning, point cloud data processing, discrete element simulation and strength reduction method, the quantitative evaluation of slope stability is realized, the slope safety factor is obtained, and a scientific basis is provided for geological disaster early warning. Through the construction of seepage field simulation, consolidation calculation and three-dimensional settlement prediction model, the dynamic prediction of foundation settlement is realized, and the foundation settlement trend curve is generated to provide prediction data for geological disaster prevention and control. Taking into account the geological disaster risk and ecological vulnerability, the geological ecological health model is constructed and the health index is calculated, which realizes the quantitative evaluation of the health status of the geological ecosystem, and provides scientific guidance and decision-making support for geological ecological protection and restoration.

[0010] Preferably, step S1 comprises the following steps: Step S11: Acquire a topographic map of the monitoring area; perform sensor network deployment planning according to the topographic map of the monitoring area to obtain a sensor deployment plan; Step S12: Install and calibrate sensors according to the sensor deployment plan to obtain a calibrated sensor network; Step S13: using the calibrated sensor network to collect real-time data and perform data preprocessing to obtain preprocessed sensor data; Step S14: performing multi-source physical quantity data fusion on the pre-processed sensor data to obtain a fused physical quantity data set; Step S15: constructing a geotechnical physical characteristic matrix for the fused physical quantity data set according to the time and space dimensions to obtain a geotechnical physical characteristic matrix.

[0011] The present invention can achieve comprehensive coverage of the monitoring area by combining terrain, geological conditions and monitoring targets to plan the deployment of sensor networks, and deploy sensors according to the risk levels of different areas, thereby improving monitoring efficiency, avoiding sensor redundancy and saving costs. The sensors are installed and calibrated to ensure the accuracy and reliability of sensor data, provide a high-quality data basis for subsequent data analysis and model building, reduce error transmission, and improve the accuracy of monitoring results. By using the calibrated sensor network for real-time data acquisition and preprocessing, the latest status of the monitoring area can be obtained in a timely manner, and the quality of the data can be improved through preprocessing operations such as data cleaning and outlier removal, providing reliable data support for subsequent data fusion and analysis. Through multi-source physical quantity data fusion, the information of different sensors can be comprehensively utilized to make up for the shortcomings of single sensor data, improve the integrity and accuracy of data, and more comprehensively reflect the geotechnical physical characteristics of the monitoring area. By constructing a geotechnical physical characteristic matrix according to the time and space dimensions, multi-source sensor data can be effectively organized and managed to form a structured data set, which is convenient for subsequent analysis and modeling. At the same time, it can intuitively reflect the changes of geotechnical physical characteristics at various locations in the monitoring area over time, and provide data support for geological disaster risk assessment and ecological health assessment.

[0012] Preferably, step S2 comprises the following steps: Step S21: adaptively generate a finite element mesh according to the geotechnical physical characteristic matrix to obtain an adaptive finite element mesh; Step S22: mapping the geotechnical physical characteristic matrix to the adaptive finite element mesh, assigning material properties, and obtaining an assigned finite element model; Step S23: applying boundary conditions and loads to the attributed finite element model to obtain a loaded finite element model; Step S24: solving the mechanical response and extracting data of the loaded finite element model to obtain the mechanical response data of the rock mass; Step S25: constructing a geotechnical mechanics response spectrum for the rock mass mechanics response data according to different stress conditions to obtain a geotechnical mechanics response spectrum.

[0013] The present invention can generate finite element grids according to the geotechnical physical characteristic matrix adaptively, and can encrypt the grids in the area where the physical quantity changes violently to improve the calculation accuracy under the condition of limited computing resources, while keeping a coarser grid in the area where the physical quantity changes gently, thereby optimizing the calculation efficiency and saving the calculation time and resources. The geotechnical physical characteristic matrix is ​​mapped to the adaptive finite element grid and the material attribute is assigned, so that the finite element model can more accurately reflect the actual geological conditions, ensure the reliability of the model and the accuracy of the calculation results. Boundary conditions and loads are applied to the assigned finite element model so that the model can simulate the real stress state, such as the constraint of bedrock, the constraint of lateral soil, and the influence of loads such as deadweight and rainfall, so as to more accurately predict the mechanical response of the geotechnical body. By solving the mechanical response and extracting data of the loaded finite element model, the stress and strain data of each unit in the model and the displacement data of each node can be obtained, which are the basis for constructing the geotechnical mechanical response spectrum and also provide important data support for the subsequent geological disaster risk assessment. Constructing the geotechnical mechanical response spectrum according to different stress conditions can comprehensively reflect the mechanical behavior of geotechnical materials under different stress states, such as the changes in parameters such as elastic modulus and Poisson's ratio, providing an important reference for understanding the deformation characteristics and stability of geotechnical bodies, and helping to more accurately predict the occurrence of geological disasters.

[0014] Preferably, step S3 comprises the following steps: Step S31: performing a three-dimensional laser scan of the ground surface according to the topographic map of the monitoring area to obtain three-dimensional laser scan data of the ground surface; Step S32: constructing a slope geometric model by processing point cloud data based on the surface three-dimensional laser scanning data to obtain a three-dimensional slope geometric model; Step S33: using the geotechnical physical characteristic matrix to assign soil parameters to the three-dimensional geometric model of the slope to obtain an assigned slope model; discretizing the assigned slope model to obtain a discrete unit model; Step S34: performing unit force analysis on the discrete unit model to obtain unit force data; Step S35: Perform strength reduction calculation according to unit force data to obtain reduced strength parameters; Step S36: Establishing an equilibrium equation according to the reduced strength parameter to obtain an equilibrium equation; solving node displacement and unit stress according to the equilibrium equation to obtain equilibrium state data; determining the slope safety factor according to the equilibrium state data to obtain the slope safety factor.

[0015] The present invention uses high-precision three-dimensional laser scanning to obtain surface data, which can accurately capture the topographic details of the slope surface, including tiny undulations and cracks, etc., and provide a reliable data basis for the subsequent construction of a high-precision slope geometric model. The three-dimensional geometric model of the slope is constructed using point cloud data processing technology, and the discrete point cloud data can be converted into a continuous three-dimensional surface model, which can intuitively show the geometric form of the slope and provide necessary geometric information for subsequent stability analysis. The three-dimensional geometric model of the slope is assigned soil parameters using the geotechnical physical characteristic matrix, and the model is discretized, so that the discrete unit model can more accurately reflect the geotechnical mechanical properties inside the slope and provide real material parameters for unit force analysis. Unit force analysis of the discrete unit model can obtain information such as the force size, direction and point of action of each unit, which provides an important data basis for subsequent strength reduction calculation and stability evaluation. Through strength reduction calculation, the strength parameters of the slope in the limit equilibrium state, that is, the reduced strength parameters, can be determined, providing key parameters for slope stability evaluation. By establishing the equilibrium equation based on the reduced strength parameters and solving the node displacement and unit stress, it is possible to determine whether the slope has reached the limit equilibrium state and ultimately calculate the slope safety factor, thereby quantitatively evaluating the stability of the slope and providing a scientific basis for geological disaster early warning.

[0016] Preferably, step S34 includes the following steps: Step S341: selecting a constitutive model and determining unit material parameters according to the discrete unit model to obtain unit material parameters; Step S342: integrating the unit stiffness matrix according to the unit material parameters and the discrete unit model to obtain the overall stiffness matrix; Step S343: Calculate the load vector according to the discrete element model to obtain the node load vector; Step S344: Solving the node displacement according to the overall stiffness matrix and the node load vector to obtain the node displacement data; Step S345: Calculate the unit stress and strain according to the node displacement data and the unit material parameters to obtain the unit stress and strain data; Step S346: Arrange the unit force data according to the unit stress-strain data to obtain the unit force data.

[0017] The present invention can accurately describe the mechanical behavior of the particle unit in the discrete unit model by selecting a suitable constitutive model and determining the unit material parameters, provide reliable material parameters for subsequent mechanical analysis, and make the calculation result closer to the actual situation. The overall stiffness matrix is ​​obtained by integrating the unit stiffness matrix, and the mechanical characteristics of the single unit can be assembled into the mechanical characteristics of the entire model, providing the necessary stiffness matrix for solving the node displacement. The node load vector is calculated, and various loads acting on the model, such as gravity, can be converted into equivalent loads on the node, providing the necessary load input for solving the node displacement. Solving the node displacement according to the overall stiffness matrix and the node load vector can obtain the displacement of each node in the model under the load, which is the basis for calculating the unit stress and strain. Calculating the unit stress and strain according to the node displacement data and the unit material parameters can obtain the stress and strain state of each unit in the model, providing key data for analyzing the mechanical behavior of the model. The unit stress and strain data are sorted into unit force data, and the subsequent strength reduction calculation and stability evaluation can be more conveniently performed. For example, the stress distribution inside the slope and whether there is a potential slip surface can be judged according to the unit force data.

[0018] Preferably, step S35 includes the following steps: Step S351: Initializing the reduction coefficient according to the unit force data to obtain the initial reduction coefficient; Step S352: Calculate the reduced strength parameter according to the unit force data and the initial reduction coefficient to obtain the trial reduced strength parameter; Step S353: solving the slope equilibrium state according to the unit force data and the trial-calculated reduced strength parameter to obtain the slope equilibrium state data; Step S354: performing limit equilibrium judgment on the slope equilibrium state data according to a preset limit equilibrium criterion to obtain a limit equilibrium judgment result; Step S355: updating the reduction coefficient according to the limit equilibrium judgment result, and generating the number of iterations to obtain the number of iterations and update the reduction coefficient; Step S356: When the number of iterations is less than the preset maximum number of iterations, the updated reduction coefficient is used as the initial reduction coefficient, and the process returns to step S352; otherwise, the reduced strength parameter is generated according to the updated reduction coefficient and the unit force data to obtain the reduced strength parameter.

[0019] The present invention provides a starting value for subsequent iterative calculations by initializing the reduction coefficient, and usually sets the initial value to 1.0, indicating that no strength reduction is performed in the initial state. The trial calculation of the reduced strength parameter is performed according to the initial reduction coefficient or the updated reduction coefficient, and the necessary parameter input is provided for solving the equilibrium state of the slope. The slope equilibrium state is solved by using the trial calculation of the reduced strength parameter, and the deformation and stress state of the slope under the current strength parameter can be obtained, providing a data basis for the limit equilibrium judgment. According to the preset limit equilibrium criterion, it is judged whether the slope reaches the limit equilibrium state. For example, it can be judged according to whether the unit displacement exceeds the threshold value, so as to determine whether the current strength parameter causes the slope to be unstable. According to the limit equilibrium judgment result, the reduction coefficient is updated, and the number of iterations is recorded, so that the limit equilibrium state of the slope can be gradually approached, and the number of iterative calculations can be controlled to ensure the calculation efficiency. Through iterative calculation, the reduction coefficient is gradually adjusted until the critical reduction coefficient that makes the slope reach the limit equilibrium state is found, and finally the reduced strength parameter is calculated according to the coefficient, and the parameter reflects the actual stability of the slope. The setting of the iterative process and the maximum number of iterations ensures the convergence of the calculation results and the calculation efficiency.

[0020] Preferably, step S4 comprises the following steps: Step S41: constructing a three-dimensional geological model according to the geotechnical physical characteristic matrix to obtain a three-dimensional geological model; Step S42: Initializing the model parameters of the three-dimensional geological model using finite element technology according to the slope safety factor to obtain initial model parameters; Step S43: performing seepage field simulation according to the initial model parameters to obtain seepage field data; performing consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; Step S44: predicting foundation settlement based on the seepage field data and the consolidation data according to the initial model parameters to obtain foundation settlement prediction data; Step S45: generating a three-dimensional settlement prediction model according to the foundation settlement prediction data to obtain a three-dimensional settlement prediction model; Step S46: extracting deep displacement data and surface settlement data from the preprocessed sensor data to obtain preprocessed deep displacement data and preprocessed surface settlement data; Step S47: Based on the pre-processed deep displacement data and the pre-processed surface settlement data, a three-dimensional settlement prediction model is used to perform settlement prediction and generate a trend curve to obtain a foundation settlement trend curve.

[0021] The present invention can accurately reflect the geological structure and distribution of rock and soil in the monitoring area by constructing a three-dimensional geological model according to the geotechnical physical characteristic matrix, and provide a realistic geological model foundation for subsequent seepage field simulation and consolidation calculation. The three-dimensional geological model is initialized by using the slope safety factor, and the results of the slope stability analysis can be integrated into the foundation settlement prediction, so that the initial state of the model is closer to the actual situation and the prediction accuracy is improved. By performing seepage field simulation and consolidation calculation, the temporal and spatial distribution law of pore water pressure and soil settlement in the monitoring area can be obtained, providing important data support for foundation settlement prediction. Combining initial model parameters, seepage field data and consolidation data to predict foundation settlement, the influence of various factors on foundation settlement can be comprehensively considered to improve the accuracy and reliability of the prediction. Constructing a three-dimensional settlement prediction model based on foundation settlement prediction data can establish a quantitative relationship between settlement and factors such as time, spatial position, and pore water pressure, providing an effective tool for predicting future foundation settlement trends. By extracting deep displacement data and surface settlement data from preprocessed sensor data, we can obtain the actual settlement information of the monitoring area and provide measured data for verifying and correcting the three-dimensional settlement prediction model. Using the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves can predict the settlement trend of the foundation in the future, providing a scientific basis for early warning and prevention of geological disasters.

[0022] Preferably, step S43 includes the following steps: Step S431: establishing a seepage model according to the initial model parameters to obtain a seepage model; performing seepage model grid division and parameter setting on the seepage model to obtain seepage calculation parameters; Step S432: performing seepage calculation using the seepage model and the seepage calculation parameters to obtain node water head data; performing pore water pressure calculation based on the node water head data to obtain pore water pressure data; Step S433: generating seepage field data according to the pore water pressure data to obtain seepage field data; Step S434: establishing a consolidation model according to the initial model parameters and the seepage field data to obtain a consolidation model; determining a control equation according to the consolidation model to obtain a consolidation control equation; Step S435: numerically discretizing and solving the consolidation model and the consolidation control equation to obtain time-step settlement data; accumulating the settlement amounts of the time-step settlement data to obtain accumulated settlement data; Step S436: generating consolidation data according to the accumulated settlement data to obtain consolidation data.

[0023] The present invention provides necessary model and parameter input for seepage calculation by establishing a seepage model and setting seepage calculation parameters according to initial model parameters, thereby ensuring the accuracy and reliability of calculation. By using the seepage model and seepage calculation parameters to perform seepage calculation, the water head value of each node in the model can be obtained, and then the pore water pressure can be calculated, providing data support for analyzing the law of groundwater seepage. The pore water pressure data is sorted into seepage field data, which can clearly show the spatiotemporal distribution law of pore water pressure and provide necessary input conditions for subsequent consolidation calculation. According to the initial model parameters and seepage field data, a consolidation model is established and a control equation is determined, and the seepage field is coupled with soil deformation, so that the deformation process of the soil under the action of seepage can be more accurately simulated. The consolidation model and the control equation are numerically discretized and solved, and the settlement data of each time step can be obtained, and the total settlement of each node is obtained by accumulation, providing data support for analyzing the law of soil settlement. The accumulated settlement data is sorted into consolidation data, which can fully describe the settlement process of the soil under the action of seepage, and provide key data for foundation settlement prediction.

[0024] Preferably, step S5 comprises the following steps: Step S51: collecting ecological environment data using remote sensing technology according to the topographic map of the monitoring area to obtain ecological environment data; Step S52: Performing a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor, and foundation settlement trend curve to obtain a geological disaster risk map; Step S53: Performing ecological vulnerability assessment based on ecological environment data to obtain an ecological vulnerability map; Step S54: constructing a geological ecological health model according to the geological disaster risk map and the ecological vulnerability map to obtain a geological ecological health model; Step S55: Calculate the geological ecological health index using the geological ecological health model to obtain the geological ecological health index.

[0025] The present invention can quickly and efficiently obtain a wide range of ecological and environmental information, such as vegetation coverage, land use type, etc., by using remote sensing technology to collect ecological and environmental data, and provide a data basis for ecological vulnerability assessment. Taking into account factors such as rock and soil physical characteristics, mechanical response, slope stability and foundation settlement trend, geological disaster risk assessment can be carried out, and geological disaster risks can be more comprehensively and accurately identified and assessed. The generated geological disaster risk map can provide an intuitive reference for disaster warning and prevention. Ecological vulnerability assessment can be carried out according to ecological and environmental data, and the vulnerability level of the ecosystem can be quantitatively assessed. The generated ecological vulnerability map can intuitively display the degree of ecological vulnerability in different regions, providing guidance for ecological protection and restoration. By combining the geological disaster risk map and the ecological vulnerability map to construct a geological ecological health model, the two key factors of geological disaster risk and ecological vulnerability can be organically combined to establish a more comprehensive geological ecological health assessment model, thereby more accurately reflecting the health status of the geological ecosystem. The geological ecological health index can be calculated using the geological ecological health model, and the geological ecological health status can be quantified and intuitively displayed through the geological ecological health index map, providing a scientific basis for geological ecological protection and restoration, and helping to formulate more effective ecological management strategies.

[0026] Preferably, the present invention also provides a geoecological monitoring system for executing the geoecological monitoring method as described above, the geoecological monitoring system comprising: The geological parameter mapping module is used to obtain the topographic map of the monitoring area; based on the topographic map of the monitoring area, the sensor network is used to collect real-time data and construct a geotechnical physical characteristic matrix to obtain the geotechnical physical characteristic matrix; Mechanical response modeling module, used to construct the geotechnical mechanical response spectrum according to the geotechnical physical characteristic matrix to obtain the geotechnical mechanical response spectrum; The slope stability assessment module is used to construct a slope geometry model based on the topographic map of the monitoring area by processing point cloud data to obtain a three-dimensional slope geometry model; perform unit force analysis based on the three-dimensional slope geometry model to obtain unit force data; perform strength reduction calculation based on the unit force data to obtain reduced strength parameters; perform slope stability assessment based on the reduced strength parameters to obtain a slope safety factor; The foundation settlement prediction module is used to initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; The geological ecological health index assessment module is used to conduct geological disaster risk assessment based on the rock and soil physical characteristic matrix, rock and soil mechanical response spectrum, slope safety factor and foundation settlement trend curve to obtain a geological disaster risk map; and to conduct geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index.

[0027] The present invention realizes the effective integration and management of multi-source sensor data in the monitoring area by constructing a geotechnical physical characteristic matrix, provides a complete and accurate data basis for subsequent analysis, and can intuitively reflect the temporal and spatial variation laws of geotechnical physical characteristics in the region. The construction of geotechnical mechanical response spectrum can comprehensively characterize the mechanical behavior of geotechnical materials under different stress conditions, and provide key mechanical parameters and reference basis for subsequent geological disaster risk assessment. Through three-dimensional laser scanning, point cloud data processing, discrete element simulation and strength reduction method, the quantitative evaluation of slope stability is realized, the slope safety factor is obtained, and a scientific basis is provided for geological disaster early warning. Through the construction of seepage field simulation, consolidation calculation and three-dimensional settlement prediction model, the dynamic prediction of foundation settlement is realized, and the foundation settlement trend curve is generated to provide prediction data for geological disaster prevention and control. Taking into account the geological disaster risk and ecological vulnerability, the geological ecological health model is constructed and the health index is calculated, which realizes the quantitative evaluation of the health status of the geological ecosystem, and provides scientific guidance and decision-making support for geological ecological protection and restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the steps of a geoecological monitoring method; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0029] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0030] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0032] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0033] To achieve this, please refer to Figures 1 to 3 , a geological ecological monitoring method, comprising the following steps: Step S1: obtaining a topographic map of the monitoring area; according to the topographic map of the monitoring area, using a sensor network to collect real-time data, and constructing a geotechnical physical characteristic matrix to obtain a geotechnical physical characteristic matrix; Step S2: constructing a geotechnical response spectrum according to the geotechnical physical characteristic matrix to obtain a geotechnical response spectrum; Step S3: Based on the topographic map of the monitoring area, the geometric model of the slope is constructed by processing the point cloud data to obtain a three-dimensional geometric model of the slope; unit force analysis is performed based on the three-dimensional geometric model of the slope to obtain unit force data; strength reduction calculation is performed based on the unit force data to obtain a reduced strength parameter; slope stability assessment is performed based on the reduced strength parameter to obtain a slope safety factor; Step S4: Initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; Step S5: Conduct a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor, and foundation settlement trend curve to obtain a geological disaster risk map; conduct a geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index to implement geological ecological monitoring operations.

[0034] In the embodiment of the present invention, remote sensing technology is first used to obtain ecological environment data, and then the geohazard risk assessment is performed in combination with the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor and foundation settlement trend curve to generate a geohazard risk map. Next, an ecological vulnerability assessment is performed based on the ecological environment data to generate an ecological vulnerability map. Finally, a geoecological health model is constructed in combination with the geological hazard risk map and the ecological vulnerability map, and the geoecological health index is calculated to evaluate the geoecological health status.

[0035] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of the steps of the geoecological monitoring method of the present invention. In this example, the geoecological monitoring method includes the following steps: Step S1: obtaining a topographic map of the monitoring area; according to the topographic map of the monitoring area, using a sensor network to collect real-time data, and constructing a geotechnical physical characteristic matrix to obtain a geotechnical physical characteristic matrix; In an embodiment of the present invention, a high-precision topographic map is first obtained, and then a sensor network is planned and deployed according to the topographic map to perform data collection and preprocessing. Finally, multi-source sensor data are fused and a geotechnical physical characteristic matrix is ​​constructed in the spatiotemporal dimension. The matrix includes the changes in the geotechnical physical characteristics of each location in the monitoring area over time.

[0036] Step S2: constructing a geotechnical response spectrum according to the geotechnical physical characteristic matrix to obtain a geotechnical response spectrum; In the embodiment of the present invention, an adaptive finite element grid is generated based on the geotechnical physical characteristic matrix, and the matrix data is mapped to the grid to assign material properties. After that, boundary conditions and loads are applied, mechanical response is solved, data is extracted, and geotechnical mechanical response spectra are constructed according to different stress conditions to reflect the mechanical response characteristics of geotechnical materials under different stresses.

[0037] Step S3: Based on the topographic map of the monitoring area, the geometric model of the slope is constructed by processing the point cloud data to obtain a three-dimensional geometric model of the slope; unit force analysis is performed based on the three-dimensional geometric model of the slope to obtain unit force data; strength reduction calculation is performed based on the unit force data to obtain a reduced strength parameter; slope stability assessment is performed based on the reduced strength parameter to obtain a slope safety factor; Step S4: Initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; In the embodiment of the present invention, the surface data is first acquired by using 3D laser scanning technology to construct a 3D geometric model of the slope. Then, the model is parameterized and discretized in combination with the geotechnical physical characteristic matrix to generate a discrete unit model. Next, the unit force analysis is performed, and the reduced strength parameter is calculated by the strength reduction method. Finally, the slope safety factor is obtained by solving the equilibrium equation and judging the limit equilibrium criterion.

[0038] Step S5: Perform a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor and foundation settlement trend curve to obtain a geological disaster risk map; perform a geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index to implement geological ecological monitoring operations; In the embodiment of the present invention, the three-dimensional geological model parameters are first initialized based on the geotechnical physical characteristic matrix and the slope safety factor. Then, the seepage field simulation and consolidation calculation are performed to obtain the seepage field data and consolidation data. The initial model parameters, seepage field data and consolidation data are then combined to predict the foundation settlement, and a three-dimensional settlement prediction model is constructed. Finally, the deep displacement and surface settlement data are extracted from the sensor data, and the foundation settlement trend curve is generated using the prediction model.

[0039] Preferably, step S1 comprises the following steps: Step S11: Acquire a topographic map of the monitoring area; perform sensor network deployment planning according to the topographic map of the monitoring area to obtain a sensor deployment plan; Step S12: Install and calibrate sensors according to the sensor deployment plan to obtain a calibrated sensor network; Step S13: using the calibrated sensor network to collect real-time data and perform data preprocessing to obtain preprocessed sensor data; Step S14: performing multi-source physical quantity data fusion on the pre-processed sensor data to obtain a fused physical quantity data set; Step S15: constructing a geotechnical physical characteristic matrix for the fused physical quantity data set according to the time and space dimensions to obtain a geotechnical physical characteristic matrix.

[0040] In an embodiment of the present invention, first, a high-precision laser scanner is used to carry out aerial photography and mapping of the monitoring area using an unmanned aerial vehicle (UAV) to obtain a digital elevation model (DEM) and an orthophoto map with a resolution better than 5 cm, and a topographic map of the monitoring area is generated. Then, based on the acquired topographic map of the monitoring area, ArcGIS software is used for spatial analysis in combination with the monitoring target and regional geological conditions. The monitoring area is divided into several 10m*10m grids, and the type and number of sensors for each grid are determined based on factors such as the terrain slope, elevation change, vegetation coverage, and potential geological disaster risks of each grid. For example, in areas with steep slopes and prone to landslides, displacement sensors and soil moisture sensors are preferentially deployed; in areas with low terrain and prone to ground subsidence, settlement sensors and pore water pressure sensors are preferentially deployed. Finally, the sensor type, quantity, and specific deployment location information are integrated to output a sensor deployment plan, which includes information such as the unique identifier, coordinate position, monitoring parameter type, and data acquisition frequency of each sensor.

[0041] According to the sensor deployment plan, various sensors are installed at designated locations. For example, the displacement sensor is fixed on the bedrock, the settlement sensor is buried at a specific depth below the surface, and the soil moisture sensor is inserted into the soil. After the sensor is installed, the sensor is calibrated. Taking the displacement sensor as an example, a high-precision total station is used to measure the three-dimensional coordinates of the sensor, and the measurement results are compared with the initial values ​​of the sensor to correct the initial values ​​of the sensor to ensure its measurement accuracy. Other types of sensors, such as soil moisture sensors, are calibrated using standard solutions to establish a corresponding relationship between the sensor readings and the actual moisture content. After all sensors are calibrated, the sensors are connected to the data collector, and the data collector is connected to the data center via a wireless network or a wired network to form a calibrated sensor network.

[0042] The calibrated sensor network automatically collects data at a preset collection frequency (e.g. once an hour). The data collector timestamps the collected raw data and transmits it to the data center through the network. In the data center, the received raw data is preprocessed. The preprocessing process includes data cleaning, outlier removal, and data format conversion. For example, the Raida criterion is used to remove obvious outliers, and linear interpolation is used to supplement missing data. The data of different types of sensors are uniformly converted into a standard data format and stored in a database to generate preprocessed sensor data.

[0043] Multi-source physical quantity data fusion is performed on the pre-processed sensor data. For example, the data of the displacement sensor, soil moisture sensor and pore water pressure sensor at the same location are correlated and analyzed, and the Kalman filter algorithm is used to perform weighted fusion of data from different sensors to improve data accuracy and reliability. At the same time, considering the time synchronization of data from different sensors, the time series analysis method is used to interpolate and align data with different time steps, and finally a fused physical quantity data set containing multiple physical quantities and time synchronization is generated.

[0044] Based on the fused physical quantity data set, a geotechnical physical characteristic matrix is ​​constructed according to the time and space dimensions. The monitoring area is divided into several spatial units, each unit corresponding to a matrix element. The rows of the matrix represent different time steps, and the columns represent different spatial units. The value of each matrix element is a vector containing the physical quantity data of the spatial unit at the time step, such as displacement, water content, pore water pressure, etc. For example, the matrix element (i, j) represents the physical quantity data vector of the jth spatial unit at the i-th time step. The geotechnical physical characteristic matrix finally generated fully reflects the changes of geotechnical physical characteristics at various locations in the monitoring area over time.

[0045] Preferably, step S2 comprises the following steps: Step S21: adaptively generate a finite element mesh according to the geotechnical physical characteristic matrix to obtain an adaptive finite element mesh; Step S22: mapping the geotechnical physical characteristic matrix to the adaptive finite element mesh, assigning material properties, and obtaining an assigned finite element model; Step S23: applying boundary conditions and loads to the attributed finite element model to obtain a loaded finite element model; Step S24: solving the mechanical response and extracting data of the loaded finite element model to obtain the mechanical response data of the rock mass; Step S25: constructing a geotechnical mechanics response spectrum for the rock mass mechanics response data according to different stress conditions to obtain a geotechnical mechanics response spectrum.

[0046] In an embodiment of the present invention, based on the geotechnical physical characteristic matrix, spatial dimension information is extracted, and the initial finite element mesh is generated using the Gmsh software. The initial mesh uses triangular units, and the average side length is set to 2 meters. Then, the initial mesh is adaptively encrypted according to the gradient of the physical quantity changes of different spatial units in the geotechnical physical characteristic matrix. Specifically, the standard deviation of the physical quantity in each unit is calculated. If the standard deviation is greater than a preset threshold (for example, 0.5), the unit is binary encrypted to generate four new sub-units. Repeat this process until the standard deviation of the physical quantity of all units is less than the preset threshold, or the maximum number of iterations (for example, 10 times) is reached. The finite element mesh finally generated has a higher mesh density in areas where the physical quantity changes dramatically, and a lower mesh density in areas where the physical quantity changes gently, thereby achieving optimal configuration of computing resources and obtaining an adaptive finite element mesh.

[0047] The physical quantity data in the geotechnical physical characteristic matrix are mapped to the nodes of the adaptive finite element grid. Using the inverse distance weighted interpolation method, the physical quantity value of each grid node is calculated as the weighted average of all matrix elements containing the physical quantity data within a certain range around it, and the weight is inversely proportional to the distance. Then, according to the physical quantity value of each unit node, the material properties of the unit are calculated. For example, according to the soil moisture content and pore water pressure data of the unit node, the elastic modulus and Poisson's ratio of the unit are calculated using empirical formulas. The calculated material properties are assigned to the corresponding unit to generate an assigned finite element model.

[0048] On the basis of the attributed finite element model, boundary conditions and loads are applied. Boundary conditions include displacement boundaries and stress boundaries. For example, the bottom boundary of the model is set as a fixed displacement boundary to simulate the constraint of the bedrock; the side boundary of the model is set as a fixed displacement boundary in the horizontal direction to simulate the constraint of the lateral soil. Loads include self-weight loads and external loads. The self-weight load is automatically calculated based on the volume and density of the unit. External loads such as rainfall loads are converted into uniform surface loads based on rainfall amount and rainfall duration, and applied to the top boundary of the model. After applying boundary conditions and loads, a loaded finite element model is obtained.

[0049] The finite element analysis software Abaqus is used to solve the mechanical response of the loaded finite element model. The static analysis method is used to solve the displacement, stress and strain field of the model under load. After the solution is completed, the stress and strain data of each unit in the model and the displacement data of each node are extracted. The extracted data are sorted according to the time step and spatial position to generate the rock mass mechanical response data.

[0050] Based on the rock mass mechanical response data, the geotechnical mechanical response spectrum is constructed according to different stress conditions. The stress range is divided into several intervals, such as 0-1MPa, 1-2MPa, 2-3MPa, etc. For each stress interval, the strain values ​​of all units in the interval are counted, and their average value and standard deviation are calculated. The curve is drawn with the stress interval as the horizontal axis and the average strain as the vertical axis, and the error range is expressed by the standard deviation. The curves of different stress intervals are combined to form the geotechnical mechanical response spectrum. The response spectrum reflects the mechanical response characteristics of geotechnical materials under different stress conditions.

[0051] Preferably, step S3 comprises the following steps: Step S31: performing a three-dimensional laser scan of the ground surface according to the topographic map of the monitoring area to obtain three-dimensional laser scan data of the ground surface; Step S32: constructing a slope geometric model by processing point cloud data based on the surface three-dimensional laser scanning data to obtain a three-dimensional slope geometric model; Step S33: using the geotechnical physical characteristic matrix to assign soil parameters to the three-dimensional geometric model of the slope to obtain an assigned slope model; discretizing the assigned slope model to obtain a discrete unit model; Step S34: performing unit force analysis on the discrete unit model to obtain unit force data; Step S35: Perform strength reduction calculation according to unit force data to obtain reduced strength parameters; Step S36: Establishing an equilibrium equation according to the reduced strength parameter to obtain an equilibrium equation; solving node displacement and unit stress according to the equilibrium equation to obtain equilibrium state data; determining the slope safety factor according to the equilibrium state data to obtain the slope safety factor.

[0052] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes: Step S31: performing a three-dimensional laser scan of the ground surface according to the topographic map of the monitoring area to obtain three-dimensional laser scan data of the ground surface; In an embodiment of the present invention, a ground three-dimensional laser scanner (for example, Leica ScanStation P40) is used to scan the monitoring area. During the scanning process, the scanning density is set to 1,000 points per square meter, and the scanning angle range is 360 degrees horizontally and 90 degrees vertically. The topographic map of the monitoring area is imported into the scanner control software to assist in determining the scanning range and aligning the point cloud data. After the scanning is completed, the original point cloud data containing information such as three-dimensional coordinates and reflection intensity is obtained. The original point cloud data is pre-processed by denoising, filtering, etc. to remove non-ground points such as vegetation and buildings to obtain clean surface three-dimensional laser scanning data.

[0053] Step S32: constructing a slope geometric model by processing point cloud data based on the surface three-dimensional laser scanning data to obtain a three-dimensional slope geometric model; In an embodiment of the present invention, the three-dimensional laser scanning data of the surface is imported into a point cloud processing software (e.g., CloudCompare). The point cloud segmentation function in the software is used to extract point cloud data representing the slope area. A three-dimensional geometric model of the slope is constructed based on the extracted slope point cloud data. The discrete point cloud data is connected into triangular patches using a triangular meshing method to form a continuous slope surface. By adjusting parameters such as the side length and angle of the triangular mesh, the accuracy and detail of the model are controlled, and finally a high-precision three-dimensional geometric model of the slope is generated.

[0054] Step S33: using the geotechnical physical characteristic matrix to assign soil parameters to the three-dimensional geometric model of the slope to obtain an assigned slope model; discretizing the assigned slope model to obtain a discrete unit model; In an embodiment of the present invention, the geotechnical physical characteristic matrix and the three-dimensional geometric model of the slope are spatially matched. According to the center coordinates of each unit, the corresponding physical quantity data are found in the geotechnical physical characteristic matrix. These physical quantity data, such as cohesion, internal friction angle, density, etc., are assigned to the corresponding three-dimensional geometric model units of the slope to obtain an attributed slope model. Then, the attributed slope model is discretized using discrete element software (e.g., PFC). The slope model is discretized into a number of circular or polygonal particle units, and the interaction force between the particle units is defined according to the contact model, and finally a discrete unit model is generated.

[0055] Step S34: performing unit force analysis on the discrete unit model to obtain unit force data; In the embodiment of the present invention, in the discrete element software, a gravity load is applied to the discrete unit model. The force state of the slope under the action of its own weight is simulated. The discrete element simulation is run to calculate the contact force and volume force on each particle unit. The force magnitude, direction, and coordinates of the action point of each unit are recorded. The force information of all units is summarized to generate unit force data.

[0056] Step S35: Perform strength reduction calculation according to unit force data to obtain reduced strength parameters; In the embodiment of the present invention, the strength reduction method is used to calculate the stability of the slope based on the unit force data. The initial reduction coefficient is set to 1.0. The shear strength parameters of the slope soil (cohesion c and internal friction angle ) are divided by the reduction coefficient to obtain the reduced strength parameter. The reduced strength parameter is used to recalculate the unit force and determine whether the slope has reached the limit equilibrium state. If the slope has not reached the limit equilibrium state, the reduction coefficient is increased and the above calculation process is repeated. Until the slope reaches the limit equilibrium state, the reduction coefficient at this time is recorded, which is the stability safety factor of the slope. The reduced strength parameter finally obtained is the reduced strength parameter.

[0057] Step S36: Establishing an equilibrium equation according to the reduced strength parameter to obtain an equilibrium equation; solving node displacement and unit stress according to the equilibrium equation to obtain equilibrium state data; determining the slope safety factor according to the equilibrium state data to obtain the slope safety factor; In an embodiment of the present invention, an overall equilibrium equation of the slope is established based on the reduced strength parameter. The equilibrium equation contains the force equilibrium and moment equilibrium equations of all units. The equilibrium equation is solved using a numerical method to obtain the node displacement and unit stress of the slope in the limit equilibrium state. The node displacement and unit stress data are summarized to generate equilibrium state data. Based on the equilibrium state data, the safety factor of the slope is calculated. The safety factor is defined as the ratio of the shear strength of the soil to the shear stress. If the safety factor is less than 1.0, it indicates that the slope is in an unstable state; if the safety factor is greater than 1.0, it indicates that the slope is in a stable state.

[0058] Preferably, step S34 includes the following steps: Step S341: selecting a constitutive model and determining unit material parameters according to the discrete unit model to obtain unit material parameters; Step S342: integrating the unit stiffness matrix according to the unit material parameters and the discrete unit model to obtain the overall stiffness matrix; Step S343: Calculate the load vector according to the discrete element model to obtain the node load vector; Step S344: Solving the node displacement according to the overall stiffness matrix and the node load vector to obtain the node displacement data; Step S345: Calculate the unit stress and strain according to the node displacement data and the unit material parameters to obtain the unit stress and strain data; Step S346: Arrange the unit force data according to the unit stress-strain data to obtain the unit force data.

[0059] In an embodiment of the present invention, for a discrete unit model, a linear elastic constitutive model is selected to describe the mechanical behavior between particle units. The material parameters of each discrete unit are determined according to the physical and mechanical parameters corresponding to each unit in the geotechnical physical characteristic matrix. Specifically, parameters such as density, elastic modulus, Poisson's ratio, etc. are directly assigned to the corresponding discrete units. For shear strength parameters, the Mohr-Coulomb model is used to assign cohesion c and internal friction angle φ to the corresponding discrete units. The material parameters of all discrete units are summarized to obtain unit material parameters.

[0060] Based on the element material parameters and the topological structure of the discrete element model, the stiffness matrix of each discrete element is calculated. For the linear elastic constitutive model, the calculation formula of the element stiffness matrix is: , where E is the elastic modulus, ν is Poisson's ratio, I is the unit matrix, and (1) is the all-one vector. The stiffness matrices of all units are assembled according to the node degrees of freedom to obtain the overall stiffness matrix According to the volume and density of each unit in the discrete unit model, the gravity of each unit is calculated. The gravity of each unit is distributed according to the node degrees of freedom to obtain the load of each node. The loads of all nodes are summed up to form a node load vector.

[0061] Using the linear equation solver, solve the following linear equations: , where K is the global stiffness matrix obtained in step S342, F is the node load vector obtained in step S343, and U is the node displacement vector. The node displacement vector U is obtained by solving, which contains the displacement values ​​of each node in the x, y, and z directions. The displacement data of all nodes are summarized to obtain the node displacement data.

[0062] Based on the node displacement data and unit material parameters, the strain and stress of each unit are calculated. The calculation formula of unit strain is: , where B is the unit strain matrix and U is the unit node displacement vector. The calculation formula for unit stress is: , where D is the unit elastic matrix. The stress-strain data of all units are summarized to obtain the unit stress-strain data.

[0063] Based on the unit stress-strain data, calculate the force of each unit. The calculation formula of unit force is: ,in is the unit stress, and A is the unit area. The force magnitude, direction, and coordinates of the action point of each unit are summarized to obtain the unit force data.

[0064] Preferably, step S35 includes the following steps: Step S351: Initializing the reduction coefficient according to the unit force data to obtain the initial reduction coefficient; Step S352: Calculate the reduced strength parameter according to the unit force data and the initial reduction coefficient to obtain the trial reduced strength parameter; Step S353: solving the slope equilibrium state according to the unit force data and the trial-calculated reduced strength parameter to obtain the slope equilibrium state data; Step S354: performing limit equilibrium judgment on the slope equilibrium state data according to a preset limit equilibrium criterion to obtain a limit equilibrium judgment result; Step S355: updating the reduction coefficient according to the limit equilibrium judgment result, and generating the number of iterations to obtain the number of iterations and the updated reduction coefficient; Step S356: When the number of iterations is less than the preset maximum number of iterations, the updated reduction coefficient is used as the initial reduction coefficient, and the process returns to step S352; otherwise, the reduced strength parameter is generated according to the updated reduction coefficient and the unit force data to obtain the reduced strength parameter.

[0065] In the embodiment of the present invention, the initial reduction coefficient is set to 1.0, which means that in the first iterative calculation, the original strength parameters of the unit are used without any reduction.

[0066] The cohesion c and internal friction angle in the element material parameters Divide by the initial reduction factor (or the reduction factor updated in the previous iteration) to obtain the trial reduced strength parameters and For example, if the initial cohesion c is 10 kPa, the initial internal friction angle is 20°, the initial reduction factor is 1.0, then the reduced strength parameter c' is 10kPa / 1.0=10kPa, is 20°.

[0067] Calculate the reduced strength parameters and 'Apply to discrete element model. Use discrete element software, such as PFC, to recalculate the force state of the slope under the action of its own weight and solve the equilibrium state of the slope. During the solution process, the contact force between the elements is calculated using the trial reduced strength parameters. After the solution is completed, the displacement, stress, strain and other information of each element are extracted and summarized to obtain the slope equilibrium state data.

[0068] The preset limit equilibrium criterion is: when the displacement of any unit in the slope exceeds the preset threshold (for example, 10 cm), the slope is considered to have reached the limit equilibrium state. According to the slope equilibrium state data, check whether the displacement of each unit exceeds the preset threshold. If there is a unit whose displacement exceeds the threshold, the judgment result is "reaching the limit equilibrium"; otherwise, the judgment result is "not reaching the limit equilibrium".

[0069] Update the reduction factor according to the limit equilibrium judgment result. If the judgment result is "limit equilibrium not reached", multiply the reduction factor by an increment factor (for example, 1.05) to increase the value of the reduction factor; if the judgment result is "limit equilibrium reached", multiply the reduction factor by a reduction factor (for example, 0.95) to reduce the value of the reduction factor. At the same time, the number of iterations is increased by 1. For example, if the initial number of iterations is 0, and the judgment result is "limit equilibrium not reached", the number of iterations is updated to 1, and the reduction factor is updated to 1.0*1.05=1.05.

[0070] Set the maximum number of iterations to 100. If the number of iterations is less than 100, the reduction factor is used as the new initial reduction factor, and the process returns to step S352 to perform the next round of iterative calculation. If the number of iterations reaches 100, the iterative calculation is stopped. The final reduction factor is recorded as Fs, and the original strength parameters c and Divide by Fs respectively to obtain the final reduced strength parameters c / Fs and .

[0071] Preferably, step S4 comprises the following steps: Step S41: constructing a three-dimensional geological model according to the geotechnical physical characteristic matrix to obtain a three-dimensional geological model; Step S42: Initializing the model parameters of the three-dimensional geological model using finite element technology according to the slope safety factor to obtain initial model parameters; Step S43: performing seepage field simulation according to the initial model parameters to obtain seepage field data; performing consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; Step S44: predicting foundation settlement based on the seepage field data and the consolidation data according to the initial model parameters to obtain foundation settlement prediction data; Step S45: generating a three-dimensional settlement prediction model according to the foundation settlement prediction data to obtain a three-dimensional settlement prediction model; Step S46: extracting deep displacement data and surface settlement data from the preprocessed sensor data to obtain preprocessed deep displacement data and preprocessed surface settlement data; Step S47: Based on the pre-processed deep displacement data and the pre-processed surface settlement data, a three-dimensional settlement prediction model is used to perform settlement prediction and generate a trend curve to obtain a foundation settlement trend curve.

[0072] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes: Step S41: constructing a three-dimensional geological model according to the geotechnical physical characteristic matrix to obtain a three-dimensional geological model; In an embodiment of the present invention, a three-dimensional geological model is constructed by using three-dimensional modeling software (e.g., SketchUp) in combination with a topographic map of the monitoring area and a geophysical characteristic matrix. According to the spatial distribution information of the geophysical characteristic matrix, the monitoring area is divided into different geological layers, and each layer is assigned corresponding material properties, such as soil type, density, porosity, etc. The model contains information such as surface topography, geological layer interface, and groundwater level. Ensure that the model accurately reflects the geological structure and distribution of rock and soil bodies in the monitoring area, and finally obtain a high-precision three-dimensional geological model.

[0073] Step S42: Initializing the model parameters of the three-dimensional geological model using finite element technology according to the slope safety factor to obtain initial model parameters; In an embodiment of the present invention, a three-dimensional geological model is imported into a finite element analysis software (e.g., Abaqus). The model parameters are initialized according to the slope safety factor. Specifically, the safety factor is used as an initial parameter, and the physical and mechanical parameters in the geotechnical physical characteristic matrix are combined to determine the initial stress state of each unit in the model. For example, the initial pore water pressure and effective stress are calculated based on the safety factor and the shear strength parameter of the soil. The calculated initial parameters are assigned to the corresponding units in the model to obtain a finite element model containing the initial stress state, and the model parameters are extracted as initial model parameters.

[0074] Step S43: performing seepage field simulation according to the initial model parameters to obtain seepage field data; performing consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; In an embodiment of the present invention, a seepage field simulation is performed using finite element software based on the initial model parameters. The boundary conditions of the model are set, such as groundwater level and rainfall. The seepage control equation is solved to obtain the pore water pressure distribution of each node in the model, and the pore water pressure distribution data is used as the seepage field data. Then, based on the initial model parameters and the seepage field data, consolidation calculation is performed. Considering the compressibility and permeability of the soil, the consolidation control equation is solved to obtain the changes in the settlement of each unit in the model and the pore water pressure over time, and the calculation results are organized as consolidation data.

[0075] Step S44: predicting foundation settlement based on the seepage field data and the consolidation data according to the initial model parameters to obtain foundation settlement prediction data; In the embodiment of the present invention, the foundation settlement is predicted by combining the initial model parameters, the seepage field data and the consolidation data. Specifically, the pore water pressure at each position in the soil is calculated according to the seepage field data, and the settlement of the soil under different pore water pressures is calculated according to the consolidation data. The settlement at different positions is superimposed to obtain the overall settlement of the foundation. The settlement data at different time steps are summarized to obtain the foundation settlement prediction data.

[0076] Step S45: generating a three-dimensional settlement prediction model according to the foundation settlement prediction data to obtain a three-dimensional settlement prediction model; In the embodiment of the present invention, a three-dimensional settlement prediction model is constructed based on foundation settlement prediction data. For example, a multivariate linear regression method is used to establish the relationship between settlement and factors such as time, spatial position, and pore water pressure. The model can be expressed as: , where S is the settlement, t is the time, x and y are the spatial coordinates, and p is the pore water pressure. By fitting the foundation settlement prediction data, the parameters in the model are determined and the three-dimensional settlement prediction model is obtained.

[0077] Step S46: extracting deep displacement data and surface settlement data from the preprocessed sensor data to obtain preprocessed deep displacement data and preprocessed surface settlement data; In the embodiment of the present invention, deep displacement data and surface settlement data are extracted from the preprocessed sensor data. The deep displacement data comes from a displacement sensor buried deep in the stratum, and the surface settlement data comes from a surface settlement sensor. The extracted data is screened and sorted, and outliers and noise data are removed to obtain preprocessed deep displacement data and preprocessed surface settlement data.

[0078] Step S47: Based on the pre-processed deep displacement data and the pre-processed surface settlement data, a three-dimensional settlement prediction model is used to perform settlement prediction and generate a trend curve to obtain a foundation settlement trend curve; In an embodiment of the present invention, the pre-processed deep displacement data and the pre-processed surface settlement data are input into a three-dimensional settlement prediction model to perform settlement prediction. The settlement of the foundation in a future period of time is calculated according to the model. The predicted settlement data is plotted into a time series curve to obtain a foundation settlement trend curve. This curve can intuitively show the trend of foundation settlement over time, such as whether the settlement rate is accelerating or slowing down, and the future settlement prediction.

[0079] Preferably, step S43 includes the following steps: Step S431: establishing a seepage model according to the initial model parameters to obtain a seepage model; performing seepage model grid division and parameter setting on the seepage model to obtain seepage calculation parameters; Step S432: performing seepage calculation using the seepage model and the seepage calculation parameters to obtain node water head data; performing pore water pressure calculation based on the node water head data to obtain pore water pressure data; Step S433: generating seepage field data according to the pore water pressure data to obtain seepage field data; Step S434: establishing a consolidation model according to the initial model parameters and the seepage field data to obtain a consolidation model; determining a control equation according to the consolidation model to obtain a consolidation control equation; Step S435: numerically discretizing and solving the consolidation model and the consolidation control equation to obtain time-step settlement data; accumulating the settlement amounts of the time-step settlement data to obtain accumulated settlement data; Step S436: generating consolidation data according to the accumulated settlement data to obtain consolidation data.

[0080] In an embodiment of the present invention, a seepage model is established in a finite element software (e.g., Abaqus) based on the initial model parameters. The seepage model has the same geometric shape as the three-dimensional geological model, but only considers the permeability of the soil, not its mechanical properties. The grid division scheme of the three-dimensional geological model is applied to the seepage model, and the seepage calculation parameters are set. The seepage calculation parameters include the permeability coefficient, porosity, saturation and boundary conditions of the soil, such as groundwater level, rainfall intensity and surface runoff coefficient.

[0081] Use finite element software to perform steady-state or transient seepage calculations on the seepage model. According to the set seepage calculation parameters and boundary conditions, solve the seepage control equation (for example, Darcy's law), obtain the head value of each node in the model, and generate node head data. Then, calculate the pore water pressure of each node based on the node head data and groundwater level. The pore water pressure calculation formula is: , where u is the pore water pressure, is the density of water, h is the node head value, and z is the node elevation.

[0082] The pore water pressure data are sorted according to spatial position and time step to generate seepage field data. The seepage field data contains the pore water pressure value of each node at different time steps, which fully describes the spatiotemporal distribution of pore water pressure in the monitoring area.

[0083] Based on the initial model parameters and seepage field data, a consolidation model is established in the finite element software. The consolidation model has the same geometric shape as the three-dimensional geological model, and the permeability and mechanical properties of the soil are considered at the same time. The consolidation control equation is determined using the Biot consolidation theory. The consolidation control equation describes the deformation and seepage coupling process of the soil under the combined action of pore water pressure and effective stress.

[0084] The consolidation control equation is numerically discretized, for example, by using the finite difference method or the finite element method. The consolidation model is discretized into a number of units, and the discretized control equation is solved at each time step to obtain the settlement of each node and generate time step settlement data. Then, the settlement of each time step is accumulated to obtain the accumulated settlement of each node and generate accumulated settlement data.

[0085] The accumulated settlement data and the pore water pressure data corresponding to each time step are sorted to generate consolidation data. The consolidation data contains the settlement and pore water pressure values ​​of each node at different time steps, which fully describes the change process of foundation settlement and pore water pressure over time in the monitoring area.

[0086] Preferably, step S5 comprises the following steps: Step S51: collecting ecological environment data using remote sensing technology based on the topographic map of the monitoring area to obtain ecological environment data; Step S52: Performing a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor, and foundation settlement trend curve to obtain a geological disaster risk map; Step S53: Performing ecological vulnerability assessment based on ecological environment data to obtain an ecological vulnerability map; Step S54: constructing a geological ecological health model according to the geological disaster risk map and the ecological vulnerability map to obtain a geological ecological health model; Step S55: Calculate the geological ecological health index using the geological ecological health model to obtain the geological ecological health index.

[0087] In an embodiment of the present invention, satellite remote sensing images (e.g., Landsat-8 and Sentinel-2 satellite images) are used in combination with a topographic map of the monitoring area to obtain ecological and environmental data. The spatial resolution of the remote sensing image is set to 10 meters. Remote sensing image interpretation software (e.g., ENVI) is used to extract vegetation index (e.g., normalized difference vegetation index NDVI), land use type, water area and other information. The extracted ecological and environmental information is spatially aligned with the topographic map of the monitoring area and converted into a raster data format. The size of each raster unit is 10 meters * 10 meters, and finally a raster data set containing a variety of ecological and environmental information is generated, i.e., ecological and environmental data.

[0088] Based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor and foundation settlement trend curve, the geological hazard risk assessment is carried out. The analytic hierarchy process (AHP) is used to determine the weights of various indicators. For example, the weight of the slope safety factor is set to 0.4, the weight of the foundation settlement trend curve is set to 0.3, and the weights of the geotechnical physical characteristic matrix and the geotechnical mechanical response spectrum are set to 0.15 respectively. According to the values ​​and weights of each indicator, the geological hazard risk index of each spatial unit is calculated. The risk index is spatially visualized to generate a geological hazard risk map, in which different colors represent different risk levels, such as red for high risk, yellow for medium risk, and green for low risk.

[0089] Based on ecological environment data, ecological vulnerability assessment is carried out. Using the comprehensive index method, indicators such as vegetation coverage, soil erosion degree, and land use type are selected, and each indicator is assigned a corresponding weight. For example, the weight of vegetation coverage is set to 0.4, the weight of soil erosion degree is set to 0.35, and the weight of land use type is set to 0.25. According to the value and weight of each indicator, the ecological vulnerability index of each spatial unit is calculated. The vulnerability index is spatially visualized to generate an ecological vulnerability map, in which different colors represent different levels of vulnerability, such as red for high vulnerability, yellow for moderate vulnerability, and green for low vulnerability.

[0090] Combined with the geological hazard risk map and the ecological vulnerability map, the geological ecological health model is constructed. The model adopts the form of weighted linear combination, and the geological hazard risk index and the ecological vulnerability index are weighted and summed to obtain the geological ecological health index. The model can be expressed as: , where HEI is the geological ecological health index, RI is the geological hazard risk index, VI is the ecological vulnerability index, w1 and w2 are the weights of the geological hazard risk index and the ecological vulnerability index, respectively. For example, w1 is set to 0.6 and w2 is set to 0.4.

[0091] Substitute the geological disaster risk index and ecological vulnerability index into the geological ecological health model to calculate the geological ecological health index of each spatial unit. The calculation results are spatially visualized to generate a geological ecological health index map, where different colors represent different health levels, such as green for health, yellow for sub-health, and red for unhealthy. This allows the geological ecological health status to be intuitively displayed, providing a scientific basis for subsequent ecological protection and geological disaster prevention and control.

[0092] Preferably, the present invention also provides a geoecological monitoring system for executing the geoecological monitoring method as described above, the geoecological monitoring system comprising: The geological parameter mapping module is used to obtain the topographic map of the monitoring area; based on the topographic map of the monitoring area, the sensor network is used to collect real-time data and construct a geotechnical physical characteristic matrix to obtain the geotechnical physical characteristic matrix; Mechanical response modeling module, used to construct the geotechnical mechanical response spectrum according to the geotechnical physical characteristic matrix to obtain the geotechnical mechanical response spectrum; The slope stability assessment module is used to construct a slope geometry model based on the topographic map of the monitoring area by processing point cloud data to obtain a three-dimensional slope geometry model; perform unit force analysis based on the three-dimensional slope geometry model to obtain unit force data; perform strength reduction calculation based on the unit force data to obtain reduced strength parameters; perform slope stability assessment based on the reduced strength parameters to obtain a slope safety factor; The foundation settlement prediction module is used to initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; The geological ecological health index assessment module is used to conduct geological disaster risk assessment based on the rock and soil physical characteristic matrix, rock and soil mechanical response spectrum, slope safety factor and foundation settlement trend curve to obtain a geological disaster risk map; and to conduct geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index.

[0093] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0094] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A geological ecological monitoring method, characterized in that: The following steps are involved: Step S1: obtaining a topographic map of the monitoring area; according to the topographic map of the monitoring area, using a sensor network to collect real-time data, and constructing a geotechnical physical characteristic matrix to obtain a geotechnical physical characteristic matrix; Step S2: constructing a geotechnical response spectrum according to the geotechnical physical characteristic matrix to obtain a geotechnical response spectrum; Step S3: Based on the topographic map of the monitoring area, the geometric model of the slope is constructed by processing the point cloud data to obtain a three-dimensional geometric model of the slope; unit force analysis is performed based on the three-dimensional geometric model of the slope to obtain unit force data; strength reduction calculation is performed based on the unit force data to obtain a reduced strength parameter; slope stability assessment is performed based on the reduced strength parameter to obtain a slope safety factor; Step S4: Initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; Step S5: Conducting geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor and foundation settlement trend curve to obtain a geological disaster risk map; Conduct a geological ecological health assessment based on the geological hazard risk map and obtain the geological ecological health index to realize geological ecological monitoring operations.

2. The geological ecological monitoring method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a topographic map of the monitoring area; perform sensor network deployment planning according to the topographic map of the monitoring area to obtain a sensor deployment plan; Step S12: Install and calibrate sensors according to the sensor deployment plan to obtain a calibrated sensor network; Step S13: using the calibrated sensor network to collect real-time data and perform data preprocessing to obtain preprocessed sensor data; Step S14: performing multi-source physical quantity data fusion on the pre-processed sensor data to obtain a fused physical quantity data set; Step S15: constructing a geotechnical physical characteristic matrix for the fused physical quantity data set according to the time and space dimensions to obtain a geotechnical physical characteristic matrix.

3. The geological ecological monitoring method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: adaptively generate a finite element mesh according to the geotechnical physical characteristic matrix to obtain an adaptive finite element mesh; Step S22: mapping the geotechnical physical characteristic matrix to the adaptive finite element mesh, assigning material properties, and obtaining an assigned finite element model; Step S23: applying boundary conditions and loads to the attributed finite element model to obtain a loaded finite element model; Step S24: solving the mechanical response and extracting data of the loaded finite element model to obtain the mechanical response data of the rock mass; Step S25: constructing a geotechnical mechanics response spectrum for the rock mass mechanics response data according to different stress conditions to obtain a geotechnical mechanics response spectrum.

4. The geological ecological monitoring method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing a three-dimensional laser scan of the ground surface according to the topographic map of the monitoring area to obtain three-dimensional laser scan data of the ground surface; Step S32: constructing a slope geometric model by processing point cloud data based on the surface three-dimensional laser scanning data to obtain a three-dimensional slope geometric model; Step S33: using the geotechnical physical characteristic matrix to assign soil parameters to the three-dimensional geometric model of the slope to obtain an assigned slope model; discretizing the assigned slope model to obtain a discrete unit model; Step S34: performing unit force analysis on the discrete unit model to obtain unit force data; Step S35: Perform strength reduction calculation according to unit force data to obtain reduced strength parameters; Step S36: Establishing an equilibrium equation according to the reduced strength parameter to obtain an equilibrium equation; solving node displacement and unit stress according to the equilibrium equation to obtain equilibrium state data; determining the slope safety factor according to the equilibrium state data to obtain the slope safety factor.

5. The geological ecological monitoring method according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: selecting a constitutive model and determining unit material parameters according to the discrete unit model to obtain unit material parameters; Step S342: integrating the unit stiffness matrix according to the unit material parameters and the discrete unit model to obtain the overall stiffness matrix; Step S343: Calculate the load vector according to the discrete element model to obtain the node load vector; Step S344: Solving the node displacement according to the overall stiffness matrix and the node load vector to obtain the node displacement data; Step S345: Calculate the unit stress and strain according to the node displacement data and the unit material parameters to obtain the unit stress and strain data; Step S346: Arrange the unit force data according to the unit stress-strain data to obtain the unit force data.

6. The method for geoecological monitoring according to claim 4, characterized in that: Step S35 includes the following steps: Step S351: Initializing the reduction coefficient according to the unit force data to obtain the initial reduction coefficient; Step S352: Calculate the reduced strength parameter according to the unit force data and the initial reduction coefficient to obtain the trial reduced strength parameter; Step S353: solving the slope equilibrium state according to the unit force data and the trial-calculated reduced strength parameter to obtain the slope equilibrium state data; Step S354: performing limit equilibrium judgment on the slope equilibrium state data according to a preset limit equilibrium criterion to obtain a limit equilibrium judgment result; Step S355: updating the reduction coefficient according to the limit equilibrium judgment result, and generating the number of iterations to obtain the number of iterations and the updated reduction coefficient; Step S356: When the number of iterations is less than the preset maximum number of iterations, the updated reduction coefficient is used as the initial reduction coefficient, and the process returns to step S352; otherwise, the reduced strength parameter is generated according to the updated reduction coefficient and the unit force data to obtain the reduced strength parameter.

7. The geological ecological monitoring method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a three-dimensional geological model according to the geotechnical physical characteristic matrix to obtain a three-dimensional geological model; Step S42: Initializing the model parameters of the three-dimensional geological model using finite element technology according to the slope safety factor to obtain initial model parameters; Step S43: performing seepage field simulation according to the initial model parameters to obtain seepage field data; performing consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; Step S44: predicting foundation settlement based on the seepage field data and the consolidation data according to the initial model parameters to obtain foundation settlement prediction data; Step S45: generating a three-dimensional settlement prediction model according to the foundation settlement prediction data to obtain a three-dimensional settlement prediction model; Step S46: extracting deep displacement data and surface settlement data from the preprocessed sensor data to obtain preprocessed deep displacement data and preprocessed surface settlement data; Step S47: Based on the pre-processed deep displacement data and the pre-processed surface settlement data, a three-dimensional settlement prediction model is used to perform settlement prediction and generate a trend curve to obtain a foundation settlement trend curve.

8. The method for geoecological monitoring according to claim 7, characterized in that: Step S43 includes the following steps: Step S431: establishing a seepage model according to the initial model parameters to obtain a seepage model; performing seepage model grid division and parameter setting on the seepage model to obtain seepage calculation parameters; Step S432: performing seepage calculation using the seepage model and the seepage calculation parameters to obtain node water head data; performing pore water pressure calculation based on the node water head data to obtain pore water pressure data; Step S433: generating seepage field data according to the pore water pressure data to obtain seepage field data; Step S434: establishing a consolidation model according to the initial model parameters and the seepage field data to obtain a consolidation model; determining a control equation according to the consolidation model to obtain a consolidation control equation; Step S435: numerically discretizing and solving the consolidation model and the consolidation control equation to obtain time-step settlement data; accumulating the settlement amounts of the time-step settlement data to obtain accumulated settlement data; Step S436: generating consolidation data according to the accumulated settlement data to obtain consolidation data.

9. The method for geoecological monitoring according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: collecting ecological environment data using remote sensing technology based on the topographic map of the monitoring area to obtain ecological environment data; Step S52: Performing a geological disaster risk assessment based on the geotechnical physical characteristic matrix, geotechnical mechanical response spectrum, slope safety factor, and foundation settlement trend curve to obtain a geological disaster risk map; Step S53: Performing ecological vulnerability assessment based on ecological environment data to obtain an ecological vulnerability map; Step S54: constructing a geological ecological health model according to the geological disaster risk map and the ecological vulnerability map to obtain a geological ecological health model; Step S55: Calculate the geological ecological health index using the geological ecological health model to obtain the geological ecological health index.

10. A geological ecological monitoring system, characterized in that: Used to perform the geoecological monitoring method as claimed in claim 1, the geoecological monitoring system comprises: The geological parameter mapping module is used to obtain the topographic map of the monitoring area; based on the topographic map of the monitoring area, the sensor network is used to collect real-time data and construct a geotechnical physical characteristic matrix to obtain the geotechnical physical characteristic matrix; Mechanical response modeling module, used to construct the geotechnical mechanical response spectrum according to the geotechnical physical characteristic matrix to obtain the geotechnical mechanical response spectrum; The slope stability assessment module is used to construct a slope geometry model based on the topographic map of the monitoring area by processing point cloud data to obtain a three-dimensional slope geometry model; perform unit force analysis based on the three-dimensional slope geometry model to obtain unit force data; perform strength reduction calculation based on the unit force data to obtain reduced strength parameters; perform slope stability assessment based on the reduced strength parameters to obtain a slope safety factor; The foundation settlement prediction module is used to initialize the three-dimensional geological model parameters according to the geotechnical physical characteristic matrix and the slope safety factor to obtain the initial model parameters; perform seepage field simulation according to the initial model parameters to obtain seepage field data; perform consolidation calculation according to the initial model parameters and the seepage field data to obtain consolidation data; construct a three-dimensional settlement prediction model according to the seepage field data and the consolidation data to obtain a three-dimensional settlement prediction model; use the three-dimensional settlement prediction model to perform settlement prediction and generate trend curves to obtain a foundation settlement trend curve; The geological ecological health index assessment module is used to conduct geological disaster risk assessment based on the rock and soil physical characteristic matrix, rock and soil mechanical response spectrum, slope safety factor and foundation settlement trend curve to obtain a geological disaster risk map; and to conduct geological ecological health assessment based on the geological disaster risk map to obtain a geological ecological health index.

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