Geological disaster real-time monitoring platform and method based on intelligent AI

Abstract

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster real-time monitoring platform and method based on intelligent AI. The method comprises the following steps: acquiring multi-source monitoring data; analyzing the multi-source monitoring data, and determining the stability of the soil body; determining soil body characteristic parameters based on soil body stability; obtaining environmental hydrological data, and determining a geologic structure dynamic response mode according to the environmental hydrological data; and determining a geological disaster risk level according to the soil body characteristic parameters and the geological structure dynamic response mode. Soil body characteristic parameters are determined, quantitative indexes of building specificity are provided, and customized risk assessment is supported. The method comprises the following steps: acquiring environmental hydrological data, determining a dynamic response mode of a geologic structure according to the environmental hydrological data, capturing real-time behavior characteristics of the geologic structure under environmental change, and identifying a weak area of the geologic structure. And according to the soil body characteristic parameters and the geological structure dynamic response mode, determining the geological disaster risk level, realizing building level early warning, and quantifying the risk evolution rate.

Description

Technical Field

[0001] The present application relates to the field of geological disaster monitoring technology, and in particular to a real-time geological disaster monitoring platform and method based on intelligent AI. Background Art

[0002] With the acceleration of urbanization, underground infrastructure excavation is becoming increasingly frequent, leading to a significant increase in the incidence of urban geological disasters. These geological disasters not only cause ground deformation, but also lead to building foundation sinking, wall cracking, foundation instability, and even collapse.

[0003] In existing technologies, geological disaster monitoring mainly relies on a single data source, resulting in low monitoring accuracy and delayed early warning, which cannot meet the urgent needs of modern cities for "early identification, early warning, and early disposal" of geological disasters. Summary of the Invention

[0004] This application provides a real-time geological disaster monitoring platform and method based on intelligent AI to solve the above problems.

[0005] In a first aspect, the present application provides a method for real-time monitoring of geological disasters based on intelligent AI, the method comprising:

[0006] Acquiring multi-source monitoring data; analyzing the multi-source monitoring data to determine soil stability;

[0007] determining soil characteristic parameters based on the soil stability;

[0008] Acquiring environmental hydrological data, and determining a geological structure dynamic response mode based on the environmental hydrological data;

[0009] The geological disaster risk level is determined based on the soil characteristic parameters and the dynamic response mode of the geological structure.

[0010] This solution allows for the acquisition of multi-source monitoring data, avoiding the one-sidedness caused by a single data source, thereby supporting a comprehensive assessment of soil stability. Multi-source monitoring data is analyzed to determine soil stability and quantify the integrity of soil stress conduction paths, thereby improving monitoring accuracy. Based on soil stability, soil characteristic parameters are determined, providing building-specific quantitative indicators to support customized risk assessments. Environmental hydrological data is obtained, and based on this data, the dynamic response pattern of the geological structure is determined, capturing the real-time behavioral characteristics of the geological structure under environmental changes and identifying weak areas of the geological structure. Based on the soil characteristic parameters and the dynamic response pattern of the geological structure, the geological hazard risk level is determined, building-level early warnings are achieved, and the risk evolution rate is quantified.

[0011] Optionally, the multi-source monitoring data includes soil stress data, and determining soil characteristic parameters based on the soil stability includes:

[0012] Analyzing the multi-source monitoring data to obtain underground structure deformation data;

[0013] Analyzing the underground structure deformation data to determine the spatiotemporal distribution characteristics of the underground structure;

[0014] Analyzing the soil stress data based on the spatiotemporal distribution characteristics to determine the soil stress conduction path;

[0015] According to the soil stress conduction path, soil characteristic parameters are determined.

[0016] Optionally, the multi-source monitoring data further includes rock and soil layer distribution parameters, and the analyzing the soil stress data based on the spatiotemporal distribution characteristics to determine the soil stress conduction path includes:

[0017] constructing three-dimensional geological data based on the rock and soil layer distribution parameters and the soil stress data;

[0018] constructing a stress transfer attenuation function based on the three-dimensional geological data;

[0019] A soil stress conduction path is constructed according to the spatiotemporal distribution characteristics and the stress transfer attenuation function.

[0020] Optionally, determining the geological structure dynamic response mode according to the environmental hydrological data includes:

[0021] determining a rate of change of osmotic pressure based on the environmental hydrological data;

[0022] Determining a weak geological structure area based on the soil stress conduction path and the seepage pressure change rate;

[0023] The dynamic response mode of the geological structure is determined according to the seepage pressure change rate and the weak area of ​​the geological structure.

[0024] Optionally, determining the geological disaster risk level according to the soil characteristic parameters and the geological structure dynamic response mode includes:

[0025] Obtain historical disaster cases, analyze the historical disaster cases, and determine disaster process data;

[0026] Constructing a spatiotemporal coupling prediction model based on the disaster process data;

[0027] Inputting the soil characteristic parameters into the space-time coupling prediction model, and obtaining the displacement gradient change rate and energy accumulation coefficient output by the space-time coupling prediction model;

[0028] Calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient and the seepage pressure change rate;

[0029] The geological disaster risk level is determined based on the dynamic response mode of the geological structure and the risk evolution rate.

[0030] Optionally, constructing a spatiotemporal coupling prediction model using the disaster process data includes:

[0031] Classifying the disaster process data to determine disaster precursor parameters and disaster development data;

[0032] Analyzing the disaster precursor parameters and the disaster development data based on the time series to determine the correlation characteristics between the disaster precursor parameters and the disaster development time series;

[0033] Based on the correlation characteristics, a long short-term memory network is used to construct a time correlation sub-model according to the disaster precursor parameters and the disaster development data to obtain a time-space coupling prediction model.

[0034] Optionally, calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient, and the seepage pressure change rate includes:

[0035] Analyzing the environmental hydrological data to determine historical rainfall intensity for the current season;

[0036] determining a rainfall-permeability coupling coefficient according to the energy accumulation coefficient, the weak geological structure area, and the historical rainfall intensity;

[0037] Obtain basic information of each building to be monitored;

[0038] Analyze the basic information to determine the buried depth characteristics of each building to be monitored;

[0039] generating a burial depth influence weight matrix according to the burial depth characteristics and the displacement gradient change rate;

[0040] Establishing a three-dimensional risk evolution model based on the rainfall-infiltration coupling coefficient and the burial depth influence weight matrix;

[0041] According to the three-dimensional risk evolution model, the risk evolution rate of each building to be monitored is output.

[0042] Optionally, determining the building to be monitored includes:

[0043] Analyze the weak geological structure areas and determine the weak structural scope;

[0044] Acquire city structure information, analyze the city structure information, and determine city buildings and coordinate information of each city building;

[0045] The structural weakness range and the coordinate information are coupled, and the building to be monitored is determined according to the coupling result.

[0046] Optionally, the environmental hydrological data includes groundwater level monitoring data, and determining the seepage pressure change rate based on the environmental hydrological data includes:

[0047] Analyzing the groundwater level monitoring data to determine the soil pore water pressure gradient;

[0048] Determine the permeability coefficient and thickness of each soil layer according to the rock and soil layer distribution parameters;

[0049] According to Darcy's law, the infiltration velocity equation of each soil layer is established;

[0050] Calculating the stratified seepage pressure attenuation rate based on the seepage velocity equation and the groundwater level monitoring data;

[0051] The osmotic pressure transfer function is generated by weighted superposition of the osmotic pressure decay rate of each layer;

[0052] A seepage pressure change rate is generated according to the seepage pressure transfer function and the soil pore water pressure change gradient.

[0053] In a second aspect, the present application provides a real-time geological disaster monitoring platform based on intelligent AI, the platform comprising:

[0054] A data analysis module is used to obtain multi-source monitoring data; analyze the multi-source monitoring data to determine soil stability;

[0055] A parameter determination module, configured to determine soil characteristic parameters based on the soil stability;

[0056] A mode determination module is used to obtain environmental hydrological data and determine a geological structure dynamic response mode based on the environmental hydrological data;

[0057] The level determination module is used to determine the geological disaster risk level based on the soil characteristic parameters and the dynamic response mode of the geological structure.

[0058] Optionally, the multi-source monitoring data includes soil stress data, and the parameter determination module determines soil characteristic parameters based on the soil stability, and is used to:

[0059] Analyzing the multi-source monitoring data to obtain underground structure deformation data;

[0060] Analyzing the underground structure deformation data to determine the spatiotemporal distribution characteristics of the underground structure;

[0061] Analyzing the soil stress data based on the spatiotemporal distribution characteristics to determine the soil stress conduction path;

[0062] According to the soil stress conduction path, soil characteristic parameters are determined.

[0063] Optionally, the multi-source monitoring data further includes geotechnical layer distribution parameters. When the parameter determination module analyzes the soil stress data based on the spatiotemporal distribution characteristics and determines the soil stress conduction path, it is used to:

[0064] constructing three-dimensional geological data based on the rock and soil layer distribution parameters and the soil stress data;

[0065] constructing a stress transfer attenuation function based on the three-dimensional geological data;

[0066] A soil stress conduction path is constructed according to the spatiotemporal distribution characteristics and the stress transfer attenuation function.

[0067] Optionally, when the mode determination module determines the dynamic response mode of the geological structure according to the environmental hydrological data, it is used to:

[0068] determining a rate of change of osmotic pressure based on the environmental hydrological data;

[0069] Determining a weak geological structure area based on the soil stress conduction path and the seepage pressure change rate;

[0070] The dynamic response mode of the geological structure is determined according to the seepage pressure change rate and the weak area of ​​the geological structure.

[0071] Optionally, when determining the geological hazard risk level based on the soil characteristic parameters and the geological structure dynamic response mode, the level determination module is used to:

[0072] Obtain historical disaster cases, analyze the historical disaster cases, and determine disaster process data;

[0073] Constructing a spatiotemporal coupling prediction model based on the disaster process data;

[0074] Inputting the soil characteristic parameters into the space-time coupling prediction model, and obtaining the displacement gradient change rate and energy accumulation coefficient output by the space-time coupling prediction model;

[0075] Calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient and the seepage pressure change rate;

[0076] The geological disaster risk level is determined based on the dynamic response mode of the geological structure and the risk evolution rate.

[0077] Optionally, when the level determination module constructs a spatiotemporal coupling prediction model using the disaster process data, it is used to:

[0078] Classifying the disaster process data to determine disaster precursor parameters and disaster development data;

[0079] Analyzing the disaster precursor parameters and the disaster development data based on the time series to determine the correlation characteristics between the disaster precursor parameters and the disaster development time series;

[0080] Based on the correlation characteristics, a long short-term memory network is used to construct a time correlation sub-model according to the disaster precursor parameters and the disaster development data to obtain a time-space coupling prediction model.

[0081] Optionally, when the level determination module calculates the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient, and the seepage pressure change rate, it is configured to:

[0082] Analyzing the environmental hydrological data to determine historical rainfall intensity for the current season;

[0083] determining a rainfall-permeability coupling coefficient according to the energy accumulation coefficient, the weak geological structure area, and the historical rainfall intensity;

[0084] Obtain basic information of each building to be monitored;

[0085] Analyze the basic information to determine the buried depth characteristics of each building to be monitored;

[0086] generating a burial depth influence weight matrix according to the burial depth characteristics and the displacement gradient change rate;

[0087] Establishing a three-dimensional risk evolution model based on the rainfall-infiltration coupling coefficient and the burial depth influence weight matrix;

[0088] According to the three-dimensional risk evolution model, the risk evolution rate of each building to be monitored is output.

[0089] Optionally, the intelligent AI-based real-time geological disaster monitoring platform further includes a building determination module, which is used to:

[0090] Analyze the weak geological structure areas and determine the weak structural scope;

[0091] Acquire city structure information, analyze the city structure information, and determine city buildings and coordinate information of each city building;

[0092] The structural weakness range and the coordinate information are coupled, and the building to be monitored is determined according to the coupling result.

[0093] Optionally, the environmental hydrological data includes groundwater level monitoring data, and the mode determination module is used to determine the seepage pressure change rate based on the environmental hydrological data:

[0094] Analyzing the groundwater level monitoring data to determine the soil pore water pressure gradient;

[0095] Determine the permeability coefficient and thickness of each soil layer according to the rock and soil layer distribution parameters;

[0096] According to Darcy's law, the infiltration velocity equation of each soil layer is established;

[0097] Calculating the stratified seepage pressure attenuation rate based on the seepage velocity equation and the groundwater level monitoring data;

[0098] The osmotic pressure transfer function is generated by weighted superposition of the osmotic pressure decay rate of each layer;

[0099] A seepage pressure change rate is generated according to the seepage pressure transfer function and the soil pore water pressure change gradient. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0101] Figure 1 A schematic diagram of an application scenario provided in one embodiment of the present application;

[0102] Figure 2 A flowchart of a method for real-time monitoring of geological disasters based on intelligent AI provided in one embodiment of the present application;

[0103] Figure 3 A schematic diagram of the structure of a real-time geological disaster monitoring platform based on intelligent AI provided in one embodiment of the present application. DETAILED DESCRIPTION

[0104] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0105] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0106] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0107] In existing technologies, geological disaster monitoring mainly relies on a single data source, resulting in low monitoring accuracy and delayed early warning, which cannot meet the urgent needs of modern cities for "early identification, early warning, and early disposal" of geological disasters.

[0108] Based on this, the present application provides a real-time monitoring platform and method for geological disasters based on intelligent AI to obtain multi-source monitoring data, avoid the one-sidedness caused by a single data source, and thus support a comprehensive assessment of soil stability. Analyze multi-source monitoring data, determine soil stability, and quantify the integrity of soil stress conduction paths, thereby improving monitoring accuracy. Based on soil stability, determine soil characteristic parameters, provide building-specific quantitative indicators, and support customized risk assessment. Obtain environmental hydrological data, and determine the dynamic response pattern of the geological structure based on the environmental hydrological data, capture the real-time behavioral characteristics of the geological structure under environmental changes, and identify weak areas of the geological structure. Determine the geological disaster risk level based on the soil characteristic parameters and the dynamic response pattern of the geological structure, achieve building-level early warning, and quantify the risk evolution rate.

[0109] Figure 1 This is a schematic diagram of an application scenario provided by this application. The method provided by this application is applied when conducting real-time monitoring of geological disasters.

[0110] Specifically, the method provided in this application is applied to any server, and the server interacts with the IoT sensor to obtain multi-source monitoring data through the IoT sensor. Analyze the multi-source monitoring data to determine the stability of the soil. Based on the stability of the soil, determine the soil characteristic parameters, provide building-specific quantitative indicators, and support customized risk assessment. Obtain environmental hydrological data, and determine the dynamic response pattern of the geological structure based on the environmental hydrological data, capture the real-time behavioral characteristics of the geological structure under environmental changes, and identify weak areas of the geological structure. Based on the soil characteristic parameters and the dynamic response pattern of the geological structure, determine the geological disaster risk level, achieve building-level early warning, and quantify the risk evolution rate.

[0111] For specific implementation methods, please refer to the following embodiments.

[0112] Figure 2 This is a flowchart of a method for real-time monitoring of geological disasters based on intelligent AI provided in one embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes:

[0113] S201, obtaining multi-source monitoring data; analyzing the multi-source monitoring data to determine soil stability;

[0114] Multi-source monitoring data can be a heterogeneous data set from multiple sensors.

[0115] Soil stability can be the ability of soil to resist deformation under external loads.

[0116] Specifically, IoT sensors collect multi-source monitoring data, including soil stress, displacement, and hydrological data, in real time. The system then analyzes this multi-source data, correlates soil stress with displacement data, and calculates a stress-displacement coupling index. Hydrological data is also incorporated to assess the impact of groundwater infiltration on pore pressure. Subsequently, a spatiotemporal coupled prediction model is trained based on historical disaster case data, and soil stability is output through a machine learning model.

[0117] S202. Determine soil characteristic parameters based on soil stability;

[0118] Soil characteristic parameters can be quantitative indicators derived from soil stability analysis.

[0119] Specifically, based on soil stability, the displacement gradient change rate is determined through time series analysis of displacement data; and the stress data is numerically integrated to calculate the cumulative value of stress in the time domain, namely the energy accumulation coefficient; thereby determining the soil characteristic parameters including the displacement gradient change rate and the energy accumulation coefficient.

[0120] S203, obtaining environmental hydrological data, and determining a geological structure dynamic response mode based on the environmental hydrological data;

[0121] Environmental hydrological data can be data related to the hydrological environment that is monitored in real time.

[0122] The dynamic response mode of geological structure can be the real-time behavioral characteristics of geological structure under environmental changes.

[0123] Specifically, environmental hydrological data is obtained in real time through hydrological monitoring equipment; then, the seepage pressure change rate is calculated based on the environmental hydrological data; through the seepage pressure change rate, weak areas of geological structure are identified and the dynamic response mode of the geological structure is determined.

[0124] S204. Determine the geological disaster risk level based on soil characteristic parameters and the dynamic response model of the geological structure.

[0125] The geological disaster risk level can be the probability and severity level of the disaster.

[0126] Specifically, the soil characteristic parameters and the dynamic response pattern of the geological structure are input into the risk assessment model; then, the soil characteristic parameters and the dynamic response pattern of the geological structure are correlated to quantify the dynamic changes in risk, thereby determining the geological hazard risk level.

[0127] This solution allows for the acquisition of multi-source monitoring data, avoiding the one-sidedness caused by a single data source, thereby supporting a comprehensive assessment of soil stability. Multi-source monitoring data is analyzed to determine soil stability and quantify the integrity of soil stress conduction paths, thereby improving monitoring accuracy. Based on soil stability, soil characteristic parameters are determined, providing building-specific quantitative indicators to support customized risk assessments. Environmental hydrological data is obtained, and based on this data, the dynamic response pattern of the geological structure is determined, capturing the real-time behavioral characteristics of the geological structure under environmental changes and identifying weak areas of the geological structure. Based on the soil characteristic parameters and the dynamic response pattern of the geological structure, the geological hazard risk level is determined, building-level early warnings are achieved, and the risk evolution rate is quantified.

[0128] In some embodiments, multi-source monitoring data is parsed to obtain underground structure deformation data; the underground structure deformation data is analyzed to determine the spatiotemporal distribution characteristics of the underground structure; based on the spatiotemporal distribution characteristics, soil stress data is analyzed to determine the soil stress conduction path; and based on the soil stress conduction path, soil characteristic parameters are determined.

[0129] The underground structure deformation data may be time series data of underground structure deformation information.

[0130] An underground structure may be an underground infrastructure or a geological formation.

[0131] The spatiotemporal distribution characteristics can be the distribution patterns of data in the time and space dimensions.

[0132] Soil stress data can be quantitative data of the stress on the soil.

[0133] The soil stress conduction path can be the trajectory of stress propagation in the soil.

[0134] Specifically, noise filtering and data alignment algorithms are used to analyze multi-source monitoring data, separating and outputting underground structure deformation data. Time series analysis methods are then applied to process underground structure deformation data, calculating the temporal trend of deformation. Furthermore, spatial interpolation algorithms are used to analyze the spatial distribution of deformation data and generate spatiotemporal distribution characteristics. Based on these spatiotemporal distribution characteristics, a path tracing algorithm is used to analyze soil stress data and construct soil stress conduction paths. Finally, based on the soil stress conduction paths, the displacement gradient change rate is determined using the displacement gradient change rate; the energy accumulation coefficient is also determined using the energy accumulation coefficient. Finally, soil characteristic parameters, including the displacement gradient change rate and the energy accumulation coefficient, are determined.

[0135] This solution analyzes multi-source monitoring data to obtain underground structure deformation data, enabling quantitative extraction of underground structure deformation information and enhancing the comprehensive assessment of soil stability. Analyze underground structure deformation data to determine the spatiotemporal distribution characteristics of underground structures, revealing the temporal and spatial variation patterns of underground structure deformation, assist in identifying weak areas of geological structure, and provide support for the construction of soil stress conduction paths. Based on the spatiotemporal distribution characteristics, analyze soil stress data, determine soil stress conduction paths, quantify the dynamic trajectory of stress propagation, and compensate for deficiencies in soil stress data processing, thereby improving the accuracy of dynamic monitoring of soil stability. Based on the soil stress conduction paths, determine soil characteristic parameters to eliminate inaccurate risk assessments.

[0136] In some embodiments, three-dimensional geological data is constructed based on rock and soil layer distribution parameters and soil stress data; a stress transfer attenuation function is constructed based on the three-dimensional geological data; and a soil stress conduction path is constructed based on the spatiotemporal distribution characteristics and the stress transfer attenuation function.

[0137] Geotechnical layer distribution parameters can be parameters that describe the characteristics of geotechnical layers.

[0138] The three-dimensional geological data may be three-dimensional model data representing a geological structure.

[0139] The stress transfer attenuation function can be a function that describes the attenuation law of stress when it is transferred in soil.

[0140] Specifically, a spatial interpolation algorithm is used to fuse the distribution parameters of geotechnical layers with soil stress data in three-dimensional space to generate three-dimensional geological data. The continuity of the soil layers in the three-dimensional geological data is then analyzed, and combined with the soil constitutive relationship, the stress attenuation coefficient is derived to form a stress transfer attenuation function. Furthermore, a path tracing algorithm is used to simulate the propagation of stress in the soil and construct the soil stress conduction path. First, the spatiotemporal distribution characteristics and the stress transfer attenuation function are used to determine the stress source point using the temporal distribution characteristics. Second, the stress transfer attenuation function is applied to calculate the propagation direction and attenuation degree of stress in three-dimensional space. Finally, the soil stress conduction path is output.

[0141] This solution constructs three-dimensional geological data based on geotechnical layer distribution parameters and soil stress data, eliminating the problem of insufficient integration of multi-source monitoring data and laying the foundation for comprehensive soil stability assessment. A stress transfer attenuation function is constructed based on the three-dimensional geological data, eliminating the problem of insufficient dynamic response pattern recognition, identifying weak areas of geological structure, and capturing the dynamic response patterns of geological structures. Based on the spatiotemporal distribution characteristics and the stress transfer attenuation function, soil stress conduction paths are constructed, providing input to address the inaccurate and lack of targeted risk assessment and supporting the quantification of geological hazard risks.

[0142] In some embodiments, the seepage pressure change rate is determined based on environmental hydrological data; the weak areas of geological structure are determined based on the soil stress conduction path and the seepage pressure change rate; and the dynamic response mode of the geological structure is determined based on the seepage pressure change rate and the weak areas of geological structure.

[0143] The osmotic pressure change rate can be the change in pore water pressure gradient over time.

[0144] Weak geological structure areas can be karst areas or soil layer boundaries.

[0145] Specifically, pore water pressure gradient data is extracted from environmental hydrological data and its rate of change over time is calculated to obtain the seepage pressure change rate. Furthermore, by combining the soil stress conduction path and the seepage pressure change rate, areas of stress concentration and permeability anomalies are analyzed to identify weak areas of geological structure. Finally, based on the seepage pressure change rate and the weak areas of geological structure, the real-time response mechanism of the geological body to hydrological changes is derived, thereby forming a dynamic response model of the geological structure.

[0146] This solution determines the seepage pressure change rate based on environmental hydrological data, providing a measure of the impact of hydrological factors and eliminating the lack of quantitative analysis of the seepage pressure change rate. Based on the soil stress conduction path and the seepage pressure change rate, weak areas of geological structure are identified, eliminating the problem of accurately identifying weak areas of geological structure and providing key input for determining the dynamic response model of geological structure. Based on the seepage pressure change rate and the weak areas of geological structure, the dynamic response model of geological structure is determined, thereby describing the dynamic response of geological hazards, eliminating the problem of being unable to determine the dynamic response model of geological structure and providing a reliable basis for risk warning.

[0147] In some embodiments, historical disaster cases are obtained and analyzed to determine disaster process data; a space-time coupling prediction model is constructed based on the disaster process data; soil characteristic parameters are input into the space-time coupling prediction model, and the displacement gradient change rate and energy accumulation coefficient output by the space-time coupling prediction model are obtained; the risk evolution rate of each building to be monitored is calculated based on the displacement gradient change rate, energy accumulation coefficient and seepage pressure change rate; and the geological disaster risk level is determined based on the dynamic response pattern of the geological structure and the risk evolution rate.

[0148] Historical disaster cases can be records of historical geological disaster events.

[0149] Disaster process data can be a complete sequence of recorded data in historical disaster events.

[0150] A spatiotemporal coupled prediction model can be a mathematical model that combines the time and space dimensions.

[0151] The displacement gradient change rate may be the soil displacement rate.

[0152] The energy accumulation factor can be the degree of stress accumulation.

[0153] The building to be monitored may be a high-risk target building.

[0154] The risk evolution rate can be the speed at which disasters develop.

[0155] Specifically, historical disaster cases are retrieved from disaster databases and monitoring records. Data mining techniques are used to extract multi-source data, including the time of occurrence, spatial location, soil stress changes, and groundwater level fluctuations. Subsequently, this multi-source data is integrated to determine the disaster process data. Based on this disaster process data, a machine learning algorithm is trained using a supervised learning framework. Through spatiotemporal correlation analysis, the disaster development pattern is learned, and a spatiotemporal coupled prediction model integrating spatiotemporal dimensions is constructed. Real-time soil characteristic parameters are input into the spatiotemporal coupled prediction model, which then outputs the displacement gradient change rate and energy accumulation coefficient using the spatiotemporal correlation analysis algorithm. For each monitored building, the risk evolution rate is calculated: first, the displacement gradient change rate, energy accumulation coefficient, and seepage pressure change rate are combined; then, a weighted or normalized algorithm is applied to calculate the risk evolution rate. By matching the dynamic response pattern of the geological structure with the risk evolution rate value, a risk level is assigned to each monitored building using a pre-set threshold rule for the risk evolution rate based on historical disaster statistics and empirical knowledge.

[0156] Through this solution, historical disaster cases are obtained and analyzed, and disaster process data is determined to ensure that the prediction model is based on the actual disaster evolution process. Through the disaster process data, a spatiotemporal coupling prediction model is constructed to provide a computational framework for the dynamic prediction of soil behavior. The soil characteristic parameters are input into the spatiotemporal coupling prediction model, and the displacement gradient change rate and energy accumulation coefficient output by the spatiotemporal coupling prediction model are obtained to quantify the dynamic changes of the soil and provide real-time parameters for risk calculation. Based on the displacement gradient change rate, energy accumulation coefficient and seepage pressure change rate, the risk evolution rate of each building to be monitored is calculated to achieve personalized risk assessment for different buildings. Based on the dynamic response pattern of the geological structure and the risk evolution rate, the geological disaster risk level is determined to protect the safety of buildings in a targeted manner.

[0157] In some embodiments, disaster process data is classified to determine disaster precursor parameters and disaster development data; based on the time series, disaster precursor parameters and disaster development data are analyzed to determine the correlation characteristics between disaster precursor parameters and disaster development time series; based on the correlation characteristics, a long short-term memory network is used to construct a time correlation sub-model according to disaster precursor parameters and disaster development data to obtain a time-space coupling prediction model.

[0158] Disaster precursor parameters can be a monitoring data sequence before the disaster occurs.

[0159] Disaster development data can be a series of monitoring data during the disaster expansion process.

[0160] A time series can be a sequence of data points arranged in time order.

[0161] The disaster development time series can be a sequence of disaster development data arranged in chronological order.

[0162] The correlation characteristics can be the correlation pattern or dependence between disaster precursor parameters and disaster development time series in the time dimension.

[0163] Long short-term memory networks (LSTMs) are recurrent neural networks that can be used to process time series data and capture long-term dependencies.

[0164] The time-related sub-model can be a model component obtained by training a long short-term memory network for predicting the evolution of disasters in the time dimension.

[0165] Specifically, using predefined classification rules established based on physical properties and historical data patterns in existing disaster monitoring theory, disaster process data is separated into disaster precursor parameters and disaster development data. Then, within a time series framework, a time axis alignment analysis is performed on the time series of disaster precursor parameters and disaster development data. Furthermore, using time series analysis methods, the changing patterns of disaster precursor parameters are compared with the evolutionary trends of disaster development data. Subsequently, pattern matching is used to identify the correlation features between the disaster precursor parameters and the disaster development time series. The disaster precursor parameters and the disaster development time series are then input into a long-short-term memory network (LSTM) and the network weights are initialized using the correlation features. Subsequently, using the LSTM network's gating mechanism, the hidden states of the disaster precursor parameters and the disaster development time series are cyclically updated to capture long- and short-term temporal dependencies. Finally, after the LSTM network is trained, a time-dependent sub-model focusing on the temporal dimension is constructed. This set of time-dependent sub-models is then embedded as a core module in the spatiotemporal coupling prediction model.

[0166] This solution classifies disaster process data, identifies precursor parameters and disaster development data, and provides structured input for time series analysis. Based on the time series, these parameters and development data are analyzed to determine the correlation characteristics between these parameters and the time series of disaster development. This ensures that the timestamps of the data points match and eliminates the interference of time offsets on the analysis. Based on these correlation characteristics, a long-short-term memory network is used to construct a time-correlation submodel based on the precursor parameters and development data, resulting in a spatiotemporal coupled prediction model. This model provides dynamic prediction capabilities in the time dimension and provides time series evolution input for the spatial analysis module.

[0167] In some embodiments, environmental hydrological data are analyzed to determine the historical rainfall intensity of the current season; the rainfall penetration coupling coefficient is determined based on the energy accumulation coefficient, weak geological structure areas, and historical rainfall intensity; basic information of each building to be monitored is obtained; the basic information is parsed to determine the burial depth characteristics of each building to be monitored; a burial depth influence weight matrix is ​​generated based on the burial depth characteristics and the displacement gradient change rate; a three-dimensional risk evolution model is established based on the rainfall penetration coupling coefficient and the burial depth influence weight matrix; and the risk evolution rate of each building to be monitored is output based on the three-dimensional risk evolution model.

[0168] The current season may be a season determined by a clock or user input.

[0169] The historical rainfall intensity may be a scalar value obtained by statistically analyzing historical rainfall data.

[0170] The rainfall-infiltration coupling coefficient can be used to quantify the coupling effect of rainfall-infiltration on soil stability.

[0171] The basic information may be the unique identification, location coordinates and infrastructure data of the building to be monitored.

[0172] The buried depth feature can be a normalized value of the depth of the building foundation.

[0173] The depth impact weight matrix may be in a matrix form for quantifying the weighted impact of depth and displacement on risk.

[0174] The three-dimensional risk evolution model can be a three-dimensional space-time model for predicting risk distribution.

[0175] Specifically, historical rainfall records are extracted from environmental hydrological data. Based on the current season, rainfall data for the same season is retrieved from the historical database. A statistical method is then used to perform time-series averaging of the rainfall data to determine the historical rainfall intensity for the current season. Attribute data for geologically weak areas is obtained from the geological exploration database. The rainfall-infiltration coupling coefficient is then calculated using a coupling function, combining the energy accumulation coefficient and historical rainfall intensity. Furthermore, the basic information of each monitored building is retrieved in batches from the urban building information database based on a predefined building list. This basic information is then analyzed algorithmically to extract the "foundation depth" field, which is then normalized to output the burial depth characteristics of each monitored building. Furthermore, for each monitored building, a burial depth influence weight matrix is ​​calculated, combining the burial depth characteristics and the displacement gradient change rate. The rainfall-infiltration coupling coefficient is then used as a global coupling factor. Combined with the burial depth influence weight matrix, a three-dimensional risk evolution model is constructed using a numerical integration method. Finally, a time series of risk values ​​corresponding to each monitored building location is extracted from the three-dimensional risk evolution model to calculate the risk evolution rate per unit time.

[0176] This solution analyzes environmental hydrological data to determine the historical rainfall intensity for the current season, avoiding delayed warnings caused by ignoring seasonal rainfall. Based on the energy accumulation coefficient, areas of weak geological structure, and historical rainfall intensity, the rainfall-infiltration coupling coefficient is determined to quantify the coupling effect of rainfall-infiltration on soil stability and correct the inability to construct soil stress conduction paths. Basic information for each monitored building is obtained to avoid overly broad warnings or missed warnings due to a lack of coupling with urban structural information. This basic information is analyzed to determine the burial depth characteristics of each monitored building, eliminating inaccurate and untargeted risk level predictions. Based on the burial depth characteristics and displacement gradient change rate, a burial depth impact weight matrix is ​​generated to quantify the weights assigned to risk by burial depth and displacement, thereby eliminating the inability to accurately calculate the displacement gradient change rate and energy accumulation coefficient. Based on the rainfall-infiltration coupling coefficient and the burial depth impact weight matrix, a three-dimensional risk evolution model is established to integrate multi-source data and correct the inability to couple urban structural information with real-time parameters. Based on the three-dimensional risk evolution model, the risk evolution rate for each monitored building is output, enabling personalized warnings for each monitored building.

[0177] In some embodiments, the weak areas of geological structure are analyzed to determine the weak range of structure; urban structure information is obtained, analyzed to determine the coordinate information of urban buildings and each urban building; the weak range of structure and coordinate information are coupled, and the buildings to be monitored are determined based on the coupling results.

[0178] The structural weakness range may be geographic polygon data generated by analyzing the geological structural weakness area.

[0179] Urban structure information can be a raw dataset containing a list of buildings and their attributes.

[0180] City buildings can be a structured list of buildings.

[0181] The coordinate information may be location data of each city building.

[0182] The coupling result can be a Boolean value list generated by the structural weak range and coordinate information.

[0183] Specifically, based on the attribute data of weak geological structures, a spatial parsing algorithm is used to read the coordinate point set of the attribute data and calculate the boundary to form a continuous geographic range, thereby determining the weak structural range. Next, the urban building information database is accessed and urban structural information is retrieved according to predefined query conditions. Based on this urban structural information, the algorithm traverses the building list, extracts the "coordinate" field, and verifies the data integrity. A unique identifier is then assigned to each urban building, determining the urban building and its corresponding coordinate information. Finally, the weak structural range is spatially coupled with the coordinate information. The algorithm traverses the coordinate points of each urban building to check whether they fall within the weak structural range, thereby determining the coupling result. Based on the coupling result, urban buildings whose coordinates fall within the weak structural range are screened out. Finally, urban buildings within the weak structural range are marked as buildings to be monitored.

[0184] This solution analyzes weak geological structures, identifies their scope, and quantifies the spatial distribution of high-risk geological disaster areas. Urban structural information is acquired and analyzed, identifying urban buildings and their coordinates, ensuring the comprehensiveness and real-time nature of the data source and eliminating the problem of overly broad early warning coverage. The scope of weak structural structures and coordinate information are coupled, and based on the coupling results, buildings to be monitored are identified. The correlation between buildings and geological risk areas is quantified, eliminating the problem of underreporting within the early warning range.

[0185] In some embodiments, groundwater level monitoring data is analyzed to determine the gradient of soil pore water pressure change; the permeability coefficient and thickness of each soil layer are determined based on the rock and soil layer distribution parameters; the infiltration velocity equation of each soil layer is established based on Darcy's law; the layered infiltration pressure attenuation rate is calculated based on the infiltration velocity equation and groundwater level monitoring data; the infiltration pressure transfer function is generated by weighted superposition of the infiltration pressure attenuation rate of each layer; and the infiltration pressure change rate is generated based on the infiltration pressure transfer function and the soil pore water pressure gradient.

[0186] The groundwater level monitoring data may be groundwater level time series value data.

[0187] The soil pore water pressure change gradient can be the rate of change of soil pore water pressure in the vertical direction per unit time.

[0188] Soil layers can be layered units with physical properties in geological structures.

[0189] The permeability coefficient can be an indicator of the rate at which fluid passes through a soil layer.

[0190] Thickness can be the vertical height of the soil layer.

[0191] Darcy's law can be a principle that describes that fluid flow is proportional to hydraulic gradient.

[0192] The seepage velocity equation can be an equation that describes the relationship between the flow velocity and pressure of a fluid in a soil layer.

[0193] The layered seepage pressure attenuation rate can be the drop in fluid pressure per unit path length in different soil layers.

[0194] The osmotic pressure transfer function may be a function that describes the relationship between the total pressure decay and depth.

[0195] Specifically, groundwater level monitoring data is differentially processed to calculate the water level change per unit time and determine the vertical gradient of the soil pore water pressure. Then, the distribution parameters of the rock and soil layers are analyzed to obtain the physical properties of several soil layers, and the permeability coefficient and thickness of each soil layer are determined. Furthermore, based on Darcy's law and the permeability coefficient of each soil layer, an infiltration velocity equation for each soil layer is constructed. Subsequently, the groundwater level monitoring data is input into the infiltration velocity equation for each soil layer, and the pressure change as the fluid passes through the different soil layers is calculated to determine the layered seepage pressure decay rate for each soil layer. Then, based on the depth sequence of the soil layers and using the thickness of each soil layer as a weight, the seepage pressure decay rates of each layer are linearly superimposed to generate a seepage pressure transfer function that comprehensively represents the entire geological profile. Finally, the soil pore water pressure gradient is input into the seepage pressure transfer function to calculate the comprehensive change in seepage pressure per unit time under the current hydrological conditions, thereby determining the seepage pressure change rate.

[0196] This solution analyzes groundwater level monitoring data to determine the pore water pressure gradient within the soil, quantifying the temporal variation of the pore water pressure gradient and eliminating the core limitation of the lack of seepage pressure change rate analysis. Based on the distribution parameters of the geostrata, the permeability coefficient and thickness of each soil layer are determined, eliminating the limitation of constructing three-dimensional geological data based on these parameters, ensuring the feasibility of stratified analysis and providing attribute data for Darcy's law. Based on Darcy's law, a seepage velocity equation is established for each soil layer, eliminating the limitation of calculating the seepage pressure decay rate within each layer. Based on the seepage velocity equation and groundwater level monitoring data, the seepage pressure decay rate within each layer is calculated, quantifying the vertical decay of seepage pressure and capturing the cumulative effect of hydrological changes across different soil layers. By weightedly superimposing the seepage pressure decay rates within each layer, a seepage pressure transfer function is generated, providing a unified representation of the vertical cumulative effect of hydrological pressure and eliminating blind spots in overall response analysis. Based on the seepage pressure transfer function and the pore water pressure gradient within the soil, the seepage pressure change rate is generated, enabling real-time assessment of the risk of soil instability caused by hydrological changes, thereby improving the accuracy and timeliness of geological hazard warnings.

[0197] Figure 3 A schematic diagram of the structure of a real-time geological disaster monitoring platform based on intelligent AI provided in one embodiment of the present application is shown in FIG. Figure 3As shown, the intelligent AI-based real-time geological disaster monitoring platform 300 of this embodiment includes: a data analysis module 301, a parameter determination module 302, a pattern determination module 303, and a level determination module 304.

[0198] The data analysis module 301 is used to obtain multi-source monitoring data; analyze the multi-source monitoring data to determine soil stability;

[0199] A parameter determination module 302 is configured to determine soil characteristic parameters based on the soil stability;

[0200] The mode determination module 303 is used to obtain environmental hydrological data and determine the geological structure dynamic response mode based on the environmental hydrological data;

[0201] The level determination module 304 is used to determine the geological disaster risk level according to the soil characteristic parameters and the geological structure dynamic response mode.

[0202] Optionally, the multi-source monitoring data includes soil stress data, and the parameter determination module 302 is used to determine soil characteristic parameters based on the soil stability:

[0203] Analyzing the multi-source monitoring data to obtain underground structure deformation data;

[0204] Analyzing the underground structure deformation data to determine the spatiotemporal distribution characteristics of the underground structure;

[0205] Analyzing the soil stress data based on the spatiotemporal distribution characteristics to determine the soil stress conduction path;

[0206] According to the soil stress conduction path, soil characteristic parameters are determined.

[0207] Optionally, the multi-source monitoring data further includes geotechnical layer distribution parameters. When the parameter determination module 302 analyzes the soil stress data based on the spatiotemporal distribution characteristics and determines the soil stress conduction path, it is used to:

[0208] constructing three-dimensional geological data based on the rock and soil layer distribution parameters and the soil stress data;

[0209] constructing a stress transfer attenuation function based on the three-dimensional geological data;

[0210] A soil stress conduction path is constructed according to the spatiotemporal distribution characteristics and the stress transfer attenuation function.

[0211] Optionally, when determining the geological structure dynamic response mode according to the environmental hydrological data, the mode determination module 303 is configured to:

[0212] determining a rate of change of osmotic pressure based on the environmental hydrological data;

[0213] Determining a weak geological structure area based on the soil stress conduction path and the seepage pressure change rate;

[0214] The dynamic response mode of the geological structure is determined according to the seepage pressure change rate and the weak area of ​​the geological structure.

[0215] Optionally, when determining the geological hazard risk level based on the soil characteristic parameters and the geological structure dynamic response mode, the level determination module 304 is configured to:

[0216] Obtain historical disaster cases, analyze the historical disaster cases, and determine disaster process data;

[0217] Constructing a spatiotemporal coupling prediction model based on the disaster process data;

[0218] Inputting the soil characteristic parameters into the space-time coupling prediction model, and obtaining the displacement gradient change rate and energy accumulation coefficient output by the space-time coupling prediction model;

[0219] Calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient and the seepage pressure change rate;

[0220] The geological disaster risk level is determined based on the dynamic response mode of the geological structure and the risk evolution rate.

[0221] Optionally, when the level determination module 304 constructs a spatiotemporal coupling prediction model using the disaster process data, it is configured to:

[0222] Classifying the disaster process data to determine disaster precursor parameters and disaster development data;

[0223] Analyzing the disaster precursor parameters and the disaster development data based on the time series to determine the correlation characteristics between the disaster precursor parameters and the disaster development time series;

[0224] Based on the correlation characteristics, a long short-term memory network is used to construct a time correlation sub-model according to the disaster precursor parameters and the disaster development data to obtain a time-space coupling prediction model.

[0225] Optionally, when the level determination module 304 calculates the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient, and the seepage pressure change rate, it is configured to:

[0226] Analyzing the environmental hydrological data to determine historical rainfall intensity for the current season;

[0227] determining a rainfall-permeability coupling coefficient according to the energy accumulation coefficient, the weak geological structure area, and the historical rainfall intensity;

[0228] Obtain basic information of each building to be monitored;

[0229] Analyze the basic information to determine the buried depth characteristics of each building to be monitored;

[0230] generating a burial depth influence weight matrix according to the burial depth characteristics and the displacement gradient change rate;

[0231] Establishing a three-dimensional risk evolution model based on the rainfall-infiltration coupling coefficient and the burial depth influence weight matrix;

[0232] According to the three-dimensional risk evolution model, the risk evolution rate of each building to be monitored is output.

[0233] Optionally, the intelligent AI-based real-time geological disaster monitoring platform further includes a building determination module 305 for:

[0234] Analyze the weak geological structure areas and determine the weak structural scope;

[0235] Acquire city structure information, analyze the city structure information, and determine city buildings and coordinate information of each city building;

[0236] The structural weakness range and the coordinate information are coupled, and the building to be monitored is determined according to the coupling result.

[0237] Optionally, the environmental hydrological data includes groundwater level monitoring data, and the mode determination module 303 is used to determine the seepage pressure change rate based on the environmental hydrological data:

[0238] Analyzing the groundwater level monitoring data to determine the soil pore water pressure gradient;

[0239] Determine the permeability coefficient and thickness of each soil layer according to the rock and soil layer distribution parameters;

[0240] According to Darcy's law, the infiltration velocity equation of each soil layer is established;

[0241] Calculating the stratified seepage pressure attenuation rate based on the seepage velocity equation and the groundwater level monitoring data;

[0242] The osmotic pressure transfer function is generated by weighted superposition of the osmotic pressure decay rate of each layer;

[0243] A seepage pressure change rate is generated according to the seepage pressure transfer function and the soil pore water pressure change gradient.

[0244] The platform of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A real-time monitoring method for geological disasters based on intelligent AI, characterized in that: include: Acquire multi-source monitoring data; Analyzing the multi-source monitoring data to determine soil stability; determining soil characteristic parameters based on the soil stability; Acquiring environmental hydrological data, and determining a geological structure dynamic response mode based on the environmental hydrological data; The geological disaster risk level is determined based on the soil characteristic parameters and the dynamic response mode of the geological structure.

2. The method according to claim 1, characterized in that The multi-source monitoring data includes soil stress data, and determining soil characteristic parameters based on the soil stability includes: Analyzing the multi-source monitoring data to obtain underground structure deformation data; Analyzing the underground structure deformation data to determine the spatiotemporal distribution characteristics of the underground structure; Analyzing the soil stress data based on the spatiotemporal distribution characteristics to determine the soil stress conduction path; According to the soil stress conduction path, soil characteristic parameters are determined.

3. The method according to claim 2, characterized in that The multi-source monitoring data also includes rock and soil layer distribution parameters. The soil stress data is analyzed based on the spatiotemporal distribution characteristics to determine the soil stress conduction path, including: constructing three-dimensional geological data based on the rock and soil layer distribution parameters and the soil stress data; constructing a stress transfer attenuation function based on the three-dimensional geological data; A soil stress conduction path is constructed according to the spatiotemporal distribution characteristics and the stress transfer attenuation function.

4. The method according to claim 3, characterized in that Determining the geological structure dynamic response mode according to the environmental hydrological data includes: determining a rate of change of osmotic pressure based on the environmental hydrological data; Determining a weak geological structure area based on the soil stress conduction path and the seepage pressure change rate; The dynamic response mode of the geological structure is determined according to the seepage pressure change rate and the weak area of ​​the geological structure.

5. The method according to claim 4, characterized in that Determining the geological disaster risk level according to the soil characteristic parameters and the geological structure dynamic response mode includes: Obtain historical disaster cases, analyze the historical disaster cases, and determine disaster process data; Constructing a spatiotemporal coupling prediction model based on the disaster process data; Inputting the soil characteristic parameters into the space-time coupling prediction model, and obtaining the displacement gradient change rate and energy accumulation coefficient output by the space-time coupling prediction model; Calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient and the seepage pressure change rate; The geological disaster risk level is determined based on the dynamic response mode of the geological structure and the risk evolution rate.

6. The method according to claim 5, characterized in that The method of constructing a spatiotemporal coupling prediction model based on the disaster process data includes: Classifying the disaster process data to determine disaster precursor parameters and disaster development data; Analyzing the disaster precursor parameters and the disaster development data based on the time series to determine the correlation characteristics between the disaster precursor parameters and the disaster development time series; Based on the correlation characteristics, a long short-term memory network is used to construct a time correlation sub-model according to the disaster precursor parameters and the disaster development data to obtain a time-space coupling prediction model.

7. The method according to claim 5, characterized in that Calculating the risk evolution rate of each building to be monitored based on the displacement gradient change rate, the energy accumulation coefficient, and the seepage pressure change rate includes: Analyzing the environmental hydrological data to determine historical rainfall intensity for the current season; determining a rainfall-permeability coupling coefficient according to the energy accumulation coefficient, the weak geological structure area, and the historical rainfall intensity; Obtain basic information of each building to be monitored; Analyze the basic information to determine the buried depth characteristics of each building to be monitored; generating a burial depth influence weight matrix according to the burial depth characteristics and the displacement gradient change rate; Establishing a three-dimensional risk evolution model based on the rainfall-infiltration coupling coefficient and the burial depth influence weight matrix; According to the three-dimensional risk evolution model, the risk evolution rate of each building to be monitored is output.

8. The method according to claim 5, characterized in that The determination of the building to be monitored includes: Analyze the weak geological structure areas and determine the weak structural scope; Acquire city structure information, analyze the city structure information, and determine city buildings and coordinate information of each city building; The structural weakness range and the coordinate information are coupled, and the building to be monitored is determined according to the coupling result.

9. The method according to claim 4, characterized in that The environmental hydrological data includes groundwater level monitoring data, and determining the seepage pressure change rate based on the environmental hydrological data includes: Analyzing the groundwater level monitoring data to determine the soil pore water pressure gradient; Determine the permeability coefficient and thickness of each soil layer according to the rock and soil layer distribution parameters; According to Darcy's law, the infiltration velocity equation of each soil layer is established; Calculating the stratified seepage pressure attenuation rate based on the seepage velocity equation and the groundwater level monitoring data; The osmotic pressure transfer function is generated by weighted superposition of the osmotic pressure decay rate of each layer; A seepage pressure change rate is generated according to the seepage pressure transfer function and the soil pore water pressure change gradient.

10. A real-time geological disaster monitoring platform based on intelligent AI, applied to the method according to any one of claims 1 to 9, characterized in that: include: Data analysis module, used to obtain multi-source monitoring data; Analyzing the multi-source monitoring data to determine soil stability; A parameter determination module, configured to determine soil characteristic parameters based on the soil stability; A mode determination module is used to obtain environmental hydrological data and determine a geological structure dynamic response mode based on the environmental hydrological data; The level determination module is used to determine the geological disaster risk level based on the soil characteristic parameters and the dynamic response mode of the geological structure.

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