Hydraulic ring geological environment detection method and system

Abstract

The invention provides a hydraulic ring geological environment detection method and system, and belongs to the technical field of geological detection.The method comprises the steps that original geological environment data and historical geological abnormal data of a target area are obtained, space-time alignment processing operation is conducted on the original geological environment data and the historical geological abnormal data, and a three-dimensional geological parameter grid is obtained; obtaining a standard geological parameter template and a geological anomaly mode based on the three-dimensional geological parameter grid and the historical geological anomaly data; obtaining geological instability probability parameters and an optimization monitoring strategy table based on the standard geological parameter template and the geological abnormal mode; according to the method, monitoring indexes and space deployment schemes can be adjusted according to distribution hot spots and risk levels of geological anomalies, waste of monitoring resources and insufficient monitoring of high-risk areas are avoided, a real-time geological environment data set is obtained according to an optimized monitoring strategy table, and the problem that a traditional geological environment detection method is prone to waste monitoring resources is solved.

Description

Technical Field

[0001] This invention belongs to the field of geological testing technology, and more specifically, relates to a method and system for testing hydrogeological and environmental geological conditions. Background Technology

[0002] The stability of the hydrogeological environment is related to the safety of engineering construction, prevention and control of geological disasters, and maintenance of the ecological environment. Its detection work requires multi-dimensional data collection, analysis and evaluation to achieve accurate grasp of changes in the geological environment and risk warning.

[0003] Existing geological environment monitoring methods mainly rely on a combination of manual on-site investigation and single-point sensor monitoring. For example, local geological parameters are collected by deploying equipment such as displacement gauges and water level gauges, and then empirical risk assessments are conducted in conjunction with historical disaster records. However, once the monitoring area of ​​traditional geological environment monitoring methods is determined, it remains fixed and cannot be dynamically optimized according to the distribution hotspots and risk levels of geological anomalies. In high-risk areas, insufficient monitoring density may lead to missed early warning opportunities, while in low-risk areas, monitoring resources are wasted. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for detecting hydrogeological and environmental conditions. This addresses the issue that in existing technologies, traditional geological environment detection methods have fixed monitoring areas once determined, making it impossible to dynamically optimize based on the distribution hotspots and risk levels of geological anomalies. In high-risk areas, insufficient monitoring density may lead to missed early warning opportunities, while in low-risk areas, it results in a waste of monitoring resources.

[0005] The purpose and effectiveness of this invention's method and system for detecting hydrogeological and environmental conditions are achieved through the following specific technical means:

[0006] A method for detecting hydrogeological and environmental conditions, the method comprising:

[0007] Acquire the original geological environment data and historical geological anomaly data of the target area, perform spatiotemporal alignment processing on the original geological environment data and historical geological anomaly data, and obtain a three-dimensional geological parameter mesh.

[0008] Standard geological parameter templates and geological anomaly patterns are obtained based on three-dimensional geological parameter grids and historical geological anomaly data; geological instability probability parameters and optimized monitoring strategy tables are obtained based on standard geological parameter templates and geological anomaly patterns.

[0009] Based on the optimized monitoring strategy table, a real-time geological environment dataset is obtained. Based on the real-time geological environment dataset, the geological entropy value is obtained. Based on the real-time geological environment dataset and the geological instability probability parameter, an anomaly zone marker dataset is obtained.

[0010] According to a preferred embodiment, the step of acquiring the original geological environment data and historical geological anomaly data of the target area, and performing spatiotemporal alignment processing on the original geological environment data and historical geological anomaly data to obtain a three-dimensional geological parameter mesh includes:

[0011] The original geological environment data includes single-point monitoring data and regional monitoring data. The single-point monitoring data refers to the monitoring data of the monitoring station on the target spatial point, and the regional monitoring data refers to the monitoring data of the monitoring station on the target area.

[0012] The target area is uniformly divided into spatial grids. Based on the target spatial points, the single-point monitoring data is located within the spatial grids. Based on the target area range, the regional monitoring data is located within the spatial grids. The regional monitoring data is divided according to the spatial grid division format and adjusted according to the area ratio of the divided regional monitoring data in a single grid of the spatial grid to obtain the geological parameter grid.

[0013] Extract the spatiotemporal coordinates of anomalous events contained in historical geological anomaly data, map the historical geological anomaly data to a geological parameter grid based on the spatiotemporal coordinates of the anomalous events, add a timestamp axis to the geological parameter grid based on the spatiotemporal coordinates of the anomalous events, arrange the historical geological anomaly data along the timestamp axis according to the spatiotemporal coordinates of the anomalous events, and obtain a three-dimensional geological parameter grid.

[0014] According to a preferred embodiment, the step of obtaining standard geological parameter templates and geological anomaly patterns based on a three-dimensional geological parameter grid and historical geological anomaly data, and obtaining geological instability probability parameters and an optimized monitoring strategy table based on the standard geological parameter templates and geological anomaly patterns, includes:

[0015] Based on the three-dimensional geological parameter grid and historical geological anomaly data, anomaly event feature separation operation is performed to obtain a steady-state geological parameter pool and anomaly evolution feature sequence. Based on the steady-state geological parameter pool and anomaly evolution feature sequence, standard geological parameter templates and geological anomaly patterns are obtained.

[0016] A dynamic risk assessment framework is obtained by integrating standard geological parameter templates and geological anomaly models. The standard geological parameter templates and geological anomaly models are then input into the dynamic risk assessment framework to obtain geological instability probability parameters and optimized monitoring strategy tables.

[0017] According to a preferred embodiment, the step of obtaining a steady-state geological parameter pool and anomaly evolution feature sequence by performing anomaly event feature separation operation based on a three-dimensional geological parameter grid and historical geological anomaly data, and obtaining a standard geological parameter template and geological anomaly pattern based on the steady-state geological parameter pool and anomaly evolution feature sequence, includes:

[0018] Grids that do not contain historical geological anomaly data are extracted from the three-dimensional geological parameter grid as steady-state grids, and the original geological environment data contained in each steady-state grid are extracted to construct a steady-state geological parameter pool.

[0019] The grid containing historical geological anomaly data in the three-dimensional geological parameter grid is extracted as anomaly grid, and the historical geological anomaly data contained in each anomaly grid is extracted to generate anomaly evolution feature sequence.

[0020] Several parameter features are obtained by extracting features from several steady-state grids contained in the steady-state geological parameter pool. A standard geological parameter template is generated based on the several parameter features. The parameter features include at least the interval mean, the extreme value fluctuation range and the covariance matrix between parameters.

[0021] Based on the several anomalous grids contained in the anomalous evolution feature sequence, sensitive mutation parameters in historical geological anomalous data are identified, and dynamic pattern coding is performed based on the sensitive mutation parameters to obtain geological anomalous patterns.

[0022] According to a preferred embodiment, the step of performing a fusion modeling operation based on a standard geological parameter template and a geological anomaly model to obtain a dynamic risk assessment framework, and inputting the standard geological parameter template and the geological anomaly model into the dynamic risk assessment framework to obtain geological instability probability parameters and an optimized monitoring strategy table, includes:

[0023] A risk assessment baseline is obtained by constructing a parameter space based on a standard geological parameter template, and an anomaly monitoring response map is obtained by locating sensitive parameters based on a geological anomaly pattern. A dynamic risk assessment framework is then synthesized based on the risk assessment baseline and the anomaly monitoring response map.

[0024] Historical safety benchmark values ​​are calculated using the mean of parameter intervals, parameter offset risk weights are derived using the covariance matrix, geological instability probability parameters are obtained based on historical safety benchmark values ​​and parameter offset risk weights, core monitoring indicators are defined based on combinations of sensitive parameters, spatial deployment plans are formulated based on the distribution of hotspot areas, and an optimized monitoring strategy table is generated based on the core monitoring indicators and spatial deployment plans.

[0025] According to a preferred embodiment, the step of obtaining a risk assessment baseline by constructing a parameter space based on a standard geological parameter template, and obtaining an anomaly monitoring response map by locating sensitive parameters based on a geological anomaly pattern, includes:

[0026] Steady-state parameter coordinate axes are obtained based on the mean of parameter intervals, parameter correlation constraint domains are generated based on the covariance matrix, and risk assessment baselines are constructed based on the steady-state parameter coordinate axes and parameter correlation constraint domains.

[0027] All abnormal event records in the abnormal evolution feature sequence are selected. A mutation judgment operation is performed on each geological parameter in the abnormal event record to obtain an abnormal parameter frequency statistics table. Based on the abnormal parameter frequency statistics table and historical geological abnormal data, the spatial grid coordinates of the abnormal parameters are obtained. The spatial grid coordinates of the abnormal parameters are marked in the three-dimensional geological parameter grid to form an abnormal monitoring response map.

[0028] According to a preferred embodiment, the step of obtaining a real-time geological environment dataset based on an optimized monitoring strategy table, obtaining geological entropy values ​​based on the real-time geological environment dataset, and obtaining anomaly zone marker datasets based on the real-time geological environment dataset and geological instability probability parameters includes:

[0029] Based on the optimized monitoring strategy table, a real-time geological environment dataset is obtained. Coupled analysis is performed on the real-time geological environment dataset to obtain the geological state evolution trajectory. Chaotic quantification is performed on the geological state evolution trajectory to obtain the geological entropy value. A grid risk index matrix is ​​obtained based on the real-time geological environment dataset and the three-dimensional geological parameter grid. Neighborhood gradient calculation is performed on the grid risk index matrix to obtain the grid neighborhood gradient difference matrix. Anomaly boundary marking is performed on the grid neighborhood gradient difference matrix to obtain the grid anomaly mutation boundary identifier set.

[0030] According to a preferred embodiment, the steps of obtaining a real-time geological environment dataset based on an optimized monitoring strategy table, performing a coupled analysis operation based on the real-time geological environment dataset to obtain a geological state evolution trajectory, and performing a chaotic quantification operation based on the geological state evolution trajectory to obtain a geological entropy value include:

[0031] The monitoring data in the real-time geological environment dataset is extracted to construct a three-dimensional phase space coordinate axis. The monitoring data is then used to generate several phase space trajectory points based on the time series. Based on the three-dimensional phase space coordinate axis and the several phase space trajectory points, the geological state evolution trajectory is constructed.

[0032] A three-dimensional phase space grid is generated based on the geological state evolution trajectory. Several phase space trajectory points are located in the three-dimensional phase space grid. The dwell frequency parameter of the phase space trajectory points in each grid of the three-dimensional phase space grid is counted. The total number parameter of phase space trajectory points is obtained. The dwell frequency parameter corresponding to each grid is divided by the total number parameter of phase space trajectory points to obtain the cell occurrence probability distribution table. The geological entropy value is obtained based on the cell occurrence probability distribution table.

[0033] According to a preferred embodiment, the step of obtaining a grid risk index matrix based on a real-time geological environment dataset and a three-dimensional geological parameter grid, obtaining a grid neighborhood gradient difference matrix by performing a neighborhood gradient calculation operation based on the grid risk index matrix, marking abnormal boundaries on the grid neighborhood gradient difference matrix, and obtaining a grid abnormal mutation boundary identifier set includes:

[0034] Based on the real-time geological environment dataset and the three-dimensional geological parameter grid, the real-time monitoring parameter values ​​of each spatial grid are extracted, and the spatial grid risk index matrix is ​​obtained by combining the geological instability probability parameter. Each grid cell in the spatial grid risk index matrix is ​​traversed, and the geological entropy value of the current grid cell is extracted. The difference between the geological entropy value of the current grid cell and the geological entropy value of the adjacent grid cells is calculated to obtain the grid neighborhood gradient difference matrix. The gradient change threshold is set to 0.3. If the gradient difference in a certain direction is greater than 0.3 in the six neighborhood directions of each grid cell, the grid boundary in that direction is marked as an abnormal mutation boundary to obtain the grid abnormal mutation boundary identifier set.

[0035] A hydrogeological environment monitoring system, comprising:

[0036] The data acquisition module is used to acquire the original geological environment data and historical geological anomaly data of the target area, as well as to acquire the real-time geological environment dataset based on the optimized monitoring strategy table.

[0037] The processing module is used to perform spatiotemporal alignment processing of raw geological environment data and historical geological anomaly data; obtain standard geological parameter templates and geological anomaly patterns based on three-dimensional geological parameter grids and historical geological anomaly data; obtain geological instability probability parameters and optimized monitoring strategy tables based on standard geological parameter templates and geological anomaly patterns; obtain geological entropy values ​​based on real-time geological environment datasets; and obtain anomaly zone marker datasets based on real-time geological environment datasets and geological instability probability parameters.

[0038] The data storage unit, connected to the processing module and the data acquisition module, is used to store raw geological environment data, historical geological anomaly data, three-dimensional geological parameter grids, standard geological parameter templates, geological anomaly patterns, geological instability probability parameters, optimized monitoring strategy tables, real-time geological environment datasets, and anomaly zone marker datasets.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] First, a dynamic risk assessment framework can be constructed, and an optimized monitoring strategy table can be developed by combining geological anomaly patterns and hotspot distribution. This allows monitoring indicators and spatial deployment plans to be adjusted according to the distribution hotspots and risk levels of geological anomalies. In high-risk areas with concentrated geological anomalies, monitoring coverage can be strengthened by increasing the density of monitoring points and the frequency of parameter collection, ensuring timely capture of subtle changes in the geological environment. In low-risk areas with stable geological conditions, monitoring resource input can be reasonably reduced to avoid unnecessary resource consumption. This achieves precise allocation and efficient utilization of monitoring resources, dynamic optimization of monitoring strategies, and avoids waste of monitoring resources and insufficient monitoring in high-risk areas.

[0041] Secondly, risk assessment baselines and anomaly monitoring response maps can be constructed based on standard geological parameter templates and geological anomaly models. Historical safety benchmark values ​​can be calculated using the mean of parameter intervals, and parameter offset risk weights can be derived using the covariance matrix to comprehensively form geological instability probability parameters. This assessment method no longer relies on fixed thresholds but fully considers the coupling effect and nonlinear changes between geological parameters. The assessment standards can be dynamically adjusted according to real-time monitoring data, so that the risk assessment results can more accurately reflect the actual state of the geological environment, reduce false alarms and omissions caused by rigid assessment standards, and improve the accuracy and adaptability of risk assessment.

[0042] Finally, the geological state evolution trajectory can be obtained through coupled analysis of real-time geological environment datasets, the degree of chaos can be quantified by combining geological entropy values, and the boundary of abnormal mutation can be marked based on the gradient calculation of grid neighborhood. The dynamic changes of geological parameters can be tracked, the abnormal evolution trend can be identified in a timely manner, and the scope and boundary of abnormal areas can be clarified by identifying abnormal boundaries. This allows the early warning information to be directed to high-risk areas, providing a more targeted decision-making basis for the early prevention and control of geological disasters and enhancing the timeliness of abnormal early warning. Attached Figure Description

[0043] Figure 1 This is a flowchart of the steps of a hydrogeological and environmental detection method according to the present invention.

[0044] Figure 2 This is a flowchart of the steps for obtaining geological anomaly patterns in a hydrogeological environment detection method of the present invention.

[0045] Figure 3 This is a schematic diagram of a hydrogeological environment monitoring system according to the present invention. Detailed Implementation

[0046] To further understand the present invention, preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings and examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, and are not intended to limit the scope of the claims of the present invention.

[0047] Example:

[0048] Please see as follows Figure 1 As shown, the present invention provides a method for detecting hydrogeological and environmental conditions, the method comprising:

[0049] Step S100: Obtain the original geological environment data and historical geological anomaly data of the target area, perform spatiotemporal alignment processing on the original geological environment data and historical geological anomaly data, and obtain a three-dimensional geological parameter mesh;

[0050] Specifically, the raw geological environment data includes single-point monitoring data and regional monitoring data. Single-point monitoring data is collected by monitoring stations distributed within the target area, such as displacement data of soil and rock mass obtained by displacement gauges installed at key points on slopes, and groundwater depth data recorded by water level gauges buried in underground aquifers. This type of data directly corresponds to specific target spatial points. Regional monitoring data covers the entire range of the target area, such as surface deformation images obtained by UAV remote sensing and underground soil and rock mass structure data obtained by ground-penetrating radar. This type of data reflects the geological characteristics of a certain area.

[0051] In practice, the target area is first divided into spatial grids at fixed intervals, with each grid having a clear latitude and longitude or planar coordinate range, which serves as the spatial reference for data integration. For single-point monitoring data, the corresponding target spatial point coordinates are used to directly locate the data within its respective spatial grid. For regional monitoring data, the data is divided into sub-data blocks corresponding to the grid according to the spatial grid division format. Then, the data values ​​are weighted and adjusted according to the area ratio of each sub-data block in the corresponding single grid.

[0052] For example, if the coverage area of ​​the monitoring data in a certain grid is one-third, then the monitoring data of that grid is one-third of the value of the corresponding sub-data block. This process ensures that the regional data and the single-point data are spatially consistent, and then integrates them to form a geological parameter grid.

[0053] Simultaneously, the spatiotemporal coordinates of anomalous events contained in historical geological anomaly data are extracted, such as the occurrence time of a landslide event (e.g., a specific year, month, and day) and the boundary coordinates of the affected area. Based on these coordinates, relevant data of the anomalous event, such as landslide thickness and sliding distance, are mapped to the corresponding spatial grid. On this basis, a timestamp axis is added to the geological parameter grid based on the temporal distribution characteristics of the anomalous events. The timestamp axis is divided into weekly and monthly intervals according to fixed time intervals. Historical geological anomaly data are arranged along the timestamp axis according to their occurrence time, so that each spatial grid contains the corresponding geological parameters and anomaly records at different timestamps, ultimately forming a three-dimensional geological parameter grid.

[0054] Its function is to solve the problem of fusion difficulties caused by different spatial benchmarks between single-point monitoring data and regional monitoring data through spatiotemporal alignment processing, so that raw data from different sources can be presented collaboratively in a unified spatial grid; while the addition of the timestamp axis links the temporal attributes and spatial distribution of historical geological anomaly data, forming a three-dimensional data structure containing spatiotemporal dimensions, providing a foundation for subsequent analysis of the evolution of the geological environment in time and space.

[0055] For example, in a target area in a mountainous region, the monitoring data of a single-point displacement meter is located to a specific grid, and the regional deformation data of the UAV remote sensing is integrated into the same grid after the area ratio is adjusted. The three landslide events that occurred in history are arranged in the corresponding grids according to their occurrence time along the timestamp axis. The resulting three-dimensional geological parameter grid can intuitively show the geological parameters and anomalies at different locations in the region at different times.

[0056] Step S101: Obtain standard geological parameter templates and geological anomaly patterns based on three-dimensional geological parameter grids and historical geological anomaly data; obtain geological instability probability parameters and optimized monitoring strategy tables based on standard geological parameter templates and geological anomaly patterns;

[0057] Specifically, based on the three-dimensional geological parameter grid and historical geological anomaly data, anomaly event feature separation operation is performed to obtain a steady-state geological parameter pool and anomaly evolution feature sequence. Based on the steady-state geological parameter pool and anomaly evolution feature sequence, standard geological parameter templates and geological anomaly patterns are obtained.

[0058] In practice, firstly, grids that do not contain historical geological anomaly data are selected from the three-dimensional geological parameter grid as steady-state grids. These grids represent areas where the geological environment is in a stable state. The original geological environment data, such as soil and rock density, porosity, and groundwater flow velocity, are extracted from each steady-state grid. These data are then classified and summarized by grid to construct a steady-state geological parameter pool. At the same time, grids that contain historical geological anomaly data are selected as anomalous grids. Geological parameter change data related to anomalous events, such as displacement increments before landslides and groundwater pressure fluctuations, are extracted from these grids and arranged in chronological order to form an anomalous evolution characteristic sequence. The sequence contains the changing trends of parameters over time and abrupt change nodes.

[0059] When generating standard geological parameter templates based on steady-state geological parameter pools, feature extraction is performed on the parameters of all steady-state grids in the pool, and the interval mean, extreme value fluctuation range, and covariance matrix between parameters are calculated for each parameter.

[0060] It should be noted that the interval mean represents the range of average values ​​of the same parameter in different steady-state grids, the extreme value fluctuation range represents the difference between the maximum and minimum values ​​of the parameter in the steady-state state, and the covariance matrix between parameters represents the degree of correlation between different parameters in the steady state.

[0061] These features are integrated into a standard template as a benchmark for judging whether the geological environment is normal. When generating geological anomaly models based on the anomaly evolution feature sequence, the sensitive mutation parameters that play a dominant role in the abnormal event are identified by analyzing the variation law of parameters in the sequence. Sensitive mutation parameters are represented by the sudden change in soil moisture content that leads to slope instability, the sudden change in permeability coefficient that triggers piping, etc. The mutation amplitude, duration and linkage relationship of these parameters with other parameters are recorded, and dynamic pattern coding is performed to form a geological anomaly model, which is used to identify the characteristics of the geological environment evolving towards an unstable state.

[0062] When obtaining the geological instability probability parameters and optimized monitoring strategy table, the standard geological parameter template and geological anomaly model are input into the dynamic risk assessment framework: the historical safety benchmark value is calculated based on the interval mean in the standard template, and the risk weight when different parameters deviate from the benchmark value is derived through the covariance matrix. The two are combined to obtain the geological instability probability parameters, quantifying the possibility of geological environmental instability in different regions. At the same time, the core monitoring indicators are determined based on the sensitive mutation parameters in the geological anomaly model. High-frequency monitoring items are set for soil moisture content mutations. Hotspot areas are determined by combining the spatial distribution of historical anomaly events. A spatial deployment plan including the density of monitoring points and the frequency of parameter collection is formulated and integrated to form an optimized monitoring strategy table.

[0063] Its functions are as follows: the standard geological parameter template provides a quantifiable reference standard for the normal state of the geological environment, avoiding the subjectivity of relying on experience-based judgment; the geological anomaly model clarifies the change law of characteristic parameters before geological instability, making risk identification more targeted; the geological instability probability parameter can intuitively reflect the risk level of different areas, providing a basis for resource allocation; and the optimized monitoring strategy table ensures that monitoring work can focus on high-risk areas and key parameters, improving monitoring efficiency.

[0064] For example, after processing the steady-state grid data of a river valley area, the standard geological parameter template includes the average range of groundwater level and the fluctuation range of soil compressive strength under normal conditions in the area. The abnormal evolution characteristic sequence shows that the groundwater level will rise sharply in a short period of time before historical piping events. Based on this, the geological anomaly model lists groundwater level mutation as a sensitive parameter. The dynamic risk assessment framework combines these data to calculate the probability of geological instability in different sections along the river valley. The optimized monitoring strategy table specifies to increase the density of water level monitoring points in high-probability areas and reduce the monitoring frequency in low-probability areas.

[0065] In this implementation, obtaining the geological anomaly model includes the following steps:

[0066] Step S200: Extract grids from the three-dimensional geological parameter grid that do not contain historical geological anomaly data as steady-state grids. These grids have not experienced any abnormal events such as landslides, piping, or ground subsidence in the historical records. Extract the original geological environment data contained in each steady-state grid. The original geological environment data includes at least the density, cohesion, internal friction angle, groundwater depth, and permeability coefficient of the soil and rock mass. Classify and store these data according to grid number to construct a steady-state geological parameter pool. This parameter pool is used to reflect the parameter distribution characteristics when the geological environment is in a stable state.

[0067] Step S201: Extract the grid containing historical geological anomaly data from the three-dimensional geological parameter grid as anomaly grids. These grids correspond to areas where anomalous events have occurred in the past. Extract the historical geological anomaly data contained in each anomaly grid. The historical geological anomaly data includes at least the sliding distance and sliding velocity when the landslide occurs, and the inflow volume and pore water pressure changes when piping occurs. Arrange them in the time sequence of the occurrence of the anomaly events. The time sequence is represented as from the stable stage before the event to the abrupt stage during the event, and then to the stable stage after the event. Generate an anomaly evolution feature sequence. This sequence is used to show the parameter changes of the entire process from the incubation to the occurrence of the geological anomaly.

[0068] Step S202: Extract features from several steady-state grids contained in the steady-state geological parameter pool to obtain several parameter features. Calculate the interval mean and extreme value fluctuation range of each parameter in all steady-state grids, and calculate the covariance matrix between different parameters. Generate a standard geological parameter template based on these parameter features. This template serves as a reference standard for judging whether the current geological parameters are within the normal range.

[0069] For example, in a certain region, the mean value of the cohesion of the soil and rock mass in the steady-state grid is 15-20 kPa, and the extreme value fluctuation range is 5 kPa. The covariance matrix of cohesion and internal friction angle shows that the two are positively correlated. In step S203, based on several anomalous grids contained in the anomalous evolution characteristic sequence, sensitive abrupt change parameters in historical geological anomalous data are identified by comparing parameter changes before and after the occurrence of the anomalous event. Assuming that the information contained in this sensitive abrupt change parameter is that the shear stress at the sliding surface increased from 10 kPa to 30 kPa in the 24 hours before the occurrence of the anomalous event, this shear stress is the sensitive abrupt change parameter. The data is processed using dynamic pattern encoding based on the change characteristics of sensitive mutation parameters. These change characteristics include at least the time node of the mutation, the magnitude of the change, and the linkage with other parameters. For example, if the change characteristic is a synchronous rise of 0.5m in groundwater level during a shear stress mutation, these characteristics are converted into structured data sequences to obtain geological anomaly patterns. These patterns are used to identify the typical parameter change patterns when the geological environment evolves into an anomalous state. For example, a geological anomaly pattern records that the change magnitude of a sensitive mutation parameter within 3 days reaches twice the range of steady-state extreme fluctuations, and is accompanied by synchronous changes in two other parameters.

[0070] Furthermore, a dynamic risk assessment framework is obtained by performing a fusion modeling operation based on the standard geological parameter template and the geological anomaly model. The standard geological parameter template and the geological anomaly model are then input into the dynamic risk assessment framework to obtain the geological instability probability parameters and the optimized monitoring strategy table.

[0071] Specifically, the process begins by separating anomalous event features based on a 3D geological parameter grid and historical geological anomaly data. Steady-state grids without historical anomaly data are then selected from the 3D geological parameter grid, and their original geological environment data is extracted to construct a steady-state geological parameter pool. Simultaneously, anomalous grids containing historical anomaly data are selected, and their data is extracted to generate anomaly evolution feature sequences. Standard geological parameter templates are then generated based on the parameter features of the steady-state geological parameter pool. Sensitive mutation parameters are identified and encoded based on the anomaly evolution feature sequences to obtain geological anomaly patterns. Finally, a parameter space construction operation is performed based on the standard geological parameter templates to obtain a risk assessment baseline.

[0072] Furthermore, a steady-state parameter coordinate axis is established based on the interval mean of each parameter in the template. For example, the interval mean of the soil and rock water content of 10%-15% is used as the baseline of the coordinate axis of this parameter. Then, the correlation strength of different parameters is calculated through the covariance matrix between parameters. For example, the covariance value of water content and porosity is 0.6, indicating that the two are strongly correlated. Based on this, a parameter correlation constraint domain is generated. The steady-state parameter coordinate axis is combined with the parameter correlation constraint domain to construct a risk assessment baseline. This baseline is used to define the range of geological parameters in a normal state. At the same time, sensitive parameter location operations are performed based on the geological anomaly model to obtain anomaly monitoring response maps.

[0073] It should be noted that all abnormal event records in the abnormal evolution feature sequence are selected, and a mutation judgment operation is performed on the geological parameters in each record. By calculating whether the change of the parameter in a unit time exceeds 1.5 times the steady-state extreme value fluctuation range, the number of mutations of each parameter is counted to form an abnormal parameter frequency statistics table. Combined with the spatial coordinates of historical geological anomaly data, the spatial grid positions corresponding to these sensitive mutation parameters are determined, that is, the spatial grid coordinates of the abnormal parameters. These coordinates are marked in the three-dimensional geological parameter grid to form an anomaly monitoring response map. This map intuitively shows the spatial distribution hotspots of sensitive mutation parameters.

[0074] Furthermore, a dynamic risk assessment framework is synthesized based on the risk assessment baseline and the anomaly monitoring response map. This framework uses the risk assessment baseline as a reference for the normal state and the anomaly monitoring response map as a spatial guide for the distribution of anomalies, achieving full coverage of geological states from normal to anomaly. Historical safety benchmark values ​​are calculated using the interval mean values ​​of parameters in the standard geological parameter template. Parameter offset risk weights are derived using the covariance matrix between parameters. The parameter offset risk weight is expressed as follows: the larger the absolute value of the covariance between water content and shear strength, the more significant the impact of water content shift on shear strength, and the higher the corresponding offset risk weight. The historical safety benchmark values ​​and parameter offset risk weights are combined to calculate the geological instability probability parameters for each spatial grid. Simultaneously, core monitoring indicators are defined based on the combination of sensitive mutation parameters in the geological anomaly model. Based on the spatial distribution hotspots of sensitive parameters in the anomaly monitoring response map, an optimized monitoring strategy table is generated based on the core monitoring indicators and spatial deployment scheme, clarifying the parameters to be monitored, the acquisition frequency, and the data transmission requirements for each area.

[0075] Its role is that the dynamic risk assessment framework breaks through the limitations of traditional fixed threshold assessment. By integrating the normal state benchmark and the characteristics of abnormal distribution, the risk assessment can be dynamically adjusted with changes in geological parameters. The geological instability probability parameter quantifies the risk level of each region, providing a basis for resource allocation. The optimized monitoring strategy table ensures that the monitoring work focuses on key parameters and high-risk areas, reducing resource waste while ensuring the effectiveness of monitoring.

[0076] For example, in the monitoring of landslides in a certain mountainous area, the probability of instability in the slope toe area was calculated to be 0.35, which is higher than 0.1 in other areas. The core monitoring indicators were set as displacement and groundwater pressure. The spatial deployment plan set up a monitoring point every 30 meters at the slope toe. The final optimized monitoring strategy table made the monitoring data collection in the area more accurate and provided a reliable basis for landslide early warning.

[0077] Step S102: Obtain the real-time geological environment dataset based on the optimized monitoring strategy table, obtain the geological entropy value based on the real-time geological environment dataset, and obtain the anomaly zone marker dataset based on the real-time geological environment dataset and the geological instability probability parameter.

[0078] Specifically, based on the monitoring point locations, parameter types, and acquisition frequencies specified in the optimized monitoring strategy table, real-time geological environment datasets are acquired through deployed sensors. These datasets contain geological parameters for each monitoring point at different time points. Coupled analysis is performed on the real-time geological environment datasets to obtain the geological state evolution trajectory. That is, by analyzing the changes of each parameter over time and the correlation between parameters, a trajectory reflecting the evolution of the geological state over time is constructed. Chaotic quantification is performed on the geological state evolution trajectory to obtain the geological entropy value, which is used to measure the stability of the geological state.

[0079] Furthermore, three core monitoring parameters are extracted from the real-time geological environment dataset and used as the X, Y, and Z axes to construct a three-dimensional phase space coordinate system. The three parameter values ​​at each time point are combined into a three-dimensional coordinate point, and several phase space trajectory points are generated by arranging them in chronological order. These trajectory points are then connected in chronological order, and a geological state evolution trajectory is constructed based on the three-dimensional phase space coordinate system and the trajectory points. This trajectory can intuitively display the change path of the geological state in the three-dimensional parameter space. A three-dimensional phase space grid is generated based on the coverage of the geological state evolution trajectory. Each phase space trajectory point is mapped to its corresponding small grid, and the number of trajectory points contained in each small grid is counted. The ratio of this number to the total number of trajectory points is calculated to obtain the unit occurrence probability of each small grid. This is integrated to form a unit occurrence probability distribution table. Based on this table, the geological entropy value is calculated using the information entropy formula. The higher the entropy value, the higher the chaos of the geological state, that is, the more disordered the parameter changes and the more unstable the state.

[0080] Specifically, based on real-time geological environment datasets and 3D geological parameter grids, real-time monitoring parameter values ​​are extracted from each spatial grid. Combined with the geological instability probability parameter of that grid, a risk index for each grid is obtained through weighted calculation. The risk indices of all grids are then integrated to form a grid risk index matrix. Based on the grid risk index matrix, a neighborhood gradient calculation operation is performed. That is, each grid cell is traversed, its geological entropy value is extracted, and the difference is calculated with the geological entropy values ​​of the six adjacent grids. All differences are integrated to form a grid neighborhood gradient difference matrix. A gradient change threshold of 0.3 is set, and the six neighborhood directions of each grid cell are judged. If the gradient difference in a certain direction is greater than 0.3, the grid boundary in that direction is marked as an anomalous mutation boundary. All anomalous mutation boundaries are integrated to obtain a grid anomalous mutation boundary identifier set. This identifier set can clearly define the range and boundary of the anomalous area.

[0081] Its role is that the real-time geological environment dataset provides the latest data support for dynamic analysis, and the geological entropy value can identify the unstable trend of geological state at an early stage by quantifying the degree of chaos, avoiding the limitation of relying on the change of a single parameter; the anomaly area marking dataset can clarify the spatial range of the anomaly area by accurately marking the anomaly boundary, providing a basis for targeted prevention and control measures.

[0082] A hydrogeological environment monitoring system, comprising:

[0083] The data acquisition module consists of various monitoring devices distributed in the target area, including sensors for collecting single-point monitoring data and devices for collecting area monitoring data.

[0084] Its usage is as follows: geological parameters of target spatial points are collected in real time through sensors, geological data of the target area are obtained through remote sensing equipment and ground-penetrating radar, and historical geological anomaly data are retrieved from the system database. After the optimized monitoring strategy table is generated, the data acquisition module will adjust the working mode of the equipment according to the monitoring point location, parameter type and acquisition frequency specified in the table, and continuously acquire real-time geological environment datasets. Its function is to provide the system with full and real-time raw data input to ensure that subsequent analysis is based on the latest geological parameters.

[0085] The processing module automates data processing using built-in algorithms. Its operation includes: first, performing spatiotemporal alignment processing on the raw geological environment data and historical geological anomaly data. This involves uniformly dividing the target area into spatial grids based on latitude and longitude, locating single-point monitoring data to their corresponding grids according to coordinates, integrating regional monitoring data into the grid after dividing it into grids and adjusting it according to area ratios, and then adding timestamp axes to the grids based on the time coordinates of historical anomaly events to form a three-dimensional geological parameter grid. Next, it extracts steady-state and anomaly grids from the three-dimensional geological parameter grid, generates a standard geological parameter template by calculating the interval mean, extreme value fluctuation range, and covariance matrix of the steady-state grid parameters, and then analyzing... The process involves identifying sensitive mutation parameters from abnormal grid parameters and encoding them to generate geological anomaly patterns. Then, combining standard geological parameter templates and these patterns, historical safety benchmarks are calculated using the mean of parameter intervals. Parameter offset risk weights are derived using the covariance matrix to obtain geological instability probability parameters. Simultaneously, an optimized monitoring strategy table is developed based on sensitive parameters and hotspot area distribution. Finally, based on a real-time geological environment dataset, a three-dimensional phase space coordinate axis is constructed, trajectory points are generated, and their dwell frequency within the grid is statistically analyzed to calculate geological entropy values. These are then combined with real-time parameters and geological instability probability parameters to generate a grid risk index matrix. Anomaly boundaries are marked by calculating neighborhood gradient differences, resulting in an anomaly zone labeling dataset. Its function is to transform raw data into structured information usable for risk assessment, enabling end-to-end processing from data acquisition to anomaly identification. For example, in a river valley area, the processing module can align collected water level data with historical piping event data to generate a three-dimensional grid. By analyzing the mean water level of the steady-state grid and the water level mutation characteristics of the abnormal grid, the geological instability probability of different sections along the river is calculated, and potential piping risks are identified in real-time monitoring through changes in geological entropy values.

[0086] The data storage unit is connected to the processing module and the data acquisition module via wired or wireless communication. Its usage is as follows: it receives and stores in real time the raw geological environment data, historical geological anomaly data, and real-time geological environment dataset transmitted by the data acquisition module, as well as the three-dimensional geological parameter grid, standard geological parameter template, geological anomaly mode, geological instability probability parameters, optimized monitoring strategy table, and anomaly area marker dataset output by the processing module. During storage, it is categorized and indexed by data type and timestamp. Its function is to provide data persistence support for the system, ensure that the data can be traced back and queried, and provide data support for the repeated calculations and model optimization of the processing module.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting hydrogeological and environmental conditions, characterized in that, The method includes: Acquire the original geological environment data and historical geological anomaly data of the target area, perform spatiotemporal alignment processing on the original geological environment data and historical geological anomaly data, and obtain a three-dimensional geological parameter mesh. Standard geological parameter templates and geological anomaly patterns are obtained based on three-dimensional geological parameter grids and historical geological anomaly data; geological instability probability parameters and optimized monitoring strategy tables are obtained based on standard geological parameter templates and geological anomaly patterns. Based on the optimized monitoring strategy table, a real-time geological environment dataset is obtained. Based on the real-time geological environment dataset, the geological entropy value is obtained. Based on the real-time geological environment dataset and the geological instability probability parameter, an anomaly zone marker dataset is obtained.

2. The method for detecting hydrogeological and environmental conditions according to claim 1, characterized in that, The process of acquiring the original geological environment data and historical geological anomaly data of the target area, performing spatiotemporal alignment processing on the original geological environment data and historical geological anomaly data, and obtaining a three-dimensional geological parameter mesh includes: The original geological environment data includes single-point monitoring data and regional monitoring data. The single-point monitoring data refers to the monitoring data of the monitoring station on the target spatial point, and the regional monitoring data refers to the monitoring data of the monitoring station on the target area. The target area is uniformly divided into spatial grids. Based on the target spatial points, the single-point monitoring data is located within the spatial grids. Based on the target area range, the regional monitoring data is located within the spatial grids. The regional monitoring data is divided according to the spatial grid division format and adjusted according to the area ratio of the divided regional monitoring data in a single grid of the spatial grid to obtain the geological parameter grid. Extract the spatiotemporal coordinates of anomalous events contained in historical geological anomaly data, map the historical geological anomaly data to a geological parameter grid based on the spatiotemporal coordinates of the anomalous events, add a timestamp axis to the geological parameter grid based on the spatiotemporal coordinates of the anomalous events, arrange the historical geological anomaly data along the timestamp axis according to the spatiotemporal coordinates of the anomalous events, and obtain a three-dimensional geological parameter grid.

3. The method for detecting hydrogeological and environmental conditions according to claim 1, characterized in that, The standard geological parameter template and geological anomaly pattern are obtained based on a three-dimensional geological parameter grid and historical geological anomaly data. A table of geological instability probability parameters and optimized monitoring strategies, obtained based on standard geological parameter templates and geological anomaly models, includes: Based on the three-dimensional geological parameter grid and historical geological anomaly data, anomaly event feature separation operation is performed to obtain a steady-state geological parameter pool and anomaly evolution feature sequence. Based on the steady-state geological parameter pool and anomaly evolution feature sequence, standard geological parameter templates and geological anomaly patterns are obtained. A dynamic risk assessment framework is obtained by integrating standard geological parameter templates and geological anomaly models. The standard geological parameter templates and geological anomaly models are then input into the dynamic risk assessment framework to obtain geological instability probability parameters and optimized monitoring strategy tables.

4. The method for detecting hydrogeological and environmental conditions according to claim 3, characterized in that, The process involves separating anomalous event features based on a 3D geological parameter grid and historical geological anomaly data to obtain a steady-state geological parameter pool and anomaly evolution feature sequence. Based on the steady-state geological parameter pool and anomaly evolution feature sequence, standard geological parameter templates and geological anomaly patterns are then obtained, including: Grids that do not contain historical geological anomaly data are extracted from the three-dimensional geological parameter grid as steady-state grids, and the original geological environment data contained in each steady-state grid are extracted to construct a steady-state geological parameter pool. The grid containing historical geological anomaly data in the three-dimensional geological parameter grid is extracted as anomaly grid, and the historical geological anomaly data contained in each anomaly grid is extracted to generate anomaly evolution feature sequence. Several parameter features are obtained by extracting features from several steady-state grids contained in the steady-state geological parameter pool. A standard geological parameter template is generated based on the several parameter features. The parameter features include at least the interval mean, the extreme value fluctuation range and the covariance matrix between parameters. Based on the several anomalous grids contained in the anomalous evolution feature sequence, sensitive mutation parameters in historical geological anomalous data are identified, and dynamic pattern coding is performed based on the sensitive mutation parameters to obtain geological anomalous patterns.

5. The method for detecting hydrogeological and environmental conditions according to claim 3, characterized in that, The process involves fusing standard geological parameter templates and geological anomaly models to obtain a dynamic risk assessment framework. The standard geological parameter templates and geological anomaly models are then input into the dynamic risk assessment framework to obtain geological instability probability parameters and an optimized monitoring strategy table, including: A risk assessment baseline is obtained by constructing a parameter space based on a standard geological parameter template, and an anomaly monitoring response map is obtained by locating sensitive parameters based on a geological anomaly pattern. A dynamic risk assessment framework is then synthesized based on the risk assessment baseline and the anomaly monitoring response map. Historical safety benchmark values ​​are calculated using the mean of parameter intervals, parameter offset risk weights are derived using the covariance matrix, geological instability probability parameters are obtained based on historical safety benchmark values ​​and parameter offset risk weights, core monitoring indicators are defined based on combinations of sensitive parameters, spatial deployment plans are formulated based on the distribution of hotspot areas, and an optimized monitoring strategy table is generated based on the core monitoring indicators and spatial deployment plans.

6. The method for detecting hydrogeological and environmental conditions according to claim 5, characterized in that, The process of constructing a parameter space based on a standard geological parameter template to obtain a risk assessment baseline, and locating sensitive parameters based on geological anomaly patterns to obtain an anomaly monitoring response map, includes: Steady-state parameter coordinate axes are obtained based on the mean of parameter intervals, parameter correlation constraint domains are generated based on the covariance matrix, and risk assessment baselines are constructed based on the steady-state parameter coordinate axes and parameter correlation constraint domains. All abnormal event records in the abnormal evolution feature sequence are selected. A mutation judgment operation is performed on each geological parameter in the abnormal event record to obtain an abnormal parameter frequency statistics table. Based on the abnormal parameter frequency statistics table and historical geological abnormal data, the spatial grid coordinates of the abnormal parameters are obtained. The spatial grid coordinates of the abnormal parameters are marked in the three-dimensional geological parameter grid to form an abnormal monitoring response map.

7. The method for detecting hydrogeological and environmental conditions according to claim 1, characterized in that, The process involves obtaining a real-time geological environment dataset based on an optimized monitoring strategy table, acquiring geological entropy values ​​based on the real-time geological environment dataset, and obtaining anomaly zone marker datasets based on the real-time geological environment dataset and geological instability probability parameters, including: Based on the optimized monitoring strategy table, a real-time geological environment dataset is obtained. Coupled analysis is performed on the real-time geological environment dataset to obtain the geological state evolution trajectory. Chaotic quantification is performed on the geological state evolution trajectory to obtain the geological entropy value. A grid risk index matrix is ​​obtained based on the real-time geological environment dataset and the three-dimensional geological parameter grid. Neighborhood gradient calculation is performed on the grid risk index matrix to obtain the grid neighborhood gradient difference matrix. Anomaly boundary marking is performed on the grid neighborhood gradient difference matrix to obtain the grid anomaly mutation boundary identifier set.

8. The method for detecting hydrogeological and environmental conditions according to claim 7, characterized in that, The process of obtaining a real-time geological environment dataset based on an optimized monitoring strategy table, performing coupled analysis on the real-time geological environment dataset to obtain the geological state evolution trajectory, and performing chaotic quantification on the geological state evolution trajectory to obtain the geological entropy value includes: The monitoring data in the real-time geological environment dataset is extracted to construct a three-dimensional phase space coordinate axis. The monitoring data is then used to generate several phase space trajectory points based on the time series. Based on the three-dimensional phase space coordinate axis and the several phase space trajectory points, the geological state evolution trajectory is constructed. A three-dimensional phase space grid is generated based on the geological state evolution trajectory. Several phase space trajectory points are located in the three-dimensional phase space grid. The dwell frequency parameter of the phase space trajectory points in each grid of the three-dimensional phase space grid is counted. The total number parameter of phase space trajectory points is obtained. The dwell frequency parameter corresponding to each grid is divided by the total number parameter of phase space trajectory points to obtain the cell occurrence probability distribution table. The geological entropy value is obtained based on the cell occurrence probability distribution table.

9. A method for detecting hydrogeological and environmental conditions according to claim 7, characterized in that, The process involves obtaining a grid risk index matrix based on a real-time geological environment dataset and a 3D geological parameter grid, performing neighborhood gradient calculations based on the grid risk index matrix to obtain a grid neighborhood gradient difference matrix, marking anomaly boundaries on the grid neighborhood gradient difference matrix, and obtaining a set of grid anomaly mutation boundary identifiers, including: Based on the real-time geological environment dataset and the three-dimensional geological parameter grid, the real-time monitoring parameter values ​​of each spatial grid are extracted, and the spatial grid risk index matrix is ​​obtained by combining the geological instability probability parameter. Each grid cell in the spatial grid risk index matrix is ​​traversed, and the geological entropy value of the current grid cell is extracted. The difference between the geological entropy value of the current grid cell and the geological entropy value of the adjacent grid cells is calculated to obtain the grid neighborhood gradient difference matrix. The gradient change threshold is set to 0.

3. If the gradient difference in a certain direction is greater than 0.3 in the six neighborhood directions of each grid cell, the grid boundary in that direction is marked as an abnormal mutation boundary to obtain the grid abnormal mutation boundary identifier set.

10. A hydrogeological environment monitoring system, characterized in that, include: The data acquisition module is used to acquire the original geological environment data and historical geological anomaly data of the target area, as well as to acquire the real-time geological environment dataset based on the optimized monitoring strategy table. The processing module is used to perform spatiotemporal alignment processing between the raw geological environment data and historical geological anomaly data; Standard geological parameter templates and geological anomaly patterns are obtained based on three-dimensional geological parameter grids and historical geological anomaly data; geological instability probability parameters and optimized monitoring strategy tables are obtained based on standard geological parameter templates and geological anomaly patterns; geological entropy values ​​are obtained based on real-time geological environment datasets; and anomaly zone marker datasets are obtained based on real-time geological environment datasets and geological instability probability parameters. The data storage unit, connected to the processing module and the data acquisition module, is used to store raw geological environment data, historical geological anomaly data, three-dimensional geological parameter grids, standard geological parameter templates, geological anomaly patterns, geological instability probability parameters, optimized monitoring strategy tables, real-time geological environment datasets, and anomaly zone marker datasets.

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