Digital twinborn basin geological disaster early warning, prevention and control system

Abstract

The invention discloses a digital twinning basin geological disaster early-warning prevention and control system, which comprises rainfall, soil humidity, air temperature, earthquake, landslide, debris flow and other monitoring data, a risk judgment module, an early-warning module, an early-warning module, an early-warning module, an early-warning module, an early-warning module, an early-warning module, an early-warning module, an early-warning module and an early-warning module, the digital twinborn modeling module is used for establishing a virtual river basin geological disaster model by using various collected data and simulating various disaster scenes, including landslide, debris flow and flood, occurring in a river basin; according to the invention, the digital twinborn model is established to be combined with the collected various data, and the digital twinborn model is subjected to time sequence analysis, so that data closer to actual geological conditions can be obtained, the accuracy of geological disaster monitoring is further improved, early warning is carried out on geological disasters, and the digital twinborn model supports simulation of various disaster scenarios.

Description

Technical Field

[0001] This invention relates to the field of geological disaster early warning technology, and in particular to a digital twin watershed geological disaster early warning and prevention system. Background Technology

[0002] Geological disasters are natural disasters primarily caused by geological dynamic activities or abnormal changes in the geological environment. Under the influence of internal or external forces or anthropogenic geological forces, the Earth experiences abnormal energy release, material movement, deformation and displacement of rock and soil masses, and abnormal environmental changes, which endanger human life and property, livelihoods and economic activities, or damage the resources and environment upon which humanity depends for survival and development. Geological disasters refer to geological processes (phenomena) that, under the influence of natural or anthropogenic factors, cause damage and loss to human life, property, and the environment. Examples include landslides, mudslides, debris flows, ground fissures, ground subsidence, ground collapse, rock bursts, tunnel water inrush, mudslides, gas outbursts, spontaneous combustion of coal seams, loess subsidence, rock and soil swelling, sand liquefaction, land freeze-thaw cycles, soil erosion, desertification and marshland formation, soil salinization, as well as earthquakes, volcanoes, and geothermal hazards.

[0003] The Chinese patent CN117830050A cannot perform comprehensive disaster prediction, and the early warning of general geological disasters mainly adopts the method of on-site survey, which is not only time-consuming, labor-intensive, and costly, but also difficult to meet the needs of dynamic investigation of large-scale geological disasters. In particular, it is difficult to obtain geological disaster information in geologically complex areas such as high mountains and canyons, high altitudes, and cold regions with poor transportation. Therefore, this invention proposes a digital twin watershed geological disaster early warning and prevention system to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to propose a digital twin watershed geological disaster early warning and prevention system. This system establishes a digital twin model and combines it with various collected data. By performing time-series analysis on the digital twin model, data that more closely approximates actual geological conditions can be obtained, thereby improving the accuracy of geological disaster monitoring and providing early warnings for geological disasters. The digital twin model supports simulation of various disaster scenarios.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a digital twin watershed geological disaster early warning and prevention system, comprising a data acquisition module, a risk assessment module, a digital twin modeling module, a disaster prediction and early warning module, and a risk evaluation module;

[0006] The data acquisition module collects various environmental data related to geological disasters in the basin in real time, including monitoring data on rainfall, soil moisture, temperature, earthquakes, landslides, and debris flows. It also collects data on the geological structure, land use, and vegetation cover of the basin.

[0007] The risk assessment module analyzes the collected data to determine whether there is a risk of geological disaster.

[0008] The digital twin modeling module uses collected data to construct a virtual watershed geological hazard model, simulating various disaster scenarios that occur in the watershed, including landslides, debris flows, and floods.

[0009] The disaster prediction and early warning module, when there is a risk of geological disaster, determines the geological disaster sub-region from the monitoring area, generates a disaster early warning signal, and sends it to multiple online mobile terminals to provide direct early warning of geological disasters;

[0010] The risk assessment module evaluates the potential risks of various geological hazards within the watershed, and after simulating different hazard scenarios, predicts their impact range, probability of occurrence, and hazard losses.

[0011] A further improvement is that the data acquisition module includes geological sensors, meteorological sensors, and hydrological monitoring sensors.

[0012] Further improvements include: the geological sensor is a seismograph used to detect seismic fluctuations and analyze the vibration situation within the basin; the meteorological sensor monitors rainfall in real time; and the hydrological monitoring sensor is a water level gauge used to monitor water level changes in rivers, lakes, and reservoirs in real time.

[0013] Further improvements are made in that: the risk assessment module includes a standard acquisition module for acquiring multidimensional standard data, a comparison and analysis module for comparing and analyzing the multidimensional monitoring data based on the multidimensional standard data and recording the data comparison results, and a geological risk assessment module for determining whether there is a geological disaster risk based on the data comparison results.

[0014] A further improvement is that the digital twin modeling module imports data collected from various sensors into the modeling system in real time, integrates the data through a data fusion platform, and forms an accurate digital twin model.

[0015] Further improvements include: the disaster prediction and early warning module includes a data filtering module, which filters disaster risk data from multidimensional monitoring data when there is a geological disaster risk, and a location acquisition module, which is used to acquire multiple risk location data from the disaster risk data.

[0016] A further improvement is that the risk assessment module assesses the potential risks of various geological disasters within the watershed based on model simulation and prediction data.

[0017] A further improvement is made in the risk assessment formula within the risk assessment module:

[0018]

[0019] in The hazard value of the i-th type of disaster, Let i be the vulnerability value for the i-th type of disaster. Let be the exposure value for the i-th type of disaster. The weight value for each type of disaster is calculated based on the severity and probability of occurrence of different disasters.

[0020] A further improvement is that the data acquisition module, risk assessment module, digital twin modeling module, disaster prediction and early warning module, and risk evaluation module establish a communication channel through the communication module to indirectly send disaster early warning signals to multiple offline mobile terminals.

[0021] A further improvement is that the disaster prediction and early warning module obtains the offline addresses of multiple offline mobile terminals, and uses these addresses to indirectly send disaster early warning signals to multiple offline mobile terminals for geological disaster early warning.

[0022] The beneficial effects of this invention are as follows: By coordinating the data acquisition module, risk assessment module, digital twin modeling module, disaster prediction and early warning module, and risk assessment module, this invention establishes a digital twin model that combines with various collected data. Through time-series analysis of the digital twin model, data that more closely approximates actual geological conditions can be obtained, thereby improving the accuracy of geological disaster monitoring and providing early warnings for geological disasters. The digital twin model supports simulation of various disaster scenarios, including extreme weather events and long-term rainfall, and can accurately assess the impact of various disasters. It integrates multi-dimensional data for risk assessment, providing disaster risk levels for different areas within the basin, and, in conjunction with meteorological information, predicts the future geological conditions of the target area. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the modules of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0027] In document CN117830050A, the platform connects to multi-source data interfaces, including data from the Zhejiang Provincial Geological Disaster Risk Intelligent Control Platform, rainfall, GPS, crack gauge monitoring data, and field survey data. It integrates geological disaster information, risk prevention zone information, hazard point information, and disaster-bearing body information. This platform focuses on geological disasters induced by human engineering activities and constructs corresponding digital application scenarios. Mountain road slope cutting early warning is based on the road section's susceptibility level, using the daily rainfall and the effective rainfall of the previous three days as early warning criteria, and employing quantile regression to determine the threshold. Rural slope cutting for housing construction management, based on survey data of slope cutting for housing construction, classifies slope cutting patterns, statistically analyzes the relationship between slope cutting height, slope gradient, and safe slope cutting distance, and performs nonlinear fitting to obtain recommended slope cutting height, gradient, and safe distance. However, the predicted disaster distance and range in this application are limited. In this application, by constructing a digital twin model of the watershed, it is possible to highly simulate changes in the watershed's geological environment, hydrology, and meteorology. This model can simulate different types of disasters (landslides, debris flows, floods) and predict the likelihood, scale, and affected areas of disasters. The digital twin model supports simulation of various disaster scenarios, such as extreme weather events and long-term rainfall, and can accurately assess the impact of various disasters and develop emergency plans in advance. Example 1

[0028] according to Figure 1 As shown, this embodiment provides a digital twin watershed geological disaster early warning and prevention system, including a data acquisition module, a risk assessment module, a digital twin modeling module, a disaster prediction and early warning module, and a risk evaluation module;

[0029] The data acquisition module collects various environmental data related to geological disasters in the basin in real time, including monitoring data on rainfall, soil moisture, temperature, earthquakes, landslides, and debris flows. It also collects data on the geological structure, land use, and vegetation cover of the basin.

[0030] The risk assessment module analyzes the collected data to determine whether there is a risk of geological disaster.

[0031] The digital twin modeling module uses collected data to construct a virtual watershed geological hazard model, simulating various disaster scenarios that occur in the watershed, including landslides, debris flows, and floods.

[0032] The disaster prediction and early warning module, when there is a risk of geological disaster, determines the geological disaster sub-region from the monitoring area, generates a disaster early warning signal, and sends it to multiple online mobile terminals to provide direct early warning of geological disasters;

[0033] The risk assessment module evaluates the potential risks of various geological hazards within the watershed, and after simulating different hazard scenarios, predicts their impact range, probability of occurrence, and hazard losses.

[0034] The data acquisition module includes geological sensors, meteorological sensors, and hydrological monitoring sensors.

[0035] Geological sensors are seismographs used to detect seismic fluctuations and analyze vibrations within a watershed. Meteorological sensors monitor rainfall in real time, while hydrological monitoring sensors are water level gauges used to monitor water level changes in rivers, lakes, and reservoirs in real time.

[0036] The risk assessment module includes a standard acquisition module for acquiring multidimensional standard data, a comparative analysis module for comparing and analyzing multidimensional monitoring data based on the multidimensional standard data and recording the comparison results, and a geological risk assessment module for determining whether there is a geological hazard risk based on the comparison results. The module standardizes the multidimensional monitoring data according to corresponding safety thresholds to obtain standardized ratios for each parameter. It then calls a preset geomechanical model based on the geological data of the monitoring area to generate a coupling index representing the interaction strength between parameters within the monitoring area. Using the standardized ratio as the base value and the corresponding coupling index as the index, the standardized ratio is amplified to obtain an amplified ratio. These amplified ratios are then merged to reflect the static risk level under the synergistic effect of multiple parameters, resulting in a baseline risk value. Finally, the module acquires geological data from historical disasters, matches the current monitoring area's geological data with historical disaster data to generate a mutation response coefficient, acquires multidimensional monitoring data for a given time period, and calculates the rate of change of each parameter in the multidimensional monitoring data within that time period.

[0037] The digital twin modeling module imports data collected from various sensors into the modeling system in real time. The data is then integrated through a data fusion platform to form an accurate digital twin model. By setting up virtual sensors, it simulates areas that cannot be covered in reality, generates hypothetical data, and continuously updates and corrects the model by combining it with real-time data, providing a dynamic and realistic virtual environment.

[0038] The disaster prediction and early warning module includes a data filtering module, which filters disaster risk data from multi-dimensional monitoring data when there is a risk of geological disaster; a location acquisition module, which acquires multiple risk location data from the disaster risk data; a sub-region determination module, which determines the geological disaster sub-region from the target monitoring area based on the multiple risk location data; and a signal generation module, which generates a disaster early warning signal based on the geological disaster sub-region.

[0039] The risk assessment module assesses the potential risks of various geological hazards within the watershed based on model simulation and prediction data.

[0040] The risk assessment formula in the risk assessment module is:

[0041]

[0042] in The hazard value of the i-th type of disaster, Let i be the vulnerability value for the i-th type of disaster. Let be the exposure value for the i-th type of disaster. The weight value for each type of disaster is calculated based on the severity and probability of occurrence of different disasters.

[0043] The data acquisition module, risk assessment module, digital twin modeling module, disaster prediction and early warning module, and risk evaluation module establish a communication channel through the communication module to indirectly send disaster early warning signals to multiple offline mobile terminals.

[0044] The disaster prediction and early warning module obtains the offline addresses of multiple offline mobile devices, and uses these addresses to indirectly send disaster early warning signals to multiple offline mobile devices for indirect geological disaster early warning.

[0045] Table 1 Comparison of Standard Prediction and Digital Twin Prediction

[0046] Example 2

[0047] Based on meteorological information, the warning module generates a geological disaster warning when one or more of the following exceed a set threshold: soil moisture, soil pressure change, surface vegetation coverage, thickness of loose deposits, or surface deformation. It also generates a geological disaster warning when soil moisture does not exceed the set threshold, but the predicted soil moisture value will exceed the set threshold after rainfall is predicted in the near future. Furthermore, it generates a geological disaster warning when one or more of the following exceed a set threshold: rate of change of soil moisture, rate of change of soil pressure, or rate of surface deformation. No warning is generated if any of the above conditions are not met.

[0048] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin watershed geological disaster early warning and prevention system, characterized in that, It includes a data acquisition module, a risk assessment module, a digital twin modeling module, a disaster prediction and early warning module, and a risk evaluation module; The data acquisition module collects various environmental data related to geological disasters in the basin in real time, including rainfall, soil moisture, temperature, earthquake, landslide and debris flow monitoring data, as well as geological structure, land use and vegetation cover data in the basin. The risk assessment module analyzes the collected data to determine whether there is a risk of geological disaster. The digital twin modeling module uses collected data to construct a virtual watershed geological hazard model, simulating various disaster scenarios that occur in the watershed, including landslides, debris flows, and floods. The disaster prediction and early warning module, when there is a risk of geological disaster, determines the geological disaster sub-region from the monitoring area, generates a disaster early warning signal, and sends it to multiple online mobile terminals to provide direct early warning of geological disasters; The risk assessment module evaluates the potential risks of various geological hazards within the watershed, and after simulating different hazard scenarios, predicts their impact range, probability of occurrence, and hazard losses.

2. The digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The data acquisition module includes geological sensors, meteorological sensors, and hydrological monitoring sensors.

3. The digital twin watershed geological disaster early warning and prevention system according to claim 2, characterized in that: The geological sensor is a seismograph used to detect seismic fluctuations and analyze the vibration situation within the basin. The meteorological sensor monitors rainfall in real time, and the hydrological monitoring sensor is a water level gauge used to monitor water level changes in rivers, lakes and reservoirs in real time.

4. The digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The risk assessment module includes a standard acquisition module for acquiring multidimensional standard data, a comparison and analysis module for comparing and analyzing multidimensional monitoring data based on the multidimensional standard data and recording the data comparison results, and a geological risk assessment module for determining whether there is a geological disaster risk based on the data comparison results.

5. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The digital twin modeling module imports data collected from various sensors into the modeling system in real time, and integrates the data through a data fusion platform to form an accurate digital twin model.

6. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The disaster prediction and early warning module includes a data filtering module, which filters disaster risk data from multidimensional monitoring data when there is a geological disaster risk, and a location acquisition module, which is used to acquire risk location data from the disaster risk data.

7. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The risk assessment module assesses the potential risks of various geological disasters within the watershed based on model simulation and prediction data.

8. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The risk assessment formula in the risk assessment module is: in The hazard value of the i-th type of disaster, Let i be the vulnerability value for the i-th type of disaster. Let be the exposure value for the i-th type of disaster. The weight value for each type of disaster is calculated based on the severity and probability of occurrence of different disasters.

9. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The data acquisition module, risk assessment module, digital twin modeling module, disaster prediction and early warning module, and risk evaluation module establish a communication channel through the communication module to indirectly send disaster early warning signals to multiple offline mobile terminals.

10. A digital twin watershed geological disaster early warning and prevention system according to claim 1, characterized in that: The disaster prediction and early warning module obtains the offline addresses of multiple offline mobile terminals and uses them to indirectly send disaster early warning signals to multiple offline mobile terminals according to the multiple offline addresses, so as to carry out geological disaster early warning.

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