Construction geological disaster early warning system and method

Abstract

The invention discloses a construction geological disaster early warning system and method, relates to the technical field of geological disaster early warning, and solves the problem that a construction geological disaster early warning system and method for performing effective prospective early warning on landslide in combination with various landslide influence factors lacks in the prior art. The system comprises a mountain information acquisition module, a weather information acquisition module, an information storage module, a data analysis processing module and an information display early warning module. The objective of the invention is to accurately obtain the deformation information of the mountain, the physical state change of the mountain can be comprehensively mastered through the multi-dimensional monitoring mode, and abundant basic data is provided for landslide early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological disaster warning, and particularly relates to a construction geological disaster warning system and method. Background Art

[0002] Common construction geological disasters include landslides, collapses, debris flows, ground settlement and subsidence, water inrush and leakage, and outburst of harmful gases, etc.; among them, a landslide refers to a geological disaster in which the rock and soil mass on a slope slides down the slope as a whole or dispersedly along a certain weak surface or weak zone under the action of gravity. When a landslide occurs, it is often accompanied by strong vibrations and sounds. The movement speed and scale of the landslide body vary due to factors such as geological conditions, topography, and hydrometeorology. For construction workers during engineering construction, a landslide is one of the important factors that may threaten personal safety and property safety during the construction process. Therefore, the warning of landslides is an important part of construction geological disaster warning; traditional landslide monitoring mainly relies on manual on-site measurement, but the work intensity is large, the risk coefficient is high, the measurement results are interfered by various factors, the accuracy is not high and the error is relatively large. At the same time, the terrain of the mountain area is complex, and personnel cannot reach special sections. Under bad weather conditions, personnel cannot obtain monitoring data in time and cannot effectively give warning information. In addition, the archiving and sorting of traditional manual monitoring data are time-consuming and laborious and relatively cumbersome; there are also existing surface displacement monitoring instruments and video surveillance cameras set up for landslide monitoring and warning; However, the current landslide monitoring and warning still have relatively high requirements for the accuracy and foresight of the monitoring results. At present, there is a lack of a construction geological disaster warning system and method that can effectively and prospectively warn of landslides by combining various factors affecting landslides. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a construction geological disaster warning system and method. The present invention realizes the all-round monitoring of the mountain state by integrating a macroscopic crack monitoring module, a microscopic crack monitoring module, a groundwater level monitoring module, a soil moisture content monitoring module, and an SBAS - InSAR detection module; the macroscopic and microscopic crack monitoring modules capture the changes in the mountain structure from different scales, the groundwater level and soil moisture content monitoring modules reflect the hydrogeological characteristics of the mountain, and the SBAS - InSAR detection module uses synthetic aperture radar interferometry technology to accurately obtain the deformation information of the mountain. This multi-dimensional monitoring method can comprehensively master the physical state changes of the mountain and provide rich basic data for landslide warning.

[0004] The technical solution adopted by the present invention is as follows: A construction geological disaster warning system, comprising a mountain body information acquisition module, a meteorological information acquisition module, an information storage module, a data analysis and processing module, and an information display and warning module; The mountain body information acquisition module and the meteorological information acquisition module are respectively connected to the information storage module through a radio signal transceiver terminal, and are used for real-time monitoring of the mountain body information and the meteorological information of the corresponding area, and transmitting the information to the information storage module for information analysis and storage; The information storage module is electrically connected to the data analysis and processing module, and is used for real-time comprehensive analysis of the mountain body information and the meteorological information to judge the landslide data of the corresponding area; The data analysis and processing module is electrically connected to the information display and warning module, and is used for grading and warning the signals of landslides in a specific area in combination with pre-set warning indicators.

[0005] Preferably, the mountain body information acquisition module includes a macroscopic crack monitoring module, a microscopic crack monitoring module, a groundwater level monitoring module, a soil moisture content monitoring module, and an SBAS-InSAR detection module; the macroscopic crack monitoring module, the microscopic crack monitoring module, the groundwater level monitoring module, the soil moisture content monitoring module, and the SBAS-InSAR detection module are respectively electrically connected to the information storage module.

[0006] Preferably, the meteorological information module includes a groundwater flow monitoring module and a rainfall prediction module.

[0007] Preferably, a plurality of the groundwater level monitoring modules and the soil moisture content detection modules are provided, and are respectively arranged in a matrix in the mountain body of the construction radiation area.

[0008] Preferably, a plurality of the groundwater flow monitoring modules are provided, and are all arranged in a matrix in the mountain body of the construction radiation area.

[0009] A construction geological disaster warning method, adopting the above-mentioned construction geological disaster warning system, and adopting the following steps: Step 1: The mountain body in the construction radiation area is accurately divided into several matrix areas, the mountain body information acquisition module and the meteorological information acquisition module are arranged in the mountain body of the construction radiation area, and each warning index is set in the data analysis and processing module; Step 2: The mountain body information acquisition module and the meteorological information acquisition module respectively acquire the monitored mountain body information and the meteorological information of the corresponding area in real time, and transmit the information to the information storage module in real time; Step 3: The information storage module transmits the stored various monitoring information to the data analysis and processing module in real time, and the data analysis and processing module performs real-time comprehensive analysis and processing on the monitored information, so as to judge the corresponding landslide data in real time; Step 4: The data analysis and processing module compares the real-time landslide data with the warning indicators to analyze the real-time mountain risk levels of each construction radiation area; Step 5: The data analysis and processing module transmits the real-time mountain risk levels of each construction radiation area to the information display and warning module in real time, and relevant management personnel conduct safety scheduling work for construction personnel according to the display of the information display and warning module.

[0010] Preferably, in Step 1, the method for setting the warning indicators is as follows: The future natural landslide degree risk percentages are sequentially set as natural landslide risk levels: Z 1 、Z 2 、Z 3 、 Z 4 ; The rainfall landslide risk percentages are sequentially set as rainfall landslide risk levels: J 1 、J 2 、J 3 、J 4 .

[0011] Preferably, in Step 3, the method for the data analysis and processing module to conduct real-time comprehensive analysis and processing on the monitored information is as follows: Step 301: The SBAS-InSAR detection module, the macroscopic crack monitoring module, and the microscopic crack monitoring module conduct real-time monitoring and analysis on the real-time natural landslide situation in the construction radiation area, so as to obtain the natural landslide degrees that will reach each matrix area within the future t, 2t, and 3t. t is the time span, and through calculation, the natural landslide degree risk percentages within t, 2t, and 3t for each matrix area are further obtained; Step 302: The rainfall prediction module predicts the unit time rainfall, rainfall duration, and rainfall coverage area of the real-time mountain body in each corresponding construction radiation area, and respectively obtains the average rainfall within the future t, 2t, and 3t for each matrix area; Step 303: Based on the average rainfall within the future t, 2t, and 3t for each matrix area, combined with the real-time data measured by the groundwater level monitoring module, the soil moisture content monitoring module, and the groundwater flow monitoring module, the real-time average groundwater level, the real-time average soil moisture content, and the real-time average groundwater flow within the future t, 2t, and 3t are obtained; Step 304: Combine the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the future t, 2t, and 3t, and analyze the mountain data measured in real time in the construction radiation area by the SBAS-InSAR detection module, macroscopic crack monitoring module, and microscopic crack monitoring module in Step 301, respectively obtain the landslide risks generated by the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the future t, 2t, and 3t, and compare them with their respective landslide critical thresholds, respectively obtain the respective landslide risk percentages a, b, c; set the weight percentages of the landslide risk impacts of the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow as m, n, and k in sequence, so as to obtain the rainfall landslide risk percentage within the future t, 2t, and 3t = m * a + n * b + k * c.

[0012] Preferably, in Step 4, the method for judging the real-time mountain risk level of each construction radiation area includes the following steps: Step 401: Combine the natural landslide degree and natural landslide risk level that will be reached in each matrix area within the future t, 2t, and 3t, and judge the natural landslide risk level of the matrix area within the corresponding construction radiation area within the future t, 2t, and 3t; Step 402: Combine the rainfall landslide risk percentage and rainfall landslide risk level within the future t, 2t, and 3t, and judge the rainfall landslide risk level of the matrix area within the corresponding construction radiation area within the future t, 2t, and 3t.

[0013] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: 1. The present invention realizes the all-round monitoring of the mountain state by integrating the macroscopic crack monitoring module, microscopic crack monitoring module, groundwater level monitoring module, soil moisture content monitoring module, and SBAS-InSAR detection module; the macroscopic and microscopic crack monitoring modules capture the changes in the mountain structure from different scales, the groundwater level and soil moisture content monitoring modules reflect the hydrogeological characteristics of the mountain, and the SBAS-InSAR detection module uses synthetic aperture radar interferometry technology to accurately obtain the deformation information of the mountain. This multi-dimensional monitoring method can comprehensively master the physical state changes of the mountain and provide rich basic data for mountain landslide early warning; 2. The present invention closely associates meteorological factors with the mountain state. The rainfall prediction module provides information on the time, intensity, and area of future rainfall, and the groundwater flow monitoring module provides real-time feedback on the dynamic changes of groundwater caused by factors such as rainfall. By comprehensively analyzing meteorological information and mountain information, it is possible to more accurately evaluate the impacts of rainfall and multiple natural factors on mountain landslides; 3. The data analysis and processing module of the present invention adopts a unique algorithm to comprehensively analyze mountain body and meteorological information. By combining rainfall prediction data with real-time monitoring data of groundwater level, soil moisture content, and groundwater flow rate, and comparing them with their respective landslide critical thresholds and assigning corresponding weights to different factors, it accurately calculates the rainfall landslide risk percentage. At the same time, using the monitoring data of natural landslide conditions from multiple modules, it analyzes and obtains the degree of natural landslide. This method of comprehensive multi-factor analysis fully considers various factors affecting mountain landslides and improves the accuracy of risk assessment; 4. The present invention arranges the groundwater level monitoring module, soil moisture content detection module, and groundwater flow rate monitoring module in a matrix form within the mountain body in the construction radiation area. This layout method can evenly distribute monitoring points in space, ensuring comprehensive and accurate monitoring of hydrogeological parameters at different positions inside the mountain body, effectively reducing monitoring blind spots, and improving the representativeness and reliability of monitoring data; 5. The present invention dynamically grades and warns the mountain body in the construction radiation area according to the pre-set rainfall landslide risk level and natural landslide risk level, combined with the risk percentage and landslide degree calculated in real time. The information display and warning module visually presents the warning information to the management personnel, facilitating the timely adoption of safety dispatching measures to achieve effective warning and prevention of mountain landslides. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be described by way of examples with reference to the accompanying drawings, wherein: Figure 1 is a schematic diagram of the overall framework of a construction geological disaster warning system in the present invention; Figure 2 is a schematic diagram of the groundwater level monitoring module matrix in Embodiment 1 of the present invention; Figure 3 is a schematic diagram of the soil moisture content monitoring module matrix in Embodiment 1 of the present invention; Figure 4 is a schematic diagram of the groundwater flow rate monitoring module matrix in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Usually, the components of the embodiments of this application described and marked in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but only represents the selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts belong to the scope of protection of this application.

[0016] In the description of the embodiments of this application, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is usually placed during use. It is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of this application. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0017] The following will be combined with Figures 1 to 4 to describe the present invention in detail.

[0018] Embodiment 1 A construction geological disaster warning system, referring to the attached Figure 1 , includes a mountain body information acquisition module, a meteorological information acquisition module, an information storage module, a data analysis and processing module, and an information display and warning module; The mountain body information acquisition module and the meteorological information acquisition module are respectively connected to the information storage module through a radio signal transceiver terminal, and are used to monitor the mountain body information and the meteorological information of the corresponding area in real time, and transmit the information to the information storage module for information analysis and storage; The information storage module is electrically connected to the data analysis and processing module, and is used to comprehensively analyze the mountain body information and the meteorological information in real time to judge the landslide data of the corresponding area; The data analysis and processing module is electrically connected to the information display and warning module, and is used to classify and warn the signals of landslides in a specific area in combination with the pre-set warning indicators.

[0019] In this embodiment, the mountain information acquisition module includes a macroscopic crack monitoring module, a microscopic crack monitoring module, a groundwater level monitoring module, a soil water content monitoring module, and an SBAS-InSAR detection module; the macroscopic crack monitoring module, the microscopic crack monitoring module, the groundwater level monitoring module, the soil water content monitoring module, and the SBAS-InSAR detection module are respectively electrically connected to the information storage module.

[0020] Among them, the total station is used in the macroscopic crack monitoring module, the fiber optic sensor is used in the microscopic crack monitoring module, and the groundwater level monitor is installed in the groundwater level monitoring module; The TH302 soil water content monitoring system is used in the soil water content monitoring module. The TH302 soil water content monitoring system consists of a data collector and a soil moisture sensor. The sensor uses the dielectric method measurement principle, is applicable to various soil types, and can be buried in the soil for a long time for continuous monitoring; The SBAS-InSAR detection module is an existing technology in this field, and the specific structure and connection implementation method are not described in this application; In this embodiment, the meteorological information module includes a groundwater flow monitoring module and a rainfall prediction module.

[0021] Among them, the AquaVISION groundwater flow velocity and direction meter is used in the meteorological information module. The AquaVISION groundwater flow velocity and direction meter uses video pipeline microscopy technology to photograph the movement trajectory of colloidal particles in water through a microscopic lens, and measures the groundwater flow velocity and direction in real time; The rainfall prediction module docks with the official website of the China Meteorological Administration to obtain relevant rainfall information.

[0022] In this embodiment, referring to Appendix Figure 2 ~Appendix Figure 3 , a plurality of the groundwater level monitoring modules and the soil water content detection modules are provided, and are respectively arranged in a matrix in the mountain body of the construction radiation area.

[0023] In this embodiment, referring to Appendix Figure 4 , a plurality of the groundwater flow monitoring modules are provided, and are all arranged in a matrix in the mountain body of the construction radiation area.

[0024] Embodiment 2 A construction geological disaster early warning method uses the above-mentioned construction geological disaster early warning system and adopts the following steps: Step 1: The mountain body in the construction radiation area is accurately divided into several matrix areas, the mountain information acquisition module and the meteorological information acquisition module are arranged in the mountain body of the construction radiation area, and each early warning index is set in the data analysis and processing module; Step 2: The mountain information acquisition module and the meteorological information acquisition module respectively acquire the monitored mountain information and the meteorological information of the corresponding area in real time, and transmit the information to the information storage module in real time; Step 3: The information storage module transmits the stored various monitoring information to the data analysis and processing module in real time. The data analysis and processing module conducts real-time comprehensive analysis and processing on the monitored information, so as to judge the corresponding landslide data in real time; Step 4: The data analysis and processing module compares the real-time landslide data with the warning indicators, so as to analyze the real-time mountain risk level of each construction radiation area; Step 5: The data analysis and processing module transmits the real-time mountain risk level of each construction radiation area to the information display and warning module in real time. Relevant management personnel conduct safety scheduling work for construction personnel according to the display of the information display and warning module.

[0025] In this embodiment, in Step 1, the method for setting the warning indicators is as follows: The future natural landslide degree risk percentages are sequentially set as natural landslide risk levels: Z 1 、Z 2 、Z 3 、 Z 4 ; The rainfall landslide risk percentages are sequentially set as rainfall landslide risk levels: J 1 、J 2 、J 3 、J 4 .

[0026] In this embodiment, in Step 3, the method for the data analysis and processing module to conduct real-time comprehensive analysis and processing on the monitored information is as follows: Step 301: The SBAS-InSAR detection module, the macroscopic crack monitoring module, and the microscopic crack monitoring module conduct real-time monitoring and analysis on the real-time natural landslide situation in the construction radiation area, so as to obtain the natural landslide degrees that will reach each matrix area within the future t, 2t, and 3t. t is the time span, and through calculation, the natural landslide degree risk percentages within t, 2t, and 3t reaching each matrix area are further obtained; Step 302: The rainfall prediction module predicts the unit time rainfall, rainfall duration, and rainfall coverage area of the real-time mountain body in each corresponding construction radiation area, and respectively obtains the average rainfall within the future t, 2t, and 3t of each matrix area; Step 303: Analyze by combining the average rainfall within the next t, 2t, and 3t in each matrix region with the real-time data measured by the groundwater level monitoring module, soil moisture content monitoring module, and groundwater flow monitoring module, so as to obtain the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the next t, 2t, and 3t; Step 304: Combine the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the next t, 2t, and 3t with the mountain data measured in real time in the construction radiation area by the SBAS-InSAR detection module, macro crack monitoring module, and micro crack monitoring module in Step 301, and respectively obtain the landslide risks generated by the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the next t, 2t, and 3t, and compare them with their respective landslide critical thresholds respectively to obtain their respective landslide risk percentages a, b, and c; Set the weight percentages for the impacts of the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow on the landslide risk to m, n, and k in sequence. Therefore, the rainfall landslide risk percentage within the next t, 2t, and 3t = m * a + n * b + k * c; Among them, in Step 301, the judgment steps for the natural landslide degree to be reached in each matrix region within t, 2t, and 3t are as follows: A1: The SBAS-InSAR detection module uses the SBAS algorithm to solve the surface deformation rate through continuous time-series SAR images, and the deformation resolution needs to reach ±1 mm / yr to generate a deformation rate map and a cumulative deformation time-series curve; The macro crack monitoring module measures the three-dimensional coordinates of the crack through a total station, calculates the crack opening change rate ΔW, and establishes a crack propagation speed model: V_w = ΔW / Δt; The micro crack monitoring module collects strain data through a fiber optic sensor, obtains the strain distribution through Brillouin optical time domain analysis, and calculates the strain gradient ▽ε (με / m); A2: Establish a landslide dynamics characteristic matrix: F = [f1, f2, f3]^T; where: f1 = deformation acceleration α (mm / day²) = d²D / dt²; f2 = crack propagation acceleration β (mm / day²) = dV_w / dt; f3 = strain gradient change rate γ (με / m·day) = d(▽ε) / dt; A3: Use the improved Newmark slider model for state assessment: Landslide potential energy index = w1·tanh(α / α_c)+ w2·erf(β / β_c) + w3·sigmoid(γ / γ_c); where: α_c = 0.05 mm / day² (critical deformation acceleration); β_c = 0.1 mm / day² (critical crack propagation acceleration); γ_c = 50 με / m·day (critical strain gradient change rate); weight coefficients w1 = 0.5, w2 = 0.3, w3 = 0.2; among them, according to the weight distribution principle in the "Geotechnical Engineering Monitoring Manual". A4: Establish a dual-benchmark system of engineering geological exploration data and machine learning dynamic correction as the benchmark for the natural landslide risk percentage: Initial stable state LSI_0 ≤ 0.2, warning threshold LSI_c = 0.8 (corresponding to safety factor FOS = 1.0), use the LSTM network to train historical monitoring data, and dynamically update the threshold: LSI_c(t) = LSI_c(t - 1) + Δ (meteorological influence factor). A5: Calculate the natural landslide risk percentage R reaching each matrix area within t, 2t, and 3t: R = min{100%, 125%× [LSI(t) - LSI_0] / [LSI_c - LSI_0]}; Time extrapolation method: R(t + nt) = R(t) × exp(n·k·Δt); where: k = 0.15 (empirical decay coefficient, according to "Research on Landslide Time Prediction"); n = 1, 2, 3 (time multiples).

[0027] Among them, in step 303, the calculation steps of the real-time average groundwater level, real-time average soil moisture content, and real-time average groundwater flow within the future t, 2t, and 3t are as follows: B1: Extract the average rainfall intensity P(t + nt) (unit: mm / h) for the future t, 2t, and 3t time periods from the meteorological prediction module, n = 1, 2, 3; B2: The groundwater level monitoring module, soil moisture content monitoring module, and groundwater flow monitoring module monitor the current groundwater level H 0 (unit: m), the current soil volume moisture content θ 0 (unit: %), and the current groundwater flow Q 0 (unit: m³ / s); B3: Retrieve geological parameters: soil saturated hydraulic conductivity K_s (m / s), specific yield μ, and permeability coefficient C, where the permeability coefficient C is obtained according to the "Engineering Geological Handbook". B4: The improved Green-Ampt infiltration model is adopted to obtain the soil water content prediction model: Δθ(t+nt) = [P(t+nt) × η_inf - E(t)] / Z_root; where: - η_inf = dynamic infiltration rate (%), calculated by the following formula: η_inf(t) = 1 - exp(-K_s × Δt / ψ_f) (ψ_f is the soil matrix potential, taking the typical value of -10 kPa) - E(t) = evapotranspiration (mm), calculated in real time using the Penman-Monteith formula - Z_root = root zone thickness (m), determined according to on-site investigation. To determine the future soil water content: θ(t+nt) = θ 0 + ΣΔθ(t+it) (i=1→n) Based on the simplified form of the Boussinesq equation, the groundwater level prediction model is obtained: H(t+nt) = H 0 + [R(t+nt) - Q(t) × S_y] / (A × μ); where: - R(t+nt) = effective recharge volume (m³) = P(t+nt) × A× η_recharge (η_recharge is the rainfall infiltration coefficient, obtained by fitting historical data) - S_y = specific storage coefficient (1 / m) - A = aquifer area (m²), obtained by inverting the InSAR deformation field - μ = porosity (%); The modified Darcy's law - Forchheimer model is adopted to obtain the groundwater flow prediction model: Q(t+nt) = K × i(t+nt) × A_flow × [1 + β × |i(t+nt)|]; where: - i(t+nt) = hydraulic gradient = [H(t+nt) - H_downstream] / L (H_downstream is the downstream boundary water level, L is the flow path length) - K = hydraulic conductivity (m / s), calibrated by pumping tests - β = turbulence coefficient (s² / m³), taking the empirical value of 0.001~0.01 - A_flow = cross-sectional area of flow (m²), modeled based on borehole data.

[0028] In step 304, the steps to obtain the respective landslide risk percentages a, b, c are as follows: C1: Construct a critical value threshold library for groundwater level, soil water content, and groundwater flow: The critical value of the groundwater level \(H_c = (\gamma_{sat}-\gamma_d) / (\gamma_w)\times h\times\cos\alpha\times\tan\varphi\), where: \(\gamma_{sat}=\) saturated unit weight (\(kN / m³\)), \(\gamma_d=\) dry unit weight, \(\gamma_w=\) unit weight of water, \(h=\) depth of the slip surface (\(m\)), \(\alpha=\) slope angle, \(\varphi=\) internal friction angle; The critical value of soil moisture content \(\theta_c=\theta_p + 0.5(\theta_L-\theta_p)\); \(\theta_p=\) plastic limit, \(\theta_L=\) liquid limit (measured by Atterberg limit test); The critical value of groundwater flow velocity \(Q_c = K\times i_c\times A\); \(i_c=\) critical hydraulic gradient \(=(\gamma_{sat}-\gamma_w) / \gamma_w\); C2: Perform parameter normalization on the critical value of the groundwater level \(H_c\), the critical value of soil moisture content \(\theta_c\), and the critical value of soil moisture content \(\theta_c\). Parameter normalization includes dimensionless processing of the predicted values: \(H_n(t + nt)=H(t + nt) / H_c\) \(\theta_n(t + nt)=\theta(t + nt) / \theta_c\), \(Q_n(t + nt)=Q(t + nt) / Q_c\); and dynamic threshold correction: \(H_c' = H_c\times(1 - 0.2\times\Delta D / 10mm)\) (\(\Delta D\) is the monthly deformation); C3: Use the S-shaped response function to quantify the risk growth and obtain the groundwater level risk percentage \(a\): \(a(t + nt)=100\%\times[1+\exp(-k_H\times(H_n - 1))]^{-1}\), where: \(k_H=\) steepness coefficient \(= 3.0\) (determined by fitting historical landslide data); soil moisture content risk percentage \(b\): \(b(t + nt)=100\%\times\min\{1,[1.5\times(\theta_n - 0.8)]^2\}\), (quadratic growth is activated when \(\theta_n\geq0.8\)), \(c(t + nt)=100\%\times\tanh(2.5\times(Q_n - 0.7))\) (the tanh function controls the risk upper limit).

[0029] In step 304, the steps for setting the weight percentages \(m\), \(n\), and \(k\) are as follows: Based on historical data analysis of the sensitivity of the groundwater level, soil moisture content, and groundwater flow rate, construct a judgment matrix, and then obtain the initial weight distribution; dynamically correct the obtained weight distribution data using the entropy weight method, and then perform coupled weight synthesis on the obtained weight distribution data to obtain \(m = 46.3\%\), \(n = 16.9\%\), \(k = 28.6\%\).

[0030] In this embodiment, in step 4, the method for judging the real-time mountain risk level of each construction radiation area includes the following steps: Step 401: Based on the natural landslide degree and natural landslide risk level that will be reached in each matrix area within the future t, 2t, and 3t, determine the natural landslide risk levels of the matrix areas within the corresponding construction radiation area within the future t, 2t, and 3t. Step 402: Based on the rainfall-induced landslide risk percentage and rainfall-induced landslide risk level within the future t, 2t, and 3t, determine the rainfall-induced landslide risk levels of the matrix areas within the corresponding construction radiation area within the future t, 2t, and 3t.

[0031] It should be noted that: The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A construction geological disaster early warning system, characterized in that: It includes a mountain information acquisition module, a meteorological information acquisition module, an information storage module, a data analysis and processing module, and an information display and warning module; The mountain information acquisition module and the meteorological information acquisition module are respectively connected to the information storage module through a radio signal transceiver terminal, and are used to monitor the mountain information and the meteorological information of the corresponding area in real time, and transmit the information to the information storage module for information analysis and storage; The information storage module is electrically connected to the data analysis and processing module, and is used for real-time comprehensive analysis of mountain information and meteorological information to determine the landslide data of the corresponding area; The data analysis and processing module is electrically connected to the information display and early warning module, and is used to perform graded early warning on signals of landslides in specific areas in combination with early warning indicators set in advance.

2. A construction geological disaster early warning system according to claim 1, characterized in that: The mountain information acquisition module includes a macro crack monitoring module, a micro crack monitoring module, a groundwater level monitoring module, a soil moisture monitoring module and a SBAS-InSAR detection module; the macro crack monitoring module, the micro crack monitoring module, the groundwater level monitoring module, the soil moisture monitoring module and the SBAS-InSAR detection module are electrically connected to the information storage module respectively.

3. A construction geological disaster early warning system according to claim 1, characterized in that: The meteorological information module includes a groundwater flow monitoring module and a rainfall prediction module.

4. A construction geological disaster early warning system according to claim 2, characterized in that: The groundwater level monitoring modules and soil moisture content detection modules are provided in plurality and are arranged in a matrix in the mountain within the construction radiation area.

5. A construction geological disaster early warning system according to claim 3, characterized in that: The groundwater flow monitoring modules are provided in plurality and are arranged in a matrix in the mountain within the construction radiation area.

6. A construction geological disaster early warning method, characterized in that: Using a construction geological disaster early warning system as described in any one of claims 1 to 5, the following steps are adopted: Step 1: The mountain in the construction radiation area is accurately divided into several matrix areas, the mountain information acquisition module and the meteorological information acquisition module are deployed in the mountain in the construction radiation area, and various early warning indicators are set in the data analysis and processing module; Step 2: The mountain information acquisition module and the meteorological information acquisition module respectively acquire the monitored mountain information and the meteorological information of the corresponding area in real time, and transmit the information to the information storage module in real time; Step 3: The information storage module transmits the stored monitoring information to the data analysis and processing module in real time. The data analysis and processing module performs real-time comprehensive analysis and processing on the monitored information, thereby determining the corresponding landslide data in real time; Step 4: The data analysis and processing module compares the real-time landslide data with the early warning indicators to analyze the real-time mountain risk level of each construction radiation area; Step 5: The data analysis and processing module transmits the real-time mountain risk level of each construction radiation area to the information display and early warning module in real time, and the relevant management personnel carry out the safety dispatch of the construction personnel according to the display of the information display and early warning module.

7. A construction geological disaster early warning method according to claim 6, characterized in that: In step 1, the method for setting the early warning indicator is: The risk percentage of natural landslide in the future is set in the range of 0-25%, 25%-50%, 50%-75% and 75%-100% from small to large as natural landslide risk levels: Z1, Z2, Z3, Z4; The rainfall-landslide risk percentages are set in the range of 0-25%, 25%-50%, 50%-75%, and 75%-100% from small to large as rainfall-landslide risk levels: J1, J2, J3, and J4.

8. A construction geological disaster early warning method according to claim 6, characterized in that: In step 3, the data analysis and processing module performs real-time comprehensive analysis and processing on the monitored information as follows: Step 301: The SBAS-InSAR detection module, the macro crack monitoring module, and the micro crack monitoring module perform real-time monitoring and analysis on the real-time natural landslide situation in the construction radiation area, thereby obtaining the degree of natural landslide that will be reached in each matrix area within the future t, 2t, and 3t, where t is the time span, and further obtains the risk percentage of the degree of natural landslide that will be reached in each matrix area within t, 2t, and 3t through calculation; Step 302: The rainfall prediction module predicts the rainfall per unit time, rainfall duration, and rainfall coverage area of ​​each corresponding construction radiation area, and obtains the average rainfall in the future t, 2t, and 3t of each matrix area respectively; Step 303: According to the average rainfall in the future t, 2t, and 3t of each matrix area, the real-time data measured by the groundwater level monitoring module, the soil moisture content monitoring module, and the groundwater flow monitoring module are combined and analyzed to obtain the real-time average groundwater level, the real-time average soil moisture content, and the real-time average groundwater flow in the future t, 2t, and 3t; Step 304: Analyze the real-time average groundwater level, real-time average soil moisture content and real-time average groundwater flow in the future t, 2t and 3t in combination with the mountain data measured in real time in the construction radiation area by the SBAS-InSAR detection module, the macro crack monitoring module and the micro crack monitoring module in step 301, respectively obtain the landslide risks caused by the real-time average groundwater level, real-time average soil moisture content and real-time average groundwater flow in the future t, 2t and 3t, and compare them with their respective landslide critical thresholds, respectively, to obtain their respective landslide risk percentages a, b and c; The weight percentages of landslide risk impacts caused by real-time average groundwater level, real-time average soil moisture content and real-time average groundwater flow are set as m, n and k respectively, so the rainfall landslide risk percentage within the future t, 2t and 3t is obtained = m*a+ n* b+ k* c.

9. A construction geological disaster early warning method according to claim 6, characterized in that: In step 4, the real-time mountain risk level judgment method of each construction radiation area includes the following steps: Step 401: Based on the degree of natural landslides that will be reached in each matrix area within the next t, 2t, and 3t and the natural landslide risk level, determine the natural landslide risk level of the matrix area in the corresponding construction radiation area within the next t, 2t, and 3t; Step 402: Based on the percentage of rainfall landslide risk and the rainfall landslide risk level within the next t, 2t, and 3t, determine the rainfall landslide risk level within the next t, 2t, and 3t of the corresponding matrix area in the construction radiation area.

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