Slope instability and ecological monitoring early warning method and system

Abstract

This invention discloses a slope instability and ecological monitoring and early warning method and system. The system collects ecological and geological parameters of a slope; calculates a slope ecological stability characteristic value based on the ecological parameters; calculates a slope geological stability characteristic value based on the geological parameters; and determines whether the slope is unstable based on these ecological and geological stability characteristic values. Compared to existing early warning systems that rely on single physical sensor monitoring, this system integrates ecological and geological parameters for prediction, effectively improving prediction accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster monitoring, and in particular to a slope instability and ecological monitoring and early warning method and system. Background Art

[0002] The current technical solutions commonly used in slope monitoring suffer from the following systemic flaws, severely hindering the accuracy and timeliness of geological disaster warnings. These flaws are primarily manifested in the following: Single-dimensional physical sensor systems ignore ecological factors such as vegetation transpiration and root development, resulting in a 34% error in rainfall-induced landslide predictions; static warning models are unable to dynamically adapt to climate change and human engineering disturbances, resulting in a 15% decrease in prediction accuracy after three years; rigid sensor networks are unreliable in complex geological environments, with even 15% node damage causing a 58% performance degradation, and significant power supply and maintenance risks; and centralized cloud-based data processing takes up to 8.2 minutes, exceeding the critical 5-7 minute warning window for landslide instability. These multi-dimensional flaws severely hinder the accuracy and timeliness of geological disaster prevention and control.

[0003] Therefore, how to improve the accuracy and stability of slope detection has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] The present invention provides a slope instability and ecological monitoring early warning method and system, which are used to solve the technical problem of low accuracy of slope monitoring methods.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0006] A slope instability prediction method, comprising:

[0007] Collect ecological and geological parameters of the slope;

[0008] Calculating an ecological stability characteristic value of the slope according to the ecological parameters of the slope;

[0009] Calculating a geological stability characteristic value of the slope according to the geological parameters of the slope;

[0010] Whether the slope is unstable is determined according to the ecological stability characteristic value and the geological stability characteristic value.

[0011] Preferably, calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope comprises the following steps:

[0012] The ecological parameters include biodiversity characteristic values D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R ;

[0013] According to the biodiversity characteristic value D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R Calculate the ecological stability characteristic value of the slope.

[0014] Preferably, the biodiversity characteristic value D The calculation method uses Simpson's diversity index;

[0015] and / or

[0016] The ecological parameters include ecological functional indicators, which include eutrophication indicators and aquatic biological indicators; the ecosystem functional characteristic values F The calculation method is as follows:

[0017]

[0018] in, n Represents the total number of functional indicators, T i For the i Functional indicators, W i is the weight of the corresponding indicator;

[0019] and / or

[0020] The environmental carrying capacity characteristic value C Calculated by the following formula:

[0021]

[0022] in, A is the resource consumption, including the consumption of reinforcement materials and plant resources used to reinforce the slope. B Represents the total amount of resources, k is the environmental coefficient, the value range is 0<k<1, and the value is determined according to the environmental conditions;

[0023] and / or

[0024] The recovery capability characteristic value R By obtaining the evaluation parameters of the slope plants, animals, soil and water bodies, and using the comprehensive index evaluation method to calculate the recovery capacity characteristic value R ;

[0025] and / or

[0026] According to the biodiversity characteristic value D , ecosystem function characteristic values F, Environmental carrying capacity characteristic value C and the recovery capability characteristic value R Calculating the ecological stability characteristic value of the slope by using a weighted fusion algorithm;

[0027] In the weighted fusion algorithm, the biodiversity feature value D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R The weight is a dynamic weight, which is updated periodically over time.

[0028] Preferably, the geological parameters include the real-time displacement, displacement velocity and other parameters of the slope; and calculating the geological stability characteristic value of the slope based on the geological parameters of the slope includes:

[0029] Calculating geological stability characteristic values ​​based on the real-time displacement, displacement velocity, and other parameters of the slope;

[0030] The other parameters include: pore water pressure, saturation, and the geological stability characteristic value is calculated based on the real-time displacement, displacement speed and other parameters of the slope through a weighted fusion algorithm.

[0031] Preferably, judging whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value includes:

[0032] Constructing a correlation matrix of the ecological stability characteristic values ​​and the geological stability characteristic values;

[0033] The correlation matrix is ​​input into a trained deep learning prediction model to obtain the instability probability of the slope.

[0034] Preferably, the ecological parameters and geological parameters of the slope are collected by a multimodal bionic sensor network, which includes:

[0035] Flexible fractal sensing unit and bioelectric collaborative monitoring system;

[0036] The flexible fractal sensing unit is used to collect ecological parameters and geological parameters of the slope;

[0037] The bioelectric collaborative monitoring system is used to perform noise removal and data conversion on the ecological parameters and geological parameters collected by the flexible fractal sensing unit.

[0038] Preferably, the flexible fractal sensing unit includes a flexible covering layer and a root-simulating distributed optical fiber sensor arranged inside the flexible covering layer, wherein the root-simulating distributed optical fiber sensor is distributedly laid along the deep layer and the surface layer of the slope to simulate the morphology of the plant root network;

[0039] and / or

[0040] The bioelectric collaborative monitoring system includes an implantable distributed electrode and a signal processing unit; the implantable distributed electrode is embedded in the slope and is used to simultaneously obtain the collected signals of the root-simulated distributed optical fiber sensor from multiple locations, and the collected signals include ecological parameters and geological parameters of the slope;

[0041] The signal processing unit is used to denoise the collected signal, eliminate interference signals from the environment or equipment, and extract effective ecological parameters and geological parameters of the slope.

[0042] Preferably, the multimodal bionic sensor network also includes an intelligent dynamic network, which is used to dynamically adjust the network topology and data transmission path of the implantable distributed electrodes in real time according to the working status of the root distributed optical fiber sensor and the bioelectric collaborative monitoring system.

[0043] A slope ecological monitoring and early warning method, which uses the slope instability prediction method to calculate the instability probability of the slope;

[0044] Obtaining a loss coefficient after instability, and determining a risk level of the slope according to the loss coefficient after instability and the instability probability; the risk levels include at least two, and different risk levels correspond to different response measures;

[0045] Execute corresponding countermeasures according to the risk level.

[0046] Preferably, the risk level includes no risk, low risk and high risk; determining the risk level of the slope according to the loss coefficient after instability and the instability probability includes:

[0047] Calculating a risk index based on the loss coefficient after the instability and the instability probability;

[0048] When the risk indicator is less than a first preset value, the risk level is determined to be no risk;

[0049] When the risk indicator is greater than or equal to a first preset value and less than or equal to a second preset value, the risk level is judged to be low risk; and the first preset value is less than the second preset value;

[0050] When the risk indicator is greater than a second preset value, the risk level is determined to be high risk;

[0051] When the risk is low, the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit are increased, focusing on the stability changes of the slope and restricting surrounding high-risk activities;

[0052] When the risk is high, initiate comprehensive emergency response activities, including evacuation of personnel, traffic control, and deployment of emergency resources.

[0053] Preferably, the method further comprises the following steps:

[0054] Constructing a digital twin model of the slope, and upon warning of a risk of instability of the slope, constructing a repair strategy using the digital twin model and executing the repair strategy;

[0055] The repair strategies include but are not limited to one or a combination of the following:

[0056] Initiate reinforcement support, adjust drainage or enhance soil stability.

[0057] A computer system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the computer program.

[0058] The present invention has the following beneficial effects:

[0059] 1. The present invention collects ecological and geological parameters of a slope; calculates the slope's ecological stability characteristic value based on the ecological parameters; calculates the slope's geological stability characteristic value based on the geological parameters; and determines whether the slope is unstable based on these ecological and geological stability characteristic values. Compared to existing early warning systems that rely on single physical sensor monitoring, the present invention integrates ecological and geological parameters for prediction, effectively improving prediction accuracy and stability.

[0060] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 This is a system framework diagram of a slope ecological monitoring and early warning system in a preferred embodiment of the present invention;

[0063] Figure 2 This is a calculation flow chart of the ecological-mechanical coupling analysis model in a preferred embodiment of the present invention;

[0064] Figure 3 Flowchart of calculation of adaptive decision algorithm in a preferred embodiment of the present invention; DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0066] Example 1:

[0067] A slope instability prediction method, comprising:

[0068] Collect ecological and geological parameters of the slope;

[0069] Calculating an ecological stability characteristic value of the slope according to the ecological parameters of the slope;

[0070] Calculating a geological stability characteristic value of the slope according to the geological parameters of the slope;

[0071] Whether the slope is unstable is determined according to the ecological stability characteristic value and the geological stability characteristic value.

[0072] In a preferred embodiment, calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope includes the following steps:

[0073] Calculating the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value, and restoration capacity characteristic value of the slope; calculating the ecological stability characteristic value of the slope based on the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value, and restoration capacity characteristic value;

[0074] Specifically, the biodiversity characteristic value D The calculation method can be calculated using the Simpson diversity index;

[0075] Specifically, the ecosystem function characteristic value F The calculation method can be referred to as follows:

[0076]

[0077] in, n Represents the total number of functional indicators, T i For the i Functional indicators (such as eutrophication indicators, aquatic biological indicators, etc.), W i is the weight of the corresponding indicator;

[0078] Specifically, the environmental carrying capacity characteristic value C The calculation method can be referred to as follows:

[0079]

[0080] in, A The resource consumption includes the consumption of reinforcement materials and plant resources used to reinforce the slope; B Represents the total amount of resources; k is the environmental coefficient, the value range is 0<k<1, and the value is determined according to the environmental conditions;

[0081] The reinforcement material consumption of the slope reinforcement is specifically the amount of steel strands, and the plant resource consumption is the number of plants required to be planted;

[0082] Specifically, the amount of steel strand is obtained by the following formula:

[0083] Steel strand usage = anchor cable length × number of strands × theoretical weight × (1 + tension loss rate)

[0084] Specifically, the plant resource consumption is obtained by the following formula:

[0085] Number of seedlings = green area ÷ single-tree planting spacing ^ 2 × (1 + survival loss rate);

[0086] Specifically, the total amount of resources is obtained by the following formula:

[0087] Total resources = total water resources + total land area + total biological resources + total other resources.

[0088] Specifically, the recovery capability characteristic value R By obtaining the evaluation parameters of the slope plants, animals, soil and water bodies, and using the comprehensive index evaluation method to calculate the recovery capacity characteristic value R ;

[0089] The plant evaluation parameters include: coverage, species richness, biomass and other related parameters; the soil evaluation parameters include soil nutrients, organic matter, porosity and other related parameters;

[0090] Animal parameters include the number of major species, species richness, and habitat area; water parameters include hydrological parameters, water quality parameters, and sediment quality;

[0091] The comprehensive index assessment method can refer to the Technical Guidelines for Ecological Environmental Damage Identification and Assessment GB / T39791.3-2024;

[0092] Specifically, the ecological stability characteristic value of the slope is calculated based on the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value and restoration capacity characteristic value, and is implemented by a weighted fusion algorithm. In the weighted fusion algorithm, the weights of the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value and restoration capacity characteristic value are dynamic weights, and the dynamic weights are periodically updated over time.

[0093] Specifically, the ecological stability characteristic value of the slope is calculated based on the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value and restoration capacity characteristic value through the following ecological stability index model ( ESI )accomplish:

[0094] ESI=w 1 D+w 2 F+w 3 C+w 4 R

[0095] in, ESI is the characteristic value of ecological stability, D stands for Biodiversity Index; F represents the ecosystem function index; C stands for environmental carrying capacity index; R stands for resilience index; w 1 、w 2 、w 3 、w 4 is the dynamic weight of each factor, which is updated every 24 hours to adapt to seasonal changes. By consulting relevant literature and combining natural laws, it can be found that in spring, species germination and migration will cause rapid changes in biodiversity, and at the same time, resources (water and nutrients) begin to show an upward trend, that is, w 1 、w 2 rise, and w 3 、w 4 unchanged; in summer, ecosystem functions (such as photosynthesis, material circulation) and resources (water, nutrients) reach their peak, that is, w 1 rise, w 3Real-time changes (such as heavy rain, drought weather), w 2 、w 4 unchanged, the opposite in autumn and winter.

[0096] This method quantitatively assesses ecosystem stability through a comprehensive analysis of various ecological parameters. Specifically, these parameters include species diversity index, species resilience, and vegetation coverage. By combining these ecological parameters with existing ecological stability index models, a computational model comprehensively assesses the stability of regional ecosystems, yielding standardized ecological parameter results that reflect the ecosystem's stability and self-regulation capacity under different conditions.

[0097] In a preferred embodiment, the geological parameters include the real-time displacement and displacement velocity of the slope and environmental hydrological parameters (pore water pressure, saturation, etc.); and calculating the geological stability characteristic value of the slope based on the geological parameters of the slope includes:

[0098] Calculate the geological stability characteristic value based on the real-time displacement, displacement speed and environmental hydrological parameters (pore water pressure, saturation, etc.) of the slope GIS .

[0099]

[0100] in W i、 S i They represent the influence weights and dimensionless indicators of displacement, velocity, pore water pressure, saturation, etc.

[0101] As a preferred embodiment of the present invention, GNSS is used to extract dynamic parameters related to slope changes, accurately determine slope changes, synchronize and calibrate data from multiple sensors, and infer the mechanical parameters of the object through mechanical modeling methods to ensure data accuracy.

[0102] As a preferred embodiment of the present invention, judging whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value includes:

[0103] Constructing a correlation matrix of the ecological stability characteristic values ​​and the geological stability characteristic values;

[0104] The correlation matrix is ​​input into a trained deep learning prediction model to obtain the instability probability of the slope.

[0105] The present invention achieves a deep fusion of ecological and mechanical parameters by constructing a correlation matrix of the ecological stability characteristic values ​​and geological stability characteristic values ​​obtained by the above calculations. By adopting cross-modal spatiotemporal feature fusion technology, the fusion layer quantifies the correlation between these two types of characteristic values ​​through a mathematical model, constructs a correlation matrix, encodes the relationship between each pair of ecological and mechanical parameters, and reflects how they jointly affect the stability of the slope. Then, the fused data will be processed by a deep algorithm (such as the gradient boosting tree method, which iteratively fits the residuals of multiple decision trees to achieve multi-feature nonlinear combination) to output a probability value of slope stability. P f ∈[0,1], where P f The closer it is to 1, the higher the stability of the slope, and the closer it is to 0, the greater the instability of the slope. It can quantify the stability risk of the slope, and then provide accurate early warning information to help formulate effective risk management and disaster prevention and mitigation measures.

[0106] As a preferred embodiment of the present invention, the collection of ecological and geological parameters of the slope is achieved through a multimodal biomimetic sensor network. This network first uses sensors to collect real-time environmental and structural data on the slope. These sensors mimic the diverse ways in which organisms perceive the external world, capturing information on changes in different dimensions of the slope. The sensor network then uses advanced data fusion algorithms to comprehensively analyze the data from these different sensors. This multi-layered, multimodal data fusion enables comprehensive monitoring and accurate assessment of the slope's condition.

[0107] Specifically, the multimodal bionic sensor network includes:

[0108] Flexible fractal sensing unit and bioelectric collaborative monitoring system;

[0109] The flexible fractal sensing unit is used to collect ecological parameters and geological parameters of the slope;

[0110] The bioelectric collaborative monitoring system is used to perform noise removal and data conversion on the ecological parameters and geological parameters collected by the flexible fractal sensing unit.

[0111] Specifically, the flexible fractal sensing unit includes a flexible covering layer and a root-simulating distributed optical fiber sensor disposed inside the flexible covering layer. The root-simulating distributed optical fiber sensor is distributed along the deep layer and surface layer of the slope to simulate the morphology of the plant root network.

[0112] The flexible fractal sensing unit uses high-performance flexible materials as the outer covering layer of the sensor. These materials have excellent bendability and adaptability, and can effectively fit closely with the complex terrain of the slope. By closely integrating with the slope surface, the flexible material enables the sensor to monitor tiny deformations and displacements in real time, improving the overall perception and early warning capabilities of slope stability and meeting the needs of different types of slope monitoring.

[0113] The root-like distributed optical fiber sensor is set inside the flexible fractal sensing unit. First, the fiber optic sensor is distributed along the deep and surface layers of the slope to simulate the plant root network to form a wide-area monitoring network. Then, the reflectometer injects pulsed laser into the optical fiber. When the laser propagates along the optical fiber, it generates backscattered light when it encounters scattering points. The system captures the intensity, temperature and strain changes of the scattered light in real time. LiDAR After preprocessing through denoising, filtering, and amplification, the technology identifies the spatiotemporal characteristics of deformation areas through multi-node data correlation analysis. This is then integrated and verified with multi-source data, such as bioelectric signals and inclinometers. Ultimately, a thermal map of slope deformation is generated, triggering a real-time warning when the deformation rate exceeds a threshold. This technology, through its biomimetic distributed structure, enables continuous monitoring of the entire slope cross-section, overcoming the limitations of traditional single-point sensors and achieving highly sensitive monitoring of environmental factors.

[0114] As a preferred embodiment of the present invention, the bioelectric collaborative monitoring system includes an implantable distributed electrode and a signal processing unit; the implantable distributed electrode is embedded in the slope and is used to simultaneously obtain the collected signals of the root-simulated distributed optical fiber sensor from multiple locations, and the collected signals include ecological parameters and geological parameters of the slope;

[0115] The implantable distributed electrodes in the present invention are embedded in the soil and rock formations to form a wide monitoring network, ensuring that the root-like distributed optical fiber sensor monitoring electrical signals can be obtained from multiple locations at the same time. Through the real-time collection of electrical signals and the use of distributed electrode design, comprehensive monitoring and collection can be carried out.

[0116] The signal processing unit is used to denoise the collected signal, eliminate interference signals from the environment or equipment, and extract effective ecological parameters and geological parameters of the slope (for example, wavelet threshold denoising method).

[0117] In the present invention, after collecting the electrical signals, the signal processing unit removes interference signals from the environment or equipment through an intelligent algorithm, and then applies an adaptive algorithm analysis method to effectively extract the electrical signals and identify key information of the slope area.

[0118] The bioelectricity collaborative monitoring system collects real-time electrical signals from the slopes through distributed fiber optic sensors that mimic the root system. Using advanced electrodes and signal processing, it captures weak electrical signals, converts them into digital signals, and transmits them to the monitoring system for analysis.

[0119] Specifically, the multimodal bionic sensor network also includes an intelligent dynamic network, which is used to dynamically adjust the network topology and data transmission path of the implantable distributed electrodes in real time according to the working status of the root distributed optical fiber sensor and the bioelectric collaborative monitoring system.

[0120] In the present invention, the intelligent dynamic network, by combining the working status of the root distributed optical fiber sensors and the bioelectric collaborative system, adopts intelligent algorithms (such as ant colony optimization algorithm, etc., dynamically adjusting the deployment density of sensor nodes according to the nonlinear strain data characteristics of the distributed optical fiber sensors) and adaptive technologies (such as Q-learning algorithm, each node dynamically adjusts the cycle through reinforcement learning) to dynamically adjust the network topology and data transmission path in real time. At the same time, the network topology will also be adjusted according to the distribution of sensors and real-time data traffic to avoid data transmission bottlenecks or network congestion, thereby maximizing resource utilization and optimizing overall network performance.

[0121] In summary, the slope instability prediction method proposed in this paper dynamically integrates multiple factors, including vegetation cover, microbial activity, and mechanical parameters, to comprehensively quantify the interaction between ecology and mechanics. This method can deeply reveal the mutual influence between vegetation, microorganisms, and soil mechanical properties, thereby more accurately assessing slope stability. Furthermore, by comprehensively analyzing the changes in these ecological and mechanical factors, it is possible to accurately predict slope stability.

[0122] On the basis of the above, the present invention also provides a slope ecological monitoring and early warning method, which uses the above slope instability prediction method to calculate the instability probability of the slope;

[0123] Obtaining a loss coefficient after instability, and determining a risk level of the slope according to the loss coefficient after instability and the instability probability; the risk levels include at least two, and different risk levels correspond to different response measures;

[0124] Execute corresponding countermeasures according to the risk level.

[0125] Preferably, the risk level includes no risk, low risk and high risk; determining the risk level of the slope according to the loss coefficient after instability and the instability probability includes:

[0126] A risk index is calculated based on the loss coefficient after the instability and the instability probability, and then the risk level is determined based on the risk index.

[0127] The specific calculation method of the risk indicator is as follows:

[0128] R=C·P f

[0129] in, R is a risk indicator, C is the loss coefficient after instability, which is a constant. Its value is affected by many factors, such as the soil conditions of the slope, the slope, and the construction conditions of the surrounding environment; P f represents the probability of instability, which is obtained by the above-mentioned slope instability prediction method of this embodiment. For details, reference may be made to the research report on landslide early warning and forecasting in the Three Gorges Reservoir area by Academician Zheng Yingren.

[0130] When the risk indicator is less than a first preset value, the risk level is determined to be no risk;

[0131] When the risk indicator is greater than or equal to a first preset value and less than or equal to a second preset value, the risk level is judged to be low risk; and the first preset value is less than the second preset value;

[0132] When the risk indicator is greater than a second preset value, the risk level is determined to be high risk;

[0133] When the risk is low, the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit are increased, focusing on the stability changes of the slope and restricting surrounding high-risk activities;

[0134] When the risk is high, initiate comprehensive emergency response activities, including evacuation of personnel, traffic control, and deployment of emergency resources.

[0135] For example, when the risk assessment value R is less than 60%, the system issues a green alert, indicating no risk. Routine slope monitoring and inspections continue, and regular risk assessments are maintained to ensure timely detection of potential changes. When R is between 60% and 85%, the system initiates a yellow alert, indicating low risk. At this point, it is necessary to increase the monitoring frequency, increase the sensor sampling rate, focus on changes in slope stability, and appropriately restrict surrounding high-risk activities to reduce interference from external factors and ensure slope safety. When R is greater than 85%, the system initiates a red alert, indicating high risk, and immediately initiates a comprehensive emergency response, including personnel evacuation, traffic control, and emergency resource deployment. At the same time, on-site monitoring data will be transmitted through the edge-cloud collaborative computing system 4 for further emergency response.

[0136] Preferably, the method further comprises the following steps:

[0137] A digital twin model of the slope is constructed. When a warning is given that the slope is at risk of instability, a repair strategy is constructed using the digital twin model and executed. The repair strategy includes, but is not limited to, one or a combination of the following:

[0138] Initiate reinforcement support, adjust drainage or enhance soil stability.

[0139] As a preferred embodiment of the present invention, the present invention also uses cloud computing technology to achieve efficient data collaboration and intercommunication between local edge nodes and the remote cloud. The system can collect and process large amounts of complex dynamic data in real time, and use intelligent algorithms and virtual models for in-depth analysis and modeling.

[0140] In addition, the present invention can also obtain early warning information of slope areas, environmental change data, etc. in real time. By constructing a three-level progressive twin model system, namely the data acquisition layer, the cloud-based high-precision twin (for example, Biot Consolidation theory), edge lightweight twin (for example, model reduction technology), virtual-real interactive closed loop (real-time rendering of the sliding surface expansion path predicted by the twin model), to achieve dynamic mapping of all elements from macro to micro. The system can compare and integrate these real-time data with historical information to establish an accurate slope virtual system. Biot Consolidation Theory:

[0141] σ=σ'+u

[0142] v=k·j

[0143] Where: σ' is the effective stress, u is the pore water pressure, v is the seepage velocity, k is the permeability coefficient, and j is the hydraulic gradient.

[0144] Based on models such as seepage-deformation coupling analysis, the system not only predicts potential slope risks but also automatically generates repair plans tailored to current geological conditions through big data analysis and simulation. In practice, the system responds to changes in slope risk in real time and promptly adjusts repair plans based on early warning information, ensuring accurate and efficient implementation of repair work.

[0145] In addition, the present invention can achieve real-time repair and continuous monitoring of slope repair through intelligent algorithms. When the system detects signs of instability in the slope, it can immediately initiate repair measures, such as reinforcing support, adjusting the drainage system or enhancing soil stability, to ensure that the slope problem can be quickly dealt with. At the same time, the repair system continuously monitors various key indicators of the slope, such as geological changes, groundwater levels, etc., and transmits data to the cloud platform in real time. Through the analysis of real-time data, the system can adaptively adjust according to the dynamic changes of the slope (for example, reinforcement learning algorithms, etc., through mechanism training of repair strategies, so that the system selects the optimal repair strategy under different slope conditions), ensuring the effectiveness and pertinence of the repair measures. In addition, after the repair work is completed, the system continues to monitor the status of the slope and collect feedback data in order to optimize and adjust the repair plan to ensure that the slope is in a stable state for a long time.

[0146] Example 2:

[0147] In this embodiment, Figure 1 As shown, a slope ecological monitoring and early warning system is provided, including a multimodal bionic sensor network 1, an ecological-mechanical coupling analysis model 2, a dynamic adaptive early warning system 3, a cloud-edge collaboration system 4, and an adaptive repair network 5.

[0148] Specifically, the multimodal biomimetic sensor network 1 first uses sensors to collect real-time environmental and structural data on the slope. These sensors simulate the diverse ways organisms perceive the external world, capturing information on slope changes across multiple dimensions. The sensor network then uses advanced data fusion algorithms to comprehensively analyze the data from these sensors. Through multi-level, multimodal data fusion, it achieves comprehensive monitoring and accurate assessment of the slope's condition. This network primarily comprises flexible fractal sensor units 6, a bioelectric collaborative monitoring system 7, and an intelligent dynamic network 8.

[0149] Specifically, the flexible fractal sensing unit 6 uses high-performance flexible materials as the outer covering layer of the sensor. These materials have excellent bendability and adaptability, and can effectively fit closely with the complex terrain of the slope. By being tightly integrated with the slope surface, the flexible material enables the sensor to monitor tiny deformations and displacements in real time, thereby improving the overall perception and early warning capabilities of slope stability and meeting the needs of different types of slope monitoring.

[0150] Specifically, the root-like distributed optical fiber sensor 9 is set inside the flexible fractal sensing unit. First, the plant root network morphology is simulated, and the optical fiber sensor is distributed along the deep and surface layers of the slope to form a wide-area monitoring network. Then, the reflectometer injects pulsed laser into the optical fiber. When the laser propagates along the optical fiber, it encounters scattering points and generates backscattered light. The system captures the intensity, temperature and strain changes of the scattered light in real time. LiDARAfter preprocessing through denoising, filtering, and amplification, the technology identifies the spatiotemporal characteristics of deformation areas through multi-node data correlation analysis. This is then integrated and verified with multi-source data, such as bioelectric signals and inclinometers. Ultimately, a thermal map of slope deformation is generated, triggering a real-time warning when the deformation rate exceeds a threshold. This technology, through its biomimetic distributed structure, enables continuous monitoring of the entire slope cross-section, overcoming the limitations of traditional single-point sensors and achieving highly sensitive monitoring of environmental factors.

[0151] Specifically, the bioelectricity collaborative monitoring system 7 collects electrical signals from the slope in real time through monitoring by distributed root-like fiber optic sensors 9. Using electrodes and signal processing, it captures weak electrical signals, converts them into digital signals, and transmits them to the monitoring system for analysis.

[0152] Specifically, the implantable distributed electrode 10 is embedded in the soil and rock formation to form a wide monitoring network, ensuring that the monitoring electrical signals of the root-like distributed optical fiber sensor 9 can be obtained from multiple locations at the same time. Through the real-time collection of electrical signals and the use of distributed electrode design, comprehensive monitoring and collection can be carried out.

[0153] Specifically, after collecting the electrical signals, the signal processing unit 11 removes interference signals from the environment or equipment through an intelligent algorithm, and then applies an adaptive algorithm analysis method to effectively extract the electrical signals and identify key information of the slope area.

[0154] Specifically, the intelligent dynamic network 8, by combining the working status of the root distributed optical fiber sensors 9 and the bioelectric collaborative system 7, adopts intelligent algorithms (such as ant colony optimization algorithm, etc., dynamically adjusting the deployment density of sensor nodes based on the nonlinear strain data characteristics of distributed optical fiber sensors) and adaptive technologies (such as Q-learning algorithm, each node dynamically adjusts the cycle through reinforcement learning) to dynamically adjust the network topology and data transmission path in real time. At the same time, the network topology will also be adjusted according to the distribution of sensors and real-time data traffic to avoid data transmission bottlenecks or network congestion, thereby maximizing resource utilization and optimizing overall network performance.

[0155] Specifically, such as Figure 2 As shown in Figure 2, the ecological-mechanical coupling analysis model, by dynamically integrating multiple factors such as vegetation cover, microbial activity, and mechanical parameters, can comprehensively quantify the interaction mechanisms between ecology and mechanics, deeply revealing the mutual influence between vegetation, microorganisms, and soil mechanical properties, thereby more accurately assessing slope stability. Furthermore, by comprehensively analyzing the changes in these ecological and mechanical factors, it is possible to accurately predict slope stability. This model primarily includes the construction of an ecological stability index model12 and a dual-stream spatiotemporal intelligent network13.

[0156] Specifically, the ecological stability index model ( ESI )12The calculation formula is:

[0157] ESI=w 1 D+w 2 F+w 3 C+w 4 R

[0158] in, D stands for Biodiversity Index; F represents the ecosystem function index; C stands for environmental carrying capacity index; R stands for resilience index; w 1 、w 2 、w 3 、w 4 is the dynamic weight of each factor, which is calculated by the entropy weight method. The weight is updated every 24 hours to adapt to seasonal changes.

[0159] Specifically, the dual-stream spatiotemporal intelligent network 13, through GNSS The device acquires precise location information and time-series data of the slope area in real time to accurately monitor changes in slope position and its dynamic characteristics. Simultaneously, it extracts relevant mechanical parameters from information such as soil deformation and moisture fluctuations fed by a multimodal biomimetic sensor network. These mechanical parameters are then combined with components of the ecological stability index to form a comprehensive ecological-mechanical assessment model. Furthermore, cross-modal spatiotemporal feature fusion technology is used to deeply integrate spatiotemporal features from different sources, forming a multidimensional, dynamically changing stability assessment system.

[0160] Specifically, the physical data flow branch 14, through GNSS The equipment extracts dynamic parameters related to slope changes, monitors the slope's position and deformation in real time, and accurately determines the slope's displacement and displacement velocity at different points in time, thereby capturing its changing trends. The equipment then uses data from a multimodal bionic sensor network1, combined with mechanical modeling methods and the slope's geological characteristics, to infer relevant mechanical parameters. These mechanical parameters comprehensively describe the slope's mechanical behavior and stability, providing a scientific basis for further assessment of its safety.

[0161] Specifically, the ecological data flow branch 15 comprehensively collects ecological parameters (including but not limited to species diversity index, species recovery ability, vegetation coverage, etc.) in the slope area through various ecological monitoring methods, combines these ecological parameters with the existing ecological stability index model, and comprehensively evaluates the stability of the regional ecosystem through the calculation model to obtain a standardized ecological parameter result.

[0162] Specifically, the cross-modal fusion layer 16 achieves a deep fusion of ecological and mechanical parameters by constructing a correlation matrix for the ecological data stream and the physical data stream obtained by the above calculations. By adopting cross-modal spatiotemporal feature fusion technology, the fusion layer quantifies the correlation between the two types of data streams through a mathematical model, constructs a correlation matrix, encodes the relationship between each pair of ecological and mechanical parameters, and reflects how they jointly affect the stability of the slope. Then, the fused data will be processed by a deep algorithm (such as the gradient boosting tree method, which iteratively fits the residuals of multiple decision trees to achieve multi-feature nonlinear combination) to output a probability value of slope stability. P f ∈[0,1], where P f The closer it is to 1, the higher the stability of the slope, and the closer it is to 0, the greater the instability of the slope. It can quantify the stability risk of the slope, and then provide accurate early warning information to help formulate effective risk management and disaster prevention and mitigation measures.

[0163] Specifically, the dynamic adaptive early warning system3 integrates multiple real-time monitoring data sources and employs decision-making algorithms to integrate and analyze these data to accurately identify and warn of slope hazards. The system dynamically adjusts its warning model based on changes in real-time data and provides real-time assessments of potential hazards such as landslides and collapses based on risk levels. Based on this risk assessment, the system automatically triggers differentiated response mechanisms to ensure timely and effective response measures. These mechanisms primarily include an adaptive decision-making algorithm17 and a tiered response mechanism18.

[0164] Specifically, the adaptive decision algorithm calculates 17 to obtain the risk index ( R ):

[0165] R=C·P f

[0166] in, C It is a constant, the loss coefficient after instability, but its value will be affected by many factors, such as the soil conditions of the slope, the slope, and the construction conditions of the surrounding environment; P f A value representing the probability of slope failure, estimated using probabilistic models or risk analysis tools. It considers both ecological and mechanical indices and is calculated through coupled ecological-mechanical analysis2.

[0167] Specifically, as shown in FIG3 , the hierarchical response mechanism 18 of the present invention, when the risk assessment value R is less than 60%, the system is in green warning, and continues to conduct routine slope monitoring and inspections, and maintains regular risk assessments to ensure that potential changes are discovered in a timely manner. When R is between 60% and 85%, the system starts a yellow warning. At this time, it is necessary to increase the monitoring frequency, increase the sampling rate of the sensor, focus on the stability changes of the slope, and appropriately restrict surrounding high-risk activities to reduce interference from external factors and ensure slope safety. When R is greater than 85%, the system starts a red warning and immediately initiates a comprehensive emergency response, including personnel evacuation, traffic control, and emergency resource allocation. At the same time, the on-site monitoring data will be transmitted to the edge-cloud collaborative computing system 4 for the next emergency response.

[0168] Specifically, the cloud-edge collaboration system 4, through cloud computing technology, realizes efficient data collaboration and intercommunication between local edge nodes and remote cloud. The system can collect and process a large amount of complex dynamic data in real time, and use intelligent algorithms and virtual models for in-depth analysis and modeling. The cloud digital model engine 19 obtains early warning information of the slope area, environmental change data, etc. in real time through the dynamic adaptive early warning system 3. By constructing a three-level progressive twin model system, namely the data acquisition layer, the cloud high-precision twin (for example, Biot Consolidation theory), edge lightweight twin (for example, model reduction technology), virtual-real interactive closed loop (real-time rendering of the sliding surface expansion path predicted by the twin model), to achieve dynamic mapping of all elements from macro to micro. The system can compare and integrate these real-time data with historical information to establish an accurate slope virtual system. Biot Consolidation Theory:

[0169] σ=σ'+u

[0170] v=k·j

[0171] Where: σ' is the effective stress, u is the pore water pressure, v is the seepage velocity, k is the permeability coefficient, and j is the hydraulic gradient.

[0172] Based on models such as seepage-deformation coupling analysis, the system not only predicts potential slope risks but also automatically generates repair plans tailored to current geological conditions through big data analysis and simulation. In practice, the system responds to changes in slope risk in real time and promptly adjusts repair plans based on early warning information, ensuring accurate and efficient implementation of repair work.

[0173] Specifically, the adaptive repair network 5 can achieve real-time repair and continuous monitoring of slope repair through intelligent algorithms. When the system detects signs of instability in the slope, it can immediately initiate repair measures, such as reinforcing support, adjusting the drainage system, or enhancing soil stability, to ensure that slope problems can be quickly addressed. At the same time, the repair system continuously monitors various key indicators of the slope, such as geological changes, groundwater levels, etc., and transmits data to the cloud platform in real time. Through the analysis of real-time data, the system can adaptively adjust the repair strategy according to the dynamic changes of the slope (for example, reinforcement learning algorithms, etc., through mechanism training of repair strategies, so that the system can select the optimal repair strategy under different slope conditions), ensuring the effectiveness and pertinence of the repair measures. In addition, after the repair work is completed, the system continues to monitor the status of the slope and collect feedback data in order to optimize and adjust the repair plan to ensure that the slope is in a stable state for a long time.

[0174] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A slope instability prediction method, characterized in that: include: Collect ecological and geological parameters of the slope; Calculating an ecological stability characteristic value of the slope according to the ecological parameters of the slope; Calculating a geological stability characteristic value of the slope according to the geological parameters of the slope; Determining whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value; Calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope includes the following steps: The ecological parameters include biodiversity characteristic values D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R ; According to the biodiversity characteristic value D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R Calculating the ecological stability characteristic value of the slope; The biodiversity characteristic values D The Simpson diversity index was used for calculation; The ecological parameters include ecological functional indicators, which include eutrophication indicators and aquatic biological indicators; the ecosystem functional characteristic values F The calculation method is as follows: in, n Represents the total number of functional indicators, T i For the i Functional indicators, W i is the weight of the corresponding indicator; The environmental carrying capacity characteristic value C Calculated by the following formula: in, A is the resource consumption, including the consumption of reinforcement materials and plant resources used to reinforce the slope. B Represents the total amount of resources, k is the environmental coefficient, the value range is 0<k<1, and the value is determined according to the environmental conditions; The recovery capability characteristic value R By obtaining the evaluation parameters of the slope plants, animals, soil and water bodies, and using the comprehensive index evaluation method to calculate the recovery capacity characteristic value R ; According to the biodiversity characteristic value D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R Calculating the ecological stability characteristic value of the slope by using a weighted fusion algorithm; In the weighted fusion algorithm, the biodiversity feature value D , ecosystem function characteristic values F , Environmental carrying capacity characteristic value C and the recovery capability characteristic value R The weight is a dynamic weight, which is updated periodically over time; Determining whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value includes: Constructing a correlation matrix of the ecological stability characteristic values ​​and the geological stability characteristic values; Inputting the correlation matrix into a trained deep learning prediction model to obtain the instability probability of the slope; The ecological and geological parameters of the slope are collected through a multimodal bionic sensor network, which includes: Flexible fractal sensing unit and bioelectric collaborative monitoring system; The flexible fractal sensing unit is used to collect ecological parameters and geological parameters of the slope; The bioelectric collaborative monitoring system is used to perform noise removal and data conversion on the ecological parameters and geological parameters collected by the flexible fractal sensing unit.

2. The slope instability prediction method according to claim 1, characterized in that: The geological parameters include the real-time displacement, displacement velocity, and other parameters of the slope; and calculating the geological stability characteristic value of the slope based on the geological parameters of the slope includes: Calculating geological stability characteristic values ​​based on the real-time displacement, displacement velocity, and other parameters of the slope; Said other parameters include: pore water pressure, saturation; The geological stability characteristic value is calculated based on the real-time displacement, displacement speed and other parameters of the slope through a weighted fusion algorithm.

3. The slope instability prediction method according to claim 1, characterized in that: The flexible fractal sensing unit includes a flexible covering layer and a root-simulating distributed optical fiber sensor disposed inside the flexible covering layer. The root-simulating distributed optical fiber sensor is distributed along the deep layer and surface layer of the slope to simulate the morphology of the plant root network. and / or The bioelectric collaborative monitoring system includes an implantable distributed electrode and a signal processing unit; the implantable distributed electrode is embedded in the slope and is used to simultaneously obtain the collected signals of the root-simulated distributed optical fiber sensor from multiple locations, and the collected signals include ecological parameters and geological parameters of the slope; The signal processing unit is used to denoise the collected signal, eliminate interference signals from the environment or equipment, and extract effective ecological parameters and geological parameters of the slope.

4. The slope instability prediction method according to claim 3, characterized in that: The multimodal bionic sensor network also includes an intelligent dynamic network, which is used to dynamically adjust the network topology and data transmission path of the implantable distributed electrodes in real time according to the working status of the root distributed optical fiber sensor and the bioelectric collaborative monitoring system.

5. A slope ecological monitoring and early warning method, characterized in that: Calculating the instability probability of the slope using the slope instability prediction method according to any one of claims 1 to 4; Obtaining a loss coefficient after instability, and determining a risk level of the slope according to the loss coefficient after instability and the instability probability; the risk levels include at least two, and different risk levels correspond to different response measures; Execute corresponding countermeasures according to the risk level.

6. The slope ecological monitoring and early warning method according to claim 5 is characterized in that: The risk level includes no risk, low risk, and high risk. The risk level of the slope is determined based on the loss coefficient after instability and the instability probability, including: Calculating a risk index based on the loss coefficient after the instability and the instability probability; When the risk indicator is less than a first preset value, the risk level is determined to be no risk; When the risk indicator is greater than or equal to a first preset value and less than or equal to a second preset value, the risk level is judged to be low risk; and the first preset value is less than the second preset value; When the risk indicator is greater than a second preset value, the risk level is determined to be high risk; When the risk is low, the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit are increased, focusing on the stability changes of the slope and restricting surrounding high-risk activities; When the risk is high, initiate comprehensive emergency response activities, including evacuation of personnel, traffic control, and deployment of emergency resources.

7. The slope ecological monitoring and early warning method according to claim 6, characterized in that: The following steps are also included: Constructing a digital twin model of the slope, and upon warning of a risk of instability of the slope, constructing a repair strategy using the digital twin model and executing the repair strategy; The repair strategies include but are not limited to one or a combination of the following: Initiate reinforcement support, adjust drainage or enhance soil stability.

8. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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