Slope instability and ecology monitoring and early warning method and system

Abstract

The invention discloses a slope instability and ecological monitoring and early warning method and system. The method comprises the following steps: collecting ecological parameters and geological parameters of a slope; calculating an ecological stability characteristic value of the side slope according to the ecological parameters of the side slope; calculating a geological stability characteristic value of the side slope according to the geological parameters of the side slope; and judging whether the slope is unstable or not according to the ecological stability characteristic value and the geological stability characteristic value. Compared with an existing early warning system depending on a single physical sensor for monitoring, the ecological parameters and the geological parameters are subjected to fusion prediction, and the prediction accuracy and stability can be effectively improved.

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 technical solutions commonly used in the current slope monitoring field have the following systematic defects, which seriously restrict the accuracy and timeliness of geological disaster warnings, mainly reflected in the following: the single-dimensional physical sensor system ignores ecological factors such as vegetation transpiration and root development, resulting in a 34% prediction error in rainfall-type landslides; the static warning model cannot dynamically adapt to climate change and human engineering disturbances, and the prediction accuracy rate decreases by 15% after 3 years; the rigid sensor network has poor reliability in complex geological environments, and 15% node damage can cause 58% performance degradation, and the power supply and operation risks are prominent; the cloud-based centralized data processing time is as long as 8.2 minutes, which exceeds the critical warning window of 5-7 minutes for landslide instability. These multi-dimensional defects seriously restrict 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 the slope monitoring method.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is: A slope instability prediction method, comprising: 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; Whether the slope is unstable is determined according to the ecological stability characteristic value and the geological stability characteristic value.

[0006] Preferably, calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope comprises 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 Cand the restoration ability characteristic value R Calculate the ecological stability characteristic value of the slope.

[0007] Preferably, the biodiversity characteristic value D is calculated using the Simpson diversity index; and / or The ecological parameters include ecological functional indicators, and the functional indicators include eutrophication indicators and aquatic biological indicators; the ecological system function characteristic value F The calculation method is as follows:

[0008] Among them, n represents the total number of functional indicators, T i is the i th functional indicator, W i is the weight of the corresponding indicator; and / or The environmental carrying capacity characteristic value C is calculated by the following formula:

[0009] Among them, A is the resource consumption, including the consumption of reinforcement materials for slope reinforcement and the consumption of plant resources, B represents the total amount of resources, k is the environmental coefficient, and the value range is 0 < k < 1, which is determined according to the environmental situation; and / or The restoration ability characteristic value R is calculated by obtaining the evaluation parameters of the slope plants, animals, soil and water bodies, and using the comprehensive index evaluation method to calculate the restoration ability characteristic value R ; and / or According to the biodiversity characteristic value D and the ecological system function characteristic value F and the environmental carrying capacity characteristic value C and the restoration ability characteristic value R calculate the ecological stability characteristic value of the slope, which is realized by using a weighted fusion algorithm; In the weighted fusion algorithm, the weights of the biodiversity characteristic value D and the ecological system function characteristic value F and the environmental carrying capacity characteristic value C and the restoration ability characteristic value R are dynamic weights, and the dynamic weights are updated periodically with time.

[0010] Preferably, the geological parameters include the real-time displacement, displacement velocity of the slope, and other parameters; calculating the geological stability characteristic value of the slope according to the geological parameters of the slope includes: Calculating the geological stability characteristic value according to the real-time displacement, displacement velocity of the slope, and other parameters; The other parameters include pore water pressure and saturation degree. Calculating the geological stability characteristic value according to the real-time displacement, displacement velocity of the slope, and other parameters is realized by a weighted fusion algorithm.

[0011] Preferably, judging whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value includes: Constructing an association matrix of the ecological stability characteristic value and the geological stability characteristic value; Inputting the association matrix into a trained deep learning prediction model to obtain the instability probability of the slope.

[0012] Preferably, collecting the ecological parameters and geological parameters of the slope is realized by a multi-modal bionic sensor network, and the multi-modal bionic sensor network includes: A flexible fractal sensing unit and a bioelectricity collaborative monitoring system; The flexible fractal sensing unit is used to collect the ecological parameters and geological parameters of the slope; The bioelectricity collaborative monitoring system is used to denoise and perform data conversion on the ecological parameters and geological parameters collected by the flexible fractal sensing unit.

[0013] Preferably, the flexible fractal sensing unit includes a flexible covering layer and a root-like distributed optical fiber sensor arranged inside the flexible covering layer. The root-like distributed optical fiber sensor is distributed along the deep and surface layers of the slope to simulate the morphological pattern of a plant root network; and / or The bioelectricity collaborative monitoring system includes implanted distributed electrodes and a signal processing unit; the implanted distributed electrodes are embedded into the slope and are used to simultaneously obtain the acquisition signals of the root-like distributed optical fiber sensor from multiple positions. The acquisition signals include the ecological parameters and geological parameters of the slope; The signal processing unit is used to denoise the acquisition signals, exclude interference signals from the environment or equipment, and extract the effective ecological parameters and geological parameters of the slope.

[0014] Preferably, the multi-modal bionic sensor network further includes an intelligent dynamic network, and the intelligent dynamic network is used to dynamically adjust the network topology structure and data transmission path of the implanted distributed electrodes in real time according to the working states of the root-like distributed optical fiber sensor and the bioelectricity collaborative monitoring system.

[0015] A slope ecological monitoring and early warning method calculates the instability probability of the slope by using the slope instability prediction method described above. Obtain the loss coefficient after instability, and determine the risk level of the slope according to the loss coefficient after instability and the instability probability; at least two risk levels are included, and different risk levels correspond to different response measures. Execute the corresponding response measures according to the risk level.

[0016] Preferably, the risk levels include 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: Calculate a risk index according to the loss coefficient after instability and the instability probability. When the risk index is less than a first preset value, determine that the risk level is no risk. When the risk index is greater than or equal to the first preset value and less than or equal to a second preset value, determine that the risk level is low risk; the first preset value is less than the second preset value. When the risk index is greater than the second preset value, determine that the risk level is high risk. When in a low-risk state, increase the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit, focus on the stability change of the slope, and restrict high-risk activities around. When in a high-risk state, initiate a comprehensive emergency response activity, notify personnel evacuation, traffic control, and emergency resource allocation.

[0017] Preferably, the following steps are further included: Construct a digital twin model of the slope. Under the state of warning that the slope has an instability risk, construct a repair strategy through the digital twin model and execute the repair strategy. The repair strategy includes, but is not limited to, one or a combination of the following: Initiate reinforcement support, adjust drainage, or enhance soil stability.

[0018] A computer system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0019] The present invention has the following beneficial effects: 1. The present invention collects ecological parameters and geological parameters of a slope; calculates the ecological stability characteristic value of the slope according to the ecological parameters of the slope; calculates the geological stability characteristic value of the slope according to the geological parameters of the slope; determines whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value. Compared with the existing early warning system that relies on the monitoring of a single physical sensor, the present invention combines ecological parameters and geological parameters for prediction, which can effectively improve the accuracy and stability of prediction.

[0020] In addition to the purposes, features and advantages described above, the present invention has other purposes, features and advantages. The following will refer to the accompanying drawings for a further detailed description of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 is a system framework diagram of a slope ecological monitoring and early warning system in a preferred embodiment of the present invention; Figure 2 is a calculation flow chart of an ecological-mechanical coupling analysis model in a preferred embodiment of the present invention; Figure 3 is a calculation flow chart of an adaptive decision-making algorithm in a preferred embodiment of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS The following will describe the embodiments of the present invention in detail with reference to the accompanying drawings. However, the present invention can be implemented in many different ways defined and covered by the claims.

[0022] Embodiment 1: A method for predicting slope instability includes: Collecting ecological parameters and geological parameters of a slope; Calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope; Calculating the 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.

[0023] In a preferred solution, calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope includes the following steps: Calculating the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value and recovery ability characteristic value of the slope; calculating the ecological stability characteristic value of the slope according to the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value and recovery ability characteristic value; Specifically, the biodiversity characteristic value D can be calculated using the Simpson diversity index; Specifically, the ecosystem function characteristic value F The calculation method can be referred to as follows, that is:

[0024] Wherein, n represents the total number of function indicators, T i is the i th function indicator (such as eutrophication index, aquatic organism index, etc.), W i is the weight of the corresponding indicator; Specifically, the environmental carrying capacity characteristic value C The calculation method can be referred to as follows, that is:

[0025] Wherein, A is the resource consumption, and the resource consumption includes the consumption of reinforcement materials for slope reinforcement and the consumption of plant resources; B represents the total amount of resources; k is the environmental coefficient, and the value range is 0 < k < 1, which is determined according to the environmental situation; Wherein, the consumption of reinforcement materials for slope reinforcement is specifically the amount of steel strands used, and the consumption of plant resources is the number of plants to be planted; Specifically, the amount of steel strands used is obtained through the following formula: Amount of steel strands used = cable length × number of strands × theoretical weight × (1 + tension loss rate) Specifically, the consumption of plant resources is obtained through the following formula: Number of seedlings = greening area ÷ square of single-plant planting spacing × (1 + survival loss rate); Specifically, the total amount of resources is obtained through the following formula: Total amount of resources = total amount of water resources + total land area + total biological resources + total other resources.

[0026] Specifically, the restoration ability characteristic value R is obtained by acquiring the evaluation parameters of the slope plants, animals, soil and water bodies, and calculating the restoration ability characteristic value using the comprehensive index evaluation method R ; Wherein, the plant evaluation parameters include: coverage, species richness, biomass and other relevant parameters; the soil evaluation parameters include soil nutrients, organic matter, porosity and other relevant parameters; Animal parameters include the number of major species, species richness, and habitat area; water body parameters include hydrological parameters, water quality parameters, and sediment quality; The comprehensive index assessment method can refer to the Technical Guidelines for the Identification and Assessment of Ecological Environment Damage GB / T 39791.3-2024; Specifically, calculating the ecological stability characteristic value of the slope according to the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value, and recovery ability characteristic value is achieved by using 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 recovery ability characteristic value are dynamic weights, and the dynamic weights are updated periodically over time.

[0027] Specifically, calculating the ecological stability characteristic value of the slope according to the biodiversity characteristic value, ecosystem function characteristic value, environmental carrying capacity characteristic value, and recovery ability characteristic value is achieved through the following ecological stability index model ( ESI ) ESI = w 1 D + w 2 F + w 3 C + w 4 R where, ESI is the ecological stability characteristic value, D represents the biodiversity index; F represents the ecosystem function index; C represents the environmental carrying capacity index; R represents the recovery ability index; w 1 、w 2 、w 3 、w 4 are the dynamic weights of each factor, and the weights are updated every 24 hours to facilitate adaptation to seasonal changes. It can be obtained by referring to relevant literature and combining natural laws. In spring, species germination, migration, etc. will cause rapid changes in biodiversity, and at the same time, resources (water, nutrients) begin to show an upward trend, that is, w 1 、w 2 increases, while w 3 、w 4 remains unchanged; in summer, ecosystem functions (such as photosynthesis, material cycling), resources (water, nutrients) reach their peaks, that is, w 1 increases, w 3 changes in real time (such as heavy rain, drought weather), w 2 、w 4 remains unchanged, and the above is the opposite in autumn and winter.

[0028] Through the comprehensive analysis of different ecological parameters, the present invention can quantitatively evaluate the stability of the ecosystem. Specifically, these parameters include species diversity index, species recovery ability, vegetation coverage, etc. By combining these ecological parameters with the existing ecological stability index model, the stability of the regional ecosystem is comprehensively evaluated through calculating the model, and a standardized ecological parameter result is obtained, so as to reflect the stability and self-regulation ability of the ecosystem under different conditions.

[0029] In a preferred embodiment, the geological parameters include the real-time displacement, displacement velocity of the slope and environmental hydrological parameters (pore water pressure, water saturation, etc.); calculating the geological stability characteristic value of the slope according to the geological parameters of the slope includes: Calculating the geological stability characteristic value according to the real-time displacement, displacement velocity of the slope and environmental hydrological parameters (pore water pressure, water saturation, etc.) GIS 。

[0030]

[0031] Where W i、 S i respectively represent the influence weights and dimensionless indexes of displacement, velocity, pore water pressure, water saturation, etc.

[0032] As a preferred embodiment of the present invention, GNSS is used to extract dynamic parameters related to the slope change, accurately determine the slope change, synchronize and calibrate the data of multiple sensors, and calculate the mechanical parameters of the object through the mechanical modeling method to ensure the accuracy of the data.

[0033] 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: Constructing the correlation matrix of the ecological stability characteristic value and the geological stability characteristic value; Inputting the correlation matrix into the trained deep learning prediction model to obtain the instability probability of the slope.

[0034] The present invention realizes the deep fusion of ecological and mechanical parameters by constructing an association matrix of the previously calculated ecological stability characteristic values and geological stability characteristic values. By adopting the cross-modal spatio-temporal feature fusion technology, the fusion layer quantifies the correlation between these two types of characteristic values through a mathematical model, constructs an association 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 realizes the non-linear combination of multiple features by iteratively fitting the residuals of multiple decision trees), and a probability value of the slope stability will be output 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

[0035] As a preferred embodiment of the present invention, the ecological parameters and geological parameters of the slope are collected through a multi-modal bionic sensor network. The multi-modal bionic sensor network first collects the environmental and structural data of the slope in real time through sensors. These sensors simulate various ways of organisms perceiving the outside world and can capture the change information in different dimensions of the slope. Then, the sensor network uses an advanced data fusion algorithm to comprehensively analyze the data from different sensors, and through multi-level and multi-modal data fusion, realizes the comprehensive monitoring and accurate assessment of the slope state

[0036] Specifically, the multi-modal bionic sensor network includes: a flexible fractal sensing unit and a bioelectricity collaborative monitoring system; The flexible fractal sensing unit is used to collect the ecological parameters and geological parameters of the slope; The bioelectricity collaborative monitoring system is used to denoise and convert the ecological parameters and geological parameters collected by the flexible fractal sensing unit

[0037] Specifically, the flexible fractal sensing unit includes a flexible covering layer and a root-like distributed optical fiber sensor arranged inside the flexible covering layer. The root-like distributed optical fiber sensor is distributed along the deep and shallow layers of the slope to simulate the morphological pattern of the plant root network; The flexible fractal sensing unit uses high-performance flexible materials as the external covering layer of the sensor. These materials have excellent flexibility and adaptability, and can effectively fit closely with the complex terrain of the slope. By closely combining with the slope surface, the flexible materials enable the sensor to monitor minute deformations and displacements in real time, enhancing the overall perception and early warning capabilities for slope stability and meeting the requirements of different types of slope monitoring.

[0038] The root-like distributed optical fiber sensor is arranged inside the flexible fractal sensing unit. First, it simulates the morphological pattern of the plant root network, and distributes the optical fiber sensor 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, backward scattered light is generated when it encounters scattering points. The system captures the intensity, temperature, and strain changes of the scattered light in real time. Secondly, after LiDAR preprocessing such as denoising, filtering, and amplification through techniques, the spatio-temporal characteristics of the deformation area are identified through multi-node data correlation analysis, and are fused and verified with multi-source data such as bioelectric signals and inclinometers; finally, a slope deformation thermal map is generated, and a real-time early warning is triggered when the monitored deformation rate exceeds the threshold. This technology realizes continuous monitoring of the entire cross-section of the slope through a bionic distributed structure, breaking through the limitations of traditional single-point sensors, thereby achieving high-sensitivity monitoring of environmental factors.

[0039] As a preferred embodiment of the present invention, the bioelectricity collaborative monitoring system includes implanted distributed electrodes and a signal processing unit; the implanted distributed electrodes are embedded into the slope for simultaneously obtaining the acquisition signals of the root-like distributed optical fiber sensor from multiple positions, and the acquisition signals include the ecological parameters and geological parameters of the slope; In the present invention, the implanted distributed electrodes form a wide monitoring network by being embedded into the soil and rock strata, ensuring that the monitoring electrical signals of the root-like distributed optical fiber sensor can be obtained simultaneously from multiple positions. Through the real-time acquired electrical signals and the distributed electrode design, comprehensive monitoring and acquisition are carried out.

[0040] The signal processing unit is used for denoising the acquisition signals, excluding interference signals from the environment or equipment, and extracting the effective ecological parameters and geological parameters of the slope (such as the wavelet threshold denoising method).

[0041] In the present invention, after the electrical signal is acquired, the signal processing unit removes the interference signals from the environment or equipment through intelligent algorithms, and then applies an adaptive algorithm analysis method to effectively extract the electrical signals and identify the key information of the slope area.

[0042] The bioelectricity collaborative monitoring system collects the electrical signals from the slope in real time through the monitoring of the root-like distributed optical fiber sensors. By using electrodes and signal processing, weak electrical signals are captured and converted into digital signals, which are then transmitted to the monitoring system for analysis.

[0043] Specifically, the multimodal bionic sensor network further includes an intelligent dynamic network, which is used to dynamically adjust the network topology structure and data transmission path of the implanted distributed electrodes in real time according to the working states of the root distributed optical fiber sensors and the bioelectricity collaborative monitoring system.

[0044] In the present invention, the intelligent dynamic network combines the working states of the root distributed optical fiber sensors and the bioelectricity collaborative system, and adopts intelligent algorithms (such as the ant colony optimization algorithm, etc., for the non-linear strain data characteristics of the distributed optical fiber sensors, dynamically adjusting the deployment density of sensor nodes) and adaptive technologies (such as the Q-learning algorithm, each node dynamically adjusts the period through reinforcement learning), to dynamically adjust the network topology structure and data transmission path in real time. At the same time, the network topology structure will also be adjusted according to the distribution of sensors and the real-time data traffic, avoiding the occurrence of data transmission bottlenecks or network congestion phenomena, thereby maximizing the resource utilization rate and optimizing the overall network performance.

[0045] In summary, the slope instability prediction method of the present invention can comprehensively quantify the interaction mechanism between ecology and mechanics, deeply reveal the mutual influence between vegetation, microorganisms and soil mechanical properties by dynamically integrating various factors such as vegetation cover, microbial activities and mechanical parameters, so as to more accurately evaluate the slope stability. On this basis, by comprehensively analyzing the changes of these ecological and mechanical factors, the accurate prediction of the slope stability can be realized.

[0046] On this basis, the present invention also provides a slope ecological monitoring and early warning method, which calculates the instability probability of the slope by using the above slope instability prediction method; Obtain the loss coefficient after instability, and determine the risk level of the slope according to the loss coefficient after instability and the instability probability; the risk level includes at least two types, and different risk levels correspond to different countermeasures; Execute the corresponding countermeasures according to the risk level.

[0047] 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: Calculate the risk index according to the loss coefficient after instability and the instability probability, and then judge the risk level according to the risk index.

[0048] The specific calculation method of the risk index is as follows: R = C·P f Among them, R is the risk index, C is the loss coefficient after instability, which is a constant, and its value is affected by various factors, such as the soil conditions, slope, and construction conditions of the surrounding environment of the slope; P f represents the probability of instability, which is obtained through the above-mentioned slope instability prediction method of this embodiment. Specifically, reference can be made to the research report on landslide early warning in the Three Gorges Reservoir Area by Academician Zheng Yingren.

[0049] When the risk index is less than the first preset value, it is determined that the risk level is risk-free; When the risk index is greater than or equal to the first preset value and less than or equal to the second preset value, it is determined that the risk level is low risk; the first preset value is less than the second preset value; When the risk index is greater than the second preset value, it is determined that the risk level is high risk; When in a low-risk state, increase the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit, focus on the stability change of the slope, and restrict the surrounding high-risk activities; When in a high-risk state, initiate a comprehensive emergency response activity, notify personnel evacuation, traffic control, and emergency resource allocation.

[0050] For example, when the risk assessment value R is less than 60%, the system gives a green warning, that is, it is risk-free, and continue with the regular slope monitoring and inspection, and maintain regular risk assessment to ensure timely detection of potential changes. When R is between 60% and 85%, the system initiates a yellow warning, that is, low risk. At this time, it is necessary to increase the monitoring frequency, increase the sampling rate of the sensor, focus on the stability change of the slope, and appropriately restrict the surrounding high-risk activities to reduce the interference of external factors and ensure the safety of the slope. When R is greater than 85%, the system initiates a red warning, that is, high risk, and immediately initiate a comprehensive emergency response, including measures such as personnel evacuation, traffic control, and emergency resource allocation. At the same time, the on-site monitoring data will be processed through the edge-cloud collaborative computing system 4 for the next emergency response.

[0051] Preferably, it further includes the following steps: Construct a digital twin model of the slope. When warning that the slope is in a state of instability risk, construct a repair strategy through the digital twin model and execute the repair strategy. The repair strategy includes, but is not limited to, one or several combinations of the following: Initiate reinforcement support, adjust drainage, or enhance soil stability.

[0052] As a preferred embodiment of the present invention, the present invention also realizes efficient data collaboration and intercommunication between local edge nodes and remote cloud through cloud computing technology. 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.

[0053] In addition, the present invention can also obtain early warning information, environmental change data, etc. of the slope area in real time. By constructing a three-level progressive twin model system, namely the data acquisition layer, high-precision cloud twin (for example, Biot consolidation theory), edge lightweight twin (for example, model reduction technology), virtual-real interaction closed loop (real-time rendering of the slip surface expansion path predicted by the twin model), to achieve full-element dynamic mapping from macro to micro. The system can compare and fuse these real-time data with historical information, so as to establish an accurate slope virtual system. Among them Biot Consolidation theory: σ = σ' + u v = k·j In the formula: 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.

[0054] Based on models such as seepage-deformation coupling analysis, the system can not only predict potential slope risks, but also automatically generate repair plans suitable for the current geological conditions through big data analysis and simulation technology. In practical applications, the system can respond to changes in slope risks in real time and adjust the repair plan in a timely manner according to the early warning information, so as to ensure that the repair work can be carried out accurately and efficiently.

[0055] In addition, through intelligent algorithms, the present invention can realize real-time repair and continuous monitoring and repair of slopes. When the system detects unstable signs of the slope, it can immediately start repair measures, such as reinforcement support, adjustment of the drainage system, or enhancement of soil stability, etc., to ensure that slope problems can be quickly handled. At the same time, the repair system continuously monitors key indicators of the slope, such as geological changes, groundwater level, 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., training repair strategies through mechanisms, so that the system selects the optimal repair strategy under different slope states), to ensure the effectiveness and pertinence of the repair measures. In addition, after the repair work is completed, the system continues to monitor the state of the slope and collect feedback data to optimize and adjust the repair plan to ensure that the slope is in a stable state for a long time.

[0056] Embodiment 2: In this embodiment, as Figure 1As 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.

[0057] Specifically, the multimodal bionic sensor network 1 first collects the environmental and structural data of the slope in real time through sensors. These sensors simulate various ways for organisms to perceive the outside world and can capture the change information of different dimensions of the slope. Then, the sensor network uses advanced data fusion algorithms to comprehensively analyze the data from different sensors. Through multi-level and multimodal data fusion, it realizes the comprehensive monitoring and accurate evaluation of the slope state. It mainly includes a flexible fractal sensing unit 6, a bioelectricity collaborative monitoring system 7, and an intelligent dynamic network 8.

[0058] Specifically, the flexible fractal sensing unit 6 uses high-performance flexible materials as the external covering layer of the sensor. These materials have excellent flexibility and adaptability and can effectively fit closely with the complex terrain of the slope. By closely combining with the slope surface, the flexible materials enable the sensor to monitor minute deformations and displacements in real time, improving the overall perception and early warning ability of the slope stability and meeting the monitoring requirements of different types of slopes.

[0059] Specifically, the root-like distributed fiber optic sensor 9 is arranged inside the flexible fractal sensing unit. First, it simulates the morphological pattern of the plant root network and distributes the fiber optic sensor along the deep and surface layers of the slope to form a wide-area monitoring network. Then, the reflectometer injects pulsed laser into the fiber optic. When the laser propagates along the fiber optic, backward scattered light is generated when it encounters scattering points. The system captures the intensity, temperature, and strain changes of the scattered light in real time. Secondly, after LiDAR technical denoising, filtering, amplification and other preprocessing, it identifies the spatio-temporal characteristics of the deformation area through multi-node data correlation analysis and fuses and verifies with multi-source data such as bioelectric signals and inclinometers; finally, it generates a slope deformation thermal map, and triggers a real-time early warning when the monitored deformation rate exceeds the threshold. This technology realizes the continuous monitoring of the entire cross-section of the slope through a bionic distributed structure, breaking through the limitations of traditional single-point sensors, so as to achieve high-sensitivity monitoring of environmental factors.

[0060] Specifically, the bioelectricity collaborative monitoring system 7, through the monitoring of the root-like distributed fiber optic sensor 9, collects the electrical signals from the slope in real time. Using electrodes and signal processing, it captures weak electrical signals and converts them into digital signals, which are transmitted to the monitoring system for analysis.

[0061] Specifically, the implantable distributed electrode 10 is embedded in the soil and rock formations to form a wide monitoring network, ensuring that the electrical signals monitored by the root-like distributed optical fiber sensor 9 can be obtained simultaneously from multiple locations. Through the real-time acquisition of electrical signals and the use of distributed electrode design, comprehensive monitoring and acquisition can be carried out.

[0062] Specifically, after the electrical signals are collected, the signal processing unit 11 removes the interference signals from the environment or equipment through intelligent algorithms, and then applies the adaptive algorithm analysis method to effectively extract the electrical signals and identify the key information in the slope area.

[0063] Specifically, the intelligent dynamic network 8 combines the working states of the root distributed optical fiber sensor 9 and the bioelectricity cooperation system 7, and uses intelligent algorithms (such as the ant colony optimization algorithm, etc., for the non-linear strain data characteristics of the distributed optical fiber sensor, dynamically adjusting the deployment density of sensor nodes) and adaptive technologies (such as the Q-learning algorithm, each node dynamically adjusts the period through reinforcement learning) to dynamically adjust the network topology structure and data transmission path in real time. At the same time, the network topology structure will also be adjusted according to the distribution of sensors and real-time data traffic, avoiding data transmission bottlenecks or network congestion phenomena, so as to maximize resource utilization and optimize the overall network performance.

[0064] Specifically, as Figure 2 shown, the ecological-mechanical coupling analysis model 2 can comprehensively quantify the interaction mechanism between ecology and mechanics by dynamically integrating multiple factors such as vegetation cover, microbial activities, and mechanical parameters, deeply revealing the mutual influence between vegetation, microorganisms and soil mechanical properties, so as to more accurately evaluate the slope stability. On this basis, by comprehensively analyzing the changes of these ecological and mechanical factors, the accurate prediction of slope stability can be realized. It mainly includes the construction of the ecological stability index model 12 and the dual-stream spatio-temporal intelligent network 13.

[0065] Specifically, the calculation formula of the ecological stability index model ( ESI ) 12 is: ESI = w 1 D + w 2 F + w 3 C + w 4 R Among them, D represents the biodiversity index; F represents the ecosystem function index; C represents the environmental carrying capacity index; R represents the recovery ability index; w 1 、w 2 、w 3 、w4 is the dynamic weight of each factor, calculated by the entropy weight method, and the weight is updated every 24 hours to facilitate adaptation to seasonal changes.

[0066] Specifically, the dual-stream spatio-temporal intelligent network 13, through GNSS devices to obtain accurate position information and time-series data of the slope area in real time, so as to accurately monitor the position changes and dynamic characteristics of the slope. At the same time, combined with the multi-modal bionic sensor network 1 to feedback information such as soil deformation and humidity fluctuation, and extract relevant mechanical parameters from it. Then, these mechanical parameters are combined with the ecological stability index components to form a comprehensive ecological-mechanical evaluation model. On this basis, using the cross-modal spatio-temporal feature fusion technology, the spatio-temporal features from different sources are deeply fused to form a multi-dimensional and dynamically changing stability evaluation system.

[0067] Specifically, the physical data flow branch 14, through GNSS devices to extract dynamic parameters related to slope changes, monitor the position information and deformation of the slope in real time, accurately determine the displacement and displacement velocity of the slope at different time points, so as to capture its change trend. Then, through the data of the multi-modal bionic sensor network 1 combined with the mechanical modeling method, it is combined with the geological characteristics of the slope to deduce relevant mechanical parameters. These mechanical parameters can comprehensively describe the mechanical behavior and stability of the slope, providing a scientific basis for further evaluating the safety of the slope.

[0068] 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 a variety of ecological monitoring means, 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.

[0069] Specifically, the cross-modal fusion layer 16 realizes the deep fusion of ecological and mechanical parameters by constructing an association matrix of the aforementioned calculated ecological data flow and physical data flow. By adopting the cross-modal spatio-temporal feature fusion technology, the fusion layer quantifies the correlation of these two types of data flows through a mathematical model, constructs an association 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 through multiple decision trees to achieve multi-feature non-linear combination) to output a probability value of slope stability P f ∈[0,1], where P fThe closer it is to 1, the higher the stability of the slope. 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.

[0070] Specifically, the dynamic adaptive early warning system 3 integrates multiple real-time monitoring data sources and uses decision-making algorithms to fuse and analyze these data to achieve accurate identification and early warning of slope disasters. The system can dynamically adjust the early warning model according to the changes in real-time data, and conduct real-time assessment of potential slope landslides or collapses and other disasters according to the risk level. Based on the risk level assessment, the system can automatically trigger a differential response mechanism to ensure the timeliness and effectiveness of response measures, mainly including the adaptive decision-making algorithm 17 and the hierarchical response mechanism 18.

[0071] Specifically, the adaptive decision-making algorithm calculates 17 to obtain the risk index ( R ): R = C·P f Among them, C is a constant, the loss coefficient after instability, but its value is affected by various factors, such as the soil conditions of the slope, the slope, and the construction conditions of the surrounding environment; P f represents the probability value of slope instability, which is estimated through a probability model or a risk analysis tool. It comprehensively considers ecological and mechanical indices and is obtained through ecological-mechanical coupling analysis 2.

[0072] Specifically, as shown in Figure 3, in the present invention, the hierarchical response mechanism 18, when the risk assessment value R is less than 60%, the system gives a green early warning, continues with routine slope monitoring and inspections, and maintains regular risk assessments to ensure timely detection of potential changes. When R is between 60% and 85%, the system activates a yellow early warning. At this time, it is necessary to increase the monitoring frequency, increase the sampling rate of sensors, focus on the stability changes of the slope, and appropriately restrict high-risk activities in the surrounding area to reduce external interference and ensure slope safety. When R is greater than 85%, the system activates a red early warning and immediately launches a comprehensive emergency response, including measures such as personnel evacuation, traffic control, and emergency resource allocation. At the same time, the on-site monitoring data will be processed through the edge-cloud collaborative computing system 4 for the next step of emergency handling.

[0073] Specifically, the cloud-edge collaboration system 4, through cloud computing technology, realizes efficient data collaboration and interconnection between local edge nodes and remote cloud platforms. This system can collect and process a large amount of complex dynamic data in real time, and conduct in-depth analysis and modeling using intelligent algorithms and virtual models. The cloud digital model engine 19 obtains early warning information, environmental change data, etc. of the slope area 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, high-precision cloud twin (for example, Biot consolidation theory), edge lightweight twin (for example, model reduction technology), and virtual-real interaction closed loop (real-time rendering of the slip surface expansion path predicted by the twin model), to achieve a full-element dynamic mapping from macro to micro. The system can compare and fuse these real-time data with historical information, thereby establishing an accurate virtual slope system. Among them Biot Consolidation theory: σ = σ' + u v = k·j In the formula: 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.

[0074] Based on models such as seepage-deformation coupling analysis, the system can not only predict potential slope risks, but also automatically generate repair plans suitable for the current geological conditions through big data analysis and simulation technology. In practical applications, the system can respond to changes in slope risks in real time and adjust the repair plan in a timely manner according to the early warning information, so as to ensure that the repair work can be carried out accurately and efficiently.

[0075] Specifically, the adaptive repair network 5 can achieve real-time repair and continuous monitoring and repair of the slope through intelligent algorithms. When the system detects unstable signs on the slope, it can immediately initiate repair measures, such as reinforcement support, adjusting the drainage system, or enhancing soil stability, etc., to ensure that slope problems can be quickly addressed. At the same time, the repair system continuously monitors 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., training the repair strategy through mechanisms to enable the system to select the optimal repair strategy under different slope states), to ensure the effectiveness and pertinence of the repair measures. In addition, after the repair work is completed, the system continues to monitor the slope status and collect feedback data to optimize and adjust the repair plan to ensure that the slope remains stable in the long term.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A slope instability prediction method, characterized in that, Including: Collecting ecological parameters and geological parameters of the slope; Calculating the ecological stability characteristic value of the slope according to the ecological parameters of the slope; Calculating the geological stability characteristic value of the slope according to the geological parameters of the slope; Judging 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, including the following steps: The ecological parameters include biodiversity characteristic values D , ecosystem function characteristic values F , environmental carrying capacity characteristic values C and resilience characteristic values R ; According to the biodiversity characteristic value D , ecosystem function characteristic value F , environmental carrying capacity characteristic value C and recovery ability characteristic value R calculate the ecological stability characteristic value of the slope; The biodiversity characteristic value D The calculation method uses the Simpson diversity index for calculation; and / or The ecological parameters include ecological functional indicators, and the functional indicators include eutrophication indicators and aquatic biological indicators; the ecological system function characteristic values F The calculation method is as follows: Among them, n represents the total number of functional indicators, T i is the i th functional indicator, W i is the weight of the corresponding indicator; and / or The characteristic value of the environmental carrying capacity C is calculated by the following formula: Among them, A is the resource consumption, including the consumption of reinforcement materials for slope reinforcement and the consumption of plant resources, B represents the total amount of resources, k is the environmental coefficient, and its value range is 0 < k < 1, which is determined according to the environmental situation; and / or The recovery ability 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 ability characteristic value R ; and / or According to the biodiversity characteristic value D , ecosystem function characteristic value F , environmental carrying capacity characteristic value C and recovery ability characteristic value R Calculate the ecological stability characteristic value of the slope, which is realized by using a weighted fusion algorithm; In the weighted fusion algorithm, the biodiversity eigenvalue D , the ecosystem function eigenvalue F , the environmental carrying capacity eigenvalue C and the resilience eigenvalue R have dynamic weights, and the dynamic weights are updated periodically over time.

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

3. The slope instability prediction method according to claim 1, wherein Judging whether the slope is unstable according to the ecological stability characteristic value and the geological stability characteristic value includes: Constructing an association matrix of the ecological stability characteristic value and the geological stability characteristic value; Inputting the association matrix into a trained deep learning prediction model to obtain the instability probability of the slope.

4. The slope instability prediction method according to claim 1, characterized in that Collecting ecological parameters and geological parameters of the slope is realized by a multi-modal bionic sensor network, and the multi-modal bionic sensor network includes: A flexible fractal sensing unit and a bioelectricity collaborative monitoring system; The flexible fractal sensing unit is used to collect the ecological parameters and geological parameters of the slope; The bioelectricity collaborative monitoring system is used to denoise and perform data conversion on the ecological parameters and geological parameters collected by the flexible fractal sensing unit.

5. The slope instability prediction method according to claim 4, characterized in that, The flexible fractal sensing unit includes a flexible covering layer and a root-like distributed optical fiber sensor arranged inside the flexible covering layer, and the root-like distributed optical fiber sensor is distributed along the deep and surface layers of the slope to simulate the morphological pattern of a plant root network; and / or The bioelectricity collaborative monitoring system includes implanted distributed electrodes and a signal processing unit; the implanted distributed electrodes are embedded into the slope and are used to simultaneously obtain the acquisition signals of the root-like distributed optical fiber sensor from multiple positions, and the acquisition signals include the ecological parameters and geological parameters of the slope; The signal processing unit is used to denoise the acquisition signals, exclude interference signals from the environment or equipment, and extract effective ecological parameters and geological parameters of the slope.

6. The slope instability prediction method according to claim 5, characterized in that The multi-modal bionic sensor network further includes an intelligent dynamic network, and the intelligent dynamic network is used to dynamically adjust the network topology structure and data transmission path of the implanted distributed electrodes in real time according to the working states of the root-like distributed optical fiber sensor and the bioelectricity collaborative monitoring system.

7. A slope ecological monitoring and early warning method, characterized in that, Calculating the instability probability of the slope by using the slope instability prediction method according to any one of claims 1-6; Obtaining the loss coefficient after instability, and determining the risk level of the slope according to the loss coefficient after instability and the instability probability; the risk level includes at least two types, and different risk levels correspond to different countermeasures; Execute corresponding countermeasures according to the risk level.

8. The slope ecological monitoring and early warning method according to claim 7, characterized in that, 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 probability of instability includes: Calculate a risk index according to the loss coefficient after instability and the probability of instability; When the risk index is less than a first preset value, determine that the risk level is no risk; When the risk index is greater than or equal to the first preset value and less than or equal to a second preset value, determine that the risk level is low risk; the first preset value is less than the second preset value; When the risk index is greater than the second preset value, determine that the risk level is high risk; When in a low-risk state, increase the monitoring frequency of the slope and the sampling rate of the flexible fractal sensing unit, focus on the stability changes of the slope, and restrict high-risk activities in the surrounding area; When in a high-risk state, initiate a comprehensive emergency response activity, notify personnel evacuation, traffic control, and emergency resource allocation.

9. The slope ecological monitoring and early warning method according to claim 8, wherein It further includes the following steps: Construct a digital twin model of the slope, and under the state of warning that the slope has an instability risk, construct a repair strategy through the digital twin model and execute the repair strategy; The repair strategy includes, but is not limited to, one or a combination of the following: Initiate reinforcement support, adjust drainage, or enhance soil stability.

10. A computer system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 9 above.

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