Dammed lake disaster prevention method and system based on disaster-causing chain

Abstract

The invention is suitable for the field of natural disaster prevention, and provides a barrier lake disaster prevention method and system based on a disaster-causing chain, and the system comprises a quay wall stability monitoring module, a barrier risk analysis module, a disaster warning broadcast module, and a disaster mitigation scheme planning module. Through disaster simulation in advance, emergency managers are allowed to conceal and design evacuation and rescue routes in advance, and precious preparation time is provided for the public. By planning a direct and efficient route in advance for the evacuation and rescue process, the pressure of rescue resources at the emergency moment can be greatly reduced, and rescue teams are allowed to distribute and use scarce resources more accurately.

Description

Technical Field

[0001] The present invention belongs to the field of natural disaster prevention, and in particular relates to a method and system for preventing barrier lake disasters based on a disaster chain. Background Art

[0002] From the perspective of emergency response, the barrier lake disaster is caused by a chain of multiple emergencies, such as the formation of a barrier dam by landslides on the bank slope, the blockage of the river channel by the barrier dam, the rise of the lake water, the collapse of the barrier dam caused by the rising lake water, and the flooding of the downstream caused by the flood. The formation of a barrier dam by landslides on the bank slope is an instantaneous emergency, and the subsequent rise of the lake water, the collapse of the barrier dam and the flooding of the downstream are all instantaneous emergencies. To eliminate or reduce the huge disaster risks brought by the incident, scientific preventive measures and strong emergency measures must be taken.

[0003] Many systems lack real-time monitoring and dynamic feedback mechanisms, making it difficult to keep up with the rapidly changing natural environment. In terms of landslide lake warning and disaster response, time is life, and the ability to process data in real time is crucial. Traditional technologies may rely on preset evacuation routes and lack the flexibility to adjust the route based on real-time disaster assessments, making it difficult for action planning to effectively respond to complex and changing actual conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a method for preventing barrier lake disasters based on a disaster chain, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is achieved by a method for preventing barrier lake disasters based on a disaster chain, the method comprising: Implement monitoring of the slope status and environmental data of the river channel connecting rod, the slope status includes: topographic status, soil moisture, displacement status and crack change status; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establish a historical data archive library, store the data collected at all historical time points as historical data in the historical data archive library, and add a time tag to each section of historical data; Establish a risk prediction model and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential barrier lake. At the same time, establish a disaster simulation model to simulate the landslide process based on the prediction results, and evaluate the size of the barrier lake caused by the landslide and the flooded area after the breach. Generate and send warning information based on the prediction results of the risk prediction model; Plan evacuation routes and rescue routes based on the evaluation results of the disaster simulation model.

[0006] Another object of the present invention is to provide a barrier lake disaster prevention system based on a disaster chain, the system comprising: The bank wall stability monitoring module is used to monitor the slope status and environmental data of the river channel connecting rod. The slope status includes: topographic status, soil moisture, displacement status and crack change status; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establish a historical data archive library to store the data collected at all historical time points as historical data in the historical data archive library, and add a time tag to each section of historical data; The damming risk analysis module is used to establish a risk prediction model and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential damming lake. At the same time, a disaster simulation model is established to simulate the landslide process based on the prediction results, and the size of the damming lake caused by the landslide and the flooded area after the breach are evaluated; Disaster warning broadcast module, used to generate and send warning information based on the prediction results of the risk prediction model; The disaster mitigation program planning module is used to plan evacuation routes and rescue routes based on the evaluation results of the disaster simulation model.

[0007] The beneficial effects of the present invention are: A systematic and scientific approach is adopted to integrate multi-source data, not only to identify risks from a single perspective but also to comprehensively evaluate potential impacts. Dynamic feedback adjustments are then made using data-driven models, making disaster management more sophisticated and interactive.

[0008] The disaster simulation formula constructed based on the laws of physics not only ensures the in-depth understanding of the disaster process, but also greatly increases the accuracy of the prediction results in fitting the actual scenario. It also provides the in-depth evolution process of landslides and possible floods after the bursting of barrier lakes, and makes a detailed assessment of the affected areas, which is of vital importance for the advance planning of emergency measures and post-disaster recovery.

[0009] Through advance disaster simulation, emergency managers can conceive and design evacuation and rescue routes in advance, providing the public with valuable preparation time. By planning direct and efficient routes for evacuation and rescue processes in advance, the pressure on rescue resources in emergencies can be greatly reduced, allowing rescue teams to distribute and use scarce resources more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a method for preventing barrier lake disasters based on a disaster chain provided in an embodiment of the present invention; Figure 2 A flow chart for implementing monitoring of the slope status and environmental data of a river channel connecting rod provided in an embodiment of the present invention; Figure 3A flowchart for predicting the probability of formation of a potential barrier lake provided by an embodiment of the present invention, establishing a disaster simulation model, simulating the landslide process based on the prediction results, and evaluating the size of the barrier lake caused by the landslide and the flooded area after the collapse; Figure 4 A flowchart of generating and sending warning information based on the prediction results of the risk prediction model provided in an embodiment of the present invention; Figure 5 A flowchart of planning evacuation routes and rescue routes provided by an embodiment of the present invention; Figure 6 A structural block diagram of a barrier lake disaster prevention system based on a disaster chain provided in an embodiment of the present invention; Figure 7 A structural block diagram of a quay wall stability monitoring module provided in an embodiment of the present invention; Figure 8 A structural block diagram of a damming risk analysis module provided in an embodiment of the present invention; Fig. 9 A structural block diagram of a disaster warning broadcast module provided by an embodiment of the present invention; Fig.10 A structural block diagram of a disaster mitigation program planning module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0012] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0013] Figure 1 A flow chart of a method for preventing barrier lake disasters based on a disaster chain provided in an embodiment of the present invention, such as Figure 1 As shown, a method for preventing barrier lake disasters based on a disaster chain comprises: S100, monitoring the slope status and environmental data of the river channel connecting rod, wherein the slope status includes: topographic status, soil moisture, displacement status and crack change status; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establishing a historical data archive library, storing the data collected at all historical time points as historical data in the historical data archive library, and adding a time tag to each section of historical data; This step first focuses on monitoring the physical parameters of the slope, trying to obtain detailed information such as topography, soil moisture, displacement status, and crack changes, which form the cornerstone of subsequent judgment and analysis. The depth of slope status monitoring ranges from the detection of small displacement changes on the surface to the microseismic monitoring of deep geological activities, comprehensively evaluating the instability indicators that the slope may present. The acquisition of longitude and latitude coordinates is extremely critical for understanding and predicting the function of potential landslide sites, and the integrated analysis of seismic data, meteorological data, and hydrological data can be regarded as a prediction system for changes in risk levels caused by environmental changes.

[0014] The use of monitoring equipment and data management systems not only improves the comprehensiveness and accuracy of data collection, but also improves the flexibility of prediction and response mechanisms through time series analysis, effectively improves the timeliness of disaster prevention and preparedness actions, greatly reduces the warning time, and effectively prevents and mitigates the damage that may be caused by barrier lake disasters. The depth and breadth of such comprehensive monitoring and data management methods play an inestimable value in modern high-risk natural disaster management.

[0015] S200, establish a risk prediction model, and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential barrier lake. At the same time, establish a disaster simulation model to simulate the landslide process based on the prediction results, and evaluate the size of the barrier lake caused by the landslide and the flooded area after the breach; This step combines the weights and data scores of different influencing factors to calculate the overall risk score, providing a numerical judgment benchmark, simply and clearly identifying the existence of risks, and determining the size of disaster risks based on thresholds. This prediction mechanism increases the accuracy and applicability of risk assessment, can promptly point out the probability of disasters, and plays a key role in public safety and resource allocation. At the same time, a disaster simulation model was established. After substituting the slope and environmental data, the process of landslide occurrence and the possibility of barrier lake formation can be specifically simulated. The model uses the principles of fluid mechanics - continuity equations and momentum equations to describe the changes in flow and velocity. This can not only reproduce the dynamic process of landslide events and simulate the flooding problems that may be caused by the collapse of the barrier lake, but also provide a scientific basis for emergency planning and evacuation strategy formulation.

[0016] The disaster simulation formula constructed by physical laws not only ensures the in-depth understanding of the disaster process, but also greatly increases the accuracy of the prediction results in fitting the actual situation. Providing the in-depth evolution of landslides and floods that may occur after the bursting of barrier lakes, and carefully assessing the affected areas, is of vital importance for planning emergency measures and post-disaster recovery in advance.

[0017] S300, generating and sending warning information based on the prediction result of the risk prediction model; This step starts with receiving the output of the risk prediction model and making a logical judgment. When the total probability risk score obtained exceeds the pre-set probability score threshold, the system generates an early warning notification. This means that the possibility of the formation of a potential barrier lake has reached a warning level and appropriate action needs to be taken. Then a more detailed alarm level system is established by setting a series of difference intervals. These stepped intervals provide corresponding early warning notification content for different risk levels, so that the early warning information is not just a general notification, but detailed guidance on specific measures to respond to risks at all levels. According to the difference between the total probability score and the threshold, the emergency level corresponding to the event is determined to guide further response measures.

[0018] Setting the difference range and corresponding warning content means that graded warnings can be implemented based on the real-time risk score. This flexible multi-level warning mechanism allows targeted emergency arrangements for different risk levels, improving the practicality and pertinence of warning information.

[0019] S400, planning evacuation routes and rescue routes according to the evaluation results of the disaster simulation model.

[0020] This step emphasizes the feasibility of the action plan. After simulating landslides and flood inundation assessments, this stage assumes the key function of providing route planning for actual disaster response. This step involves receiving disaster simulation results and selecting safe areas that are not expected to be affected in disaster scenarios. These areas may serve as evacuation assembly points, temporary shelters, or important bases for further rescue operations. Subsequently, based on the determined safe zone range, evacuation routes and rescue routes are drawn and planned. When planning, this step needs to comprehensively consider factors such as road conditions, traffic flow, ease rate, route directness, and shelter carrying capacity.

[0021] Disaster occurrence is a dynamic process. This step provides the ability to update and adjust evacuation and rescue routes in real time, making adaptive adjustments based on the latest results of actual simulation, which can more accurately reflect the actual needs of emergency evacuation and rescue.

[0022] Through advance disaster simulation, emergency managers can conceive and design evacuation and rescue routes in advance, providing the public with valuable preparation time. By planning direct and efficient routes for evacuation and rescue processes in advance, the pressure on rescue resources in emergencies can be greatly reduced, allowing rescue teams to distribute and use scarce resources more accurately.

[0023] Figure 2 A flow chart for implementing monitoring of the slope status and environmental data of a river channel connecting rod provided by an embodiment of the present invention, such as Figure 2 As shown, the implementation of monitoring the slope status and environmental data of the river channel connecting rod specifically includes: S110, obtaining terrain change data, slope slope, evaluating soil moisture change data, monitoring slope displacement distance and whether cracks appear and changes in existing cracks; S120, locate the precise coordinates of the slope and obtain the spatial coordinates of the slope; record the earthquake activity data of the current area; collect the rainfall, temperature, wind speed data, and water flow depth and flow rate of the current area; S130, building a historical data archive library, and storing the collected slope status and environmental data in the historical data archive library as historical data, and marking the historical data with the collection time as a label during storage.

[0024] Figure 3 A flowchart for predicting the probability of formation of a potential barrier lake provided by an embodiment of the present invention, establishing a disaster simulation model, simulating the landslide process according to the prediction results, and evaluating the size of the barrier lake caused by the landslide and the flooded area after the collapse, such as Figure 3 As shown, the prediction of the formation probability of a potential barrier lake is carried out, and a disaster simulation model is established. The landslide process is simulated according to the prediction results, and the size of the barrier lake caused by the landslide and the flooded area after the collapse are evaluated, which specifically includes: S210, establishing a risk prediction model, substituting the collected slope status and environmental data into the risk prediction model, and calculating the probability of formation of a potential barrier lake; Set probability score thresholds; If the total probability score is lower than the probability score threshold, it is confirmed that there is no risk; If the total probability score exceeds the probability score threshold, it is confirmed that there is a risk, and the risk probability increases as the difference between the total probability score and the probability score threshold increases; S220, building a disaster simulation model, and simulating the landslide process by substituting the state of the slope with risks and environmental data into the disaster simulation model; S230, based on the simulated landslide process, evaluate the size of the barrier lake caused by the landslide and the flooded area after the breach.

[0025] The risk prediction model is established, and the collected slope status and environmental data are substituted into the risk prediction model to calculate the probability of formation of a potential barrier lake, specifically including: The slope state and environmental data are changed using kernel functions to measure the similarity between the two features and obtain kernel features. The corresponding process has the following relationship: ; in, represents the nuclear characteristics, Represent two different features in slope state and environmental data, represents the coefficient of the kernel function, Represents the Gaussian kernel function. The calculation process of the Gaussian kernel function has the following relationship: ; in, represents the Gaussian kernel parameter; The slope state and environmental data are analyzed by wavelet transform at multiple scales to obtain wavelet coefficients. The corresponding process has the following relationship: ; in, represents the wavelet coefficients, denote the mean and standard deviation respectively, represents the coefficients of wavelet transform, represents the Morlet wavelet function; The slope state and environmental data are multiplied to capture the complex interaction between features and obtain the interactive features. The corresponding process has the following relationship: ; in, represents the interaction feature, represents the coefficient of interaction; The kernel features, wavelet coefficients and interaction features are combined in a linear manner, and the combined results are normalized to probability to obtain the formation probability of a potential barrier lake. The corresponding process has the following relationship: ; in, represents the formation probability of a potential barrier lake, represents the normalized exponential function, represents the weight of the feature, represents the number of input features, Indicates n The input features.

[0026] In this step, the nonlinear relationship may be ignored in the traditional linear model, while the present invention uses kernel techniques and wavelet transform to capture the nonlinear relationship between features, which is particularly suitable for processing the interaction of complex environmental variables. And considering the problem of feature interaction, the model is allowed to identify the impact of different feature combinations on the results, thereby improving the prediction ability of the formation of barrier lakes under complex backgrounds. And the wavelet transform allows multi-scale analysis, thereby capturing the changing characteristics at different scales, which is more suitable for processing multi-scale phenomena in natural geographic data.

[0027] The disaster simulation model is constructed by substituting the risky slope state and environmental data into the disaster simulation model to simulate the landslide process, specifically including: Each feature in the risky slope state and environmental data at different time nodes is taken as a node to construct the node feature matrix at different time nodes; According to the correlation between the slope state and each feature in the environmental data at different time nodes, the adjacency matrix of different time nodes is constructed; The node feature matrix and adjacency matrix of different time nodes are used as input, and the node feature matrix is ​​updated using the DCNN network to obtain the updated node feature matrix of different time nodes; The node feature matrices of different time nodes and the updated node features of different time nodes are concatenated in the time dimension, and then the time convolution operation and the graph convolution operation are performed in sequence to obtain the node embedding; Use the risky slope status and environmental data to build a physical model and obtain the mechanical effect; The node embedding and mechanical effects are weightedly combined to obtain the final simulation results.

[0028] In this step, the nodes, edges and overall structure of the graph in the dynamic graph may change over time. The present invention uses an adjacency matrix that changes dynamically over time to adapt to structural changes, combining time convolution and graph convolution to effectively extract local patterns and short-term trends in time series. By convolving in the time dimension, the model can identify important time-dependent features, thereby more accurately reflecting the relationship between nodes at different time steps. In addition, since the DCNN network is suitable for the task of modeling long-distance dependencies, it can be suitable for processing diffusion processes in graphs, and therefore, it is more applicable when simulating propagation effects with physical models.

[0029] Figure 4 A flowchart of generating and sending warning information based on the prediction results of the risk prediction model provided in an embodiment of the present invention, such as Figure 4 As shown, the prediction result based on the risk prediction model generates and sends warning information, specifically including: S310, receiving the prediction result of the risk prediction model, and generating a warning notification when the total probability score exceeds the probability score threshold; S320, setting a difference interval, and setting corresponding warning notification content for each difference interval, obtaining the difference between the total probability score and the probability score threshold, and determining the difference interval in which the difference lies.

[0030] Among them, the process of receiving the prediction results of the risk prediction model and generating an early warning notification when the total probability score exceeds the probability score threshold is as follows: ; in, Indicates the trigger value of the early warning notification; Represents the hyperbolic tangent function to limit the calculation result to between -1 and 1; represents the number of different factors involved in the calculation of the risk prediction model, represents the weight of each factor, Indicates m The risk score of each factor is represents the power exponent, represents the adjustment factor, represents the environmental impact gain parameter, Indicates m Environmental risk correction factor for each factor, represents the probability score threshold, represents the global adjustment factor, represents the threshold adjustment parameter; In this step, the present invention uses the power exponent to allow nonlinear adjustment of the risk scores of different factors, thereby being able to more accurately reflect the relative importance of certain factors in different situations, thereby improving the accuracy of risk assessment; and introduces environmental risk correction factors and adjustment parameters to dynamically respond to external environmental changes (such as earthquakes or extreme weather conditions). This makes risk assessment more real-time and accurate, and finally the hyperbolic tangent function is used to limit the calculation results to a reasonable range to ensure the stability of the results. This helps to avoid the impact of extreme values ​​on decision-making and maintain the reliability of early warning information.

[0031] Figure 5 A flowchart of planning evacuation routes and rescue routes provided by an embodiment of the present invention, such as Figure 5 As shown, the planned evacuation route and rescue route specifically include: S410, receiving simulation results of a disaster simulation model, and identifying safe areas in the model that are not affected by the disaster; S420, planning evacuation routes and rescue routes in the disaster area based on the scope of the safe area.

[0032] The receiving simulation results of the disaster simulation model and identifying the safe area in the model that is not affected by the disaster specifically includes: Obtain risk data of each location at the current time point from the disaster simulation model, and analyze the impact of terrain factors and environmental factors on the risk based on the risk data at the current time point to obtain terrain impact factors and environmental impact factors; Use terrain influence factors and environmental influence factors to adjust risk data and obtain revised risk data; A buffer adjustment parameter is introduced, and the corrected risk data is adjusted using the buffer adjustment parameter to obtain adjusted risk data; Based on the spread of risk in space, the adjusted risk data of each neighboring location is integrated in the neighborhood; The adjusted risk value is compared with the set safety threshold. If the adjusted risk level is lower than the safety threshold and is also lower than the threshold after neighborhood integration, the location is marked as a safe area, otherwise, it is marked as an unsafe area.

[0033] In this step, the present invention introduces buffer adjustment parameters to add a layer of protection mechanism. This method can provide additional safety guarantees under conditions of greater uncertainty and help reduce misjudgments. In addition, the present invention considers the neighborhood risk integral, which means that the model not only focuses on the risk of a single point, but also considers the risk distribution of the surrounding area. This helps to identify potential risk hotspots and conduct more effective regional planning.

[0034] Figure 6 The structural block diagram of the barrier lake disaster prevention system based on the disaster chain provided by the embodiment of the present invention is as follows: Figure 6 As shown, a barrier lake disaster prevention system based on a disaster chain comprises: The bank wall stability monitoring module 100 is used to monitor the slope state and environmental data of the river channel connecting rod, wherein the slope state includes: topographic state, soil moisture, displacement state and crack change state; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establish a historical data archive library, store the data collected at all historical time points as historical data in the historical data archive library, and add a time tag to each section of historical data; This module first focuses on monitoring the physical parameters of the slope, trying to obtain detailed information such as topography, soil moisture, displacement status, and crack changes, which form the cornerstone of subsequent judgment and analysis. The depth of slope status monitoring ranges from the detection of small displacement changes on the surface to the microseismic monitoring of deep geological activities, comprehensively evaluating the instability indicators that the slope may present. The acquisition of longitude and latitude coordinates is extremely critical for understanding and predicting the function of potential landslide sites, and the integrated analysis of seismic data, meteorological data, and hydrological data can be regarded as a prediction system for changes in risk levels caused by environmental changes.

[0035] The use of monitoring equipment and data management systems not only improves the comprehensiveness and accuracy of data collection, but also improves the flexibility of prediction and response mechanisms through time series analysis, effectively improves the timeliness of disaster prevention and preparedness actions, greatly reduces the warning time, and effectively prevents and mitigates the damage that may be caused by barrier lake disasters. The depth and breadth of such comprehensive monitoring and data management methods play an inestimable value in modern high-risk natural disaster management.

[0036] The damming risk analysis module 200 is used to establish a risk prediction model, and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential damming lake. At the same time, a disaster simulation model is established to simulate the landslide process based on the prediction results, and the size of the damming lake caused by the landslide and the flooded area after the collapse are evaluated; This module combines the weights and data scores of different influencing factors to calculate the overall risk score, providing a numerical judgment benchmark, simply and clearly identifying the existence of risks, and determining the size of disaster risks based on thresholds. This prediction mechanism increases the accuracy and applicability of risk assessment, can promptly point out the probability of disasters, and plays a key role in public safety and resource allocation. At the same time, a disaster simulation model was established. After substituting slope and environmental data, the process of landslide occurrence and the possibility of barrier lake formation can be specifically simulated. The model uses the principles of fluid mechanics - continuity equation and momentum equation to describe the changes in flow and velocity. This can not only reproduce the dynamic process of landslide events and simulate the flooding problems that may be caused by the collapse of the barrier lake, but also provide a scientific basis for emergency planning and evacuation strategy formulation.

[0037] The disaster simulation formula constructed by physical laws not only ensures the in-depth understanding of the disaster process, but also greatly increases the accuracy of the prediction results in fitting the actual situation. Providing the in-depth evolution of landslides and floods that may occur after the bursting of barrier lakes, and carefully assessing the affected areas, is of vital importance for planning emergency measures and post-disaster recovery in advance.

[0038] A disaster warning broadcast module 300 is used to generate and send warning information based on the prediction results of the risk prediction model; The module starts by receiving the output of the risk prediction model and making a logical judgment. When the total probability risk score obtained exceeds the pre-set probability score threshold, the system generates an early warning notification. This means that the possibility of the formation of a potential barrier lake has reached a warning level and appropriate action needs to be taken. Then a more detailed alarm level system is established by setting a series of difference intervals. These stepped intervals provide corresponding early warning notification content for different risk levels, so that the early warning information is not just a general notification, but detailed guidance on specific measures to deal with risks at all levels. According to the difference between the total probability score and the threshold, the emergency level corresponding to the event is determined to guide further response measures.

[0039] Setting the difference range and corresponding warning content means that graded warnings can be implemented based on the real-time risk score. This flexible multi-level warning mechanism allows targeted emergency arrangements for different risk levels, improving the practicality and pertinence of warning information.

[0040] The disaster mitigation program planning module 400 is used to plan evacuation routes and rescue routes according to the evaluation results of the disaster simulation model.

[0041] This module emphasizes the feasibility of the action plan. After simulating landslides and flood inundation assessments, this stage assumes the key function of providing route planning for actual disaster response. This module involves receiving disaster simulation results and selecting safe areas that are not expected to be affected in disaster scenarios. These areas may serve as evacuation assembly points, temporary shelters, or important bases for further rescue operations. Subsequently, based on the determined safe zone range, evacuation routes and rescue routes are drawn and planned. When planning, this step needs to comprehensively consider factors such as road conditions, traffic flow, ease rate, route directness, and shelter carrying capacity.

[0042] Disaster occurrence is a dynamic process. This module provides the ability to update and adjust evacuation and rescue routes in real time, making adaptive adjustments based on the latest results of actual simulation, and can more accurately reflect the actual needs of emergency evacuation and rescue.

[0043] Through advance disaster simulation, emergency managers can conceive and design evacuation and rescue routes in advance, providing the public with valuable preparation time. By planning direct and efficient routes for evacuation and rescue processes in advance, the pressure on rescue resources in emergencies can be greatly reduced, allowing rescue teams to distribute and use scarce resources more accurately.

[0044] Figure 7 The structural block diagram of the quay wall stability monitoring module provided by the embodiment of the present invention is as follows: Figure 7 As shown, the quay wall stability monitoring module includes: The slope landform detection unit 110 is used to obtain terrain change data, slope slope, evaluate soil moisture change data, monitor slope displacement distance and whether cracks appear and changes in existing cracks; The environmental data integration unit 120 is used to locate the precise coordinates of the slope and obtain the spatial coordinates of the slope; record the seismic activity data of the current area; collect the rainfall, temperature, wind speed data, and water flow depth and flow rate of the current area; The data archiving and time indexing unit 130 is used to build a historical data archiving library, and store the collected slope status and environmental data in the historical data archiving library as historical data, and mark the historical data with the collection time as a label during storage.

[0045] Figure 8 The structural block diagram of the damming risk analysis module provided by the embodiment of the present invention is as follows: Figure 8 As shown, the damming risk analysis module includes: The accumulation disaster probability analysis unit 210 is used to establish a risk prediction model, substitute the collected slope state and environmental data into the risk prediction model, and calculate the probability of the formation of a potential barrier lake; Set the probability score threshold, If the total probability score is below the probability score threshold, it means there is no risk; If the total probability score exceeds the probability score threshold, it indicates that there is a risk, and the probability increases as the difference between the total probability score and the probability score threshold increases; The disaster dynamic rehearsal unit 220 is used to build a disaster simulation model by substituting the state of the slope with risks and environmental data into the disaster simulation model; The disaster spreading impact unit 230 is used to evaluate the size of the barrier lake caused by the landslide and the flooded area after the collapse according to the simulated landslide process.

[0046] In this step, the nonlinear relationship may be ignored in the traditional linear model, while the present invention uses kernel techniques and wavelet transform to capture the nonlinear relationship between features, which is particularly suitable for processing the interaction of complex environmental variables. And considering the problem of feature interaction, the model is allowed to identify the impact of different feature combinations on the results, thereby improving the prediction ability of the formation of barrier lakes under complex backgrounds. And the wavelet transform allows multi-scale analysis, thereby capturing the changing characteristics at different scales, which is more suitable for processing multi-scale phenomena in natural geographic data.

[0047] The disaster simulation model is constructed by substituting the risky slope state and environmental data into the disaster simulation model to simulate the landslide process, specifically including: Each feature in the risky slope state and environmental data at different time nodes is used as a node to construct a node feature matrix at different time nodes; The correlation between each feature of the slope state and environmental data at different time nodes is used to construct the adjacency matrix of different time nodes; The node feature matrix and adjacency matrix of different time nodes are used as input, and the node feature matrix is ​​updated using the DCNN network to obtain the updated node feature moments of different time nodes; The node feature matrices of different time nodes and the updated node features of different time nodes are concatenated in the time dimension, and then the time convolution operation and the graph convolution operation are performed in sequence to obtain the node embedding; Use the risky slope status and environmental data to build a physical model and obtain the mechanical effect; The node embedding and mechanical effects are weightedly combined to obtain the final simulation results.

[0048] In this step, the nodes, edges and overall structure of the graph in the dynamic graph may change over time. The present invention uses an adjacency matrix that changes dynamically over time to adapt to structural changes, combining time convolution and graph convolution to effectively extract local patterns and short-term trends in time series. By convolving in the time dimension, the model can identify important time-dependent features, thereby more accurately reflecting the relationship between nodes at different time steps. In addition, since the DCNN network is suitable for the task of modeling long-distance dependencies, it can be suitable for processing diffusion processes in graphs, and therefore, it is more applicable when simulating propagation effects with physical models.

[0049] Fig. 9 A structural block diagram of a disaster warning broadcast module provided by an embodiment of the present invention, such as Fig. 9 As shown, the disaster warning broadcast module includes: The crisis prediction signal integration unit 310 is used to receive the prediction results of the risk prediction model and generate an early warning notification when the total probability score exceeds the probability score threshold; The warning notification editing unit 320 is used to set the difference interval, set the corresponding warning notification content for each difference interval, obtain the difference between the total probability score and the probability score threshold, and determine the difference interval in which the difference lies.

[0050] Fig.10 A structural block diagram of a disaster mitigation program planning module provided in an embodiment of the present invention, such as Fig.10 As shown, the disaster mitigation program planning module includes: The non-disaster area identification and marking unit 410 is used to receive the simulation results of the disaster simulation model and identify the safe area in the model that is not affected by the disaster; The disaster area evacuation and rescue route planning unit 420 is used to plan the evacuation route and rescue route of the disaster area according to the scope of the safe area.

[0051] The receiving simulation results of the disaster simulation model and identifying the safe area in the model that is not affected by the disaster specifically includes: Obtain risk data of each location at the current time point from the disaster simulation model, and analyze the impact of terrain factors and environmental factors on the risk based on the risk data at the current time point to obtain terrain impact factors and environmental impact factors; Use terrain influence factors and environmental influence factors to adjust risk data and obtain revised risk data; A buffer adjustment parameter is introduced, and the corrected risk data is adjusted using the buffer adjustment parameter to obtain adjusted risk data; Based on the spread of risk in space, the adjusted risk data of each neighboring location is integrated in the field; The adjusted risk value is compared with the set safety threshold. If the adjusted risk level is lower than the safety threshold and is also lower than the threshold after neighborhood integration, the location is marked as a safe area, otherwise, it is marked as an unsafe area.

[0052] In this step, the present invention introduces buffer adjustment parameters to add a layer of protection mechanism. This method can provide additional safety guarantees under conditions of greater uncertainty and help reduce misjudgments. In addition, the present invention considers the neighborhood risk integral, which means that the model not only focuses on the risk of a single point, but also considers the risk distribution of the surrounding area. This helps to identify potential risk hotspots and conduct more effective regional planning.

[0053] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0054] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0055] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preventing barrier lake disasters based on a disaster chain, characterized in that: The method comprises the following steps: Implement monitoring of the slope status and environmental data of the river channel connecting rod, the slope status includes: topographic status, soil moisture, displacement status and crack change status; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establish a historical data archive library, store the data collected at all historical time points as historical data in the historical data archive library, and add a time tag to each section of historical data; Establish a risk prediction model and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential barrier lake. At the same time, establish a disaster simulation model to simulate the landslide process based on the prediction results, and evaluate the size of the barrier lake caused by the landslide and the flooded area after the breach. Generate and send warning information based on the prediction results of the risk prediction model; Plan evacuation routes and rescue routes based on the evaluation results of the disaster simulation model; The implementation of monitoring the slope status and environmental data of the river channel connecting rod specifically includes: Obtain terrain change data, slope slope, evaluate soil moisture change data, monitor slope displacement distance and whether cracks appear and changes in existing cracks; Locate the precise coordinates of the slope and obtain the spatial coordinates of the slope; record the seismic activity data of the current area; collect the rainfall, temperature, wind speed data, as well as the depth and velocity of the water flow in the current area; Build a historical data archive library, and store the collected slope status and environmental data in the historical data archive library as historical data, and mark the historical data with the collection time as a label during storage.

2. The method for preventing barrier lake disasters based on the disaster chain according to claim 1, characterized in that: The prediction of the formation probability of a potential barrier lake and the establishment of a disaster simulation model are carried out to simulate the landslide process based on the prediction results, and to evaluate the size of the barrier lake caused by the landslide and the flooded area after the collapse, which specifically include: Establish a risk prediction model, substitute the collected slope status and environmental data into the risk prediction model, and calculate the probability of the formation of a potential barrier lake; Set probability score thresholds; If the total probability score is lower than the probability score threshold, it is confirmed that there is no risk; If the total probability score exceeds the probability score threshold, it is confirmed that there is a risk, and the risk probability increases as the difference between the total probability score and the probability score threshold increases; Build a disaster simulation model and simulate the landslide process by substituting the risky slope status and environmental data into the disaster simulation model; Based on the simulated landslide process, the size of the barrier lake caused by the landslide and the flooded area after the breach are evaluated.

3. The method for preventing barrier lake disasters based on the disaster chain according to claim 2 is characterized in that: The risk prediction model is established, and the collected slope status and environmental data are substituted into the risk prediction model to calculate the probability of formation of a potential barrier lake, specifically including: The slope state and environmental data are changed using kernel functions to measure the similarity between the two features and obtain kernel features. The corresponding process has the following relationship: ； in, represents the nuclear characteristics, Represent two different features in slope state and environmental data, represents the coefficient of the kernel function, Represents the Gaussian kernel function. The calculation process of the Gaussian kernel function has the following relationship: ； in, represents the Gaussian kernel parameter; The slope state and environmental data are analyzed by wavelet transform at multiple scales to obtain wavelet coefficients. The corresponding process has the following relationship: ； in, represents the wavelet coefficients, denote the mean and standard deviation respectively, represents the coefficients of wavelet transform, represents the Morlet wavelet function; The slope state and environmental data are multiplied to capture the complex interaction between features and obtain the interactive features. The corresponding process has the following relationship: ； in, represents the interaction feature, represents the coefficient of interaction; The kernel features, wavelet coefficients and interaction features are combined in a linear manner, and the combined results are normalized to probability to obtain the formation probability of a potential barrier lake. The corresponding process has the following relationship: ； in, represents the formation probability of a potential barrier lake, represents the normalized exponential function, represents the weight of the feature, represents the number of input features, Indicates n The input features.

4. The method for preventing barrier lake disasters based on the disaster chain according to claim 3 is characterized in that: The disaster simulation model is constructed by substituting the risky slope state and environmental data into the disaster simulation model to simulate the landslide process, specifically including: Each feature in the risky slope state and environmental data at different time nodes is taken as a node to construct the node feature matrix at different time nodes; According to the correlation between the slope state and each feature in the environmental data at different time nodes, the adjacency matrix of different time nodes is constructed; The node feature matrix and adjacency matrix of different time nodes are used as input, and the node feature matrix is ​​updated using the DCNN network to obtain the updated node feature matrix of different time nodes; The node feature matrices of different time nodes and the updated node features of different time nodes are concatenated in the time dimension, and then the time convolution operation and the graph convolution operation are performed in sequence to obtain the node embedding; Use the risky slope status and environmental data to build a physical model and obtain the mechanical effect; The node embedding and mechanical effects are weightedly combined to obtain the final simulation results.

5. The method for preventing barrier lake disasters based on the disaster chain according to claim 4 is characterized in that: The prediction results based on the risk prediction model generate and send warning information, specifically including: Receive the prediction results of the risk prediction model and generate an early warning notification when the total probability score exceeds the probability score threshold; Set the difference interval and set the corresponding early warning notification content for each difference interval, obtain the difference between the total probability score and the probability score threshold, and determine the difference interval in which the difference lies.

6. The method for preventing barrier lake disasters based on the disaster chain according to claim 5 is characterized in that: The process of receiving the prediction results of the risk prediction model and generating an early warning notification when the total probability score exceeds the probability score threshold is as follows: ； in, Indicates the trigger value of the early warning notification; Represents the hyperbolic tangent function to limit the calculation result to between -1 and 1; represents the number of different factors involved in the calculation of the risk prediction model, represents the weight of each factor, Indicates m The risk score of each factor is represents the power exponent, represents the adjustment factor, represents the environmental impact gain parameter, Indicates m Environmental risk correction factor for each factor, represents the probability score threshold, represents the global adjustment factor, Represents the threshold adjustment parameter.

7. The method for preventing barrier lake disasters based on the disaster chain according to claim 6, characterized in that: The planned evacuation routes and rescue routes specifically include: Receive simulation results from the disaster simulation model and identify safe areas in the model that are not affected by the disaster; Plan evacuation and rescue routes in the disaster-stricken area based on the scope of the safe area.

8. The method for preventing barrier lake disasters based on the disaster chain according to claim 7 is characterized in that: The receiving simulation results of the disaster simulation model and identifying the safe area in the model that is not affected by the disaster specifically includes: Obtain risk data of each location at the current time point from the disaster simulation model, and analyze the impact of terrain factors and environmental factors on the risk based on the risk data at the current time point to obtain terrain impact factors and environmental impact factors; Use terrain influence factors and environmental influence factors to adjust risk data and obtain revised risk data; A buffer adjustment parameter is introduced, and the corrected risk data is adjusted using the buffer adjustment parameter to obtain adjusted risk data; Based on the spread of risk in space, the adjusted risk data of each adjacent location is integrated over the neighborhood. The adjusted risk value is compared with the set safety threshold. If the adjusted risk level is lower than the safety threshold and is also lower than the threshold after neighborhood integration, the location is marked as a safe area; otherwise, it is marked as an unsafe area.

9. A barrier lake disaster prevention system based on a disaster chain, the system being applied to the barrier lake disaster prevention method based on a disaster chain according to any one of claims 1 to 8, characterized in that: The system comprises: The bank wall stability monitoring module is used to monitor the slope status and environmental data of the river channel connecting rod. The slope status includes: topographic status, soil moisture, displacement status and crack change status; the environmental data includes: earthquake data, longitude and latitude coordinates, meteorological data and hydrological data, and establish a historical data archive library to store the data collected at all historical time points as historical data in the historical data archive library, and add a time tag to each section of historical data; The damming risk analysis module is used to establish a risk prediction model and substitute the collected slope status and environmental data into the risk prediction model to predict the probability of the formation of a potential damming lake. At the same time, a disaster simulation model is established to simulate the landslide process based on the prediction results, and the size of the damming lake caused by the landslide and the flooded area after the breach are evaluated; Disaster warning broadcast module, used to generate and send warning information based on the prediction results of the risk prediction model; The disaster mitigation program planning module is used to plan evacuation routes and rescue routes based on the evaluation results of the disaster simulation model.

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