Dynamic planning method for risk avoiding path of high-risk barrier lake

Abstract

The invention discloses a dynamic planning method for a risk avoiding path of a high-risk dammed lake. The method comprises the following steps: S1, establishing a dammed lake downstream risk area range calculation model coupling a dam break flood outer envelope line and a secondary disaster range; s2, establishing a segmented risk avoiding window time calculation model; s3, establishing a crowd-based path planning model based on dynamic toughness weight and multi-objective collaborative optimization; and S4, performing dynamic rolling updating based on the flood routing process. According to the method, the downstream risk avoiding range of the barrier lake can be accurately defined, and the accuracy of the downstream risk avoiding range of the barrier lake is improved through superposition of the outsourcing line and the secondary disaster range through multi-method flood inrush simulation; a crowd-based path planning method based on dynamic toughness weight and multi-objective collaborative optimization is established, the limitation that only the shortest path is considered in traditional path planning is broken through, and differential risk avoiding path dynamic generation is realized in combination with crowd characteristics, real-time road conditions and secondary disaster risks.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency management of water conservancy project disasters, and in particular relates to a dynamic planning method for avoiding risk paths in high-risk barrier lakes. Background Art

[0002] A barrier lake is formed when a river is blocked by natural processes such as landslides, collapses, and debris flows. The resulting water-blocking accumulation is called a barrier dam. High-risk barrier lakes generally refer to those with the potential to burst in the short term. These lakes have large water storage capacity, poor dam stability, a short burst duration, and high destructive power. Typical high-risk barrier lakes are often caused by landslides or debris flows triggered by earthquakes and heavy rainfall, but can also be formed by factors such as glacial collapse or moraine blocking the river. These natural dams have loose structures, poor cementation, and a wide particle size distribution. They lack the planning and core wall anti-seepage measures of artificial earth-rockfill dams. Once water inflow increases, the dam crest will continue to rise, making it highly susceptible to collapse. The downstream areas of high-risk barrier lakes are often located in mountainous and canyon areas, with a relatively high concentration of residential areas and critical infrastructure along the downstream coast. If a barrier lake bursts, secondary disasters such as upstream flooding and dam collapse, as well as severe flood peaks, will pose a serious threat to the safety of life, property, and infrastructure downstream. High-risk barrier lake outburst floods are characterized by suddenness, wide-area destruction, and complex disaster chains.

[0003] Traditional methods of defining evacuation areas downstream of a landslide-dammed lake and planning evacuation routes for personnel have certain limitations: first, calculations of flood inundation caused by dam breaches often rely on a single hydrodynamic model, resulting in a small or redundant evacuation area; second, the risk assessment system does not consider the impact of secondary disasters such as bank collapse and landslides, causing the evacuation area to be often smaller than the actual disaster-affected area; third, existing models lack a hierarchical transfer route planning algorithm based on population density and mobility capabilities. In scenarios where data is missing or the disaster situation evolves dynamically, existing technologies make it difficult to accurately define evacuation areas and efficiently evacuate different groups of people.

[0004] Existing technologies for evacuating people downstream of high-risk landslide-dammed lakes have significant shortcomings and are unable to meet emergency evacuation needs. A dynamic planning method for evacuation routes downstream of high-risk landslide-dammed lakes is urgently needed to overcome these limitations. This method, which simultaneously considers factors such as the impact of secondary disasters, population heterogeneity, changes in road access, and real-time monitoring data updates in both evacuation area delineation and evacuation route planning, dynamically integrates multi-source information, accurately delineates evacuation areas, and plans evacuation routes in real time, thereby improving the accuracy and timeliness of downstream evacuation decisions. Summary of the Invention

[0005] The present invention is proposed to solve the above-mentioned shortcomings, and its purpose is to provide a dynamic planning method for evacuation paths of high-risk landslide-dammed lakes. This method can accurately define the evacuation range downstream of the landslide-dammed lake, and improve the accuracy of the evacuation range downstream of the landslide-dammed lake by superimposing the outer envelope of multi-method flood simulation and the secondary disaster range; establish a population-based path planning method based on dynamic resilience weights and multi-objective collaborative optimization, breaking through the limitation of traditional path planning that only considers the "shortest path", and combining population characteristics, real-time road conditions and secondary disaster risks to realize the dynamic generation of differentiated evacuation paths.

[0006] In order to achieve the above purpose, the present invention adopts the following scheme:

[0007] A method for dynamically planning a high-risk barrier lake avoidance path includes the following steps:

[0008] S1: Multiple outburst flood numerical simulation models are used to calculate the outer envelope of the weir-break flood. The outer envelopes of the weir-break flood obtained by coupling multiple models are used to form the maximum envelope of the flood inundation range. Obtain the impact range of secondary disasters, superimpose the maximum envelope of the flood inundation range and the impact range of secondary disasters, and calculate the scope of the dangerous area downstream of the barrier lake;

[0009] S2: Taking the location of the landslide barrier as the starting point, the downstream area of ​​the landslide lake is divided into several sections. Based on the flood propagation speed and safety redundancy time of each section, the safety window time of each section is calculated, and finally the safety window time of the entire flood propagation line is obtained;

[0010] S3: Divide the risk-avoiding population by movement speed, dynamically calculate the path safety factor, path travel time, and road carrying capacity, determine the comprehensive resilience score of each alternative path, dynamically adjust the path resilience weight, and use the reverse gradient search method to continuously optimize the candidate risk-avoiding path set for each group of people;

[0011] S4: During emergency response to a landslide lake, the scope of the hazard zone downstream of the landslide lake, the evacuation window, and a set of candidate evacuation routes are updated at preset intervals based on real-time flood monitoring data. This allows for dynamic optimization of safe evacuation routes for people within the hazard zone downstream of the landslide lake. This approach, by integrating multiple flood simulations and real-time monitoring information, enables precise planning of the hazard zone and evacuation routes. This method, by combining multi-model flood simulation results with secondary hazard analysis, comprehensively depicts the impact of the dam-break flood and geological risks, significantly improving the accuracy of downstream hazard zone delineation. Furthermore, by segmenting the downstream region into calculated evacuation windows and combining them with population groups for multi-objective route optimization, it overcomes the limitations of traditional planning methods that rely solely on the "shortest path" approach. Furthermore, the system integrates real-time flood monitoring data to update the hazard zone and evacuation routes on a rolling basis, effectively improving emergency response speed and adapting to changing disaster conditions. This makes the evacuation process downstream of the landslide lake more timely, safe, and efficient, demonstrating significant engineering application value.

[0012] As a preferred embodiment, in step S1, the BREACH model, the HEC-RAS one-dimensional hydrodynamic model, and the two-dimensional shallow water equation are used to calculate the outer envelope of the weir-break flood. In this way, by introducing a variety of numerical simulation tools such as the BREACH model, the HEC-RAS one-dimensional hydrodynamic model, and the two-dimensional shallow water equation, it is used to calculate the inundation range of the weir-break flood. Compared with the traditional method that relies only on a single model, this multi-model coupling scheme can combine the advantages of different algorithms to obtain a more complete flood inundation envelope, thereby improving the reliability and accuracy of the danger zone delineation. By more comprehensively simulating the propagation of floods, this implementation method avoids the drawbacks of the existing technology of a small or excessively redundant risk avoidance range, enhances the rigor of disaster prediction, and helps to formulate risk avoidance plans more accurately in engineering practice.

[0013] As a preferred embodiment, in step S1, the impact range of secondary hazards such as bank collapse and landslides is determined through InSAR monitoring and mountain stability analysis along the downstream flood path. This inclusion of secondary hazard impact ranges (e.g., bank collapse and landslides) through InSAR monitoring and mountain stability analysis addresses the risk factors caused by geological instability, addressing the current art's tendency to neglect secondary hazards. By integrating flood peaks and potential landslides into the same hazard zone calculation model, the method ensures that the delineated hazard zones account for worst-case scenarios while also addressing potential blind spots. This significantly enhances the scientific nature and integrity of risk assessments and provides a more reliable basis for emergency response deployment.

[0014] As a preferred embodiment, in step S1, the calculation model of the scope of the dangerous area downstream of the barrier lake is as follows: ; Where, F down The scope of the dangerous area downstream of the barrier lake; Method i is the outer envelope of the weir-break flood inundation range obtained by the i-th calculation model; Q peak is the peak flow rate; t is the peak propagation time; C j is the impact range of the jth secondary disaster along the flood propagation line downstream of the barrier lake.

[0015] By establishing a clear calculation model for the downstream hazard zone of a barrier lake, the multiple outburst flood envelopes and the impact ranges of secondary disasters are systematically integrated. This unified model provides a standardized calculation process for engineering calculations and simplifies the process of assessing the hazard range. In practical applications, it has effectively improved calculation efficiency and accuracy, enhancing the method's operability and engineering practicality.

[0016] As a preferred embodiment, in step S2, the downstream area of ​​the barrier lake is divided into segments of 10 km, and the calculation formula for the risk avoidance window time of each segment is as follows: ; Where: T i V is the time window for avoiding danger in the i-th section downstream of the barrier lake; i is the flood propagation speed of the i-th section downstream of the barrier lake; max() is the maximum value function; T safe Redundant time for safety.

[0017] By dividing the downstream area of ​​the landslide lake into 10-kilometer intervals and calculating the evacuation window time for each segment, a 10km-level segmented evacuation time window model was established. This model can predict the flood propagation process along the entire line and generate differentiated "available time windows" for different sections. In engineering practice, this helps command departments organize personnel transfers in batches and segments, effectively alleviating traffic congestion and resource waste caused by simultaneous evacuation of the entire line, thereby improving overall evacuation efficiency and the orderliness of evacuation organization.

[0018] As a preferred embodiment, in step S3, the evacuation crowd is divided into the elderly, the weak, the sick and the disabled K1, the ordinary walking crowd K2 and the crowd K3 that can be transferred by vehicle according to the movement speed. The candidate path set for K1, K2 and K3 is expressed as follows: ; Where, P k1 is the candidate path set of K1 crowd; P k2 is the candidate path set of K2 crowd; P k3 is the candidate path set for K3 people; K1 is the elderly, weak, sick and disabled people; K2 is the ordinary walking crowd; K3 is the crowd that can be transferred by vehicle; n is the total number of paths in the candidate path set; λk1,i is the resilience weight of the i-th path of the K1 population; k2,i is the resilience weight of the i-th path of K2 population; k3,i is the resilience weight of the i-th path of K3 population; F i (p) Calculate a composite resilience score for each pathway.

[0019] In this way, the evacuees are divided into three categories based on their travel speed: the elderly, the sick, and the disabled (K1); ordinary pedestrians (K2); and those who can be evacuated by vehicle (K3). A set of candidate routes is then constructed for each category. This population-based path planning method provides differentiated evacuation routes for people of different abilities, making it more targeted than traditional path planning. This not only ensures the safety of slower-moving individuals but also fully utilizes vehicle resources to expedite the evacuation of large crowds, thereby improving the overall efficiency of evacuation.

[0020] As a preferred embodiment, in step S3, the path resilience weight is dynamically adjusted based on the crowd characteristics and the real-time environment according to the following formula: ; Where λ k1,i is the resilience weight of the i-th path of the K1 population; k2,i is the resilience weight of the i-th path of K2 population; k3,i is the resilience weight of the i-th path of K3 population; Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i The carrying capacity of the road.

[0021] In this way, path resilience weights are dynamically adjusted using indicators such as safety factor, travel time, and road carrying capacity, allowing path selection to adapt to the real-time environment and population characteristics. The introduction of dynamic weights significantly increases the flexibility of evacuation path planning: in the event of traffic congestion, flooding changes, or changes in mountain risks, the algorithm can respond instantly, prioritizing safer and more accessible routes. This significantly enhances the adaptability and robustness of path planning, ensuring the continuous provision of optimal evacuation solutions even in complex and changing disaster environments, further improving the accuracy and reliability of evacuation.

[0022] As a preferred embodiment, in step S3, F i (p) is calculated by the following formula: ; Where, Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -iThe carrying capacity of the road.

[0023] The path comprehensive resilience score F is given by i The calculation formula for (p) quantifies and integrates multiple factors, including path safety, travel time, and road carrying capacity. This scoring mechanism objectively quantifies and ranks the pros and cons of each candidate path, allowing for a more systematic assessment of the comprehensive performance of different paths during the planning process. This evaluation method provides a clear basis for decision-making, making path optimization measurable, avoiding the one-sided reliance on experience or the shortest distance, and significantly improving the scientific nature of risk-avoidance path selection.

[0024] As a preferred embodiment, in step S3, the reverse gradient search method is used to traverse the path backward from the risk avoidance end point, and the candidate path set P corresponding to different groups of people K1, K2, and K3 is calculated based on the comprehensive resilience score and path resilience weight through the established population-based path planning model. k1 、P k2 and P k3 , and dynamically adjust the candidate path set P according to the latest downstream danger zone range updated in a rolling manner k1 、P k2 and P k3 , achieving dynamic optimization and generation of differentiated evacuation routes. This method uses a reverse gradient search method to traverse the path backward from the evacuation endpoint to find the optimal solution. Unlike traditional forward search, this method transcends the limitations of local shortest paths and more comprehensively searches for the global optimal route. In complex road networks, this technology helps find safer and more accessible evacuation routes, ensuring rapid evacuation while avoiding potential risk points, further improving the accuracy and efficiency of evacuation.

[0025] As a preferred embodiment, in step S4, the preset time period is 30 to 45 minutes. By rolling updating relevant data in a period of 30 to 45 minutes during the emergency response to the landslide lake. By introducing this dynamic iteration mechanism, the scope of the downstream danger zone, the risk avoidance time window and the path set can be continuously refreshed using real-time monitoring data. It ensures that in the event of emergencies such as landslide collapse, sudden increase in heavy rain or blocked roads, the system can quickly provide a new feasible route, effectively improve the timeliness of emergency response and the timeliness of decision-making, and significantly reduce the risk of casualties caused by sudden changes in on-site conditions.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] First, the present invention establishes a calculation model for the range of the dangerous area downstream of the landslide lake, which couples the outer envelope line of the dam-break flood and the range of secondary disasters. By superimposing the outer envelope line of the multi-method flood burst simulation and the secondary disaster, it improves the calculation accuracy of the risk avoidance range downstream of the landslide lake, and realizes rolling updates every 30 minutes in combination with the flood evolution process.

[0028] Secondly, this invention combines multiple models for secondary disaster monitoring, unifying secondary risks such as dam breach floods, bank collapse, and landslides onto a single "maximum envelope." Compared to single hydrodynamic models or those that consider only the flood itself, this approach accounts for both worst-case scenarios and geological instability, limiting the area requiring evacuation to a minimum without overexpansion or missing any blind spots, thus enhancing the scientific and reliable nature of risk assessment.

[0029] Third, the present invention establishes a 10km-level segmented evacuation window time calculation model, realizes the segmented evacuation window time prediction of the entire flood propagation line, and combines the flood evolution process to achieve rolling updates every 30 minutes. It can generate differentiated "available time windows" for each section, and guide the command department to start the transfer in batches and sections, reducing congestion and waste of resources caused by simultaneous evacuation of the entire line.

[0030] Fourthly, the population-based path planning method established by the present invention based on dynamic resilience weight and multi-objective collaborative optimization breaks through the limitation of traditional path planning that only considers the "shortest path" and combines crowd characteristics, real-time road conditions and secondary disaster risks to achieve a candidate path set P k1 、P k2 and P k3 Dynamic adjustment to achieve dynamic optimization of differentiated risk hedging paths.

[0031] Fifth, the present invention introduces reverse gradient retrieval to infer the optimal path from the end point, which helps to find a globally safer and smoother solution in a complex road network, rather than being limited to the local shortest path.

[0032] Sixth, the present invention uses real-time monitoring data on a rolling basis to update flood evolution, mountain stability, and traffic conditions, simultaneously refreshing danger zones, evacuation windows, and route sets. If the dam collapses further, rainfall suddenly increases, or roads become blocked, the system can adaptively propose new feasible routes, significantly reducing secondary casualties caused by sudden changes in on-site conditions. This system demonstrates dynamic decision-making and resilient response capabilities, and has broad engineering applicability for high-risk dammed lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of the dynamic planning method for avoiding high-risk barrier lake hazards of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] The method for dynamically planning a high-risk barrier lake avoidance path of the present invention comprises the following steps:

[0036] S1: Establish a calculation model for the scope of the dangerous area downstream of the barrier lake by coupling the outer envelope of the dam-break flood and the scope of secondary disasters

[0037] This step uses a variety of dam-break flood numerical simulation models to fully simulate the dam-break process. The BREACH model, the HEC-RAS one-dimensional hydrodynamic model, and the two-dimensional shallow-water equation are used to calculate the outer inundation envelope of the dam-break flood. The BREACH model is used to physically simulate the erosion process of earth dams, the HEC-RAS one-dimensional hydrodynamic model calculates the propagation of river floods, and a hydrodynamic model based on the two-dimensional shallow-water equation is used to model the flood stagnation area. Each model inputs basic data such as the dam body parameters (dam height, dam width, dam structure), initial water storage, local hydrological rainfall series, elevation DEM, and roughness. Numerical calculations are then performed to determine the flood inundation depth and inundation range using different methods. The results of multiple models are combined to form a maximum inundation envelope, which reflects the potential flood impact range under the most unfavorable circumstances. The outer envelope of the dam-break flood obtained by coupling various models is used to form the maximum envelope of the flood inundation range. The impact range of secondary disasters such as bank collapse and landslides is obtained through InSAR monitoring and mountain stability analysis along the downstream flood line. The maximum envelope of the flood inundation range is superimposed with the impact range of secondary disasters such as bank collapse and landslides to calculate the range of the dangerous area downstream of the barrier lake. The calculation model is shown in Equation (1): (Formula 1); Where: F down The scope of the dangerous area downstream of the barrier lake; Method i is the outer envelope of the weir-break flood inundation range obtained by the i-th calculation method; Q peak is the peak flow rate, m 3 / s; t is the peak propagation time, s; C j is the impact range of the jth secondary disaster along the flood propagation line downstream of the barrier lake.

[0038] At the same time, this step uses InSAR satellite monitoring and mountain stability analysis along the route to obtain the impact range of secondary disasters downstream of the landslide lake (such as bank collapse, landslides, etc.). These secondary disaster areas are superimposed with the maximum flood envelope obtained by simulation to obtain the final range of the danger zone downstream of the landslide lake. The range of the danger zone downstream of the landslide lake can be defined as the union of the outer envelope of each model and each secondary disaster area, that is, the downstream danger zone covers the areas affected by both floods and geological disasters. This range can be used as the spatial input for delineating the evacuation road network. The models and analysis results can be communicated with each other through GIS or database interfaces to achieve modular processing. The maximum inundation range output by the dam break model can be saved as a vector layer for subsequent regional demarcation.

[0039] S2: Establish a segmented risk hedging window time calculation model

[0040] Taking the location of the landslide body as the starting point, the downstream area of ​​the landslide lake is divided into several sections, and the 10 km distance is used as the segment. The risk avoidance window time of each segment, such as 0~10 km, 10~20 km, and 20~30 km, is constructed as shown in formula (2): (Formula 2); Where: T i is the time window for avoiding danger in the downstream of the barrier lake, s; V i is the flood propagation velocity of the i-th section downstream of the barrier lake, which can be predicted using a wave-breaking model, m / s; max() is the maximum value function; T safe This is the safety redundancy time, ranging from 3600s to 7200s.

[0041] For example, T1 is the evacuation window time for the first section (i.e. 0~10km) downstream of the landslide lake, V1 is the flood propagation speed in the first section downstream of the landslide lake, and the same applies to other sections, ultimately obtaining the evacuation window time for the entire flood propagation line.

[0042] This step starts from the location of the landslide dam and extends downstream, dividing the downstream area into fixed distance segments (preferably 10 km per segment). For the i-th segment (such as 0~10km, 10~20km, etc.), the corresponding risk avoidance window time is calculated based on the flood propagation speed and safety redundancy time of the segment. Among them, the safety redundancy time T safe The value is 3600s~7200s to ensure sufficient evacuation time. The flood propagation speed can be obtained through the wave-break theory or historical data fitting. For example, if the length of the first section is 10km, if V1≈2.8m / s, then the flood intrinsic arrival time is about 3600s, which is consistent with T safe If the value is consistent, then T1≈3600s; if V1 is lower, then T1 is T safeSimilarly, the available evacuation time window for each segment can be calculated. The window timetable (e.g., in CSV format) calculated in step S2 provides timing constraints for evacuation in different zones and batches and serves as an input parameter for subsequent route planning. If necessary, GIS software can be used to spatially overlay the danger zone with the road network to clarify the road extents and nodes of each segment for subsequent analysis.

[0043] S3: Establish a population-based path planning model based on dynamic resilience weights and multi-objective collaborative optimization

[0044] The population-based path planning model based on dynamic resilience weights and multi-objective collaborative optimization breaks through the limitation of traditional path planning that only considers the "shortest path". It combines population characteristics, real-time road conditions, and secondary disaster risks to achieve differentiated risk avoidance path generation, as shown in formula (3): (Formula 3); In the formula: K1 represents the elderly, the weak, the sick and the disabled, that is, they move slowly (v≤1 m / s), and they prefer flat, short-distance routes; K2 represents ordinary pedestrians, with a medium movement speed (1 m / s<v≤2 m / s), and the route should balance time and risk; K3 represents people who can be transferred by vehicle, and the route planning depends on the road capacity, so high-grade roads are preferred. k1 represents the candidate path set of K1 people, n represents the total number of paths in the candidate path set, P k2 and P k3 And so on. k1,i is the resilience weight of the i-th path of the K1 population; k2,i is the resilience weight of the i-th path of K2 population; k3,i is the resilience weight of the i-th path of K3 population.

[0045] The weights are dynamically adjusted according to the crowd characteristics and real-time environment, as shown in formula (4): (Formula 4); Where, Safety -i is the path safety factor, which is calculated based on the real-time probability of secondary disasters and flood inundation depth; Time -i RoadCapacity is the travel time of the path, which is calculated by combining the crowd speed and congestion factor; -i The road bearing capacity is calculated taking into account the road width, slope and damage status.

[0046] F i (p) is the comprehensive resilience score calculated for each path. The higher the score, the stronger the path resilience and the better the path. The calculation method is shown in formula (5): (Formula 5); Where, Safety-i is the path safety factor; Time -i is the path travel time; Road Capacity -i is the road carrying capacity.

[0047] Adopt the reverse gradient search method, traverse the path backward from the hazard avoidance end point, and calculate the candidate path sets P corresponding to different populations of K1, K2, and K3 through the established path planning model for different populations according to the comprehensive resilience score and the path resilience weight. k1 , P k2 and P k3 , and dynamically adjust the candidate path sets P k1 , P k2 and P k3 to achieve the dynamic optimization generation of differentiated hazard avoidance paths.

[0048] In this step, people are classified according to different population characteristics, and hazard avoidance paths are planned specifically. The population is divided into three categories: K1 (the elderly, the weak, the sick, and the disabled, with a walking speed v ≤ 1 m / s), K2 (ordinary walking, with a population speed of 1 < v ≤ 2 m / s), and K3 (able to use motor vehicles, with a road passing speed v > 2 m / s). For each category of population, construct the corresponding candidate path sets P k1 , P k2 and P k3 . For each path in the set, dynamically calculate its comprehensive safety and passing characteristics. Specifically, the path safety factor Safety -i is evaluated based on the probability of real-time secondary disasters occurring along the path and the flood inundation depth; the path travel time Time -i is calculated in combination with the population speed and the road congestion factor; the road carrying capacity RoadCapacity -i considers the road width, slope, and damage condition. For example, a narrow section or a high slope will significantly reduce the capacity. For example, a mountain road with a width of only 3 m, a slope of 15%, and a general damage condition has a relatively low vehicle passing capacity; while a road with a shallow flood depth and no risk of landslides has a higher path safety factor. Then, calculate the comprehensive resilience score F i (p) of each path through formula (5). This score is positively correlated with Safety -i and RoadCapacity -i , and negatively correlated with Time -i .

[0049] To achieve multi-objective optimization, the resilience weights can be dynamically adjusted for different groups of people, and preferences can be set for the emphasis on safety, time, and capacity indicators. The system uses a reverse gradient search method, which starts from each refuge point (evacuation end point) and searches backward along the road network, traversing the upstream paths and calculating their comprehensive resilience scores. Finally, for the three groups of people K1, K2, and K3, a set of candidate paths P is obtained. k1 、P k2 and P k3 The highest-scoring routes are then used as differentiated optimal risk-avoidance routes. For example, for K1 riders traveling at a speed of 0.5 m / s, the system prefers short, flat routes; for K3 riders who can drive, it prioritizes routes with higher-grade roads and faster travel. All routes and their evaluation results are stored in a graph database or road network topology format and can be dynamically updated and used in the next step.

[0050] S4: Dynamic rolling update based on flood evolution process

[0051] During the emergency response to the landslide lake, real-time monitoring of the flood's evolution is used to update the danger zone downstream of the lake, the evacuation window, and a set of candidate evacuation routes every 30 minutes. This allows for the dynamic optimization of safe evacuation routes for people within the evacuation zone. Ultimately, the calculated evacuation windows for the entire flood propagation route and a set of evacuation routes for different groups of people are used to ensure the safe evacuation of people within the evacuation zone downstream of the lake.

[0052] During the emergency response to a landslide lake, the system dynamically updates the input parameters and results of each step based on real-time monitoring data at a preset interval (preferably 30-45 minutes). Specifically, whenever new flood evolution information (such as water level and flow) or secondary disaster monitoring results are generated, step S1 is re-executed to correct the downstream inundation range and danger zone, update the flood propagation speed and window time for each segment, and simultaneously update the road network status (such as reduced capacity or road blockage). These updated results serve as new inputs to steps S2 and S3, triggering the recalculation of each segment's hazard avoidance window and candidate path set. For example, if upstream rainfall intensifies, causing a higher peak flow, the system automatically recalculates the flood arrival time and danger zone range. If Road A is interrupted by a landslide, its corresponding path safety factor and road carrying capacity will be reduced or even eliminated from the candidate set. This rolling closed-loop update ensures that the latest data always guides path planning as the disaster situation evolves, enabling the path planning solution to be adaptively adjusted to on-site conditions. Finally, based on the window time and crowd grouping path set calculated at each moment, a safe transfer plan for different groups downstream of the high-risk landslide lake is output to ensure that people are evacuated in batches and in an orderly manner.

[0053] Each step in this process enables information sharing through GIS and databases. The maximum flood inundation area and secondary disaster zones calculated in step S1 are transmitted to S2 and S3 as GIS vector layers. The window time vector table output in step S2 is passed to the path planning module via a database or CSV file. The candidate paths generated in step S3 are stored in the GIS as polyline elements in the road network. Each module uses a standardized data format (such as CSV) to exchange results, ensuring the universality and operability of the data interface. This creates a closed-loop implementation logic from dam break simulation to segmented evacuation timing and then to crowd path optimization.

[0054] The above embodiments are merely illustrative of the technical solutions of the present invention. The present invention is not limited to the contents described in the above embodiments, but is subject to the scope defined by the claims. Any modifications, supplements, or equivalent substitutions made by those skilled in the art based on these embodiments are within the scope of protection claimed in the claims of the present invention.

Claims

1. A dynamic planning method for avoiding dangerous paths in high-risk barrier lakes, characterized by: The steps include: S1: Multiple outburst flood numerical simulation models are used to calculate the outer envelope of the weir-break flood. The outer envelopes of the weir-break flood obtained by coupling multiple models are used to form the maximum envelope of the flood inundation range. Obtain the impact range of secondary disasters, superimpose the maximum envelope of the flood inundation range and the impact range of secondary disasters, and calculate the scope of the dangerous area downstream of the barrier lake; S2: Taking the location of the landslide barrier as the starting point, the downstream area of ​​the landslide lake is divided into several sections. Based on the flood propagation speed and safety redundancy time of each section, the safety window time of each section is calculated, and finally the safety window time of the entire flood propagation line is obtained; S3: Divide the risk-avoiding population by movement speed, dynamically calculate the path safety factor, path travel time, and road carrying capacity, determine the comprehensive resilience score of each alternative path, dynamically adjust the path resilience weight, and use the reverse gradient search method to continuously optimize the candidate risk-avoiding path set for each group of people; S4: During the emergency response to the landslide lake, the disaster data based on real-time monitoring of the flood evolution process is rolled out every preset time period to update the scope of the dangerous area downstream of the landslide lake, the evacuation window time and the set of candidate evacuation paths, so as to dynamically optimize the safe transfer routes of people in the dangerous area downstream of the landslide lake.

2. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 1, characterized in that: In step S1, the BREACH model, the HEC-RAS one-dimensional hydrodynamic model, and the two-dimensional shallow water equation are used to calculate the outer envelope of the weir-break flood.

3. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 1, characterized in that: In step S1, the impact range of secondary disasters such as bank collapse and landslide is obtained through InSAR monitoring and mountain stability analysis along the downstream flood line.

4. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 1, characterized in that: In step S1, the calculation model of the scope of the dangerous area downstream of the barrier lake is as follows: ； Where, F down The scope of the dangerous area downstream of the barrier lake; Method i is the outer envelope of the weir-break flood inundation range obtained by the i-th calculation model; Q peak is the peak flow rate; t is the peak propagation time; C j is the impact range of the jth secondary disaster along the flood propagation line downstream of the barrier lake.

5. The method for dynamically planning a high-risk landslide lake avoidance path according to any one of claims 1 to 4, characterized in that: In step S2, the downstream area of ​​the barrier lake is divided into sections of 10 km, and the calculation formula for the risk avoidance window time of each section is as follows: ； Where: T i V is the time window for avoiding danger in the i-th section downstream of the barrier lake; i is the flood propagation speed of the i-th section downstream of the barrier lake; max() is the maximum value function; T safe Redundant time for safety.

6. The method for dynamically planning a high-risk landslide lake avoidance path according to claim 5, characterized in that: In step S3, the evacuees are divided into the elderly, the weak, the sick and the disabled K1, the ordinary walking crowd K2 and the crowd K3 that can be transferred by vehicle according to their movement speed. The candidate path set for K1, K2 and K3 is expressed as follows: ； Where, P k1 is the candidate path set of K1 crowd; P k2 is the candidate path set of K2 crowd; P k3 is the candidate path set for K3 people; K1 is the elderly, weak, sick and disabled people; K2 is the ordinary walking crowd; K3 is the crowd that can be transferred by vehicle; n is the total number of paths in the candidate path set; λ k1,i is the resilience weight of the i-th path of population K1; λ k2,i is the resilience weight of the i-th path of K2 population; k3,i is the resilience weight of the i-th path of K3 population; F i (p) Calculate a composite resilience score for each pathway.

7. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 6, characterized in that: In step S3, based on the crowd characteristics and the real-time environment, the path resilience weight is dynamically adjusted according to the following formula: ； Where λ k1,i is the resilience weight of the i-th path of population K1; λ k2,i is the resilience weight of the i-th path of K2 population; k3,i is the resilience weight of the i-th path of K3 population; Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i The carrying capacity of the road.

8. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 7, characterized in that: In step S3, F i (p) is calculated by the following formula: ； Where, Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i The carrying capacity of the road.

9. The method for dynamically planning a high-risk barrier lake avoidance path according to claim 8, characterized in that: In step S3, the reverse gradient search method is used to traverse the path backward from the safe haven endpoint. According to the comprehensive resilience score and the path resilience weight, the candidate path set P corresponding to different groups of K1, K2, and K3 is calculated through the established group-based path planning model. k1 、P k2 and P k3 , and dynamically adjust the candidate path set P according to the latest downstream danger zone range updated in a rolling manner k1 、P k2 and P k3 , to achieve dynamic optimization and generation of differentiated risk hedging paths.

10. The method for dynamically planning a high-risk barrier lake avoidance path according to any one of claims 1 to 4, characterized in that: In step S4, the preset time period is 30 to 45 minutes.

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