Dynamic planning method for dangerous dammed lake escape route

Abstract

The application discloses a high-risk dammed lake danger-avoiding path dynamic planning method, which comprises the following steps: S1, establishing a dammed lake downstream danger zone range calculation model coupling a dam-broken flood envelope line and a secondary disaster range; S2, establishing a segmented danger-avoiding window time calculation model; S3, establishing a segmented population path planning model based on dynamic resilience weight and multi-target collaborative optimization; and S4, based on dynamic rolling update of a flood evolution process. The application can accurately define a danger-avoiding range downstream of a dammed lake, superimpose a dam-broken flood simulation envelope line and a secondary disaster range by multiple methods, and improve the accuracy of the danger-avoiding range downstream of the dammed lake. The segmented population path planning method based on dynamic resilience weight and multi-target collaborative optimization breaks through the limitation of traditional path planning which only considers a shortest path, combines population characteristics, real-time road conditions and secondary disaster risks, and realizes dynamic generation of differentiated danger-avoiding paths.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water conservancy disaster emergency management, and particularly relates to a high-risk barrier lake refuge path dynamic planning method. BACKGROUND

[0002] A barrier lake is a lake formed by natural processes such as landslides, collapses and debris flows blocking a river, and the water-blocking accumulation body is called a barrier dam. A high-risk barrier lake generally refers to a type of barrier lake that may collapse in a short period of time. Such a barrier lake has a large water storage capacity, poor dam stability, a short duration of dam collapse and strong destructive power. A typical high-risk barrier lake is often caused by landslides or debris flows triggered by earthquakes and heavy rainfall, or may be formed by factors such as glacier collapse or moraine blocking rivers. Such natural dam structures are loose, poorly cemented and have a wide particle size distribution, and there is no planning and design of artificial earth-rock dams or core wall seepage prevention measures. Once the water level rises due to the overflow of the dam top, the dam is prone to collapse. The downstream of a high-risk barrier lake is usually a high mountain and valley area, and there are relatively concentrated residential areas and important infrastructure along the coast. Once the barrier lake collapses, the secondary disasters such as upstream flooding and dam collapse, and the severe flood peak will pose a serious threat to the safety of people's lives and property and infrastructure downstream. The high-risk barrier lake collapse flood has the characteristics of strong suddenness, wide destruction range and complex disaster chain.

[0003] Traditional barrier lake downstream refuge range delineation and personnel refuge path planning has certain limitations: first, the calculation of barrier lake flood inundation relies on a single hydrodynamic model, resulting in a small or redundant refuge range; second, the risk assessment system does not consider the influence of secondary disasters such as landslides, resulting in a refuge range smaller than the actual disaster affected area; third, the existing model lacks a hierarchical transfer path planning algorithm based on population density and action ability, and in the case of data missing or dynamic evolution of disaster conditions, the existing technology is difficult to achieve accurate delineation of the refuge range and efficient evacuation of different populations.

[0004] The existing technology for personnel refuge downstream of a high-risk barrier lake has obvious deficiencies and cannot meet the emergency refuge needs. There is an urgent need for a high-risk barrier lake refuge path dynamic planning method that can overcome the above limitations, simultaneously consider the influence of secondary disasters, population heterogeneity, road traffic changes and real-time monitoring data updates in refuge range delineation and evacuation path planning, dynamically integrate multi-source information, accurately delineate the refuge area and real-time plan the personnel evacuation path, to improve the accuracy and timeliness of downstream refuge decision-making. SUMMARY

[0005] The present application is proposed to solve the above-mentioned deficiencies, and aims to provide a high-risk barrier lake refuge path dynamic planning method, which can accurately define the barrier lake downstream refuge range, superimpose the multi-method flood outburst simulation envelope line and the secondary disaster range, and improve the accuracy of the barrier lake downstream refuge range; a path planning method based on dynamic resilience weight and multi-objective collaborative optimization is established, which breaks through the limitation of traditional path planning only considering the "shortest path", combines the characteristics of the crowd, real-time traffic and secondary disaster risk, and realizes the dynamic generation of differentiated refuge paths.

[0006] In order to achieve the above-mentioned purposes, the present application adopts the following scheme:

[0007] A high-risk barrier lake refuge path dynamic planning method comprises the following steps:

[0008] S1: adopt a plurality of flood outburst numerical simulation models to calculate the outburst flood inundation envelope line, couple the outburst flood inundation envelope lines obtained by the plurality of models, and form a maximum envelope line of the flood inundation range;

[0009] Obtain the secondary disaster influence range, superimpose the maximum envelope line of the flood inundation range and the secondary disaster influence range, and calculate the range of the downstream dangerous area of the barrier lake;

[0010] S2: divide the downstream area of the barrier lake into several sections with the location of the barrier body as the starting point, calculate the refuge window time of each section based on the flood propagation speed and safety redundancy time of each section, and finally obtain the segmented refuge window time of the whole flood propagation line;

[0011] S3: divide the refuge crowd according to the action speed, dynamically calculate the path safety coefficient, path passing time and road carrying capacity, determine the comprehensive resilience score of each alternative path, dynamically adjust the path resilience weight, and continuously optimize the candidate refuge path set of each type of crowd by using the reverse gradient search method;

[0012] S4: In the process of emergency disposal of barrier lake, the disaster data monitored in real time based on the flood evolution process is used to update the dangerous area range, the evacuation window time and the candidate evacuation path set of the downstream dangerous area of the barrier lake every preset time period, so as to dynamically optimize the personnel safety transfer route in the downstream dangerous area of the barrier lake. In this way, by fusing multiple flood simulation and real-time monitoring information, the accurate planning of the dangerous area range and the evacuation path is realized. On the one hand, the method uses the superposition analysis of multiple model flood simulation results and secondary disasters, which can comprehensively depict the barrier lake dam-break flood and the geological risk affected area, thereby significantly improving the accuracy of the downstream dangerous area division; on the other hand, the evacuation time window is calculated for the downstream area in sections, and the multi-objective path optimization is carried out in combination with population grouping, which overcomes the limitations of the traditional single "shortest path" planning. In addition, the system also updates the dangerous area range and the evacuation path in a rolling manner combined with the real-time monitoring data of the flood evolution, effectively improves the emergency response speed and the ability to adapt to the disaster changes, makes the personnel evacuation process of the downstream of the barrier lake more timely, safe and efficient, and has obvious engineering application value.

[0013] As a preferred embodiment, in the step S1, the BREACH model, the HEC-RAS one-dimensional hydrodynamic model and the two-dimensional shallow water equation are used to calculate the dam-break flood inundation envelope. In this way, by introducing multiple numerical simulation tools such as the BREACH model, the HEC-RAS one-dimensional hydrodynamic model and the two-dimensional shallow water equation, the dam-break flood inundation range is calculated. Compared with the traditional method which only relies on a single model, this multi-model coupling scheme can integrate the advantages of different algorithms to obtain a more complete flood inundation envelope, thereby improving the reliability and accuracy of the dangerous area division. By more comprehensively simulating the flood propagation, this embodiment avoids the disadvantages of the prior art that the evacuation range is too small or excessively redundant, enhances the rigor of disaster prediction, and helps to more accurately develop an evacuation plan in engineering practice.

[0014] As a preferred embodiment, in the step S1, the secondary disaster influence range of landslides and bank collapses is obtained through InSAR monitoring and downstream mountain stability analysis. In this way, by introducing the secondary disaster influence range (such as landslides and bank collapses) through InSAR monitoring and mountain stability analysis, the risk factors caused by geological instability can be covered, which makes up for the defects of the prior art that usually ignores secondary disasters. By integrating the flood peak and potential landslides and other risks into the same dangerous area calculation model, the method ensures that the dangerous area considered is not only the worst case, but also does not miss potential blind areas, thereby significantly improving the scientificity and integrity of risk assessment, and providing a more reliable basis for emergency deployment.

[0015] As a preferred embodiment, in the step S1, the calculation model of the downstream dangerous area range of the barrier lake is as follows:

[0016] ;

[0017] In the formula, F down is the range of the dangerous area downstream of the barrier lake; Method i is the outer envelope line of the inundation area of the breach flood obtained by the i-th calculation model; Q peak is the peak flow; t arrival is the peak propagation time; C j is the j-th secondary disaster influence range along the flood propagation line downstream of the barrier lake.

[0018] In this way, by establishing a calculation model of the range of the dangerous area downstream of the barrier lake, a plurality of breach flood outer envelope lines and secondary disaster influence ranges are systematically integrated. The unified model provides a standardized calculation process for engineering calculation, simplifying the risk avoidance range assessment process. In actual application, the calculation efficiency and accuracy are effectively improved, and the operability and engineering practicability of the method are enhanced.

[0019] As a preferred embodiment, in the step S2, the downstream area of the barrier lake is segmented at a distance of 10 km, and the calculation formula of the risk avoidance window time of each segment is as follows:

[0020] ;

[0021] In the formula, T i is the i-th segment risk avoidance window time downstream of the barrier lake; V i is the flood propagation speed of the i-th segment downstream of the barrier lake; max() is the maximum function; T safe is the safety redundancy time; and i is the number of segments of the downstream area of the barrier lake segmented at a distance of 10 km.

[0022] In this way, by segmenting the downstream area of the barrier lake at a distance of 10 km and calculating the risk avoidance window time of each segment, a 10-km segmented risk avoidance time window model is established, which can predict the entire flood propagation process and generate differentiated "available time windows" for different segments. In engineering practice, this helps the command department to organize personnel transfer in batches and segments for each segment, effectively alleviating traffic congestion and resource waste caused by simultaneous evacuation of the entire line, thereby improving the overall risk avoidance efficiency and the orderliness of evacuation organization.

[0023] As a preferred embodiment, in the step S3, the risk avoidance population is divided into the old, weak, sick and disabled population K1, the ordinary walking population K2 and the vehicle transfer population K3 according to the action speed, and the candidate path set of K1, K2 and K3 is represented as follows:

[0024] ;

[0025] In the formula, P k1is the candidate path set of K1 population; P k2 is the candidate path set of K2 population; P k3 is the candidate path set of K3 population; K1 is the weak and sick population; K2 is the ordinary walking population; K3 is the vehicle transferable population; n is the total path number of the candidate path set; λ k1,i is the i-th path resilience weight of K1 population; λ k2,i is the i-th path resilience weight of K2 population; λ k3,i is the i-th path resilience weight of K3 population; F(p) i The comprehensive resilience score of each path is calculated.

[0026] In this way, the evacuation population is divided into three categories of weak and sick population K1, ordinary walking population K2 and vehicle transferable population K3 according to the walking speed, and a candidate path set is constructed for each category of population. The population-based path planning method can provide differentiated evacuation routes for different ability populations, which is more targeted than traditional path planning. Not only the priority safety of slow-moving personnel is ensured, but also the vehicle resources are fully utilized to speed up the evacuation speed of large-scale population, thereby improving the efficiency of personnel transfer as a whole.

[0027] As a preferred embodiment, in the step S3, the path resilience weight is dynamically adjusted based on the population characteristics and the real-time environment according to the following formula:

[0028] ;

[0029] In the formula, λ k1,i is the i-th path resilience weight of K1 population; λ k2,i is the i-th path resilience weight of K2 population; λ k3,i is the i-th path resilience weight of K3 population; Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i is the road carrying capacity.

[0030] In this way, the path resilience weight is dynamically adjusted by the safety factor, travel time and road carrying capacity, so that the path selection adapts to the real-time environment and population characteristics. After introducing the dynamic weight, the planning flexibility of the evacuation path is greatly improved: once the traffic congestion, flood change or mountain risk changes, the algorithm can respond immediately, and preferentially select safer and more open routes, significantly enhancing the adaptability and robustness of the path planning scheme, ensuring that the optimal evacuation scheme can be continuously provided in complex and variable disaster environments, thereby further improving the accuracy and reliability of the evacuation.

[0031] As a preferred embodiment, in the step S3, F(p)i is calculated by the following formula:

[0032] ;

[0033] In the formula, Safety -i is the path safety factor; Time -i is the path travel time; and RoadCapacity -i is the road carrying capacity.

[0034] By giving the calculation formula of the path comprehensive resilience score F(p) i , the formula quantitatively integrates various factors such as path safety, travel time and road carrying capacity. Using this scoring mechanism, the advantages and disadvantages of each candidate path can be objectively quantified and ranked, so that the comprehensive performance of different paths can be more systematically evaluated in the planning process. This evaluation method provides clear decision-making basis, making the path optimization measurable, avoiding the one-sidedness of simply relying on experience or shortest distance, and significantly improving the scientificity of the risk avoidance path selection.

[0035] As a preferred embodiment, in the step S3, the reverse gradient search method is used to traverse the path from the risk avoidance endpoint, and according to the comprehensive resilience score and the path resilience weight, the candidate path set P k1 , P k2 and P k3 corresponding to K1, K2 and K3 different groups of people are calculated by the established path planning model for different groups of people, and the candidate path set P k1 , P k2 and P k3 are dynamically adjusted according to the latest downstream risk area range of the rolling update, so as to realize the dynamic optimization generation of differentiated risk avoidance paths. By using the reverse gradient search method to traverse the path from the risk avoidance endpoint to find the optimal solution. Unlike traditional forward search, this method can jump out of the limitation of local shortest path and more comprehensively search for global optimal route. In a complex road network, this technology helps to find safer and more unobstructed risk avoidance routes, ensuring that personnel evacuation can be both rapid and avoid potential risk points, thereby further improving the risk avoidance accuracy and efficiency.

[0036] As a preferred embodiment, the preset time period in the step S4 is 30-45 minutes. By rolling updating the relevant data in the dammed lake emergency disposal process in 30-45 minutes as a period, the dynamic iteration mechanism is introduced, the downstream danger zone range, the danger avoidance time window and the path set can be constantly refreshed by using the real-time monitoring data. In the case of sudden situations such as dam collapse, sudden rainstorm increase or road blockage, the system can quickly give 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 field conditions.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] Firstly, the dammed lake downstream danger zone range calculation model coupled with the breach dam flood envelope line and the secondary disaster range is established, the calculation accuracy of the dammed lake downstream danger avoidance range is improved by superimposing the multi-method flood simulation envelope line and the secondary disaster, and the rolling update is realized every 30 minutes in combination with the flood evolution process.

[0039] Secondly, the present application monitors the secondary disaster by multi-model coupling, and unifies the breach dam flood and the secondary risks such as bank collapse and landslide to a "maximum envelope line". Compared with a single hydrodynamic model or only considering the flood body, this method can take into account the worst case and the geological instability factor, limit the area to be evacuated without over-expansion and missing blind areas, and improve the scientificity and reliability of risk determination.

[0040] Thirdly, the present application establishes a segmented danger avoidance window time calculation model of 10km level, realizes the segmented danger avoidance window time prediction of the whole line of flood propagation, and realizes the rolling update every 30 minutes in combination with the flood evolution process, can generate differentiated "available time window" for each section, guide the command department to start transfer in batches and segments, and reduce the congestion and resource waste brought by simultaneous evacuation of the whole line.

[0041] Fourthly, the present application establishes a path planning method based on dynamic resilience weight and multi-objective collaborative optimization, breaks through the limitation of traditional path planning only considering "shortest path", and realizes the dynamic adjustment of candidate path set P k1 , P k2 and P k3 by combining the characteristics of the crowd, the real-time traffic and the secondary disaster risk, realizes the dynamic optimization of differentiated danger avoidance path.

[0042] Fifthly, the present application introduces reverse gradient retrieval to inversely deduce the optimal path from the end point, which is helpful to find a globally safer and more unblocked solution in a complex road network, rather than being limited to a local shortest path.

[0043] Sixthly, the application uses real-time monitoring data to update the flood evolution, mountain stability state and traffic condition in a rolling cycle, synchronously refreshes the dangerous area, risk-avoiding window and path set. When the dam body further collapses, rainfall suddenly increases or the road is blocked, the system can adaptively give a new feasible route, significantly reduces the secondary casualties caused by the sudden change of the field conditions, embodies the dynamic decision and the resilience coping capacity, and has wide engineering applicability to the high-risk barrier lake. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 It is a flowchart of the high-risk barrier lake risk-avoiding path dynamic planning method of the application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0046] The high-risk barrier lake risk-avoiding path dynamic planning method of the application comprises the following steps:

[0047] S1: establishing a barrier lake downstream dangerous area range calculation model coupling the dam-break flood inundation envelope and the secondary disaster range

[0048] This step comprehensively simulates the dam-break process of the barrier lake by using multiple dam-break flood numerical simulation models, calculates the dam-break flood inundation envelope by using the BREACH model, the HEC-RAS one-dimensional water dynamic model and the two-dimensional shallow water equation, physically simulates the dam erosion process by using the BREACH model, calculates the river flood propagation by using the HEC-RAS one-dimensional water dynamic model, and simulates the flood inundation area based on the two-dimensional shallow water equation. The dam body parameters (dam height, dam width, dam structure), initial water storage, local hydrological rainfall sequence, elevation DEM and roughness and other basic data are input into each model, and the flood inundation depth and inundation range under different methods are obtained through numerical calculation. The multiple model results are taken as a set to form the maximum inundation envelope, reflecting the potential flood influence range under the most unfavorable condition. The dam-break flood inundation envelope obtained by coupling various models forms the maximum flood inundation range envelope. The InSAR monitoring and downstream flood along the mountain stability analysis are used to obtain the secondary disaster influence range such as bank collapse and landslide. The maximum flood inundation envelope and the secondary disaster influence range such as bank collapse and landslide are superimposed to calculate the barrier lake downstream dangerous area range. The calculation model is shown in formula (1):

[0049] (Formula 1)

[0050] 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 arrival 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.

[0051] 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.

[0052] S2: Establish a segmented risk hedging window time calculation model

[0053] 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):

[0054] (Formula 2);

[0055] 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 is the safety redundancy time, which ranges from 3600s to 7200s; i is the number of segments of the downstream area of ​​the barrier lake divided into 10km intervals.

[0056] 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.

[0057] This step takes the location of the barrier dam as the starting point and extends downstream. The downstream area is segmented by a fixed distance (preferably 10 km per segment). For the ith segment (e.g., 0-10 km, 10-20 km, etc.), the corresponding risk avoidance window time is calculated based on the flood propagation speed and the safety redundancy time for that segment. The safety redundancy time T safe is valued between 3600s and 7200s to ensure sufficient evacuation time. The flood propagation speed can be obtained through the method of similar wave theory or historical data fitting. For example, the length of the first segment is 10 km, and if V1≈2.8 m / s, the intrinsic arrival time of the flood is about 3600s, which is consistent with T safe . Therefore, T1≈3600s; if V1 is lower, T1 takes T safe . Similarly, the available risk avoidance time window for each segment can be calculated. The window time table obtained in step S2 (such as in CSV format) provides timing constraints for zoned and batch evacuation and serves as an input parameter for subsequent path planning. If necessary, use GIS software to superimpose the dangerous area range and the road network space to clearly define the road range and nodes of each segment for subsequent analysis.

[0058] S3: Establish a path planning model for different groups of people based on dynamic resilience weight and multi-objective collaborative optimization

[0059] The path planning model for different groups of people based on dynamic resilience weight and multi-objective collaborative optimization breaks through the limitations of traditional path planning, which only considers the "shortest path". It combines the characteristics of different groups of people, real-time traffic conditions, and secondary disaster risks to generate differentiated risk avoidance paths, as shown in equation (3):

[0060] Equation (3)

[0061] In the equation, K1 represents the old, weak, sick, and disabled population, i.e., slow action speed (v≤1 m / s), and they prefer to choose flat and short distance paths; K2 represents the ordinary walking population, with medium action speed (1 m / s < v≤2 m / s), and the route should balance time and risk; K3 represents the population that can be transferred by vehicles, and the route planning depends on road traffic capacity, and high-grade highways are preferred. k1 P k2 represents the candidate path set for K1 population, and n represents the total number of candidate paths in the set, P k3 and P k1,i are the resilience weights of the ith path for K1 population; λ k2,i is the resilience weight of the ith path for K2 population; λ k3,i is the resilience weight of the ith path for K3 population; F(p) i is the comprehensive resilience score calculated for each path.

[0062] The weights are dynamically adjusted according to the characteristics of different groups of people and the real-time environment, as shown in equation (4):

[0063] (Formula 4);

[0064] Safety -i is the path safety factor, calculated based on real-time secondary disaster occurrence probability and flood inundation depth; Time -i is the path travel time, calculated in combination with crowd speed and congestion factor; RoadCapacity -i is the road carrying capacity, calculated considering road width, slope and damage state.

[0065] F(p) i is the comprehensive resilience score of each path, with a higher score indicating stronger path resilience and better path. The calculation method is shown in Formula (5):

[0066] (Formula 5);

[0067] Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i is the road carrying capacity.

[0068] Using the reverse gradient search method, the path is traversed in reverse from the safety end. According to the comprehensive resilience score and path resilience weight, the candidate path sets P k1 , P k2 and P k3 corresponding to K1, K2 and K3 different crowds are calculated through the established crowd path planning model, and the candidate path sets P k1 , P k2 and P k3 are dynamically adjusted to realize dynamic optimization generation of differentiated safety path.

[0069] This step classifies personnel according to different crowd characteristics and plans safety paths accordingly. The crowd is divided into three categories: K1 (old, weak, sick and disabled, action speed v≤1m / s), K2 (ordinary walking, crowd speed 1 k1 , P k2 and P k3 are constructed for each type of crowd. For each path in the set, its comprehensive safety and travel characteristics are dynamically calculated. Specifically, the path safety factor Safety -i is evaluated based on the real-time secondary disaster occurrence probability and flood inundation depth along the path; the path travel time Time -i is calculated in combination with crowd speed and road congestion factor; and the road carrying capacity RoadCapacity-i Consideration of road width, slope and damage conditions, such as narrow sections or high slopes, will significantly reduce the capacity. For example, a mountain road with a width of only 3m, a slope of 15%, and a general damage condition, its vehicle passing capacity is relatively low; while a road with a shallow flood depth and no landslide risk, the path safety factor is higher. Then the comprehensive resilience score F(p) of each path is calculated by formula (5) i , which is positively correlated with Safety -i and RoadCapacity -i , and inversely correlated with Time -i .

[0070] To achieve multi-objective optimization, the resilience weight can be dynamically adjusted for different groups of people, and the focus of safety, time, and capacity indicators can be set with preference. The system uses a reverse gradient search method, i.e. starting from each shelter (evacuation endpoint) and searching along the road network in reverse, traversing upstream paths and calculating their comprehensive resilience scores. Finally, for K1, K2, and K3 groups of people, candidate path sets P k1 , P k2 and P k3 are obtained, respectively, where the top several paths with the highest scores are the best differentiated risk avoidance routes. For example, for K1 people with a speed of 0.5m / s, the system prefers flat and short paths; while for K3 groups who can drive, routes with higher highway grades and faster traffic will be prioritized. All paths and their evaluation results are saved in a graph database or road network topology, which can be dynamically updated for use in the next step.

[0071] S4: Dynamic rolling update based on flood evolution process

[0072] During the emergency disposal of the dammed lake, the disaster data monitored in real time based on the flood evolution process is updated every 30 minutes to update the range of the dangerous area downstream of the dammed lake, the risk avoidance window time, and the candidate risk avoidance path set, in order to dynamically optimize the personnel safety transfer route in the dangerous area downstream of the dammed lake. Finally, according to the calculated flood propagation section avoidance window time and different groups of people's risk avoidance path set, the personnel safety transfer in the dangerous area downstream of the dammed lake is realized.

[0073] In the dam-break lake emergency response process, the system dynamically updates the input parameters and results of each step according to the preset period (preferably 30-45 minutes) based on real-time monitoring data. Specifically, whenever new flood evolution information (such as water level, flow) or secondary disaster monitoring results are generated, step S1 needs to be re-executed to correct the downstream inundation range and danger zone, and update the flood propagation speed and window time of each section; at the same time, update the road network state (such as reduced traffic capacity or road blockage). These updated results are fed back as new inputs to steps S2 and S3, triggering the recalculation of each segmented refuge window and path candidate set. For example, if the upstream rainfall intensifies, causing the flood peak flow to increase, 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 value will decrease or even be excluded from the candidate. Through rolling closed-loop updating, it ensures that the latest data is used to guide path planning during the evolution of the disaster, and realizes the synchronous adaptive adjustment of the path planning scheme and the field conditions. Finally, according to the window time and crowd grouping path set calculated at each time, the safety transfer scheme for different groups downstream of the high-risk dam-break lake is output, ensuring the orderly evacuation of personnel in batches.

[0074] The steps of the present process can realize information sharing through GIS and database. The maximum flood inundation range and secondary disaster zone calculated in step S1 are transmitted to S2 and S3 in the form of GIS vector layers; the window time vector table output by step S2 is transmitted to the path planning module through a database or a CSV file; the candidate paths generated by step S3 are stored in GIS as polyline elements in the road network. Each module exchanges results using standardized data formats (such as CSV), ensuring the universality and operability of the data interface, thereby forming a closed-loop implementation logic from dam-break simulation to segmented refuge timing, and then to crowd path optimization.

[0075] The above embodiments are only exemplary descriptions of the technical solutions of the present application. The present application is not limited to only the content described in the above embodiments, but is subject to the scope defined by the claims. Any modification or supplement or equivalent replacement made by a person skilled in the art based on the embodiments is within the scope claimed by the claims of the present application.

Claims

1. A high-risk dammed lake refuge path dynamic planning method, characterized in that: The method comprises the following steps: S1: calculating the flood submerged outer envelope of the breached dam by using a plurality of numerical simulation models of breached dam flood, coupling the flood submerged outer envelope of the breached dam obtained by using the plurality of models to form a maximum envelope of the flood submerged range; obtaining the influence range of secondary disasters, superimposing the maximum envelope of the flood submerged range and the influence range of secondary disasters, and calculating the range of the dangerous area downstream of the dammed lake; S2: dividing the area downstream of the dammed lake into a plurality of sections with the location of the dammed lake as the starting point, calculating the refuge window time of each section based on the flood propagation speed and the safety redundancy time of each section, and finally obtaining the segmented refuge window time of the flood propagation line; S3: dividing the refuge population according to the action speed, dynamically calculating the path safety factor, path travel time and road carrying capacity, determining the comprehensive resilience score of each candidate path, and continuously optimizing the candidate refuge path set of each type of population by dynamically adjusting the path resilience weight and using the reverse gradient search method; S4: in the process of emergency disposal of the dammed lake, the disaster data monitored in real time based on the flood evolution process is used to update the range of the dangerous area downstream of the dammed lake, the refuge window time and the candidate refuge path set every preset time period, so as to dynamically optimize the personnel safety transfer route in the dangerous area downstream of the dammed lake.

2. The high-risk dammed lake refuge path dynamic planning method according to claim 1, characterized in that: In the step S1, the BREACH model, the HEC-RAS one-dimensional hydrodynamic model and the two-dimensional shallow water equation are used to calculate the flood submerged outer envelope of the breached dam.

3. The high-risk dammed lake refuge path dynamic planning method according to claim 1, characterized in that: In the step S1, the influence range of secondary disasters such as bank collapse and landslide is obtained by InSAR monitoring and analysis of the stability of mountains along the downstream flood line.

4. The high-risk dammed lake refuge path dynamic planning method according to claim 1, characterized in that: In the step S1, the calculation model of the dangerous area downstream of the dammed lake is as follows: ； In the formula, F down is the range of dangerous area downstream of the barrier lake; Method i is the outer envelope line of the inundation area of the breach flood obtained by the i th calculation model; Q peak is the peak flow; t arrival is the peak propagation time; C j is the j th secondary disaster influence range along the flood propagation line downstream of the barrier lake.

5. The high-risk dammed lake refuge path dynamic planning method according to any one of claims 1-4, characterized in that: In the step S2, the downstream area of the dammed lake is divided into sections with a distance of 10 km, and the calculation formula of the refuge window time of each section is as follows: ； In the formula: T i is the safety window time of the i th section downstream of the barrier lake; V i is the flood propagation speed of the i th section downstream of the barrier lake; max() is the maximum function; T safe is the safety redundancy time; i is the section number of the downstream area of the barrier lake segmented by 10 km.

6. The method for dynamically planning a high-risk landslide lake avoidance path according to claim 5, characterized in that: In the step S3, the refuge population is divided into the old, weak, sick and disabled population K1, the ordinary walking population K2 and the vehicle transfer population K3 according to the action speed, and the candidate path set of K1, K2 and K3 is represented as follows: ； In the formula, P k1 is a candidate path set for the K1 population; P k2 P is the candidate path set for K2 population; k3 P is the candidate path set for K3 population; K1 is the old, weak, sick and disabled population; K2 is the ordinary walking population; K3 is the population that can be transferred by vehicles; n is the total path number of the candidate path set; λ k1,i is the resilience weight of the i-th path for K1 population; λ k2,i λi is the resilience weight for the ith path for the K2 population; λ k3,i λi is the resilience weight for the ith path for the K3 population; F(p) i Calculate the overall resilience score for each path.

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

8. The high-risk dammed lake escape route dynamic planning method according to claim 7, characterized in that: In the step S3, F(p) i is calculated from the following equation: ； In the formula, Safety -i is the path safety factor; Time -i is the path travel time; RoadCapacity -i is the road carrying capacity.

9. The high-risk dammed lake escape route dynamic planning method according to claim 8, characterized in that: In the step S3, the reverse gradient search method is adopted to traverse the path reversely from the safety end, and the candidate path set P corresponding to K1, K2 and K3 different populations is calculated through the established population path planning model according to the comprehensive resilience score and the path resilience weight. k1 k2 k3 According to the latest downstream danger area range updated by rolling update, the candidate path set P k1 k2 k3 is dynamically adjusted to realize dynamic optimization generation of differentiated safety path.​​​​ 10. The high-risk dammed lake refuge path dynamic planning method according to any one of claims 1-4, characterized in that: In the step S4, the preset time period is 30-45 minutes.

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