Anti-typhoon emergency transfer real-time decision-making method for mountainous villages and towns

Abstract

The invention discloses an anti-typhoon emergency transfer real-time decision-making method for mountainous villages and towns, and the method comprises the steps: building probability rainfall forecast: obtaining a rainfall forecast sample through a sampling method, and inputting the rainfall forecast sample into a physical simulation model for the dynamic forecast of a random scene; establishing a transfer decision-making unit: rasterizing the research area in space, and performing spatial clustering block division on the research area based on grids to obtain the transfer decision-making unit; performing risk forecasting on the transfer decision-making unit: calculating the impending damage level of the residents due to the fact that the residents are not transferred, integrating the impending damage level of the residents on the transfer decision-making unit so as to obtain comprehensive risk index forecasting of the transfer decision-making unit, and performing risk forecasting on all random scenes; establishing a multi-stage random decision model, and making a decision based on the uncertainty risk; and forecasting rolling decisions. According to the method, fine decision making is carried out on small-scale transfer, and optimization is sought between excessive prevention and untimely transfer.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and in particular to a real-time decision-making method for typhoon emergency transfer in mountainous villages and towns. Background Art

[0002] Coastal areas are mostly mountainous and hilly, and typhoons are prone to occur in summer. Typhoons are different from sudden disasters such as earthquakes, tsunamis and landslides. Typhoon disasters usually take several days from formation, approach, landing to decay. During this period, a reasonable and effective method of emergency personnel transfer can well ensure the safety of people's lives and social stability. However, the existing research on typhoon emergency evacuation decision-making lacks systematic modeling from the disaster end to the decision-making end, and the uncertainty of meteorological forecasts and the complexity of mountainous terrain are intertwined and superimposed, which aggravates the difficulty of residents' emergency evacuation decision-making.

[0003] From the characteristics of emergency transfer command in mountainous areas: First, during the evolution of typhoons, the trajectory of uncertainty forecasts and rainfall intensity develop dynamically, the forecast is highly random, and the degree of evacuation necessity in various areas changes in real time; second, the rainfall field will become fragmented under the influence of complex terrain, and the spatial differences in disaster conditions are large, which is manifested in that the transfer within mountainous villages and towns is usually limited to the villages and towns, and there is no need to transfer the entire town in batches. The spatial differences in risks require high decision-making precision, which is not suitable for the design concept of large-scale evacuation. Therefore, the specific emergency transfer command faces two key problems: "uncertain transfer timing" and "unclear transfer areas".

[0004] In existing research results, decision optimization for emergency evacuation usually focuses on two key factors: evacuation path optimization and evacuation start time. For the evacuation path optimization problem, the time consumption of evacuation is analyzed by establishing network flow models, dynamic traffic distribution models or agent models, so as to optimize the transfer path of individuals (or families), aiming to transfer the affected people more efficiently and safely within a limited time. However, this type of work implies the assumption that evacuation is necessary, so it only focuses on how to effectively clear the dangerous area, but does not discuss whether a certain area needs to be evacuated from the perspective of the uncertainty of disaster occurrence. This problem is particularly prominent in the disaster context with significant spatial differences. There are two types of research methods for optimizing the evacuation start time: one is to make deterministic dynamic decisions. This method builds a decision model based on known information from preset or historical reanalysis. It lacks the ability to respond flexibly to random problems and is therefore usually only used for large-scale (regional scope of more than hundreds of square kilometers) evacuation guidance in the early stages of a disaster. The second is to make single-stage or two-stage transfer decisions. Although it takes into account a variety of scenarios, it does not consider the development of randomness and cannot make dynamic adjustments to highly changing problems. Typhoon disasters generally have an evolution process of 2 to 3 days. Although the two-stage decision-making method can quickly evaluate the potential risks and benefits under different decision paths, it obviously ignores the changes in the necessity of transfer during the disaster. Summary of the invention

[0005] In order to effectively consider the uncertainty faced by transfer decisions during the dynamic evolution of typhoons, and to address the spatial heterogeneity of risks in mountainous areas, refined decisions are made on small-scale (regional scope of tens of square kilometers) transfers on the front line to serve village and town emergency management. The present invention provides a real-time decision-making method for typhoon emergency transfers for mountainous villages and towns. The multi-stage random decision-making model established by this method seeks optimization between excessive prevention and untimely transfer, and directly provides real-time, multi-stage, and refined emergency transfer plans for management decision-makers.

[0006] Terminology explanation:

[0007] 1. SCS-CN: Soil Conservation Service Curve Number, runoff coefficient curve method confluence model.

[0008] 2. LisFlood-FP: A semi-open source two-dimensional hydrodynamic model.

[0009] The technical solution adopted by the present invention to overcome the technical problems is:

[0010] A real-time decision-making method for typhoon emergency evacuation in mountainous villages and towns, comprising the following steps:

[0011] S1. Establishing a probabilistic rainfall forecast: obtaining rainfall forecast samples through Monte Carlo sampling, and inputting the rainfall forecast samples into the physical simulation model for dynamic forecasting of random scenarios;

[0012] S2. Establishing a transfer decision unit: rasterizing the study area spatially, and performing spatial clustering and dividing the study area into blocks based on the raster to obtain the smallest monomer of the transfer decision, i.e., the transfer decision unit;

[0013] S3. Risk forecasting for the transfer decision unit: Calculate the level of harm that residents will face if they do not transfer, integrate the level of harm that residents will face on the transfer decision unit to obtain a comprehensive risk index forecast for the transfer decision unit, and conduct risk forecasting for all random scenarios;

[0014] S4, establish a multi-stage random decision model, and make decisions based on the uncertainty risk obtained in step S3;

[0015] S5. Rolling decision forecast.

[0016] Further, step S1 includes:

[0017] S11. Establish a random error model: collect official rainfall forecast data and meteorological station observation data during historical typhoons in the study area, use the original forecast value as the independent variable, regress the mean and variance of the error, and then normalize the errors under different forecast values ​​based on the mean and variance obtained by regression. Then fit the error distribution obtained after processing to establish a random error model.

[0018] S12, generating rainfall forecast samples: based on the deterministic rainfall forecast, extracting error samples from the random error model of step S11 by Monte Carlo method, combining the original forecast samples with the error samples to obtain several groups of rainfall forecast samples;

[0019] S13. Flash flood forecasting through physical simulation models: The physical simulation models include the SCS-CN model and the LisFlood-FP model. The surface runoff is estimated based on the SCS-CN model and the dynamic flood process is simulated in combination with the LisFlood-FP model. The SCS-CN model and the LisFlood-FP model are driven by several groups of rainfall forecast samples obtained in step S12, respectively, to obtain a set of random scenarios represented by the dynamic water depth of the grid.

[0020] Further, step S2 includes:

[0021] S21. Without considering the village administrative boundaries, the study area is spatially gridded based on a preset resolution, and traffic control nodes are set to reflect the surrounding road construction conditions of the study area. The nearest road topological node that can be reached by any grid is taken as the traffic control node. The study area is divided into several areas according to the traffic control nodes, and the areas controlled by different traffic control nodes are studied independently.

[0022] S22, first-level clustering: according to the elevation of the grid and the distance from the river, the study area is divided into two categories: river valley area and riverside area;

[0023] S23, Secondary clustering: For river valleys, clustering is performed based on the slope at the grid position, the roughness at the grid position, the relative height difference with the nearest river channel, and the slope of the nearest river section; for inter-river areas, clustering is performed based on the elevation at the grid position, the slope at the grid position, the curvature at the grid position, and the historical landslide density;

[0024] S24. When the block obtained after the secondary clustering has a village-level administrative boundary passing through it, post-processing is performed.

[0025] Furthermore, in step S24, post-processing specifically refers to: further dividing the blocks clustered in step S23 according to the village-level administrative boundaries to ensure clear administrative jurisdiction.

[0026] Further, step S3 includes:

[0027] S31. Based on the building function vulnerability curve, calculate the level of damage that residents will face if they fail to relocate using the maximum flood depth in the next few hours;

[0028] S32, integrating the level of harm that the residents will face obtained in step S31 on the transfer decision unit to obtain a comprehensive risk index forecast of the transfer decision unit;

[0029] S33. Perform risk forecasts for all random scenarios obtained in step S1.

[0030] Furthermore, step S3 specifically includes:

[0031] S31. Based on the building function vulnerability curve, the maximum flood depth in the next few hours is used to calculate the level of damage that residents will face if they are not relocated, which is expressed by the following formula:

[0032] (1)

[0033] (2)

[0034] (3)

[0035] (4)

[0036] Formula (1)-Formula (4), represents the base of natural logarithms; Indicates the current moment; express The grid water depth at the time, express The grid water depth at the time, express Grid water depth at the moment; Indicates the functional loss rate of the building at different times; Indicates that the actual transfer process takes time to command. The forecast value of future risk level at all times; represents the building function loss function; The flood model predicts The maximum flooding depth of the building during the period, Indicates a preset time interval;

[0037] S32, integrating the damage level that the residents will face obtained in step S31 on the transfer decision unit, that is, weighting all the affected buildings in the transfer decision unit by population to obtain a comprehensive risk index forecast of the transfer decision unit, which is expressed by the following formula:

[0038] (5)

[0039] In formula (5), Indicates The transfer decision unit The comprehensive risk index at the moment; Indicates The transfer decision unit Population of buildings; Indicates the number of flooded buildings within a transfer decision unit; Indicates Residents of the building The degree of harm that is about to happen; Indicates The total population within the transfer decision unit;

[0040] S33, based on all random scenarios obtained in step S1, risk forecast is performed respectively, through It indicates that, , , Represents a collection of samples of random scenes.

[0041] Further, step S4 includes:

[0042] S41. Establish a multi-stage random decision model:

[0043] (1) Clarify decision variables: Multiple stages include the current stage and several future stages; decision variables include whether the transfer decision unit will transfer in the current stage and several future stages, and the corresponding opening status of each shelter in each stage;

[0044] (2) Design objective function: Calculate the optimal transfer time of the decision-making units to be evacuated in the study area from the perspective of overall utility maximization, and comprehensively consider the trade-offs between the safety of life and property that can be protected by evacuation, the assets abandoned by evacuation, and the cost of evacuation, and select the strategy with the greatest benefit to establish the objective function;

[0045] (3) Design the constraints of the objective function;

[0046] S42. Find the solution to the multi-stage random decision model.

[0047] Furthermore, step S4 specifically includes:

[0048] S41. Establish a multi-stage random decision model:

[0049] (1) Clarify decision variables: multiple stages include the current stage and several future stages; decision variables include transfer decision units At the current stage The time and future stages of whether to transfer, as well as the corresponding opening status of each shelter in each stage, are expressed as and Two sets of 0-1 binary decision variables, where , , , represents the number of future stages, It means that residents are staying at home at this time. Indicates that the resident has left home and any transfer decision unit , the first The moment is the immediate transfer time point, The evacuation shelters are open. It means that the shelter is not open;

[0050] (2) Design of objective function: Calculate the optimal transfer time of the decision-making units to be evacuated in the study area from the perspective of overall utility maximization, and comprehensively consider the trade-offs between the safety of life and property that can be protected by evacuation, the assets abandoned by evacuation, and the cost of evacuation, and select the strategy with the greatest benefit to establish the objective function. The objective function is expressed as follows:

[0051] (6)

[0052] (7)

[0053] (8)

[0054] In formula (6)-formula (8), formula (6) indicates that in order to postpone the transfer action to reduce unnecessary production losses and reduce the transfer and resettlement costs, and control the activation costs by opening the number of shelters; formula (7) indicates that in order to reduce the expected cumulative damage within the decision-making forecast period, It means to find the expected value; It indicates the time consumption of a single person unit caused by the transfer; represents the fixed cost of activating a single shelter; It represents the number of people expected to be transferred during the decision forecast period; Indicates The total population living in the transfer decision unit;

[0055] (3) Design the constraints of the objective function as follows:

[0056] (9)

[0057] (10)

[0058] (11)

[0059] (12)

[0060] (13)

[0061] Formula (9) and Formula (10) represent the decision variables respectively. and The value range of ; Formula (11) and Formula (12) indicate that once a decision is implemented, the value of the decision variable cannot be returned to the unimplemented state; In Formula (13), Indicates a refuge point At the maximum capacity, formula (13) indicates that the number of residents to be transferred cannot exceed the total capacity of the shelter;

[0062] S42. Solve the solution of the multi-stage random decision model:

[0063] (1) Variables is a random variable, and the The function expectation is converted into multiple random samples and then averaged. At this time, formula (7) is converted into the following expression:

[0064] (14)

[0065] (2) Taking minimizing the expected cumulative damage within the decision forecast period as the main objective, the relaxation degree is controlled based on the ε-constraint algorithm, and ε is adjusted to generate a series of Pareto optimization solutions.

[0066] Further, step S5 includes:

[0067] S51, based on the latest rainfall forecast, sequentially executing steps S1, S3, and S4, selecting a solution from the Pareto solution set of the multi-stage random decision model of step S4 as a transfer plan, the solution including the location that needs to be transferred immediately in the current stage and the location that is expected to be transferred in the future stage, and issuing an "immediate transfer instruction" and a "preparatory transfer notice" for the current stage and the future stage respectively;

[0068] S52. Repeat step S51 based on a preset time interval. During each rolling decision process, the previous round of "preparatory transfer notification" is replaced by the current round of "immediate transfer instruction". Each decision is a multi-stage rolling decision. Each transfer command only corresponds to the current stage in the execution decision solution, and the future stage is a predictive result.

[0069] The beneficial effects of the present invention are:

[0070] 1. By establishing a high-resolution transfer decision unit, the present invention can capture the spatial distribution structure of potential dangers in advance, which helps the multi-stage random decision model to accurately identify the disaster-stricken locations; then the flooding depth of each building point obtained by physical simulation is integrated into the transfer decision unit for risk assessment, so as to take into account the precision and reliability of risk forecasting.

[0071] 2. The present invention focuses on the uncertainty in the dynamic evolution of disasters, and connects rolling decisions through the dual instructions of "immediate transfer" and "preparatory transfer", which helps to promote the dynamic information disclosure between managers and residents and solve practical problems such as command difficulties and time-consuming communication.

[0072] 3. The present invention establishes a multi-stage random decision model, decomposing the dynamically changing process into multiple interrelated decision links, thereby decomposing future uncertainties into each stage, gradually reducing the cumulative effect of decision deviations, and the deviations in the first stage are relatively small, while the strategies in future stages can be adjusted in real time according to updated information. The multi-stage random decision model weighs the retention risk and transfer cost, optimizes the optimal transfer time for each transfer decision unit, and thus provides a batch transfer solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1The present invention is a flowchart of a real-time decision-making method for typhoon emergency evacuation in mountainous villages and towns according to an embodiment of the present invention.

[0074] Figure 2 This is a curve chart of the first round of probabilistic rainfall forecast described in an embodiment of the present invention.

[0075] Figure 3 This is a curve chart of the second round of probabilistic rainfall forecast described in an embodiment of the present invention.

[0076] Figure 4 This is a curve chart of the third round of probabilistic rainfall forecast described in an embodiment of the present invention.

[0077] Figure 5 This is a curve chart of the fourth round of probabilistic rainfall forecast described in an embodiment of the present invention.

[0078] Figure 6 The diagram is a clustering flow diagram of the transfer decision unit according to an embodiment of the present invention.

[0079] Figure 7 This is a schematic diagram of a transfer decision according to an embodiment of the present invention.

[0080] Figure 8 This is a rolling decision effect diagram made during the typhoon evolution process described in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to facilitate those skilled in the art to better understand the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0082] The present invention discloses a real-time decision-making method for typhoon emergency evacuation in mountainous villages and towns. Figure 1 As shown, the following steps are included:

[0083] S1. Establishing a probabilistic rainfall forecast: obtaining rainfall forecast samples through Monte Carlo sampling, and inputting the rainfall forecast samples into the physical simulation model for dynamic forecasting of random scenarios;

[0084] S2. Establishing a transfer decision unit: rasterizing the study area spatially, and performing spatial clustering and dividing the study area into blocks based on the raster to obtain the smallest monomer of the transfer decision, i.e., the transfer decision unit;

[0085] S3. Risk forecasting for the transfer decision unit: Calculate the level of harm that residents will face if they do not transfer, integrate the level of harm that residents will face on the transfer decision unit to obtain a comprehensive risk index forecast for the transfer decision unit, and conduct risk forecasting for all random scenarios;

[0086] S4, establish a multi-stage random decision model, and make decisions based on the uncertainty risk obtained in step S3;

[0087] S5. Rolling decision forecast.

[0088] In order to better understand the above technical solution, the exemplary embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. These are only exemplary embodiments of the present invention. However, it should be understood that the present invention can also be implemented in various forms and is not limited to the embodiments described herein. These embodiments are intended to enable those skilled in the art to understand the present invention more clearly and thoroughly.

[0089] This embodiment takes a town (set as Town B, which has more mountainous terrain) in a coastal province (set as Province A) as an example, and takes the typhoon (set as Typhoon C) that occurred in the province in 2019 as the disaster background to illustrate the real-time decision-making method for typhoon emergency transfer for mountainous villages and towns described in the present invention. It should be noted that the maps involved in the drawings of this embodiment are all virtual maps, not real areas, and are only for the purpose of more intuitively illustrating and reflecting the real-time decision-making method for typhoon emergency transfer for mountainous villages and towns described in this embodiment.

[0090] S1. Establish probabilistic rainfall forecast: obtain rainfall forecast samples through Monte Carlo sampling, and input the rainfall forecast samples into the physical simulation model for dynamic forecasting of random scenarios.

[0091] In this embodiment, in order to quantify the uncertainty faced by the decision, it is first necessary to simulate the uncertain disaster scene, which specifically includes the following steps:

[0092] S11. Establish a random error model: collect rainfall forecast data and meteorological station observation data released by the local meteorological bureau during Typhoon C in the study area (Town B), take the original forecast value as the independent variable, regress the mean and variance of the error, and then normalize the errors under different forecast values ​​based on the mean and variance obtained by regression. Then fit the error distribution after processing to establish a random error model.

[0093] S12, generating rainfall forecast samples: Based on the deterministic rainfall forecast, error samples are extracted from the random error model of step S11 by using the Monte Carlo method, and the original forecast samples and the error samples are combined to obtain several groups of rainfall forecast samples. In this embodiment, 2000 groups of rainfall forecast samples are taken as an example. Figure 2-Figure 5 As shown, it is a curve chart of four rounds of probabilistic rainfall forecasts obtained in this embodiment, Figure 2-Figure 5 The curve graphs represent the first, second, third and fourth rounds of probabilistic rainfall forecasts respectively.

[0094] S13. Forecasting flash floods through physical simulation models: The physical simulation models include the SCS-CN model and the LisFlood-FP model. The surface runoff is estimated based on the SCS-CN model and the dynamic flood process is simulated in combination with the LisFlood-FP model. The 2000 sets of rainfall forecast samples obtained in step S12 are used to drive the SCS-CN model and the LisFlood-FP model respectively, thereby obtaining a set of random scenes represented by the dynamic water depth of the grid, where the dynamics here are based on a time resolution of one minute.

[0095] S2. Establish a transfer decision unit: rasterize the study area B town spatially, and divide the study area into blocks by spatial clustering based on the grid to obtain the smallest monomer of the transfer decision, namely the transfer decision unit.

[0096] Due to the significant differences in the distribution of disasters in mountainous areas, the transfer decisions made based on administrative villages generally cannot match the actual situation. The high spatial resolution disaster prediction specific to building locations has extremely strong uncertainty. In order to find a balance between forecast uncertainty and the refined needs of transfer, this embodiment selects key factors that can reflect the disaster-prone environment to establish a transfer decision unit, such as Figure 6 As shown, the details are as follows:

[0097] S21. Without considering the village-level administrative boundaries, the town B described in this embodiment is spatially rasterized (including topography, roads, buildings, etc.) based on a resolution of 30m, and traffic control nodes are set to reflect the surrounding road construction conditions of the study area. The nearest road topology node that can be reached by any grid is taken as the corresponding traffic control node. The town B is divided into v areas according to the traffic control nodes, and the areas controlled by different traffic control nodes are studied independently to ensure that the transfer decision-making unit is actually feasible in action command.

[0098] S22, first-level clustering: According to the elevation of the grid and the distance from the river, Town B is divided into two categories: river valley area and riverside area. The disaster types of river valley area and riverside area are different.

[0099] S23. Secondary clustering: For river valley areas, clustering is performed based on four indicators: the slope at the grid position, the roughness at the grid position, the relative height difference with the nearest river channel, and the slope of the nearest river section. For inter-river areas, clustering is performed based on four indicators: the elevation at the grid position, the slope at the grid position, the curvature at the grid position, and the historical landslide density. Among them, the historical landslide density is used to characterize the stability of the slope unit.

[0100] S24. When a village-level administrative boundary passes through the block obtained after the secondary clustering, the block clustered in step S23 is further divided according to the village-level administrative boundary to ensure clear administrative jurisdiction.

[0101] S3. Make risk forecasts for the transfer decision unit: calculate the level of harm that residents will face if they fail to transfer, integrate the level of harm that residents will face on the transfer decision unit to obtain a comprehensive risk index forecast for the transfer decision unit, and make risk forecasts for all random scenarios separately.

[0102] In this embodiment, step S3 includes:

[0103] S31, based on building function vulnerability curve , the maximum flood depth in the next few hours is used to calculate the level of damage that residents will face if they do not evacuate (that is, "stay" and carry out normal activities at home). The purpose is to make a forward-looking assessment of potential risks in the future, which is specifically expressed by the following formula:

[0104] (1)

[0105] (2)

[0106] (3)

[0107] (4)

[0108] Formula (1)-Formula (4), represents the base of natural logarithms; Indicates the current moment; express The grid water depth at the time, express The grid water depth at the time, express Grid water depth at the moment; Indicates the functional loss rate of the building at different times; Indicates that the actual transfer process takes time to command. The forecast value of future risk level at all times; represents the building function loss function; The flood model predicts The maximum flooding depth of the building during the period, represents the preset time interval. The time from when the village cadres receive the superior's instruction to when the transfer is completed is usually no more than 6 hours. As a calculation parameter, this parameter can be modified according to actual conditions to ensure that decision makers can take timely response measures.

[0109] S32, integrating the damage level that the residents will face obtained in step S31 on the transfer decision unit, that is, weighting all the affected buildings in the transfer decision unit by population to obtain a comprehensive risk index forecast of the transfer decision unit. The comprehensive risk index changes over time and is expressed by the following formula:

[0110] (5)

[0111] In formula (5), Indicates The transfer decision unit The comprehensive risk index at the moment; Indicates The transfer decision unit Population of buildings; Indicates the number of flooded buildings within a transfer decision unit; Indicates Residents of the building The degree of harm that is about to happen; Indicates The total population within a transfer decision unit.

[0112] S33, based on all random scenarios obtained in step S1, risk forecast is performed respectively, through It indicates that, , , Represents a collection of samples of random scenes.

[0113] S4. Establish a multi-stage random decision-making model and make decisions based on the uncertainty risk obtained in step S3.

[0114] In this embodiment, step S4 specifically includes:

[0115] S41. Establish a multi-stage random decision model:

[0116] (1) Clarify decision variables: Multiple stages include the current stage and several future stages. In this embodiment, there are 7 decision stages in total. Decision variables include the transfer decision unit At the current stage The time and future stages of whether to transfer, as well as the corresponding opening status of each shelter in each stage, are expressed as and Two sets of 0-1 binary decision variables, where , , In this embodiment, the interval between stages is 4 hours. It means that residents are staying at home at this time. Indicates that the resident has left home and any transfer decision unit , the first The moment is the immediate transfer time point, The evacuation shelters are open. Indicates that the shelter is not open.

[0117] (2) Design of objective function: Calculate the optimal transfer time of the decision-making units to be evacuated in the study area from the perspective of overall utility maximization, and comprehensively consider the trade-offs between the safety of life and property that can be protected by evacuation, the assets abandoned by evacuation, and the cost of evacuation, and select the strategy with the greatest benefit to establish the objective function. The objective function is expressed as follows:

[0118] (6)

[0119] (7)

[0120] (8)

[0121] In formula (6)-formula (8), formula (6) indicates that in order to postpone the transfer action to reduce unnecessary production losses and reduce the transfer and resettlement costs, and control the activation costs by opening the number of shelters; formula (7) indicates that in order to reduce the expected cumulative damage within the decision-making forecast period, It means to find the expected value; It indicates the time consumption of a single person unit caused by the transfer; represents the fixed cost of activating a single shelter; It represents the number of people expected to be transferred during the decision forecast period; Indicates The total population living in the transfer decision unit. The purpose of formula (6) is to postpone the transfer action as much as possible to reduce unnecessary production losses and reduce the cost of transfer and resettlement, and to control the activation cost by opening the number of shelters; formula (7) is to reduce the expected cumulative damage within the decision forecast period.

[0122] (3) Design the constraints of the objective function as follows:

[0123] (9)

[0124] (10)

[0125] (11)

[0126] (12)

[0127] (13)

[0128] Formula (9) and Formula (10) represent the decision variables respectively. and The value range of ; Formula (11) and Formula (12) indicate that once a decision is implemented, the value of the decision variable cannot be returned to the unimplemented state; In Formula (13), Indicates a refuge point Formula (13) indicates that the number of residents to be transferred cannot exceed the total capacity of the shelter.

[0129] S42. Solve the solution of the multi-stage random decision model:

[0130] (1) Variables is a random variable, and the The function expectation is converted into multiple random samples and then averaged. At this time, formula (7) is converted into the following expression:

[0131] (14)

[0132] (2) Taking minimizing the expected cumulative damage within the decision forecast period as the main objective, the relaxation degree is controlled based on the ε-constraint algorithm. The ε-constraint algorithm is a multi-objective optimization algorithm used to solve optimization problems with multiple decision variables and multiple objective functions. It is based on the idea of ​​constrained optimization and introduces a parameter ε to control the weight of the objective function. In this way, an approximate solution set of the optimal solution is found while ensuring that the constraints are met. That is, ε is adjusted to generate a series of Pareto optimization solutions.

[0133] S5. Rolling decision forecast.

[0134] In this embodiment, step S5 includes:

[0135] S51. Based on the latest rainfall forecast, execute steps S1, S3, and S4 in sequence, and select a solution from the Pareto solution set of the multi-stage random decision model of step S4 as a transfer plan. The solution includes locations that need to be transferred immediately in the current stage and locations that are expected to need to be transferred in the future stage. An "immediate transfer instruction" and a "preparatory transfer notice" are issued for the current stage and the future stage, respectively.

[0136] S52. In this embodiment, step S51 is repeatedly executed based on a preset time interval of 4 hours. In each rolling decision-making process, the previous round of "preparatory transfer notification" is replaced by this round of "immediate transfer instruction". Each decision is a multi-stage rolling decision. Each transfer command only corresponds to the current stage in the execution decision solution, and the future stage is a predictive result.

[0137] The transfer decision diagram is as follows: Figure 7 As shown, this embodiment uses time intervals , each decision-making design of the next 6 stages of transfer action as an example, of course, it is not limited to the time interval of 4 hours and the design of the next 6 stages, and can be adjusted according to actual conditions. The horizontal axis represents the current decision optimization solution, the current stage ( ) is the area that should be transferred, and an "immediate transfer order" is issued and the transfer is directed immediately. The future stage is the area that is expected to need to be transferred, and a "preparatory transfer notice" is issued. The vertical axis represents the actual transfer command process. As the decision base point moves forward, a new optimization plan is generated, and the previous round of "preparatory transfer notice" is replaced by the current round of "immediate transfer order". This embodiment shows the effect of rolling decisions made during the evolution of a typhoon, such as Figure 8 As shown in Figure 1, a total of seven rounds of evacuation decisions are given, summarizing the warning release situation in each period of the emergency process. Figure 8 Each column represents a transfer decision unit, for example, "P-23" represents the 23rd transfer decision unit. Figure 8 Each row represents each round of notifications received continuously by the transfer decision unit. Red represents the area that needs to be transferred immediately, orange represents the first-level pre-transfer area, yellow represents the second-level pre-transfer area, and gray represents the evacuated area.

[0138] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art may make many changes and improvements based on the above description, and these changes and improvements should fall within the protection scope of the present invention.

Claims

1. A real-time decision-making method for typhoon emergency evacuation in mountainous villages and towns, characterized in that: The steps include: S1. Establishing probabilistic rainfall forecast: Obtain rainfall forecast samples through Monte Carlo sampling, and input the rainfall forecast samples into the physical simulation model for dynamic forecasting of random scenarios; S2. Establishing a transfer decision unit: rasterizing the study area spatially, and performing spatial clustering and dividing the study area into blocks based on the raster to obtain the smallest monomer of the transfer decision, i.e., the transfer decision unit; S3. Risk forecasting for the transfer decision unit: Calculate the level of harm that residents will face if they do not transfer, integrate the level of harm that residents will face on the transfer decision unit to obtain a comprehensive risk index forecast for the transfer decision unit, and conduct risk forecasting for all random scenarios; S4, establish a multi-stage random decision model, and make decisions based on the uncertainty risk obtained in step S3; S5. Rolling decision forecast.

2. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 1 is characterized in that: Step S1 includes: S11. Establish a random error model: collect official rainfall forecast data and meteorological station observation data during historical typhoons in the study area, use the original forecast value as the independent variable, regress the mean and variance of the error, and then normalize the errors under different forecast values ​​based on the mean and variance obtained by regression. Then fit the error distribution obtained after processing to establish a random error model. S12, generating rainfall forecast samples: based on the deterministic rainfall forecast, extracting error samples from the random error model of step S11 by Monte Carlo method, combining the original forecast samples with the error samples to obtain several groups of rainfall forecast samples; S13. Flash flood forecasting through physical simulation models: The physical simulation models include the SCS-CN model and the LisFlood-FP model. The surface runoff is estimated based on the SCS-CN model and the dynamic flood process is simulated in combination with the LisFlood-FP model. The SCS-CN model and the LisFlood-FP model are driven by several groups of rainfall forecast samples obtained in step S12, respectively, to obtain a set of random scenarios represented by the dynamic water depth of the grid.

3. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 1 is characterized in that: Step S2 includes: S21. Without considering the village administrative boundaries, the study area is spatially gridded based on a preset resolution, and traffic control nodes are set to reflect the surrounding road construction conditions of the study area. The nearest road topological node that can be reached by any grid is taken as the traffic control node. The study area is divided into several areas according to the traffic control nodes, and the areas controlled by different traffic control nodes are studied independently. S22, first-level clustering: according to the elevation of the grid and the distance from the river, the study area is divided into two categories: river valley area and riverside area; S23, Secondary clustering: For river valleys, clustering is performed based on the slope at the grid position, the roughness at the grid position, the relative height difference with the nearest river channel, and the slope of the nearest river channel section; for inter-river areas, clustering is performed based on the elevation at the grid position, the slope at the grid position, the curvature at the grid position, and the historical landslide density; S24. When the block obtained after the secondary clustering has a village-level administrative boundary passing through it, post-processing is performed.

4. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 3 is characterized in that: In step S24, post-processing specifically refers to: further dividing the blocks clustered in step S23 according to the village-level administrative boundaries to ensure clear administrative jurisdiction.

5. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 1 is characterized in that: Step S3 includes: S31. Based on the building function vulnerability curve, calculate the level of damage that residents will face if they fail to relocate using the maximum flood depth in the next few hours; S32, integrating the level of harm that the residents will face obtained in step S31 on the transfer decision unit to obtain a comprehensive risk index forecast of the transfer decision unit; S33. Perform risk forecasts for all random scenarios obtained in step S1.

6. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 5 is characterized in that: Step S3 specifically includes: S31. Based on the building function vulnerability curve, the maximum flood depth in the next few hours is used to calculate the level of damage that residents will face if they are not relocated, which is expressed by the following formula: （1） （2） （3） （4） Formula (1)-Formula (4), represents the base of natural logarithms; Indicates the current moment; express The grid water depth at the time, express The grid water depth at the time, express Grid water depth at the moment; Indicates the functional loss rate of the building at different times; Indicates that the actual transfer process takes time to command. The forecast value of future risk level at all times; represents the building function loss function; The flood model predicts The maximum flooding depth of the building during the period, Indicates a preset time interval; S32, integrating the damage level that the residents will face obtained in step S31 on the transfer decision unit, that is, weighting all the affected buildings in the transfer decision unit by population to obtain a comprehensive risk index forecast of the transfer decision unit, which is expressed by the following formula: （5） In formula (5), Indicates The transfer decision unit The comprehensive risk index at the moment; Indicates The transfer decision unit Population of buildings; Indicates the number of flooded buildings within a transfer decision unit; Indicates Residents of the building The degree of harm that is about to happen; Indicates The total population within the transfer decision unit; S33, based on all random scenarios obtained in step S1, risk forecast is performed respectively, through It indicates that, , , Represents a collection of samples of random scenes.

7. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 6 is characterized in that: Step S4 includes: S41. Establish a multi-stage random decision model: (1) Clarify decision variables: Multiple stages include the current stage and several future stages; decision variables include whether the transfer decision unit will transfer in the current stage and several future stages, and the corresponding opening status of each shelter in each stage; (2) Design objective function: Calculate the optimal transfer time of the decision-making units to be evacuated in the study area from the perspective of overall utility maximization, and comprehensively consider the trade-offs between the safety of life and property that can be protected by evacuation, the assets abandoned by evacuation, and the cost of evacuation, and select the strategy with the greatest benefit to establish the objective function; (3) Design the constraints of the objective function; S42. Find the solution to the multi-stage random decision model.

8. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 7 is characterized in that: Step S4 specifically includes: S41. Establish a multi-stage random decision model: (1) Clarify decision variables: multiple stages include the current stage and several future stages; decision variables include transfer decision units At the current stage The time and future stages of whether to transfer, as well as the corresponding opening status of each shelter in each stage, are expressed as and Two sets of 0-1 binary decision variables, where , , , represents the number of future stages, It means that residents are staying at home at this time. Indicates that the resident has left home and any transfer decision unit , the first The moment is the immediate transfer time point, The evacuation shelters are open. It means that the shelter is not open; (2) Design of objective function: Calculate the optimal transfer time of the decision-making units to be evacuated in the study area from the perspective of overall utility maximization, and comprehensively consider the trade-offs between the safety of life and property that can be protected by evacuation, the assets abandoned by evacuation, and the cost of evacuation, and select the strategy with the greatest benefit to establish the objective function. The objective function is expressed as follows: （6） （7） （8） In formula (6)-formula (8), formula (6) indicates that in order to postpone the transfer action to reduce unnecessary production losses and reduce the transfer and resettlement costs, and control the activation costs by opening the number of shelters; formula (7) indicates that in order to reduce the expected cumulative damage within the decision-making forecast period, It means to find the expected value; It indicates the time consumption of a single person unit caused by the transfer; represents the fixed cost of activating a single shelter; It represents the number of people expected to be transferred during the decision forecast period; Indicates The total population living in the transfer decision unit; (3) Design the constraints of the objective function as follows: （9） （10） （11） （12） （13） Formula (9) and Formula (10) represent the decision variables respectively. and The value range of ; Formula (11) and Formula (12) indicate that once a decision is implemented, the value of the decision variable cannot be returned to the unimplemented state; In Formula (13), Indicates a refuge point At the maximum capacity, formula (13) indicates that the number of residents to be transferred cannot exceed the total capacity of the shelter; S42. Solve the solution of the multi-stage random decision model: (1) Variables is a random variable, and the The function expectation is converted into multiple random samples and then averaged. At this time, formula (7) is converted into the following expression: （14） (2) Taking minimizing the expected cumulative damage within the decision forecast period as the main objective, the relaxation degree is controlled based on the ε-constraint algorithm, and ε is adjusted to generate a series of Pareto optimization solutions.

9. The real-time decision-making method for typhoon emergency evacuation for mountainous villages and towns according to claim 8 is characterized in that: Step S5 includes: S51, based on the latest rainfall forecast, sequentially executing steps S1, S3, and S4, selecting a solution from the Pareto solution set of the multi-stage random decision model of step S4 as a transfer plan, the solution including the position that needs to be transferred immediately in the current stage and the position that is expected to be transferred in the future stage, and issuing an "immediate transfer instruction" and a "preparatory transfer notice" for the current stage and the future stage respectively; S52. Repeat step S51 based on a preset time interval. During each rolling decision process, the previous round of "preparatory transfer notification" is replaced by the current round of "immediate transfer instruction". Each decision is a multi-stage rolling decision. Each transfer command only corresponds to the current stage in the execution decision solution, and the future stage is a predictive result.

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