Method and System for Layout of Emergency Supplies Points for Responding to Urban Rainstorm Flood Disasters

Abstract

The present invention discloses a method and system for arranging emergency material points to cope with urban rainstorm and flood disasters, belonging to the technical field of flood disaster rescue prediction and scheduling. The method includes the following steps: obtaining the following information about the research area under the flood disaster scenario: flood inundation range, distribution of existing material points, and ground traffic network status; determining the coverable area for material transportation and the area to be supplemented with material points within a specified time; establishing a double-constraint optimization model, including two objective functions of newly added service population and newly added service range and constraint conditions; solving the double-constraint optimization model to obtain a Pareto solution set; normalizing and scoring the elements in the Pareto solution set, and selecting the solution with the highest score as the optimal solution, and the candidate points for arranging the material points in this solution are the supplementary material points. The present invention constructs a double-constraint optimization model, and solving the model can obtain a reasonable layout plan for emergency materials that balances the rescue range and the rescue population under a specific flood scenario.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood disaster rescue prediction and scheduling, and particularly relates to a method and system for arranging emergency material points to cope with urban rainstorm flood disasters. Background Art

[0002] Affected by the dual influences of climate change and urbanization, urban flood disasters occur frequently, and the risks and losses brought by them increase year by year, seriously affecting the normal operation of cities. In order to effectively cope with such disasters and ensure the safety of residents' lives and property, the reasonable deployment of emergency materials is particularly important. The deployment of emergency materials not only concerns the rescue efficiency, but also is an important part of the urban emergency management system.

[0003] In the deployment of emergency materials, the coverage area and the covered population are two crucial attributes. However, the current settings of the positions of emergency materials and rescue personnel are mostly based on historical experience, and such settings may lead to the failure of materials to be delivered in time in uncertain flood disaster scenarios. The population distribution patterns of different cities are different, and the service requirements for emergency materials are also different. If the population is concentrated, the main consideration is that the emergency materials can cover these population areas; if the population is relatively dispersed, while ensuring the coverage of emergency materials in the main population areas, the ability of the dispersed population to obtain emergency materials also needs to be considered.

[0004] Therefore, it is particularly important to develop a method for arranging emergency material points that can adapt to different urban population distribution patterns and consider different flood disaster scenarios. Summary of the Invention

[0005] Object of the Invention: To solve the above problems, the present invention provides a method and system for arranging emergency material points to cope with urban rainstorm flood disasters.

[0006] Technical Solution: A method for arranging emergency material points to cope with urban rainstorm flood disasters includes the following steps:

[0007] Step 1: In a flood disaster scenario, obtain basic information about the research area, where the basic information at least includes: flood inundation area, distribution of existing material points, and ground traffic network status;

[0008] Step 2: Based on the flood inundation area and the ground traffic network status, determine the coverage range that the existing material points can cover through ground transportation within a predetermined rescue time, and define the coverage range as the material coverage area; based on the material coverage area, determine the area where the rescue materials cannot be transported and arrived within the predetermined rescue time, and define the area where the rescue materials cannot be transported and arrived as the material shortage area;

[0009] Step 3: Based on the material shortage area, set up a set of material candidate points. Define the corresponding new service area and new service population for each material candidate point; establish a double-constraint optimization model, which includes two objective functions regarding the new service population and the new service scope, as well as constraint conditions regarding the maximum rescue time and the points to be built.

[0010] Step 4: Solve the double-constraint optimization model to obtain the Pareto solution set regarding the new service population and the new service scope. The Pareto solution set contains m groups of non-inferior solutions; the Pareto solution set contains the following elements: the area of the new service area and the number of the new service population; normalize and score the elements in the m groups of non-inferior solutions, and select the non-inferior solution with the highest score as the optimal solution, and the candidate points for the material point layout in this solution are the new supplementary material points.

[0011] In a further embodiment, the following steps are further included:

[0012] Step 5: Define the material coverage area as the set and the material shortage area as the set and the existing material points as the set ; incorporate the new supplementary material points into the set , and based on the new service area corresponding to the new supplementary material points, update the set , the set , to obtain the new sets , the set ;

[0013] Based on the new sets , the set , repeat Step 3 to Step 5 until the constraint conditions are met.

[0014] In a further embodiment, Step 3 further includes the following steps:

[0015] Set the set of material candidate points as , where n is the number of material candidate points, n≥1; divide the research area into several grid areas to obtain the set G;

[0016] Define the expression of the objective function of the double-constraint optimization model as:

[0017] ;

[0018] ;

[0019] In the formula, represents the area growth of the new service area; G uncoveredis the area that has not been covered by existing supply points within the scheduled rescue time currently; is an indication parameter, representing the selected supply candidate point When used as a supply point, whether it can cover the grid area g within the scheduled rescue time, the value is 1 or 0; is the area of the grid area g; represents the growth in the number of newly added service population; is the population quantity within the grid area g.

[0020] In a further embodiment, the expression of the Pareto solution set in step four is:

[0021] ;

[0022] In the formula, m is the number of non-inferior solutions obtained.

[0023] In a further embodiment, the constraint conditions in step three include a first constraint condition and a second constraint condition; the first constraint condition is the set rescue time threshold;

[0024] The second constraint condition is that until the newly added is 0 or is 0, the model solving is completed.

[0025] In a further embodiment, the normalization processing and scoring of the elements in the m groups of non-inferior solutions in step four include the following steps:

[0026] Adopt the method of vector normalization, and use the newly added service area and the newly added service population quantity in the m groups of non-inferior solutions as two vectors for normalization calculation respectively;

[0027] The calculation formula is as follows:

[0028] ;

[0029] In the formula, is the total number of newly added service population brought by the i-th solution in the m groups of non-inferior solutions, is the normalization result of the newly added service population quantity; is the total number of newly added service population brought by the j-th solution in the m groups of non-inferior solutions;

[0030] ;

[0031] In the formula, is the newly added service area brought by the i-th solution in the m groups of non-inferior solutions, is the normalization result of the newly added service area; is the newly added service area area brought by the j-th plan among the m sets of non-inferior plans;

[0032] Perform weight division on the newly added service area area and the newly added service population quantity that are normalized, and calculate scores for each of the m sets of non-inferior plans one by one, and select the non-inferior plan with the highest score; the calculation formula is as follows:

[0033] ;

[0034] ;

[0035] In the formula, is the score of the i-th set of non-inferior plans, is the weight of the newly added service population quantity, is the weight of the newly added service area area; is the maximum score of the non-inferior plan, is the score of the m-th set of non-inferior plans.

[0036] In a further embodiment, the determination of the material coverage area in the second step includes the following steps:

[0037] Obtain the distribution locations of existing material points and the ground traffic network status under the flood disaster scenario; define the rescue time threshold and the path passing weights in the traffic network;

[0038] Within the rescue time threshold, identify the flooded areas that can be covered by each existing material point, summarize the coverage ranges of all existing material points, and determine the total flood-flooded areas that can be covered and their service populations.

[0039] In a further embodiment, the flood disaster scenario in the first step is derived from the simulation results of a one-two dimensional coupled hydrological and hydrodynamic model or a real flood event; wherein, the simulation process of the simulation results includes the following steps:

[0040] Obtain the topographic data, pipe network data, and rainstorm conditions of the research area, and use a one-two dimensional coupled hydrological and hydrodynamic model to perform flood disaster simulation to obtain the flood inundation results of the research area. The flood inundation results at least include flood inundation areas and ground traffic network status.

[0041] In another technical solution, an emergency material point layout system for coping with urban rainstorm floods is provided, which is used to implement the emergency material point layout method for coping with urban rainstorm floods as described above, including:

[0042] The first module is configured to obtain basic information about the research area under the flood disaster scenario, and the basic information at least includes: flood inundation areas, distribution of existing material points, and ground traffic network status;

[0043] The second module is configured to determine the coverage range that the existing material points can reach through ground transportation within a predetermined rescue time based on the flood inundation area and the ground traffic network status, and define the coverage range as the material coverage area; determine the area where the rescue materials cannot be transported to within the predetermined rescue time based on the material coverage area, and define the area where the rescue materials cannot be transported to as the material shortage area;

[0044] The third module is configured to set a set of candidate material points based on the material shortage area, and define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, where the double-constraint optimization model includes two objective functions regarding the new service population and the new service range, and constraint conditions regarding the maximum rescue time and the points to be built;

[0045] The fourth module is configured to solve the double-constraint optimization model to obtain the Pareto solution set regarding the new service population and the new service range, where the Pareto solution set contains m groups of non-dominated solutions; the Pareto solution set contains the following elements: the area of the new service area and the number of the new service population; normalize and score the elements in the m groups of non-dominated solutions, and select the non-dominated solution with the highest score as the optimal solution, and the candidate points for the material point layout in this solution are the new supplementary material points.

[0046] Beneficial effects: The present invention aims to improve the rescue level of the city in response to flood disasters through scientific and reasonable layout of emergency rescue material points, ensure that emergency materials can be transported to the required areas in a timely and effective manner, and provide necessary rescue and support for the affected people.

[0047] The method proposed by the present invention combines the influence range of flood disasters and constructs a double-constraint optimization model considering the coverage range of material points and the number of covered population. Solving the double-constraint optimization model can obtain a reasonable layout plan for emergency materials that balances the rescue range and the rescue population under a specific flood scenario, thereby helping to improve the rescue service level within the specified rescue time. Description of the Drawings

[0048] Figure 1 is the method logic flow chart of the present invention;

[0049] Figure 2 is the flood simulation result diagram under the compound of 10-year rainfall and 5-year tide;

[0050] Figure 3 is the flood simulation result diagram under the compound of 20-year rainfall and 5-year tide;

[0051] Figure 4It is a flood simulation result map under the combination of 50-year rainfall and 5-year tide;

[0052] Figure 5 It is a population quantity simulation map;

[0053] Figure 6 It is an optimized configuration plan map of medical emergency supplies under the 10-year flood scenario;

[0054] Figure 7 It is an optimized configuration plan map of medical emergency supplies under the 20-year flood scenario;

[0055] Figure 8 It is an optimized configuration plan map of medical emergency supplies under the 50-year flood scenario;

[0056] Figure 9 It is a comparison map of the newly added service population quantity brought by the newly added emergency supply resettlement points under different flood scenarios;

[0057] Figure 10 It is a comparison map of the newly added service area area brought by the newly added emergency supply resettlement points under different flood scenarios. Specific implementation manner

[0058] Example 1

[0059] As Figure 1 shown, this example provides a method for arranging emergency supply points to cope with urban rainstorm flood disasters (hereinafter referred to as this method), including Step 1 to Step 4. The following details Step 1 to Step 4 step by step.

[0060] Step 1: In the flood disaster scenario, obtain the basic information about the research area, and the basic information at least includes: flood inundation area, distribution of existing supply points, ground traffic network status.

[0061] The above flood disaster scenario comes from the simulation results of the hydrological and hydrodynamic model or the real flood events. Among them, when using the hydrological and hydrodynamic model to simulate the flood disaster scenario, the simulation process of the simulation results includes the following steps:

[0062] Obtain the terrain data, pipe network data, rainfall conditions, and tide level information of the research area, and use a one-two dimensional coupled hydrological and hydrodynamic model to conduct flood disaster simulation to obtain the flood inundation results of the research area. The flood inundation results at least include the flood inundation area and the ground traffic network status.

[0063] That is to say, use the hydrological and hydrodynamic model combined with the terrain and pipe network of the research area, according to the input rainfall conditions and tide level information, to obtain the corresponding maximum flood inundation scenario. As Figure 2 、 Figure 3 、 Figure 4As shown in the figure, the 5-year return period tide levels are combined with the 10-year return period rainfall, 20-year return period rainfall, and 50-year return period rainfall events respectively. The rainfall duration is 1 hour, and three flood occurrence conditions are constructed using the Chicago rain pattern. Through a one-two-dimensional coupled hydrological and hydrodynamic model, combined with pipe network information, the urban flood results are obtained, and the flood inundation area and inundation depth are obtained.

[0064] Taking intersections as nodes and traffic lines as edges, a traffic network model of the research area is constructed. The research area is divided into independent small intervals of 300m * 300m, and the travel time from each small interval to the nearest traffic network is identified using big travel data. The traffic network with an inundation depth deeper than 30 cm is removed using spatial analysis, and the remaining traffic road network forms a topological structure with travel weights (here, travel time is used as the weight). If the nearest traffic network is inundated, it is considered that this area loses contact with the outside world and becomes an isolated area. Combining the shortest path analysis algorithm, the ground traffic network state under flood scenarios is constructed. As Figure 5 shown in the figure, it is a simulation diagram of the population quantity under flood scenarios. The obtained big population data is assigned to the corresponding small intervals using spatial identification to obtain the population in need of emergency supplies.

[0065] The simulation based on the hydrological and hydrodynamic model has the advantages of forward-looking prediction and controllable variables. It can early warn of future possible flood events, and through adjusting parameters, conduct various scenario analyses to optimize disaster prevention and mitigation strategies. It provides sufficient time for emergency preparation, and can evaluate potential risk areas, so as to formulate effective preventive measures.

[0066] When using real flood events, it is necessary to clarify the specific time and affected areas of the events, collect the disaster area maps and inundation depth maps released by the local government, and record data such as the actual inundation range and water depth.

[0067] Step 2: Based on the flood inundation area and the ground traffic network state, determine the coverage range that the existing material points can cover through ground transportation within the predetermined rescue time, and define the coverage range as the material coverage area; based on the material coverage area, determine the areas where the rescue materials cannot be transported and arrived within the predetermined rescue time, and define the areas where the rescue materials cannot be transported and arrived as the material shortage areas.

[0068] Among them, the determination of the material coverage area includes the following steps:

[0069] Obtain the distribution locations of the existing material points and the ground traffic network state under flood disaster scenarios; define the rescue time threshold and the path travel weights in the traffic network;

[0070] Within the rescue time threshold, identify the flooded areas that can be covered by each existing material point, summarize the coverage of all existing material points, and determine the total flood-flooded areas that can be covered and their served population.

[0071] Step 2 is to evaluate the service status of existing emergency materials in the study area under the flood disaster scenario, and determine the flood-flooded areas that can be covered and the areas lacking materials. In this embodiment, the layout of medical emergency resources is taken as an example for illustration: Suppose a serious flood disaster occurs in a certain city, affecting the normal operation of multiple hospitals. In order to ensure that these hospitals can obtain necessary emergency materials (such as medicines, medical equipment, etc.) in a timely manner, it is necessary to optimize the coverage and service efficiency of existing material points, as well as add new material points.

[0072] First, determine the distribution locations of existing material points in the study area; then, according to the real-time traffic monitoring system, obtain the road traffic conditions during the flood, including which sections are flooded, which roads are still passable and their passing speeds. Set the rescue time threshold (i.e., the predetermined rescue time) to 15 minutes, that is, 15 minutes is the maximum rescue time, and each material point must deliver materials to the target hospital within 15 minutes. According to the ground traffic network status, define the passing weights of different sections; for example, the weights of unaffected roads are lower (the passing speed is fast), while the weights of partially flooded or congested roads are higher (the passing speed is slow). Use the simulation results of the hydrological and hydrodynamic model or actual flood data to identify the flooded areas that each existing material point can reach within 15 minutes, and determine the served population it covers. Summarize the coverage of all existing material points to form the total flood-flooded areas that can be covered and their served population. Set the flooded areas that cannot be covered by all existing material points as the material candidate areas, and the material candidate areas wait for the replenishment of subsequent newly added material points.

[0073] Step 3: Based on the material shortage area, set a set of material candidate points, and define the corresponding newly added service area and newly added served population for each material candidate point; establish a double-constraint optimization model, and the double-constraint optimization model includes two objective functions regarding the newly added served population and the newly added service scope, as well as constraint conditions regarding the maximum rescue time and the locations to be built.

[0074] Step 4: Solve the double-constraint optimization model to obtain the Pareto solution set regarding the newly added served population and the newly added service scope. The Pareto solution set contains m groups of non-inferior solutions; the Pareto solution set contains the following elements: the area of the newly added service area and the number of the newly added served population; normalize and score the elements in the m groups of non-inferior solutions, and select the non-inferior solution with the highest score as the optimal solution, and the candidate points for the layout of material points in this solution are the new supplementary material points.

[0075] In steps three and four, the areas and populations with rescue needs are weighed, and additional emergency supply points are set up. The aim is to enable residents to receive the services provided by emergency supplies within the specified time limit in the flood scenario. This method considers two attributes, namely the area served by emergency supplies and the population served. Therefore, when determining the new supply points, it is necessary to determine the importance of the new service area and the new service population as weight constraint conditions. At the same time, it is also necessary to determine the scheduled rescue time t required for rescue according to different flood disaster scenarios, and use the scheduled rescue time t as the time constraint condition; the ground traffic network status under flood disasters is used as the input condition. During the solution process, there may be multiple non-dominated solutions in each solution, such as a large population but a small rescue area, or a small population but a large rescue area. Such solutions constitute the pareto solution set. The vector normalization method and comprehensive scoring are used to obtain the solution with the highest score in the pareto solution set as the optimal solution in the current iteration process. Then, using the idea of the greedy algorithm, the iteration is completed until all areas can obtain emergency supplies within the specified time.

[0076] In step three, set the set of candidate supply points as , where n is the number of candidate supply points and n≥1; divide the research area into several grid areas to obtain the set G.

[0077] Define the expression of the objective function of the double-constraint optimization model as:

[0078] ;

[0079] ;

[0080] In the formula, represents the area growth of the new service area; G uncovered is the area that has not been covered by the existing supply points within the scheduled rescue time; is an indicator parameter, indicating whether the grid area g can be covered within the scheduled rescue time when selecting the candidate supply point as a supply point, The value of is 1 or 0; is the area of the grid area g; represents the population growth of the new service population; is the population quantity within the grid area g.

[0081] The first constraint condition is the set rescue time threshold. Use the rescue time threshold to determine is 0 or 1; in other words, when selecting the candidate supply point as a supply point, if the grid area g can be covered within the rescue time threshold, then The value of is 1, otherwise it is 0.

[0082] The second constraint condition is that until the newly added is 0 or is 0, the calculation is completed.

[0083] The expression of the above-mentioned Pareto solution set is:

[0084] .

[0085] Solve the bi-constrained optimization model to obtain the Pareto solution set. Among them, m is the number of non-inferior solution sets sought.

[0086] Normalize and score the elements in the m groups of non-inferior solutions, including the following steps:

[0087] Adopt the method of vector normalization, and take the newly added service area and the newly added service population in the m groups of non-inferior solutions as two vectors respectively for normalization calculation;

[0088] The calculation formula is as follows:

[0089] ;

[0090] In the formula, is the total newly added service population brought by the i-th solution in the m groups of non-inferior solutions, is the normalization result of the newly added service population; is the total newly added service population brought by the j-th solution in the m groups of non-inferior solutions;

[0091] ;

[0092] In the formula, is the newly added service area brought by the i-th solution in the m groups of non-inferior solutions, is the normalization result of the newly added service area; is the newly added service area brought by the j-th solution in the m groups of non-inferior solutions;

[0093] Conduct weight division on the newly added service area and the newly added service population after normalization calculation respectively, and conduct score calculation for each of the m groups of non-inferior solutions, and select the non-inferior solution with the highest score; the calculation formula is as follows:

[0094] ;

[0095] ;

[0096] In the formula, is the score of the i-th group of non-inferior solutions, is the weight of the newly added service population, is the weight of the newly added service area is the maximum score of the non-inferior solutions, is the score of the non-inferior solutions in the m-th group.

[0097] If it is considered that the scope of the rescue area is as important as the rescued population, the weights of the newly added population covered by the newly added material points and the weight of the newly added service area are each 0.5. In other embodiments, the calculation weights of the newly added coverage area and the newly added service population for the deployment of emergency materials are confirmed through expert experience: if the weight of the newly added coverage area of the service is 0, the proposed algorithm becomes the optimal identification algorithm considering the covered population; if the area of the newly added service population of the service is 0, the proposed algorithm becomes the optimal identification algorithm considering the coverage area. The model supports adjusting the weights of these two attributes during the calculation process. For example, if more newly added population is prioritized in the early stage, the weight of the coverage scope can be appropriately reduced. During the operation process, more consideration is given to the growth of the service area, and the weight of the newly added population is reduced, thereby increasing the weight of the newly added coverage area. In the case of limited resources, reasonable resource deployment can be carried out.

[0098] Furthermore, the method further includes the following steps:

[0099] Step Five: Define the material coverage area as set , the material replenishment area as set , and the existing material points as set ; Incorporate the new supplementary material points into set and update set , set based on the newly added service area corresponding to the new supplementary material points, to obtain new sets , set ;

[0100] Based on the new sets , set , repeat steps three to five until the constraint conditions are met.

[0101] In the above technical solution, the new supplementary material points obtained in step four are incorporated into set , and then the contents of set , set are updated; using the idea of the greedy algorithm, a new round of calculation is then restarted until the constraint conditions (i.e., the second constraint condition) are met such that no new rescue area and rescue population can be added, and the calculation stops.

[0102] Through the calculation and iteration of the above formulas and models, the optimal deployment plan of material points under different flood disaster scenarios can be obtained. The specific results are as Figures 6 to 10 shown:

[0103] Figure 6 Shows the optimized plan for the allocation of medical emergency supplies under the flood scenario with a return period of 10 years. Figure 7 Shows the optimized plan for the allocation of medical emergency supplies under the flood scenario with a return period of 20 years. Figure 8 Shows the optimized plan for the allocation of medical emergency supplies under the flood scenario with a return period of 50 years.

[0104] Figure 9 Provides a comparison of the number of newly served populations brought about by the newly added emergency supply placement points under different flood scenarios. Figure 10 Shows a comparison of the areas of newly added service regions brought about by the newly added emergency supply placement points under different flood scenarios.

[0105] Embodiment 2

[0106] This embodiment provides an emergency supply location layout system for coping with urban rainstorm flood disasters, which is used to implement the emergency supply location layout method for coping with urban rainstorm flood disasters as described in Embodiment 1, including:

[0107] The first module is configured to obtain basic information about the research area under the flood disaster scenario, and the basic information at least includes: flood inundation area, distribution of existing supply points, and ground traffic network status;

[0108] The second module is configured to determine the coverage range that the existing supply points can cover through ground transportation within a predetermined rescue time based on the flood inundation area and the ground traffic network status, and define the coverage range as the supply coverage area; determine the area where the rescue supplies cannot be transported and reached within the predetermined rescue time based on the supply coverage area, and define the area where the rescue supplies cannot be transported and reached as the supply shortage area;

[0109] The third module is configured to set a set of supply candidate points based on the supply shortage area, define the corresponding newly added service area and newly added service population for each supply candidate point; establish a double-constraint optimization model, and the double-constraint optimization model includes two objective functions regarding the newly added service population and the newly added service range, as well as constraint conditions regarding the maximum rescue time and the points to be built;

[0110] The fourth module is configured to solve the double-constraint optimization model to obtain the Pareto solution set regarding the newly added service population and the newly added service range, and the Pareto solution set contains m groups of non-inferior solutions; the Pareto solution set contains the following elements: the area of the newly added service area, the number of newly added service populations; normalize and score the elements in the m groups of non-inferior solutions, and select the non-inferior solution with the highest score as the optimal solution, and the candidate points for the supply location layout in this solution are the new supplementary supply points.

Claims

1. A method for arranging emergency material points to cope with urban rainstorm and flood disasters, characterized in that, It includes the following steps: Step 1: In the flood disaster scenario, obtain the basic information about the research area, where the basic information includes at least: the flood inundation area, the distribution of existing material points, and the status of the ground traffic network; Step 2: Based on the flood inundation area and the ground traffic network status, determine the coverage range that the existing material points can cover through ground transportation within the predetermined rescue time, and define the coverage range as the material coverage area; Based on the material coverage area, determine the area where the rescue materials cannot be transported and arrived within the predetermined rescue time, and define the area where the rescue materials cannot be transported and arrived as the material to be supplemented area; Step 3: Set a set of material candidate points based on the material to be supplemented area, and define the corresponding new service area and new service population for each material candidate point; Establish a double-constrained optimization model, where the double-constrained optimization model includes two objective functions regarding the new service population and the new service range, and constraint conditions regarding the maximum rescue time and the points to be built; Step 4: Solve the double-constrained optimization model to obtain the Pareto solution set regarding the new service population and the new service range, where the Pareto solution set contains m groups of non-inferior solutions; The Pareto solution set contains the following elements: the area of the new service area and the number of the new service population; Normalize and score the elements in the m groups of non-inferior solutions, and select the non-inferior solution with the highest score as the optimal solution, and the candidate point for the material point layout in this solution is the new supplementary material point; The Step 3 includes: Set the set of candidate material points as , where n is the number of candidate material points and n≥1; divide the research area into several grid regions to obtain the set G; Define the objective function expression of the double-constrained optimization model as: ； ； In the formula, represents the area growth of the newly added service area; G uncovered is the area that is not covered by the existing supply points within the scheduled rescue time currently; is an indication parameter, representing whether the selected supply candidate point can cover the grid area g within the scheduled rescue time when it is used as a supply point, and its value is 1 or 0; is the area of the grid area g; represents the population growth of the newly added service population; is the population quantity within the grid area g.

2. The method for arranging emergency material points for coping with urban rainstorm and flood disasters according to claim 1, characterized in that It also includes the following steps: Step 5: Define the material coverage area as set , the material to-be-supplemented area as set , and the existing material points as set ; Incorporate the new supplementary material points into set , and update set , set based on the new service areas corresponding to the new supplementary material points, to obtain the new sets , set ; Based on the new set and set , repeat steps three to five until the constraint condition is satisfied.

3. The method for arranging emergency material points to cope with urban rainstorm and flood disasters according to claim 1, wherein, The expression of the Pareto solution set in the Step 4 is: ； In the formula, m is the number of non-inferior solutions obtained.

4. The emergency material location layout method for coping with urban rainstorm and flood disasters according to claim 1, wherein, The constraint conditions in the Step 3 include the first constraint condition and the second constraint condition; The first constraint condition is the set rescue time threshold; The second constraint condition is that until the newly added is 0 or is 0 in the Pareto solution set, the model solution is completed.

5. The method for arranging emergency material points for coping with urban rainstorm and flood disasters according to claim 1, characterized in that, The step of normalizing and scoring the elements in the m groups of non-inferior solutions in the Step 4 includes the following steps: Adopt the method of vector normalization, and use the area of the new service area and the number of the new service population in the m groups of non-inferior solutions as two vectors respectively for normalization calculation; The calculation formula is as follows: ； In the formula, is the total number of newly served population brought by the i-th solution among m groups of non-inferior solutions, is the normalization result of the number of newly served population; is the total number of newly served population brought by the j-th solution among m groups of non-inferior solutions; ； In the formula, is the newly added service area area brought by the \(i\)-th solution among \(m\) groups of non-inferior solutions, is the normalization result of the newly added service area area; is the newly added service area area brought by the \(j\)-th solution among \(m\) groups of non-inferior solutions; Conduct weight division on the area of the new service area and the number of the new service population after normalization calculation respectively, conduct score calculation on each of the m groups of non-inferior solutions one by one, and select the non-inferior solution with the highest score; The calculation formula is as follows: ； ； In the formula, is the score of the i-th group of non-inferior solutions, is the weight of the newly added service population, is the weight of the newly added service area; is the maximum score of the non-inferior solutions, is the score of the m-th group of non-inferior solutions.

6. The method for arranging emergency material points to cope with urban rainstorm and flood disasters according to claim 1, characterized in that The determination of the material coverage area in the Step 2 includes the following steps: Obtain the distribution locations of the existing material points and the status of the ground traffic network in the flood disaster scenario; Define the rescue time threshold and the path passing weight in the traffic network; Within the rescue time threshold, identify the inundation area that each existing material point can cover, summarize the coverage ranges of all existing material points, and determine the total flood inundation area that can be covered and its service population.

7. The method for arranging emergency material points to cope with urban rainstorm and flood disasters according to claim 1, characterized in that, The flood disaster scenario in the Step 1 comes from the simulation results of a one-two dimensional coupled hydrological and hydrodynamic model or a real flood event; Among them, the simulation process of the simulation results includes the following steps: Obtain the topographic data, pipe network data, and rainstorm conditions of the research area, and use a one-dimensional and two-dimensional coupled hydrological and hydrodynamic model to simulate flood disasters and obtain the flood inundation results of the research area. The flood inundation results at least include the flood inundation area and the ground traffic network status.

8. An emergency material point layout system for coping with urban rainstorm and flood disasters, which is used to implement the emergency material point layout method for coping with urban rainstorm and flood disasters as described in any one of claims 1 to 7, and is characterized in that, It includes: The first module is configured to obtain basic information about the research area in the flood disaster scenario. The basic information at least includes: the flood inundation area, the distribution of existing material points, and the ground traffic network status; The second module is configured to determine the coverage range that can be covered by the existing material points through ground transportation within a predetermined rescue time based on the flood inundation area and the ground traffic network status, and define the coverage range as the material coverage area; determine the area where it is impossible to transport rescue materials to within the predetermined rescue time based on the material coverage area, and define the area where it is impossible to transport rescue materials to as the material to-be-supplemented area; The third module is configured to set a set of candidate material points based on the material to-be-supplemented area, and define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, where the double-constraint optimization model includes two objective functions regarding the new service population and the new service range, and constraint conditions regarding the maximum rescue time and the to-be-built points; The fourth module is configured to solve the double-constraint optimization model to obtain the Pareto solution set regarding the new service population and the new service range. The Pareto solution set contains m sets of non-inferior solutions; the Pareto solution set contains the following elements: the area of the new service area and the number of the new service population; normalize and score the elements in the m sets of non-inferior solutions, and select the non-inferior solution with the highest score as the optimal solution, and the candidate points for the material point layout in this solution are the new supplementary material points.

Images