Emergency material point location layout method and system for coping with urban rainstorm flood disasters

Abstract

The invention discloses an emergency material point location layout method and system for coping with urban rainstorm flood disasters, and belongs to the technical field of flood disaster rescue prediction scheduling. The method comprises the following steps: acquiring the following information about a research area in a flood disaster scene: a flood submerging range, distribution of existing material points and a ground traffic network state; determining a coverage area of material transportation and a material point location to-be-supplemented area within the specified time; establishing a double-constraint optimization model, wherein the double-constraint optimization model comprises two objective functions and constraint conditions of a newly-added service population and a newly-added service range; solving the double-constraint optimization model to obtain a Pareto solution set; and elements in the Pareto solution set are subjected to normalization processing and score assignment, the scheme with the highest score is selected as the optimal solution, and material point location arrangement candidate points in the scheme are material supplement points. According to the method, a double-constraint optimization model is constructed, and a reasonable arrangement scheme of emergency materials for balancing the rescue range and the rescue population in a specific flood scene can be obtained by solving the model.

Description

Technical Field

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

[0002] Under the dual influence of climate change and urbanization, urban flood disasters occur frequently, and the risks and losses they bring are increasing year by year, seriously affecting the normal operation of cities. In order to effectively respond to such disasters and ensure the safety of residents' lives and property, the reasonable deployment of emergency supplies is particularly important. The deployment of emergency supplies is not only related to rescue efficiency, but also an important part of the city's emergency management system.

[0003] In the deployment of emergency supplies, coverage and population coverage are two crucial attributes. However, the existing locations of emergency supplies and rescue personnel are mostly based on historical experience, which may result in the failure of timely delivery of supplies in uncertain flood disaster scenarios. The population distribution patterns of different cities vary, and the service requirements for emergency supplies are also different. If the population is concentrated, the main consideration is whether the emergency supplies can cover these population areas; if the population is dispersed, while meeting the coverage of emergency supplies in the main population areas, it is also necessary to consider the ability of dispersed populations to obtain emergency supplies.

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

[0005] Purpose of the invention: In order to solve the above problems, the present invention provides a method and system for deploying emergency materials to deal with urban rainstorm and flood disasters.

[0006] Technical solution: A method for deploying emergency supplies to deal with urban rainstorm and flood disasters, including the following steps: Step 1: Under the flood disaster scenario, obtain basic information about the study area, the basic information at least including: flooded area, distribution of existing material points, and ground transportation network status; Step 2: Based on the flooded area and the ground transportation network status, determine the coverage range of the existing material points through ground transportation within the scheduled rescue time, and define the coverage range as a material coverage area; based on the material coverage area, determine the area where the rescue materials cannot be transported to within the scheduled rescue time, and define the area where the rescue materials cannot be transported to as a material waiting area; Step 3: Set a set of candidate material points based on the material replenishment area, and define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions on the new service population and the new service range, as well as constraints on the maximum rescue time and the points to be built; Step 4: Solve the dual-constraint optimization model to obtain the Pareto solution set about the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area, the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

[0007] In a further embodiment, the following steps are also included: Step 5: Define the material coverage area as a collection , the material replenishment area is the gathering area , the existing material point is the collection point ; Add new supply points to the collection Based on the newly added service area corresponding to the new replenishment point, update the collection ,gather , get a new set ,gather ; Based on the new collection ,gather , repeat steps 3 to 5 until the constraints are met.

[0008] In a further embodiment, the step three further comprises the following steps: Set the material candidate point set to , n is the number of candidate material points, n≥1; the study area is divided into a number of grid areas to obtain a set G; The expression for defining the objective function of the dual-constraint optimization model is: ; ; In the formula, It indicates the area growth of the newly added service area; G uncovered These are all areas that are not currently covered by existing material points within the scheduled rescue time; Is an indicator parameter, indicating the selection of candidate material points When used as a material point, whether the grid area g can be covered within the scheduled rescue time, The value of is 1 or 0; is the area of ​​the grid region g; It indicates the increase in the number of newly added service population; is the population in the grid area g.

[0009] In a further embodiment, the expression of the Pareto solution set in step 4 is: ; In the formula, m is the number of non-inferior solutions obtained.

[0010] In a further embodiment, the constraint condition in step three includes a first constraint condition and a second constraint condition; the first constraint condition is a set rescue time threshold; The second constraint is to solve the Pareto solution set until the newly added 0 or If it is 0, the model is solved.

[0011] In a further embodiment, the step 4 normalizes and scores the elements in the m groups of non-inferior solutions, comprising the following steps: Using the vector normalization method, the newly added service area and the newly added service population in the m groups of non-inferior solutions are treated as two vectors for normalization calculation; The calculation formula is as follows: ; In the formula, is the total number of newly added service population brought by the i-th plan among the m groups of non-inferior plans, It is the normalized result of the number of newly added service population; is the total number of newly served population brought by the jth plan among the m groups of non-inferior plans; ; In the formula, is the newly added service area brought by the i-th solution among the m groups of non-inferior solutions, is the normalized result of the newly added service area; is the newly added service area brought by the jth solution among the m groups of non-inferior solutions; The newly added service area and the newly added service population for normalization calculation are weighted respectively, and the m groups of non-inferior plans are scored one by one, and the non-inferior plan with the highest score is selected; the calculation formula is as follows: ; ; In the formula, is the score of the non-inferior regimen in group i, is the weight of the number of newly added service population, is the weight of the newly added service area; is the maximum score of the non-inferior solution, is the score of the non-inferior regimen in group m.

[0012] In a further embodiment, the determination of the material coverage area in step 2 includes the following steps: Obtain the distribution location of existing material points and the status of the ground transportation network under flood disaster scenarios; define the rescue time threshold and the path traffic weight in the transportation network; Within the rescue time threshold, the inundated area that can be covered by each existing material point is identified, the coverage of all existing material points is summarized, and the total coverable flood inundated area and the population it serves are determined.

[0013] In a further embodiment, the flood disaster scenario in step 1 is derived from a simulation result of a one- and two-dimensional coupled hydrological and hydrodynamic model or a real flood event; wherein the simulation process of the simulation result includes the following steps: The terrain data, pipe network data, and rainstorm conditions of the study area are obtained, and a one- and two-dimensional coupled hydrological and hydrodynamic model is used to simulate flood disasters to obtain flood inundation results in the study area, which at least include the flood inundation area and the status of the ground transportation network.

[0014] In another technical solution, a system for distributing emergency materials in response to urban rainstorm and flood disasters is provided, which is used to implement the above-mentioned method for distributing emergency materials in response to urban rainstorm and flood disasters, including: The first module is configured to obtain basic information about the study area in a flood disaster scenario, wherein the basic information includes at least: the flooded area, the distribution of existing material points, and the status of the ground transportation network; The second module is configured to determine the coverage range of the existing material points through ground transportation within the scheduled rescue time based on the flooded area and the ground transportation network status, and define the coverage range as a material coverage area; determine the area where the rescue materials cannot be transported to within the scheduled rescue time based on the material coverage area, and define the area where the rescue materials cannot be transported to as a material waiting area; The third module is configured to set a set of candidate material points based on the material replenishment area, define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions on the new service population and the new service range, as well as constraints on the maximum rescue time and the points to be built; The fourth module is configured to solve the dual-constraint optimization model to obtain a Pareto solution set regarding the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area and the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

[0015] Beneficial effects: The present invention aims to improve the city's rescue level in response to flood disasters through the 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.

[0016] The method proposed in the present invention combines the impact range of flood disasters and constructs a double-constraint optimization model that takes into account the coverage range of material points and the number of covered population. Solving the double-constraint optimization model can obtain a reasonable deployment plan for emergency materials that balances the rescue scope and the rescue population in response to specific flood scenarios, thereby helping to improve the rescue service level within the specified rescue time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a method logic flow chart of the present invention; Figure 2 This is a flood simulation result diagram under the combination of 10-year rainfall and 5-year tide; Figure 3 This is a flood simulation result diagram under the combination of 20-year rainfall and 5-year tide; Figure 4 This is a flood simulation result diagram under the combination of 50-year rainfall and 5-year tide; Figure 5 It is a population simulation diagram; Figure 6 This is the optimal allocation plan for medical emergency supplies under a 10-year flood scenario; Figure 7 This is the optimized configuration diagram of medical emergency supplies under the 20-year flood scenario; Figure 8 This is the optimal configuration diagram of medical emergency supplies under the 50-year flood scenario; Fig. 9 A comparison chart of the number of additional service populations brought about by the addition of emergency material settlement sites under different flood scenarios; Fig.10 A comparison chart of the newly added service areas brought by the new emergency material placement points under different flood scenarios. DETAILED DESCRIPTION

[0018] Example 1 like Figure 1 As shown, this embodiment provides a method for deploying emergency supplies to cope with urban rainstorm and flood disasters (hereinafter referred to as this method), including steps 1 to 4. Steps 1 to 4 are described in detail step by step below.

[0019] Step 1: Under flood disaster scenarios, obtain basic information about the study area, which at least includes: flood-inundated areas, distribution of existing material points, and ground transportation network status.

[0020] The above-mentioned flood disaster scenarios are derived from the simulation results of the hydrological and hydrodynamic models or real flood events. When the hydrological and hydrodynamic models are used to simulate flood disaster scenarios, the simulation process of the simulation results includes the following steps: The terrain data, pipe network data, rainfall conditions, and tide information of the study area are obtained, and a one- and two-dimensional coupled hydrological and hydrodynamic model is used to simulate flood disasters to obtain flood inundation results of the study area, which at least include the flood inundation area and the status of the ground transportation network.

[0021] In other words, the hydrological and hydrodynamic model is combined with the topography and pipe network of the study area, and the corresponding maximum flood inundation scenario is obtained according to the input rainfall conditions and tide level information. Figure 2 , Figure 3 , Figure 4 As shown in the figure, the 5-year tide level is combined with the 10-year rainfall, 20-year rainfall, and 50-year rainfall events, and the rainfall duration is 1 hour. The Chicago rain shape is used to construct the conditions for the occurrence of three flood disasters; and through a one- and two-dimensional coupled hydrological and hydrodynamic model, combined with the pipe network information, the urban flood results are obtained, and the flood inundation area and inundation depth are obtained.

[0022] The traffic network model of the study area is constructed with intersections as nodes and traffic routes as edges. The study area is divided into independent small areas of 300m*300m, and travel big data is used to identify the travel time from each small area to the nearest traffic network. Spatial analysis is used to remove traffic networks with a flood depth deeper than 30cm, and the remaining traffic road networks are formed into a topological structure with travel weights (travel time is used as weight here). If the nearest traffic network is flooded, it is considered that this area has lost contact with the outside world and becomes an isolated area. Combined with the shortest path analysis algorithm, the state of the ground transportation network under the flood scenario is constructed. Figure 5 The figure below is a population simulation diagram under flood scenarios. The obtained population big data is assigned to the corresponding small areas by using spatial recognition to obtain the population in need of emergency supplies.

[0023] The simulation based on the hydrological and hydrodynamic model has the advantages of forward-looking prediction and controllable variables, which can provide early warning of possible flood events in the future, and optimize disaster prevention and mitigation strategies by adjusting parameters for multiple scenario analysis. It provides sufficient time for emergency preparation and can assess potential risk areas, thereby formulating effective preventive measures.

[0024] When using real flood events, it is necessary to clarify the specific time of the event and the affected area, collect maps of the disaster area and flooding depth maps issued by the local government, and record data such as the actual flooding range and water depth.

[0025] Step 2: Based on the flooded area and the ground transportation network status, determine the coverage range of the existing material points through ground transportation within the scheduled rescue time, and define the coverage range as a material coverage area; based on the material coverage area, determine the area where rescue materials cannot be transported to within the scheduled rescue time, and define the area where rescue materials cannot be transported to as a material waiting area.

[0026] The determination of the material coverage area includes the following steps: Obtain the distribution location of existing material points and the status of the ground transportation network under flood disaster scenarios; define the rescue time threshold and the path traffic weight in the transportation network; Within the rescue time threshold, the inundated area that can be covered by each existing material point is identified, the coverage of all existing material points is summarized, and the total coverable flood inundated area and the population it serves are determined.

[0027] Step 2 is to evaluate the existing emergency material service status in the study area under the flood disaster scenario, and determine the flood-inundated areas that can be covered and the areas that lack materials. In this embodiment, the deployment of medical emergency resources is used as an example: suppose a serious flood disaster occurs in a city, affecting the normal operation of multiple hospitals. In order to ensure that these hospitals can obtain the 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.

[0028] First, determine the distribution of existing material points in the study area; then, based on the real-time traffic monitoring system, obtain the road traffic conditions during the flood period, including which sections are flooded, which roads are still passable, and their traffic speeds. Set the rescue time threshold (i.e., the scheduled 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. Define the traffic weights of different sections based on the status of the ground transportation network; for example, unaffected roads have a lower weight (fast traffic speed), while partially flooded or traffic-congested roads have a higher weight (slow traffic speed). Using the simulation results of the hydrological and hydrodynamic model or actual flood data, identify the flooded area that each existing material point can reach within 15 minutes, and determine the service population it covers. Summarize the coverage of all existing material points to form the total coverable flood-inundated area and its service population. Set the flooded area that all existing material points cannot cover as a material standby area, and the material standby area waits for the supply of subsequent new material points.

[0029] Step three, set a set of candidate material points based on the material replenishment area, and define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions about the new service population and the new service scope, as well as constraints on the maximum rescue time and the points to be built.

[0030] Step 4: Solve the dual-constraint optimization model to obtain the Pareto solution set about the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area, the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

[0031] In step three and step four, the area and population with rescue are weighed, and emergency material points are added. The purpose is to enable residents to receive services provided by emergency materials within the specified time limit under the flood scenario. This method takes into account the two attributes of the area and population served by emergency materials. Therefore, when determining the new material points, it is necessary to determine the importance of the new service area and the new service population as weight constraints. 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 a time constraint; the ground transportation network state under flood disasters is used as an input condition. In the solution process, there may be multiple non-dominated solutions for each solution, such as a large population and a small rescue area, a small population and a large rescue area. Such solutions constitute the Pareto solution set. Using the vector normalization method and comprehensive scoring, the highest-scoring solution in the Pareto solution set is obtained as the optimal solution in the current iteration process. Then, the idea of ​​the greedy method is used until all areas can obtain emergency materials within the specified time, and the iteration is completed.

[0032] In step 3, set the material candidate point set to , n is the number of candidate material points, n≥1; the study area is divided into several grid areas to obtain a set G.

[0033] The expression for defining the objective function of the dual-constraint optimization model is: ; ; In the formula, It indicates the area growth of the newly added service area; G uncovered These are all areas that are not currently covered by existing material points within the scheduled rescue time; Is an indicator parameter, indicating the selection of candidate material points When used as a material point, whether the grid area g can be covered within the scheduled rescue time, The value of is 1 or 0; is the area of ​​the grid region g; It indicates the increase in the number of newly added service population; is the population in the grid area g.

[0034] The first constraint is the rescue time threshold. Is it 0 or 1; in other words, select the candidate point for the material When used as a material point, the grid area g that can be covered within the rescue time threshold is The value of is 1, otherwise it is 0.

[0035] The second constraint is to solve the Pareto solution set until the newly added is 0 or If it is 0, the calculation is complete.

[0036] The expression of the above Pareto solution set is: .

[0037] Solve the double-constrained optimization model and obtain the Pareto solution set. Where m is the number of non-inferior solutions required.

[0038] The elements in the m groups of non-inferior solutions are normalized and scored, including the following steps: Using the vector normalization method, the newly added service area and the newly added service population in the m groups of non-inferior solutions are treated as two vectors for normalization calculation; The calculation formula is as follows: ; In the formula, is the total number of newly added service population brought by the i-th plan among the m groups of non-inferior plans, It is the normalized result of the number of newly added service population; is the total number of newly served population brought by the jth plan among the m groups of non-inferior plans; ; In the formula, is the newly added service area brought by the ith solution among the m groups of non-inferior solutions, is the normalized result of the newly added service area; is the newly added service area brought by the jth solution among the m groups of non-inferior solutions; The newly added service area and the newly added service population for normalization calculation are weighted respectively, and the m groups of non-inferior plans are scored one by one, and the non-inferior plan with the highest score is selected; the calculation formula is as follows: ; ; In the formula, is the score of the non-inferior regimen in group i, is the weight of the number of newly added service population, is the weight of the newly added service area; is the maximum score of the non-inferior solution, is the score of the non-inferior regimen in group m.

[0039] If the scope of the rescue area is considered to be as important as the rescue population, the weight of the number of new population covered by the newly added material points and the weight of the newly added service area area are respectively 0.5. In other embodiments, the calculation weights of the newly added coverage area and the newly added service population when deploying 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, more new population can be given priority in the early stage, and the weight of the coverage range will be appropriately reduced. During the operation process, more consideration is given to the growth of the service area, and the weight of the new population is reduced, thereby increasing the weight of the new coverage area. In the case of limited resources, resources can be deployed reasonably.

[0040] Furthermore, the method further comprises the following steps: Step 5: Define the material coverage area as a collection , the material replenishment area is the gathering area , the existing material point is the collection point ; Add new supply points to the collection Based on the newly added service area corresponding to the new replenishment point, update the collection ,gather , get a new set ,gather ; Based on the new collection ,gather , repeat steps 3 to 5 until the constraints are met.

[0041] In the above technical solution, the new replenishment point obtained in step 4 is included in the collection Then update the collection ,gather ; use the idea of ​​greedy algorithm, and then restart a new round of calculation until the constraint condition (that is, the second constraint condition) is met so that it is impossible to increase the rescue area and the rescue population, and then stop the calculation.

[0042] After calculation and iteration of the above formulas and models, the optimal material point deployment plan under different flood disaster scenarios can be obtained. The specific results are as follows: Figures 6 to 10 As shown: Figure 6 It demonstrates the optimization plan for the allocation of medical emergency supplies under a flood scenario that occurs once every 10 years. Figure 7 It demonstrates the optimization plan for the allocation of medical emergency supplies under a flood scenario that occurs once every 20 years. Figure 8 It demonstrates the optimization plan for the allocation of medical emergency supplies under a once-in-50-year flood scenario.

[0043] Fig. 9 A comparison of the number of additional service populations created by new emergency material resettlement sites under different flood scenarios is provided. Fig.10 It shows the comparison of the newly added service area brought by the new emergency material resettlement points under different flood scenarios.

[0044] Example 2 This embodiment provides an emergency material point deployment system for responding to urban rainstorm and flood disasters, which is used to implement the emergency material point deployment method for responding to urban rainstorm and flood disasters as described in Example 1, including: The first module is configured to obtain basic information about the study area in a flood disaster scenario, wherein the basic information includes at least: the flooded area, the distribution of existing material points, and the status of the ground transportation network; The second module is configured to determine the coverage range of the existing material points through ground transportation within the scheduled rescue time based on the flooded area and the ground transportation network status, and define the coverage range as a material coverage area; determine the area where the rescue materials cannot be transported to within the scheduled rescue time based on the material coverage area, and define the area where the rescue materials cannot be transported to as a material waiting area; The third module is configured to set a set of candidate material points based on the material replenishment area, define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions on the new service population and the new service range, as well as constraints on the maximum rescue time and the points to be built; The fourth module is configured to solve the dual-constraint optimization model to obtain a Pareto solution set regarding the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area and the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

Claims

1. A method for deploying emergency supplies to cope with urban rainstorm and flood disasters, characterized in that: The following steps are involved: Step 1: Under the flood disaster scenario, obtain basic information about the study area, the basic information at least including: flooded area, distribution of existing material points, and ground transportation network status; Step 2: Based on the flooded area and the ground transportation network status, determine the coverage range of the existing material points through ground transportation within the scheduled rescue time, and define the coverage range as a material coverage area; based on the material coverage area, determine the area where the rescue materials cannot be transported to within the scheduled rescue time, and define the area where the rescue materials cannot be transported to as a material waiting area; Step 3: Set a set of candidate material points based on the material replenishment area, and define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions on the new service population and the new service range, as well as constraints on the maximum rescue time and the points to be built; Step 4: Solve the dual-constraint optimization model to obtain the Pareto solution set about the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area, the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

2. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters as claimed in claim 1, characterized in that: The following steps are also included: Step 5: Define the material coverage area as a collection , the material replenishment area is the gathering area , the existing material point is the collection point ; Add new supply points to the collection Based on the newly added service area corresponding to the new replenishment point, update the collection ,gather , get a new set ,gather ; Based on the new collection ,gather , repeat steps 3 to 5 until the constraints are met.

3. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters as claimed in claim 1, characterized in that: The step 3 further comprises the following steps: Set the material candidate point set to , n is the number of candidate material points, n≥1; the study area is divided into a number of grid areas to obtain a set G; The objective function expression of the dual-constraint optimization model is defined as: ； ； In the formula, It indicates the area growth of the newly added service area; G uncovered These are all areas that are not currently covered by existing material points within the scheduled rescue time; Is an indicator parameter, indicating the selection of candidate material points When used as a material point, whether the grid area g can be covered within the scheduled rescue time, The value of is 1 or 0; is the area of ​​the grid region g; It indicates the increase in the number of newly added service population; is the population in the grid area g.

4. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters according to claim 1, characterized in that: The expression of the Pareto solution set in step 4 is: ； In the formula, m is the number of non-inferior solutions obtained.

5. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters as claimed in claim 1, characterized in that: The constraint conditions in step three include a first constraint condition and a second constraint condition; The first constraint condition is a set rescue time threshold; The second constraint is to solve the Pareto solution set until the newly added is 0 or If it is 0, the model is solved.

6. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters according to claim 1, characterized in that: In the step 4, the elements in the m groups of non-inferior solutions are normalized and scored, including the following steps: Using the vector normalization method, the newly added service area and the newly added service population in the m groups of non-inferior solutions are treated as two vectors for normalization calculation; the calculation formula is as follows: ； In the formula, is the total number of newly added service population brought by the i-th plan among the m groups of non-inferior plans, It is the normalized result of the number of newly added service population; is the total number of newly served population brought by the jth plan among the m groups of non-inferior plans; ； In the formula, is the newly added service area brought by the ith solution among the m groups of non-inferior solutions, is the normalized result of the newly added service area; is the newly added service area brought by the jth solution among the m groups of non-inferior solutions; The newly added service area and the newly added service population for normalization calculation are weighted respectively, and the m groups of non-inferior plans are scored one by one, and the non-inferior plan with the highest score is selected; the calculation formula is as follows: ； ； In the formula, is the score of the non-inferior regimen in group i, is the weight of the number of newly added service population, is the weight of the newly added service area; is the maximum score of the non-inferior solution, is the score of the non-inferior regimen in group m.

7. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters according to claim 1, characterized in that: The determination of the material coverage area in step 2 includes the following steps: Obtain the distribution location of existing material points and the status of the ground transportation network under flood disaster scenarios; define the rescue time threshold and the path traffic weight in the transportation network; Within the rescue time threshold, the inundated area that can be covered by each existing material point is identified, the coverage of all existing material points is summarized, and the total coverable flood inundated area and the population it serves are determined.

8. The method for deploying emergency supplies to cope with urban rainstorm and flood disasters as claimed in claim 1, characterized in that: The flood disaster scenario in step 1 is derived from the simulation results of a one- and two-dimensional coupled hydrological and hydrodynamic model or a real flood event; wherein the simulation process of the simulation results includes the following steps: The terrain data, pipe network data, and rainstorm conditions of the study area are obtained, and a one- and two-dimensional coupled hydrological and hydrodynamic model is used to simulate flood disasters to obtain flood inundation results in the study area, which at least include the flood inundation area and the status of the ground transportation network.

9. A system for distributing emergency supplies in response to urban rainstorm and flood disasters, used to implement a method for distributing emergency supplies in response to urban rainstorm and flood disasters as claimed in any one of claims 1 to 8, characterized in that: include: The first module is configured to obtain basic information about the study area in a flood disaster scenario, wherein the basic information includes at least: the flooded area, the distribution of existing material points, and the status of the ground transportation network; The second module is configured to determine the coverage range of the existing material points through ground transportation within the scheduled rescue time based on the flooded area and the ground transportation network status, and define the coverage range as a material coverage area; determine the area where the rescue materials cannot be transported to within the scheduled rescue time based on the material coverage area, and define the area where the rescue materials cannot be transported to as a material waiting area; The third module is configured to set a set of candidate material points based on the material replenishment area, define the corresponding new service area and new service population for each candidate material point; establish a double-constraint optimization model, which includes two objective functions on the new service population and the new service range, as well as constraints on the maximum rescue time and the points to be built; The fourth module is configured to solve the dual-constraint optimization model to obtain a Pareto solution set regarding the newly added service population and the newly added service scope, wherein the Pareto solution set includes m groups of non-inferior solutions; the Pareto solution set includes the following elements: the area of ​​the newly added service area and the number of newly added service population; the elements in the m groups of non-inferior solutions are normalized and scored, and the non-inferior solution with the highest score is selected as the optimal solution, and the candidate points for the material point layout in the solution are the new material replenishment points.

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