An urban waterlogging emergency management system based on an internet of things

Abstract

The application relates to the technical field of urban waterlogging treatment, in particular to an urban waterlogging emergency management system based on the Internet of Things. The system comprises the following: a data acquisition module that acquires first data of drainage hydraulic states and water accumulation distribution and second data of waterlogging drainage construction conditions and drainage surplus; a region division module that divides the urban region into a plurality of waterlogging island units based on the calculation of connectivity of the first data and the calculation of conflict degree of the second data; a key node identification module that identifies key nodes in the units, calculates the overlapping degree and the coordination coefficient between the nodes, and determines the node emptying sequence; a cross-unit scheduling module that calculates the unit lever density according to the sum of the node emptying lever rate and the average overlapping degree, and determines the unit processing sequence; and a processing module that sequentially carries out waterlogging drainage. The application can significantly improve the utilization efficiency of waterlogging drainage resources by means of two-dimensional region division, key node identification and lever density sorting, and preferentially processing high-lever nodes and units.

Description

Technical Field

[0001] This invention relates to the field of urban flood control technology, and in particular to an urban flood emergency management system based on the Internet of Things. Background Technology

[0002] With rapid urbanization and frequent extreme rainfall, urban flooding has become an increasingly prominent problem, posing a serious threat to the safety of people's lives and property and the normal operation of cities. Traditional flood emergency management mainly relies on manual inspections and experience-based judgment, which suffers from slow response speed, blind resource allocation, and low drainage efficiency. Although some existing flood monitoring systems can collect data such as water depth and pipe network levels in real time, they lack in-depth analysis of the hydraulic relationships between flooded areas. They cannot identify which drainage points can alleviate the situation in surrounding areas simultaneously, resulting in drainage resources often being prioritized for the areas with the deepest or largest water accumulation, rather than the key nodes that can generate the most overall benefits. In addition, the resource competition problem arising when multiple flooded areas share the same drainage outlet also lacks an effective quantitative assessment and scheduling mechanism, which can easily lead to drainage conflicts and decreased efficiency.

[0003] Chinese Patent Publication No. CN116469234A discloses an emergency management system for urban rainstorm and flood disasters, including a weather information acquisition module, a drainage information acquisition module, a road information acquisition module, an equipment information acquisition module, a power information acquisition module, a data processing module, a central control module, and an information transmission module. The weather information acquisition module collects weather information for the monitored area, including real-time weather information and weather information within a preset time period. The drainage information acquisition module collects drainage outlet information for the monitored area, including the number of drainage outlets, unit drainage volume information, and real-time flow information. The road information acquisition module collects road information for the monitored area, including real-time water depth information, number of vehicles on the road, and road congestion length information.

[0004] Therefore, the existing technology has the following problems: The lack of quantitative analysis on the hydraulic connectivity and drainage resource competition among flood-prone areas makes it impossible to identify high-leverage key nodes that can drive other areas to alleviate flooding simultaneously, and makes it difficult to solve the problems of drainage conflicts and priority scheduling when multiple areas share the same drainage outlet. Summary of the Invention

[0005] To address this, the present invention provides an Internet of Things-based urban flooding emergency management system to overcome the lack of quantitative analysis of the hydraulic connectivity and drainage resource competition relationships between flooded areas in the existing technology, the inability to identify high-leverage key nodes that can drive other areas to alleviate flooding simultaneously, and the difficulty in solving the problems of drainage conflicts and priority scheduling when multiple areas share the same drainage outlet.

[0006] To achieve the above objectives, the present invention provides an urban flooding emergency management system based on the Internet of Things, comprising: The data acquisition module is used to acquire first data representing the hydraulic state of the urban drainage system and the distribution of surface water in each flood-prone area, and second data representing the real-time operating conditions and drainage capacity of the drainage facilities. The region division module is used to calculate the connectivity of each flooded area based on the first data, calculate the conflict degree of each flooded area based on the second data, and divide the urban area into several flooded island units according to the connectivity and the conflict degree. The critical node identification module is used to identify one or more critical nodes affecting the waterlogged island unit based on the first data and the second data, calculate the overlap and coordination coefficient between each critical node, and determine the drainage order between each critical node in the waterlogged island unit based on the overlap and the coordination coefficient. The cross-unit scheduling module is used to calculate the unit leverage density based on the sum of the drainage leverage ratios of all key nodes in each flooded island unit and the average overlap between the key nodes, and to determine the processing order among several flooded island units based on the unit leverage density. The processing module is used to perform drainage processing based on the processing order among the several waterlogged island units and the drainage order among the key nodes within the waterlogged island units.

[0007] Furthermore, the region division module calculates the connectivity of each flooded area, wherein, The regional division module extracts the surface runoff paths and pipeline connection relationships between each flooded area from the first data. The region division module constructs a hydraulic topology map between various waterlogged areas based on the surface runoff path and the pipeline connection relationship; The region division module calculates the shortest path length between any two waterlogged areas in the hydraulic topology map, and uses the reciprocal of the shortest path length as the connectivity between the two waterlogged areas.

[0008] Furthermore, the region division module calculates the conflict degree of each flooded area, wherein, The area division module extracts the drainage outlet information corresponding to each flooded area and the remaining drainage capacity of each drainage outlet from the second data. For any two flooded areas sharing the same drainage outlet, the area division module adds the estimated drainage water demand of the first flooded area to the estimated drainage water demand of the second flooded area to obtain the total water demand. The region division module divides the total water demand by the remaining drainage capacity of the drainage outlet to obtain the conflict degree between the two flooded areas.

[0009] Furthermore, the area division module divides the urban area into several waterlogged island units, wherein, The region division module uses each flooded area as a node and edges with connectivity greater than a preset connectivity threshold to construct a first association graph, and calculates the first set of connected components of the first association graph. The region division module uses each flooded area as a node and edges with a conflict degree greater than a preset conflict threshold to construct a second association graph, and calculates the second connected component set of the second association graph. The region division module aggregates the flooded areas in the intersection of the first connected component set and the second connected component set into a single flooded island unit.

[0010] Furthermore, the key node identification module calculates the overlap between each key node, wherein, The key node identification module determines the set of flooded areas affected by each key node; The key node identification module calculates the intersection and union of the sets of waterlogged areas affected by any two key nodes; The key node identification module divides the intersection size by the union size, and the resulting ratio is used as the overlap between the two key nodes.

[0011] Furthermore, the key node identification module calculates the coordination coefficient between each key node, wherein, The critical node identification module determines the number of flooded areas affected by each critical node after it is drained individually. The critical node identification module determines the number of flooded areas affected by the simultaneous drainage of any two critical nodes. The critical node identification module subtracts the sum of the number of flooded areas affected by the simultaneous drainage of the two critical nodes from the number of flooded areas affected by the individual drainage of the two critical nodes to obtain the difference. The critical node identification module divides the difference by the sum of the number of flooded areas affected by the drainage of the two critical nodes individually, and the resulting ratio is used as the coordination coefficient between the two critical nodes.

[0012] Furthermore, the key node identification module determines the drainage sequence among the key nodes within the flooded island unit, wherein, The key node identification module constructs a key node relationship graph based on the overlap and coordination coefficient between each key node, with the reciprocal of the overlap as the initial weight and the coordination coefficient as the correction factor. The key node identification module calculates the minimum spanning tree of the key node relationship graph and uses the optimal path order of the minimum spanning tree as the emptying order among the key nodes.

[0013] Furthermore, the cross-unit scheduling module calculates the unit leverage density, wherein, The cross-unit scheduling module calculates the sum of the drainage leverage ratios of all key nodes in each waterlogged island unit, where the drainage leverage ratio of each key node is equal to the number of other waterlogged areas that can be alleviated after the key node is emptied, divided by the estimated time required to empty the key node. The cross-unit scheduling module calculates the overlap between any two key nodes within each waterlogged island unit, where the overlap between any two key nodes is equal to the number of waterlogged areas jointly affected by the two key nodes divided by the union of the number of waterlogged areas each key node individually affects. The cross-unit scheduling module calculates the average overlap of all combinations of any two key nodes as the average overlap of the waterlogged island unit. The cross-unit scheduling module divides the sum of the drainage leverage ratios by the sum of the average overlap to obtain the unit leverage density of the flooded island unit.

[0014] Furthermore, the cross-unit scheduling module determines the processing order among several isolated flood-prone units, wherein, The cross-unit scheduling module sorts each waterlogged island unit according to its unit leverage density from high to low to obtain the initial processing order. The cross-unit scheduling module identifies waterlogged island units that depend on the same drainage outlet, assigns them to the same resource conflict group, and determines the serial processing order within the same resource conflict group according to the unit leverage density from high to low. The cross-unit scheduling module determines the waterlogged island units in different resource conflict groups as being processed in parallel, and merges the serial processing order with the parallel processing relationship to generate the final processing order.

[0015] Furthermore, when the cross-unit scheduling module determines the serial processing order in the same resource conflict group according to the unit leverage density from high to low, if there are two or more waterlogged island units with the same unit leverage density, the serial processing order is determined according to the estimated total drainage time of each waterlogged island unit from small to large.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a hydraulic topology map and calculating the reciprocal of the shortest path length as connectivity, this invention quantifies the strength of hydraulic connectivity between any two flooded areas into a continuous value between 0 and 1. This overcomes the shortcomings of traditional methods, which can only determine whether connectivity exists but cannot measure the strength of connectivity, thus providing a refined quantitative basis for subsequent regional aggregation. Simultaneously, by calculating the conflict degree by estimating the ratio of drainage demand to the remaining drainage capacity of the drainage outlet, the intensity of resource competition among multiple flooded areas for the same drainage outlet is quantified into a comparable value. When the conflict degree is greater than 1, resource competition is automatically determined to exist, providing a quantitative criterion for identifying resource coupling relationships. Based on this, a first association graph is constructed with edges having a connectivity greater than a preset connectivity threshold, and a second association graph is constructed with edges having a conflict degree greater than a preset conflict threshold. The intersection of the connected component sets of the two association graphs is then used to aggregate them into waterlogged island units. This ensures that the regions within each waterlogged island unit satisfy both hydraulic connectivity and resource competition conditions. That is, these regions not only allow water to flow between each other physically, but also compete for the same drainage outlet. This avoids separating or incorrectly aggregating regions that are hydraulically tightly coupled but have no resource competition from regions that are hydraulically disconnected but have strong dependencies due to sharing a drainage outlet. This ensures the internal connectivity and physical integrity of the waterlogged island units.

[0017] Furthermore, this invention quantifies the impact of each key node's individual drainage on the water levels of other flooded areas by simulating the impact of each key node's drainage on the water levels of other flooded areas, and constructs a set of impact areas. Based on this, by calculating the ratio of the intersection to the union of the impact area sets of any two key nodes, the overlap value is obtained, which reflects the redundancy or complementarity of the two key nodes in the drainage function. This provides a basis for determining the drainage sequence between key nodes, allowing drainage scheduling to be optimized based on the degree of overlap, thereby achieving precise allocation of drainage resources and maximizing overall efficiency.

[0018] Furthermore, this invention uses a hydraulic model to simulate and obtain the number of flooded areas affected by individual drainage at each key node and by the simultaneous drainage of any two key nodes. It then calculates the ratio of the difference between the number of areas affected by simultaneous drainage and the sum of the number of areas affected by individual drainage to the sum of the number of areas affected by individual drainage, thus obtaining a synergy coefficient. This synergy coefficient quantitatively characterizes the interaction between two key nodes, providing a basis for optimizing the drainage sequence among key nodes. Positive synergy nodes with high synergy coefficients should be drained adjacently to fully utilize synergistic effects, while negative synergy nodes with low synergy coefficients should be drained separately to avoid resource waste, thereby maximizing the marginal benefit of each drainage action and improving the overall utilization efficiency of limited drainage resources.

[0019] Furthermore, this invention uses the reciprocal of the overlap degree as the initial weight to give nodes with highly overlapping influence areas lower edge weights in the graph. The synergy coefficient is used as a correction factor to further reduce the weight of nodes with positive synergy effects and increase the weight of nodes with negative synergy effects. This automatically connects key nodes with high synergy and high overlap in the minimum spanning tree. The optimal path order obtained by traversing the minimum spanning tree ensures that nodes with strong synergy in the drainage sequence are closely adjacent, fully utilizing the positive synergy between nodes to expand the drainage impact range. Simultaneously, mutually inhibiting nodes are appropriately separated to avoid resource waste and redundant investment. This achieves automatic quantitative optimal determination of the drainage sequence among key nodes without human intervention, significantly improving the overall efficiency of drainage operations.

[0020] Furthermore, this invention effectively solves the resource competition problem of multiple units sharing a limited drainage outlet by grouping waterlogged island units that rely on the same drainage outlet into resource conflict groups and processing them serially within the group and in parallel between different groups. This avoids drainage stagnation and efficiency decline caused by resource conflicts. At the same time, by using the sorting rules of prioritizing unit leverage density and prioritizing the estimated total drainage time when the density is the same, the invention ensures that the overall drainage benefits are maximized and the queuing waiting time is minimized, thus achieving efficient scheduling of drainage resources across units. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the Internet of Things-based urban flooding emergency management system in this embodiment; Figure 2 This is a flowchart of the area division module in the Internet of Things-based urban flooding emergency management system of this embodiment; Figure 3 This is a schematic diagram of the processing module in the Internet of Things-based urban flooding emergency management system of this embodiment. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] Please see Figures 1-3 As shown, Figure 1 This is a schematic diagram of the Internet of Things-based urban flooding emergency management system in this embodiment; Figure 2This is a flowchart of the area division module in the Internet of Things-based urban flooding emergency management system of this embodiment; Figure 3 This is a schematic diagram of the processing module in the Internet of Things-based urban flooding emergency management system of this embodiment.

[0025] This embodiment provides an Internet of Things-based urban flooding emergency management system, including: The data acquisition module is used to acquire first data representing the hydraulic state of the urban drainage system and the distribution of surface water in each flood-prone area, and second data representing the real-time operating conditions and drainage capacity of the drainage facilities. The area division module, connected to the data acquisition module, is used to calculate the connectivity of each flooded area based on the first data, calculate the conflict degree of each flooded area based on the second data, and divide the urban area into several flooded island units according to the connectivity and the conflict degree. The key node identification module is connected to the data acquisition module and the area division module respectively. It is used to identify one or more key nodes affecting the waterlogged island unit based on the first data and the second data, calculate the overlap and coordination coefficient between each key node, and determine the drainage order between each key node in the waterlogged island unit based on the overlap and coordination coefficient. The cross-unit scheduling module is connected to the region division module and the key node identification module respectively. It is used to calculate the unit leverage density based on the sum of the drainage leverage ratios of all key nodes in each waterlogged island unit and the average overlap between key nodes, and to determine the processing order among several waterlogged island units based on the unit leverage density. The processing module, connected to the cross-unit scheduling module, is used to perform drainage processing based on the processing order among the several isolated waterlogged island units and the drainage order among the key nodes within the isolated waterlogged island units.

[0026] In this embodiment of the invention, an IoT sensor network deployed at various urban flood monitoring points collects and aggregates multi-source sensing data in real time. The first data includes: pressure, level, and full-flow status information of the pipe network nodes collected by pressure and level sensors deployed at underground pipe network nodes; surface water depth, water accumulation range, and water accumulation location information collected by water level sensors deployed in flood-prone areas such as low-lying roads, underpasses, and underground parking garages; and regional topographic elevation feature data obtained through a digital elevation model. The second data includes: real-time operating condition data such as the start / stop status, current drainage flow rate, and gate opening of the drainage pumping station obtained through the pumping station PLC control system; and data on the current drainage flow rate and remaining drainage capacity at the drainage outlet obtained through flow monitoring equipment installed at the drainage outlet, wherein the remaining drainage capacity is calculated based on the difference between the designed maximum drainage capacity of the drainage outlet and the currently used drainage capacity. All data is uploaded in real time to a cloud platform or local server via an IoT communication protocol at a preset sampling frequency (e.g., once every 1 minute) for subsequent module analysis.

[0027] Specifically, the region division module calculates the connectivity of each flooded area, wherein, The regional division module extracts the surface runoff paths and pipeline connection relationships between each flooded area from the first data. The region division module constructs a hydraulic topology map between various waterlogged areas based on the surface runoff path and the pipeline connection relationship; The region division module calculates the shortest path length between any two waterlogged areas in the hydraulic topology map, and uses the reciprocal of the shortest path length as the connectivity between the two waterlogged areas.

[0028] In this embodiment of the invention, the region division module extracts the surface runoff paths and pipeline connection relationships between the flooded areas from the first data. Specifically, it analyzes the surface water flow direction based on a digital elevation model to identify whether there are surface runoff channels between waterlogged areas; simultaneously, it extracts the connection relationships of each pipeline node and the pipeline direction from pipeline monitoring data to determine whether different flooded areas are connected by underground pipelines. Next, the region division module constructs a hydraulic topology map between the flooded areas based on the extracted surface runoff paths and pipeline connection relationships. In this hydraulic topology map, each flooded area is used as a node; if there is a surface runoff path or pipeline connection channel between two flooded areas, an undirected edge is established between the corresponding nodes. Then, the region division module calculates the shortest path length between any two flooded areas in the hydraulic topology map. For example, it uses Dijkstra's algorithm or Floyd's algorithm to calculate the path from node i to node j with the fewest edges; the number of edges traversed by this path is the shortest path length. If there is no connecting path between two flooded areas, the shortest path length is set to infinity, and the corresponding connectivity is zero.

[0029] Finally, the region division module uses the reciprocal of the shortest path length as the connectivity between the two flooded areas. When the shortest path length is 1, the connectivity is 1, indicating that the two flooded areas are directly connected, representing the strongest hydraulic connectivity. When the shortest path length is 2, the connectivity is 0.5, indicating that the two flooded areas are indirectly connected through an intermediate area, representing the next strongest hydraulic connectivity. The longer the shortest path length, the lower the connectivity and the weaker the hydraulic connectivity. Through the above calculations, the connectivity value between any two flooded areas is obtained, which is used for subsequent region aggregation judgment.

[0030] Specifically, the area division module calculates the conflict degree of each flooded area, wherein, The area division module extracts the drainage outlet information corresponding to each flooded area and the remaining drainage capacity of each drainage outlet from the second data. For any two flooded areas sharing the same drainage outlet, the area division module adds the estimated drainage water demand of the first flooded area to the estimated drainage water demand of the second flooded area to obtain the total water demand. The region division module divides the total water demand by the remaining drainage capacity of the drainage outlet to obtain the conflict degree between the two flooded areas.

[0031] In this embodiment of the invention, the region division module extracts the drainage outlet information corresponding to each flooded area and the remaining drainage capacity of each drainage outlet from the second data. Specifically, each flooded area is connected to one or more drainage outlets (such as drainage pumping stations, river outlets, rainwater pipe network outlets, etc.) through an underground pipe network. The region division module determines the main drainage outlet of each flooded area based on the pipe network topology. Simultaneously, it obtains the remaining drainage capacity of each drainage outlet from the second data. This remaining drainage capacity is equal to the maximum designed drainage capacity of the drainage outlet minus the currently used drainage capacity. For example, if a pumping station has a designed drainage capacity of 1000 cubic meters per hour and is currently handling 600 cubic meters per hour of drainage, then the remaining drainage capacity is 400 cubic meters per hour. Next, the region division module identifies flooded areas sharing the same drainage outlet. For each drainage outlet, the region division module traverses all flooded areas and filters out all flooded areas with that drainage outlet as the main drainage outlet, forming the set of flooded areas corresponding to that drainage outlet.

[0032] Then, for any two flooded areas sharing the same drainage outlet, the area division module calculates the estimated drainage water demand. The estimated drainage water demand for each flooded area is calculated based on the area's water depth, water area, and target drainage time, for example, using the formula: Estimated drainage water demand = Water area × Average water depth ÷ Target drainage time. The area division module adds the estimated drainage water demand of the first flooded area to the estimated drainage water demand of the second flooded area to obtain the total water demand. Finally, the area division module divides the total water demand by the remaining drainage capacity of the drainage outlet to obtain the conflict degree between the two flooded areas. When the conflict degree is less than or equal to 1, it indicates that the remaining drainage capacity of the drainage outlet can simultaneously meet the drainage needs of two flooded areas, and there is no substantial resource competition between them. When the conflict degree is greater than 1, it indicates that the remaining drainage capacity of the drainage outlet is insufficient to simultaneously meet the drainage needs of both areas, and it is determined that there is a drainage resource competition relationship between the two flooded areas. The higher the conflict degree, the more intense the competition. For example, if the water demand of the two areas is 200 cubic meters / hour and 300 cubic meters / hour respectively, and the remaining drainage capacity of the drainage outlet is 400 cubic meters / hour, then the conflict degree is (200+300) / 400=1.25, which is greater than 1, indicating that there is resource competition.

[0033] Specifically, the area division module divides the urban area into several waterlogged island units, wherein, The region division module uses each flooded area as a node and edges with connectivity greater than a preset connectivity threshold to construct a first association graph, and calculates the first set of connected components of the first association graph. The region division module uses each flooded area as a node and edges with a conflict degree greater than a preset conflict threshold to construct a second association graph, and calculates the second connected component set of the second association graph. The region division module aggregates the flooded areas in the intersection of the first connected component set and the second connected component set into a single flooded island unit.

[0034] In this embodiment of the invention, the region division module constructs a first association graph using each flooded area as a node and edges with a connectivity greater than a preset connectivity threshold. The preset connectivity threshold can be set based on the density of the urban pipe network and historical flooding data, for example, 0.3. For any two flooded areas, if their connectivity is greater than 0.3, an edge is established between the corresponding nodes, indicating that there is a hydraulic connection between the two areas. After determining all node pairs, the first association graph is formed. Then, the region division module uses a depth-first search or disjoint-set data structure algorithm to calculate the connected components of the first association graph. Nodes within each connected component are directly or indirectly connected to each other, and the set of these nodes is denoted as the first connected component set. For example, if areas A and B are connected, and B and C are connected, then A, B, and C belong to the same first connected component. Next, the region division module constructs a second association graph using each flooded area as a node and edges with a conflict degree greater than a preset conflict threshold. The preset conflict threshold is usually set to 1, as mentioned earlier, a conflict degree greater than 1 indicates insufficient drainage resources. For any two flooded areas, if they share the same drainage outlet and their conflict degree is greater than 1, an edge is established between the corresponding nodes, indicating that there is a competition for drainage resources between the two areas. After completing the judgment of all node pairs, a second association graph is formed.

[0035] Then, using the same connected component calculation method, the second connected component set of the second association graph is obtained. For example, if regions A and B share the same pumping station with a conflict degree of 1.25, and B and C share the same pumping station with a conflict degree of 1.1, then A, B, and C belong to the same second connected component. Next, the region partitioning module performs an intersection operation on the first connected component set and the second connected component set. Specifically, for a certain set of regions in the first connected component and a certain set of regions in the second connected component, if the two sets contain the same regions, then these regions constitute an intersection. For example, if the first connected component contains regions {A, B, C}, and the second connected component also contains regions {A, B, C}, then the intersection contains regions {A, B, C}. If the first connected component contains {A, B, C}, and the second connected component contains {A, B}, then the intersection contains regions {A, B}. Finally, the region partitioning module aggregates the flooded areas in the intersection into the same flooded island unit. Each isolated flood-prone area unit satisfies both hydraulic connectivity (connectivity greater than a threshold) and drainage resource competition (conflict greater than a threshold), meaning that these areas not only allow water to flow between each other but also compete for the same drainage outlet resource. Isolated areas that do not belong to any intersection are treated as independent drainage units.

[0036] In this embodiment of the invention, the preset connectivity threshold is set based on the urban pipe network density and historical flooding data. Specifically, the connectivity distribution between all pairs of flooded areas within the city is statistically analyzed, and the average connectivity μ is calculated. c and standard deviation σ c Set the preset connectivity threshold to μ c -0.5σ c When the connectivity exceeds this threshold, it indicates that the hydraulic connectivity between the two areas is higher than the city average, making them worthy of aggregation into the same pre-divided unit. For older urban areas with dense pipe networks, connectivity is generally higher, and this threshold will be automatically adjusted; for newer urban areas with sparse pipe networks, connectivity is generally lower, and the threshold will be lower accordingly. The preset conflict threshold is fixed at 1. Its physical meaning is: when the total water demand of two flooded areas at the same drainage outlet exceeds the remaining drainage capacity of that outlet, drainage resources cannot simultaneously meet the drainage needs of both areas, inevitably leading to resource competition, requiring them to be included in the same conflict association diagram for unified scheduling.

[0037] This invention constructs a hydraulic topology map and calculates the reciprocal of the shortest path length as connectivity, quantifying the strength of hydraulic connectivity between any two flooded areas into a continuous value between 0 and 1. This overcomes the limitation of traditional methods, which can only determine connectivity but not its strength, providing a refined quantitative basis for subsequent regional aggregation. Simultaneously, by calculating the conflict degree by estimating the ratio of drainage demand to the remaining drainage capacity of the drainage outlet, the intensity of resource competition among multiple flooded areas for the same drainage outlet is quantified into a comparable value. When the conflict degree is greater than 1, resource competition is automatically determined, providing a quantitative criterion for identifying resource coupling relationships. Based on this, a first association graph is constructed with edges having a connectivity greater than a preset connectivity threshold, and a second association graph is constructed with edges having a conflict degree greater than a preset conflict threshold. The intersection of the connected component sets of the two association graphs is then used to aggregate them into waterlogged island units. This ensures that the regions within each waterlogged island unit satisfy both hydraulic connectivity and resource competition conditions. That is, these regions not only allow water to flow between each other physically, but also compete for the same drainage outlet. This avoids separating or incorrectly aggregating regions that are hydraulically tightly coupled but have no resource competition from regions that are hydraulically disconnected but have strong dependencies due to sharing a drainage outlet. This ensures the internal connectivity and physical integrity of the waterlogged island units.

[0038] Specifically, the key node identification module calculates the overlap between each key node, wherein... The key node identification module determines the set of flooded areas affected by each key node; The key node identification module calculates the intersection and union of the sets of waterlogged areas affected by any two key nodes; The key node identification module divides the intersection size by the union size, and the resulting ratio is used as the overlap between the two key nodes.

[0039] In this embodiment of the invention, the key node identification module determines the set of flooded areas affected by each key node. A key node refers to a node within a flooded island unit whose water level drop significantly leads to a synchronous drop in water levels in other flooded areas, such as a pipe network confluence point, a bottleneck pipe section, a shared pump node, or the lowest point. The key node identification module uses a hydraulic model simulation. For each candidate key node, it simulates completely draining the water from that node and monitors the water level changes in other flooded areas within the flooded island unit. When the water level drop in a flooded area exceeds a preset water level drop threshold, it is determined that the flooded area is affected by the key node and included in the set of areas affected by that key node. In this way, each key node corresponds to a set of areas affected, and the elements in the set are other flooded areas affected by the drainage of that node.

[0040] Secondly, for any two key nodes, such as key node A and key node B, the key node identification module obtains the set S of the influence region of key node A. A and the set of influence regions S of the key node B B Then, the key node identification module calculates the intersection S of the two sets. A ∩S B The size of the set, i.e., the number of flooded areas simultaneously affected by the drainage of both critical nodes A and B; and the union S of the two sets is calculated simultaneously. A ∪S B The size of the intersection is the number of flooded areas affected by the drainage of at least one of the key nodes, A or B. Then, the key node identification module divides the intersection size by the union size, and the resulting ratio is taken as the overlap between the two key nodes. That is, overlap = |S| A ∩S B | / |S A ∪S B The overlap value ranges from 0 to 1. When the overlap is close to 1, it indicates that the flooded areas affected by the two key nodes highly overlap, meaning that the areas alleviated after the two nodes drain water are almost identical, indicating that the two nodes are functionally redundant. When the overlap is close to 0, it indicates that the flooded areas affected by the two key nodes hardly overlap, meaning that the two nodes affect different flooded areas, indicating that the two nodes are functionally complementary.

[0041] In this embodiment of the invention, the method for determining the preset water level drop threshold is as follows: When calculating the flood-prone area affected by key nodes, it is necessary to determine whether the water level drop has reached the mitigation standard. The preset water level drop threshold is set to 5 centimeters. The basis for this determination is: according to urban road design specifications, the curb height of general roads is 15 centimeters. When the water depth drops by 5 centimeters, it can significantly improve traffic conditions for flooded areas with a depth of 5-20 centimeters, and this value is easy to measure and verify in engineering. For different cities, it can be adjusted within the range of 3-10 centimeters according to local road conditions and drainage standards.

[0042] This invention simulates the impact of each key node's individual drainage on water levels in other flood-prone areas, quantifies the impact range of each key node, and constructs a set of affected areas. Based on this, by calculating the ratio of the intersection to the union of the affected area sets of any two key nodes, an overlap value is obtained, reflecting the redundancy or complementarity of the two key nodes in their drainage functions. This provides a basis for determining the drainage sequence between key nodes, allowing drainage scheduling to be optimized based on the degree of overlap, thereby achieving precise allocation of drainage resources and maximizing overall efficiency.

[0043] Specifically, the key node identification module calculates the coordination coefficient between each key node, wherein... The critical node identification module determines the number of flooded areas affected by each critical node after it is drained individually. The critical node identification module determines the number of flooded areas affected by the simultaneous drainage of any two critical nodes. The critical node identification module subtracts the sum of the number of flooded areas affected by the simultaneous drainage of the two critical nodes from the number of flooded areas affected by the individual drainage of the two critical nodes to obtain the difference. The critical node identification module divides the difference by the sum of the number of flooded areas affected by the drainage of the two critical nodes individually, and the resulting ratio is used as the coordination coefficient between the two critical nodes.

[0044] In this embodiment of the invention, the key node identification module determines the number of flooded areas affected by each key node after it is individually drained. Based on hydraulic model simulation, for each key node, the simulation is performed to completely drain the water from that node, and the water level changes of other flooded areas within the flooded island unit are monitored. When the water level drop in a certain flooded area exceeds a preset water level drop threshold, it is determined that the flooded area is affected by the key node, and the number of flooded areas affected by the key node is counted and denoted as A. i For example, if the water level in three flooded areas drops beyond the threshold after key node 1 is drained alone, then A1=3; if the water level in four flooded areas drops beyond the threshold after key node 2 is drained alone, then A2=4. Next, the key node identification module determines the number of flooded areas affected by the simultaneous draining of any two key nodes. Based on hydraulic model simulation, for any two key nodes, the simulation shows the simultaneous and complete draining of the accumulated water from both nodes, monitoring the water level changes in other flooded areas within the flooded island unit. When the water level drop in a certain flooded area exceeds a preset water level drop threshold, it is determined that the flooded area is affected by the simultaneous draining of these two key nodes, and the number of flooded areas affected by the simultaneous draining of these two key nodes is counted and denoted as A. ij For example, if critical node 1 and critical node 2 are drained simultaneously, and the water level in 5 flooded areas drops above the threshold, then A... 12 =5.

[0045] Then, the critical node identification module subtracts the sum of the number of flooded areas affected by the simultaneous drainage of the two critical nodes from the number of flooded areas affected by the individual drainage of the two critical nodes, obtaining the difference. That is, difference = A. ij -(A i +A jFollowing the example above, the difference = 5 - (3 + 4) = -2. Finally, the key node identification module divides the difference by the sum of the number of flooded areas affected by the individual drainage of the two key nodes, and the resulting ratio is used as the coordination coefficient between the two key nodes. Using the example above, the synergy coefficient is approximately -2 / 7 to -0.286.

[0046] The synergy coefficient ranges from -1 to 1. When the synergy coefficient is greater than 0, it indicates a positive synergistic effect from simultaneous drainage by two key nodes. This means the number of flooded areas affected by simultaneous drainage is greater than the sum of the effects of individual drainage, indicating a mutually reinforcing synergy between the two nodes. For example, drainage by one node improves water flow channels, expanding the influence range of the other node. When the synergy coefficient is equal to 0, it indicates no synergistic effect from simultaneous drainage by two key nodes. This means the impact of simultaneous drainage is exactly equal to the sum of the effects of individual drainage, indicating that the two nodes function independently. When the synergy coefficient is less than 0, it indicates a negative synergistic effect from simultaneous drainage by two key nodes. This means the number of flooded areas affected by simultaneous drainage is less than the sum of the effects of individual drainage, indicating a mutually inhibiting relationship between the two nodes. For example, the flooded areas affected by the two nodes highly overlap, resulting in redundancy and waste during simultaneous drainage.

[0047] This invention uses a hydraulic model to simulate and obtain the number of flooded areas affected by individual drainage at each key node and by the simultaneous drainage of any two key nodes. It then calculates the ratio of the difference between the number of areas affected by simultaneous drainage and the sum of the number affected by individual drainage to the sum of the number of areas affected by individual drainage, thus obtaining a synergy coefficient. This synergy coefficient quantitatively characterizes the interaction between two key nodes, providing a basis for optimizing the drainage sequence among key nodes. Positive synergy nodes with high synergy coefficients should be drained adjacently to fully utilize synergistic effects, while negative synergy nodes with low synergy coefficients should be drained separately to avoid resource waste. This maximizes the marginal benefit of each drainage action and improves the overall utilization efficiency of limited drainage resources.

[0048] Specifically, the key node identification module determines the drainage sequence among the key nodes within the flooded island unit, wherein, The key node identification module constructs a key node relationship graph based on the overlap and coordination coefficient between each key node, with the reciprocal of the overlap as the initial weight and the coordination coefficient as the correction factor. The key node identification module calculates the minimum spanning tree of the key node relationship graph and uses the optimal path order of the minimum spanning tree as the emptying order among the key nodes.

[0049] In this embodiment of the invention, the key node identification module constructs a key node relationship graph based on the overlap and coordination coefficient between key nodes. Specifically, each key node within the flooded island unit is taken as a node in the graph, and for any two key nodes i and j, their initial weights are calculated. The initial weights are the reciprocal of the overlap, i.e. The higher the overlap, the lower the initial weight, indicating that the influence areas of the two nodes highly overlap and should be closely adjacent in the emptying order. Conversely, the lower the overlap, the higher the initial weight, indicating that the influence areas of the two nodes are complementary and can be sorted relatively separately. Then, the key node identification module uses the synergy coefficient as a correction factor to adjust the initial weights. The correction rules are as follows: when the synergy coefficient is greater than 0, it indicates that emptying the two nodes simultaneously has a mutually reinforcing effect, and their weights should be further reduced to make them more closely adjacent in the emptying order. The correction method is to divide the initial weight by (1 + synergy coefficient). When the synergy coefficient is less than 0, it indicates that emptying the two nodes simultaneously has a mutually inhibiting or redundant waste effect, and their weights should be increased to appropriately separate them in the emptying order. The correction method is to multiply the initial weight by (1 + |synergy coefficient|). When the synergy coefficient equals 0, the weight remains unchanged. Through the above corrections, the final weight between any two key nodes is obtained. The lower the weight, the more adjacent the two nodes should be in the emptying order.

[0050] Secondly, the key node identification module constructs a weighted undirected graph, i.e., a key node relationship graph, using all key nodes as vertices and the corrected weights as edge weights. Then, the key node identification module calculates the minimum spanning tree of the key node relationship graph. The minimum spanning tree is a connected subgraph that connects all vertices and minimizes the sum of the weights of all edges. Commonly used algorithms include Prim's algorithm or Kruskal's algorithm. By calculating the minimum spanning tree, a set of paths with minimum weights connecting all key nodes is obtained. The edges in this set represent the optimal adjacency relationships between key nodes; that is, edges with lower weights are more likely to be retained in the minimum spanning tree, thus ensuring that key nodes with high synergy and high overlap are directly connected in the spanning tree. Finally, the key node identification module uses the optimal path order of the minimum spanning tree as the order of clearing key nodes. Specifically, a depth-first traversal or Hamiltonian path is performed on the minimum spanning tree to generate a path that passes through all key nodes and follows the edges of the minimum spanning tree as much as possible. The order in which nodes are visited on this path is the order of clearing key nodes.

[0051] This invention uses the reciprocal of the overlap degree as the initial weight, giving nodes with highly overlapping influence areas lower edge weights in the graph. A synergy coefficient is used as a correction factor to further reduce the weight of nodes with positive synergy effects and increase the weight of nodes with negative synergy effects. This automatically connects key nodes with high synergy and high overlap in the minimum spanning tree. The optimal path order obtained by traversing the minimum spanning tree ensures that nodes with strong synergy in the drainage sequence are closely adjacent, fully utilizing the positive synergy between nodes to expand the drainage impact range. Simultaneously, mutually inhibiting nodes are appropriately separated to avoid resource waste and redundant investment. This achieves automatic quantitative optimal determination of the drainage sequence among key nodes without human intervention, significantly improving the overall efficiency of drainage operations.

[0052] Specifically, the cross-unit scheduling module calculates the unit leverage density, wherein, The cross-unit scheduling module calculates the sum of the drainage leverage ratios of all key nodes in each waterlogged island unit, where the drainage leverage ratio of each key node is equal to the number of other waterlogged areas that can be alleviated after the key node is emptied, divided by the estimated time required to empty the key node. The cross-unit scheduling module calculates the overlap between any two key nodes within each waterlogged island unit, where the overlap between any two key nodes is equal to the number of waterlogged areas jointly affected by the two key nodes divided by the union of the number of waterlogged areas each key node individually affects. The cross-unit scheduling module calculates the average overlap of all combinations of any two key nodes as the average overlap of the waterlogged island unit. The cross-unit scheduling module divides the sum of the drainage leverage ratios by the sum of the average overlap to obtain the unit leverage density of the flooded island unit.

[0053] In this embodiment of the invention, the cross-unit scheduling module calculates the sum of the drainage leverage ratios of all key nodes within each isolated flooded unit. For each key node, its drainage leverage ratio equals the number of other flooded areas that can be alleviated after the key node is drained, divided by the estimated time required to drain the key node. The "number of other flooded areas that can be alleviated" is determined based on a hydraulic model simulation: the simulation completely drains the water from the key node, monitors the water level changes in other flooded areas within the isolated flooded unit, and counts the number of flooded areas whose water level drop exceeds a preset water level drop threshold (e.g., 5 cm), denoted as N. i The "estimated time required to drain this critical node" is calculated based on the total amount of water accumulated at the critical node and the drainage capacity of available drainage equipment, for example, T. i =Water volume / Rated flow rate of drainage equipment. Then the evacuation leverage ratio L of a single critical node. i =Ni / T i Then, the drainage leverage ratios of all key nodes within the flooded island unit are summed to obtain the sum of the drainage leverage ratios L. sum =ΣL i .

[0054] Secondly, the cross-unit scheduling module calculates the overlap between any two key nodes within each flood-prone island unit. The overlap between any two key nodes is O. ij The overlap is equal to the number of flooded areas jointly affected by the two key nodes divided by the union of the number of flooded areas individually affected by each key node. Here, "number of flooded areas jointly affected" refers to the intersection of the sets of areas affected by each key node individually, and "union" refers to the union of the sets of areas affected by each key node individually. The overlap value ranges from 0 to 1; a higher overlap value indicates a greater overlap in the flooded areas affected by the two key nodes. Then, the cross-unit scheduling module calculates the average overlap value of all combinations of any two key nodes, which is taken as the average overlap value of the flooded island unit. Assuming there are k key nodes in the flooded island unit, there are a total of... Key node pairs. Average overlap. The average overlap reflects the overall degree of repetition of the influence areas between key nodes within the unit. The higher the average overlap, the more severe the functional redundancy between key nodes.

[0055] Finally, the cross-unit scheduling module divides the sum of the drainage leverage ratios by the sum of the average overlap ratios to obtain the unit leverage density of the flooded island unit. That is, the unit leverage density. Adding 1 to the denominator prevents the denominator from being zero when the average overlap is 1, while also ensuring that the unit leverage density is always positive. The physical meaning of unit leverage density is: considering the functional redundancy of key nodes within a unit, the net driving capacity of those key nodes. The greater the sum of the exhaust leverage ratios, the stronger the overall driving capacity of the unit; the greater the average overlap, the more severe the functional redundancy between key nodes within the unit, the higher the degree of dilution of net benefits, and the lower the unit leverage density.

[0056] Specifically, the cross-unit scheduling module determines the processing order among several isolated flood-prone units, wherein, The cross-unit scheduling module sorts each waterlogged island unit according to its unit leverage density from high to low to obtain the initial processing order. The cross-unit scheduling module identifies waterlogged island units that depend on the same drainage outlet, assigns them to the same resource conflict group, and determines the serial processing order within the same resource conflict group according to the unit leverage density from high to low. The cross-unit scheduling module determines the waterlogged island units in different resource conflict groups as being processed in parallel, and merges the serial processing order with the parallel processing relationship to generate the final processing order.

[0057] Specifically, when the cross-unit scheduling module determines the serial processing order in the same resource conflict group according to the unit leverage density from high to low, if there are two or more waterlogged island units with the same unit leverage density, the serial processing order is determined according to the estimated total drainage time of each waterlogged island unit from small to large.

[0058] In this embodiment of the invention, the estimated total drainage time refers to the total time required for all key nodes within the unit to complete drainage sequentially according to the determined drainage order. Specifically, the calculation method is as follows: the estimated drainage times of each key node within the isolated flooded island unit are added together to obtain the estimated total drainage time of the unit. That is, the estimated total drainage time T. unit =ΣT nodej T nodej This represents the estimated emptying time for the j-th critical node within this unit. The estimated emptying time T for each critical node is... nodej The drainage capacity is calculated based on the total water accumulation at the critical node and the available drainage equipment. Specifically, the total water accumulation at the critical node equals the water area at the node's location multiplied by the average water depth. The available drainage equipment includes fixed pumping stations and mobile pump trucks. The drainage capacity is determined based on the real-time operating status of the drainage facilities in the second data obtained in real time. For example, if a pumping station has a rated drainage capacity of 500 cubic meters per hour and is currently handling 200 cubic meters per hour of drainage, then the remaining available drainage capacity is 300 cubic meters per hour. The estimated emptying time T is then calculated. nodej = Total water accumulation at critical nodes / Remaining drainage capacity of drainage equipment allocated to that critical node.

[0059] In this embodiment of the invention, the cross-unit scheduling module sorts the isolated flooded island units from high to low according to their unit leverage density to obtain an initial processing order. Unit leverage density reflects the net driving capacity of key nodes within an isolated flooded island unit; units with higher density have higher overall drainage efficiency and therefore higher priority in the initial processing order. Secondly, the cross-unit scheduling module identifies isolated flooded island units that depend on the same drainage outlet and groups them into the same resource conflict group. Specifically, each isolated flooded island unit is associated with one or more drainage outlets through its internal key nodes. The cross-unit scheduling module groups units according to the main drainage outlets they depend on, grouping all isolated flooded island units that depend on the same drainage outlet into the same resource conflict group. For example, if units A, B, and C all depend on pump station X for drainage, these three units are grouped into the same resource conflict group; if unit D depends on pump station Y and unit E depends on pump station Y, then units D and E are grouped into another resource conflict group.

[0060] Then, the cross-unit scheduling module determines the serial processing order within the same resource conflict group according to the unit leverage density from high to low. Since the isolated flooded island units within the same resource conflict group share the same drainage outlet, and the remaining drainage capacity of this outlet is limited and cannot simultaneously meet the drainage needs of multiple units, these units must be processed serially; that is, the next unit can only begin using the drainage outlet after one unit has completed drainage. The serial processing order is determined according to the unit leverage density from high to low, with units of higher density processed first to ensure maximum overall drainage efficiency. When two or more isolated flooded island units within the same resource conflict group have the same unit leverage density, the cross-unit scheduling module determines the serial processing order according to the estimated total drainage time of each isolated flooded island unit from short to long; that is, units with shorter estimated total drainage time are processed first to quickly release drainage outlet resources for subsequent units, reducing overall queuing time. The cross-unit scheduling module determines that isolated flooded island units in different resource conflict groups are processed in parallel. Since different resource conflict groups rely on different drainage outlets, and each drainage outlet is independent of the others, there is no resource competition. Therefore, the isolated waterlogging units in different resource conflict groups can be processed simultaneously in parallel without waiting for each other. Finally, the cross-unit scheduling module merges the serial processing order with the parallel processing relationship to generate the final processing order.

[0061] This invention effectively solves the resource competition problem of multiple units sharing a limited drainage outlet by grouping waterlogged island units that rely on the same drainage outlet into resource conflict groups and processing them serially within each group and in parallel between different groups. This avoids drainage stagnation and efficiency decline caused by resource conflicts. At the same time, by prioritizing unit leverage density and, when the density is the same, prioritizing the estimated total drainage time, the invention ensures the maximization of overall drainage benefits and the minimization of queuing time, thus achieving efficient scheduling of drainage resources across units.

[0062] In this embodiment of the invention, the processing module includes a task queue generation unit, an equipment scheduling unit, and a status monitoring unit. The task queue generation unit receives the final processing order output by the cross-unit scheduling module and parses the final processing order into a drainage task queue with timestamps. Each task item in the drainage task queue includes an identifier of an isolated flooded area, the drainage sequence of key nodes within that area, the estimated drainage time of each key node, and the type and quantity of drainage equipment required. The equipment scheduling unit is connected to the task queue generation unit and is used to issue control commands to the drainage equipment sequentially according to the drainage task queue, the processing order between isolated flooded areas, and the drainage order between key nodes within each isolated flooded area. The control commands include equipment start commands, equipment stop commands, flow adjustment commands, and equipment... The status monitoring unit is connected to the equipment scheduling unit and the data acquisition module respectively, and is used to collect the operating status of the drainage equipment and the water level change data of each flooded area in real time. When the deviation between the actual drainage progress and the estimated drainage progress exceeds the preset deviation threshold, a rescheduling request is generated and sent to the cross-unit scheduling module. During the drainage process, the processing module performs drainage operations on each key node in the current flooded island unit according to the drainage order between the key nodes in the unit. When the water depth of the previous key node drops below the preset safety threshold, the equipment scheduling unit automatically transfers the drainage resources to the next key node. After all key nodes in the current flooded island unit are emptied, the processing module automatically switches to the next flooded island unit in the drainage task queue.

[0063] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An urban flooding emergency management system based on the Internet of Things, characterized in that, include: The data acquisition module is used to acquire first data representing the hydraulic state of the urban drainage system and the distribution of surface water in each flood-prone area, and second data representing the real-time operating conditions and drainage capacity of the drainage facilities. The region division module is used to calculate the connectivity of each flooded area based on the first data, calculate the conflict degree of each flooded area based on the second data, and divide the urban area into several flooded island units according to the connectivity and the conflict degree. The critical node identification module is used to identify one or more critical nodes affecting the waterlogged island unit based on the first data and the second data, calculate the overlap and coordination coefficient between each critical node, and determine the drainage order between each critical node in the waterlogged island unit based on the overlap and the coordination coefficient. The cross-unit scheduling module is used to calculate the unit leverage density based on the sum of the drainage leverage ratios of all key nodes in each flooded island unit and the average overlap between the key nodes, and to determine the processing order among several flooded island units based on the unit leverage density. The processing module is used to perform drainage processing based on the processing order among the several waterlogged island units and the drainage order among the key nodes within the waterlogged island units.

2. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The region division module calculates the connectivity of each flooded area, wherein... The regional division module extracts the surface runoff paths and pipeline connection relationships between each flooded area from the first data. The region division module constructs a hydraulic topology map between various waterlogged areas based on the surface runoff path and the pipeline connection relationship; The region division module calculates the shortest path length between any two waterlogged areas in the hydraulic topology map, and uses the reciprocal of the shortest path length as the connectivity between the two waterlogged areas.

3. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The region division module calculates the conflict degree of each flooded area, wherein... The area division module extracts the drainage outlet information corresponding to each flooded area and the remaining drainage capacity of each drainage outlet from the second data. For any two flooded areas sharing the same drainage outlet, the area division module adds the estimated drainage water demand of the first flooded area to the estimated drainage water demand of the second flooded area to obtain the total water demand. The region division module divides the total water demand by the remaining drainage capacity of the drainage outlet to obtain the conflict degree between the two flooded areas.

4. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The area division module divides the urban area into several waterlogged island units, among which, The region division module uses each flooded area as a node and edges with connectivity greater than a preset connectivity threshold to construct a first association graph, and calculates the first set of connected components of the first association graph. The region division module uses each flooded area as a node and edges with a conflict degree greater than a preset conflict threshold to construct a second association graph, and calculates the second connected component set of the second association graph. The region division module aggregates the flooded areas in the intersection of the first connected component set and the second connected component set into a single flooded island unit.

5. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The key node identification module calculates the overlap between each key node, wherein... The key node identification module determines the set of flooded areas affected by each key node; The key node identification module calculates the intersection and union of the sets of waterlogged areas affected by any two key nodes; The key node identification module divides the intersection size by the union size, and the resulting ratio is used as the overlap between the two key nodes.

6. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The key node identification module calculates the coordination coefficient between each key node, where... The critical node identification module determines the number of flooded areas affected by each critical node after it is drained individually. The critical node identification module determines the number of flooded areas affected by the simultaneous drainage of any two critical nodes. The critical node identification module subtracts the sum of the number of flooded areas affected by the simultaneous drainage of the two critical nodes from the number of flooded areas affected by the individual drainage of the two critical nodes to obtain the difference. The critical node identification module divides the difference by the sum of the number of flooded areas affected by the drainage of the two critical nodes individually, and the resulting ratio is used as the coordination coefficient between the two critical nodes.

7. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The key node identification module determines the drainage sequence among the key nodes within the flooded island unit, wherein... The key node identification module constructs a key node relationship graph based on the overlap and coordination coefficient between each key node, with the reciprocal of the overlap as the initial weight and the coordination coefficient as the correction factor. The key node identification module calculates the minimum spanning tree of the key node relationship graph and uses the optimal path order of the minimum spanning tree as the emptying order among the key nodes.

8. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The cross-unit scheduling module calculates the unit lever density, wherein, The cross-unit scheduling module calculates the sum of the drainage leverage ratios of all key nodes in each waterlogged island unit, where the drainage leverage ratio of each key node is equal to the number of other waterlogged areas that can be alleviated after the key node is emptied, divided by the estimated time required to empty the key node. The cross-unit scheduling module calculates the overlap between any two key nodes within each waterlogged island unit, where the overlap between any two key nodes is equal to the number of waterlogged areas jointly affected by the two key nodes divided by the union of the number of waterlogged areas each key node individually affects. The cross-unit scheduling module calculates the average overlap of all combinations of any two key nodes as the average overlap of the waterlogged island unit. The cross-unit scheduling module divides the sum of the drainage leverage ratios by the sum of the average overlap to obtain the unit leverage density of the flooded island unit.

9. The urban flooding emergency management system based on the Internet of Things according to claim 1, characterized in that, The cross-unit scheduling module determines the processing order among several isolated flood-prone units, wherein... The cross-unit scheduling module sorts each waterlogged island unit according to its unit leverage density from high to low to obtain the initial processing order. The cross-unit scheduling module identifies waterlogged island units that depend on the same drainage outlet, assigns them to the same resource conflict group, and determines the serial processing order within the same resource conflict group according to the unit leverage density from high to low. The cross-unit scheduling module determines the waterlogged island units in different resource conflict groups as being processed in parallel, and merges the serial processing order with the parallel processing relationship to generate the final processing order.

10. The urban flooding emergency management system based on the Internet of Things according to claim 9, characterized in that, When the cross-unit scheduling module determines the serial processing order in the same resource conflict group according to the unit leverage density from high to low, if there are two or more waterlogged island units with the same unit leverage density, the serial processing order is determined according to the estimated total drainage time of each waterlogged island unit from small to large.

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