Urban flood prevention early warning and emergency command integrated system and method

Abstract

The invention relates to the technical field of flood prevention early warning and emergency command, in particular to an urban flood prevention early warning and emergency command integrated system and method, which collects meteorology, hydrology, urban drainage network and video monitoring in real time through a multi-source data acquisition module, and reports data through a mobile terminal. The dynamic early warning analysis module couples a meteorological time sequence predicted value based on a gating circulation unit, drainage pipe network hydraulic model output and a terrain influence factor function, a weight coefficient is calibrated in real time through KL divergence, a high-precision dynamic early warning map is generated, the three-dimensional visual command module generates a dynamic submerging area three-dimensional model, and the dynamic submerging area three-dimensional model is subjected to dynamic early warning. And in the intelligent scheduling decision module, an improved ant colony optimization algorithm is combined with an environment risk factor to realize path planning and real-time catastrophe dynamic coupling. The urban flood prevention early warning accuracy and the emergency command efficiency are greatly improved, and urban flood disasters are powerfully handled.

Description

Technical Field

[0001] The present invention relates to the technical field of flood prevention warning and emergency command, and in particular to an integrated system and method for urban flood prevention warning and emergency command. Background Art

[0002] With global climate change, extreme weather events are becoming more frequent, and the probability and intensity of urban flood disasters have increased significantly. The rapid development of urban construction has changed the original underlying surface conditions, the impervious area has increased significantly, and the rainwater runoff coefficient has increased, making the urban drainage system face unprecedented pressure.

[0003] The existing urban flood prevention and early warning system has many shortcomings. On the one hand, multi-source data collection lacks systematicity and real-time performance. Meteorological departments, hydrological monitoring stations, drainage management departments, etc. collect data independently, with inconsistent data formats and update frequencies, making it difficult to achieve efficient integration and sharing. For example, meteorological data may not be able to accurately reflect changes in the local microclimate of the city in a timely manner, and hydrological data has a lag in monitoring the water flow status inside the urban drainage network, which leads to deviations in the judgment of flood conditions.

[0004] On the other hand, the accuracy of the early warning analysis model is poor. The traditional meteorological forecast model and the urban waterlogging model have a low degree of coupling and cannot fully consider the interaction of multiple factors such as meteorological factors, topography, and drainage network operation conditions. For example, in some complex terrain areas, the existing models are difficult to accurately simulate the formation and diffusion process of water accumulation, and the early warning map cannot accurately reflect the actual flood situation, resulting in insufficient reliability of early warning information and failure to provide strong support for flood control decision-making.

[0005] In the emergency command link, the problems are also prominent. It is difficult for various departments to coordinate, and there is a lack of a unified visual command platform. When dealing with floods, urban planning, transportation, emergency rescue and other departments have poor information communication and low command and dispatch efficiency. For example, when deploying emergency resources, the lack of real-time and comprehensive understanding of traffic conditions, resource distribution and needs of the affected areas leads to unreasonable resource allocation and poor transportation route planning, making it difficult to deliver resources to the disaster site within the optimal time, which seriously affects the timeliness and effectiveness of emergency rescue work. At the same time, the communication guarantee between the site and the command center is unstable. In extreme weather, communication interruptions occur from time to time, affecting the timely feedback of on-site information and the transmission of command instructions. Therefore, in response to the above problems, an integrated system and method for urban flood control warning and emergency command is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an integrated system and method for urban flood prevention warning and emergency command to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An integrated system for urban flood prevention warning and emergency command, comprising:

[0009] Multi-source data acquisition module: used to obtain meteorological data, hydrological data, urban drainage network data and video surveillance data in real time, and report the data using mobile terminals;

[0010] Dynamic warning analysis module: Generates dynamic warning maps by coupling the meteorological forecast model with the urban waterlogging model. The coupled meteorological forecast model uses machine learning algorithms to correct forecast parameters in real time.

[0011] 3D visualization command module: Integrates BIM city model and GIS geographic information data to realize 3D spatial visualization of flood situation and multi-department collaborative operation interface;

[0012] Intelligent scheduling decision module: including emergency resource path planning unit based on improved ant colony optimization algorithm and resource matching decision unit based on dynamic weight multi-objective optimization;

[0013] Cross-platform communication module: supports 5G-MEC edge computing and satellite communication dual-link guarantee, realizes multimedia transmission of command instructions and augmented reality (AR) interaction of on-site terminals.

[0014] As a preferred solution, the dynamic warning analysis module adopts an improved spatiotemporal fusion prediction model, and its core algorithm is defined as: , in, for The predicted value at the moment; , , is the dynamic weight coefficient, which is calibrated in real time through KL divergence; is the meteorological time series prediction value based on the gated recurrent unit, For meteorological data; Output the rate of change of the hydraulic model of the drainage network with respect to time, is the water level in the pipe, is the surface runoff flow; is the integral value of terrain influence, is the terrain influence factor function, and is the integration interval.

[0015] As a preferred solution, the improved ant colony optimization algorithm in the intelligent scheduling decision module meets the following requirements: , in, for Time Path pheromone concentration on; for Time Path pheromone concentration on; is the pheromone volatility coefficient; For the Ants on the path The increase of pheromones left on the is the total number of ants; is the environmental risk adjustment factor; for Joint risk assessment value of road water depth and bridge load-bearing capacity at all times.

[0016] As a preferred solution, the resource matching decision unit adopts a dynamic weight multi-objective optimization model: , in, , , It is a dynamically adjusted weight parameter linked to the warning level; For emergency response time; Cost of emergency resources; To improve the efficiency of emergency resource deployment;

[0017] Constraints: ,in, For the The number of emergency resources; is the number of types of emergency resources; is the total amount of emergency resource demand; Demand volatility is updated in real time based on public opinion data; The arrival time of emergency resources; The length of the transportation route for emergency resources; for The speed of emergency resource transportation at all times; is the path waterlogging influence coefficient; The depth of water accumulation on the road section.

[0018] As a preferred solution, in the spatiotemporal fusion prediction model:

[0019] The dual-channel attention mechanism is used to improve the LSTM network, and its attention weight is calculated as: ,in, for The attention weight of the moment; , , is the trainable parameter matrix; for Hidden layer state at all times; Enter features for the current moment; is the bias term;

[0020] The SWMM model incorporates a pipe siltation correction factor: ,in, is the actual flow rate; is the theoretical flow rate; is the pipeline aging coefficient; To maintain the impact factor; The time since the last maintenance.

[0021] As a preferred solution, the three-dimensional visualization command module includes:

[0022] The three-dimensional model of the dynamic flooded area is generated based on the improved Marching Cubes algorithm, and the isosurface extraction formula is: ,in, For space point The isosurface function value at ; is the octree node weight; It is an improved Sigmoid function with increased terrain mutation response; is the water level height at the grid point; is the water level threshold;

[0023] Path planning visualization uses particle swarm optimization algorithm to generate navigation beams: , in, For the Daidi The particle in The speed of the dimension; is the inertia weight; For the Daidi The particle in The speed of the dimension; , , is the learning factor; , , for Random numbers in the interval; For the The particle in The optimal position of an individual in the dimension; For the Daidi The particle in The location of the dimension; The global optimal position is Dimensional coordinates; It is a hazard gradient item, which can avoid high-risk areas in real time.

[0024] As a preferred solution, it also includes:

[0025] The evaluation feedback module uses the improved TOPSIS algorithm to calculate the comprehensive score of the disposal plan: ,in, For the The overall score of the treatment options; For the The weight of each indicator is adjusted dynamically by introducing a time decay function; For the The ideal solution is the The distance on the indicator; For the The solution to the negative ideal solution is The distance on the indicator; is the risk penalty factor; is the number of indicators.

[0026] The invention discloses a method for urban flood prevention early warning and emergency command, which utilizes an integrated system of urban flood prevention early warning and emergency command to carry out urban flood prevention early warning and emergency command.

[0027] It can be seen from the technical solutions provided by the present invention that the integrated system and method for urban flood prevention warning and emergency command provided by the present invention has the following beneficial effects:

[0028] High-precision early warning: The dynamic early warning analysis module integrates meteorological forecasts and urban waterlogging models, and uses an innovative spatiotemporal fusion prediction model to combine LSTM meteorological time series predictions, drainage network hydraulic model outputs, and terrain influencing factor functions. Through real-time calibration of the dynamic weight coefficient through KL divergence, the model can flexibly adjust the weights of various factors according to different scenarios, greatly improving the accuracy and timeliness of waterlogging warnings, and providing solid and reliable data support for flood control decisions.

[0029] Efficient resource allocation: In the intelligent scheduling decision module, the path planning unit of the improved ant colony optimization algorithm innovatively introduces an environmental risk adjustment factor that is positively correlated with the real-time rainfall intensity, and combines the joint risk assessment value of road water depth and bridge load-bearing capacity to allow the emergency resource transportation route to avoid dangerous areas in real time, ensuring efficient and safe transportation. At the same time, the resource matching decision unit constructs a three-objective optimization system with time-varying weights, dynamically adjusts the weight parameters according to the warning level, comprehensively weighs the emergency response time, resource cost and allocation efficiency, and achieves the optimal allocation of emergency resources.

[0030] Intuitive visual command: The 3D visual command module integrates BIM city model and GIS geographic information data, uses the improved MarchingCubes algorithm to generate a dynamic 3D model of the flooded area, and uses the improved Sigmoid function to enhance the response to sudden changes in terrain, making the display of the flooded area more accurate and intuitive; the path planning visualization uses the particle swarm optimization algorithm to generate navigation beams, and adds a new danger gradient item to avoid high-risk areas in real time, providing commanders with clear dispatch guidance, effectively improving the efficiency and accuracy of multi-department collaborative operations;

[0031] Reliable communication guarantee: The cross-platform communication module adopts 5G-MEC edge computing and satellite communication dual-link guarantee mechanism to ensure that the multimedia transmission of command instructions is stable and reliable in complex and harsh environments; the augmented reality interaction function of the on-site terminal enables on-site staff to intuitively obtain command information and on-site flood data, significantly improving the timeliness and accuracy of emergency response;

[0032] Continuously optimize decision-making: The evaluation and feedback module uses the improved TOPSIS algorithm to calculate the comprehensive score of the treatment plan, dynamically adjusts the weight by introducing the time decay function, and considers the risk penalty factor to conduct a comprehensive and dynamic evaluation of different treatment plans; this provides a strong data basis for the improvement and optimization of subsequent flood prevention work, and promotes the continuous improvement of the overall level of urban flood prevention warning and emergency command. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a schematic diagram of the overall structure of an integrated system and method for urban flood prevention early warning and emergency command. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] like Figure 1 As shown, an embodiment of the present invention provides an integrated system for urban flood prevention warning and emergency command, including a multi-source data acquisition module, a dynamic warning analysis module, a three-dimensional visualization command module, an intelligent scheduling decision module and a cross-platform communication module.

[0037] In this embodiment, the multi-source data acquisition module is used to obtain meteorological data, hydrological data, urban drainage network data and video surveillance data in real time, and report the data using a mobile terminal;

[0038] Furthermore, the multi-source data acquisition module is responsible for collecting and integrating various key data in the integrated system of urban flood prevention warning and emergency command, providing indispensable basic support for subsequent warning analysis and command decision-making, and is an important link in ensuring the efficient implementation of urban flood prevention work; specifically:

[0039] 1. Data collection equipment and technology:

[0040] Meteorological data collection equipment: Use professional meteorological monitoring stations equipped with rain sensors, wind speed and direction instruments, temperature and pressure monitors and other equipment to accurately collect meteorological data; rain sensors can monitor precipitation and precipitation intensity in real time, providing a key basis for predicting waterlogging risks; for example, before a rainstorm arrives, they can capture precipitation changes in time and warn of possible waterlogging in advance; at the same time, use satellite meteorological cloud image receiving equipment to obtain macro-meteorological information, combined with ground monitoring data, to improve the accuracy of meteorological forecasts; meteorological data collection frequency is high, usually updated in minutes or even seconds, to ensure that meteorological changes can be reflected in a timely manner;

[0041] Hydrological data collection facilities: Hydrological monitoring equipment such as water level gauges and flow meters are installed in rivers, lakes, reservoirs and other water bodies. Water level gauges can monitor water level changes in real time, while flow meters can accurately measure water flow. For example, at river confluences or in areas prone to flooding, these devices can be used to keep abreast of water conditions. In addition, advanced technologies such as acoustic Doppler current profilers (ADCPs) are used to obtain more comprehensive hydrological information, including flow velocity distribution, to provide detailed data for flood evolution simulation. These devices transmit data to the system in real time through wired or wireless transmission methods.

[0042] Urban drainage network data collection technology: Use pipeline robots, liquid level sensors and other equipment to collect drainage network data; pipeline robots can go deep into the drainage pipes, take pictures of the internal conditions of the pipes, and detect whether there are problems such as damage and siltation; liquid level sensors are installed at key nodes of the pipes to monitor the water level of the pipes in real time; at the same time, combined with geographic information system (GIS) technology, the layout, pipe diameter, slope and other information of the drainage network are digitally managed to build an accurate drainage network model, which provides a basis for analyzing drainage capacity and waterlogging risks;

[0043] Video surveillance data collection equipment: Install high-definition cameras in key areas such as urban roads, bridges, and low-lying areas to form a video surveillance network; these cameras have night vision and automatic zoom capabilities, and can clearly capture images such as the depth of water accumulation, water flow speed, and road traffic conditions; through video analysis technology, they can automatically identify abnormal situations such as severe water accumulation and trapped vehicles, and issue alarms in a timely manner; video data is transmitted to the command center in real time through the network, providing intuitive on-site information for staff;

[0044] 2. Data collection and transmission process:

[0045] Data collection: Various data collection devices continuously collect corresponding data according to preset time intervals or trigger conditions; meteorological monitoring stations, hydrological monitoring equipment, drainage network sensors, and video surveillance cameras work simultaneously to ensure all-round and multi-dimensional data collection; during the data collection process, the collected data is subjected to preliminary quality inspection to eliminate data with obvious errors or abnormalities to ensure data accuracy;

[0046] Data transmission: The collected data is transmitted to the data processing center through a variety of communication methods. For data with short distances and high real-time requirements, such as urban drainage network data and some video surveillance data, wired networks (such as optical fibers) are used for transmission to ensure the stability and high speed of data transmission. For meteorological data, hydrological data, and widely distributed video surveillance data, wireless communication technology (such as 4G / 5G, Wi-Fi, etc.) is used for transmission. In addition, to prevent data loss due to communication interruption, some devices also have local storage functions, and automatically resend data after communication is restored.

[0047] Data integration: Data transmitted to the data processing center is classified and integrated according to different types and sources; meteorological data, hydrological data, drainage network data and video surveillance data are correlated and matched to form a unified data set; through data cleaning and preprocessing, duplicate data is further removed and missing data is filled, providing a high-quality data foundation for subsequent analysis and application;

[0048] 3. Importance in flood prevention work:

[0049] Accurate early warning support: The rich data collected by the multi-source data acquisition module provides strong support for the dynamic early warning analysis module; by coupling the meteorological forecast model with the urban waterlogging model, combined with real-time meteorological, hydrological and drainage network data, it can generate more accurate dynamic early warning maps, predict the time, location and severity of waterlogging in advance, and buy more preparation time for urban flood prevention work;

[0050] Scientific decision-making basis: Provide accurate data basis for the intelligent scheduling decision module; based on the collected real-time data, the intelligent scheduling decision module can plan the emergency resource path through the improved ant colony optimization algorithm, use the dynamic weight multi-objective optimization model to make resource matching decisions, realize the reasonable allocation of emergency resources, and improve the efficiency and effectiveness of flood control emergency response;

[0051] Real-time monitoring and command: The 3D visualization command module uses the data provided by the multi-source data acquisition module, integrates the BIM city model and GIS geographic information data, and realizes the 3D spatial visualization display of the flood situation. The staff can intuitively understand the flood situation in the city, conduct multi-department coordinated command, and issue accurate command instructions in a timely manner, thus improving the scientificity and timeliness of flood control command.

[0052] The multi-source data acquisition module plays a key role in urban flood prevention warning and emergency command through diversified data acquisition equipment and technologies, and rigorous data collection and transmission processes, providing solid data support for ensuring the city's safety during the flood season.

[0053] In this embodiment, the dynamic warning analysis module generates a dynamic warning map by coupling the meteorological prediction model with the urban waterlogging model, and the coupled meteorological prediction model uses a machine learning algorithm to correct the prediction parameters in real time;

[0054] Furthermore, the dynamic early warning analysis module plays a core role in the integrated system of urban flood prevention early warning and emergency command. It integrates multiple models and algorithms, deeply analyzes multi-source data, generates accurate dynamic early warning maps, provides key decision-making basis for urban flood prevention work, and effectively improves the city's ability to respond to flood disasters. Specifically:

[0055] 1. Core Algorithm and Model Fusion:

[0056] Improved spatiotemporal fusion prediction model: This module adopts an improved spatiotemporal fusion prediction model, which innovatively integrates multiple data prediction methods; the core algorithm formula is ; Among them, among them, for The predicted value at the moment; , , is a dynamic weight coefficient, which is calibrated in real time through KL divergence; is the meteorological time series prediction value based on the gated recurrent unit, For meteorological data; Output the rate of change of the hydraulic model of the drainage network with respect to time, is the water level in the pipe, is the surface runoff flow; is the integral value of terrain influence, is the terrain influence factor function, and is the integration interval;

[0057] Time series forecasting of meteorological data based on gated recurrent units can effectively capture the time series characteristics in meteorological data and accurately predict future meteorological trends, such as the prediction of key meteorological elements such as rainfall intensity and duration, providing meteorological basic data for subsequent waterlogging risk assessment;

[0058] Coupling meteorological and urban waterlogging models: By coupling meteorological forecasting models with urban waterlogging models, accurate mapping from meteorological data to urban waterlogging risks is achieved; meteorological forecasting models provide meteorological information such as rainfall, and urban waterlogging models (such as SWMM models) simulate surface runoff formation, drainage network water flow movement, and other processes based on these meteorological data combined with urban drainage network data and terrain data; Output the time-dependent rate of change for the drainage network hydraulic model, which reflects the operation status of the drainage network at different times, helps analyze whether the drainage capacity of the network can cope with the runoff generated by the current rainfall, and thus predicts the area and time where waterlogging may occur;

[0059] Dynamic weight coefficient calibration: , , is a dynamic weight coefficient, which is calibrated in real time through KL divergence. This calibration mechanism can dynamically adjust the weight of each part of the data in the prediction results according to the changes in the importance of meteorological data, drainage network data and terrain data at different times. In the early stage of heavy rain, meteorological data has a greater impact on the prediction of waterlogging risk. The weight may be relatively high; as rainfall continues, the importance of drainage network operation status data increases. The weights will be adjusted accordingly to ensure that the forecasting model can adapt to changes in actual conditions in real time and improve forecast accuracy;

[0060] 2. Algorithm optimization and improvement:

[0061] Dual-channel attention mechanism improves LSTM network: In the spatiotemporal fusion prediction model, a dual-channel attention mechanism is used to improve the LSTM network; the attention weight calculation method is ,in, for The attention weight of the moment; , , is the trainable parameter matrix; for Hidden layer state at all times; Enter features for the current moment; is a bias term. This mechanism assigns different attention weights to the LSTM network by learning the importance of input features at different times. When processing meteorological data, it can focus on meteorological elements that are more critical to rainfall prediction, such as air pressure changes and humidity anomalies, and ignore some relatively minor information, thereby more accurately capturing effective information in meteorological data and improving the accuracy of meteorological time series prediction.

[0062] SWMM model introduces pipeline sedimentation correction factor: The SWMM model is optimized and the pipeline sedimentation correction factor is introduced; the actual flow calculation formula is: ,in, is the actual flow rate; is the theoretical flow rate; is the pipeline aging coefficient; To maintain the impact factor; is the time since the last maintenance; considering that the drainage capacity of the pipeline will decrease due to siltation during long-term use, this correction factor can correct the theoretical flow rate according to the aging degree and maintenance status of the pipeline, so that the model is more in line with the actual drainage situation and improves the reliability of waterlogging prediction;

[0063] 3. Generation and application of early warning maps:

[0064] Dynamic warning map generation: Based on the above algorithms and models, the dynamic warning analysis module generates a dynamic warning map; the map presents the waterlogging risk levels of different areas of the city at different times in the future in an intuitive and visual way. Combined with GIS geographic information, it can accurately locate the risk areas; for example, different colors are used to represent different risk levels, red represents high-risk areas, yellow represents medium-risk areas, and green represents low-risk areas, so that decision makers can quickly understand the overall distribution and changing trends of urban waterlogging risks;

[0065] Provide support for emergency command: The generated early warning map provides an important basis for subsequent emergency command work; the intelligent dispatch decision module plans the emergency resource transportation route based on the early warning map and the emergency resource reserve situation using the improved ant colony optimization algorithm, and makes resource matching decisions through the dynamic weight multi-objective optimization model to achieve efficient deployment of emergency resources; the three-dimensional visualization command module displays the flood situation based on the early warning map, providing intuitive information support for multi-department coordinated command, so that each department can carry out flood prevention and rescue work in a targeted manner according to the risk area and level;

[0066] The dynamic warning analysis module uses complex and sophisticated algorithm models to achieve accurate prediction and dynamic warning of urban waterlogging risks. It plays an indispensable and key role in the urban flood warning and emergency command system, and provides strong support for ensuring urban safety and the safety of people’s lives and property.

[0067] In this embodiment, the three-dimensional visualization command module integrates the BIM city model and GIS geographic information data to achieve a three-dimensional spatial visualization display of the flood situation and a multi-department collaborative operation interface;

[0068] Furthermore, the 3D visualization command module is a key component of the integrated system of urban flood warning and emergency command. With the help of advanced technical means, it presents complex flood information in an intuitive and vivid 3D form, providing an efficient collaborative operation platform for flood control command, greatly improving the scientificity and accuracy of command decision-making; specifically:

[0069] 1. Data integration and fusion:

[0070] Integration of BIM and GIS data: This module integrates BIM city models and GIS geographic information data; BIM models record the three-dimensional structural information of urban buildings and infrastructure in detail, accurately recording the location and attributes of each floor of the building and each key facility; while GIS geographic information data covers macro-geographic information such as topography, water system distribution, and transportation network; integrating the two can build a comprehensive and accurate three-dimensional urban geographic information scene; for example, when checking the flood situation in a certain area of ​​the city, you can not only see the terrain undulations and road directions of the area, but also clearly understand the structure and layout of the buildings, providing a comprehensive data basis for analyzing the impact range of the flood situation and formulating response strategies;

[0071] Multi-source data access and association: In addition to BIM and GIS data, the 3D visualization command module also accesses multi-source information such as meteorological data, hydrological data, urban drainage network data, and video surveillance data, and realizes real-time association with the 3D scene; for example, real-time water level data is associated with rivers and lakes in the 3D scene. When the water level changes, the height of the water body in the 3D scene will also change accordingly; the rainfall area and intensity in the meteorological data are intuitively displayed on the 3D map, combined with the terrain and drainage network information, the waterlogging area and waterlogging risk can be more accurately predicted; video surveillance data can be played in real time at the corresponding points in the 3D scene, providing the commander with a real-life scene on the scene and enhancing the intuitive feeling of the flood situation;

[0072] 2. 3D visualization display function:

[0073] Real-time display of flood situation: Using the integrated data, the 3D visualization command module realizes the 3D spatial visualization display of flood situation; presents the flood inundation range, water level change trend, road water depth and other information in an intuitive way; for example, through rendering with different colors and transparency, the water depth in different areas is displayed, red represents areas with deeper water and higher risk, and blue represents areas with shallower water; dynamic water flow effects are used to simulate the flow direction and speed of the flood, so that the commander can quickly grasp the development trend of the flood situation; at the same time, combined with the timeline function, historical flood data can be traced back to analyze the evolution of the flood situation, providing a reference for predicting the future development of the flood situation;

[0074] Construction of dynamic flooding area 3D model: The dynamic flooding area 3D model is generated based on the improved MarchingCubes algorithm. The isosurface extraction formula is: ,in, For space point The isosurface function value at ; is the octree node weight; It is an improved Sigmoid function with increased terrain mutation response; is the water level height at the grid point; is the water level threshold; the algorithm is based on the water level height of different grid points With water level threshold The comparison result is obtained by using the octree node weight And the improved Sigmoid function Calculate spatial points The isosurface function value at , and then generate a dynamically changing three-dimensional model of the flooded area; the improved Sigmoid function adds a sudden terrain response, so that the model can more accurately reflect the flood inundation situation under complex terrain conditions, providing a more accurate basis for command decision-making;

[0075] 3. Path planning and collaborative operation:

[0076] Path planning visualization: Path planning visualization uses particle swarm optimization algorithm to generate navigation beams. The formula is , in, For the Daidi The particle in The speed of the dimension; is the inertia weight; For the Daidi The particle in The speed of the dimension; , , is the learning factor; , , for Random numbers in the interval; For the The particle in The optimal position of an individual in the dimension; For the Daidi The particle in The location of the dimension; The global optimal position is Dimensional coordinates; It is a dangerous gradient item to avoid high-risk areas in real time; the algorithm takes inertia weight into account when planning the path , learning factor , , and the individual optimal positions of the particles , global optimal position and the hazard gradient term Through the synergy of these parameters, the optimal path that avoids high-risk areas (such as areas with severe waterlogging and road damage) can be generated and displayed in the three-dimensional scene in the form of a navigation beam; for example, when deploying emergency resources, the path planning function can be used to plan the best driving route for rescue vehicles to improve resource transportation efficiency;

[0077] Multi-department collaborative operation interface: The 3D visual command module provides a collaborative operation interface for multiple departments, integrating the business functions of multiple departments such as emergency management, fire protection, transportation, and water conservancy. Personnel from various departments can share information and conduct collaborative operations in the same 3D scene, such as marking key protection areas, issuing task instructions, and reporting work progress. For example, the emergency management department can mark areas that require emergency rescue in the 3D scene and issue rescue tasks to the fire department. The transportation department can adjust the traffic control plan in real time and display the control area and diversion route through a 3D map. The water conservancy department can remotely control the operation of water conservancy facilities in the 3D scene according to water level changes, realize efficient collaborative operations among multiple departments, and improve the overall efficiency of flood control emergency command.

[0078] The three-dimensional visual command module provides an all-round, visual decision-making support platform for urban flood control command through data integration, visual display and collaborative operation functions, effectively improving the ability and level of urban flood control emergency command, and playing an important role in ensuring the city's safety during the flood season.

[0079] In this embodiment, the intelligent scheduling decision module includes an emergency resource path planning unit based on an improved ant colony optimization algorithm and a resource matching decision unit based on dynamic weight multi-objective optimization;

[0080] Furthermore, the intelligent dispatching decision-making module is the core component of the integrated system of urban flood prevention warning and emergency command, and plays a key role in flood emergency response. It integrates advanced algorithms and comprehensively considers multiple factors to provide scientific decision-making basis for emergency resource allocation and path planning, ensuring rapid and efficient response in the face of floods and minimizing losses. Specifically:

[0081] 1. Emergency Resource Path Planning Unit:

[0082] Principle of improved ant colony optimization algorithm: This unit uses the improved ant colony optimization algorithm to plan the emergency resource transportation path. Its core formula is , in, for Time Path The pheromone concentration on the surface will evaporate over time. The pheromone volatility coefficient , ; for Time Path pheromone concentration on; For the Ants on the path The increase of pheromones left on the is the total number of ants; is the environmental risk adjustment factor; for The combined risk assessment value of road water depth and bridge load at all times, each ant On the path It will leave pheromone increments , the sum of the pheromone increments left by all ants will affect the pheromone concentration on the path; at the same time, the environmental risk adjustment factor is introduced and Joint risk assessment value of road water depth and bridge load at any given moment , which enables the algorithm to fully consider environmental risk factors when calculating the pheromone concentration of the path;

[0083] Algorithm advantages and application scenarios: Compared with traditional algorithms, the improved ant colony optimization algorithm can better adapt to the complex and changing environment of urban flood control; for example, when heavy rain causes waterlogging on roads and the load-bearing capacity of bridges is limited, the algorithm will The algorithm evaluates the value, reduces the pheromone concentration of high-risk paths, and guides ants (representing the choice of emergency resource transportation paths) to avoid roads with serious waterlogging or insufficient bridge load-bearing capacity, and choose safer and more efficient paths. This algorithm can ensure that emergency resources are quickly and safely transported to disaster-stricken areas under severe flood conditions, thereby improving rescue efficiency. In practical applications, whether it is fire trucks heading to areas with concurrent fires and floods, or medical supplies being transported to centralized resettlement sites for disaster-stricken people, the algorithm can help plan the best path.

[0084] 2. Resource matching decision unit:

[0085] Dynamic weight multi-objective optimization model construction: The resource matching decision unit uses the dynamic weight multi-objective optimization model to make resource matching decisions. The objective function is ,in, , , It is a dynamically adjusted weight parameter linked to the warning level; For emergency response time; Cost of emergency resources; For the efficiency of emergency resource allocation, when the warning level is high and the disaster is urgent, the emergency response time Weight will increase, emphasizing that resources should be put into use as soon as possible; when the warning level is relatively low and resource allocation focuses more on cost-effectiveness, the cost of emergency resources will increase. Weight The efficiency of emergency resource allocation will be improved accordingly. is also considered in the model, Give it a weight to ensure that the resource allocation process is efficient;

[0086] Constraint setting and significance: The model sets a series of constraints, including , ensuring that the amount of emergency resources can meet actual demand, taking into account demand fluctuations , updated in real time based on public opinion data to cope with possible changes in resource requirements; , taking into account the length of the emergency resource transportation route , Transport speed at all times and path waterlogging influence coefficient , Depth of waterlogging on road section , ensuring that resources can reach the disaster-stricken areas within the specified time; , ensuring the rationality and effectiveness of the weight parameters; these constraints provide comprehensive restrictions and guidance for resource matching decisions in terms of resource quantity, arrival time and weight balance, making the decision results more in line with actual flood control needs (among which, For the The number of emergency resources; is the number of types of emergency resources; is the total amount of emergency resource demand; Demand volatility is updated in real time based on public opinion data; The arrival time of emergency resources; The length of the transportation route for emergency resources; for The speed of emergency resource transportation at all times; is the path waterlogging influence coefficient; is the depth of water accumulation on the road section);

[0087] 3. Module synergy and overall performance:

[0088] Data interaction with other modules: The intelligent dispatching decision module works closely with other modules in the system; the multi-source data acquisition module provides it with meteorological, hydrological, drainage network and on-site real-time video data, which are used to assess the severity of floods, road traffic conditions, etc., and provide basic information for path planning and resource matching; the warning maps and forecast data generated by the dynamic warning analysis module help the intelligent dispatching decision module determine the emergency response priority and resource demand scale of different regions; the three-dimensional visualization command module displays the results of the intelligent dispatching decision module in an intuitive three-dimensional form, which facilitates the commander to make decisions and coordinate commands, and realizes information sharing and collaborative work among modules;

[0089] Improve the effectiveness of flood control emergency command: Through the collaborative work of the emergency resource path planning unit and the resource matching decision unit, the intelligent scheduling decision module can quickly plan the optimal emergency resource transportation route according to the real-time flood situation, reasonably allocate various emergency resources, and realize efficient use of resources; in actual flood control work, this module can greatly improve the emergency response speed, ensure that rescue forces arrive at the disaster-stricken areas in a timely manner, improve the scientificity and accuracy of flood control emergency command, and play an important role in ensuring urban safety and the safety of people’s lives and property.

[0090] In this embodiment, the cross-platform communication module supports 5G-MEC edge computing and satellite communication dual-link guarantee, realizing multimedia transmission of command instructions and augmented reality AR interaction of on-site terminals;

[0091] Furthermore, as the key hub of the integrated system of urban flood warning and emergency command, the cross-platform communication module plays an indispensable role in ensuring efficient and stable information transmission. It integrates advanced communication technologies, builds a multi-link guarantee system, realizes multimedia transmission of command instructions and augmented reality (AR) interaction of on-site terminals, and effectively promotes the coordination and efficient development of flood emergency command work. Specifically:

[0092] 1. Communication link guarantee:

[0093] 5G-MEC edge computing link: The cross-platform communication module uses the high-speed and low-latency characteristics of the 5G network to ensure that data can be transmitted quickly. The large bandwidth capacity of the 5G network can meet the needs of fast uploading and downloading of large amounts of real-time data (such as high-definition video surveillance data, high-resolution GIS map data, etc.), allowing the command center to obtain detailed information on the on-site flood situation in real time, such as the depth of road waterlogging and drainage conditions at the drainage outlets, providing an accurate basis for decision-making. At the same time, combined with MEC (mobile edge computing) technology, computing and storage capabilities are moved to the edge of the network to reduce data transmission delays and improve system response speed. For example, data collected by on-site terminal devices can be preliminarily processed and analyzed on the local edge server, and key information can be quickly generated before being transmitted to the command center, reducing the burden on the core network and further improving communication efficiency. This enables command instructions to be quickly transmitted to on-site terminals, realizing real-time command of flood control work.

[0094] Satellite communication link: To cope with complex flood control environments, such as when 5G network coverage is insufficient or ground communication networks are damaged due to disasters, satellite communication links are used as backup guarantees. Satellite communication has the advantages of wide coverage and is not restricted by geographical conditions, ensuring that the command center maintains contact with remote disaster-stricken areas, mountainous areas and other areas that are difficult to communicate through ground networks. It can transmit a variety of information such as voice, data, and images to ensure the transmission of key command instructions and important disaster information. When flood disasters cause base stations in some areas to be unusable, on-site rescue personnel can use satellite communication equipment to report the disaster situation to the command center and request support. The command center can also issue rescue missions and resource allocation instructions through satellite communications to maintain the continuity of flood control command work.

[0095] 2. Command transmission function:

[0096] Multimedia transmission: The cross-platform communication module supports multimedia transmission of command instructions, and can integrate and transmit information in various forms such as text, voice, image, and video. Commanders can quickly convey decision-making deployments through voice commands, improve communication efficiency, and avoid wasting time due to text input. At the same time, combined with image and video materials, such as maps with key rescue areas marked and on-site disaster videos, on-site personnel can more intuitively understand task requirements and accurately perform rescue operations. When commanding the rescue of trapped people, the command center can send map images containing the location of trapped people and on-site rescue guidance videos to help rescue personnel quickly locate and implement rescue operations, thereby improving the accuracy and efficiency of rescue operations.

[0097] Accurate transmission and reception of commands: This module uses advanced communication protocols and signal processing technologies to ensure the accuracy and integrity of command instructions during transmission. Through data verification, error correction coding and other mechanisms, the transmitted data is monitored and repaired in real time to avoid command loss or miscommunication due to signal interference or transmission errors. At the same time, the on-site terminal equipment has intelligent reception and decoding functions, which can quickly and accurately parse the received multimedia commands and present them to on-site staff in an intuitive way, such as displaying clear text prompts on the terminal equipment, playing voice commands, etc., to ensure that the command instructions can be correctly understood and executed by on-site personnel.

[0098] 3. On-site terminal interactive function:

[0099] Augmented reality (AR) interaction: The cross-platform communication module realizes the AR interaction function of the on-site terminal, combining virtual information with real scenes through AR technology; on-site staff wearing AR devices (such as smart glasses) can view virtual annotation information in the real environment, such as danger zone warnings, rescue route planning, equipment operation guides, etc.; when repairing drainage equipment, AR devices can display the equipment's structural diagram, troubleshooting steps and maintenance guidance information in the real scene, helping maintenance personnel to quickly locate the fault point and repair it, thereby improving maintenance efficiency; at the same time, staff can also interact with the command center in real time through AR devices, such as transmitting the on-site perspective back to the command center in real time, so that the command personnel can understand the on-site situation more intuitively and make more accurate decisions;

[0100] Improve on-site work efficiency and safety: AR interactive function greatly improves the efficiency and safety of on-site work; it reduces the time for staff to consult paper materials or operate complex terminal equipment, allowing staff to focus more on actual work tasks; through real-time display of risk warnings and operation guides, it effectively avoids safety accidents caused by human negligence or unfamiliarity with complex operations; when conducting rescue in dangerous flooded areas, AR equipment can display information such as water depth and potential dangers in real time, providing safety protection for rescuers, while improving the efficiency and success rate of rescue operations;

[0101] The cross-platform communication module breaks the barriers to information transmission and interaction through dual-link security, multimedia command transmission, and AR interactive functions of on-site terminals, enabling efficient communication and collaboration between the flood control command center and the site, providing strong communication support for urban flood control warning and emergency command work, and effectively improving flood control emergency response capabilities.

[0102] In this embodiment, the evaluation feedback module uses an improved TOPSIS algorithm to calculate the comprehensive score of the disposal plan;

[0103] Furthermore, the evaluation and feedback module plays an important role in the integrated system of urban flood prevention warning and emergency command. It uses scientific algorithms to quantitatively evaluate flood prevention and disposal plans and feeds back the evaluation results to the system to promote continuous optimization of the system and improve the overall effectiveness of flood prevention work. Specifically:

[0104] Solution evaluation algorithm: The evaluation feedback module uses the improved TOPSIS algorithm to calculate the comprehensive score of the disposal solution. The formula is: ,in, For the The comprehensive score of each disposal plan is the core indicator to measure the quality of the plan; For the The weights of the indicators are dynamically adjusted by introducing the time decay function; in the early stage of flood prevention, the weights of some indicators related to rapid response will be relatively high; as flood prevention work progresses, the weights of indicators such as resource utilization efficiency may change to reflect the focus of flood prevention work at different stages; For the The ideal solution is the The distance on the indicator; For the The solution to the negative ideal solution is The two distances are used to measure the gap between the solution and the best and worst cases in each indicator; As a risk penalty factor, for high-risk solutions, the penalty will be increased to ensure that the evaluation results can truly reflect the risk level of the solution; The number of indicators covers emergency response time, resource allocation efficiency, disaster loss control and other aspects to comprehensively evaluate the overall effect of the plan;

[0105] Dynamic adjustment of indicators and feedback optimization: The evaluation indicators will be dynamically adjusted according to the actual flood control situation and system operation feedback; by analyzing historical flood control data and comparing the implementation effects of different plans, new indicators with significant impact on flood control results can be discovered, or the weights of existing indicators can be redistributed; if it is found that a certain area is delayed in rescue due to poor information transmission during a flood control, the weight of information transmission efficiency in the evaluation indicators will be increased in the future; the evaluation feedback module will feed back the results of the program evaluation to all aspects of the system; provide reference for the intelligent scheduling decision-making module to help it optimize resource allocation and path planning strategies; assist the dynamic early warning analysis module to improve the early warning model and improve the accuracy of the early warning; and enable the three-dimensional visualization command module to display more targeted information, improve the scientific nature of command decisions, and promote the continuous optimization of the entire flood control system;

[0106] Improve the overall effectiveness of flood prevention work: Through the evaluation and feedback of the disposal plan, this module effectively improves the overall effectiveness of flood prevention work; in the actual flood prevention process, different emergency rescue plans are evaluated and the plan with high comprehensive score is selected for implementation, which can significantly improve the rescue efficiency and reduce casualties and property losses; after each flood prevention work, the evaluation and feedback module reviews and evaluates the entire process, summarizes the experience and lessons, and provides valuable reference for subsequent flood prevention work, so that the city's flood prevention warning and emergency command system can be continuously improved to better cope with future flood disasters.

[0107] A method for urban flood prevention warning and emergency command, which realizes real-time monitoring, accurate warning, efficient command and scientific evaluation of urban flood conditions through multi-module collaborative work to ensure urban safety; the following are its detailed operation steps:

[0108] Step S1, data collection and transmission: The multi-source data collection module uses professional equipment and mobile terminals to collect meteorological, hydrological, drainage network and video monitoring data in real time; the meteorological monitoring station collects data such as precipitation and wind speed, the hydrological monitoring equipment monitors water level and flow, the drainage network sensor obtains pipeline water level and drainage capacity information, and the video monitoring camera captures the scene; these data are stably transmitted to the data processing center through wired or wireless communication to provide comprehensive and accurate data support for subsequent analysis;

[0109] Step S2, dynamic warning analysis: The dynamic warning analysis module receives the collected data and processes it using the improved spatiotemporal fusion prediction model; by coupling the meteorological prediction model with the urban waterlogging model and combining the terrain data, the waterlogging risk values ​​at different times and regions are calculated to generate a dynamic warning map; for example, the LSTM network is used to predict meteorological data, and the SWMM model is combined to simulate the water flow of the drainage network, and the prediction results are corrected according to the terrain influence factors; if the waterlogging risk in a certain area is predicted to be high, the system will issue a warning message in a timely manner to notify the relevant departments and personnel;

[0110] Step S3, visual command and decision-making: The three-dimensional visual command module integrates the BIM city model and GIS geographic information data to display the flood situation; the commander views the flooded area, water level changes and other information in the three-dimensional scene, and conducts multi-department coordinated command; the intelligent scheduling decision-making module plans the emergency resource transportation route based on the early warning information and resource reserves, and makes resource matching decisions through the dynamic weight multi-objective optimization model; such as determining the deployment plan of fire trucks, rescue materials, etc., and issuing command instructions;

[0111] Step S4, cross-platform communication and execution: The cross-platform communication module transmits the command instructions to the on-site terminal in multimedia form through 5G-MEC edge computing and satellite communication dual links; the on-site staff receives the instructions with the help of the augmented reality (AR) interactive function of the terminal, checks the task information and on-site annotations, performs emergency rescue tasks such as drainage and rescue of trapped persons, and feeds back the on-site situation to the command center in real time;

[0112] Step S5, evaluation feedback and optimization: The evaluation feedback module uses the improved TOPSIS algorithm to comprehensively evaluate the disposal scheme and calculate the score; based on the evaluation results, the advantages and disadvantages of the scheme are analyzed, and feedback information is provided to other modules; for example, the intelligent scheduling decision-making module optimizes the resource allocation strategy based on the feedback, and the dynamic early warning analysis module improves the prediction model to continuously enhance the flood control capability of the system.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated system for urban flood prevention warning and emergency command, characterized in that: include: Multi-source data acquisition module: used to obtain meteorological data, hydrological data, urban drainage network data and video surveillance data in real time, and report the data using mobile terminals; Dynamic warning analysis module: Generates dynamic warning maps by coupling the meteorological forecast model with the urban waterlogging model. The coupled meteorological forecast model uses machine learning algorithms to correct forecast parameters in real time. 3D visualization command module: integrates BIM city model and GIS geographic information data to realize 3D spatial visualization display of flood situation and multi-department collaborative operation interface; Intelligent scheduling decision module: including emergency resource path planning unit based on improved ant colony optimization algorithm and resource matching decision unit based on dynamic weight multi-objective optimization; Cross-platform communication module: supports 5G-MEC edge computing and satellite communication dual-link guarantee, realizes multimedia transmission of command instructions and augmented reality (AR) interaction of on-site terminals.

2. The integrated system of urban flood prevention warning and emergency command according to claim 1 is characterized in that: The dynamic warning analysis module adopts an improved spatiotemporal fusion prediction model, and its core algorithm is defined as: ， in, for The predicted value at the moment; , , is the dynamic weight coefficient, which is calibrated in real time through KL divergence; is the meteorological time series prediction value based on the gated recurrent unit, For meteorological data; Output the rate of change of the hydraulic model of the drainage network with respect to time, is the water level in the pipe, is the surface runoff flow; is the integral value of terrain influence, is the terrain influence factor function, and is the integration interval.

3. The integrated system of urban flood prevention warning and emergency command according to claim 2 is characterized in that: The improved ant colony optimization algorithm in the intelligent scheduling decision module satisfies: ,in, for Time Path pheromone concentration on; for Time Path pheromone concentration on; is the pheromone volatility coefficient; For the Ants on the path The increase of pheromones left on the is the total number of ants; is the environmental risk adjustment factor; for Joint risk assessment value of road water depth and bridge load-bearing capacity at all times.

4. The integrated system of urban flood prevention early warning and emergency command according to claim 3 is characterized in that: The resource matching decision unit adopts a dynamic weight multi-objective optimization model: ,in, , , It is a dynamically adjusted weight parameter linked to the warning level; For emergency response time; Cost of emergency resources; To improve the efficiency of emergency resource deployment; Constraints: ,in, For the the number of emergency resources; is the number of types of emergency resources; is the total amount of emergency resource demand; Demand volatility is updated in real time based on public opinion data; The arrival time of emergency resources; The length of the transportation route for emergency resources; for The speed of emergency resource transportation at all times; is the path waterlogging influence coefficient; The depth of water accumulation on the road section.

5. The integrated system of urban flood prevention early warning and emergency command according to claim 4 is characterized in that: In the spatiotemporal fusion prediction model: The dual-channel attention mechanism is used to improve the LSTM network, and its attention weight is calculated as: ,in, for The attention weight of the moment; , , is the trainable parameter matrix; for Hidden layer state at all times; Enter features for the current moment; is the bias term; The SWMM model incorporates a pipe siltation correction factor: ,in, is the actual flow rate; is the theoretical flow rate; is the pipeline aging coefficient; To maintain the impact factor; The time since the last maintenance.

6. The integrated system of urban flood prevention early warning and emergency command according to claim 5 is characterized in that: The three-dimensional visualization command module includes: The three-dimensional model of the dynamic flooded area is generated based on the improved Marching Cubes algorithm, and the isosurface extraction formula is: ,in, For space point The isosurface function value at ; is the octree node weight; It is an improved Sigmoid function with increased terrain mutation response; is the water level height at the grid point; is the water level threshold; Path planning visualization uses particle swarm optimization algorithm to generate navigation beams: ， in, For the Daidi The particle in The speed of the dimension; is the inertia weight; For the Daidi The particle in The speed of the dimension; , , is the learning factor; , , for Random numbers in the interval; For the The particle in The optimal position of an individual in the dimension; For the Daidi The particle in The location of the dimension; The global optimal position is Dimensional coordinates; It is a hazard gradient item, which can avoid high-risk areas in real time.

7. The integrated system of urban flood prevention early warning and emergency command according to claim 6 is characterized by: Also includes: The evaluation feedback module uses the improved TOPSIS algorithm to calculate the comprehensive score of the disposal plan: ,in, For the The comprehensive score of the treatment options; For the The weight of each indicator is adjusted dynamically by introducing a time decay function; For the The ideal solution is the The distance on the indicator; For the The solution to the negative ideal solution is The distance on the indicator; is the risk penalty factor; is the number of indicators.

8. A method for urban flood prevention early warning and emergency command, characterized in that: The urban flood warning and emergency command method utilizes the urban flood warning and emergency command integrated system described in any one of claims 1-7 to perform urban flood warning and emergency command.

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