An urban smart drainage management system based on big data analysis

Abstract

This invention provides a smart urban drainage management system based on big data analysis, which relates to the technical field of urban drainage management. The system includes: a multi-source data acquisition module that collects historical climate change, rainfall curves, satellite meteorology, and water level, flow, and topological structure data at pipe network nodes in real time; a dynamic three-dimensional modeling module that constructs a three-dimensional pipe network model based on fluid mechanics principles and integrates real-time sensor data using a spatiotemporal data fusion algorithm; a drainage limit analysis module that calculates the maximum drainage threshold and failure probability of nodes under extreme rainfall; an intelligent early warning module that integrates LSTM and ARIMA models, combines satellite meteorological data, generates a heat map of drainage pressure for the next 72 hours, and triggers early warnings; a health assessment module that generates node health indexes and prioritizes maintenance; and a resource scheduling engine that generates dynamic scheduling plans. This invention effectively solves urban drainage problems, improves the drainage system's ability to cope with extreme weather and aging pipe networks, and enables accurate early warnings and efficient resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of urban drainage management, and in particular to an urban smart drainage management system based on big data analysis. Background Art

[0002] With the acceleration of urbanization, the continuous expansion of city size and the continuous growth of population density, urban drainage systems are facing unprecedented challenges. Traditional urban drainage systems have exposed many problems in responding to increasingly complex drainage needs.

[0003] On the one hand, extreme weather events are becoming more frequent, with a significant increase in the intensity and frequency of rainstorms. Such extreme rainfall can easily lead to urban flooding, causing huge losses to urban infrastructure, residents' lives, and economic development.

[0004] On the other hand, due to the long-term influence of factors such as sewage corrosion, mechanical wear and underground soil displacement, the structural integrity of the pipeline network is easily damaged, and problems such as pipe wall corrosion and sediment accumulation occur frequently, resulting in a significant decline in drainage capacity. The drainage pipeline network in some old urban areas even has serious leakage and blockage.

[0005] Furthermore, the existing drainage system's management model is relatively extensive, lacking effective real-time monitoring and precise control methods. Key parameters such as water levels and flow rates in the pipe network cannot be accurately and timely acquired, making it difficult to conduct a comprehensive, real-time assessment of the drainage system's operational status. When faced with sudden drainage issues, management departments often fail to make swift and informed decisions, resulting in delayed resolution and further exacerbating the impact of disasters.

[0006] To effectively address these issues and improve the safety, reliability, and operational efficiency of urban drainage systems, it is imperative to develop a smart urban drainage management system based on big data analysis. This system integrates multi-source data and utilizes advanced algorithms and models to achieve refined management and intelligent decision-making for urban drainage systems, providing a strong foundation for sustainable urban development. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an urban smart drainage management system based on big data analysis, which can improve fleet management efficiency and operational benefits.

[0008] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0009] An urban smart drainage management system based on big data analysis, including:

[0010] Multi-source data acquisition module, used to obtain real-time historical climate change data, high-precision historical rainfall curves, satellite meteorological monitoring data, water level sensor data at each node of the drainage network, drainage flow sensor data, and network topology data;

[0011] The dynamic 3D modeling module builds a 3D model of the urban drainage network based on the principles of fluid mechanics, including network material properties, terrain slope, and node connection relationships. It also dynamically binds real-time sensor data to the 3D model of the urban drainage network through a data fusion algorithm.

[0012] The drainage limit analysis module uses a pressure-flow coupling algorithm to calculate the maximum drainage threshold of each node under extreme rainfall conditions and generates a node failure probability distribution map based on Monte Carlo simulation;

[0013] The intelligent early warning module generates a heat map of drainage pressure for the next 72 hours by integrating the long short-term memory network (LSTM) and the ARIMA time series model, combined with satellite weather forecast data. When the real-time data of a node exceeds the preset safety threshold, a multi-level early warning mechanism is triggered.

[0014] The health assessment module uses the random forest algorithm to analyze the correlation between historical node fault data and real-time performance indicators, generates a node health index including pipe wall corrosion rate and sediment accumulation coefficient, and outputs a maintenance priority ranking;

[0015] The resource scheduling engine optimizes maintenance path planning based on genetic algorithms, generates dynamic scheduling plans based on comprehensive warning levels, health indexes, and emergency resource distribution, and achieves real-time optimal configuration of personnel, equipment, and materials.

[0016] Furthermore, the multi-source data acquisition module includes:

[0017] Multi-parameter sensor arrays deployed at key nodes in the drainage network, including ultrasonic flow meters, laser turbidity meters, and pressure transmitters;

[0018] Meteorological satellite data interface, supporting the reception of high-resolution synthetic aperture radar (SAR) rainfall monitoring data;

[0019] The municipal geographic information system GIS data fusion unit realizes the three-dimensional coordinate calibration of the pipeline network topology structure.

[0020] Furthermore, the dynamic 3D modeling module adopts a spatiotemporal data fusion algorithm, and its fusion weight coefficient is calculated as follows:

[0021] Sensor data contribution part: Multiply the real-time reading of each sensor by the geographical influence factor and sum it, then divide it by the maximum reading of the sensor for normalization, and multiply it by the dynamic adjustment parameter α;

[0022] Satellite data contribution: The rainfall forecast value of each satellite meteorological channel is multiplied by the time decay coefficient and then summed. The sum is then divided by the maximum value of the satellite data for normalization and multiplied by the dynamic adjustment parameter β.

[0023] The final fusion weight coefficient is the weighted sum of the above two parts, where α+β=1 and is updated every 5 minutes;

[0024] The geographical impact factor is calculated based on the node altitude and surrounding building density, with a value range of 0.8-1.2;

[0025] The time attenuation coefficient decays dynamically according to an exponential function, expressed as 0.5 multiplied by e to the power of -0.1t.

[0026] Furthermore, the failure probability calculation method of the drainage limit analysis module is:

[0027] Conduct at least 10,000 Monte Carlo simulations, and calculate the actual drainage flow at the node in each simulation;

[0028] The actual discharge is compared with a threshold value obtained by subtracting a historical bias correction term from the theoretical maximum discharge;

[0029] The proportion of the number of simulations in which the actual traffic exceeds the threshold to the total number of simulations is counted as the node failure probability;

[0030] Correction mechanism: The historical deviation correction term is calculated by multiplying the historical maximum flow fluctuation value by the dynamic adjustment factor γ. γ is adaptively adjusted through the hyperbolic tangent function, and the adjustment amplitude is affected by the difference between the current flow fluctuation and the standard deviation of the historical data.

[0031] Furthermore, the hybrid prediction model of the intelligent early warning module performs the following operations:

[0032] Prediction formula logic:

[0033] LSTM prediction component: generates predictions based on drainage pressure data in a historical time window (24-72 hours);

[0034] ARIMA forecast component: Generates benchmark forecast values ​​through the autoregressive difference moving average model;

[0035] Satellite correction component: the impact of future rainfall intensity predicted by satellite on drainage pressure;

[0036] Weight distribution rules:

[0037] The weights of LSTM and ARIMA are dynamically calculated by the ratio of their prediction errors and normalized using the sigmoid function;

[0038] The meteorological sensitivity coefficient controls the contribution strength of the satellite correction term and has a value range of 0.2-0.8.

[0039] Furthermore, the health index calculation rules of the health assessment module are as follows:

[0040] Calculate the ratio of the current value to the maximum allowable value for indicators such as corrosion depth and deposition thickness;

[0041] Each indicator is given a dynamic weight, which is calculated by the entropy weight method and the sum is 1;

[0042] Multiply the score of each indicator by the exponential decay factor. The decay coefficient λ reflects the aging rate of the pipeline network. The value range of λ is 0.05-0.2 / year.

[0043] The final health index is the standardized value of the weighted sum of all indicator scores.

[0044] Furthermore, the optimization objective function of the resource scheduling engine is defined as:

[0045] Total path time: the sum of the path distances to each rescue point divided by the dynamic driving speed;

[0046] Time breach cost: Penalties are imposed on response times exceeding the time limit of the plan. The penalty coefficient η is positively correlated with the warning level.

[0047] Total resource constraints: Available resources must meet the demand threshold;

[0048] Decision variable constraints: Resource allocation adopts 0-1 integer programming.

[0049] Furthermore, the geographic impact factor is calculated using the following rules:

[0050] Altitude impact term: the normalized value of the node altitude relative to the regional extreme value multiplied by 0.2;

[0051] Building density impact term: 1 minus the ratio of the building density around the node to the maximum density of the city, multiplied by 0.8;

[0052] The final geographic impact factor is the sum of the above two items.

[0053] Furthermore, the update rule of the dynamic adjustment factor γ is:

[0054] Based on the benchmark value of 0.8;

[0055] The adjustment range is calculated by the difference between the current flow fluctuation and the historical fluctuation through the hyperbolic tangent function;

[0056] Adjust the amplitude coefficient μ to control the change range, with a value of 0.2-0.6.

[0057] Furthermore, the optimization based on the genetic algorithm performs the following steps:

[0058] The non-dominated sorting genetic algorithm NSGA-II is used to generate the Pareto optimal solution set;

[0059] Define a dual objective vector: total path time and time breach cost;

[0060] The optimal solution is selected based on the real-time weather warning level, with priority given to minimizing the time default cost during red warnings.

[0061] The above solution of the present invention includes at least the following beneficial effects:

[0062] Accurate data collection and analysis: The multi-source data acquisition module comprehensively collects various data, including meteorological, network status, and topological structure, providing a solid data foundation for subsequent modules. The dynamic 3D modeling module builds a precise 3D model based on this data and updates it in real time through a spatiotemporal data fusion algorithm. This module accurately reflects the actual operation of the drainage network and facilitates more precise analysis and decision-making. For example, when predicting extreme conditions such as floods, it can provide accurate network water flow simulation, providing strong support for early prevention.

[0063] Efficient drainage limit assessment: The drainage limit analysis module uses a pressure-flow coupling algorithm and an improved Monte Carlo simulation algorithm to accurately calculate the maximum drainage threshold and failure probability of each node under extreme rainfall. The generated node failure probability distribution map allows managers to intuitively understand the weak links in the pipeline network, formulate countermeasures in advance, and reduce the risk of disasters such as waterlogging.

[0064] Timely and accurate early warning: The intelligent early warning module integrates LSTM and ARIMA models with satellite meteorological data to accurately predict drainage pressure for the next 72 hours. The generated heat map intuitively displays the pressure distribution. When node data is abnormal, the multi-level early warning mechanism responds quickly, allowing management departments to prepare in advance and reduce disaster losses.

[0065] Scientific health assessment: The health assessment module uses a multi-feature fusion evaluation algorithm to comprehensively consider real-time performance indicators, historical fault data, and aging attenuation effects to accurately calculate and rank node health indexes. This helps management departments rationally arrange maintenance plans, prioritize nodes with serious problems, improve maintenance efficiency, and extend the service life of the pipeline network.

[0066] Optimize resource scheduling: The resource scheduling engine integrates warning levels, node health indexes, and emergency resource distribution, and uses multi-objective optimization algorithms and genetic algorithms to achieve optimal resource allocation. It also generates dynamic scheduling plans to ensure that personnel, equipment, and supplies can quickly reach the fault point in an emergency, improving emergency response capabilities and ensuring the stable operation of the urban drainage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a schematic diagram of an urban smart drainage management system based on big data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0069] like Figure 1 As shown, an embodiment of the present invention proposes an urban smart drainage management system based on big data analysis, including a multi-source data acquisition module, a dynamic three-dimensional modeling module, a drainage limit analysis module, an intelligent early warning module, a health assessment module and a resource scheduling engine.

[0070] In this embodiment, the multi-source data acquisition module is used to obtain historical climate change data, high-precision historical rainfall curves, satellite meteorological monitoring data, water level sensor data at each node of the drainage network, drainage flow sensor data, and network topology data in real time;

[0071] Furthermore, the multi-source data acquisition module includes:

[0072] Multi-parameter sensor arrays deployed at key nodes in the drainage network, including ultrasonic flow meters, laser turbidity meters, and pressure transmitters;

[0073] Meteorological satellite data interface, supporting the reception of high-resolution synthetic aperture radar (SAR) rainfall monitoring data;

[0074] Municipal Geographic Information System (GIS) data fusion unit, which realizes the three-dimensional coordinate calibration of the pipe network topology;

[0075] The multi-source data acquisition module is a fundamental component of the urban smart drainage management system based on big data analysis. Its main function is to obtain all types of urban drainage-related data in real time and comprehensively, providing data support for subsequent analysis, modeling, and decision-making. The following is a detailed description of this module:

[0076] Overall function overview:

[0077] The multi-source data acquisition module is responsible for collecting data from various sources. These data cover multiple aspects such as meteorology, drainage network status, and network topology, which helps the system fully understand the current status and potential influencing factors of urban drainage.

[0078] Specific data collection content:

[0079] Weather-related data:

[0080] Historical climate change data: Collect long-term climate records for the city and surrounding areas, including information such as temperature, humidity, air pressure, wind direction, and wind speed. This data can help analyze the long-term impacts of climate change on urban drainage systems. For example, temperature changes may affect the rate of snow and ice melting, which in turn affects drainage flow.

[0081] High-precision historical rainfall curves: Record information such as rainfall intensity, duration, and rainfall distribution over different time periods in the past. High-precision rainfall curves provide an important basis for predicting future rainfall conditions and evaluating the performance of drainage systems under different rainfall conditions.

[0082] Satellite meteorological monitoring data: Receive high-resolution synthetic aperture radar (SAR) rainfall monitoring data through the meteorological satellite data interface. Satellite data can provide large-scale, real-time meteorological information, helping to timely understand the spatial distribution and changing trends of rainfall, especially for monitoring and early warning of sudden heavy rainfall events.

[0083] Drainage network status data:

[0084] Water level sensor data at each node of the drainage network: Water level sensors are deployed at key nodes of the drainage network to monitor the water level at the nodes in real time. Water level data can reflect the drainage capacity and operating status of the network. When the water level exceeds a certain threshold, it may indicate poor drainage or blockage.

[0085] Drainage flow sensor data: Install drainage flow sensors to measure the flow rate at each node in the pipe network. Flow data can help assess the actual drainage load of the pipe network, and combined with water level data, it can more accurately determine the operating status of the pipe network.

[0086] Pipeline network topology data:

[0087] The municipal geographic information system (GIS) data fusion unit is used to obtain topological data of the drainage network, including information such as the network layout, pipe diameter, pipe material, and node connection relationships. This data is the basis for constructing a three-dimensional model of the urban drainage network and helps to accurately simulate the flow of water in the network.

[0088] Data acquisition equipment and interface:

[0089] Multi-parameter sensor array:

[0090] Deploy multi-parameter sensor arrays at key nodes in the drainage network, including ultrasonic flow meters, laser turbidity meters, and pressure transmitters;

[0091] Ultrasonic flow meter: It uses the characteristics of ultrasonic waves propagating in fluids to measure the flow velocity and flow rate of fluids. It has the advantages of non-contact measurement, high accuracy and strong reliability.

[0092] Laser turbidimeter: measures the concentration of suspended particles in water through the principle of laser scattering, reflecting the turbidity of water quality; turbidity data can help determine whether there are problems such as sediment accumulation or sewage leakage in the pipe network;

[0093] Pressure transmitter: measures pressure changes in the pipe network. Pressure data can reflect the operating pressure status of the pipe network and plays an important role in detecting failures such as leakage and blockage in the pipe network.

[0094] Meteorological satellite data interface:

[0095] A meteorological satellite data interface that supports receiving high-resolution synthetic aperture radar (SAR) rainfall monitoring data. This interface enables real-time data transmission with the meteorological satellite system, ensuring that the system can obtain the latest meteorological information in a timely manner.

[0096] Municipal Geographic Information System (GIS) Data Fusion Unit:

[0097] Achieve three-dimensional coordinate calibration of the pipe network topology; through data fusion with the GIS system, combine the topological structure data of the drainage pipe network with geographic spatial information, improve data accuracy and visualization, and provide more accurate basic data for subsequent analysis and modeling;

[0098] Real-time and accuracy of data collection:

[0099] The multi-source data acquisition module emphasizes real-time and accuracy. By adopting advanced sensor technology and data transmission technology, it ensures timely and accurate acquisition of various types of data. At the same time, the collected data is pre-processed and quality controlled to remove noise and outliers, thereby improving data reliability and availability.

[0100] In summary, the multi-source data acquisition module uses a variety of data acquisition devices and interfaces to comprehensively and in real time acquire multi-source data such as meteorological conditions, drainage network status, and network topology, providing a solid data foundation for subsequent analysis and decision-making of the urban smart drainage management system.

[0101] In this embodiment, the dynamic 3D modeling module constructs a 3D model of the urban drainage network based on the principles of fluid mechanics, including network material properties, terrain slope, and node connection relationships. It also dynamically binds real-time sensor data to the 3D model of the urban drainage network through a data fusion algorithm.

[0102] Furthermore, the dynamic 3D modeling module adopts a spatiotemporal data fusion algorithm, and its fusion weight coefficient is calculated as follows:

[0103] Sensor data contribution part: Multiply the real-time reading of each sensor by the geographical influence factor and sum it, then divide it by the maximum reading of the sensor for normalization, and multiply it by the dynamic adjustment parameter α;

[0104] Satellite data contribution: The rainfall forecast value of each satellite meteorological channel is multiplied by the time decay coefficient and then summed. The sum is then divided by the maximum value of the satellite data for normalization and multiplied by the dynamic adjustment parameter β.

[0105] The final fusion weight coefficient is the weighted sum of the above two parts, where α+β=1 and is updated every 5 minutes;

[0106] The geographical impact factor is calculated based on the node altitude and surrounding building density, with a value range of 0.8-1.2;

[0107] The time attenuation coefficient decays dynamically according to an exponential function, expressed as 0.5 multiplied by e to the power of -0.1t;

[0108] The geographic impact factor is calculated using the following rules:

[0109] Altitude impact term: the normalized value of the node altitude relative to the regional extreme value multiplied by 0.2;

[0110] Building density impact term: 1 minus the ratio of the building density around the node to the maximum density of the city, multiplied by 0.8;

[0111] The final geographic impact factor is the sum of the above two items;

[0112] Specifically, the dynamic 3D modeling module is a core component of the urban smart drainage management system based on big data analysis. It is primarily responsible for constructing a 3D model of the urban drainage network and dynamically binding real-time sensor data to it to achieve accurate simulation and real-time monitoring of the drainage system. The following is a detailed description of this module:

[0113] Overall function overview:

[0114] The dynamic 3D modeling module uses data acquired by the multi-source data acquisition module and combines it with principles of fluid mechanics to construct a 3D model that reflects the true state of the urban drainage network. This model not only takes into account the physical structure of the network but also dynamically adjusts based on real-time sensor data, providing accurate foundational data for subsequent drainage limit analysis, intelligent early warning, and other modules.

[0115] Basic data for modeling:

[0116] The construction of the dynamic 3D modeling module relies on a variety of data provided by the multi-source data acquisition module, including:

[0117] Pipeline material properties: Pipelines made of different materials have different properties, such as resistance to water flow and corrosion resistance, which can affect the flow of water in the pipe network. For example, the inner wall of a plastic pipe network is relatively smooth, resulting in less water flow resistance; whereas metal pipe networks may experience increased water flow resistance due to corrosion.

[0118] Topographic slope: Topographic slope determines the natural flow direction and speed of water flow and is an important factor affecting drainage effectiveness. In low-lying areas, water flow tends to accumulate, requiring more reasonable drainage design.

[0119] Node connection relationships: Clarifying the connection methods and topological structures between nodes in the drainage network is crucial for accurately simulating the flow path of water in the network. For example, node connection methods may include series and parallel connections, and different connection methods will result in different water distribution and flow conditions at the nodes.

[0120] Real-time sensor data: including water level sensor data and drainage flow sensor data at each node of the drainage network; this data reflects the real-time operating status of the network and is the key to dynamic model updates;

[0121] 3D model construction:

[0122] Based on the principles of fluid mechanics:

[0123] This module constructs a three-dimensional model of an urban drainage network based on the principles of fluid mechanics. Fluid mechanics describes the flow of fluids (such as water) through pipes. By applying these principles, a mathematical model can be established that accurately simulates the flow of water in a drainage network. For example, using the Bernoulli equation and the continuity equation, parameters such as flow velocity and pressure at different locations in the network can be calculated.

[0124] Model building process:

[0125] Geometric modeling: Based on the network topology data, a three-dimensional geometric model of the drainage network is established to determine the shape, size and spatial location of the network;

[0126] Physical property assignment: Assign data such as pipe network material properties and terrain slope to each element in the geometric model so that the model can reflect real physical characteristics;

[0127] Boundary condition setting: Set the boundary conditions of the model according to the actual situation, such as inlet flow, outlet pressure, etc., to ensure that the model can accurately simulate the actual drainage process;

[0128] Data fusion algorithm:

[0129] In order to enable the 3D model to reflect the operating status of the drainage network in real time, this module uses a spatiotemporal data fusion algorithm to dynamically bind real-time sensor data to the model; the specific formula is as follows:

[0130] (in, is the fusion weight coefficient, which is used to quantify the contribution of sensor and satellite data; For the The sensors in Real-time reading at the moment, in m³ / s; is the geographical influence factor, with a value range of 0.8-1.2, calculated from the node altitude and the surrounding building density; For the The rainfall intensity forecast value of each satellite meteorological channel, in mm / h; is the time attenuation coefficient, according to Dynamic attenuation; 、 To dynamically adjust the parameters, And it is updated every 5 minutes; It is the maximum reading of the sensor and is used for normalization processing to ensure that the data are compared under a unified standard; is the maximum value of satellite data, used for normalization);

[0131] Through this algorithm, the data collected by sensors in real time and the satellite meteorological forecast data are integrated, and the model is dynamically adjusted according to the fusion weight coefficient, so that the model can reflect the actual operation status of the drainage network in real time.

[0132] Dynamic update and application of models:

[0133] Dynamic Updates: As real-time sensor data is continuously updated, the model will adjust in real time based on the data fusion algorithm to ensure that the model always remains consistent with the actual operating status of the drainage network. For example, when the water level sensor data at a node changes, the model will adjust the water flow simulation results at that node and its surrounding area accordingly.

[0134] Application scenarios: The dynamic 3D model can provide an accurate pipe network model for the drainage limit analysis module, used to calculate the maximum drainage threshold of each node under extreme rainfall conditions; it can also provide real-time drainage status information for the intelligent early warning module to generate drainage pressure heat maps and trigger early warning mechanisms; it can also provide data support for the health assessment module and resource scheduling engine, helping to assess the health of the pipe network and formulate reasonable scheduling plans;

[0135] In summary, the dynamic 3D modeling module provides a solid foundation for the efficient operation of the urban smart drainage management system by constructing an accurate 3D model of the urban drainage network and dynamically binding it with real-time sensor data.

[0136] In this embodiment, the drainage limit analysis module uses a pressure-flow coupling algorithm to calculate the maximum drainage threshold of each node under extreme rainfall conditions and generates a node failure probability distribution map based on Monte Carlo simulation;

[0137] Furthermore, the failure probability calculation method of the drainage limit analysis module is:

[0138] Conduct at least 10,000 Monte Carlo simulations, and calculate the actual drainage flow at the node in each simulation;

[0139] The actual discharge is compared with a threshold value obtained by subtracting a historical bias correction term from the theoretical maximum discharge;

[0140] The proportion of the number of simulations in which the actual traffic exceeds the threshold to the total number of simulations is counted as the node failure probability;

[0141] Correction mechanism: The historical deviation correction term is calculated by multiplying the historical maximum flow fluctuation value by the dynamic adjustment factor γ. γ is adaptively adjusted using the hyperbolic tangent function. The adjustment range is affected by the difference between the current flow fluctuation and the standard deviation of the historical data.

[0142] The update rule of the dynamic adjustment factor γ is:

[0143] Based on the benchmark value of 0.8;

[0144] The adjustment range is calculated by the difference between the current flow fluctuation and the historical fluctuation through the hyperbolic tangent function;

[0145] Adjust the amplitude coefficient μ to control the range of change, with a value of 0.2-0.6;

[0146] Specifically, the drainage limit analysis module plays a key role in the urban smart drainage management system based on big data analysis. Its main function is to accurately assess the carrying capacity of the urban drainage network under extreme rainfall conditions, identify potential risk nodes, and provide an important basis for subsequent early warning and scheduling. The following is a detailed description of this module:

[0147] Overall function overview:

[0148] The drainage limit analysis module uses a pressure-flow coupling algorithm and an improved Monte Carlo simulation algorithm, combined with various data provided by the multi-source data acquisition module, to calculate the maximum drainage threshold of each node in the drainage network under extreme rainfall conditions and generate a node failure probability distribution map, helping the system identify the weak links of the drainage network under extreme conditions.

[0149] Pressure-flow coupling algorithm:

[0150] Algorithm principle:

[0151] This algorithm is based on the principles of fluid mechanics and takes into account the relationship between pressure and flow in a drainage network. In a drainage system, changes in pressure affect the flow rate, while changes in flow rate in turn affect the pressure distribution. By establishing a pressure-flow coupling equation, the pressure and flow values ​​of each node under different operating conditions can be accurately calculated.

[0152] Maximum drainage threshold calculation:

[0153] Using a pressure-flow coupling algorithm, combined with network parameters such as material properties, pipe diameter, and slope, we calculate the maximum drainage flow rate each node can withstand under extreme rainfall conditions. This is known as the maximum drainage threshold. This threshold is an important indicator of a node's drainage capacity. When the actual drainage flow rate approaches or exceeds this threshold, the node may experience poor drainage or even fail.

[0154] Improved Monte Carlo simulation algorithm:

[0155] Simulation principle:

[0156] Monte Carlo simulation is a method that simulates the behavior of complex systems through random sampling. In drainage limit analysis, the improved Monte Carlo simulation algorithm considers the impact of various uncertain factors on the drainage system through a large number of random simulations, such as the randomness of rainfall intensity and the uncertainty of pipe network parameters.

[0157] Failure probability calculation:

[0158] The failure probability calculation formula is:

[0159] (in, is the node failure probability, and its value range is ; For the number of simulations, set Second-rate; For the The actual drainage flow of the node in this simulation, in units of ; is the theoretical maximum drainage capacity, calculated based on the pipe diameter and slope; is the historical deviation correction factor, according to Adaptive adjustment; is the historical maximum flow fluctuation value, taking the standard deviation of the data of the past five years; is an indicator function, which takes 1 when the condition is met and 0 otherwise);

[0160] Through multiple simulations, the number of times the actual drainage flow exceeds the corrected threshold is counted, thereby calculating the failure probability of the node;

[0161] Historical bias correction factor:

[0162] Historical bias correction factor Adopting the adaptive adjustment strategy, the formula is:

[0163] (in, is the baseline correction value, the default value is 0.8; To adjust the amplitude coefficient, the value range is 0.2-0.6; The current monitored flow fluctuation value, in m³ / s; is the standard deviation of historical data, updated quarterly);

[0164] The introduction of this correction factor makes the prediction of failure probability more accurate and can better reflect the actual situation;

[0165] Generation of node failure probability distribution graph:

[0166] Based on the failure probability of each node calculated using an improved Monte Carlo simulation algorithm, a node failure probability distribution map is generated. This map intuitively illustrates the failure risk of each node in the drainage network under extreme rainfall conditions, with darker colors indicating higher failure probability. This failure probability distribution map allows system administrators to quickly identify weak links in the drainage network and take appropriate preventive measures.

[0167] Application value of the module:

[0168] The output of the drainage limit analysis module provides an important basis for the intelligent early warning module and resource scheduling engine. The intelligent early warning module can set reasonable early warning thresholds based on the node failure probability and the maximum drainage threshold. When the actual operating data of a node approaches or exceeds the warning threshold, it will promptly issue an early warning message. The resource scheduling engine can rationally allocate personnel, equipment, and materials based on the failure probability distribution map, prioritizing the maintenance and treatment of nodes with high failure probabilities, thereby improving the urban drainage system's ability to cope with extreme rainfall events.

[0169] In summary, the drainage limit analysis module uses advanced algorithms and simulation technologies to conduct a comprehensive and accurate analysis of the drainage capacity of the urban drainage network under extreme rainfall conditions, providing strong support for the efficient operation of the urban smart drainage management system.

[0170] In this embodiment, the intelligent early warning module generates a heat map of drainage pressure for the next 72 hours by integrating the long short-term memory network LSTM and the ARIMA time series model, combined with satellite meteorological forecast data. When the real-time data of the node exceeds the preset safety threshold, a multi-level early warning mechanism is triggered;

[0171] Furthermore, the hybrid prediction model of the intelligent early warning module performs the following operations:

[0172] Prediction formula logic:

[0173] LSTM prediction component: generates predictions based on drainage pressure data in a historical time window (24-72 hours);

[0174] ARIMA forecast component: Generates benchmark forecast values ​​through the autoregressive difference moving average model;

[0175] Satellite correction component: the impact of future rainfall intensity predicted by satellite on drainage pressure;

[0176] Weight distribution rules:

[0177] The weights of LSTM and ARIMA are dynamically calculated by the ratio of their prediction errors and normalized using the sigmoid function;

[0178] The meteorological sensitivity coefficient controls the contribution strength of the satellite correction term and has a value range of 0.2-0.8;

[0179] Specifically, the intelligent early warning module is a key part of the urban smart drainage management system based on big data analysis. Its main function is to use multi-source data, combined with advanced prediction models, to accurately predict the future status of the urban drainage system and issue multi-level early warnings in a timely manner when abnormal conditions occur, to ensure the safe and stable operation of the urban drainage system. The following is a detailed description of this module:

[0180] Overall function overview:

[0181] The intelligent early warning module integrates the long short-term memory (LSTM) network with the ARIMA time series model, combined with satellite weather forecast data, to generate a heat map of drainage pressure for the next 72 hours. When real-time data from drainage network nodes exceeds preset safety thresholds, a multi-level early warning mechanism is triggered, providing timely and effective decision-making support for urban drainage management departments.

[0182] Data source:

[0183] The data of this module mainly comes from the multi-source data acquisition module, including:

[0184] Satellite weather forecast data: Meteorological information for the coming period provided by meteorological satellites, such as rainfall intensity, rainfall timing, and spatial distribution, is crucial for predicting pressure changes in drainage systems.

[0185] Real-time monitoring data of the drainage network: including water level sensor data and drainage flow sensor data at each node of the drainage network, reflecting the current operating status of the drainage system;

[0186] Hybrid prediction model:

[0187] The intelligent early warning module uses a hybrid prediction model to predict drainage pressure. The formula is as follows:

[0188] (in, for The predicted drainage pressure at the moment, in kPa; is the model weight factor, through Dynamic calculation; is the LSTM time window length, set to 24-72 hours; Satellite prediction correction item, reflecting the future rainfall intensity after time; is the meteorological sensitivity coefficient, ranging from 0.2 to 0.8; is the sigmoid function, used to normalize weight distribution);

[0189] LSTM model:

[0190] Long Short-Term Memory (LSTM) is a specialized recurrent neural network that excels at processing long-term dependencies in sequential data. In this module, the LSTM model is used to learn complex patterns and trends in historical drainage system data to predict future drainage pressure.

[0191] ARIMA Model:

[0192] The Autoregressive Integrated Moving Average (ARIMA) model is a classic time series forecasting model that captures linear trends and seasonal changes in data through autoregression and moving average processing of historical data. In hybrid forecasting models, the ARIMA model is used to provide a relatively stable forecast basis.

[0193] Satellite prediction corrections:

[0194] Satellite prediction corrections Satellite meteorological forecast data was introduced to consider the impact of future rainfall on drainage pressure; meteorological sensitivity coefficient Used to adjust the weight of satellite data in the forecast, and flexibly adjust according to different meteorological conditions;

[0195] Model weight factors:

[0196] Model weight factor Through dynamic calculation, the contribution of the LSTM model and the ARIMA model to the forecast is adjusted based on their root mean square error (RMSE). This adaptive adjustment mechanism enables the hybrid forecasting model to fully leverage the advantages of both models based on different data characteristics and forecasting scenarios, thereby improving forecast accuracy.

[0197] Drainage pressure heat map generation:

[0198] A drainage pressure heat map is generated based on the predicted drainage pressure for the next 72 hours obtained from the hybrid prediction model. This heat map uses a visual color distribution to display the drainage pressure at each node in the drainage network at different times. Darker colors indicate greater drainage pressure. This heat map allows urban drainage management departments to quickly understand the pressure distribution of the drainage system and promptly identify potential risk areas.

[0199] Multi-level early warning mechanism:

[0200] The intelligent early warning module sets a preset safety threshold. When the real-time data of the node exceeds the threshold, a multi-level early warning mechanism is triggered. The warning level can be divided according to the degree of exceeding the threshold, for example:

[0201] Level 1 warning: A slight exceedance of the safety threshold may indicate that the drainage system has experienced some minor abnormalities, but has not yet seriously affected the normal operation of the system. At this time, the system can issue a prompt message to remind managers to pay attention to the operation of related nodes;

[0202] Level 2 warning: If the safety threshold is exceeded significantly, it indicates that the drainage system may be at risk and requires appropriate measures. The system can automatically notify relevant personnel to conduct on-site inspections and provide preliminary treatment suggestions.

[0203] Level 3 Warning: If the safety threshold is seriously exceeded, it means that the drainage system may have failed or is facing a major risk, and the emergency plan needs to be activated immediately. The system can quickly notify the emergency rescue team and provide detailed fault information and rescue plans.

[0204] Application value of the module:

[0205] Through accurate predictions and timely warnings, the intelligent early warning module helps urban drainage management departments identify potential problems in the drainage system in advance, take effective measures to prevent and deal with them, and reduce the occurrence of disasters such as urban waterlogging. At the same time, the module's multi-level early warning mechanism can provide corresponding response strategies based on different risk levels, improving the emergency response capabilities and management efficiency of the urban drainage system.

[0206] In this embodiment, the health assessment module uses the random forest algorithm to analyze the correlation between the node's historical fault data and real-time performance indicators, generates a node health index including the pipe wall corrosion rate and sediment accumulation coefficient, and outputs a maintenance priority ranking;

[0207] Furthermore, the health index calculation rules of the health assessment module are as follows:

[0208] Calculate the ratio of the current value to the maximum allowable value for indicators such as corrosion depth and deposition thickness;

[0209] A dynamic weight is assigned to each indicator, which is calculated by the entropy weight method and the sum is 1;

[0210] Multiply the score of each indicator by the exponential decay factor. The decay coefficient λ reflects the aging rate of the pipeline network. The value range of λ is 0.05-0.2 / year.

[0211] The final health index is the standardized value of the weighted sum of all indicator scores;

[0212] Specifically, the health assessment module plays a key role in the urban smart drainage management system based on big data analysis. It primarily uses the random forest algorithm to analyze historical node fault data and real-time performance indicators, thereby generating a node health index and providing maintenance priority rankings, providing a key basis for the maintenance and management of urban drainage networks. The following is a detailed and specific description of this module:

[0213] Overall function overview:

[0214] The core task of the health assessment module is to comprehensively evaluate the health status of each node in the drainage network. By deeply mining and analyzing historical fault data and real-time performance indicators, the module can accurately identify potential problems and risks at each node, providing scientific and reasonable decision support for subsequent maintenance work.

[0215] Data source:

[0216] The data of this module mainly comes from the multi-source data acquisition module, specifically covering the following categories:

[0217] Node historical failure data: This includes information such as the time, type, cause, and maintenance records of past node failures. This data reflects the stability and reliability of the node during its historical operation and can provide important clues for analyzing node failure modes and patterns.

[0218] Real-time performance indicators: These include real-time monitoring data such as pipe wall corrosion rate, sediment accumulation coefficient, water level, flow rate, and pressure. These indicators directly reflect the current operating status and physical characteristics of the node and are an important basis for assessing the node's health status.

[0219] Multi-feature fusion evaluation algorithm:

[0220] The health assessment module uses a multi-feature fusion assessment algorithm to calculate the health index of the node. The specific formula is as follows:

[0221] (in, is the health index, standardized to 0-100 points; For the The dynamic weight of each indicator is calculated by entropy weight method and meets ; Indicates the various physical quantities currently detected, including corrosion depth, deposition thickness and other physical quantities; This is the maximum value allowed by industry standards; is the aging attenuation coefficient, ranging from 0.05 to 0.2 per year; The current timestamp, accurate to the day; The time for pipe network installation; represents the number of metrics used to evaluate the health of a node);

[0222] Dynamic weight of indicators:

[0223] The first The entropy weight method is an objective weighting method that determines the weight according to the discrete degree of the indicator data. The greater the discrete degree of the indicator, the higher its weight. This can avoid the influence of subjective factors and make the weight distribution more reasonable.

[0224] Current test values ​​and industry standards:

[0225] Indicates the currently detected physical quantities, such as the corrosion depth of the pipe wall, the thickness of the deposit, etc. is the maximum value allowed for these physical quantities as specified by industry standards; by calculating , we can get the degree of deviation of each indicator relative to the standard value. The greater the deviation, the greater the impact of the indicator on the node health status;

[0226] Aging degradation effects:

[0227] It reflects the aging and attenuation effect of the pipeline network as it grows in use; is the aging attenuation coefficient, which reflects the aging rate of the pipeline network; as time goes by, The value of increases, The value of decreases, which means that the health of the node will gradually decline due to aging;

[0228] Random Forest Algorithm Analysis:

[0229] This module uses the random forest algorithm to analyze the correlation between historical node failure data and real-time performance indicators. Random forest is an ensemble learning method composed of multiple decision trees. By learning from a large amount of historical data, the random forest algorithm can discover the complex relationships between different indicators and their correlation patterns with node failures.

[0230] During training, the random forest algorithm randomly selects a portion of data and features to construct a decision tree, and then combines the results of multiple decision trees. This improves the accuracy and stability of the model and reduces the risk of overfitting. By analyzing the output of the random forest algorithm, it is possible to determine which indicators have a greater impact on the health of the node, providing a more scientific basis for calculating the health index.

[0231] Health Index and Maintenance Priority Ranking:

[0232] Health Index Calculation:

[0233] According to the multi-feature fusion evaluation algorithm, the various indicators of each node are comprehensively calculated to obtain the node health index The health index ranges from 0 to 100. A higher score indicates a better health condition for the node, while a lower score indicates that the node has more problems and requires timely maintenance.

[0234] Maintenance Prioritization:

[0235] Based on the calculated health index, all nodes are prioritized for maintenance; nodes with lower health indexes are prioritized for maintenance. This ensures that limited maintenance resources are allocated to the nodes most in need, improving maintenance efficiency and reducing the risk of drainage network failures.

[0236] Application value of the module:

[0237] The health assessment module provides urban drainage management departments with a clear basis for maintenance decision-making by scientifically and accurately evaluating the health status of drainage network nodes. It can help management departments identify potential problems in advance, reasonably arrange maintenance plans, extend the service life of the network, and ensure the stable operation of the urban drainage system. At the same time, by sorting maintenance priorities, it optimizes the allocation of maintenance resources and improves the efficiency and effectiveness of maintenance work.

[0238] In this embodiment, the resource scheduling engine optimizes maintenance path planning based on a genetic algorithm, generates a dynamic scheduling plan based on the warning level, health index, and emergency resource distribution, and achieves real-time optimal configuration of personnel, equipment, and supplies;

[0239] Furthermore, the optimization objective function of the resource scheduling engine is defined as:

[0240] Total path time: the sum of the path distances to each rescue point divided by the dynamic driving speed;

[0241] Time breach cost: Penalties are imposed on response times exceeding the time limit of the plan. The penalty coefficient η is positively correlated with the warning level.

[0242] Total resource constraints: Available resources must meet the demand threshold;

[0243] Decision variable constraints: Resource allocation uses 0-1 integer programming;

[0244] Furthermore, the optimization based on the genetic algorithm performs the following steps:

[0245] The non-dominated sorting genetic algorithm NSGA-II is used to generate the Pareto optimal solution set;

[0246] Define a dual objective vector: total path time and time breach cost;

[0247] Select the optimal solution based on the real-time weather warning level, giving priority to minimizing the time default cost when the red warning occurs;

[0248] Specifically, the resource scheduling engine is one of the core modules in the urban smart drainage management system based on big data analysis. Its main function is to make real-time and optimal allocation of emergency resources such as personnel, equipment, and supplies based on the information provided by each module in the system, so as to effectively respond to various problems arising in the urban drainage system and ensure the stable operation of the urban drainage system. The following is a detailed and specific description of this module:

[0249] Overall function overview:

[0250] The resource scheduling engine comprehensively considers multiple factors such as warning levels, node health index, and emergency resource distribution, and uses genetic algorithms to optimize maintenance paths and generate dynamic scheduling plans to achieve the optimal allocation of emergency resources in time and space, thereby improving the urban drainage system's ability to respond to emergencies.

[0251] Data input:

[0252] The operation of the resource scheduling engine relies on data provided by multiple modules, as follows:

[0253] Warning level: generated by the intelligent warning module based on drainage pressure prediction and real-time monitoring data, reflecting the degree of risk faced by each node in the drainage system; warning levels are divided into different levels, such as level 1 warning, level 2 warning, and level 3 warning, with higher levels indicating more severe risks;

[0254] Node Health Index: Calculated by the health assessment module, it reflects the health status of each node in the drainage network. The lower the health index, the more serious the problem at the node, and the more priority maintenance is needed.

[0255] Emergency resource distribution: This includes the location and quantity of emergency resources such as personnel, equipment, and supplies within the city; these resources are essential for responding to drainage system failures and disasters.

[0256] Multi-objective optimization algorithm:

[0257] The resource scheduling engine uses a multi-objective optimization algorithm to determine the optimal resource scheduling solution. The objective function is as follows:

[0258] (in, For the The path distance to each rescue point, in km; Dynamic driving speed, obtained through the real-time traffic API, in km / h; is the time penalty factor, which is set to 10-50 and is positively correlated with the warning level; is the actual response time in minutes; Set time limits for emergency plans, divided by warning levels; represents the number of rescue points);

[0259] Path time optimization:

[0260] Indicates the total time required for rescue personnel or equipment to reach the fault node from the resource point; dynamic driving speed is obtained through the real-time traffic API , the path time can be calculated more accurately, thus selecting the optimal driving path and reducing response time;

[0261] Time penalty:

[0262] It is a time penalty item, which is used to punish the situation where the actual response time exceeds the time limit specified in the plan; is the time penalty factor, which is positively correlated with the warning level. The higher the warning level, The larger the value, the more severe the penalty for timeouts; this helps ensure timely response in high-risk situations and reduce losses;

[0263] Constraints:

[0264] When scheduling resources, the following constraints must be met:

[0265] Resource requirement constraints: ,in, is the amount of available resources, is the demand threshold; this ensures that the scheduled resources can meet the maintenance and processing needs of the faulty node;

[0266] Resource allocation constraints: , indicating whether to start from the resource point To the node Allocate resources; Indicates the allocation of resources, Indicates that no resources are allocated;

[0267] Genetic algorithm optimization:

[0268] To solve multi-objective optimization problems, the resource scheduling engine uses a genetic algorithm to optimize maintenance paths. A genetic algorithm is an optimization algorithm that simulates natural selection and heredity mechanisms, finding the optimal solution through continuous iterative evolution.

[0269] coding:

[0270] The resource scheduling scheme is encoded as a chromosome, each chromosome represents a possible scheduling scheme; the genes in the chromosome represent the allocation of resources and path selection;

[0271] Fitness function:

[0272] The fitness value of each chromosome is calculated according to the objective function. The higher the fitness value, the better the scheduling scheme.

[0273] Selection, crossover, and mutation:

[0274] Through genetic operations such as selection, crossover, and mutation, new chromosomes are continuously generated, gradually approaching the optimal solution; the selection operation selects excellent chromosomes according to their fitness values, the crossover operation exchanges the genes of excellent chromosomes, and the mutation operation randomly mutates the genes of chromosomes to increase the diversity of the population;

[0275] Pareto optimal solution screening mechanism:

[0276] In order to strike a balance between multiple optimization objectives, the resource scheduling engine introduces a Pareto optimal solution screening mechanism; the optimization objective function is defined as:

[0277] ,in, It is Path distance to each rescue point (in km), is the dynamic driving speed;

[0278] The Pareto front is solved using the non-dominated sorting genetic algorithm (NSGA-II) to obtain a set of non-dominated solutions. The final solution is selected by the decision maker based on the real-time weather warning level and actual conditions to achieve a balanced decision between efficiency and safety.

[0279] Dynamic scheduling plan generation:

[0280] The resource scheduling engine generates a dynamic scheduling plan based on the results of multi-objective optimization and genetic algorithms, combined with real-time data changes. This plan specifies the type and quantity of resources required for each faulty node, as well as the resource allocation path and schedule. Furthermore, as the warning level, node health index, and emergency resource distribution change, the scheduling plan is updated in real time to ensure optimal resource allocation.

[0281] Application value of the module:

[0282] The resource scheduling engine improves the emergency response capability and maintenance efficiency of urban drainage systems through scientific and reasonable resource allocation and route planning. It can allocate emergency resources to where they are most needed in the shortest possible time, reducing the impact of disasters such as urban waterlogging, and protecting the lives and property of urban residents and the normal operation of the city. At the same time, the real-time update mechanism of the dynamic scheduling plan ensures the flexibility and adaptability of the system, and can cope with various complex and changing situations.

[0283] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An urban smart drainage management system based on big data analysis, characterized in that: include: Multi-source data acquisition module, used to obtain real-time historical climate change data, high-precision historical rainfall curves, satellite meteorological monitoring data, water level sensor data at each node of the drainage network, drainage flow sensor data, and network topology data; The dynamic 3D modeling module constructs a 3D model of the urban drainage network based on the principles of fluid mechanics, including network material properties, terrain slope, and node connection relationships. It also dynamically binds real-time sensor data to the 3D model of the urban drainage network through a data fusion algorithm. The dynamic 3D modeling module uses a spatiotemporal data fusion algorithm to dynamically bind real-time sensor data to the model. The formula is as follows: ;in, is the fusion weight coefficient; For the The sensors in Real-time readings at all times; is the geographical impact factor; For the Predicted rainfall intensity values ​​from satellite meteorological channels; is the time attenuation coefficient, according to Dynamic attenuation; 、 To dynamically adjust the parameters, And it is updated every 5 minutes; is the maximum reading of the sensor; is the maximum value of satellite data; The drainage limit analysis module uses a pressure-flow coupling algorithm to calculate the maximum drainage threshold of each node under extreme rainfall conditions, and generates a node failure probability distribution map based on an improved Monte Carlo simulation algorithm; The actual discharge is compared with a threshold value obtained by subtracting a historical bias correction term from the theoretical maximum discharge; The proportion of the number of simulations in which the actual traffic exceeds the threshold to the total number of simulations is counted as the node failure probability; Correction mechanism: The historical deviation correction term is calculated by multiplying the historical maximum flow fluctuation value by the dynamic adjustment factor γ. γ is adaptively adjusted using the hyperbolic tangent function. The adjustment range is affected by the difference between the current flow fluctuation and the standard deviation of the historical data. The failure probability calculation formula of the improved Monte Carlo simulation algorithm is: ,in, is the node failure probability; For the number of simulations, set Second-rate; For the The actual drainage flow of the node in the simulation; is the theoretical maximum drainage capacity, calculated based on the pipe diameter and slope; is the dynamic adjustment factor; This is the maximum historical flow fluctuation value; is an indicator function, which takes 1 when the condition is met and 0 otherwise; Dynamic adjustment factor Adopting the adaptive adjustment strategy, the formula is: ,in, is the baseline correction value; is the adjustment amplitude coefficient; is the current monitoring flow fluctuation value; is the standard deviation of historical data, updated quarterly; The intelligent early warning module generates a heat map of drainage pressure for the next 72 hours by integrating the long short-term memory network (LSTM) and the ARIMA time series model, combined with satellite meteorological forecast data. The intelligent early warning module uses a hybrid prediction model to predict drainage pressure. The formula is as follows: ,in, for The predicted drainage pressure at the moment, in kPa; is the model weight factor, through Dynamic calculation; is the LSTM time window length; Satellite prediction correction item, reflecting the future rainfall intensity after time; is the meteorological sensitivity coefficient; is the sigmoid function, which is used to normalize the weight distribution; When the node's real-time data exceeds the preset safety threshold, a multi-level warning mechanism is triggered; The health assessment module uses the random forest algorithm to analyze the correlation between historical node fault data and real-time performance indicators, generates a node health index including pipe wall corrosion rate and sediment accumulation coefficient, and outputs a maintenance priority ranking; The specific formula is as follows: ,in, is the health index, standardized to 0-100 points; For the The dynamic weight of each indicator is calculated by entropy weight method and meets ; Indicates the various physical quantities currently detected, including corrosion depth and deposition thickness; This is the maximum value allowed by industry standards; is the aging attenuation coefficient; is the current timestamp; The time for pipe network installation; Represents the number of indicators used to evaluate the health of the node; The resource scheduling engine optimizes maintenance path planning based on genetic algorithms, generates dynamic scheduling plans based on comprehensive warning levels, health indexes, and emergency resource distribution, and achieves real-time optimal configuration of personnel, equipment, and materials.

2. The urban smart drainage management system based on big data analysis according to claim 1 is characterized in that: The optimization objective function of the resource scheduling engine is defined as: Total path time: the sum of the path distances to each rescue point divided by the dynamic driving speed; Time breach cost: Penalties are imposed on response times exceeding the time limit of the plan. The penalty coefficient η is positively correlated with the warning level. Total resource constraints: Available resources must meet the demand threshold; Decision variable constraints: Resource allocation adopts 0-1 integer programming.

3. The urban smart drainage management system based on big data analysis according to claim 2 is characterized in that: The geographic impact factor is calculated using the following rules: Altitude impact term: the normalized value of the node altitude relative to the regional extreme value multiplied by 0.2; Building density impact term: 1 minus the ratio of the building density around the node to the maximum density of the city, multiplied by 0.8; The final geographic impact factor is the sum of the above two items.

4. The urban smart drainage management system based on big data analysis according to claim 3 is characterized in that: The update rule of the dynamic adjustment factor γ is: Based on the benchmark value of 0.8; The adjustment range is calculated by the difference between the current flow fluctuation and the historical fluctuation through the hyperbolic tangent function; Adjust the amplitude coefficient μ to control the change range, with a value of 0.2-0.6.

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