A method and system for optimizing dispatching based on urban power plant network river integration

Abstract

The present invention relates to the technical field of urban water management, and in particular to an optimization scheduling method and system based on the integration of urban power plants, networks and rivers. The method comprises the following steps: constructing a network topology structure according to metadata of the urban drainage network, generating drainage network topology structure data; estimating the pollution source strength according to the drainage network topology structure data, and obtaining regional pollution source strength distribution data; constructing a network drainage water hydraulic model according to the drainage network topology structure data and the regional pollution source strength distribution data, and generating a plant-network-river integrated hydraulic water quality model; conducting an overflow pollution impact assessment based on the plant-network-river integrated hydraulic water quality model, and generating overflow pollution impact level data; generating a multi-objective optimization scheduling scheme according to the overflow pollution impact level data, and generating an overflow risk control strategy. The present invention realizes intelligent early warning scheduling of urban drainage by finely constructing an integrated plant-network-river hydraulic water quality model, and effectively reduces the risk of overflow.

Description

Technical Field

[0001] The present invention relates to the technical field of urban water management, and in particular to an optimization dispatching method and system based on urban plant network river integration. Background Art

[0002] The application of integrated dispatching of urban power plants, networks and rivers can not only improve the efficiency of water resource utilization, but also bring significant economic and social benefits. Through coordinated dispatching, water resource waste can be reduced, water supply energy consumption can be reduced, and water quality compliance rate can be improved, thereby reducing operating costs and improving service quality. At the same time, it also helps to improve the urban water environment, reduce water pollution, and improve the quality of life of urban residents. However, the traditional integrated dispatching method of urban power plants, networks and rivers often relies on experience judgment and extensive dispatching, and focuses on a single goal (such as sewage treatment efficiency). This static or simple dispatching strategy has a low degree of dispatching refinement, resulting in dispatching decisions that are not scientific and accurate enough. In the event of emergencies such as extreme weather, it is impossible to provide early risk warnings through weather forecasts, resulting in insufficient flexibility and adaptability. Summary of the invention

[0003] Based on this, the present invention provides a method and system for optimizing scheduling based on urban plant-grid-river integration to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for optimizing dispatching based on urban power plant network river integration includes the following steps:

[0005] Step S1: Obtaining metadata of the urban drainage network; constructing a network topology structure according to the metadata of the urban drainage network to generate preliminary drainage network topology data; performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology structure data;

[0006] Step S2: Drainage areas are divided according to the drainage network topology data to generate urban drainage grid area data; a rainfall-pollution-erosion relationship model is constructed based on the urban drainage grid area data; pollution source strength is estimated according to the urban drainage grid area data to generate urban pollution source strength data and non-point source pollution source strength data respectively; time series transformation trend analysis is performed according to the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data;

[0007] Step S3: construct a drainage water hydraulic model for the drainage network according to the drainage network topology data and the regional pollution source intensity distribution data to obtain the drainage water hydraulic model for the drainage network; couple the drainage water hydraulic model for the drainage network and the rainfall pollution flushing relationship model with a three-dimensional hydraulic water quality model to generate a plant-network-river integrated hydraulic water quality model; perform network load prediction based on the plant-network-river integrated hydraulic water quality model to generate rainfall network load prediction data;

[0008] Step S4: perform overflow pollution impact assessment based on rainfall pipe network load forecast data to generate overflow pollution impact level data; perform risk rapid forecast processing based on overflow pollution impact level data to generate rapid risk forecast data; generate a multi-objective optimization scheduling plan based on the overflow pollution impact level data to obtain multi-objective optimization scheduling plan data;

[0009] Step S5: Carry out emergency plan processing based on rapid risk forecast data, and carry out coordinated linkage control of drainage facilities through multi-objective optimization scheduling plan data to generate overflow risk control strategy.

[0010] The present invention can construct a preliminary topological structure of the drainage network by acquiring and analyzing the metadata of the urban drainage network, which is the basis for the physical layout of the urban drainage system. The subsequent topological verification process ensures the accuracy and logic of the network structure. The division of drainage areas according to the topological structure of the drainage network not only helps to refine the management area, but also facilitates the targeted analysis and treatment of drainage problems in different areas. The constructed rainfall-pollution-scouring relationship model can scientifically predict the generation and migration laws of pollutants under rainfall conditions, which is of great significance for understanding the emission of pollutants in the rainy season. The estimation of the intensity of pollution sources and the analysis of their temporal changes provide key information for quantifying pollution contributions and identifying high-risk areas, which helps to formulate more accurate pollution prevention and control measures. Through the construction of the hydraulic model of the drainage water network, the water flow dynamics of the drainage system under different conditions are simulated, which can reveal the drainage efficiency and identify potential bottleneck areas. This hydraulic model is coupled with the rainfall-pollution-scouring relationship model into a plant-network-river integrated hydraulic water quality model, which realizes the comprehensive simulation of the urban drainage system, river water quality and the operation status of the sewage treatment plant, greatly improving the comprehensiveness and accuracy of the simulation. The network load forecast based on this model provides data support for advance planning and adjustment of drainage strategies, which helps prevent system overload problems caused by rainfall. By evaluating the possibility of overflow events under different rainfall scenarios and the extent of their impact on the environment, high-risk areas can be quickly identified and response measures can be initiated in a timely manner. The generation of multi-objective optimization scheduling schemes provides decision makers with optimal resource allocation suggestions based on multiple factors such as environmental protection, economic costs and public safety, and enhances the flexibility and resilience of the drainage system. The emergency plan based on rapid risk forecast data ensures a rapid response to emergencies and minimizes environmental damage and social impact. Through the coordinated linkage control of drainage facilities through multi-objective optimization scheduling schemes, effective control of overflow risks is achieved, which not only improves the overall efficiency of the urban drainage system, but also strengthens its sustainability and resilience, and ensures the safety and health of the urban water environment. Therefore, the present invention is based on an integrated optimization scheduling method for urban power plants, networks and rivers. By constructing a network topology structure, and after verification and correction, accurate drainage network topology data is formed; the domestic, industrial and urban runoff drainage grid areas are divided in detail, and the emission intensity of various pollution sources is estimated. By establishing a network drainage water hydraulic model, and coupling it with the drainage scheduling purification model and the rainfall pollution flushing relationship model, a plant network river integrated hydraulic water quality model is formed. The model can predict the network load under rainfall conditions through simulation and real-time data access, and realize the spatiotemporal dynamic prediction of pollutant emissions; through rapid risk forecasting, combined with a multi-objective optimization scheduling algorithm, a dynamic scheduling plan is generated to minimize the risk of overflow, thereby significantly improving the flexibility of the system and its ability to respond to emergencies.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: acquiring metadata of the urban drainage network according to the urban drainage system to obtain metadata of the urban drainage network;

[0013] Step S12: Drainage nodes are identified according to the metadata of the urban drainage network, and inspection wells, interception wells, reservoirs, and water outlets are used as drainage element nodes to obtain drainage node data;

[0014] Step S13: Drainage pipelines are identified according to the metadata of the urban drainage network, and drainage pipes, pumping stations, orifices and weirs are used as drainage element pipelines to obtain drainage pipeline data;

[0015] Step S14: constructing a topological structure based on the drainage node data and the drainage pipeline data to generate preliminary drainage network topology data;

[0016] Step S15: performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology verification data;

[0017] Step S16: Associating drainage network attributes according to the drainage network topology verification data to generate drainage network topology data.

[0018] The present invention obtains the metadata of the urban drainage network by performing comprehensive data collection on the urban drainage system. The metadata is a collection of basic information of the drainage system. By identifying and marking key nodes such as inspection wells, interception wells, reservoirs and outlets in the metadata of the urban drainage network, the positions and roles of various functional structures in the drainage system are clarified, providing basic units for constructing the network topology structure, which is helpful for accurately locating and understanding the functions and effects of each node. The flow path inside the drainage system is refined, and the necessary line information is provided for simulating water flow dynamics. By distinguishing the characteristics of different types of pipelines, the water flow velocity, flow rate and the blockage that occurs can be simulated more accurately. The topological structure is constructed based on the node and pipeline data collected previously, and the interconnection relationship inside the drainage system is depicted from a macro perspective. The process of checking and correcting the topological data initially constructed ensures the logic and consistency of the data, ensures that the network truly reflects the structure of the actual drainage system, and reduces the analysis deviation caused by data errors. By combining specific attribute information (such as pipe diameter size, material durability, etc.) with the network structure, the network can not only reflect the physical structure, but also reflect the functional characteristics and status information.

[0019] Preferably, step S15 comprises the following steps:

[0020] Step S151: assigning elevation data to the preliminary drainage network topology data through a preset terrain elevation model to obtain elevation drainage topology network data;

[0021] Step S152: Calculate the pipeline slope according to the elevation drainage topology network data, and perform pipeline flow direction analysis to generate preliminary confluence direction data;

[0022] Step S153: performing pipeline longitudinal section segmentation based on the preliminary confluence direction data to generate pipeline longitudinal section data;

[0023] Step S154: identifying and marking abnormal pipe sections according to the pipeline longitudinal section data to obtain abnormal pipe section data to be checked;

[0024] Step S155: conducting on-site pipe segment review on the abnormal pipe segment data to be checked, and performing abnormal error correction processing to generate corrected drainage pipe network topology data;

[0025] Step S156: performing blind pipe re-correction processing according to the corrected drainage network topology data to generate re-corrected drainage network topology data;

[0026] Step S157: Perform a topology integrity check based on the revised drainage network topology data to obtain drainage network topology check data.

[0027] The present invention combines the terrain elevation model with the preliminary drainage network topology data to give actual elevation information to each part of the network, which helps to simulate the rainwater flow and sewage discharge process more accurately. Calculating the pipeline slope and analyzing the flow direction helps to identify the water flow convergence points and dispersion points, ensuring the accuracy of the hydrodynamic simulation. Identifying and marking abnormal pipe sections helps to quickly locate those pipe sections that do not conform to hydraulic principles or design specifications. Through the longitudinal section division of the pipeline, the geometric characteristics of the pipeline are further refined, which helps to deeply analyze the capacity, blockage points and cleaning and maintenance needs of the pipeline. Through field verification and error correction, the accuracy and reliability of the drainage network data are significantly improved, decision-making errors caused by data errors are avoided, and the management level of the entire drainage system is improved. Supplementary corrections are made to those pipe sections that are not correctly identified or connected to ensure the integrity and continuity of the drainage network topology. The drainage network topology verification data confirms the rationality and logic of the entire drainage network structure, which is an important guarantee for implementing efficient drainage scheduling, preventing overflow pollution and improving the quality of urban water environment.

[0028] Preferably, step S156 includes the following steps:

[0029] Step S1561: Identify the outlet nodes according to the modified drainage network topology data, and obtain the outlet node data and the water outlet node data respectively;

[0030] Step S1562: performing upstream recursive tracing processing on the drainage outlet node data based on the modified drainage network topology data to obtain drainage path tree data;

[0031] Step S1563: performing downstream recursive tracing processing on the outlet node data based on the modified drainage network topology data to obtain water catchment path tree data;

[0032] Step S1564: perform reachability calculation based on the drainage path tree data and the catchment path tree data, and generate drainage reachability probability matrix data and catchment reachability probability matrix data respectively;

[0033] Step S1565: Performing connectivity evaluation on the drainage reachability probability matrix data, and performing dead-end detection on the drainage path tree data, to generate drainage potential blind pipe data; performing connectivity evaluation on the catchment reachability probability matrix data, and performing dead-end detection on the catchment path tree data, to generate catchment potential blind pipe data;

[0034] Step S1566: Perform cross-comparison processing based on the drainage potential blind pipe data and the water collection potential blind pipe data to generate the pipe network blind pipe data;

[0035] Step S1567: Perform pipeline isolation processing on the blind pipe data of the pipe network, and perform secondary correction processing to obtain the re-corrected drainage pipe network topology data.

[0036] The present invention provides clear starting and ending points by identifying and modifying the outlet nodes and outlet nodes in the drainage network topology data. The acquisition of the outlet node data and the outlet node data is crucial to understanding the way rainwater flows into and out of the system. Through upstream recursive tracing and downstream recursive tracing processing, drainage path tree data and water catchment path tree data are generated, and the complete path of the water flow from the source to the outlet is systematically depicted. The accessibility calculation is performed using the path tree data, and the drainage reachable probability matrix data and water catchment reachable probability matrix data are obtained, which quantify the probability of water flow connectivity between each node, and provide quantitative indicators for analyzing the overall connectivity and potential flow bottlenecks of the drainage system. Connectivity evaluation and dead-end detection, especially the dead-end detection of the drainage path tree and the water catchment path tree, find the neglected or forgotten parts, that is, potential blind pipes. By integrating the potential blind pipe information in both the drainage and water catchment directions, it is ensured that no key problem points will be missed when optimizing the pipe network. By isolating and re-correcting the blind pipe data of the pipeline network, the final corrected drainage network topology data not only eliminates the blind pipe problem that causes poor drainage, but also further optimizes the structure of the entire drainage system, ensuring its efficient and safe operation.

[0037] Preferably, step S2 comprises the following steps:

[0038] Step S21: Identify the main pipe network water confluence points according to the drainage pipe network topology data, divide the drainage area through the geographic information system, and generate urban drainage area data;

[0039] Step S22: Performing Thiessen polymorphic grid division on the urban drainage area data to generate urban drainage grid area data, wherein the urban drainage grid area data includes domestic drainage grid area data, industrial drainage grid area data and urban runoff drainage grid area data;

[0040] Step S23: Estimating the pollution source strength according to the domestic drainage grid area data and the industrial drainage grid area data to generate urban pollution source strength data;

[0041] Step S24: constructing a rainfall-pollution-scouring relationship model based on the urban runoff drainage grid area data; performing non-point source pollution concentration analysis based on the rainfall-pollution-scouring relationship model to obtain non-point source pollution source strength data;

[0042] Step S25: Performing grid spatiotemporal superposition processing on the urban drainage grid area data through the urban pollution source strength data and the non-point source pollution source strength data, and performing drainage volume and pollution concentration characteristic analysis to obtain grid water volume boundary data and grid water quality boundary data respectively;

[0043] Step S26: Perform time series transformation trend analysis based on the grid water quantity boundary data and the grid water quality boundary data to obtain regional pollution source intensity distribution data.

[0044] The present invention helps to clarify the scope of responsibility of each area by identifying the water confluence point of the main pipeline network and using the geographic information system to divide the drainage area. The urban drainage area is gridded by the Thiessen polygon method to achieve the subdivision management of the drainage area. This subdivision not only helps to accurately grasp the distribution of drainage of different properties, but also provides more accurate geographical positioning for the identification and control of pollution sources. According to the data of the domestic drainage grid area and the industrial drainage grid area, combined with information such as land use type and population density, the pollution source intensity of each grid is estimated, including point source pollution source and non-point source pollution source. A rainfall-pollution flushing relationship model is established, and the non-point source pollution source intensity is estimated. Accurate pollutant input data is provided for construction and pollution load prediction, and the generation, propagation and emission of pollutants in the urban drainage process are effectively simulated. By gridding the urban pollution source intensity data and the non-point source pollution source intensity data for spatiotemporal superposition processing, detailed data is provided for quantitative analysis of the drainage characteristics and pollution levels of different grid areas, which helps to identify pollution hotspot areas and optimize resource allocation to more effectively deal with water pollution problems. By conducting time series transformation trend analysis based on the grid water quantity boundary data and the grid water quality boundary data, we can understand the distribution of pollution source intensity in different time periods and different regions.

[0045] Preferably, step S23 includes the following steps:

[0046] Step S231: Obtaining the grid resident population density according to the living drainage grid area data, and calculating the water consumption of different types of living areas according to the preset urban water quota standard data to obtain the total water consumption data of the living areas, wherein the different types of living areas include public buildings, residential areas, commercial areas and mixed residential areas;

[0047] Step S232: Perform quarterly typical daily pollutant measurement on the living drainage grid area data through preset living source strength measurement indicators to obtain living area pollutant concentration data, wherein the preset living source strength measurement indicators include water volume, BOD5, COD, ammonia nitrogen, total phosphorus and surfactant;

[0048] Step S233: Calculate the total amount of sewage by using the total amount of water used in the living area and the pollutant concentration data in the living area to obtain the domestic sewage discharge data; integrate the domestic sewage source strength according to the domestic sewage discharge data and the pollutant concentration data in the living area to generate the domestic sewage source strength data;

[0049] Step S234: classifying enterprises according to the industrial drainage grid area data, and obtaining drainage grid data of key enterprises and drainage grid data of non-key enterprises respectively;

[0050] Step S235: Access the drainage grid data of key enterprises online, and perform manual monitoring and supplementary processing to obtain the pollution discharge data of key enterprises; collect environmental protection supervision data based on the drainage grid data of non-key enterprises to obtain the pollution discharge data of non-key enterprises;

[0051] Step S236: Integrate the industrial pollution source strengths based on the pollution data of key enterprises and non-key enterprises to generate industrial wastewater source strength data.

[0052] The present invention calculates the water consumption of different types of living areas by obtaining the resident population density of the living drainage grid area and combining it with the urban water quota standard, which helps to rationally plan water resource allocation. The data of the living drainage grid area are measured for the quarterly typical daily level pollutants through the preset living source strength measurement indicators (water volume, BOD5, COD, ammonia nitrogen, total phosphorus and surfactant), and the pollutant concentration information is provided for the calculation of sewage discharge. By combining the total water consumption data of the living area with the pollutant concentration data, the domestic sewage discharge is calculated and integrated into the domestic sewage source strength data, which directly links the water use behavior and pollution output, and provides accurate input parameters. The enterprise classification of the industrial drainage grid area distinguishes between key enterprises and non-key enterprises. This classification helps to take different regulatory intensities and strategies in a targeted manner, improve management efficiency, and ensure key monitoring of major pollution sources. The online drainage data access and manual monitoring supplement of key enterprises, as well as the environmental protection supervision data collection of non-key enterprises, ensure the comprehensiveness and accuracy of industrial sewage discharge data, and reveal the specific impact of industrial activities on the water environment by integrating the sewage discharge data of key enterprises and non-key enterprises.

[0053] Preferably, step S24 comprises the following steps:

[0054] Step S241: Divide the urban runoff drainage grid area data into minimum water catchment units to generate hydrological monitoring grid data;

[0055] Step S242: Acquire historical hydrological flow data based on hydrological monitoring grid data;

[0056] Step S243: classify the underlying surface type according to the hydrological monitoring grid data, and calculate the time series runoff coefficient through the historical hydrological flow data to generate surface runoff coefficient data;

[0057] Step S244: Obtain historical water quality monitoring data; draw a non-point source pollution curve based on the historical water quality monitoring data, and analyze the pollutant growth time series characteristics through the surface runoff coefficient data to obtain runoff non-point source pollution parameters, wherein the runoff non-point source pollution parameters include pollutant growth type, growth rate, saturation constant and maximum growth rate;

[0058] Step S245: constructing a rainfall-pollution-erosion relationship model based on the runoff surface source pollution parameters and the surface runoff coefficient data through a preset corresponding rainfall event;

[0059] Step S246: Estimating the scour coefficient according to the rainfall-pollution scour relationship model to obtain the rainfall scour coefficient;

[0060] Step S247: Analyze the surface source pollution concentration according to the rainfall erosion coefficient to obtain surface source pollution source strength data.

[0061] The present invention divides the urban runoff drainage grid area into the smallest water catchment unit, and the generated hydrological monitoring grid data provides a fine spatial scale basis for subsequent hydrological analysis. This subdivision helps to accurately capture the hydrological characteristics of different regions and improves the accuracy of hydrological monitoring and simulation. The underlying surface type is divided according to the hydrological monitoring grid data, and the time series runoff coefficient is calculated using historical hydrological flow data to reflect the runoff characteristics of different types of underlying surfaces under different rainfall conditions. The drawing of the non-point source pollution curve and the determination of characteristic parameters clarify the dynamic law of pollutant changes over time, and provide key parameters for understanding the generation mechanism of non-point source pollution, such as the growth type and rate of pollutants, which helps to quantify the role of rainfall in promoting pollutant release. The constructed rainfall-pollution-scouring relationship model establishes a quantitative connection between rainfall and pollutant emissions by integrating runoff coefficients and pollution parameters. The scouring coefficient is estimated based on the rainfall-pollution-scouring relationship model to obtain the rainfall scouring coefficient, which reflects the intensity of pollutant scouring under different rainfall intensities. The non-point source pollution concentration analysis based on the rainfall erosion coefficient directly guides the formulation and implementation of non-point source pollution control measures, ensuring the improvement and protection of urban water environment quality.

[0062] Preferably, step S3 comprises the following steps:

[0063] Step S31: performing sewage node association processing according to the drainage network topology data and the regional pollution source intensity distribution data, and constructing a sewage force model of the network to obtain a sewage force model of the network;

[0064] Step S32: obtaining a drainage scheduling and purification model, wherein the drainage scheduling and purification model includes a pump station model, a water treatment plant model, and an inland river model;

[0065] Step S33: coupling the pipe network drainage hydraulic model, drainage scheduling and purification model, and rainfall pollution flushing relationship model with a three-dimensional hydraulic and water quality model to generate an initial integrated hydraulic and water quality model of the plant, network, and river;

[0066] Step S34: simulating the initial integrated hydraulic and water quality model of the plant, network and river using the preset rainfall and sewage sample data, and adjusting the model parameters to obtain the integrated hydraulic and water quality model of the plant, network and river;

[0067] Step S35: accessing real-time monitoring data according to the integrated hydraulic and water quality model of the plant, network and river, and performing real-time water discharge simulation to generate real-time pipe network operation status data;

[0068] Step S36: Obtain short-term weather forecast data; use the plant-network-river integrated hydraulic and water quality model to predict the pipeline load based on the short-term weather forecast data based on the real-time pipeline network operation status data to generate rainfall pipeline network load prediction data.

[0069] The present invention performs sewage node association processing on the drainage network topology data and the regional pollution source intensity distribution data, establishes a mathematical description of water flow and pollutant transmission in the network, and ensures the accurate matching between pollution source discharge and network flow. The obtained drainage scheduling and purification model includes models of key components such as pumping stations, water treatment plants and inland rivers. The integration of this model comprehensively covers the whole process from sewage collection to treatment and discharge. The network drainage water hydraulic model, drainage scheduling and purification model and rainfall pollution flushing relationship model are integrated into one to form a comprehensive model that comprehensively reflects the dynamic interaction between hydraulic power and water quality of the urban drainage system. This coupling model can not only simulate the complex behavior of the urban drainage system more realistically, but also effectively evaluate the impact of different strategies on water quality and water level. Through simulation and model parameter adjustment, it is ensured that the model can accurately reflect the actual situation and improve the accuracy of model prediction. The real-time water discharge simulation using the real-time monitoring data access model provides managers with instant system operation feedback. Based on short-term meteorological forecast data and real-time pipeline operation status data, pipeline load forecasting using the integrated hydraulic and water quality model of the plant, network and river can help deploy flood control and drainage measures in advance and reduce the probability of urban waterlogging.

[0070] Preferably, step S4 comprises the following steps:

[0071] Step S41: identifying the potential overflow risk of the combined pipe by using the preset overflow risk threshold value for the rainfall pipe network load forecast data, and generating potential overflow risk point data;

[0072] Step S42: performing overflow period time series analysis on potential overflow risk point data through rainfall pipe network load forecast data to generate potential overflow risk period data;

[0073] Step S43: evaluating the impact of overflow pollution according to the potential overflow risk point data and the potential overflow risk period data, and classifying the risk levels to generate overflow pollution impact level data;

[0074] Step S44: using a preset risk forecast model library to perform risk rapid forecast processing on the overflow pollution impact level data to generate rapid risk forecast data;

[0075] Step S45: Perform risk reduction scheduling analysis on potential overflow risk point data and potential overflow risk period data through the integrated hydraulic and water quality model of the plant, network and river, and generate a multi-objective optimization scheduling plan through preset scheduling control rules and preset constraints to obtain multi-objective optimization scheduling plan data.

[0076] The present invention analyzes the rainfall pipe network load forecast data through a preset overflow risk threshold, identifies potential overflow risk points, helps decision makers lock in existing hidden danger areas in advance, and provides a clear goal for formulating emergency response measures. By performing a time series analysis on potential overflow risk points, a time window for overflow occurrence is provided. Overflow pollution impact assessment and risk level classification not only quantify the potential degree of harm at different risk points, but also ensure that limited resources can be used for prevention and control of high-risk areas first. The use of a risk forecast model library to quickly forecast and process overflow pollution impact level data greatly improves the speed and efficiency of risk response. This instant forecast capability provides a valuable time window for urban managers to quickly initiate emergency plans and reduce or avoid environmental and economic losses caused by overflow. Taking into account multiple dimensions such as system operation efficiency, environmental impact, and economic benefits, the system strives to find the best balance between multiple goals, effectively reduce overflow risks through scientific dispatching of pump stations, regulating water flow, and other measures, and optimize water resource allocation to ensure the safe, efficient, and sustainable operation of urban drainage systems.

[0077] The present invention also provides an optimized dispatching system based on the integration of urban power plants, grids and rivers, which executes the optimized dispatching method based on the integration of urban power plants, grids and rivers as described above. The optimized dispatching system based on the integration of urban power plants, grids and rivers includes:

[0078] The pipe network topology construction module is used to obtain the metadata of the urban drainage pipe network; construct the pipe network topology structure according to the metadata of the urban drainage pipe network, and generate preliminary drainage pipe network topology data; perform drainage topology verification on the preliminary drainage pipe network topology data, and generate drainage pipe network topology structure data;

[0079] The pollution source strength analysis module is used to divide the drainage area according to the drainage network topology data and generate urban drainage grid area data; build a rainfall pollution erosion relationship model based on the urban drainage grid area data; estimate the pollution source strength according to the urban drainage grid area data, and generate urban pollution source strength data and non-point source pollution source strength data respectively; perform time series transformation trend analysis based on the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data;

[0080] The hydraulic and water quality model integration module is used to construct a hydraulic model of the drainage network according to the drainage network topology data and the regional pollution source intensity distribution data to obtain the hydraulic model of the drainage network; the hydraulic model of the drainage network and the rainfall pollution flushing relationship model are coupled with a three-dimensional hydraulic and water quality model to generate a plant-network-river integrated hydraulic and water quality model; the load of the network is predicted based on the plant-network-river integrated hydraulic and water quality model to generate rainfall network load prediction data;

[0081] Overflow risk assessment module, used to evaluate the impact of overflow pollution according to the rainfall pipe network load forecast data, and generate overflow pollution impact level data; to perform risk rapid forecast processing according to the overflow pollution impact level data, and generate rapid risk forecast data; to generate multi-objective optimization scheduling scheme according to the overflow pollution impact level data, and obtain multi-objective optimization scheduling scheme data;

[0082] The collaborative dispatching control module is used to process emergency plans based on rapid risk forecast data, and to coordinate and control drainage facilities through multi-objective optimization dispatching plan data to generate overflow risk control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A schematic diagram of the steps of an optimization scheduling method based on urban power plant network river integration of the present invention;

[0084] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0085] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0086] Figure 4 It is a detailed schematic diagram of the mechanism of the integrated hydraulic and water quality model of the plant, network and river;

[0087] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0088] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0089] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0090] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0091] To achieve this, please refer to Figures 1 to 4 The present invention provides an optimization scheduling method based on urban power plant network river integration, comprising the following steps:

[0092] Step S1: Obtaining metadata of the urban drainage network; constructing a network topology structure according to the metadata of the urban drainage network to generate preliminary drainage network topology data; performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology structure data;

[0093] Step S2: Drainage areas are divided according to the drainage network topology data to generate urban drainage grid area data; a rainfall-pollution-erosion relationship model is constructed based on the urban drainage grid area data; pollution source strength is estimated according to the urban drainage grid area data to generate urban pollution source strength data and non-point source pollution source strength data respectively; time series transformation trend analysis is performed according to the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data;

[0094] Step S3: construct a drainage water hydraulic model for the drainage network according to the drainage network topology data and the regional pollution source intensity distribution data to obtain the drainage water hydraulic model for the drainage network; couple the drainage water hydraulic model for the drainage network and the rainfall pollution flushing relationship model with a three-dimensional hydraulic water quality model to generate a plant-network-river integrated hydraulic water quality model; perform network load prediction based on the plant-network-river integrated hydraulic water quality model to generate rainfall network load prediction data;

[0095] Step S4: perform overflow pollution impact assessment based on rainfall pipe network load forecast data to generate overflow pollution impact level data; perform risk rapid forecast processing based on overflow pollution impact level data to generate rapid risk forecast data; generate a multi-objective optimization scheduling plan based on the overflow pollution impact level data to obtain multi-objective optimization scheduling plan data;

[0096] Step S5: Carry out emergency plan processing based on rapid risk forecast data, and carry out coordinated linkage control of drainage facilities through multi-objective optimization scheduling plan data to generate overflow risk control strategy.

[0097] In the embodiment of the present invention, reference Figure 1The above is a schematic diagram of the steps of an integrated optimization dispatching method based on urban power plant network and river of the present invention. In this embodiment, the integrated optimization dispatching method based on urban power plant network and river includes the following steps:

[0098] Step S1: Obtaining metadata of the urban drainage network; constructing a network topology structure according to the metadata of the urban drainage network to generate preliminary drainage network topology data; performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology structure data;

[0099] In an embodiment of the present invention, GIS software and database are used to obtain metadata of urban drainage pipe networks from relevant departments, and data cleaning and format conversion are performed. Graph theory methods are used to construct a pipe network topology structure based on the connection relationship in the pipe network metadata. The pipe network nodes and pipes are abstracted as nodes and edges in the graph, and the topological relationship between nodes and edges is established based on the connection relationship in the pipe network metadata to generate preliminary pipe network topology data. The preliminary pipe network topology data is checked for connectivity, directionality, closure, etc., and topological errors are identified and corrected. Corrections can be made according to actual conditions, such as adding missing pipes or nodes, or modifying the connection relationship between pipes or nodes. Finally, complete drainage pipe network topology data is generated.

[0100] Step S2: Drainage areas are divided according to the drainage network topology data to generate urban drainage grid area data; a rainfall-pollution-erosion relationship model is constructed based on the urban drainage grid area data; pollution source strength is estimated according to the urban drainage grid area data to generate urban pollution source strength data and non-point source pollution source strength data respectively; time series transformation trend analysis is performed according to the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data;

[0101] In the embodiment of the present invention, drainage area division is performed based on drainage network topological structure data, combined with factors such as urban terrain and population distribution, and urban drainage grid area data is generated. Model software is used to construct a rainfall pollution scouring relationship model based on grid area data to simulate the amount of pollutants generated under different rainfall conditions. According to grid area data, population density, industrial enterprise distribution, etc., urban pollution source strength data and non-point source pollution source strength data are estimated respectively. For example, urban sewage discharge statistical data, industrial enterprise pollution discharge permit information, etc. can be used in combination with the emission coefficient method to estimate point source pollution load; land use type, rainfall, pollutant scouring coefficient, etc. are used in combination with model simulation to estimate non-point source pollution load. Time series transformation trend analysis is performed on pollution source strength data, and time series analysis methods are used to analyze the daily, weekly, monthly and annual changes in pollution source strength, and combined with factors such as population density and land use changes, regional pollution source strength distribution data at different time scales in the future are predicted to obtain regional pollution source strength distribution data.

[0102] Step S3: construct a drainage water hydraulic model for the drainage network according to the drainage network topology data and the regional pollution source intensity distribution data to obtain the drainage water hydraulic model for the drainage network; couple the drainage water hydraulic model for the drainage network and the rainfall pollution flushing relationship model with a three-dimensional hydraulic water quality model to generate a plant-network-river integrated hydraulic water quality model; perform network load prediction based on the plant-network-river integrated hydraulic water quality model to generate rainfall network load prediction data;

[0103] In the embodiment of the present invention, hydraulic model software is used to construct a hydraulic model of pipe network drainage water according to the drainage pipe network topology data and the regional pollution source intensity distribution data to simulate the hydraulic characteristics under different pollution load conditions. The hydraulic model of pipe network drainage water is coupled with the rainfall pollution flushing relationship model, and the treatment capacity of the sewage treatment plant and the hydraulic characteristics of the river are considered to construct a hydraulic water quality model of the integrated plant network river. For example, the EPASWMM model can be used as a platform to couple the pipe network drainage system and the river system, wherein the pipe network drainage system is calculated using the EPANET engine, and the river system is simulated using the dynamic wave equation, and the rainfall runoff, overflow pollution and other data are transmitted through the interface to realize the integrated simulation of the plant network river. The model is verified using historical data, and the pipe network load is predicted according to different rainfall scenarios to generate rainfall pipe network load prediction data.

[0104] Step S4: perform overflow pollution impact assessment based on rainfall pipe network load forecast data to generate overflow pollution impact level data; perform risk rapid forecast processing based on overflow pollution impact level data to generate rapid risk forecast data; generate a multi-objective optimization scheduling plan based on the overflow pollution impact level data to obtain multi-objective optimization scheduling plan data;

[0105] In the embodiment of the present invention, the node location where overflow occurs, the overflow volume and the overflow pollutant concentration are identified based on the rainfall pipe network load prediction data, and the diffusion range and concentration change of the overflow pollutants in the receiving water body are calculated in combination with the water quality model, and the impact of overflow pollution on the water environment is evaluated, and it is divided into different levels (such as mild, moderate, and severe). Using the risk forecast model library, the risk rapid forecast processing is performed according to the overflow pollution impact level data, and the built-in forecast database is used to achieve pattern matching based on the actual rapid forecast of ground rain, and generate rapid risk forecast data. Using the optimization algorithm, multi-objective optimization scheduling plan data is generated according to the overflow pollution impact level data.

[0106] Step S5: Carry out emergency plan processing based on rapid risk forecast data, and carry out coordinated linkage control of drainage facilities through multi-objective optimization scheduling plan data to generate overflow risk control strategy.

[0107] In the embodiment of the present invention, the corresponding emergency plan is initiated according to the rapid risk forecast data, such as personnel evacuation, pollutant collection, environmental monitoring and other measures. According to the multi-objective optimization scheduling plan data, the drainage facilities are coordinated and controlled, such as adjusting the operating status of the pump station, controlling the opening and closing of the gate valve, coordinating the treatment capacity of the sewage treatment plant, etc., to reduce the impact of overflow pollution. According to the execution of the emergency plan and the effect of coordinated linkage control, the overflow risk control strategy is continuously optimized.

[0108] Preferably, step S1 comprises the following steps:

[0109] Step S11: acquiring metadata of the urban drainage network according to the urban drainage system to obtain metadata of the urban drainage network;

[0110] Step S12: Drainage nodes are identified according to the metadata of the urban drainage network, and inspection wells, interception wells, reservoirs, and water outlets are used as drainage element nodes to obtain drainage node data;

[0111] Step S13: Drainage pipelines are identified according to the metadata of the urban drainage network, and drainage pipes, pumping stations, orifices and weirs are used as drainage element pipelines to obtain drainage pipeline data;

[0112] Step S14: constructing a topological structure based on the drainage node data and the drainage pipeline data to generate preliminary drainage network topology data;

[0113] Step S15: performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology verification data;

[0114] Step S16: Associating drainage network attributes according to the drainage network topology verification data to generate drainage network topology data.

[0115] In the embodiment of the present invention, the metadata of the urban drainage network is first obtained, including information of network nodes, pipe sections, pump stations, gate valves and other facilities, as well as related attribute data. The obtained metadata is cleaned to remove duplicate data, erroneous data, missing data, etc., and coordinate system conversion, attribute information standardization and other processing are performed to ensure the accuracy and consistency of the data. According to the defined node type, the corresponding node element is identified from the metadata. For example, according to the "element type" field in the attribute information, the element with the value of "inspection well", "interception well", "reservoir" and "outlet" can be identified as a drainage node. According to the defined pipeline type, the corresponding pipeline element is identified from the metadata. For example, according to the "element type" field in the attribute information, the element with the value of "drainage pipe", "pump station", "orifice" and "weir" can be identified as a drainage pipeline. According to the starting point and end point coordinates of the pipeline data, the pipeline is connected to the corresponding node to form a preliminary network topology structure. According to the actual situation of the drainage network, the corresponding topology rules are set, for example, each node must be connected to at least one pipeline, each pipeline must be connected to two nodes, and there cannot be isolated nodes or pipelines. For violations of topological rules, error correction is performed, for example, connecting isolated nodes to corresponding pipelines, reconnecting incorrectly connected pipelines, etc. According to the numbering information of nodes and pipelines, the topological verification data is associated with the original metadata, and the attribute information in the metadata is added to the corresponding nodes and pipelines. The associated topological relationship, node attributes, pipeline attributes and other information are integrated together to form a complete drainage network topological structure data.

[0116] Preferably, step S15 comprises the following steps:

[0117] Step S151: assigning elevation data to the preliminary drainage network topology data through a preset terrain elevation model to obtain elevation drainage topology network data;

[0118] Step S152: Calculate the pipeline slope according to the elevation drainage topology network data, and perform pipeline flow direction analysis to generate preliminary confluence direction data;

[0119] Step S153: performing pipeline longitudinal section segmentation based on the preliminary confluence direction data to generate pipeline longitudinal section data;

[0120] Step S154: identifying and marking abnormal pipe sections according to the pipeline longitudinal section data to obtain abnormal pipe section data to be checked;

[0121] Step S155: conducting on-site pipe segment review on the abnormal pipe segment data to be checked, and performing abnormal error correction processing to generate corrected drainage pipe network topology data;

[0122] Step S156: performing blind pipe re-correction processing according to the corrected drainage network topology data to generate re-corrected drainage network topology data;

[0123] Step S157: Perform a topology integrity check based on the revised drainage network topology data to obtain drainage network topology check data.

[0124] In an embodiment of the present invention, a geographic information system (GIS) software, such as ArcGIS, QGIS, etc., is used to perform spatial overlay analysis on DEM data and preliminary drainage network topology data. According to the spatial coordinates of the network node, the DEM elevation value of the corresponding position is extracted, and the elevation value is assigned to the corresponding network node to generate drainage network node data with elevation information. According to the elevation difference of the nodes at both ends of the pipeline and the length of the pipeline, the slope value of each pipeline is calculated. For example, the slope can be calculated using the following formula: slope = (starting point elevation - end point elevation) / pipeline length. According to the elevation values ​​of the nodes at both ends of the pipeline, the flow direction of the pipeline is determined. For example, if the elevation of the starting point node is higher than the elevation of the end point node, the flow direction of the pipeline is from the starting point node to the end point node. The calculated slope value and flow direction information are added to the elevation drainage topology network data to generate preliminary confluence direction data. Using GIS software, such as the "Create Longitudinal Section" tool of ArcGIS, the longitudinal section information of each pipeline is extracted according to the preliminary confluence direction data. The longitudinal section information includes information such as the distance along the pipeline direction, the ground elevation at the corresponding distance, and the buried depth of the pipeline. According to the actual situation, set the rules for identifying abnormal pipe sections, such as: the pipeline slope is too large or too small; the pipeline has a reverse slope phenomenon; the pipeline is too shallow or too deep. Analyze the longitudinal section data of the pipeline, identify the pipe sections that do not meet the rules, and mark them as abnormal pipe sections to be checked. According to the data of the abnormal pipe sections to be checked, organize personnel to review on site, and use measurement tools such as total stations and RTK to accurately measure the actual elevation, slope, buried depth and other information of the abnormal pipe sections. Compare and analyze the data collected on site with the original data to determine whether the abnormal situation is true. For the pipe section information that is confirmed to be erroneous, correct it according to the on-site measurement data, such as correcting the elevation value of the nodes at both ends of the pipeline, the length of the pipeline, etc. Connectivity analysis is performed on the corrected drainage network topology data to identify the pipe sections that are not connected to other pipelines or nodes, that is, blind pipes. According to the specific situation of the blind pipe, formulate corresponding correction plans. For example, for short blind pipes, you can consider deleting them; for long blind pipes, you can extend them to the pipelines or nodes they should be connected to according to the actual situation; for blind pipes whose connection relationship cannot be determined, you can conduct on-site verification and handle them according to the actual situation. According to the formulated correction plan, the blind pipes are corrected, such as extending the pipelines, adding nodes, etc. The attribute information in the revised drainage network topology data is verified, such as checking whether the attribute information such as pipe diameter, slope, and burial depth meets the specification requirements and whether there are logical errors.

[0125] Preferably, step S156 includes the following steps:

[0126] Step S1561: Identify the outlet nodes according to the modified drainage network topology data, and obtain the outlet node data and the water outlet node data respectively;

[0127] Step S1562: performing upstream recursive tracing processing on the drainage outlet node data based on the modified drainage network topology data to obtain drainage path tree data;

[0128] Step S1563: performing downstream recursive tracing processing on the outlet node data based on the modified drainage network topology data to obtain water catchment path tree data;

[0129] Step S1564: perform reachability calculation based on the drainage path tree data and the catchment path tree data, and generate drainage reachability probability matrix data and catchment reachability probability matrix data respectively;

[0130] Step S1565: Performing connectivity evaluation on the drainage reachability probability matrix data, and performing dead-end detection on the drainage path tree data, to generate drainage potential blind pipe data; performing connectivity evaluation on the catchment reachability probability matrix data, and performing dead-end detection on the catchment path tree data, to generate catchment potential blind pipe data;

[0131] Step S1566: Perform cross-comparison processing based on the drainage potential blind pipe data and the water collection potential blind pipe data to generate the pipe network blind pipe data;

[0132] Step S1567: Perform pipeline isolation processing on the blind pipe data of the pipe network, and perform secondary correction processing to obtain the re-corrected drainage pipe network topology data.

[0133] In an embodiment of the present invention, whether a node is a drain node or an outlet node is determined based on the attribute information of the node, such as the node type, the type of the connecting pipeline, etc. For example, according to the "node type" field in the attribute information, the node with the value of "rainwater outlet" or "discharge outlet" can be identified as a drain node, and the node with the value of "outlet" or "river outlet" can be identified as an outlet node. Using graph theory algorithms such as depth-first search or breadth-first search, starting from each drain node, recursively trace the flow direction information of the pipeline upstream until it is impossible to continue to trace upstream. In the process of recursive tracing, all upstream nodes and pipelines corresponding to each drain node are recorded, and this information is stored in the drainage path tree data. Using graph theory algorithms such as depth-first search or breadth-first search, starting from each outlet node, recursively trace the flow direction information of the pipeline downstream until it is impossible to continue to trace downstream. In the process of recursive tracing, all downstream nodes and pipelines corresponding to each outlet node are recorded, and this information is stored in the water catchment path tree data. Define the accessibility between nodes. For example, if node A can reach node B through the pipe network path, node A is said to be reachable to node B. Calculate the reachability probability between any two nodes based on the drainage path tree data and the catchment path tree data. The reachability probability can be weighted based on factors such as path length, pipe diameter, and flow direction relationship. For example, the shorter the path, the larger the pipe diameter, and the more consistent the flow direction, the higher the reachability probability between nodes. Evaluate the connectivity of the pipe network based on the drainage reachability probability matrix and the catchment reachability probability matrix. For example, the average reachability probability of each node can be calculated. If the average reachability probability is lower than the preset threshold, it is considered that the connectivity of the node is poor and there is a blind pipe. Perform dead-end analysis on the drainage path tree data and the catchment path tree data to identify pipe sections that are not connected to the drain node or the outlet node. These pipe sections are blind pipes. Cross-check the drainage potential blind pipe data and the catchment potential blind pipe data to find out the pipe sections that appear in both data sets. A confidence level can be calculated for each suspected blind pipe based on factors such as the length of the pipe segment, the probability of being reachable, and the connection relationship with other pipe segments. The higher the confidence level, the greater the possibility that the pipe segment is a blind pipe. For pipe segments that are confirmed to be blind pipes, they are isolated from the revised drainage network topology data, such as marking them as deleted or moving them to a separate layer. According to the actual situation, the blind pipes are corrected for the second time. For example, for blind pipes with shorter lengths, they can be directly deleted. For blind pipes with longer lengths, they can be extended to the pipelines or nodes that they should be connected to according to the on-site conditions. For blind pipes whose connection relationships cannot be determined, on-site verification can be carried out and handled according to the actual situation.

[0134] Preferably, step S2 comprises the following steps:

[0135] Step S21: Identify the main pipe network water confluence points according to the drainage pipe network topology data, divide the drainage area through the geographic information system, and generate urban drainage area data;

[0136] Step S22: Performing Thiessen polymorphic grid division on the urban drainage area data to generate urban drainage grid area data, wherein the urban drainage grid area data includes domestic drainage grid area data, industrial drainage grid area data and urban runoff drainage grid area data;

[0137] Step S23: Estimating the pollution source strength according to the domestic drainage grid area data and the industrial drainage grid area data to generate urban pollution source strength data;

[0138] Step S24: constructing a rainfall-pollution-scouring relationship model based on the urban runoff drainage grid area data; performing non-point source pollution concentration analysis based on the rainfall-pollution-scouring relationship model to obtain non-point source pollution source strength data;

[0139] Step S25: Performing grid spatiotemporal superposition processing on the urban drainage grid area data through the urban pollution source strength data and the non-point source pollution source strength data, and performing drainage volume and pollution concentration characteristic analysis to obtain grid water volume boundary data and grid water quality boundary data respectively;

[0140] Step S26: Perform time series transformation trend analysis based on the grid water quantity boundary data and the grid water quality boundary data to obtain regional pollution source intensity distribution data.

[0141] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0142] Step S21: Identify the main pipe network water confluence points according to the drainage pipe network topology data, divide the drainage area through the geographic information system, and generate urban drainage area data;

[0143] In an embodiment of the present invention, the trunk pipe network is identified from the drainage pipe network topological structure data based on information such as pipe diameter, flow direction, and service area, using hydrological analysis or graph theory algorithms. For example, a pipe diameter threshold can be set to identify pipe sections with a pipe diameter greater than the threshold as the trunk pipe network. Nodes on the trunk pipe network that collect sewage from a larger area are identified as water confluence points. For example, by analyzing the topological relationship of the pipe network, trunk pipe network nodes with a larger degree (connecting multiple upstream pipe sections) can be identified as water confluence points. Combined with digital elevation model (DEM) data, automatic watershed division is performed to generate urban drainage area data. Each drainage area represents a water catchment range, and its boundaries are determined by the terrain and the direction of the pipe network.

[0144] Step S22: Performing Thiessen polymorphic grid division on the urban drainage area data to generate urban drainage grid area data, wherein the urban drainage grid area data includes domestic drainage grid area data, industrial drainage grid area data and urban runoff drainage grid area data;

[0145] In an embodiment of the present invention, GIS software, such as the "Create Thiessen Polygon" tool of ArcGIS, is used to perform Thiessen polygon division on urban drainage area data, and each drainage area is divided into several smaller grid areas. Thiessen polygon division can ensure that the distance from the point inside each grid area to the center of the area is less than the distance to the center of other areas, thereby optimizing spatial division. According to information such as land use type, pollution emission characteristics, etc., urban drainage grid area data is classified into domestic drainage grid area data, industrial drainage grid area data, and urban runoff drainage grid area data. For example, land use data can be used to divide the grid area mainly based on residential land into the domestic drainage grid area, the grid area mainly based on industrial land is divided into the industrial drainage grid area, and the grid area mainly based on roads, green land, etc. is divided into the urban runoff drainage grid area.

[0146] Step S23: Estimating the pollution source strength according to the domestic drainage grid area data and the industrial drainage grid area data to generate urban pollution source strength data;

[0147] In an embodiment of the present invention, relevant information such as urban population statistics, water consumption data, and industrial enterprise pollution data are collected. According to the data situation and accuracy requirements, a suitable pollution source strength estimation method is selected, for example: Population equivalent method: Estimate the pollution source strength of domestic sewage according to the population size of different types and the corresponding pollutant emission coefficient. Drainage coefficient method: Estimate the pollution source strength of industrial wastewater according to the water consumption of different types of industrial enterprises and the corresponding pollutant emission coefficient. Material balance method: Construct a material balance model based on the production process, raw and auxiliary material consumption, pollutant removal rate and other information of industrial enterprises to estimate the pollution source strength of industrial wastewater.

[0148] Step S24: constructing a rainfall-pollution-scouring relationship model based on the urban runoff drainage grid area data; performing non-point source pollution concentration analysis based on the rainfall-pollution-scouring relationship model to obtain non-point source pollution source strength data;

[0149] In an embodiment of the present invention, a suitable rainfall-runoff model and pollutant accumulation and erosion model are selected according to the characteristics of the study area and the data situation. For example, the SWMM model can be selected to simulate the rainfall-runoff process, and the EPR model can be selected to simulate the pollutant accumulation and erosion process. The model parameters are calibrated using historical rainfall-runoff data, water quality monitoring data, etc. to improve the simulation accuracy of the model. The constructed rainfall-pollution-erosion relationship model is used to simulate the runoff pollutant concentration in each urban runoff drainage grid area under different rainfall conditions. The simulated runoff pollutant concentration is multiplied by the corresponding grid area and rainfall to obtain the non-point source pollutant emissions in each urban runoff drainage grid area, and generate non-point source pollution source strength data.

[0150] Step S25: Performing grid spatiotemporal superposition processing on the urban drainage grid area data through the urban pollution source strength data and the non-point source pollution source strength data, and performing drainage volume and pollution concentration characteristic analysis to obtain grid water volume boundary data and grid water quality boundary data respectively;

[0151] In the embodiment of the present invention, the spatial analysis function of GIS software is used to spatially overlay the urban pollution source intensity data, the surface source pollution source intensity data and the urban drainage grid area data, and the pollution source intensity information is allocated to the corresponding grid area. According to the drainage of the three pollution sources of life, industry and urban runoff, the total drainage of each grid area at different times is calculated to generate grid water volume boundary data. According to the pollutant emissions and drainage of different pollution sources, the pollutant concentration of each grid area at different times is calculated to generate grid water quality boundary data.

[0152] Step S26: Perform time series transformation trend analysis based on the grid water quantity boundary data and the grid water quality boundary data to obtain regional pollution source intensity distribution data.

[0153] In the embodiment of the present invention, the drainage volume and pollutant concentration time series of each grid area are analyzed by using time series analysis methods, such as ARIMA model, wavelet analysis, etc., to identify its periodicity, trend and other characteristics. The discrete grid data are interpolated into continuous pollution source intensity distribution data by using spatial interpolation methods, such as Kriging interpolation, inverse distance weighted interpolation, etc., so as to more intuitively display the spatial distribution characteristics of pollution source intensity.

[0154] Preferably, step S23 includes the following steps:

[0155] Step S231: Obtaining the grid resident population density according to the living drainage grid area data, and calculating the water consumption of different types of living areas according to the preset urban water quota standard data to obtain the total water consumption data of the living areas, wherein the different types of living areas include public buildings, residential areas, commercial areas and mixed residential areas;

[0156] Step S232: Perform quarterly typical daily pollutant measurement on the living drainage grid area data through preset living source strength measurement indicators to obtain living area pollutant concentration data, wherein the preset living source strength measurement indicators include water volume, BOD5, COD, ammonia nitrogen, total phosphorus and surfactant;

[0157] Step S233: Calculate the total amount of sewage by using the total amount of water used in the living area and the pollutant concentration data in the living area to obtain the domestic sewage discharge data; integrate the domestic sewage source strength according to the domestic sewage discharge data and the pollutant concentration data in the living area to generate the domestic sewage source strength data;

[0158] Step S234: classifying enterprises according to the industrial drainage grid area data, and obtaining drainage grid data of key enterprises and drainage grid data of non-key enterprises respectively;

[0159] Step S235: Access the drainage grid data of key enterprises online, and perform manual monitoring and supplementary processing to obtain the pollution discharge data of key enterprises; collect environmental protection supervision data based on the drainage grid data of non-key enterprises to obtain the pollution discharge data of non-key enterprises;

[0160] Step S236: Integrate the industrial pollution source strengths based on the pollution data of key enterprises and non-key enterprises to generate industrial wastewater source strength data.

[0161] In an embodiment of the present invention, using demographic data, building area data, etc., combined with GIS spatial analysis functions, population information is allocated to each living drainage grid area, and the resident population density of each grid area is calculated. According to the preset urban water quota standard data, combined with the area proportion of different types of living areas in each living drainage grid area, the water consumption of public buildings, residential areas, commercial areas and mixed residential areas is calculated respectively. For example, assuming that the area proportion of residential areas in a certain living drainage grid area is 60%, the area proportion of commercial areas is 30%, and the area proportion of mixed residential areas is 10%, then according to the preset residential area water quota, commercial area water quota and mixed residential area water quota, the water consumption of the three types of living areas is calculated respectively, and the total living water consumption of the grid area is obtained by adding up. According to the preset living source strength measurement indicators (water volume, BOD5, COD, ammonia nitrogen, total phosphorus, surfactant, etc.), the pollutant measurement scheme of each living drainage grid area is determined, including sampling point layout, sampling frequency, sampling time, measurement method, etc. For example, sampling points can be set up at the outlet of the sewage network in each domestic drainage grid area, and a typical day can be selected in each quarter for 24-hour continuous sampling, and the water volume and the concentration of each pollutant can be measured using standard methods. The sewage discharge data of each domestic drainage grid area are integrated with the corresponding pollutant concentration data to generate domestic sewage source strength data. Enterprise classification standards can be formulated based on local industrial enterprise types, pollution discharge scale, environmental risks and other factors to divide industrial enterprises into key enterprises and non-key enterprises. For example, enterprise classification standards can be formulated based on indicators such as the industry category, pollutant discharge, and environmental risk level of the enterprise, and enterprises with large emissions and high environmental risks can be classified as key enterprises, and enterprises with small emissions and low environmental risks can be classified as non-key enterprises. According to the enterprise classification standards and enterprise spatial distribution information, the industrial drainage grid area data is classified to obtain drainage grid data for key enterprises and drainage grid data for non-key enterprises. For key enterprises that have installed online monitoring equipment, their online drainage data are connected to obtain real-time sewage discharge information. For key enterprises that have not installed online monitoring equipment or whose online monitoring data are missing, manual monitoring is supplemented, such as regularly collecting water samples for laboratory analysis to obtain their sewage concentration and water volume information. Collect the supervisory monitoring data, pollutant discharge permit data, self-monitoring data, etc. of non-key enterprises by the environmental protection department to obtain their pollutant discharge information. Standardize the collected pollutant discharge data of key and non-key enterprises, and associate the standardized pollutant discharge data with the corresponding grid area, for example, associate the pollutant discharge data of key enterprises with the corresponding grid data of key enterprise drainage, integrate the pollutant discharge data of key and non-key enterprises, and generate industrial wastewater source strength data, which contains the industrial wastewater discharge and main pollutant concentration information of each industrial drainage grid area.

[0162] Preferably, step S24 comprises the following steps:

[0163] Step S241: Divide the urban runoff drainage grid area data into minimum water catchment units to generate hydrological monitoring grid data;

[0164] Step S242: Acquire historical hydrological flow data based on hydrological monitoring grid data;

[0165] Step S243: classify the underlying surface type according to the hydrological monitoring grid data, and calculate the time series runoff coefficient through the historical hydrological flow data to generate surface runoff coefficient data;

[0166] Step S244: Obtain historical water quality monitoring data; draw a non-point source pollution curve based on the historical water quality monitoring data, and analyze the pollutant growth time series characteristics through the surface runoff coefficient data to obtain runoff non-point source pollution parameters, wherein the runoff non-point source pollution parameters include pollutant growth type, growth rate, saturation constant and maximum growth rate;

[0167] Step S245: constructing a rainfall-pollution-erosion relationship model based on the runoff surface source pollution parameters and the surface runoff coefficient data through a preset corresponding rainfall event;

[0168] Step S246: Estimating the scour coefficient according to the rainfall-pollution scour relationship model to obtain the rainfall scour coefficient;

[0169] Step S247: Analyze the surface source pollution concentration according to the rainfall erosion coefficient to obtain surface source pollution source strength data.

[0170] In an embodiment of the present invention, for example, the "Hydrological Analysis" tool of ArcGIS is used, combined with an actual hydrological monitoring station, and each urban runoff drainage grid area is divided into a plurality of minimum catchment units with a small catchment area and relatively uniform terrain by operations such as filling depressions, calculating flow directions, and extracting watershed boundaries. The historical hydrological flow data of each hydrological monitoring grid is obtained, and the historical monitoring flow data of the hydrological monitoring station is directly obtained. According to land use data, remote sensing images, etc., the underlying surface of each hydrological monitoring grid is divided into different types, such as: impermeable areas (such as buildings, roads), permeable areas (such as green spaces, water bodies), etc. According to the flow characteristics of different underlying surface types and historical hydrological flow data, the SCS model, the Phipps model, and other methods are used to calculate the time series runoff coefficient of each hydrological monitoring grid under different rainfall conditions. For example, the SCS model can be used to determine the corresponding curve number (CN value) according to the land use type of each grid, and the runoff depth of each grid is calculated according to the rainfall and CN value, and then the runoff coefficient is calculated. According to the historical water quality monitoring data and the corresponding rainfall events, the non-point source pollution curves of different pollutants are drawn. For example, the cumulative runoff of the rainfall event is used as the horizontal axis, and the pollutant concentration at the corresponding time is used as the vertical axis to draw a scatter plot, and the curve of the pollutant concentration changing with the runoff is fitted, that is, the non-point source pollution curve. According to the non-point source pollution curve and the time series characteristics of pollutant growth, the runoff non-point source pollution parameters are determined, such as: pollutant growth type (linear, exponential, logarithmic, etc.), growth rate, saturation constant, maximum growth rate, etc. According to the runoff non-point source pollution parameters and surface runoff coefficient data, empirical formulas or machine learning methods are used to construct a rainfall pollution erosion relationship model to simulate the runoff pollutant concentration of each hydrological monitoring grid under different rainfall conditions. The constructed rainfall pollution erosion relationship model is used to simulate the runoff pollutant concentration of each hydrological monitoring grid under different rainfall intensities and durations. According to the simulation results and the corresponding rainfall conditions, the erosion coefficient of each grid and each pollutant is calculated. The erosion coefficient of each hydrological monitoring grid is multiplied by its area and the corresponding rainfall to obtain the pollutant load of the grid under the rainfall event. The pollutant load of each hydrological monitoring grid is summarized in time series to generate non-point source pollution intensity data.

[0171] Preferably, step S3 comprises the following steps:

[0172] Step S31: performing sewage node association processing according to the drainage network topology data and the regional pollution source intensity distribution data, and constructing a sewage force model of the network to obtain a sewage force model of the network;

[0173] Step S32: obtaining a drainage scheduling and purification model, wherein the drainage scheduling and purification model includes a pump station model, a water treatment plant model, and an inland river model;

[0174] Step S33: coupling the pipe network drainage hydraulic model, drainage scheduling and purification model, and rainfall pollution flushing relationship model with a three-dimensional hydraulic and water quality model to generate an initial integrated hydraulic and water quality model of the plant, network, and river;

[0175] Step S34: simulating the initial integrated hydraulic and water quality model of the plant, network and river using the preset rainfall and sewage sample data, and adjusting the model parameters to obtain the integrated hydraulic and water quality model of the plant, network and river;

[0176] Step S35: accessing real-time monitoring data according to the integrated hydraulic and water quality model of the plant, network and river, and performing real-time water discharge simulation to generate real-time pipe network operation status data;

[0177] Step S36: Obtain short-term weather forecast data; use the plant-network-river integrated hydraulic and water quality model to predict the pipeline load based on the short-term weather forecast data based on the real-time pipeline network operation status data to generate rainfall pipeline network load prediction data.

[0178] In an embodiment of the present invention, the pollution source load is associated with the corresponding drainage network node. For example, the pollution load of each pollution source grid can be loaded onto its nearest drainage network node. Using hydraulic model software, the drainage network topology data, pipe segment attribute data (such as length, diameter, material, slope, etc.), pump station, gate valve and other information are input into the model, and the pollutant emission of each node is set according to the associated pollution source strength data. A drainage scheduling and purification model is obtained to simulate the hydraulic transportation and purification effect of sewage on drainage facilities such as pump stations, water treatment plants, and inner rivers. The network drainage water hydraulic model, drainage scheduling and purification model and rainfall pollution scouring relationship model are coupled, and these models are integrated into a unified framework to form an initial plant network river integrated hydraulic water quality model. Determine the data exchange interface between different models, for example: between the network drainage water hydraulic model and the drainage scheduling and purification model: data exchange can be carried out through information such as node flow and water quality concentration. Between the network drainage water hydraulic model and the rainfall pollution scouring relationship model: data exchange can be carried out through information such as overflow volume and overflow pollutant concentration of the overflow node. Using the collected historical data, the initial integrated hydraulic and water quality model of the plant network and river is simulated, and the simulation results are output, such as: pipe network water level, flow, pollutant concentration, etc. The model simulation results are compared with the actual monitoring data, the model error is analyzed, and the model parameters are adjusted according to the error, such as: pipeline roughness coefficient, pollutant degradation coefficient, model boundary conditions, etc., and the simulation is re-performed until the model accuracy meets the requirements. The real-time data collected by the equipment such as the pipe network level monitoring data, pipe network water quality monitoring data, pump station water quality and quantity monitoring data, water treatment plant online monitoring data, river water level and water quality monitoring data, assessment section water quality monitoring data and outer river tide level monitoring data in the pipe network are connected to the integrated hydraulic and water quality model of the plant network and river through the SCADA system, data transmission network, etc. Obtain rainfall forecast data for a period of time in the future, such as: rainfall amount, rainfall intensity, time distribution, etc. The acquired short-term weather forecast data is used as the input condition of the model, and the constructed integrated hydraulic and water quality model of the plant, network and river is used for simulation to predict the changing trends of the water level, flow, pollutant concentration, etc. of the pipeline network in the future, and the load of the pipeline network is evaluated based on the prediction results.

[0179] Preferably, step S4 comprises the following steps:

[0180] Step S41: identifying the potential overflow risk of the combined pipe by using the preset overflow risk threshold value for the rainfall pipe network load forecast data, and generating potential overflow risk point data;

[0181] Step S42: performing overflow period time series analysis on potential overflow risk point data through rainfall pipe network load forecast data to generate potential overflow risk period data;

[0182] Step S43: evaluating the impact of overflow pollution according to the potential overflow risk point data and the potential overflow risk period data, and classifying the risk levels to generate overflow pollution impact level data;

[0183] Step S44: using a preset risk forecast model library to perform risk rapid forecast processing on the overflow pollution impact level data to generate rapid risk forecast data;

[0184] Step S45: Perform risk reduction scheduling analysis on potential overflow risk point data and potential overflow risk period data through the integrated hydraulic and water quality model of the plant, network and river, and generate a multi-objective optimization scheduling plan through preset scheduling control rules and preset constraints to obtain multi-objective optimization scheduling plan data.

[0185] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0186] Step S41: identifying the potential overflow risk of the combined pipe by using the preset overflow risk threshold value for the rainfall pipe network load forecast data, and generating potential overflow risk point data;

[0187] In an embodiment of the present invention, a combined pipe overflow risk threshold is preset based on factors such as the pipe network design standard and the environmental capacity of the receiving water body, such as a water level threshold in the pipe and an overflow flow threshold. The rainfall pipe network load forecast data is compared and analyzed with the preset overflow risk threshold to identify the combined pipe nodes that exceed the threshold during the forecast period and mark them as potential overflow risk points. For example, nodes that exceed the water level threshold at any time step during the forecast period, or nodes whose cumulative overflow exceeds the flow threshold, can be identified as potential overflow risk points. For example, if the water depth of a node at a certain time step exceeds 90% of the design flow of the node section, then it is determined that the node has an overflow risk at that time step.

[0188] Step S42: performing overflow period time series analysis on potential overflow risk point data using rainfall pipe network load forecast data to generate potential overflow risk period data;

[0189] In the embodiment of the present invention, a time series analysis method, such as a sliding window method, is used to analyze the overflow period of each potential overflow risk point. For example, the sliding window size is set to 3 hours, and the sliding is performed in steps of 1 hour to count the number of overflow risks of the node in each window. If the number of overflow risks in a window exceeds a preset threshold, such as 2 times, the time period is determined to be a potential overflow risk period for the node.

[0190] Step S43: evaluating the impact of overflow pollution according to the potential overflow risk point data and the potential overflow risk period data, and classifying the risk levels to generate overflow pollution impact level data;

[0191] In an embodiment of the present invention, the pollutant emissions of each potential overflow risk point in each overflow risk period are calculated based on the rainfall pipe network load forecast data and the potential overflow risk period data. For example, the total amount of pollutant emissions in each risk period can be calculated based on the overflow flow and the overflow pollutant concentration. The water quality model is used to simulate the diffusion, degradation and other processes after the overflow pollutants enter the receiving water body, and the degree of their impact on the water quality of the receiving water body is evaluated. For example, the concentration of major pollutants in the receiving water body can be calculated. Indicators such as the range of exceeding the standard, the multiple of exceeding the standard, and the duration of exceeding the standard can be calculated. According to pre-set risk assessment standards, such as pollutant emissions, the sensitivity of the receiving water body, etc., each potential overflow risk point is divided into risk levels, such as high risk, medium risk, low risk, etc.

[0192] Step S44: using a preset risk forecast model library to perform risk rapid forecast processing on the overflow pollution impact level data to generate rapid risk forecast data;

[0193] In an embodiment of the present invention, a built-in forecast database is used to implement a mode of rapid forecasting based on actual ground rain: the simulation results of typical working conditions are embedded in the pipe network model as a database for rapid prediction, and the actual working conditions are matched from the database according to the measured ground rain data, and the forecast results are quickly given to achieve rapid forecasting. For example, a Bayesian network model or a decision tree model is used. The selected forecast model is trained and verified based on historical rainfall data, overflow event records, and corresponding risk level data. Then, using the trained forecast model, real-time rainfall forecast data and current pipe network operation status data are input to quickly predict the risk level of each overflow risk point in the future, and generate rapid risk forecast data, for example, displayed in the form of a map, with different colors marking overflow risk points of different risk levels.

[0194] Step S45: Perform risk reduction scheduling analysis on potential overflow risk point data and potential overflow risk period data through the integrated hydraulic and water quality model of the plant, network and river, and generate a multi-objective optimization scheduling plan through preset scheduling control rules and preset constraints to obtain multi-objective optimization scheduling plan data.

[0195] In the embodiment of the present invention, the integrated hydraulic water quality model of the plant network river integrates the pipe network model, the sewage treatment plant model and the river model, and can simulate the impact of rainfall, overflow, treatment and other processes on water quantity and water quality. Then, the scheduling objectives are set, such as minimizing the total amount of overflow, minimizing the total amount of pollutant emissions, maximizing the load balance of the sewage treatment plant, etc., and real-time scheduling capacity analysis is performed. According to the preset scheduling control rules, including but not limited to the pipe network control rules, the pump station control rules and the gate station control rules, such as adjusting the gate opening according to the risk level, allocating the pump station flow according to the water level, etc., and the preset constraints, such as the pipe network water level limit, the treatment plant processing capacity limit, etc., a multi-objective optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, is used to optimize the scheduling scheme of the plant network river system. Finally, the scheduling scheme data that meets the constraints and can achieve multi-objective collaborative optimization is obtained, for example, stored in a table form, including the scheduling strategies of each gate and pump station, such as start and stop time, flow control and other information.

[0196] The present invention also provides an optimized dispatching system based on the integration of urban power plants, grids and rivers, which executes the optimized dispatching method based on the integration of urban power plants, grids and rivers as described above. The optimized dispatching system based on the integration of urban power plants, grids and rivers includes:

[0197] The pipe network topology construction module is used to obtain the metadata of the urban drainage pipe network; construct the pipe network topology structure according to the metadata of the urban drainage pipe network, and generate preliminary drainage pipe network topology data; perform drainage topology verification on the preliminary drainage pipe network topology data, and generate drainage pipe network topology structure data;

[0198] The pollution source strength analysis module is used to divide the drainage area according to the drainage network topology data and generate urban drainage grid area data; build a rainfall pollution erosion relationship model based on the urban drainage grid area data; estimate the pollution source strength according to the urban drainage grid area data, and generate urban pollution source strength data and non-point source pollution source strength data respectively; perform time series transformation trend analysis based on the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data;

[0199] The hydraulic and water quality model integration module is used to construct a hydraulic model of the drainage network according to the drainage network topology data and the regional pollution source intensity distribution data to obtain the hydraulic model of the drainage network; the hydraulic model of the drainage network and the rainfall pollution flushing relationship model are coupled with a three-dimensional hydraulic and water quality model to generate a plant-network-river integrated hydraulic and water quality model; the load of the network is predicted based on the plant-network-river integrated hydraulic and water quality model to generate rainfall network load prediction data;

[0200] Overflow risk assessment module, used to evaluate the impact of overflow pollution according to the rainfall pipe network load forecast data, and generate overflow pollution impact level data; to perform risk rapid forecast processing according to the overflow pollution impact level data, and generate rapid risk forecast data; to generate multi-objective optimization scheduling scheme according to the overflow pollution impact level data, and obtain multi-objective optimization scheduling scheme data;

[0201] The collaborative dispatching control module is used to process emergency plans based on rapid risk forecast data, and to coordinate and control drainage facilities through multi-objective optimization dispatching plan data to generate overflow risk control strategies.

[0202] The beneficial effect of this application is that by constructing a high-precision drainage network topology, identifying and analyzing the strength of domestic, industrial and non-point source pollution, and combining historical hydrological data and predicted rainfall data, an accurate and reliable data basis is provided for model simulation and scheduling optimization. Integrating drainage networks, pumping stations, water treatment plants, inland rivers and other elements into a unified model framework can simulate the operating status of the entire drainage system and the migration and transformation process of pollutants under different rainfall scenarios, significantly improving the simulation accuracy and prediction capabilities. Taking into account multiple objectives such as overflow pollution control, scheduling operation costs, and water resource utilization efficiency, and combining preset scheduling control rules and constraints, using genetic algorithms, particle swarm algorithms and other intelligent optimization algorithms, the optimal scheduling plan that can effectively respond to different rainfall scenarios is automatically generated, effectively improving the scheduling efficiency of the drainage system, and realizing the refined management and intelligent scheduling of the urban drainage system.

[0203] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0204] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing dispatching based on urban power plant network river integration, characterized in that: The following steps are involved: Step S1: Obtaining metadata of the urban drainage network; constructing a network topology structure based on the metadata of the urban drainage network to generate preliminary drainage network topology data; Perform drainage topology verification on the preliminary drainage network topology data to generate drainage network topology structure data; Step S2: Drainage areas are divided according to the drainage network topology data to generate urban drainage grid area data; a rainfall-pollution flushing relationship model is constructed based on the urban drainage grid area data; pollution source strength is estimated according to the urban drainage grid area data to generate urban pollution source strength data and surface source pollution source strength data respectively; Based on the urban pollution source intensity data and non-point source pollution source intensity data, the time series transformation trend analysis is carried out to obtain the regional pollution source intensity distribution data; Step S3 includes the following steps: Step S31: performing sewage node association processing according to the drainage network topology data and the regional pollution source intensity distribution data, and constructing a sewage force model of the network to obtain a sewage force model of the network; Step S32: obtaining a drainage scheduling and purification model, wherein the drainage scheduling and purification model includes a pump station model, a water treatment plant model, and an inland river model; Step S33: coupling the pipe network drainage hydraulic model, drainage scheduling and purification model, and rainfall pollution flushing relationship model with a three-dimensional hydraulic and water quality model to generate an initial integrated hydraulic and water quality model of the plant, network, and river; Step S34: simulating the initial integrated hydraulic and water quality model of the plant, network and river using the preset rainfall and sewage sample data, and adjusting the model parameters to obtain the integrated hydraulic and water quality model of the plant, network and river; Step S35: accessing real-time monitoring data according to the integrated hydraulic and water quality model of the plant, network and river, and performing real-time water discharge simulation to generate real-time pipe network operation status data; Step S36: Obtain short-term weather forecast data; use the integrated hydraulic and water quality model of the plant, network and river to predict the pipe network load based on the real-time pipe network operation status data, and generate rainfall pipe network load prediction data; Step S4: perform overflow pollution impact assessment based on rainfall pipe network load forecast data to generate overflow pollution impact level data; perform risk rapid forecast processing based on overflow pollution impact level data to generate rapid risk forecast data; generate a multi-objective optimization scheduling plan based on the overflow pollution impact level data to obtain multi-objective optimization scheduling plan data; Step S5: Carry out emergency plan processing based on rapid risk forecast data, and carry out coordinated linkage control of drainage facilities through multi-objective optimization scheduling plan data to generate overflow risk control strategy.

2. The method for optimizing dispatching based on urban power plant network and river integration according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: acquiring metadata of the urban drainage network according to the urban drainage system to obtain metadata of the urban drainage network; Step S12: Drainage nodes are identified according to the metadata of the urban drainage network, and inspection wells, interception wells, reservoirs, and water outlets are used as drainage element nodes to obtain drainage node data; Step S13: Drainage pipelines are identified according to the metadata of the urban drainage network, and drainage pipes, pumping stations, orifices and weirs are used as drainage element pipelines to obtain drainage pipeline data; Step S14: constructing a topological structure based on the drainage node data and the drainage pipeline data to generate preliminary drainage network topology data; Step S15: performing drainage topology verification processing on the preliminary drainage network topology data to generate drainage network topology verification data; Step S16: Associating drainage network attributes according to the drainage network topology verification data to generate drainage network topology data.

3. The method for optimizing dispatching based on urban power plant network and river integration according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: assigning elevation data to the preliminary drainage network topology data through a preset terrain elevation model to obtain elevation drainage topology network data; Step S152: Calculate the pipeline slope according to the elevation drainage topology network data, and perform pipeline flow direction analysis to generate preliminary confluence direction data; Step S153: performing pipeline longitudinal section segmentation based on the preliminary confluence direction data to generate pipeline longitudinal section data; Step S154: identifying and marking abnormal pipe sections according to the pipeline longitudinal section data to obtain abnormal pipe section data to be checked; Step S155: conducting on-site pipe segment review on the abnormal pipe segment data to be checked, and performing abnormal error correction processing to generate corrected drainage pipe network topology data; Step S156: performing blind pipe re-correction processing according to the corrected drainage network topology data to generate re-corrected drainage network topology data; Step S157: Perform a topology integrity check based on the revised drainage network topology data to obtain drainage network topology check data.

4. The method for optimizing scheduling based on urban power plant network river integration according to claim 3 is characterized in that: Step S156 includes the following steps: Step S1561: Identify the outlet nodes according to the modified drainage network topology data, and obtain the outlet node data and the water outlet node data respectively; Step S1562: performing upstream recursive tracing processing on the drainage outlet node data based on the modified drainage network topology data to obtain drainage path tree data; Step S1563: performing downstream recursive tracing processing on the outlet node data based on the modified drainage network topology data to obtain water catchment path tree data; Step S1564: perform reachability calculation based on the drainage path tree data and the catchment path tree data, and generate drainage reachability probability matrix data and catchment reachability probability matrix data respectively; Step S1565: Performing connectivity evaluation on the drainage reachability probability matrix data, and performing dead-end detection on the drainage path tree data, to generate drainage potential blind pipe data; performing connectivity evaluation on the catchment reachability probability matrix data, and performing dead-end detection on the catchment path tree data, to generate catchment potential blind pipe data; Step S1566: Perform cross-comparison processing based on the drainage potential blind pipe data and the water collection potential blind pipe data to generate the pipe network blind pipe data; Step S1567: Perform pipeline isolation processing on the blind pipe data of the pipe network, and perform secondary correction processing to obtain the re-corrected drainage pipe network topology data.

5. The method for optimizing dispatching based on urban power plant network and river integration according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Identify the main pipe network water confluence points according to the drainage pipe network topology data, divide the drainage area through the geographic information system, and generate urban drainage area data; Step S22: Performing Thiessen polymorphic grid division on the urban drainage area data to generate urban drainage grid area data, wherein the urban drainage grid area data includes domestic drainage grid area data, industrial drainage grid area data and urban runoff drainage grid area data; Step S23: Estimating the pollution source strength according to the domestic drainage grid area data and the industrial drainage grid area data to generate urban pollution source strength data; Step S24: constructing a rainfall-pollution-scouring relationship model based on the urban runoff drainage grid area data; performing non-point source pollution concentration analysis based on the rainfall-pollution-scouring relationship model to obtain non-point source pollution source strength data; Step S25: Performing grid spatiotemporal superposition processing on the urban drainage grid area data through the urban pollution source strength data and the non-point source pollution source strength data, and performing drainage volume and pollution concentration characteristic analysis to obtain grid water volume boundary data and grid water quality boundary data respectively; Step S26: Perform time series transformation trend analysis based on the grid water quantity boundary data and the grid water quality boundary data to obtain regional pollution source intensity distribution data.

6. The method for optimizing dispatching based on urban power plant network and river integration according to claim 5 is characterized in that: The urban pollution source strength data includes domestic sewage source strength data and industrial wastewater source strength data. Step S23 includes the following steps: Step S231: Obtaining the grid resident population density according to the living drainage grid area data, and calculating the water consumption of different types of living areas according to the preset urban water quota standard data to obtain the total water consumption data of the living areas, wherein the different types of living areas include public buildings, residential areas, commercial areas and mixed residential areas; Step S232: Perform quarterly typical daily pollutant measurement on the living drainage grid area data through preset living source strength measurement indicators to obtain living area pollutant concentration data, wherein the preset living source strength measurement indicators include water volume, BOD5, COD, ammonia nitrogen, total phosphorus and surfactant; Step S233: Calculate the total amount of sewage by using the total amount of water used in the living area and the pollutant concentration data in the living area to obtain the domestic sewage discharge data; integrate the domestic sewage source strength according to the domestic sewage discharge data and the pollutant concentration data in the living area to generate the domestic sewage source strength data; Step S234: classifying enterprises according to the industrial drainage grid area data, and obtaining drainage grid data of key enterprises and drainage grid data of non-key enterprises respectively; Step S235: Access the drainage grid data of key enterprises online, and perform manual monitoring and supplementary processing to obtain the pollution discharge data of key enterprises; collect environmental protection supervision data based on the drainage grid data of non-key enterprises to obtain the pollution discharge data of non-key enterprises; Step S236: Integrate the industrial pollution source strengths based on the pollution data of key enterprises and non-key enterprises to generate industrial wastewater source strength data.

7. The method for optimizing dispatching based on urban power plant network river integration according to claim 5 is characterized in that: Step S24 includes the following steps: Step S241: Divide the urban runoff drainage grid area data into minimum water catchment units to generate hydrological monitoring grid data; Step S242: Acquire historical hydrological flow data based on hydrological monitoring grid data; Step S243: classify the underlying surface type according to the hydrological monitoring grid data, and calculate the time series runoff coefficient through the historical hydrological flow data to generate surface runoff coefficient data; Step S244: Obtain historical water quality monitoring data; draw a non-point source pollution curve based on the historical water quality monitoring data, and analyze the pollutant growth time series characteristics through the surface runoff coefficient data to obtain runoff non-point source pollution parameters, wherein the runoff non-point source pollution parameters include pollutant growth type, growth rate, saturation constant and maximum growth rate; Step S245: constructing a rainfall-pollution-erosion relationship model based on the runoff surface source pollution parameters and the surface runoff coefficient data through a preset corresponding rainfall event; Step S246: Estimating the scour coefficient according to the rainfall-pollution scour relationship model to obtain the rainfall scour coefficient; Step S247: Analyze the surface source pollution concentration according to the rainfall erosion coefficient to obtain surface source pollution source strength data.

8. The method for optimizing dispatching based on urban power plant network river integration according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: identifying the potential overflow risk of the combined pipe by using the preset overflow risk threshold value for the rainfall pipe network load forecast data, and generating potential overflow risk point data; Step S42: performing overflow period time series analysis on potential overflow risk point data through rainfall pipe network load forecast data to generate potential overflow risk period data; Step S43: evaluating the impact of overflow pollution according to the potential overflow risk point data and the potential overflow risk period data, and classifying the risk levels to generate overflow pollution impact level data; Step S44: using a preset risk forecast model library to perform risk rapid forecast processing on the overflow pollution impact level data to generate rapid risk forecast data; Step S45: Perform risk reduction scheduling analysis on potential overflow risk point data and potential overflow risk period data through the integrated hydraulic and water quality model of the plant, network and river, and generate a multi-objective optimization scheduling plan through preset scheduling control rules and preset constraints to obtain multi-objective optimization scheduling plan data.

9. An optimization dispatching system based on urban power plant network river integration, characterized in that: Used to execute the optimization dispatching method based on the integration of urban power plant network and river as claimed in claim 1, the optimization dispatching system based on the integration of urban power plant network and river includes: The pipe network topology construction module is used to obtain the metadata of the urban drainage pipe network; construct the pipe network topology structure according to the metadata of the urban drainage pipe network, and generate preliminary drainage pipe network topology data; perform drainage topology verification on the preliminary drainage pipe network topology data, and generate drainage pipe network topology structure data; The pollution source strength analysis module is used to divide the drainage area according to the drainage network topology data and generate urban drainage grid area data; build a rainfall pollution erosion relationship model based on the urban drainage grid area data; estimate the pollution source strength according to the urban drainage grid area data, and generate urban pollution source strength data and non-point source pollution source strength data respectively; perform time series transformation trend analysis based on the urban pollution source strength data and non-point source pollution source strength data to obtain regional pollution source strength distribution data; The hydraulic and water quality model integration module is used to perform sewage node association processing according to the drainage network topological structure data and the regional pollution source intensity distribution data, and to construct the hydraulic model of the drainage network to obtain the hydraulic model of the drainage network; obtain the drainage scheduling and purification model, wherein the drainage scheduling and purification model includes the pump station model, the water treatment plant model and the inner river model; couple the drainage network hydraulic model, the drainage scheduling and purification model and the rainfall pollution flushing relationship model with the three-dimensional hydraulic water quality model to generate an initial plant-network-river integrated hydraulic and water quality model; simulate the initial plant-network-river integrated hydraulic and water quality model using the preset rainfall sewage sample data, and adjust the model parameters to obtain the plant-network-river integrated hydraulic and water quality model; access the real-time monitoring data according to the plant-network-river integrated hydraulic and water quality model, and perform real-time water discharge simulation to generate real-time pipeline network operation status data; obtain short-term meteorological forecast data; use the plant-network-river integrated hydraulic and water quality model to predict the pipeline load based on the short-term meteorological forecast data based on the real-time pipeline network operation status data to generate rainfall pipeline load prediction data; Overflow risk assessment module, used to evaluate the impact of overflow pollution according to the rainfall pipe network load forecast data, and generate overflow pollution impact level data; to perform risk rapid forecast processing according to the overflow pollution impact level data, and generate rapid risk forecast data; to generate multi-objective optimization scheduling scheme according to the overflow pollution impact level data, and obtain multi-objective optimization scheduling scheme data; The collaborative dispatching control module is used to process emergency plans based on rapid risk forecast data, and to coordinate and control drainage facilities through multi-objective optimization dispatching plan data to generate overflow risk control strategies.

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