Urban power grid-lake integrated intelligent dispatching system and method

Abstract

The city plant network river lake integration intelligent dispatching system and method belong to the city water intelligent dispatching field, and solve the problems of plant network river lake separate system management and control, dispatching fragmentation, and insufficient pollution control precision. The system contains five interactive modules of load monitoring analysis, multi-source coupling modeling, water environment capacity estimation, pollution load scheduling, and collaborative control feedback; pollution load is analyzed by four levels of grid, then a global water quantity-water quality coupling model is constructed to deduce the working condition, combined with hierarchical weighted accounting of water body environmental capacity balance, to solve the time storage and peak shifting time sequence of facilities and pollution boundary, and to issue multiple instructions and rely on field feedback closed loop iteration optimization. The invention realizes unified and collaborative scheduling of plant network river lake, accurately traces the pollution of the area, the model simulation error is less than 10%, the overflow pollution is reduced by rigid constraint of water pollution capacity, the rainfall and pollution fluctuation can be dynamically adapted, the drainage, sewage disposal and river water quality long-term standard are simultaneously considered, and the fine management and control level of urban water environment is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent urban water management scheduling technology, and in particular to an integrated intelligent scheduling system and method for urban water plants, networks, rivers and lakes. Background Technology

[0002] The urban water system comprises multiple business segments, including water supply, drainage, sewage treatment, river and lake water quality management, and flood control and drainage. Each facility has a clear division of labor and is interconnected: the water supply network is responsible for the distribution of urban water and the regulation of network pressure; the drainage network is responsible for the collection and transfer of rainwater, domestic and industrial wastewater; sewage treatment plants adjust their operating conditions according to the influent volume, pollutant concentration, and their own rated process load; pumping stations, sluice gates, and regulating reservoirs work together to regulate network water levels, distribute runoff, and manage flood drainage during the flood season; rivers and lakes receive sewage treatment effluent, road runoff, and ecological water replenishment. The water exchange, pollutant dilution, and self-purification capacity directly determine the regional water environment quality.

[0003] With the implementation of smart city water management informatization, online water quality analyzers, electromagnetic flowmeters, rain gauges, liquid level monitors, video surveillance, remote sensing inspections, and various IoT sensing terminals have been widely deployed. Most cities have built independent management platforms for different business functions, covering drainage monitoring platforms, sewage treatment plant operation and maintenance management platforms, river and lake water quality monitoring platforms, urban flooding early warning platforms, and remote automatic control systems for pumps and gates. At present, routine water management scheduling integrates rainfall forecasts, pipeline liquid levels, sewage treatment plant influent loads, river and lake water levels, water quality at assessment sections, and electromechanical equipment operation data to adjust pump station start-up and shutdown, gate opening, sewage treatment plant load, storage and release rhythm of regulating reservoirs, and ecological water replenishment of rivers and lakes. Some platforms further integrate pipeline hydrodynamic models and water quality simulation models, and conduct operational scenario simulations with fixed scheduling rules to provide auxiliary decision support for manual management.

[0004] However, existing urban water management generally adopts a separate management model with independent control of subsystems and links, and a unified dynamic collaborative scheduling mechanism has not been established between plants, networks, rivers, and lakes. Water supply, drainage, sewage treatment plants, pumping stations, and river and lake management belong to different business platforms, each generating control instructions based on its own data. The scheduling objects, constraints, and on-site execution feedback data are fragmented, and the pipeline transportation conditions, sewage treatment plant load, pollution processes entering rivers, and the pollution limits of rivers and lakes cannot be integrated into a unified scheduling logic for calculation. Once there are changes in operating conditions such as sudden rainfall, fluctuations in sewage inflow load, and rises and falls in river and lake water levels, the local control instructions issued by each system cannot match the overall operational needs of the urban water system. The asynchronous control responses of each link can easily lead to problems such as pipeline overflow, sewage treatment plant overload, excessive total pollution entering rivers, and conflicts between flood drainage and water quality control targets, significantly reducing the overall operational efficiency of urban water management and the effectiveness of water environment governance.

[0005] Existing integrated scheduling technologies, such as CN119047724B (disclosing a scheduling decision-making method and system applicable to integrated management of power plants, networks, and rivers), CN119047742B (disclosing an optimized scheduling method and system based on integrated urban power plant, network, and river management), and CN118886325B (disclosing a joint control method and system for urban drainage systems), while initially achieving basic simulation and coordinated control of power plants, networks, and rivers, still have significant inherent defects: CN119047724B discloses a scheduling decision-making method and system applicable to integrated management of plants, networks, and rivers. It establishes multiple business function modules and divides the system into four basic scheduling scenarios, relying on a mechanistic model to complete scheduling rehearsals. However, it only adopts a macro-level scenario control approach, failing to conduct refined confluence grid subdivision at four levels: watershed, region, street, and community, thus unable to quantify the pollution contribution of each area to river and lake assessment sections. Scheduling optimization only considers overflow and energy consumption, failing to differentiate between national, provincial, and municipal control sections and set hard constraints on water body environmental capacity. It uses fixed experience-based scheduling rules, failing to break down the day into short, discrete time periods for rolling optimization. The model simulation and on-site control lack a real-time feedback automatic correction link; after operational deviations occur, adjustments rely solely on manual consultation, resulting in insufficient adaptive control capabilities.

[0006] CN119047742B discloses a method and system for integrated urban plant-network-river optimization scheduling. This method uses pipeline topology verification and Thiessen polygon mesh to estimate source strength and constructs a pollution flushing model to predict overflow risks. However, this scheme can only statically estimate the total regional pollution discharge and cannot fully reconstruct the entire transport path, segmented load, and transport duration of pollution from the source to rivers and lakes. The hydraulic and water quality model is only used for risk prediction and is not deeply integrated with time-sharing regulation and peak-shaving optimization algorithms, making it difficult to fully release the emission reduction potential of the pipeline network and storage tanks. Furthermore, it does not set tiered pollution carrying weights for each level of assessment section, and the uniform fixed threshold does not meet the requirements of graded environmental protection assessments. The monitoring, simulation, capacity calculation, time-series optimization, and command issuance processes are independent of each other, lacking a standardized, integrated, and automatic scheduling process.

[0007] CN118886325B discloses a joint control method and system for urban drainage systems, focusing on the hydraulic control of the pipe network to achieve the linkage of pump station gate valves. Its core emphasis is on urban flood control, but it lacks a significant dimension in water environment management. This scheme only simulates the hydraulic state of the pipe network, without establishing an integrated coupled simulation system for water quantity and quality, making it impossible to simultaneously deduce the complete migration process of pollution; it completely fails to incorporate the daily environmental capacity and remaining environmental margin of water bodies as scheduling constraints, making it unable to control the total pollution load entering rivers; it lacks a short-term discrete time-sharing scheduling mechanism, resulting in poor control of pollution flushing during the initial stages of rainfall; and it cannot uniformly generate multiple collaborative commands for water supply, drainage, sewage, and river / lake replenishment, making it prone to conflicts and imbalances in multi-platform control commands.

[0008] The common shortcomings of traditional sub-platform scheduling models and existing integrated technical solutions are as follows: existing technologies lack a four-level confluence grid for refined pollution source tracing, making it impossible to quantify pollution contributions by region; the models of pipe networks, sewage treatment plants, and rivers and lakes are independent of each other, making it impossible to simultaneously complete the joint simulation of water quantity and quality across the entire chain; the weighted calculation of differentiated water bodies' pollution carrying capacity does not take into account environmental protection classification and control requirements; scheduling relies on fixed empirical rules and lacks a short-term discrete time-series high-frequency rolling optimization mechanism; most scheduling is open-loop, and on-site measured deviations cannot automatically update the model and rearrange scheduling schemes; instructions from multiple business systems are scattered, making it difficult to simultaneously balance multiple objectives such as flood control, pollution treatment, and ecological water replenishment.

[0009] Therefore, there is an urgent need to develop an integrated intelligent scheduling system and method for urban water plants, networks, rivers and lakes that can perform four-level grid pollution source tracing, full-area water quantity and quality coupling simulation, rigid constraints on graded cross-sectional environmental capacity, time-sharing discrete rolling optimization, closed-loop iterative correction of scheduling, and unified collaborative management and control of multiple objectives. This system will make up for the various deficiencies of existing technologies and improve the level of refined and intelligent management and control of urban water environment. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides an integrated intelligent scheduling system and method for urban power plants, networks, rivers, and lakes. The technical problem this invention aims to solve is: how to address the issues of decentralized scheduling of urban power plants, networks, rivers, and lakes, inconsistent scheduling rules, and difficulty in matching local scheduling instructions with the overall operational status through pollution load monitoring and analysis, water quantity-water quality coupling modeling, daily environmental capacity constraints of urban water bodies, finite discrete scheduling time-series solution, and execution feedback updates.

[0011] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This invention provides an integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes, comprising a load monitoring and analysis module, a multi-source coupling modeling module, a water environment capacity estimation module, a pollution load scheduling module, and a collaborative control feedback module that sequentially interact with each other. The load monitoring and analysis module outputs the pollution load contribution ratio and pollution load migration and transformation results to the multi-source coupling modeling module. The multi-source coupling modeling module receives the pollution load migration and transformation results and outputs the overall system operating status to the water environment capacity estimation module and the pollution load scheduling module. The water environment capacity estimation module outputs the daily environmental capacity of urban water bodies to the pollution load scheduling module. The pollution load scheduling module solves the discrete scheduling time sequence based on the aforementioned data, generates an optimized pollution load scheduling scheme, and sends it to the collaborative control feedback module. The collaborative control feedback module issues scheduling instructions and sends the equipment execution feedback back to the multi-source coupling modeling module to achieve closed-loop iteration.

[0012] The load monitoring and analysis module is used to access multi-source water data, divide the runoff grid into four levels: watershed, region, street, and community, and analyze pollution load-related parameters. The pollution load contribution ratio is the ratio of the pollution load flowing into the corresponding river / lake assessment section of the target runoff grid to the total pollution load received by that section. The pollution load of a single grid is calculated from the discharge volume and pollutant concentration. The pollution load migration and transformation results include the pollution load transport path, segmented arrival time, segmented pollution load, and pollution load entering the river. The transport path is determined by the runoff grid pointing to the corresponding river / lake assessment section based on the pipeline network topology.

[0013] The multi-source coupling modeling module is used to construct a full-element water quantity-water quality coupled unsteady flow model, complete parameter calibration, boundary updates, and output the pipe network, sewage treatment plant, overflow, river inflow load, and river and lake water quality response status. The module is configured with four types of model boundary conditions: runoff inflow boundary formed by rainfall zoning data, pollution load input boundary formed by drainage user data, facility operation boundary formed by facility condition data, and water body outflow boundary formed by measured river and lake water level data. After model calibration, the simulation errors of pipe network water level, sewage treatment plant inflow, and river and lake cross-section pollutant concentration are all controlled within 10%.

[0014] The water environment capacity estimation module is used to calculate the daily environmental capacity of water bodies by combining ecological flow and cross-sectional constraints. The module defines the level coefficients of national, provincial and municipal control cross-sections, and constructs a weight matrix by calculating the weight of each cross-section through the coefficient ratio. The daily environmental capacity of each cross-section is weighted to obtain the overall daily environmental capacity of the urban water body.

[0015] The pollution load scheduling module is used to solve the timing sequence of storage and peak-shaving scheduling with environmental capacity margin and facility operation boundary as constraints. The module divides the scheduling day into several consecutive scheduling periods of equal duration, and a single scheduling day is evenly divided into 96 scheduling periods of 15 minutes each. The core pollutant indicators for scheduling control are COD and ammonia nitrogen. The interception, pipeline storage and peak-shaving transportation volume are calculated in different time periods. When the pollution load entering the river during the predicted period exceeds the environmental capacity margin of the section, the discrete scheduling timing sequence is readjusted within the load operation boundary of the pipeline, interception structure and sewage treatment plant.

[0016] The collaborative management and control feedback module comprises four subsystems: water supply, plant-network joint operation, urban flood control, and river and lake water quality management. It is used to issue multi-dimensional water dispatch instructions and provide feedback on execution data to iteratively update model boundaries and dispatch sequence. The module outputs four types of dispatch instructions: water supply pressure control instructions, sewage treatment plant influent load control instructions, urban flood control instructions for pump and gate flow control, and river and lake water quality management instructions for gate water replenishment regulation. The module compares the equipment execution feedback data with the model output operating status. When the deviation of a single indicator exceeds a preset threshold, the multi-source coupled modeling module is triggered to update all boundary conditions, simultaneously updating the boundaries of four types of models: network water level, overflow flow, sewage treatment plant load, and river and lake section concentration. The pollution load dispatch module recalculates the dispatch sequence for periods when dispatching was not executed.

[0017] The system comprehensively covers runoff source facilities, pipelines, pumping stations, storage tanks, sewage treatment plants, rivers, lakes, and water quality assessment sections at all levels, achieving unified and coordinated scheduling of plants, networks, rivers, and lakes, and solving problems such as load mismatch, excessive overflow during rainy days, and imbalance in water environment management caused by decentralized scheduling of multiple systems.

[0018] This invention also provides an integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes, which is implemented using the aforementioned integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes, and includes the following steps: Step 1: Access the basic data of drainage users, pipeline topology data, water quality, water quantity, and hydrological multi-source water affairs monitoring basic data. Divide the river confluence grid into four levels: watershed, region, street, and community. Determine the pollution load contribution ratio and pollution load migration and transformation results corresponding to the grid. For drainage users without actual measured pollutant concentrations, use the historical monitoring average concentration of similar drainage users to calculate the pollution load.

[0019] Step 2: Construct a water quantity-water quality coupled unsteady flow model covering runoff source facilities, pipe networks, pumping stations, storage tanks, sewage treatment plants, rivers, and lakes. Use urban measured data, facility operating data, rainfall zoning data, drainage user data, and pollution load migration and transformation results as inputs to complete model parameter calibration and boundary updates. Output the pipe network transport status, sewage treatment plant influent load status, interception and overflow status, pollution load entering the river status, and river and lake water quality response status.

[0020] Step 3: Combine river ecological flow and the control constraints of river and lake assessment sections at all levels to calculate the daily environmental capacity of urban water bodies, and calculate the environmental capacity margin of each river and lake section in real time.

[0021] Step 4: Combining the overall system operation status output in Step 2 with the environmental capacity margin obtained in Step 3, and using the environmental capacity margin, pipeline storage capacity, operating boundary of interception and overflow structures, and treatment load boundary of sewage treatment plants as scheduling constraints, solve the finite discrete scheduling time sequence corresponding to interception and storage, pipeline storage, and peak-shifting transportation to generate an optimized scheduling scheme for pollution load.

[0022] Step 5: Based on the pollution load optimization scheduling plan, generate four types of scheduling instructions for water supply scheduling, sewage treatment plant network operation, urban flood control, and river and lake water quality management, and issue them for execution. Collect equipment execution status feedback data. When the deviation between the execution feedback data and the model prediction status exceeds the preset threshold, simultaneously update the four types of model boundaries: pipeline water level, overflow flow, sewage treatment plant influent load, and river and lake section pollutant concentration. Resolve the optimized scheduling sequence, iteratively update the model input and boundary conditions for the next scheduling period, and recalculate the scheduling sequence for the unexecuted period to ensure that the total daily pollution load entering the river is lower than the daily environmental capacity limit of the water body.

[0023] The present invention provides an integrated intelligent scheduling system and method for urban power plants, networks, rivers, and lakes, which has the following beneficial effects: 1. This invention sets up a load monitoring and analysis module, a multi-source coupling modeling module, a water environment capacity estimation module, a pollution load scheduling module, and a collaborative management and control feedback module to interact sequentially to form a closed-loop data link, unifying and integrating the scheduling logic of the entire process of plants, networks, rivers, and lakes, and solving the problems of decentralized management and control, fragmented scheduling rules, and inconsistent responses of various links in the existing water affairs subsystem: breaking down the independent scheduling barriers of multiple platforms such as water supply, drainage, sewage treatment plants, pump gates, rivers and lakes, and realizing integrated collaborative management and control of all water affairs elements.

[0024] 2. This invention divides river confluence into four levels: watershed, region, street, and community. It quantifies the pollution load contribution ratio of each grid and fully analyzes the entire process of pollution load migration and transformation. This solves the problems of traditional technologies that cannot accurately trace the source of pollution in a region and cannot quantify the pollution contribution of each region to the assessment section. It accurately locates the pollution source region, fully restores the pollution transport path, arrival time, and segmented load, and provides a quantitative basis for precise interception of pollution in different zones.

[0025] 3. This invention calculates grid pollution load based on drainage volume and pollutant concentration. For drainage users without actual measured concentrations, the parameters are supplemented using historical averages of similar drainage users, which solves the problem of pollution load calculation distortion caused by the lack of monitoring of some drainage users: ensuring the completeness and continuity of pollution load calculation across the entire area, and avoiding the impact of missing source load data on the accuracy of scheduling decisions.

[0026] 4. This invention constructs a water quantity-quality coupled unsteady flow model covering source runoff facilities, pipe networks, pumping stations, storage tanks, sewage treatment plants, rivers and lakes, and integrates multiple boundary conditions to synchronously simulate the operating status of the entire system. It solves the problem that existing pipe network, sewage, and river and lake models are independent and cannot be synchronously coupled to simulate water quantity and quality. It synchronously outputs multi-dimensional operating conditions of pipe network water level, sewage treatment plant load, overflow flow, pollution entering the river, and river and lake water quality, realizing synchronous simulation of hydrodynamics and pollution across the entire chain.

[0027] 5. This invention configures four types of boundary conditions for the coupled model: rainfall inflow, pollution input, facility operation, and river / lake outflow, and continuously calibrates the parameters. The model simulation error is controlled within 10%, solving the problems of fixed parameters, large simulation deviation, and low reliability of prediction results in traditional models. It significantly improves the accuracy of predicting the operating conditions of pipe networks, sewage treatment plants, and river / lake water quality, and provides reliable data support for scheduling optimization.

[0028] 6. This invention distinguishes between national, provincial, and municipal control river and lake assessment sections and sets grade coefficients. It calculates the daily environmental capacity of urban water bodies through a weighted matrix, solving the problems of existing technologies that do not consider the graded control requirements of the sections and have a single and crude calculation of the water body's pollution carrying capacity. It conforms to the environmental protection assessment and control standards at all levels and accurately calculates the total amount of pollution that the water body can accept in the entire region.

[0029] 7. This invention calculates the environmental capacity margin of river and lake sections in real time and uses it as the core scheduling constraint, solving the problem that traditional scheduling does not include the water body's pollution carrying capacity in the hard constraints, which easily leads to excessive pollution entering the river and substandard water quality: optimizing the entire scheduling process to strictly control the total amount of pollution entering the river and ensuring that the cumulative daily load entering the river does not exceed the water body's carrying capacity limit.

[0030] 8. This invention divides the daily scheduling into 96 finely discrete scheduling periods of 15 minutes each. With COD and ammonia nitrogen as the core control indicators, it simultaneously optimizes the timing of interception, pipeline regulation and storage, and staggered peak transportation. This solves the problems of the traditional coarse scheduling period division and the inability to fully tap the potential of peak-shaving and emission reduction of storage facilities. It achieves fine-grained time-sharing control, balances the daily influent load of sewage treatment plants, and minimizes overflow pollution during rainy days.

[0031] 9. This invention solves the optimal scheduling sequence under multiple boundary constraints such as environmental capacity margin, pipeline storage capacity, interception structures, and sewage treatment plant load, and solves the problem that traditional scheduling relies only on fixed empirical rules and does not take into account the operating limits of various facilities: the scheduling scheme fully conforms to the upper limit of facility hardware operation, and takes into account both pollution control effect and equipment operation safety.

[0032] 10. The collaborative control and feedback module of this invention integrates four subsystems: water supply, plant and network joint operation, urban flood control, and river and lake water quality management. A set of optimized scheduling schemes simultaneously outputs four types of scheduling instructions, solving the problems of multiple systems requiring manual issuance of instructions and cumbersome operation and maintenance processes: unified instruction output simplifies water affairs operation and maintenance operations and reduces the management cost of switching between multiple platforms.

[0033] 11. This invention compares the equipment execution feedback data with the model prediction working conditions in real time. When the index deviation exceeds the threshold, the model automatically updates four types of boundary conditions, solving the problem that traditional scheduling is an open-loop control and cannot dynamically correct the scheme according to the actual working conditions on site. It forms a closed-loop iterative mechanism of scheduling-execution-feedback-re-optimization, which is adapted to the real-time fluctuations of rainfall and drainage load.

[0034] 12. This invention automatically re-solves the discrete scheduling sequence of the unexecuted period after the feedback deviation exceeds the standard, and synchronously updates the boundary of the concentration model of the pipeline network, overflow, sewage treatment plant, and river and lake, so as to solve the problem of scheduling scheme lagging and unable to adapt dynamically after sudden changes in local operating conditions: real-time correction of the remaining cycle scheduling strategy to continuously maintain the pollution load entering the river in compliance with the standard.

[0035] 13. The system of this invention fully covers the runoff source, pipeline network, pumping station, storage tank, sewage treatment plant, river and lake and water quality sections at all levels, and coordinates multiple objectives of flood drainage, sewage treatment and river and lake water replenishment. It solves the problem of conflict and incomplete attention between flood control and water quality management in traditional scheduling: it simultaneously achieves multiple management objectives of flood prevention during rainy days, pollution reduction at the source and long-term water quality protection of rivers and lakes.

[0036] 14. The integrated intelligent scheduling method and system module functions of this invention correspond one-to-one, and the entire process is standardized and executed step by step, solving the problem of traditional water affairs scheduling lacking a standardized and implementable full-chain operation process: the process is clear, can be deployed programmatically, and is easy to be applied to various urban water affairs scheduling scenarios on a large scale.

[0037] 15. This invention relies on the analysis of the pollution load contribution ratio and pollution load migration and transformation results of the four-level river confluence grid, and combines the water quantity-water quality coupled unsteady flow model to uniformly represent the overall operation status of the pipeline network, sewage treatment plant, interception and overflow facilities, and river and lake sections. It solves the problem of fragmented models of each link of plant, network, river and lake and the inability to uniformly represent the operation status: realizes the integration of all facilities of plant, network, river and lake into the same set of scheduling logic for coordinated operation.

[0038] 16. This invention uses the daily environmental capacity and environmental capacity margin of urban water bodies as rigid scheduling constraints to solve the finite discrete scheduling time sequence of interception and storage, pipeline storage, and peak-shaving scheduling. At the same time, it uses equipment execution status feedback to update the input parameters for the next scheduling period, solving the problems of traditional scheduling lacking long-term load control constraints, poor scheduling continuity, and insufficient water environment control precision. It effectively reduces the pollution load entering the river, improves the continuity of scheduling time sequence, and comprehensively improves the level of refined management and control of urban water environment. Attached Figure Description

[0039] The present invention will be further described below with reference to the accompanying drawings: Figure 1 This is a schematic diagram showing the relationship between the core modules of the scheduling system of the present invention; Figure 2 This is the main flowchart of the scheduling method of the present invention; Figure 3 This is a schematic diagram of the pollution load scheduling decision-making process of the present invention. Detailed Implementation

[0040] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments: Example 1 like Figures 1 to 3 As shown, this embodiment provides an integrated intelligent scheduling system for urban water treatment plants, networks, rivers, and lakes, achieving unified scheduling of all elements including plants, networks, rivers, and lakes, and solving the core problems of decentralized management and fragmented scheduling logic in traditional water management systems. This intelligent scheduling system comprises five core modules with sequential data exchange: a load monitoring and analysis module, a multi-source coupling modeling module, a water environment capacity estimation module, a pollution load scheduling module, and a collaborative management and control feedback module. The data flow relationships between these modules are as follows: Figure 1 As shown, the functions and data output logic of each module are as follows: 1. Load monitoring and analysis module The module uniformly accesses the basic ledger of drainage users, pipeline topology drawings, and real-time monitoring data of water quality / quantity / hydrology across the entire chain. It performs fine-grained subdivision of the entire catchment area according to a four-level grid of watershed-region-street-community, and calculates two types of core parameters layer by layer: pollution load contribution ratio and pollution load migration and transformation results.

[0041] The pollution load contribution ratio is calculated as follows: the pollution load of a single confluence grid flowing into a designated river or lake assessment section ÷ the total pollution load received by that section from all grids; single grid pollution load = discharge volume × pollutant concentration.

[0042] The pollution load migration and transformation results include four core pieces of information: the pipeline transport path from the grid to the assessment section, the sewage arrival time of each pipe section, the pollution load of each segment, and the final pollution load entering the river. All paths are automatically identified and generated based on the pipeline topology connectivity.

[0043] For scattered wastewater dischargers without online water quality monitoring equipment, the system automatically retrieves the historical average concentration of wastewater dischargers of the same type to complete the calculation parameters, ensuring that the load accounting of the entire area is complete.

[0044] 2. Multi-source coupling modeling module A water quantity-quality coupled unsteady flow model is constructed, covering all elements including source runoff facilities, stormwater / sewage pipe networks, booster pumping stations, storage tanks, sewage treatment plants, urban rivers, and lakes. The model is input with measured hydrological data, pump / sewage treatment plant facility operating conditions, rainfall zoning forecasts, sewage discharge data from drainage users, and pollution migration results output from the load analysis module. The model continuously performs parameter calibration and dynamically updates boundary conditions, and outputs five categories of system operating status: pipe network water level and flow status, sewage treatment plant influent load status, interceptor well overflow prediction status, segmented pollution entering the river status, and river and lake section water quality response status.

[0045] The model is configured with four types of independent boundary conditions: inflow boundary generated by rainfall zones, pollution input boundary formed by sewage discharge from drainage users, operating condition boundary of pumping stations / gates / storage tanks, and outflow boundary of water bodies formed by measured water levels in rivers and lakes, to ensure that the model closely matches the actual on-site conditions.

[0046] 3. Water Environment Capacity Estimation Module The daily environmental capacity of urban water bodies in the entire region is calculated by weighting the ecological flow baseline of rivers and the control standards of water quality assessment sections of rivers and lakes at all levels.

[0047] The module classifies the assessment sections into three categories: national control, provincial control, and municipal control, and assigns differentiated grade coefficients to each category. The weight of a single section is calculated as the grade coefficient of that section divided by the sum of the grade coefficients of all sections. The weights of all sections are combined to form the weight matrix of the assessment sections. The total daily environmental capacity of the water body in the entire area is calculated by weighting the daily pollution carrying capacity of each section in combination with the weight matrix, which is in line with the requirements of environmental protection graded management and control.

[0048] 4. Pollution load scheduling module Receives pollution load migration data, overall system operating status, and daily environmental capacity of water bodies; calculates the environmental capacity margin of cross-sections in real time; the margin value equals the daily environmental capacity of the water body. The cumulative pollution load entering the river during the current period has been recorded.

[0049] The module breaks down the complete daily scheduling into 96 consecutive 15-minute discrete scheduling periods, calculating the optimal interception and storage capacity, pipeline storage capacity, and wastewater peak-shifting transport capacity for each period. Constraints include: the upper limit of the cross-sectional environmental capacity margin, the maximum storage volume of the pipeline network, the overflow limit of interception structures, and the rated treatment load of the wastewater treatment plant. If the predicted pollution load entering the river exceeds the remaining environmental capacity margin for a certain period, the discrete scheduling sequence is re-optimized within the operational boundaries of all facilities, outputting the globally optimal pollution load scheduling scheme.

[0050] 5. Collaborative Management and Feedback Module Internally, it integrates four major business subsystems: water supply scheduling subsystem, sewage treatment plant network scheduling subsystem, urban flood control subsystem, and river and lake water quality management subsystem; based on the discrete time sequence of the optimized scheduling plan, it automatically generates four types of on-site execution instructions and sends them to the automatic control equipment.

[0051] The module collects real-time execution feedback data from pump gates, sewage treatment plants, storage tanks, and water supply networks, and compares the measured operating conditions with the model predictions. When any index deviation exceeds the preset threshold, the multi-source coupled modeling module is automatically driven to update all boundary conditions, and the pollution load scheduling module re-solves the timing sequence for all scheduling periods that have not yet been executed that day, forming a closed-loop iterative scheduling mechanism.

[0052] This system is equipped with a standardized five-step scheduling process, which corresponds one-to-one with the functions of the five major modules. The overall scheduling logic chain is as follows: Figure 2 As shown: Step 1: Access basic data of drainage users, pipeline topology data, and water quality, quantity, and hydrological monitoring data for all stages; divide the runoff grid into four levels: watershed, region, street, and community; calculate the pollution load contribution ratio of the grid; and determine the pollution load contribution and migration transformation. Step 2: Load the full-element water quantity-water quality coupled unsteady flow model, input multi-source measured and analytical data to complete model calibration and update, establish the water quantity-water quality model and output the status, including the full-dimensional operating status of pipe network, sewage treatment plant, overflow, river inflow load, and river and lake water quality; Step 3: Combine ecological flow and national / provincial / municipal control section classification assessment standards to calculate the daily environmental capacity of the water body, estimate the daily environmental capacity and determine the margin; Step 4: The integrated model outputs operating conditions and cross-sectional environmental margins. Constrained by the pipeline network's storage capacity, interception structures, wastewater treatment plant operating boundaries, and the water body's pollution carrying capacity, the discrete time series of time-sharing interception, pipeline network storage, and peak-shaving transport are solved to generate a unified pollution load optimization scheduling scheme. The internal time series judgment logic of this step is as follows: Figure 3 As shown: retrieve the current environmental capacity margin at the cross-section, compare the margin value with the predicted pollution load entering the river for this period. If the margin is less than the predicted river load, adjust the scheduling sequence within the operating limits of the pipeline network, intercepting structures, and sewage treatment plants, generate an optimized scheduling plan, and then proceed to the next period for cyclical calculation; if the margin is greater than or equal to the predicted river load, directly use the original scheduling plan and proceed to the next period. Step 5: Issue four types of execution instructions based on the scheduling plan: water supply, plant-network joint operation, drainage, and river and lake water replenishment. This generates multiple types of instructions and updates them. Collect feedback data from equipment and dynamically update the model input parameters for the next scheduling cycle. If the deviation exceeds the standard, recalculate the scheduling sequence for the remaining time period. Send the updated boundary data back to the water quantity-water quality coupling model in Step 2 to form a closed-loop feedback iteration throughout the process.

[0053] This embodiment integrates pollution load grid analysis, water quantity and quality coupled simulation, graded water body environmental capacity constraints, time-sharing discrete optimization, and scheduling closed-loop feedback into a unified scheduling link, completely changing the traditional independent management and control mode of plant, network, river, and lake subsystems. Pollution load source data provides quantitative positioning basis for precise interception and control of pollution in different zones. The coupled model realizes the synchronous extrapolation of hydrodynamics and pollution conditions across the entire chain. The graded weighted environmental capacity serves as a hard constraint to control the total amount of pollution entering the river from the source. Figure 3 The residual comparison branch logic shown can identify the risk of water quality exceeding the standard in advance and automatically optimize the storage and peak-shifting transportation strategy. Time-sharing fine scheduling can fully tap the emission reduction potential of various water facilities. The feedback iteration mechanism built in step 5 can dynamically adapt to the real-time fluctuations of rainfall, sewage discharge and water level, and realize multi-objective continuous collaborative management and control of drainage and flood control, sewage treatment and river and lake water quality protection.

[0054] Example 2 In another preferred embodiment, based on the above embodiment 1, this embodiment provides an integrated intelligent scheduling method for urban plant, network, river and lake systems. This method is implemented based on the integrated intelligent scheduling system for urban plant, network, river and lake systems described in embodiment 1. This embodiment selects an actual urban polder case to verify how the pollution load scheduling module uses the environmental capacity margin of river and lake sections as the core constraint to solve the staggered scheduling sequence of interception, pipeline network, and sewage treatment plant, so as to achieve the reduction of rainwater overflow and the control of pollution load entering the river to meet standards.

[0055] 1. Project Basic Information and Grid Division The total area of ​​the target urban area is 42.6 square kilometers. The area has a permanent population of 360,000; a total of 318 km of drainage pipelines have been laid, with 1,260 pipeline nodes, and is equipped with 2 sewage treatment plants, 12 booster pump stations, 8 control gate stations, 4 large regulating reservoirs, and 6 river and lake water quality assessment sections.

[0056] The system uniformly divides the scheduling day into 96 15-minute fine scheduling periods, with the core pollutant indicators being COD (Chemical Oxygen Demand) and ammonia nitrogen.

[0057] The load monitoring and analysis module accesses basic data from drainage users, pipeline topology data, water quality data, water quantity data, and hydrological data, and divides the river confluence grid according to a four-level grid partitioning rule: watershed, region, street, and community. The dispatch area is divided into 4 watershed grids, 18 region grids, 76 street grids, and 326 community grids.

[0058] Taking grid A-2-15 in the community as an example: the total daily sewage discharge is 720. The COD concentration was 230 mg / L and the ammonia nitrogen concentration was 28 mg / L. The module automatically calculated the daily COD load of the grid as 165.6 kg and the ammonia nitrogen load as 20.16 kg. For individual households in the area without online monitoring, the historical average concentration of similar businesses was used to supplement the load calculation data.

[0059] 2. Analysis of the contribution and migration path of pollution load across the entire region The system aggregates pollution loads at the community, street, region, and watershed grid levels along the confluence direction of the pipeline network, and quantifies the proportion of pollution contribution of each area to the assessment section.

[0060] In the case study, the total daily COD load received by the national monitoring section D1 of the river and lake was 4250 kg, of which 680 kg of COD load was received by the regional grid R3. The calculation showed that the contribution of the R3 grid to the pollution of the D1 section was 16%.

[0061] The system automatically identifies the complete transport chain from grid R3 to section D1, dividing it into four segments: pipeline transport, pump station lifting, interception and overflow, and river discharge. It outputs the flow rate, pollutant concentration, and wastewater arrival time for each segment: Pipeline transport segment 4200 COD 210 mg / L, arrival time 35 min; pump station lifting section 4050 COD 205 mg / L, arrival time 50 min; overflow section 3900 COD 198 mg / L, arrival time 70 min; discharge section into river 3600 COD 185 mg / L, arrival time 95 min, complete output of full data on pollution load migration and transformation.

[0062] 3. Calibration and Operating Condition Output of the Water Quantity-Quality Coupled Model The multi-source coupling modeling module imports the topology and pollution migration data of the entire facility to complete the model calibration. After calibration, the simulation error is controlled within 10%: the simulation error of pipeline water level is 7.8%, the simulation error of sewage treatment plant influent flow rate is 6.5%, and the simulation error of river and lake section COD concentration is 8.2%. The simulation accuracy meets the requirements of scheduling decision-making.

[0063] The model output conditions were checked during the peak rainfall period from 08:00 to 09:00: Main pipe P12 water level 1.64m (82% of pipe diameter), No. 1 sewage treatment plant influent 68,000 cubic meters per second. (Design load 85%), predicted overflow rate of O3 at the overflow outlet is 1200. Without scheduling intervention, the total daily COD load entering the river at section D1 was 510 kg, and the predicted COD concentration at the section was 18.6 mg / L, indicating a risk of water quality exceeding standards.

[0064] 4. Calculation of daily environmental capacity and surplus of water bodies at graded cross-sections The water environment capacity estimation module estimates the daily environmental capacity of urban water bodies based on ecological flow and river and lake assessment sections. River and lake assessment sections D1 is a national control section, D2 is a provincial control section, and D3 is a municipal control section. The section grade coefficients for the national, provincial, and municipal control sections are set to 3, 2, and 1, respectively. After dividing each section grade coefficient by the sum of all section grade coefficients, the section weights for D1, D2, and D3 are 0.50, 0.33, and 0.17, respectively, as shown in Table 1. Table 1. Weight of River and Lake Assessment Sections and Daily Environmental Capacity Record of COD

[0065] At 08:00, the daily environmental capacity for COD at the river / lake assessment section D1 was 480 kg / d, and the COD pollution load already entering the river was 350 kg / d. The calculated remaining environmental capacity margin is 480 kg / d. 350 = 130 kg / d. Without scheduling measures, the predicted additional COD entering the river during the remaining scheduling period is 160 kg / d, and the predicted cumulative COD entering the river for the whole day is 510 kg / d. This exceeds the upper limit of the margin, and time-sharing optimization scheduling needs to be initiated.

[0066] 5. Finite Discrete Scheduling: Timing Solution and Scheduling Results The pollution load scheduling module uses environmental capacity margin, pipeline storage capacity, interceptor well overflow limit, and wastewater treatment plant load as constraints to optimize the solution for four 15-minute time periods from 08:00 to 09:00, generating scheduling schemes as shown in Table 2: Table 20:00-09:00 Pollution Load Optimization and Dispatch Scheme

[0067] After implementing the optimized scheduling plan, the remaining period's additional COD load entering the river decreased from 160 kg / d to 115 kg / d, and the total daily COD load entering the river at section D1 was 465 kg / d, lower than the environmental capacity limit of 480 kg / d; the overflow of O3 at the overflow outlet decreased from 1200 kg / d to 115 kg / d. Reduced to 620 The influent load of Wastewater Treatment Plant No. 1 is stably controlled at 72,000 kW. Within design limits.

[0068] This embodiment verifies that the present invention can incorporate pollution load tracing, coupled model operating conditions, graded water body environmental capacity, and facility operation boundaries into a unified scheduling logic, and through time-sharing regulation and peak-shaving control, stably control the pollution load entering the river within the carrying capacity of the river and lake water bodies.

[0069] Example 3 In another preferred embodiment, based on the above embodiment 1, this embodiment provides an integrated intelligent scheduling method for urban power plants, networks, rivers and lakes, which is implemented based on the integrated intelligent scheduling system for urban power plants, networks, rivers and lakes described in embodiment 1. This embodiment verifies the closed-loop correction mechanism of the collaborative control feedback module. When the deviation between the actual on-site working conditions and the model prediction exceeds the standard, the model boundary is automatically updated, the remaining time period scheduling sequence is recalculated, and the dynamic adaptation to sudden changes in on-site working conditions is achieved.

[0070] 1. Project Overview and Initial Scheduling Plan Another polder town was selected as the verification area, with a total area of ​​31.8 square kilometers. It serves a population of 240,000; the total length of the pipeline network is 226km, with 940 pipeline nodes, and is equipped with 1 sewage treatment plant, 9 pumping stations, 6 gate stations, and 3 regulating reservoirs. It has 4 water quality assessment sections, and the scheduling period is also 96 sections × 15min. The core control indicator is COD, and the target assessment section D4 has a daily COD environmental capacity of 360kg / d.

[0071] At 10:00 AM, the load analysis module reported a COD load of 245 kg entering the river at section D4. The model predicted a total daily inflow of 382 kg without optimization. The water level at main pipe Q8 was 1.38 m, and the predicted operating flow rate at pump station No. 2 was 1.4 kg. The predicted overflow rate of O6 at the overflow outlet is 520. The influent load of the wastewater treatment plant is 51,000 kW. .

[0072] The pollution load scheduling module calculates that the environmental capacity margin at section D4 is only 115 kg / d, while the predicted additional COD inflow to the river during the remaining period is 137 kg / d, exceeding the environmental capacity margin. The pollution load scheduling module then solves for the finite discrete scheduling time sequence for the 10:00-11:00 period. In the initial scheduling scheme, the total interception and storage capacity for the 10:00-11:00 period is 1780 kg / d. The total storage capacity of the pipeline network is 1220. The total off-peak transport volume was 520. .

[0073] The collaborative control and feedback module simultaneously issued four types of supporting dispatch instructions: the water supply dispatch instruction was to reduce the water supply pressure in area B from 0.31MPa to 0.30MPa; the wastewater treatment plant network coordination instruction was to control the influent load of the wastewater treatment plant at 53,000 kW. Within this area, the flood control command is to activate Pump Station No. 2 and set the operating flow rate to 1.6. The water quality control order for rivers and lakes is to open sluice gate G5 and set the water replenishment flow rate to 0.4. .

[0074] 2. On-site execution feedback collection and deviation exceeding judgment At 10:30, after the first scheduling period ends, the collaborative management and control feedback module receives execution status feedback data. Comparing this data with the model's initial predictions, deviation record table 3 is generated. The COD inflow load in table 3 represents the total predicted cumulative COD inflow load for the entire day, extrapolated from the current feedback data, and does not represent the actual pollution load that has already entered the river and cannot be changed.

[0075] Table 310: Execution Status Feedback Data and Deviation Record Table for 30-Hour Period

[0076] System preset thresholds: network water level deviation 0.15m, wastewater treatment plant load deviation 0.4kWh. Overflow deviation 180 COD load deviation 20 kg / d, COD concentration deviation 1.5 mg / L; if all four indicators in the table exceed the threshold, the system will automatically trigger a closed-loop correction process.

[0077] 3. Model boundary update and time series re-optimization during non-execution periods The multi-source coupled modeling module synchronously updates four types of model boundary conditions based on measured feedback: the water level boundary of the main pipe Q8 from 1.38m to 1.55m; the overflow boundary of the overflow outlet O6 at 520m. →760 Wastewater treatment plant influent load boundary: 51,000 kW →56,000 COD concentration boundary at section D4: 17.2 mg / L → 19.4 mg / L.

[0078] The pollution load scheduling module, constrained by the updated model conditions and a fixed environmental capacity of 360 kg / d at section D4, re-solved the discrete time series for all unexecuted scheduling periods from 10:30 to 24:00; the key adjustment was to the control plan from 10:30 to 11:30: the total pipeline storage capacity was increased from 1420. Upgraded to 1960 The interception and storage of water has been reduced from 1680. Upgraded to 2180 The off-peak transport volume was increased from 520 Upgraded to 940 The influent load of the wastewater treatment plant has been adjusted back to 52,000 kilowatts. The operating flow rate of pump station #2 has been adjusted to 1.9. Increase efforts in pipeline transfer, regulation, and interception of sewage.

[0079] 4. Scheduling effect after closed-loop correction After time-series re-optimization, the daily predicted cumulative COD inflow at section D4 decreased from 405 kg to 352 kg, below the daily environmental capacity limit of 360 kg / d; the water level at main pipe Q8 dropped from 1.55 m to 1.42 m, eliminating the risk of flooding due to high water levels in the pipe network; and the subsequent predicted overflow at overflow outlet O6 decreased from 760 kg / d. Dropped to 510 This significantly reduces pollution from rainwater overflows; the influent load of the wastewater treatment plant has been stably maintained at 52,000 kilowatts. Within a reasonable range, avoid overloading.

[0080] This embodiment demonstrates that by dynamically correcting model boundaries and recalculating remaining scheduling timelines based on feedback data, the present invention can quickly adjust scheduling strategies when there are sudden deviations in pipeline water levels, overflows, sewage treatment plant loads, and water quality loads, thereby continuously ensuring that the overall operating conditions of the plant network, rivers, and lakes match the water body's pollution carrying capacity constraints and achieving closed-loop, adaptive, and intelligent scheduling throughout the entire process.

[0081] Example 4 In another preferred embodiment, based on the above embodiment 1, this embodiment provides an integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes. This embodiment establishes an integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes that can be directly implemented. The data flow relationships between the various modules of the system are as follows: Figure 1 As shown.

[0082] The entire system consists of five modules: a load monitoring and analysis module, a multi-source coupled modeling module, a water environment capacity estimation module, a pollution load scheduling module, and a collaborative control and feedback module. These five modules establish a complete data interaction path. The load monitoring and analysis module generates two types of data: pollution load contribution ratio and pollution load migration and transformation data, and transmits them to the multi-source coupled modeling module. The multi-source coupled modeling module calculates and outputs the overall system operating status, which is simultaneously pushed to the water environment capacity estimation module and the pollution load scheduling module. The water environment capacity estimation module calculates the daily environmental capacity of urban water bodies and sends it to the pollution load scheduling module. The pollution load scheduling module integrates all input data to solve the discrete scheduling time sequence, outputs a complete optimized pollution load scheduling scheme, and transmits it to the collaborative control and feedback module. The collaborative control and feedback module issues scheduling instructions to field equipment and simultaneously collects equipment operation feedback data and sends it back to the multi-source coupled modeling module, forming a continuous closed-loop iterative mechanism.

[0083] The load monitoring and analysis module connects to the city's water affairs IoT sensing terminals and business database, uniformly accessing basic ledgers of drainage users, pipeline topology maps, and real-time monitoring data on water quality, quantity, and hydrology at each stage. It divides the entire river network into confluence grids according to four levels: watershed, region, street, and community. The module calculates the grid pollution load by multiplying the drainage volume of a single grid by the corresponding pollutant concentration, and obtains the pollution load contribution ratio by the ratio of the pollution load at the assessment section of a single grid flowing into a river or lake to the total pollution load received at that section. For drainage areas without online water quality monitoring points, it automatically retrieves the historical average monitoring concentration of similar drainage users to complete the load supplementation. Based on the pipeline topology connectivity, it identifies the complete transport link from each grid to the corresponding assessment section of a river or lake, outputting a complete set of migration and transformation results, including the pollution load transport path, segmented sewage arrival time, segmented pollution load volume, and final pollution load entering the river.

[0084] The multi-source coupling modeling module constructs a water quantity-water quality coupled unsteady flow model covering runoff source control facilities, drainage networks, booster pumping stations, storage tanks, sewage treatment plants, and urban rivers and lakes. The model is configured with four types of independent boundary conditions: runoff inflow boundary generated from rainfall zoning data, pollution input boundary formed from sewage discharge data from drainage users, operational boundary of equipment such as pumping stations and sewage treatment plants, and outflow boundary formed by measured water levels in rivers and lakes. The system uses urban measured hydrological data, equipment operating conditions, rainfall zoning information, drainage user data, and load analysis results as model inputs, continuously performs parameter calibration and dynamic boundary updates, and outputs five types of system operating conditions in real time: network transport status, sewage treatment plant influent load status, interception and overflow status, pollution load entering rivers status, and river and lake water quality response status.

[0085] The water environment capacity estimation module calculates the daily environmental capacity of urban water bodies based on river ecological flow and the requirements of water quality control sections at all levels of rivers and lakes. The module divides river and lake assessment sections into national, provincial, and municipal control sections, assigning corresponding level coefficients to each. The section weight is obtained by dividing the single section level coefficient by the sum of all section levels. All section weights are combined to form an assessment section weight matrix. The daily environmental capacity of each section is then weighted by the weight matrix to obtain a unified daily environmental capacity value for the entire urban water body.

[0086] The pollution load scheduling module reads the daily environmental capacity of the water body and the cumulative pollution load entering the river at the cross-section, with the difference serving as the environmental capacity margin. The complete daily scheduling is divided into multiple continuous scheduling periods, within which the interception and storage capacity, pipeline storage capacity, and peak-shifting transport capacity are calculated separately. The scheduling process uses environmental capacity margin, maximum pipeline storage capacity, operating limits of interception and overflow structures, and rated treatment load of wastewater treatment plants as multiple constraints to solve for the finite discrete scheduling sequence. If the predicted pollution load entering the river exceeds the remaining environmental capacity margin at a certain time period, the time-sharing scheduling scheme is readjusted within the operating boundaries of all facilities, and the optimal pollution load scheduling strategy is output.

[0087] The collaborative control and feedback module integrates subsystems for water supply scheduling, wastewater treatment plant network operation, urban flooding prevention, and river and lake water quality management. Based on the discrete time sequence within the optimized scheduling scheme, it automatically generates four types of on-site execution instructions and issues them to the automated control equipment. The module collects real-time feedback data on the execution status of all water facilities, calculating the deviation between measured operating conditions and model-predicted operating conditions item by item. When any indicator deviation exceeds a system-preset threshold, it drives the multi-source coupled modeling module to synchronously update all model boundary conditions. The pollution load scheduling module then re-solves the discrete scheduling time sequence for all unexecuted scheduling periods of the day, achieving dynamic adaptive correction of the scheduling scheme.

[0088] This embodiment of the system can integrate all business operations of urban water supply, drainage, sewage treatment, river and lake water quality, and urban flood control, eliminating the problems of traditional multi-platform decentralized scheduling and mismatch in operation of each link. It achieves integrated intelligent management and control of plant, network, river and lake by relying on full-chain pollution source tracing, synchronous simulation of water quantity and quality, graded water body pollution control, time-sharing regulation and storage optimization, and closed-loop feedback correction.

[0089] Example 5 In another preferred embodiment, based on embodiments 1 to 4 above, this embodiment provides an integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes, implemented based on the integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes described in embodiment 4. The overall scheduling process is as follows: Figure 2As shown, the scheduling day is uniformly divided into 96 continuous scheduling periods of 15 minutes each. The core pollutants controlled during the scheduling process are COD and ammonia nitrogen. The entire method consists of five standardized execution steps.

[0090] Step 1: Integrate comprehensive water resources infrastructure and monitoring data to complete pollution load analysis of the four-level catchment grid. The system uniformly retrieves basic information of drainage users, pipeline topology data, and comprehensive water quality, water quantity, and hydrological monitoring data, dividing the river catchment grid into four levels: watershed, region, street, and community. The pollution load of a single grid is calculated by multiplying the drainage volume by the pollutant concentration. For drainage users lacking real-time water quality monitoring, the average historical monitoring concentration of similar drainage users is used in the load calculation. Based on the pipeline catchment relationship, the pollution contribution ratio of each grid to the river and lake assessment sections is statistically analyzed. At the same time, the complete pollution load migration and transformation results, including pollution load transport path, segmented arrival time, segmented pollution load amount, and pollution load amount entering the river, are output.

[0091] Step 2: Build and calibrate a water quantity-water quality coupled unsteady flow model to output the real-time operating status of the entire system. Construct a comprehensive water quantity-water quality coupled unsteady flow model covering runoff source facilities, pipe networks, pumping stations, storage tanks, sewage treatment plants, rivers, and lakes. Import urban measured data, facility operating conditions, rainfall zones, drainage user information, and pollution load migration and transformation results obtained in Step 1 to complete model parameter calibration and boundary updates. The model includes four types of boundary conditions: runoff inflow, pollution input, facility operation, and river / lake outflow. After model calibration, the simulation errors for pipe network water level, sewage treatment plant influent flow, and river / lake cross-section pollutant concentration can be controlled within 10%. Stable output data for five operating conditions: pipe network transport status, sewage treatment plant influent load status, interception and overflow status, pollution load entering the river status, and river / lake water quality response status.

[0092] Step 3: Combine control constraints to calculate the daily environmental capacity of urban water bodies and calculate the environmental capacity surplus of each section. Based on the ecological flow baseline of rivers, the control standards of national, provincial, and municipal river and lake assessment sections, set the section level coefficients at different levels and construct the section weight matrix. The overall daily environmental capacity of urban water bodies is calculated by weighting the coefficients. The remaining environmental capacity surplus of each river and lake assessment section is updated in real time by subtracting the cumulative pollution load that has entered the river on that day from the daily environmental capacity of the water body.

[0093] Step 4: Solve the time-sharing discrete scheduling sequence under multiple constraints to generate an optimal pollution load scheduling scheme. Combining the overall system operating status output in Step 2 with the cross-sectional environmental capacity margin obtained in Step 3, and using the environmental capacity margin, pipeline storage capacity, operating boundaries of interception and overflow structures, and the upper limit of wastewater treatment plant load as scheduling constraints, solve the finite discrete scheduling sequence corresponding to interception and storage volume, pipeline storage volume, and peak-shifting transport volume for each time period. If the predicted pollution load entering the river in a single time period exceeds the remaining environmental capacity margin of the cross-section, readjust the time-sharing scheduling scheme within the operating limits of all facilities, and output the globally optimal pollution load scheduling scheme.

[0094] Step 5: Issue multiple types of dispatch instructions, and achieve closed-loop iteration of dispatch based on equipment feedback data. Based on the optimized dispatch plan, generate water supply dispatch instructions, sewage treatment plant network operation instructions, urban flood control instructions, and river and lake water quality management instructions, and issue them to on-site pumping stations, gates, sewage treatment plants, and regulating reservoirs. Continuously collect equipment execution status feedback data, compare measured values ​​with model prediction values ​​to calculate deviations; once any indicator deviation exceeds a preset threshold, simultaneously update the boundaries of four types of models: pipeline water level, overflow flow, sewage treatment plant load, and river and lake section pollutant concentration. Re-optimize the dispatch sequence for all dispatch periods not executed that day to ensure that the total daily pollution load entering the river is below the daily environmental capacity limit of the urban water body.

[0095] The method described in this embodiment is complete and can be programmed and automatically run. It can dynamically adjust the coordinated scheduling strategy of plant, network and river / lake according to rainfall fluctuations and changes in sewage load, and simultaneously take into account multiple control objectives such as urban flood control, stable sewage treatment and long-term compliance of river and lake water quality.

[0096] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art can make various modifications, substitutions, and equivalent adjustments to system module parameters, scheduling period duration, cross-sectional grade coefficients, and water storage facility scheduling thresholds without departing from the technical principles and core scheduling logic of the present invention. All equivalent technical solutions based on the concept of the present invention fall within the scope of protection defined by the claims of the present invention.

[0097] In the preferred embodiment, the load monitoring and analysis module is used to access basic data of drainage users, pipeline topology data, and water quality, quantity, and hydrological data of each stage from the source, transportation, treatment, and river channels. It also divides the river confluence grid into four levels: watershed, region, street, and community, determining the pollution load contribution ratio and pollution load migration and transformation results of each confluence grid. This configuration enables refined source tracing and dynamic tracking of pollution load across the entire city, from drainage sources to river and lake receiving ends. It provides clear and quantifiable control targets for subsequent scheduling decisions, effectively solving the problems of unclear pollution sources and ambiguous responsibilities in existing technologies.

[0098] In the preferred embodiment, the pollution load contribution ratio is the ratio of the pollution load entering the corresponding river / lake assessment section from the target confluence grid to the total pollution load received by the corresponding river / lake assessment section. The pollution load is calculated based on the discharge volume in the water volume data and the pollutant concentration in the water quality data. The above settings can quantify the specific contribution of different regions (from small areas to watersheds) to the pollution problem of specific river / lake sections, thereby accurately locating key control areas of pollution sources and improving the accuracy and effectiveness of pollution prevention and control measures.

[0099] In the preferred embodiment, the pollution load migration and transformation results include the pollution load transport path, segmented arrival time, segmented pollution load amount, and pollution load entering the river. The pollution load transport path is determined by the pipeline network topology path from each confluence grid to the corresponding river and lake assessment section. The above settings can not only describe the spatial migration process of the pollution load, but also dynamically predict its transport time and segmented changes in facilities such as pipelines and pumping stations, providing a key data foundation and predictive capability for realizing time control and peak-shaving scheduling of the pollution load transport process.

[0100] In the preferred embodiment, the multi-source coupling modeling module is used to establish a water quantity-water quality coupled unsteady flow model covering runoff source control facilities, pipe networks, pumping stations, storage tanks, sewage treatment plants, rivers, and lakes. It takes urban measured data, facility operating data, rainfall zoning data, drainage user data, and pollution load migration and transformation results as inputs to perform parameter calibration, boundary condition updates, and state output on the water quantity-water quality coupled unsteady flow model. The state output includes pipe network transport status, sewage treatment plant influent load status, interception and overflow status, pollution load entering the river status, and river and lake water quality response status. This configuration enables unified, synchronous, and dynamic simulation of the operating status (such as water level, flow rate, and pollutant concentration) of each subsystem (plant, network, river, and lake), providing a reliable "digital twin" that maps to the real system for integrated collaborative scheduling and decision-making.

[0101] In a preferred embodiment, the boundary conditions include the inflow boundary determined by rainfall zoning data, the pollution load input boundary determined by drainage user data, the facility operation boundary determined by facility condition data, and the outflow boundary determined by river and lake water level data from urban measured data. These settings ensure that the water quantity-water quality coupled model can fully consider multiple dynamic factors encountered in actual operation, such as external rainfall input, pollution source input, internal facility conditions, and downstream river backwater, thereby significantly improving the accuracy of model simulation and its adaptability to real-time operating conditions.

[0102] In the preferred embodiment, the water environment capacity estimation module is used to estimate the daily environmental capacity of urban water bodies based on ecological flow and river and lake assessment section constraints. The above settings establish an objective and quantitative core objective for the integrated scheduling process—to control the total daily pollution load entering the river within the self-purification capacity (daily environmental capacity) of the river and lake water bodies, so that the originally dispersed plant and grid scheduling behaviors are all unified to the ultimate goal of protecting the water environment quality of rivers and lakes.

[0103] In the preferred embodiment, the water environment capacity estimation module divides river and lake assessment sections into national, provincial, and municipal control sections, and sets a section level coefficient for each. The section level coefficient of each section is divided by the sum of all section level coefficients to obtain the section weight of each river and lake assessment section. The section weights constitute the assessment section weight matrix, and the daily environmental capacity of urban water bodies is the result of weighting the daily environmental capacity of each section according to the assessment section weight matrix. The above settings provide a scientific weight allocation method for complex water systems containing multiple assessment sections of different levels, ensuring that during overall scheduling, the minimum compliance requirements of all sections are met, and strategies can be optimized according to the differences in the policy importance of section management, thereby improving the scientificity and compliance of scheduling.

[0104] In the preferred embodiment, the pollution load scheduling module is used to determine the environmental capacity margin of river and lake sections based on the pollution load migration and transformation results, status output, and daily environmental capacity of urban water bodies. The environmental capacity margin is the difference between the daily environmental capacity of urban water bodies and the amount of pollution load that has entered the river at the corresponding river and lake section. Using the environmental capacity margin, pipeline storage capacity, the operating boundary of interception and overflow structures, and the treatment load boundary of sewage treatment plants as scheduling constraints, the module solves the finite discrete scheduling sequence of interception and storage, pipeline storage, and peak-shaving scheduling to generate an optimized pollution load scheduling scheme. The above settings couple the key indicator of "environmental capacity margin" with the objective capacity constraints of physical facilities such as pumping stations, pipelines, sewage treatment plants, and storage tanks in the system to automatically generate specific scheduling instruction schemes. This enables the disposal of remaining pollutants at what time, where, and in what way (such as storage, peak-shaving, and enhanced treatment) to ultimately ensure that the total amount of pollution load entering the river throughout the day does not exceed the standard.

[0105] In the preferred embodiment, the pollution load scheduling module divides a scheduling day into multiple continuous scheduling periods, and determines the interception and storage volume, pipeline storage volume, and peak-shifting transport volume within each continuous scheduling period. When the environmental capacity margin is less than the predicted pollution load entering the river within the corresponding continuous scheduling period determined based on the pollution load entering the river, the finite discrete scheduling sequence is adjusted within the operating boundaries of the interception overflow structure, the pipeline storage capacity, and the treatment load boundaries of the sewage treatment plant. This configuration achieves high-frequency, rolling optimization of the scheduling. It can address deviations between real-time monitoring and prediction. Once it is predicted that the environmental capacity of rivers and lakes will be insufficient for a certain period, the scheduling strategy for subsequent periods can be dynamically adjusted to store sewage that might otherwise be released during non-exceeding periods, or to guide the load to facilities with sufficient treatment capacity, ensuring the achievement of the overall objectives.

[0106] In the preferred scheme, the scheduling day is evenly divided into 96 15-minute scheduling periods, and the core pollutant indicators for scheduling control are COD and ammonia nitrogen. The above settings clarify that the frequency of scheduling decisions is sufficient to respond to rapid changes such as rainfall and load fluctuations (once every 15 minutes), and incorporates key representative water quality indicators (COD and ammonia nitrogen) into the model and optimization, so that the scheduling scheme can simultaneously take into account the refined control of water quantity processes and water quality targets.

[0107] In the preferred embodiment, the collaborative control and feedback module includes a water supply scheduling subsystem, a sewage treatment plant network coordination subsystem, an urban flood control subsystem, and a river and lake water quality control subsystem. This subsystem is used to generate corresponding scheduling instructions based on the finite discrete scheduling sequence in the pollution load optimization scheduling scheme, and to update the scheduling input for the next scheduling period based on execution status feedback data. This setup establishes a closed loop from "optimization decision-making" to "instruction issuance and execution," enabling a unified mathematical model solution to drive the collaborative work of business systems such as water supply, drainage, plant-network coordination, urban flood control, and water quality control, which were originally managed by different departments, truly achieving "integrated" intelligent scheduling.

[0108] In the preferred embodiment, the collaborative control feedback module calculates the deviation between the execution status feedback data and the status output. When any deviation exceeds a preset deviation threshold, the multi-source coupling modeling module updates the boundary conditions, and the pollution load scheduling module re-solves the finite discrete scheduling sequence for the unexecuted scheduling period. These settings constitute the adaptive, closed-loop feedback mechanism of the entire system. This mechanism can immediately correct the model's input parameters and dynamically recalculate the optimal scheduling scheme for the remaining time period when execution deviations significantly deviate from model predictions occur at various stages (e.g., abnormal rainfall, abnormal sewage treatment plant load, pipeline blockage, etc.). This greatly enhances the robustness of the scheduling system in responding to sudden operating conditions and ensuring the stability of the daily scheduling objectives.

[0109] In the preferred scheme, if the wastewater discharger mentioned in step 1 has no measured pollutant concentration, the average historical monitoring concentration of the same type of wastewater discharger is used to replace the pollution load in the calculation. The above settings solve the technical problem of missing source monitoring data in practical applications. By using the historical statistical patterns of the same type of wastewater discharger to supplement the data, the load analysis module can operate stably and generate a continuous and comparable pollution load data foundation covering the entire area.

[0110] In the preferred embodiment, after model calibration in step 2, the simulation errors of pipeline water level, sewage treatment plant influent flow, and river and lake cross-section pollutant concentration are all controlled within 10%. The above settings, through quantitative accuracy requirements (error <10%), ensure that the constructed water quantity-water quality coupled unsteady flow model has high credibility and reliability, and can provide sufficiently accurate system state predictions for subsequent optimization scheduling. It is a key quality control link supporting efficient and reliable scheduling decisions.

[0111] In the preferred scheme, when the feedback deviation exceeds the standard in step 5, the boundaries of the four types of models—pipeline water level, overflow flow, sewage treatment plant load, and river and lake section concentration—are updated simultaneously to re-optimize the scheduling sequence and ensure that the total daily pollution load entering the river is lower than the daily environmental capacity limit of the water body. The above settings clarify that during the closed-loop correction process, the update of the model boundary conditions must simultaneously cover the water quantity and quality status of multiple core links such as pipeline network, overflow, sewage treatment, and river and lake sections. This comprehensive synchronous update ensures that the re-solved scheduling scheme can systematically and globally respond to real-world changes and ultimately lock in the ultimate goal of "the total daily pollution load does not exceed the standard."

[0112] In summary, this invention provides an integrated intelligent scheduling system and method for urban power plants, networks, rivers, and lakes, effectively solving the technical problems of decentralized scheduling of multiple systems such as power plants, networks, rivers, and lakes, inconsistent scheduling rules, and mismatch between local scheduling instructions and overall operating conditions.

[0113] The core of this system and method lies in constructing a complete, dynamic, and closed-loop collaborative scheduling logic. First, it adopts a refined management approach from source to end, using a four-level river confluence grid (basin, region, street, and community) to quantify and predict the contribution ratio and migration / transformation paths of pollution loads, thereby accurately locating the control targets. Second, by establishing a comprehensive water quantity-quality coupled unsteady flow model covering sources, pipe networks, pumping stations, sewage treatment plants, and even rivers and lakes, it can perform unified and quantitative dynamic simulation and evaluation of the operational status of key facilities and the water quality response of rivers and lakes, providing a scientific digital foundation for integrated decision-making.

[0114] At the scheduling decision-making level, this invention introduces and calculates the daily environmental capacity of urban water bodies based on ecological flow and cross-sectional constraints, and compares it in real time with the predicted inflow load for the remaining period to derive a clear "environmental capacity margin" as the core scheduling objective. Around this objective, the system, within a unified mathematical framework, integrates multiple physical constraints such as pipeline storage capacity, interception facility operation boundaries, and sewage treatment plant capacity. It decomposes the scheduling behavior for the next day into multiple (e.g., 96) consecutive time periods for solution, generating specific time-series schemes for interception storage, pipeline storage, and peak-shaving transportation, achieving high-frequency, rolling optimization of scheduling decisions. Simultaneously, by weighting different levels of assessment sections (national, provincial, and municipal), this invention can scientifically coordinate and differentiate to meet the pollution control requirements of different sections.

[0115] To ensure the effective implementation of the scheduling plan, this invention breaks down the barriers between systems, enabling the time-series plan generated by the above-mentioned optimized calculations to be issued as commands to multiple actual business subsystems belonging to different management domains, such as water supply scheduling, plant-grid joint operation, urban flood control, and water quality management, forming a unified set of execution commands. More importantly, it establishes a closed-loop feedback iteration mechanism. By continuously comparing the execution status of the scheduling commands with the prediction results of the model, when the deviation exceeds a preset threshold, the model boundary conditions are dynamically updated and the optimal plan for the remaining scheduling period is recalculated. This ensures that the overall daily target (such as the total amount of pollution load entering the river not exceeding the daily environmental capacity of the water body) is not disturbed by real-time operating condition fluctuations, guaranteeing the resilience and reliability of the scheduling strategy.

[0116] This invention achieves unified and coordinated scheduling of all elements in an urban water system—sources, networks, plants, rivers, and lakes—under the same scheduling logic by employing a four-level confluence grid pollution load analysis, comprehensive water quantity-water quality coupled modeling, graded cross-sectional water body environmental capacity constraints, time-segmented discrete scheduling sequence optimization, and collaborative application of closed-loop feedback iteration. This enables scheduling decisions to not only focus on local optima but also pursue overall system optimization, ultimately achieving the comprehensive goals of reducing pollution entering rivers at the source, ensuring stable river and lake water quality, reducing the risk of rainwater overflow, and balancing urban drainage safety with the efficient and stable operation of wastewater treatment plants.

Claims

1. An integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes, characterized by: It includes a load monitoring and analysis module that interacts with data sequentially, a multi-source coupling modeling module, a water environment capacity estimation module, a pollution load scheduling module, and a collaborative management and control feedback module; The load monitoring and analysis module outputs the pollution load contribution ratio and pollution load migration and transformation results to the multi-source coupled modeling module. The multi-source coupled modeling module receives the pollution load migration and transformation results and outputs the overall system operating status to the water environment capacity estimation module and the pollution load scheduling module. The water environment capacity estimation module outputs the daily environmental capacity of urban water bodies to the pollution load scheduling module. The pollution load scheduling module solves the discrete scheduling time sequence based on the aforementioned data, generates an optimized pollution load scheduling scheme, and sends it to the collaborative control feedback module. The collaborative control feedback module issues scheduling instructions and sends the equipment execution feedback back to the multi-source coupled modeling module to achieve closed-loop iteration.

2. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The load monitoring and analysis module is used to access basic data of drainage users, pipeline topology data, and water quality, water quantity and hydrological data of each link of the source, transportation, treatment and river channel. It divides the river channel into four levels: watershed, region, street and community, and determines the pollution load contribution ratio and pollution load migration and transformation results of each confluence grid.

3. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The pollution load contribution ratio is the ratio of the pollution load entering the corresponding river / lake assessment section from the target confluence grid to the total pollution load received by the corresponding river / lake assessment section. The pollution load is calculated based on the discharge volume in the water volume data and the pollutant concentration in the water quality data.

4. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The pollution load migration and transformation results include the pollution load transport path, segmented arrival time, segmented pollution load amount, and pollution load entering the river. The pollution load transport path is determined by the pipeline topology path from each confluence grid to the corresponding river and lake assessment section.

5. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The multi-source coupling modeling module is used to establish a water quantity-water quality coupled unsteady flow model covering runoff source control facilities, pipe networks, pumping stations, storage tanks, sewage treatment plants, rivers and lakes. It takes urban measured data, facility operating data, rainfall zoning data, drainage user data and pollution load migration and transformation results as inputs to perform parameter calibration, boundary condition update and state output on the water quantity-water quality coupled unsteady flow model. The state output includes pipe network transport status, sewage treatment plant influent load status, interception overflow status, pollution load entering river status and river and lake water quality response status.

6. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 5, characterized in that: The boundary conditions include the inflow boundary determined by rainfall zoning data, the pollution load input boundary determined by drainage user data, the facility operation boundary determined by facility condition data, and the outflow boundary determined by river and lake water level data from urban measured data.

7. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The water environment capacity estimation module is used to estimate the daily environmental capacity of urban water bodies based on ecological flow and river and lake assessment section constraints.

8. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The water environment capacity estimation module divides river and lake assessment sections into national control sections, provincial control sections, and municipal control sections, and sets section grade coefficients for each section. The section grade coefficient of each section is divided by the sum of all section grade coefficients to obtain the section weight of each river and lake assessment section. The section weights of each section form an assessment section weight matrix. The daily environmental capacity of urban water bodies is the result of weighting the daily environmental capacity of each section according to the assessment section weight matrix.

9. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The pollution load scheduling module is used to determine the environmental capacity margin of river and lake sections based on the pollution load migration and transformation results, status output, and daily environmental capacity of urban water bodies. The environmental capacity margin is the difference between the daily environmental capacity of urban water bodies and the amount of pollution load that has entered the river at the corresponding river and lake section. Using the environmental capacity margin, pipeline storage capacity, the operating boundary of interception and overflow structures, and the treatment load boundary of sewage treatment plants as scheduling constraints, the module solves the finite discrete scheduling time sequence of interception and storage, pipeline storage, and peak-shaving scheduling to generate an optimized pollution load scheduling scheme.

10. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The pollution load scheduling module divides a scheduling day into multiple continuous scheduling periods and determines the interception and storage capacity, pipeline storage capacity, and peak-shifting transport capacity within each continuous scheduling period. When the environmental capacity margin is less than the predicted pollution load entering the river within the corresponding continuous scheduling period determined based on the pollution load entering the river, the finite discrete scheduling sequence is adjusted within the operating boundary of the interception overflow structure, the pipeline storage capacity, and the treatment load boundary of the sewage treatment plant.

11. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The collaborative control and feedback module includes a water supply scheduling subsystem, a sewage treatment plant network scheduling subsystem, an urban flood control subsystem, and a river and lake water quality control subsystem. It is used to generate corresponding scheduling instructions based on the finite discrete scheduling sequence in the pollution load optimization scheduling scheme, and to update the scheduling input for the next scheduling period based on the execution status feedback data.

12. The integrated intelligent dispatching system for urban power plants, networks, rivers, and lakes according to claim 1, characterized in that: The collaborative control feedback module will calculate the deviation between the execution status feedback data and the status output. When any deviation exceeds the preset deviation threshold, the multi-source coupling modeling module updates the boundary conditions, and the pollution load scheduling module re-solves the finite discrete scheduling sequence of the unexecuted scheduling period.

13. An integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes, implemented based on the integrated intelligent scheduling system for urban power plants, networks, rivers, and lakes as described in claims 1-12, characterized in that, Includes the following steps: Step 1: Access basic data of drainage users, pipeline topology data, water quality data, water quantity data and hydrological data, divide the river confluence grid into four levels: watershed, region, street, and community, and determine the pollution load contribution ratio and pollution load migration and transformation results; Step 2: Establish a water quantity-water quality coupled unsteady flow model, and take urban measured data, facility operating data, rainfall zoning data, drainage user data and pollution load migration and transformation results as inputs, and output the pipeline transportation status, sewage treatment plant influent load status, interception and overflow status, pollution load entering the river status and river and lake water quality response status. Step 3: Estimate the daily environmental capacity of urban water bodies based on ecological flow and river and lake assessment section constraints, and determine the environmental capacity margin of river and lake sections; Step 4: Based on the pipeline transportation status, sewage treatment plant influent load status, interception and overflow status, pollution load entering the river status, river and lake water quality response status, and environmental capacity margin, using environmental capacity margin, pipeline storage capacity, interception and overflow structure operation boundary, and sewage treatment plant treatment load boundary as scheduling constraints, solve the finite discrete scheduling time series of interception and storage, pipeline storage, and peak-shaving scheduling to generate an optimized scheduling scheme for pollution load; Step 5: Generate water supply scheduling instructions, sewage treatment plant network operation instructions, urban flood control instructions, and river and lake water quality management instructions based on the pollution load optimization scheduling plan, and update the scheduling input for the next scheduling period based on the execution status feedback data.

14. The integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes according to claim 13, characterized in that: For wastewater dischargers mentioned in step 1 who do not have actual measured pollutant concentrations, the average historical monitoring concentration of similar wastewater dischargers shall be used to calculate the pollution load.

15. The integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes according to claim 13, characterized in that: After model calibration in step 2, the simulation errors of pipeline water level, sewage treatment plant influent flow rate, and pollutant concentration at river and lake sections are all controlled within 10%.

16. The integrated intelligent scheduling method for urban power plants, networks, rivers, and lakes according to claim 13, characterized in that: When the feedback deviation exceeds the standard in step 5, the boundaries of the four types of models, namely, pipeline water level, overflow flow, sewage treatment plant load, and river and lake section concentration, are updated simultaneously, and the scheduling sequence is re-optimized to ensure that the cumulative daily pollution load entering the river is lower than the daily environmental capacity limit of the water body.

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