A comprehensive real-time simulation control method for joint scheduling of urban drainage systems and the system used therein

Abstract

A comprehensive real-time simulation control method for the joint scheduling of urban drainage systems includes the following steps: parsing an online mechanism model to construct an online mechanism model of the drainage system; simplifying the online mechanism model; constructing an offline simulation generalization model; correcting model parameters; establishing logical relationships between facilities; establishing communication command relationships; establishing a rainfall process; establishing system boundary conditions and control objectives; determining an optimized control method; coupling the online mechanism model, the simplified online mechanism model, and the offline simulation generalization model; performing real-time simulation of joint scheduling; and performing data analysis and display. This invention aims to establish a simulation platform that integrates algorithms, has strong applicability, and has a rich set of control strategies, integrating the SWMM engine. This platform can thus achieve efficient real-time simulation and optimized control of all elements, across the entire time domain, and for multiple objectives in urban power plants, networks, and rivers.

Description

Technical Field

[0001] The present invention relates to the field of real-time control of urban drainage systems, and in particular to a comprehensive urban drainage system joint scheduling real-time simulation control method and a system used therein. Background Art

[0002] Real-time control of drainage systems refers to a control method that develops optimal operational strategies for the coordinated scheduling of drainage systems based on real-time monitoring data, fully utilizing the efficiency of existing facilities to achieve preset operational objectives. Real-time control is a future development trend for drainage systems. Currently, in the field of urban drainage modeling, simulation research on drainage networks, LID facilities, pumping stations, storage tanks, sewage treatment plants, and other facilities primarily utilizes the SWMM software developed by the US EPA. This software provides excellent simulation results for the performance of each catchment area during rainfall and the dynamic flow of water in pipes, but it has significant limitations in simulating real-time control systems in the drainage sector.

[0003] Currently, SWMM software and secondary development software based on SWMM on the market can only achieve simple logical control of the drainage process, and cannot achieve real-time global multi-objective optimization control. At the same time, due to the complex distributed model calculations and slow operation speed of software such as SWMM, it cannot meet the requirements of real-time control of drainage systems (usually requiring the entire system to complete the optimization calculation within 5 minutes and feedback the control strategy). Therefore, optimizing and improving the SWMM model is an important improvement direction of this patent.

[0004] In the field of simulation and control, many commercial simulation platforms offer high levels of visualization and are compatible with multiple programming languages ​​(C / C++, Python, etc.), enabling simulation and research of numerous physical processes. However, these platforms typically lack application modules for simulating urban drainage systems and are currently unable to directly simulate the hydraulic processes of urban drainage systems. Summary of the Invention

[0005] The main purpose of this invention is to provide a comprehensive real-time simulation control method for the joint scheduling of urban drainage systems. This method is a universal drainage system simulation method, which aims to establish an algorithm-integrated, highly applicable, and rich control strategy simulation platform that integrates the SWMM engine, thereby realizing efficient real-time simulation and optimization control of all elements, all time domains, and multiple objectives of urban power plants, networks, and rivers.

[0006] Another object of the present invention is to provide a system for the above-mentioned comprehensive urban drainage system joint scheduling real-time simulation control method.

[0007] As conceived above, the technical solution of the present invention is: a comprehensive urban drainage system joint scheduling real-time simulation control method, comprising the following steps:

[0008] Step 1: Analyze the online mechanism model and construct the online mechanism model of the drainage system;

[0009] Step 2: Simplify the online mechanism model;

[0010] Step 3: Construct an offline simulation generalization model;

[0011] Step 4: Correct model parameters;

[0012] Step 5: Establish logical relationships between facilities;

[0013] Step 6: Establish communication command relationship;

[0014] Step 7: Establish rainfall process;

[0015] Step 8: Establish system boundary conditions and control objectives;

[0016] Step 9: Determine the optimal control method;

[0017] Step 10: Coupling the online mechanism model, simplified online mechanism model and offline simulation generalization model;

[0018] Step 11: Conduct joint scheduling real-time simulation;

[0019] Step 12: Data analysis and presentation.

[0020] Furthermore, the step 1 includes the following steps:

[0021] ① Calling and running the online mechanism model file (.inp);

[0022] ②Collation and analysis of simulation process data and result data;

[0023] ③ The water volume, water level and water quality information of each important node are exported in a time series manner as boundary conditions and correction references for subsequent simulation control steps.

[0024] Furthermore, the method for simplifying the online mechanism model in step 2 is: first simplify the model facilities, and then linearize the model, that is, use the Muskingum model based on mass conservation, use the runoff coefficient R, the number of sub-segments N, the transmission time K and the weight coefficient X to define the catchment area, pipeline and river model, and convert the complex nonlinear physical relationship in the mechanism model into a linear relationship containing the above parameters. Finally, use the linear fitting method to linearize the process curve of the facility.

[0025] Furthermore, the method of constructing the offline simulation generalization model in step 3 is: based on the online mechanism model facilities, constructing the offline simulation generalization model, including all types of facilities of the online mechanism model.

[0026] Furthermore, the step 4 of correcting the model parameters includes:

[0027] ① Parameter calibration of online mechanism model;

[0028] ②Simplify the parameter calibration of the online mechanism model;

[0029] ③ Correction of setting parameters of each module in the offline simulation generalization model.

[0030] Furthermore, the method of establishing the logical relationship between facilities in step 5 is: the logical rules between the established modules include the relationship between the inlet and outlet water flow, and the relationship between the maximum inlet / outlet capacity and the flow.

[0031] Furthermore, the step 6 of establishing the communication instruction relationship specifically includes: the status parameter uploading relationship of each facility, the instruction issuing relationship of the central controller, the instruction issuing relationship of the local controller, the wireless signal simulation and the external instruction access simulation.

[0032] Furthermore, the step 7 of establishing the rainfall process specifically includes: calling an external rainfall file, calling a typical rainfall type database, and generating rainfall using a given formula.

[0033] Furthermore, the step 8 of establishing the system boundary conditions and control objectives specifically includes: the system's logical rules, the system's boundary conditions and the system's joint control objectives, namely, overflow, waterlogging and water quality objectives of the plant network river system.

[0034] Furthermore, the optimization control method determined in step 9 specifically includes:

[0035] ① Determine the control rules and parameters based on the optimization algorithm: select the controlled facilities, select the monitoring locations and the required monitoring operation time, water quality, water level and flow information, select the optimization algorithm and adjust the default parameters of the algorithm. Based on the monitoring information and the system joint control objectives, the selected optimization algorithm will calculate the optimal control value of the controlled location in real time during the simulation process; use the PID control method to control the facilities to smoothly reach the control value;

[0036] ② Determination of control rules and parameters based on rule control or fuzzy logic: Select the controlled facility, the monitoring location, and the required monitoring operating time, water quality, water level, and flow information; automatically generate the response relationship between the monitoring information and the control instruction; adjust the response relationship as the control basis; use the real-time monitoring value and the control basis to obtain the facility control value during simulation control; and use the PID control method to control the facility to smoothly reach the control value;

[0037] The above two optimization methods can both realize the control of weirs, gates, orifices, valves, diversion (interception) wells, pumping stations and other facilities based on the operating time, depth, head, inflow, outflow, water accumulation, water quality and working status information of each location in the system; both optimization control methods can be combined with PID control to achieve accurate and stable control.

[0038] Furthermore, the coupling of the online mechanism model, the simplified online mechanism model and the offline simulation generalization model in step 10 specifically includes:

[0039] ①Use the online mechanism model to simulate the real-time operation status of the plant network river system, and output the water volume, water level and water quality information of each important node to the simplified online mechanism model in the form of time series;

[0040] ② The simplified online mechanism model simulates the system operation in the future time period based on the input status data, and outputs the water volume, water level and water quality information of each important node in the form of time series to the offline simulation generalization model;

[0041] ③ The offline simulation generalization model optimizes and controls the operation of the plant-grid-river system within the simulation time period based on the selected control objectives and optimization methods and the input data, and calculates the control strategy under the optimal operating state of the system;

[0042] ④ The calculated control strategy is transmitted back to the online mechanism model, and simulation is performed under the optimal control strategy to obtain real-time optimized control effect and complete simulation control under one time step.

[0043] Furthermore, the joint scheduling real-time simulation in step 11 specifically includes: joint scheduling real-time simulation under a user-defined time step and joint scheduling real-time simulation under a selected control target and method.

[0044] Furthermore, the data analysis and display in step 12 specifically includes:

[0045] ①Data recording and calling of each node;

[0046] ②Calculation of universality index;

[0047] ③Custom formula calculation;

[0048] ④Generation of common data charts.

[0049] The system used in the comprehensive real-time simulation control method for joint scheduling of urban drainage systems includes an online mechanism model module, an online mechanism model simplification module, a facility editing and management module, a model parameter correction module, a target optimization control module, and a simulation and result display module;

[0050] Furthermore, ① the online mechanism model module includes: a calling module for the system call of the online mechanism model file (.inp), an operation module for the system operation of the online mechanism model file (.inp), a data processing module for collating and analyzing simulation process data and result data, and a data export module for exporting the water volume, water level and water quality information of each important node in a time series manner;

[0051] ②The functions of the online mechanism model simplification module include:

[0052] A. Based on the online mechanism model simplification method, merge, delete or simplify the facilities in the online mechanism model;

[0053] B. Modify the catchment area, pipeline and river model parameters to generate a model that meets the linearization requirements;

[0054] C. Modify the model parameters of gate and orifice facilities to generate a model that meets the linearization requirements of the process curve;

[0055] ③The functions of the facility editing and management module include:

[0056] A. Construction of offline simulation generalization model, including adding and editing attributes of pipelines, LID facilities, wells, orifice gate valves, water storage facilities, pumps, sewage treatment plants, and river channels;

[0057] B. Establishment of logical relationships between facilities, including logical rules between modules, relationship between inlet and outlet water flow, and relationship between maximum inlet / outlet capacity and flow;

[0058] C. Establishment of communication command relationships, including the relationship between status parameter uploads of each facility, the relationship between command issuance by the central controller and local controllers, wireless signal simulation, and external command access simulation;

[0059] D. Establishment of rainfall process, including calling external rainfall files, calling typical rainfall type database, and generating rainfall based on given formula.

[0060] ④ The functions of the model parameter correction module include: parameter calibration of the online mechanism model, parameter calibration of the simplified online mechanism model and parameter correction of each module in the offline simulation generalization model;

[0061] ⑤The functions of the target optimization control module include:

[0062] A. Establishment of the system's logical rules and boundary conditions;

[0063] B. Establishment of joint control objectives of the system;

[0064] C. Use control rules and parameter determination based on optimization algorithms: select the controlled facility; select the monitoring location and the required monitoring information such as the operating time, water quality, water level and flow; select the optimization algorithm and adjust the default parameters of the algorithm; based on the monitoring information and combined with the system joint control objectives, the selected optimization algorithm will calculate the optimal control value of the controlled location in real time during the simulation process; use the PID control method to control the facility to smoothly reach the control value; or use control rules and parameter determination based on rule control method or fuzzy logic method: select the controlled facility; select the monitoring location and the required monitoring information such as the operating time, water quality, water level and flow; automatically generate the response relationship between the monitoring information and the control instruction; adjust the response relationship as the control basis; use the real-time monitoring value and the control basis to obtain the facility control value during simulation control; use the PID control method to control the facility to smoothly reach the control value;

[0065] ⑥The functions of the simulation and result display module include:

[0066] A. Online mechanism model, simplified online mechanism model and offline model coupling;

[0067] B. Real-time simulation of joint scheduling under custom time steps;

[0068] C. Real-time simulation of joint scheduling under the selected control objectives and methods;

[0069] D. Data recording and calling of each node;

[0070] E. Calculation of universality index;

[0071] F. Custom formula calculation;

[0072] G.Generate common data charts.

[0073] The present invention has the following advantages and positive effects: (1) The present invention uses a linearization method to simplify the mechanism model, which can significantly improve the model operation speed and realize efficient and reliable system state prediction simulation. (2) The present invention constructs an offline simulation generalization model on the basis of not changing the main logical structure of the traditional mechanism model, outputs the system simulation results to the simulation model for simulation, and uses the optimization algorithm to quickly obtain the optimization control strategy. The calculated optimization control strategy results can be fed back to the online mechanism model to simulate / verify the optimization control strategy effect. This method solves the problem that the traditional mechanism model has a slow operation rate and cannot realize global multi-objective optimization control under dynamic conditions. (3) After the online mechanism model, the simplified online mechanism model and the offline simulation generalization model of the present invention are coupled, they still have a relatively fast calculation rate. For example, when the control is completed once every 5 minutes, the model operation and control strategy optimization can be completed within 3 minutes. (4) The present invention integrates multiple optimization algorithms, and can select appropriate optimization methods according to different control objectives, realize the calculation of optimal control rules, and transmit the optimized control strategy to the actuator. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the module diagram of online mechanism model calling;

[0075] Figure 2 It is a schematic diagram of the system offline simulation model;

[0076] Figure 3 It is a schematic diagram of the offline simulation model of the sewage treatment plant;

[0077] Figure 4 It is the model parameter correction diagram module;

[0078] Figure 5 It is a coupling flow chart of online mechanism model, simplified online mechanism model and offline simulation model;

[0079] Figure 6 It is a flow chart of an example of the present invention. DETAILED DESCRIPTION

[0080] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0081] As shown in the figure: The present invention proposes a comprehensive urban drainage system joint scheduling real-time simulation control method, which includes the following steps:

[0082] Step 1: Analyze the online mechanism model and construct the online mechanism model of the drainage system;

[0083] Step 2: Online mechanism model simplification;

[0084] Step 3: Offline simulation generalization model construction;

[0085] Step 4: Model parameter calibration;

[0086] Step 5: Establish logical relationships between facilities;

[0087] Step 6: Communication command relationship is established;

[0088] Step 7: Rainfall process establishment;

[0089] Step 8: Establish system boundary conditions and control objectives;

[0090] Step 9: Determine the optimal control method;

[0091] Step 10: Coupling the online mechanism model, simplified online mechanism model and offline simulation generalization model;

[0092] Step 11: Real-time simulation of joint scheduling;

[0093] Step 12: Data analysis and presentation.

[0094] Reference Figure 1 , online mechanism model file analysis, including:

[0095] ① Calling and running the online mechanism model file (.inp);

[0096] ②Collation and analysis of simulation process data and result data;

[0097] ③ The water volume, water level and water quality information of each important node are exported in a time series manner as boundary conditions and correction references for subsequent simulation control steps.

[0098] The online mechanism model is simplified, including:

[0099] ① Generalization of model facilities: moderately simplify the plant-grid-river system model, retain key nodes, pipelines, and facilities, and reasonably merge, simplify, or delete the rest;

[0100] ② Model linearization: Utilizing methods such as the Muskingum model based on mass conservation, the runoff coefficient R, number of subsegments N, transmission time K, and weight coefficient X are used to define the catchment, pipeline, and river models. Complex nonlinear physical relationships in the mechanism model, such as rainfall-runoff and inflow-outflow relationships, are converted into linear relationships that incorporate these parameters. Linear fitting methods are used to linearize the process curves of facilities such as gates and orifices. This method significantly improves computational speed and efficiency without significantly compromising simulation accuracy, meeting the computational speed requirements of real-time control.

[0101] Reference Figure 2 and Figure 3 The offline simulation generalization model construction includes building an offline simulation generalization model based on the online mechanism model facility, including all types of online mechanism model facilities.

[0102] Reference Figure 4 , the model parameter correction includes:

[0103] ① Parameter calibration of online mechanism model;

[0104] ②Simplify the parameter calibration of the online mechanism model;

[0105] ③ Correction of setting parameters of each module in the offline simulation generalization model.

[0106] The establishment of the logical relationship between the facilities includes: logical rules between the established modules, including the relationship between the inlet and outlet water flow, the relationship between the maximum inlet / outlet capacity and the flow, etc.

[0107] The communication instruction relationship establishment includes:

[0108] ① Upload relationship of status parameters of each facility;

[0109] ②The relationship between the central controller’s instructions;

[0110] ③ Local controller command issuing module;

[0111] ④Wireless signal simulation;

[0112] ⑤External command access simulation.

[0113] The rainfall process establishment includes:

[0114] ① Calling external rainfall files;

[0115] ② Calling the typical rainfall type database;

[0116] ③Generate rainfall using the given formula.

[0117] The establishment of the system boundary conditions and control objectives includes:

[0118] ①Logical rules of the system;

[0119] ②Boundary conditions of the system;

[0120] ③The joint control objectives of the system mainly include overflow, waterlogging and water quality objectives of the plant network river system.

[0121] The optimization control method comprises:

[0122] ① Determine the control rules and parameters based on the optimization algorithm (genetic algorithm, population dynamics algorithm, linear solver method, etc.): select the controlled facility; select the monitoring location and the required monitoring information such as operating time, water quality, water level and flow; select the optimization algorithm and adjust the default parameters of the algorithm; based on the monitoring information and the system joint control objectives, the selected optimization algorithm will calculate the optimal control value of the controlled location in real time during the simulation process; use the PID control method to control the facility to smoothly reach the control value;

[0123] ② Determine the control rules and parameters based on rule-based control or fuzzy logic: select the controlled facility; select the monitoring location and the required monitoring information such as operating time, water quality, water level and flow; automatically generate the response relationship between monitoring information and control instructions; adjust the response relationship as the control basis; use the real-time monitoring value and control basis to obtain the facility control value during simulation control; use the PID control method to control the facility to smoothly reach the control value;

[0124] The above two optimization methods can both realize the control of weirs, gates, orifices, valves, diversion (interception) wells, pumping stations and other facilities based on the operating time, depth, head, inflow, outflow, water accumulation, water quality, and working status (opening, working flow, etc.) of each location in the system; both optimization control methods can be combined with PID control to achieve accurate and stable control.

[0125] Reference Figure 5 The coupling of the online mechanism model, the simplified online mechanism model and the offline simulation generalization model includes:

[0126] ①Use the online mechanism model to simulate the real-time operation status of the plant network river system, and output the water volume, water level and water quality information of each important node to the simplified online mechanism model in the form of time series;

[0127] ② The simplified online mechanism model simulates the system operation in the future time period based on the input status data, and outputs the water volume, water level and water quality information of each important node in the form of time series to the offline simulation generalization model;

[0128] ③ The offline simulation generalization model optimizes and controls the operation of the plant-grid-river system within the simulation time period based on the selected control objectives and optimization methods and the input data, and calculates the control strategy under the optimal operating state of the system;

[0129] ④ The calculated control strategy is transmitted back to the online mechanism model, and simulation is performed under the optimal control strategy to obtain real-time optimized control effect and complete simulation control under one time step.

[0130] The joint scheduling real-time simulation includes:

[0131] ① Real-time simulation of joint scheduling under custom time steps;

[0132] ② Real-time simulation of joint scheduling under the selected control objectives and methods.

[0133] The data analysis presentation includes:

[0134] ①Data recording and calling of each node;

[0135] ②Calculation of universality index;

[0136] ③Custom formula calculation;

[0137] ④Generation of common data charts.

[0138] Reference Figure 6 The present invention proposes a system for a comprehensive real-time simulation control method for joint scheduling of urban drainage systems. The system includes an online mechanism model module, an online mechanism model simplification module, a facility editing and management module, a model parameter correction module, a target optimization control module, and a simulation and result display module.

[0139] The system implementation process includes:

[0140] S10: Construct drainage system online mechanism module;

[0141] S20: Online mechanism model simplification module;

[0142] S30: constructing an offline simulation generalization module;

[0143] S40: Correct model parameters;

[0144] S50: Establish logical relationships between facilities;

[0145] S60: establishing a communication instruction relationship;

[0146] S70: Establishing rainfall process;

[0147] S80: Establish system boundary conditions and control objectives;

[0148] S90: Selecting an optimization control method;

[0149] S100: coupling of online mechanism model, simplified online mechanism model and offline simulation model;

[0150] S110: Performing joint scheduling real-time simulation;

[0151] S120: Data analysis presentation.

[0152] 1. S10: Build the drainage system online mechanism module, which simulates the real-time operation of the drainage system and provides boundary conditions and calibration references for simulation control. In this example, the online mechanism model module is used to fully import the existing SWMM model file.

[0153] 2. S20: Online Mechanism Model Simplification Module, used to simulate the operation of the drainage system in future time periods. In this example, the mechanism model simplification module is used to simplify the mechanism model and obtain a linearized model file. This process includes: merging, deleting, or simplifying facilities within the online mechanism model module; modifying the model parameters of the catchment area, pipeline, and river channel to generate a model that meets the linearization requirements; and modifying the model parameters of facilities such as gates and orifices to generate a model that meets the linearization requirements of the process curve.

[0154] 3. S30: Construct an offline simulation generalization module, which is used to formulate an optimization control strategy. In this example, the facility editing and management modules are used to construct an offline simulation generalization model. The offline simulation generalization model includes a pipeline module, a LID module, a well module, an orifice gate valve module, a water storage module, a pump module, a sewage treatment plant module, a river channel module, and a communication control module. Among them, the pipeline module simulates the rainwater pipes, sewage pipes, combined pipes, other connecting pipes, and pipes with certain storage capacity in the pipe network system; the LID module simulates the LID facilities in the system, including common LID facility types such as rainwater gardens, permeable grass ditches, ecological dry creeks, and sunken green spaces; the well module simulates common facilities such as diversion wells, interception wells, and inspection wells; the orifice gate valve module includes common orifices, various gates, various valves, etc.; the water storage module simulates storage facilities such as regulating reservoirs and regulating pipelines; the pump module simulates the water level of various water pumps and pump station forebays; the sewage treatment plant module simulates the treatment capacity, hierarchical structure, and treatment effect of the sewage treatment plant; the river module simulates the river water level, water volume, and water quality; the communication control module sets the rules for transmitting data and instructions between the various facilities in the system.

[0155] 4. S40: Calibrate model parameters. In this example, the model parameter calibration module is used to adjust model parameters. Field monitoring data is used to calibrate the parameters of the online and simplified online mechanism models, as well as the parameters of the offline simulation generalization model. This parameter adjustment ensures that the model accurately reflects actual operating conditions.

[0156] 5. S50: Establishing logical relationships between facilities. In this example, the facility editing and management module is used to establish logical relationships between facilities.

[0157] 6. S60: Establishing a communication command relationship. In this example, the facility editing and management modules are used to establish a communication command relationship. The central controller and local controllers are set up. The operating data recorded by the monitoring equipment is uploaded to the central controller and local controllers. The central controller and local controllers then issue operating commands to each controlled facility.

[0158] 7. S70: Establishing a rainfall process. In this example, the facility editing and management module is used to establish a rainfall process, and the data is derived from actual local rainfall data.

[0159] 8. S80: Establish system boundary conditions and control objectives. In this example, the target optimization module is used to establish the system boundary conditions and control objectives. The system boundary condition is the maximum sewage treatment capacity of the system; the control objective is a multi-objective function, including reducing combined sewer overflow, reducing waterlogging, and fully utilizing the sewage treatment plant's treatment capacity. The multi-objective function is constructed by assigning weights to different control objectives and then summing them.

[0160] 9. S90: Selecting an optimization control method. In this example, the target optimization module is used to select the rule-based control method as the control method for the simulation run. Then, multiple optimization algorithms are switched to compare the effects of different control methods on system operation.

[0161] 10. S100: Simplify the coupling of the online mechanism model and the offline model. In this example, the model coupling function in the simulation and result display module is used to couple the simplified online mechanism model, the online mechanism model, and the offline simulation generalization model.

[0162] 11. S110: Perform real-time joint debugging and control simulation. In this example, the simulation and results display module is used: the simulation time step is set to 5 minutes; the online mechanism model's real-time simulation results of the system are passed as input data to the simplified online mechanism model; the simplified online mechanism model's simulation results of future rainfall are passed as input data to the generalized simulation model; the generalized simulation model optimizes the control method and continuously tests and calculates the optimal control strategy that meets the set control objectives; the optimal control strategy is passed to the online mechanism model, which then runs the optimal control strategy and simulates the control effect.

[0163] 12. S120: Data analysis and display. In this example, the result display function of the simulation and result display module is used to display the simulation results through data images.

[0164] The above-described embodiment merely represents one embodiment of the present application. While the description is relatively specific and detailed, it should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A comprehensive real-time simulation control method for joint scheduling of urban drainage systems, characterized by: The following steps are involved: Step 1: Analyze the online mechanism model and construct the online mechanism model of the drainage system; Step 2: Simplify the online mechanism model; Step 3: Construct an offline simulation generalization model; Step 4: Correct model parameters; Step 5: Establish logical relationships between facilities; Step 6: Establish communication command relationship; Step 7: Establish rainfall process; Step 8: Establish system boundary conditions and control objectives; Step 9: Determine the optimal control method; Step 10: Coupling the online mechanism model, simplified online mechanism model and offline simulation generalization model; Step 11: Conduct joint scheduling real-time simulation; Step 12: Data analysis and presentation; The step 1 comprises the following steps: ① Calling and running the online mechanism model file (.inp); ②Collation and analysis of simulation process data and result data; ③ The water volume, water level and water quality information of each important node are exported in a time series format and used as boundary conditions and correction references for subsequent simulation control steps; The method for simplifying the online mechanism model in step 2 is: first simplify the model facilities, and then linearize the model, that is, using the Muskingum model based on mass conservation, using the runoff coefficient R, the number of sub-segments N, the transmission time K and the weight coefficient X to define the catchment area, pipeline and river model, converting the complex nonlinear physical relationship in the mechanism model into a linear relationship containing the above parameters, and finally using the linear fitting method to linearize the process curve of the facility; The method of constructing the offline simulation generalization model in step 3 is: based on the online mechanism model facilities, constructing the offline simulation generalization model, including all types of facilities of the online mechanism model; The step 4 of correcting the model parameters includes: ① Parameter calibration of online mechanism model; ②Simplify the parameter calibration of the online mechanism model; ③ Correction of setting parameters of each module in the offline simulation generalization model; The method of establishing the logical relationship between facilities in step 5 is as follows: the logical rules between the modules are established, including the relationship between the inlet and outlet water flow, and the relationship between the maximum inlet / outlet capacity and the flow; The step 6 of establishing the communication command relationship specifically includes: the status parameter uploading relationship of each facility, the command issuing relationship of the central controller, the command issuing relationship of the local controller, the wireless signal simulation and the external command access simulation; The step 7 of establishing the rainfall process specifically includes: calling an external rainfall file, calling a typical rainfall type database, and generating rainfall using a given formula; The step 8 of establishing the system boundary conditions and control objectives specifically includes: the system logic rules, the system boundary conditions and the system joint control objectives, namely, overflow, waterlogging and water quality objectives of the plant network river system; The optimization control method determined in step 9 specifically includes: ① Determine the control rules and parameters based on the optimization algorithm: select the controlled facilities, select the monitoring locations and the required monitoring operation time, water quality, water level and flow information, select the optimization algorithm and adjust the default parameters of the algorithm. Based on the monitoring information and the system joint control objectives, the selected optimization algorithm will calculate the optimal control value of the controlled location in real time during the simulation process; use the PID control method to control the facilities to smoothly reach the control value; ② Determination of control rules and parameters based on rule control or fuzzy logic: Select the controlled facility, the monitoring location, and the required monitoring operating time, water quality, water level, and flow information; automatically generate the response relationship between the monitoring information and the control instruction; adjust the response relationship as the control basis; use the real-time monitoring value and the control basis to obtain the facility control value during simulation control; and use the PID control method to control the facility to smoothly reach the control value; Both optimization methods can realize the control of weirs, gates, orifices, valves, diversion (interception) wells, pumping stations and other facilities based on the operating time, depth, head, inflow, outflow, water accumulation, water quality and working status information of each location in the system; both optimization control methods can be combined with PID control to achieve accurate and stable control. The coupling of the online mechanism model, the simplified online mechanism model and the offline simulation generalization model in step 10 specifically includes: ①Use the online mechanism model to simulate the real-time operation status of the plant network river system, and output the water volume, water level and water quality information of each important node to the simplified online mechanism model in the form of time series; ② The simplified online mechanism model simulates the system operation in the future time period based on the input status data, and outputs the water volume, water level and water quality information of each important node in the form of time series to the offline simulation generalization model; ③ The offline simulation generalization model optimizes and controls the operation of the plant-grid-river system within the simulation time period based on the selected control objectives and optimization methods and the input data, and calculates the control strategy under the optimal operating state of the system; ④ The calculated control strategy is transmitted back to the online mechanism model, and simulation is performed under the optimal control strategy to obtain real-time optimized control effect and complete simulation control under one time step; The joint scheduling real-time simulation simulation in step 11 specifically includes: joint scheduling real-time simulation under a custom time step and joint scheduling real-time simulation under a selected control target and method; The data analysis and display in step 12 specifically includes: ①Data recording and calling of each node; ②Calculation of universality index; ③Custom formula calculation; ④Generation of common data charts.

2. A system for use in the comprehensive urban drainage system joint scheduling real-time simulation control method according to claim 1, characterized in that: It includes online mechanism model module, online mechanism model simplification module, facility editing and management module, model parameter correction module, target optimization control module and simulation and result display module.

3. The system used in the comprehensive urban drainage system joint scheduling real-time simulation control method according to claim 2 is characterized by: ① The online mechanism model module includes: a calling module for the system call of the online mechanism model file (.inp), an operation module for the system operation of the online mechanism model file (.inp), a data processing module for collating and analyzing the simulation process data and result data, and a data export module for exporting the water volume, water level and water quality information of each important node in a time series manner; ②The functions of the online mechanism model simplification module include: A. Based on the online mechanism model simplification method, merge, delete or simplify the facilities in the online mechanism model; B. Modify the catchment area, pipeline and river model parameters to generate a model that meets the linearization requirements; C. Modify the model parameters of gate and orifice facilities to generate a model that meets the linearization requirements of the process curve; ③The functions of the facility editing and management module include: A. Construction of offline simulation generalization model, including adding and editing attributes of pipelines, LID facilities, wells, orifice gate valves, water storage facilities, pumps, sewage treatment plants, and river channels; B. Establishment of logical relationships between facilities, including logical rules between modules, relationship between inlet and outlet water flow, and relationship between maximum inlet / outlet capacity and flow; C. Establishment of communication command relationships, including the relationship between status parameter uploads of each facility, the relationship between command issuance by the central controller and local controllers, wireless signal simulation, and external command access simulation; D. Establishment of rainfall process, including calling external rainfall files, calling typical rainfall type database, and generating rainfall based on given formula. ④ The functions of the model parameter correction module include: parameter calibration of the online mechanism model, parameter calibration of the simplified online mechanism model and parameter correction of each module in the offline simulation generalization model; ⑤The functions of the target optimization control module include: A. Establishment of the system's logical rules and boundary conditions; B. Establishment of joint control objectives of the system; C. Use control rules and parameter determination based on optimization algorithms: select the controlled facility; select the monitoring location and the required monitoring information such as the operating time, water quality, water level and flow; select the optimization algorithm and adjust the default parameters of the algorithm; based on the monitoring information and combined with the system joint control objectives, the selected optimization algorithm will calculate the optimal control value of the controlled location in real time during the simulation process; use the PID control method to control the facility to smoothly reach the control value; or use control rules and parameter determination based on rule control method or fuzzy logic method: select the controlled facility; select the monitoring location and the required monitoring information such as the operating time, water quality, water level and flow; automatically generate the response relationship between the monitoring information and the control instruction; adjust the response relationship as the control basis; use the real-time monitoring value and the control basis to obtain the facility control value during simulation control; use the PID control method to control the facility to smoothly reach the control value; ⑥The functions of the simulation and result display module include: A. Online mechanism model, simplified online mechanism model and offline model coupling; B. Real-time simulation of joint scheduling under custom time steps; C. Real-time simulation of joint scheduling under the selected control objectives and methods; D. Data recording and calling of each node; E. Calculation of universality index; F. Custom formula calculation; G.Generate common data charts.

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