CN113947034A - Smart city wading link overall process coupling simulation method - Google Patents
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Abstract
The invention relates to the technical field of smart city construction, and particularly discloses a coupling simulation method for the whole wading link process of a smart city, wherein the method comprises the steps of acquiring meteorological data in real time; acquiring regional hydrology and non-point source data according to the meteorological data; the regional hydrology and non-point source data comprise non-built-up region hydrology and non-point source data and built-up region hydrology and non-point source data; obtaining sewage treatment data according to the pipe network drainage data in the hydrology and non-point source data of the built-up area; acquiring water quality and water ecological data of a water body according to non-pipe network drainage data and meteorological data in the hydrology and non-area source data of the non-built area and the hydrology and non-area source data of the built area; and generating pipe network water supply data according to the water quality and water ecology data. The invention can be used for water supply management, flood control and drainage, city combined dispatching, sponge city LID scheme optimization, pollution source tracing, management and control and the like, and improves the fine management level of urban water resources, water environment and water ecology.
Description
Technical Field
The invention relates to the technical field of smart city construction, in particular to a whole-process coupling simulation method for a wading link of a smart city.
Background
Water is the source of life, the essence of production, the basis of ecology, and is indispensable and not available for replacement. The development of cities is closely related to water and cannot be divided, and currently, water resource shortage, water environment pollution, water ecological damage and frequent occurrence of flood and drought disasters become important restriction factors of the development of the cities. How to deal with global climate change and improve the urban water supply guarantee rate; how to evaluate the occurrence range and degree of urban flood disasters, the overflow degree of a pipe network and the influence of the overflow degree on a water environment and a water ecosystem under the future rainfall condition; how to quickly trace the source of sudden pollution accidents and make effective prevention and control measures at the same time; how to identify the area source pollution key source area, evaluate the effectiveness of different sponge city LID measures and the like become important aspects of city wading management. Model simulation is the most important means for solving such problems, and the whole wading link coupling simulation system has become an important kernel of the smart city.
A wading link whole-process coupling simulation system relates to various simulation technologies of meteorology, hydrology, pipe networks, water environment, ecology and the like. At present, WRF is often used for forecasting future weather conditions and outputting indexes such as air pressure, air temperature, rainfall, relative humidity, cloud cover and the like; models such as SWAT, HSPF, SWMM and the like are adopted to forecast hydrological processes such as drainage basin rainfall runoff and the like, pollution load production, and migration and transformation processes of water and pollutants in a river channel and a drainage pipe network; simulating the water supply process by using EPANET; physical processes such as diffusion and migration in a water body, biochemical reaction processes and growth and death change processes of aquatic organisms such as algae are simulated and calculated by adopting water environments such as WASP, EFDC, Delft3D, MIKE3D and HEC-RAS and an aquatic attitude simulation model.
The application of the current model is single, most models are one model simulation or two model coupling simulation, some wading problems can be solved to a certain extent, but aiming at urban areas, the model is used as a complex, open and multidimensional synthesis, and the coupling of the single model or the models is not enough to support urban fine wading link management work.
Disclosure of Invention
The invention aims to provide a whole-process coupling simulation method for a wading link of a smart city, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a coupling simulation method for the whole process of a wading link of a smart city comprises the following steps:
acquiring meteorological data in real time;
acquiring regional hydrology and non-point source data according to the meteorological data; the regional hydrology and non-point source data comprise non-built-up region hydrology and non-point source data and built-up region hydrology and non-point source data;
obtaining sewage treatment data according to the pipe network drainage data in the hydrology and non-point source data of the built-up area;
acquiring water quality and water ecological data of a water body according to non-pipe network drainage data and meteorological data in the hydrology and non-area source data of the non-built area and the hydrology and non-area source data of the built area;
and generating pipe network water supply data according to the water quality and water ecology data.
As a further limitation of the technical scheme of the invention: the step of acquiring meteorological data in real time comprises the following steps:
establishing a power equation set, wherein the power equation set comprises a large aerodynamic equation set, a motion equation set and a continuous equation, and when coordinate systems are different, the equation sets are different;
discretizing the system of power equations;
and acquiring boundary conditions, and determining meteorological data according to the boundary conditions.
As a further limitation of the technical scheme of the invention: the step of obtaining the boundary condition and determining the meteorological data according to the boundary condition comprises the following steps:
creating a mode area, and receiving related parameters set by a user based on the mode area, wherein the related parameters comprise a projection mode parameter, an area range size parameter, an area position parameter and a nesting relation parameter;
interpolating the related parameters into discrete calculation grid points based on an inverse distance weighted interpolation method, and determining an initial field and boundary conditions to obtain a prediction grid;
and interpolating the forecast grids into normal grids, and calculating and diagnosing to obtain meteorological data.
As a further limitation of the technical scheme of the invention: the method for acquiring the hydrological and non-established area source data comprises the following steps:
collecting the underlying surface of the research area and human activity information to generate original data;
performing watershed segmentation and aggregation on the research area according to the original data to generate processing data;
simulating a hydrologic balance result according to the processing data to generate theoretical data;
and acquiring actual measurement data, and correcting the theoretical data according to the actual measurement data.
As a further limitation of the technical scheme of the invention: the step of acquiring the hydrological and non-point source data of the built-up area comprises the following steps:
processing various hydrological processes generated by urban regional runoff to obtain runoff production data;
simulating the flow of runoff and external water flow in pipelines, channels, water storage and treatment units and water diversion buildings in drainage pipelines to obtain confluence data;
and simulating the water pollution load amount generated along the production and confluence process according to the production flow data and the confluence data.
As a further limitation of the technical scheme of the invention: the step of obtaining sewage treatment data according to the pipe network drainage data in the hydrology and non-point source data of the built-up area comprises the following steps:
constructing and simulating a sewage treatment process based on an activated sludge mathematical model (ASM);
the control process is built and simulated based on a timer.
As a further limitation of the technical scheme of the invention: the sewage treatment process comprises an activated sludge process, a biological filter, a trickling filter, an MBR, anaerobic fermentation and precipitation; the activated sludge process comprises AO, AAO, oxidation ditch, SBR and deformation process thereof.
As a further limitation of the technical scheme of the invention: the step of acquiring water quality and water ecological data of the water body according to the non-pipe network drainage data and meteorological data in the hydrology and non-source data of the non-built area and the hydrology and non-source data of the built area comprises the following steps:
collecting regional data;
outputting a water quality and water ecology forecasting result according to the regional data;
and acquiring actual measurement data, and correcting the water quality and water ecology forecasting results according to the actual measurement data.
As a further limitation of the technical scheme of the invention: the step of generating the pipe network water supply data according to the water quality and water ecology data of the water body further comprises a hydraulic simulation step and a water quality simulation step, wherein the hydraulic simulation step at least comprises the following steps:
calculating the frictional head loss based on Hazen-Williams, Darcy-Weisbach or Chezy-Manning formula;
simulating a constant-speed and variable-speed water pump, and analyzing the lifting energy and cost of the water pump;
the water quality simulation step comprises:
simulating the motion change of the reaction substance;
simulating the water age of the whole pipe network;
tracking the percentage of water flow from a known node;
simulating a reaction in the mainstream water body based on n-level reaction kinetics;
the reaction at the tube wall is simulated based on zero order or first order reaction kinetics.
As a further limitation of the technical scheme of the invention: the water quality simulation step further comprises:
setting a mass transfer limit value to simulate the reaction at the pipe wall to generate an increase or attenuation reaction which continuously reaches the limit concentration;
and (4) opening a time-varying concentration or quality input port at any position in the pipe network.
Compared with the prior art, the invention has the beneficial effects that: the intelligent water affair and intelligent environment-friendly central service module in the intelligent city system is formed by coupling and simulating the atmospheric rainfall, surface runoff, confluence, pipe network water supply and drainage, discharge after treatment of a sewage treatment plant, regulation and control of river and lake water body hydrodynamics and water environment and water ecology system changes related to the river and lake hydrodynamics by water conservancy facilities such as gates, dams, pump stations and the like, can serve the work of water supply management, flood control and drainage, factory-net-river-pool-gate-dam combined dispatching, sponge city LID scheme optimization, pollution traceability, management and control and the like, and improves the fine management level of urban water resources, water environment and water ecology.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 shows a WRF model framework and workflow.
FIG. 2 is a schematic structural diagram of what is in the whole process coupling simulation method of the intelligent city wading link.
FIG. 3 is a schematic structural diagram of what is in the whole process coupling simulation method of the intelligent city wading link.
FIG. 4 is an architecture diagram of a coupling simulation method for the whole process of a wading link of a smart city.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment of the invention provides a coupling simulation method for the whole process of a wading link of a smart city, which comprises the following steps:
step S100: acquiring meteorological data in real time;
step S200: acquiring regional hydrology and non-point source data according to the meteorological data; the regional hydrology and non-point source data comprise non-built-up region hydrology and non-point source data and built-up region hydrology and non-point source data;
step S300: obtaining sewage treatment data according to the pipe network drainage data in the hydrology and non-point source data of the built-up area;
step S400: acquiring water quality and water ecological data of a water body according to non-pipe network drainage data and meteorological data in the hydrology and non-area source data of the non-built area and the hydrology and non-area source data of the built area;
step S500: and generating pipe network water supply data according to the water quality and water ecology data.
The invention integrates simulation modules of weather, hydrology, non-point source, water supply and drainage pipe network, sewage treatment, water quality and water ecology, and the like, and data among the modules are automatically matched and effectively transmitted, so that the whole process coupling simulation of atmospheric rainfall, surface runoff, confluence, pipe network water delivery and drainage, sewage treatment plant treatment and discharge, water conservancy facilities such as gates, dams, pump stations and the like for regulating and controlling the water power of rivers and lakes and the water environment and water ecology system change related to the water conservancy facilities is realized.
For acquiring meteorological data, the meteorological module is developed by adopting the current international advanced mesoscale meteorological model WRF (weather Research and Forecast model). The WRF is a new generation of mesoscale meteorological numerical model jointly developed by a plurality of scientific research institutions such as the national atmospheric research center (NCAR), the National Oceanic Atmospheric Administration (NOAA), the national environmental protection center (NCEP) and the like, and a WRF model system has good simulation and forecast performance in forecasting various weather situations and is widely applied to regional and refined weather forecasts all over the world. Initial field and boundary conditions were measured using GFS data provided by the national oceanic and atmospheric administration NOAA and FNL data was used for simulations.
The advantages of the WRF model over other models are mainly expressed in:
1) the WRF-ARW is used as a public mode, is responsible for maintenance and technical support by the NCAR and is freely and externally released;
2) the WRF mode system is a tool for improving the precision of forecasting important weather features of different scales from a cloud scale to a weather scale and the like, and a horizontal grid of 1-10km is mainly considered;
3) the mode combines an advanced numerical method and a data assimilation technology, adopts an improved physical process scheme, and simultaneously has the capabilities of multiple nesting and easy positioning at different geographic positions;
4) the WRF mode system has the characteristics of portability, easiness in maintenance, expandability, high efficiency, convenience and the like, and is more convenient for applying a new scientific research result to a service forecasting mode;
5) the method can well meet the requirements of applications from idealized research to business forecasting and the like, and has the flexibility of facilitating further strengthening and perfecting.
Referring to fig. 2, for non-built area hydrologic and non-point source data, the non-built area hydrologic and non-point source data are generally obtained by a non-built area hydrologic and non-point source calculation module, which is based on United States Environmental Protection Agency (USEPA) support development and widely applied HSPF (distributed networking Program-FORTRAN) development, and has the following specific advantages:
1) the HSPF model has a clear and complete structure, the hydrological module and the soil erosion and sediment migration module adopt a mechanistic sub-model, the accuracy in the simulation of runoff and sediment is higher than that of other models, and the simulation of runoff and sediment is the basis of the simulation accuracy of non-point source pollution load;
2) the water quality module of the HSPF model covers the balance of various complex pollutants such as nitrogen and phosphorus nutritive salts, BOD, DO, pesticides and the like, and particularly considers the migration and transformation process of nitrogen and phosphorus in soil and water;
3) the HSPF model is integrated into BASINS at present, has good user application interface and spatial data management, analysis and expression capacity, and can be suitable for simulation research of non-point source pollution load in tidal river basin.
The hydrologic and non-point source data of the built-up area are generally obtained by a built-up area hydrologic and non-point source calculation module, the built-up area hydrologic and non-point source calculation module is developed based on SWMM (storm water management model), and the SWMM is a dynamic rainfall-runoff simulation model which is supported and developed by EPA and is mainly used for simulating a single rainfall event or long-term water quantity and water quality simulation of a city. The runoff module part comprehensively treats precipitation, runoff and pollution loads generated by each sub-basin. The confluence module part carries out water quantity transmission through a pipe network, a channel, a water storage and treatment facility, a water pump, an adjusting gate and the like. The model can track and simulate the water quality and the water quantity of runoff generated by each sub-basin at any time with different time step lengths, and the conditions of the flow, the water depth, the water quality and the like of water in each pipeline and each river channel. SWMM has undergone multiple upgrades since its development in 1971. The method is widely applied to planning, analyzing and designing storm flood, combined sewer, blow-down pipeline and other drainage systems in urban areas worldwide, and also widely applied to other non-urban areas. The concrete advantages are that:
1) SWMM is a source opening software, and becomes a drainage calculation engine preferred by a plurality of professional software companies in the industry by the openness, flexibility and stability of the SWMM;
2) since the development in 1971, the functions are improved and improved continuously, and meanwhile, the application and the verification are also widely carried out.
After the data acquisition is completed, the most main step is to generate water supply data according to the data, which is the final purpose of the invention, wherein two modules are involved, namely a water body quality module and a water attitude module, and a pipe network water supply module.
Referring to fig. 3, the water quality and water status module is developed by using (Environmental fluidic Codes) EFDC. The concrete advantages are that:
the EFDC model was originally developed by the American Virginia oceanographic institute and is now supported by the United states environmental protection agency (US EPA). The EFDC model has been widely validated over 100 model studies and is currently used by many universities, research institutions, government departments, and commercial companies.
EFDC is free open source software, and secondary development and packaging are very convenient;
the EFDC has strong function, and can be used for three-dimensional flow, transmission and biological geochemical process simulation of surface water bodies such as rivers, lakes, estuaries, reservoirs, wetlands, coastal areas and the like. It is under single source code frame, has coupled sub-models such as hydrodynamic force, quality of water and eutrophication, silt transport, toxic chemical substance transport and conversion, and the EFDC model includes 4 submodule: hydrodynamic force module, water quality module, silt transport module, noxious material module. The 4 sub-modules form a unique model set, and complex interfaces required by a composite model to describe different processes are avoided.
4. The model can be used for carrying out simulation calculation on water conservancy facilities such as a sluice, an overflow weir, a pump station and the like at present.
The pipe network water supply module is developed based on EPANET, which is an open source software package for performing simulation analysis on hydraulic power and water quality of a pressure pipe network and developed by a National Risk Management Research Laboratory (National Risk Management Research Laboratory) set under the United States Environmental Protection Agency (USEPA). The concrete advantages are that:
1) EPANET is used as an open-source software package, and becomes a preferred hydraulic computing engine for a plurality of professional software companies in the industry due to the openness, flexibility and stability of the EPANET;
2) the EPANET program's Toolkit is also released along with EPANET software, and encapsulates a plurality of functions in a hydraulic computing engine in a Dynamic Linking Library (DLL) mode, so that developers can perform secondary development according to different requirements by using an integrated development environment of a third party.
It is worth mentioning that in the water link simulation process, a very important point lies in sewage, and for this, the invention carries out treatment through a sewage treatment module; the sewage treatment module adopts a WEST model cooperatively developed by HEMMIS and DHI company of Denmark, the WEST is a flexible and powerful sewage treatment system simulation tool, can simulate almost all sewage treatment processes, such as various traditional activated sludge methods, A2O processes, oxidation ditch processes, SBR processes and deformation thereof, and can also simulate the process treatment processes of a biological filter, anaerobic fermentation, filtration, precipitation and the like. WEST is a powerful tool for eliminating operation faults, analyzing working conditions and controlling cost of a sewage treatment plant, and is the most effective decision support tool for operation managers, engineering consultations and research and development personnel. The concrete advantages are that:
1) by utilizing a built-in model library and a built-in process node library of WEST, an operator can flexibly construct a large-scale complex sewage treatment plant scheme;
2) dynamically simulating the sewage treatment process in real time, dynamically displaying the simulation result, and adjusting the operation parameters at any time in the simulation process;
3) calculating absolute and relative sensitivity of a certain variable to the change of a certain parameter, automatically determining important model parameters, and finding out the parameter with the largest influence on the processing effect;
4) the optimal or the most unfavorable working condition is quickly found by calculating the working condition combination of a plurality of values of a plurality of parameters;
5) fitting parameter values according to experimental data, and the method is a powerful tool for carrying out model correction;
6) the system can be connected with other application programs such as Excel, SCADA, GIS and AutoCAD to realize functions such as automatic control and design support;
7) the user can edit and add the model according to the needs.
As shown in fig. 4, a brief summary of the data transmission process is as follows:
the forecasting result of the meteorological module is used as the input of a hydrological and non-point source module of the built-up area and the non-built-up area on one hand and is used as the input of a water quality and water ecology module on the other hand; outputting results of the hydrological and non-point source module of the non-built area as water quality and water attitude module input; one part of the output of the hydrological and non-point source module (pipe network drainage part) of the built-up area is used as the input of a sewage treatment module, and the other part of the output of the hydrological and non-point source module is used as the input of a water quality and water attitude module of the water body; the output results of the water quality and water ecology module are used as the input of the pipe network water supply module.
The constitution of each module is described in detail below.
1) The meteorological module includes: the system comprises a power frame submodule, an equation discretization submodule and a weather forecast initialization submodule.
A1: the power frame submodule is used for establishing a power equation set, the power equation set comprises an atmospheric power equation set, a motion equation set and a continuous equation, and when coordinate systems are different, the equation sets are different;
a2: the equation discretization submodule is used for discretizing a power equation set in the power frame submodel, the equation set is a nonlinear partial differential equation set and needs to be discretized, and discretization methods are generally difference methods and spectrum methods and must follow the principles of compatibility, accuracy, convergence and stability;
a3: the weather forecast initialization submodule is used for determining a model parameterization scheme through boundary conditions, namely under the condition that the boundary conditions are given, the boundary conditions comprise a vertical boundary condition and a horizontal boundary condition, and parameterization schemes such as a time integration scheme and a nesting scheme are determined, the time integration scheme comprises a time separation scheme and a semi-Lagrange scheme, and the nesting scheme comprises a multiple, same and different and single and double modes.
The weather forecast initialization process in the weather forecast initialization submodule can be specifically divided into the following steps:
s11, defining and creating a mode area, namely setting relevant parameters of each simulation area by a user, wherein the relevant parameters comprise a projection mode parameter, an area range size parameter, an area position parameter, a nesting relation parameter and the like;
s12, performing data interpolation, applying an inverse distance weighting interpolation method, interpolating data into discrete calculation grid points, including interpolation in the horizontal direction and the vertical direction, and determining an initial field and boundary conditions;
and S13, parameterizing, interpolating the forecast grid into a normal grid, and calculating the diagnosis output quantity.
2) The non-built-up area hydrology and non-point source module includes: data collection, research area generalization, calculation construction, calibration and verification sub-modules;
b1: the data collection submodule is used for collecting the underlying surface of the research area and human activity information, including DEM, land utilization rate, soil parameters, agricultural management parameters, hydrological parameters and the like;
b2: the research area generalization submodule is used for carrying out basin segmentation and aggregation on a research area according to data collected by the data collection submodel, the generalization method is to carry out basin segmentation and aggregation on the research area, the basin response condition is predicted according to input data and parameters, the whole research area basin is divided into spatially dispersed units, the units can be divided into two types, namely land units and water body units, wherein the land units have the same topographic characteristics, land covering characteristics and soil characteristics, and the water body units are river reach with the same hydraulic characteristics, such as hydraulic gradient, water cross section, flow velocity and the like;
b3: and the construction calculation submodule is used for simulating a hydrologic balance result according to the output of the research area generalization submodel.
In other words, after the cells are generalized, the precipitation existence form, such as rainfall and snowmelt, is determined according to the weather station position and weather factor information in the simulation area; simulating hydrologic balance conditions through a generalization unit, wherein the hydrologic balance conditions comprise canopy interception, precipitation distribution, soil water distribution, evapotranspiration, lateral subsurface flow of soil water, shallow groundwater backflow and the like; estimating the growth of crops through a crop growth equation, and simulating the sowing, harvesting, farming, fertilizer and pesticide application conditions of a farming system; and calculating the transport, loss and the like of the sediment, nitrogen and phosphorus loads through a soil loss equation and a river calculation equation, applying a conceptual lumped model to each sub-basin to calculate during calculation, performing convergence calculation through a Masjing's method, and finally obtaining the data of the section flow and the pollutant mass.
B4: the calibration and verification sub-module is used for repeatedly calculating the constructed calculation sub-model according to the actually measured data, and parameters are adjusted in the calculation process, so that the simulated hydrologic balance result is consistent with the actually measured data.
In the calibration stage, the model is subjected to trial calculation repeatedly according to actually measured hydrological and water quality data, the calculation result of the model is enabled to be consistent with the actually measured result by adjusting the parameters of the model, then the use parameters of the model are determined, in the verification stage, the model is driven by adopting data in another time period, the actually measured value is compared with the simulated value, and how to verify and evaluate the simulation effect of the model.
3) The hydrology and non-point source module of the built-up area comprises a hydrology submodule, a hydraulic submodule and a water quality submodule.
C1: the hydrology submodule is mainly used for processing the runoff producing and converging processes of surface runoff of urban areas.
The runoff producing process comprises time-varying rainfall, surface water evaporation, accumulated snow and snow melting, depression storage and the like; the confluence process comprises the steps of water flow exchange between rainfall infiltration to an unsaturated soil layer, runoff water infiltration to an underground water layer, underground water and a drainage pipeline, slope confluence and various micro-image response processes for reducing or delaying precipitation and runoff.
C2: the hydraulic submodule is used for simulating the flow of runoff and foreign water in pipelines, channels, water storage and treatment units, water diversion buildings and the like in drainage pipelines.
Including water flow in closed and open channel pipes of various shapes, water flow in specific parts (such as storage and treatment units, flow divider valves, pumps, weirs, and drain ports, etc.), input of external water flow and water quality, confluence, and various forms of water flow (such as backwater, counter-flow, and overflow, etc.).
C3: the water proton module is used to simulate the water pollution load accompanying the production and confluence process.
The same drainage cell can be divided into different hydrological response units according to the functional area or the land cover type, and accumulation models and scouring models of various surface pollutants are defined according to the hydrological response units so as to simulate the growth, scouring, transportation and treatment processes of the pollutants in surface runoff.
4) The water supply module comprises a hydraulic simulation submodule and a water quality simulation submodule.
D1: the hydraulic simulation submodule can calculate the friction head loss by utilizing a Hazen-Williams, Darcy-Weisbach or Chezy-Manning formula; local head loss calculation at elbows, accessories and the like is included; the water pump can be simulated at a constant speed and a variable speed, and the lifting energy and the cost of the water pump can be analyzed; various types of valves can be simulated, including a masking valve, a check valve, a pressure regulating valve and a flow control valve; considering various water demand types of nodes, each node can have a time-varying mode; pressure dependent flow rates can be simulated, such as diffusers (shower head); the system operation can be based on simple pool level or timer control, as well as on complex control based on rules.
D2: the water quality simulation submodule can simulate the movement of the non-reactive tracer in the pipe network along with time; simulating the motion change of the reaction substance; simulating the water age of the whole pipe network; tracking the percentage of water flow from a known node; simulating the reaction in the mainstream water body by using n-level reaction kinetics; simulating the reaction at the tube wall by using zero-order or first-order reaction kinetics; mass transfer limits may be considered in simulating reactions at the tube wall; allowing a growth or decay response that continues to reach a threshold concentration; allowing time-varying concentration or mass input at any position in the pipe network; the reservoir is designed as a complete mixing, plug flow or dual chamber reactor.
D3: the key steps are as follows:
data gathering
The following data needs to be collected and collated:
(i) characteristic data of each entity in the water supply system (namely basic data of pipelines, water pumps, valves, pools and the like);
(ii) the water consumption data and the water consumption change mode of the nodes;
(iii) elevation data of the nodes;
(iv) control information of the operation of the water supply system.
(II) model building and solving
Establishing a hydraulic model of a complex system consisting of a pump station, a regulating and storing water body, a water diversion point, a water conveying pipeline and the like, and solving by adopting a high-precision numerical algorithm; according to research needs, water quality indexes such as nitrogen and phosphorus are selected, a raw water quality model is established, and finally, solution is carried out.
(III) model verification
And calibrating the model parameters according to the actually measured hydraulic power (including water level, water quantity and the like) and water quality data, and verifying the model by adopting the actually measured data at different time intervals.
5) The sewage treatment module comprises 26 major categories including a materialization unit sub-module, a biochemical unit sub-module, an auxiliary function sub-module and the like, wherein each major category comprises a plurality of minor categories.
E1: the biochemical unit module can construct and simulate almost all sewage treatment processes such as various activated sludge processes (AO, AAO, oxidation ditch, SBR and deformation processes thereof and the like), biofilters, trickling filters, MBRs, anaerobic fermentation, sedimentation and other processes based on an activated sludge mathematical model (ASM).
E2: the auxiliary function module is very abundant. The controller comprises multiple functions of switch control, P, PI, PID and the like, and can construct and simulate various control processes. The timer is used to control and set the time of the different processes. Cost calculations may estimate costs for aeration, return flow, sludge disposal, etc. The process calculator can custom calculate various process variables.
E3: the key steps are as follows:
analyzing the actual process flow, selecting a model mechanism (ASM1, ASM2, ASM2d or ASM3) according to requirements,
and then establishing the process configuration required by the user by utilizing various process component units in the model library.
(II) inputting the relation between the size of the sewage treatment unit structure and the flow of the system according to the design parameters of the sewage plant, and combining the size of the sewage treatment unit structure with the flow of the system
And converting the conventional carbon, nitrogen and phosphorus water quality parameters into model components, and determining and inputting the quality of the inlet water of the sewage plant.
(III) determining the stoichiometric coefficient and the kinetic parameter of the model, selecting a calculation method and a step length, performing steady-state model operation,
and outputs the graphic or numerical results of various processes as required.
And (IV) using the steady-state simulation operation result as an initial value of dynamic simulation, carrying out dynamic simulation analysis on the dynamic inlet water quality, and repeatedly correcting and verifying parameter values.
And (V) combining actual data to perform result analysis and parameter correction, determining the reliability of the model, and realizing reasonable simulation.
6) The water quality and water attitude module comprises: and a data collection, model construction, calibration and verification submodule.
F1: the data collection submodel is used for collecting regional data, and the data to be collected comprises: the data acquisition system comprises topographic data, meteorological data, hydrological data and water quality data, and if the data are missing, the work of field survey needs to be organized to acquire related data.
F2: and the water quality forecasting construction submodule is used for configuring a water quality model by adopting the collected data and outputting a calculation result. The construction method comprises the steps of regional generalization, establishment of solution conditions, model solution and the like. The regional generalization refers to dividing the water body into a plurality of units according to topographic data of the water body, and the process is also called subdivision of a model computing grid; the solution conditions comprise initial conditions and boundary conditions; the initial condition refers to the initial state of the water body and comprises initial water depth, flow field, concentration field and other data; boundary conditions refer to the course of hydrodynamic and water quality factors at the model boundary over time.
F3: the calibration and verification sub-module is used for repeatedly calculating the water quality model according to the actually measured data, adjusting parameters in the calculation process to enable the simulation result to be consistent with the actually measured data, namely calibrating the model parameters according to the actually measured hydraulic power (including water level, water amount and other data) and water quality data, and then verifying the model by adopting the actually measured data in different time periods.
The following describes a specific coupling manner between the modules:
the method adopts WRF, HSPF, SWMM, EPANET, WEST, EFDC and other models to construct meteorological, non-built area hydrological and non-source calculation, pipe network water supply, sewage treatment, water quality and water attitude modules, and carries out whole-process coupling simulation of the wading link of the smart city, the input and output files of the models are shown in table 1, and as can be seen, 6 modules do not have unified or standard data structures.
Table 1 input and output file data structure of model
Therefore, to develop these modules into a series-connected coupling simulation, one problem that must be solved is data exchange between the computing modules, i.e., a data interface problem. And writing a computer program by adopting a Python programming language to realize a data interface between each computing module.
The main data interface is realized as follows:
s1: writing a program, reading a NetCFD data file generated by WRF, and generating a text file and a WDM data system file in a specific format which can be identified by HSPF, and a specific format file which can be identified by SWMM and EFDC;
s2: writing a program, reading a WDM data system file unique to the HSPF, and generating a text file with a specific format which can be identified by the EFDC;
s3: writing a program, reading a binary data file with a specific format generated by SWMM, and generating a text file with a specific format which can be identified by WEST and EFDC;
s4: writing a program, reading a binary data file generated by WEST, and generating a text file with a specific format which can be identified by EFDC;
s5: and writing a program, reading the binary data file generated by the EFDC, and generating a text file which can be identified by the EPANET and has a specific format.
The specific modules operate according to the following steps:
s1: weather forecast data output by the weather forecast model, including rainfall, air temperature, wind speed, sunshine, transpiration, dew point, cloud layer and other parameters, generate a text file and a WDM data system file in a specific format recognizable by HSPF, and a specific format file recognizable by SWMM and EFDC, and store the files in a database.
S2: when hydrological and non-point source calculation is carried out in a non-built area, firstly, meteorological forecast data in a database is read, the meteorological forecast data and collected pollution source sample data are written into a model input basin data management, then, an HSPF model is driven to carry out calculation, runoff and pollution load forecast data are output, the data comprise pollution load data and FLOW data of each branch of a basin, such as parameters related to CHLA-chlorophyll, TN-total nitrogen, FLOW-FLOW, ORN-organic nitrogen, ORP-organic phosphorus, DO-dissolved oxygen, PO 4-inorganic phosphorus, TEMP-water temperature, NH 4-ammonia nitrogen, TP-total phosphorus, BOD-biological oxygen demand, NO 3-nitrate nitrogen, TAM-ammonia nitrogen and the like, and the data are stored in the database.
S3: when hydrological and surface source calculation of a built-up area is performed, firstly, meteorological forecast data in a database are read, a watershed is divided and aggregated, and a pipe network is arranged to perform production convergence calculation and hydraulic calculation in the pipe network. The runoff producing process comprises time-varying rainfall, surface water evaporation, accumulated snow and melting of the accumulated snow, depression storage and the like; the confluence process comprises the steps of water flow exchange between rainfall infiltration to an unsaturated soil layer, runoff water infiltration to an underground water layer, underground water and a drainage pipeline, slope confluence and various micro-image response processes for reducing or delaying precipitation and runoff. Hydraulic calculations in pipe networks include water flow in closed and open channel pipes of various shapes, water flow in specific sections (e.g., impoundment and treatment units, diverter valves, pumps, weirs, and drainage ports, etc.), external water flow and water quality inputs, confluence, and various forms of water flow (e.g., backwater, counter-flow, and overflow, etc.). On the basis of hydrological calculation and hydraulic calculation, water quality calculation is carried out, the same drainage cell can be divided into different hydrological response units according to the functional area or the land cover type, and accumulation models and scouring models of various surface pollutants are defined according to the hydrological response units so as to simulate the growth, scouring, transportation and treatment processes of the pollutants in surface runoff. And outputting and storing the flow of the basin outlet or the sewage treatment plant inlet and the concentration data of the pollutants into a database.
S4: when calculating the water supply of the pipe network, firstly reading the water quantity data in the database, and utilizing the following data:
(i) characteristic data of each entity in the water supply system (namely basic data of pipelines, water pumps, valves, pools and the like);
(ii) the water consumption data and the water consumption change mode of the nodes;
(iii) elevation data of the nodes;
(iv) control information of the operation of the water supply system;
and (II) establishing and solving a model.
Establishing a hydraulic model of a complex system consisting of a pump station, a regulating and storing water body, a water diversion point, a water conveying pipeline and the like, and solving by adopting a high-precision numerical algorithm; according to research needs, water quality indexes such as nitrogen and phosphorus are selected, a raw water quality model is established, and finally, solution is carried out.
S5: when in sewage treatment calculation, firstly, reading the water quantity and water quality data of a pipe network accessed to a sewage treatment plant in a database, analyzing the actual process flow, selecting a model mechanism (ASM1, ASM2, ASM2d or ASM3) according to the requirement, and then establishing a process structure required by a user by utilizing various process component units in the model library; inputting the relation between the size of a sewage treatment unit structure and the flow of a system according to the design parameters of the sewage plant, converting the water quality parameters of conventional carbon, nitrogen and phosphorus into model components, and determining and inputting the water quality of inlet water of the sewage plant; determining the stoichiometric coefficient and the kinetic parameter of the model, selecting a calculation method and a step length, performing steady-state model operation, and outputting graphs or numerical results of various processes according to requirements; using the steady state simulation operation result as an initial value of dynamic simulation, carrying out dynamic simulation analysis on the dynamic inlet water quality, and repeatedly correcting and verifying parameter values; and (4) combining actual data to carry out result analysis and parameter correction, determining the reliability of the model and realizing reasonable simulation. Finally, the water quantity and the water quality of the water outlet of the sewage treatment plant are output and stored in a database.
S6: when the water quality and water ecology of the water body are calculated, the meteorological data of a database, the water quantity and water quality data of the water body discharged from the drain outlets of the built-up area and the non-built-up area, the water quantity and water quality data of the water body discharged into the water body by a sewage treatment plant, the water quantity data of different water taking points and the like are respectively read and written into model input, then the EFDC model is driven to calculate, the water quality and water ecology change result of each grid of the water body is obtained, and the result is stored in the database.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A coupling simulation method for the whole process of a wading link of a smart city is characterized by comprising the following steps:
acquiring meteorological data in real time;
acquiring regional hydrology and non-point source data according to the meteorological data; the regional hydrology and non-point source data comprise non-built-up region hydrology and non-point source data and built-up region hydrology and non-point source data;
obtaining sewage treatment data according to the pipe network drainage data in the hydrology and non-point source data of the built-up area;
acquiring water quality and water ecological data of a water body according to non-pipe network drainage data and meteorological data in the hydrology and non-area source data of the non-built area and the hydrology and non-area source data of the built area;
and generating pipe network water supply data according to the water quality and water ecology data.
2. The smart city wading link overall-process coupling simulation method of claim 1, wherein the step of acquiring weather data in real time comprises:
establishing a power equation set, wherein the power equation set comprises a large aerodynamic equation set, a motion equation set and a continuous equation, and when coordinate systems are different, the equation sets are different;
discretizing the system of power equations;
and acquiring boundary conditions, and determining meteorological data according to the boundary conditions.
3. The method of claim 2, wherein the step of obtaining boundary conditions and determining weather data according to the boundary conditions comprises:
creating a mode area, and receiving related parameters set by a user based on the mode area, wherein the related parameters comprise a projection mode parameter, an area range size parameter, an area position parameter and a nesting relation parameter;
interpolating the related parameters into discrete calculation grid points based on an inverse distance weighted interpolation method, and determining an initial field and boundary conditions to obtain a prediction grid;
and interpolating the forecast grids into normal grids, and calculating and diagnosing to obtain meteorological data.
4. The smart city wading link overall-process coupling simulation method of claim 1, wherein the step of obtaining non-built-up area hydrology and non-point source data comprises:
collecting the underlying surface of the research area and human activity information to generate original data;
performing watershed segmentation and aggregation on the research area according to the original data to generate processing data;
simulating a hydrologic balance result according to the processing data to generate theoretical data;
and acquiring actual measurement data, and correcting the theoretical data according to the actual measurement data.
5. The smart city wading link overall-process coupling simulation method of claim 1, wherein the step of obtaining the hydrologic and non-point source data of the built-up area comprises:
processing various hydrological processes generated by urban regional runoff to obtain runoff production data;
simulating the flow of runoff and external water flow in pipelines, channels, water storage and treatment units and water diversion buildings in drainage pipelines to obtain confluence data;
and simulating the water pollution load amount generated along the production and confluence process according to the production flow data and the confluence data.
6. The smart city wading link overall-process coupling simulation method according to claim 1, wherein the step of obtaining sewage treatment data according to pipe network drainage data in the built-up area hydrology and non-point source data comprises:
constructing and simulating a sewage treatment process based on an activated sludge mathematical model (ASM);
the control process is built and simulated based on a timer.
7. The smart city wading link overall-process coupling simulation method of claim 6, wherein the sewage treatment process comprises an activated sludge process, a biological filter, a trickling filter, an MBR, anaerobic fermentation and sedimentation; the activated sludge process comprises AO, AAO, oxidation ditch, SBR and deformation process thereof.
8. The smart city wading link overall-process coupling simulation method according to claim 1, wherein the step of obtaining water quality and water ecological data of the water body according to non-pipe network drainage data and meteorological data in the non-built area hydrology and non-area source data and the built area hydrology and non-area source data comprises:
collecting regional data;
outputting a water quality and water ecology forecasting result according to the regional data;
and acquiring actual measurement data, and correcting the water quality and water ecology forecasting results according to the actual measurement data.
9. The smart city wading link overall-process coupling simulation method according to claim 1, wherein the step of generating pipe network water supply data according to the water quality and water ecological data further comprises a hydraulic simulation step and a water quality simulation step, wherein the hydraulic simulation step at least comprises:
calculating the frictional head loss based on Hazen-Williams, Darcy-Weisbach or Chezy-Manning formula;
simulating a constant-speed and variable-speed water pump, and analyzing the lifting energy and cost of the water pump;
the water quality simulation step comprises:
simulating the motion change of the reaction substance;
simulating the water age of the whole pipe network;
tracking the percentage of water flow from a known node;
simulating a reaction in the mainstream water body based on n-level reaction kinetics;
the reaction at the tube wall is simulated based on zero order or first order reaction kinetics.
10. The smart city wading link overall-process coupling simulation method according to claim 9, wherein the water quality simulation step further comprises:
setting a mass transfer limit value to simulate the reaction at the pipe wall to generate an increase or attenuation reaction which continuously reaches the limit concentration;
and (4) opening a time-varying concentration or quality input port at any position in the pipe network.
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| CN115809749B (en) * | 2023-02-09 | 2023-09-12 | 四川文韬工程技术有限公司 | Method for establishing comprehensive online prediction model of sewage treatment and prediction and early warning method |
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