CN109558973B - Water quality and water ecology integrated early warning system, control equipment and storage medium - Google Patents

Water quality and water ecology integrated early warning system, control equipment and storage medium Download PDF

Info

Publication number
CN109558973B
CN109558973B CN201811360834.8A CN201811360834A CN109558973B CN 109558973 B CN109558973 B CN 109558973B CN 201811360834 A CN201811360834 A CN 201811360834A CN 109558973 B CN109558973 B CN 109558973B
Authority
CN
China
Prior art keywords
water
model
water quality
forecasting
submodel
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811360834.8A
Other languages
Chinese (zh)
Other versions
CN109558973A (en
Inventor
尹魁浩
赵鑫
林青
李再华
彭盛华
孙滔滔
练海贤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Yuegang Water Supply Co ltd
Shenzhen Linkpoi Technology Co ltd
SHENZHEN ACADEMY OF ENVIRONMENTAL SCIENCES
Original Assignee
Guangdong Yuegang Water Supply Co ltd
Shenzhen Linkpoi Technology Co ltd
SHENZHEN ACADEMY OF ENVIRONMENTAL SCIENCES
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Yuegang Water Supply Co ltd, Shenzhen Linkpoi Technology Co ltd, SHENZHEN ACADEMY OF ENVIRONMENTAL SCIENCES filed Critical Guangdong Yuegang Water Supply Co ltd
Priority to CN201811360834.8A priority Critical patent/CN109558973B/en
Publication of CN109558973A publication Critical patent/CN109558973A/en
Application granted granted Critical
Publication of CN109558973B publication Critical patent/CN109558973B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Development Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Educational Administration (AREA)
  • Primary Health Care (AREA)
  • Physiology (AREA)
  • Genetics & Genomics (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a water quality and water ecology integrated early warning system, which comprises a meteorological forecasting model, a runoff and pollution load forecasting model, a water intake area water quality forecasting model, a water pipeline water quality forecasting model and a water receiving area water ecology change forecasting model, wherein a water quality and water ecology integrated early warning system is established through coupling, water quality variable parameters among different models are mutually converted, synchronous calibration of the parameters of the integrated forecasting system is carried out through a genetic algorithm, measured data is substituted in the forecasting process of each model, the simulation forecasting result is continuously corrected, the precision of the subsequent calculation result is further improved, a more accurate water quality and water ecology integrated early warning result is obtained, the water ecology change process of the water receiving area in the next several days can be forecasted through a rainfall driving system, and in order to timely prejudge the water environment and water ecology risks of the water receiving area, and aiming at various risks which may occur, an optimized scheduling scheme is formulated, and important technical support is provided for guaranteeing the water supply safety of drinking water.

Description

Water quality and water ecology integrated early warning system, control equipment and storage medium
Technical Field
The invention relates to the field of water quality early warning, in particular to a water quality and water ecology integrated early warning system, control equipment and a storage medium.
Background
At present, China enters a high-incidence period of environmental pollution events, most of which are related to water, and some water pollution events occur in drinking water source places, so that the safety of regional water supply and the health of surrounding residents are greatly influenced. In addition, in some lakes, large-scale reservoirs and estuaries, cyanobacterial bloom disasters are caused due to the large accumulation of nitrogen and phosphorus nutritive salts, and the serious water environment problem is also brought. Compared with the increasingly severe water environment risk situation of the drinking water source area, the early warning and forecasting capability of the water environment risk of the drinking water source area in China is still weak, and the requirement of drinking water source area management cannot be effectively supported.
The early warning and forecasting of the water environment risk of the drinking water source area relates to various forecasting and simulating technologies of meteorology, hydrology, water environment, water ecology and the like. The current common meteorological model is WRF, which is used for forecasting future weather conditions and outputting indexes such as air pressure, air temperature, rainfall, relative humidity, cloud cover and the like. Common hydrological models include SWAT, HSPF, SWMM and the like and are used for forecasting rainfall runoff and pollution load production of a drainage basin, and water environment and water attitude models include WASP, EFDC, Delft3D, MIKE3D, HEC-RAS and the like and are used for simulating and calculating 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 and the like.
Most of the water environment risk early warning work carried out at present mainly takes a water environment and water ecological model as tools, when an emergent pollution event occurs, the migration and diffusion processes of a pollution group, the influence degree, the range, the action time and other conditions on a sensitive object are forecasted, but an integrated coupling model of various influencing factors is established, such as weather forecast, runoff and pollution load forecast, water quality forecast of a water intake area, water quality forecast of a water conveying pipeline, water ecological change forecast of a water receiving area and other factors, so that a calculation coupling integrated early warning forecasting technology is necessary.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a water quality and water ecology integrated early warning system, a control device and a storage medium of a coupling model.
The technical scheme adopted by the invention is as follows:
in a first aspect, the invention provides a water quality and water ecology integrated early warning system, which comprises a weather forecasting model, a runoff and pollution load forecasting model, a water intake area water quality forecasting model, a water pipeline water quality forecasting model and a water receiving area water ecology change forecasting model;
the output of the meteorological forecasting model is used as the input of a runoff and pollution load forecasting model, the output of the runoff and pollution load forecasting model is used as the input of a water quality forecasting model of a water intake area, the output of the water quality forecasting model of the water intake area is used as the input of a water quality forecasting model of a water conveying pipeline, and the output of the water quality forecasting model of the water conveying pipeline is used as the input of a water ecological change forecasting model of a water receiving area; the input parameters of the meteorological forecasting model are rainfall parameters.
Further, the weather forecast model includes: the system comprises a dynamic framework sub-model, an equation discretization sub-model and a weather forecast initialization sub-model;
the dynamic frame submodel is used for establishing a dynamic equation set, wherein the dynamic equation set comprises an atmospheric force equation set, a motion equation set and a continuous equation;
the equation discretization sub-model is used for discretizing a power equation set in the power frame sub-model;
the weather forecast initialization submodel is used for determining a model parameterization scheme through boundary conditions.
Further, the runoff and pollution load forecasting model comprises: the method comprises the following steps of collecting data, researching a regional generalization submodel, constructing a calculation submodel and a rating and verification submodel;
wherein the data collection submodel is for collecting underlying surfaces of the study area and human activity information;
the research area generalization submodel is used for performing watershed segmentation and aggregation on the research area according to the data collected by the data collection submodel;
constructing an arithmetic submodel for simulating a hydrologic balance result according to the output of the research area generalization submodel;
the calibration and verification submodel is used for repeatedly calculating the constructed calculation submodel 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.
Further, the water quality forecasting model of the water intake area comprises: a water intake area water quality data collection sub-model, a water intake area water quality prediction construction sub-model and a water intake area water quality prediction calibration and verification sub-model;
the water quality data collection sub-model of the water taking area is used for collecting the area data of the water taking area;
the water intake area water quality forecasting construction sub-model is used for outputting a simulated water intake area water quality forecasting result according to the water intake area water quality data;
the water intake area water quality prediction and calibration and verification submodel is used for repeatedly calculating the water intake area water quality prediction construction submodel according to the actually measured data, and parameters are adjusted in the calculation process to enable the simulated water intake area water quality prediction result to be consistent with the actually measured data.
Further, the water quality forecasting model of the water pipeline comprises: the water pipe data collection sub-model, the water pipe water quality prediction construction sub-model and the water pipe water quality prediction calibration and verification sub-model;
the water pipeline data collection sub-model is used for collecting water pipeline region data;
the water quality forecasting construction sub-model of the water conveying pipeline is used for outputting a simulated water quality forecasting result of the water conveying pipeline according to the data of the water conveying pipeline area;
the water quality prediction and calibration and verification submodel of the water conveying pipeline is used for repeatedly calculating the water quality prediction and construction submodel of the water conveying pipeline according to the actually measured data, and parameters are adjusted in the calculation process so that the simulated water quality prediction result of the water conveying pipeline is consistent with the actually measured data.
Further, the water ecological change forecasting model of the water receiving area comprises: a water quality data collection sub-model of the water receiving area, a water quality forecast construction sub-model of the water receiving area and a water quality forecast calibration and verification sub-model of the water receiving area;
the water quality data collection submodel of the water receiving area is used for collecting the data of the water receiving area;
the water quality forecasting construction sub-model of the water receiving area is used for outputting a simulated water quality forecasting result of the water receiving area according to the water quality data of the water receiving area;
the water quality forecast calibration and verification submodel of the water receiving area is used for repeatedly calculating the water quality forecast construction submodel of the water receiving area according to the actually measured data, and parameters are adjusted in the calculation process, so that the simulated water quality forecast result of the water receiving area is consistent with the actually measured data.
Further, a genetic algorithm is adopted for parameter synchronization calibration.
Further, the parameters for calibration include: the rated storage capacity of the lower soil layer and/or the rated storage capacity of the upper soil layer and/or the soil infiltration coefficient and/or the interflow recession coefficient and/or the basal flow evaporation coefficient and/or the riverway roughness coefficient and/or the nitrification reaction rate and/or the algae growth rate.
In a second aspect, the present invention provides a control apparatus for a water quality and water ecology integrated early warning system, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to implement the system of the first aspect.
In a third aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to implement the system of the first aspect.
The invention has the beneficial effects that:
the invention establishes a water quality and water ecology integrated early warning system by coupling a meteorological forecast model, a runoff and pollution load forecast model, a water intake area water quality forecast model, a water delivery pipeline water quality forecast model and a water receiving area water ecology change forecast model, carries out mutual conversion processing on water quality variable parameters among different models, synchronously calibrates the parameters of the integrated early warning system by a genetic algorithm, substitutes measured data in the forecasting process of each model and continuously corrects the simulated forecasting result so as to further improve the precision of the subsequent calculation result and obtain a more accurate water quality and water ecology integrated early warning result, can forecast the water ecology change process of the water receiving area in the future for several days by a rainfall driving system, and sets up an optimized scheduling scheme aiming at various risks which possibly occur, the important technical support is provided for guaranteeing the safety of drinking water supply.
Drawings
FIG. 1 is a schematic diagram of an integrated early warning system for water quality and water ecology according to an embodiment of the present invention;
FIG. 2 is a flow chart of parameter synchronization calibration using a genetic algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data correction result of the water quality and water ecology integrated early warning system according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The first embodiment is as follows:
as shown in fig. 1, the configuration diagram of the water quality and water ecology integrated early warning system of the embodiment shows that the sub-models 1-5 are a weather forecasting model, a runoff and pollution load forecasting model, a water intake area water quality forecasting model, a water pipe water quality forecasting model and a water receiving area water ecology change forecasting model respectively, wherein the weather forecasting model outputs weather forecasting data to the runoff and pollution load forecasting model, the runoff and pollution load forecasting model outputs runoff and pollution load forecasting data to the water intake area water quality forecasting model, the water intake area water quality forecasting model outputs water intake area water quality forecasting data to the water pipe water quality forecasting model, the water pipe water quality forecasting model outputs water pipe water quality forecasting data to the water receiving area water ecology change forecasting model, the water receiving area water ecology change forecasting model outputs water receiving area water ecology change forecasting data, wherein the water taking area is mainly a river flow type water taking area.
The constitution of each submodel is described in detail below.
1) The weather forecast model comprises: the dynamic framework sub-model, the equation discretization sub-model and the weather forecast initialization sub-model. The input parameters are rainfall parameters, namely, the rainfall parameters drive the system to complete water quality forecast.
A1: the dynamic frame submodel is used for establishing a dynamic equation set, the dynamic equation set comprises an atmospheric force 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 submodel is used for discretizing a power equation set in the power framework 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 submodel 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 parameterization schemes such as a vertical boundary condition and a horizontal boundary condition, a time integration scheme and a nesting scheme are determined, the time integration scheme comprises a time separation scheme and a half Lagrange scheme, and the nesting scheme comprises multiple, same and different modes and a single and double mode.
The weather forecast initialization process in the weather forecast initialization submodel can be specifically divided into the following steps:
and 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 the 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 runoff and pollution load forecasting model comprises: the method comprises the following steps of collecting data, researching a regional generalization submodel, constructing a calculation submodel and a rating and verification submodel;
b1: the data collection submodel 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 submodel 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 terrain characteristics, land coverage characteristics and soil characteristics, and the water body units are river reach with the same hydraulic characteristics, such as hydraulic gradient, water cross section and flow speed.
B3: and constructing an arithmetic submodel for simulating a hydrologic balance result according to the output of the research area generalization submodel.
After the cells are generalized, determining the existing forms of rainfall, such as rainfall and snowmelt, according to the positions of weather stations in the simulation area and weather factor information; 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 submodel is used for repeatedly calculating the constructed calculation submodel 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 repeatedly calculated according to the measured hydrology and water quality data, the calculation result of the model is consistent with the measured result by adjusting the model parameters, then the use parameters of the model are determined,
in the verification stage, the model is driven by adopting data in another time period, and the measured value is compared with the simulation value to verify the simulation effect of the evaluation model.
3) The water quality forecasting model of the water intake area comprises: a water intake area water quality data collection sub-model, a water intake area water quality prediction construction sub-model and a water intake area water quality prediction calibration and verification sub-model;
c1: the water quality data collection submodel of the water taking area is used for collecting the regional data of the water taking area, and the data needing to be collected comprises the following steps: 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.
C2: the water intake area water quality prediction construction sub-model is used for outputting a simulated water intake area water quality prediction result according to water intake area water quality data, and the construction of the model can be generally divided into 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.
C3: the water intake area water quality prediction calibration and verification submodel is used for repeatedly calculating the water intake area water quality prediction construction submodel according to the actually measured data, adjusting parameters in the calculation process to enable the simulated water intake area water quality prediction result to be consistent with the actually measured data, namely calibrating the model parameters according to the actually measured hydraulic power (including data of water level, water quantity and the like) and the water quality data, and then verifying the model by adopting the actually measured data of different time periods.
4) The water quality forecasting model of the water pipeline comprises: the water pipe data collection sub-model, the water pipe water quality prediction construction sub-model and the water pipe water quality prediction calibration and verification sub-model;
d1: the water pipeline data collecting sub-model is used for collecting water pipeline region data, and the data to be collected comprises the following data: 1. characteristic data of each entity in the water supply system, namely basic data of pipelines, water pumps, valves, pools and the like; 2. the water consumption data and the water consumption change mode parameters of the nodes; 3. elevation data of the nodes; 4. control information of the operation of the water supply system, etc.
D2: the water quality forecasting construction sub-model of the water conveying pipeline is used for outputting a simulated water quality forecasting result of the water conveying pipeline according to the data of the area of the water conveying pipeline, namely selecting water quality indexes such as nitrogen, phosphorus and the like according to research needs, and establishing a water quality model composed of a pump station, a regulating and storing water body, a water distribution point, the water conveying pipeline and the like.
D3: the water quality prediction and calibration and verification submodel of the water conveying pipeline is used for repeatedly calculating the water quality prediction and construction submodel of the water conveying pipeline according to the actually measured data, and parameters are adjusted in the calculation process so that the simulated water quality prediction result of the water conveying pipeline is consistent with the actually measured data.
5) The water ecological change forecasting model of the water receiving area comprises the following steps: a water quality data collection sub-model of the water receiving area, a water quality forecast construction sub-model of the water receiving area and a water quality forecast calibration and verification sub-model of the water receiving area;
e1: the water quality data collection submodel of the water receiving area is used for collecting the data of the water receiving area;
e2: the water quality forecasting construction sub-model of the water receiving area is used for outputting a simulated water quality forecasting result of the water receiving area according to the water quality data of the water receiving area;
e3: the water quality forecast calibration and verification submodel of the water receiving area is used for repeatedly calculating the water quality forecast construction submodel of the water receiving area according to the actually measured data, and parameters are adjusted in the calculation process, so that the simulated water quality forecast result of the water receiving area is consistent with the actually measured data.
The following describes a mode of establishing a water quality and water ecology integrated early warning system through coupling among the concrete models.
Firstly, analyzing the formats of input and output files of each model, and realizing the uniform format of each model simulation variable according to the conversion relation among the model simulation variables.
S1: the meteorological forecast data output by the meteorological forecast model comprises parameters such as rainfall, air temperature, wind speed, sunshine, transpiration, dew point and cloud layer, and the data are imported into a database.
S2: when the runoff and pollution load forecasting model is calculated, firstly, the meteorological forecasting data in the database is read, the meteorological forecasting data and the collected pollution source sample data are written into the model and input into the basin data management, then the HSPF model is driven to calculate, and runoff and pollution load forecasting data are output, wherein the runoff and pollution load forecasting data comprise pollution load data and FLOW data of each branch of the 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 the water quality prediction model of the water intake area is calculated, firstly reading runoff and pollution load prediction data calculated by HSPF, and writing the data into the model to form input data of an EFDC model, wherein the input data comprises the following steps: meteorological data, tracer time sequence, flow data, water temperature data, pollutant input concentration data and the like, then an EFDC model is driven to calculate, results are stored in a database, and water quality forecast data of a water intake area comprise water quality data results of a plurality of different water intake areas and are stored in the database.
S4: the water quality forecasting model of the water conveying pipeline comprises the steps of firstly reading water quality data of a plurality of different water taking area areas and water pumping two-sample data, inputting the data into the model, then respectively calculating water quality pollution data of different water conveying pipeline outlets, and storing the water quality forecasting data of the water conveying pipelines in a database.
S5: the water ecological change forecasting model of the water receiving area is characterized in that water quality data of different water taking points in a database and water quality forecasting data of a water conveying pipeline, such as pollution load concentration data, flow data and the like, are read, water quantity data of each water taking area and the like are written into model input, then an EFDC model is driven to carry out calculation, water quality water ecological change results of each grid of different water taking areas are obtained, water quality conditions of each water taking area are obtained, and the water ecological change forecasting data of the water receiving area are stored in the database.
As shown in fig. 2, which is a flow chart of performing parameter synchronization calibration by using a genetic algorithm in this embodiment, the water quality and water ecology integrated early warning system performs parameter synchronization calibration by using a genetic algorithm, which is used to continuously correct model parameters, so as to obtain a more accurate model prediction result compared with measured data, where the parameters for calibration include: the method comprises the steps of obtaining an initialized population, carrying out fitness calculation on the initialized population, carrying out selection, intersection and variation operation on the population which does not reach an expected value after the fitness is passed or the iteration frequency does not reach the maximum value, judging whether the population meets a termination condition, carrying out fitness detection again if the termination condition is not met, repeating the selection, the intersection and the variation operation, and carrying out the termination operation if the termination condition is met, wherein the termination condition is that the iteration frequency is reached or the fitness reaches the expected value.
As shown in fig. 3, which is a schematic diagram of data correction results of the water quality and water ecology integrated early warning system in this embodiment, actual measurement data is substituted in the prediction process of each model, and simulation result data is continuously corrected, so that the accuracy of subsequent calculation results is further improved. It can be seen that the middle solid line is the result of the measured data, the top line is the result of the data correction, and the deviation is larger than that of the measured data, and the lower dotted line is the forecast result after the data correction, so that the deviation is smaller than that of the measured data.
Example two:
in addition, the invention also provides a control device of the water quality and water ecology integrated early warning system, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to implement the system according to embodiment one.
Example three:
in addition, the present invention also provides a computer-readable storage medium, which stores computer-executable instructions for causing a computer to implement the implementation described in the first embodiment.
The invention establishes a water quality and water ecology integrated early warning system by coupling a meteorological forecast model, a runoff and pollution load forecast model, a water intake area water quality forecast model, a water delivery pipeline water quality forecast model and a water receiving area water ecology change forecast model, carries out mutual conversion processing on water quality variable parameters among different models, synchronously calibrates the parameters of the integrated early warning system by a genetic algorithm, substitutes measured data in the forecasting process of each model and continuously corrects the simulated forecasting result so as to further improve the precision of the subsequent calculation result and obtain a more accurate water quality and water ecology integrated early warning result, can forecast the water ecology change process of the water receiving area in the future for several days by a rainfall driving system, and sets up an optimized scheduling scheme aiming at various risks which possibly occur, the important technical support is provided for guaranteeing the safety of drinking water supply.
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 (5)

1. A water quality and water ecology integrated early warning system is characterized by comprising a weather forecasting model, a runoff and pollution load forecasting model, a water intake area water quality forecasting model, a water pipeline water quality forecasting model and a water receiving area water ecological change forecasting model;
the output of the meteorological forecasting model is used as the input of a runoff and pollution load forecasting model, the output of the runoff and pollution load forecasting model is used as the input of a water quality forecasting model of a water intake area, the output of the water quality forecasting model of the water intake area is used as the input of a water quality forecasting model of a water conveying pipeline, and the output of the water quality forecasting model of the water conveying pipeline is used as the input of a water ecological change forecasting model of a water receiving area;
the input parameters of the meteorological forecasting model are rainfall parameters;
the weather forecast model includes: the system comprises a dynamic framework sub-model, an equation discretization sub-model and a weather forecast initialization sub-model;
the dynamic frame submodel is used for establishing a dynamic equation set, wherein the dynamic equation set comprises an atmospheric force equation set, a motion equation set and a continuous equation;
the equation discretization sub-model is used for discretizing a power equation set in the power frame sub-model;
the weather forecast initialization submodel is used for determining a model parameterization scheme through boundary conditions;
the runoff and pollution load forecasting model comprises: the method comprises the following steps of collecting data, researching a regional generalization submodel, constructing a calculation submodel and a rating and verification submodel;
wherein the data collection submodel is for collecting underlying surfaces of the study area and human activity information;
the research area generalization submodel is used for performing watershed segmentation and aggregation on the research area according to the data collected by the data collection submodel;
constructing an arithmetic submodel for simulating a hydrologic balance result according to the output of the research area generalization submodel;
the calibration and verification submodel is used for repeatedly calculating the constructed calculation submodel according to the actually measured data, and parameters are adjusted in the calculation process to enable the simulated hydrologic balance result to be consistent with the actually measured data;
the water quality forecasting model of the water intake area comprises: a water intake area water quality data collection sub-model, a water intake area water quality prediction construction sub-model and a water intake area water quality prediction calibration and verification sub-model;
the water quality data collection sub-model of the water taking area is used for collecting the area data of the water taking area;
the water intake area water quality forecasting construction sub-model is used for outputting a simulated water intake area water quality forecasting result according to the water intake area water quality data;
the water intake area water quality prediction and calibration and verification submodel is used for repeatedly calculating the water intake area water quality prediction construction submodel according to the actually measured data, and parameters are adjusted in the calculation process to enable the simulated water intake area water quality prediction result to be consistent with the actually measured data;
the water quality forecasting model of the water pipeline comprises: the water pipe data collection sub-model, the water pipe water quality prediction construction sub-model and the water pipe water quality prediction calibration and verification sub-model;
the water pipeline data collection sub-model is used for collecting water pipeline region data;
the water quality forecasting construction sub-model of the water conveying pipeline is used for outputting a simulated water quality forecasting result of the water conveying pipeline according to the data of the water conveying pipeline area;
the water quality prediction and calibration and verification submodel of the water conveying pipeline is used for repeatedly calculating the water quality prediction and construction submodel of the water conveying pipeline according to the actually measured data, and parameters are adjusted in the calculation process so that the simulated water quality prediction result of the water conveying pipeline is consistent with the actually measured data;
the water ecological change forecasting model of the water receiving area comprises: a water quality data collection sub-model of the water receiving area, a water quality forecast construction sub-model of the water receiving area and a water quality forecast calibration and verification sub-model of the water receiving area;
the water quality data collection submodel of the water receiving area is used for collecting the data of the water receiving area;
the water quality forecasting construction sub-model of the water receiving area is used for outputting a simulated water quality forecasting result of the water receiving area according to the water quality data of the water receiving area;
the water quality forecast calibration and verification submodel of the water receiving area is used for repeatedly calculating the water quality forecast construction submodel of the water receiving area according to the actually measured data, and parameters are adjusted in the calculation process, so that the simulated water quality forecast result of the water receiving area is consistent with the actually measured data.
2. A water quality and water ecology integrated early warning system according to claim 1, wherein a genetic algorithm is adopted for parameter synchronization calibration.
3. A water quality and water ecology integrated early warning system according to claim 1, wherein the parameters for synchronous calibration comprise: the rated storage capacity of the lower soil layer and/or the rated storage capacity of the upper soil layer and/or the soil infiltration coefficient and/or the interflow recession coefficient and/or the basal flow evaporation coefficient and/or the riverway roughness coefficient and/or the nitrification reaction rate and/or the algae growth rate.
4. The utility model provides a quality of water and water ecology integration early warning system's controlgear which characterized in that includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to implement the system of any one of claims 1 to 3.
5. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to implement the system of any one of claims 1 to 3.
CN201811360834.8A 2018-11-15 2018-11-15 Water quality and water ecology integrated early warning system, control equipment and storage medium Active CN109558973B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811360834.8A CN109558973B (en) 2018-11-15 2018-11-15 Water quality and water ecology integrated early warning system, control equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811360834.8A CN109558973B (en) 2018-11-15 2018-11-15 Water quality and water ecology integrated early warning system, control equipment and storage medium

Publications (2)

Publication Number Publication Date
CN109558973A CN109558973A (en) 2019-04-02
CN109558973B true CN109558973B (en) 2021-07-06

Family

ID=65866278

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811360834.8A Active CN109558973B (en) 2018-11-15 2018-11-15 Water quality and water ecology integrated early warning system, control equipment and storage medium

Country Status (1)

Country Link
CN (1) CN109558973B (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111260208B (en) * 2020-01-14 2020-09-22 生态环境部环境规划院 Regional gridding accumulative environment risk assessment system and method based on risk field
CN111310976B (en) * 2020-01-19 2023-10-27 北京耘数科技有限责任公司 Quick water quality forecasting and early warning method and system based on distributed computing
CN111428972B (en) * 2020-03-10 2022-09-13 中国环境科学研究院 Coupling model-based storage regulation engineering environmental effect evaluation method and device
CN111444595B (en) * 2020-03-10 2022-07-19 中国环境科学研究院 Sewage interception engineering environmental effect evaluation method and device based on SWMM and EFDC coupling model
CN111353718B (en) * 2020-03-10 2022-08-30 中国环境科学研究院 Wetland and water replenishing engineering environmental effect evaluation method and device based on SWMM and EFDC
CN111428914B (en) * 2020-03-10 2022-06-14 中国环境科学研究院 Method and device for determining water environment treatment engineering scheme and storage medium
CN111428350A (en) * 2020-03-10 2020-07-17 中国环境科学研究院 Method and device for constructing water environment detection coupling model and computer equipment
CN112116229A (en) * 2020-09-08 2020-12-22 深圳市广汇源环境水务有限公司 Drainage basin water quality scheduling management method, system and platform
CN113762729A (en) * 2021-08-16 2021-12-07 河海大学 Comprehensive evaluation method for urban drainage system modification project
CN113947034A (en) * 2021-08-26 2022-01-18 深圳市广汇源环境水务有限公司 Smart city wading link overall process coupling simulation method
CN114878651B (en) * 2022-05-18 2024-03-08 西安热工研究院有限公司 Condensate water quality deterioration early warning system and method based on big data

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1900956A (en) * 2006-07-11 2007-01-24 南京大学 Design method for improved mixed genetic algorithm optimizing water quality model parameter
TW201014798A (en) * 2008-10-14 2010-04-16 Pei-Jie Meng Early warning mode for eutrophication of water quality and detection method thereof
CN102289570A (en) * 2011-07-23 2011-12-21 浙江大学 Flood forecast method based on rainfall-runoff-flood routing calculation
CN104318325A (en) * 2014-10-14 2015-01-28 广东省环境监测中心 Multi-basin real-time intelligent water quality predication method and system
CN104460577A (en) * 2014-07-24 2015-03-25 上海市城市排水有限公司 Water quality safety early warning decision making system
CN108009736A (en) * 2017-12-13 2018-05-08 北京北华中清环境工程技术有限公司 A kind of water quality early-warning and predicting system and water quality early-warning and predicting method
CN108256249A (en) * 2018-01-26 2018-07-06 重庆市环境保护信息中心 A kind of reservoir area of Three Gorges EFDC model integrated methods
CN108459989A (en) * 2018-03-19 2018-08-28 中国气象科学研究院 A kind of unstrctured grid meteorology numerical model computing system
CN108765880A (en) * 2018-07-13 2018-11-06 夏璐 Geological disaster monitors in real time and early warning system

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020642B (en) * 2012-10-08 2016-07-13 江苏省环境监测中心 Monitoring water environment Quality Control data analysing method
US9772609B2 (en) * 2013-05-24 2017-09-26 Optirtc, Inc. Optimized hydromodification management with active stormwater controls
US20160115675A1 (en) * 2014-10-28 2016-04-28 Marcus Quigley Method for automated control of a combined greywater/stormwater system with forecast integration
US10645911B2 (en) * 2017-04-27 2020-05-12 International Business Machines Corporation Automated aquaculture pen location
CN107992960A (en) * 2017-11-03 2018-05-04 中山大学 Hekou District salty tide traces back chlorosity Simulation prediction method
CN108550096A (en) * 2018-03-06 2018-09-18 河海大学 Water transfer method is drawn to the ecology of waterfront body regulating water environment
CN108665100A (en) * 2018-05-09 2018-10-16 中国农业大学 A kind of water quality prediction technique, system and device

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1900956A (en) * 2006-07-11 2007-01-24 南京大学 Design method for improved mixed genetic algorithm optimizing water quality model parameter
TW201014798A (en) * 2008-10-14 2010-04-16 Pei-Jie Meng Early warning mode for eutrophication of water quality and detection method thereof
CN102289570A (en) * 2011-07-23 2011-12-21 浙江大学 Flood forecast method based on rainfall-runoff-flood routing calculation
CN104460577A (en) * 2014-07-24 2015-03-25 上海市城市排水有限公司 Water quality safety early warning decision making system
CN104318325A (en) * 2014-10-14 2015-01-28 广东省环境监测中心 Multi-basin real-time intelligent water quality predication method and system
CN108009736A (en) * 2017-12-13 2018-05-08 北京北华中清环境工程技术有限公司 A kind of water quality early-warning and predicting system and water quality early-warning and predicting method
CN108256249A (en) * 2018-01-26 2018-07-06 重庆市环境保护信息中心 A kind of reservoir area of Three Gorges EFDC model integrated methods
CN108459989A (en) * 2018-03-19 2018-08-28 中国气象科学研究院 A kind of unstrctured grid meteorology numerical model computing system
CN108765880A (en) * 2018-07-13 2018-11-06 夏璐 Geological disaster monitors in real time and early warning system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Alkaline solubilization of excess mixed sludge and the recovery of released phosphorus as magnesium ammonium phosphat;Xu De-chao;《Bioresource Technology》;20180228;全文 *
Ecological monitoring and assessment network’s proposed core monitoring variables: an early warning of environmental change;B Tegler;《Environmental Monitoring & Assessment》;20011231;全文 *
水环境风险源识别和评估研究进展综述;孙滔滔;《中国水利》;20180812;全文 *
深圳市石岩水库富营养化主控因素分析及潜在风险预测;彭溢;《安全与环境学报》;20120825;全文 *

Also Published As

Publication number Publication date
CN109558973A (en) 2019-04-02

Similar Documents

Publication Publication Date Title
CN109558973B (en) Water quality and water ecology integrated early warning system, control equipment and storage medium
Wu et al. Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction
Schulze Transcending scales of space and time in impact studies of climate and climate change on agrohydrological responses
Li et al. A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain
Vazifedoust et al. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts
Noory et al. Distributed agro-hydrological modeling with SWAP to improve water and salt management of the Voshmgir Irrigation and Drainage Network in Northern Iran
Raneesh et al. A study on the impact of climate change on streamflow at the watershed scale in the humid tropics
Pennock et al. The role of field studies in landscape-scale applications of process models: an example of soil redistribution and soil organic carbon modeling using CENTURY
CN105912836A (en) Pure remote sensing data driven drainage basin water circulation simulation method
CN113011993A (en) Method for measuring and calculating water-entering load of agricultural pollution source based on standard data
CN114881544B (en) Reservoir flow regulation and control method and device, electronic equipment and storage medium
CN112784395B (en) Method for predicting and simulating total phosphorus concentration of river water body
Irmak et al. Nebraska agricultural water management demonstration network (NAWMDN): Integrating research and extension/outreach
Al-Kufaishi et al. The feasibility of using variable rate water application under a central pivot irrigation system
Thorp et al. Using cross-validation to evaluate CERES-Maize yield simulations within a decision support system for precision agriculture
Amiri et al. The performance of fuzzy regression method for estimating of reference evapotranspiration under controlled environment
Qiu et al. Quantifying the space–time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework
CN112541611B (en) Rain-raising agricultural ground source pollution emission prediction method and system
CN117494419A (en) Multi-model coupling drainage basin soil erosion remote sensing monitoring method
CN116595333B (en) Soil-climate intelligent rice target yield and nitrogen fertilizer consumption determination method
Yurova et al. Using soil hydromorphy degree for adjusting steady-state water table simulations along catenas in semiarid Russia
Zeberie Modelling of rainfall-runoff relationship in Big-Akaki watershed, upper Awash Basin, Ethiopia
Malamos et al. Modelling irrigation management services: The IRMA_SYS case
CN112949182B (en) Method for calculating regional scale maximum pore conductance of coupling surface soil water content
Manos et al. A decision support system approach for rivers monitoring and sustainable management

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant