CN117491585A - Water ecological pollution monitoring method, device and system based on time sequence network - Google Patents
Water ecological pollution monitoring method, device and system based on time sequence network Download PDFInfo
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Abstract
The invention discloses a water ecological pollution monitoring method, a device and a system based on a time sequence network, comprising the following steps: obtaining topographic survey data of a target river reach, and constructing a two-dimensional river reach model; determining a calculation region of a target river reach; performing grid division on the calculation region of the target river reach; setting relevant parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach, and outputting hydrodynamic analysis result data; constructing a water quality prediction model based on a time sequence network; according to pollution source data generated by the hydrodynamic analysis result data and the historical monitoring data, a time sequence data set of historical water quality indexes of each outlet of the target river reach is obtained, and the time sequence data set is input into the water quality prediction model based on the time sequence network for training and testing, so that the water quality monitoring data of the target river reach are predicted; and generating monitoring report data according to the predicted water quality monitoring data. The method can accurately monitor the water quality condition of the water ecology, and is beneficial to the diagnosis and treatment of the water ecology problem.
Description
Technical Field
The invention relates to the technical field of river water pollution control and treatment, in particular to a method, a device and a system for monitoring water ecological pollution based on a time sequence network.
Background
Currently, urban infrastructures are continuously built and increased, manually excavated landscape waters are usually arranged in public facilities such as parks, and water bodies of most landscape waters mainly flow into peripheral rainwater runoffs, and are usually closed water bodies. Due to the property of the closed water body, if the regional rainfall surface source is polluted or the sewage drainage is influenced, the self-cleaning capacity of the whole water body is often reduced, the load for carrying tourists is heavy, the park water ecology is polluted, the water quality is rapidly deteriorated, and the landscape effect is greatly reduced. The traditional method which relies on manpower to monitor and evaluate the quality of the ecological environment is low in efficiency, and the water quality of the park water area cannot be accurately monitored, so that the problem diagnosis cannot be predicted and carried out, and the problem is brought to ecological management.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a water ecological pollution monitoring method and device based on a time sequence network, an intelligent water quality monitoring system and a construction method of the water ecological system, and the method can intelligently and accurately monitor the water ecological water quality condition and is beneficial to diagnosis and treatment of water ecological problems; meanwhile, a four-level network system of source reduction, interception, purification and repair is built, and a multi-mode water ecological system is built.
In a first aspect, a method for monitoring water ecological pollution based on a time sequence network provided by an embodiment of the present invention includes:
obtaining topographic survey data of a target river reach, and constructing a two-dimensional river reach model;
determining a calculation area of the target river reach according to the two-dimensional river reach model;
performing grid division on the calculation region of the target river reach to obtain a division result of the calculation region;
setting relevant parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach based on the dividing result, and outputting hydrodynamic analysis result data, wherein the relevant parameters comprise boundary conditions, initial conditions and river bed roughness parameters; the hydrodynamic analysis result data comprise river water level, river water depth, water flow velocity, flow direction, drainage precipitation runoff information and afflux information of each drainage outlet of a river reach area;
constructing a water quality prediction model based on a time sequence network;
according to pollution source data generated by the hydrodynamic analysis result data and the historical monitoring data, a time sequence data set of historical water quality indexes of each outlet of the target river reach is obtained, the data set is divided into a training set and a testing set, and the training set and the testing set are input into the water quality prediction model based on the time sequence network to train and test, so that the water quality monitoring data of the target river reach are predicted;
and generating monitoring report data according to the predicted water quality monitoring data.
Optionally, the building of the water quality prediction model based on a time sequence network is defined as:
in the method, in the process of the invention,for the output value of the current time, < > and->Is the output value of the historical time, k isThe delay order, f (x), is the network output function.
Optionally, the pollution source data is generated according to the historical monitoring data, wherein the pollution source data comprises point pollution source and surface pollution source data.
Optionally, the water quality prediction model is used for generating prediction data according to the point pollution source data and the surface pollution source data in the pollution source data; the water quality prediction model is also used for inputting the input quantity and pollution source data in a time sequence mode according to grid information of the water outlet and the river reach position where the water outlet is located.
Optionally, when the water quality prediction model generates prediction data, water level monitoring data, water quality monitoring data and ecological current situation data of the target river reach are comprehensively utilized, flow field calculation is completed based on the two-dimensional river reach model, time-space distribution characteristic data of the flow velocity field are obtained, and dynamic change data of each pollution index is calculated according to the data.
Optionally, the method further includes: evaluating the risk of the pollution source of the target river reach, and predicting the change of the pollutant concentration of each outlet of the target river reach area by using a water quality prediction model:
ΔC i ＝C i -C 0
in DeltaC i Refers to the concentration change value of the ith outlet of the target river reach; c (C) 0 The concentration value of the river reach pollutant is simulated by using a water quality model under the condition that only upstream water comes; c (C) i Refers to the concentration value of the river reach pollutant simulated by using a water quality model under the condition that the pollution load of the whole area is only upstream water and the ith discharge.
Optionally, based on the current water pollution condition and the problem diagnosis in the monitoring report data, making a river reach treatment decision to complete construction of a river reach water ecological system comprises the following steps: water purification, ecological system construction and hydrodynamic system construction.
In a second aspect, an embodiment of the present invention provides a water ecological pollution monitoring device based on a time sequence network, including:
the river reach model building module is used for obtaining the topography measurement data of the target river reach and building a two-dimensional river reach model;
the calculation region determining module is used for determining a calculation region of the target river reach according to the two-dimensional river reach model;
the network dividing module is used for carrying out grid division on the calculation region of the target river reach to obtain a division result of the calculation region;
the hydrodynamic analysis module is used for setting relevant parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach based on the dividing result, and outputting hydrodynamic analysis result data, wherein the relevant parameters comprise boundary conditions, initial conditions and river bed roughness parameters; the hydrodynamic analysis result data comprise river water level, river water depth, water flow velocity, flow direction, drainage precipitation runoff information and afflux information of each drainage outlet of a river reach area;
the prediction model construction module is used for constructing a water quality prediction model based on a time sequence network;
the water quality prediction module is used for obtaining a time sequence data set of each drainage port historical water quality index of the target river reach according to pollution source data generated by the hydrodynamic analysis result data and the historical monitoring data, dividing the data set into a training set and a testing set, inputting the training set and the testing set into the water quality prediction model based on the time sequence network, and further predicting the water quality monitoring data of the target river reach;
and the report generation module is used for generating monitoring report data according to the predicted water quality monitoring data, wherein the monitoring report data comprises river reach pollution conditions, current situation conditions and problem diagnosis.
In a third aspect, an intelligent water quality monitoring system provided by an embodiment of the present invention includes: the system comprises a cloud server, an intelligent control terminal, a water level and quality acquisition terminal and a water ecological pollution monitoring device based on a time sequence network according to the second aspect; wherein,
the water ecological pollution monitoring device based on the time sequence network is used for performing water quality monitoring by the water ecological pollution monitoring method based on the time sequence network according to the second aspect to obtain predicted monitoring information data;
the water level and water quality acquisition terminal comprises a water level monitoring unit and a water quality monitoring unit,
the water level monitoring unit is used for monitoring the water level change condition of the target river reach in real time;
the water quality monitoring unit is used for monitoring the water quality change condition of the target river reach in real time;
the intelligent control terminal is used for receiving and displaying the water level change condition and the water quality change condition obtained by the water level and water quality acquisition terminal through real-time monitoring, and receiving and displaying the water quality monitoring data and the monitoring report data predicted by the water ecological pollution monitoring device based on the time sequence network;
the intelligent control terminal is also used for analyzing the water level change condition and the water quality change condition obtained by combining the predicted monitoring report data with the real-time monitoring to obtain the pollution condition, the current situation condition and the problem diagnosis of the river reach, and generating decision advice according to the pollution condition, the current situation condition and the problem diagnosis of the river reach, wherein the decision advice comprises ecological improvement measures, in-situ treatment, strengthening treatment and water ecosystem reconstruction;
the intelligent control terminal is also used for carrying out information interaction with the cloud server.
In a fourth aspect, the method for constructing an water ecology system according to the embodiment of the present invention is based on the intelligent water quality monitoring system to intelligently monitor the water pollution condition of the water ecology system, and at least one of the following treatment measures is executed according to the decision advice:
establishing an artificial wetland and an integrated treatment station;
an ecological buffer zone is established along the river, runoff rainwater is purified through the physical, chemical and biological actions of vegetation and soil, and pollutants are intercepted and blocked;
an ecological rapid reconstruction technology is applied to recover the water ecosystem of the river reach;
and purifying surface runoff and lake area polluted water by utilizing a microorganism system and a new mode ecological buffer zone.
Compared with the prior art, the invention has the following technical effects:
1. the invention can intelligently and accurately monitor the water ecological quality condition, and is beneficial to the diagnosis and treatment of water ecological problems;
2. the method can evaluate the risk of the pollution source of the target river reach, give early warning information in time and facilitate the timely treatment of the subsequent river reach;
3. the intelligent water quality monitoring system of the invention is linked with the water ecological pollution monitoring device and the water level and quality acquisition terminal based on the time sequence network through the intelligent control terminal, and the water body change condition is monitored by combining the prediction and actual measurement data, so that the state of the aerator can be adjusted, and the remote intelligent control of the aerator is realized.
4. A four-level network system of 'source reduction-interception-purification-restoration' is built, a water quality improvement and ecological restoration are taken as main treatment targets, a 'multi-mode water ecological system' is built, ecological cycle development new mode taking ecological restoration and pollution control as cores is realized, ecological value conversion is realized, and an ecological clear water space template is created.
Drawings
FIG. 1 is a schematic flow chart of a method for monitoring water pollution based on a time series network according to a first embodiment of the present invention;
FIG. 2 is a two-dimensional model grid topographic map of a river reach provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a water quality prediction model establishment based on a time sequence network according to an embodiment of the present invention;
FIG. 4 shows the positions of 6 ports on a topographic map of a river reach according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a water pollution monitoring device based on a time-series network according to a second embodiment of the present invention;
FIG. 6 is a schematic diagram of a third embodiment of an intelligent water quality monitoring system according to the present invention;
FIG. 7 is a flow chart of a method for constructing an aquatic ecosystem according to a fourth embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a flow chart of a water ecology pollution monitoring method 100 based on a time sequence network according to a first embodiment of the invention is shown, the method 100 includes:
s110: obtaining topographic survey data of a target river reach, and constructing a two-dimensional river reach model;
in this embodiment, the modeling river reach is a park range, and the terrain measurement data of the simulated river reach is obtained, including slope data and total length data (e.g. 750 m), and the flow field simulation is calculated by using a Mike21 two-dimensional unsteady flow method, and the calculation needs to input the downstream water level boundary, the flow inflow of the upstream flow-producing area, the section of the river reach and the like.
S120: determining a calculation region of the target river reach according to the two-dimensional river reach model;
after the two-dimensional river reach model of the target river reach is obtained, the calculation area is determined according to the two-dimensional river reach model, and the drainage position can be set for the target two-dimensional river reach model.
S130: performing grid division on the calculation region of the target river reach to obtain a division result;
the model terrain is set according to the engineering design slope, and the terrain (underwater terrain) of the area is digitally processed. Dividing the calculation region into grids, wherein each grid consists of triangle units, 14893 modeling grid units and 15m of maximum control area 2 The two-dimensional model grid topography of the river reach is shown in fig. 2.
S140: setting related parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach based on the dividing result, and outputting hydrodynamic analysis result data including river water level, river water depth, water flow speed, flow direction, precipitation runoff information of each drainage port of a river reach area and afflux amount information (simulation data of pollutant movement), wherein the related parameters comprise boundary conditions, initial conditions and river bed roughness parameters;
setting related parameters for the two-dimensional river reach model, wherein the related parameters comprise boundary conditions, initial conditions and river bed roughness parameters, and the setting process is as follows:
boundary conditions: taking the simulated dry season hydrologic condition as an example, according to related data, upstream inflow is divided into wetland water outlet and drain water outlet, the wetland water outlet amount is 1.7 ten thousand m < 3 >/d, the drain water amount is 71.6m < 3 >/d, and the converted flow is 0.198m < 3 >/s; the lower boundary condition adopts a park water system to control the water surface elevation, namely 48.9m; initial conditions: given an initial water level of 48.9m in the calculation region, the initial flow rate is 0; river bed roughness: referring to the natural river reach brown rate Table, 0.030 is taken in combination with the river reach vegetation condition.
And then carrying out hydrodynamic analysis on the target river reach, and outputting hydrodynamic analysis result data including river water level, river water depth, water flow velocity, water flow direction, and rainfall runoff information and afflux amount information (simulation data of pollutant movement) of each drainage outlet of the river reach area.
Dry season: the flow rate of the engineering river reach is generally smaller, the upstream flow rate is obviously larger than the downstream flow rate, the flow rate of the upstream main river reach can reach between 0.01 and 0.025m/s, the downstream flow rate is lower, the middle river reach is relatively uniform in flow rate distribution due to wider river grooves and no other protruding parts, the flow rate is lower, the flow rate is basically 0.003 to 0.005m/s, the fluidity is poor, and the flow rate of the downstream main river reach can be basically kept between 0.005 and 0.01 m/s. And (3) rainy season: it is necessary to consider alone for the region exceeding 0.1m/s of flow rate in combination with the results of the twenty year flood analysis.
S150: constructing a water quality prediction model based on a time sequence network;
optionally, in some embodiment examples, the timing network is defined as follows:
in the method, in the process of the invention,for the output value of the current time, < > and->K is the delay order and f is the output function.
In the prediction process of the time sequence, continuous iterative training update is needed, and the prediction accuracy of the prediction model is high.
Fig. 3 is a schematic flow chart of a water quality prediction model based on a time sequence network according to an embodiment of the present invention, where the model is obtained by continuous training and optimization, and prediction data can be imported into the model for prediction, and then judgment is performed according to the prediction result.
S160, obtaining a data set of time series data of historical water quality indexes of each drainage port according to the drainage port rainfall runoff information, the afflux amount information and the pollution source data generated by the historical monitoring data of the river reach area, dividing the data set into a training set and a testing set, inputting the training set and the testing set into the water quality prediction model based on the time sequence network for training and testing, and predicting the water quality monitoring data of the target river reach;
performing data preprocessing according to the original pollution source data generated by the drainage rainfall runoff information and the remittance information of the river reach areas and the historical monitoring data to obtain a data set of the drainage historical water quality index time series data,
for example, by analyzing historical rainfall data in 1991-2020 in the area, 2015 is selected as a horizontal year, the annual rainfall is 501.8mm, the total rainfall of 2 hours in 2 years is 42.4mm, the total rainfall of 24 hours in 20 years is 175.2mm, the precipitation area is 419.2ha, and the total inlet amount of each row of outlets is 97757.44 m 3 And/d, see Table 1. As in fig. 4, the positions of 6 ports displayed on the topographic map of the river reach provided by the embodiment of the present invention are shown.
TABLE 1 precipitation area and sink volume information for each discharge port
Wherein pollution source data is generated from the historical monitoring data, wherein the pollution source data comprises point pollution source and surface pollution source data, as shown in tables 2 and 3, respectively.
Table 2 dry season point source pollution source data
The method comprises the steps of calculating the non-point source pollution, wherein the rainfall of 42.4mm in 2 hours in one year is selected as basic data for calculation, and the rainfall covers more than 90% of the rainfall in 2015 (except 2 times of stormwater weather).
TABLE 3 non-point source pollution source data for rainy season
After the construction of the water quality prediction model, the data set of the historical water quality index time series data of each outlet is divided into a training set and a test set based on the historical data, the water quality data of 6 outlets from 1 month in 1991 to 31 months in 2020 are used as the training set of the model, and the data from 1 month in 2015 to 31 months in 12 are used as the test set. And training the water quality prediction model by using the training set and the testing set.
The time sequence network comprises 6 nodes and continuous edges which appear or disappear intermittently along with time change among the nodes. The data are cut according to the time succession by day, the layer number of the time sequence network is set to be 2, wherein each layer corresponds to a network formed by 6 discharge ports from 1 month, 1 day, 1991 to 12 months, 31 days 2020. Each layer comprises 6 nodes and the link relation among the nodes in the time corresponding to each layer.
In some embodiments, the river water quality time series data is input, and the river water quality time series data mainly comprises three water quality indexes of chemical oxygen demand, ammonia nitrogen and dissolved oxygen index at a certain time, and in order to ensure the accuracy of data operation processing, the original river water quality time series data set needs to be preprocessed, which specifically includes but is not limited to: carrying out abnormal value elimination and original data normalization processing on the data set, taking the normalized data as input, taking the data at a plurality of continuous moments as output, and obtaining an input data set and an output data set; splitting an input data set and an output data set into a training set and a testing set, inputting the training set and the testing set into the water quality prediction model based on the time sequence network for training and testing, and further predicting the water quality monitoring number of the target river reach.
In this embodiment, by acquiring the historical data and then cleaning and normalizing the historical data, a training set and a testing set are generated, so that the accuracy of the neural network model prediction result can be significantly improved.
Optionally, in some embodiments, after the water quality prediction model and the training set are obtained, a training sample is selected from the training set, historical input data in the training sample is input into the water quality prediction model, and an error between the predicted data of the predicted target river reach and the historical real data in the training sample corrects the network weight coefficient. Because the prediction result of the water quality prediction model may have deviation, in order to improve the accuracy of prediction of the prediction model, the invention further comprises performing correlation analysis on the predicted water quality data and the real water quality data to optimize the model.
Optionally, in some embodiments, pollution source data is generated from historical monitoring data, wherein the pollution source data includes point pollution sources and face pollution source data.
The water quality prediction model is used for generating prediction data according to the point pollution source data and the surface pollution source data in the pollution source data; the water quality prediction model is also used for inputting the input quantity and pollution source data in a time sequence mode according to grid information of the water outlet and the river reach position where the water outlet is located.
S170: and generating monitoring report data according to the predicted water quality monitoring data.
The monitoring report data is helpful for a manager to timely master the pollution condition, the current situation and the problem diagnosis of the river reach.
Pollution analysis, such as dry season sewage inline: COD:16.06kg/d, TP:0.13kg/d, NH3-N:3.82kg/d; surface runoff pollution in rainy season: COD:7820.60kg, TP:29.33kg, NH3-N:1173.09kg. The status quo situation can be derived, for example: in dry seasons, the water flow in the stagnant water area is slow, the flow speed is lower than 0.003 m/s, the water body basically does not flow, and chlorophyll a is easy to rise reversely and explode; after rain stop, the water quality of the main river channel starts to recover from top to bottom, and the recovery time is about 7 days; the water flow in the stagnant water area is slow, and the water quality recovery time is far longer than 7 days. Problem diagnosis, for example, can be derived: insufficient hydrodynamic force, low transparency of water, poor self-cleaning capability of water, non-point source pollution impact in rainy season, and the like.
According to the embodiment, the water quality prediction model obtained based on the training can accurately predict the water quality of the river reach, and further, monitoring report data is generated according to the predicted water quality monitoring data, and the monitoring report data provides a basis for constructing a water ecological system, repairing vegetation in a stagnant water area, maintaining water quality, improving water transparency and improving the overall environment of a park.
Optionally, in some embodiments, when the water quality prediction model generates prediction data, water level monitoring data, water quality monitoring data and ecological current situation data of the target river reach are comprehensively utilized, flow field calculation is completed based on the two-dimensional river reach model, time-space distribution characteristic data of the flow velocity field are obtained, and dynamic change data of each pollution index is calculated according to the data.
Optionally, in some embodiments, the method further includes: evaluating the risk of the pollution source of the target river reach, and predicting the change of the pollutant concentration of each outlet of the target river reach area by using a water quality prediction model:
ΔC i ＝C i -C 0
wherein DeltaC i Refers to the concentration change value of the ith outlet of the target river reach; c (C) 0 The concentration value of the river reach pollutant is simulated by using a water quality model under the condition that only upstream water comes; c (C) i Refers to the concentration value of the river reach pollutant simulated by using a water quality model under the condition that the pollution load of the whole area is only upstream water and the ith discharge.
In this embodiment, the risk of the pollution source of the target river reach can be evaluated, and early warning information can be timely given, so that the subsequent river reach can be treated in time.
Optionally, in some embodiments, based on the current condition of water pollution of the river reach in the monitoring report data and the problem diagnosis, making a river reach treatment decision to complete construction of a river reach water ecosystem includes: water purification, ecological system construction and hydrodynamic system construction.
In this embodiment, after diagnosing the water quality pollution data of water ecological pollution monitoring and prediction, aiming at the existing problems: (1) low risk of water transparency: the water body is turbid and green, and blue-green algae burst phenomenon exists; (2) the self-cleaning capability of the water body is insufficient: the water body lacks submerged plants, plankton, benthonic animals and the like, the self-cleaning capability is poor, and the water ecological system of the water area needs to be optimized; (3) non-point source pollution: the pollution of the first rain enters the river and lacks a purifying means; (4) fluidity of water: the water surface width of the river reach is more than 30 m, the water flow rate in the dead water period is low, the flood flow rate in the rainy season is high, and the like, and a river reach treatment decision is made, so that the construction of a river reach water ecological system is completed, and a treatment scheme such as water purification, ecological system construction and hydrodynamic system construction is provided, so that the water ecology is effectively recovered.
Further, after the water ecological measures are restored, the method further comprises the steps of evaluating the water ecological restoration condition and the treatment scheme system, firstly, constructing indexes of an evaluation project system, determining weights of all evaluation indexes, calculating an evaluation value of ecological system restoration according to an algorithm formula of the water ecological system restoration condition, and finally, evaluating the effectiveness of the water ecological system treatment scheme system according to the evaluation value of ecological system restoration.
The algorithm formula of the water ecological system recovery condition is as follows:
wherein P is an evaluation value of the recovery condition of the water ecosystem; x is X i Evaluating the index value for the ith item; k (K) i To be matched with the ith evaluation index pairThe weight value to be applied can be set to a positive number smaller than 1; n is the number of indexes; i is the serial number of the current evaluation index.
Because the types of the evaluation indexes are different, in order to more directly reflect the recovery condition and quantitative comparison of the water ecological environment, the grid units divided for the river reach calculation area in step S130 are taken as the water ecological evaluation units, and the recovery evaluation values under the water ecological grids are processed according to the following algorithm:
wherein S is jh The value after processing the evaluation value corresponding to the evaluation index is recovered for the jth water ecological grid in the jth year, and the value range is [0,10]；P jh Representing an evaluation value corresponding to the h water ecological grid recovery evaluation index in the j th year; p (P) max Representing historical maximum values of all water ecological grid recovery evaluation indexes over the years; p (P) min Representing historical minimum values of all water ecological grid recovery assessment indexes over the years.
Fig. 5 is a schematic block diagram of a water ecology pollution monitoring device 500 based on a time series network according to a second embodiment of the invention, wherein the device 500 comprises:
the river reach model building module 510 is used for obtaining the topography measurement data of the target river reach and building a two-dimensional river reach model;
the calculation region determining module 520 is configured to determine a calculation region of the target river reach according to the two-dimensional river reach model;
the network dividing module 530 is configured to grid-divide the calculation region of the target river reach to obtain a division result of the calculation region;
the hydrodynamic analysis module 540 is configured to set relevant parameters for the two-dimensional river reach model, perform hydrodynamic analysis of the target river reach based on the division result, and output hydrodynamic analysis result data, where the relevant parameters include boundary conditions, initial conditions, and parameters of the river bed roughness; the hydrodynamic analysis result data comprise river water level, river water depth, water flow velocity, flow direction, drainage precipitation runoff information and afflux information of each drainage outlet of a river reach area;
a prediction model construction module 550 for constructing a water quality prediction model based on a time sequence network;
the water quality prediction module 560 is configured to obtain a time series data set of historical water quality indexes of each drainage port of the target river reach according to the hydrodynamic analysis result data and the pollution source data generated by the historical monitoring data, divide the data set into a training set and a testing set, input the training set and the testing set into the water quality prediction model based on the time series network, and further predict the water quality monitoring data of the target river reach;
a report generation module 570 for generating monitoring report data based on the predicted water quality monitoring data.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the modules described in the water ecological pollution monitoring device 500 based on a time sequence network in the embodiment of the present invention may refer to the corresponding process in the foregoing method embodiment, and the same parts and advantages as those in the method embodiment are not repeated here.
As shown in fig. 6, a schematic frame diagram of an intelligent water quality monitoring system 600 according to a third embodiment of the present invention includes: the system comprises a cloud server 610, an intelligent control terminal 620, a water level and quality acquisition terminal 630 and the water ecological pollution monitoring device 500 based on a time sequence network according to the second embodiment; wherein,
the water ecological pollution monitoring device 500 based on a time sequence network is configured to perform water quality monitoring according to the water ecological pollution monitoring method based on the time sequence network according to the first embodiment, so as to obtain predicted monitoring information data;
the water level and quality acquisition terminal 630 includes a water level monitoring unit 6301 and a water quality monitoring unit 6302,
the water level monitoring unit 6301 is used for monitoring the water level change condition of the target river reach in real time;
the water quality monitoring unit 6302 is used for monitoring the water quality change condition of the target river reach in real time;
the intelligent control terminal 620 is configured to receive and display the water level change condition and the water quality change condition obtained by the water level and water quality acquisition terminal through real-time monitoring, and receive and display the water quality monitoring data and the monitoring report data predicted by the water ecological pollution monitoring device based on the time sequence network;
the intelligent control terminal 620 is further configured to analyze the water level change situation and the water quality change situation obtained by combining the predicted monitoring report data with the real-time monitoring to obtain a river reach pollution situation, a current situation and a problem diagnosis, and generate a decision suggestion according to the river reach pollution situation, the current situation and the problem diagnosis, where the decision suggestion includes an ecological improvement measure, an in-situ treatment, an enhancement treatment, and an aqueous ecological system reconstruction;
the intelligent control terminal 620 is further configured to interact with the cloud server 610.
In this embodiment, the intelligent water quality monitoring system 600 is linked with the water ecological pollution monitoring device 500 and the water level and quality acquisition terminal 630 based on the time sequence network in the second embodiment through the intelligent control terminal 620, and the state of the aerator is adjusted by combining water monitoring data, so that remote intelligent control of the aerator can be realized.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the intelligent water quality monitoring system 600 in the third embodiment of the present invention may refer to the corresponding processes in the first and second embodiments, and the same parts and advantages as those in the previous embodiments will not be repeated here.
The intelligent control terminal 600 may be a digital computer of various forms, and may also be a mobile device of various forms, such as a smart phone, a wearable device, and other similar computing devices. This intelligent control terminal includes: at least one processing unit, a memory unit communicatively connected to the at least one processing unit, a ROM, a bus, an input unit (e.g., keyboard, mouse, etc.), an output unit (e.g., various types of displays, speakers, etc.), a user interface, a communication unit, each unit, interface being connected to the bus. The storage unit stores instructions executable by the at least one processing unit, so that the at least one processing unit can execute the steps of the method and achieve the same technical effects, and detailed descriptions of the same parts and advantages as those of the method embodiment in this embodiment are omitted.
Fig. 7 is a schematic flow chart of a construction method of an water ecology system according to a fourth embodiment of the invention, wherein the intelligent water quality monitoring system is used for intelligently monitoring the water pollution condition of the water ecology system according to the third embodiment, and at least one of the following treatment measures is executed according to the decision proposal:
establishing an artificial wetland and an integrated treatment station;
an ecological buffer zone is established along the river, runoff rainwater is purified through the physical, chemical and biological actions of vegetation and soil, and pollutants are intercepted and blocked;
an ecological rapid reconstruction technology is applied to recover the water ecosystem of the river reach;
and purifying surface runoff and lake area polluted water by utilizing a microorganism system and a new mode ecological buffer zone.
According to the embodiment, a four-level network system of source reduction, interception, purification and restoration is established, a water quality improvement and ecological restoration are taken as main treatment targets, a multi-mode water ecological system is established, ecological cycle development new mode with ecological restoration and pollution control as cores is achieved, ecological value conversion is achieved, and an ecological clear water space template is created. Specifically, the pollution load of the river channel is reduced from the source through measures such as artificial wetland, integrated treatment station and the like. An ecological buffer zone is established along the river, runoff rainwater is purified through the physical, chemical and biological actions of vegetation and soil, and pollutants are intercepted and blocked, so that the pollutants cannot be directly discharged into the river. The microbial system and the new-mode ecological buffer zone are utilized to purify surface runoff and lake area polluted water bodies, and the purified water bodies are discharged into a water return open channel as river channel water supplementing.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the construction method of the water ecological system of the present invention may refer to the corresponding process in the foregoing embodiments, and the parts and advantages same as those of the foregoing embodiments will not be described herein again.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems set forth in the system embodiments may also be implemented by one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The water ecological pollution monitoring method based on the time sequence network comprises the following steps:
obtaining topographic survey data of a target river reach, and constructing a two-dimensional river reach model;
determining a calculation area of the target river reach according to the two-dimensional river reach model;
performing grid division on the calculation region of the target river reach to obtain a division result of the calculation region;
setting related parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach based on the dividing result, and outputting hydrodynamic analysis result data, wherein the related parameters comprise boundary conditions, initial conditions and river bed roughness parameters; the hydrodynamic analysis result data comprise river water level, river water depth, water flow velocity, flow direction, drainage precipitation runoff information and afflux information of each drainage outlet of a river reach area;
constructing a water quality prediction model based on a time sequence network;
according to pollution source data generated by the hydrodynamic analysis result data and the historical monitoring data, a time sequence data set of historical water quality indexes of each outlet of the target river reach is obtained, the data set is divided into a training set and a testing set, and the training set and the testing set are input into the water quality prediction model based on the time sequence network to train and test, so that the water quality monitoring data of the target river reach are predicted;
and generating monitoring report data according to the predicted water quality monitoring data.
2. The method of claim 1, wherein said constructing a water quality prediction model based on a time series network is defined as:
；
in the method, in the process of the invention,for the output value of the current time, < > and->K is the delay order and f is the output function.
3. The method of claim 1, wherein pollution source data is generated from historical monitoring data, wherein the pollution source data includes point pollution source and face pollution source data.
4. A method according to claim 3, wherein the water quality prediction model is used to generate prediction data from the point pollution source data and the surface pollution source data in the pollution source data; the water quality prediction model is also used for inputting the input quantity and pollution source data in a time sequence mode according to grid information of the water outlet and the river reach position where the water outlet is located.
5. The method according to claim 1, wherein when the water quality prediction model generates prediction data, water level monitoring data, water quality monitoring data and ecology current situation data of the target river reach are comprehensively utilized, flow field calculation is completed based on the two-dimensional river reach model, time-space distribution characteristic data of a flow velocity field are obtained, and dynamic change data of each pollution index is calculated according to the data.
6. The method of claim 1, further comprising: evaluating the risk of the pollution source of the target river reach, and predicting the change of the pollutant concentration of each outlet of the target river reach area by using a water quality prediction model:
ΔC i ＝C i -C 0；
in DeltaC i Refers to the concentration change value of the ith outlet of the target river reach; c (C) 0 The concentration value of the river reach pollutant is simulated by using a water quality model under the condition that only upstream water comes; c (C) i Refers to the concentration value of the river reach pollutant simulated by using a water quality model under the condition that the pollution load of the whole area is only upstream water and the ith discharge.
7. The method of claim 1, wherein making a river reach governance decision to complete construction of a river reach water ecosystem based on the condition of the present state of river reach water pollution in the monitoring report data and the diagnosis of problems, comprises: water purification, ecological system construction and hydrodynamic system construction.
8. A water ecology pollution monitoring device based on a time sequence network, comprising:
the river reach model building module is used for obtaining the topography measurement data of the target river reach and building a two-dimensional river reach model;
the calculation region determining module is used for determining a calculation region of the target river reach according to the two-dimensional river reach model;
the network dividing module is used for carrying out grid division on the calculation region of the target river reach to obtain a division result of the calculation region;
the hydrodynamic analysis module is used for setting relevant parameters for the two-dimensional river reach model, carrying out hydrodynamic analysis of the target river reach based on the dividing result, and outputting hydrodynamic analysis result data, wherein the relevant parameters comprise boundary conditions, initial conditions and river bed roughness parameters; the hydrodynamic analysis result data comprise river water level, river water depth, water flow velocity, flow direction, drainage precipitation runoff information and afflux information of each drainage outlet of a river reach area;
the prediction model construction module is used for constructing a water quality prediction model based on a time sequence network;
the water quality prediction module is used for obtaining a time sequence data set of each drainage port historical water quality index of the target river reach according to pollution source data generated by the hydrodynamic analysis result data and the historical monitoring data, dividing the data set into a training set and a testing set, inputting the training set and the testing set into the water quality prediction model based on the time sequence network, and further predicting the water quality monitoring data of the target river reach;
and the report generation module is used for generating monitoring report data according to the predicted water quality monitoring data.
9. Intelligent water quality monitoring system, its characterized in that includes: the system comprises a cloud server, an intelligent control terminal, a water level and quality acquisition terminal and the water ecological pollution monitoring device based on the time sequence network as claimed in claim 8; wherein,
the water ecological pollution monitoring device based on the time sequence network is used for performing water quality monitoring by the water ecological pollution monitoring method based on the time sequence network according to any one of claims 1-6 to obtain predicted monitoring information data;
the water level and water quality acquisition terminal comprises a water level monitoring unit and a water quality monitoring unit,
the water level monitoring unit is used for monitoring the water level change condition of the target river reach in real time;
the water quality monitoring unit is used for monitoring the water quality change condition of the target river reach in real time;
the intelligent control terminal is used for receiving and displaying the water level change condition and the water quality change condition obtained by the water level and water quality acquisition terminal through real-time monitoring, and receiving and displaying the water quality monitoring data and the monitoring report data predicted by the water ecological pollution monitoring device based on the time sequence network;
the intelligent control terminal is also used for analyzing the water level change condition and the water quality change condition obtained by combining the predicted monitoring report data with the real-time monitoring to obtain the pollution condition, the current situation condition and the problem diagnosis of the river reach, and generating decision advice according to the pollution condition, the current situation condition and the problem diagnosis of the river reach, wherein the decision advice comprises ecological improvement measures, in-situ treatment, strengthening treatment and water ecosystem reconstruction;
the intelligent control terminal is also used for carrying out information interaction with the cloud server.
10. The construction method of the water ecological system is characterized in that the intelligent water quality monitoring system is used for intelligently monitoring the water pollution condition of the water ecological system, and at least one of the following treatment measures is executed according to the decision proposal:
establishing an artificial wetland and an integrated treatment station;
an ecological buffer zone is established along the river, runoff rainwater is purified through the physical, chemical and biological actions of vegetation and soil, and pollutants are intercepted and blocked;
an ecological rapid reconstruction technology is applied to recover the water ecosystem of the river reach;
and purifying surface runoff and lake area polluted water by utilizing a microorganism system and a new mode ecological buffer zone.
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