CN113125355A - Water quality supervision system - Google Patents
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- CN113125355A CN113125355A CN201911408161.3A CN201911408161A CN113125355A CN 113125355 A CN113125355 A CN 113125355A CN 201911408161 A CN201911408161 A CN 201911408161A CN 113125355 A CN113125355 A CN 113125355A
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Abstract
The utility model relates to a water quality supervisory systems for supervise the quality of water of the waters that await measuring, include: the spectrum detection device is used for collecting spectrum data of the water area to be detected at a first sampling frequency, wherein the first sampling frequency is higher than a preset sampling frequency; and the server is used for determining at least two water quality index data of the water area to be detected according to the spectral data of the water area to be detected and supervising the water quality of the water area to be detected according to the at least two water quality index data of the water area to be detected. Therefore, the applicability of the water quality index in real-time water quality abnormity detection can be improved.
Description
Technical Field
The disclosure relates to the technical field of water quality supervision, in particular to a water quality supervision system.
Background
With the rapid development of global industry and the rapid growth of urban population, the discharge amount of factory and domestic sewage is increased rapidly, so that the current situation of global water resources is worsened continuously, frequent water pollution events bring new requirements for the supervision and emergency capability of relevant departments, and the construction of a real-time detection system for river and lake water environment becomes the urgent need and inevitable choice for current environmental supervision.
The main functions of the existing water quality monitoring system are to collect the conventional water quality index data at different time intervals through daily sampling and measurement, for example: water temperature, Turbidity (TURB), Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), pH, conductivity, etc.
Disclosure of Invention
In view of this, the present disclosure provides a water quality monitoring system for monitoring water quality of a water area to be measured, including: the spectrum detection device is used for collecting spectrum data of the water area to be detected at a first sampling frequency, wherein the first sampling frequency is higher than a preset sampling frequency; and the server is used for determining at least two water quality index data of the water area to be detected according to the spectral data of the water area to be detected and supervising the water quality of the water area to be detected according to the at least two water quality index data of the water area to be detected.
In one possible implementation, the spectrum detection apparatus is further configured to: collecting spectral data of a water area sample at the first sampling frequency; and the server is further configured to: acquiring at least two water quality index data of the water area sample; establishing a water quality index soft measurement model according to the spectral data of the water area sample and at least two kinds of water quality index data of the water area sample, wherein the input of the water quality index soft measurement model is the spectral data of the water area and the output of the water quality index soft measurement model is the at least two kinds of water quality index data of the water area, and the determining of the at least two kinds of water quality index data of the water area to be measured according to the spectral data of the water area to be measured comprises the following steps: and determining at least two kinds of water quality index data of the water area to be detected according to the spectral data of the water area to be detected and the water quality index soft measurement model.
In a possible implementation manner, the server is configured to acquire acquired data and peripheral data, where the acquired data includes water quality index data obtained according to spectral data of a water area; the peripheral data comprises dynamic data and/or static data; the dynamic data comprises one or more of meteorological data, water temperature data, hydrological data and offline calibration data; the static data includes one or more of river geometry, location and distribution of nearby possible pollution sources, meteorological index, river biological index.
In a possible implementation manner, the collected data further includes water quality index data obtained according to other data of the water area to be detected, which is different from the spectral data of the water area to be detected.
In one possible implementation manner, the monitoring the water quality of the water area to be measured according to at least two water quality index data of the water area to be measured includes: and the server judges whether the water area to be detected is polluted or not according to the peripheral data of the water area to be detected and at least two water quality index data.
In one possible implementation, the spectrum detection apparatus is further configured to: collecting spectral data of a water area sample at the first sampling frequency; and the server is further configured to: determining at least two kinds of water quality index data of the water area sample according to the spectral data of the water area sample, and acquiring peripheral data of the water area sample; establishing a water quality index prediction model according to the peripheral data of the water area sample and at least two kinds of water quality index data, wherein the input of the water quality index prediction model comprises the peripheral data of the water area and the at least two kinds of water quality index data, and the output of the water quality index prediction model comprises an abnormal threshold of the peripheral data of the water area and an abnormal threshold of each kind of water quality index data of the at least two kinds of water quality index data of the water area, wherein the server judges whether the water area to be detected is polluted according to the peripheral data of the water area to be detected and the at least two kinds of water quality index data, and the method comprises the following steps: the server determines an abnormal threshold value of the peripheral data of the water area to be detected and an abnormal threshold value of each water quality index data according to the peripheral data of the water area to be detected, at least two water quality index data and the water quality index prediction model; and the server judges whether the water area to be detected is polluted or not according to the peripheral data and the abnormal threshold value of the water area to be detected, and at least two kinds of water quality index data and the abnormal threshold value of each kind of water quality index data of the water area to be detected.
In one possible implementation, the server is further configured to: and under the condition that the water area to be detected is polluted, determining the spatial position of the water area to be detected as a pollution position, and determining the pollution characteristic and the pollution grade according to the peripheral data of the water area to be detected and at least two kinds of water quality index data.
In one possible implementation, the server is further configured to: and under the condition that the water area to be detected is polluted, carrying out parameter calibration on the established hydrodynamic model according to the peripheral data of the water area to be detected and at least two kinds of water quality index data.
In one possible implementation, the server is further configured to: and predicting relevant pollution information of the water area to be measured according to the peripheral data of the water area to be measured, the at least two kinds of water quality index data and the hydrodynamic model with the parameters calibrated.
In one possible implementation, the information related to the contamination includes at least one of a duration, a spatial extent, and a contamination level of the contamination.
In one possible implementation, the spectrum detection apparatus is further configured to: collecting spectral data of a contaminated water field sample at the first sampling frequency; and the server is further configured to: determining at least two kinds of water quality index data of the water area sample according to the spectral data of the water area sample, and acquiring the position, the initial time and the concentration of the polluted source; establishing a water area pollution distribution function according to the position of the polluted source, the starting time, the concentration of the polluted source and at least two kinds of water quality index data; and under the condition that the water area to be detected is polluted, determining the position, the initial time and the concentration of a pollution source of the pollution corresponding to the water area to be detected according to the water area pollution distribution function and at least two kinds of water quality index data of the water area to be detected.
In one possible implementation, the server is further configured to: and providing a countermeasure aiming at the pollution corresponding to the water area to be detected according to the determined pollution source position, the starting time and the pollution source concentration.
According to the water quality supervision system, the spectrum detection device such as the micro spectrum sensor collects the spectrum data of the water area to be measured at a higher sampling frequency, the server determines at least two kinds of water quality index data of the water area to be measured according to the collected spectrum data, and supervises the water quality of the water area to be measured according to the at least two kinds of water quality index data. Since the spectrum detection device (such as a quantum dot spectrum sensor) can collect the spectrum data of the water area to be detected at a high sampling frequency, the high-frequency sampling in time or the real-time sampling and the data efficient measurement can be realized. Since the spectrum detection device is small in size, the spectrum detection device, that is, the sampling positions can be arranged in space at high density in the space of the water area to be measured. In addition, a plurality of water quality index data can be obtained simultaneously by the spectrum detection device.
Therefore, the water quality monitoring data has the characteristic of density in time, space and data types, the water quality of the water area to be detected is supervised by using the water quality monitoring data with the characteristic, and the applicability of the water quality index in real-time water quality abnormity detection can be improved compared with the existing water quality monitoring system.
In addition, the water quality supervision system disclosed by the invention can be used for excavating the dynamic association condition among the water quality change rule, the pollution characteristics and various water quality indexes, so that more timely and accurate water quality pollution identification, pollution early warning and pollution tracing can be realized.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Figure 1 shows a block diagram of a water quality supervision system according to an embodiment of the present disclosure.
Fig. 2 shows a schematic application diagram of a water quality supervision system according to an embodiment of the present disclosure.
Fig. 3 shows a flow chart of a method for establishing a water quality index soft measurement model according to an embodiment of the present disclosure.
Fig. 4 shows a flow chart of a water quality supervision method according to an embodiment of the present disclosure.
Fig. 5 shows a flow chart of a water quality supervision method according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
COD (Chemical Oxygen Demand) represents the amount of Oxygen required by potassium dichromate to oxidize organic substances in one liter of wastewater under strongly acidic conditions, and may approximately represent the amount of organic substances in wastewater.
TN (Total Nitrogen) represents the Total amount of various forms of inorganic and organic Nitrogen in a water sample. Comprising NO3 -、NO2 -And NH4 +Inorganic nitrogen and organic nitrogen such as protein, amino acid and organic amine are measured in milligrams of nitrogen per liter of water.
TP (Total Phosphorus) represents the results of measurements after various forms of Phosphorus are converted into orthophosphate by digestion of water samples, measured in milligrams of Phosphorus per liter of water sample.
TSS (Total Suspended Solids) represents the Total Suspended matter in a water sample, measured in milligrams of Suspended matter per liter of water sample.
BOD (Biochemical Oxygen Demand) can be measured in milligrams per liter by culturing microorganisms in a water sample at a certain temperature and measuring the consumption of dissolved Oxygen in the water.
With the increasing importance of the water quality management of rivers in China, water monitoring infrastructures are increasingly healthy, data accumulation is increasingly abundant, and the realization of water environment management under the support of big data is increasingly mature. The monitoring points of the water quality monitoring system for monitoring the water body are mainly a national surface water examination section (a national examination section for short), a water outlet and the like, and the sampling periods of different water quality indexes are different. For example, for a water quality index with short detection time, such as a water quality index such as conductivity and water temperature that can be measured quickly, the sampling frequency is high; for water quality indexes with long detection time (for example, more than 1 hour), such as COD and TOC which need long-time digestion measurement, the sampling frequency is low (for example, 4 hours/time); for a water quality index with longer detection time (for example, 5 days), such as a water quality index like BOD which requires longer digestion measurement, the sampling frequency is lower (for example, 6 days/time).
However, the conventional detection method used by the existing water quality monitoring system limits the improvement of the sampling frequency of part of the water quality indexes, and the lower sampling frequency limits the applicability of the water quality indexes in real-time water quality abnormity detection.
Therefore, the present disclosure provides a water quality monitoring system, wherein a spectrum detection device such as a micro spectrum sensor collects spectrum data of a water area to be detected with a higher sampling frequency, a server determines at least two water quality index data of the water area to be detected according to the collected spectrum data, and monitors the water quality of the water area to be detected according to the at least two water quality index data.
Since the spectrum detection device (such as a quantum dot spectrum sensor) can collect the spectrum data of the water area to be detected at a high sampling frequency, the high-frequency sampling in time or the real-time sampling and the data efficient measurement can be realized. Since the spectrum detection device is small in size, the spectrum detection device, that is, the sampling positions can be arranged in space at high density in the space of the water area to be measured. In addition, a plurality of water quality index data can be obtained simultaneously by the spectrum detection device.
Therefore, the water quality monitoring data has the characteristic of density in time, space and data types, the water quality of the water area to be detected is supervised by using the water quality monitoring data with the characteristic, and the applicability of the water quality index in real-time water quality abnormity detection can be improved compared with the existing water quality monitoring system.
The embodiments of the present disclosure are explained below with reference to the drawings attached to the specification.
Fig. 1 shows a block diagram of a water quality supervision system for supervising the water quality of a water area to be measured according to an embodiment of the present disclosure. As shown in fig. 1, the water quality monitoring system includes a spectrum detection device 110 and a server 130. The spectrum detection device 110 is used for collecting the spectrum data of the water area to be detected at a first sampling frequency, wherein the first sampling frequency is higher than the preset sampling frequency; the server 130 is configured to determine at least two water quality index data of the water area to be detected according to the spectral data of the water area to be detected, and supervise the water quality of the water area to be detected according to the at least two water quality index data of the water area to be detected.
In this embodiment, the spectrum detecting device 110 includes, but is not limited to, a spectrum detecting device capable of acquiring spectrum data of the water area to be detected at a high frequency and/or a spectrum detecting device with a small volume for acquiring spectrum data of the water area to be detected and/or simultaneously acquiring multiple water quality index data of the water area to be detected. In one implementation, the spectral detection device 110 includes a micro-spectral sensor. Illustratively, the spectral detection device 110 is a quantum dot spectral sensor.
It should be understood that the spectrum detection device 110 collects the spectrum data of the water area to be detected at a first sampling frequency higher than a predetermined sampling frequency, which may be a preset sampling frequency value or a sampling frequency value arbitrarily set according to practical application requirements, such as water quality detection requirements, and the like, and that the spectrum detection device 110 collects the spectrum data of the water area to be detected at a higher sampling frequency.
In one possible implementation, the server 130 may be configured to obtain collected data and peripheral data, where the collected data may include water quality indicator data obtained from spectral data of a water area; the peripheral data may include dynamic data and/or static data; the dynamic data can refer to data with frequent change and fluctuation, and comprise one or more of meteorological data, water temperature data, hydrological data and offline calibration data; the static data refers to data which is basically maintained unchanged for a certain time and comprises one or more of the shape of a river channel, the position and distribution of a possible pollution source nearby, a meteorological index and a river biological index.
In a possible implementation manner, the collected data further includes water quality index data obtained according to other data of the water area to be detected, which is different from the spectral data of the water area to be detected.
In this embodiment, the server 130 may acquire the collected data as the water quality index data and the peripheral data as the non-water quality index data.
The collected data not only can include water quality index data obtained according to the spectral data of the water area, but also can include water quality index data obtained according to other data of the water area. That is, the server 130 may optionally acquire water quality index data acquired by other means, for example, water quality index data acquired from other data of the water area different from the spectrum data, according to the actual application requirements, in addition to the water quality index data acquired from the spectrum data of the water area.
Exemplary water quality indicator data obtained by other means include, but are not limited to, conductivity, pH, dissolved oxygen, ammonia nitrogen content, total nitrogen content, nitrate content, nitrite content, organic phosphorus content, residual chlorine content, heavy metal content, chlorophyll content, blue-green algae content, total phosphorus content, total organic carbon content, chemical oxygen demand, biochemical oxygen demand, CO2Content, H2One or more of S content, oil content in water, floating pollutants and the like.
The peripheral data may include not only dynamic data but also static data. It should be understood that dynamic data represents data in which the number of times data changes per unit time substantially exceeds a threshold value (i.e., fluctuates frequently per unit time), and static data represents data in which the number of times data changes per unit time substantially does not exceed the threshold value (i.e., remains substantially unchanged per unit time).
Illustratively, the dynamic data includes, but is not limited to, meteorological data, water temperature data, hydrological data, offline calibration data, and the like, and the static data includes, but is not limited to, river geometry, location and distribution of nearby potential pollution sources (location of potentially polluting entities such as sewage drains and the like in the area of the water body), meteorological indices, river biological indices, and the like.
For convenience of description, the water quality monitoring system of the present embodiment will be described below by taking a quantum dot spectrum sensor as an example of a spectrum detection device.
The method comprises the steps that a plurality of quantum dot spectrum sensors are arranged in the space of a water area to be measured in a high-density mode, each quantum dot spectrum sensor in the plurality of quantum dot spectrum sensors collects spectrum data of water quality around the position where the quantum dot spectrum sensor is located at a high sampling frequency, and the spectrum data can be sent to a server. The server determines at least two kinds of water quality index data of the water area to be detected according to the received spectrum data, and therefore the water quality of the water area to be detected is supervised according to the determined water quality index data.
Therefore, the water quality monitoring data of the water quality monitoring system has the characteristic of density in time, space and data types, the water quality monitoring data with the characteristics is used for monitoring the water quality of the water area to be detected, and the applicability of water quality indexes in real-time water quality abnormity detection can be improved compared with the existing water quality monitoring system.
In addition, because the water quality monitoring system enriches the water quality monitoring information, compared with the existing water quality monitoring system, the water quality monitoring system not only can simplify the material resources and the human resources required by periodic sampling and reduce the errors in the sampling and recording processes, but also can improve the monitoring frequency and shorten the delay time for acquiring data.
In one implementation, the server 130 determines whether the water area to be detected is polluted according to the acquired peripheral data of the water area to be detected and the determined at least two water quality index data of the water area to be detected.
Fig. 2 shows a schematic application diagram of a water quality supervision system according to an embodiment of the present disclosure. As shown in fig. 2, the data stored in the database of the server 130 includes, but is not limited to, meteorological data, hydrographic data, offline calibration data, and real-time acquisition data, and optionally, the peripheral data is, for example, meteorological data and hydrographic data. The water quality supervision system can establish a water quality index soft measurement model according to data stored in the database, wherein the input of the water quality index soft measurement model is spectral data of a water area, and the output of the water quality index soft measurement model is at least two kinds of water quality index data of the water area, such as TOC, BOD, TP and the like.
The water quality supervision system can pre-establish a water quality index soft measurement model, so that when the water quality of the water area to be monitored needs to be supervised, at least two kinds of water quality index data of the water area to be monitored can be obtained only by inputting the collected spectral data of the water area to be monitored into the pre-established water quality index soft measurement model.
Fig. 3 is a flowchart illustrating a method for establishing a water quality indicator soft measurement model according to an embodiment of the present disclosure, and the method illustrated in fig. 3 may be used to establish the water quality indicator soft measurement model.
In step S210, the spectrum detection apparatus 110 acquires spectrum data of the water area sample at a first sampling frequency.
In step S220, the server 130 obtains at least two types of water quality index data of the water area sample.
Although step S210 is first executed and then step S220 is executed in fig. 3, it should be understood that the present disclosure is not limited to the order of steps S210 and S220, for example, step S220 may be first executed and then step S210 may be executed, and for example, steps S210 and S220 may be executed simultaneously.
In step S230, the server 130 establishes a water quality index soft measurement model according to the spectrum data collected by the spectrum detection device 110 and at least two types of water quality index data acquired by the server 130.
Thus, the present disclosure can build a soft measurement model of water quality index data such as turbidity, COD, TOC, BOD, TP, etc. by employing means such as regression analysis, correlation analysis, etc., based on the internal relationship between the abundant-information-amount spectral data and the water quality index data.
In one implementation, the server 130 supervises the water quality of the water area to be tested according to at least two water quality index data of the water area to be tested, including: the server 130 determines whether the water area to be detected is polluted according to the peripheral data of the water area to be detected and the at least two water quality index data.
Fig. 4 shows a flow chart of a water quality supervision method according to an embodiment of the present disclosure.
In step S310, the spectrum detecting device 110 collects spectrum data of the water area sample at a first sampling frequency.
In step S320, the server 130 determines at least two types of water quality index data of the water area sample according to the spectral data of the water area sample, and acquires the peripheral data of the water area sample.
In step S330, the server establishes a water quality index prediction model according to the peripheral data of the water area sample and the at least two types of water quality index data.
In this embodiment, the steps S310, S320, and S330 may be executed to establish a water quality indicator prediction model in advance, wherein the input of the water quality indicator prediction model includes the peripheral data of the water area and at least two types of water quality indicator data, and the output of the water quality indicator prediction model includes the abnormal threshold of the peripheral data of the water area and the abnormal threshold of each of the at least two types of water quality indicator data of the water area.
After the peripheral data of the water area sample are collected and various water quality index data are calculated according to the spectral data, a water quality index prediction model can be established by using a time series analysis method.
In step S340, the spectrum detecting apparatus collects the spectrum data of the water area to be detected at a first sampling frequency.
In step S350, the server determines at least two types of water quality index data of the water area to be measured according to the spectral data of the water area to be measured, and obtains peripheral data of the water area to be measured.
In step S360, the server determines an abnormal threshold of the peripheral data of the water area to be measured and an abnormal threshold of each water quality index data according to the peripheral data of the water area to be measured, the at least two water quality index data, and the water quality index prediction model.
Since the water quality index prediction model is pre-established, the server 130 may determine the abnormal threshold of the peripheral data of the water area to be measured and the abnormal threshold of each water quality index data according to the peripheral data of the water area to be measured, the at least two water quality index data and the pre-established water quality index prediction model.
In step S370, the server determines whether the water area to be measured is polluted.
And comparing the pre-established water quality index prediction model with actual monitoring data (including peripheral data of the water area to be detected and at least two kinds of water quality index data) of the water area to be detected to obtain a residual time sequence, determining the residual distribution type and a corresponding abnormal threshold value through residual distribution fitting, and measuring the abnormal probability of a single parameter level.
Therefore, the server 130 can determine whether the water area to be detected is polluted according to the peripheral data of the water area to be detected and the abnormal threshold thereof, and the at least two types of water quality index data of the water area to be detected and the abnormal threshold of each type of water quality index data.
For example, if the measured value of the peripheral data of the water area to be measured exceeds the abnormal threshold of the peripheral data, and the measured value of each water quality index data exceeds the abnormal threshold of the water quality index data, the server 130 determines that the water area to be measured is polluted. Otherwise, the server 130 determines that the water area to be detected is not polluted.
If the server 130 determines that the water area to be measured is polluted, yes in step S370, and the process proceeds to step S380 and/or S390 described below. If the server 130 determines that the water area to be measured is not polluted, no in step S370, and the process proceeds to step S340.
In step S380, the server determines the spatial position of the water area to be detected as a pollution position, and determines the pollution characteristic and the pollution level according to the peripheral data of the water area to be detected and the at least two types of water quality index data.
In the embodiment, the dynamic correlation coefficients of different positions and different water quality indexes can be calculated according to a dynamic time warping algorithm, so that the real-time tracking and accurate judgment of the surface water quality change and pollution of the monitoring position are realized, and the polluted position, the polluted characteristic and the polluted grade are determined.
The data fusion pollution discrimination model can be pre-established, wherein the input of the data fusion pollution discrimination model is the spatial position of the water area, the peripheral data and at least two kinds of water quality index data, and the output of the data fusion pollution discrimination model is the pollution characteristic and the pollution level of the water area, so that the output of the pre-established data fusion pollution discrimination model can be determined as the pollution characteristic and the pollution level of the water area to be detected by inputting the spatial position of the water area to be detected, the peripheral data of the water area to be detected and at least two kinds of water quality index data into the pre-established data fusion pollution discrimination model when the server 130 determines that the water area to be detected is polluted.
Illustratively, the data fusion contamination discrimination model may be pre-established by: the spectrum detection device 110 collects the spectrum data of the polluted water area sample at a first sampling frequency; the server 130 determines at least two kinds of water quality index data of the water area sample according to the spectrum data of the water area sample, and obtains the spatial position of the water area sample, the peripheral data of the water area sample, the pollution characteristics and the pollution level; the server 130 establishes a data fusion pollution discrimination model in advance according to the spatial position of the acquired water area sample, the peripheral data of the water area sample, the pollution characteristics, the pollution level and at least two water quality index data of the water area sample.
As shown in fig. 2, the polluted river reach positioning, event classification and pollution feature analysis can be implemented according to the pre-established data fusion pollution discrimination model, so that the server 130 can determine the pollution feature and the pollution level of the water area to be detected according to the pre-established data fusion pollution discrimination model.
In step S390, the server performs parameter calibration on the established hydrodynamic model ("hydrohydrodynamic coupling model" in fig. 2) according to the peripheral data of the water area to be measured and the at least two types of water quality index data.
As shown in fig. 2, the off-line calibration parameters/data can be corrected, the water flow in the water area can be simulated, and the pollution migration in the water area can be predicted based on the pre-established hydrodynamic model.
The parameter calibration is a precondition for the application of a pollutant migration and diffusion model and a hydrodynamic model, and the accuracy of the parameter calibration directly influences the accuracy of a prediction result. In the existing pollution early warning scheme, most of parameter calibration methods are offline calibration algorithms, and when the algorithms are applied to sudden water pollution events, the parameters of a pollutant migration diffusion model cannot change along with the change of hydrological conditions. Because the water environment is a complex dynamic system, weather and hydrological conditions have great difference in time and space, nonlinearity and non-stationarity are inherent characteristics of the system, and the method of neglecting the change of model parameters inevitably has poor dynamic adaptability, so that the accuracy of a calculation result is difficult to ensure. In contrast, according to the present disclosure, the parameter calibration method in step S390 is based on high-frequency, gridded detection data, and thus dynamic parameter calibration is possible.
In step S391, the server predicts the relevant information of the pollution of the water area to be measured according to the peripheral data of the water area to be measured, the at least two types of water quality index data, and the hydrodynamic model after parameter calibration. Wherein the information related to contamination comprises at least one of a duration of contamination, a spatial extent, and a contamination level.
In this embodiment, when the server 130 determines that the water area to be measured is polluted, the server 130 may correct the calibration parameter, simulate the water flow movement, and predict the pollution migration according to the pre-established hydrodynamic coupling model.
After the server 130 identifies the pollution event, the dynamic calibration of the key parameters in the pollutant migration and diffusion model is quickly completed by using high frequency, real-time, gridding, multi-parameter data and an intelligent optimization algorithm, the offline calibration parameters are corrected and brought into the water quality hydrodynamics coupling model, and therefore the future diffusion rule of the pollutants can be simulated and analyzed, and the influence time, the space range and the severity of the pollutants can be predicted.
Fig. 5 shows a flow chart of a water quality supervision method according to an embodiment of the present disclosure.
In step S310, the spectrum detecting device 110 collects spectrum data of the water area sample at a first sampling frequency.
In step S320, the server 130 determines at least two types of water quality index data of the water area sample according to the spectral data of the water area sample, and acquires the peripheral data of the water area sample.
In step S330, the server establishes a water quality index prediction model according to the peripheral data of the water area sample and the at least two types of water quality index data.
In step S340, the spectrum detecting apparatus collects the spectrum data of the water area to be detected at a first sampling frequency.
In step S350, the server determines at least two types of water quality index data of the water area to be measured according to the spectral data of the water area to be measured, and obtains peripheral data of the water area to be measured.
In step S360, the server determines an abnormal threshold of the peripheral data of the water area to be measured and an abnormal threshold of each water quality index data according to the peripheral data of the water area to be measured, the at least two water quality index data, and the water quality index prediction model.
In step S370, the server determines whether the water area to be measured is polluted.
The descriptions of step S310 to step S370 can refer to the detailed description related to fig. 3, and are not repeated herein.
If the server 130 determines that the water area to be measured is polluted, yes in step S370, and the process proceeds to step S410 described below. If the server 130 determines that the water area to be measured is not polluted, no in step S370, and the process proceeds to step S340.
In step S410, a pollution source location, a start time, and a pollution source concentration of the pollution corresponding to the water area to be measured are determined according to the water area pollution distribution function ("source tracing inversion model" in fig. 2) and the at least two types of water quality index data of the water area to be measured.
In this embodiment, the water pollution distribution function may be pre-established as follows: the spectrum detection device 110 collects the spectrum data of the polluted water area sample at a first sampling frequency; the server 130 determines at least two water quality index data of the water area sample according to the spectral data of the water area sample, and acquires the position, the initial time and the concentration of a polluted source; the server 130 establishes a water area pollution distribution function according to the position of the polluted source, the starting time, the concentration of the polluted source and at least two kinds of water quality index data.
Thus, when the server 130 determines that the water area to be detected is polluted, the position, the starting time and the concentration of the pollution source of the polluted water area to be detected can be determined according to the pre-established water area pollution distribution function and the at least two kinds of water quality index data of the water area to be detected, so that the spatial positioning of the pollution source, the backtracking of the pollution time and the calculation of the concentration of the pollution source are realized.
In step S420, the server 130 provides a countermeasure for the pollution corresponding to the water area to be measured according to the determined pollution source location, start time, and pollution source concentration.
Pollution tracing refers to tracing and positioning the source of pollution by various ways after a river has a pollution accident, and the main work of the pollution tracing comprises the following steps: analyzing the source and the type of the pollutant, and searching key information such as the position, the leakage time, the concentration and the like of the pollutant source.
Therefore, the water quality supervision system of the embodiment can track and trace the canal sudden water pollution event from the perspective of combining probability and optimization, establish a distribution function of the tracing result (including the position of the pollution source, the initial pollution time and the concentration of the pollution source) of the water pollution event based on a large amount of accumulated data, a random sampling method and a Bayesian theory, and quantify uncertainty of the result, thereby providing a basis and an emergency response for emergency decision.
From this, the water quality supervisory systems of this embodiment can excavate the dynamic correlation condition between water quality change law, pollution characteristics and multiple water quality index to can realize more timely, accurate water quality pollution discernment, pollution early warning and pollution traceability.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
1. A water quality supervision system is used for supervising the water quality of a water area to be measured, and is characterized by comprising:
the spectrum detection device is used for collecting spectrum data of the water area to be detected at a first sampling frequency, wherein the first sampling frequency is higher than a preset sampling frequency;
and the server is used for determining at least two water quality index data of the water area to be detected according to the spectral data of the water area to be detected and supervising the water quality of the water area to be detected according to the at least two water quality index data of the water area to be detected.
2. The water quality supervision system according to claim 1,
the spectrum detection device is also used for: collecting spectral data of a water area sample at the first sampling frequency; and
the server is further configured to: acquiring at least two water quality index data of the water area sample; establishing a water quality index soft measurement model according to the spectral data of the water area sample and at least two kinds of water quality index data of the water area sample, wherein the input of the water quality index soft measurement model is the spectral data of the water area and the output of the water quality index soft measurement model is the at least two kinds of water quality index data of the water area,
determining at least two water quality index data of the water area to be detected according to the spectral data of the water area to be detected, wherein the determining comprises the following steps: and determining at least two kinds of water quality index data of the water area to be detected according to the spectral data of the water area to be detected and the water quality index soft measurement model.
3. The water quality supervision system according to claim 1, wherein the server is configured to obtain acquisition data and peripheral data,
the collected data comprises water quality index data obtained according to the spectral data of the water area;
the peripheral data comprises dynamic data and/or static data;
the dynamic data comprises one or more of meteorological data, water temperature data, hydrological data and offline calibration data;
the static data includes one or more of river geometry, location and distribution of nearby possible pollution sources, meteorological index, river biological index.
4. The water quality supervision system according to claim 3,
the collected data also comprises water quality index data obtained according to other data of the water area to be detected, which is different from the spectral data of the water area to be detected.
5. The water quality monitoring system according to claim 3, wherein monitoring the water quality of the water area to be tested according to at least two water quality index data of the water area to be tested comprises:
and the server judges whether the water area to be detected is polluted or not according to the peripheral data of the water area to be detected and at least two water quality index data.
6. The water quality supervision system according to claim 5,
the spectrum detection device is also used for: collecting spectral data of a water area sample at the first sampling frequency; and
the server is further configured to:
determining at least two kinds of water quality index data of the water area sample according to the spectral data of the water area sample, and acquiring peripheral data of the water area sample;
establishing a water quality index prediction model according to the peripheral data of the water area sample and at least two kinds of water quality index data, wherein the input of the water quality index prediction model comprises the peripheral data of the water area and the at least two kinds of water quality index data, and the output of the water quality index prediction model comprises an abnormal threshold of the peripheral data of the water area and an abnormal threshold of each kind of water quality index data of the at least two kinds of water quality index data of the water area,
wherein, the server judges whether the water area to be detected is polluted according to the peripheral data of the water area to be detected and at least two water quality index data, and the method comprises the following steps:
the server determines an abnormal threshold value of the peripheral data of the water area to be detected and an abnormal threshold value of each water quality index data according to the peripheral data of the water area to be detected, at least two water quality index data and the water quality index prediction model;
and the server judges whether the water area to be detected is polluted or not according to the peripheral data and the abnormal threshold value of the water area to be detected, and at least two kinds of water quality index data and the abnormal threshold value of each kind of water quality index data of the water area to be detected.
7. The water quality supervision system of claim 5, wherein the server is further configured to:
and under the condition that the water area to be detected is polluted, determining the spatial position of the water area to be detected as a pollution position, and determining the pollution characteristic and the pollution grade according to the peripheral data of the water area to be detected and at least two kinds of water quality index data.
8. The water quality supervision system of claim 5, wherein the server is further configured to:
and under the condition that the water area to be detected is polluted, carrying out parameter calibration on the established hydrodynamic model according to the peripheral data of the water area to be detected and at least two kinds of water quality index data.
9. The water quality supervision system of claim 8, wherein the server is further configured to:
and predicting relevant pollution information of the water area to be measured according to the peripheral data of the water area to be measured, the at least two kinds of water quality index data and the hydrodynamic model with the parameters calibrated.
10. The water quality supervision system according to claim 9, wherein the information relating to pollution comprises at least one of a duration, a spatial extent and a pollution level of the pollution.
11. The water quality supervision system according to claim 5,
the spectrum detection device is also used for: collecting spectral data of a contaminated water field sample at the first sampling frequency; and
the server is further configured to:
determining at least two kinds of water quality index data of the water area sample according to the spectral data of the water area sample, and acquiring the position, the initial time and the concentration of the polluted source;
establishing a water area pollution distribution function according to the position of the polluted source, the starting time, the concentration of the polluted source and at least two kinds of water quality index data;
and under the condition that the water area to be detected is polluted, determining the position, the initial time and the concentration of a pollution source of the pollution corresponding to the water area to be detected according to the water area pollution distribution function and at least two kinds of water quality index data of the water area to be detected.
12. The water quality supervision system of claim 11, wherein the server is further configured to:
and providing a countermeasure aiming at the pollution corresponding to the water area to be detected according to the determined pollution source position, the starting time and the pollution source concentration.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117173580A (en) * | 2023-11-03 | 2023-12-05 | 芯视界（北京）科技有限公司 | Water quality parameter acquisition method and device, image processing method and medium |
| CN117370751A (en) * | 2023-09-13 | 2024-01-09 | 浙江天禹信息科技有限公司 | Cross-validation hydrologic data elasticity monitoring method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117370751A (en) * | 2023-09-13 | 2024-01-09 | 浙江天禹信息科技有限公司 | Cross-validation hydrologic data elasticity monitoring method and system |
| CN117370751B (en) * | 2023-09-13 | 2024-03-19 | 浙江天禹信息科技有限公司 | Cross-validation hydrologic data elasticity monitoring method and system |
| CN117173580A (en) * | 2023-11-03 | 2023-12-05 | 芯视界（北京）科技有限公司 | Water quality parameter acquisition method and device, image processing method and medium |
| CN117173580B (en) * | 2023-11-03 | 2024-01-30 | 芯视界（北京）科技有限公司 | Water quality parameter acquisition method and device, image processing method and medium |
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