CN114485781A - Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method - Google Patents
Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method Download PDFInfo
- Publication number
- CN114485781A CN114485781A CN202111646479.2A CN202111646479A CN114485781A CN 114485781 A CN114485781 A CN 114485781A CN 202111646479 A CN202111646479 A CN 202111646479A CN 114485781 A CN114485781 A CN 114485781A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- information
- unit
- water quality
- forecasting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 190
- 238000013277 forecasting method Methods 0.000 title claims abstract description 12
- 238000007667 floating Methods 0.000 title claims description 21
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 106
- 241000192700 Cyanobacteria Species 0.000 claims abstract description 18
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 238000012806 monitoring device Methods 0.000 claims abstract description 10
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 14
- 229910052760 oxygen Inorganic materials 0.000 claims description 14
- 239000001301 oxygen Substances 0.000 claims description 14
- 238000007781 pre-processing Methods 0.000 claims description 14
- 238000004146 energy storage Methods 0.000 claims description 13
- 238000012800 visualization Methods 0.000 claims description 12
- 238000005286 illumination Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 11
- 241000195493 Cryptophyta Species 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 5
- 239000000523 sample Substances 0.000 claims description 4
- 238000004148 unit process Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 238000007726 management method Methods 0.000 abstract description 5
- 241000192710 Microcystis aeruginosa Species 0.000 description 6
- 238000001514 detection method Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 241000251468 Actinopterygii Species 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000012851 eutrophication Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000000034 method Methods 0.000 description 2
- 206010021143 Hypoxia Diseases 0.000 description 1
- 201000002451 Overnutrition Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229930002875 chlorophyll Natural products 0.000 description 1
- 235000019804 chlorophyll Nutrition 0.000 description 1
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 235000020823 overnutrition Nutrition 0.000 description 1
- 230000029553 photosynthesis Effects 0.000 description 1
- 238000010672 photosynthesis Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/06—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a liquid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/08—Systems determining position data of a target for measuring distance only
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Hydrology & Water Resources (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physical Water Treatments (AREA)
Abstract
The invention discloses a floating-sinking type cyanobacteria bloom monitoring system and a forecasting method for a shallow lake, which comprise a cyanobacteria bloom monitoring device module and a cyanobacteria bloom forecasting module, wherein a cyanobacteria bloom monitoring system is perfected by monitoring weather, hydrology and water quality at different water depths, a multi-element, deep and low-cost monitoring device is constructed, the monitoring of water quality, weather and hydrology data in a fixed-point area of the lake can be realized, and the device can automatically cruise to monitor the occurrence condition of cyanobacteria bloom in the lake timely and comprehensively according to actual requirements; through the treatment of weather, hydrology and water quality, the occurrence condition of cyanobacterial bloom in a period of time in the future is predicted by utilizing a big data technology, and the early warning grade is divided by referring to the actual management demand and is sent to the corresponding administrative management department in a grading manner.
Description
Technical Field
The invention relates to the technical field of cyanobacterial bloom monitoring and early warning, in particular to a floating cyanobacterial bloom monitoring system and a forecasting method applied to shallow lakes.
Background
In recent years, the eutrophication of water bodies in China is increasingly serious, and most freshwater lakes and reservoirs in China are in a mild or moderate eutrophication state. In some water bodies with rich nutrition, nutrients are difficult to digest to cause overnutrition, blue algae are usually propagated in summer, and the mass propagation of the blue algae deteriorates ventilation, illumination and oxygen deficiency in water, so that plankton in the water grows and propagates, thereby inhibiting the photosynthesis of the algae, reducing the living space of fish and other organisms, forming a layer of blue-green and foul-smelling froth called water bloom during sleep, and causing water quality deterioration. The water body biological diversity caused by the cyanobacterial bloom is reduced rapidly, and the death of fish caused by the exhaustion of oxygen in water is caused seriously, so that the instant monitoring of the cyanobacterial and the water body with the cyanobacterial bloom phenomenon is developed, the instant analysis and feedback of the monitoring data are carried out, and the instant early warning and effective treatment means are facilitated to be established.
The current cyanobacterial bloom monitoring mainly comprises manual cruising and fixed-point automatic monitoring, but the manual cruising monitoring needs a large amount of manpower and material resources; the automatic fixed-point monitoring needs high-density arrangement, and the monitoring cost is higher.
Disclosure of Invention
The invention discloses a sinking-floating type cyanobacteria bloom monitoring system and a forecasting method for a shallow lake, which solve the problems that a large amount of manpower and material resources are consumed for the current manual cruising monitoring, the automatic fixed-point monitoring needs high-density arrangement, and the monitoring cost is higher.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the invention discloses a floating type cyanobacteria water bloom monitoring system for a shallow lake, which comprises a cyanobacteria water bloom monitoring device module and a cyanobacteria water bloom forecasting module, wherein the cyanobacteria water bloom monitoring device module comprises a weather monitoring unit, a hydrological monitoring unit, a water quality monitoring unit and a cruising unit, wherein the weather monitoring unit is used for acquiring weather information of a monitored area; the hydrologic monitoring unit is used for acquiring hydrologic information of a monitored area; the water quality monitoring unit is used for acquiring water quality information of a monitored area; the cruise unit comprises a laser range finder and a GPS positioning device and is used for realizing cruise monitoring at a specified place in a monitored area; the cyanobacterial bloom forecasting module comprises a detection information preprocessing unit, a detection information visualization unit, a forecasting information unit and an information sending unit, wherein the monitoring information preprocessing unit is used for processing monitoring dirty data in real time and improving the quality of the monitoring data; the monitoring information visualization unit is used for visualizing the water quality and hydrological conditions of the monitored area; the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, and a forecasting model of the cyanobacterial bloom is constructed by a big data technology to obtain forecasting information; and the information sending unit carries out hierarchical processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments.
Further, the floating type cyanobacterial bloom monitoring system for the shallow lake further comprises an energy storage unit, wherein the energy storage unit is used for providing electric energy for the cruise unit.
Further, the energy storage unit comprises a solar panel and an energy storage battery, wherein the solar panel is used for converting solar energy into electric energy; the energy storage battery is used for storing the electric energy converted by the solar cell panel.
Further, the meteorological monitoring unit comprises a wind direction sensor, a wind speed sensor, a rainfall sensor, a light illumination sensor and a pressure sensor, wherein the wind direction sensor is used for acquiring a wind direction; the wind speed sensor is used for acquiring wind speed; the rainfall sensor is used for acquiring rainfall; the illumination sensor is used for acquiring illumination intensity; the sunshine duration sensor is used for acquiring sunshine duration; the air pressure sensor is used for acquiring atmospheric pressure.
Further, the hydrological monitoring unit comprises a water depth detector, a flow rate monitor and a flow direction monitor, wherein the water depth detector is used for detecting the depth of the water area; the flow rate monitor is used for monitoring the water flow rate in the area; the flow direction monitor is used for monitoring the water flow direction in the area.
Furthermore, the water quality monitoring unit comprises a dissolved oxygen monitoring sensor, a turbidity monitoring sensor and a conductivity monitoring sensor, wherein the dissolved oxygen monitoring sensor is used for monitoring the milligrams of oxygen in each liter of water in the water area; the turbidity monitoring sensor is used for monitoring the water turbidity in the water area; the conductivity monitoring sensor is used for monitoring the conductivity in the water area.
Furthermore, the dissolved oxygen monitoring sensor, the turbidity monitoring sensor and the conductivity monitoring sensor are all retractable structures.
Furthermore, the water quality monitoring unit also comprises a cleaning spray head used for cleaning the probes of the dissolved oxygen monitoring sensor, the turbidity monitoring sensor and the conductivity monitoring sensor at regular time.
The invention also discloses a forecasting method of the floating type cyanobacterial bloom monitoring system for the shallow lake, which comprises the following steps:
the weather monitoring unit acquires weather information of a monitored area in real time; the hydrological monitoring unit acquires hydrological information of a monitored area in real time; the water quality monitoring unit acquires water quality information of a monitored area in real time;
the monitoring information preprocessing unit processes monitoring dirty data and improves the quality of the monitoring data;
the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, a forecasting model of the cyanobacterial bloom is constructed through a big data technology, the forecasting information is obtained, and information visualization is realized;
the information sending unit carries out grading processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments.
Further, the method for constructing the cyanobacterial bloom forecasting model by the forecasting information unit comprises the following steps:
s1: integrating meteorological information acquired by a meteorological monitoring unit in real time, hydrological information acquired by a hydrological monitoring unit in real time and water quality information acquired by a water quality monitoring unit in real time to form a data set D, wherein D is [ water quality, hydrology and meteorology ];
s2: preprocessing data;
s21: data outlier screening
S22: completing missing data values;
s3: screening important influence variables of the cyanobacterial bloom;
s4: constructing a blue algae bloom forecasting model;
s41: dividing a data variable and a data label;
s42: dividing a data set;
s43: building a model;
s44: verifying the model;
s5: forecasting the water quality;
s6: and visualizing forecast information.
The beneficial technical effects are as follows:
the invention discloses a floating-sinking type cyanobacteria bloom monitoring system and a forecasting method for a shallow lake, which comprise a cyanobacteria bloom monitoring device module and a cyanobacteria bloom forecasting module, wherein the cyanobacteria bloom monitoring device module comprises a weather monitoring unit, a hydrological monitoring unit, a water quality monitoring unit and a cruising unit, wherein the weather monitoring unit is used for acquiring weather information of a monitored area; the hydrologic monitoring unit is used for acquiring hydrologic information of a monitored area; the water quality monitoring unit is used for acquiring water quality information of a monitored area; the cruise unit comprises a laser range finder and a GPS positioning device and is used for realizing cruise monitoring at a specified place in a monitored area; the cyanobacterial bloom forecasting module comprises a detection information preprocessing unit, a detection information visualization unit, a forecasting information unit and an information sending unit, wherein the monitoring information preprocessing unit is used for processing monitoring dirty data in real time and improving the quality of the monitoring data; the monitoring information visualization unit is used for visualizing the water quality and hydrological conditions of the monitored area; the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, and a forecasting model of the cyanobacterial bloom is constructed by a big data technology to obtain forecasting information; and the information sending unit carries out hierarchical processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiments will be briefly introduced below.
FIG. 1 is a block diagram of a floating cyanobacterial bloom monitoring system for a shallow lake according to the present invention;
FIG. 2 is a flow chart of the steps of the forecasting method of the ups and downs type cyanobacterial bloom monitoring system for shallow lakes.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention discloses a sinking and floating type cyanobacterial bloom monitoring system for a shallow lake, which comprises a cyanobacterial bloom monitoring device module and a cyanobacterial bloom forecasting module, wherein the cyanobacterial bloom monitoring system for the shallow lake comprises a meteorological monitoring unit, a hydrological monitoring unit, a water quality monitoring unit and a cruising unit, wherein the meteorological monitoring unit is used for acquiring meteorological information of a monitored area and acquiring meteorological information including but not limited to wind direction, wind speed, rainfall, illumination intensity, illumination duration, atmospheric pressure and the like, preferably, the meteorological monitoring unit comprises a wind direction sensor, a wind speed sensor, a rainfall sensor, a light illumination sensor, a sunshine duration sensor and an air pressure sensor, wherein the wind direction sensor is used for acquiring wind direction; the wind speed sensor is used for acquiring wind speed; the rainfall sensor is used for acquiring rainfall; the illumination sensor is used for acquiring illumination intensity; the sunshine duration sensor is used for acquiring sunshine duration; the air pressure sensor is used for acquiring atmospheric pressure; the hydrologic monitoring unit is used for acquiring hydrologic information of a monitored area, and acquiring hydrologic information including but not limited to water depth, water flow velocity and water flow direction, preferably, the hydrologic monitoring unit comprises a water depth detector, a flow velocity monitor and a flow direction detector, wherein the water depth detector is used for detecting the depth of the water area; the flow rate monitor is used for monitoring the water flow rate in the area; the flow direction monitor is used for monitoring the water flow direction in the area; the water quality monitoring unit is used for acquiring water quality information of a monitored area, and acquiring water quality information including but not limited to dissolved oxygen, conductivity, turbidity, chlorophyll and the like, preferably, the water quality monitoring unit comprises a dissolved oxygen monitoring sensor, a turbidity monitoring sensor and a conductivity monitoring sensor, wherein the dissolved oxygen monitoring sensor is used for monitoring the milligrams of oxygen in each liter of water in the water area; the turbidity monitoring sensor is used for monitoring the water turbidity in the water area; the conductivity monitoring sensor is used for monitoring the conductivity in the water area, the water quality sensors are all arranged in a telescopic mode, monitoring in different water depths is achieved, and in order to avoid the probe from being shielded by phytoplankton underwater, the water quality monitoring unit is further provided with a cleaning nozzle which can clean the probe at regular time; the cruise unit comprises a laser range finder and a GPS positioning device, and is used for realizing cruise monitoring at a specified place in a monitored area, specifically, the GPS positioning device can position the position of the device, meanwhile, the position of the area required to be monitored can be input according to actual requirements, the cruise unit can automatically cruise to the specified area for monitoring, the laser range finder can calculate the distance between a cyanobacteria bloom monitoring system and an obstacle, a distance matrix is generated, an intensity-based abnormal value detection algorithm is applied to the distance matrix, an LOF algorithm is adopted for abnormal value detection, whether the obstacle exists in the front is judged, if the obstacle exists in the front, the cruise unit can send an obstacle avoidance instruction cyanobacteria bloom forecasting module to the system, and the cyanobacteria bloom forecasting module comprises a monitoring information preprocessing unit, a monitoring information visualization unit, a forecasting information unit and an information sending unit, wherein the monitoring information preprocessing unit is used for processing the monitoring dirty data in real time, improving the quality of monitoring data, specifically, screening abnormal values through an LOF algorithm based on density monitoring, removing the abnormal values, and completing with a front average value and a rear average value; the monitoring information visualization unit is used for visualizing the water quality and hydrological conditions of the monitored area, and particularly, the visualization of the water quality and hydrological information is realized through a GIS difference method; the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, a forecasting model of the cyanobacterial bloom is constructed through a big data technology, the forecasting information is obtained, specifically, the forecasting information unit integrates water quality, hydrological and meteorological data, and the relation among water quality, hydrological and meteorological data is mined through big data technologies such as machine learning and deep learning, so that the cyanobacterial bloom forecasting model is established; the information sending unit carries out classification processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments, and specifically, the information sending unit divides the early warning levels into a first-level early warning, a second-level early warning and a third-level early warning according to the explosion degree of the cyanobacterial bloom in the actual lake and pushes the early warning levels to different administrative management departments.
As a preferred embodiment of the present invention, the floating and sinking type cyanobacteria water bloom monitoring system for shallow lakes further comprises an energy storage unit, the energy storage unit is used for providing electric energy for the cruise unit, preferably, the energy storage unit comprises a solar cell panel and an energy storage battery, wherein the solar cell panel is used for converting solar energy into electric energy; the energy storage battery is used for storing the electric energy converted by the solar panel.
The invention discloses a forecasting method of a floating type cyanobacterial bloom monitoring system for a shallow lake on the other hand, which is shown in figure 2 and specifically comprises the following steps:
s1: the weather monitoring unit acquires weather information of a monitored area in real time; the hydrologic monitoring unit acquires hydrologic information of a monitored area in real time; the water quality monitoring unit acquires water quality information of a monitored area in real time;
s2: the monitoring information preprocessing unit processes monitoring dirty data and improves the quality of the monitoring data;
s3: the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, a forecasting model of the cyanobacterial bloom is constructed through a big data technology, the forecasting information is obtained, and information visualization is realized;
specifically, the specific steps of constructing the forecasting model of the cyanobacterial bloom comprise:
integrating meteorological information acquired by a meteorological monitoring unit in real time, hydrological information acquired by a hydrological monitoring unit in real time and water quality information acquired by a water quality monitoring unit in real time to form a data set D, wherein D is [ water quality, hydrology and meteorology ];
preprocessing data;
specifically, the data preprocessing comprises data outlier screening and data missing value completion;
screening important influence variables of the cyanobacterial bloom;
constructing a blue algae bloom forecasting model;
specifically, the construction of the blue algae bloom forecasting model comprises data variable and data label division, data set division, model building, model verification and model analysis,
Forecasting the water quality;
forecasting information visualization;
s4: the information sending unit carries out grading processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments.
The invention discloses a floating-sinking type cyanobacteria bloom monitoring system and a forecasting method for a shallow lake, which perfects a cyanobacteria bloom monitoring system by monitoring weather, hydrology and water quality at different water depths, constructs a multi-element, deep and low-cost monitoring device, can realize monitoring of water quality, weather and hydrology data in a fixed-point area of the lake, and can automatically cruise to monitor the cyanobacteria bloom occurrence condition of the lake timely and comprehensively; through the treatment of weather, hydrology and water quality, the occurrence condition of cyanobacterial bloom in a period of time in the future is predicted by utilizing a big data technology, and the early warning grade is divided by referring to the actual management demand and is sent to the corresponding administrative management department in a grading manner.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above examples are only for describing the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (10)
1. The utility model provides a heavy floating type cyanobacterial bloom monitoring system for shallow lake which characterized in that, includes cyanobacterial bloom monitoring devices module and cyanobacterial bloom forecast module, wherein, cyanobacterial bloom monitoring devices module includes:
the meteorological monitoring unit is used for acquiring meteorological information of a monitored area;
the hydrologic monitoring unit is used for acquiring hydrologic information of a monitored area;
the water quality monitoring unit is used for acquiring water quality information of a monitored area;
the cruise unit comprises a laser range finder and a GPS positioning device and is used for realizing cruise monitoring at a specified place in a monitored area;
the blue algae bloom forecasting module comprises:
the monitoring information preprocessing unit is used for processing and monitoring dirty data in real time and improving the quality of the monitoring data;
the monitoring information visualization unit is used for visualizing the water quality and hydrologic conditions of the monitored area;
the forecasting information unit is used for constructing a forecasting model of the cyanobacterial bloom through a big data technology by combining the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit to obtain forecasting information;
and the information sending unit is used for carrying out hierarchical processing on the forecast information acquired by the forecast information unit and sending the forecast information to different departments.
2. The system for monitoring the floating cyanobacterial bloom in a shallow lake according to claim 1, further comprising:
and the energy storage unit is used for providing electric energy for the cruise unit.
3. The system for monitoring the floating cyanobacterial bloom in a shallow lake of claim 2, wherein the energy storage unit comprises:
the solar cell panel is used for converting solar energy into electric energy;
and the energy storage battery is used for storing the electric energy converted by the solar panel.
4. The system for monitoring the floating cyanobacterial bloom in a shallow lake of claim 1, wherein the meteorological monitoring unit comprises:
a wind direction sensor for acquiring a wind direction;
the wind speed sensor is used for acquiring wind speed;
a rainfall sensor for acquiring rainfall;
the illumination sensor is used for acquiring illumination intensity;
the sunshine duration sensor is used for acquiring sunshine duration;
and the air pressure sensor is used for acquiring atmospheric pressure.
5. The system for monitoring the floating cyanobacterial bloom in a shallow lake of claim 1, wherein the hydrological monitoring unit comprises:
a water depth detector for detecting the depth of the water area;
a flow rate monitor for monitoring the flow rate of water in the area;
and the flow direction monitor is used for monitoring the water flow direction in the area.
6. The system for monitoring the floating cyanobacterial bloom in the shallow lake as claimed in claim 1, wherein the water quality monitoring unit comprises:
a dissolved oxygen monitoring sensor for monitoring milligrams of oxygen per liter of water in the water region;
a turbidity monitoring sensor for monitoring the turbidity of the water in the water region;
a conductivity monitoring sensor to monitor conductivity within the water region.
7. The system for monitoring the floating and sinking type cyanobacterial bloom in a shallow lake of claim 6, wherein the dissolved oxygen monitoring sensor, the turbidity monitoring sensor and the conductivity monitoring sensor are all retractable structures.
8. The system for monitoring the floating and sinking type cyanobacterial bloom in a shallow lake as claimed in claim 6, further comprising a cleaning nozzle for cleaning the probes of the dissolved oxygen monitoring sensor, the turbidity monitoring sensor and the conductivity monitoring sensor at regular time.
9. The forecasting method of the floating cyanobacterial bloom monitoring system for shallow lakes as claimed in any one of claims 1 to 8, comprising the following steps:
the meteorological monitoring unit acquires meteorological information of a monitored area in real time; the hydrologic monitoring unit acquires hydrologic information of a monitored area in real time; the water quality monitoring unit acquires water quality information of a monitored area in real time;
the monitoring information preprocessing unit processes monitoring dirty data and improves the quality of the monitoring data;
the forecasting information unit is combined with the meteorological information monitored by the meteorological monitoring unit, the hydrological information monitored by the hydrological monitoring unit and the water quality information monitored by the water quality monitoring unit, a forecasting model of the cyanobacterial bloom is constructed through a big data technology, the forecasting information is obtained, and information visualization is realized;
the information sending unit carries out hierarchical processing on the forecast information acquired by the forecast information unit and sends the forecast information to different departments.
10. The forecasting method of the floating cyanobacteria bloom monitoring system for the shallow lake of claim 9, wherein the construction of the cyanobacteria bloom forecasting model by the forecasting information unit comprises the following steps:
s1: integrating meteorological information acquired by a meteorological monitoring unit in real time, hydrological information acquired by a hydrological monitoring unit in real time and water quality information acquired by a water quality monitoring unit in real time to form a data set D, wherein D is [ water quality, hydrology and meteorology ];
s2: preprocessing data;
s21: data outlier screening
S22: completing missing data values;
s3: screening important influence variables of the cyanobacterial bloom;
s4: constructing a blue algae bloom forecasting model;
s41: dividing a data variable and a data label;
s42: dividing a data set;
s43: building a model;
s44: verifying the model;
s5: forecasting the water quality;
s6: and visualizing forecast information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111646479.2A CN114485781A (en) | 2021-12-30 | 2021-12-30 | Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111646479.2A CN114485781A (en) | 2021-12-30 | 2021-12-30 | Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114485781A true CN114485781A (en) | 2022-05-13 |
Family
ID=81507516
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111646479.2A Pending CN114485781A (en) | 2021-12-30 | 2021-12-30 | Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114485781A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN103604423A (en) * | 2013-11-22 | 2014-02-26 | 中国科学院南京地理与湖泊研究所 | Remote-sensing wild automatic monitoring system and method for shallow lakes |
| CN104197983A (en) * | 2014-03-18 | 2014-12-10 | 王雅娜 | Ecological restoration monitoring system for early warning outbreak of water blue algae |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
| CN108830425A (en) * | 2018-06-26 | 2018-11-16 | 北京高能时代环境技术股份有限公司 | Prediction of Reservoir Water Quality system and method |
-
2021
- 2021-12-30 CN CN202111646479.2A patent/CN114485781A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN103604423A (en) * | 2013-11-22 | 2014-02-26 | 中国科学院南京地理与湖泊研究所 | Remote-sensing wild automatic monitoring system and method for shallow lakes |
| CN104197983A (en) * | 2014-03-18 | 2014-12-10 | 王雅娜 | Ecological restoration monitoring system for early warning outbreak of water blue algae |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
| CN108830425A (en) * | 2018-06-26 | 2018-11-16 | 北京高能时代环境技术股份有限公司 | Prediction of Reservoir Water Quality system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jónsdóttir et al. | Fish welfare based classification method of ocean current speeds at aquaculture sites | |
| CN101520449B (en) | Immediate offshore area water-quality sampling point optimization device and optimization method thereof | |
| CN104197983A (en) | Ecological restoration monitoring system for early warning outbreak of water blue algae | |
| CN110126993A (en) | A kind of automatic operating system waterborne | |
| CN112801381A (en) | Jellyfish disaster early warning method | |
| CN116467565B (en) | Enteromorpha green tide plaque optimal search area forecasting method | |
| Mu et al. | Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences | |
| Codiga | A marine autonomous surface craft for long-duration, spatially explicit, multidisciplinary water column sampling in coastal and estuarine systems | |
| Veytia et al. | Overwinter sea-ice characteristics important for Antarctic krill recruitment in the southwest Atlantic | |
| Gouezo et al. | Modelled larval supply predicts coral population recovery potential following disturbance | |
| Stelfox et al. | Minimum drift times infer trajectories of ghost nets found in the Maldives | |
| CN114049243B (en) | Enteromorpha accumulation amount estimation method, system and storage medium | |
| Johannessen et al. | Remote sensing and model simulation studies of the Norwegian coastal current during the algal bloom in May 1988 | |
| CN114485781A (en) | Floating type cyanobacterial bloom monitoring system for shallow lake and forecasting method | |
| CN105070102A (en) | Ship navigation dynamic risk inference system and method based on fuzzy logic | |
| Thompson et al. | Nearshore circulation and synthetic aperture radar: an exploratory study | |
| Thomas | Modelling marine connectivity in the Great Barrier Reef and exploring its ecological implications | |
| Nichols et al. | Remote sensing technologies to monitor harmful algal blooms in offshore aquaculture | |
| Xue et al. | Busy fishing area recognition based on improved K-means with random walking centroid | |
| Klebert et al. | Response of Atlantic salmon in flexible sea cages to waves and currents: Measurements at farm-level and modeling | |
| Kumar et al. | Detection of Cyclones and Extreme Weather Events Using Advanced Deep Learning Methods | |
| Handegard et al. | Toward a global ocean ecosystem mid-trophic automatic acoustic sampler (MAAS) | |
| Scannell | Seas in Hot Water: Marine Heatwaves on the Move; Following the Heat | |
| Wu et al. | Characteristics and distribution model of wind and wave in Shengsi mussel culture area under the impacts of tropical cyclones | |
| Sundermann | The changing North Sea: knowledge, speculation and new challenges |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |