CN112964843A - Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method - Google Patents
Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method Download PDFInfo
- Publication number
- CN112964843A CN112964843A CN202110103573.7A CN202110103573A CN112964843A CN 112964843 A CN112964843 A CN 112964843A CN 202110103573 A CN202110103573 A CN 202110103573A CN 112964843 A CN112964843 A CN 112964843A
- Authority
- CN
- China
- Prior art keywords
- data
- water quality
- effluent
- sensor
- monitoring
- 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
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 209
- 238000012544 monitoring process Methods 0.000 title claims abstract description 109
- 239000010865 sewage Substances 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000013136 deep learning model Methods 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 238000013441 quality evaluation Methods 0.000 claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 238000007726 management method Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 10
- 230000005540 biological transmission Effects 0.000 claims abstract description 8
- 238000013500 data storage Methods 0.000 claims abstract description 8
- 229910019142 PO4 Inorganic materials 0.000 claims description 10
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 9
- MMDJDBSEMBIJBB-UHFFFAOYSA-N [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] Chemical compound [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] MMDJDBSEMBIJBB-UHFFFAOYSA-N 0.000 claims description 8
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 claims description 8
- 239000010452 phosphate Substances 0.000 claims description 8
- 238000012217 deletion Methods 0.000 claims description 6
- 230000037430 deletion Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 3
- 230000009469 supplementation Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims description 2
- 238000001303 quality assessment method Methods 0.000 claims 2
- 238000001514 detection method Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 description 14
- 238000013135 deep learning Methods 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 230000010354 integration Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 239000013589 supplement Substances 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 240000007651 Rubus glaucus Species 0.000 description 1
- 235000011034 Rubus glaucus Nutrition 0.000 description 1
- 235000009122 Rubus idaeus Nutrition 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 239000007853 buffer solution Substances 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 239000007791 liquid phase Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 230000033116 oxidation-reduction process Effects 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
Images
Classifications
-
- 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
- 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
- G01N33/1806—Biological oxygen demand [BOD] or chemical oxygen demand [COD]
-
- 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
- G01N33/1826—Organic contamination in water
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Immunology (AREA)
- Engineering & Computer Science (AREA)
- Pathology (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Biomedical Technology (AREA)
- Emergency Medicine (AREA)
- Molecular Biology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention discloses an Internet of things sensor system for monitoring water quality of a sewage treatment facility and a monitoring method, wherein the system comprises: detecting water data in real time through a sensor terminal; the data acquisition module is used for acquiring water outlet data detected by the sensor terminal in real time; the data storage module is used for storing the water outlet data; the data preprocessing module is used for preprocessing the water outlet data; the water quality monitoring module is used for processing the pretreated effluent data through the deep learning model to obtain effluent water quality information; the water quality evaluation module is used for carrying out graded evaluation on the water quality information through a water quality evaluation algorithm; the data transmission module is used for sending the data of the water quality monitoring terminal to the Internet of things platform, and remote monitoring and management are carried out through the Internet of things platform. The method can realize the online real-time monitoring of the effluent quality of rural sewage treatment facilities and perform standard evaluation on the effluent quality.
Description
Technical Field
The invention relates to the technical field of water environment quality monitoring, in particular to an Internet of things sensor system and a monitoring method for monitoring water quality of a sewage treatment facility.
Background
Along with the gradual deepening of water environment treatment in China, the rural sewage treatment is gradually emphasized. A large number of rural sewage treatment facilities are built in more and more areas, and rural domestic sewage is collected and treated, so that the rural domestic sewage treatment facility becomes an important link for water environment protection. At the present stage, rural sewage treatment facilities in China have the characteristics of small monomer scale, large integral quantity and scattered distribution, so that the integral operation and maintenance supervision is very difficult, and the development of the Internet of things technology is urgently needed.
At present, an internet of things remote management platform is developed in part of regions, and stable and reliable information such as the running state, water quantity and electric quantity of equipment of rural sewage treatment facilities is acquired to the management platform. However, the effluent quality of the treatment facility is mainly detected by manual sampling, and almost no automatic monitoring instrument and equipment exist, so that the effluent monitoring of the treatment facility is vacant. The main reasons are that a set of outlet water quality monitoring instrument with complete supervision indexes is expensive in manufacturing cost and cannot be deployed in a large range, and meanwhile, the existing water quality online monitoring instrument needs to replace chemical reagents regularly, and has the problems of high maintenance, operation and maintenance cost, high requirement on the operation skills of maintenance personnel and the like. The actual constraint conditions directly cause that the effluent quality information of a large amount of rural sewage treatment facilities is in a blank state, the daily operation management is seriously influenced, the supervision requirements cannot be met, and a solution is urgently needed to be found.
In recent years, in the field of water treatment, a method for realizing water quality monitoring based on a soft measurement technology is researched, and the main research directions are as follows: (1) in the field of sewage treatment plants, the water quality data of other treatment units of the water-free instrument is predicted by the data of a known online water quality monitoring instrument, for example, the water quality data of each treatment unit is predicted and analyzed according to the data of a water inlet and outlet water quality instrument of the sewage treatment plant; (2) forecasting unmonitored water quality data according to data of an installed water quality meter, such as forecasting BOD water quality data through an online monitoring meter such as COD/SS; (3) and predicting the water quality data of certain effluent according to the data of the installed monitoring instrument, such as total nitrogen, COD (chemical oxygen demand) and the like of the effluent according to the pH, conductivity and ammonia nitrogen instrument data of the inlet and outlet water. The water quality monitoring method based on the soft measurement technology still needs to utilize the water quality monitoring instrument to predict the effluent water quality, not only does not solve the problems of expensive manufacturing cost and high deployment cost of the water quality instrument in the rural sewage water quality monitoring field, but also needs to carry out modeling correction on each set of soft measurement technology even if part of the water quality instrument is deployed in each treatment facility, needs a large amount of manpower and material resources, and does not meet the actual condition of the rural sewage effluent water quality monitoring in China at present.
Therefore, how to realize the monitoring of the effluent quality of the rural sewage treatment facility by deploying a small amount of monitoring instruments which are cheap, stable and have the attributes of the Internet of things is an urgent problem to be solved in the field of rural sewage treatment, and has important significance for the stable standard-reaching operation of the treatment facility, the improvement of the operation and maintenance management level of the rural sewage and the effective supervision of water pollution treatment.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide an Internet of things sensor system for monitoring the water quality of a sewage treatment facility, which can realize online real-time monitoring of the effluent quality of the rural sewage treatment facility and perform standard evaluation on the effluent quality.
The invention also aims to provide a water quality monitoring method for the sewage treatment facility.
In order to achieve the above object, an embodiment of the invention provides an internet of things sensor system for monitoring water quality of a sewage treatment facility, which includes:
the system comprises a sensor terminal, a water quality monitoring terminal, a power supply module and an Internet of things platform;
the water quality monitoring terminal comprises a data acquisition module, a data storage module, a data preprocessing module, a water quality monitoring module, a water quality evaluation module and a data transmission module;
the sensor terminal and the water quality monitoring terminal are connected with a power module, and the sensor terminal is connected with the water quality monitoring terminal;
the sensor terminal comprises a plurality of sensors and is used for detecting water data in real time;
the data acquisition module is used for acquiring water outlet data detected by the sensor terminal in real time;
the data storage module is used for storing the water outlet data;
the data preprocessing module is used for preprocessing the water outlet data;
the water quality monitoring module is used for processing the pretreated effluent data through a deep learning model to obtain effluent water quality information;
the water quality evaluation module is used for carrying out graded evaluation on the water quality information through a water quality evaluation algorithm;
the data transmission module is used for transmitting the data of the water quality monitoring terminal to the Internet of things platform, and remote monitoring and management are carried out through the Internet of things platform.
According to the Internet of things sensor system for monitoring the water quality of the sewage treatment facility, the corresponding low-cost instrument is selected according to the sewage treatment reaction mechanism, the cost is low, and the large-scale deployment is facilitated; the online real-time monitoring is realized by continuous monitoring and characteristic identification of simple indexes and integration of the attributes of the Internet of things; a deep learning algorithm is adopted to represent the effluent quality condition, and the water quality is subjected to standard-reaching grading evaluation to realize real-time monitoring of the effluent quality; the low-cost instrument is integrated into the Internet of things sensor terminal, so that the installation is convenient, the sensor is free from daily maintenance, and the operation and maintenance cost is effectively reduced.
In addition, the internet of things sensor system for monitoring the water quality of the sewage treatment facility according to the embodiment of the invention can also have the following additional technical characteristics:
further, in one embodiment of the present invention, the sensor terminal includes: a PH sensor, an ORP sensor, a conductivity sensor, and a turbidity sensor;
the pH sensor is used for detecting the pH of the effluent, the ORP sensor is used for detecting the ORP of the effluent, the conductivity sensor is used for detecting the conductivity of the effluent, and the turbidity sensor is used for detecting the turbidity of the effluent.
Further, in an embodiment of the present invention, the data preprocessing module is configured to preprocess the effluent data, including timestamp inspection, data determination, error data deletion, missing data supplementation, and obtain the pH of the cleaned sensor data after preprocessingc、ORPc、CONDc、TURBc。
Further, in an embodiment of the present invention, a deep learning model for post-training and parameter optimization is built in the water quality monitoring module, and the pH after pretreatment is processed by the deep learning modelc、ORPc、CONDc、TURBcAnd importing the data into a deep learning model to obtain the water quality data of effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate.
Further, in an embodiment of the present invention, the water quality evaluation module is configured to compare the water quality information of the effluent with a discharge standard to obtain a water quality index of each index, select a maximum value of the index as a comprehensive water quality index to represent a treatment effect of the sewage treatment facility, and rank the comprehensive water quality index.
In order to achieve the above object, an embodiment of another aspect of the present invention provides a method for monitoring water quality of a sewage treatment facility, including:
collecting water outlet data through a plurality of sensors, and preprocessing the water outlet data;
processing the pretreated effluent data through a deep learning model to obtain effluent quality information;
grading and evaluating the water quality information through a water quality evaluation algorithm;
and sending the pretreated effluent data, the water quality information and the grading evaluation result to an Internet of things platform, and carrying out real-time monitoring and management through the Internet of things platform.
According to the water quality monitoring method for the sewage treatment facility, the corresponding low-cost instrument is selected according to the sewage treatment reaction mechanism, the cost is low, and the large-scale deployment is facilitated; the online real-time monitoring is realized by continuous monitoring and characteristic identification of simple indexes and integration of the attributes of the Internet of things; a deep learning algorithm is adopted to represent the effluent quality condition, and the water quality is subjected to standard-reaching grading evaluation to realize real-time monitoring of the effluent quality; the low-cost instrument is integrated into the Internet of things sensor terminal, so that the installation is convenient, the sensor is free from daily maintenance, and the operation and maintenance cost is effectively reduced.
In addition, the method for monitoring the water quality of the sewage treatment facility according to the embodiment of the invention may further have the following additional technical features:
further, in an embodiment of the present invention, the collecting water data by a plurality of sensors includes:
the pH sensor is used for detecting the pH of the water, the ORP sensor is used for detecting the ORP of the water, the conductivity sensor is used for detecting the conductivity of the water, and the turbidity sensor is used for detecting the turbidity of the water.
Further, in an embodiment of the present invention, the effluent data is preprocessed, including timestamp inspection, data determination, error data deletion, and missing data supplement, to obtain the pH of the cleaned sensor datac、ORPc、CONDc、TURBc。
Further, in an embodiment of the present invention, the processing the pretreated effluent data by the deep learning model to obtain the effluent quality information includes:
pretreated pH by deep learning modelc、ORPc、CONDc、TURBcAnd importing the data into a deep learning model to obtain the water quality data of effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate.
Further, in an embodiment of the present invention, the performing a graded evaluation on the water quality information by a water quality evaluation algorithm includes:
and comparing the water quality information of the effluent with the discharge standard to obtain the water quality index of each index, selecting the maximum value of each index as a comprehensive water quality index to represent the treatment effect of the sewage treatment facility, and grading the comprehensive water quality index.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic structural diagram of a sensor system of the Internet of things for monitoring water quality of a sewage treatment facility according to one embodiment of the invention;
fig. 2 is a schematic structural diagram of a sensor terminal of the internet of things according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a water quality monitoring module of a sensor of the Internet of things according to an embodiment of the invention;
FIG. 4 is a data curve of the Internet of things sensor after data preprocessing according to an embodiment of the invention;
fig. 5 is water quality monitoring data of a sensor of the internet of things according to an embodiment of the invention;
FIG. 6 is water quality monitoring rating data of an Internet of things sensor according to an embodiment of the invention;
FIG. 7 is a flow chart of a method for monitoring water quality in a sewage treatment facility according to an embodiment of the present invention.
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 drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes an internet of things sensor system and a monitoring method for monitoring water quality of a sewage treatment facility according to an embodiment of the invention with reference to the accompanying drawings.
First, an internet of things sensor system for monitoring water quality of a sewage treatment facility according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of a sensor system of the internet of things for monitoring water quality of a sewage treatment facility according to an embodiment of the invention.
As shown in fig. 1, the internet of things sensor system for monitoring water quality of a sewage treatment facility comprises: sensor terminal, water quality monitoring terminal, power module and thing networking platform.
The water quality monitoring terminal comprises a data acquisition module, a data storage module, a data preprocessing module, a water quality monitoring module, a water quality evaluation module and a data transmission module;
the sensor terminal and the water quality monitoring terminal are connected with the power supply module, and the sensor terminal is connected with the water quality monitoring terminal;
the sensor terminal comprises a plurality of sensors and is used for detecting water data in real time;
the data acquisition module is used for acquiring water outlet data detected by the sensor terminal in real time;
the data storage module is used for storing the water outlet data;
the data preprocessing module is used for preprocessing the water outlet data;
the water quality monitoring module is used for processing the pretreated effluent data through the deep learning model to obtain effluent water quality information;
the water quality evaluation module is used for carrying out graded evaluation on the water quality information through a water quality evaluation algorithm;
the data transmission module is used for sending the data of the water quality monitoring terminal to the Internet of things platform, and remote monitoring and management are carried out through the Internet of things platform.
Further, the sensor terminal is shown in fig. 2 and includes a PH sensor for detecting PH of the effluent, an ORP (oxidation reduction potential) sensor for detecting ORP of the effluent, a conductivity sensor for detecting conductivity of the effluent, and a turbidity sensor for detecting turbidity of the effluent.
It is understood that PH sensors, ORP sensors, conductivity sensors, turbidity sensors are conventional sensors with ModbusRTU communication capability. The water quality monitoring terminal adopts an industrial controller, such as an embedded industrial computer, a raspberry edge gateway and the like. The sensor terminal is connected with the water quality monitoring terminal through an RS485 communication line for communication and is connected with the power supply module. And the water quality monitoring terminal sends the water quality data to the Internet of things platform through 4G or Ethernet.
The water quality monitoring terminal acquires the monitoring data of the sensor terminal in real time through the data acquisition module to obtain the pH, ORP and electricity of the effluentConductivity, turbidity data: pH valueoriUnit dimensionless, ORPoriUnit mV, CONDoriUnit S/m, TURBoriAnd the unit NTU stores the data into a database of the water quality monitoring terminal through the data storage module.
The water quality monitoring terminal carries out data preprocessing on sensor terminal data acquired in real time through a data preprocessing module to obtain cleaned data, the preprocessing comprises timestamp inspection, data judgment, error data deletion, missing data supplement and the like, and the cleaned sensor data pH is obtainedc、ORPc、CONDc、TURBc。
As shown in fig. 3, a pH sensor is used for representing a discharged water liquid phase ion buffer system, ORP represents organic matter concentration, conductivity represents ammonia nitrogen, nitrate nitrogen and phosphate concentration conditions, and turbidity represents SS concentration, separation effect and the like, effluent COD concentration is obtained through pH, ORP and turbidity, effluent ammonia nitrogen concentration is obtained through ORP and conductivity, effluent TN concentration is obtained through effluent ORP, conductivity and turbidity, and effluent TP concentration is obtained through effluent conductivity/turbidity. A deep learning model for post-training and parameter optimization is arranged in the water quality monitoring module, and the pretreated pH value is usedc、ORPc、CONDc、TURBcAfter the data are imported into a deep learning model, the water quality data of effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate are obtained: COD and NH3-N、NO3-N、PO4P, the units are mg/L. And the TN, TP and SS concentration of the effluent are obtained by calculation, and the calculation formula is as follows:
SS＝1.5+TURBc*0.45
the water quality evaluation module obtains COD and NH of the effluent water through deep learning3N, TN, TP, SS concentration and emissionAnd (3) comparing the standards to obtain a water quality index tau of each index, selecting the maximum value of the index as a comprehensive water quality index xi to represent the treatment effect of the sewage treatment facility, grading the comprehensive water quality index, and grading the comprehensive water quality index by adopting 100 grades, wherein the average grade is 5 and the like, the quality is good, the middle grade is bad, and the standard exceeding 100 is over standard.
Wherein: c. CiCOD and NH of the effluent3-N, TN, TP, SS concentration in mg/L;
csfor treating COD and NH in emission standard of facilities3-limiting concentrations in mg/L for N, TN, TP, SS;
τiCOD and NH of the effluent3-N, TN, TP, SS index, dimensionless;
′＝max{τi}
the following describes a water quality monitoring internet of things sensor system for a sewage treatment facility in a specific embodiment.
A sewage treatment facility in a certain rural area adopts an AO + MBR process, the design scale is 24 tons/day, no water quality monitoring instrument is arranged at an original water outlet, the sensor of the Internet of things is arranged, a sensor terminal shown in figure 3 is arranged at the water outlet, and the water quality monitoring terminal acquires pH in real time through the sensor terminalori、ORPori、CONDori、TURBoriMonitoring data, storing the data in a database, preprocessing the original data by a data preprocessing module to obtain a cleaned data curve as shown in FIG. 4, and monitoring pH value by a water quality monitoring modulec、ORPc、CONDc、TURBcAnd importing the data into a deep learning model to obtain the water quality data of the treatment facility effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate, as shown in figure 5. The effluent standard of the treatment facility is a first-grade A standard, namely COD is 50mg/L and NH is3N is 5mg/L, TN is 15mg/L, TP is 0.5mg/L, SS is 10mg/L, the emission standard is input into the water quality evaluation module, and the effluent quality evaluation condition in 4137 data monitored in real time is as follows：
| Rank of | Superior food | Good wine | In | Difference (D) | Bad quality | Out of limits |
| Number of | 0 | 1394 | 192 | 242 | 2222 | 87 |
Meanwhile, a water outlet rating data curve shown in fig. 6 can be obtained, and it can be known from the curve that the treatment effect of the site is poor from 24 days in 9 months to 3 days in 10 months, the water quality of the outlet water is in a poor grade, the treatment effect is good from 4 days in 10 months to 12 days in 10 months, and the water quality of the outlet water is in a good grade.
The data transmission module of the sensor of the Internet of things can be used for preprocessing the pH valuec、ORPc、CONDc、TURBcMonitoring indicators, COD, NH3-N、NO3-N、PO4Effluent concentration and rating conditions of-P, TN, TP and SSThe effluent quality change condition of the treatment facility can be conveniently and timely mastered by operation and maintenance management personnel by transmitting the effluent quality change condition to the Internet of things remote management platform in a 4G or Ethernet mode, and corresponding adjustment can be timely made.
According to the Internet of things sensor system for monitoring the water quality of the sewage treatment facility, provided by the embodiment of the invention, the corresponding low-cost instrument is selected according to the sewage treatment reaction mechanism, so that the cost is low, and the large-scale deployment is facilitated; the online real-time monitoring is realized by continuous monitoring and characteristic identification of simple indexes and integration of the attributes of the Internet of things; a deep learning algorithm is adopted to represent the effluent quality condition, and the water quality is subjected to standard-reaching grading evaluation to realize real-time monitoring of the effluent quality; the low-cost instrument is integrated into the Internet of things sensor terminal, so that the installation is convenient, the sensor is free from daily maintenance, and the operation and maintenance cost is effectively reduced.
Next, a water quality monitoring method for a sewage treatment facility according to an embodiment of the present invention will be described with reference to the drawings.
FIG. 7 is a flow chart of a method for monitoring water quality in a sewage treatment facility according to an embodiment of the present invention.
As shown in fig. 7, the method for monitoring the water quality of the sewage treatment facility comprises the following steps:
s1, collecting water outlet data through a plurality of sensors, and preprocessing the water outlet data;
s2, processing the pretreated effluent data through a deep learning model to obtain effluent quality information;
s3, grading and evaluating the water quality information through a water quality evaluation algorithm;
and S4, sending the pretreated effluent data, the water quality information and the grading evaluation result to an Internet of things platform, and carrying out real-time monitoring and management through the Internet of things platform.
Further, in one embodiment of the present invention, the collecting water data by a plurality of sensors includes:
the pH sensor is used for detecting the pH of the water, the ORP sensor is used for detecting the ORP of the water, the conductivity sensor is used for detecting the conductivity of the water, and the turbidity sensor is used for detecting the turbidity of the water.
Further, in an embodiment of the present invention, the effluent data is preprocessed, including timestamp inspection, data determination, error data deletion, missing data supplement, to obtain the pH of the cleaned sensor datac、ORPc、CONDc、TURBc。
Further, in an embodiment of the present invention, the processing the pretreated effluent data by the deep learning model to obtain the effluent quality information includes:
pretreated pH by deep learning modelc、ORPc、CONDc、TURBcAnd importing the data into a deep learning model to obtain the water quality data of effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate.
Further, in an embodiment of the present invention, the graded evaluation of the water quality information by the water quality evaluation algorithm includes:
and comparing the water quality information of the effluent with the discharge standard to obtain the water quality index of each index, selecting the maximum value of each index as a comprehensive water quality index to represent the treatment effect of the sewage treatment facility, and grading the comprehensive water quality index.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
According to the water quality monitoring method for the sewage treatment facility, provided by the embodiment of the invention, the corresponding low-cost instrument is selected according to the sewage treatment reaction mechanism, so that the cost is low, and the large-scale deployment is facilitated; the online real-time monitoring is realized by continuous monitoring and characteristic identification of simple indexes and integration of the attributes of the Internet of things; a deep learning algorithm is adopted to represent the effluent quality condition, and the water quality is subjected to standard-reaching grading evaluation to realize real-time monitoring of the effluent quality; the low-cost instrument is integrated into the Internet of things sensor terminal, so that the installation is convenient, the sensor is free from daily maintenance, and the operation and maintenance cost is effectively reduced.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
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 are not necessarily intended to 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. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. The utility model provides a sewage treatment facility water quality monitoring's thing networking sensor system which characterized in that includes:
the system comprises a sensor terminal, a water quality monitoring terminal, a power supply module and an Internet of things platform;
the water quality monitoring terminal comprises a data acquisition module, a data storage module, a data preprocessing module, a water quality monitoring module, a water quality evaluation module and a data transmission module;
the sensor terminal and the water quality monitoring terminal are connected with a power module, and the sensor terminal is connected with the water quality monitoring terminal;
the sensor terminal comprises a plurality of sensors and is used for detecting water data in real time;
the data acquisition module is used for acquiring water outlet data detected by the sensor terminal in real time;
the data storage module is used for storing the water outlet data;
the data preprocessing module is used for preprocessing the water outlet data;
the water quality monitoring module is used for processing the pretreated effluent data through a deep learning model to obtain effluent water quality information;
the water quality evaluation module is used for carrying out graded evaluation on the water quality information through a water quality evaluation algorithm;
the data transmission module is used for transmitting the data of the water quality monitoring terminal to the Internet of things platform, and remote monitoring and management are carried out through the Internet of things platform.
2. The system of claim 1, wherein the sensor terminal comprises: a PH sensor, an ORP sensor, a conductivity sensor, and a turbidity sensor;
the pH sensor is used for detecting the pH of the effluent, the ORP sensor is used for detecting the ORP of the effluent, the conductivity sensor is used for detecting the conductivity of the effluent, and the turbidity sensor is used for detecting the turbidity of the effluent.
3. The system of claim 1, wherein the data preprocessing module is configured to preprocess the effluent data, including timestamp detection, data determination, error data deletion, missing data supplementation, to obtain the cleaned sensor data pHc、ORPc、CONDc、TURBc。
4. The system of claim 3, wherein the water quality monitoring module is provided with a deep learning model for training and parameter optimization, and the pH value after pretreatment is obtained through the deep learning modelc、ORPc、CONDc、TURBcData guideAnd entering a deep learning model to obtain the water quality data of COD, ammonia nitrogen, nitrate nitrogen and phosphate in the effluent.
5. The system of claim 1, wherein the water quality assessment module is configured to compare the water quality information of the effluent with a discharge standard to obtain a water quality index for each index, select a maximum value of the index as a comprehensive water quality index to characterize a treatment effect of the sewage treatment facility, and rank the comprehensive water quality index.
6. A method for monitoring water quality of a sewage treatment facility is characterized by comprising the following steps:
collecting water outlet data through a plurality of sensors, and preprocessing the water outlet data;
processing the pretreated effluent data through a deep learning model to obtain effluent quality information;
grading and evaluating the water quality information through a water quality evaluation algorithm;
and sending the pretreated effluent data, the water quality information and the grading evaluation result to an Internet of things platform, and carrying out real-time monitoring and management through the Internet of things platform.
7. The method of claim 6, wherein the collecting water data with a plurality of sensors comprises:
the pH sensor is used for detecting the pH of the water, the ORP sensor is used for detecting the ORP of the water, the conductivity sensor is used for detecting the conductivity of the water, and the turbidity sensor is used for detecting the turbidity of the water.
8. The method of claim 6, wherein the effluent data is preprocessed, including time stamp checking, data determination, error data deletion, missing data supplementation, to obtain the cleaned sensor data pHc、ORPc、CONDc、TURBc。
9. The method of claim 8, wherein the processing of the pre-processed effluent data by the deep learning model to obtain effluent quality information comprises:
pretreated pH by deep learning modelc、ORPc、CONDc、TURBcAnd importing the data into a deep learning model to obtain the water quality data of effluent COD, ammonia nitrogen, nitrate nitrogen and phosphate.
10. The method of claim 6, wherein the grading assessment of the water quality information by a water quality assessment algorithm comprises:
and comparing the water quality information of the effluent with the discharge standard to obtain the water quality index of each index, selecting the maximum value of each index as a comprehensive water quality index to represent the treatment effect of the sewage treatment facility, and grading the comprehensive water quality index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110103573.7A CN112964843A (en) | 2021-01-26 | 2021-01-26 | Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110103573.7A CN112964843A (en) | 2021-01-26 | 2021-01-26 | Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112964843A true CN112964843A (en) | 2021-06-15 |
Family
ID=76272656
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110103573.7A Pending CN112964843A (en) | 2021-01-26 | 2021-01-26 | Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112964843A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113943045A (en) * | 2021-11-26 | 2022-01-18 | 浙江一谦生态农业科技有限责任公司 | System and method for removing ammonia nitrogen and total nitrogen in medical product wastewater based on Internet of things |
| CN114910622A (en) * | 2022-06-22 | 2022-08-16 | 清华苏州环境创新研究院 | Calibration device and method for water quality monitoring Internet of things sensor |
| CN115575594A (en) * | 2022-09-22 | 2023-01-06 | 呼伦贝尔安泰热电有限责任公司海拉尔热电厂 | Heating station water quality monitoring and evaluating method and system based on Internet of things technology |
| CN116282455A (en) * | 2023-05-18 | 2023-06-23 | 交通运输部天津水运工程科学研究所 | Intelligent operation and maintenance system and method for bulk cargo port sewage treatment station |
| CN117164103A (en) * | 2023-07-03 | 2023-12-05 | 广西智碧达智慧环境科技有限公司 | Intelligent control method, terminal and system of domestic sewage treatment system |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103645289A (en) * | 2013-12-02 | 2014-03-19 | 中山欧麦克仪器设备有限公司 | Intelligent on-line turbidity monitoring system based on internet of things |
| CN105323321A (en) * | 2015-11-16 | 2016-02-10 | 清华大学 | Water networking system |
| CN107358048A (en) * | 2017-07-14 | 2017-11-17 | 广东省环境科学研究院 | A kind of high-precision Pollution From Ships thing Emission amount calculation method based on AIS data |
| CN107357840A (en) * | 2017-06-23 | 2017-11-17 | 广东开放大学（广东理工职业学院） | A kind of fishery big data determination method and system |
| CN208239436U (en) * | 2018-06-12 | 2018-12-14 | 桂林电子科技大学 | A kind of Water quality ammonia nitrogen detection system |
| CN109060920A (en) * | 2018-07-24 | 2018-12-21 | 清华大学 | A kind of microorganism electrochemical water quality monitoring system based on Internet of Things control |
| CN109467283A (en) * | 2018-12-25 | 2019-03-15 | 湖南智水环境工程有限公司 | Sewage disposal device suitable for more family sanitary sewage disposals |
| CN109534501A (en) * | 2018-12-03 | 2019-03-29 | 浙江清华长三角研究院 | A kind of monitoring and managing method of rural domestic sewage treatment facility |
| CN110132629A (en) * | 2019-06-06 | 2019-08-16 | 浙江清华长三角研究院 | A method of utilizing SVM prediction rural domestic sewage treatment facility operation validity |
| CN110146122A (en) * | 2019-06-06 | 2019-08-20 | 浙江清华长三角研究院 | A kind of prediction technique of rural domestic sewage treatment facility operation validity |
| CN110186505A (en) * | 2019-06-06 | 2019-08-30 | 浙江清华长三角研究院 | A kind of prediction technique of the rural domestic sewage treatment facility standard water discharge situation based on support vector machines |
| CN110322027A (en) * | 2019-07-04 | 2019-10-11 | 南京大学 | A kind of domestic sewage in rural areas Internet of Things management platform |
| CN111596028A (en) * | 2020-07-02 | 2020-08-28 | 上海交通大学 | Water quality on-line monitoring system for distributed sewage discharge source |
| CN111858715A (en) * | 2020-07-24 | 2020-10-30 | 青岛洪锦智慧能源技术有限公司 | Sewage treatment plant water inlet quality prediction method based on data mining |
| CN111965125A (en) * | 2020-09-03 | 2020-11-20 | 沈阳农业大学 | Online monitoring system and monitoring method for water quality |
-
2021
- 2021-01-26 CN CN202110103573.7A patent/CN112964843A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103645289A (en) * | 2013-12-02 | 2014-03-19 | 中山欧麦克仪器设备有限公司 | Intelligent on-line turbidity monitoring system based on internet of things |
| CN105323321A (en) * | 2015-11-16 | 2016-02-10 | 清华大学 | Water networking system |
| CN107357840A (en) * | 2017-06-23 | 2017-11-17 | 广东开放大学（广东理工职业学院） | A kind of fishery big data determination method and system |
| CN107358048A (en) * | 2017-07-14 | 2017-11-17 | 广东省环境科学研究院 | A kind of high-precision Pollution From Ships thing Emission amount calculation method based on AIS data |
| CN208239436U (en) * | 2018-06-12 | 2018-12-14 | 桂林电子科技大学 | A kind of Water quality ammonia nitrogen detection system |
| CN109060920A (en) * | 2018-07-24 | 2018-12-21 | 清华大学 | A kind of microorganism electrochemical water quality monitoring system based on Internet of Things control |
| CN109534501A (en) * | 2018-12-03 | 2019-03-29 | 浙江清华长三角研究院 | A kind of monitoring and managing method of rural domestic sewage treatment facility |
| CN109467283A (en) * | 2018-12-25 | 2019-03-15 | 湖南智水环境工程有限公司 | Sewage disposal device suitable for more family sanitary sewage disposals |
| CN110132629A (en) * | 2019-06-06 | 2019-08-16 | 浙江清华长三角研究院 | A method of utilizing SVM prediction rural domestic sewage treatment facility operation validity |
| CN110146122A (en) * | 2019-06-06 | 2019-08-20 | 浙江清华长三角研究院 | A kind of prediction technique of rural domestic sewage treatment facility operation validity |
| CN110186505A (en) * | 2019-06-06 | 2019-08-30 | 浙江清华长三角研究院 | A kind of prediction technique of the rural domestic sewage treatment facility standard water discharge situation based on support vector machines |
| CN110322027A (en) * | 2019-07-04 | 2019-10-11 | 南京大学 | A kind of domestic sewage in rural areas Internet of Things management platform |
| CN111596028A (en) * | 2020-07-02 | 2020-08-28 | 上海交通大学 | Water quality on-line monitoring system for distributed sewage discharge source |
| CN111858715A (en) * | 2020-07-24 | 2020-10-30 | 青岛洪锦智慧能源技术有限公司 | Sewage treatment plant water inlet quality prediction method based on data mining |
| CN111965125A (en) * | 2020-09-03 | 2020-11-20 | 沈阳农业大学 | Online monitoring system and monitoring method for water quality |
Non-Patent Citations (2)
| Title |
|---|
| 李宁，徐连明，邓中亮: "《物联网基础理论与应用》", 31 July 2012, 北京邮电大学出版社 * |
| 林强: "《机器学习、深度学习与强化学习》", 31 May 2019 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113943045A (en) * | 2021-11-26 | 2022-01-18 | 浙江一谦生态农业科技有限责任公司 | System and method for removing ammonia nitrogen and total nitrogen in medical product wastewater based on Internet of things |
| CN114910622A (en) * | 2022-06-22 | 2022-08-16 | 清华苏州环境创新研究院 | Calibration device and method for water quality monitoring Internet of things sensor |
| CN114910622B (en) * | 2022-06-22 | 2023-08-08 | 清华苏州环境创新研究院 | Calibration device and method for water quality monitoring Internet of things sensor |
| CN115575594A (en) * | 2022-09-22 | 2023-01-06 | 呼伦贝尔安泰热电有限责任公司海拉尔热电厂 | Heating station water quality monitoring and evaluating method and system based on Internet of things technology |
| CN116282455A (en) * | 2023-05-18 | 2023-06-23 | 交通运输部天津水运工程科学研究所 | Intelligent operation and maintenance system and method for bulk cargo port sewage treatment station |
| CN117164103A (en) * | 2023-07-03 | 2023-12-05 | 广西智碧达智慧环境科技有限公司 | Intelligent control method, terminal and system of domestic sewage treatment system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112964843A (en) | Internet of things sensor system for monitoring water quality of sewage treatment facility and monitoring method | |
| Borzooei et al. | Data scarcity in modelling and simulation of a large-scale WWTP: stop sign or a challenge | |
| Haiyan | Assessment and prediction of overall environmental quality of Zhuzhou City, Hunan Province, China | |
| US20200071209A1 (en) | Intelligent early warning method of membrane fouling | |
| CN114047719A (en) | Remote monitoring and evaluating system and operation method for rural domestic sewage treatment facility | |
| CN111489271B (en) | Sewage management cloud platform | |
| CN107677614A (en) | Heavy metal pollution risk on-line early warning system and method in a kind of water | |
| CN108088974B (en) | Soft measurement method for effluent nitrate nitrogen in anaerobic simultaneous denitrification methanogenesis process | |
| CN101021543A (en) | Water polletion source on-line dynamic tracking monitoring method and system | |
| CN103632032A (en) | Effluent index online soft measurement prediction method in urban sewage treatment process | |
| CN106600509A (en) | Method for determining water-mixed sewage discharge behavior of enterprise based on basic data analysis | |
| CN111612678B (en) | Implementation method of sewage management cloud platform | |
| CN112786119B (en) | Method, device and medium for predicting TN (twisted nematic) treatment effect of multi-process type agricultural sewage facility | |
| CN115754207A (en) | Simulation method and system for biological sewage treatment process | |
| CN110146122B (en) | Method for predicting operation effectiveness of rural domestic sewage treatment facility | |
| CN207516235U (en) | Heavy metal pollution risk on-line early warning system in a kind of water | |
| JP2003245653A (en) | Operation supporting method for treatment system, operation supporting method for water treatment system and equipment therefor | |
| CN108803533A (en) | The online environment monitoring system measured by wisdom management process | |
| CN110790368B (en) | Regional supervision method for rural domestic sewage treatment facility | |
| CN208432896U (en) | The online environment monitoring system measured by wisdom management process | |
| Olsson | The potential of control and monitoring | |
| CN114149076B (en) | Intelligent debugging system of anaerobic ammonia oxidation sewage treatment system | |
| CN106021884A (en) | Neural network principle-based method for predicting effluent concentration of subsurface flow wetland | |
| Holloway et al. | Exploring the use of water resource recovery facility instrument data to visualise dynamic resilience to environmental stressors | |
| CN112707508B (en) | BP-ANN-based agricultural sewage facility COD treatment effect prediction method, device and platform |
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 |