CN117193224B - Sewage treatment intelligent monitoring system based on Internet of things - Google Patents
Sewage treatment intelligent monitoring system based on Internet of things Download PDFInfo
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- CN117193224B CN117193224B CN202311467988.8A CN202311467988A CN117193224B CN 117193224 B CN117193224 B CN 117193224B CN 202311467988 A CN202311467988 A CN 202311467988A CN 117193224 B CN117193224 B CN 117193224B
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- 239000010865 sewage Substances 0.000 title claims abstract description 112
- 238000011282 treatment Methods 0.000 title claims abstract description 61
- 238000012544 monitoring process Methods 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 27
- 238000007405 data analysis Methods 0.000 claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims abstract description 12
- 238000010219 correlation analysis Methods 0.000 claims abstract description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 58
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 44
- 238000011156 evaluation Methods 0.000 claims description 32
- 229910052757 nitrogen Inorganic materials 0.000 claims description 22
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 21
- 229910052698 phosphorus Inorganic materials 0.000 claims description 21
- 239000011574 phosphorus Substances 0.000 claims description 21
- 229910001385 heavy metal Inorganic materials 0.000 claims description 18
- 230000003204 osmotic effect Effects 0.000 claims description 16
- 238000003912 environmental pollution Methods 0.000 claims description 12
- 230000001419 dependent effect Effects 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 6
- 230000006855 networking Effects 0.000 claims 1
- 238000011269 treatment regimen Methods 0.000 abstract description 2
- 239000001257 hydrogen Substances 0.000 description 5
- 229910052739 hydrogen Inorganic materials 0.000 description 5
- -1 hydrogen ions Chemical class 0.000 description 3
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 239000007864 aqueous solution Substances 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 229910052793 cadmium Inorganic materials 0.000 description 2
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 description 2
- 229910052804 chromium Inorganic materials 0.000 description 2
- 239000011651 chromium Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 2
- 229910052753 mercury Inorganic materials 0.000 description 2
- 238000000034 method Methods 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 150000004767 nitrides Chemical class 0.000 description 1
- 125000001477 organic nitrogen group Chemical group 0.000 description 1
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
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Abstract
The invention relates to the technical field of sewage treatment, in particular to an intelligent sewage treatment monitoring system based on the Internet of things, which comprises the following components: the system comprises a data acquisition module, a data transmission module, a data analysis processing module and a safety early warning module; the data acquisition module comprises a first data acquisition module and a second data acquisition module; the data transmission module is used for transmitting the sewage flow parameters and the sewage internal parameters to the data analysis processing module; the data analysis processing module performs directional data processing on the parameters from the data transmission module, and further performs correlation analysis by adopting SPSS analysis software to obtain pearson correlation coefficient r; the safety early warning module selects different safety early warning processing strategies according to the correlation coefficient r; the invention can timely and accurately acquire the real-time condition of sewage treatment, further judge and analyze the condition, evaluate and classify risks according to different influence degrees, and pertinently adopt different subsequent treatment strategies.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to an intelligent sewage treatment monitoring system based on the Internet of things.
Background
The development of sewage treatment and recycling technology is increasingly emphasized in all countries of the world. The sustainable utilization of water resources is realized, the trend of aggravation of water pollution is restrained, and even a good water environment is recovered, so that the problem which needs to be solved urgently is solved.
For sewage treatment processes, there are a number of important on-site parameters and information (such as sewage flow, liquid level, PH, temperature dissolved oxygen concentration, etc.) that need to be monitored and shared in real time, and the time-varying nature of these data and information places demands on the real-time nature of the monitoring system.
In addition, the sewage treatment monitoring system should have the following functions: the monitoring means which is not limited by the geographic position can be used for knowing the running condition of the equipment and making related statistical information with related management and maintenance personnel; the resource sharing can be realized, and the purposes of management, control and monitoring are achieved.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an intelligent sewage treatment monitoring system based on the Internet of things, which is used for regularly monitoring sewage treatment conditions through the Internet of things technology, so that real-time sewage treatment conditions can be timely and accurately acquired, further judgment and analysis can be carried out on the real-time sewage treatment conditions, and a sewage treatment scheme can be timely adjusted according to the real-time sewage treatment conditions, so that the problems in the technical background are solved.
(II) technical scheme
In order to achieve the above purpose, the invention provides an intelligent sewage treatment monitoring system based on the Internet of things, comprising: the system comprises a data acquisition module, a data transmission module, a data analysis processing module and a safety early warning module; the data acquisition module comprises a first data acquisition module and a second data acquisition module; the first data acquisition module is used for acquiring sewage flow parameters, including acquisition water flow speed V, water level increment Q and osmotic pressure F; the second data acquisition module is used for acquiring internal parameters of the sewage, including an average PH value HP, nitrogen content N, phosphorus content P and heavy metal index SJ;
the data transmission module is used for transmitting the sewage flow parameters acquired by the first data acquisition module and the sewage internal parameters acquired by the second data acquisition module to the data analysis processing module;
the data analysis processing module performs directional data processing on the sewage flow parameters and the internal parameters from the data transmission module to obtain a sewage water quantity evaluation index SV and an environmental pollution evaluation index HJ, and further performs correlation analysis on the sewage water quantity evaluation index SV dependent variable and the environmental pollution evaluation index HJ independent variable by adopting SPSS analysis software to obtain pearson correlation coefficient r;
and the safety early warning module selects different safety early warning processing strategies according to pearson correlation coefficient r calculated and processed by the data analysis processing module.
Further, the saidThe water flow velocity V of the device is monitored in real time through a flowmeter, and the flowmeter comprises a rotary wing type flowmeter, a vortex street flowmeter or an ultrasonic flowmeter; said water level increasing amountThe real-time water level is monitored in real time through a water level sensor; the osmotic pressure F is the pressure of water to a unit volume pipeline in the seepage direction, and is monitored in real time through an osmotic pressure sensor.
Further, the average PH value HP is monitored in real time through a PH detector, and the PH detector comprises a hydrogen electrode PH detector, a glass electrode detector and the like; the nitrogen content N is the total content of nitrogen elements in the sewage in unit volume, and is monitored in real time through a total nitrogen detector; the phosphorus content P is the total content of phosphorus elements in the sewage in unit volume, and the total phosphorus content P is monitored in real time by a total phosphorus rapid detector; the heavy metal index SJ is the total content of heavy metal elements such as lead, mercury, cadmium, chromium and the like in the sewage in a unit volume, and the total content is monitored in real time by a heavy metal detector.
Further, obtaining sewage flow parameters, and obtaining sewage water quantity evaluation index SV after carrying out dimensionless treatment on water flow speed V, water level increment Q and osmotic pressure F;
the acquisition mode of the sewage water quantity evaluation index SV accords with the following formula:
wherein, the parameter meaning is: flow velocity influencing factor，/>Water level influencing factor>，/>Osmotic pressure influencing factor->，/>C is a constant correction coefficient.
Further, obtaining internal parameters of sewage, and obtaining an environmental pollution evaluation index HJ after carrying out dimensionless treatment on an average PH value HP, a nitrogen content N, a phosphorus content P and a heavy metal index SJ; the acquisition mode of the environmental pollution evaluation index HJ accords with the following formula:
wherein, the parameter meaning is: pH value influencing factor，/>Nitrogen content influencing factor->，/>Phosphorus content influencing factor->，/>Heavy metal index influencing factor->，/>D is a constant correction coefficient.
Further, the security early warning module selects different security early warning processing strategies according to pearson correlation coefficient r calculated and processed by the data analysis processing module, specifically:
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are expressed to be in a weak correlation, so that the influence degree of the sewage comprehensive parameter in the current monitoring period on the external environment is lower, correspondingly, the safety early warning module sends out three-level early warning signals, the problem of mild potential safety hazard caused by the current sewage comprehensive parameter on the external environment is fed back, the treatment speed of the current sewage treatment system can be improved, and the subsequent sewage treatment condition can be dynamically observed and detected in real time;
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are represented to be in a moderate correlation, so that the influence degree of the comprehensive sewage parameters in the current monitoring period on the external environment is proved to be moderate, correspondingly, the safety early warning module sends out a secondary early warning signal, the problem of moderate potential safety hazard caused by the comprehensive sewage parameters to the external environment is fed back, the treatment speed of the current sewage treatment system can be improved, the standby sewage treatment system is started, and the subsequent sewage treatment condition is dynamically observed and detected in real time;
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are expressed to be in a strong correlation, so that the influence degree of the comprehensive sewage parameters in the current monitoring period on the external environment is higher, correspondingly, the safety early warning module sends out a first-level early warning signal, and feeds back the problem of serious potential safety hazard caused by the comprehensive sewage parameters on the external environment, so that the treatment speed of the current sewage treatment system can be improved, the standby sewage treatment system can be started, the treatment speed of the standby sewage treatment system can be improved, and the subsequent sewage treatment conditions can be dynamically observed and detected in real time;
(III) beneficial effects
According to the invention, the sewage flow parameters including the water flow speed V, the water level increment Q and the osmotic pressure F are collected to obtain the sewage water quantity evaluation index SV, the internal parameters of the sewage including the average sewage PH value HP, the nitrogen content N, the phosphorus content P and the heavy metal index SJ are collected to obtain the environment pollution evaluation index HJ, the correlation analysis is carried out on the two parameters by adopting SPSS analysis software, the influence degree of the comprehensive sewage parameters in the current monitoring period on the external environment is accurately and efficiently estimated, the risk assessment and classification are carried out according to different influence degrees, different subsequent treatment strategies are pertinently adopted, and real-time visual decision support is provided for sewage treatment staff.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of the operation of a module unit of an intelligent sewage treatment monitoring system based on the internet of things.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, the present invention provides an intelligent sewage treatment monitoring system based on the internet of things, which includes: the system comprises a data acquisition module, a data transmission module, a data analysis processing module and a safety early warning module;
the data acquisition module comprises a first data acquisition module and a second data acquisition module; the first data acquisition module is used for acquiring sewage flow parameters, including acquisition water flow speed V, water level increment Q and osmotic pressure F; the second data acquisition module is used for acquiring internal parameters of the sewage, including an average PH value HP, nitrogen content N, phosphorus content P and heavy metal index SJ;
the pH value refers to the index of the concentration of hydrogen ions in the aqueous solution, and is one method for indicating the concentration of hydrogen ions. It is the negative value of the usual logarithm of the concentration (activity) of hydrogen ions in aqueous solutions, commonly referred to as "pH" or "pH value". The average PH value HP is monitored in real time through a PH detector, and the PH detector comprises a hydrogen electrode PH detector, a glass electrode detector and the like.
The nitrogen content N is the total content of nitrogen elements in the sewage in unit volume, the nitrogen content in the water refers to the sum of the contents of organic nitrogen and various inorganic nitrides, the nitrogen content is high, the dissolved oxygen in the water is low, and the water quality is deteriorated, so that the total nitrogen is one of important indexes for measuring the water quality, and the total nitrogen is monitored in real time through a total nitrogen detector.
The phosphorus content P is the total content of phosphorus elements in the sewage per unit volume, and the total phosphorus is the measurement result of the water sample after being digested and converted into orthophosphate, and is measured in milligrams of phosphorus per liter of the water sample. And monitoring in real time by a total phosphorus rapid detector. The heavy metal index SJ is the total content of heavy metal elements such as lead, mercury, cadmium, chromium and the like in the sewage in a unit volume, and the total content is monitored in real time by a heavy metal detector. The water flow velocity V is monitored in real time through a flowmeter, and the flowmeter comprises a rotary wing type flowmeter, a vortex street flowmeter or an ultrasonic flowmeter.
Water level incrementAnd the real-time water level is monitored in real time through the water level sensor. The osmotic pressure F is the pressure of water to the unit volume pipeline in the seepage direction, and is monitored in real time through an osmotic pressure sensor. Obtaining sewage flow parameters, carrying out dimensionless treatment on the water flow speed V, the water level increment Q and the osmotic pressure F, and obtaining a sewage water quantity evaluation index SV;
the acquisition mode of the sewage water quantity evaluation index SV accords with the following formula:
wherein, the parameter meaning is: flow velocity influencing factor，/>Water level influencing factor>，/>Osmotic pressure influencing factor->，/>C is a constant correction coefficient.
Acquiring internal parameters of sewage, and acquiring an environmental pollution evaluation index HJ after carrying out dimensionless treatment on an average PH value HP, a nitrogen content N, a phosphorus content P and a heavy metal index SJ; the acquisition mode of the environmental pollution evaluation index HJ accords with the following formula:
wherein, the parameter meaning is: pH value influencing factor，/>Nitrogen content influencing factor->，/>Phosphorus content influencing factor->，/>Heavy metal index influencing factor->，/>D is a constant correction coefficient.
The data transmission module is used for transmitting the sewage flow parameters acquired by the first data acquisition module and the sewage internal parameters acquired by the second data acquisition module to the data analysis processing module;
the data analysis processing module performs directional data processing on the sewage flow parameters and the internal parameters from the data transmission module to obtain a sewage water quantity evaluation index SV and an environmental pollution evaluation index HJ, and further performs correlation analysis on the sewage water quantity evaluation index SV dependent variable and the environmental pollution evaluation index HJ independent variable by adopting SPSS analysis software to obtain pearson correlation coefficient r; the safety early warning module selects different safety early warning processing strategies according to pearson correlation coefficient r calculated and processed by the data analysis processing module, and specifically comprises the following steps:
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are expressed to be in a weak correlation, so that the influence degree of the sewage comprehensive parameter in the current monitoring period on the external environment is lower, correspondingly, the safety early warning module sends out three-level early warning signals, the problem of mild potential safety hazard caused by the current sewage comprehensive parameter on the external environment is fed back, the treatment speed of the current sewage treatment system can be improved, and the subsequent sewage treatment condition can be dynamically observed and detected in real time;
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are represented to be in a moderate correlation, so that the influence degree of the comprehensive sewage parameters in the current monitoring period on the external environment is proved to be moderate, correspondingly, the safety early warning module sends out a secondary early warning signal, the problem of moderate potential safety hazard caused by the comprehensive sewage parameters to the external environment is fed back, the treatment speed of the current sewage treatment system can be improved, the standby sewage treatment system is started, and the subsequent sewage treatment condition is dynamically observed and detected in real time;
when (when)When the environment pollution monitoring system is used, the SV dependent variable and the HJ independent variable of the sewage water quantity evaluation index are expressed to be in a strong correlation, so that the influence degree of the comprehensive sewage parameters in the current monitoring period on the external environment is higher, correspondingly, the safety early warning module sends out a first-level early warning signal, and feeds back the problem of serious potential safety hazard caused by the comprehensive sewage parameters on the external environment, so that the treatment speed of the current sewage treatment system can be improved, the standby sewage treatment system can be started, the treatment speed of the standby sewage treatment system can be improved, and the subsequent sewage treatment conditions can be dynamically observed and detected in real time.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (1)
1. Sewage treatment intelligent monitoring system based on thing networking, its characterized in that: the system comprises a data acquisition module, a data transmission module, a data analysis processing module and a safety early warning module;
the data acquisition module comprises a first data acquisition module and a second data acquisition module; the first data acquisition module is used for acquiring sewage flow parameters, including acquisition water flow speed V, water level increment Q and osmotic pressure F; the second data acquisition module is used for acquiring internal parameters of the sewage, including an average PH value HP, nitrogen content N, phosphorus content P and heavy metal index SJ;
the water flow speed V is monitored in real time through a flowmeter; said water level increasing amountThe real-time water level is monitored in real time through a water level sensor; the osmotic pressure F is monitored in real time through an osmotic pressure sensor;
the average PH value HP is monitored in real time through a PH detector; the nitrogen content N is monitored in real time through a total nitrogen detector; the phosphorus content P is monitored in real time through a total phosphorus rapid detector; the heavy metal index SJ is monitored in real time through a heavy metal detector;
the data transmission module is used for transmitting the sewage flow parameters acquired by the first data acquisition module and the sewage internal parameters acquired by the second data acquisition module to the data analysis processing module;
the data analysis processing module performs directional data processing on the sewage flow parameters and the internal parameters from the data transmission module to obtain a sewage water quantity evaluation index SV and an environmental pollution evaluation index HJ, and further performs correlation analysis on the sewage water quantity evaluation index SV dependent variable and the environmental pollution evaluation index HJ independent variable by adopting SPSS analysis software to obtain pearson correlation coefficient r;
obtaining sewage flow parameters, carrying out dimensionless treatment on the water flow speed V, the water level increment Q and the osmotic pressure F, and obtaining a sewage water quantity evaluation index SV;
the acquisition mode of the sewage water quantity evaluation index SV accords with the following formula:
；
wherein, the parameter meaning is: flow velocity influencing factor，/>Water level influencing factor>，/>Osmotic pressure influencing factor->，/>C is a constant correction coefficient;
acquiring internal parameters of sewage, and acquiring an environmental pollution evaluation index HJ after carrying out dimensionless treatment on an average PH value HP, a nitrogen content N, a phosphorus content P and a heavy metal index SJ;
the acquisition mode of the environmental pollution evaluation index HJ accords with the following formula:
；
wherein, the parameter meaning is: pH value influencing factor，/>Nitrogen content influencing factor->，/>Phosphorus content influencing factor->，/>Heavy metal index influencing factor->，/>C is a constant correction coefficient;
the safety early warning module selects different safety early warning processing strategies according to pearson correlation coefficient r calculated and processed by the data analysis processing module;
the security early warning module selects different security early warning processing strategies according to pearson correlation coefficient r calculated and processed by the data analysis processing module, and specifically comprises the following steps:
when (when)When the sewage treatment system is used, the safety early warning module does not send out an early warning signal, and the current sewage treatment condition is fed back well, so that obvious potential safety hazard problems are avoided;
when (when)When the sewage treatment system is in use, the safety early warning module sends out three-level early warning signals to feed back mild safety to the environment caused by the current sewage treatment conditionHidden trouble problems;
when (when)When the sewage treatment system is in use, the safety early warning module sends out a secondary early warning signal to feed back the problem of moderate potential safety hazard to the environment caused by the current sewage treatment condition;
when (when)And when the sewage treatment system is in use, the safety early warning module sends out a first-level early warning signal to feed back the problem of serious potential safety hazard to the environment caused by the current sewage treatment condition.
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| CN102463037A (en) * | 2010-11-10 | 2012-05-23 | 东丽纤维研究所（中国）有限公司 | Method for evaluating polluting property of filtered liquid |
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