CN101718774A - Diagnostic method for validity of online collected water quality data - Google Patents
Diagnostic method for validity of online collected water quality data Download PDFInfo
- Publication number
- CN101718774A CN101718774A CN200910185421A CN200910185421A CN101718774A CN 101718774 A CN101718774 A CN 101718774A CN 200910185421 A CN200910185421 A CN 200910185421A CN 200910185421 A CN200910185421 A CN 200910185421A CN 101718774 A CN101718774 A CN 101718774A
- Authority
- CN
- China
- Prior art keywords
- water quality
- quality parameter
- parameter
- monitoring
- time
- 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.)
- Granted
Links
Landscapes
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a diagnostic method for online collected validity of water quality data in an unmanned water quality monitoring station. Via statistical analysis of the historical monitoring data of main water quality parameter of raw water in multitude water quality monitoring stations, the regression curve between every two different water quality parameters is fitted by a linear regression model, the linear correlated coefficient between the parameters is calculated, and another water quality parameter with the maximum correlated coefficient (greater than or equal to 0.7) is defined as the mutual correlated water quality parameter of the water quality parameter. The diagnostic method for the validity of collected water quality data in the unmanned water quality monitoring station has good practical and promotional values and has auxiliary effect for diagnosing and maintaining the fault of the water quality parameter measuring instrument in the unmanned raw water quality monitoring station.
Description
Technical field
The present invention relates to a kind of diagnostic method of water quality data validity, especially be particularly related to a kind of unmanned water quality monitoring and stand in the diagnostic method that line is gathered water quality data validity.
Background technology
In recent years, the lasting expansion of the fast development of China's industrial or agricultural and cities and towns scale has caused serious water environment pollution, and the continuing of drinking water source ground water quality condition becomes bad, caused showing great attention to of compatriots especially.Environmental administration of governments at all levels is in order to improve the drinking water emergency mechanism that strick precaution, early warning and disposal combine of polluting, progressively (from the beginning) water factory sets up former water intake upstream the long-range monitoring water quality on line of unmanned station in this area, and nearly 20 kinds of main water quality parameters such as the PH of former water, temperature, turbidity, ammonia nitrogen, oxygen utilization, dissolved oxygen DO, total nitrogen, total phosphorus, chlorophyll, algae density are implemented continuous real time on-line monitoring.All online water quality parameter water quality datas that surveying instrument is gathered of long-range water quality monitoring station are all by wired or wireless mode, be sent to Running-water Company's water quality information management center server (some area is an environmental administration), use for relevant water factory of Running-water Company, as the important evidence of tap water production run control.Unmanned long-range water quality monitoring station and remote monitoring IT application in management system architecture thereof are as shown in Figure 1.
Gathering accurate raw water quality parameter changes control of tap water production process optimization and assurance effluent water quality most important.Be example now with the ammonia nitrogen parameter, be described as follows: the ammonia nitrogen in the former water can be used to characterize the contaminated degree of water body, it mainly relies on the bioprocess degraded, the existence of ammonia nitrogen can react with the chlorine that adds in the tap water production run, thereby the chlorine-throwed quantity that needing in the disinfection of tap water process to cause increases, and then generates more harmful to health DBPs.Ammonia nitrogen in the former water can be removed preferably by BAC process, but too high influent ammonia nitrogen can cause the accumulation of filter tank water outlet nitrous acid nitrogen, and influences biological activated carbon filter to removal effect of organic matter.
The large dead time, big inertia, the nonlinear feature that the significant impact and the potable water technological process of production of potable water production technology are had in view of each raw water quality parameter, usually wait when water factory's internal procedure measuring instrument detects raw water quality generation fluctuation even suddenly change this situation, operating personnel of water factory and robot control system(RCS) have lost the time of response in advance, even take emergency measures according to situation of change immediately, also can cause the water quality fluctuation even exceed standard in short-term of dispatching from the factory because of the characteristics of tap water production procedure large dead time, big inertia.After the long-range monitoring water quality on line of unmanned station is built up, can realize the main water quality parameter of former water is implemented continuous real time on-line monitoring, the waterworks can shift to an earlier date (more than about 5-10 hour) and accurately obtain the raw water quality situation of change, actively take counter-measure thereby can have, ensure water factory's production run safety than plenty of time.
Setting up of the long-range monitoring water quality on line of unmanned station perfect polluted the drinking water emergency mechanism that strick precaution, early warning and disposal combine, and improved the ability that potable water is produced solution of emergent event.But the water quality monitoring station is built up in remote place usually, and long-term work is in the unmanned state, though present environmental monitoring center, usually artificial on-site sampling is carried out once at the long-range monitoring water quality on line station of can sending someone weekly, the laboratory assay compare of analysis, with check water quality instrument operation irregularity whether, but this method cycle is oversize, if certain instrument breaks down, be difficult in time be found and get rid of fault by the people.
Because each water quality parameter surveying instrument of long-range water quality monitoring station, need long-term real-time online continuously to detect, and the suspended ring particle, the phycophyta that often have grain size to differ in the former water enter sampler through screen pack, if can not in time and effectively clean, then can stop up water quality instrument collection tube, adhere to the sampler phenomenon, thereby influence normal, the accurately measurement of water quality parameter surveying instrument; And reasons such as water quality measurement instrument electronic devices and components are aging, line fault very easily cause the water quality parameter surveying instrument to produce abnormal data.If the waterworks takes corresponding operating (because the big inertia that has of drinking water treatment technology, characteristics and water factory's adding equipment of large dead time are arranged to automatic running status usually according to this type of abnormal data, therefore, employee on duty is difficult in and finds and correct the faulty operation that adding equipment takes place automatically in the short time), bring serious harmful effect then may for tap water lean production and drinking water safety.
Summary of the invention
Technical matters: the invention provides and a kind ofly utilize correlativity between the raw water quality parameter long-range water quality monitoring stands in line and gathers the method that water quality data validity is diagnosed to unmanned, this method is on existing hardware condition basis, need not additionally to increase any hardware, can realize by software algorithm fully, statistical study by to the historical Monitoring Data of each main water quality parameter of monitoring station calculates the linearly dependent coefficient between per two kinds of different quality parameters.
Technical scheme: when certain water quality parameter Monitoring Data validity need be diagnosed, then diagnose: if similar fluctuation situation also appears in the comparison water quality parameter by best another water quality parameter situation of change in the identical time period of check and this water quality parameter correlationship, this time Monitoring Data is normal, effective then to be diagnosed as this water quality parameter, and this water quality parameter detecting instrument is working properly; Otherwise this time Monitoring Data is unusual, invalid then to be diagnosed as this water quality parameter, should abandon, and this water quality parameter detecting instrument operation irregularity, the while sends the fault alarm information of respective detection instrument to relevant department.
The present invention is a kind of validity identification and diagnostic method that is used for the long-range raw water quality of unmanned monitoring station online acquisition water quality data, specifically may further comprise the steps:
1. it is the statistical study time period) with nearest a certain section time Δ t, choose PH, temperature, turbidity, COD, dissolved oxygen DO, ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll and algae density parameter that water quality monitoring stands in main raw water quality in the Δ t time period, historical Monitoring Data is carried out statistical study, utilize the regression curve between the per two kinds of different quality parameters of linear regression model (LRM) match, and obtain the linearly dependent coefficient between them respectively;
2.) each water quality parameter of participation statistical study, definition is the simple crosscorrelation water quality parameter of this water quality parameter with another parameter of its related coefficient maximum;
3.) according to nearest twice subnormal Monitoring Data of each water quality parameter of real time record, renewal and monitoring time thereof, calculate the mean change speed in the nearest monitoring time section of each water quality parameter, and define this rate of change and be this water quality parameter reference change speed; When monitoring central station water quality information administrative center receives in the online acquisition data of the long-range raw water quality of certain unmanned monitoring station, a certain water quality parameter rate of change surpasses this water quality parameter reference change speed more than 2 times the time, then need to this water quality parameter this time Monitoring Data validity diagnose;
4. fluctuation appears as a certain water quality parameter A), when its Monitoring Data validity need be diagnosed, then by checking procedure 2) in simple crosscorrelation water quality parameter B situation of change in the identical time period of this water quality parameter of determining judge: if similar situation of change also appears in water quality parameter B, then diagnose water quality parameter A sudden change this time really since the sampling raw water quality occur due to the sudden change; Otherwise judge that then water quality parameter A sudden change this time is because this water quality parameter surveying instrument induced fault.
The statistical study time period is chosen as arbitrary time span in 1 to 3 year scope.Linearly dependent coefficient between each water quality parameter and its simple crosscorrelation water quality parameter must be more than or equal to 0.7.The water quality monitoring station is monitored main water quality parameter and is comprised at least: PH, temperature, turbidity, COD, dissolved oxygen DO, ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll and algae density.
Beneficial effect: enforcement of the present invention, can quick and precisely must diagnose out the validity of online acquisition water quality data, eliminated the serious harmful effect that monitoring station exception water quality data bring for tap water lean production and drinking water safety, simultaneously can in time find the monitoring station instrument failure, and send corresponding water quality instrument warning message to the environmental monitoring center, ensured reliable, the effectively operation of water quality monitoring head of a station phase.The present invention has good practical and popularizing value, be not only applicable to the long-range water quality monitoring of unmanned and stand in the differentiation that line is gathered water quality data validity, equally also be applicable to other similar applications, as differentiation of the long-range air quality monitoring station of unmanned online acquisition data validity etc.
Description of drawings
Fig. 1 is unmanned long-range water quality monitoring station and remote monitoring IT application in management system architecture synoptic diagram thereof.
Embodiment
The present invention is a kind of method for distinguishing validity that is used for the long-range raw water quality of unmanned monitoring station online acquisition water quality data, on the existing hardware platform base of water quality monitoring station, utilize correlativity between each water quality parameter, realize that by software algorithm water quality monitoring is stood in line collection water quality data validity to be diagnosed, now elaborate this process, specifically comprise the steps: in conjunction with example
1), chooses nearest 3 years, the historical Monitoring Data of ten kinds of main raw water quality parameters in long-range raw water quality monitoring station (PH, temperature, turbidity, COD, dissolved oxygen DO, ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll, algae density) is carried out statistical study, utilize the regression curve Y=kX+b between the per two kinds of different quality parameters of linear regression model (LRM) match, k, b can be tried to achieve by calculating, and obtain linearly dependent coefficient between them respectively, and carry out significance test, remain with the meaning correlationship.
2), participate in each water quality parameter of statistical study, definition is the simple crosscorrelation water quality parameter of this water quality parameter with (must satisfy 〉=0.7) another parameter of its linearly dependent coefficient maximum.
3), water quality parameter A, nearest two subnormal Monitoring Data are X
A1, X
A2, corresponding monitoring time is respectively t
1, t
2,
Calculate this water quality parameter at t
1To t
2Mean speed is V in time period
1=| X
A2-X
A1|/(t
2-t
1), and with V
1Be this water quality parameter reference change speed, i.e. V
RF=V
1After nearest twice normal data of water quality parameter A upgraded, this parameter reference change speed then needed to recomputate.Monitoring central station water quality information administrative center receives the up-to-date image data X of A parameter
A3, monitoring time t
3After, calculate it at t
2To t
3Mean speed is V in time period
2=| X
A3-X
A2|/(t
3-t
2), if V
2＞2*V
RF, then fluctuation by a relatively large margin appears in the A parameter, and this Monitoring Data validity need enter (4) and diagnose; Otherwise, think that then this Monitoring Data of A parameter is normal.
4) Monitoring Data (X fluctuation appears, as certain water quality parameter A,
A3) when validity need be diagnosed, find out this parameter simple crosscorrelation water quality parameter B that determines in (2), the B parameter is Y in the corresponding Monitoring Data of identical monitoring time
B3, by the A of (1) kind match, the linear regression curve Y=kX+b of B parameter and B parameter be Monitoring Data Y this time
B3It is that 0.95 prediction is estimated that the A parameter is carried out confidence level, calculate fiducial interval for (m, n), if X
A3(m n), then is diagnosed as water quality parameter A to ∈, B, this time Monitoring Data X
A3, Y
B3, when confidence level 0.95, satisfy the regression curve equation, promptly similar fluctuation situation appears in water quality parameter A, B, and this fluctuation is that A parameter this time Monitoring Data is normal, effective because raw water quality occurs belonging to normal variation due to the bigger variation; Otherwise, think that then similar situation of change does not appear in water quality parameter A, B, the A parameter fluctuation is because due to the instrument failure, A parameter this time Monitoring Data is an abnormal data, should abandon, and sends corresponding water quality instrument failure warning message to the environmental monitoring center simultaneously.
Claims (4)
1. the diagnostic method of an online acquisition water quality data validity is characterized in that this method may further comprise the steps:
1. it is the statistical study time period) with nearest a certain section time Δ t, choose PH, temperature, turbidity, COD, dissolved oxygen DO, ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll and algae density parameter that water quality monitoring stands in main raw water quality in the Δ t time period, historical Monitoring Data is carried out statistical study, utilize the regression curve between the per two kinds of different quality parameters of linear regression model (LRM) match, and obtain the linearly dependent coefficient between them respectively;
2.) each water quality parameter of participation statistical study, definition is the simple crosscorrelation water quality parameter of this water quality parameter with another parameter of its related coefficient maximum;
3.) according to nearest twice subnormal Monitoring Data of each water quality parameter of real time record, renewal and monitoring time thereof, calculate the mean change speed in the nearest monitoring time section of each water quality parameter, and define this rate of change and be this water quality parameter reference change speed; When monitoring central station water quality information administrative center receives in the online acquisition data of the long-range raw water quality of certain unmanned monitoring station, a certain water quality parameter rate of change surpasses this water quality parameter reference change speed more than 2 times the time, then need to this water quality parameter this time Monitoring Data validity diagnose;
4. fluctuation appears as a certain water quality parameter A), when its Monitoring Data validity need be diagnosed, then by checking procedure 2) in simple crosscorrelation water quality parameter B situation of change in the identical time period of this water quality parameter of determining judge: if similar situation of change also appears in water quality parameter B, then diagnose water quality parameter A sudden change this time really since the sampling raw water quality occur due to the sudden change; Otherwise judge that then water quality parameter A sudden change this time is because this water quality parameter surveying instrument induced fault.
2. the diagnostic method of online acquisition water quality data validity according to claim 1 is characterized in that the statistical study time period is chosen as arbitrary time span in 1 to 3 year scope.
3. the diagnostic method of online acquisition water quality data validity according to claim 1 is characterized in that the linearly dependent coefficient between each water quality parameter and its simple crosscorrelation water quality parameter must be more than or equal to 0.7.
4. the diagnostic method of online acquisition water quality data validity according to claim 1 is characterized in that the water quality monitoring station monitors main water quality parameter and comprise at least: PH, temperature, turbidity, COD, dissolved oxygen DO, ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll and algae density.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009101854215A CN101718774B (en) | 2009-11-09 | 2009-11-09 | Diagnostic method for validity of online collected water quality data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009101854215A CN101718774B (en) | 2009-11-09 | 2009-11-09 | Diagnostic method for validity of online collected water quality data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101718774A true CN101718774A (en) | 2010-06-02 |
| CN101718774B CN101718774B (en) | 2012-11-28 |
Family
ID=42433378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2009101854215A Expired - Fee Related CN101718774B (en) | 2009-11-09 | 2009-11-09 | Diagnostic method for validity of online collected water quality data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101718774B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102947690A (en) * | 2010-06-22 | 2013-02-27 | 株式会社日立高新技术 | Automatic analysis device |
| CN103175941A (en) * | 2012-12-29 | 2013-06-26 | 聚光科技(杭州)股份有限公司 | Analysis device data validity judgment device and method |
| CN103235096A (en) * | 2013-04-16 | 2013-08-07 | 广州铁路职业技术学院 | Sewage water quality detection method and apparatus |
| CN103398743A (en) * | 2013-08-07 | 2013-11-20 | 广州中国科学院沈阳自动化研究所分所 | Mobile water quality monitoring device, system and method |
| CN103728429A (en) * | 2013-12-25 | 2014-04-16 | 力合科技（湖南）股份有限公司 | Water quality online monitoring method and monitoring system |
| CN103743878A (en) * | 2013-12-26 | 2014-04-23 | 力合科技（湖南）股份有限公司 | Water quality monitoring data processing method and device |
| WO2018027517A1 (en) * | 2016-08-09 | 2018-02-15 | 曹鸿鹏 | On-site water source pollution monitoring method and system |
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN108226437A (en) * | 2018-04-09 | 2018-06-29 | 山东建筑大学 | A kind of method of amendment water quality monitoring result applied to lake library water body |
| CN108665119A (en) * | 2018-08-03 | 2018-10-16 | 清华大学 | A kind of water supply network unusual service condition method for early warning |
| CN109520567A (en) * | 2018-11-27 | 2019-03-26 | 深圳先进技术研究院 | A kind of sea-farming water quality early-warning method |
| CN110278287A (en) * | 2019-07-24 | 2019-09-24 | 浙江创韵环境科技有限公司 | A kind of sewage monitoring system |
| CN110751853A (en) * | 2019-10-25 | 2020-02-04 | 百度在线网络技术（北京）有限公司 | Parking space data validity identification method and device |
| CN112037106A (en) * | 2020-08-07 | 2020-12-04 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on characteristic cross correlation and probability density |
| CN112540050A (en) * | 2020-11-24 | 2021-03-23 | 威海精讯畅通电子科技有限公司 | Water quality detector and detection method thereof |
| CN112858619A (en) * | 2021-03-03 | 2021-05-28 | 华南农业大学 | Prediction and early warning method for prawn culture water quality |
| CN115508522A (en) * | 2021-06-07 | 2022-12-23 | 芯视界（北京）科技有限公司 | Water quality detection method and device, electronic device and storage medium |
| WO2023000503A1 (en) * | 2021-07-19 | 2023-01-26 | 上海亨通海洋装备有限公司 | Online self-detection control system for water quality testing instrument |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19824609C2 (en) * | 1998-06-02 | 2001-09-06 | Wolfram Weis | Process for online monitoring of compliance with specified limit values and parameters in fluids and device for carrying out the process |
| CN201110857Y (en) * | 2007-10-29 | 2008-09-03 | 于明 | Water source on-line monitoring early-warning system |
| CN201222059Y (en) * | 2008-06-20 | 2009-04-15 | 东莞市凌峰信息科技有限公司 | Remote wireless multi-point detection system for water quality |
-
2009
- 2009-11-09 CN CN2009101854215A patent/CN101718774B/en not_active Expired - Fee Related
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102947690A (en) * | 2010-06-22 | 2013-02-27 | 株式会社日立高新技术 | Automatic analysis device |
| CN102947690B (en) * | 2010-06-22 | 2015-03-11 | 株式会社日立高新技术 | Automatic analysis device |
| CN103175941A (en) * | 2012-12-29 | 2013-06-26 | 聚光科技(杭州)股份有限公司 | Analysis device data validity judgment device and method |
| CN103175941B (en) * | 2012-12-29 | 2016-01-20 | 聚光科技(杭州)股份有限公司 | The judgment means of analytical equipment data validity and method |
| CN103235096A (en) * | 2013-04-16 | 2013-08-07 | 广州铁路职业技术学院 | Sewage water quality detection method and apparatus |
| CN103398743A (en) * | 2013-08-07 | 2013-11-20 | 广州中国科学院沈阳自动化研究所分所 | Mobile water quality monitoring device, system and method |
| CN103398743B (en) * | 2013-08-07 | 2015-12-23 | 广州中国科学院沈阳自动化研究所分所 | Mobile water quality monitoring device, system and method |
| CN103728429A (en) * | 2013-12-25 | 2014-04-16 | 力合科技（湖南）股份有限公司 | Water quality online monitoring method and monitoring system |
| CN103728429B (en) * | 2013-12-25 | 2016-06-01 | 力合科技（湖南）股份有限公司 | On-line water quality monitoring method and Monitoring systems |
| CN103743878A (en) * | 2013-12-26 | 2014-04-23 | 力合科技（湖南）股份有限公司 | Water quality monitoring data processing method and device |
| WO2018027517A1 (en) * | 2016-08-09 | 2018-02-15 | 曹鸿鹏 | On-site water source pollution monitoring method and system |
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN108226437A (en) * | 2018-04-09 | 2018-06-29 | 山东建筑大学 | A kind of method of amendment water quality monitoring result applied to lake library water body |
| CN108665119A (en) * | 2018-08-03 | 2018-10-16 | 清华大学 | A kind of water supply network unusual service condition method for early warning |
| CN108665119B (en) * | 2018-08-03 | 2021-05-28 | 清华大学 | Water supply pipe network abnormal working condition early warning method |
| CN109520567A (en) * | 2018-11-27 | 2019-03-26 | 深圳先进技术研究院 | A kind of sea-farming water quality early-warning method |
| CN110278287A (en) * | 2019-07-24 | 2019-09-24 | 浙江创韵环境科技有限公司 | A kind of sewage monitoring system |
| CN110751853A (en) * | 2019-10-25 | 2020-02-04 | 百度在线网络技术（北京）有限公司 | Parking space data validity identification method and device |
| CN112037106A (en) * | 2020-08-07 | 2020-12-04 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on characteristic cross correlation and probability density |
| CN112037106B (en) * | 2020-08-07 | 2023-12-15 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on feature cross-correlation and probability density |
| CN112540050A (en) * | 2020-11-24 | 2021-03-23 | 威海精讯畅通电子科技有限公司 | Water quality detector and detection method thereof |
| CN112540050B (en) * | 2020-11-24 | 2022-12-27 | 威海精讯畅通电子科技有限公司 | Water quality detector and detection method thereof |
| CN112858619A (en) * | 2021-03-03 | 2021-05-28 | 华南农业大学 | Prediction and early warning method for prawn culture water quality |
| CN115508522A (en) * | 2021-06-07 | 2022-12-23 | 芯视界（北京）科技有限公司 | Water quality detection method and device, electronic device and storage medium |
| WO2023000503A1 (en) * | 2021-07-19 | 2023-01-26 | 上海亨通海洋装备有限公司 | Online self-detection control system for water quality testing instrument |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101718774B (en) | 2012-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101718774B (en) | Diagnostic method for validity of online collected water quality data | |
| CN112863134B (en) | Intelligent diagnosis system and method for rural sewage treatment facility abnormal operation | |
| CN110297018A (en) | A kind of method and device that drainage pipeline networks pollutant emission is traced to the source | |
| CN111596025B (en) | Water environment pollution analysis system based on big data | |
| CN103728428A (en) | Water quality real-time online monitoring system | |
| CN203101366U (en) | Online water quality detecting device and online water quality monitoring system for water supplying network | |
| JP2005250557A (en) | Daily water quality monitoring system by mobile technology | |
| CN117630319B (en) | Big data-based water quality monitoring and early warning method and system | |
| JP4964273B2 (en) | Water treatment facility management system | |
| CN111121874A (en) | Water quality monitoring and evaluating system and method for water source area | |
| CN106225832A (en) | A kind of water quality monitoring system for Landscape Park and method | |
| CN208588936U (en) | A kind of monitoring system in waste water processing | |
| CN211825988U (en) | Water surface pollutant monitoring system | |
| CN110850051A (en) | Water quality environment monitoring method | |
| CN115330575A (en) | Three-dimensional visual distributed sewage plant management system | |
| CN109870989A (en) | A kind of method and its system of comprehensively monitoring sewage discharge | |
| CN112327791A (en) | Environmental monitoring platform based on big data | |
| KR100523856B1 (en) | A remote control system in the quality of water | |
| CN111240256A (en) | Building engineering on-site monitoring management system | |
| CN211374726U (en) | Water quality monitoring system | |
| CN112699003A (en) | Monitoring management method and system for rural small-sized sewage treatment facilities | |
| CN117853055A (en) | Pollution tracing management and control system | |
| CN104750144B (en) | Method of early warning operating condition of equipment | |
| CN217765080U (en) | Water meter and pipe network running state monitoring system | |
| JP2007087117A (en) | Data collection device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20121128 Termination date: 20151109 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |