CN109444232A - A kind of multichannel intelligent polluted gas monitoring device and diffusion source tracing method - Google Patents
A kind of multichannel intelligent polluted gas monitoring device and diffusion source tracing method Download PDFInfo
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Abstract
The present invention relates to a kind of multichannel intelligent polluted gas monitoring devices, including gasmetry sensing unit, data acquisition and procession unit, gas extraction and pretreatment unit, exceeded gas alarm and sampling unit, wherein, gasmetry sensing unit, design have multiple measurement acquisition channels；Data acquisition and procession unit will test signal and be converted to digital signal and be handled, and gas concentration is calculated, and gas concentration data are transmitted to data network platform by remote transmission mode or are stored in local storage unit；According to gas concentration, processor of single chip computer sends alarm signal；Gas extraction and pretreatment unit, including aspiration pump, flow controller, gas oil water separator, particulate filter, moisture filter, gas conduit and heating tape.The present invention provides a kind of polluted gas diffusion source tracing method simultaneously.The present invention accurately samples after multi-channel sampling and alarm may be implemented.
Description
Technical field
The present invention relates to a kind of multichannel intelligent polluted gas monitoring devices and diffusion source tracing method.
Background technique
Atmospheric environment is the necessary condition that the mankind depend on for existence and development, and protection and improvement atmosphere quality are for promoting
Human society, expanding economy and guarantee human health all have a very important significance.As China's process of industrialization adds
Fastly, pollution pressure caused by the discharge of various industrial waste gases increasingly increases, and the gaseous species of various pollution environment are more
(including NH3, HF, HCl, acid mist etc.), emission behaviour is complicated, and to environmental gas online monitoring instruments, more stringent requirements are proposed.
Air pollution emission feature is often multiple emission sources within a certain area, this also gives inspective regulation, especially positioning specific
Disposal of pollutants source is finely divided Classification Management to disposal of pollutants source and brings difficulty.
For the feature of environmental pollution gas multiplicity, needs measuring device can be and realize that multiple gases multi-channel parallel is surveyed
Amount reduces measurement cost.For the feature of ambient gas composition complexity, it is desirable that there is measuring device certain gas to pre-process energy
Power removes moisture in air, the influence that particulate matter etc. detects its target component, and extends measurement sensing to a certain extent
The device service life.Furthermore intelligent measuring requires measuring device exceeded to pollution can alarm, and measurement data is deposited in real time
Storage is transmitted to the network platform and is handled in local storage, to provide foundation for supervision.
In addition, atmosphere pollution has, diffusion is fast, the big feature of range, therefore is directed to the diffusion analysis of pollutant and traces to the source
It is very crucial, in the prior art, gridding setting monitoring base station (or site in a network) is generallyd use, base will be monitored
The monitoring data stood are uploaded to server or referred to as data processing centre, and data processing centre is distributed according to monitoring data to be determined
The high site of monitor value, to determine contamination approximate region.In practical applications, grid dividing, source tracing method and classification
Method accurately identifies pollutant key that is particularly significant, and improving precision management.
Summary of the invention
In view of this, the main purpose of the present invention is to provide a kind of multichannel intelligent environmental gas measuring device,
Feature is that there is sampling pretreatment and multi-channel gas sampling functions, measurement data to be transmitted to network number by remote transmission mode
There is the Intelligent environment gas of the exceeded sampling of polluted gas and warning function into local storage unit according to platform, or storage
Fluid measurement device.Meanwhile the present invention also provides one kind based on related to multi-channel detection data time series point of gridded data
The air pollution diffusion of analysis and pollutant clustering is traced to the source and subdivision monitoring method.Above-mentioned purpose of the invention be by with
What lower technical solution was realized:
A kind of multichannel intelligent polluted gas monitoring device, including gasmetry sensing unit, data acquisition and procession
Unit, gas extraction and pretreatment unit, exceeded gas alarm and sampling unit, which is characterized in that
Gasmetry sensing unit, design have multiple measurement acquisition channels, configure gas with various sensing according to actual needs
Device realizes that multiple gases detect simultaneously, and detection signal is admitted to data acquisition and procession unit；
Data acquisition and procession unit will test signal and be converted to digital signal and be handled, it is dense that gas is calculated
Degree, gas concentration data are transmitted to data network platform by remote transmission mode or are stored in local storage unit；According to
Gas concentration, processor of single chip computer send alarm signal；
Gas extraction and pretreatment unit, including aspiration pump, flow controller, gas oil water separator, particulate matter filtering
Device, moisture filter, gas conduit and heating tape；Gas extraction and pretreatment unit each section pass through the gas with heating tape
Conduit connection, heating tape prevent gas componant from changing, and improve detection accuracy to keep gas temperature；Aspiration pump extracts outer
Gas in portion's environment sequentially enters moisture filter, particulate filter, gas oil water separator and flow by gas conduit
Controller finally enters gasmetry sensing unit and measures；
Exceeded gas alarm and sampling unit, including combined aural and visual alarm, sampling pump, gas conduit, check valve and sampling gas
Bag, the unit start combined aural and visual alarm and sampling pump, sampling pump after the alarm signal for the sending for receiving processor of single chip computer
It extracts gas and is sent into sampling airbag, wherein connected between air pump and sampling airbag by check valve, prevent sample gas from flowing back, and
Guarantee sample precision.
The present invention also provides a kind of polluted gas to spread source tracing method, comprising the following steps:
(1) region to be measured divide for gridding, establish lattice vector map, and define lattice vector map matrix
Coordinate, if i represents line number, j represents row number, and concentration data of the n kind monitoring pollution object in certain time t indicates are as follows:Base
The pollution on the lattice vector map of pollutant monitoring Data Matching to region to be measured, will be obtained in GPS latitude and longitude coordinates
Source grid map；
(2) Regressive averaging model ARMA processing is carried out to each node polluted gas gas-monitoring data, obtained every
A node difference pollutant concentration time series indicates are as follows:
Wherein [β0, β1... βp] it is regression coefficient, [α0, α1... αp] it is mean coefficient, [e0, e1... ep] it is white noise
Sound, it is assumed that various noise profiles are similar to white noise in monitoring process；
(3) in the maximum node of grid range searching pollutant concentration, which is obtained into pollutant concentration time series Y
(t) pollutant time series Y ' (t) adjacent with surrounding carries out correlation analysis, and pollutant concentration maximum is pointed out from monitoring result
Hair, it is successively all around search to close on domain point, it is available to have a series of related coefficients, take maximum phase relation numerical digit
It sets, obtains pollutant and spread a path, be expressed asThen in being with new starting point (i, j+1)
The heart obtains data with neighbouring monitoring point and is scanned for, and finds related coefficient maximum position.Above step is repeated, until
All monitoring points in traversal detection grid, obtain diffusion path of the different pollutants in monitoring region, and acquired pollutant expands
Dissipate path of tracing to the source.
In addition, the different pollutant kinds that can be also obtained according to the measurement of each gridding monitoring point, to every in monitoring region
The data of a monitoring point carry out clustering, and the different pollutant concentration sequences that each monitoring point is obtained are adopted as sample value
Sample Similarity Principle carries out matching classification to sample, using using euclidean planes be the Similarity Principle of module as Classification and Identification
Criterion, net region contamination characteristics whether having the same where determining two monitoring points.
The present invention is needed for environmental gas detection, is had the advantage that compared with prior art
(1) multiple measurement acquisition channels are designed, different sensors can be configured according to actual needs, to realize multiple gases
Parallel detection.
(2) gas extraction and pretreatment are handled using multistage filtering, and are connected by the gas conduit of heating tape, heat tracing
Band main function is to maintain gas temperature, prevents gas componant from changing, and improve detection accuracy.
(3) there are exceeded gas alarm and sampling functions, sound-light alarm can be started automatically according to the gas concentration measured
Device and air pump, air pump extract gas and are sent into sampling airbag, are wherein connected between air pump and sampling airbag by check valve, prevent from adopting
Sample gas backstreaming, and guarantee sample precision.
(4) gasmetry result can be sent into data acquisition platform by remote transmission module, or be stored in and be locally stored
In unit, it is convenient for data inspection and monitoring.
(5) it traces to the source/broadcast algorithm the present invention provides a kind of pollutant based on correlation analysis, is polluted from monitoring result
Object concentration maximum point sets out, successively all around search to close on domain point, and calculates separately pollutant measurement time series correlation letter
Number, it is available to have a series of related coefficients, it takes the adjacent monitoring point for wherein corresponding to maximum correlation coefficient to be used as and spreads/trace to the source road
Diameter carries out the above processing for grid monitoring point datas all in detection zone, obtains different pollutants in monitoring region
Diffusion path.
(6) it traces to the source/broadcast algorithm the present invention provides a kind of pollutant based on correlation analysis, is polluted from monitoring result
Object concentration maximum point sets out, successively all around search to close on domain point, and calculates separately pollutant measurement time series correlation letter
Number, it is available to have a series of related coefficients, it takes the adjacent monitoring point for wherein corresponding to maximum correlation coefficient to be used as and spreads/trace to the source road
Diameter carries out the above processing for grid monitoring point datas all in detection zone, obtains different pollutants in monitoring region
Diffusion path.
(7) clustering is carried out according to pollutant concentrations different in monitoring data the present invention provides a kind of, to realize
Monitor contamination characteristics identification and classification in region.The different pollutant concentration sequences that this method is obtained based on monitoring point, using with
Euclidean planes are the Similarity Principle of module as Classification and Identification criterion, classification accuracy with higher.
Detailed description of the invention
Fig. 1 multichannel intelligent environmental gas measuring device composition figure
Fig. 2 gridding monitoring and measuring device cloth point diagram
Trace to the source broadcast algorithm flow chart of the Fig. 3 based on measuring contamination time series correlation analysis
Fig. 4 traces to the source/diffusion path figure
Fig. 5 cluster analysis result
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in further detail.
Multichannel intelligent polluted gas monitoring device of the invention, is integrally placed in industrial waterproof cabinet, such as Fig. 1 institute
Show, be mainly made of following components:
(1) gasmetry sensing unit, including gas chamber, gas sensor, Current Voltage convert amplification module, filter module
Composition, wherein gas sensor principal mode is electrochemical sensor, and oxidation is generated between gas flows through sensor and membrane electrode also
Original reaction, to generate micro-current, by Current Voltage converter amplifier circuit, is converted to voltage signal for gas concentration, passes through
Signal-to-Noise is improved after filtering processing.In apparatus of the present invention, design has multiple measurement acquisition channels, can be according to actual needs
Different sensors are configured, to realize multiple gases while detect.
(2) data acquisition and procession unit is made of A/D conversion module and processor of single chip computer, passes through A/D modulus of conversion
Voltage value is converted to digital signal by block, is then fed into processor of single chip computer and is carried out algorithm process, gas concentration is calculated.Number
According to data network platform can be transmitted to by remote transmission mode, or it is stored in local storage unit.It is single according to gas concentration
The also transmittable alarm of piece machine processor or sampled signal.
(3) gas extraction and pretreatment unit, including, aspiration pump, flow controller, gas oil water separator, particulate matter
Filter, moisture filter, gas conduit and heating tape.Gas extraction and pretreatment unit each section are by having heating tape
Gas conduit connection, heating tape main function is to maintain gas temperature, prevents gas componant from changing, and improves detection accuracy.It takes out
Air pump extracts gas in external environment, is sequentially entered by gas conduit, moisture filter, particulate filter, gas grease
Separator, flow controller finally enter gasmetry sensing unit and measure.
(4) exceeded gas alarm and sampling unit, mainly form sound light crossing-signal, sampling pump, gas conduit, unidirectionally
Valve samples airbag.The unit is after the exceeded signal for the sending for receiving processor of single chip computer, starting combined aural and visual alarm and sampling
Pump, sampling pump extract gas and are sent into sampling airbag, are wherein connected between sampling pump and sampling airbag by check valve, prevent from sampling
Gas backstreaming, and guarantee sample precision.
The invention further relates to a kind of based on gridded data and the analysis of multi-channel detection data time series relevant cluster
Atmosphere pollution is traced to the source and detecting/monitoring method, is achieved through the following technical solutions:
(1) region to be measured divide for gridding, and define grid map matrix coordinate, i.e., be matrix by north to south
Row (line number is represented using i), by west to east be matrix column (row number is represented using j), n kind monitoring pollution object is in certain time
Concentration data in t indicates are as follows:Based on GPS latitude and longitude coordinates by pollutant monitoring Data Matching to the net in region to be measured
On lattice vector quantity map, the pollution sources grid map is obtained, as shown in Figure 2
(2) in order to reduce random errors affect, ARMA is carried out to each node polluted gas gas-monitoring data first
It is different dirty to obtain each node for (Autoregressive moving average model, Regressive averaging model) processing
Object concentration-time sequence is contaminated, is indicated are as follows:
Wherein [β0, β1... βp] it is regression coefficient, [α0, α1... αp] it is mean coefficient, [e0, e1... ep] it is white noise
Sound, it is assumed that various noise profiles are similar to white noise in monitoring process, then obtaining time series will be reduced after ARMA is handled
Influence of noise changes over time trend more representative of pollutant actual concentration.
(3) in the maximum node of grid range searching pollutant concentration, which is obtained into pollutant concentration time series Y
(t) pollutant time series Y ' (t) adjacent with surrounding carries out correlation analysis, and related coefficient calculation formula is as follows:
The pollutant concentration maximum point from monitoring result, it is successively all around search to close on domain point, it is available to have
A series of related coefficients take maximum related coefficient position, so that it may obtain pollutant and spread a path, it is assumed that indicate
For
Then centered on new starting point (i, j+1), data is obtained with neighbouring monitoring point and are scanned for, and are found
Related coefficient maximum position.Above step is repeated, until all monitoring points, algorithm flow are as shown in Figure 3 in traversal detection grid.
Diffusion path of the last available different pollutants in monitoring region, acquired pollutant spread the path such as Fig. 4 institute that traces to the source
Show.
(4) the different pollutant kinds obtained according to the measurement of each gridding monitoring point, to each monitoring in monitoring region
The data of point carry out clustering.The different pollutant concentration sequences that each monitoring point is obtained are selected close as sample value, sampling
Principle carries out matching classification to sample, using using euclidean planes be the Similarity Principle of module as Classification and Identification criterion,
Euclidean distance calculation formula is as follows:
If can determine that Y and Y if ρ (Y, Y ') < α ' it is classified as in same fuzzy set, that is, can be determined that two monitorings
Net region contamination characteristics having the same where point.
The data monitored in different grids are traversed using above method, calculate European approach degree set, so as to
Monitoring point classification results are obtained according to the matching classification results of Similarity Principle, as shown in Figure 5.It can be more acurrate according to classification results
Monitoring point contamination characteristics are extracted, and more fine management can be carried out for each monitoring point.
The instrument course of work is that air pump extracts ambient atmos by sampling gas nozzle, then passes through moisture filter respectively,
Grain object filter, gas pressure regulator and gas oil water separator, carry out gas pretreatment, remove moisture and particulate matter in gas
Deng the ingredient for influencing measurement result, then by flow controller, gas is controlled with certain flow and enters gas measurement unit.
Atmosphere pollution based on gridded data and the analysis of multi-channel detection data time series relevant cluster of the invention
It traces to the source and detecting/monitoring method, specific steps are as follows:
Region to be measured divide for gridding, and defines grid map matrix coordinate, i.e., is matrix by north to south
Row (representing line number using i), by being matrix column (representing row number using j) west to east, n kind monitoring pollution object is in certain time t
Interior concentration data indicates are as follows:Based on GPS latitude and longitude coordinates by pollutant monitoring Data Matching to the grid in region to be measured
On map vector, the pollution sources grid map is obtained, as shown in Figure 2
Measurement process is that gas flows through the sensor in measuring unit, redox reaction is generated, to generate micro- electricity
Stream, by Current Voltage converter amplifier circuit, is converted to voltage signal for gas concentration, and signal letter is improved after filtering processing
It makes an uproar ratio.In apparatus of the present invention, settable multiple measurement acquisition channels configure different sensors according to actual needs, gas according to
It is secondary to flow through each measuring device, to realize multiple gases while detect.Voltage value is converted into number by A/D conversion module
Signal is then fed into processor of single chip computer and carries out algorithm process, gas concentration is calculated.Data can pass through remote transmission mode
It is transmitted to data network platform, or is stored in local storage unit.According to gas concentration, the also transmittable report of processor of single chip computer
Alert or sampled signal.
After data are transferred to platform, ARMA is carried out to each node polluted gas gas-monitoring data first
It is different dirty to obtain each node for (Autoregressive moving average model, Regressive averaging model) processing
Object concentration-time sequence is contaminated, is indicated are as follows:
In the maximum node of grid range searching pollutant concentration, which is obtained into pollutant concentration time series Y (t)
Correlation function calculating is carried out with adjacent pollutant time series Y ' (t) around, pollutant concentration maximum is pointed out from monitoring result
Hair, it is successively all around search to close on domain point, it is available to have a series of related coefficients, take maximum phase relation numerical digit
It sets, so that it may it obtains pollutant and spreads a path, repeat above step, until traversing all monitoring points in detection grid, from
And obtain diffusion path of the different pollutants in monitoring region.
The different pollutant concentration sequences that each monitoring point is obtained sample Similarity Principle and carry out to sample as sample value
Matching classification, using using euclidean planes be the Similarity Principle of module as Classification and Identification criterion.It traverses in different grids
Obtained data are monitored, European approach degree set are calculated, so as to be monitored according to the matching classification results of Similarity Principle
Point classification results, can more acurrate extraction monitoring point contamination characteristics according to classification results.
Claims (3)
1. a kind of multichannel intelligent polluted gas monitoring device, including gasmetry sensing unit, data acquisition and procession list
Member, gas extraction and pretreatment unit, exceeded gas alarm and sampling unit, which is characterized in that
Gasmetry sensing unit, design have multiple measurement acquisition channels, configure gas with various sensor according to actual needs, real
Existing multiple gases detect simultaneously, and detection signal is admitted to data acquisition and procession unit；
Data acquisition and procession unit will test signal and be converted to digital signal and be handled, gas concentration, gas is calculated
Bulk concentration data are transmitted to data network platform by remote transmission mode or are stored in local storage unit；It is dense according to gas
Degree, processor of single chip computer send alarm signal；
Gas extraction and pretreatment unit, including aspiration pump, flow controller, gas oil water separator, particulate filter, water
Divide filter, gas conduit and heating tape；Gas extraction and pretreatment unit each section pass through the gas conduit with heating tape
Connection, heating tape prevent gas componant from changing, and improve detection accuracy to keep gas temperature；Aspiration pump extracts external rings
Gas in border sequentially enters moisture filter, particulate filter, gas oil water separator and flow control by gas conduit
Device finally enters gasmetry sensing unit and measures；
Exceeded gas alarm and sampling unit, including combined aural and visual alarm, sampling pump, gas conduit, check valve and sampling airbag, should
Unit starts combined aural and visual alarm and sampling pump after the alarm signal for the sending for receiving processor of single chip computer, and sampling pump extracts
Gas is sent into sampling airbag, is wherein connected between air pump and sampling airbag by check valve, prevents sample gas from flowing back, and guarantee
Sample precision.
2. a kind of polluted gas spreads source tracing method, comprising the following steps:
(1) region to be measured is carried out establishing lattice vector map, and define lattice vector map matrix seat for gridding division
Mark, if i represents line number, j represents row number, and concentration data of the n kind monitoring pollution object in certain time t indicates are as follows:It is based on
GPS latitude and longitude coordinates on the lattice vector map of pollutant monitoring Data Matching to region to be measured, will obtain the pollution sources
Grid map；
(2) Regressive averaging model ARMA processing is carried out to each node polluted gas gas-monitoring data, obtains each section
The different pollutant concentration time serieses of point, indicate are as follows:
Wherein [β0, β1... βp] it is regression coefficient, [α0, α1... αp] it is mean coefficient, [e0, e1... ep] it is white noise, it is false
If various noise profiles are similar to white noise in monitoring process；
(3) in the maximum node of grid range searching pollutant concentration, which is obtained into pollutant concentration time series Y (t)
With adjacent pollutant time series Y ' (t) progress correlation analysis around, the pollutant concentration maximum point from monitoring result,
It is successively all around search to close on domain point, it is available to have a series of related coefficients, maximum related coefficient position is taken, is obtained
A path is spread to pollutant, is expressed asThen centered on new starting point (i, j+1), with neighbour
Nearly monitoring point obtains data and is scanned for, and finds related coefficient maximum position.Above step is repeated, until traversal detects
All monitoring points in grid, obtain diffusion path of the different pollutants in monitoring region, and acquired pollutant spreads road of tracing to the source
Diameter.
3. according to the method described in claim 2, it is characterized in that, dirty according to the difference that the measurement of each gridding monitoring point obtains
Species are contaminated, clustering are carried out to the data of each monitoring point in monitoring region, the different pollutions that each monitoring point is obtained
Object concentration sequence samples Similarity Principle and carries out matching classification to sample as sample value, is measurement mark using with euclidean planes
For quasi- Similarity Principle as Classification and Identification criterion, whether having the same the pollution of net region where determining two monitoring points be special
Sign.
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| CN110531032A (en) * | 2019-09-20 | 2019-12-03 | 哈尔滨学院 | Pollutant testing apparatus for mathematical modeling and the unmanned plane with it |
| CN110596327A (en) * | 2019-06-25 | 2019-12-20 | 北京机械设备研究所 | Method for detecting components and concentration of polluted gas |
| CN110596328A (en) * | 2019-06-25 | 2019-12-20 | 北京机械设备研究所 | Integrated multichannel polluted gas concentration detection device |
| TWI698760B (en) * | 2019-03-12 | 2020-07-11 | 寰宇國際地球資訊有限公司 | Pollution source tracking system and method thereof |
| CN111695712A (en) * | 2019-03-12 | 2020-09-22 | 寰宇国际地球资讯有限公司 | Pollution source tracking system and method thereof |
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| CN113203775A (en) * | 2021-04-30 | 2021-08-03 | 国网上海市电力公司 | A multi-gas monitoring intelligent module |
| CN113284244A (en) * | 2021-04-20 | 2021-08-20 | 四川大学 | Atmospheric pollutant tracing method based on lattice diffusion simulation |
| CN113607765A (en) * | 2021-08-03 | 2021-11-05 | 亚翔系统集成科技（苏州）股份有限公司 | Pollution source searching method based on poor products in semiconductor production line |
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| CN114778648A (en) * | 2022-04-24 | 2022-07-22 | 深圳科瑞德健康科技有限公司 | Test system and measurement method for oxidation-reduction potential value of aqueous solution |
| CN115238807A (en) * | 2022-07-29 | 2022-10-25 | 中用科技有限公司 | AMC detection method based on artificial intelligence |
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Citations (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1441336A (en) * | 2002-02-26 | 2003-09-10 | 北京汇冠科技有限公司 | Positioning system and method for detecting coordinates of vibration source position |
| JP2003302342A (en) * | 2003-04-09 | 2003-10-24 | Hitachi Metals Ltd | Calculation method for prediction expression of dust generation quantity from dust generation source |
| US20040254740A1 (en) * | 2003-06-16 | 2004-12-16 | Ryohji Ohba | Diffusion status prediction method and diffusion status prediction system for diffused substance |
| CN1575194A (en) * | 2001-09-24 | 2005-02-02 | 提取系统有限公司 | System and method for monitoring contamination |
| CN201402268Y (en) * | 2009-02-25 | 2010-02-10 | 北京泰华恒越科技发展有限责任公司 | Intelligent environmental air quality monitor |
| US20100332210A1 (en) * | 2009-06-25 | 2010-12-30 | University Of Tennessee Research Foundation | Method and apparatus for predicting object properties and events using similarity-based information retrieval and modeling |
| JP2012047498A (en) * | 2010-08-24 | 2012-03-08 | Meijo University | Method for predicting movement of chemical substance in soil gas, method for calculating removal period of chemical substance in soil gas, and method for determining disposition of suction well for removal of chemical substance in soil gas |
| CN102708245A (en) * | 2012-05-09 | 2012-10-03 | 浙江大学 | Sudden water pollution accident analog simulation and visualization service system |
| CN104217040A (en) * | 2014-10-11 | 2014-12-17 | 清华大学 | Rapid pollution incident detection method based on traditional online monitor |
| JP2015040814A (en) * | 2013-08-23 | 2015-03-02 | 紀明 岡崎 | Radioactive material moving path estimation method and decontamination method |
| JP2015040815A (en) * | 2013-08-23 | 2015-03-02 | 紀明 岡崎 | Radioactive material moving path estimation method and decontamination method |
| CN204255708U (en) * | 2014-11-21 | 2015-04-08 | 天津圣纳科技有限公司 | Based on the portable exhaust analyzer sampling box of ZigBee wireless telecommunications |
| CN204758358U (en) * | 2015-06-17 | 2015-11-11 | 陕西钢铁集团有限公司 | Directly extract flue gas sampling device of formula CEMS system |
| CN105656693A (en) * | 2016-03-15 | 2016-06-08 | 南京联成科技发展有限公司 | Regression-based information safety and anomaly detection method and system |
| US20160161456A1 (en) * | 2014-12-01 | 2016-06-09 | St. Francis Xavier University | Gas emission detection device, system and method |
| CN106290094A (en) * | 2015-06-29 | 2017-01-04 | 天津同阳科技发展有限公司 | The mie being applied to airborne dust particulate matter on-line monitoring scatters quick calculation method |
| CN106368813A (en) * | 2016-08-30 | 2017-02-01 | 北京协同创新智能电网技术有限公司 | Abnormal alarm data detection method based on multivariate time series |
| CN106448080A (en) * | 2016-09-20 | 2017-02-22 | 北京工业大学 | Small-space volatile inflammable liquid detection system based on sensor-network sniffing |
| CN106442031A (en) * | 2016-10-13 | 2017-02-22 | 北京市环境保护监测中心 | Automatic sampling device for malodorous gas |
| CN106605225A (en) * | 2014-08-27 | 2017-04-26 | 日本电气株式会社 | Simulation device, simulation method, and memory medium |
| CN106651036A (en) * | 2016-12-26 | 2017-05-10 | 东莞理工学院 | Air quality forecasting system |
| CN106920198A (en) * | 2015-12-24 | 2017-07-04 | 日本电气株式会社 | For the apparatus and method that pollutant is traced to the source |
| CN107631960A (en) * | 2017-08-18 | 2018-01-26 | 浙江海洋大学 | Ocean material disperse and transport coefficient CCD monitors experimental provision and its monitoring method automatically |
| CN107633148A (en) * | 2017-10-16 | 2018-01-26 | 核工业北京地质研究院 | A kind of method for numerical simulation for nuclear facilities exhaust gas diffusion |
| CN107704966A (en) * | 2017-10-17 | 2018-02-16 | 华南理工大学 | A kind of Energy Load forecasting system and method based on weather big data |
| CN108008705A (en) * | 2017-11-23 | 2018-05-08 | 武汉英伦丰创软件有限公司 | A kind of atmosphere pollution visualizing monitor assessment system |
| CN108062454A (en) * | 2018-01-19 | 2018-05-22 | 宁波市镇海规划勘测设计研究院 | Pollutant spatial and temporal distributions uncertainty characteristic analysis method, system and storage medium |
| CN108664647A (en) * | 2018-05-17 | 2018-10-16 | 环境保护部环境规划院 | A kind of basin fine-grained management system of integrated Model of Water Environment |
| CN109085641A (en) * | 2018-08-02 | 2018-12-25 | 北京大学深圳研究生院 | Monitoring data processing method, method for forecasting earthquake and system for earthquake prediction |
| CN209372744U (en) * | 2018-12-26 | 2019-09-10 | 苏州同阳科技发展有限公司 | A kind of multichannel intelligent polluted gas monitoring device |
-
2018
- 2018-12-26 CN CN201811602569.XA patent/CN109444232B/en active Active
Patent Citations (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1575194A (en) * | 2001-09-24 | 2005-02-02 | 提取系统有限公司 | System and method for monitoring contamination |
| CN1441336A (en) * | 2002-02-26 | 2003-09-10 | 北京汇冠科技有限公司 | Positioning system and method for detecting coordinates of vibration source position |
| JP2003302342A (en) * | 2003-04-09 | 2003-10-24 | Hitachi Metals Ltd | Calculation method for prediction expression of dust generation quantity from dust generation source |
| US20040254740A1 (en) * | 2003-06-16 | 2004-12-16 | Ryohji Ohba | Diffusion status prediction method and diffusion status prediction system for diffused substance |
| CN201402268Y (en) * | 2009-02-25 | 2010-02-10 | 北京泰华恒越科技发展有限责任公司 | Intelligent environmental air quality monitor |
| US20100332210A1 (en) * | 2009-06-25 | 2010-12-30 | University Of Tennessee Research Foundation | Method and apparatus for predicting object properties and events using similarity-based information retrieval and modeling |
| JP2012047498A (en) * | 2010-08-24 | 2012-03-08 | Meijo University | Method for predicting movement of chemical substance in soil gas, method for calculating removal period of chemical substance in soil gas, and method for determining disposition of suction well for removal of chemical substance in soil gas |
| CN102708245A (en) * | 2012-05-09 | 2012-10-03 | 浙江大学 | Sudden water pollution accident analog simulation and visualization service system |
| JP2015040814A (en) * | 2013-08-23 | 2015-03-02 | 紀明 岡崎 | Radioactive material moving path estimation method and decontamination method |
| JP2015040815A (en) * | 2013-08-23 | 2015-03-02 | 紀明 岡崎 | Radioactive material moving path estimation method and decontamination method |
| CN106605225A (en) * | 2014-08-27 | 2017-04-26 | 日本电气株式会社 | Simulation device, simulation method, and memory medium |
| CN104217040A (en) * | 2014-10-11 | 2014-12-17 | 清华大学 | Rapid pollution incident detection method based on traditional online monitor |
| CN204255708U (en) * | 2014-11-21 | 2015-04-08 | 天津圣纳科技有限公司 | Based on the portable exhaust analyzer sampling box of ZigBee wireless telecommunications |
| US20160161456A1 (en) * | 2014-12-01 | 2016-06-09 | St. Francis Xavier University | Gas emission detection device, system and method |
| CN204758358U (en) * | 2015-06-17 | 2015-11-11 | 陕西钢铁集团有限公司 | Directly extract flue gas sampling device of formula CEMS system |
| CN106290094A (en) * | 2015-06-29 | 2017-01-04 | 天津同阳科技发展有限公司 | The mie being applied to airborne dust particulate matter on-line monitoring scatters quick calculation method |
| CN106920198A (en) * | 2015-12-24 | 2017-07-04 | 日本电气株式会社 | For the apparatus and method that pollutant is traced to the source |
| CN105656693A (en) * | 2016-03-15 | 2016-06-08 | 南京联成科技发展有限公司 | Regression-based information safety and anomaly detection method and system |
| CN106368813A (en) * | 2016-08-30 | 2017-02-01 | 北京协同创新智能电网技术有限公司 | Abnormal alarm data detection method based on multivariate time series |
| CN106448080A (en) * | 2016-09-20 | 2017-02-22 | 北京工业大学 | Small-space volatile inflammable liquid detection system based on sensor-network sniffing |
| CN106442031A (en) * | 2016-10-13 | 2017-02-22 | 北京市环境保护监测中心 | Automatic sampling device for malodorous gas |
| CN106651036A (en) * | 2016-12-26 | 2017-05-10 | 东莞理工学院 | Air quality forecasting system |
| CN107631960A (en) * | 2017-08-18 | 2018-01-26 | 浙江海洋大学 | Ocean material disperse and transport coefficient CCD monitors experimental provision and its monitoring method automatically |
| CN107633148A (en) * | 2017-10-16 | 2018-01-26 | 核工业北京地质研究院 | A kind of method for numerical simulation for nuclear facilities exhaust gas diffusion |
| CN107704966A (en) * | 2017-10-17 | 2018-02-16 | 华南理工大学 | A kind of Energy Load forecasting system and method based on weather big data |
| CN108008705A (en) * | 2017-11-23 | 2018-05-08 | 武汉英伦丰创软件有限公司 | A kind of atmosphere pollution visualizing monitor assessment system |
| CN108062454A (en) * | 2018-01-19 | 2018-05-22 | 宁波市镇海规划勘测设计研究院 | Pollutant spatial and temporal distributions uncertainty characteristic analysis method, system and storage medium |
| CN108664647A (en) * | 2018-05-17 | 2018-10-16 | 环境保护部环境规划院 | A kind of basin fine-grained management system of integrated Model of Water Environment |
| CN109085641A (en) * | 2018-08-02 | 2018-12-25 | 北京大学深圳研究生院 | Monitoring data processing method, method for forecasting earthquake and system for earthquake prediction |
| CN209372744U (en) * | 2018-12-26 | 2019-09-10 | 苏州同阳科技发展有限公司 | A kind of multichannel intelligent polluted gas monitoring device |
Non-Patent Citations (4)
| Title |
|---|
| FRYER RJ. ET AL: ""Using smoothers for comprehensive assessments of contaminant time series in marine biota"", 《ICES JOURNAL OF MARINE SCIENCE》, vol. 56, no. 5, pages 779 - 790 * |
| 孟尚臻等: ""基于时间序列的高层建筑物沉降预测"", 《北京测绘》, vol. 32, no. 6, pages 725 - 729 * |
| 李晨曦等: ""近红外光谱主要成分分析与模糊聚类的典型地面目标物识别"", 《光谱学与光谱分析》, vol. 37, no. 11, pages 3386 - 3390 * |
| 潘楠等: ""非线性线条痕迹小波流域特征快速溯源算法研究"", 《中国测量与仪器学报》, vol. 31, no. 6, pages 899 - 908 * |
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