CN108519465A - A kind of air pollution intelligent monitor system based on big data - Google Patents
A kind of air pollution intelligent monitor system based on big data Download PDFInfo
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- CN108519465A CN108519465A CN201810270125.4A CN201810270125A CN108519465A CN 108519465 A CN108519465 A CN 108519465A CN 201810270125 A CN201810270125 A CN 201810270125A CN 108519465 A CN108519465 A CN 108519465A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
- G01N33/004—Specially adapted to detect a particular component for CO, CO2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The present invention provides a kind of air pollution intelligent monitor system based on big data, including multiple atmosphere quality monitoring devices and big data processing center, each atmosphere quality monitoring device is all connected to big data processing center, and each atmosphere quality monitoring device is used to acquire the air pollution parameter of multiple air pollution monitoring nodes in an air pollution monitoring region；Big data processing center is used to carry out processing analysis to the air pollution parameter of acquisition, realizes the real-time monitoring to air pollution；Big data processing center includes data pre-processing unit, data clusters processing unit, outlier detection unit, database.
Description
Technical field
The present invention relates to air pollution monitoring technical fields, and in particular to a kind of air pollution intelligence prison based on big data
Examining system.
Background technology
In the related technology, mainly have to the method for urban air pollution monitoring：
(1) conventional method, the i.e. method of manual sampling lab analysis.This method can only obtain air pollution monitoring area
Monitor value in domain in certain time can not be monitored in real time, monitoring result by it is artificial influenced it is very big, meanwhile, work as air
The health of meeting grievous injury monitoring personnel when pollution monitoring region harmful gas concentration is very high；
(2) on-line monitoring popular at present, the automation air environment monitoring equipment for mostly using external import carry out
Monitoring, this monitoring method, although real-time monitoring can be realized, device therefor is complicated, it is expensive, be difficult to safeguard,
Operation cost is high and its working environment is harsh.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of air pollution intelligent monitor system based on big data.
The purpose of the present invention is realized using following technical scheme：
Provide a kind of air pollution intelligent monitor system based on big data, including multiple atmosphere quality monitoring devices and
Big data processing center, each atmosphere quality monitoring device are all connected to big data processing center, each air quality monitoring dress
Set the air pollution parameter for acquiring multiple air pollution monitoring nodes in an air pollution monitoring region；At big data
Reason center is used to carry out processing analysis to the air pollution parameter of acquisition, realizes the real-time monitoring to air pollution.
Preferably, each atmosphere quality monitoring device includes the sensor section for being set to each air pollution monitoring node
Point further includes base station, and the air pollution parameter of air pollution monitoring node where sensor node is used to acquire, biography is responsible in base station
Two-way information interaction between sensor node and big data processing center.
When air pollution parameter amount reaches certain threshold value, sensor node by collected air pollution model parameter at
Air pollution Parameter File is simultaneously sent to base station, and then the air pollution Parameter File of reception is sent in data by base station
The heart.
Preferably, big data processing center includes data pre-processing unit, data clusters processing unit, outlier detection list
Member, database, data pre-processing unit is for pre-processing the air pollution parameter that atmosphere quality monitoring device acquires；Number
According to clustering processing unit by improved global K-means clustering algorithm to the pretreated air pollution of data pre-processing unit
Parameter carries out clustering processing；Outlier detection unit carries out outlier detection to the air pollution parameter after clustering processing, obtains
Peel off point set, and the point set that will peel off be marked after storage in database.
Beneficial effects of the present invention are：Based on big data treatment technology and wireless sensor network technology, realize that air is dirty
The real-time monitoring of dye and the united analysis of related data are handled, and improve the monitoring capability to air pollution, intelligence is convenient, saves people
Power, advantage of lower cost.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the structural schematic block diagram of the air pollution intelligent monitor system of an illustrative embodiment of the invention；
Fig. 2 is the structural schematic block diagram of the big data processing center of an illustrative embodiment of the invention.
Reference numeral：
Atmosphere quality monitoring device 1, big data processing center 2, data pre-processing unit 10, data clusters processing unit
20, outlier detection unit 30, database 40.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of air pollution intelligent monitor system based on big data provided in this embodiment, including multiple skies
Gas quality monitoring device 1 and big data processing center 2, each atmosphere quality monitoring device 1 are all connected to big data processing center
2, each atmosphere quality monitoring device 1 is used to acquire multiple air pollution monitoring nodes in an air pollution monitoring region
Air pollution parameter；Big data processing center 2 is used to carry out processing analysis to the air pollution parameter of acquisition, realizes to air dirt
The real-time monitoring of dye.
In one embodiment, each atmosphere quality monitoring device 1 includes being set to each air pollution monitoring node
Sensor node further includes base station, the air pollution parameter of air pollution monitoring node, base where sensor node is used to acquire
It stands and is responsible for two-way information interaction between sensor node and big data processing center 2.
Wherein, sensor node includes at least one following sensor：
Dust sensor, the concentration for detecting the dust pollution object in the air pollution monitoring region in real time；
PM2.5 sensors, the concentration for detecting the PM2.5 pollutants in the air pollution monitoring region in real time；
Formaldehyde sensor, the concentration for detecting the formaldehyde pollutants in the air pollution monitoring region in real time；
Toxic gas sensor, the concentration for detecting the toxic gas in the air pollution monitoring region in real time；
Peculiar smell sensor, the concentration for detecting the peculiar smell in the air pollution monitoring region in real time；
Carbon dioxide sensor, the concentration for detecting the carbon dioxide in the air pollution monitoring region in real time.
In one embodiment, as shown in Fig. 2, big data processing center 2 includes data pre-processing unit 10, data clusters
Processing unit 20, outlier detection unit 30, database 40, data pre-processing unit 10 is for adopting atmosphere quality monitoring device
The air pollution parameter of collection is pre-processed；Data clusters processing unit 20 passes through improved global K-means clustering algorithm logarithm
10 pretreated air pollution parameter of Data preprocess unit carries out clustering processing；After outlier detection unit 30 is to clustering processing
Air pollution parameter carry out outlier detection, obtain the point set that peels off, and the point set that will peel off be marked after storage arrive database
In 40.
The above embodiment of the present invention is based on big data treatment technology and wireless sensor network technology, realizes air pollution
The united analysis of monitoring and related data is handled in real time, improves the monitoring capability to air pollution, and intelligence is convenient, saves manpower,
Advantage of lower cost.
In one embodiment, the air pollution parameter to atmosphere quality monitoring device acquisition pre-processes, and has
Body is：The air pollution parameter of atmosphere quality monitoring device acquisition is detected according to acquisition time sequence, air pollution is joined
Number xaWith its previous air pollution parameter xa-1It is compared, calculates air pollution parameter xaWhether meet data and merges condition, if
Air pollution parameter xaMeet data and merge condition, then by air pollution parameter xaIt rejects, continues to next air pollution parameter
It is detected.
Wherein, data merge condition and are set as：
In formula, δ is the change rate threshold value of setting.
The present embodiment pre-processes the air pollution parameter that atmosphere quality monitoring device acquires, and change rate is smaller
Air pollution parameter is rejected, and can be reduced at air pollution parameter under the premise of ensureing air pollution parameter precision itself
The quantity of reason is beneficial to the memory space of saving system, reduces the calculation amount of data clusters processing unit 20, improves data clusters
The efficiency of processing.
In one embodiment, the improved global K-means clustering algorithm, specifically includes：
(1) the air pollution parameter of pretreated set period of time is extracted as an air pollution parameter set, is set as
X；
(2) by the air pollution parameter in air pollution parameter set X according to being ranked up from small to large, choose it is therein in
Digit as air pollution parameter set X cluster centre and enable λ=1；
(3) λ=λ+1 is enabled, if λ ＞ M, M are the iterations threshold value of setting, then algorithm terminates；
(4) first λ -1 times initial cluster center C are taken1,C2,…,Cλ-1, and take the air pollution in air pollution parameter set
Parameter xiAs the λ initial cluster center, xi∈ X, i=1 ..., N, N is the air pollution ginseng that air pollution parameter set X has
Quantity calculates B according to the following formulai, selection is so that BiValue maximum group cluster center is as optimal initial cluster center：
In formula, xj∈ X, BiFor measuring in xiThe amount of cluster error reduction after a cluster centre is added in place,It indicates
xjTo in C1,C2,…,Cλ-1The square distance of the nearest initial cluster center of middle distance；
(5) optimal initial cluster center application K- mean algorithms are clustered, and preserves cluster result, remember the first of them
Beginning cluster centre is Y1,Y2,…,Yλ；
(6) if obtained cluster result has the cluster for only including an air pollution parameter, the corresponding B of this cluster is enablediIt is 0,
It goes to (4)；Otherwise it goes to (7)；
(7) C is enabledk=Yk, k=1 ..., λ are gone to step (3).
Wherein, the K- mean algorithms are existing algorithm, and the algorithm is by making cluster error minimum carry out drawing for cluster
Point.
The present embodiment is by improved global K-means clustering algorithm to 10 pretreated air of data pre-processing unit
Pollution parameters carry out clustering processing, and innovatively propose improved global K-means clustering algorithm, which can avoid
There is situation of the independent air pollution parameter as a cluster, and utilizes BiTo measure in xiA cluster centre is added in place
The amount for clustering error reduction afterwards, optimizes the solution efficiency of clustering problem, so that the improved overall situation that the present embodiment proposes
K- means clustering algorithms have more preferably Clustering Effect relative to existing global K-means clustering algorithm, and can be applicable in
In larger air pollution parameter set.
In one embodiment, the air pollution parameter to after clustering processing carries out outlier detection, specifically includes：
(1) the air pollution parameter of the same cluster is ranked up according to sequence from small to large, obtains middle position therein
Number xmed；
(2) if air pollution parameter xiMeet the following condition that peels off, then by air pollution parameter xiIt is considered as outlier, returns
Enter the point set that peels off：
,(Dxi,o-D2)[|xi-xmed|-|x21-xT2|] ＞ 0
In formula, xT1For section [x1,xmed] median, xT2For section [xmed,xn] median, xi∈{x1,x2,…,
xn, n xiThe air pollution number of parameters of place cluster,For air pollution parameter xiTo the distance of the cluster centre of its cluster,
DTFor the distance threshold of setting,For the judgement value function of setting, whenWhen, WhenWhen,
In an optional mode, by the DTIt is set as xiIn the cluster of place air pollution parameter to cluster centre away from
From average value.
The present embodiment carries out outlier detection to the air pollution parameter after clustering processing, therefrom innovatively proposes use
In detection air pollution parameter whether be outlier the condition that peels off, the condition that peels off is according to air pollution parameter and cluster centre
Distance and the magnitude relationships of air pollution parameter and place cluster air pollution parameter sets judge the air pollution parameter
Whether it is outlier, due to judging that air pollution parameter and the size of place cluster air pollution parameter sets are closed using median
System enables to detection not limited by air pollution parameter distribution in cluster, has certain robustness, detection mode is simple
Effectively.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (6)
1. a kind of air pollution intelligent monitor system based on big data, characterized in that including big data processing center and multiple
Atmosphere quality monitoring device, each atmosphere quality monitoring device are all connected to big data processing center, each air quality monitoring
Device is used to acquire the air pollution parameter of multiple air pollution monitoring nodes in an air pollution monitoring region；Big data
Processing center is used to carry out processing analysis to the air pollution parameter of acquisition, realizes the real-time monitoring to air pollution；Big data
Processing center includes data pre-processing unit, data clusters processing unit, outlier detection unit, database, data prediction
Unit is for pre-processing the air pollution parameter that atmosphere quality monitoring device acquires；Data clusters processing unit is by changing
Into global K-means clustering algorithm to the pretreated air pollution parameter of data pre-processing unit carry out clustering processing；It peels off
Point detection unit carries out outlier detection to the air pollution parameter after clustering processing, obtains the point set that peels off, and the point set that will peel off
After being marked in storage to database.
2. a kind of air pollution intelligent monitor system based on big data according to claim 1, characterized in that Mei Gekong
Gas quality monitoring device includes the sensor node for being set to each air pollution monitoring node, further includes base station, sensor section
Air pollution parameter of the point for air pollution monitoring node where acquiring, base station are responsible in sensor node and big data processing
Two-way information interaction between the heart.
3. a kind of air pollution intelligent monitor system based on big data according to claim 2, characterized in that work as air
When pollution parameters amount reaches certain threshold value, sensor node is by collected air pollution model parameter at air pollution parameter text
Part is simultaneously sent to base station, and then the air pollution Parameter File of reception is sent to data center by base station.
4. according to a kind of air pollution intelligent monitor system based on big data of claim 1-3 any one of them, feature
It is that the air pollution parameter to atmosphere quality monitoring device acquisition pre-processes, specially：Air quality monitoring is filled
The air pollution parameter for setting acquisition is detected according to acquisition time sequence, by air pollution parameter xaJoin with its previous air pollution
Number xa-1It is compared, calculates air pollution parameter xaWhether meet data and merge condition, if air pollution parameter xaMeet data
Merging condition, then by air pollution parameter xaIt rejects, continues to be detected next air pollution parameter.
5. a kind of air pollution intelligent monitor system based on big data according to claim 4, characterized in that wherein,
Data merge condition and are set as：
In formula, δ is the change rate threshold value of setting.
6. according to a kind of air pollution intelligent monitor system based on big data of claim 1-3 any one of them, feature
It is that the improved global K-means clustering algorithm specifically includes：
(1) the air pollution parameter of pretreated set period of time is extracted as an air pollution parameter set, is set as X；
(2) the air pollution parameter in air pollution parameter set X is chosen into median therein according to being ranked up from small to large
As air pollution parameter set X cluster centre and enable λ=1；
(3) λ=λ+1 is enabled, if λ ＞ M, M are the iterations threshold value of setting, then algorithm terminates；
(4) first λ -1 times initial cluster center C are taken1,C2,…,Cλ-1, and take the air pollution parameter in air pollution parameter set
xiAs the λ initial cluster center, xi∈ X, i=1 ..., N, N is the air pollution parameter that air pollution parameter set X has
Amount, calculates B according to the following formulai, selection is so that BiValue maximum group cluster center is as optimal initial cluster center：
In formula, xj∈ X, BiFor measuring in xiThe amount of cluster error reduction after a cluster centre is added in place,Indicate xjIt arrives
In C1,C2,…,Cλ-1The square distance of the nearest initial cluster center of middle distance；
(5) optimal initial cluster center application K- mean algorithms are clustered, and preserves cluster result, remember the initial poly- of them
Class center is Y1,Y2,…,Yλ；
(6) if obtained cluster result has the cluster for only including an air pollution parameter, the corresponding B of this cluster is enablediIt is 0, goes to
(4)；Otherwise it goes to (7)；
(7) C is enabledk=Yk, k=1 ..., λ are gone to step (3).
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| CN108519465B (en) | 2019-12-17 |
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