CN108414016A - A kind of sewage network monitoring system based on big data technology - Google Patents
A kind of sewage network monitoring system based on big data technology Download PDFInfo
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Abstract
The present invention provides a kind of, and the sewage network based on big data technology monitors system, including multiple sewage network monitoring subsystems and big data processing center, each sewage network monitoring subsystem is all connected to big data processing center, and each sewage network monitoring subsystem is used to acquire the sewage network monitoring data of multiple sewage monitoring nodes in a sewage network monitoring region；Big data processing center is used to carry out processing analysis to the sewage network monitoring data of acquisition, realizes the real-time monitoring to sewage network.The present invention is based on big data treatment technologies, and the data of numerous data collecting module collecteds summarize and united analysis is handled, data analysis utilization can be carried out, improve the monitoring capability to sewage network.
Description
Technical field
The present invention relates to sewage networks to monitor field, and in particular to a kind of sewage network monitoring system based on big data technology
System.
Background technology
Waste pipe network system is the fundamental equipments in urban construction, environmental protection, the municipal projects such as control flood and drain flooded fields, and is built
The monitoring system of vertical municipal sewage pipe network provides the environmental information of observation sewage network, analysis in real time for municipal drainage manager
The monitoring management function of sewage network dynamic operation situation, has become the active demand of modern city drainage management.It is another
Aspect, in such a way that setting monitoring point is monitored, because underground sewage pipe network monitoring point is numerous and distributed pole is wide, the number of acquisition
Big, difficult according to measuring, monitoring effect is poor.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of sewage network monitoring system based on big data technology.
The purpose of the present invention is realized using following technical scheme：
Provide a kind of sewage network monitoring system based on big data technology, including multiple sewage network monitoring subsystems
With big data processing center, each sewage network monitoring subsystem is all connected to big data processing center, each sewage network prison
Survey the sewage network monitoring data for multiple sewage monitoring nodes that subsystem is used to acquire in a sewage network monitoring region；Greatly
Data processing centre is used to carry out processing analysis to the sewage network monitoring data of acquisition, realizes the real-time prison to sewage network
It surveys.
Preferably, each sewage network monitoring subsystem includes the data acquisition module for being set to each sewage monitoring node
1 data acquisition module is arranged in block, optionally, each sewage monitoring node.
Beneficial effects of the present invention are：Based on big data treatment technology, the data that numerous sewage network monitoring points are acquired
Summarize and united analysis processing is, it can be achieved that macroscopic view, microcosmic a variety of monitorings, improve the monitoring capability to sewage network.
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 sewage network monitoring system of an illustrative embodiment of the invention；
Fig. 2 is the structural schematic block diagram of the data acquisition module of an illustrative embodiment of the invention；
Fig. 3 is the structural schematic block diagram of the big data processing center of an illustrative embodiment of the invention；
Fig. 4 is the structural schematic block diagram of the data preprocessing module of an illustrative embodiment of the invention.
Reference numeral：
Sewage network monitoring subsystem 1, big data processing center 2, data acquisition module 3, sensing unit 10, positioning unit
20, communication unit 30, data pre-processing unit 40, data reception module 100, data preprocessing module 200, data analysis module
300, data compressing module 400, data memory module 500, abnormal deciding means 50, outlier processing unit 60, Supplementing Data
Unit 70.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of sewage network monitoring system based on big data technology provided in this embodiment, including multiple dirts
Grid monitoring subsystem 1 and big data processing center 2, each sewage network monitoring subsystem 1 are all connected to big data processing
Center 2, each sewage network monitoring subsystem 1 are used to acquire multiple sewage monitoring nodes in a sewage network monitoring region
Sewage network monitoring data；Big data processing center 2 is real for carrying out processing analysis to the sewage network monitoring data of acquisition
Now to the real-time monitoring of sewage network.
In one embodiment, each sewage network monitoring subsystem 1 includes the number for being set to each sewage monitoring node
According to acquisition module 3, optionally, 1 data acquisition module 3 is arranged in each sewage monitoring node.
Optionally, as shown in Fig. 2, data acquisition module 3 includes sensing unit 10, positioning unit 20,30 and of communication unit
The output end of data pre-processing unit 40, sensing unit 10 is connect with the input terminal of data pre-processing unit 40, and the data are pre-
Processing unit 40 is connect with communication unit 30, and the big data processing center 2 connects with positioning unit 20 and communication unit 30 respectively
It connects.
Wherein, sensing unit 10 includes temperature sensor, humidity sensor, water level sensor, flow sensor or an oxygen
Change carbon sensor.
In one embodiment, as shown in figure 3, big data processing center 2 includes sequentially connected data reception module
100, data preprocessing module 200, data analysis module 300.
Wherein, data reception module 100 is used to receive the sewage network monitoring that multiple sewage network monitoring subsystems 1 are sent
Data.
Wherein, data preprocessing module 200 is used to carry out the sewage network monitoring data that data reception module 100 receives
Pretreatment.
Wherein, data analysis module 300 is used to carry out analyzing processing to pretreated sewage network monitoring data.
In one embodiment, big data processing center 2 further includes the data compressing module 400 being connected and data storage
Module 500, data compressing module 400 are connect with data reception module 100, multiple for being received to data reception module 100
The sewage network monitoring data that sewage network monitoring subsystem 1 is sent carry out compression processing, and by the sewage pipe after compression processing
Net monitoring data are sent in data memory module 500 and store.
In one embodiment, as shown in figure 4, data preprocessing module 200 includes abnormal deciding means 50, at exceptional value
Manage unit 60 and Supplementing Data unit 70；Wherein abnormal deciding means 50 is used to carry out exception to sewage network monitoring data to sentence
It is disconnected, determine the exceptional value in sewage network monitoring data；The outlier processing unit 60 is used to monitor number to sewage network
Exceptional value in is handled；The Supplementing Data unit 70 is used to carry out deletion analysis to sewage network monitoring data,
And completion processing is carried out to the missing item in sewage network monitoring data.
In one embodiment, when abnormal deciding means 50 judges sewage network monitoring data progress exception, by one
The sewage network monitoring data that data acquisition module 3 acquires in a collection period are as a sewage network monitoring data
Group carries out anomaly analysis to each sewage network monitoring data group, exports the exceptional value in sewage network monitoring data group.
Wherein, anomaly analysis is carried out to sewage network monitoring data group, specifically included：
(1) using the time as horizontal axis, sewage network monitoring data size is the longitudinal axis, and wave is drawn to sewage network monitoring data group
Shape figure；
(2) abnormal judgement is carried out to the corresponding sewage network monitoring data of each Wave crest and wave trough point in oscillogram, according to
Improved Grubbs test method calculates their G values, if the G values of the corresponding sewage network monitoring data of a Wave crest and wave trough point are big
In the decision threshold of setting, then the corresponding sewage network monitoring data of Wave crest and wave trough point are determined as exceptional value, wherein G values
Calculation formula is：
In formula, GiIndicate the G values of the corresponding sewage network monitoring data of i-th of Wave crest and wave trough point, yiIndicate i-th of wave crest
The corresponding sewage network monitoring data of trough point,For the average value of sewage network monitoring data group；ymedTo be supervised to sewage network
After sewage network monitoring data in measured data group are ranked up according to ascending sequence, it is formed by the middle position in sequence
Number；yjFor j-th of sewage network monitoring data in sewage network monitoring data group, n is the dirt in sewage network monitoring data group
Grid monitoring data number, S are the standard deviation of sewage network monitoring data group.
Preferably, the decision threshold is set asWherein Gi' in Grubbs table with GiCorresponding critical value.
Large amount of sewage pipe network monitoring data are generated from true external environment, due to the failure of data acquisition module 3, respectively
There is more or less exceptional value rather in a sewage network monitoring data group, if to exceptional value without processing, it will certainly be right
The precision of subsequent Data Analysis Services impacts.
The present embodiment is using sewage network monitoring data group as an anomaly analysis unit, to sewage network monitoring data group
Anomaly analysis is carried out, when carrying out anomaly analysis, corresponding oscillogram is drawn to sewage network monitoring data group, only to oscillogram
In the corresponding sewage network monitoring data of each Wave crest and wave trough point carry out abnormal judgement, monitored relative to all sewage networks
Data carry out the mode of anomaly analysis, the anomaly analysis efficiency of sewage network monitoring data are improved, wherein to each wave crest wave
When the corresponding sewage network monitoring data in valley point carry out anomaly analysis, improved Grubbs test method is used.
In the prior art, the Grubbs test method based on average and standard deviation is the typical exception based on parametric statistics
Point detecting method.This implementation is on the basis of existing Grubbs test method, by the way of weighting, increases in the calculation formula of G values
Weighting coefficient is addedThe introducing of the weighting coefficient so that the G values of the normal value in sewage network monitoring data group
Smaller, it is possible to reduce the erroneous judgement of normal value improves the robustness and robustness of Grubbs test method, and then improves to sewage network
Monitoring data carry out the precision of abnormal determination.
In one embodiment, outlier processing unit 60 handles the exceptional value in sewage network monitoring data
When, it is specific to execute：
(1) exceptional value x is setiThe sewage network monitoring data group at place is Γρ, sewage network monitoring data group ΓρUpper one
The corresponding sewage network monitoring data group of a collection period is Γρ-1, calculate sewage network monitoring data group ΓρAverage valueAnd calculate sewage network monitoring data group Γρ-1Average value
(2) x is calculated according to the following formulaiSubstitution value xi', by xi' substitute xi：
In formula, max expressions are maximized, and min expressions are minimized.
When handling in the prior art exceptional value, exceptional value is directly typically subjected to rejecting processing, this mode
The missing that can cause sewage network monitoring data, to influence the time response of sewage network monitoring data, after further influencing
The continuous precision that sewage network monitoring data are carried out with modeling analysis.The present embodiment to the exceptional value in sewage network monitoring data into
When row processing, substitution value is calculated according to the formula of setting, substitution value is replaced into the exceptional value in sewage network monitoring data group,
The sewage network monitoring data advantageously allowed in sewage network monitoring data group tend to be steady, and avoid that sewage network is caused to monitor
Shortage of data and the time response for influencing sewage network monitoring data.
In one embodiment, described that deletion analysis is carried out to sewage network monitoring data, specially：To each sewage pipe
Net monitoring data group is analyzed, if there are two adjacent sewage network monitoring data in sewage network monitoring data group, it
Sampling time interval be more than the time threshold of setting, then judge there is missing between the adjacent sewage network monitoring data
.
Wherein, the missing item in the monitoring data to sewage network carries out completion processing, specifically includes：
(1) the sewage network monitoring data group in the presence of missing item is set as Γυ={ y1,y2,..,ya,yb,..,ym, m is dirt
Grid monitoring data group ΓυIncluding sewage network monitoring data number, ya,ybBetween exist missing item, according to following normalizing
Change processing formula to sewage network monitoring data group ΓυIn sewage network monitoring data be normalized, obtain normalizing
Change treated sequence Γυ'={ y1′,y2′,..,ya′,yb′,..,ym′}：
In formula, ykFor sewage network monitoring data group ΓυIn k-th of sewage network monitoring data, ymaxFor sewage network
Monitoring data group ΓυThe maximum value of middle sewage network monitoring data, yminFor sewage network monitoring data group ΓυMiddle sewage network prison
The minimum value of measured data；
(2) normalized value of missing item is calculated according to following equation：
In formula, yab' indicate ya,ybBetween missing item normalized value, yβ' indicate { y1′,y2′,..,ya-1' between
The normalized value of the β sewage network monitoring data, yα' indicate { yb+1′,..,ym' between the α sewage network monitor number
According to normalized value；
(3) renormalization is carried out to the normalized value for lacking item, obtains the completion value of missing item；
(4) obtained completion value is added into corresponding missing item, to obtain complete sewage network monitoring data group.
The missing of sewage network monitoring data can influence the time response of sewage network monitoring data, further influence follow-up
Sewage network monitoring data are carried out with the precision of modeling analysis.The present embodiment is missing item completion with sewage network monitoring data group
The unit of processing, it is proposed that a kind of new Supplementing Data mechanism monitors number using the mechanism to the sewage network that there is missing item
Completion is carried out according to group, ensure that the integrality of sewage network monitoring data group.The present embodiment is restored with sewage network monitoring data
Angle completion sewage network monitoring data, it is proposed that the calculation formula for lacking the normalized value of item, by missing item return
The renormalization of one change value obtains the completion value of missing item, calculates simply, has higher sewage network monitoring data completion effect
Rate, due to calculating returning for missing item using the sewage network monitoring data in the sewage network monitoring data group where missing item
One change value so that the data variation rule of place sewage network monitoring data group can be met by lacking the completion value of item, be protected as possible
Hinder the precision of Supplementing Data.
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 (7)
1. a kind of sewage network based on big data technology monitors system, characterized in that monitor subsystem including multiple sewage networks
System and big data processing center, each sewage network monitoring subsystem are all connected to big data processing center, each sewage network
Monitoring subsystem is used to acquire the sewage network monitoring data of multiple sewage monitoring nodes in a sewage network monitoring region；
Big data processing center is used to carry out processing analysis to the sewage network monitoring data of acquisition, realizes the real-time prison to sewage network
It surveys.
2. a kind of sewage network based on big data technology according to claim 1 monitors system, characterized in that each dirty
Grid monitoring subsystem includes the data acquisition module for being set to each sewage monitoring node, and data acquisition module includes sensing
Unit, positioning unit, communication unit and data pre-processing unit, the input of the output end and data pre-processing unit of sensing unit
End connection, the data pre-processing unit connect with communication unit, the big data processing center respectively with positioning unit and lead to
Believe unit connection；Wherein data pre-processing unit is for pre-processing the sewage network monitoring data that sensing unit acquires.
3. a kind of sewage network based on big data technology according to claim 2 monitors system, characterized in that sensing is single
Member includes temperature sensor, humidity sensor, water level sensor, flow sensor or carbon monoxide transducer.
4. monitoring system, feature according to a kind of sewage network based on big data technology of claim 1-3 any one of them
It is that big data processing center includes sequentially connected data reception module, data preprocessing module, data analysis module, wherein
Data reception module is used to receive the sewage network monitoring data that multiple sewage network monitoring subsystems are sent；The data are located in advance
Reason module is for pre-processing the sewage network monitoring data that data reception module receives；The data analysis module is used
In to the progress analyzing processing of pretreated sewage network monitoring data.
5. a kind of sewage network based on big data technology according to claim 4 monitors system, characterized in that big data
Processing center further includes the data compressing module being connected and data memory module, and data compressing module connects with data reception module
It connects, the sewage network monitoring data that multiple sewage network monitoring subsystems for being received to data reception module are sent are pressed
Contracting is handled, and the sewage network monitoring data after compression processing are sent in data memory module and are stored.
6. a kind of sewage network based on big data technology according to claim 4 monitors system, characterized in that data are pre-
Processing module includes abnormal deciding means, outlier processing unit and Supplementing Data unit；Wherein abnormal deciding means for pair
Sewage network monitoring data carry out abnormal judgement, determine the exceptional value in sewage network monitoring data；The outlier processing
Unit is for handling the exceptional value in sewage network monitoring data；The Supplementing Data unit is used for sewage network
Monitoring data carry out deletion analysis, and carry out completion processing to the missing item in sewage network monitoring data.
7. a kind of sewage network based on big data technology according to claim 5 monitors system, characterized in that abnormal to sentence
When disconnected unit judges sewage network monitoring data progress exception, a data acquisition module is acquired in a collection period
Sewage network monitoring data as a sewage network monitoring data group, each sewage network monitoring data group is carried out abnormal
Analysis exports the exceptional value in sewage network monitoring data group；
Wherein, anomaly analysis is carried out to sewage network monitoring data group, specifically included：
(1) using the time as horizontal axis, sewage network monitoring data size is the longitudinal axis, and waveform is drawn to sewage network monitoring data group
Figure；
(2) abnormal judgement is carried out to the corresponding sewage network monitoring data of each Wave crest and wave trough point in oscillogram, according to improvement
Grubbs test method calculate their G values, if the G values of the corresponding sewage network monitoring data of a Wave crest and wave trough point be more than set
The corresponding sewage network monitoring data of Wave crest and wave trough point are then determined as exceptional value, the wherein calculating of G values by fixed decision threshold
Formula is：
In formula, GiIndicate the G values of the corresponding sewage network monitoring data of i-th of Wave crest and wave trough point, yiIndicate i-th of Wave crest and wave trough
The corresponding sewage network monitoring data of point,For the average value of sewage network monitoring data group；ymedTo monitor number to sewage network
After being ranked up according to ascending sequence according to the sewage network monitoring data in group, the median that is formed by sequence；
yjFor j-th of sewage network monitoring data in sewage network monitoring data group, n is the sewage pipe in sewage network monitoring data group
Net monitoring data number, S are the standard deviation of sewage network monitoring data group.
Preferably, the decision threshold is set asWherein Gi' in Grubbs table with GiCorresponding critical value.
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Application publication date: 20180817 |