CN108958130A - A kind of Intelligent sewage processing method for early warning - Google Patents
A kind of Intelligent sewage processing method for early warning Download PDFInfo
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- CN108958130A CN108958130A CN201810882152.7A CN201810882152A CN108958130A CN 108958130 A CN108958130 A CN 108958130A CN 201810882152 A CN201810882152 A CN 201810882152A CN 108958130 A CN108958130 A CN 108958130A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25314—Modular structure, modules
Abstract
In order to avoid the characteristic of sensor oneself requirement fixed form installation monitors the blind area that may cause to blowdown, the present invention provides a kind of Intelligent sewages to handle method for early warning, including (1) determines sewage location；(2) warning information is sent to the administrative staff in sewage location.The present invention can be obtained by machine learning the characteristics of image such as color, the growth conditions of surface vegetation with whether be that sewage causes being associated between this possible cause, the matching that possible cause and season are carried out based on 6 rank depth probability analysis methods, so that way compared with prior art reduces the operand more than 37% or so.
Description
Technical field
The present invention relates to vision signal control technology fields, handle method for early warning more particularly, to a kind of Intelligent sewage.
Background technique
Currently, it has been recognized that sewage discharge for various negative effects caused by environment, is just taking measures pair
The discharge of sewage is limited.So far, the measure taken substantially has the following two kinds: one is must be managed by environmental protection
Personnel carry out personal monitoring to enterprise's effluent discharge outlet, the information of enterprise's sewage effluent are obtained, to the enterprise of discharge in violation of regulations sewage
Industry is punished.Such measure is not only time-consuming and laborious, not can guarantee continuous monitoring in daily 24 hours.And due to human factor compared with
It is more, it is difficult to ensure that preventing blowdown and accurate to the punishment of blowdown enterprise, rationally.Another measure is then for personal monitoring institute
There are the shortcomings that and design, it be sewage monitoring instrument is mounted on sewage disposal device, and by sewage monitoring instrument with
Water pump and the valve being mounted on blowoff line connect, and water pump and valve can only be realized according to the output signal of sewage monitoring instrument
It opens or closes, will be emitted in time by the middle water for handling, meeting pollution emission standard, raw sewerage is prevented to discharge,
Its high degree of automation can save a large amount of manpowers.But due to sewage disposal device all be installation enterprise place in, it is a
Other enterprise illegally installs around the sewage pipe of sewage disposal device, valve, finally by raw sewerage from enterprise
Effluent discharge outlet discharge；Moreover, because set valve does not have power-off restoration function, when valve is in the open state and prominent
So when power-off, valve will still be kept it turned on.In this way, being in valve if artificially sewage disposal device is allowed to power off
Then raw sewerage is discharged by the valve opened from enterprise's effluent discharge outlet for open state.
In this regard, in the prior art, application No. is the Chinese invention patent applications of CN03238004.6 to disclose a kind of sewage
Automatic monitoring device is discharged, sensor is equipped with, is connected to signal processor, the output of signal processor and power-off bullet with sensor
Spring homing position type executing agency connects, and spring to break homing position type executing agency connects with sewage discharge valve.Enterprise can be installed in
Industry effluent discharge outlet, the water timing monitoring that enterprise is discharged.When the water of enterprise's discharge meets pollution emission standard, valve is beaten
Open, otherwise valve close, alarm, can prevent enterprise illegally install bypass sewage disposal device drainage pipeline and will be unprocessed
Sewage discharge.However, this method still will use sensor, and the detection position of sensor is fixed, as long as sewage
Discharger gets around the position, then still can not effectively monitor the truth of sewage discharge.
Summary of the invention
In order to avoid the characteristic of sensor oneself requirement fixed form installation monitors the blind area that may cause, this hair to blowdown
It is bright to provide a kind of Intelligent sewage processing method for early warning, comprising:
(1) sewage location is determined；
(2) warning information is sent to the administrative staff in sewage location.
Further, the step (1) includes:
(10) geographical information library is established；
(20) video information identification model is established according to the geographical information library by machine learning mode；
(30) UAV Video information is obtained.
Further, the step (10) includes: that at least two width obtained around Sewage outlet are continuously clapped in time
The image taken the photograph, the image can uniquely identify corresponding Sewage outlet, and position a certain in image and image is corresponding
Latitude and longitude information be saved in database jointly, as mark sewage discharge ground geographical information library.
Further, the machine learning is that engineering is carried out in a manner of unsupervised learning according to vegetation growth state image
It practises.
Further, the machine learning is to carry out engineering to vegetation growth state image using stochastic gradient descent method
It practises.
Further, the step (20) includes:
(2021) key frame information determines: assuming that earth's surface vegetation map corresponds to vegetation health status Cj as Ei；Vegetation health shape
The corresponding possible cause Sm of state Cj constitutes set { Sm, Pm }, then using vegetation health status Cj as key frame, wherein Pm is possible former
The probability of vegetation health status Cj caused by becoming because of Sm, i, j and m are the natural number since 1；
(2022) probability of the appearance for possible cause of vegetation health status Cj is defined:
p(Sm|Cj)=χgh(pj),
Wherein
M=1,2,3,4,5,6；AndFor with for
Mean value, ξmFor the m rank diagonal matrix of variance,
(2023) according to Probability p (Sm|Cj) determine when vegetation health status Cj takes current meaning and the matching degree in season:
It calculatesWherein p ' indicates to carry out difference to p；
It calculatesWhether less than the first preset threshold: when small
Yu Shi determines that the serial number for the possible cause that j is indicated in Cj meets Ei corresponding season, otherwise enables j=j+1, jump to step
(2022), it if j reaches its maximum value by traversal, enables j=1 and continues step (2024), u and v are nature
Number；
(2024) when correction corresponding possible cause of the Sm as Cj and the matching degree in season:
It calculatesWhether less than second
Preset threshold: it when being less than, determines that Sm meets season as the corresponding possible cause of Cj, otherwise enables m=m+1, jump to step
Suddenly (2022) enable m=1 if m reaches its maximum value by traversal.
Further, the step (30) includes:
(301) framing sampling is carried out to the video of camera acquisition；
(302) sample image is normalized；
(303) feature extraction is carried out to the image after normalization using convolutional neural networks.
Further, the step (2) includes:
The video information obtained based on the unmanned plane and the model, when determining that the corresponding possible cause of certain image is dirty
When water, that is, eliminate the surface vegetation indicated according to image after the predetermined reason such as weather, pest and disease damage color and
The comparison of the characteristics of image such as growth conditions determines that possible cause is caused by sewage discharge, then according to the figure of figure unmanned plane acquisition
As corresponding second latitude and longitude information of information, make again unmanned plane shooting and second latitude and longitude information it is immediate, described
The image of the corresponding sewage draining exit of already existing latitude and longitude information in geographical information library determines that Location for Sewage and sewage draining exit may
The location being related to issues warning information to the relevant monitoring of the sewage draining exit or administrative staff.
The beneficial effect comprise that the figure such as color, growth conditions of surface vegetation can be obtained by machine learning
As feature with whether be that sewage causes being associated between this possible cause, based on 6 rank depth probability analysis methods carry out may
The matching of reason and season, so that way compared with prior art reduces the operand more than 37% or so.
Detailed description of the invention
Fig. 1 shows the flow chart of the method for the present invention.
Specific embodiment
As shown in Figure 1, preferred embodiment in accordance with the present invention, the present invention provides a kind of pre- police of Intelligent sewage processing
Method, comprising:
(1) sewage location is determined；
(2) warning information is sent to the administrative staff in sewage location.
Preferably, the step (1) includes:
(10) geographical information library is established；
(20) video information identification model is established according to the geographical information library by machine learning mode；
(30) UAV Video information is obtained.
Preferably, the step (10) includes: that at least two width obtained around Sewage outlet are continuously shot in time
Image, which can uniquely identify corresponding Sewage outlet, and position a certain in image and image is corresponding
Latitude and longitude information is saved in database jointly, the geographical information library as mark sewage discharge ground.
Preferably, the machine learning is that engineering is carried out in a manner of unsupervised learning according to vegetation growth state image
It practises.
Preferably, the machine learning is to carry out engineering to vegetation growth state image using stochastic gradient descent method
It practises.
Preferably, the step (20) includes:
(2021) key frame information determines: assuming that earth's surface vegetation map corresponds to vegetation health status Cj as Ei；Vegetation health shape
The corresponding possible cause Sm of state Cj constitutes set { Sm, Pm }, then using vegetation health status Cj as key frame, wherein Pm is possible former
The probability of vegetation health status Cj caused by becoming because of Sm, i, j and m are the natural number since 1；
(2022) probability of the appearance for possible cause of vegetation health status Cj is defined:
p(Sm|Cj)=χgh(pj),
Wherein
M=1,2,3,4,5,6；AndFor with for
Mean value, ξmFor the m rank diagonal matrix of variance,
(2023) according to Probability p (Sm|Cj) determine when vegetation health status Cj takes current meaning and the matching degree in season:
It calculatesWherein p ' indicates to carry out difference to p；
It calculatesWhether less than the first preset threshold: when small
Yu Shi determines that the serial number for the possible cause that j is indicated in Cj meets Ei corresponding season, otherwise enables j=j+1, jump to step
(2022), it if j reaches its maximum value by traversal, enables j=1 and continues step (2024), u and v are nature
Number；
(2024) when correction corresponding possible cause of the Sm as Cj and the matching degree in season:
It calculatesWhether less than second
Preset threshold: it when being less than, determines that Sm meets season as the corresponding possible cause of Cj, otherwise enables m=m+1, jump to step
Suddenly (2022) enable m=1 if m reaches its maximum value by traversal.
Preferably, the step (30) includes:
(301) framing sampling is carried out to the video of camera acquisition；
(302) sample image is normalized；
(303) feature extraction is carried out to the image after normalization using convolutional neural networks.
Preferably, the step (2) includes:
The video information obtained based on the unmanned plane and the model, when determining that the corresponding possible cause of certain image is dirty
When water, that is, eliminate the surface vegetation indicated according to image after the predetermined reason such as weather, pest and disease damage color and
The comparison of the characteristics of image such as growth conditions determines that possible cause is caused by sewage discharge, then according to the figure of figure unmanned plane acquisition
As corresponding second latitude and longitude information of information, make again unmanned plane shooting and second latitude and longitude information it is immediate, described
The image of the corresponding sewage draining exit of already existing latitude and longitude information in geographical information library determines that Location for Sewage and sewage draining exit may
The location being related to issues warning information to the relevant monitoring of the sewage draining exit or administrative staff.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (8)
1. a kind of Intelligent sewage handles method for early warning, comprising:
(1) sewage location is determined；
(2) warning information is sent to the administrative staff in sewage location.
2. the method according to claim 1, wherein the step (1) includes:
(10) geographical information library is established；
(20) video information identification model is established according to the geographical information library by machine learning mode；
(30) UAV Video information is obtained.
3. according to the method described in claim 2, it is characterized in that, the step (10) includes: to obtain around Sewage outlet
The image that is continuously shot in time of at least two width, which can uniquely identify corresponding Sewage outlet, will
The corresponding latitude and longitude information in a certain position is saved in database jointly in image and image, the ground as mark sewage discharge ground
Manage information bank.
4. according to the method described in claim 2, it is characterized in that, the machine learning be according to vegetation growth state image with
Unsupervised learning mode carries out machine learning.
5. according to the method described in claim 2, it is characterized in that, the machine learning is using stochastic gradient descent method to plant
Object upgrowth situation image carries out machine learning.
6. according to the method described in claim 2, it is characterized in that, the step (20) includes:
(2021) key frame information determines: assuming that earth's surface vegetation map corresponds to vegetation health status Cj as Ei；Vegetation health status Cj
Corresponding possible cause Sm constitutes set { Sm, Pm }, then using vegetation health status Cj as key frame, wherein Pm is possible cause Sm
As the probability for causing vegetation health status Cj, i, j and m are the natural number since 1；
(2022) probability of the appearance for possible cause of vegetation health status Cj is defined:
p(Sm|Cj)=χgh(pj),
Wherein
M=1,2,3,4,5,6；AndFor withFor mean value,
ξmFor the m rank diagonal matrix of variance,
(2023) according to Probability p (Sm|Cj) determine when vegetation health status Cj takes current meaning and the matching degree in season:
It calculatesWherein p ' indicates to carry out difference to p；
It calculatesWhether less than the first preset threshold: when being less than,
It determines that the serial number for the possible cause that j is indicated in Cj meets Ei corresponding season, otherwise enables j=j+1, jump to step (2022),
If j reaches its maximum value by traversal, enables j=1 and continue step (2024), u and v are natural number；
(2024) when correction corresponding possible cause of the Sm as Cj and the matching degree in season:
It calculatesIt is whether default less than second
Threshold value: it when being less than, determines that Sm meets season as the corresponding possible cause of Cj, otherwise enables m=m+1, jump to step
(2022), if m reaches its maximum value by traversal, m=1 is enabled.
7. according to the method described in claim 6, it is characterized in that, the step (30) includes:
(301) framing sampling is carried out to the video of camera acquisition；
(302) sample image is normalized；
(303) feature extraction is carried out to the image after normalization using convolutional neural networks.
8. the method according to the description of claim 7 is characterized in that the step (2) includes:
The video information obtained based on the unmanned plane and the model, when determining that the corresponding possible cause of certain image is sewage
When, that is, eliminate the color and life of the surface vegetation indicated after the predetermined reason such as weather, pest and disease damage according to image
The comparison of the characteristics of image such as long status determines that possible cause is caused by sewage discharge, then according to the image of figure unmanned plane acquisition
Corresponding second latitude and longitude information of information makes unmanned plane shooting and second latitude and longitude information immediate, described again
The image for managing the corresponding sewage draining exit of already existing latitude and longitude information in information bank, determines that Location for Sewage and sewage draining exit may relate to
And location, to the sewage draining exit it is relevant monitoring or administrative staff issue warning information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810882152.7A CN108958130B (en) | 2018-08-04 | 2018-08-04 | Intelligent sewage treatment early warning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810882152.7A CN108958130B (en) | 2018-08-04 | 2018-08-04 | Intelligent sewage treatment early warning method |
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| CN108958130A true CN108958130A (en) | 2018-12-07 |
| CN108958130B CN108958130B (en) | 2021-07-09 |
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ID=64467704
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| CN201810882152.7A Active CN108958130B (en) | 2018-08-04 | 2018-08-04 | Intelligent sewage treatment early warning method |
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Effective date of registration: 20210618 Address after: Room 901, Shandong digital industry building, 28-1 Jingqi Road, Shizhong District, Jinan City, Shandong Province Applicant after: Jinyun Data Technology Co.,Ltd. Applicant after: SHANDONG INTELLIGENCE GATHERING CLOUD BUILDING INFORMATION TECHNOLOGY Co.,Ltd. Address before: No. 28-2, Zhongtian village group, Qinggang village committee, Tianxing Town, Daguan County, Zhaotong City, Yunnan Province Applicant before: Xiao hengnian |
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