CN109187534A - Water quality detection method and its water sample pattern recognition device - Google Patents
Water quality detection method and its water sample pattern recognition device Download PDFInfo
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Abstract
The embodiment of the invention provides a kind of water quality detection method and its water sample pattern recognition devices.The described method includes: obtaining several water sample image patterns；Based on the water sample image pattern, corresponding neural network model is constructed；Acquisition obtains current water sample image；According to the neural network model, the recognition result of current water sample image is determined；The recognition result is sent to remote terminal, real-time detection water quality.This method provides water sample detection result by the identification to water sample image.The reaction speed of the water sample detection result is very fast, is able to solve the problem of water treatment system water quality mutation can not be found in time.In addition, the water quality detection method can also provide real-time detection data for environmental protection, it is ensured that can timely find the exceeded problem of blowdown.
Description
Technical field
The present invention relates to water quality inspection technique fields more particularly to a kind of water quality detection method and its water sample image recognition to fill
It sets.
Background technique
In water treatment field, the real-time detection result of water quality is very important Con trolling index, for water quality detection knot
The real-time and accuracy requirement of fruit are higher.Existing water quality detection (or detection) method is all based on the principles of chemistry, with various
Different sensors is the chemical analysis method of hardware foundation.
The result that the chemical analysis method obtains is although comprehensive and accurate, but since chemical reaction occurs to need centainly
Time, such chemical analysis method have certain hysteresis quality, can not grasp the mutation of water quality, water quality detection result meeting in time
There are a fixed response time, cause in special circumstances, some water treatment facilities cannot achieve quick combustion adjustment, cause equipment pollution
And water process can not be up to standard etc. problem.
In addition, enterprise and environmental protection administration can not all grasp waste water row in real time since the real-time of water quality detection result is bad
To one's heart's content condition often will appear the phenomenon that waste water discharge beyond standards pollute environment.
Therefore, there are also to be developed for the prior art.
Summary of the invention
Place in view of above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide water quality detection method and its water sample figures
As identification device, it is intended to which the testing result real-time for solving the problems, such as that chemical analysis provides in the prior art is bad.
In order to achieve the above object, this invention takes following technical schemes:
A kind of water quality detection method, which comprises obtain several water sample image patterns；With the water sample image pattern
Based on, construct corresponding neural network model；Acquisition obtains current water sample image；According to the neural network model, determine
The recognition result of current water sample image；The recognition result is sent to remote terminal, real-time detection water quality.
The water quality detection method, wherein it is described based on the water sample image pattern, construct corresponding nerve net
Network model, specifically includes: generating multiple dimensioned training sample data corresponding with the water sample image pattern；Use more rulers
Degree training sample data are trained neural network, obtain corresponding neural network model.
The water quality detection method, wherein it is described according to the neural network model, determine the knowledge of current water sample image
Not as a result, specifically including: extracting the crucial point data of the current water sample image；By the neural network model, to described
Crucial point data carries out consistency matching, obtains matching result；According to the matching result, the identification of current water sample image is determined
As a result.
The water quality detection method, wherein the key point data include: the water sample color of water sample image, turbidity, drift
Floating object and spray.
The water quality detection method, wherein described to generate multiple dimensioned training corresponding with the water sample image pattern
Sample data specifically includes: extracting the crucial point data of each water sample image pattern；The water sample image pattern is not
Of the same trade or same industry different quality water sample image pattern；The crucial point data of the water sample image template is returned
One change processing.
A kind of water sample pattern recognition device, wherein the pattern recognition device includes: online image capture module and image
Identification module；The online image capture module is for acquiring several water sample image patterns and current water sample image；
Described image identification module includes: sample acquisition unit, for obtaining several water sample image patterns；Model generates single
Member, for constructing corresponding neural network model based on the water sample image pattern；Sampling unit is obtained for acquiring
Current water sample image；Image analyzing unit, for determining the identification knot of current water sample image according to the neural network model
Fruit；And long-range detection unit, for receiving the recognition result, real-time detection water quality.
The water sample pattern recognition device, wherein the model generation unit specifically includes: determining sample subelement,
For generating multiple dimensioned training sample data corresponding with the water sample image pattern；Training subelement, for described in use
Multiple dimensioned training sample data are trained neural network, obtain corresponding neural network model.
The water sample pattern recognition device, wherein the sampling unit is also used to, and extracts the current water sample image
Crucial point data；Described image analytical unit is specifically used for: by the neural network model, carrying out to the crucial point data
Consistency matching, obtains matching result；According to the matching result, the recognition result of current water sample image is determined.
The water sample pattern recognition device, wherein the key point data include: the water sample color, turbid of water sample image
Degree, floating material and spray.
The water sample pattern recognition device, wherein the sample acquisition unit is also used to, and extracts each described water sample
The crucial point data of image pattern；The water sample image pattern is the water sample image of different industries or same industry different quality
Sample；The trained subelement is specifically used for, and the crucial point data of the water sample image template is normalized.
The utility model has the advantages that water quality detection method provided by the invention and its water sample pattern recognition device, in conjunction with based on nerve net
The image analysis method of network provides water sample detection result by the identification to water sample image.The reaction of the water sample detection result
Fast speed is able to solve the problem of water treatment system water quality mutation can not be found in time.In addition, the water quality detection method may be used also
To provide real-time detection data for environmental protection, it is ensured that can timely find the exceeded problem of blowdown.
Detailed description of the invention
Fig. 1 is the method flow diagram of the water quality detection method of the specific embodiment of the invention.
Fig. 2 is the functional block diagram of the water quality detection method water sample pattern recognition device of the specific embodiment of the invention.
Specific embodiment
The present invention provides a kind of water quality detection method and its water sample pattern recognition device.To make the purpose of the present invention, technology
Scheme and effect are clearer, clear, and the present invention is described in more detail as follows in conjunction with drawings and embodiments.It should manage
Solution, described herein specific examples are only used to explain the present invention, is not intended to limit the present invention.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", "outside", " up time
The orientation or positional relationship of the instructions such as needle ", " counterclockwise ", " axial direction ", " radial direction ", " circumferential direction " be orientation based on the figure or
Positional relationship is merely for convenience of description of the present invention and simplification of the description, rather than the device or element of indication or suggestion meaning must
There must be specific orientation, be constructed and operated in a specific orientation, therefore be not considered as limiting the invention.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more, unless separately
There is clearly specific restriction.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
In the present invention unless specifically defined or limited otherwise, fisrt feature in the second feature " on " or " down " can be with
It is that the first and second features directly contact or the first and second features pass through intermediary mediate contact.Moreover, fisrt feature exists
Second feature " on ", " top " and " above " but fisrt feature be directly above or diagonally above the second feature, or be merely representative of
First feature horizontal height is higher than second feature.Fisrt feature can be under the second feature " below ", " below " and " below "
One feature is directly under or diagonally below the second feature, or is merely representative of first feature horizontal height less than second feature.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
Fig. 1 is the method flow diagram of water quality detection method provided in an embodiment of the present invention.As shown in Figure 1, the water quality detection
Method includes the following steps:
110, several water sample image patterns are obtained.
The water sample image pattern can be from different industries, for example, thermal power plant's water process Water purification raw water water sample
Saliva sample discharges in image, the water outlet water sample image of desulfurization process system, steel industry wastewater effluent water sample image, sewage treatment plant
In image and/or same industry, the water sample image of different pollutant loads.
Specifically, crucial point data can be therefrom extracted, to realize neural network after obtaining water sample image pattern
The training of model.
In the present embodiment, which refers to the organoleptic indicator that can be estimated in water sample, including color, turbid
Degree, floating material, spray etc..After obtaining a large amount of crucial point data, can further it be distributed according to color gradient, spray shape
These water sample samples are divided into water quality qualification, overproof water quality, water quality three class of serious ultrafiltration by state, quantity situation etc., and with
The modes such as artificial or machinery correspond to labeled data boundary.
In some embodiments, which can also include the identification process for water sample picture traverse.For example,
It can be then further straight according to normal pressure streamflow regime, different pipelines using water sample width in Hough transform method processing picture
The parameters such as diameter calculate water velocity and moment flow.
120, based on the water sample image pattern, corresponding neural network model is constructed.
A large amount of water sample image pattern can be used as training data, realized to training neural network model it is subsequent
Line deterministic process.
Specifically, based on, (as extracted crucial point data), can be generated after some pretreatments to water sample image pattern
Multiple dimensioned training sample data corresponding with the water sample image pattern.
In some embodiments, the database of multiple dimensioned training sample data composition can by normalized and
Multiple dimensioned determination method etc. extracts generation from crucial point data, such as can be according to the crucial point data of water sample image to image
It is normalized, obtains standard water sample image.The standard water sample image refers to each key point possessed by water sample image
There are unique mapping relations between parameter value and the water sample image.It in further embodiments, can also be using manually
The mode of input inputs generation.
Then, using these multiple dimensioned training sample data as training data set, neural network is trained, is obtained
Corresponding neural network model.
The consistency principle based on crucial point data and output result can be closed using multiple dimensioned training sample water sample image
Key point data.In some embodiments, the crucial point data of multiple dimensioned training sample water sample image may include in different scale
Sample data under the color gradient data of the sample image observed, spray morphological data and different resolution ratio, and
These data bands have the water pollution data manually marked.
Specifically, the purpose in order to realize water sample to be measured matched in neural network model, corresponding neural network
Model may include the essential informations such as water sample image Classification of water Qualities, the industry, pollution level.
130, acquisition obtains current water sample image.
Current water sample image be by high-definition camera or other types of image capture device to current water sample into
The acquisition of row real-time online obtains.Certainly, it carries out needing to protect using suitable mode in image acquisition process in high-definition camera
Brightness, the clarity for demonstrate,proving image, reduce the influence from vapor, light, guarantee the consistency of sampled images, obtain accurate
Identify judging result.
140, according to the neural network model, the recognition result of current water sample image is determined.
Specifically, in order to realize the identification to current water sample image, it is also necessary to execute similar crucial point data and extract
Operation.In the present embodiment, corresponding with the extraction of above-mentioned key point, the key point data can also include: water sample image
Water sample color, turbidity, floating material and spray.
After crucial point data extracts completion, by the neural network model, one is carried out to the crucial point data
The matching of cause property, obtains matching result.Finally, determining the recognition result of current water sample image according to the matching result.
150, the recognition result is sent to remote terminal, real-time detection water quality.
Remote terminal is the remote terminal platform positioned at backstage.It can be received by wireless communication technique in remote terminal
Real-time detection is taken as a result, providing early warning and water sample the qualitative analysis for testing staff.
In water quality detection method provided in this embodiment, polluted using what is be obtained ahead of time from different industries and difference
The water sample image of degree is trained neural network model as tranining database.Later neural network mould is completed in training
Type is used to carry out matched to the crucial point data extracted in current water sample image, provides current water sample figure in conjunction with output result
The water quality characteristics of picture or the qualitative results of pollutant load.
The water quality detection method uses the image recognition based on neural network model to realize the inspection for water sample quality
The case where surveying, having the reaction speed being exceedingly fast, can timely feed back change of water quality, has a good application prospect.
Fig. 2 is a kind of water sample pattern recognition device provided in an embodiment of the present invention.As shown in Fig. 2, the pattern recognition device
Including 220 two modules of image capture module 210 and picture recognition module.
Wherein, described image acquisition module 210 is for acquiring several water sample image patterns and current water sample image.
Specifically, training and recognition effect in order to ensure neural network model.The image capture module 210 can lead to
The machine algorithms such as image normalization processing are crossed, obtain the key point parameter of water sample image, the key point includes the range estimation of water sample
Organoleptic indicator, such as color, turbidity, floating material, spray characteristic parameter.
Described image identification module 220 includes: sample acquisition unit 221, model generation unit 222, sampling unit 223,
Image analyzing unit 224 and long-range detection unit 225.
Wherein, sample acquisition unit 221 is for obtaining several water sample image patterns.Model generation unit 222 is used for institute
Based on stating water sample image pattern, corresponding neural network model is constructed.
In some embodiments, the sample acquisition unit 221 is also used to extract each water sample image pattern
Crucial point data.Specifically, the water sample image pattern is the water sample image sample of different industries or same industry different quality
This.
Crucial point data based on acquisition, the trained subelement 222 are specifically used for the pass to the water sample image template
Key point data is normalized, and generates multiple dimensioned training sample data.
Specifically, the model generation unit, which specifically includes, determines sample subelement and training subelement.The judgement sample
Subunit is for generating multiple dimensioned training sample data corresponding with the water sample image pattern.The trained subelement is used
Neural network is trained in using the multiple dimensioned training sample data, obtains corresponding neural network model.
By the step of above-mentioned training, the monitoring and warning data storehouse of water quality image can be established based on training data, and
Water sample image data to be measured is identified accordingly.
In identification matching process, sampling unit 223 obtains current water sample image for acquiring.Image analyzing unit 224
For determining the recognition result of current water sample image according to the neural network model.
In some embodiments, the sampling unit 223 is also used to, and extracts the crucial points of the current water sample image
According to.After determining key point data, described image analytical unit 224 is used for by the neural network model, to the key
Point data carries out consistency matching, obtains matching result, and according to the matching result, output determines water sample pollution level
Final recognition result.
Specifically, the key point data may include water sample color, turbidity, floating material and spray of water sample image etc.
Visual detection parameter.
Long-range detection unit 225 is for receiving the recognition result, real-time detection water quality.In the present embodiment, this is long-range
The mode that detection unit 225 can connect by wireless communication is connect with image analyzing unit, be arranged in one it is additional long-range
On platform.
In further embodiments, image capture module 210 and picture recognition module 220 can also be by wireless communication
Mode connect, thus by image capture module 210 be arranged in detected place, and by picture recognition module 220 setting remote
In Cheng Pingtai.
It, can according to the technique and scheme of the present invention and this hair it is understood that for those of ordinary skills
Bright design is subject to equivalent substitution or change, and all these changes or replacement all should belong to the guarantor of appended claims of the invention
Protect range.
Claims (10)
1. a kind of water quality detection method, which is characterized in that the described method includes:
Obtain several water sample image patterns；
Based on the water sample image pattern, corresponding neural network model is constructed；
Acquisition obtains current water sample image；
According to the neural network model, the recognition result of current water sample image is determined；
The recognition result is sent to remote terminal, real-time detection water quality.
2. water quality detection method according to claim 1, which is characterized in that described using the water sample image pattern as base
Plinth constructs corresponding neural network model, specifically includes:
Generate multiple dimensioned training sample data corresponding with the water sample image pattern；
Neural network is trained using the multiple dimensioned training sample data, obtains corresponding neural network model.
3. water quality detection method according to claim 2, which is characterized in that it is described according to the neural network model, really
The recognition result of settled preceding water sample image, specifically includes:
Extract the crucial point data of the current water sample image；
By the neural network model, consistency matching is carried out to the crucial point data, obtains matching result；
According to the matching result, the recognition result of current water sample image is determined.
4. water quality detection method according to claim 3, which is characterized in that the key point data include: water sample image
Water sample color, turbidity, floating material and spray.
5. water quality detection method according to claim 4, which is characterized in that the generation and the water sample image pattern phase
Corresponding multiple dimensioned training sample data, specifically include:
Extract the crucial point data of each water sample image pattern；The water sample image pattern is different industries or same
The water sample image pattern of industry different quality；
The crucial point data of the water sample image template is normalized.
6. a kind of water sample pattern recognition device characterized by comprising online image capture module and picture recognition module；
The online image capture module is for acquiring several water sample image patterns and current water sample image；
Described image identification module includes: sample acquisition unit, for obtaining several water sample image patterns；Model generation unit,
For constructing corresponding neural network model based on the water sample image pattern；Sampling unit obtains currently for acquiring
Water sample image；Image analyzing unit, for determining the recognition result of current water sample image according to the neural network model；With
And long-range detection unit, for receiving the recognition result, real-time detection water quality.
7. water sample pattern recognition device according to claim 6, which is characterized in that the model generation unit is specific to wrap
It includes:
Sample subelement is determined, for generating multiple dimensioned training sample data corresponding with the water sample image pattern；
Training subelement obtains corresponding mind for being trained using the multiple dimensioned training sample data to neural network
Through network model.
8. water sample pattern recognition device according to claim 7, which is characterized in that
The sampling unit is also used to, and extracts the crucial point data of the current water sample image；Described image analytical unit is specific
For: by the neural network model, consistency matching is carried out to the crucial point data, obtains matching result；According to institute
Matching result is stated, determines the recognition result of current water sample image.
9. water sample pattern recognition device according to claim 8, which is characterized in that the key point data include: water sample
Water sample color, turbidity, floating material and the spray of image.
10. water sample pattern recognition device according to claim 9, which is characterized in that
The sample acquisition unit is also used to, and extracts the crucial point data of each water sample image pattern；The water sample figure
Decent is different industries or the water sample image pattern of same industry different quality；
The trained subelement is specifically used for, and the crucial point data of the water sample image template is normalized.
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|---|---|---|---|---|
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100008576A1 (en) * | 2008-07-11 | 2010-01-14 | Robinson Piramuthu | System and method for segmentation of an image into tuned multi-scaled regions |
| CN101655462A (en) * | 2009-09-11 | 2010-02-24 | 中国科学院地理科学与资源研究所 | Apparatus for obtaining water quality information, method and system for recognizing water body eutrophication degree |
| CN103229197A (en) * | 2010-11-18 | 2013-07-31 | （株）此后 | Integrated pipe management system and method using information identification means |
| CN104268521A (en) * | 2014-09-23 | 2015-01-07 | 朱毅 | Image recognition method based on convolutional neural network in non-finite category |
| CN105824280A (en) * | 2016-03-16 | 2016-08-03 | 宁波市江东精诚自动化设备有限公司 | IoT (Internet of Things) environment protective monitoring system |
| CN106295653A (en) * | 2016-07-29 | 2017-01-04 | 宁波大学 | A kind of water quality image classification method |
| CN106526112A (en) * | 2016-10-25 | 2017-03-22 | 浙江工业大学 | Water toxicity detection method based on fish activity analysis |
| CN107527037A (en) * | 2017-08-31 | 2017-12-29 | 苏州市数字城市工程研究中心有限公司 | Blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data |
| CN107664680A (en) * | 2016-07-27 | 2018-02-06 | 复凌科技（上海）有限公司 | A kind of adaptive acquiring method and device of water quality soft-sensing model |
| CN107944601A (en) * | 2017-11-07 | 2018-04-20 | 中国石油天然气集团公司 | Using characteristic contamination Source Tracing as the sewage disposal appraisal procedure and device instructed |
| US20180130193A1 (en) * | 2016-11-09 | 2018-05-10 | Regents Of The University Of Minnesota | Simultaneous estimation of location elevations and water levels |
| CN108038880A (en) * | 2017-12-20 | 2018-05-15 | 百度在线网络技术（北京）有限公司 | Method and apparatus for handling image |
| CN108229509A (en) * | 2016-12-16 | 2018-06-29 | 北京市商汤科技开发有限公司 | For identifying object type method for distinguishing and device, electronic equipment |
-
2018
- 2018-08-01 CN CN201810867078.1A patent/CN109187534A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100008576A1 (en) * | 2008-07-11 | 2010-01-14 | Robinson Piramuthu | System and method for segmentation of an image into tuned multi-scaled regions |
| CN101655462A (en) * | 2009-09-11 | 2010-02-24 | 中国科学院地理科学与资源研究所 | Apparatus for obtaining water quality information, method and system for recognizing water body eutrophication degree |
| CN103229197A (en) * | 2010-11-18 | 2013-07-31 | （株）此后 | Integrated pipe management system and method using information identification means |
| CN104268521A (en) * | 2014-09-23 | 2015-01-07 | 朱毅 | Image recognition method based on convolutional neural network in non-finite category |
| CN105824280A (en) * | 2016-03-16 | 2016-08-03 | 宁波市江东精诚自动化设备有限公司 | IoT (Internet of Things) environment protective monitoring system |
| CN107664680A (en) * | 2016-07-27 | 2018-02-06 | 复凌科技（上海）有限公司 | A kind of adaptive acquiring method and device of water quality soft-sensing model |
| CN106295653A (en) * | 2016-07-29 | 2017-01-04 | 宁波大学 | A kind of water quality image classification method |
| CN106526112A (en) * | 2016-10-25 | 2017-03-22 | 浙江工业大学 | Water toxicity detection method based on fish activity analysis |
| US20180130193A1 (en) * | 2016-11-09 | 2018-05-10 | Regents Of The University Of Minnesota | Simultaneous estimation of location elevations and water levels |
| CN108229509A (en) * | 2016-12-16 | 2018-06-29 | 北京市商汤科技开发有限公司 | For identifying object type method for distinguishing and device, electronic equipment |
| CN107527037A (en) * | 2017-08-31 | 2017-12-29 | 苏州市数字城市工程研究中心有限公司 | Blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data |
| CN107944601A (en) * | 2017-11-07 | 2018-04-20 | 中国石油天然气集团公司 | Using characteristic contamination Source Tracing as the sewage disposal appraisal procedure and device instructed |
| CN108038880A (en) * | 2017-12-20 | 2018-05-15 | 百度在线网络技术（北京）有限公司 | Method and apparatus for handling image |
Cited By (24)
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| CN111104860A (en) * | 2019-11-19 | 2020-05-05 | 浙江工业大学 | Unmanned aerial vehicle water quality chromaticity monitoring method based on machine vision |
| CN111104860B (en) * | 2019-11-19 | 2022-02-15 | 浙江工业大学 | Unmanned aerial vehicle water quality chromaticity monitoring method based on machine vision |
| CN110955166A (en) * | 2019-11-27 | 2020-04-03 | 上海达显智能科技有限公司 | Intelligent washing device based on image recognition and intelligent washing control method |
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| CN112834450A (en) * | 2020-12-31 | 2021-05-25 | 剑科云智(深圳)科技有限公司 | Sensor, sewage measuring system and method |
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