CN111398189A - Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method - Google Patents
Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method Download PDFInfo
- Publication number
- CN111398189A CN111398189A CN202010209472.3A CN202010209472A CN111398189A CN 111398189 A CN111398189 A CN 111398189A CN 202010209472 A CN202010209472 A CN 202010209472A CN 111398189 A CN111398189 A CN 111398189A
- Authority
- CN
- China
- Prior art keywords
- image
- fluorescence
- light source
- optical imaging
- pollution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N21/3151—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths using two sources of radiation of different wavelengths
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
- G01N21/6402—Atomic fluorescence; Laser induced fluorescence
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/20—Controlling water pollution; Waste water treatment
Abstract
The invention discloses a multimode optical imaging water quality remote sensing detection device and a pollution feature extraction method, wherein the device comprises: a light source combining a white light source and a fluorescence excitation light source, and an image acquisition device combining an imaging camera and a filter wheel; a filter wheel is adopted to switch a narrow-band filter to obtain spectral imaging signals with different wavelengths, so that a multi-mode optical imaging mode combining an absorption spectrum and a fluorescence spectrum is realized; images obtained by each imaging camera are programmed into digital signals and transmitted to a data acquisition and processing system for processing, and pollutant characteristics are extracted. The method comprises the following steps: registering the measured absorption spectrum and fluorescence spectrum images by adopting an affine edge energy conversion method to realize the consistency of the image measurement range and resolution; counting the fused image features one by one; and (3) taking the energy and the correlation of the gray level mean value, the variance and the gray level co-occurrence matrix as feature vectors, and classifying the fused image features by using a support vector machine, thereby realizing the extraction of different pollution features in a wide water area.
Description
Technical Field
The invention relates to the field of water quality on-line monitoring, in particular to a multi-mode optical imaging water quality remote sensing detection device and a pollution feature extraction method.
Background
Water resources are an indispensable part of the earth ecosystem and a ring on which the human civilization development is relied. Based on the Chinese situation, the total amount of fresh water resources in China lists the fourth world, but the per capita water resource amount in China is only 1/4 which is the average level in the world, and is one of the most impoverished countries in the per capita water resource in the world. Under the condition of insufficient water per capita in China, the water consumption of people in China in living, agriculture and artificial ecology is still very large. China is facing to the problem of serious shortage of water resources, and the pollution of the water resources is also serious. According to the Chinese water resource bulletin of 2018, the proportion of the seriously polluted poor V-class water quality is higher, and the severely polluted shallow groundwater, rivers and lakes respectively account for 46.9%, 5.5% and 16.1%. Such severe water pollution causes the situation of scarce fresh water resources to be frosted.
At present, the problem of water resource pollution in China is mainly reflected by improper treatment of industrial wastewater and agricultural polluted water and incomplete water resource monitoring, treatment and management system. Contaminants in water are largely classified into physical, chemical and biological contaminants. The physical pollutants are mainly suspended particles and radioactive substances; the chemical pollutants are divided into inorganic pollutants such as phosphorus and nitrogen and organic pollutants such as benzene and oil; the biological pollutants are mainly domestic sewage and wastewater.
In order to develop and use water resources reasonably, water quality monitoring is not slow enough, and the purpose is to master real-time dynamic change of water quality and change conditions and rules thereof by quantitatively analyzing the content of various pollutants in the water, thereby providing scientific basis for monitoring and water pollution prevention and control for relevant government departments. The online conventional water quality monitoring water quality parameters are as follows: conventional five parameters (pH, water temperature, dissolved oxygen, turbidity, conductivity), oxidation-reduction potential (ORP), Total Organic Carbon (TOC), Chemical Oxygen Demand (COD), Total Phosphorus (TP), Total Nitrogen (TN), total chlorine, nitrate nitrogen, hardness, salinity, etc. Currently, common monitoring methods include chemical methods, electrochemical methods, biosensor methods, and spectroscopic methods. Chemical methods are currently the most widely used analytical methods, such as measuring Chemical Oxygen Demand (COD) using potassium dichromate or potassium permanganate as the oxidizing agent. This approach has significant drawbacks: the detection speed is slow, secondary pollution exists, the cost is high, and real-time online detection cannot be realized. The electrochemical method utilizes the electrochemical reaction of substances in a solution to detect the content of the substances, has short measurement time and accurate measurement result, but has short service life of the electrode. The biosensor method is based on molecular recognition elements such as enzymes and microorganisms to recognize different bioactive substances, and is limited to the stability of biological response.
The technology for detecting water pollution components by using a spectrum detection principle in water quality parameter detection mainly comprises an absorption spectrum method, a fluorescence spectrum method, an atomic absorption spectrum method and a Raman spectrum method, wherein the fluorescence spectrum method mainly comprises the steps of detecting the concentration of an element to be detected by using the fluorescence characteristics and the intensity of different substances after passing through specific wavelengths, but not all substances can generate fluorescence, and the species of the detectable substance is few, the atomic absorption spectrum method (AAS) is used for detecting the concentration of the element according to the absorption degree of atomic steam generated by a sample under the specific wavelengths of the element to be detected, the AAS is high in measurement accuracy and simple to operate, but the AAS cannot detect insoluble elements and nonmetal elements and is mainly used for detecting metal elements, the Raman spectrum method is mainly used for detecting metal elements, the Raman scattering spectrum of the substance to be detected by detecting Raman scattering spectra, molecular structure characteristics are good in characteristics, the detection sensitivity is high, the Raman spectrum method is combined with other technologies at present, the Raman spectrum method is widely used for measuring the concentrations of impurities contained in different water samples, the absorption spectrum measurement is combined with chemical metrology and other analysis methods, the method can effectively improve the reliability of online detection of water quality parameters, the online detection, the detection of nitrate, the nitrate content of the nitrate, the hydrocarbon pollutants are measured by using a Bayesian spectrum method, the visible spectrum method, the method of the visible spectrum method of detecting the visible spectrum method of the visible pollutant detection, the hydrocarbon pollutant detection method of the pollutant detection, the pollutant detection method of the pollutant detection method.
The main problems faced by the existing water quality monitoring device and method based on the spectrum method are that sampling is needed, and remote sensing detection of a wider water area is difficult to realize; in the measurement mode, only absorption signals or fluorescence signals are usually targeted, and various optical signals cannot be acquired at the same time for comprehensive analysis; due to the complex water pollution components, different components are mutually interfered during measurement, and the identification precision of the pollution characteristics in remote sensing imaging is poor.
Disclosure of Invention
The invention provides a multimode optical imaging water quality remote sensing detection device and a pollution characteristic extraction method, which utilize two optical detection imaging devices to simultaneously realize spectral absorption and fluorescence imaging of a wide water area, and extract pollution characteristics of different components from a multimode optical image by combining image fusion to realize remote sensing monitoring of water quality pollution of the wide water area, and are described in detail as follows:
a multimode optical imaging water quality remote sensing detection device, the device includes: a light source combining a white light source and a fluorescence excitation light source, and an image acquisition device combining an imaging camera and a filter wheel;
the filter wheels are adopted to respectively switch the narrow-band filters to obtain spectral imaging signals with different wavelengths, so that a multi-mode optical imaging mode combining absorption spectrum and fluorescence spectrum can be realized;
images obtained by each imaging camera are programmed into digital signals and then transmitted to a data acquisition and processing system for processing, and pollutant characteristics are extracted.
A multi-modal optical imaging contamination feature extraction method comprises the following steps:
registering the measured absorption spectrum and fluorescence spectrum images by adopting an affine edge energy conversion method to realize the consistency of the image measurement range and resolution;
counting the fused image features one by one;
and (3) taking the energy and the correlation of the gray level mean value, the variance and the gray level co-occurrence matrix as feature vectors, and classifying the fused image features by using a support vector machine, thereby realizing the extraction of different pollution features in a wide water area.
Wherein, the registering of the measured absorption spectrum and fluorescence spectrum image specifically comprises:
performing correlation calculation on the absorption spectrum image and the fluorescence image;
and obtaining image correlation coefficients in different directions according to calculation, then carrying out coordinate transformation registration on the two-needle images, and when the calculated correlation coefficient is 1, achieving the image registration precision.
Further, the image features after the one-by-one statistical fusion are specifically:
and selecting a pixel pitch d as 1, and calculating energy, information entropy and contrast characteristics in a 0-degree direction.
The classification of the fused image features by using the support vector machine specifically comprises the following steps:
classifying local texture features of the sequence images in the infrared image space by adopting a support vector machine;
taking energy, contrast and entropy as input parameters, and inputting the parameters into a found optimal separation hyperplane w.x + b ═ 0, wherein the plane is a decision boundary of classification, w is a normal vector, and b is an intercept;
and (5) the characteristic values are arranged on two sides of the plane, so that different pollution image characteristics are identified and classified.
The technical scheme provided by the invention has the beneficial effects that:
1. the invention has wider spectrum measurement range, and realizes the simultaneous acquisition of absorption spectrum and fluorescence signal by adopting a filter combination mode;
2. the invention can realize remote sensing monitoring of a wide water area by adopting an imaging mode;
3. the invention adopts a multimode image fusion mode, can accurately extract the pollutant characteristics and improves the monitoring and identifying precision;
4. the invention realizes the multi-parameter rapid, real-time and remote sensing detection of the water quality of a wide water area without pretreatment, reagent and secondary pollution.
Drawings
FIG. 1 is a schematic structural diagram of a multi-modal optical imaging water quality remote sensing detection device;
in the drawings, the components represented by the respective reference numerals are listed below:
1: an imaging camera; 2: a light filtering wheel;
3: a beam splitter; 4: a collimating mirror;
5: a white light source; 6: a fluorescence excitation light source;
7: a data acquisition and processing system.
The filter wheel 2 is composed of band-pass filters and band-stop filters with different wavelengths, and the filter wheel 2 is adopted to switch different filters, so that the imaging camera 1 can acquire images with different wavelengths.
In the invention, the filter wheel 2 is matched with the white light source 5 for illumination, and an absorption spectrum image can be obtained. When the fluorescence excitation light source 6 is adopted, the filter wheel 2 selects the band elimination filter, and a fluorescence image can be obtained. The image obtained by the imaging camera 1 is converted into a digital signal by an image acquisition device, and the digital signal enters a data acquisition and processing system for further processing.
FIG. 2 is a flow chart of a water pollution feature extraction method based on multi-mode image fusion designed by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
The invention develops a detection device capable of realizing simultaneous remote sensing imaging of absorbed light and fluorescence signals and a pollution characteristic extraction method based on a multimode optical imaging principle aiming at the online remote sensing detection requirements of various pollutants in a wide water area in environmental monitoring.
Wherein, multimode optical imaging quality of water remote sensing detection device includes: the light source formed by combining the white light source 5 and the fluorescence excitation light source 6 and the image acquisition device formed by combining the imaging camera 1 and the filter wheel 2 can realize a multi-mode optical imaging mode combining an absorption spectrum and a fluorescence spectrum.
The invention also designs a multi-modal water pollution quantitative analysis method based on the absorption signal fluorescence signal characteristic extraction and quantitative analysis. The turbidity change and the absorption characteristic of the water quality sample are separated, the quantitative analysis precision is improved, and the quantitative analysis of various water quality pollution parameters can be realized.
Example 1
The invention designs a multimode optical imaging water quality remote sensing detection device, which comprises: the filter wheel 2 and the imaging camera 1 are combined to form the image acquisition device, and the filter wheel 2 is adopted to respectively switch the narrow-band filters, so that the acquisition of spectral imaging signals with different wavelengths is realized.
The light source adopts the combination of a white light source 5 and a fluorescence excitation light source 6, and is in switching fit with the filter wheel 2, so that a multi-mode optical imaging mode combining an absorption spectrum and a fluorescence spectrum can be realized, and images obtained by each imaging camera 1 are programmed into digital signals through an image acquisition device and then are transmitted to a data acquisition and processing system 7 for further processing.
Example 2
Based on the device, the invention also designs a multi-mode optical imaging pollution feature extraction method, and an affine edge energy exchange method is adopted to register the measured absorption spectrum and fluorescence spectrum images. After registration, different images are consistent in measurement range and resolution, then image fusion is carried out, the fused images are subjected to statistical analysis, the fused image features are counted one by one, the energy and the correlation of the gray level mean value, the variance and the gray level co-occurrence matrix are taken as feature vectors, and a support vector machine is adopted to classify the fused image features, so that the extraction of different pollution features in a wide water area is realized.
The method for extracting contamination characteristics based on multi-modal optical imaging provided by the invention is described with reference to the accompanying drawings and embodiments, as shown in fig. 2, the method can be divided into the following steps:
(1) multi-modal optical image acquisition
The white light source irradiates a measurement target, and the filter wheels 2 are adopted to respectively switch the narrow-band filters, so that the absorption spectrum imaging with different wavelengths is realized. The fluorescence excitation light source 6 irradiates a measurement target, the filter wheel 2 is adopted to respectively switch the narrow-band filters, the fluorescence imaging measurement is realized, and an image obtained by the imaging camera 1 enters the data acquisition and processing system 7 to be processed in the next step after a digital signal is programmed by the image acquisition device.
(2) Registration of absorption spectrum image with fluorescence image
According to the machine vision theory, an object image in a three-dimensional space is acquired, the coordinates of a target in an absorption spectrum image are (x, y, z), the coordinates of the target in a fluorescence image are (x ', y ', z '), and basic coordinate transformation is respectively carried out on the positions of the target in the two images, wherein the transformation can be expressed as the following formula:
translation along the x-axis:
translation along the y-axis:
translation along the z-axis:
rotation about the x-axis:
rotation about the y-axis:
rotation about the z-axis:
wherein p is x-axis translationAn amount; q is the translation amount in the y-axis direction; r is the translation amount in the z-axis direction; theta is the rotation angle around the x axis; omega is the rotation angle around the y axis;
is the angle of rotation about the z-axis.
In the invention, firstly, correlation calculation is carried out on the absorption spectrum image and the fluorescence image:
wherein A ismnAnd BmnThe gray values of the pixel points of the absorption spectrum image A and the fluorescence spectrum image B are respectively,
and
the return means of the two images are respectively. m is the pixel row position and n is the pixel column position.
And obtaining image correlation coefficients in different directions according to calculation, then carrying out coordinate transformation registration on the two-needle images by using the change formula, and when the calculated correlation coefficient is 1, achieving the image registration precision.
(3) Multimodal optical image fusion
Firstly, obtaining a correlation coefficient matrix between the absorption spectrum image and the fluorescence image through an image fusion method, calculating a characteristic value and a characteristic vector according to the correlation coefficient matrix, and then obtaining each principal component image; then, contrast stretching is carried out on the fluorescence image data to enable the fluorescence image data to have the same mean value and variance with the absorption spectrum image data set; and finally, adding the two principal component matrixes, and performing PCA inverse transformation to obtain a fused image.
(4) Post-fusion image feature computation
For texture distribution with high precision requirement and fine texture, selecting a pixel distance d as 1, and calculating energy, entropy and correlation texture characteristics in a 0-degree direction. p (i, j) represents the value of the ith row and jth column of the fused image.
The energy calculation formula is as follows:
the contrast calculation formula is as follows:
the information entropy calculation formula is as follows:
(5) object feature extraction based on support vector machine
And classifying local texture features of the sequence images in the infrared image space by adopting a support vector machine technology. And taking energy, contrast and entropy as input parameters, inputting the input parameters into a found optimal separation hyperplane w.x + b ═ 0, wherein the plane is a decision boundary for classification, w is a normal vector, b is an intercept, and the eigenvalues are positioned at two sides of the plane, so that different pollution image characteristics are identified and classified.
In the embodiment of the present invention, except for the specific description of the model of each device, the model of other devices is not limited, as long as the device can perform the above functions.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. The utility model provides a multimode optical imaging quality of water remote sensing detection device which characterized in that, the device includes: a light source combining a white light source and a fluorescence excitation light source, and an image acquisition device combining an imaging camera and a filter wheel;
the filter wheels are adopted to respectively switch the narrow-band filters to obtain spectral imaging signals with different wavelengths, so that a multi-mode optical imaging mode combining absorption spectrum and fluorescence spectrum can be realized;
images obtained by each imaging camera are programmed into digital signals and then transmitted to a data acquisition and processing system for processing, and pollutant characteristics are extracted.
2. A multi-modal optical imaging pollution feature extraction method is characterized by comprising the following steps:
registering the measured absorption spectrum and fluorescence spectrum images by adopting an affine edge energy conversion method to realize the consistency of the image measurement range and resolution;
counting the fused image features one by one;
and (3) taking the energy and the correlation of the gray level mean value, the variance and the gray level co-occurrence matrix as feature vectors, and classifying the fused image features by using a support vector machine, thereby realizing the extraction of different pollution features in a wide water area.
3. The method according to claim 2, wherein the registering the measured absorption spectrum and fluorescence spectrum images specifically comprises:
performing correlation calculation on the absorption spectrum image and the fluorescence image;
and obtaining image correlation coefficients in different directions according to calculation, then carrying out coordinate transformation registration on the two-needle images, and when the calculated correlation coefficient is 1, achieving the image registration precision.
4. The method according to claim 2, wherein the image features after statistical fusion one by one are specifically:
and selecting a pixel pitch d as 1, and calculating energy, information entropy and contrast characteristics in a 0-degree direction.
5. The method according to claim 2, wherein the classifying the fused image features by using a support vector machine specifically comprises:
classifying local texture features of the sequence images in the infrared image space by adopting a support vector machine;
taking energy, contrast and entropy as input parameters, and inputting the parameters into a found optimal separation hyperplane w.x + b ═ 0, wherein the plane is a decision boundary of classification, w is a normal vector, and b is an intercept;
and (5) the characteristic values are arranged on two sides of the plane, so that different pollution image characteristics are identified and classified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010209472.3A CN111398189B (en) | 2020-03-23 | 2020-03-23 | Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010209472.3A CN111398189B (en) | 2020-03-23 | 2020-03-23 | Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111398189A true CN111398189A (en) | 2020-07-10 |
| CN111398189B CN111398189B (en) | 2023-03-14 |
Family
ID=71434417
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010209472.3A Active CN111398189B (en) | 2020-03-23 | 2020-03-23 | Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111398189B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116973348A (en) * | 2023-09-20 | 2023-10-31 | 恒天益科技（深圳）有限公司 | Water quality analysis method and system for COD (chemical oxygen demand) on line by ultraviolet light method |
| CN117233116A (en) * | 2023-11-09 | 2023-12-15 | 江西洪城检测有限公司 | Water quality analysis method and system based on machine vision |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447703A (en) * | 2016-08-31 | 2017-02-22 | 北京数字精准医疗科技有限公司 | Near infrared fluorescence and Cherenkov fluorescence fused imaging method and apparatus |
| CN108507955A (en) * | 2018-03-22 | 2018-09-07 | 上海交通大学 | The device and method of multispectral synchronous detection chemical oxygen demand of water body |
| CN108801985A (en) * | 2017-05-03 | 2018-11-13 | 中国科学院大连化学物理研究所 | A kind of spectrometer of fluorescence spectrum and absorption spectrum together as one |
| CN108830858A (en) * | 2018-06-20 | 2018-11-16 | 天津大学 | It is a kind of based on infrared and optical image double-mode imaging information living body method for counting colonies |
| CN110018123A (en) * | 2019-05-16 | 2019-07-16 | 厦门斯坦道科学仪器股份有限公司 | A kind of In situ spectroscopic fado parameter water quality monitoring method and device |
-
2020
- 2020-03-23 CN CN202010209472.3A patent/CN111398189B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447703A (en) * | 2016-08-31 | 2017-02-22 | 北京数字精准医疗科技有限公司 | Near infrared fluorescence and Cherenkov fluorescence fused imaging method and apparatus |
| CN108801985A (en) * | 2017-05-03 | 2018-11-13 | 中国科学院大连化学物理研究所 | A kind of spectrometer of fluorescence spectrum and absorption spectrum together as one |
| CN108507955A (en) * | 2018-03-22 | 2018-09-07 | 上海交通大学 | The device and method of multispectral synchronous detection chemical oxygen demand of water body |
| CN108830858A (en) * | 2018-06-20 | 2018-11-16 | 天津大学 | It is a kind of based on infrared and optical image double-mode imaging information living body method for counting colonies |
| CN110018123A (en) * | 2019-05-16 | 2019-07-16 | 厦门斯坦道科学仪器股份有限公司 | A kind of In situ spectroscopic fado parameter water quality monitoring method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116973348A (en) * | 2023-09-20 | 2023-10-31 | 恒天益科技（深圳）有限公司 | Water quality analysis method and system for COD (chemical oxygen demand) on line by ultraviolet light method |
| CN116973348B (en) * | 2023-09-20 | 2023-12-05 | 恒天益科技（深圳）有限公司 | Water quality analysis method and system for COD (chemical oxygen demand) on line by ultraviolet light method |
| CN117233116A (en) * | 2023-11-09 | 2023-12-15 | 江西洪城检测有限公司 | Water quality analysis method and system based on machine vision |
| CN117233116B (en) * | 2023-11-09 | 2024-02-02 | 江西鼎智检测有限公司 | Water quality analysis method and system based on machine vision |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111398189B (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Moo et al. | New development of optical fibre sensor for determination of nitrate and nitrite in water | |
| He et al. | Water quality monitoring in a slightly-polluted inland water body through remote sensing—Case study of the Guanting Reservoir in Beijing, China | |
| Mesquita et al. | Activated sludge characterization through microscopy: A review on quantitative image analysis and chemometric techniques | |
| Shafique et al. | Hyperspectral remote sensing of water quality parameters for large rivers in the Ohio River basin | |
| CN111398189B (en) | Multi-modal optical imaging water quality remote sensing detection device and pollution feature extraction method | |
| Martsenyuk et al. | Multispectral control of water bodies for biological diversity with the index of phytoplankton | |
| Doǧan et al. | A field-deployable water quality monitoring with machine learning-based smartphone colorimetry | |
| Zhu et al. | Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm | |
| Nguyen et al. | Smartphone nano-colorimetry for on-demand multiplex lead and mercury detection and quantitation in drinking water | |
| Pan et al. | A hand-held optoelectronic tongue for the identification of heavy-metal ions | |
| Liu et al. | Tracing sources of oilfield wastewater based on excitation-emission matrix fluorescence spectroscopy coupled with chemical pattern recognition techniques | |
| CN108801361B (en) | Water quality monitoring device that physics and chemistry biology combines | |
| Lee et al. | Automatic classification of microplastics and natural organic matter mixtures using a deep learning model | |
| Fogelman et al. | Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks | |
| Xing et al. | A cellphone-based colorimetric multi-channel sensor for water environmental monitoring | |
| CN104931488A (en) | Indicator-array-based anion detection method | |
| Hakiki et al. | the Prospect of Digitally Enhanced Colorimetry As an Analytical Method for Water Quality Determination | |
| CN110501329B (en) | Colorimetric chemical sensor array, method for detecting metal ions, and method for evaluating and optimizing sensor array | |
| Li et al. | Tap water fingerprinting using a convolutional neural network built from images of the coffee-ring effect | |
| Banerjee et al. | A portable spectroscopic instrument for multiplexed monitoring of acute water toxicity: Design, testing, and evaluation | |
| Heidari-Bafroui et al. | Infrared Lightbox and iPhone App for Improving Detection Limit of Phosphate Detecting Dip Strips. Int. J. Chem. Mol. Eng. 2020, 14 (7), 179–185. | |
| CN115219472A (en) | Method and system for quantitatively identifying multiple pollution sources of mixed water body | |
| Gimenez et al. | Turbidimetry by image degradation analysis | |
| Yin et al. | Open-set recognition for unknown organic pollutants in drinking water with three-dimensional fluorescence spectroscopy | |
| Liu et al. | Underappreciated microplastic galaxy biases the filter-based quantification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |