CN115508276A - Water quality detection method, detection device and detection system - Google Patents
Water quality detection method, detection device and detection system Download PDFInfo
- Publication number
- CN115508276A CN115508276A CN202110696024.5A CN202110696024A CN115508276A CN 115508276 A CN115508276 A CN 115508276A CN 202110696024 A CN202110696024 A CN 202110696024A CN 115508276 A CN115508276 A CN 115508276A
- Authority
- CN
- China
- Prior art keywords
- chromaticity
- water quality
- standard
- laboratory
- reference object
- 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.)
- Pending
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
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/94—Investigating contamination, e.g. dust
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides a water quality detection method, a detection device and a detection system, wherein the method comprises the following steps: constructing a standard curve between the concentration and the chromaticity component of the target object; selecting at least one reference object, and acquiring the laboratory standard chromaticity of the reference object; acquiring actual chromaticity of a reference object corresponding to standard chromaticity of a reference object laboratory on a water quality detection site; under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory is larger than a set value, constructing a correction model to obtain the conversion relation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory; acquiring field chrominance components of an image of a water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component by using the conversion relation and the standard curve. The method provided by the invention is simple, high in testing speed, wide in application range, independent of specific shooting conditions, good in applicability to different shooting equipment, light sources and shooting methods, and high in detection accuracy.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a water quality detection method, a water quality detection device and a water quality detection system.
Background
The quantitative determination of the target object in the water body is widely applied to the processes of industrial water quality detection, environmental detection, biochemical analysis, accident investigation, domestic water detection, sewage treatment and the like, and relates to the fields of industrial production, medical health, daily life and the like. While large-scale and precise monitoring systems continue to be developed, research on small-scale portable, automatic, continuous, simple and rapid monitoring technologies is gradually receiving attention.
The combination of optical analysis methods such as chromaticity, gray scale, turbidity and the like and portable water quality detection equipment is an important research and application trend in the water quality detection field, can fully exert the advantages in the aspects of intellectualization, miniaturization, automation, integration, portability and the like, and has wide application prospect.
The key of the quantitative detection of the water body target object by using the optical method is to quickly and accurately identify the color development information. However, most of the detection methods in the prior art require a fixed light source, consistent photographing equipment and settings, and even require complicated additional equipment for fixing the equipment and the position, photographing angle and distance of the object to be measured, and once the actual conditions are different from the standard conditions, the photographing accuracy cannot be guaranteed.
Disclosure of Invention
The invention provides a water quality detection method, a water quality detection device and a water quality detection system, aiming at the technical problems of complex test equipment, narrow detection application range and high detection environment requirement in the prior art.
In order to achieve the purpose, the water quality detection method provided by the invention comprises the following steps: constructing a standard curve between the concentration and the chromaticity component of the target object; selecting at least one reference object according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference object in a laboratory; acquiring the actual chromaticity of the reference object corresponding to the standard chromaticity of the reference object laboratory on a water quality detection site; under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory is larger than a set value, constructing a correction model to obtain the conversion relation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory; acquiring the field chromaticity component of the object to be detected on the water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component by using the conversion relation and the standard curve.
Further, the optical environment for obtaining the standard chromaticity of the reference laboratory is the same as the optical environment for constructing the standard curve.
Further, determining a chromaticity deviation of the reference real chromaticity from a corresponding reference laboratory standard chromaticity comprises: judging whether the shooting conditions of the water quality detection site meet the detection requirements or not; and determining the chromaticity deviation of the actual chromaticity of the reference object and the laboratory standard chromaticity of the corresponding reference object under the condition that the detection requirement is satisfied.
Further, the photographing conditions include at least: image pixel, illumination intensity and light uniformity of a water quality detection site.
Further, the correction model is constructed through a neural network algorithm or a multivariate nonlinear fitting method.
Further, the determining the concentration of the target corresponding to the field chromaticity component by using the conversion relation and the standard curve includes: converting the field chrominance component into a standard chrominance component by using the conversion relation; and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
Further, the method further comprises: under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
A second aspect of the present invention provides a water quality detecting apparatus, comprising: the standard curve building module is used for building a standard curve between the concentration and the chromaticity component of the target object; the standard chromaticity acquisition module of the reference substance laboratory is used for selecting at least one reference substance according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference substance laboratory of the reference substance; the reference object actual chromaticity acquisition module is used for acquiring the reference object actual chromaticity corresponding to the reference object laboratory standard chromaticity on a water quality detection site; the conversion relation acquisition module is used for constructing a correction model under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory is greater than a set value, and acquiring the conversion relation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory; the field chromaticity component acquisition module is used for acquiring the field chromaticity component of the object to be detected on the water quality detection field; and the target concentration determining module is used for determining the target concentration corresponding to the field chromaticity component by using the conversion relation and the standard curve.
Further, the optical environment for obtaining the standard chromaticity of the reference laboratory is the same as the optical environment for constructing the standard curve.
Further, the conversion relation obtaining module determines the chromaticity deviation between the actual chromaticity of the reference object and the corresponding laboratory standard chromaticity of the reference object, and includes: judging whether the shooting conditions of the water quality detection site meet the detection requirements or not; and determining the chromaticity deviation of the actual chromaticity of the reference object and the laboratory standard chromaticity of the corresponding reference object under the condition that the detection requirement is satisfied.
Further, the photographing conditions include at least: image pixel, illumination intensity and light uniformity of a water quality detection site.
Further, the conversion relation obtaining module constructs the correction model through a neural network algorithm or a multivariate nonlinear fitting method.
Further, the target concentration determination module determines a target concentration corresponding to the field chromaticity component using the conversion relationship and the standard curve, including: converting the field chrominance component into a standard chrominance component by using the conversion relation; and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
Further, the target concentration determination module is further configured to: under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
A third aspect of the invention provides a water quality detection system comprising the water quality detection device described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the water quality detection method described above.
Through the technical scheme provided by the invention, the invention at least has the following technical effects:
the water quality detection method provided by the invention starts from the actual image result, and utilizes the standard object with fixed chromaticity to carry out correction and matching with the laboratory conditions, so that the required color information is obtained quickly and efficiently. The method has wide application range, does not depend on specific shooting conditions, has good applicability to different shooting equipment, light sources and shooting methods, can be used for various detection and analysis methods related to shooting quantification, and has low requirements on detection conditions. And the detection effect is good, the utilization of the chromaticity change caused by chemical reaction is improved, the action relation between the sensitive chromaticity component and the concentration is more deeply excavated, and the quantitative and accurate determination of the concentration is facilitated.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
FIG. 1 is a flow chart of a water quality detection method according to an embodiment of the present invention;
FIG. 2 is a schematic view of a water quality detecting apparatus according to an embodiment of the present invention;
FIG. 3 is a standard graph of an object according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a chromaticity correction effect according to an embodiment of the present invention;
FIG. 5 is a schematic diagram showing the comparison of the concentrations measured before and after the chromaticity correction with the national standard method in accordance with one embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a chromaticity correction effect according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a chromaticity correction effect according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the present invention, unless specified to the contrary, use of the terms "upper, lower, top and bottom" in the orientation illustrated in the drawings generally refers to the orientation of the components as shown in the drawings or to the orientation of the components relative to each other in the vertical, vertical or gravitational direction.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, an embodiment of the present invention provides a water quality detection method, including the following steps: s101: constructing a standard curve between the concentration and the chromaticity component of the target object; s102: selecting at least one reference object according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference object in a laboratory; s103: acquiring the actual chromaticity of the reference object corresponding to the standard chromaticity of the reference object laboratory on a water quality detection site; s104: under the condition that the chromaticity deviation between the actual chromaticity of the reference substance and the standard chromaticity of the corresponding laboratory of the reference substance is larger than a set value, constructing a correction model to obtain the conversion relation between the actual chromaticity of the reference substance and the standard chromaticity of the corresponding laboratory of the reference substance; s105: acquiring the field chromaticity component of the object to be detected on the water quality detection field; s106: and determining the concentration of the target object corresponding to the field chromaticity component by using the conversion relation and the standard curve.
Specifically, in the embodiment of the present invention, the chromaticity component of the analyte including different concentrations of the target object is detected for the target object to be detected, and a standard curve between the concentration of the target object and the chromaticity component is constructed. And then selecting a reference object according to the attributes of the target object and the object to be measured containing the target object.
The reference object needs to basically cover key chromaticity characteristics possibly involved in the actual shooting process, meanwhile, the material is as close as possible to a system involved in an experiment, the reference object can be solid or liquid, the reference object can be directly a test device subjected to color reaction, a test device body subjected to different chromaticity dyes or a system made of similar materials can be adopted, and for some systems, reference objects which are beneficial to obtaining accurate color information and can be stored for a long time can be adopted, such as a standard color card. Sensitive chromaticity components of different reaction systems are different, common chromaticity systems comprise RGB, HSV, CMYK and the like, and a complex relational expression of various chromaticity components is involved in the actual process.
And shooting the reference object in a laboratory to obtain the laboratory standard chromaticity of the reference object. And (4) screening the material of the test equipment, designing the structure, designing the operation flow and the like according to the characteristics of the reaction system. The shooting equipment comprises various designs which can obtain color information, such as a camera, a mobile phone, a camera, a scanner, monitoring and the like.
And then, photographing the reference object at a photographing site of the object to be measured containing the target object to obtain the actual chromaticity of the reference object. There is no hard requirement for shooting conditions during shooting, but optimizing shooting conditions is helpful to improve result accuracy, such as keeping the reference level, the shooting angle and distance of the mobile phone are appropriate, and the light source illumination is not too high or too low.
And comparing the actual chromaticity of the reference object with the standard chromaticity of the reference object in the laboratory, and constructing a correction model to obtain the conversion relation between the actual chromaticity of the reference object and the standard chromaticity of the reference object in the laboratory under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object in the laboratory is more than a set value. And then, acquiring an image of the object to be detected on a water quality detection site, acquiring site chromaticity components of the object to be detected, and determining the corresponding target object concentration by using the conversion relation and the standard curve.
According to the water quality detection method provided by the invention, starting from an actual image result, the reference object with fixed chromaticity is used for correcting and matching with laboratory conditions, so that the required color information is obtained quickly and efficiently. The method has wide application range, does not depend on specific shooting conditions, has good applicability to different shooting equipment, light sources and shooting methods, can be used for various detection and analysis methods related to shooting quantification, and has low requirements on detection conditions. The detection effect is good, the utilization of the chromaticity change caused by chemical reaction is improved, the action relation between the sensitive chromaticity component and the concentration is further excavated, and the quantitative and accurate determination of the concentration is facilitated.
Further, the optical environment for obtaining the standard chromaticity of the reference laboratory is the same as the optical environment for constructing the standard curve.
Specifically, in the embodiment of the present invention, in order to ensure the consistency between the standard chromaticity of the reference object laboratory and the standard chromaticity of the target object laboratory, the reference object is photographed in the laboratory optical environment where the standard curve is obtained, so as to obtain the standard chromaticity of the reference object laboratory.
Further, determining a chromaticity deviation of the reference substance actual chromaticity from a corresponding reference substance laboratory standard chromaticity, comprising: judging whether the shooting conditions of the water quality detection site meet the detection requirements or not; and determining the chromaticity deviation of the actual chromaticity of the reference object and the laboratory standard chromaticity of the corresponding reference object under the condition that the detection requirement is satisfied.
Specifically, in the embodiment of the invention, after shooting at the water quality detection site, whether shooting conditions such as image pixels, illumination intensity and light uniformity of the water quality detection site meet detection requirements or not is judged. If the pixel of the water quality detection field image is too small, the resolution of the camera is low or the shooting distance is too far, so that the information amount required by detection is lacked. Whether the illumination intensity meets the requirements or not is analyzed, the noise information occupation ratio is too high due to too high or too low illumination on the water quality detection site, the identification of the information to be detected is seriously disturbed, and the illumination condition or the settings such as exposure time and light sensitivity of shooting equipment need to be adjusted. The light uniformity also affects the data accuracy, if the illumination is uneven, different groups of shooting conditions are very easy to generate great difference, the chromaticity information of different positions of a water quality detection field in a single shooting process under severe conditions can have great deviation, and if the problem occurs, the light source condition, the position of an object to be detected, the shooting angle and the like need to be adjusted according to the conditions.
According to the water quality detection method provided by the invention, the shooting condition can be optimized, and the accuracy of the detection result is improved.
Further, the correction model is constructed through a neural network algorithm or a multivariate nonlinear fitting method.
Specifically, in the embodiment of the invention, the actual chromaticity is the comprehensive reflection of conditions such as a shooting light source, hardware setting, a shooting method and the like, the shooting method can be adjusted and optimized according to the actual chromaticity, the actual chromaticity and the laboratory standard chromaticity are compared and analyzed, the difference information between a water quality detection field and a laboratory condition can be obtained, and further the chromaticity correction is carried out to carry out matching association on the actual chromaticity and the laboratory condition. The optional chromaticity correction algorithm comprises a neural network algorithm, a multivariate nonlinear fitting method and the like, and the chromaticity conversion can be directly carried out by using optical analysis for a system with clear illumination conditions.
Further, the determining the concentration of the target corresponding to the field chromaticity component by using the conversion relation and the standard curve includes: converting the field chrominance component into a standard chrominance component by using the conversion relation; and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
Specifically, in the embodiment of the present invention, the image chromaticity is corrected by using the conversion relationship, and the field chromaticity component is converted into the standard chromaticity component, so as to obtain the standard chromaticity component of the target object under the laboratory condition. And then, the concentration corresponding to the standard chromaticity component is searched on the standard curve, and the concentration of the target object on the water quality detection site can be obtained.
Further, the method further comprises: under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
Specifically, in the embodiment of the present invention, if the chromaticity deviation between the actual chromaticity of the standard object and the standard chromaticity of the reference object laboratory is less than or equal to the set value, it indicates that the shooting environment of the water quality detection site is approximately the same as the shooting environment of the laboratory, and the concentration corresponding to the chromaticity component on the site can be directly searched by using the standard curve to obtain the concentration of the target object on the water quality detection site.
Example 1: referring to fig. 3, taking the detection of chromium ions in water as an example, after completing the determination of a complex reagent reaction system mainly composed of diphenylcarbazide and the structural design of a paper chip, a standard curve is constructed, and correlation fitting of chromium ion concentration and chromaticity distance is performed under laboratory conditions to obtain standard curve basic data, for which the chromaticity distance is directly proportional to the chromium ion concentration in the detection linear region. Dyes with different colors are added into the microfluidic chip to serve as reference objects, and reference object shooting is carried out in a laboratory optical environment with a standard curve obtained to obtain laboratory standard chromaticity of a reference object image.
And (4) photographing and image processing the reference object under the condition of illumination on the water quality detection site. And analyzing the image result to judge whether the shot meets the analysis requirement. The actual chromaticity of the reference object is compared with the laboratory standard chromaticity of the reference object under the experimental condition, the deviation between the required chromaticity component and the laboratory result is found to be larger, the correction and matching of the field image chromaticity and the laboratory chromaticity are carried out on the two reference objects of the color card and the chip, and the conversion relation is determined by adopting an optimal multivariate nonlinear fitting method. Referring to fig. 4, the result shows that the average deviation between the actual chromaticity of the reference substance and the laboratory standard chromaticity of the reference substance can be greatly reduced to within 5% by the conversion relationship established by the method.
Further developing field detection experiment, and carrying out reaction and optical test on the chromium-containing system. And performing operations such as key area selection, edge detection, noise reduction smoothing, chromaticity enhancement and the like on the image to finally obtain various chromaticity component values, and performing matching query on the spot chromaticity components shot on the spot and the standard curve to obtain the actual concentration of the chromium ions. Referring to fig. 5, when the chromium ion concentration obtained by the method is compared with the national standard method, the average deviation is less than 7%, and if the image chromaticity is not corrected, the average deviation is greater than 15%.
Example 2: taking the detection of nickel ions in water as an example, firstly, the reaction system is determined: according to the property of a target object to be detected, a paper chip is selected for testing, the core part of the chip is filter paper subjected to hydrophobic modification (the sample inlet, the channel and the detection pool are hydrophilic, and the rest areas are hydrophobic, wherein the detection pool is preset with a compound reagent taking dimethylglyoxime as a main substance and can perform specific color reaction with nickel), a nickel-containing water sample is added into the paper chip from a sample adding area during detection, and the sample flows to the detection pool along the channel and reacts with the reagent to generate a pink substance.
And (3) carrying out correlation fitting of nickel ion concentration and colorimetric distance under a laboratory condition to obtain standard curve basic data, wherein the colorimetric distance is in direct proportion to the nickel ion concentration in the detection linear region for the system. Dyes with different colors are added into a standard color card and a microfluidic chip to serve as reference objects, and reference object shooting is carried out in a laboratory optical environment with a standard curve to obtain laboratory standard chromaticity of a reference object image.
And photographing the reference object under the condition of on-site illumination of water quality monitoring to obtain on-site chromaticity components. Comparing and analyzing the field chromaticity component and the standard chromaticity of the reference substance laboratory, finding that the deviation between the actual chromaticity of the reference substance and the standard chromaticity of the reference substance laboratory is larger, respectively utilizing multivariate nonlinear fitting and a neural network algorithm to correct and match the field image chromaticity and the laboratory chromaticity of the reference substance, and determining an optimal multivariate nonlinear fitting method. Referring to fig. 6, the result shows that the conversion relationship established by the method can greatly reduce the average deviation between the actual chromaticity of the reference substance and the laboratory standard chromaticity of the reference substance to within 7%.
And further carrying out an on-site detection experiment, carrying out reaction and optical test on the nickel-containing system to obtain each chromaticity component value, and matching the on-site chromaticity component result shot on site with the standard curve to obtain the actual concentration of the nickel ions. The nickel ion concentration obtained by the method is compared with a national standard method, the deviation is less than 9%, if image chromaticity correction is not carried out, the average deviation is more than 25%, the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time, convenience and the like.
Example 3: taking the detection of chromium ions in water as an example, a microfluidic chip is adopted for testing, a channel, a detection pool and other structures are constructed on a chip bottom sheet, and then a cover sheet is used for packaging, so that a closed space is formed finally. A complex reagent which mainly comprises diphenyl carbodihydrazide is embedded in partial areas such as a channel, a detection pool and the like of the negative, and can generate specific color reaction with chromium. And (3) performing correlation fitting of the chromium ion concentration and the chromaticity distance under a laboratory condition to obtain standard curve basic data, wherein the chromaticity distance and the chromium ion concentration are in direct proportion in a detection linear interval for the system. And selecting the microfluidic chip added with different dyes as a reference object, and shooting the reference object in the laboratory optical environment with a standard curve to obtain the laboratory standard chromaticity of the reference object.
And photographing the reference object under the field illumination condition of a certain device to obtain the actual chromaticity of the reference object. And comparing and analyzing the actual chromaticity of the reference substance and the laboratory standard chromaticity of the reference substance, finding that the deviation of the required chromaticity component and the laboratory result is large, and performing correction and matching on the spot image chromaticity and the laboratory chromaticity by using neural network algorithms with different training set numbers to determine an optimal matching method. Referring to fig. 7, the results show that the average deviation of the actual chromaticity of the reference substance from the standard chromaticity of the laboratory reference substance can be reduced to 8%.
Further developing field detection experiment, and carrying out reaction and optical test on the chromium-containing system. And performing operations such as key area selection, edge detection, noise reduction smoothing, chromaticity enhancement and the like on the image to finally obtain various chromaticity component values, and performing matching query on a detection reaction chromaticity result shot on site and a standard curve to obtain the actual concentration of the chromium ions. The chromium ion concentration obtained by the method is compared with that of a national standard method, the deviation is less than 11%, the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time, convenience and the like.
Example 4: taking the detection of hydrogen in the air as an example, a metal oxide material is used as a detection reagent, and a precipitation method and a hydrothermal method are used for preparing metal powder, wherein the powder is milky white and gradually turns into dark blue along with the contact with the hydrogen. And (3) performing correlation fitting of the hydrogen concentration and the chromaticity distance under a laboratory condition to obtain a standard curve, wherein the chromaticity distance is in direct proportion to the hydrogen concentration in the detection linear region for the system. And selecting particles with different typical colors as a reference object, and shooting the reference object in a laboratory optical environment for obtaining a standard curve to obtain the laboratory standard chromaticity of the reference object.
And photographing the reference object under the condition of detecting on-site illumination to obtain the actual chromaticity of the reference object. The actual chromaticity of the reference substance is compared and analyzed with the standard chromaticity of the reference substance laboratory, the fact that the deviation of the actual chromaticity of the reference substance and the standard chromaticity of the reference substance laboratory is large is found, the actual chromaticity of the reference substance and the standard chromaticity of the reference substance laboratory are corrected and matched by utilizing a neural network algorithm, and the result shows that the average deviation of the field shooting chromaticity and the standard chromaticity of the laboratory can be greatly reduced to be within 5% through the conversion relation established by the method.
Further carrying out an on-site detection experiment, testing the configured hydrogen with the concentrations of 1%, 2%, 4% and 10% to obtain various chromaticity component values, matching the on-site chromaticity component shot on site with a standard curve to obtain the calculated hydrogen concentrations with the deviations of 8%, 6%, 3%, 4% and 1% from the true value respectively, and if image chromaticity correction is not carried out, the average deviation of the on-site chromaticity components is more than 20%, so that the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time (less than 1 minute), convenience and the like.
Referring to fig. 2, a second aspect of the present invention provides a water quality detecting apparatus, including: the standard curve building module is used for building a standard curve between the concentration and the chromaticity component of the target object; the standard chromaticity acquisition module of the reference substance laboratory is used for selecting at least one reference substance according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference substance laboratory of the reference substance; the reference object actual chromaticity acquisition module is used for acquiring the reference object actual chromaticity corresponding to the reference object laboratory standard chromaticity on a water quality detection site; the conversion relation acquisition module is used for constructing a correction model under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory is greater than a set value, and acquiring the conversion relation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory; the field chromaticity component acquisition module is used for acquiring the field chromaticity component of the object to be detected on the water quality detection field; and the target object concentration determining module is used for determining the target object concentration corresponding to the field chromaticity component by using the conversion relation and the standard curve.
Further, the optical environment for obtaining the standard chromaticity of the reference laboratory is the same as the optical environment for constructing the standard curve.
Further, the determining the chromaticity deviation between the actual chromaticity of the reference object and the corresponding laboratory standard chromaticity of the reference object by the conversion relation obtaining module includes: judging whether the shooting conditions of the water quality detection site meet the detection requirements or not; and determining the chromaticity deviation of the actual chromaticity of the reference object and the laboratory standard chromaticity of the corresponding reference object under the condition that the detection requirement is satisfied.
Further, the photographing conditions include at least: image pixel, illumination intensity and light uniformity of a water quality detection site.
Further, the conversion relation obtaining module constructs the correction model through a neural network algorithm or a multivariate nonlinear fitting method.
Further, the target concentration determining module determines a target concentration corresponding to the field chromaticity component using the conversion relationship and the standard curve, including: converting the field chrominance component into a standard chrominance component by using the conversion relation; and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
Further, the target concentration determination module is further configured to: under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field; and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
A third aspect of the invention provides a water quality detection system comprising the water quality detection device described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the water quality detection method described above.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications all fall within the protection scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (16)
1. A water quality detection method is characterized by comprising the following steps:
constructing a standard curve between the concentration and the chromaticity component of the target object;
selecting at least one reference object according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference object in a laboratory;
acquiring the actual chromaticity of the reference object corresponding to the standard chromaticity of the reference object laboratory on a water quality detection site;
under the condition that the chromaticity deviation between the actual chromaticity of the reference substance and the standard chromaticity of the corresponding laboratory of the reference substance is larger than a set value, constructing a correction model to obtain the conversion relation between the actual chromaticity of the reference substance and the standard chromaticity of the corresponding laboratory of the reference substance;
acquiring the field chromaticity component of the object to be detected on the water quality detection field;
and determining the concentration of the target object corresponding to the field chromaticity component by using the conversion relation and the standard curve.
2. The water quality detecting method according to claim 1, wherein an optical environment for obtaining the standard chromaticity of the reference material laboratory is the same as an optical environment for constructing the standard curve.
3. The water quality detection method of claim 1, wherein determining the chromaticity deviation of the actual chromaticity of the reference substance from the corresponding laboratory standard chromaticity of the reference substance comprises:
judging whether the shooting conditions of the water quality detection site meet the detection requirements or not;
and determining the chromaticity deviation of the actual chromaticity of the reference object and the laboratory standard chromaticity of the corresponding reference object under the condition that the detection requirement is satisfied.
4. The water quality detection method according to claim 3, wherein the imaging conditions include at least: image pixel, illumination intensity and light uniformity of a water quality detection site.
5. The water quality detection method according to claim 1, wherein the correction model is constructed by a neural network algorithm or a multivariate nonlinear fitting method.
6. The water quality detection method according to claim 1, wherein the determining the concentration of the target object corresponding to the field chromaticity component by using the conversion relation and the standard curve comprises:
converting the field chrominance component into a standard chrominance component by using the conversion relation;
and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
7. The water quality detection method according to claim 1, further comprising:
under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field;
and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
8. A water quality detecting apparatus, characterized in that the water quality detecting apparatus comprises:
the standard curve building module is used for building a standard curve between the concentration and the chromaticity component of the target object;
the standard chromaticity acquisition module of the reference substance laboratory is used for selecting at least one reference substance according to the attributes of a target object and an object to be detected containing the target object, and acquiring the standard chromaticity of the reference substance laboratory of the reference substance;
the reference object actual chromaticity acquisition module is used for acquiring the reference object actual chromaticity corresponding to the reference object laboratory standard chromaticity on a water quality detection site;
the conversion relation acquisition module is used for constructing a correction model under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory is greater than a set value, and acquiring the conversion relation between the actual chromaticity of the reference object and the corresponding standard chromaticity of the reference object laboratory;
the field chrominance component acquisition module is used for acquiring the field chrominance component of the object to be detected on the water quality detection field;
and the target concentration determining module is used for determining the target concentration corresponding to the field chromaticity component by using the conversion relation and the standard curve.
9. The water quality detecting apparatus according to claim 8, wherein an optical environment for obtaining the standard chromaticity of the reference material laboratory is the same as an optical environment for constructing the standard curve.
10. The water quality detecting device according to claim 8, wherein the conversion relation obtaining module determines a chromaticity deviation of the actual chromaticity of the reference object from a corresponding laboratory standard chromaticity of the reference object, and comprises:
judging whether the shooting conditions of the water quality detection site meet the detection requirements or not;
and under the condition that the detection requirement is met, determining the chromaticity deviation of the actual chromaticity of the reference substance and the corresponding laboratory standard chromaticity of the reference substance.
11. The water quality detecting apparatus according to claim 10, wherein the photographing condition includes at least: image pixel, illumination intensity and light uniformity of a water quality detection site.
12. The water quality detection device of claim 8, wherein the conversion relation obtaining module constructs the correction model by a neural network algorithm or a multivariate nonlinear fitting method.
13. The water quality detecting apparatus according to claim 8, wherein the target concentration determining module determines the target concentration corresponding to the field chromaticity component using the conversion relationship and the standard curve, and includes:
converting the field chrominance component into a standard chrominance component by using the conversion relation;
and searching the concentration corresponding to the standard chromaticity component on the standard curve, and determining the concentration of the target object.
14. The water quality detection device of claim 8, wherein the target concentration determination module is further configured to:
under the condition that the chromaticity deviation between the actual chromaticity of the reference object and the laboratory standard chromaticity of the reference object is less than or equal to a set value, acquiring the field chromaticity component of the object to be detected on the water quality detection field;
and determining the concentration of the target object corresponding to the field chromaticity component according to the standard curve.
15. A water quality detecting system comprising the water quality detecting apparatus according to any one of claims 8 to 14.
16. A computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the water quality detection method of any one of claims 1-7.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110696024.5A CN115508276A (en) | 2021-06-23 | 2021-06-23 | Water quality detection method, detection device and detection system |
| EP22827296.9A EP4343307A1 (en) | 2021-06-23 | 2022-05-25 | Water quality testing method and water quality testing apparatus |
| PCT/CN2022/094832 WO2022267799A1 (en) | 2021-06-23 | 2022-05-25 | Water quality testing method and water quality testing apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110696024.5A CN115508276A (en) | 2021-06-23 | 2021-06-23 | Water quality detection method, detection device and detection system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115508276A true CN115508276A (en) | 2022-12-23 |
Family
ID=84500009
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110696024.5A Pending CN115508276A (en) | 2021-06-23 | 2021-06-23 | Water quality detection method, detection device and detection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115508276A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115791641A (en) * | 2023-02-06 | 2023-03-14 | 小水怪（深圳）智能科技有限公司 | Method and system for detecting liquid components based on intelligent water cup |
-
2021
- 2021-06-23 CN CN202110696024.5A patent/CN115508276A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115791641A (en) * | 2023-02-06 | 2023-03-14 | 小水怪（深圳）智能科技有限公司 | Method and system for detecting liquid components based on intelligent water cup |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9506855B2 (en) | Method and system for analyzing a colorimetric assay | |
| JP6189286B2 (en) | Test equipment | |
| CN110222698B (en) | Method and system for water quality analysis based on color information processing | |
| RU2754208C1 (en) | Methods and systems for calibration and use of camera for determination of analyte in sample | |
| KR20200078548A (en) | Methods and devices for performing analytical measurements based on color formation reactions | |
| US11531032B2 (en) | Methods for measuring analyte and/or protein in biological samples | |
| CN108152283A (en) | It is a kind of to measure Cr VI, the device of copper content and its detection method in water using camera | |
| US20190317022A1 (en) | System and method of detecting contaminants | |
| CN113945556A (en) | Water quality fluoride detection method based on digital image colorimetric analysis | |
| CN112964652A (en) | Rapid detection device, system and detection method for solution colorimetric analysis | |
| CN115508341A (en) | Water quality detection method and system based on digital image processing | |
| WO2021228730A1 (en) | Method of evaluating the quality of a color reference card | |
| CN115508276A (en) | Water quality detection method, detection device and detection system | |
| Bao et al. | A remote computing based point-of-care colorimetric detection system with a smartphone under complex ambient light conditions | |
| Doǧan et al. | A field-deployable water quality monitoring with machine learning-based smartphone colorimetry | |
| Li et al. | Development of a versatile smartphone-based environmental analyzer (vSEA) and its application in on-site nutrient detection | |
| Wang et al. | Quantification of combined color and shade changes in colorimetry and image analysis: water pH measurement as an example | |
| US20220026275A1 (en) | Systems and methods for detecting disease using mobile device | |
| Sivakumar et al. | An automated lateral flow assay identification framework: Exploring the challenges of a wearable lateral flow assay in mobile application | |
| WO2022267799A1 (en) | Water quality testing method and water quality testing apparatus | |
| Hamedpour et al. | Chemometric challenges in development of paper-based analytical devices: Optimization and image processing | |
| Xing et al. | A cellphone-based colorimetric multi-channel sensor for water environmental monitoring | |
| Martinez-Hurtado et al. | Multiplex smartphone diagnostics | |
| CN117848963A (en) | Colorimetric quantification-based method and system for detecting component content of object to be detected | |
| CN116420067A (en) | Method for controlling automatic exposure setting of mobile device with camera |
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 |