CN120385654A - A method and device for constructing a turbidity testing system based on a photoelectric detector - Google Patents
A method and device for constructing a turbidity testing system based on a photoelectric detectorInfo
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Abstract
本发明属于水质监测技术领域，具体涉及一种基于光电探测器的浊度测试系统的构建方法及装置，其中，获取光学信号数据，多波长光信号数据；根据信号数据，进行智能切换与光谱匹配，确定目标波长，结合散射透射联合测量原理，得到浊度相关的特征参数；根据特征参数，测量不同标准液，得到对应特征参数，构建浊度特征参数数据集，根据数据集，建立特征比值浑浊校准模型；根据特征比值浑浊校准模型结合动态温度补偿算法，测量计算浑浊度，同时，进行温度补偿；将参数信息，传输至云端数据库储存，进行分层式处理，同时，定期智能自检，出现异常时，反馈异常数据。由此，解决了现有技术中量程覆盖不足、自动化校准能力薄弱以及易受温度漂移影响等问题。
The present invention belongs to the field of water quality monitoring technology, and specifically relates to a method and device for constructing a turbidity test system based on a photoelectric detector, wherein optical signal data and multi-wavelength optical signal data are obtained; based on the signal data, intelligent switching and spectral matching are performed to determine the target wavelength, and the characteristic parameters related to turbidity are obtained by combining the scattering and transmission joint measurement principle; based on the characteristic parameters, different standard liquids are measured to obtain corresponding characteristic parameters, and a turbidity characteristic parameter data set is constructed; based on the data set, a characteristic ratio turbidity calibration model is established; based on the characteristic ratio turbidity calibration model combined with a dynamic temperature compensation algorithm, turbidity is measured and calculated, and temperature compensation is performed at the same time; the parameter information is transmitted to a cloud database for storage and hierarchical processing, and at the same time, regular intelligent self-checking is performed, and when an abnormality occurs, abnormal data is fed back. Thus, the problems of insufficient range coverage, weak automatic calibration capability, and susceptibility to temperature drift in the prior art are solved.
Description
Technical Field
The invention belongs to the technical field of water quality monitoring, and particularly relates to a method and a device for constructing a turbidity test system based on a photoelectric detector.
Background
Along with the improvement of drinking water safety standards and the increase of industrial wastewater discharge monitoring demands, the market is urgently required to be provided with a turbidity test system with wide range, high precision and intellectualization, the current turbidity detection technology based on a photoelectric detector is mainly developed around a photoelectric conversion principle, turbidity quantification is realized by measuring the scattering/transmission intensity change of suspended particles in water to light rays, and an intelligent turbidity test system with full range coverage, strong particle adaptability and high automation degree is constructed so as to meet the accurate monitoring demands under complex water quality scenes.
However, the traditional equipment has the defects of insufficient range coverage and scattering model failure caused by particle characteristic influence, needs to calibrate different water qualities frequently, has weak automatic calibration capability, depends on preset standard liquid, has poor long-term stability, is easily interfered by optical window pollution and device drift, is difficult to adapt to complex water quality change along with the improvement of drinking water safety standards and the rapid increase of requirements of industry and environment monitoring on wide dynamic range high-precision measurement, and has remarkable influence on stability due to optical window pollution and device drift after long-term operation.
Disclosure of Invention
The application provides a method and a device for constructing a turbidity test system based on a photoelectric detector, which are used for solving the problems of insufficient range coverage, weak automatic calibration capability, easiness in temperature drift and the like in the prior art.
The embodiment of the application provides a method for constructing a turbidity test system based on a photoelectric detector, which comprises the following steps of obtaining optical signal data, multi-wavelength optical signal data, wherein the optical signal data comprise transmission light intensity data and scattering light intensity data, the multi-wavelength optical signal data comprise three-wavelength LED light sources, intelligent switching and spectrum matching are carried out according to the optical signal data and the multi-wavelength optical signal data, target wavelength is determined, characteristic parameters related to turbidity are obtained according to the target wavelength combined with a scattering transmission combined measurement principle, corresponding characteristic parameters are obtained by measuring standard liquids of different turbidity according to the characteristic parameters related to the turbidity, a turbidity characteristic parameter data set is constructed according to the corresponding characteristic parameters, a characteristic ratio turbidity calibration model is established according to the turbidity characteristic parameter data set, turbidity is calculated by measuring in real time according to the characteristic ratio turbidity calibration model combined with a dynamic temperature compensation algorithm, meanwhile, temperature compensation is carried out, cloud turbidity, temperature, water quality and light intensity parameter information are transmitted to a database for storage, layered data processing is carried out, and meanwhile, when abnormal self-detection results occur periodically and abnormal self-detection results are fed back.
Preferably, intelligent switching and spectrum matching are carried out according to the optical signal data and the multi-wavelength optical signal data, and target wavelength is selected, wherein the method comprises the steps of constructing a dynamic spectrum matching algorithm, analyzing spectral characteristics according to the dynamic spectrum matching algorithm to obtain spectral signal response data, detecting turbidity range, particle characteristics or water quality change of a medium according to the spectral signal response data, and dynamically selecting the target wavelength with highest measurement scene matching degree.
Preferably, the formula of the dynamic spectrum matching algorithm is:
wherein, C is a correlation coefficient, R is a reference spectrum, T is a dynamic spectrum; Points for spectral data; Is the i-th wavelength point; at the ith wavelength point for the reference spectrum A light intensity value at; at the i wavelength point for dynamic spectrum A light intensity value at; Is the mean value of the reference spectrum R; For dynamic spectrum I is an index.
Preferably, the characteristic parameters related to turbidity are obtained according to the target wavelength and combined scattering transmission measurement principle, wherein the characteristic parameters comprise a polarized light measurement method, sediment or dust particles with different shapes, sizes and materials are analyzed according to the polarized light measurement method to obtain the influence of the particles on the intensity and direction of polarized light scattering and transmission, the polarization state is changed according to the influence of the particles on the intensity and direction of polarized light scattering and transmission to obtain the changed polarization state, and the shape, orientation and surface roughness information of the particles are obtained by measuring the changed polarization state, wherein the polarization state data comprise the polarization degree and polarization angle parameters.
The method comprises the steps of obtaining standard liquid measurement data, establishing a characteristic ratio turbidity calibration model according to the standard liquid measurement data and characteristic ratio parameters related to turbidity, measuring samples to be measured with different turbidity gradients according to the characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, calculating an initial turbidity value according to the corresponding characteristic parameters, carrying out temperature compensation on the initial turbidity value based on the dynamic temperature compensation algorithm to obtain a final turbidity value.
Preferably, the formula of the characteristic ratio turbidity calibration model is:
Wherein, the Is a target turbidity value; is the characteristic ratio; is a mathematical mapping function; is the real-time temperature; ,,,,, Is a linear model coefficient; Is the square of the feature ratio; Is the square of the target turbidity value.
Preferably, the turbidity characteristic parameter data set is constructed according to the corresponding characteristic parameters, and the turbidity characteristic parameter data set comprises the steps of acquiring optical characteristic parameters and environment background parameters, capturing transient characteristic data of turbidity fluctuation through high-frequency sampling based on the optical characteristic parameters and the environment background parameters, and carrying out real-time updating according to the transient characteristic data and combining with a Bayes online learning algorithm to generate the characteristic parameter data set.
The embodiment of the second aspect of the application provides a device for constructing a turbidity test system based on a photoelectric detector, which comprises an acquisition module, a determination module and a construction module, wherein the acquisition module is used for acquiring optical signal data and multi-wavelength optical signal data, the optical signal data comprise transmission light intensity data and scattering light intensity data, the multi-wavelength optical signal data comprise three-wavelength LED light sources, the determination module is used for performing intelligent switching and spectrum matching according to the optical signal data and the multi-wavelength optical signal data, determining a target wavelength, acquiring characteristic parameters related to turbidity according to the target wavelength combined with a scattering transmission combined measurement principle, the construction module is used for measuring standard liquids with different turbidity according to the characteristic parameters related to turbidity to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set according to the corresponding characteristic parameters, and establishing a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set. And the measurement module is used for measuring and calculating the turbidity in real time according to the characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm, and performing temperature compensation. And the feedback module is used for transmitting the turbidity, temperature, water quality and light intensity parameter information to the cloud database for storage, performing layered data processing, and simultaneously, performing periodic intelligent self-checking, and feeding back abnormal data when the self-checking result is abnormal.
Therefore, the application has the following beneficial effects:
The three-wavelength LED light source is combined with a dynamic spectrum matching algorithm, target wavelength can be intelligently switched according to the turbidity range and particle characteristics of a medium, adaptability and measurement accuracy of complex water quality are improved, based on a scattering transmission combined measurement principle, a polarized light measurement technology is fused, light intensity data of traditional turbidity detection are obtained, multidimensional characteristics such as particle shape, size and material are analyzed, abundant parameter support is provided for water quality analysis, the characteristic ratio turbidity calibration model is combined with a dynamic temperature compensation algorithm, the influence of temperature fluctuation on measurement results is effectively eliminated, high-accuracy real-time turbidity calculation is realized, a cloud database storage and layering data processing mechanism is matched with a periodic intelligent self-checking function, an intelligent closed loop system integrating data acquisition, analysis, storage and abnormal feedback is constructed, the requirements of industrial field real-time monitoring are met, and a solution is provided for long-term trend analysis and equipment maintenance of water quality. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, easiness in influence of temperature drift and the like in the prior art are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for constructing a turbidity test system based on a photodetector according to an embodiment of the present application;
FIG. 2 is a diagram of an example of turbidity detection for wastewater treatment provided in accordance with one embodiment of the present application;
FIG. 3 is a diagram showing an example of detection of a potable water source according to an embodiment of the present application;
FIG. 4 is a diagram of an example of industrial wastewater detection provided in accordance with one embodiment of the present application;
FIG. 5 is a diagram of an example urban potable water monitoring network provided according to one embodiment of the present application;
FIG. 6 is a flow chart of a method of constructing a turbidity test system based on photodetectors, according to one embodiment of the present application;
FIG. 7 is a schematic structural diagram of a device for constructing a turbidity test system based on a photodetector according to an embodiment of the present application;
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a method and a device for constructing a turbidity test system based on a photoelectric detector according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the problem of weak automatic calibration capability in the background technology, the application provides a method for constructing a turbidity test system based on a photoelectric detector, in the method, target wavelengths can be intelligently switched according to the turbidity range of a medium and particle characteristics by combining a three-wavelength LED light source with a dynamic spectrum matching algorithm, adaptability and measurement precision of complex water quality (such as high turbidity and multi-particle type scenes) are improved, based on a combined scattering and transmission measurement principle, the light intensity data of the traditional turbidity test are obtained by combining a polarized light measurement technology, multidimensional characteristics such as particle shape, size and material quality are analyzed, abundant parameter support is provided for water quality analysis, the characteristic ratio turbidity calibration model is combined with a dynamic temperature compensation algorithm, the influence of temperature fluctuation on a measurement result is effectively eliminated, high-precision real-time turbidity calculation is realized, a cloud database storage and layering type data processing mechanism is matched with a periodic intelligent self-checking function, an intelligent closed-loop system integrating data acquisition, analysis, storage and abnormal feedback is constructed, the real-time monitoring requirements of an industrial site are met, and a solution is provided for long-term water quality trend analysis and equipment maintenance. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, easiness in influence of temperature drift and the like in the prior art are solved.
Specifically, fig. 1 is a schematic flow chart of a method for constructing a turbidity test system based on a photoelectric detector according to an embodiment of the present application.
As shown in fig. 1, the method for constructing the turbidity test system based on the photoelectric detector comprises the following steps:
In step S101, optical signal data including transmitted light intensity data and scattered light intensity data, and multi-wavelength optical signal data including a three-wavelength LED light source are acquired.
The three-wavelength LED light source is a light source device integrating three different wavelength light emitting diodes, and can emit multi-wavelength light signals to support the system to dynamically match target wavelengths according to the characteristics of the measured medium.
It can be understood that the embodiment of the application can be used for transmitting multi-wavelength optical signals by integrating three light emitting diodes with different wavelengths, intelligently selecting the target wavelength for measurement, accurately acquiring the transmitted light intensity data and the scattered light intensity data and improving the detection precision of the optical characteristics of different media.
For example, as shown in fig. 2, in a wastewater treatment monitoring scene, spectral response data is analyzed through a dynamic spectrum matching algorithm, 850nm near infrared light with strong penetrability is automatically selected as a target wavelength, interference of particle scattering on light signals is reduced, transmission and scattering light intensity data are accurately obtained, and when low-turbidity effluent (containing colloid and microbial flocs) of a secondary sedimentation tank is monitored, the secondary sedimentation tank is switched to 450nm blue light with higher sensitivity, and scattering difference of fine particles on short-wavelength light is captured. By intelligent switching of the three-wavelength light source, the measurement error in the wide turbidity range of 0.1 NTU-1000 NTU is reduced by 40% compared with the traditional single-wavelength scheme, and the adaptability and the accuracy of turbidity detection under different water quality conditions are improved.
In step S102, intelligent switching and spectrum matching are performed according to the optical signal data and the multi-wavelength optical signal data, a target wavelength is determined, and a characteristic parameter related to turbidity is obtained according to the target wavelength combined with the scattering transmission combined measurement principle.
The scattering transmission combined measurement principle refers to a measurement method for calculating turbidity by synchronously collecting transmission light intensity and scattering light intensity signals of incident light by a medium and comprehensively analyzing parameters of particle concentration, size and distribution state by combining light intensity distribution characteristics of the transmission light intensity and the scattering light intensity signals and action rules of particles on the light.
It can be understood that by synchronously collecting the transmitted light intensity and the scattered light intensity signals and utilizing the light intensity distribution characteristics and the action rule of particles on light, the embodiment of the application comprehensively and accurately analyzes parameters such as concentration, size, distribution state and the like of the particles comprehensively and comprehensively by combining the intelligent switching and spectrum matching to determine the target wavelength, so as to obtain the characteristic parameters related to turbidity.
It should be noted that, the principle formula of the combined measurement of scattering and transmission is as follows:
Wherein, the Is the intensity of incident light; for transmitted light intensity; is the turbidity coefficient; is the optical path length; is the intensity of scattered light; is a correction factor; Is the molecular weight or volume of the particles; Is the concentration of particles; is the wavelength of incident light; Distance from the detection point to the light source; Is a scattering angle; Is a comprehensive turbidity parameter; Absorbance for transmitted light; is the intensity of scattered light; Is a weight coefficient; is the base of natural logarithms.
For example, taking a high-precision water quality monitor as an example, the water quality monitor emits green laser with the wavelength of 550nm to a water body, and the specific wavelength has good penetrability and scattering sensitivity in water. When the laser enters the water body, a part of light is scattered and deviates from the original propagation direction due to suspended particles in the water, such as silt particles with the average particle diameter of 5 mu m and microorganisms with the diameters of 0.5-10 mu m. In this body of water, when the concentration of silt particles increases from 10mg/L to 50mg/L, the intensity of the scattered light increases from 0.05. Mu.W/cm 2 to 0.2. Mu.W/cm 2 at a scattering angle of 90. At the same time, another part of light penetrates the water body, but is attenuated due to absorption and scattering of particles, and if the optical path length is 10cm, when the turbidity of the water body is increased from 5NTU to 20NTU, the transmitted light intensity is reduced from 80% to 40% of the initial light intensity. The instrument can accurately detect the transmitted light intensity and the scattered light intensity respectively through the high-sensitivity photoelectric sensor, and then the light intensity change data are combined with the scattering transmission combined measurement principle, so that the water turbidity value can be rapidly and accurately calculated, and the error range is controlled within +/-2%. And the characteristics of particle shape, surface roughness and the like can be deeply analyzed by means of the polarization characteristic of scattered light, so that the polarization degree change of the scattered light is more remarkable, high-precision data is provided for the comprehensive evaluation of water quality, and the water quality monitoring capability is improved.
According to the embodiment of the application, intelligent switching and spectrum matching are performed according to optical signal data and multi-wavelength optical signal data, and target wavelength is selected, wherein the method comprises the steps of constructing a dynamic spectrum matching algorithm, analyzing spectrum characteristics according to the dynamic spectrum matching algorithm to obtain spectrum signal response data, detecting turbidity range, particle characteristics or water quality change of a medium according to the spectrum signal response data, and dynamically selecting the target wavelength with highest measurement scene matching degree.
The spectral signal response data refers to response output data of the sensor or the detector to incident light with different wavelengths, and is used for reflecting optical characteristics of the measured medium at each wavelength.
It can be understood that the embodiment of the application accurately captures the optical characteristic differences of the measured medium such as transmission, scattering and the like under different wavelengths through real-time response output of the sensor to the multi-wavelength incident light, deeply analyzes the turbidity range, the particle characteristics and the water quality change rule of the medium, and dynamically screens the target wavelength with the highest matching degree with the current measurement scene from the three-wavelength LED light source.
The spectrum signal response data are obtained by analyzing the spectrum characteristics according to a dynamic spectrum matching algorithm, the spectrum data of a substance to be detected are collected in real time and are dynamically compared with a standard spectrum library, the difference of spectrum peak position, bandwidth and absorbance characteristic parameters is analyzed by utilizing an Euclidean distance metric matching algorithm, the most similar reference spectrum is screened out according to a matching degree threshold, and the response data of the spectrum signal are output.
Euclidean distance metric matching algorithm formula:
Wherein, the Is the i-th observed value after standardization; An original observation value of the ith dimension; Is the mean value of the ith dimension; Is the standard deviation in the ith dimension; is the normalized euclidean distance between two samples; the number of data dimensions; an original observation in the ith dimension for another sample; For weighted euclidean distance; Is the weight coefficient of the i-th dimension.
For example, as shown in fig. 3, in a monitoring scene of a drinking water source, after a three-wavelength LED light source emits multi-wavelength signals, a sensor acquires spectral signal response data under different wavelengths in real time, when high-turbidity silt water carried by surface runoff in a rainy season is detected, transmission light intensity response data of 940nm near infrared light shows smaller attenuation amplitude, and scattered light intensity response of 470nm blue light is enhanced, the data is analyzed through a dynamic spectrum matching algorithm, the current medium is judged to be mainly large-particle silt, the turbidity range is 200-500NTU, 940nm near infrared light with strong penetrating power is selected as a target wavelength to reduce particle scattering interference, when low-turbidity colloid in a water body in a low-temperature period in winter is monitored, 470nm blue light scattering light intensity response data is more sensitive to fine particles, the scattering characteristics of the colloid particles are accurately captured, and turbidity measurement errors under the water source water quality mutation scene are reduced from +/-8% to +/-3.5% of a traditional fixed wavelength scheme through real-time analysis of the spectral signal response data.
In the embodiment of the application, the formula of the dynamic spectrum matching algorithm is as follows:
wherein, C is a correlation coefficient, R is a reference spectrum, T is a dynamic spectrum; Points for spectral data; Is the i-th wavelength point; at the ith wavelength point for the reference spectrum A light intensity value at; At the ith wavelength point for dynamic spectrum A light intensity value at; Is the mean value of the reference spectrum R; For dynamic spectrum I is an index.
It can be understood that the embodiment of the application acquires the accurate spectral signal response data by analyzing the spectral characteristics in real time, can rapidly detect the turbidity range, the particle characteristics and the water quality change of the medium, automatically screens the target wavelength with the highest matching degree according to the difference of the spectral data in different scenes, and realizes the self-adaptive adjustment of the measurement wavelength. The sensitivity and the accuracy of detection are improved, the turbidity measurement error is controlled, the environmental interference is avoided, the detection efficiency is improved, and the manual intervention cost is reduced.
For example, in the water quality monitoring project of a certain urban water area, a dynamic spectrum matching algorithm is used to improve the monitoring efficiency and accuracy. The unmanned aerial vehicle is used for carrying a hyperspectral imager, so that spectrum data of different water areas are collected in real time, and the spectrum range of the hyperspectral imager covers 400-900nm. The algorithm compares the collected spectrum with a spectrum library containing various pollution characteristics, and analyzes the spectrum characteristics through the Euclidean distance measurement matching algorithm. When detecting whether algae pollution exists in the water body, aiming at a unique chlorophyll absorption peak of algae near 680nm, an algorithm accurately identifies corresponding characteristics in a spectrum, spectrum signal response data is rapidly obtained, and the variety and approximate concentration range of the algae are defined. Meanwhile, based on spectral signal response data, the turbidity range and particle characteristics of the medium can be detected, the water quality change can be monitored in all directions, and a powerful support is provided for environmental monitoring decisions.
According to the method, characteristic parameters related to turbidity are obtained according to a target wavelength and combined with a scattering transmission combined measurement principle, the method comprises the steps of obtaining a polarized light measurement method, analyzing sediment or dust particles of different shapes, sizes and materials according to the polarized light measurement method to obtain the influence of the particles on the intensity and direction generated by scattering and transmission of polarized light, changing the polarization state according to the influence of the particles on the intensity and direction generated by scattering and transmission of the polarized light to obtain the changed polarization state, and obtaining the shape, orientation and surface roughness information of the particles by measuring the changed polarization state, wherein the polarization state data comprise the polarization degree and polarization angle parameters.
The polarized light measurement method is a method for acquiring optical characteristics or physical parameters of a measured object by detecting a change in polarization state of light.
It can be understood that by detecting the change of the polarization state, the embodiment of the application analyzes the influence of particles on polarized light scattering and transmission, obtains the characteristics of particle shape, orientation and the like, provides microscopic information of particles for turbidity measurement, reduces deviation caused by the particle characteristics, and improves the recognition precision of the multi-particle mixed water body.
For example, as shown in FIG. 4, in an industrial wastewater monitoring scene, 525nm polarized green light is used as a target wavelength, particle characteristics in wastewater are detected by a polarized light measurement method, wherein when spherical silica particles exist in a water sample, the polarization degree of scattered light is reduced by 15% compared with that of incident light, the polarization angle is basically unchanged, when flaky clay particles are detected, the polarization degree of the scattered light is suddenly reduced by 30%, the polarization angle deflects by 25 degrees, spherical and non-spherical particles are accurately distinguished by analyzing the polarization state changes, and the turbidity measurement error of a water body containing mixed particles is reduced to +/-5% from +/-10% of the traditional light intensity measurement by combining scattered transmitted light intensity data.
In step S103, standard liquids with different turbidity values are measured according to the characteristic parameters related to the turbidity values, so as to obtain corresponding characteristic parameters, a turbidity characteristic parameter data set is constructed according to the corresponding characteristic parameters, and a characteristic ratio turbidity calibration model is established according to the turbidity characteristic parameter data set.
The turbidity characteristic parameter data set is a multidimensional data set for describing characteristics of different turbidity samples, comprises optical parameters, particle characteristic parameters and auxiliary environment parameters, and provides data for turbidity measurement model construction, algorithm optimization and mechanism analysis.
It can be understood that the embodiment of the application provides training and calibration data for the turbidity measurement model by collecting the multidimensional characteristic parameters (including optical characteristics, physical and chemical properties of particles and environmental parameters) of different turbidity standard liquids, solves the problem that parameter measurement is interfered by particle characteristics (particle size, color, surface charge and the like), and improves the adaptability of the model to complex water quality scenes.
In the embodiment of the application, a turbidity characteristic parameter data set is constructed according to corresponding characteristic parameters, and the turbidity characteristic parameter data set comprises the steps of acquiring optical characteristic parameters and environment background parameters, capturing transient characteristic data of turbidity fluctuation through high-frequency sampling based on the optical characteristic parameters and the environment background parameters, and carrying out real-time updating according to the transient characteristic data and combining with a Bayesian online learning algorithm to generate the characteristic parameter data set.
The optical characteristic parameters are quantifiable indexes generated by the interaction of light and substances, and comprise scattered/transmitted light intensity, absorbance, spectral wavelength, polarization state and the like, and are used for representing the optical behavior characteristics of the substances on the scattering, absorption, transmission and the like of the light.
It can be understood that the embodiment of the application comprehensively characterizes the optical behavior characteristics of the water sample by collecting the quantitative indexes of interaction of multidimensional light such as scattered/transmitted light intensity, absorbance, spectral wavelength, polarization state and the like and substances, provides basic data for a turbidity characteristic parameter data set, combines high-frequency sampling and Bayesian algorithm real-time updating, accurately captures water quality dynamics, and improves turbidity measurement precision and environmental adaptability.
It should be noted that, the formula of the bayesian online learning algorithm is as follows:
Wherein, the The mean value and the variance of the turbidity characteristic parameters are obtained; Is a transient turbidity feature; Is a priori probability; Is a likelihood function; is an evidence item; Is posterior probability.
In the embodiment of the application, the formula of the characteristic ratio turbidity calibration model is as follows:
Wherein, the Is a target turbidity value; is the characteristic ratio; is a mathematical mapping function; is the real-time temperature; ,,,,, Is a linear model coefficient; Is the square of the feature ratio; Is the square of the target turbidity value.
It can be understood that the embodiment of the application establishes quantitative association between complex spectral characteristic parameters and turbidity by measuring characteristic parameters and constructing a data set of different turbidity standard liquids, effectively eliminates the influence of environmental interference and medium difference factors on measurement results, improves the stability and accuracy of turbidity detection, can control the measurement errors in a minimum range, simplifies the data processing flow by calculating characteristic ratio, realizes rapid and automatic turbidity analysis, improves the detection efficiency, is suitable for water quality monitoring of different scenes, provides reliable turbidity data support for the fields of environmental evaluation, industrial production and the like, and has the advantages of accurate power-assisted decision and quality control.
For example, in a lake water quality monitoring project, workers use a characteristic ratio turbidity calibration model to develop and analyze water samples at different points. The method comprises the steps of firstly collecting lake water samples with different turbidity ranging from relatively clear (turbidity about 5 NTU) to moderate turbidity (turbidity up to 50 NTU), and obtaining spectral characteristic parameters of each sample in a 400-900nm wave band, such as absorbance at 680nm chlorophyll absorption peak and 750nm suspended matter sensitive wave band, by utilizing a spectrometer. The turbidity characteristic parameter data set is constructed by calculating the characteristic ratio of absorbance in a specific wave band, such as 680nm to 550nm absorbance ratio. Based on the data set, a feature ratio turbidity calibration model is established. When the method is actually applied, the spectral characteristic parameters of the newly collected water sample are substituted into the model, and the characteristic ratio is calculated and compared with the model, so that the turbidity of the lake water can be rapidly and accurately determined, the water quality condition is judged, and the water quality change of the lake is mastered in time.
In step S104, the turbidity is measured and calculated in real time according to the feature ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, temperature compensation is performed.
The dynamic temperature compensation algorithm is an adaptive algorithm for automatically correcting parameter deviation of a sensor or a measuring system caused by temperature drift and eliminating the influence of temperature on a measuring result by monitoring the change of the ambient temperature in real time.
It can be understood that by collecting temperature data in real time, the embodiment of the application dynamically adjusts the measured calibration parameters, eliminates systematic interference of temperature on turbidity measurement, measures adaptability in a wide temperature range scene of-20 ℃ to 60 ℃, avoids measurement deviation caused by temperature fluctuation, and ensures stability of a characteristic ratio turbidity calibration model.
It should be noted that, the formula of the dynamic temperature compensation algorithm:
Wherein, the For the compensated turbidity value; Is the measured turbidity value; Is the difference between the current temperature and the reference temperature; Is the temperature coefficient; is the temperature offset.
According to the embodiment of the application, turbidity is measured and calculated in real time according to a characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm, and meanwhile, temperature compensation is carried out, and the method comprises the steps of obtaining standard liquid measurement data, establishing a characteristic ratio turbidity calibration model according to the standard liquid measurement data and characteristic ratio parameters related to turbidity, measuring samples to be measured with different turbidity gradients according to the characteristic ratio turbidity calibration model and the dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, calculating an initial turbidity value according to the corresponding characteristic parameters, and carrying out temperature compensation on the initial turbidity value based on the dynamic temperature compensation algorithm to obtain a final turbidity value.
The standard solution measurement data is a result obtained by measuring standard solution with known accurate concentration or characteristic parameters (such as turbidity and absorbance), and is used for calibrating detection equipment and verifying the accuracy and precision of a measurement method.
It can be understood that by measuring the standard solution with known accurate turbidity, absorbance and other parameters, the embodiment of the application provides high-precision reference data for the characteristic ratio turbidity calibration model, ensures that the model can accurately establish the mapping relation between the multi-optical characteristic parameter and the real turbidity, reduces the initial calibration error of the measurement system in the whole range to less than +/-1%, controls the additional error caused by temperature drift in the temperature range of-20 ℃ to 60 ℃ to be within +/-0.5%, and improves the accuracy of turbidity measurement in a complex environment.
In step S105, the turbidity, temperature, water quality and light intensity parameter information is transmitted to the cloud database for storage, and layered data processing is performed, and meanwhile, periodic intelligent self-checking is performed, and when the self-checking result is abnormal, abnormal data is fed back.
The hierarchical data processing is a technical method for dividing a data processing flow into a plurality of layers with clear functions and realizing gradual extraction of the value from the original data to the information through the hierarchical cooperation of the layers.
It can be understood that the embodiment of the application processes turbidity and temperature parameters according to the steps of collection, cleaning, analysis and the like through hierarchical division, wherein the bottom layer collects and reduces noise, the middle layer cleans data and constructs characteristics, the top layer digs data association, improves processing efficiency, ensures accurate data, provides data for long-term analysis, and combines intelligent self-detection to quickly locate anomalies such as sensor drift and the like and feed back.
For example, as shown in FIG. 5, in the urban drinking water monitoring network, the layered data processing technology effectively improves the utilization value of mass water quality data, wherein a bottom data acquisition layer captures original signals such as turbidity (0.1-100 NTU), water temperature (0-40 ℃) and light intensity in real time through sensors distributed in various water source areas, high-frequency electromagnetic interference is removed through hardware filtering, a middle layer cleaning conversion layer performs standardized processing on the data, automatically identifies and repairs abnormal values (such as invalid data of a certain node burst temperature-5 ℃) caused by short-time faults of the sensors, a data set containing characteristics such as particle scattering coefficients and transmission attenuation ratios is constructed, a top layer analysis modeling layer utilizes a random forest algorithm to excavate data association, finds that the correlation between turbidity mutation in summer and biofilm shedding of the inner wall of a pipe network reaches 82%, and predicts the maintenance period of the pipe network through a long-term trend model. The effective utilization rate of data is improved from 65% to 92% by combining an intelligent self-checking mechanism of the system per hour.
According to the method for constructing the turbidity test system based on the photoelectric detector, the three-wavelength LED light source is combined with the dynamic spectrum matching algorithm, the target wavelength can be intelligently switched according to the turbidity range of a medium and the particle characteristics, the adaptability and the measurement precision of complex water quality (such as high turbidity and multi-particle type scenes) are improved, the light intensity data of traditional turbidity detection are obtained by combining the polarized light measurement technology based on the scattering transmission combined measurement principle, the multidimensional characteristics of particle shape, size, material and the like are analyzed, abundant parameter support is provided for water quality analysis, the characteristic ratio turbidity calibration model is combined with the dynamic temperature compensation algorithm, the influence of temperature fluctuation on a measurement result is effectively eliminated, high-precision real-time turbidity calculation is realized, the cloud database storage and layering type data processing mechanism is matched with the periodic intelligent self-detection function, the intelligent closed-loop system integrating data acquisition, analysis, storage and abnormal feedback is constructed, the real-time monitoring requirement of an industrial field is met, and a solution is provided for long-term trend analysis of water quality and equipment maintenance. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, easiness in influence of temperature drift and the like in the prior art are solved.
The method of constructing a turbidity test system based on a photodetector will be described by one specific embodiment, as shown in fig. 6, including:
step one, constructing system hardware.
A turbidity test system hardware platform based on a photoelectric detector is built, three LED light sources with different wavelengths (the wavelengths are respectively 450nm, 520nm and 660 nm) are adopted as multi-wavelength light signal emission sources, the LED light sources are uniformly distributed around a test cavity, and the light signals can be ensured to fully cover tested liquid. The opposite side of the test cavity is provided with a high-precision photoelectric detector for acquiring transmission light intensity data, and the opposite side of the test cavity is provided with another group of photoelectric detectors for acquiring scattering light intensity data in the direction of 90 degrees with respect to the light source. Meanwhile, a temperature sensor is integrated in the test system, and the change of the ambient temperature is monitored in real time so as to carry out temperature compensation operation subsequently.
And step two, optical signal and wavelength processing.
After the system is started, the LED light source emits light signals to the tested liquid in the testing cavity, the photoelectric detector acquires transmission light intensity data and scattering light intensity data in real time, and meanwhile, light signal data of the three-wavelength LED light source are acquired. And constructing a dynamic spectrum matching algorithm, and processing the acquired spectrum data. The reference spectrum R is assumed to be the spectrum data of a known standard water sample under different wavelengths, and the dynamic spectrum T is the measured liquid spectrum data acquired in real time. According to the formula of dynamic spectrum matching algorithmAnd calculating a correlation coefficient C of the reference spectrum and the dynamic spectrum. And judging the spectral characteristics by analyzing the magnitude of the C value to obtain spectral signal response data. If the correlation coefficient C is maximum at the wavelength of 450nm, the turbidity range, the particle characteristics or the water quality change of the current medium are detected to be more suitable for measurement by using the wavelength of 450nm, and the 450nm is dynamically selected as the target wavelength.
And step three, obtaining characteristic parameters.
The polarized light measuring method is adopted, and a polaroid is used for modulating light with target wavelength to enable the light to become polarized light and then to be injected into the tested liquid in the testing cavity. And analyzing the influence of silt or dust particles with different shapes, sizes and materials on the intensity and direction generated by polarized light scattering and transmission, wherein the polarization state of polarized light is changed due to the effect of the particles. And obtaining polarization state data such as the degree of polarization, the polarization angle and the like by measuring the changed polarization state, further obtaining particle shape, orientation and surface roughness information, and taking the information as characteristic parameters related to turbidity.
And step four, calibrating the model and calculating the turbidity.
Standard liquids with different turbidity degrees (such as standard liquids with turbidity degrees of 10NTU, 50NTU, 100NTU, 200NTU and 500 NTU) are prepared, and the built test system is used for measuring each standard liquid to obtain optical characteristic parameters (transmission light intensity, scattering light intensity, polarization state data and the like) and environmental background parameters (temperature, air pressure and the like). Transient characteristic data of turbidity fluctuations are captured by high frequency sampling (10 samples per second) based on the optical characteristic parameters and the environmental background parameters. And according to the transient characteristic data, carrying out real-time updating by combining with a Bayes online learning algorithm to generate a turbidity characteristic parameter data set. According to the turbidity characteristic parameter data set, establishing a characteristic ratio turbidity calibration modelAnd measuring samples to be measured with different turbidity gradients by combining a dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, and substituting the characteristic ratio turbidity calibration model to calculate an initial turbidity value. And carrying out temperature compensation on the initial turbidity value according to a dynamic temperature compensation algorithm to obtain a final turbidity value. For example, if the ambient temperature at the time of measurement is 25 ℃, the initial turbidity value is corrected according to the temperature compensation algorithm, and an accurate final turbidity value is obtained.
5. Data transmission and self-checking.
And transmitting the measured turbidity, temperature, water quality (preliminarily judged by characteristic parameters) and light intensity parameter information to a cloud database for storage through a wireless network (such as a 4G/5G module). And carrying out hierarchical processing on the data at the cloud, such as dividing the data into an original data layer, a preprocessed data layer and an analysis data layer. Meanwhile, the system performs intelligent self-checking at regular intervals (once per hour) to check whether hardware parameters such as sensitivity of the photoelectric detector and luminous intensity of the LED light source are normal or not. When the self-checking result is abnormal, the abnormal data is fed back to the system management terminal, and a worker is prompted to perform equipment maintenance or fault checking.
In summary, the invention ensures the comprehensive collection of the optical signals by the layout of the multi-wavelength LED light source and the multi-azimuth photoelectric detector on hardware, and utilizes advanced algorithms such as dynamic spectrum matching, polarized light measurement and the like to accurately acquire characteristic parameters on software, and combines Bayesian learning and dynamic temperature compensation to improve the accuracy and measurement stability of a calibration model. On data processing, cloud layered storage and intelligent self-checking are realized, so that the safety and effectiveness of data are guaranteed, and faults can be fed back in time. The turbidity measuring precision, reliability and intelligent degree are remarkably improved, the turbidity measuring device is suitable for complex water quality environments, the real-time and accurate turbidity monitoring requirements under different scenes are effectively met, and the turbidity measuring device has high practical value and popularization significance.
Next, a construction device of a turbidity test system based on a photoelectric detector according to an embodiment of the present application will be described with reference to the accompanying drawings.
FIG. 7 is a block schematic diagram of a construction apparatus of a turbidity test system based on a photodetector according to an embodiment of the present application.
As shown in fig. 7, the construction apparatus 10 of the turbidity test system based on the photo detector includes an acquisition module 100, a determination module 200, a construction module 300, a measurement module 400, and a feedback module 500.
The system comprises an acquisition module 100, a determination module 200, a construction module 300, a measurement module 400, a feedback module 500 and a feedback module, wherein the acquisition module 100 is used for acquiring optical signal data and multi-wavelength optical signal data, wherein the optical signal data comprises transmission light intensity data and scattering light intensity data, the multi-wavelength optical signal data comprises a three-wavelength LED light source, the determination module 200 is used for performing intelligent switching and spectrum matching according to the optical signal data and the multi-wavelength optical signal data, determining a target wavelength, obtaining characteristic parameters related to turbidity according to the target wavelength and a scattering transmission combined measurement principle, the construction module 300 is used for measuring standard liquids with different turbidity according to the characteristic parameters related to turbidity to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set according to the corresponding characteristic parameters, establishing a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set, calculating turbidity according to the characteristic ratio turbidity calibration model, measuring in real time, meanwhile performing temperature compensation, the feedback module 500 is used for transmitting turbidity, temperature, water quality and light intensity parameter information to a cloud database to store, performing layered intelligent self-test, and feeding back abnormal data when the self-test result is abnormal.
It should be noted that the foregoing explanation of the embodiment of the method for constructing the turbidity test system based on the photodetector is also applicable to the apparatus for constructing the turbidity test system based on the photodetector of this embodiment, and will not be repeated here.
According to the construction device of the turbidity test system based on the photoelectric detector, the three-wavelength LED light source is combined with the dynamic spectrum matching algorithm, the target wavelength can be intelligently switched according to the turbidity range of a medium and the particle characteristics, the adaptability and the measurement precision of complex water quality (such as high turbidity and multi-particle type scenes) are improved, the light intensity data of traditional turbidity detection are obtained by combining the polarized light measurement technology based on the scattering transmission combined measurement principle, the multidimensional characteristics of particle shape, size, material and the like are analyzed, rich parameter support is provided for water quality analysis, the characteristic ratio turbidity calibration model is combined with the dynamic temperature compensation algorithm, the influence of temperature fluctuation on a measurement result is effectively eliminated, high-precision real-time turbidity calculation is realized, the cloud database storage and layering type data processing mechanism is matched with the periodic intelligent self-detection function, the intelligent closed-loop system integrating data acquisition, analysis, storage and abnormal feedback is constructed, the real-time monitoring requirement of an industrial field is met, and a solution is provided for long-term trend analysis of water quality and equipment maintenance. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, easiness in influence of temperature drift and the like in the prior art are solved.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (8)
1. The method for constructing the turbidity test system based on the photoelectric detector is characterized by comprising the following steps of:
Acquiring optical signal data, wherein the optical signal data comprises transmission light intensity data and scattering light intensity data, and the multi-wavelength optical signal data comprises a three-wavelength LED light source;
according to the optical signal data and the multi-wavelength optical signal data, intelligent switching and spectrum matching are carried out, a target wavelength is determined, and according to the target wavelength combined with a scattering transmission combined measurement principle, characteristic parameters related to turbidity are obtained;
Measuring standard liquids with different turbidity according to the characteristic parameters related to the turbidity to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set according to the corresponding characteristic parameters, and constructing a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set;
According to the characteristic ratio turbidity calibration model, a dynamic temperature compensation algorithm is combined, the turbidity is measured and calculated in real time, and meanwhile, temperature compensation is carried out;
and transmitting the turbidity, temperature, water quality and light intensity parameter information to a cloud database for storage, performing layered data processing, and simultaneously, performing periodic intelligent self-checking, and feeding back abnormal data when the self-checking result is abnormal.
2. The method for constructing a turbidity test system based on a photodetector according to claim 1, wherein performing intelligent switching and spectrum matching according to the optical signal data and the multi-wavelength optical signal data, selecting a target wavelength comprises:
constructing a dynamic spectrum matching algorithm;
according to the dynamic spectrum matching algorithm, analyzing spectrum characteristics to obtain spectrum signal response data;
And detecting turbidity range, particle characteristics or water quality change of the medium according to the spectral signal response data, and dynamically selecting a target wavelength with highest measurement scene matching degree.
3. The method for constructing a turbidity test system based on a photoelectric detector according to claim 2, wherein the formula of the dynamic spectrum matching algorithm is:
;
wherein, C is a correlation coefficient, R is a reference spectrum, T is a dynamic spectrum; Points for spectral data; Is the i-th wavelength point; at the ith wavelength point for the reference spectrum A light intensity value at; at the i wavelength point for dynamic spectrum A light intensity value at; Is the mean value of the reference spectrum R; For dynamic spectrum I is an index.
4. The method for constructing a turbidity test system based on a photodetector according to claim 1, wherein obtaining the characteristic parameter related to turbidity according to the target wavelength combined with the scattering transmission combined measurement principle comprises:
Obtaining a polarized light measurement method;
According to the polarized light measuring method, analyzing sediment or dust particles with different shapes, sizes and materials to obtain the influence of the particles on the intensity and direction generated by polarized light scattering and transmission;
according to the influence of the particles on the intensity and direction generated by polarized light scattering and transmission, changing the polarization state to obtain the changed polarization state;
and obtaining particle shape, orientation and surface roughness information by measuring the changed polarization state, wherein the polarization state data comprises polarization degree and polarization angle parameters.
5. The method for constructing a turbidity test system based on a photoelectric detector according to claim 1, wherein the calculating of the turbidity by real-time measurement according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and the temperature compensation comprises:
Obtaining standard liquid measurement data:
Establishing a characteristic ratio turbidity calibration model according to the standard liquid measurement data and the characteristic ratio parameters related to turbidity;
measuring samples to be measured with different turbidity gradients according to the characteristic ratio turbidity calibration model and combining a dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, calculating an initial turbidity value according to the corresponding characteristic parameters,
And carrying out temperature compensation on the initial turbidity value based on the dynamic temperature compensation algorithm to obtain a final turbidity value.
6. The method for constructing a turbidity test system based on a photodetector according to claim 4, wherein the characteristic ratio turbidity calibration model is formulated as:
;
Wherein, the Is a target turbidity value; is the characteristic ratio; is a mathematical mapping function; is the real-time temperature; ,,,,, Is a linear model coefficient; Is the square of the feature ratio; Is the square of the target turbidity value.
7. The method for constructing a turbidity test system based on a photodetector according to claim 1, wherein constructing a turbidity characteristic parameter data set according to the corresponding characteristic parameter, comprises:
acquiring optical characteristic parameters and environmental background parameters;
capturing transient characteristic data of turbidity fluctuation through high-frequency sampling based on the optical characteristic parameters and the environmental background parameters;
and according to the transient characteristic data, combining with a Bayes online learning algorithm, updating in real time to generate a characteristic parameter data set.
8. A device for constructing a turbidity test system based on a photodetector, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring optical signal data and multi-wavelength optical signal data, the optical signal data comprise transmission light intensity data and scattering light intensity data, and the multi-wavelength optical signal data comprise three-wavelength LED light sources;
the determining module is used for performing intelligent switching and spectrum matching according to the optical signal data and the multi-wavelength optical signal data, determining a target wavelength and obtaining a characteristic parameter related to turbidity according to the combination of the target wavelength and a scattering transmission combined measurement principle;
The construction module is used for measuring standard liquids with different turbidity according to the characteristic parameters related to the turbidity to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set according to the corresponding characteristic parameters, and constructing a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set;
the measuring module is used for measuring and calculating turbidity in real time according to the characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm, and meanwhile, temperature compensation is carried out;
And the feedback module is used for transmitting the turbidity, temperature, water quality and light intensity parameter information to the cloud database for storage, performing layered data processing, and simultaneously, performing periodic intelligent self-checking, and feeding back abnormal data when the self-checking result is abnormal.
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