Construction method and device of turbidity test system based on photoelectric detector

Abstract

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity test system based on a photodetector. Background Art

[0002] With the improvement of drinking water safety standards and the increasing demand for industrial wastewater discharge monitoring, the market urgently needs a turbidity test system with a wide range, high precision, and intelligence. Currently, the turbidity detection technology based on photodetectors mainly focuses on the principle of photoelectric conversion. Turbidity quantification is achieved by measuring the change in the scattering / transmission intensity of light by suspended particles in water. An intelligent turbidity test system with full-range coverage, strong particle adaptability, and high automation is constructed to meet the accurate monitoring requirements in complex water quality scenarios.

[0003] However, traditional devices generally have insufficient range coverage, and the scattering model fails due to the influence of particle characteristics, requiring frequent calibration for different water qualities. At the same time, the automatic calibration ability is weak, relying on preset standard solutions and having poor long-term stability. It is easily interfered by optical window contamination and device drift. With the improvement of drinking water safety standards and the surge in the demand for wide dynamic range and high-precision measurements in industrial and environmental monitoring, it is difficult to adapt to complex water quality changes, and the optical window contamination and device drift significantly affect the stability after long-term operation. Summary of the Invention

[0004] This application provides a construction method and device of a turbidity test system based on a photodetector to solve the problems of insufficient range coverage, weak automatic calibration ability, and susceptibility to temperature drift in the prior art.

[0005] The first aspect of this application provides a construction method of a turbidity test system based on a photodetector, including the following steps: obtaining optical signal data and multi-wavelength optical signal data, where the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source; performing intelligent switching and spectral matching according to the optical signal data and the multi-wavelength optical signal data to determine the target wavelength, and obtaining characteristic parameters related to turbidity according to the target wavelength in combination with the principle of combined scattering and transmission measurement; measuring standard solutions with different turbidities 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; calculating the turbidity in real time according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and performing temperature compensation at the same time; transmitting the turbidity, temperature, water quality, and light intensity parameter information to a cloud database for storage, performing hierarchical data processing, and performing regular intelligent self-checking at the same time. When the self-check result is abnormal, abnormal data is fed back.

[0006] Preferably, intelligent switching and spectral matching are performed based on the optical signal data and the multi-wavelength optical signal data to select a target wavelength, including: constructing a dynamic spectral matching algorithm; analyzing spectral characteristics according to the dynamic spectral matching algorithm to obtain spectral signal response data; detecting the turbidity range, particle characteristics or water quality changes of the medium according to the spectral signal response data, and dynamically selecting the target wavelength with the highest matching degree for the measurement scene.

[0007] Preferably, the formula of the dynamic spectrum matching algorithm is:

[0008] Wherein, C is the correlation coefficient; R is the reference spectrum; T is the dynamic spectrum; is the number of points of spectral data; is the i-th wavelength point; is the reference spectrum at the i-th wavelength point The light intensity value at ; is the dynamic spectrum at wavelength i The light intensity value at ; is the mean value of the reference spectrum R; Dynamic spectrum The mean of ; i is an index.

[0009] Preferably, characteristic parameters related to turbidity are obtained according to the target wavelength combined with the scattering and transmission joint measurement principle, including: obtaining a polarized light measurement method; analyzing mud or dust particles of different shapes, sizes and materials according to the polarized light measurement method to obtain the intensity and direction effects of the particles on the scattering and transmission of polarized light; changing the polarization state according to the intensity and direction effects of the particles on the scattering and transmission of polarized light to obtain a changed polarization state; and obtaining particle shape, orientation, and surface roughness information by measuring the changed polarization state, wherein the polarization state data includes polarization degree and polarization angle parameters.

[0010] Preferably, the turbidity is measured and calculated in real time according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and temperature compensation is performed at the same time, including: obtaining standard solution measurement data: establishing a characteristic ratio turbidity calibration model according to the standard solution measurement data and characteristic ratio parameters related to turbidity; measuring the samples to be tested with different turbidity gradients according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, calculating an initial turbidity value according to the corresponding characteristic parameters, and performing temperature compensation on the initial turbidity value based on the dynamic temperature compensation algorithm to obtain a final turbidity value.

[0011] Preferably, the formula of the characteristic ratio turbidity calibration model is:

[0012] wherein, is the target turbidity value; is the characteristic ratio; is the mathematical mapping function; is the real-time temperature; , , , , , is the linear model coefficient; is the square of the characteristic ratio; is the square of the target turbidity value.

[0013] Preferably, according to the corresponding characteristic parameters, a turbidity characteristic parameter dataset is constructed, including: obtaining optical characteristic parameters and environmental background parameters; based on the optical characteristic parameters and the environmental background parameters, capturing transient characteristic data of turbidity fluctuations through high-frequency sampling; according to the transient characteristic data, combining with the Bayesian online learning algorithm, performing real-time update to generate a characteristic parameter dataset.

[0014] An embodiment of the second aspect of the present application provides a construction device for a turbidity test system based on a photodetector, including: an acquisition module for acquiring optical signal data and multi-wavelength optical signal data, wherein the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source; a determination module for performing intelligent switching and spectral matching according to the optical signal data and the multi-wavelength optical signal data to determine a target wavelength, and obtaining characteristic parameters related to turbidity according to the target wavelength in combination with the scattering and transmission joint measurement principle; a construction module for measuring standard solutions with different turbidities according to the characteristic parameters related to turbidity to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter dataset according to the corresponding characteristic parameters, and establishing a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter dataset. A measurement module for calculating the turbidity in real time according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and performing temperature compensation at the same time. A feedback module for transmitting the turbidity, temperature, water quality, and light intensity parameter information to a cloud database for storage, performing hierarchical data processing, and regularly performing intelligent self-checking, and when the self-check result is abnormal, feeding back abnormal data.

[0015] Thus, the present application has the following beneficial effects: The embodiment of the present application uses a three-wavelength LED light source combined with a dynamic spectrum matching algorithm to intelligently switch the target wavelength according to the turbidity range of the medium and the characteristics of the particles, thereby improving the adaptability and measurement accuracy of complex water quality. Based on the principle of combined scattering and transmission measurement, it integrates polarized light measurement technology to obtain light intensity data of traditional turbidity detection, analyzes multi-dimensional characteristics such as particle shape, size, and material, and provides rich parameter support for water quality analysis. The combination of the characteristic ratio turbidity calibration model and the dynamic temperature compensation algorithm effectively eliminates the influence of temperature fluctuations on the measurement results and realizes high-precision real-time turbidity calculation. The cloud database storage and hierarchical data processing mechanism, combined with the regular intelligent self-check function, construct an intelligent closed-loop system integrating data collection, analysis, storage, and abnormal feedback, meeting the real-time monitoring needs of industrial sites and providing solutions for long-term water quality trend analysis and equipment maintenance. In this way, the problems of insufficient range coverage, weak automatic calibration capabilities, and susceptibility to temperature drift in the existing technology are solved.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for constructing a turbidity testing system based on a photodetector according to an embodiment of the present application; Figure 2 This is a diagram showing an example of turbidity detection in wastewater treatment according to one embodiment of the present application; Figure 3 This is a diagram showing an example of drinking water source detection according to one embodiment of the present application; Figure 4 This is an example diagram of industrial wastewater detection provided according to one embodiment of the present application; Figure 5 This is an example diagram of a city drinking water monitoring network provided according to one embodiment of the present application; Figure 6 A flowchart of a method for constructing a turbidity testing system based on a photodetector according to one embodiment of the present application; Figure 7 A schematic structural diagram of a device for constructing a turbidity testing system based on a photodetector according to an embodiment of the present application; DETAILED DESCRIPTION

[0018] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0019] The following describes a method and apparatus for constructing a turbidity measurement system based on a photodetector according to an embodiment of the present application, with reference to the accompanying drawings. To address the issue of weak automated calibration capabilities mentioned in the background art, the present application provides a method for constructing a turbidity measurement system based on a photodetector. In this method, a three-wavelength LED light source combined with a dynamic spectrum matching algorithm can intelligently switch target wavelengths based on the turbidity range and particle characteristics of the medium, improving adaptability and measurement accuracy for complex water quality (such as high turbidity and multiple particle types). Based on the principle of combined scattering and transmission measurement, polarized light measurement technology is integrated to obtain light intensity data from traditional turbidity detection, analyze multidimensional characteristics such as particle shape, size, and material, and provide rich parameter support for water quality analysis. The combination of a characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm effectively eliminates the impact of temperature fluctuations on measurement results, achieving high-precision real-time turbidity calculation. Cloud-based database storage and a hierarchical data processing mechanism, combined with regular intelligent self-test functions, construct an intelligent closed-loop system integrating data acquisition, analysis, storage, and abnormality feedback, meeting the real-time monitoring needs of industrial sites and providing solutions for long-term water quality trend analysis and equipment maintenance. This solves the problems of insufficient range coverage, weak automated calibration capability, and susceptibility to temperature drift in the existing technology.

[0020] Specifically, Figure 1 This is a flow chart of a method for constructing a turbidity testing system based on a photoelectric detector provided in an embodiment of the present application.

[0021] like Figure 1 As shown, the construction method of the turbidity test system based on the photoelectric detector includes the following steps: In step S101 , optical signal data and multi-wavelength optical signal data are acquired, wherein the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source.

[0022] Among them, the three-wavelength LED light source refers to a light source device that integrates three light-emitting diodes with different wavelengths, which can emit multi-wavelength optical signals to support the system to dynamically match the target wavelength according to the characteristics of the medium being measured.

[0023] It can be understood that the embodiment of the present application integrates three light-emitting diodes with different wavelengths, emits multi-wavelength light signals, intelligently selects the target wavelength for measurement, accurately obtains transmitted light intensity data and scattered light intensity data, and improves the detection accuracy of the optical properties of different media.

[0024] For example, as Figure 2 shown, in the wastewater treatment monitoring scenario, the spectral response data is analyzed through the dynamic spectral matching algorithm, and the 850nm near-infrared light with strong penetration ability is automatically selected as the target wavelength to reduce the interference of particle scattering on the optical signal and accurately obtain the transmitted and scattered light intensity data; while when monitoring the low-turbidity effluent (containing colloids and microbial flocs) in the secondary sedimentation tank, the 450nm blue light with higher sensitivity is switched to capture the scattering difference of fine particles on the short-wavelength light. Through the intelligent switching of the three-wavelength light source, the measurement error in the wide turbidity range of 0.1NTU~1000NTU is reduced by 40% compared with the traditional single-wavelength scheme, improving the adaptability and accuracy of turbidity detection under different water quality conditions.

[0025] In step S102, based on the optical signal data and the multi-wavelength optical signal data, intelligent switching and spectral matching are performed to determine the target wavelength, and according to the target wavelength and the combined scattering and transmission measurement principle, the characteristic parameters related to turbidity are obtained.

[0026] Among them, the combined scattering and transmission measurement principle refers to a measurement method for calculating turbidity by synchronously collecting the transmitted light intensity and scattered light intensity signals of the incident light on the medium, combining the light intensity distribution characteristics of both and the law of the action of particles on light, and comprehensively analyzing the particle concentration, size, and distribution state parameters.

[0027] It can be understood that in the embodiments of the present application, by synchronously collecting the transmitted light intensity and scattered light intensity signals, using the light intensity distribution characteristics and the law of the action of particles on light, and combining the target wavelength determined by intelligent switching and spectral matching, the parameters such as the concentration, size, and distribution state of particles are comprehensively and accurately analyzed to obtain the characteristic parameters related to turbidity.

[0028] It should be noted that the formula for the combined scattering and transmission measurement principle: Among them, is the incident light intensity; is the transmitted light intensity; is the turbidity coefficient; is the optical path length; is the scattered light intensity; is the correction factor; is the particle molecular weight or volume; is the particle concentration; is the incident light wavelength; is the distance from the detection point to the light source; is the scattering angle; is the comprehensive turbidity parameter; is the absorbance of transmitted light; is the intensity of scattered light; is the weighting coefficient; is the base of the natural logarithm.

[0029] For example, taking a high-precision water quality monitor as an example, this instrument emits green laser with a wavelength of 550nm into the water body. This specific wavelength has good penetration and scattering sensitivity in water. When the laser enters the water body, part of the light is scattered by suspended particles in the water, such as sediment particles with an average particle size of 5μm and microorganisms with diameters ranging from 0.5 - 10μm, and deviates from the original propagation direction. In this water body, when the sediment particle concentration increases from 10mg / L to 50mg / L, at a scattering angle of 90°, the scattered light intensity increases from 0.05μW / cm² to 0.2μW / cm². At the same time, another part of the light penetrates the water body but is attenuated due to particle absorption and scattering. If the optical path length is 10cm, when the water turbidity rises from 5NTU to 20NTU, the transmitted light intensity will decrease from 80% of the initial light intensity to 40%. The instrument accurately detects the transmitted light intensity and the scattered light intensity respectively through a high-sensitivity photoelectric sensor, and then combines these light intensity change data with the scattering-transmission combined measurement principle to quickly and accurately calculate the water quality turbidity value, with the error range controlled within ±2%. Moreover, by leveraging the polarization characteristics of the scattered light, it can deeply analyze characteristics such as particle shape and surface roughness, making the change in the polarization degree of the scattered light more significant, providing high-precision data for the comprehensive assessment of water quality and improving the water quality monitoring ability.

[0030] In the embodiments of the present application, based on the optical signal data and multi-wavelength optical signal data, intelligent switching and spectral matching are performed to select the target wavelength, including: constructing a dynamic spectral matching algorithm; analyzing the spectral characteristics according to the dynamic spectral matching algorithm to obtain spectral signal response data; detecting the turbidity range, particle characteristics or water quality changes of the medium according to the spectral signal response data, and dynamically selecting the target wavelength with the highest measurement scenario matching degree.

[0031] Among them, the spectral signal response data refers to the response output data of the sensor or detector to incident light of different wavelengths, and is used to reflect the optical characteristics of the measured medium at each wavelength.

[0032] It can be understood that in the embodiments of the present application, through the real-time response output of the sensor to multi-wavelength incident light, the optical characteristics differences such as transmission and scattering of the measured medium at different wavelengths are accurately captured, the turbidity range, particle characteristics and water quality change rules of the medium are deeply analyzed, and the target wavelength with the highest matching degree with the current measurement scenario is dynamically selected from the three-wavelength LED light source.

[0033] It should be noted that according to the dynamic spectrum matching algorithm, the spectral characteristics are analyzed to obtain spectral signal response data. By collecting the spectral data of the substance to be measured in real time and dynamically comparing it with the standard spectral library, the Euclidean distance metric matching algorithm is used to analyze the differences in spectral peak position, bandwidth, and absorbance characteristic parameters. The most similar reference spectrum is selected according to the matching degree threshold, and the response data of the spectral signal is output.

[0034] The formula of the Euclidean distance metric matching algorithm: Among them, is the i-th standardized observation value; is the original observation value of the i-th dimension; is the mean value of the i-th dimension; is the standard deviation on the i-th dimension; is the standardized Euclidean distance between two samples; is the number of data dimensions; is the original observation value of another sample in the i-th dimension; is the weighted Euclidean distance; is the weight coefficient of the i-th dimension.

[0035] For example, as Figure 3 shown, in the monitoring scenario of drinking water source areas, after emitting multi-wavelength signals through a three-wavelength LED light source, the sensor collects the spectral signal response data at different wavelengths in real time. When detecting high-turbidity sediment-laden water carried by surface runoff during the rainy season, the response data of the transmitted light intensity of the 940 nm near-infrared light shows a small attenuation amplitude, while the response of the scattered light intensity of the 470 nm blue light increases. By analyzing the data with the dynamic spectrum matching algorithm, it is determined that the current medium is mainly large-particle sediment, and the turbidity range is between 200 - 500 NTU. Therefore, the 940 nm near-infrared light with strong penetration ability is preferably selected as the target wavelength to reduce the interference of particle scattering; when detecting low-turbidity colloids in water during the low-temperature period in winter, the response data of the scattered light intensity of the 470 nm blue light is more sensitive to fine particles. The blue light source is switched to accurately capture the scattering characteristics of colloidal particles. Through the real-time analysis of the spectral signal response data, the turbidity measurement error in the water quality mutation scenario of the water source area is reduced from ±8% of the traditional fixed-wavelength scheme to ±3.5%.

[0036] In the embodiment of the present application, the formula of the dynamic spectrum matching algorithm is:

[0037] Among them, C is the correlation coefficient; R is the reference spectrum; T is the dynamic spectrum; is the number of points of the spectral data; is the i-th wavelength point; is the light intensity value of the reference spectrum at the i-th wavelength point ; is the light intensity value of the dynamic spectrum at the i-th wavelength point ; is the mean value of the reference spectrum R; is the dynamic spectrum ; i is an index.

[0038] It can be understood that in the embodiments of the present application, by analyzing the spectral characteristics in real time to obtain accurate spectral signal response data, the turbidity range, particle characteristics, and water quality changes of the medium can be quickly detected. According to the differences in spectral data in different scenarios, the target wavelength with the highest matching degree is automatically selected to realize the adaptive adjustment of the measurement wavelength. This improves the sensitivity and accuracy of detection, controls the turbidity measurement error, avoids environmental interference, improves the detection efficiency, and reduces the cost of manual intervention.

[0039] For example, in a water quality monitoring project in a certain city, the dynamic spectrum matching algorithm is used to improve the monitoring efficiency and accuracy. With the help of an unmanned aerial vehicle carrying a hyperspectral imager, spectral data of different water areas are collected in real time, and its spectral range covers 400 - 900 nm. The algorithm compares the collected spectrum with a spectral library containing various pollution characteristics, and analyzes the spectral characteristics through the Euclidean distance metric matching algorithm. When detecting whether there is algal pollution in the water, aiming at the unique chlorophyll absorption peak of algae near 680 nm, the algorithm accurately identifies the corresponding characteristics in the spectrum, quickly obtains the spectral signal response data, and clarifies the type and approximate concentration range of algae. At the same time, based on the spectral signal response data, the turbidity range and particle characteristics of the medium can be detected, and the all-round monitoring of water quality changes provides strong support for environmental monitoring decisions.

[0040] In the embodiments of the present application, according to the target wavelength and the principle of combined scattering and transmission measurement, characteristic parameters related to turbidity are obtained, including: obtaining the polarized light measurement method; according to the polarized light measurement method, analyzing sediment or dust particles of different shapes, sizes, and materials, and obtaining the influence of the particles on the intensity and direction of polarized light scattering and transmission; according to the influence of the particles on the intensity and direction of polarized light scattering and transmission, changing the polarization state, and obtaining the changed polarization state; by measuring the changed polarization state, information on particle shape, orientation, and surface roughness is obtained, where the polarization state data includes polarization degree and polarization angle parameters.

[0041] Among them, the polarized light measurement method is a method for obtaining the optical characteristics or physical parameters of the object to be measured by detecting the change in the polarization state of light.

[0042] It can be understood that the embodiments of the present application detect changes in polarization states, analyze the effects of particles on polarized light scattering and transmission, obtain characteristics such as particle shape and orientation, provide particle microscopic information for turbidity measurement, reduce deviations caused by particle characteristics, and improve the identification accuracy of multi-particle mixed water bodies.

[0043] For example, Figure 4 As shown in the figure, in the industrial wastewater monitoring scenario, 525nm polarized green light is used as the target wavelength, and the particle characteristics in the wastewater are detected by the polarization light measurement method: when spherical silica particles are present in the water sample, the polarization degree of the scattered light is reduced by 15% compared with the incident light, and the polarization angle remains basically unchanged; when flaky clay particles are detected, the polarization degree of the scattered light drops sharply by 30%, and the polarization angle deflects by 25°; by analyzing these polarization state changes, spherical and non-spherical particles can be accurately distinguished, and combined with the scattered transmission light intensity data, the turbidity measurement error of water containing mixed particles is reduced from ±10% of traditional light intensity measurement to ±5%.

[0044] In step S103, based on the characteristic parameters related to turbidity, standard solutions with different turbidities are measured to obtain corresponding characteristic parameters, a turbidity characteristic parameter data set is constructed based on the corresponding characteristic parameters, and a characteristic ratio turbidity calibration model is established based on the turbidity characteristic parameter data set.

[0045] Among them, the turbidity characteristic parameter dataset is a multi-dimensional data set used to describe the characteristics of different turbidity samples, covering optical parameters, particle characteristic parameters and auxiliary environmental parameters, providing data for turbidity measurement model construction, algorithm optimization and mechanism analysis.

[0046] It can be understood that the embodiments of the present application provide training and calibration data for the turbidity measurement model by collecting multi-dimensional characteristic parameters (covering optical properties, particle physicochemical properties and environmental parameters) of different turbidity standard solutions, thereby solving the problem of parameter measurement being interfered with by particle characteristics (particle size, color, surface charge, etc.) and improving the model's adaptability to complex water quality scenarios.

[0047] In an embodiment of the present application, a turbidity characteristic parameter data set is constructed based on the corresponding characteristic parameters, including: obtaining optical characteristic parameters and environmental background parameters; based on the optical characteristic parameters and environmental background parameters, capturing transient characteristic data of turbidity fluctuations through high-frequency sampling; based on the transient characteristic data, combined with the Bayesian online learning algorithm, real-time updating is performed to generate a characteristic parameter data set.

[0048] Among them, optical characteristic parameters are quantifiable indicators produced by the interaction between light and matter, including scattered / transmitted light intensity, absorbance, spectral wavelength and polarization state, which are used to characterize the optical behavior characteristics of matter such as scattering, absorption and transmission of light.

[0049] It is understandable that in the embodiments of the present application, by collecting quantitative indicators of the interaction between light and matter in multiple dimensions such as scattered / transmitted light intensity, absorbance, spectral wavelength, and polarization state, the optical behavior characteristics of the water sample are comprehensively characterized, providing basic data for the turbidity characteristic parameter dataset. Combined with high-frequency sampling and the Bayesian algorithm for real-time update, the dynamic changes of water quality are accurately captured, improving the turbidity measurement accuracy and environmental adaptability.

[0050] It should be noted that the formula of the Bayesian online learning algorithm is:

[0051] Where, is the mean and variance of the turbidity characteristic parameters; is the transient turbidity characteristic; is the prior probability; is the likelihood function; is the evidence term; is the posterior probability.

[0052] In the embodiments of the present application, the formula of the characteristic ratio turbidity calibration model is:

[0053] Where, is the target turbidity value; is the characteristic ratio; is the mathematical mapping function; is the real-time temperature; , , , , , is the linear model coefficient; is the square of the characteristic ratio; is the square of the target turbidity value.

[0054] It is understandable that in the embodiments of the present application, by measuring the characteristic parameters of different turbidity standard solutions and constructing the dataset, a quantitative correlation is established between the complex spectral characteristic parameters and turbidity, effectively eliminating the influence of environmental interference and medium difference factors on the measurement results, improving the stability and accuracy of turbidity detection, and controlling the measurement error within a very small range; through the calculation of the characteristic ratio, the data processing process is simplified, realizing fast and automatic turbidity analysis and improving the detection efficiency; it is applicable to water quality monitoring in different scenarios, providing reliable turbidity data support for fields such as environmental assessment and industrial production, and assisting in precise decision-making and quality control.

[0055] For example, in a water quality monitoring project of a certain lake, the staff used the characteristic ratio turbidity calibration model to analyze water samples at different points. First, lake water samples with different turbidities were collected, covering the range from relatively clear (turbidity about 5 NTU) to moderately turbid (turbidity up to 50 NTU). The spectral characteristic parameters of each sample in the 400 - 900 nm band were obtained using a spectrometer, such as the absorbance at the chlorophyll absorption peak of 680 nm and the absorbance at the sensitive band of suspended solids of 750 nm. By calculating the characteristic ratio of the absorbance in a specific band, such as the ratio of the absorbance at 680 nm to that at 550 nm, a turbidity characteristic parameter data set was constructed. Based on this data set, a characteristic ratio turbidity calibration model was established. In actual application, the spectral characteristic parameters of the newly collected water sample were substituted into the model. By calculating the characteristic ratio and comparing it with the model, the turbidity of the lake water could be quickly and accurately determined, the water quality status could be judged, and the changes in the lake water quality could be grasped in a timely manner.

[0056] In step S104, according to the characteristic ratio turbidity calibration model combined with the dynamic temperature compensation algorithm, the turbidity is measured and calculated in real time, and at the same time, temperature compensation is performed.

[0057] Among them, the dynamic temperature compensation algorithm is an adaptive algorithm that automatically corrects the parameter deviation caused by temperature drift of the sensor or measurement system by real-time monitoring of the environmental temperature change, and eliminates the influence of temperature on the measurement result.

[0058] It can be understood that in the embodiment of the present application, by real-time collecting temperature data, the calibration parameters of the measurement are dynamically adjusted, the systematic interference of temperature on the turbidity measurement is eliminated, the adaptability in the wide temperature range scenario of -20°C - 60°C is measured, the measurement deviation caused by temperature fluctuation is avoided, and the stability of the characteristic ratio turbidity calibration model is ensured.

[0059] It should be noted that the formula of the dynamic temperature compensation algorithm: Among them, is 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.

[0060] In an embodiment of the present application, turbidity is measured and calculated in real time according to a characteristic ratio turbidity calibration model combined with a dynamic temperature compensation algorithm, and temperature compensation is performed at the same time, including: obtaining standard solution measurement data: establishing a characteristic ratio turbidity calibration model based on the standard solution measurement data and characteristic ratio parameters related to turbidity; measuring samples to be tested with different turbidity gradients according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm to obtain corresponding characteristic parameters, calculating an initial turbidity value based on the corresponding characteristic parameters, and performing temperature compensation on the initial turbidity value based on the dynamic temperature compensation algorithm to obtain a final turbidity value.

[0061] Among them, standard solution measurement data is the result obtained by measuring a standard solution with known accurate concentration or characteristic parameters (such as turbidity, absorbance), which is used to calibrate detection equipment and verify the accuracy and precision of measurement methods.

[0062] It can be understood that the embodiments of the present application provide high-precision benchmark data for the characteristic ratio turbidity calibration model by measuring standard solutions with known accurate turbidity, absorbance and other parameters, ensuring that the model can accurately establish a mapping relationship between multiple optical characteristic parameters and true turbidity, so that the initial calibration error of the measurement system within the full range is reduced to below ±1%, and the additional error caused by temperature drift in the temperature range of -20℃-60℃ is controlled within ±0.5%, thereby improving the accuracy of turbidity measurement in complex environments.

[0063] In step S105, the turbidity, temperature, water quality and light intensity parameter information is transmitted to the cloud database for storage and hierarchical data processing. At the same time, regular intelligent self-inspection is performed, and when the self-inspection results are abnormal, the abnormal data is fed back.

[0064] Among them, hierarchical data processing is a technical method that divides the data processing process into multiple levels with clear functions, and realizes the gradual extraction of information value from raw data through division of labor and cooperation among various levels.

[0065] It can be understood that the embodiment of the present application processes turbidity and temperature parameters according to the levels of collection, cleaning, and analysis through hierarchical division of labor: the bottom layer collects and reduces noise, the middle layer cleans data and constructs features, and the top layer mines data associations to improve processing efficiency, ensure data accuracy, and provide data for long-term analysis; combined with intelligent self-test, it quickly locates anomalies such as sensor drift and provides feedback.

[0066] For example, Figure 5As shown in the figure, in the urban drinking water monitoring network, the hierarchical data processing technology effectively improves the utilization value of a large amount of water quality data: the bottom data acquisition layer captures original signals such as turbidity (0.1 - 100 NTU), water temperature (0 - 40 °C), and light intensity in real time through sensors distributed in each water source, and eliminates high-frequency electromagnetic interference through hardware filtering; the middle cleaning and conversion layer standardizes the data, automatically identifies and repairs outliers caused by short-term sensor failures (such as invalid data with a sudden temperature of -5 °C at a certain node), and constructs a data set containing characteristics such as particle scattering coefficient and transmission attenuation ratio; the top analysis and modeling layer uses the random forest algorithm to mine data associations, discovers that the correlation between the sudden change of turbidity in summer and the shedding of biofilm on the inner wall of the pipe network reaches 82%, and predicts the pipe network maintenance cycle through a long-term trend model. Combining with the hourly intelligent self-check mechanism of the system, the effective data utilization rate is increased from 65% to 92%.

[0067] According to the construction method of the turbidity test system based on a photodetector proposed in the embodiment of the present application, through a three-wavelength LED light source combined with a dynamic spectral matching algorithm, the target wavelength can be intelligently switched according to the medium turbidity range and particle characteristics, improving the adaptability and measurement accuracy for complex water quality (such as high turbidity and multi-particle type scenarios); based on the principle of combined scattering and transmission measurement, integrating polarized light measurement technology, obtaining the light intensity data of traditional turbidity detection, and analyzing multi-dimensional characteristics such as particle shape, size, and material, providing rich parameter support for water quality analysis; the combination of the characteristic ratio turbidity calibration model and the dynamic temperature compensation algorithm effectively eliminates the influence of temperature fluctuations on the measurement results, realizing high-precision real-time turbidity calculation; the cloud database storage and hierarchical data processing mechanism, combined with the regular intelligent self-check function, constructs an intelligent closed-loop system integrating data acquisition, analysis, storage, and abnormal feedback, meeting the real-time monitoring requirements of the industrial field, and providing a solution for long-term water quality trend analysis and equipment maintenance. Thus, the problems of insufficient range coverage, weak automatic calibration ability, and susceptibility to temperature drift in the prior art are solved.

[0068] The construction method of the turbidity test system based on a photodetector will be elaborated through a specific embodiment below, as Figure 6 shown, including: Step 1: System hardware construction.

[0069] Build a hardware platform for a turbidity test system based on a photodetector. Use three LED light sources with different wavelengths (450 nm, 520 nm, and 660 nm respectively) as multi-wavelength optical signal emission sources, and evenly distribute them around the test cavity to ensure that the optical signal can fully cover the liquid to be measured. Install a high-precision photodetector on the opposite side of the test cavity to obtain transmitted light intensity data; install another group of photodetectors at a 90° angle to the light source to collect scattered light intensity data. At the same time, integrate a temperature sensor in the test system to monitor the ambient temperature change in real time for subsequent temperature compensation operations.

[0070] Step 2: Optical signal and wavelength processing.

[0071] After the system starts, the LED light source emits an optical signal into the liquid to be measured in the test cavity. The photodetector collects the transmitted light intensity data and the scattered light intensity data in real time, and at the same time obtains the optical signal data of the three-wavelength LED light source. Construct a dynamic spectral matching algorithm to process the collected spectral data. Assume that the reference spectrum R is the spectral data of a known standard water sample at different wavelengths, and the dynamic spectrum T is the spectral data of the liquid to be measured collected in real time. According to the dynamic spectral matching algorithm formula , calculate the correlation coefficient C between the reference spectrum and the dynamic spectrum. By analyzing the magnitude of the C value, judge the spectral characteristics and obtain the spectral signal response data. If the correlation coefficient C is the largest at the 450 nm wavelength, it is detected that the turbidity range, particle characteristics, or water quality change of the current medium is more suitable for measurement using the 450 nm wavelength, and dynamically select 450 nm as the target wavelength.

[0072] Step 3: Obtain characteristic parameters.

[0073] Adopt the polarized light measurement method. Use a polarizer to modulate the light of the target wavelength to make it polarized light and then inject it into the liquid to be measured in the test cavity. Analyze the influence of the intensity and direction of the scattering and transmission of polarized light by sediment or dust particles of different shapes, sizes, and materials. Due to the action of the particles, the polarization state of the polarized light changes. By measuring the changed polarization state, obtain polarization state data such as the degree of polarization and the polarization angle, and then obtain information on the particle shape, orientation, and surface roughness, and use this information as characteristic parameters related to turbidity.

[0074] Step 4: Calibration model and turbidity calculation.

[0075] Prepare standard solutions of different turbidity (e.g., 10NTU, 50NTU, 100NTU, 200NTU, 500NTU), and use the established test system to measure each standard solution to obtain optical characteristic parameters (transmitted light intensity, scattered light intensity, polarization state data, etc.) and environmental background parameters (temperature, air pressure, etc.). Based on the optical characteristic parameters and environmental background parameters, high-frequency sampling (10 times per second) is used to capture transient characteristic data of turbidity fluctuations. Based on the transient characteristic data, combined with the Bayesian online learning algorithm, real-time updates are performed to generate a turbidity characteristic parameter data set. Based on the turbidity characteristic parameter data set, a characteristic ratio turbidity calibration model is established. Incorporating a dynamic temperature compensation algorithm, we measure samples with varying turbidity gradients to obtain the corresponding characteristic parameters. These parameters are then substituted into the characteristic ratio turbidity calibration model to calculate the initial turbidity value. Using the dynamic temperature compensation algorithm, we then apply temperature compensation to the initial turbidity value to obtain the final turbidity value. For example, if the ambient temperature during measurement is 25°C, the temperature compensation algorithm corrects the initial turbidity value to obtain an accurate final turbidity value.

[0076] 5. Data transmission and self-test.

[0077] The measured turbidity, temperature, water quality (preliminarily determined through characteristic parameters), and light intensity parameters are transmitted via wireless networks (such as 4G / 5G modules) to a cloud database for storage. In the cloud, the data is processed in layers, such as raw data, preprocessed data, and analyzed data. The system also performs regular (hourly) intelligent self-tests to check the health of hardware parameters such as the sensitivity of the photoelectric detector and the luminous intensity of the LED light source. If any abnormalities are detected in the self-test results, the abnormal data is fed back to the system management terminal, prompting personnel to perform equipment maintenance or troubleshooting.

[0078] In summary, the present invention ensures comprehensive acquisition of optical signals through the layout of multi-wavelength LED light sources and multi-directional photodetectors in hardware. In terms of software, it uses advanced algorithms such as dynamic spectrum matching and polarized light measurement to accurately obtain characteristic parameters, and combines Bayesian learning with dynamic temperature compensation to improve the accuracy of the calibration model and measurement stability. In terms of data processing, cloud-based layered storage and intelligent self-testing are implemented, which not only ensures data security and effectiveness, but also provides timely feedback on faults. This significantly improves the accuracy, reliability, and intelligence of turbidity measurements, is suitable for complex water quality environments, and effectively meets the needs of real-time and accurate turbidity monitoring in different scenarios. It has high practical value and promotion significance.

[0079] Next, a construction device of a turbidity testing system based on a photoelectric detector proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0080] Figure 7It is a block diagram of a device for constructing a turbidity test system based on a photodetector according to an embodiment of the present application.

[0081] As Figure 7 shown, the device 10 for constructing a turbidity test system based on a photodetector includes: an acquisition module 100, a determination module 200, a construction module 300, a measurement module 400, and a feedback module 500.

[0082] Among them, the acquisition module 100 is used to acquire optical signal data, multi-wavelength optical signal data, where the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source; the determination module 200 is used to perform intelligent switching and spectral matching according to the optical signal data and the multi-wavelength optical signal data to determine the target wavelength, and obtain characteristic parameters related to turbidity according to the target wavelength in combination with the principle of combined scattering and transmission measurement; the construction module 300 is used to measure standard solutions with different turbidities according to the characteristic parameters related to turbidity to obtain corresponding characteristic parameters, construct a turbidity characteristic parameter data set according to the corresponding characteristic parameters, and establish a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set; the measurement module 400 is used to calculate the turbidity in real time according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and at the same time, perform temperature compensation; the feedback module 500 is used to transmit the turbidity, temperature, water quality, and light intensity parameter information to the cloud database for storage, perform hierarchical data processing, and at the same time, perform regular intelligent self-checking, and when the self-check result is abnormal, feedback the abnormal data.

[0083] It should be noted that the foregoing explanation of the embodiment of the method for constructing a turbidity test system based on a photodetector is also applicable to the device for constructing a turbidity test system based on a photodetector in this embodiment, and will not be repeated here.

[0084] The device for constructing a photodetector-based turbidity measurement system proposed in the embodiments of the present application uses a three-wavelength LED light source combined with a dynamic spectrum matching algorithm to intelligently switch target wavelengths based on the medium's turbidity range and particle characteristics, improving adaptability and measurement accuracy for complex water quality scenarios (such as high turbidity and multiple particle types). Based on the principle of combined scattering and transmission measurement, it integrates polarized light measurement technology to obtain light intensity data from traditional turbidity detection and analyze multidimensional characteristics such as particle shape, size, and material, providing rich parameter support for water quality analysis. The combination of a characteristic ratio turbidity calibration model and a dynamic temperature compensation algorithm effectively eliminates the impact of temperature fluctuations on measurement results, achieving high-precision real-time turbidity calculation. Cloud-based database storage and a hierarchical data processing mechanism, combined with regular intelligent self-test functions, create an intelligent closed-loop system integrating data acquisition, analysis, storage, and abnormality feedback, meeting the real-time monitoring needs of industrial sites and providing solutions for long-term water quality trend analysis and equipment maintenance. This solves the problems of insufficient range coverage, weak automated calibration capabilities, and susceptibility to temperature drift in existing technologies.

[0085] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0087] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0088] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0089] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A construction method of a turbidity test system based on a photodetector, characterized in that, Including: Obtain optical signal data, multi-wavelength optical signal data, wherein the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source; Based on the optical signal data and the multi-wavelength optical signal data, perform intelligent switching and spectral matching to determine the target wavelength, and based on the target wavelength and the scattering and transmission combined measurement principle, obtain characteristic parameters related to turbidity; Measure standard solutions with different turbidities according to the characteristic parameters related to turbidity to obtain corresponding characteristic parameters, construct a turbidity characteristic parameter data set according to the corresponding characteristic parameters, and establish a characteristic ratio turbidity calibration model according to the turbidity characteristic parameter data set; According to the characteristic ratio turbidity calibration model and the dynamic temperature compensation algorithm, measure and calculate the turbidity in real time, and at the same time, perform temperature compensation; Transmit the turbidity, temperature, water quality, and light intensity parameter information to the cloud database for storage, perform hierarchical data processing, and at the same time, perform regular intelligent self-checking. When the self-check result is abnormal, feedback the abnormal data.

2. The construction method of the turbidity test system based on a photodetector according to claim 1, characterized in that, Based on the optical signal data and the multi-wavelength optical signal data, perform intelligent switching and spectral matching to select the target wavelength, including: Construct a dynamic spectral matching algorithm; Analyze the spectral characteristics according to the dynamic spectral matching algorithm to obtain spectral signal response data; According to the spectral signal response data, detect the turbidity range, particle characteristics, or water quality change of the medium, and dynamically select the target wavelength with the highest measurement scenario matching degree.

3. The method for constructing a turbidity test system based on a photodetector according to claim 2, wherein The formula of the dynamic spectral matching algorithm is: ； Among them, C is the correlation coefficient; R is the reference spectrum; T is the dynamic spectrum; is the number of points of the spectral data; is the i-th wavelength point; is the light intensity value of the reference spectrum at the i-th wavelength point ; is the light intensity value of the dynamic spectrum at the i-th wavelength point ; is the mean value of the reference spectrum R; is the mean value of the dynamic spectrum ; i is an index.

4. The method for constructing a turbidity test system based on a photodetector according to claim 1, wherein Based on the target wavelength and the scattering and transmission combined measurement principle, obtain characteristic parameters related to turbidity, including: Obtain the polarized light measurement method; Analyze sediment or dust particles with different shapes, sizes, and materials 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; According to the influence of the particles on the intensity and direction of polarized light scattering and transmission, change the polarization state to obtain the changed polarization state; By measuring the changed polarization state, obtain particle shape, orientation, and surface roughness information, wherein the polarization state data includes polarization degree and polarization angle parameters.

5. The construction method of the turbidity test system based on a photodetector according to claim 1, characterized in that, According to the characteristic ratio turbidity calibration model and the dynamic temperature compensation algorithm, measure and calculate the turbidity in real time, and at the same time, perform temperature compensation, including: Obtain standard solution measurement data: Establish a characteristic ratio turbidity calibration model according to the standard solution measurement data and the characteristic ratio parameters related to turbidity; According to the characteristic ratio turbidity calibration model, combined with the dynamic temperature compensation algorithm, measure samples to be measured with different turbidity gradients to obtain corresponding characteristic parameters, and calculate the initial turbidity value according to the corresponding characteristic parameters; Based on the dynamic temperature compensation algorithm, perform temperature compensation on the initial turbidity value to obtain the final turbidity value.

6. The method for constructing a turbidity test system based on a photodetector according to claim 4, characterized in that, The formula of the characteristic ratio turbidity calibration model is: ； wherein, is the target turbidity value; is the characteristic ratio; is the mathematical mapping function; is the real-time temperature; , , , , , are the linear model coefficients; is the square of the characteristic ratio; is the square of the target turbidity value.

7. The construction method of the turbidity test system based on a photodetector according to claim 1, wherein Construct a turbidity characteristic parameter data set according to the corresponding characteristic parameters, including: Obtain optical characteristic parameters and environmental background parameters; Based on the optical characteristic parameters and the environmental background parameters, transient characteristic data of turbidity fluctuations are captured through high-frequency sampling; According to the transient characteristic data, combined with the Bayesian online learning algorithm, real-time updates are performed to generate a characteristic parameter data set.

8. An apparatus for constructing a turbidity test system based on a photodetector, characterized in that, It includes: An acquisition module for acquiring optical signal data and multi-wavelength optical signal data, where the optical signal data includes transmitted light intensity data and scattered light intensity data, and the multi-wavelength optical signal data includes a three-wavelength LED light source; A determination module for performing intelligent switching and spectral matching according to the optical signal data and the multi-wavelength optical signal data to determine the target wavelength, and obtaining characteristic parameters related to turbidity according to the target wavelength in combination with the scattering and transmission joint measurement principle; A construction module for measuring standard solutions with different turbidities 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; A measurement module for real-time measuring and calculating turbidity according to the characteristic ratio turbidity calibration model in combination with the dynamic temperature compensation algorithm, and performing temperature compensation at the same time; A feedback module for transmitting the turbidity, temperature, water quality and light intensity parameter information to the cloud database for storage, performing hierarchical data processing, and performing regular intelligent self-checks. When the self-check result is abnormal, abnormal data is feedback.

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