A high turbidity measurement method, a high turbidity measuring instrument, a medium and a program product

Abstract

A method, instrument, medium, and procedure for measuring high turbidity are disclosed, relating to the field of testing or analyzing materials by measuring their chemical or physical properties. In this method, the intensity of filtered transmitted light and filtered scattered light in the red light band, and the intensity of filtered transmitted light and filtered scattered light in the white light band are collected. Based on the correspondence between the light intensity ratio and turbidity value of a standard turbidity sample, a first set of weighting coefficients corresponding to a first turbidity range and a second set of weighting coefficients corresponding to a second turbidity range are determined. Based on the first set of weighting coefficients, the initial turbidity value of the water sample to be tested is calculated. It is then determined whether the initial turbidity value is greater than a preset range threshold. If so, the final turbidity value of the water sample to be tested is calculated based on the second set of weighting coefficients. If not, the initial turbidity value is used as the final turbidity value of the water sample to be tested. The final turbidity value of the water sample to be tested is displayed on a screen. This application aims to improve the accuracy of testing high-turbidity water samples.

Description

Technical Field

[0001] This application belongs to the field of testing or analyzing materials by means of measuring their chemical or physical properties, and particularly relates to a method for measuring high turbidity, a high turbidity measuring instrument, a medium, and a procedure product. Background Technology

[0002] In the field of water quality monitoring, turbidity is one of the important parameters for assessing water quality. Traditional turbidity measurement methods usually use a single light source and a fixed optical path configuration. However, due to the complex scattering and absorption characteristics of impurity particles in water samples, the measurement process is easily affected by stray light, resulting in poor stability and reliability of the test data, especially when measuring high-turbidity water samples.

[0003] A turbidity testing system based on a photodetector is proposed in the related technology. This system acquires multi-wavelength optical signal data, performs intelligent switching and spectral matching by combining the principle of scattering and transmission measurement, establishes a characteristic ratio turbidity calibration model and introduces a dynamic temperature compensation algorithm, which can improve the measurement accuracy to a certain extent.

[0004] However, in practical applications, the detection sensitivity of the photodetector is limited by its own physical characteristics. This makes it difficult for the detector to accurately respond to and distinguish dense scattered signals when testing high-turbidity water samples, resulting in reduced resolution of the measurement data. In particular, when there are a large number of suspended particles in the water sample, the multiple scattering effect can cause signal superposition and interference, further reducing the measurement accuracy. Summary of the Invention

[0005] This application provides a method for measuring high turbidity, a high turbidity measuring instrument, a medium, and a procedure product to improve the accuracy of testing high turbidity water samples.

[0006] Firstly, this application provides a method for measuring high turbidity. A high turbidity meter illuminates the water sample to be tested using red and white light sources, respectively. After optical filtering by a narrow-band filter in the optical path, the meter collects the corresponding transmitted light signal and 90° scattered light signal, obtaining the filtered transmitted light intensity, filtered scattered light intensity, and filtered transmitted light intensity in the red light band, and the filtered transmitted light intensity and filtered scattered light intensity in the white light band. The high turbidity meter uses the ratio of the filtered transmitted light intensity to the filtered scattered light intensity in the red light band as the light intensity ratio in the red light band. The high turbidity meter also uses the ratio of the filtered transmitted light intensity to the filtered scattered light intensity in the white light band as the light intensity ratio in the white light band. The high turbidity meter uses the light intensity ratio of a standard turbidity sample and the turbidity... The correspondence between turbidity values ​​is determined by establishing the red light weighting coefficient corresponding to the ratio of red light intensity in the first turbidity range and the white light weighting coefficient corresponding to the ratio of white light intensity in the second turbidity range. This yields the first set of weighting coefficients for the first turbidity range and the second set of weighting coefficients for the second turbidity range. Based on the first set of weighting coefficients, the high turbidity meter calculates the initial turbidity value of the water sample. The high turbidity meter then determines whether the initial turbidity value is greater than the preset range threshold. If so, the high turbidity meter calculates the final turbidity value of the water sample based on the second set of weighting coefficients. If not, the high turbidity meter uses the initial turbidity value as the final turbidity value of the water sample. The high turbidity meter displays the final turbidity value of the water sample on the display screen.

[0007] By employing the above technical solution, the optical characteristics of the water sample under test are obtained by irradiating the sample with red and white light sources respectively and collecting transmitted and scattered light signals. The intensity ratio of the two wavelengths is calculated, and weighting coefficients for different turbidity ranges are determined based on standard samples, establishing a mapping relationship between the intensity ratio of the two wavelengths and the turbidity value. The initial turbidity value is used to determine whether it exceeds a preset range threshold, and the appropriate weighting coefficients are selected to calculate the final turbidity value, solving the problem of saturation in high turbidity ranges in single-wavelength measurements. Dual-wavelength complementary measurement improves the dynamic range of the measurement. A dedicated second set of weighting coefficients is used for calculations in the high turbidity range, reducing measurement deviations caused by multiple scattering effects in high-turbidity water samples. By utilizing the differences in scattering characteristics of different wavelengths of light in the water sample and through a reasonable weighting calculation method, the accuracy of testing high-turbidity water samples is improved.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of the high turbidity meter calculating the initial turbidity value of the water sample to be tested based on the first set of weighting coefficients includes: the high turbidity meter multiplying the light intensity ratio of the red light band with the red light weighting coefficient in the first set of weighting coefficients to obtain the red light band turbidity component; the high turbidity meter multiplying the light intensity ratio of the white light band with the white light weighting coefficient in the first set of weighting coefficients to obtain the white light band turbidity component; and the high turbidity meter adding the red light band turbidity component and the white light band turbidity component to obtain the initial turbidity value.

[0009] By employing the above technical solution, the turbidity component is obtained by multiplying the intensity ratio of the red and white light bands by their respective weighting coefficients. The initial turbidity value is then obtained by adding the turbidity components of the two bands, thus achieving a linear combination of dual-band information. This linear weighting calculation method considers the contribution of both bands to turbidity measurement, preserving the measurement advantages of each band while adjusting their respective weights through the ratio of weighting coefficients. Because the scattering characteristics of the two bands are complementary, the linear weighting method maintains good measurement accuracy across different turbidity ranges, and the calculation results have physical meaning, reflecting the actual turbidity level of the water sample being tested.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of the high turbidity meter calculating the initial turbidity value of the water sample to be tested based on the first set of weighting coefficients includes: the high turbidity meter establishing a two-dimensional feature vector containing the light intensity ratio of the red light band and the light intensity ratio of the white light band; the high turbidity meter constructing a polynomial function matrix according to the first set of weighting coefficients, wherein the matrix elements in the polynomial function matrix include different orders of red light weighting coefficients and white light weighting coefficients; the high turbidity meter performing a convolution operation between the two-dimensional feature vector and the polynomial function matrix to obtain a turbidity feature mapping value; and the high turbidity meter correcting the turbidity feature mapping value based on the pre-calibrated correspondence between the feature mapping value and the turbidity value to obtain the initial turbidity value.

[0011] By employing the aforementioned technical solution, turbidity values ​​are calculated using convolution operations of two-dimensional eigenvectors and polynomial function matrices, establishing a nonlinear mapping relationship between the dual-band light intensity ratio and turbidity values. The polynomial function matrix contains different orders of weighting coefficients, capable of describing the complex relationship between water sample turbidity and optical properties. Turbidity feature mapping values ​​are obtained through convolution operations and corrected based on pre-calibrated correspondences, compensating for nonlinear scattering effects in the water sample. This nonlinear mapping calculation method improves the accuracy of turbidity measurements over a large range, is particularly suitable for high turbidity measurements, and reduces the limitations of simple linear calculation methods when handling nonlinear optical properties.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after obtaining the first set of weighting coefficients corresponding to the first turbidity range and the second set of weighting coefficients corresponding to the second turbidity range, the method further includes: the high turbidity meter using the ratio of the filtered scattered light intensity of the red light band to the filtered transmitted light intensity of the white light band in the first turbidity range as the first cross ratio; the high turbidity meter using the ratio of the filtered scattered light intensity of the white light band to the filtered transmitted light intensity of the red light band in the first turbidity range as the second cross ratio; the high turbidity meter calculating the difference between the first cross ratio and the second cross ratio to obtain the cross ratio difference; when the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio of the red light band exceeds a preset range, the high turbidity meter determining that the physical properties of the water sample to be tested in the first turbidity range have changed; the high turbidity meter correcting the first set of weighting coefficients according to the magnitude of the cross ratio difference and a preset correction coefficient to obtain the corrected first set of weighting coefficients, and replacing the corrected first set of weighting coefficients with the first set of weighting coefficients.

[0013] By employing the above technical solution, when the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio exceeds a preset range, it indicates that the physical properties of the water sample have changed. At this point, the weighting coefficients are corrected based on the magnitude of the cross ratio difference. This adaptive correction mechanism can address measurement errors caused by changes in the physical properties of the water sample, improving the accuracy of the measurement results. The calculation of the cross ratio utilizes information from both scattered and transmitted light in the dual-band, increasing the basis for judging changes in water sample properties, making the correction of the weighting coefficients more reasonable, and ensuring reliable measurement results even when the physical properties of the water sample change.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the high turbidity meter calculates the amplitude spectrum of the light intensity ratio of the red light band and the light intensity ratio of the white light band in the second turbidity range; the high turbidity meter extracts the characteristic frequency components in the amplitude spectrum; when the energy density of the characteristic frequency components exceeds a preset threshold, the high turbidity meter determines that the water sample to be tested in the second turbidity range has agglomerated; the high turbidity meter performs nonlinear compensation on the second set of weighting coefficients according to the energy density distribution of the characteristic frequency components to obtain a corrected second set of weighting coefficients, and replaces the corrected second set of weighting coefficients with the second set of weighting coefficients.

[0015] By employing the aforementioned technical solution, and calculating the amplitude spectrum of the intensity ratio between the red and white light bands within the second turbidity range, and extracting the characteristic frequency components, particle aggregation in the water sample can be identified. When the energy density of the characteristic frequency components exceeds a preset threshold, it indicates that particle aggregation has occurred in the water sample, and this aggregation alters the optical properties of the water sample. Nonlinear compensation is applied to the second set of weighting coefficients based on the energy density distribution of the characteristic frequency components to obtain a corrected second set of weighting coefficients, enabling the measurement results to adapt to changes in optical properties caused by aggregation. This compensation mechanism reduces the impact of aggregation on measurement accuracy and improves measurement accuracy within the high turbidity range.

[0016] In some embodiments of the first aspect, after the high turbidity meter calculates the initial turbidity value of the water sample based on the first set of weighting coefficients, the method further includes: the high turbidity meter setting a transition range with a preset range threshold as the median; when the initial turbidity value is within the transition range, the high turbidity meter calculates the relative position of the initial turbidity value within the transition range, and calculates the weighting coefficients of the first set of weighting coefficients and the second set of weighting coefficients based on the relative position, the sum of the weighting coefficients of the first set of weighting coefficients and the weighting coefficients of the second set of weighting coefficients being 1; the high turbidity meter multiplies the first set of weighting coefficients by the corresponding weighting coefficients to obtain a first weighted result, and multiplies the second set of weighting coefficients by the corresponding weighting coefficients to obtain a second weighted result; the high turbidity meter adds the first weighted result and the second weighted result to obtain a mixed weighting coefficient; the high turbidity meter calculates the final turbidity value of the water sample based on the mixed weighting coefficient.

[0017] By adopting the above technical solution, a transition interval is set near the preset measurement threshold, and the weighting coefficients of the two sets of weighting coefficients are calculated based on the relative position of the initial turbidity value within the transition interval, achieving a smooth transition between the measurement results of the two turbidity ranges. By multiplying the two sets of weighting coefficients by their corresponding weighting coefficients and summing the results, a mixed weighting coefficient is obtained, preventing abrupt changes in the measurement results during the switching between different turbidity ranges. This dynamic weighting allocation method based on relative position improves the continuity and stability of the measurement results. The method of calculating the final turbidity value using a mixed weighting coefficient improves the reliability of the measurement process, making the measurement results more consistent with the actual turbidity variation patterns of the water sample.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the high turbidity measuring instrument calculates the relative position of the initial turbidity value within the transition interval, and calculates the weighting coefficients of the first set of weighting coefficients and the weighting coefficients of the second set of weighting coefficients based on the relative position. Specifically, this includes: the high turbidity measuring instrument calculates the upper relative distance and the lower relative distance based on the difference between the initial turbidity value and the upper and lower limits of the transition interval, respectively; the high turbidity measuring instrument divides the upper relative distance by the interval length of the transition interval to obtain the relative position; the high turbidity measuring instrument uses the relative position as the weighting coefficient of the first set of weighting coefficients; the high turbidity measuring instrument subtracts the weighting coefficient of the first set of weighting coefficients from 1 to obtain the weighting coefficient of the second set of weighting coefficients.

[0019] By employing the above technical solution, a linear weighting mechanism is established by calculating the relative distance between the initial turbidity value and the upper and lower limits of the transition interval, using this relative position as the weighting coefficient of the first set of weighting coefficients, and subtracting this weighting coefficient from 1 as the weighting coefficient of the second set of weighting coefficients. This weighting method allows the weights of the two sets of weighting coefficients to change linearly and gradually as the initial turbidity value changes within the transition interval. The closer the initial turbidity value is to the upper limit of the transition interval, the greater the weight of the second set of weighting coefficients; the closer it is to the lower limit, the greater the weight of the first set of weighting coefficients, thereby improving the smoothness of the measurement results.

[0020] In a second aspect, embodiments of this application provide a high turbidity measuring instrument, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the high turbidity measuring instrument to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a high turbidity measuring instrument, cause the high turbidity measuring instrument to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a high turbidity measuring instrument, causes the high turbidity measuring instrument to perform the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. This application provides a method for measuring high turbidity. By irradiating the water sample with red and white light sources respectively and collecting transmitted and scattered light signals, the optical characteristics of the water sample at different wavelengths can be obtained. The intensity ratio of the two wavelengths is calculated, and weighting coefficients for different turbidity ranges are determined based on standard samples, establishing a mapping relationship between the intensity ratio of the two wavelengths and the turbidity value. The initial turbidity value is used to determine whether it exceeds a preset range threshold, and the appropriate weighting coefficients are selected to calculate the final turbidity value, solving the problem of saturation in high turbidity ranges caused by single-wavelength measurements. Dual-wavelength complementary measurement improves the dynamic range of the measurement. A dedicated second set of weighting coefficients is used for calculation in the high turbidity range, reducing measurement deviations caused by multiple scattering effects in high-turbidity water samples. By utilizing the differences in scattering characteristics of different wavelengths of light in the water sample and through a reasonable weighting calculation method, the accuracy of testing high-turbidity water samples is improved.

[0025] 2. This application provides a method for measuring high turbidity. When the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio exceeds a preset range, it indicates that the physical properties of the water sample have changed. At this time, the weighting coefficient is corrected according to the magnitude of the cross ratio difference. This adaptive correction mechanism can cope with the measurement error caused by changes in the physical properties of the water sample, improving the accuracy of the measurement results. The calculation of the cross ratio utilizes information from both scattered and transmitted light in two bands, increasing the basis for judging changes in water sample properties, making the correction of the weighting coefficient more reasonable, and ensuring that reliable measurement results can still be obtained when the physical properties of the water sample change.

[0026] 3. This application provides a method for measuring high turbidity. A transition interval is set near a preset measurement threshold, and the weighting coefficients of two sets of weighting coefficients are calculated based on the relative position of the initial turbidity value within the transition interval, achieving a smooth transition between measurement results of two turbidity ranges. By multiplying each set of weighting coefficients by its corresponding weighting coefficient and summing the results, a mixed weighting coefficient is obtained, preventing abrupt changes in measurement results during the switching between different turbidity ranges. This dynamic weighting allocation method based on relative position improves the continuity and stability of the measurement results. The method of calculating the final turbidity value using a mixed weighting coefficient improves the reliability of the measurement process, making the measurement results more consistent with the actual turbidity variation patterns of water samples. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of a high turbidity measuring instrument provided in this application.

[0028] Figure 2 This is a schematic diagram of the main interface of a high turbidity measuring instrument provided in this application.

[0029] Figure 3 This is a schematic flowchart of a high turbidity measurement method in an embodiment of this application.

[0030] Figure 4 This is another schematic flowchart of a high turbidity measurement method in the embodiments of this application.

[0031] Figure 5 This is a schematic diagram of the physical device structure of a high turbidity measuring instrument provided in an embodiment of this application.

[0032] Figure labeling: ①: Measurement range; ②: Remarks; ③: Actual concentration value calculation; ④: Measurement data; ⑤: Zeroing; ⑥: Measurement; ⑦: Measurement range indicator bar; ⑧: Menu; ⑨: View; ⑩: Save to mobile phone; ⑪: Save to USB flash drive; ⑫: Print. Detailed Implementation

[0033] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0034] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0035] First, combine Figure 1 For a description of a high turbidity measuring instrument provided in this application, please refer to [link to relevant documentation]. Figure 1 The diagram below illustrates a high turbidity analyzer provided in this application. It includes a touchscreen display, a built-in printer, a sample chamber, and a power button.

[0036] The entire touchscreen display is responsive to touch. You can use your fingernail, fingertip, eraser tip, or pen to tap the screen to make selections; there are no restrictions.

[0037] When the user taps the print control on the touchscreen, the built-in printer can print the content selected by the user. The built-in printer is a thermal printer.

[0038] The sample chamber is used to hold colorimetric bottles containing the water sample to be tested and other water samples.

[0039] Then combine Figure 2The main interface of the display screen of a high turbidity measuring instrument provided in this application is described below: Please refer to Figure 2 This is a schematic diagram of the main interface of a high turbidity analyzer provided in this application. It includes the following functions and corresponding controls: ①: Measurement range; ②: Remarks; ③: Actual concentration value calculation; ④: Measurement data; ⑤: Zeroing; ⑥: Measurement; ⑦: Measurement range indicator bar; ⑧: Menu; ⑨: View; ⑩: Save to mobile phone; ⑪: Save to USB flash drive; ⑫: Print.

[0040] This application provides a method for measuring high turbidity, which is applied to the high turbidity measuring instrument. An embodiment is described below in conjunction with... Figure 3 The present application describes a method for measuring high turbidity in its embodiments:

[0041] Please see Figure 3 This is a schematic flowchart of a high turbidity measurement method in an embodiment of this application.

[0042] The S301 high turbidity meter illuminates the water sample to be tested with red light source and white light source respectively. After optical filtering by a narrow band filter set in the optical path, the corresponding transmitted light signal and 90° scattered light signal are collected to obtain the filtered transmitted light intensity, filtered scattered light intensity in the red light band, and filtered transmitted light intensity and filtered scattered light intensity in the white light band.

[0043] In this step, the high turbidity analyzer illuminates the water sample with red and white light sources, respectively. After optical filtering by a narrow-band filter placed in the optical path, the corresponding transmitted light signal and 90° scattered light signal are collected to obtain the filtered transmitted light intensity, filtered scattered light intensity, and filtered transmitted light intensity and filtered scattered light intensity in the red light band and white light band. The red light source can be an LED or laser emitting a specific red light wavelength, typically within the 600-700nm range. The white light source can be a broadband halogen lamp or a white LED, typically covering the 400-700nm visible light range. A narrow-band filter is an optical element used to selectively transmit light within a specific wavelength range while blocking other wavelengths. Its bandwidth can be selected according to measurement requirements, generally 10-50nm, but is not limited here. The transmitted light signal refers to the light signal after passing through the water sample, and the 90° scattered light signal refers to the scattered light signal at a 90-degree angle to the incident light direction. The intensity of the filtered transmitted light and the intensity of the filtered scattered light represent the intensity values ​​of the transmitted light and the scattered light after being filtered by the narrowband filter, respectively. They are usually expressed in the form of voltage values ​​or digital quantities, but are not limited here.

[0044] This step can be achieved in the following specific ways: The first method is to use a time-division multiplexing method with the high turbidity meter. First, the red LED light source is turned on. The red light passes through a collimating lens to form a parallel beam, which illuminates the cuvette containing the water sample to be tested. A first photodetector is set up directly opposite the cuvette to collect the transmitted light, and a second photodetector is set up on the side at a 90-degree angle to the incident light to collect the scattered light. A corresponding red narrowband filter is set in front of both photodetectors. After the collection is completed, the red light source is turned off, and then the white LED light source is turned on. The above process is repeated, but a white narrowband filter is used, and finally four light intensity values ​​are obtained. The second implementation method involves a dual-optical-path simultaneous measurement method for the high turbidity meter. Two independent optical systems are set up. The first system includes a red laser diode, a red collimating lens, a first cuvette, a red light transmission detector, and a red light scattering detector. The second system includes a white halogen lamp, a white light collimating lens, a second cuvette, a white light transmission detector, and a white light scattering detector. The two systems work simultaneously, with narrowband filters of corresponding wavelengths placed in front of their respective detectors. The water sample to be tested is simultaneously introduced into the two cuvettes through a spectrometer to achieve synchronous measurement of red and white light. The analog signals output by each detector are converted into digital signals by an amplification circuit and an analog-to-digital converter to obtain four light intensity values.

[0045] S302. The high turbidity meter uses the ratio of the intensity of filtered transmitted light to the intensity of filtered scattered light in the red light band as the light intensity ratio in the red light band; the high turbidity meter uses the ratio of the intensity of filtered transmitted light to the intensity of filtered scattered light in the white light band as the light intensity ratio in the white light band.

[0046] In this step, the high turbidity meter uses the ratio of the filtered transmitted light intensity to the filtered scattered light intensity in the red light band as the light intensity ratio for the red light band; similarly, it uses the ratio of the filtered transmitted light intensity to the filtered scattered light intensity in the white light band as the light intensity ratio for the white light band. The light intensity ratio is a mathematical ratio of transmitted light intensity to scattered light intensity, reflecting the combined effect of the absorption and scattering characteristics of the water sample at different wavelengths. The light intensity ratio for the red light band is denoted as R_red = I_trans_red / I_scatter_red, and the light intensity ratio for the white light band is denoted as R_white = I_trans_white / I_scatter_white, where I_trans represents the transmitted light intensity and I_scatter represents the scattered light intensity. This ratio calculation method can eliminate the influence of light source intensity fluctuations and detector response differences, improving the stability and repeatability of the measurement.

[0047] This step can be implemented in the following specific ways: The first method involves the microprocessor of the high turbidity meter directly reading the digital values ​​of the four light intensity values. First, it performs numerical calculations on the filtered transmitted light intensity value and the filtered scattered light intensity value in the red light band, calculating their ratio to obtain the light intensity ratio for the red light band. Then, it performs the same calculations on the filtered transmitted light intensity value and the filtered scattered light intensity value in the white light band to obtain the light intensity ratio for the white light band. Floating-point arithmetic is used in the calculation to ensure accuracy, and exception handling is performed for division by zero. The second method involves the high turbidity meter using analog circuitry to calculate the ratio. The output signals of the transmitted light detector and the scattered light detector are respectively sent to a logarithmic amplifier to obtain voltage signals that are logarithmically related to the light intensity. Then, a differential amplifier is used to calculate the difference between the two logarithmic signals. Since log(A / B) = log(A) - log(B), the output of the differential amplifier is the logarithmic value of the light intensity ratio. Finally, the actual light intensity ratio is obtained through an antilogarithmic circuit or a lookup table. This method can expand the dynamic range and improve the resolution of small signals.

[0048] S303, the high turbidity measuring instrument determines the red light weighting coefficient corresponding to the light intensity ratio of the red light band and the white light weighting coefficient corresponding to the light intensity ratio of the white light band in the first turbidity range and the second turbidity range, respectively, based on the correspondence between the light intensity ratio of the red light band and the turbidity value of the standard turbidity sample, and obtains the first set of weighting coefficients corresponding to the first turbidity range and the second set of weighting coefficients corresponding to the second turbidity range.

[0049] In this step, the high turbidity analyzer, based on the correspondence between the light intensity ratio and turbidity value of the standard turbidity sample, determines the red light weighting coefficient corresponding to the red light intensity ratio and the white light weighting coefficient corresponding to the white light intensity ratio in the first and second turbidity ranges, respectively. This yields the first set of weighting coefficients for the first turbidity range and the second set of weighting coefficients for the second turbidity range. The standard turbidity sample refers to a standard substance with a known accurate turbidity value, typically a formalin standard solution or a polystyrene microsphere suspension. The unit of turbidity value is NTU (turbidity unit). The first turbidity range usually refers to the low turbidity range, such as 0-1000 NTU, and the second turbidity range usually refers to the high turbidity range, such as 1000-4000 NTU. The specific range can be adjusted according to actual application requirements and is not limited here. The red light weighting coefficient and the white light weighting coefficient are weighting parameters used to calculate the final turbidity value, reflecting the relative importance of the light intensity ratio of different wavelengths to the turbidity contribution. The first set of weighting coefficients applies to the first turbidity range, and the second set of weighting coefficients applies to the second turbidity range. Each set of weighting coefficients includes two parameters: red light weighting coefficient and white light weighting coefficient.

[0050] This step can be implemented in the following specific ways: The first implementation method is to use the least squares method to fit and determine the weighting coefficients of the high turbidity meter. First, a series of standard samples with different turbidity values ​​are prepared, covering the first turbidity range and the second turbidity range. Each standard sample is measured to obtain the corresponding red light band intensity ratio and white light band intensity ratio. A linear regression model is established: NTU=a1×R_red+a2×R_white+b, where a1 is the red light weighting coefficient, a2 is the white light weighting coefficient, and b is the bias term. The data of the first turbidity range and the second turbidity range are fitted respectively. By minimizing the mean square error between the predicted value and the actual value, two sets of weighting coefficients are obtained. The second implementation method involves using a neural network to determine the weighting coefficients in the high turbidity meter. A three-layer feedforward neural network is constructed, with two nodes in the input layer receiving the intensity ratio of red and white light respectively, 10 neurons in the hidden layer, and one node in the output layer outputting the turbidity value. The network is trained using a standard sample dataset, and the network weights are optimized through backpropagation. After training, the corresponding weighting coefficients are extracted from the weight matrix from the input layer to the hidden layer. Two independent networks are trained for the first turbidity range and the second turbidity range respectively to obtain two sets of weighting coefficients.

[0051] S304. Based on the first set of weighting coefficients, the high turbidity meter calculates the initial turbidity value of the water sample to be tested.

[0052] Based on the first set of weighting coefficients, the high turbidity meter calculates the initial turbidity value of the water sample. This step can be implemented in two ways:

[0053] The first method involves multiplying the intensity ratio of the red light band by the red light weighting coefficient in the first set of weighting coefficients to obtain the turbidity component of the red light band; multiplying the intensity ratio of the white light band by the white light weighting coefficient in the first set of weighting coefficients to obtain the turbidity component of the white light band; and adding the turbidity component of the red light band to the turbidity component of the white light band to obtain the initial turbidity value. The processor of the high turbidity meter reads the first set of pre-calibrated weighting coefficients from the memory, including the red light weighting coefficient k1_red and the white light weighting coefficient k1_white. It multiplies the red light intensity ratio R_red calculated in step S302 with the red light weighting coefficient k1_red to obtain the red light turbidity component T_red = k1_red × R_red. It multiplies the white light intensity ratio R_white with the white light weighting coefficient k1_white to obtain the white light turbidity component T_white = k1_white × R_white. Finally, it adds the two turbidity components to obtain the initial turbidity value T_initial = T_red + T_white. This linear weighting method is simple to calculate, has good real-time performance, and is suitable for application scenarios with high response speed requirements.

[0054] The second method involves: the high turbidity meter establishing a two-dimensional feature vector containing the intensity ratios of the red light band and the white light band; the high turbidity meter constructing a polynomial function matrix based on the first set of weighting coefficients, where the matrix elements include different orders of red light weighting coefficients and white light weighting coefficients; the high turbidity meter convolving the two-dimensional feature vector with the polynomial function matrix to obtain turbidity feature mapping values; and the high turbidity meter correcting the turbidity feature mapping values ​​based on the pre-calibrated correspondence between the feature mapping values ​​and turbidity values ​​to obtain the initial turbidity value. The high turbidity meter first constructs a two-dimensional feature vector V=[R_red, R_white]. Then, based on the first set of weighting coefficients, it constructs a 3×3 polynomial function matrix M. The matrix elements include constant terms, linear terms, and cross terms, such as M=[[1, k1_red, k1_red²], [k1_white, k1_red×k1_white, k1_red²×k1_white], [k1_white², k1_red×k1_white², k1_red²×k1_white²]]. Through the convolution operation F=V*M, a 9-dimensional turbidity feature mapping value is obtained. Based on a pre-calibrated lookup table or fitting function of the feature mapping value and turbidity value, the feature mapping value is converted into the initial turbidity value. This method can capture the nonlinear interaction between red light and white light signals and improve measurement accuracy.

[0055] When calculating the initial turbidity value, technical issues such as numerical overflow or loss of accuracy may occur. To solve this problem, high turbidity measuring instruments can employ normalization and dynamic range adjustment techniques. Before performing weighted calculations, the light intensity ratio is normalized and mapped to the [0, 1] interval. The normalization formula is R_norm=(R-R_min) / (R_max-R_min), where R_min and R_max are the minimum and maximum values ​​of historical measurement data, respectively. Fixed-point arithmetic is used instead of floating-point arithmetic, and an appropriate fixed-point bit width and decimal places are selected to ensure that overflow does not occur during the calculation. After the calculation is completed, inverse normalization is performed to obtain the actual turbidity value. This approach ensures calculation accuracy while avoiding numerical problems.

[0056] S305. The high turbidity meter determines whether the initial turbidity value is greater than the preset range threshold.

[0057] This step is a judgment step, where the high turbidity meter determines whether the initial turbidity value is greater than the preset range threshold. The preset range threshold is the critical value that distinguishes between the low and high turbidity ranges, usually set near the upper limit of the first turbidity range, such as 800-1000 NTU. The purpose of this judgment step is to determine whether it is necessary to switch to the second set of weighting coefficients for high turbidity measurement. The judgment result will determine the subsequent processing flow. If the initial turbidity value is greater than the preset range threshold, it means that the water sample to be tested belongs to the high turbidity range, and it needs to be recalculated using the second set of weighting coefficients; if the initial turbidity value is not greater than the preset range threshold, it means that the water sample to be tested belongs to the low turbidity range, and the initial turbidity value can be directly used as the final result. The specific value of the preset range threshold can be adjusted according to the measurement range and accuracy requirements of the instrument, and is not limited here.

[0058] S306, the high turbidity meter calculates the final turbidity value of the water sample to be tested based on the second set of weighting coefficients;

[0059] If so, the high turbidity meter calculates the final turbidity value of the water sample based on the second set of weighting coefficients. The second set of weighting coefficients are parameters specifically optimized for the second turbidity range (high turbidity range), taking into account the multiple scattering effect and optical saturation phenomenon under high turbidity conditions. In the high turbidity range, the light propagation characteristics differ significantly from those in the low turbidity range, requiring the use of different weighting coefficients to ensure measurement accuracy. The final turbidity value is the accurate turbidity measurement result after correction by the second set of weighting coefficients. The calculation method is similar to step S304, and can employ linear weighted summation or polynomial function mapping, etc., without limitation here.

[0060] This step can be implemented in the following specific ways: The first implementation method is that the high turbidity measuring instrument adopts a nonlinear compensation weighted calculation method, reads the pre-stored second set of weighting coefficients, including the high turbidity red light weighting coefficient k2_red and the high turbidity white light weighting coefficient k2_white, and introduces a nonlinear compensation function f(x)=x / (1+βx), where β is the compensation coefficient. First, the light intensity ratio is nonlinearly transformed, R'_red=f(R_red), R'_white=f(R_white), and then the final turbidity value T_final=k2_red×R'_red+k2_white×R'_white+c, where c is the high turbidity bias constant. This method can effectively compensate for the signal saturation effect under high turbidity. The second implementation method is that the high turbidity meter adopts a lookup table interpolation method. A two-dimensional lookup table of light intensity ratio combinations and turbidity values ​​within the high turbidity range is pre-established. The table uses the light intensity ratio of the red light band as the row index and the light intensity ratio of the white light band as the column index. During measurement, the four closest data points are located in the lookup table according to the actual light intensity ratio. The final turbidity value is calculated using a bilinear interpolation algorithm. This method avoids complex mathematical calculations and improves the real-time response speed.

[0061] S307, the high turbidity meter uses the initial turbidity value as the final turbidity value of the water sample to be tested;

[0062] If not, the high turbidity meter will use the initial turbidity value as the final turbidity value of the water sample. This means that for water samples in the low turbidity range, the initial turbidity value calculated using the first set of weighting coefficients already has sufficient accuracy, requiring no additional correction or recalculation. This approach simplifies the low turbidity measurement process and improves measurement efficiency. The initial turbidity value is directly assigned to the final turbidity value variable, ready for subsequent display output.

[0063] The S308 high turbidity meter displays the final turbidity value of the water sample on the screen.

[0064] The display screen can be an LCD, LED digital tube, or OLED screen, displaying turbidity values ​​and units (NTU), and may also include auxiliary information such as measurement time and temperature. The display format can be customized according to user needs, such as decimal places or scientific notation; this is not limited here. This step is the output stage of the measurement results, allowing users to intuitively obtain the turbidity information of the water sample. In addition to local display, the high turbidity meter can also transmit data to a host computer or cloud platform via a communication interface for recording and analysis; this is not limited here either.

[0065] In the above embodiments, by illuminating the water sample with red and white light sources respectively and collecting transmitted and scattered light signals, optical characteristic information of the water sample at different wavelengths can be obtained. The intensity ratio of the two bands is calculated, and weighting coefficients for different turbidity ranges are determined based on standard samples, establishing a mapping relationship between the intensity ratio of the two bands and the turbidity value. Based on the initial turbidity value, it is determined whether it exceeds a preset range threshold, and the corresponding weighting coefficients are selected to calculate the final turbidity value, solving the problem of saturation in high turbidity ranges in single-band measurements. Dual-band complementary measurement improves the dynamic range of the measurement. A dedicated second set of weighting coefficients is used for calculation in the high turbidity range, reducing measurement deviations caused by multiple scattering effects in high-turbidity water samples. By utilizing the differences in scattering characteristics of different wavelengths of light in the water sample and through a reasonable weighting calculation method, the accuracy of testing high-turbidity water samples is improved.

[0066] In the above embodiments, accurate measurement of the turbidity of the water sample is achieved by using dual-band measurement with red and white light, combined with weighting coefficients for different turbidity ranges. However, in practical applications, the physical properties of the water sample may change dynamically, such as the sedimentation and aggregation of particulate matter, which can affect the accuracy of optical measurements. To solve this technical problem, this application also provides an adaptive measurement method based on cross-ratio and spectral analysis, which can detect changes in the physical properties of the water sample in real time and dynamically adjust the weighting coefficients. The method is described in detail below with reference to specific embodiments. Further, after obtaining the first set of weighting coefficients corresponding to the first turbidity range and the second set of weighting coefficients corresponding to the second turbidity range, the method further includes:

[0067] The high turbidity meter uses the ratio of the filtered scattered light intensity in the red light band to the filtered transmitted light intensity in the white light band within the first turbidity range as the first cross ratio; it uses the ratio of the filtered scattered light intensity in the white light band to the filtered transmitted light intensity in the red light band within the first turbidity range as the second cross ratio; the high turbidity meter calculates the difference between the first and second cross ratios to obtain the cross ratio difference; when the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio in the red light band exceeds a preset range, the high turbidity meter determines that the physical properties of the water sample in the first turbidity range have changed; the high turbidity meter adjusts the cross ratio difference according to a preset correction coefficient. The first set of weighting coefficients is corrected to obtain the corrected first set of weighting coefficients, and the corrected first set of weighting coefficients is replaced with the first set of weighting coefficients. The high turbidity meter calculates the amplitude spectrum of the light intensity ratio of the red band and the light intensity ratio of the white band in the second turbidity range. The high turbidity meter extracts the characteristic frequency components in the amplitude spectrum. When the energy density of the characteristic frequency components exceeds the preset threshold, the high turbidity meter determines that the water sample to be tested in the second turbidity range has agglomerated. The high turbidity meter performs nonlinear compensation on the second set of weighting coefficients according to the energy density distribution of the characteristic frequency components to obtain the corrected second set of weighting coefficients, and the corrected second set of weighting coefficients is replaced with the second set of weighting coefficients.

[0068] The high turbidity meter calculates the first cross ratio, which is the ratio of the intensity of filtered scattered light in the red light band to the intensity of filtered transmitted light in the white light band. The first cross ratio is denoted as CR1 = I_scatter_red / I_trans_white, where I_scatter_red represents the intensity of scattered light in the red light band, and I_trans_white represents the intensity of transmitted light in the white light band. This cross ratio calculation method breaks the traditional ratio relationship between transmitted and scattered light within the same wavelength band, establishing a correlation between scattering and transmission across different wavelength bands. The first turbidity range typically refers to the low turbidity range of 0-1000 NTU, within which the optical properties of the water sample are relatively stable. The cross ratio reflects the interrelationship between the scattering and transmission characteristics of different wavelengths of light in the water sample and has high sensitivity to changes in particle size distribution and concentration of particulate matter in the water sample. The numerical range of the first cross ratio can be from 0.01 to 100, depending on the turbidity and particle characteristics of the water sample, and is not limited here.

[0069] This step can be implemented in the following specific ways: The first method involves the data acquisition module of the high turbidity meter synchronously reading the output signals of the red light scattering detector and the white light transmission detector. After analog-to-digital conversion, the corresponding digital values ​​are obtained. The microprocessor directly performs division to calculate the first cross ratio CR1. To avoid a zero divisor, when the white light transmission intensity is below the detection limit, it is set to the minimum non-zero value to ensure calculation stability. The second method involves the high turbidity meter using a logarithmic domain operation method. First, the red light scattering intensity and the white light transmission intensity are converted to logarithmic domain signals by logarithmic amplifiers. Then, the difference between the two is calculated using a subtraction circuit. Since log(A / B) = log(A) - log(B), the output of the subtraction circuit is the logarithmic value of the first cross ratio. Finally, the ratio in the linear domain is recovered using exponential transformation or a lookup table method. This method can expand the dynamic range and improve the calculation accuracy under small signal conditions.

[0070] The high turbidity meter calculates the second cross ratio, which is the ratio of the intensity of filtered scattered light in the white light band to the intensity of filtered transmitted light in the red light band. The second cross ratio is denoted as CR2 = I_scatter_white / I_trans_red, where I_scatter_white represents the intensity of scattered light in the white light band, and I_trans_red represents the intensity of transmitted light in the red light band. The second cross ratio complements the first cross ratio, and their combination provides a more comprehensive reflection of the optical properties of the water sample. Because red and white light have different penetrating abilities and scattering characteristics in water, the second cross ratio may have different sensitivities to changes in certain properties of particulate matter. The calculation method and numerical range of the second cross ratio are similar to those of the first cross ratio, but its physical meaning differs; it reflects the relationship between white light scattering and red light transmission, which is not limited here.

[0071] This step can be implemented in the following specific ways: The first method involves the high turbidity meter calculating the first cross ratio in parallel with the second cross ratio. Parallel computation is achieved using a high-speed processor such as an FPGA or DSP. Data on the intensity of white light scattered light and the intensity of red light transmitted light are transferred to the processor via DMA (Direct Memory Access) to reduce data transmission latency. The processor uses a pipelined architecture for division, completing the ratio calculation within one clock cycle, thus improving real-time performance. The second method involves the high turbidity meter using an analog ratio circuit. Analog multiplier and divider chips, such as AD633 or MPY634, are used. The output voltage signal from the white light scattered light detector is connected to the numerator of the divider, and the output voltage signal from the red light transmitted light detector is connected to the denominator. The divider directly outputs the analog voltage of the second cross ratio, which is then converted into a digital value by a buffer amplifier and an ADC for subsequent processing.

[0072] The high turbidity analyzer calculates the difference between the first cross-ratio CR1 and the second cross-ratio CR2, yielding the cross-ratio difference ΔCR = CR1 - CR2. This cross-ratio difference reflects the asymmetry between red and white light in the scattering-transmission cross-measurement, and this asymmetry is closely related to the characteristics of particulate matter in the water sample. When the physical properties of the water sample remain stable, the cross-ratio difference should remain within a relatively constant range; however, when particulate matter undergoes sedimentation, aggregation, or dispersion, the cross-ratio difference will change significantly. The cross-ratio difference can be positive or negative; its absolute value reflects the degree of asymmetry, while the sign indicates the direction of the asymmetry, which is not limited here.

[0073] This step can be implemented in the following specific ways: The first method involves the microprocessor of the high turbidity analyzer directly performing a subtraction operation to obtain the cross-ratio difference after obtaining the first and second cross-ratios. To improve calculation accuracy, double-precision floating-point numbers are used for the operation, and the result is checked for range to ensure the cross-ratio difference is within a reasonable range. If it exceeds the preset upper and lower limits, an anomaly is flagged and an alarm mechanism is triggered. The second method involves the high turbidity analyzer using a differential amplifier circuit to calculate the cross-ratio difference. The analog voltage signals representing the first and second cross-ratios are connected to the positive and negative input terminals of the instrumentation amplifier, respectively. The gain of the instrumentation amplifier is set to 1, and the output voltage directly represents the cross-ratio difference. This analog-domain difference calculation can reduce quantization errors caused by digital conversion.

[0074] When the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio in the red light band exceeds the preset range, the high turbidity meter determines that the physical properties of the water sample to be tested in the first turbidity range have changed.

[0075] The high turbidity meter adaptively corrects the first set of weighting coefficients based on the magnitude of the cross-ratio difference ΔCR. The preset correction coefficients are determined based on the empirical relationship between the cross-ratio difference and the weighting coefficient deviation, typically using piecewise linear or nonlinear functions. The corrected red light weighting coefficient is k1_red_modified = k1_red × (1 + f(ΔCR)), and the corrected white light weighting coefficient is k1_white_modified = k1_white × (1 + g(ΔCR)), where f(ΔCR) and g(ΔCR) are correction functions. The specific form of the correction function can be a linear function, a polynomial function, or a lookup table interpolation function, determined based on actual calibration data; no specific limitation is made here. The corrected weighting coefficients replace the original first set of weighting coefficients and are used in subsequent turbidity calculations.

[0076] This step can be implemented in the following specific ways: The first method involves the high turbidity meter employing a piecewise linear correction method, dividing the cross-ratio difference range into multiple intervals, such as [-10, -5], [-5, 0], [0, 5], [5, 10], etc. Each interval corresponds to a different correction coefficient. When ΔCR falls within a certain interval, the correction coefficient of that interval is used to linearly correct the weighting coefficients. The correction formula is k_modified = k_original × (1 + α × ΔCR), where α is the correction slope for that interval, determined through experimental calibration. The second method involves the high turbidity meter employing a neural network adaptive correction method. A small neural network is constructed, with the cross-ratio difference and the original weighting coefficients as inputs, and the corrected weighting coefficients as outputs. The network structure is a 3-layer fully connected network with 20 neurons in the hidden layers, using the ReLU activation function. The network parameters are continuously optimized through online learning to achieve intelligent correction of the weighting coefficients.

[0077] The high turbidity meter performs spectral analysis on the light intensity ratio signals within the second turbidity range (high turbidity range, such as 1000-4000 NTU), calculating the amplitude spectra of the light intensity ratios in the red and white light bands respectively. The amplitude spectrum reflects the energy distribution of the signal across different frequency components, and its analysis can identify the dynamic characteristics of particulate matter in the water sample. Under high turbidity conditions, processes such as Brownian motion, sedimentation, and aggregation of particulate matter generate specific frequency components in the light intensity ratio signal. The amplitude spectrum is typically calculated using the Fast Fourier Transform (FFT) algorithm to convert the time-domain signal to the frequency domain for analysis. The calculated amplitude spectrum includes all frequency components from the DC component to the Nyquist frequency, expressed in units of amplitude value or power spectral density, which are not specified here.

[0078] This step can be implemented in the following specific ways: The first method involves the high turbidity meter employing an FFT-based spectral analysis method. First, 1024 light intensity ratio data points are continuously collected at a sampling rate of 100Hz to form a time series. The data is then processed using a Hanning window function to reduce spectral leakage. Next, a 1024-point FFT operation is performed to obtain a complex spectrum. The modulus of the complex spectrum is calculated to obtain the amplitude spectrum, with a frequency resolution of 0.0977Hz. The same processing is applied to the light intensity ratio sequences of red and white light respectively to obtain two amplitude spectra. The second method involves the high turbidity meter using a Short-Time Fourier Transform (STFT) method. The long-term light intensity ratio signal is divided into multiple short-time windows, each with a length of 256 sampling points. Adjacent windows overlap by 50%. An FFT transformation is performed on each window to obtain a time-spectrum. By averaging the time-spectrum over the time dimension, a stable amplitude spectrum estimate is obtained. This method can track the changes in spectral characteristics over time.

[0079] The high turbidity analyzer extracts characteristic frequency components from the calculated amplitude spectrum. Characteristic frequency components refer to frequency elements in the amplitude spectrum with significant peaks or specific patterns; these characteristic frequencies are related to the physical processes of particulate matter in the water sample. For example, low-frequency components (0.1-1 Hz) may correspond to the slow settling process of particulate matter, mid-frequency components (1-5 Hz) may reflect the aggregation dynamics of particulate matter, and high-frequency components (5-10 Hz) may be related to fluid turbulence or mechanical vibration. Characteristic frequencies can be extracted using methods such as peak detection, spectral line identification, or mode matching. The extracted characteristic frequency components include frequency values, amplitude values, and phase information, which are used for subsequent water sample condition assessment and weighting coefficient compensation.

[0080] This step can be implemented in the following specific ways: The first method involves the high turbidity meter employing an adaptive threshold peak detection method. First, the statistical characteristics of the amplitude spectrum are calculated, including the mean μ and standard deviation σ. The detection threshold is set to μ+2σ. The entire amplitude spectrum is scanned, and all local maxima exceeding the threshold are identified as candidate characteristic frequencies. Cluster analysis is performed on these candidate frequencies, merging similar frequency points to obtain 3-5 main characteristic frequency components and their corresponding amplitude values. The second method involves the high turbidity meter using principal component analysis (PCA) to extract characteristic frequencies. The amplitude spectra obtained from multiple measurements are combined into a data matrix, with each row representing the amplitude spectrum of a single measurement. PCA decomposition is performed on the data matrix, and the first three principal components are selected. The frequencies corresponding to these principal components are the characteristic frequency components. The importance weight of each characteristic frequency is determined by analyzing the loading vectors of the principal components.

[0081] When the energy density of the characteristic frequency component exceeds the preset threshold, the high turbidity meter determines that the water sample in the second turbidity range has agglomerated.

[0082] The high turbidity analyzer performs nonlinear compensation on the second set of weighting coefficients based on the energy density distribution of characteristic frequency components. The energy density distribution reflects the relative intensity of different frequency components and is related to the degree and mode of particulate matter aggregation. The nonlinear compensation function is designed based on the characteristics of the energy density distribution and can take the form of a polynomial function, exponential function, or piecewise function. The compensated red light weighting coefficient is k²_red_compensated = k²_red × h(E), and the compensated white light weighting coefficient is k²_white_compensated = k²_white × j(E), where E is the energy density distribution vector, and h(E) and j(E) are the nonlinear compensation functions. The parameters of the compensation function are obtained through aggregation experiments and can effectively correct measurement deviations caused by aggregation effects.

[0083] This step can be implemented in the following specific ways: The first method involves the high turbidity meter employing a compensation method based on energy density ratio. It calculates the energy density ratio R_energy between the 1-3Hz band and the 0.1-1Hz band. Based on a pre-calibrated compensation curve, a compensation factor is determined. The compensation curve is fitted using a cubic polynomial: C(R) = a0 + a1×R + a2×R² + a3×R³, where coefficients a0 to a3 are determined using the least squares method. The original weighting coefficients are multiplied by the compensation factor to obtain the corrected weighting coefficients. The second method involves the high turbidity meter employing a fuzzy logic compensation method. Multiple features of the energy density distribution (such as dominant frequency, bandwidth, and peak value) are used as input to the fuzzy logic system. A fuzzy rule base is defined, such as "if the dominant frequency is high and the bandwidth is narrow, then the compensation coefficient is large." The compensation coefficients are obtained through fuzzy inference, and defuzzification is performed using the centroid method to obtain a definite compensation value, which is then applied to correct the weighting coefficients.

[0084] In the above embodiments, when the ratio of the rate of change of the cross ratio difference to the rate of change of the light intensity ratio exceeds a preset range, it indicates that the physical properties of the water sample have changed. At this time, the weighting coefficient is corrected according to the magnitude of the cross ratio difference. This adaptive correction mechanism can cope with the measurement error caused by changes in the physical properties of the water sample, improving the accuracy of the measurement results. The calculation of the cross ratio utilizes information from both scattered and transmitted light in the dual-band, increasing the basis for judging changes in water sample properties, making the correction of the weighting coefficient more reasonable, and ensuring that reliable measurement results can still be obtained when the physical properties of the water sample change.

[0085] The above embodiments, by setting a transition range and employing a mixed weighting coefficient method, achieve a smooth transition between different turbidity ranges, effectively avoiding abrupt measurement changes during range switching. To further improve measurement accuracy within the transition range, the following describes... Figure 4 Another method for measuring high turbidity in this application is described below: Please refer to Figure 4 This is another flowchart illustrating a high turbidity measurement method in an embodiment of this application.

[0086] S401, The high turbidity measuring instrument sets the transition range with the preset range threshold as the median value;

[0087] The preset range threshold is the critical value that distinguishes the first turbidity range from the second turbidity range. It is usually set in the range of 800-1000 NTU, with the specific value determined based on the instrument's measurement characteristics and application requirements. The transition range is a turbidity range centered on the preset range threshold, used to achieve a smooth transition between different turbidity ranges. The transition range is set symmetrically, extending the same turbidity range both above and below the preset range threshold. For example, if the preset range threshold is 900 NTU and the transition range width is 200 NTU, then the transition range is [800 NTU, 1000 NTU]. The width of the transition range can be adjusted according to actual measurement needs, generally taking 10%-30% of the preset range threshold; no specific limit is set here. The purpose of the transition range is to avoid abrupt changes or oscillations in the measured values ​​near the range switching point, improving the stability and continuity of the measurement.

[0088] This step can be implemented in the following ways: The first method involves the high turbidity meter reading the preset range threshold T_threshold from the configuration parameters during the initialization phase. Then, based on the preset transition interval width ratio coefficient α (e.g., α=0.2), it calculates the half-width ΔT=α×T_threshold / 2 of the transition interval, thereby determining the lower limit T_lower=T_threshold-ΔT and the upper limit T_upper=T_threshold+ΔT of the transition interval. These three parameters are stored in the instrument's register or memory for subsequent judgment. The second method involves the high turbidity meter employing an adaptive transition interval setting method. This method dynamically adjusts the transition interval width based on the statistical characteristics of historical measurement data. Specifically, it collects data from the last 100 measurements where the turbidity value is close to the preset range threshold, calculates the standard deviation σ of these data, and sets the transition interval to [T_threshold-3σ, T_threshold+3σ], ensuring that 99.7% of the measurements transition smoothly within the transition interval. Simultaneously, it sets minimum and maximum transition interval width limits to prevent the transition interval from being too narrow or too wide.

[0089] S402. When the initial turbidity value is in the transition range, the high turbidity measuring instrument calculates the relative position of the initial turbidity value in the transition range, and calculates the weight coefficient of the first set of weighting coefficients and the weight coefficient of the second set of weighting coefficients based on the relative position.

[0090] The high turbidity meter calculates the relative position of the initial turbidity value within the transition interval, and calculates the weighting coefficients of the first and second sets of weighted coefficients based on the relative position. Specifically, the high turbidity meter calculates the upper and lower relative distances based on the differences between the initial turbidity value and the upper and lower limits of the transition interval, respectively; the high turbidity meter divides the upper relative distance by the length of the transition interval to obtain the relative position; the high turbidity meter uses the relative position as the weighting coefficient of the first set of weighted coefficients; the high turbidity meter subtracts the weighting coefficient of the first set of weighted coefficients from 1 to obtain the weighting coefficient of the second set of weighted coefficients. The relative position refers to the normalized position of the initial turbidity value within the transition interval, with a value range of [0, 1], where 0 indicates the lower limit of the transition interval and 1 indicates the upper limit of the transition interval. The formula for calculating the relative position is: P = (T_initial - T_lower) / (T_upper - T_lower), where T_initial is the initial turbidity value, and T_lower and T_upper are the lower and upper limits of the transition interval, respectively. Weighting coefficients are used to determine the contribution ratio of the two sets of weighting coefficients in calculating the final turbidity value. The weighting coefficient of the first set of weighting coefficients is denoted as W1, and the weighting coefficient of the second set of weighting coefficients is denoted as W2. Both satisfy the constraint W1 + W2 = 1. The weighting coefficients can be calculated using linear functions, sigmoid functions, or other smooth functions; no limitation is made here.

[0091] This step can be implemented in the following specific ways: The first implementation method is that the high turbidity measuring instrument adopts a linear weighting method. First, it is determined whether the initial turbidity value is within the transition interval [T_lower, T_upper]. If it is within the interval, the relative position P=(T_initial-T_lower) / (T_upper-T_lower) is calculated. Then, the weighting coefficients are calculated according to the linear relationship: W1=1-P, W2=P. This method is simple to calculate, and the weighting coefficients change linearly with the relative position. When the initial turbidity value is close to the lower limit of the transition interval, the first set of weighting coefficients dominates. When it is close to the upper limit, the second set of weighting coefficients dominates. The second implementation method is that the high turbidity meter adopts the S-curve weight allocation method and uses the sigmoid function to calculate the weight coefficients. First, the relative position P is mapped to the interval [-6, 6]: x = 12 × P - 6. Then, the sigmoid function value is calculated: S(x) = 1 / (1 + exp(-x)). The weight of the second set of weighted coefficients is W2 = S(x), and the weight of the first set of weighted coefficients is W1 = 1 - S(x). This method has a slower change in weight at both ends of the transition interval and a faster change in the middle part, providing a smoother transition effect.

[0092] When calculating relative positions and weighting coefficients, insufficient numerical calculation precision may lead to inaccurate weight allocation. To address this issue, high turbidity analyzers can employ a combination of high-precision fixed-point arithmetic and lookup table methods. The weighting coefficients corresponding to different relative positions are pre-calculated, and a lookup table containing 1001 data points is established with a precision interval of 0.001 and stored in the instrument's ROM. During measurement, the fixed-point representation of the relative position is first calculated (using 16-bit fixed-point numbers, with 10 decimal places). Then, the corresponding weighting coefficient is obtained by looking up the table. For precise values ​​not found in the lookup table, linear interpolation is used for calculation. This approach ensures both calculation accuracy and improved computational speed.

[0093] S403, the high turbidity measuring instrument multiplies the first set of weighting coefficients with the corresponding weighting coefficients to obtain the first weighted result, and multiplies the second set of weighting coefficients with the corresponding weighting coefficients to obtain the second weighted result;

[0094] The first weighted result includes weighted red light and white light weighted coefficients after weight adjustment, calculated using the formulas: k1_red_weighted = k1_red × W1, k1_white_weighted = k1_white × W1, where k1_red and k1_white are the original weighted coefficients of the first group, and W1 is the weight coefficient of the first group. Similarly, the second weighted result is calculated using the formulas: k2_red_weighted = k2_red × W2, k2_white_weighted = k2_white × W2. This weighting process enables dynamic adjustment of the two sets of weighted coefficients, allowing the contribution ratio of the two sets of coefficients to be automatically adjusted according to the magnitude of the turbidity value within the transition range. The numerical range of the weighted result depends on the values ​​of the original weighted coefficients and the weight coefficients, and normalization is usually required to ensure numerical stability; however, this is not limited here.

[0095] This step can be implemented in the following specific ways: The first implementation method is that the high turbidity measuring instrument adopts a parallel multiplication operation mode, using the parallel multiplier unit in the DSP processor or FPGA to perform four multiplication operations simultaneously: k1_red×W1, k1_white×W1, k2_red×W2, and k2_white×W2. Each multiplier adopts 32-bit floating-point operation precision, and the operation result is stored in the corresponding register. The entire operation process is completed within one clock cycle, which improves the calculation efficiency. The second implementation method involves using matrix operations in the high turbidity analyzer to organize the weighting coefficients into a 2×2 matrix: K1=[[k1_red, 0], [0, k1_white]], K2=[[k2_red, 0], [0, k2_white]], and the weighting coefficients form a diagonal matrix: W1_matrix=[[W1, 0], [0, W1]], W2_matrix=[[W2, 0], [0, W2]]. The weighted result is obtained through matrix multiplication K1_weighted=K1×W1_matrix and K2_weighted=K2×W2_matrix, and efficient calculation is achieved using a dedicated matrix operation accelerator or SIMD instruction set.

[0096] S404, the high turbidity measuring instrument adds the first weighted result and the second weighted result to obtain the mixed weighting coefficient;

[0097] The mixed weighting coefficient is a linear combination of two sets of weighted results, including the mixed red light weighting coefficient and white light weighting coefficient. The specific calculation formulas are: k_mixed_red = k1_red_weighted + k2_red_weighted = k1_red × W1 + k2_red × W2, k_mixed_white = k1_white_weighted + k2_white_weighted = k1_white × W1 + k2_white × W2. Since the weighting coefficients satisfy the constraint W1 + W2 = 1, the mixed weighting coefficient is essentially the weighted average of the two original weighting coefficients. This mixing method ensures that within the transition range, the measurement result can smoothly transition from being dominated by the first set of weighting coefficients to being dominated by the second set, avoiding discontinuities during range switching. The value range of the mixed weighting coefficient lies between the two original weighting coefficients; the specific value depends on the allocation of the weighting coefficients and is not limited here.

[0098] This step can be implemented in the following specific ways: The first implementation method is that the high turbidity measuring instrument adopts the direct addition operation method. The processor reads the four weighted results calculated in step S403 and performs two addition operations: k_mixed_red=k1_red_weighted+k2_red_weighted, k_mixed_white=k1_white_weighted+k2_white_weighted. In order to improve the calculation accuracy, a double-precision floating-point adder is used, and rounding is performed after the addition operation to retain 6 significant digits of the result and store it in a dedicated mixing coefficient register. The second implementation method involves the high turbidity measuring instrument employing a vectorized operation method. The first and second weighted results are organized into vector forms: V1=[k1_red_weighted, k1_white_weighted], V2=[k2_red_weighted, k2_white_weighted]. Using the vector addition instruction in the SIMD (Single Instruction Multiple Data) instruction set, the addition operation of the two components is completed in one go: V_mixed=V1+V2, resulting in the mixed weighted coefficient vector [k_mixed_red, k_mixed_white]. This method can improve the computational efficiency and is particularly suitable for application scenarios that require frequent calculation of mixing coefficients.

[0099] The S405 high turbidity meter calculates the final turbidity value of the water sample based on a mixed weighting coefficient.

[0100] The final turbidity value is calculated using a similar method to the initial turbidity value, but with dynamically adjusted mixing weighting coefficients. The formula is: T_final = k_mixed_red × R_red + k_mixed_white × R_white, where k_mixed_red and k_mixed_white are the mixing weighting coefficients obtained in step S404, and R_red and R_white are the intensity ratios of the red and white light bands. This calculation method based on mixing weighting coefficients provides continuous and smooth measurement results within the transition range, effectively solving the measurement jump problem that may occur near the range switching point in traditional methods. The unit of the final turbidity value is NTU, and the numerical range depends on the instrument's measurement range, typically 0-4000 NTU, which is not limited here. After calculation, the final turbidity value will be used for display output and data recording.

[0101] This step can be implemented in the following specific ways: The first implementation method is that the high turbidity meter adopts the standard linear calculation method. The processor reads the mixing weighting coefficients k_mixed_red and k_mixed_white, as well as the current light intensity ratios R_red and R_white, from the register, and performs two multiplication operations and one addition operation: T_final=k_mixed_red×R_red+k_mixed_white×R_white. To ensure the calculation accuracy, the IEEE754 double-precision floating-point arithmetic standard is adopted. After the calculation is completed, the result is checked for range to ensure that the final turbidity value is within the effective range of 0-4000 NTU. If it exceeds the range, saturation processing is performed. The second implementation method is that the high turbidity meter adopts a lookup table interpolation optimization method. A three-dimensional lookup table of the combination of mixing weighting coefficients and light intensity ratios and turbidity values ​​is established in advance. The table is indexed by the mixing red light weighting coefficient, mixing white light weighting coefficient and light intensity ratio, and stores the corresponding turbidity values. During measurement, the eight closest data points are located in the lookup table according to the actual parameters, and the final turbidity value is calculated using a trilinear interpolation algorithm. This method can include a nonlinear correction factor to improve the measurement accuracy in the transition range.

[0102] In the above embodiments, a transition interval is set near a preset measurement threshold, and the weighting coefficients of the two sets of weighting coefficients are calculated based on the relative position of the initial turbidity value within the transition interval, achieving a smooth transition between the measurement results of the two turbidity ranges. By multiplying the two sets of weighting coefficients by their corresponding weighting coefficients and summing them, a mixed weighting coefficient is obtained, preventing abrupt changes in the measurement results during the switching between different turbidity ranges. This dynamic weighting allocation method based on relative position improves the continuity and stability of the measurement results. The method of using mixed weighting coefficients to calculate the final turbidity value improves the reliability of the measurement process, making the measurement results more consistent with the turbidity variation patterns of actual water samples.

[0103] The high turbidity measuring instrument in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 5 This is a schematic diagram of the physical device structure of a high turbidity measuring instrument provided in an embodiment of this application.

[0104] It should be noted that, Figure 5 The structure of the high turbidity measuring instrument shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0105] like Figure 5As shown, the high turbidity measuring instrument includes a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0106] The following components are connected to the I / O interface: input sections including cameras, infrared sensors, etc.; output sections including liquid crystal displays (LCDs) and speakers, etc.; storage sections including hard drives, etc.; and communication sections including network interface cards such as LAN (Local Area Network) cards and modems, etc. The communication section performs communication processing via a network such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as needed.

[0107] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the various functions defined in the present invention.

[0108] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the high turbidity measuring instrument, method, and computer program product according to various embodiments of the present invention. Each block in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0110] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the high turbidity measuring instrument described in the above embodiments; or it may exist independently and not assembled into the high turbidity measuring instrument. The storage medium carries one or more computer programs that, when executed by a processor of a high turbidity measuring instrument, cause the high turbidity measuring instrument to implement the methods provided in the above embodiments.

[0111] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0112] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0113] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A high turbidity measurement method applied to a high turbidity measuring instrument, characterized by, The method comprises: The high turbidity tester irradiates the water sample to be measured by a red light source and a white light source respectively, and collects corresponding transmission light signals and 90° scattering light signals after optical filtering by a narrow-band optical filter arranged in the light path, to obtain filtered transmission light intensity and filtered scattering light intensity of the red light band, and filtered transmission light intensity and filtered scattering light intensity of the white light band; The high turbidity tester takes the ratio of the filtered transmission light intensity and the filtered scattering light intensity of the red light band as the light intensity ratio of the red light band; and takes the ratio of the filtered transmission light intensity and the filtered scattering light intensity of the white light band as the light intensity ratio of the white light band; The high turbidity tester determines the red light weighting coefficient corresponding to the light intensity ratio of the red light band and the white light weighting coefficient corresponding to the light intensity ratio of the white light band in the first turbidity range and the second turbidity range based on the corresponding relationship between the light intensity ratio and the turbidity value of the standard turbidity sample, to obtain a first set of weighting coefficients corresponding to the first turbidity range and a second set of weighting coefficients corresponding to the second turbidity range; The high turbidity tester calculates the initial turbidity value of the water sample to be measured based on the first set of weighting coefficients; The high turbidity tester determines whether the initial turbidity value is greater than a preset range threshold; If yes, the high turbidity tester calculates the final turbidity value of the water sample to be measured based on the second set of weighting coefficients; If no, the high turbidity tester takes the initial turbidity value as the final turbidity value of the water sample to be measured; The high turbidity tester displays the final turbidity value of the water sample to be measured on the display screen.

2. The method of claim 1, wherein, The step of calculating the initial turbidity value of the water sample to be measured based on the first set of weighting coefficients comprises: The high turbidity tester multiplies the light intensity ratio of the red light band by the red light weighting coefficient in the first set of weighting coefficients to obtain a red light band turbidity component; The high turbidity tester multiplies the light intensity ratio of the white light band by the white light weighting coefficient in the first set of weighting coefficients to obtain a white light band turbidity component; The high turbidity tester adds the red light band turbidity component and the white light band turbidity component to obtain the initial turbidity value.

3. The method of claim 1, wherein, The step of calculating the initial turbidity value of the water sample to be measured based on the first set of weighting coefficients comprises: The high turbidity tester establishes a two-dimensional feature vector containing the light intensity ratio of the red light band and the light intensity ratio of the white light band; The high turbidity tester constructs a polynomial function matrix according to the first set of weighting coefficients, and the matrix elements in the polynomial function matrix include different order combinations of the red light weighting coefficient and the white light weighting coefficient; The high turbidity tester performs convolution operation on the two-dimensional feature vector and the polynomial function matrix to obtain a turbidity feature mapping value; The high turbidity tester corrects the turbidity feature mapping value based on the corresponding relationship between the feature mapping value and the turbidity value calibrated in advance to obtain the initial turbidity value.

4. The method of claim 1, wherein, After the first set of weighting coefficients corresponding to the first turbidity range and the second set of weighting coefficients corresponding to the second turbidity range are obtained, the method further comprises: The high-turbidity tester takes the ratio of the filtered scattered light intensity of the red light band to the filtered transmitted light intensity of the white light band in the first turbidity range as a first cross-ratio; The high-turbidity tester takes the ratio of the filtered scattered light intensity of the white light band to the filtered transmitted light intensity of the red light band in the first turbidity range as a second cross-ratio; The high-turbidity tester calculates the difference between the first cross-ratio and the second cross-ratio to obtain a cross-ratio difference; When the ratio of the change rate of the cross-ratio difference to the change rate of the light intensity ratio of the red light band exceeds a preset range, the high-turbidity tester determines that the physical property of the water sample to be measured in the first turbidity range has changed; The high-turbidity tester corrects the first set of weighting coefficients according to the size of the cross-ratio difference by a preset correction coefficient to obtain a corrected first set of weighting coefficients, and replaces the first set of weighting coefficients with the corrected first set of weighting coefficients.

5. The method of claim 4, wherein, The method further comprises: The high-turbidity tester calculates the amplitude spectrum of the light intensity ratio of the red light band to the light intensity ratio of the white light band in the second turbidity range, respectively; The high-turbidity tester extracts a characteristic frequency component in the amplitude spectrum; When the energy density of the characteristic frequency component exceeds a preset threshold, the high-turbidity tester determines that the water sample to be measured in the second turbidity range has agglomerated; The high-turbidity tester performs nonlinear compensation on the second set of weighting coefficients according to the energy density distribution of the characteristic frequency component to obtain a corrected second set of weighting coefficients, and replaces the second set of weighting coefficients with the corrected second set of weighting coefficients.

6. The method of claim 1, wherein, After the high-turbidity tester calculates the initial turbidity value of the water sample to be measured based on the first set of weighting coefficients, the method further comprises: The high-turbidity tester sets a transition interval with the preset range threshold as the median value; When the initial turbidity value is located in the transition interval, the high-turbidity tester calculates the relative position of the initial turbidity value in the transition interval, and calculates the weight coefficient of the first set of weighting coefficients and the weight coefficient of the second set of weighting coefficients based on the relative position, the sum of the weight coefficient of the first set of weighting coefficients and the weight coefficient of the second set of weighting coefficients being 1; The high-turbidity tester multiplies the first set of weighting coefficients by the corresponding weight coefficient to obtain a first weighted result, and multiplies the second set of weighting coefficients by the corresponding weight coefficient to obtain a second weighted result; The high-turbidity tester adds the first weighted result and the second weighted result to obtain a mixed weighting coefficient; The high-turbidity tester calculates the final turbidity value of the water sample to be measured based on the mixed weighting coefficient.

7. The method of claim 6, wherein, The high-turbidity tester calculates the relative position of the initial turbidity value in the transition interval, and calculates the weight coefficient of the first set of weighting coefficients and the weight coefficient of the second set of weighting coefficients based on the relative position, specifically comprising: The high turbidity tester calculates the upper relative distance and the lower relative distance respectively by the difference between the initial turbidity value and the upper limit value and the lower limit value of the transition interval; The high turbidity tester obtains the relative position by dividing the upper relative distance by the interval length of the transition interval; The high turbidity tester takes the relative position as the weight coefficient of the first set of weighting coefficients; The high turbidity tester obtains the weight coefficient of the second set of weighting coefficients by subtracting the weight coefficient of the first set of weighting coefficients from 1.

8. A high turbidity meter characterized by, The high turbidity tester comprises: One or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to enable the high turbidity tester to perform the method according to any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions run on the high turbidity tester, the high turbidity tester performs the method according to any one of claims 1-7.

10. A computer program product, characterised in that, When the computer program product runs on the high turbidity tester, the high turbidity tester performs the method according to any one of claims 1-7.

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