Method for calculating concentration of suspended matters in water body based on multi-wavelength detection

Abstract

The invention relates to the technical field of concentration calculation, in particular to a water suspended matter concentration calculation method based on multi-wavelength detection, which comprises the following steps of: adopting a multi-spectral measurement method, utilizing light beams with different wavelengths to penetrate through a water body, and identifying particle size distribution and morphological characteristics of suspended matter particles; separating and correcting the turbidity contribution of the particles with different particle sizes through a least square fitting algorithm; decomposing the measured optical signal into a basic scattering component, a particle size dependent component and a background signal component by adopting a multi-level signal decoupling algorithm; and in combination with data after particle characteristic correction and optical signal decoupling, constructing a suspended matter concentration calculation model based on particle dynamics, calculating the water suspended matter concentration by using the dynamic model, and outputting suspended matter concentration data. According to the method, dynamic modeling is carried out on the concentration of the suspended matter, a dynamic equilibrium equation is solved through numerical values, and the concentration change of the suspended matter in time and depth is accurately described.

Description

Technical Field

[0001] The present invention relates to the technical field of concentration calculation, and particularly to a method for calculating the concentration of suspended solids in water based on multi-wavelength detection. Background Art

[0002] The concentration of suspended solids in water is an important indicator for measuring water quality and evaluating water environment, and is widely used in fields such as river treatment, lake monitoring, industrial sewage supervision, and sewage treatment. Currently, the measurement methods for suspended solids concentration mainly include laboratory sampling analysis and on-site optical sensor detection. However, these methods have the following deficiencies in practical applications:

[0003] Laboratory analysis usually adopts the gravimetric method or the turbidimetric method, which requires collecting water samples and processing them through procedures such as centrifugation and filtration. This method is complex in operation and time-consuming, and it is difficult to meet the requirements of real-time monitoring. In addition, the water samples may change during the processes of collection, transportation, and storage, resulting in a decrease in the accuracy of concentration measurement.

[0004] On-site measurement methods usually rely on optical sensors to estimate the suspended solids concentration by measuring the intensity of transmitted light or scattered light. However, in a high turbidity environment, the light scattering effect is prone to saturation, resulting in a large deviation in concentration calculation. At the same time, the dynamic changes in the particle size distribution, shape, and composition of suspended solids in water make it impossible for single-wavelength measurement to accurately reflect the true concentration of suspended solids. In addition, background light interference, instrument noise, and signal coupling problems further reduce the stability and reliability of measurement. Summary of the Invention

[0005] The present invention provides a method for calculating the concentration of suspended solids in water based on multi-wavelength detection.

[0006] The method for calculating the concentration of suspended solids in water based on multi-wavelength detection includes the following steps:

[0007] S1, Particle characteristic correction: Adopt a multi-spectral measurement method, use light beams of different wavelengths to penetrate the water body, identify the particle size distribution and morphological characteristics of suspended solids particles, and separate and correct the turbidity contributions of particles with different particle sizes through the least squares fitting algorithm;

[0008] S2, Optical signal decoupling: On the basis of particle characteristic correction, for the optical signal coupling problem existing in the turbidity data, adopt a multi-level signal decoupling algorithm to decompose the measured optical signal into a basic scattering component, a particle size-dependent component, and a background signal component;

[0009] S3, Dynamic model construction: Combine the data after particle characteristic correction and optical signal decoupling, construct a calculation model for the concentration of suspended solids based on particle dynamics, use the dynamic model to calculate the concentration of suspended solids in water, and output the suspended solids concentration data.

[0010] Optionally, S1 specifically includes:

[0011] S11, spectral data acquisition: Emitting light beams of different wavelengths through an underwater spectrometer to obtain the transmitted light intensity and scattered light intensity of multiple wavelengths and scattered light intensity ;

[0012] S12, construction of particle optical response characteristics: According to Mie scattering theory and optical properties, establish an optical response characteristic database of particles with different particle sizes, including scattering coefficient, absorption coefficient, and spectral transmittance. Generate a particle size-optical property response curve based on the potential composition of suspended particles to describe the response law of particles with different particle sizes to light beams of different wavelengths;

[0013] S13, multi-wavelength response analysis: Input the collected multi-wavelength light intensity data into the spectral analysis model, and extract the total contribution value of particles corresponding to each wavelength of light by calculating the light attenuation ratio at each wavelength;

[0014] S14, inference of particle size distribution: Combine the spectral analysis model with the particle optical response characteristic database, and use the inversion algorithm to estimate the particle size distribution of suspended particles. Divide the particles into multiple particle size segments according to a specific particle size range (less than 10 µm, 10 - 50 µm, greater than 50 µm);

[0015] S15, least squares fitting correction: Fit the measured multi-wavelength optical response data with the inferred particle size distribution, and use the least squares fitting algorithm to adjust the contribution coefficient of particles in each particle size segment to turbidity, so that the deviation between the fitting result and the measured light intensity data is minimized, and the correction value of the contribution of each particle size particle to turbidity is separated;

[0016] S15, output of correction result: Generate the corrected particle size distribution and optical response characteristics, and mark the turbidity contribution value of each particle size segment.

[0017] Optionally, in S12:

[0018] The scattering coefficient is expressed as: , where is the scattering coefficient at wavelength and particle size , is the particle radius, is the particle volume, is the scattering efficiency coefficient, which is related to wavelength and particle size ;

[0019] The absorption coefficient is expressed as: , where is the wavelength and particle size The absorption coefficient under is the absorption efficiency coefficient.

[0020] Optionally, in S13, the total particle contribution value is calculated as:

[0021] , where Contribution represents the total contribution of particles to turbidity at wavelength , represents the transmitted light contribution, represents the scattered light contribution (integrated over the scattering angle, considering scattered light in all directions), represents a small increment of the scattering angle.

[0022] Optionally, the inversion algorithm in S14 for estimating the particle size distribution of suspended matter is expressed as:

[0023] , where is the extinction efficiency coefficient, representing the combination of absorption and scattering efficiencies, represents the particle size distribution function, describing the number of particles with a particle size of per unit volume, is the attenuation coefficient deduced from the transmitted light intensity data, represents a small increment of the particle size;

[0024] The correction value for separating the contribution of each particle size to turbidity is expressed as:

[0025] , where represents the contribution of particles within the particle size range to turbidity;

[0026] The corrected result is output in the form of a spectral model, spectral model:

[0027] , where represents the total contribution after correction at wavelength .

[0028] Optionally, S2 specifically includes:

[0029] S21, optical signal modeling: Represent the measured multi-wavelength optical signals as:

[0030] , where is the wavelength under which the total optical signal intensity is measured, is the basic scattering component, representing the contribution of the overall scattering of particles to the signal, is the particle size-dependent component, which describes the optical response differences of particles with different particle sizes. is the background signal component, including light source noise and ambient light interference.

[0031] S22, multi-level signal decoupling, including extraction of the basic scattering component, extraction of the particle size-dependent component, and extraction of the background signal component.

[0032] S23, iterative optimization: perform multiple iterations on the decoupling results to minimize the total signal error.

[0033] ;

[0034] S24, result output: output the decoupled basic scattering component , particle size-dependent component and background signal component , for calculating the concentration of suspended matter.

[0035] Optionally, the extraction of the basic scattering component: based on the constructed particle optical response database, use the least squares fitting method to fit the basic scattering model: , where is the scattering coefficient, which has been obtained through calibration, is the particle concentration, obtained through fitting optimization;

[0036] The extraction of the particle size-dependent component: after fitting the basic scattering component, use the residual signal to extract the particle size-dependent component: ;

[0037] Then decompose it through the particle size distribution model: ;

[0038] The extraction of the background signal component: after extracting the basic scattering component and the particle size-dependent component, use the remaining signal as the background signal component: .

[0039] Optionally, the specific steps of S3 include:

[0040] S31, construction of the kinetic model:

[0041] Based on the particle size distribution function corrected by particle characteristics and optical property data, establish the kinetic equation of particles: , where is the sedimentation velocity of particles with particle size , is the density of suspended particles, is the density of water, is the acceleration due to gravity, is the dynamic viscosity of the water body, is a constant factor in the sedimentation velocity formula, derived from Stokes flow theory;

[0042] According to the decoupled optical signal components, combined with the fluid turbulence characteristics, supplement the influence of turbulent diffusion effect on particle distribution: , where, is the turbulent diffusion coefficient, is the turbulent viscosity coefficient, which depends on the water body flow velocity and particle characteristics, is the particle diameter of ;

[0043] S32, Suspended sediment concentration calculation:

[0044] Combined with the sedimentation velocity of the particles and turbulent diffusion characteristics , construct the particle dynamic equilibrium equation, numerically solve the particle dynamic equilibrium equation, and obtain the depth distribution of the suspended sediment concentration :

[0045] , where, is the total concentration of suspended sediment in the water body, are the minimum and maximum particle diameters respectively, are the minimum and maximum depth ranges of the water body respectively, is a small increment of particle diameter, used to integrate the particle concentration within the particle diameter range, is a small increment of water body depth, used to integrate the particle concentration within the depth range, represents the integration over the particle diameter range to , accumulating the concentration contributions of particles with different diameters, represents the integration over the depth range to , accumulating the concentration contributions of particles at different depths.

[0046] Optionally, the particle dynamic equilibrium equation is expressed as:

[0047] , where, is the time and depth at which the particle diameter is ; is the aggregation or fragmentation source term of the particles, describing the interaction between particles, represents the particle concentration with respect to time , describing the dynamic change of particle concentration over time, represents the particle concentration For the rate of change of depth which describes the gradient of particle concentration in the water depth direction, represents the second-order rate of change of particle concentration in the depth direction, reflecting the intensity of the diffusion process.

[0048] Advantages of the present invention:

[0049] In the present invention, through the multi-spectral measurement method, by combining the transmitted light intensity and scattered light intensity data, and using the least squares fitting algorithm to correct the particle size distribution and morphological characteristics of suspended particles, the accuracy of particle characteristic analysis is significantly improved. At the same time, a multi-level signal decoupling algorithm is introduced to decompose the optical signal into a basic scattering component, a particle size-dependent component, and a background signal component, solving the error problem caused by signal coupling in traditional methods, ensuring that each component has an independent physical meaning, and making the concentration calculation more reliable, especially under complex water conditions.

[0050] In the present invention, in combination with the particle dynamics model, the particle settling velocity, turbulent diffusion coefficient, and the dynamic equilibrium mechanism of particle aggregation and fragmentation are introduced to dynamically model the suspended solid concentration. By numerically solving the dynamic equilibrium equation, the concentration change of suspended solids in time and depth is accurately described. It can not only handle the sedimentation process in static water bodies but also adapt to the changes in complex hydrodynamic characteristics in dynamic water bodies, expanding the application scope of this method in various scenarios such as rivers, lakes, and sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a schematic flowchart of the calculation method for the embodiments of the present invention;

[0053] Figure 2 It is a schematic diagram of optical signal decoupling for the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following will describe the present invention in detail with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0055] It should be noted that in the specification, terms such as "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. Additionally, when combining an embodiment to describe a specific feature, structure, or characteristic, implementing such feature, structure, or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge scope of those skilled in the relevant art.

[0056] Generally, terms can be understood at least in part from their use in context. For example, depending at least in part on the context, the term "one or more" used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey a set of exclusive factors, but rather, depending at least in part on the context, can allow for the existence of other factors that are not necessarily explicitly described.

[0057] As Figure 1 - Figure 2 shown, the method for calculating the concentration of suspended solids in water based on multi-wavelength detection includes the following steps:

[0058] S1, particle property correction: Adopt a multi-spectral measurement method, use light beams of different wavelengths to penetrate the water body, identify the particle size distribution and morphological characteristics of suspended solids particles, and perform separation and correction on the turbidity contributions of particles with different particle sizes through the least squares fitting algorithm;

[0059] S2, optical signal decoupling: On the basis of particle property correction, for the optical signal coupling problem existing in the turbidity data, adopt a multi-level signal decoupling algorithm to decompose the measured optical signal into a basic scattering component, a particle size-dependent component, and a background signal component;

[0060] S3, dynamic model construction: Combine the data after particle property correction and optical signal decoupling, construct a calculation model for the concentration of suspended solids based on particle dynamics, use the dynamic model to calculate the concentration of suspended solids in the water body, and output the suspended solids concentration data.

[0061] The specific content of S1 includes:

[0062] S11, spectral data acquisition: Use an underwater spectrometer to emit light beams of different wavelengths to obtain the transmitted light intensity of multiple wavelengths and the scattered light intensity ; The wavelength range covers the visible light and near-infrared bands, preferably 400 - 1000 nm, and the interval of each band is 5 - 10 nm;

[0063] Expression of transmitted light intensity: , where is the transmitted light intensity at wavelength The transmitted light intensity at wavelength is the incident light intensity at wavelength The incident light intensity at wavelength is the attenuation coefficient, which describes the degree of light absorption and scattering is the propagation path length of light in water

[0064] Scattered light intensity expression: where represents the scattered light intensity at wavelength and scattering angle The scattered light intensity at wavelength is the scattering coefficient at wavelength The scattering coefficient at wavelength is the scattering phase function, which represents the scattered light intensity distribution at different scattering angles and satisfies the normalization condition is the number concentration of suspended particles per unit volume

[0065] S12, Construction of particle optical response characteristics: According to Mie scattering theory and optical properties, an optical response characteristic database of particles with different particle sizes is established, including scattering coefficient, absorption coefficient and spectral transmittance. According to the potential composition of suspended particles, a particle size-optical property response curve is generated to describe the response law of particles with different particle sizes to light beams with different wavelengths

[0066] S13, Multi-wavelength response analysis: Input the collected multi-wavelength light intensity data into the spectral analysis model, and extract the total contribution value of particles corresponding to each wavelength of light by calculating the light attenuation ratio at each wavelength

[0067] S14, Inference of particle size distribution: Combine the spectral analysis model with the particle optical response characteristic database, and use the inversion algorithm to estimate the particle size distribution of suspended particles. The particles are divided into multiple particle size segments according to a specific particle size range (less than 10 µm, 10 - 50 µm, greater than 50 µm)

[0068] S15, Least squares fitting correction: Fit the measured multi-wavelength optical response data with the inferred particle size distribution, and use the least squares fitting algorithm to adjust the contribution coefficient of particles in each particle size segment to turbidity, so that the deviation between the fitting result and the measured light intensity data is minimized, and the correction value of the contribution of each particle size particle to turbidity is separated

[0069] Fitting adjustment according to the difference between the measured value and the model calculated value where is the measured value of the transmitted light intensity is the transmitted light intensity calculated based on the inverted particle size distribution

[0070] S15. Output the calibration result: Generate the calibrated particle size distribution and optical response characteristics for subsequent dynamic model construction steps, and label the turbidity contribution value for each particle size range.

[0071] In the above S12:

[0072] The scattering coefficient is expressed as: , where is the wavelength and the particle size at which the scattering coefficient is measured, is the particle radius, is the particle volume, is the scattering efficiency coefficient, which is related to the wavelength and the particle size ;

[0073] The absorption coefficient is expressed as: , where is the wavelength and the particle size at which the absorption coefficient is measured, is the absorption efficiency coefficient.

[0074] In the above S13, the total particle contribution value is calculated as:

[0075] , where Contribution represents the total contribution of particles to turbidity at wavelength , represents the transmitted light contribution, represents the scattered light contribution (integrated over the scattering angle, considering scattered light in all directions), represents the small increment of the scattering angle.

[0076] In the above S14, the particle size distribution of suspended particles estimated by the inversion algorithm is expressed as:

[0077] , where is the extinction efficiency coefficient, representing the combination of absorption and scattering efficiencies, represents the particle size distribution function, describing the number of particles with particle size per unit volume, is the attenuation coefficient deduced from the transmitted light intensity data, represents the small increment of the particle size;

[0078] The calibration value of the contribution of each particle size to turbidity is expressed as:

[0079] , where represents the particle size range Contribution of internal particles to turbidity;

[0080] The corrected result is output in the form of a spectral model. The spectral model:

[0081] , where represents the wavelength and is the total contribution after correction at this wavelength.

[0082] Specifically, S2 includes:

[0083] S21, optical signal modeling: Represent the measured multi-wavelength optical signals as:

[0084] , where is the total optical signal intensity measured at wavelength , is the basic scattering component, representing the contribution of the overall scattering of particles to the signal, is the particle size-dependent component, describing the difference in the optical response of particles with different sizes, is the background signal component, including light source noise and ambient light interference;

[0085] S22, multi-level signal decoupling, including extraction of the basic scattering component, extraction of the particle size-dependent component, and extraction of the background signal component;

[0086] S23, iterative optimization: Perform multiple iterations on the decoupled result to minimize the total signal error:

[0087] ;

[0088] S24, result output: Output the decoupled basic scattering component , particle size-dependent component and background signal component for calculating the concentration of suspended substances.

[0089] Extraction of the basic scattering component: Based on the established particle optical response database, use the least squares fitting method to fit the basic scattering model: , where is the scattering coefficient, which has been obtained through calibration, is the particle concentration, obtained through fitting optimization;

[0090] Extraction of the particle size-dependent component: After fitting the basic scattering component, use the residual signal to extract the particle size-dependent component: ;

[0091] Then decompose it through the particle size distribution model: ; ​

[0092] Extraction of the background signal component: After extracting the basic scattering component and the particle size-dependent component, the remaining signal is taken as the background signal component: .

[0093] The specific steps of S3 include:

[0094] S31, Construction of the kinetic model:

[0095] Based on the particle size distribution function corrected by particle characteristics and the optical property data, establish the kinetic equation of particles: , where is the sedimentation velocity of particles with a particle size of , is the density of suspended particles, is the density of water, is the acceleration due to gravity, is the dynamic viscosity of water, is the constant factor in the sedimentation velocity formula, derived from Stokes flow theory;

[0096] According to the decoupled optical signal components, combined with the fluid turbulence characteristics, supplement the influence of turbulent diffusion effect on particle distribution: , where is the turbulent diffusion coefficient, is the turbulent viscosity coefficient, depending on the water flow velocity and particle characteristics, is the particle concentration of particles with a particle size of ;

[0097] S32, Calculation of suspended solid concentration:

[0098] Combined with the sedimentation velocity of particles and the turbulent diffusion characteristics , construct the kinetic equilibrium equation of particles, and numerically solve the kinetic equilibrium equation of particles to obtain the depth distribution of the suspended solid concentration:

[0099] , where is the total concentration of suspended solids in water, are the minimum and maximum particle sizes of particles respectively, are the minimum and maximum depth ranges of water respectively, is the small increment of particle size, used to integrate the particle concentration within the particle size range, is the small increment of water depth, used to integrate the particle concentration within the depth range, represents the integration over the particle size range to , accumulating the concentration contributions of particles with different particle sizes, Represents the integration over the depth range to to accumulate the concentration contributions of particles at different depths.

[0100] Convert the total suspended solid concentration obtained by the solution into a real-time data format, output it to the monitoring system, and generate a concentration distribution map to show the changes in the suspended solid concentration at different depths and times.

[0101] The particle kinetic equilibrium equation is expressed as:

[0102] , where is time and depth at which the particle concentration with particle size is is the source term of particle aggregation or breakage, describing the interaction between particles, represents the particle concentration with respect to time and describes the dynamic change of particle concentration over time, represents the particle concentration with respect to depth and describes the gradient of particle concentration in the water depth direction, represents the second-order change rate of the particle concentration in the depth direction, reflecting the intensity of the diffusion process.

[0103] The present invention encompasses any substitutions, modifications, equivalent methods, and solutions made to the essence and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0104] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating the concentration of suspended solids in water based on multi-wavelength detection, characterized in that: The following steps are involved: S1, particle characteristics correction: adopt multi-spectral measurement method, use light beams of different wavelengths to penetrate the water body, identify the particle size distribution and morphological characteristics of suspended particles, and separate and correct the turbidity contribution of particles of different sizes through the least squares fitting algorithm; S2, optical signal decoupling: Based on the particle characteristic correction, a multi-level signal decoupling algorithm is used to decompose the measured optical signal into basic scattering component, particle size dependent component and background signal component to solve the optical signal coupling problem in turbidity data; S3, dynamic model construction: Combined with the data after particle characteristic correction and optical signal decoupling, a suspended matter concentration calculation model based on particle dynamics is constructed, the suspended matter concentration of the water body is calculated using the dynamic model, and the suspended matter concentration data is output.

2. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 1, characterized in that: The S1 specifically includes: S11, spectral data acquisition: The underwater spectrometer emits light beams of different wavelengths to obtain the intensity of transmitted light at multiple wavelengths and scattered light intensity ; S12, construction of particle optical response characteristics: Based on Mie scattering theory and optical properties, a database of optical response characteristics of particles of different sizes is established, including scattering coefficient, absorption coefficient and spectral transmittance. According to the potential composition of suspended particles, a particle size-optical characteristic response curve is generated to describe the response law of particles of different sizes to light beams of different wavelengths; S13, multi-wavelength response analysis: input the collected multi-wavelength light intensity data into the spectrum analysis model, calculate the light attenuation ratio at each wavelength, and extract the total particle contribution value corresponding to each wavelength of light; S14, particle size distribution inference: combining the spectral analysis model with the particle optical response characteristic database, using the inversion algorithm to estimate the particle size distribution of suspended particles, and dividing the particles into multiple particle size segments according to the particle size range; S15, least square fitting correction: fit the measured multi-wavelength optical response data with the inferred particle size distribution, and use the least square fitting algorithm to adjust the contribution coefficient of each particle size segment to the turbidity, so that the deviation between the fitting result and the measured light intensity data is minimized, and the correction value of the contribution of each particle size segment to the turbidity is separated; S15, output correction results: generate a particle size distribution and optical response characteristics based on the correction, and annotate the turbidity contribution value of each particle size segment.

3. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 2 is characterized in that: In S12: The scattering coefficient is expressed as: ,in, is the wavelength and particle size The scattering coefficient under is the particle radius, is the particle volume, is the scattering efficiency coefficient, which is related to the wavelength and particle size Related; The absorption coefficient is expressed as: ,in, is the wavelength and particle size The absorption coefficient under is the absorption efficiency coefficient.

4. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 3 is characterized in that: In S13, the total contribution value of particles is calculated as: , where Contribution Indicates wavelength The total contribution of particles to turbidity is represents the transmitted light contribution, represents the scattered light contribution (integrated over the scattering angle, considering scattered light in all directions), Represents small increments in scattering angle.

5. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 4 is characterized in that: The particle size distribution of suspended matter estimated by the inversion algorithm in S14 is expressed as: ,in, is the extinction efficiency coefficient, which represents the combination of absorption and scattering efficiency. Represents the particle size distribution function, which describes the particle size per unit volume. The number of particles, is the attenuation coefficient calculated from the transmitted light intensity data, Represents a small increment in particle size; The correction value of the contribution of each particle size to turbidity is expressed as: ,in, Indicates particle size range Contribution of internal particles to turbidity; The corrected result is output in the form of a spectral model: ,in, Indicates wavelength The total contribution after correction is given below.

6. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 1, characterized in that: The S2 specifically includes: S21, optical signal modeling: The measured multi-wavelength optical signal is expressed as: ,in, is the wavelength The total optical signal intensity measured under is the basic scattering component, which indicates the contribution of the overall scattering of particles to the signal, is the particle size-dependent component, describing the difference in optical response of particles of different sizes. It is the background signal component, including light source noise and ambient light interference; S22, multi-level signal decoupling, including basic scattering component extraction, particle size dependent component extraction and background signal component extraction; S23, iterative optimization: perform multiple iterations on the decoupling results to minimize the total signal error: ； S24, result output: output the decoupled basic scattering component , particle size dependent component and background signal component , used for suspended matter concentration calculation.

7. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 6 is characterized in that: The basic scattering component extraction: Based on the constructed particle optical response database, the basic scattering model is fitted using the least squares fitting method: ,in, is the scattering coefficient, obtained by correction, is the particle concentration, obtained by fitting optimization; The particle size dependent component extraction: After fitting the basic scattering component, the residual signal is used Extract size-dependent components: ; Then decompose it through the particle size distribution model: ; The background signal component extraction: After extracting the basic scattering component and the particle size dependent component, the remaining signal is used as the background signal component: .

8. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 1, characterized in that: The S3 specifically includes: S31, kinetic model construction: Particle size distribution function corrected for particle characteristics Based on the optical property data, the particle dynamic equation is established: ,in, The particle size is The particle settling velocity, is the density of the suspended particles, is the density of the water body, is the acceleration due to gravity, is the dynamic viscosity of water, is the constant factor in the settling velocity formula; According to the decoupled optical signal components and combined with the fluid turbulence characteristics, the influence of turbulent diffusion effect on particle distribution is supplemented: ,in, is the turbulent diffusion coefficient, is the turbulence coefficient, The particle size is The particle concentration; S32, suspended matter concentration calculation: Sedimentation velocity of bound particles and turbulent diffusion characteristics , construct the particle dynamics equilibrium equation, numerically solve the particle dynamics equilibrium equation, and obtain the depth distribution of suspended matter concentration : ,in, is the total concentration of suspended matter in water, are the minimum and maximum particle sizes, are the minimum and maximum depth ranges of the water body, is a small increment in particle size, is a small increment in the depth of the water column, Indicates the particle size range to The integral of Indicates the depth range to 's points.

9. The method for calculating the concentration of suspended solids in water based on multi-wavelength detection according to claim 8, characterized in that: The particle dynamics equilibrium equation is expressed as: ,in, It's time and depth The particle size below is The particle concentration, is the source term for particle aggregation or fragmentation, describing the interaction between particles. Indicates particle concentration About Time The rate of change of describes the dynamic change of particle concentration over time. Indicates particle concentration Depth The rate of change of describes the gradient of particle concentration in the depth direction of the water body. Indicates particle concentration The second-order rate of change in the depth direction reflects the intensity of the diffusion process.

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