A particle size and refractive index inversion method and device based on a multispectral sensing chip

Abstract

The application discloses a particle size and refractive index inversion method and device based on a multi-spectrum sensing chip, and the method comprises the following steps: measuring scattered light intensity data of particles at multiple wave bands of a wide-spectrum light source by using multiple measuring channels of the multi-spectrum sensing chip, constructing an inversion model, performing key physical parameter inversion, and acquiring particle size distribution and complex refractive index. The method disclosed by the application irradiates particles with a wide-spectrum light source, synchronously collects scattered light intensity of multiple discrete wavelength channels by using a device integrated with the multi-spectrum sensing chip, and simultaneously calculates logarithmic normal distribution parameters of the particle size distribution and the complex refractive index of particles from limited spectral data by constructing an inversion model, so that the joint inversion of characteristic parameters of particles under limited measurement information is realized.

Description

Technical Field

[0001] This invention relates to the field of particulate matter size and refractive index measurement technology, and in particular to a method and apparatus for inverting particulate matter size and refractive index based on a multispectral sensing chip. Background Technology

[0002] The particle size distribution and optical properties of particulate matter are key parameters in environmental monitoring, industrial production, and biomedicine. Traditional measurement methods are mainly divided into two categories: offline laboratory analysis and online optical measurement.

[0003] Offline laboratory methods, such as electron microscopy and dynamic light scattering, offer high precision but are expensive, involve complex sample preparation, and cannot achieve real-time online monitoring. Online optical measurement technologies, such as particle size analyzers based on laser diffraction or dynamic light scattering, can achieve real-time measurement, but typically can only invert particle size distribution and cannot simultaneously obtain the complex refractive index information of the particles. Complex refractive index is a core parameter characterizing the chemical composition and optical absorption properties of particulate matter, and is crucial for accurately identifying non-flammable aerosol particles such as pollen, dust, and soot.

[0004] In existing technologies, some studies have attempted to simultaneously invert particle size and refractive index. For example, an invention application CN104634705A from Harbin Institute of Technology discloses a measurement method based on continuous laser, which performs inversion by establishing an inverse problem model of reflection and transmission signals. However, this method relies on a complex laser system and multiple detectors, resulting in high system cost and complex optical path debugging, and does not focus on achieving miniaturization and low-cost integration.

[0005] For example, the invention patent with publication number CN112798479B discloses an online particle size measurement system for wide sieving based on three fixed wavelength lasers. It uses multi-wavelength scattering measurement technology to obtain the scattering response of particles at different wavelengths, which increases the information dimension, but fails to make full use of the spatial distribution information of scattered light.

[0006] In summary, existing technologies have the following limitations: while systems capable of simultaneously measuring particle size and refractive index are typically complex in structure and expensive; methods based on a single wavelength or limited band have limited information content, and the uniqueness and accuracy of inversion results are easily affected by noise; and there is a lack of highly integrated, low-cost, and easily deployable real-time online measurement solutions.

[0007] Therefore, there is a need for a particulate matter size and refractive index inversion method based on a multispectral sensing chip, which can simultaneously acquire the spatial distribution signal of scattered light of particulate matter in multiple continuous bands using an integrated multispectral sensing chip. Summary of the Invention

[0008] To address the aforementioned problems, the present invention aims to provide a method and apparatus for inverting particle size and refractive index based on a multispectral sensing chip. This addresses the issues of complex structure, high cost, insufficient measurement information in single or limited wavelength bands, and poor accuracy of inversion results in existing systems for simultaneously measuring particle size and refractive index. The invention achieves low-cost, high-accuracy, real-time online measurement of particle parameters, enabling system miniaturization and low-cost deployment, and meeting the application requirements for real-time online measurement.

[0009] The objective of this invention can be achieved through the following technical solution: a method for inverting particle size and refractive index based on a multispectral sensing chip, comprising:

[0010] S1: Utilize the multiple measurement channels of the multispectral sensor chip to measure the scattered light intensity data of particulate matter in multiple bands of a broadband light source;

[0011] S2: Construct an inversion model and invert key physical parameters based on scattered light intensity data;

[0012] S3: Obtain particle size distribution and complex refractive index based on key physical parameters obtained from the inversion.

[0013] As a further embodiment of the present invention, the multispectral sensing chip integrates at least eight visible light sensing channels, simultaneously measures the scattered light intensity data at at least eight specific wavelengths, and converts it into digital spectral data.

[0014] As a further embodiment of the present invention, the broadband light source adopts a white LED, and the emission spectrum covers the visible light to near-infrared band.

[0015] As a further aspect of the present invention, the inversion model construction includes:

[0016] A forward model is established to represent the key physical parameters to the theoretical scattered light intensity, and the theoretical scattered light intensity is obtained, expressed as:

[0017]

[0018] in, It is the system spectral response function of the k-th channel. The scattering intensity function This is the particle size concentration distribution function;

[0019] Construct an inversion problem to minimize the theoretical scattered light intensity The objective function values ​​of the measured light intensity and the scattered light intensity (Imeas) are used to optimize the parameters of the forward model, which are expressed as follows:

[0020]

[0021] in, It is the square of the L2 norm. These are the parameters corresponding to the forward model. The theoretical scattered light intensity corresponding to the model parameters is used to measure the goodness of fit to the data. For regularization terms;

[0022] The process of solving for the forward model parameters is iteratively optimized to obtain the optimal forward model parameters, which are then deployed to the forward model. This includes:

[0023] S21: Call the forward model and calculate the current model parameters. Theoretical scattered light intensity ;

[0024] S22: Calculate the objective function value ;

[0025] S23: Using the Levenberg-Marquardt algorithm, based on the current residuals and the model's sensitivity to the parameters, the parameter update direction and step size are determined to obtain new model parameter estimates. ;

[0026] S24: Iterate through S21 to S23 until the change in the objective function value is less than the preset threshold, then stop the iteration and output the optimal model parameter estimate x*.

[0027] As a further embodiment of the present invention, the particle size concentration distribution function is based on a log-normal distribution and is expressed as:

[0028]

[0029] Where r is the particle radius, σ is the standard deviation, and μ is the mean of a normal distribution.

[0030] As a further embodiment of the present invention, the scattering intensity function is calculated based on Mie theory and is expressed as:

[0031]

[0032] Where r is the particle radius, The center wavelength corresponding to the k-th sensing channel is The complex refractive index;

[0033]

[0034] in, For the real part of the complex refractive index, is the imaginary part of the complex refractive index, and i is the polymerization coefficient.

[0035] As a further embodiment of the present invention, the regularization term Γ(x) incorporates prior knowledge, constrains the range of solutions, and improves the ill-conditioning of the problem, and is expressed as:

[0036]

[0037] in, These are weighting coefficients, which constrain the parameters within a physically reasonable range. , ( ) is the prior estimate of the parameter.

[0038] A particulate matter size and refractive index inversion device based on a multispectral sensing chip includes an optical detection module and a signal processing and control module.

[0039] The optical detection module includes a broadband illumination source, a multispectral sensing chip, and an optical darkroom.

[0040] Broad-spectrum lighting source, using white LEDs covering the visible to near-infrared band;

[0041] The multispectral sensing chip integrates at least eight visible light sensing channels. Each channel is used to collect the intensity data of scattered light from a broadband illumination source after being scattered by particulate matter, and convert it into digital spectral data.

[0042] An optical darkroom is used to contain the particles to be measured, forming a detection space with a broadband illumination source;

[0043] Signal processing and control module: contains a microprocessor, used to control the light source drive, acquire digital spectral data, perform inversion algorithms, and output particle size distribution and complex refractive index.

[0044] The beneficial effects of this invention are:

[0045] 1. This invention uses a broadband light source to irradiate particulate matter. A highly integrated multispectral sensing chip simultaneously acquires the scattered light intensity across multiple discrete wavelength channels. An inversion model integrating Mie scattering theory, regularization constraints, and optimization algorithms is constructed. From finite spectral data, the log-normal distribution parameters of particle size (mean μ, standard deviation σ) and the complex refractive index of the particles (real part Nr, imaginary part Ni) are simultaneously calculated. This achieves joint inversion of particulate matter characteristic parameters under limited measurement information, improving the accuracy of particle size and refractive index parameter inversion. Compared to traditional single-band scattering measurement schemes, this method mines the differentiated response characteristics of multi-band scattering signals to different particle sizes and refractive indices, overcoming the inversion uncertainty caused by insufficient single-dimensional measurement information. It can effectively adapt to particulate matter measurement scenarios in different environments.

[0046] 2. This invention uses a commercial multispectral sensing chip (e.g., AS7341) as the core detector, replacing the complex spectroscopic system of traditional spectrometers or multiple monochromators plus detectors, which greatly simplifies the optical path, reduces hardware costs and size, and makes the device easy to miniaturize, modularize and mass-produce.

[0047] 3. This invention utilizes multi-wavelength information provided by a wide spectrum, and through rigorous physical models and optimization algorithms, it can simultaneously calculate four key parameters—particle size distribution (μ, σ) and complex refractive index (Nr, Ni)—from a simple measurement of scattered light intensity, providing a more comprehensive characterization of particulate matter properties.

[0048] 4. By introducing a regularization term Γ(x), this invention incorporates reasonable physical constraints (such as particle size distribution width restrictions and refractive index range priors) into the inversion process, effectively overcoming the ill-conditioned nature of the inverse problem, improving the solution stability and reliability under measurement noise, and avoiding solutions without physical meaning.

[0049] 5. The hardware system of this invention is simple and reliable, and the algorithm is highly efficient (only 4 parameters to be inverted), making rapid, online, and real-time monitoring possible. It is very suitable for scenarios that require rapid response, such as ambient air quality monitoring, industrial process control, and fire aerosol early warning.

[0050] 6. This invention makes full use of the spectral information of 8 discrete channels provided by a multispectral sensing chip (such as the AS7341 chip), which provides richer optical fingerprint information of particulate matter compared with single-wavelength or dual-wavelength methods, and enhances the ability to distinguish different types of particulate matter. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 This is a schematic diagram illustrating the parameter optimization of the inversion model of the present invention;

[0053] Figure 3 This is a schematic diagram of the device module structure of the present invention;

[0054] Figure 4 This is a schematic diagram of the application structure of the device of the present invention. Detailed Implementation

[0055] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0056] Example 1:

[0057] like Figure 1 As shown, this invention discloses a method for inverting particulate matter size and refractive index based on a multispectral sensing chip, comprising:

[0058] S1: Utilize the multiple measurement channels of a multispectral sensor chip to measure the scattered light intensity data of particulate matter in multiple bands of a broadband light source.

[0059] The multispectral sensing chip uses the AS7341 chip, an 11-channel spectral sensor that integrates multiple interference filters, with eight channels covering the visible light range. Using a broadband illumination source, this chip can simultaneously measure the intensity of scattered light at eight specific wavelengths, replacing the complex beam splitting path and multiple independent detectors of traditional systems, effectively reducing the size of the detection device.

[0060] The AS7341 chip can convert scattered light intensity data into digital spectral data. The AS7341 chip can also be used via I... 2 The C interface connects to the microprocessor, uploading digital spectral data from each channel in real time.

[0061] S2: Construct an inversion model and invert key physical parameters based on scattered light intensity data.

[0062] The purpose of constructing the inversion model is to solve for key physical parameters in reverse based on the 8-channel scattered light intensity data. Key physical parameters include: the mean μ and standard deviation σ of the log-normal distribution of particle size, the real part Nr of the complex refractive index, and the imaginary part Ni of the complex refractive index.

[0063] Based on the mean μ, standard deviation σ, real part Nr, and imaginary part Ni of the log-normal distribution of particle size, the particle size distribution and complex refractive index are obtained.

[0064] The inversion model is constructed as follows: Figure 2 As shown, it includes:

[0065] A forward model is established to represent the key physical parameters to the theoretical scattered light intensity, and the theoretical scattered light intensity is obtained, expressed as:

[0066]

[0067] in, It is the system spectral response function of the k-th channel. The scattering intensity function This is the particle size concentration distribution function.

[0068] The particulate matter group follows a log-normal distribution, and the particle size concentration distribution function is:

[0069]

[0070] Where r is the particle radius, σ is the standard deviation, and μ is the mean of a normal distribution.

[0071] The scattering intensity function is calculated based on Mie theory and is expressed as:

[0072]

[0073] Where r is the particle radius, The center wavelength corresponding to the k-th sensing channel is The complex refractive index;

[0074]

[0075] in, For the real part of the complex refractive index, is the imaginary part of the complex refractive index, and i is the polymerization coefficient.

[0076] Find a set of optimal model parameters x* such that the theoretical scattered light intensity Icalc(x) best matches the measured scattered light intensity Imeas. Wherein, These are the parameters corresponding to the forward model. Icalc(x) represents the theoretical scattered light intensity corresponding to the model parameters, and Icalc[k] corresponds to Icalc[k].

[0077] Minimize theoretical scattered light intensity Compared with the measured light intensity and the objective function value of the scattered light intensity Imeas:

[0078]

[0079] in, The squared L2 norm measures the goodness of fit to the data. This is a regularization term.

[0080] Based on the regularization term Γ(x), prior knowledge is introduced, the range of solutions is constrained, and the ill-conditioned nature of the problem is improved, which can be expressed as:

[0081]

[0082] in, These are weighting coefficients, which constrain the parameters within a physically reasonable range. , ( ) is the prior estimate of the parameter.

[0083] Optimize the forward model parameters by iteratively optimizing the solution process to obtain the optimal forward model parameters, and then deploy them to the forward model. This includes:

[0084] S21: Call the forward model and calculate the current model parameters. Theoretical scattered light intensity ;

[0085] S22: Calculate the objective function value ;

[0086] S23: Using the Levenberg-Marquardt algorithm, based on the current residuals and the model's sensitivity to the parameters (Jacobi matrix J), and combined with the objective function value, the parameter update direction and step size are determined to obtain new model parameter estimates. The Jacobian matrix can be approximated by the finite difference method.

[0087] S24: Iterate through S21 to S23 until the change in the objective function value is less than the preset threshold, then stop the iteration and output the optimal model parameter estimate x*.

[0088] S3: Obtain particle size distribution and complex refractive index based on key physical parameters obtained from the inversion.

[0089] The multi-spectral sensor chip collects multi-wavelength scattered light intensity signals of particles from different channels in real time. After denoising and normalizing the collected raw scattered light intensity data, the processed scattered light intensity data is input into a parameter-optimized forward model.

[0090] Based on the particle size distribution function and scattering intensity function, and combined with the parameter relationships in the forward model, the measured scattered light intensity of each channel of the multispectral system is inverted into a function relating to the particle size distribution function and the scattering intensity function. Four key physical parameters are used to obtain the particle size distribution and complex refractive index of particulate matter, enabling real-time detection.

[0091] based on Four key physical parameters—the correspondence between particle size distribution and μ and σ, and the relationship between complex refractive index and the definitions of Nr and Ni—are used to obtain the particle size distribution and complex refractive index of particulate matter, thus completing the real-time inversion detection of particulate matter parameters based on signals acquired by a multispectral sensor chip.

[0092] Example 2:

[0093] This embodiment discloses a particle size and refractive index inversion device based on a multispectral sensing chip, used in the method of Embodiment 1, such as... Figure 3 and Figure 4 As shown, the device includes an optical detection module and a signal processing and control module:

[0094] The optical detection module includes a broadband illumination source, a multispectral sensing chip, and an optical darkroom.

[0095] Broad-spectrum lighting sources use white LEDs as light sources, and their emission spectrum covers the visible to near-infrared bands.

[0096] The multispectral sensing chip is based on the AS7341 chip. The AS7341 is an 11-channel spectral sensor that integrates multiple interference filters, with 8 channels covering the visible light range. Using a broadband illumination source, this chip can simultaneously measure light intensity signals at 8 specific wavelengths, replacing the complex beam splitting paths and multiple independent detectors found in traditional systems.

[0097] The optical darkroom has an optical detection space to accommodate the particulate aerosol to be measured. The multispectral sensing chip and broadband illumination source are placed inside the optical darkroom. The optical darkroom has the functions of preventing interference from non-target light sources and preventing dust and insects.

[0098] Signal processing and control module: Includes a microprocessor (STM32 series MCU). The AS7341 chip connects via I... 2 The C interface connects to the microprocessor, uploading digital spectral data from each channel in real time.

[0099] The microprocessor is mounted on the substrate. It is responsible for controlling the light source drive, acquiring digital spectral data, running the inversion model algorithm, and outputting results such as particle size distribution and complex refractive index.

[0100] In use, the optical detection module acquires multispectral signals from the particulate aerosol under test entering the optical darkroom detection space. A broadband illumination source emits broadband light covering the wavelength range of each channel of the AS7341 sensor chip. After passing through the particulate aerosol under test, the light carrying particle scattering and absorption characteristics enters the AS7341 multispectral sensor chip, which simultaneously performs intensity signal conversion and digital processing at eight specific wavelengths of visible light.

[0101] After the original spectral signal is acquired, the chip uses I... 2 The C interface transmits the digitized spectral data of all channels to the signal processing and control module in real time. After receiving the spectral data, the signal processing and control module calls the pre-trained inversion model algorithm to calculate the particle size distribution and complex refractive index parameters of the current particle aerosol based on the input multi-wavelength scattered light intensity data. Finally, it outputs the processing results, completing the rapid detection of the optical properties and particle size information of the particulate matter.

[0102] This invention adopts a device architecture based on an integrated multispectral sensing chip, using the AS7341 spectral sensing chip as the scattered light detection element, combined with a white LED broadband light source, to form a minimized, integrated hardware system for particulate matter multispectral scattering measurement, which is fundamentally different from the complex systems in the prior art that use lasers, independent spectrometers and multiple detectors.

[0103] This invention adopts an orientation The regularized optimization method for four-parameter synchronous inversion uses two parameters (μ, σ) of the log-normal distribution of particle size and two parts (Nr, Ni) of complex refractive index as parameters to be inverted. By constructing a composite objective function Φ(x) that includes data fitting terms and physical constraint regularization terms Γ(x), and solving it through a nonlinear optimization algorithm, the problem of stably solving four key physical parameters from finite multispectral data is solved.

[0104] This invention adopts a complete integrated sensing-inversion solution, forming a complete technical solution from hardware architecture and data acquisition to parameter inversion algorithm, and outputting an integrated solution for particle size distribution and complex refractive index results.

[0105] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for inverting particle size and refractive index based on a multispectral sensing chip, characterized in that, include: S1: Utilize the multiple measurement channels of the multispectral sensor chip to measure the scattered light intensity data of particulate matter in multiple bands of a broadband light source; S2: Construct an inversion model and invert key physical parameters based on scattered light intensity data; S3: Obtain particle size distribution and complex refractive index based on key physical parameters obtained from the inversion.

2. The method according to claim 1, characterized in that, The multispectral sensing chip integrates at least eight visible light sensing channels, simultaneously measures the intensity data of scattered light at at least eight specific wavelengths, and converts it into digital spectral data.

3. The method according to claim 2, characterized in that, The broadband light source uses white LEDs, and its emission spectrum covers the visible to near-infrared band.

4. The method according to claim 1, characterized in that, The inversion model construction in S2 includes: A forward model is established to represent the key physical parameters to the theoretical scattered light intensity, and the theoretical scattered light intensity is obtained, expressed as: in, It is the system spectral response function of the k-th channel. The scattering intensity function This is the particle size concentration distribution function; Construct an inversion problem to minimize the theoretical scattered light intensity The objective function values ​​of the measured light intensity and the scattered light intensity (Imeas) are used to optimize the parameters of the forward model, which are expressed as follows: in, It is the square of the L2 norm. These are the parameters corresponding to the forward model. For model parameters The corresponding theoretical scattered light intensity is used to measure the goodness of fit of the data. For regularization terms; The process of solving for the forward model parameters is iteratively optimized to obtain the optimal forward model parameters, which are then deployed to the forward model. This includes: S21: Call the forward model and calculate the current model parameters. Theoretical scattered light intensity ; S22: Calculate the objective function value ; S23: Using the Levenberg-Marquardt algorithm, based on the current residuals and the model's sensitivity to the parameters, the parameter update direction and step size are determined to obtain new model parameter estimates. ; S24: Iterate through S21 to S23 until the change in the objective function value is less than the preset threshold, then stop the iteration and output the optimal model parameter estimate x*.

5. The method according to claim 4, characterized in that, The particle size concentration distribution function is based on a log-normal distribution and is expressed as follows: Where r is the particle radius, σ is the standard deviation, and μ is the mean of a normal distribution.

6. The method according to claim 4, characterized in that, The scattering intensity function is calculated based on Mie theory and is expressed as follows: Where r is the particle radius, The center wavelength corresponding to the k-th sensing channel is The complex refractive index; in, For the real part of the complex refractive index, is the imaginary part of the complex refractive index, and i is the polymerization coefficient.

7. The method according to claim 4, characterized in that, The regularization term Γ(x) introduces prior knowledge, constrains the range of solutions, and improves the ill-conditioned nature of the problem, and is expressed as: in, These are weighting coefficients, which constrain the parameters within a physically reasonable range. , ( ) is the prior estimate of the parameter.

8. A particulate matter particle size and refractive index inversion device based on a multispectral sensing chip, characterized in that, The apparatus, applicable to the method according to any one of claims 1 to 7, comprises an optical detection module and a signal processing control module, wherein: The optical detection module includes a broadband illumination source, a multispectral sensing chip, and an optical darkroom; The broadband illumination source uses white LEDs covering the visible to near-infrared bands; the multispectral sensing chip integrates at least 8 visible light sensing channels, each channel is used to collect the intensity data of the scattered light from the broadband illumination source after being scattered by particles, and convert it into digital spectral data; the optical darkroom is used to contain the particles to be measured, forming a detection space for the broadband illumination source. The signal processing and control module contains a microprocessor used to control the light source drive, acquire digital spectral data, perform inversion algorithms, and output the particle size distribution and complex refractive index.

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