Aerosol particle size distribution sensor and aerosol particle size distribution measuring method

Abstract

The invention belongs to the field of particulate matter measuring and sensing, and particularly relates to an aerosol particle size distribution sensor and an aerosol particle size distribution measuring method.The aerosol particle size distribution sensor comprises a particle containing cavity, a spectrum sensing chip and a plurality of white light LED light sources located at different scattering angles; each scattering angle is a rotation angle formed by rotating the light beam propagation direction of the light source at the particulate matter accommodating cavity to the direction pointing to the spectrum sensing chip from the center of the particulate matter accommodating cavity; the plurality of light sources are used for sequentially emitting white light into the particle accommodating cavity, and the spectrum sensing chip is used for collecting scattered light generated by to-be-detected particles in the particle accommodating cavity based on the white light to obtain an aerosol scattered spectrum signal so as to analyze aerosol particle size distribution; the scattering angle combination formed by the scattering angles is obtained through optimization by taking the minimum aerosol parameter deviation between different aerosols of which the aerosol scattering spectrum signal difference value is smaller than a threshold value as a target. Accurate measurement of aerosol particle size distribution is realized through the sensor with a simple structure.

Description

Technical Field

[0001] The present invention belongs to the field of particle measurement sensing, and more specifically, relates to an aerosol particle size distribution sensor and an aerosol particle size distribution measurement method. Background Art

[0002] Aerosols are solid or liquid particles suspended in the air, such as smoke, dust, mist, pollen and microorganisms. These particles can have a significant impact on the atmospheric environment, human health and industrial production. Specific applications include aerosol monitoring of mass concentration PM10 with a particle size of less than 10μm, mass concentration PM2.5 with a particle size of less than 2.5μm and mass concentration PM1 with a particle size of less than 1μm in the atmosphere. These aerosols are closely related to human health. PM10 particles can directly enter the human chest through the throat, PM2.5 particles can reach the human bronchi, and PM1 particles will be absorbed by the alveoli. These particles enter the human body and cause cardiovascular and respiratory diseases. According to relevant reports, 70% of industrial production is related to aerosols, and the global aerosol monitoring market size is estimated to increase from US$1.3 billion in 2022 to US$2.2 billion in 2027.

[0003] Common methods for measuring aerosol particle size distribution include differential mobility method, aerodynamic method, laser diffraction method, etc. The instruments implemented by these technologies have complex structures and large volumes, and are mainly used in laboratory scenarios, such as aerodynamic particle spectrometers, differential mobility particle spectrometers, and laser particle size analyzers. With the gradual expansion of the aerosol market, the application scenarios of aerosol measurement instruments have gradually diversified. Passive monitoring needs such as fire smoke, atmospheric particulate matter, and particle concentration in semiconductor production workshops require multiple devices to run online in real time for a long time. Large experimental instruments have the problem of high installation and maintenance costs, while small portable, low-cost sensors can effectively meet the needs of distributed monitoring.

[0004] A method for measuring the particle size distribution of a multi-wavelength particle system is disclosed in the Chinese invention patent specification CN216247609U. The method uses the scattered light intensity of particles at three different wavelengths to achieve real-time measurement of particle size. The Chinese patent with publication number CN220751904U detects the scattering spectrum of smoke through a Mini / Micro-LED multi-wavelength array light source and a three-color CCD or CMOS camera chip, and realizes smoke fire alarm through a threshold method. The patent installs LED lights of different wavelengths at different positions of the detector structure, and controls the LED lights to emit light at different times. The CCD or CMOS chip collects scattered light signals to obtain scattered light signals of multiple wavelengths. Although the patent realizes the collection of scattered light intensity data of multiple wavelengths, it does not discuss the relationship between smoke characteristics and scattered light intensity, and the structure of multiple LED arrays is complex and difficult to miniaturize. Summary of the invention

[0005] In view of the defects and improvement needs of the prior art, the present invention provides an aerosol particle size distribution sensor and an aerosol particle size distribution measurement method, the purpose of which is to achieve accurate measurement of aerosol particle size distribution through a sensor with a simple structure.

[0006] To achieve the above object, according to one aspect of the present invention, an aerosol particle size distribution sensor is provided, comprising: a particle receiving cavity, a spectrum sensing chip, and a plurality of white light LED light sources at different scattering angles;

[0007] Among them, each scattering angle is the rotation angle of the light beam propagation direction of the white light LED light source at the particle holding cavity to the direction from the center of the particle holding cavity to the spectral sensor chip; multiple white light LED light sources are used to emit white light into the particle holding cavity in sequence, and the spectral sensor chip is used to collect the scattered light generated by the particles to be tested located in the particle holding cavity based on the white light, to obtain an aerosol scattering spectrum signal to analyze the aerosol particle size distribution; the scattering angle combination formed by each scattering angle is optimized with the goal of minimizing the aerosol parameter deviation between different aerosols whose aerosol scattering spectrum signal difference is less than a threshold.

[0008] Further, the scattering angle combination is optimized in the following manner:

[0009] Determine the number of aerosol species contained in the industrial scene to which the particles to be tested belong; preset multiple candidate scattering angle combinations;

[0010] Calculate the relative deviation between the aerosol scattering spectrum signals corresponding to every two aerosols under each scattering angle combination; take each aerosol as a reference aerosol, determine the relative deviation less than a preset threshold among all relative deviations corresponding to the reference aerosol, and screen out other aerosols corresponding to all relative deviations less than the preset threshold that are paired with the reference aerosol to form an indistinguishable aerosol set of the reference aerosol; calculate the average value of the deviation between any parameter of each reference aerosol and the corresponding parameter of each indistinguishable aerosol in the indistinguishable aerosol set of the reference aerosol, add the average values ​​corresponding to each reference aerosol as the value deviation of the parameter between the reference aerosol and the indistinguishable aerosol under the scattering angle combination, thereby obtaining the value deviation of various parameters under the scattering angle combination;

[0011] The value deviations of various parameters under each scattering angle combination are integrated to obtain the parameter deviation corresponding to the scattering angle combination; and the scattering angle combination with the smallest parameter deviation is selected as the final scattering angle combination.

[0012] Furthermore, the relative deviation between the aerosol scattering spectral signals is calculated by the following calculation method:

[0013]

[0014] In the formula, is the scattering angle combination [θ1,θ2,…,θ n The aerosol scattering spectrum signal of aerosol y under ] is specifically j represents the number of wavelengths after the wavelength discretization of the aerosol spectrum signal, and y represents aerosols A and A′; represents the average relative deviation between the aerosol scattering spectral signals of aerosols A and A′, p A_k,g and p A′_k,g They represent the light intensity values ​​of two aerosols A and A′ at the same wavelength k and the same scattering angle g respectively.

[0015] Furthermore, the deviation SME of each parameter x under each candidate scattering angle combination is calculated as follows:

[0016]

[0017] In the formula, θ1, θ2, …, θ n represents the scattering angle combination, K represents the number of aerosol species, x i represents the value of parameter x of the current reference aerosol i, N represents the number of indistinguishable aerosols in the corresponding indistinguishable aerosol set when various aerosols are used as reference aerosols, x j ′ represents the value of parameter x of indistinguishable aerosol j in the indistinguishable aerosol set corresponding to the current reference aerosol i, μ, σ, and m represent the median particle size and standard deviation of the aerosol particle size distribution and the refractive index of the aerosol, respectively.

[0018] Furthermore, the refractive index m includes a real part of the refractive index and an imaginary part of the refractive index.

[0019] Further, the method further comprises: a processor: wherein the processor is used to:

[0020] According to the discrete acquisition wavelength of the spectral sensor chip, the preset discrete particle size value of the particle to be measured and the initially determined refractive index m, the scattered light matrix Q is obtained based on the Mie scattering theory: [λ,d,m] ;

[0021] According to the scattered light matrix Q [λ,d,m] and the initially determined particle size distribution vector F [μ,σ] , the formula P [λ] =Q [λ,d,m] ×F [μ,σ] The calculated theoretical aerosol scattering spectrum signal P[λ] Compare with the aerosol scattering spectrum signal collected by the sensor to update the refractive index m and particle size distribution vector F [μ,σ] , and iterate continuously until the theoretical aerosol scattering spectrum signal P [λ] The deviation from the aerosol scattering spectrum signal collected by the sensor meets the threshold requirement;

[0022] According to the theoretical aerosol scattering spectrum signal P [λ] The aerosol parameters [μ, σ, m] of the particles to be measured are obtained by analysis, thereby obtaining the aerosol particle size distribution.

[0023] Further, the processor adopts swarm intelligence ant colony algorithm to perform the analysis of the aerosol particle size distribution, specifically: the processor adopts swarm intelligence ant colony algorithm to perform the analysis of the aerosol particle size distribution, specifically: after executing the first iterative update, select L ants from ME ants as learning ants, M is the number of ants in the ant colony corresponding to each update iteration, and E is the number of elite ants in M; L is an integer multiple of E, and each learning ant has an initial value of each parameter that is a value of an elite ant in the elite ant set of the first iteration. The elite ants corresponding to the parameter values ​​of each learning ant are different, and the parameters of each learning ant start from the second iteration, and each time according to the learning speed v l =[x1(t)-x l (t)]·rand·c, randomly approaching the elite ant with the smallest F in the elite ant set in the previous (t) iteration; where x1(t) is the value of the parameter x of the elite ant with the smallest F in the t iteration, c is the preset update parameter, and x l (t) is the value of the learning ant l in t iterations, rand is a random number between 0 and 1, and the parameter x of the updated learning ant l is x′ l (t+1)=x l (t)+v l , F is the theoretical aerosol scattering spectrum signal P of each ant [λ] The error between the aerosol scattering spectrum signal collected by the sensor; in each iteration starting from the second iteration, the ants in the ant colony except the elite ants and the learning ants are updated in the same way as the elite ants.

[0024] Furthermore, the white light LED light source is a white light LED lamp bead.

[0025] The present invention also provides an aerosol particle size distribution measurement method, which uses the aerosol particle size distribution sensor as described above to achieve aerosol particle size distribution measurement.

[0026] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0027] (1) The present invention proposes an aerosol particle size distribution sensor, which obtains aerosol scattering spectrum signals by using a spectral sensor chip and a white light LED light source. Since the spectral signal collected by the spectral sensor chip is a scattering spectrum signal of discrete wavelengths, and the white light LED light source contains a wide continuous visible light spectrum, the wavelength range of the measured scattering spectrum signal is wide, and the multi-dimensional scattering spectrum signal contains rich aerosol characteristics, and with the help of these characteristics, the particle size distribution parameters can be accurately sensed. In addition, the present invention adopts multiple white light LED light sources, each of which is located at a different scattering angle. It is a sensor with a multi-scattering angle structure, thereby integrating the different spectral characteristics under different scattering angles, further enriching the aerosol characteristics, and realizing accurate sensing of aerosol particle size distribution. The sensor of the present invention has a simple structure, and uses a multi-dimensional scattering spectrum signal obtained through a small number of scattering angles to achieve accurate aerosol particle size distribution measurement, and has the advantages of simple structure and accurate measurement.

[0028] (2) The processor in the sensor of the present invention adopts a new ant colony algorithm to perform aerosol particle size analysis. Specifically, the learning ants will search for the best solution near the elite ants, that is, each learning ant will search for the best update path in its surroundings, thereby avoiding the rapid convergence of the ant colony and adding diversity to the entire ant colony, enabling the ant colony to effectively jump out of the local optimal solution trap and further converge to the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of an aerosol particle size distribution sensor provided by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of the spectrum discretization principle provided by an embodiment of the present invention;

[0031] Figure 3 The scattering spectra under different aerosol parameters when the scattering angle is 45° provided in the embodiment of the present invention;

[0032] Figure 4 The ant colony algorithm provided in the embodiment of the present invention is a result graph of the median particle size μ and the refractive index m inverted from the scattered spectrum data under ±5% and ±10% noise; wherein A is a result graph of the median particle size μ inverted from the scattered spectrum data under ±5% noise, B is a result graph of the refractive index m inverted from the scattered spectrum data under ±5% noise, C is a result graph of the median particle size μ inverted from the scattered spectrum data under ±10% noise, and D is a result graph of the refractive index m inverted from the scattered spectrum data under ±10% noise;

[0033] Figure 5 A diagram showing the difference between scattering spectrum signals of the same aerosol at different scattering angles provided by an embodiment of the present invention;

[0034] Figure 6 SME distribution result diagram of different aerosol parameters under double scattering angle combination provided by the embodiment of the present invention; wherein A is the distribution diagram of SME of median particle size distribution of aerosol particle size under double scattering angle combination, B is the distribution diagram of SME of standard deviation of aerosol particle size distribution under double scattering angle combination, C is the distribution diagram of SME of real part of aerosol refractive index under double scattering angle combination; D is the distribution diagram of SME of imaginary part of aerosol refractive index under double scattering angle combination;

[0035] Figure 7 A distribution diagram of SME' under the double scattering angle combination structure provided by an embodiment of the present invention;

[0036] Figure 8 A structural design diagram of an aerosol particle size distribution sensor provided in an embodiment of the present invention;

[0037] Fig. 9 A result diagram of an aerosol scattering spectrum signal of smoldering smoke measured by an aerosol particle size distribution sensor provided by an embodiment of the present invention;

[0038] Fig.10 A result diagram of aerosol scattering spectrum signal of open fire smoke measured by the aerosol particle size distribution sensor provided in an embodiment of the present invention;

[0039] Fig.11 Based on Fig. 9 and 10 Comparison chart of aerosol particle size distribution analyzed by the results and the measurement results of the reference aerosol particle spectrometer. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] Embodiment 1

[0042] An aerosol particle size distribution sensor, such as Figure 1 As shown, it includes: a particle receiving cavity, a spectrum sensing chip, and a plurality of white light LED light sources located at different scattering angles;

[0043] Among them, each scattering angle is the rotation angle of the light beam propagation direction of the white light LED light source at the particle holding cavity to the direction from the center of the particle holding cavity to the spectral sensor chip; multiple white light LED light sources are used to emit white light into the particle holding cavity in sequence, and the spectral sensor chip is used to collect the scattered light generated by the particles to be tested located in the particle holding cavity based on the white light, to obtain an aerosol scattering spectrum signal to analyze the aerosol particle size distribution; the scattering angle combination formed by each scattering angle is optimized with the goal of minimizing the aerosol parameter deviation between different aerosols whose aerosol scattering spectrum signal difference is less than a threshold.

[0044] This embodiment uses a spectral sensor chip and a white light LED light source to obtain an aerosol scattering spectrum signal. Since the spectral signal collected by the spectral sensor chip is a scattering spectrum signal of discrete wavelengths, and the white light LED light source contains a wide continuous visible light spectrum, the wavelength range of the measured scattering spectrum signal is wide, and the multi-dimensional scattering spectrum signal contains rich aerosol characteristics, with the help of these characteristics, the particle size distribution parameters can be accurately sensed. In addition, this embodiment uses multiple white light LED light sources, each of which is located at a different scattering angle. It is a sensor with a multi-scattering angle structure, which integrates the different spectral characteristics under different scattering angles, further enriches the aerosol characteristics, and realizes accurate sensing of aerosol particle size distribution. The sensor of this embodiment has a simple structure, uses fewer scattering angles to collect more scattering spectrum signals, and realizes accurate aerosol particle size distribution measurement. It has the advantages of simple structure and accurate measurement.

[0045] As a preferred embodiment, the white light LED light source is a white light LED lamp bead. In practice, the white light LED lamp bead has a relatively large power and generates a relatively strong scattered light signal, which can achieve the sensitivity of the spectral sensor chip at an aerosol concentration of 0.2db / m.

[0046] As a preferred embodiment, the above scattering angle combination is optimized in the following way:

[0047] Determine the number of aerosol types contained in the industrial scene to which the particles to be measured belong (each aerosol parameter value combination corresponds to an aerosol); preset multiple candidate scattering angle combinations;

[0048] Calculate the relative deviation between the aerosol scattering spectrum signals corresponding to every two aerosols under each scattering angle combination; take each aerosol as a reference aerosol, determine the relative deviation less than a preset threshold among all relative deviations corresponding to the reference aerosol, and screen out other aerosols corresponding to all relative deviations less than the preset threshold that are paired with the reference aerosol to form an indistinguishable aerosol set of the reference aerosol; calculate the average value of the deviation between any parameter of each reference aerosol and the corresponding parameter of each indistinguishable aerosol in the indistinguishable aerosol set of the reference aerosol, add the average values ​​corresponding to each reference aerosol as the value deviation of the parameter between the reference aerosol and the indistinguishable aerosol under the scattering angle combination, thereby obtaining the value deviation of various parameters under the scattering angle combination;

[0049] The value deviations of various parameters under each scattering angle combination are integrated to obtain the parameter deviation corresponding to the scattering angle combination; and the scattering angle combination with the smallest parameter deviation is selected as the final scattering angle combination.

[0050] The present embodiment aims to measure the parameter values ​​of aerosols and then obtain the aerosol particle size distribution. In order to make the parameter measurement values ​​more accurate, considering that under any scattering angle combination, there will be a problem that the scattering spectrum signals between different aerosols are similar, or in actual measurement, due to the influence of noise, the measured scattering spectrum signal deviates from the actual value. To address this problem, the present embodiment proposes to select a scattering angle combination so that even if the scattering spectrum signal deviates, the analyzed parameter is closest to the actual parameter value.

[0051] Therefore, in a specific implementation, the preferred embodiment of the present invention first selects any one aerosol from all aerosols as a reference aerosol, then calculates the relative deviation of the scattering spectrum under the same scattering angle combination of the reference aerosol and all aerosols, compares the relative deviation with a preset deviation threshold, screens aerosols with a relative deviation less than the threshold and forms a set, which is all aerosols that are difficult to distinguish from the reference aerosol in the scattering spectrum, and is defined as an indistinguishable aerosol set.

[0052] As mentioned above, the aerosol parameters in the indistinguishable aerosol set are the possible values ​​of the theoretically analyzed reference aerosol under the scattering spectrum, that is, when the object to be measured is the above-mentioned reference aerosol in an actual environment, the obtained scattering spectrum, in addition to the reference aerosol parameters characterized, also contains the values ​​of all aerosol parameters in the indistinguishable aerosol set in the case of misjudgment. Calculate the deviation between the same aerosol parameter of the reference aerosol and the same parameter of all aerosols in the indistinguishable aerosol set, and sum and average the above deviations. Sum the average deviations of all reference aerosols and divide them by the total number of aerosols to obtain the deviation of the aerosol parameters measured by the scattering spectrum under the scattering angle combination, and select the scattering angle combination with the best measurement performance from all candidate scattering angle combinations with the smallest parameter deviation.

[0053] As a preferred implementation, the relative deviation between aerosol scattering spectrum signals is calculated by the following calculation method:

[0054]

[0055] In the formula, is the scattering angle combination [θ1,θ2,…,θ n The aerosol scattering spectrum signal of aerosol y under ] is specifically j represents the number of wavelengths after the wavelength discretization of the aerosol spectrum signal, and y represents aerosols A and A′; represents the average relative deviation between the aerosol scattering spectral signals of aerosols A and A′, p A_k and p A′_k They represent the light intensity values ​​of two aerosols A and A′ at the same wavelength k and the same scattering angle g respectively.

[0056] As a preferred implementation, the deviation SME of each parameter x under each candidate scattering angle combination is calculated in the following way:

[0057]

[0058] In the formula, θ1, θ2, …, θ n represents the scattering angle combination, K represents the number of aerosol species, x i represents the value of parameter x of the current reference aerosol i, N represents the number of indistinguishable aerosols in the corresponding indistinguishable aerosol set when various aerosols are used as reference aerosols, x j' represents the value of parameter x of indistinguishable aerosol j in the indistinguishable aerosol set corresponding to the current reference aerosol i, μ, σ, m represent the median particle size and standard deviation of the aerosol particle size distribution and the refractive index of the aerosol, respectively. Preferably, the refractive index m includes a real part of the refractive index and an imaginary part of the refractive index, wherein the real part of the refractive index represents the degree of change in the propagation speed of light in the aerosol, and the imaginary part of the refractive index represents the absorption effect of the aerosol on the energy of light, which corresponds to different physical meanings. Therefore, in this embodiment, the refractive index m is further changed into a real part of the refractive index and an imaginary part of the refractive index.

[0059] When performing scattering angle combination optimization, the preset threshold ΔM of the relative deviation between the aerosol scattering spectrum signals is, for example, 0.1. In addition, the set of indistinguishable aerosols A′ of the reference aerosol A can be expressed as:

[0060] Since the imaginary part of the refractive index mI has a value of 0, in order to measure the deviation between aerosol parameters and optimize the performance of the sensor, this embodiment calculates the value deviation between aerosol parameters through the Symmetric Mean Absolute Percentage Error (SME).

[0061] As a preferred implementation, the method further includes: a processor: wherein the processor is used to:

[0062] According to the discrete acquisition wavelength of the above-mentioned spectral sensor chip, the preset discrete particle size value of the particles to be measured and the initially determined refractive index m, the scattered light matrix Q is obtained based on the Mie scattering theory: [λ,d,m] ;

[0063] According to the scattered light matrix Q [λ,d,m] and the initially determined particle size distribution vector F [μ,σ] , the formula P [λ] =Q [λ,d,m] ×F [μ,σ] The calculated theoretical aerosol scattering spectrum signal P [λ] Compare with the aerosol scattering spectrum signal collected by the sensor to update the refractive index m and particle size distribution vector F [μ,σ] , and iterate continuously until the theoretical aerosol scattering spectrum signal P [λ] The deviation from the aerosol scattering spectrum signal collected by the sensor meets the threshold requirement;

[0064] According to the theoretical aerosol scattering spectrum signal P [λ] The aerosol parameters [μ, σ, m] of the particles to be measured are obtained by analysis, thereby obtaining the aerosol particle size distribution.

[0065] According to Mie scattering theory, aerosol is a particle group composed of particles of different sizes, with particle size distribution parameters in the particle size dimension. Usually, the secondary scattering of particles can be ignored. Under this condition, the scattered light signal of aerosol can be regarded as the sum of the scattered light of all particles. The aerosol scattering spectrum can be expressed as:

[0066]

[0067] Where d represents the particle size, d min d max are the lower and upper limits of particle size, p(λ) is the scattering spectrum function, λ is the wavelength of scattered light, f(d) is the particle size distribution of aerosol, is the single particle scattering spectrum obtained based on Mie scattering theory.

[0068] It can be seen from formula (1) that the aerosol scattering spectrum is the result of the superposition of single-particle scattering spectra of different particle sizes. Since the single-particle scattering spectrum is the result of the combined coupling of aerosol parameters and optical parameters, the aerosol scattering spectrum is also affected by aerosol parameters and optical parameters.

[0069] Since the log-normal distribution is highly consistent with the smoke particle size distribution, the aerosol particle size distribution is set to the log-normal distribution in this embodiment, which is consistent with the actual aerosol distribution. The calculation formula is:

[0070]

[0071] Where μ is the median particle size and σ is the standard deviation.

[0072] Setting the particle size distribution to a log-normal distribution can simplify the aerosol scattering spectrum model, making the scattering spectrum a function related to [μ, σ, m], which is defined as:

[0073]

[0074] Since the spectral data actually obtained is discrete, equation (3) can be rewritten as:

[0075] P [λ] =Q [λ,d,m] ×F [μ,σ] (4)

[0076] Where P [λ] is j wavelengths The scattering spectrum signal is Q [λ,d,m] is j wavelengths and g particle size The j×g-order single-particle scattered light matrix composed of F [μ,σ]is the preset particle size g The particle size distribution vector after discretization is:

[0077] from Figure 2 It can be seen that the discretized aerosol scattering spectrum is a discrete single particle scattering spectrum. With discrete particle size distribution Perform relevant transformation operations on the particle size dimension. Figure 3 The scattering spectra under different aerosol parameters have significant differences, which is beneficial to the sensing of aerosol parameters [μ, σ, m]. Figure 3 It is the aerosol scattering spectrum with a scattering angle of 45° ("spectrum" is continuous, "spectral signal" is discrete), sample 1 is the aerosol scattering spectrum with a median particle size μ of 150nm, variance σ of 1.3, and a refractive index m of 1.30+0i; sample 2 is the aerosol scattering spectrum with a median particle size μ of 150nm, variance σ of 1.3, and a refractive index m of 1.55+0.5i; sample 3 is the aerosol scattering spectrum with a median particle size μ of 550nm, variance 1.3, and a refractive index of 1.3+0i; sample 4 is the aerosol scattering spectrum with a median particle size μ of 150nm, variance σ of 1.7, and a refractive index m of 1.30+0i.

[0078] To accurately decouple the scattered spectrum P [λ] Aerosol particle size distribution parameter F [μ,σ] In this embodiment, the ant colony algorithm of swarm intelligence is preferably used, which relies on the relationship between individuals in the swarm and can effectively find the correct optimization direction, and has the characteristics of high stability and strong robustness. [λ] As input, combined with the scattered light matrix Q [λ,d,m] and the particle size distribution vector F [μ,σ] , continue to iterate according to formula (4), and finally converge to P [λ] The optimal solution of the corresponding aerosol parameters [μ, σ, m]. Figure 4 The median particle size μ and refractive index m obtained by the ant colony algorithm from the scattering spectrum data with ±5% and ±10% noise are shown in Figure 2. It can be seen that the ant colony algorithm has excellent aerosol parameter decoupling ability.

[0079] As a preferred implementation, the processor uses an ant colony algorithm of swarm intelligence to perform the analysis of the aerosol particle size distribution, specifically:

[0080] The median particle size μ and the variance σ of the aerosol particle size distribution of M ants in the ant colony are randomly set, and the aerosol refractive index m is set. The above ants represent a set aerosol. [λ] =Q [λ,d,m] ×F[μ,σ] Calculate the aerosol scattering spectral signal P of M ants in the ant colony [λ] and compare it with the aerosol scattering spectrum signal measured by the aerosol particle size distribution sensor to calculate the deviation where p k,g is the light intensity value of the scattered spectrum signal measured by the aerosol sensor at wavelength k and scattering angle g, p k,g ′ is the aerosol scattering spectrum signal calculated by the ant according to the discrete aerosol scattering spectrum formula. For the entire ant colony, there are M preset ants, so there are also M calculated deviations F; the M deviations F are arranged from small to large, and the front E ants are selected as elite ants to form an elite ant set; under the set M aerosol parameter [μ, σ, m] values, the elite ant's value is closer to the scattering spectrum signal of the sensor; calculate the standard deviation of each elite ant in the elite ant set between the values ​​of each parameter and the values ​​of the same parameter of other elite ants. x h (t) is the value of the elite ant h on parameter x, x o (t) is the value of the same parameter x taken by other ants in the elite ant set except the elite ant h, t is the number of iterations, and β is the preset weight parameter to facilitate the subsequent adjustment of the width of the normal distribution. h (t) and standard deviation s h (t) is used as the parameter of the normal distribution, and the normal distribution of each parameter of each elite ant is calculated. That is, the probability density function of the value of the parameter under the elite ant is used to measure the relationship between each elite ant and the entire elite ant set. Finally, the normal distribution of each parameter of each elite ant is calculated according to the preset weights to find W = {w1, w2, ..., w E} Perform weighted summation to obtain the normal distribution used to describe the elite ant set The next generation (t+1) updates the parameter values ​​of ants other than the elite ants of the current generation. Specifically, it is based on the normal distribution of the entire elite ant set. The parameter values ​​of ME ants in the current generation except the elite ants in the current generation are updated in a random manner using a roulette wheel. However, since the update method using a normal distribution converges faster and is closely related to the number of elite ants E, when E is too small, it is easy to converge to the wrong aerosol parameter value. Therefore, this embodiment selects L ants from ME ants as learning ants, where L is an integer multiple of E. The parameter values ​​of each learning ant in the second iteration are the values ​​of an elite ant in the elite ant set of the first iteration. The parameter values ​​of each learning ant correspond to different elite ants, and they will learn at a certain learning speed v. l =[x1(t)-xl (t)]·rand·c randomly approaches the elite ant with the smallest F in the elite ant set in the previous (t) iteration, where x1(t) is the value of the parameter x of the elite ant with the smallest F in the t iteration, c is the preset update parameter, and x l (t) is the value of the learning ant l in t iterations, rand is a random number between 0 and 1, and the parameter x of the updated learning ant l is x′ l (t+1)=x l (t)+v l , so that the ant colony will look for possible parameter values ​​around the elite ants, increasing their chances of escaping from erroneous aerosol parameter values; thus, in each iteration, the elite ants will update the aerosol parameter values ​​of the MEL ants except the elite ants and the learning ants, while the learning ants will update their own values.

[0081] For the above formula (4), the scattering angle θ0 is an important parameter for calculating the scattering spectrum. It changes the aerosol characteristics by affecting the scattering spectrum characteristics, resulting in spatial differences in the scattering spectrum. Figure 5 As shown, the normalized aerosol scattering spectrum distribution with a refractive index of 1.45+0i, a median particle size of 250nm, and a variance of 1.2 at different scattering angles has significant differences in the scattering spectrum characteristics under the same aerosol parameters. The outstanding characteristic differences have richer aerosol characteristics, that is, they can characterize more diverse and complex aerosol parameters. Therefore, in order to pursue the outstanding sensing performance of the sensor, the sensor structure will be optimized through different scattering angle combinations. The aerosols in Table 1 are common aerosol samples in fire smoke detection scenes with standard references and repeated tests, and are used as the main test objects of this embodiment. The typical aerosol parameters covered in Table 1 differ in median particle size, variance and refractive index due to different components and generation methods. These differences will be further reflected in the scattering spectrum, so that the scattering spectrum can sense aerosol parameters.

[0082] Table 1 Typical aerosol parameters

[0083]

[0084] When optimizing the scattering angle combination, the scattering angle combination optimization problem can be expressed as:

[0085]

[0086] Since this embodiment focuses on the simple structure of the sensor but highlights the performance, for the fire smoke detection scenario, the optimized structure of this embodiment is a double scattering angle, that is, a combination of θ1 and θ2. The candidate scattering angle range is 40°-140°, and the value is taken every 5°. The SME distribution obtained by solving is as follows: Figure 6 As shown, Figure 5 The minimum relative deviation of μ is about 27.14%, and the best combination of scattering angles is 100° and 140°; the minimum relative deviation of σ is 11.00%, and the best combination of scattering angles is 40° and 140°; the minimum absolute deviation of mR is 0.045%, and the best combination of scattering angles is 40° and 105°; the minimum absolute deviation of mI is 1.24%, and the best combination of scattering angles is 40° and 95°.

[0087] Since the characteristics of different aerosol parameters on the scattering spectrum are different, the optimization results of different parameters are also different. In order to comprehensively evaluate and optimize the characteristics of aerosol parameters, this embodiment preferably averages the optimization results of the four parameters, which are specifically described as follows:

[0088]

[0089] Among them, SME μ SME σ SME mR and SMEs mI are the deviations of aerosol parameters μ, σ, mR and mI respectively. The final result of averaging the deviations of all typical aerosol parameters is SME′ as follows: Figure 7 The best structure is the combination of scattering angles 70° and 140°, and the average SME value of all parameter deviations is 10.85%. μ The deviation is 28.12%, with the smallest SME μ The difference is about 0.98%; the structure SME σ 13.89%, with the smallest SME σ The difference is about 2.89%; the structure SME mR 0.050%, with the smallest SME mR The difference is about 0.005%; the structure SME mI 1.33%, with the smallest SME mI The difference is about 0.09%. It can be seen that the difference between the combined structure of scattering angles 70° and 140° and the optimal characterization structure of each parameter is small, and the comprehensive characterization of aerosol multi-parameters can be achieved.

[0090] Figure 8 It is a prototype design of the scattered spectrum principle, including the sensor structure diagram and the sensor optical structure diagram. It should be noted that although the above preferred scattering angle combination is 70° and 140°, in order to facilitate the installation of LED lamp beads, this embodiment selects the suboptimal scattering angle combination of 45° and 140°. The scattering angle difference in this suboptimal structure is large, so there is no need to increase the volume of the entire sensor. In addition, this structure has good aerosol particle size distribution parameter characteristics, which can ensure the accuracy of aerosol parameter inversion. Figure 6 and Figure 7 It can be seen that the SME′ of the 45° and 140° combined structures is 11.72%, SME μ 33.47% and SME σ 11.62% and SME mR 0.13%, SME mI The SME′ of the 45° and 140° structures differs by about 0.87% compared to the 70° and 140° structures. In addition, since the light of the LED lamp beads is divergent, a lot of stray light will be generated, which will increase the background value and affect the response range and measurement accuracy of the sensor. The 45° and 140° structures can reduce the interference of stray light.

[0091] In addition, to enhance the sensitivity and stability of the sensor, e.g. Figure 8 As shown, the present embodiment adopts the following measures in the structural design:

[0092] (1) To increase the sensitivity and anti-interference ability of the prototype, a combination of multiple lenses and apertures was used to focus the LED light in the light channel design of the prototype. Similarly, an aperture with a diameter of 3 mm was also set at the front end of the AS7341 spectral sensor chip to ensure that the AS7341 receives less scattered light.

[0093] (2) In the structural design, the intersection point of light emission and light reception is set to about 7 mm, which ensures that there is no interference between the backward light emission channel and the receiving channel, and also ensures that the sensor has a strong sensitivity;

[0094] (3) A lens with a focal length of 4 mm is installed in front of the AS7341 sensor to increase the photosensitive surface of the AS7341 spectral sensor chip, thereby increasing sensitivity;

[0095] (4) To ensure that the LED lamp emits constant light, a heat sink and a fan are installed behind the LED lamp and above the PCB board to dissipate heat;

[0096] During the measurement process, the specific working process of the sensor is as follows:

[0097] (1) The LED lights at 45° and 140° work alternately;

[0098] (2) The light emitted by the LED lamp generates scattered light at the intersection, which is received by the spectral sensor chip to obtain spectral data, such as Fig. 9 and Fig.10 shown.

[0099] Finally, the ant colony algorithm was combined to decouple the aerosol particle size distribution parameters, and the measurement results of the scanning mobility particle sizer (SMPS) 3936 of TSI Company in the United States were used as a reference. Fig.11 As shown), it can be seen that the present invention exhibits good performance in actual measurement.

[0100] Embodiment 2

[0101] The present invention also provides an aerosol particle size distribution measurement method, which uses the aerosol particle size distribution sensor as described above to achieve aerosol particle size distribution measurement.

[0102] The relevant technical solution is the same as that in Embodiment 1 and will not be described in detail here.

[0103] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An aerosol particle size distribution sensor, characterized in that: include: A particle holding cavity, a spectral sensing chip, and multiple white light LED light sources at different scattering angles; Among them, each scattering angle is the rotation angle of the light beam propagation direction of the white light LED light source at the particle holding cavity to the direction from the center of the particle holding cavity to the spectral sensor chip; multiple white light LED light sources are used to emit white light into the particle holding cavity in sequence, and the spectral sensor chip is used to collect the scattered light generated by the particles to be tested located in the particle holding cavity based on the white light, to obtain an aerosol scattering spectrum signal to analyze the aerosol particle size distribution; the scattering angle combination formed by each scattering angle is optimized with the goal of minimizing the aerosol parameter deviation between different aerosols whose aerosol scattering spectrum signal difference is less than a threshold.

2. The aerosol particle size distribution sensor according to claim 1, characterized in that: The scattering angle combination is optimized in the following way: Determine the number of aerosol species contained in the industrial scene to which the particles to be tested belong; preset multiple candidate scattering angle combinations; Calculate the relative deviation between the aerosol scattering spectrum signals corresponding to each two aerosols under each scattering angle combination; take each aerosol as a reference aerosol, determine the relative deviations corresponding to the reference aerosol that are smaller than a preset threshold, and select other aerosols corresponding to all relative deviations smaller than the preset threshold that are paired with the reference aerosol to form an indistinguishable aerosol set of the reference aerosol; Calculate the average value of the deviation between any parameter of each reference aerosol and the corresponding parameter of each indistinguishable aerosol in the indistinguishable aerosol set of the reference aerosol, add the average values ​​corresponding to each reference aerosol, and use the value deviation of the parameter between the reference aerosol and the indistinguishable aerosol under the scattering angle combination as the value deviation of the parameter under the scattering angle combination; thereby obtain the value deviation of various parameters under the scattering angle combination; The value deviations of various parameters under each scattering angle combination are integrated to obtain the parameter deviation corresponding to the scattering angle combination; and the scattering angle combination with the smallest parameter deviation is selected as the optimized scattering angle combination.

3. The aerosol particle size distribution sensor according to claim 2, characterized in that: The relative deviation between aerosol scattering spectral signals is calculated by the following calculation method: In the formula, is the scattering angle combination [θ1,θ2,…,θ n The aerosol scattering spectrum signal of aerosol y under ] is specifically j represents the number of wavelengths after the wavelength discretization of the aerosol spectrum signal, and y represents aerosols A and A′; represents the average relative deviation between the aerosol scattering spectral signals of aerosols A and A′, p A_k and p A′_k They represent the light intensity values ​​of two aerosols A and A′ at the same wavelength k and the same scattering angle g respectively.

4. The aerosol particle size distribution sensor according to claim 2, characterized in that: The deviation SME of each parameter x under each candidate scattering angle combination is calculated as follows: In the formula, θ1, θ2, …, θ n represents the scattering angle combination, K represents the number of aerosol species, x i represents the value of parameter x of the current reference aerosol i, N represents the number of indistinguishable aerosols in the corresponding indistinguishable aerosol set when various aerosols are used as reference aerosols, x j ′ represents the value of parameter x of indistinguishable aerosol j in the indistinguishable aerosol set corresponding to the current reference aerosol i, μ, σ, and m represent the median particle size and standard deviation of the aerosol particle size distribution and the refractive index of the aerosol, respectively.

5. The aerosol particle size distribution sensor according to claim 4, characterized in that: The refractive index m is divided into two parameters: a real part of the refractive index and an imaginary part of the refractive index.

6. The aerosol particle size distribution sensor according to claim 1, characterized in that: Also includes: Processor: wherein the processor is used to: According to the discrete acquisition wavelength of the spectral sensor chip, the preset discrete particle size value of the particle to be measured and the initially determined refractive index m, the scattered light matrix Q is obtained based on the Mie scattering theory: [λ,d,m] ; According to the scattered light matrix Q [λ,d,m] and the initially determined particle size distribution vector F [μ,σ] , the formula P [λ] =Q [λ,d,m] ×F [μ,σ] The calculated theoretical aerosol scattering spectrum signal P [λ] Compare with the aerosol scattering spectrum signal collected by the sensor to update the refractive index m and particle size distribution vector F [μ,σ] , and iterate continuously until the theoretical aerosol scattering spectrum signal P [λ] The deviation from the aerosol scattering spectrum signal collected by the sensor meets the threshold requirement; According to the theoretical aerosol scattering spectrum signal P [λ] The aerosol parameters [μ, σ, m] of the particles to be measured are obtained by analysis, thereby obtaining the aerosol particle size distribution.

7. The aerosol particle size distribution sensor according to claim 6, characterized in that: The processor uses an ant colony algorithm of swarm intelligence to perform the analysis of the aerosol particle size distribution, specifically: after executing the first iterative update, L ants are selected from ME ants as learning ants, M is the number of ants in the ant colony corresponding to each update iteration, and E is the number of elite ants in M; L is an integer multiple of E, and each learning ant takes the initial value of each parameter as the value of an elite ant in the elite ant set of the first iteration. The elite ants corresponding to the parameter values ​​of each learning ant are different, and the parameters of each learning ant start from the second iteration, and each time according to the learning speed v l =[x1(t)-x l (t)]·rand·c, randomly approaching the elite ant with the smallest F in the elite ant set in the previous (t) iteration; where x1(t) is the value of the parameter x of the elite ant with the smallest F in the t iteration, c is the preset update parameter, and x l (t) is the value of the learning ant l in t iterations, rand is a random number between 0 and 1, and the parameter x of the updated learning ant l is x′ l (t+1)=x l (t)+v l , F is the theoretical aerosol scattering spectrum signal P of each ant [λ] The error between the aerosol scattering spectrum signal collected by the sensor; in each iteration starting from the second iteration, the ants in the ant colony except the elite ants and the learning ants are updated in the same way as the elite ants.

8. The aerosol particle size distribution sensor according to claim 1, characterized in that: The white light LED light source is a white light LED lamp bead.

9. A method for measuring aerosol particle size distribution, characterized in that: An aerosol particle size distribution sensor as claimed in any one of claims 1 to 8 is used to achieve aerosol particle size distribution measurement.

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