A dust concentration measurement method and system based on light scattering method with humidity characteristic decoupling

Abstract

This invention relates to a method and system for measuring dust concentration using light scattering based on humidity characteristic decoupling, belonging to the field of dust concentration monitoring technology. The invention involves simultaneously acquiring light intensity signals from multiple scattering angles, along with ambient humidity and temperature; constructing multiple feature quantities, and selecting the optimal feature quantity to distinguish the humid state of particulate matter through feature validity verification; constructing a humidity-particle size dynamic coupling model based on the optimal feature quantity; decomposing the measured scattered light intensity signal into multiple components to construct a decoupling model; substituting the measured light intensity signals from multiple scattering angles, along with ambient humidity and temperature, into the decoupling model to establish a multivariate equation system; solving the equation system to obtain the dust concentration-related components; and weighted fusion of the dust concentration-related components from multiple scattering angles to invert the dust concentration. This invention significantly improves measurement accuracy, reduces measurement errors in high-humidity environments, and solves the problem of scattering characteristic differences caused by dust moisture absorption.

Description

Technical Field

[0001] This invention belongs to the field of dust concentration monitoring technology, and relates to a dust concentration measurement method and system based on humidity characteristic decoupling light scattering. Background Technology

[0002] Pneumoconiosis is the most common occupational disease in my country, with cases concentrated in industries such as coal mining and tunnel construction. Among dust control methods, spray dust suppression is widely used; and for dust concentration measurement, light scattering is a common method. However, during spray dust suppression, phenomena such as changes in the pore structure of dust particles due to moisture absorption and the formation of agglomerates due to water film adhesion occur. This not only alters the mass of the dust particles but also changes their scattering characteristics, leading to significant errors in the measurement results of light scattering in high-humidity environments.

[0003] To reduce the interference of humidity on dust concentration measurement, current methods typically employ hardware dehumidification, algorithm compensation, or a combination of both. For example, Huang Zhihuang (CN202110494249.2), Huang Jing (CN202210318809.3), and Zhao Zheng (CN202510515426.9) proposed using a heating dehumidification device to control the humidity of dust-laden airflow within a humidity threshold, thereby improving the instrument's monitoring accuracy and lifespan. Guo Dongchen (CN202410149488.8) and Zhao Zheng (CN202310817588.9) calculated the real-time dust concentration value by constructing a mass concentration conversion model with humidity correction. In existing methods, heating for dehumidification causes dust particles to crack, altering their original scattering characteristics and increasing energy consumption, making it difficult to meet the low-power requirements of coal mines. Furthermore, the heating method has a lag in response and cannot track particle changes caused by sudden humidity fluctuations. When humidity correction is used, the correction coefficient, tested experimentally, cannot distinguish the coupling effect between concentration and humidity, nor does it consider dynamic changes in particle size. In high-humidity environments, the calculated concentration value deviates from the measured value by as much as 30%. Therefore, existing technologies do not fundamentally solve the difference in scattering characteristics caused by moisture absorption and cannot address the core problem of decreased measurement accuracy due to dust moisture absorption in high-humidity, high-dust environments in underground coal mines. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a dust concentration measurement technology and method based on humidity characteristic decoupling of light scattering. A humidity-particle size coupling model based on scattering characteristics is established to replace the traditional static model that only relies on the influence of humidity, so as to realize real-time inversion of dust concentration. The "concentration-related component" and "humidity interference component" are separated from the measured scattered light intensity, eliminating nonlinear interference in high humidity environment, thereby improving the measurement accuracy of light scattering method in high humidity environment.

[0005] To achieve the above objectives, the present invention provides a method for measuring dust concentration using light scattering based on humidity characteristic decoupling, comprising:

[0006] The system uses laser to irradiate the dust-laden airflow under test, and simultaneously collects light intensity signals from multiple scattering angles, as well as ambient humidity and temperature.

[0007] Multiple features were constructed to quantify the differences in light scattering characteristics of particulate matter, and the optimal feature for distinguishing the humid state of particulate matter was selected by verifying the effectiveness of the features.

[0008] Based on the optimal feature quantity, a humidity-particle size dynamic coupling model is constructed to realize real-time tracking of particle size changes with humidity.

[0009] The measured scattered light intensity signal is decomposed into multiple components to construct a decoupled model for decoupling and solving the humidity-particle size dynamic coupling model. During the solution process, the measured light intensity signals from multiple scattering angles, as well as the ambient humidity and temperature, are substituted into the decoupled model to establish a multivariate equation system. Solving this multivariate equation system yields the dust concentration-related components.

[0010] By dynamically adjusting the fusion weights using the optimal feature values, the dust concentration-related components from multiple scattering angles are weighted and fused to invert the dust concentration of the dust-laden airflow to be measured.

[0011] Furthermore, light intensity signals I were collected at four scattering angles: 60°, 90°, 120°, and 150°. 60 I 90 I 120 I 150 Based on the light intensity signals from these four scattering angles, characteristic quantities are constructed, including angle ratio feature F1, secondary peak feature F2, tail angle feature F3, and distribution standard deviation F4.

[0012] Furthermore, the effectiveness of the features was verified by obtaining the correlation coefficients between each feature quantity and the moisture content of particulate matter through comparative experiments. Finally, the secondary peak feature F2 with the highest correlation coefficient was selected as the optimal feature quantity for distinguishing the moisture content of particulate matter.

[0013] Furthermore, a humidity-particle size dynamic coupling model is constructed based on the secondary peak feature F2, as shown below:

[0014]

[0015] In the formula, Where d0 is the particle size under high humidity, k1 and k2 are coefficients, and R is the particle size reference value under low humidity. H For ambient humidity, T corr This is a temperature correction term used to compensate for the effect of temperature on scattering efficiency.

[0016] Furthermore, the measured scattered light intensity signal is decomposed into multiple components to construct a decoupling model:

[0017]

[0018] In the formula, I concentration (θ) represents the light intensity component of the dust concentration, I concentration (θ)=a(θ)×C+b(θ), where a(θ) is the angle-related sensitivity coefficient, b(θ) is the dark current compensation term, and C is the dust concentration of the dust-laden airflow to be measured; This is to couple the interference component, reflecting the effect of particle size variation on scattering efficiency. I humidity (θ,R H The humidity interference component reflects the direct scattering effect of ambient water vapor. humidity (θ,R H )=c(θ)×R H 2 +d(θ)×R H +e(θ), c(θ), d(θ), and e(θ) represent the coefficients of the angle-dependent quadratic, linear, and constant terms of humidity in the humidity interference component, respectively.

[0019] The measured light intensity signals from the four scattering angles and the ambient humidity R H and the particle size of moist particles Substituting into the decoupling model, a system of four equations is established. The weighted least squares method is used to solve this system, thus separating the light intensity component related to dust concentration. The objective function is: ω(θ) is the iteratively calculated value of the light intensity, and ω(θ) is the scattering angle weighting coefficient.

[0020] Furthermore, for the dust concentration light intensity component I at multiple scattering angles... concentration (θ) is weighted and fused to retrieve the dust concentration of the dust-laden airflow to be measured:

[0021]

[0022] Among them, the fusion weight ω(θ,F2) is dynamically adjusted with the secondary peak feature F2.

[0023] Furthermore, the fusion weight ω(θ,F2) is automatically calibrated at fixed time periods each day, including: during periods of stable humidity in the mine each day, the humidity R over the past 24 hours is retrieved. HTen sets of data with fluctuations less than 5% were used for inversion calculation of dust concentration. The particle swarm optimization algorithm was used to fine-tune the angle correlation sensitivity coefficient a(θ), the particle dimension was set to 4, the search range was ±10% of the initial value, and the fitness function was the sum of the absolute values ​​of the deviations between the inverted dust concentration and the reference value of the weighing method. The iteration continued until convergence.

[0024] Another aspect of the present invention provides a dust concentration measurement system based on humidity characteristic decoupling using light scattering, including a measurement chamber, a fan, a laser source, a photodetector, a temperature and humidity measurement unit, and a signal processing unit.

[0025] The measuring chamber is cylindrical; a fan is located at the outlet of the measuring chamber; a temperature and humidity measuring unit is located outside the measuring chamber and fixed at a certain distance from the inlet along the axial direction of the measuring chamber; a laser emitted from a laser source is focused by a lens and enters the measuring chamber through a window; multiple photodetectors are arranged at positions corresponding to different scattering angles on the measuring chamber, and all photodetectors are located on a plane perpendicular to the axis of the measuring chamber and passing through the optical axis of the laser source; a signal processing unit is connected to the temperature and humidity measuring unit, photodetectors, and laser source respectively, calculates the dust concentration of the dust-laden airflow to be measured in the measuring chamber based on the data transmitted by the temperature and humidity measuring unit and photodetectors, and simultaneously controls the output of the laser source.

[0026] In addition, the system also includes an inlet rectifier grid installed at the inlet of the measuring chamber, an outlet protection grid installed at the outlet of the measuring chamber and located at the rear end of the fan, and a fan bracket for fixing the fan. The inlet rectifier grid and the outlet protection grid keep the airflow in the measuring chamber in a laminar state.

[0027] The beneficial effects of this invention are as follows:

[0028] (1) This invention collects light intensity signals at four scattering angles of 60°, 90°, 120° and 150° simultaneously, and constructs a scattering feature vector containing angle ratio features, secondary peak features, tail angle features and distribution standard deviation, thereby realizing multi-dimensional scattering feature extraction and accurately characterizing the humidity-particle size coupling effect.

[0029] (2) Based on the secondary peak features in the scattering feature vector and the ambient humidity and temperature, this invention constructs a humidity-particle size dynamic coupling model, which can dynamically calculate the particle size of moist particles, breaks through the traditional static particle size assumption, and realizes real-time tracking of particle size changes with humidity through the humidity-particle size dynamic coupling model.

[0030] (3) This invention decomposes the measured scattered light intensity into light intensity component of dust concentration, coupling interference component and humidity interference component, and constructs a decoupling model to decouple the humidity-particle size dynamic coupling model. By establishing a four-equation system and solving it using the weighted least squares method, the pure concentration-related component is separated from the light intensity signal, thus eliminating nonlinear interference in high humidity environment.

[0031] (4) The present invention can adaptively adjust the weight of the four scattering angles based on the secondary peak characteristics, and combine it with a daily automatic calibration mechanism to achieve accurate concentration inversion under different humidity environments.

[0032] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0034] Figure 1 This is a flowchart illustrating a method for accurately measuring dust concentration based on humidity characteristic decoupling using light scattering, provided in an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of a dust concentration precision measurement system based on humidity feature decoupling provided in an embodiment of the present invention. Detailed Implementation

[0036] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0037] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0038] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0039] One embodiment of the present invention provides a method for measuring dust concentration using light scattering based on humidity characteristic decoupling, the method being as follows:

[0040] 1. Extracting multi-dimensional scattering features

[0041] The airflow containing dust particles is illuminated by a laser, and light intensity signals at four scattering angles (60°, 90°, 120°, and 150°) are collected simultaneously. 60 I 90 I 120 I 150 Ambient humidity R H And temperature T. A set of data samples is generated every 100ms, and N=20 sets are continuously collected to form a sample matrix (4×20).

[0042] 2. Construct the scattering feature vector F

[0043] Four features—angle ratio F1, secondary peak F2, tail angle F3, and distribution standard deviation F4—are constructed to quantify the differences in light scattering characteristics of particulate matter. Among them:

[0044] For dry dust, the cloth ratio is F1 = 1.8 to 2.2, and for moist dust, F1 = 1.2 to 1.6.

[0045]

[0046] R H When R = 80%, F² = 0.8–0.9; when R H When the percentage is 90%, F2 = 0.9 to 1.1.

[0047] It reaches 20% to 30%.

[0048] Distribution standard deviation F4 = std(I 60 ,I 90 ,I 120 ,I150 The standard deviation of the distribution is used to quantify the dispersion of the scattering angle distribution. The F4 of the dust containing moisture is 15% to 25% higher than that of the dry state.

[0049] Feature validity verification: Through 300 sets of control experiments (5 types of dust, 6 humidity gradients), the correlation coefficient between the secondary peak feature F2 and the dust moisture content (mass ratio) reached 0.92, which is the optimal feature quantity for distinguishing the humid state.

[0050] The scattering eigenvectors are represented as follows:

[0051] F = [F1, F2, F3, F4] = [I 90 / I 60 ,I 120 / I 90 ,I 150 / I 90 std(I 60 ,I 90 ,I 120 ,I 150 )]

[0052] 3. Construct a dynamic coupling model of humidity and particle size based on feature quantities.

[0053] When the humidity of the dust-laden airflow is relatively low (i.e., R) H When the particle size is ≤60%, the reference value d0 for its particle size is:

[0054]

[0055] Among them, T corr This is a temperature correction term used to compensate for the effect of temperature on scattering efficiency, T corr =1+0.002×(25-T).

[0056] When the humidity of the dust-laden airflow is high (i.e., R) H When the moisture content is >60%, the particle size distribution of the moist particles is measured. Dynamic calculation:

[0057]

[0058] in, The ambient humidity is represented by R. H The wet particle size is given by k1 and k2, which are coefficients, k1=0.0035 and k2=0.0012.

[0059] Combining d0 and Later:

[0060]

[0061] 4. Construct a dynamic decoupling model for humidity and particle size based on feature vectors.

[0062] The measured scattered light intensity I measurd (θ,R H It can be broken down into three parts:

[0063]

[0064] Among them, I concentration (θ) represents the light intensity component of the dust concentration, I concentration (θ) = a(θ) × C + b(θ), where a(θ) is the angle-related sensitivity coefficient, obtained through experimental calibration and combined with equipment characteristics calibration, and b(θ) is the dark current compensation term (<5mV), obtained through baseline measurement under no light illumination. This is to couple the interference component, reflecting the effect of particle size variation on scattering efficiency. I humidity (θ,R H The humidity disturbance component reflects the direct scattering effect of ambient water vapor. humidity (θ,R H )=c(θ)×R H 2 +d(θ)×R H +e(θ), c(θ), d(θ), and e(θ) represent the coefficients of the angle-dependent quadratic, linear, and constant terms of humidity in the humidity interference component, respectively, all of which can be calibrated through a humidity chamber experiment. When R H =40%~95%, under dust-free conditions, c(90°) = 0.003mV / %. 2 d(90°) = 0.02mV / %, e(90°) = 0.5mV (fit coefficient of 90°).

[0065] Comprehensive I concentration (θ) I humidity (θ,R H (The following is a list of items:)

[0066]

[0067] During the decoupling solution, the light intensity signals from the four scattering angles measured, as well as the ambient humidity R, are substituted into the input. H and the particle size of moist particles A system of four equations is established, and the weighted least squares method is used to solve for the light intensity component of the dust concentration. The objective function is: ω(θ) is the iteratively calculated value of the light intensity, and ω(θ) is the scattering angle weighting coefficient.

[0068] 5. The dust concentration C is retrieved by multi-angle dynamic weighted fusion. The fusion weight is adjusted with the secondary peak feature F2 and automatically calibrated over a fixed period of time.

[0069] The dust concentration inversion formula is as follows:

[0070]

[0071] The fusion weight ω(θ,F2) is adaptively adjusted according to the humidity state:

[0072] When the dust-laden airflow is dry, F2 < 0.8, the weight of 90° ω(90°,F2) is 0.4, the weight of 60° ω(60°,F2) is 0.3, the weight of 120° ω(120°,F2) is 0.2, and the weight of 150° ω(150°,F2) is 0.1.

[0073] When the dust-laden airflow is highly humid, F2>0.8, the weight of 120° is ω(120°,F2)=0.4, the weight of 90° is ω(90°,F2)=0.3, the weight of 150° is ω(150°,F2)=0.2, and the weight of 60° is ω(60°,F2)=0.1.

[0074] The method for automatically calibrating the weight ω(θ,F2) over a fixed time is as follows:

[0075] The calibration process automatically starts between 3 and 5 a.m. daily (during the non-production period in coal mines, when humidity is relatively stable), and retrieves data from the past 24 hours. H Ten sets of data with fluctuations less than 5% were used for inversion calculation of dust concentration. The dust concentration calculated using these data ensures calibration accuracy and avoids interference from humidity fluctuations. A particle swarm optimization algorithm was used to fine-tune the angle-related sensitivity coefficient a(θ), setting the particle dimension to 4 (corresponding to 4 angles), the search range to ±10% of the initial value, and the fitness function to be the sum of the absolute values ​​of the deviations between the inverted dust concentration and the weighing method reference value. Iteration continued until convergence.

[0076] During the system's factory delivery or installation and commissioning phase, using standard dust and a controllable temperature and humidity environment, combined with hardware parameters such as laser power, the initial a(θ) values ​​of four scattering angles of 60°, 90°, 120°, and 150° are calibrated to form the initial conversion benchmark between the light intensity signal and the single-angle concentration contribution, and also to provide the initial reference for the subsequent optimization and integration of the fusion weight ω(θ,F2).

[0077] During long-term operation, hardware drift such as laser source and detector aging can cause the initial a(θ) value to deviate. Even if ω(θ,F2) is allocated in the optimal proportion according to the humidity state, the deviation of a(θ) will amplify the error, rendering the weight optimization meaningless. Therefore, a particle swarm optimization algorithm is used to fine-tune a(θ) to correct the deviation, ensuring the accuracy of the single-angle concentration contribution value and guaranteeing the effectiveness of the weight ω(θ,F2). The fine-tuned a(θ) will immediately pass through I... concentration The concentration contribution value at a single angle is calculated using (θ) = a(θ) × C + b(θ). Then, based on the current dust moisture content, the corresponding weight ω(θ, F2) is applied, and the dust concentration inversion formula is used. Calculate the dust concentration; finally, determine convergence by summing the absolute values ​​of the deviations between the inverted dust concentration and the reference value obtained by the weighing method. If convergence is achieved, solidify the fine-tuned a(θ) and the weight ω(θ,F2) rule together as parameters for subsequent daily measurements. If the fine-tuned a(θ) is accurate, the weight ω(θ,F2) can be further improved by optimizing the ratio to enhance the accuracy of the fused concentration; if a(θ) still has a deviation, the deviation between the inverted dust concentration and the reference value will indicate the need for further fine-tuning.

[0078] Another embodiment of the present invention provides a dust concentration measurement system based on humidity characteristic decoupling using light scattering, such as... Figure 2 As shown, it includes:

[0079] (1) Measuring chamber and components

[0080] The measuring chamber is cylindrical, with a black light-absorbing coating on the inner wall to eliminate stray light, and a reflectivity of less than 5%.

[0081] An inlet rectifier is connected to the inlet of the measuring chamber, and an outlet protective grid and a fan are connected to the outlet. The fan is fixed by a fan bracket. The inlet rectifier and the outlet protective grid are slit-shaped, which keeps the airflow in a laminar state within the cylindrical measuring chamber.

[0082] (2) Laser source

[0083] In this embodiment, the laser source is a 650nm semiconductor laser with a laser output power of 5mW±0.5mW, a linewidth of less than 1nm, and a beam divergence angle of less than 0.5mrad. The laser emitted by the laser source is focused into a detection spot with a diameter of less than 1mm by an aspherical lens.

[0084] (3) Detector array

[0085] Four silicon-based photodetectors with a response wavelength of 400–1100 nm and a dark current of less than 1 nA are used and fixed at positions corresponding to four scattering angles of 60°, 90°, 120°, and 150° on the measurement chamber. To suppress ambient light interference, each detector is equipped with a bandpass filter with a center wavelength of 650 nm ± 5 nm and a bandwidth of 20 nm.

[0086] (4) Temperature and humidity measurement unit

[0087] A temperature and humidity measurement unit is installed at a certain distance from the air inlet of the measuring chamber. This unit communicates with the signal processing unit to ensure that the measurement environment is consistent with the dust's moisture absorption state. The temperature and humidity measurement range is R. H =0~100%, T=0~50℃, response time less than 50ms.

[0088] (5) Signal processing unit

[0089] A microcontroller is used as the signal processing unit. Each scattered signal is amplified by a low-noise amplifier, filtered, and then input into the microcontroller for processing to calculate the dust concentration. The microcontroller is used in conjunction with a memory to store calibration parameters, feature vector templates, and 30 days of historical data. The sampling interval can be set to 1 second.

[0090] In summary, this invention provides a method and system for accurate measurement of dust concentration using light scattering based on humidity feature decoupling. This invention simultaneously acquires light intensity signals at four scattering angles (60°, 90°, 120°, and 150°) to construct a scattering feature vector containing angle ratio features, secondary peak features, tail angle features, and distribution standard deviation, achieving multi-dimensional scattering feature extraction and enabling accurate characterization of the humidity-particle size coupling effect. Based on the secondary peak features, ambient humidity, and temperature in the scattering feature vector, this invention dynamically calculates the particle size of moist dust particles using an exponential function, breaking through the traditional static particle size assumption and achieving real-time tracking of particle size changes with humidity through a humidity-particle size dynamic coupling model. This invention decomposes the measured scattered light intensity into a concentration-related component, a particle size-humidity coupling interference component, and a direct humidity interference component. By establishing a four-equation system and solving it using weighted least squares, the pure concentration-related component is separated from the light intensity signal, eliminating nonlinear interference under high humidity conditions. This invention adaptively adjusts the weights of the four scattering angles based on the secondary peak features and combines a daily automatic calibration mechanism to achieve accurate concentration inversion under different humidity conditions.

[0091] This invention can significantly improve measurement accuracy and reduce measurement errors in high humidity environments. It fundamentally solves the problem of differences in scattering characteristics caused by dust moisture absorption. Through humidity-particle size-concentration decoupling at the algorithm level, this invention eliminates the need for heating and dehumidification modules and complex gas path systems, greatly reducing power consumption and meeting coal mine safety requirements.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for measuring dust concentration using light scattering based on humidity characteristic decoupling, characterized in that, The system uses laser to irradiate the dust-laden airflow under test, and simultaneously collects light intensity signals from multiple scattering angles, as well as ambient humidity and temperature. Multiple features were constructed to quantify the differences in light scattering characteristics of particulate matter. The optimal feature for distinguishing the humid state of particulate matter was selected by verifying the effectiveness of the features. The constructed features include angle ratio feature, secondary peak feature, tail angle feature, and distribution standard deviation. Based on the optimal feature quantity, a humidity-particle size dynamic coupling model is constructed to realize real-time tracking of particle size changes with humidity. The measured scattered light intensity signal is decomposed into multiple components to construct a decoupling model for decoupling and solving the humidity-particle size dynamic coupling model. The decomposed components include the light intensity component of dust concentration, the coupling interference component, and the humidity interference component. During the solution process, the measured light intensity signals from multiple scattering angles, as well as the ambient humidity and temperature, are substituted into the decoupling model to establish a multivariate equation system. Solving this multivariate equation system yields the dust concentration-related component. By dynamically adjusting the fusion weights using the optimal feature values, the dust concentration-related components from multiple scattering angles are weighted and fused to invert the dust concentration of the dust-laden airflow to be measured.

2. The method according to claim 1, characterized in that, Light intensity signals were collected at four scattering angles: 60°, 90°, 120°, and 150°. , , , Based on the light intensity signals from these four scattering angles, feature quantities are constructed, including angle ratio features. Secondary peak characteristics Tail horn characteristics and distribution standard deviation .

3. The method according to claim 2, characterized in that, To verify the effectiveness of the features, correlation coefficients between each feature quantity and particulate matter moisture content were obtained through control experiments. Finally, the secondary peak feature with the highest correlation coefficient was selected. As the optimal characteristic quantity for distinguishing the moist state of particulate matter.

4. The method according to claim 3, characterized in that, Based on secondary peak characteristics The humidity-particle size dynamic coupling model is constructed as follows: In the formula, This refers to the particle size under high humidity conditions. This is the particle size reference value for low humidity. , For coefficients, For ambient humidity, This is a temperature correction term used to compensate for the effect of temperature on scattering efficiency.

5. The method according to claim 4, characterized in that, The measured scattered light intensity signal is decomposed into multiple components to construct a decoupling model: In the formula, The light intensity component represents the dust concentration. , The angle-dependent sensitivity coefficient, This is a dark current compensation term. C The dust concentration in the dust-laden airflow to be measured; This is to couple the interference component, reflecting the effect of particle size variation on scattering efficiency. ; The humidity interference component reflects the direct scattering effect of ambient water vapor. , , , These represent the coefficients of the angle-dependent quadratic, linear, and constant terms of humidity in the humidity interference component, respectively. The measured light intensity signals from the four scattering angles and the ambient humidity were used to determine the light intensity signals. and the particle size of moist particles Substituting into the decoupling model, a system of four equations is established. The weighted least squares method is used to solve for the light intensity component of the dust concentration. The objective function is: , This represents the iteratively calculated value of the light intensity. This is the scattering angle weighting coefficient.

6. The method according to claim 5, characterized in that, Dust concentration light intensity components at multiple scattering angles Weighted fusion is performed to invert the dust concentration of the dust-laden airflow being measured: Among them, the fusion weight Characteristics of secondary peaks Dynamic adjustment.

7. The method according to claim 6, characterized in that, For fusion weights Automatic calibration is performed at fixed times each day, including: during periods of stable humidity underground in the mine, retrieving humidity data from the past 24 hours. Ten sets of data with fluctuations less than 5% were used for inversion calculation of dust concentration; the angle correlation sensitivity coefficient was fine-tuned using a particle swarm optimization algorithm. The particle dimension is set to 4, the search range is ±10% of the initial value, and the fitness function is the sum of the absolute values ​​of the deviations between the dust concentration calculated by inversion and the reference value by weighing method. The iteration continues until convergence.

8. A dust concentration measurement system based on humidity characteristic decoupling using light scattering for implementing the method described in any one of claims 1 to 7, characterized in that, It includes a measuring chamber, a fan, a laser source, a photodetector, a temperature and humidity measuring unit, and a signal processing unit. The fan is located at the outlet of the measuring chamber, and the temperature and humidity measuring unit is located outside the measuring chamber and fixed at a certain distance from the inlet along the axial direction of the measuring chamber. The laser emitted by the laser source is focused by a lens and enters the measuring chamber through a window. Multiple photodetectors are arranged at positions corresponding to different scattering angles on the measuring chamber, and all photodetectors are located on a plane perpendicular to the axis of the measuring chamber and passing through the optical axis of the laser source. The signal processing unit is connected to the temperature and humidity measuring unit, the photodetectors, and the laser source.

9. The system according to claim 8, characterized in that, It also includes an inlet rectifier grid installed at the inlet of the measuring gas chamber, an outlet protection grid installed at the outlet of the measuring gas chamber and located at the rear end of the fan, and a fan bracket for fixing the fan.

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