Smoke lidar ratio probability retrieval method and system based on microscopic scattering constraint

Abstract

The application discloses a smoke laser radar ratio probability inversion method and system based on microscopic scattering constraints, and the method first utilizes a polarization laser radar to scan a target combustion area, acquires a laser echo signal and pre-processes, and determines a target depolarization ratio constraint interval.Secondly, the smoke aerosol is modeled as an internal mixture containing black carbon and organic carbon, and the effective complex refractive index of the mixed aerosol under different black carbon volume fraction conditions is calculated.Then, the optical property parameter of a single smoke aerosol particle is calculated to construct an optical property database.Finally, through single smoke aerosol particle level and smoke aerosol group collection level screening, the microphysical parameter combination with the theoretical linear depolarization ratio falling into the constraint interval is obtained, the laser radar ratio probability density function is constructed, and the inversion is completed.The application significantly improves the accuracy of smoke aerosol extinction coefficient and mass concentration estimation, and is suitable for routine polarization laser radar business monitoring of smoke aerosol.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric environment monitoring and lidar remote sensing technology, specifically relating to a smoke lidar specific probability inversion method and system based on microscopic scattering constraints. Background Technology

[0002] Accurate characterization of the optical and microphysical properties of smoke aerosols is of significant scientific and practical value for forest fire detection and the formulation of atmospheric environmental governance strategies. As a core technology for acquiring three-dimensional information of particulate matter, polarized lidar, by detecting the linear depolarization ratio parameter of smoke aerosol particles, can effectively identify the non-spherical characteristics and internal mixing state of smoke aerosols, providing technical support for distinguishing between smoke and non-fire smoke.

[0003] When quantitatively retrieving the extinction coefficient and mass concentration of smoke aerosols using conventional elastic backscattering lidar, the ratio of the extinction coefficient to the backscattering coefficient, i.e., the lidar ratio, must be introduced as a core prior input parameter. Since conventional lidar systems cannot directly resolve this parameter, traditional inversion algorithms generally rely on climatological aerosol models based on long-term statistical analysis to provide fixed prior values. However, the optical and microphysical properties of smoke aerosols are comprehensively influenced by fuel type, combustion efficiency, and atmospheric aging processes, exhibiting extremely high spatiotemporal heterogeneity and dynamic evolution characteristics. General static aerosol models severely smooth out this diversity of local features, leading to significant systematic biases in inversion results based on static prior information when facing specific and fresh fire smoke events. This severely limits the accuracy of quantitative inversion of key parameters such as the smoke aerosol extinction coefficient.

[0004] Currently, while Raman lidar and hyperspectral resolution lidar possess the ability to independently detect aerosol extinction and backscattering characteristics, thus circumventing prior assumptions, they are limited by hardware bottlenecks such as complex system architecture, high manufacturing costs, and large size, making it difficult to meet the operational monitoring needs of large-scale network observation and rapid field response. Furthermore, existing spaceborne lidars are constrained by both long revisit periods and coarse spatial resolution, and the data they acquire often characterizes highly aged and mixed aerosols after long-distance transmission. Such macroscopic observation scales make it difficult to establish clear correlation mechanisms with concurrent surface combustion sources with known fuels and specific combustion conditions, lacking the ability to characterize the microphysical properties of local fresh emission sources in real time.

[0005] Current technologies, relying solely on conventional polarization lidar observations, lack dynamic constraint mechanisms for the characteristic parameters of smoke aerosols, making it difficult to achieve precise decoupling of optical parameters of smoke aerosols under complex combustion conditions. Therefore, there is an urgent need to develop a dynamic constraint method based on real-time depolarization ratio observation data and incorporating a smoke morphology physical model. This method aims to overcome the computational instability and inversion error bottlenecks caused by traditional static models, thereby meeting the refined requirements of high-precision atmospheric environment monitoring and quantitative remote sensing of smoke aerosols. Summary of the Invention

[0006] This invention addresses the core shortcomings of existing technologies: conventional elastic backscattering lidar requires a fixed lidar ratio for inverting forest fire smoke aerosols, leading to significant inversion errors; smoke aerosol models cannot characterize the microphysical differences in local fire combustion events; Raman and hyperspectral resolution lidars are costly and difficult to deploy on a large scale; and the lack of a benchmark dataset of forest fire smoke aerosol debiasing ratios bound to known combustion conditions hinders the development of source-specific inversion algorithms. This invention provides a probabilistic inversion method and system for smoke lidar ratios based on microscopic scattering constraints. To solve the above technical problems, this invention adopts the following technical solution:

[0007] In one aspect, this invention provides a probability inversion method for smoke lidar based on microscopic scattering constraints, comprising the following steps:

[0008] Step 1: Use a polarization lidar to perform continuous three-dimensional scanning of the smoke plume in the target combustion area to acquire the original laser echo signal; perform background subtraction and filtering on the laser echo signal to extract the measured linear depolarization ratio within the effective detection range. Determine the constraint interval for the target deflection ratio. .

[0009] Step 2: Model the smoke aerosol as an internal mixture containing black carbon (BC) and organic carbon (OC), and set the basic complex refractive index of each of the black carbon and organic carbon; based on the effective medium theory (such as the Maxwell-Garnett theory) and combined with the set basic complex refractive index, calculate the volume fraction of black carbon for different amounts. Effective complex refractive index of mixed smoke aerosols under certain conditions .

[0010] Step 3: Treat the smoke aerosol particles as rotationally symmetric ellipsoids and set the particle size to cover different size distributions. Scope and aspect ratio characterizing the degree of non-sphericity Range; the effective complex refractive index calculated in step 2 The particle size Scope and aspect ratio Using the range as an input parameter, the extinction cross section of a single smoke aerosol particle under the corresponding conditions is calculated using the T-matrix method. Backscattering cross section Total scattering cross section Single-scatter albedo and theoretical linear debias ratio Based on this, an optical property database covering a variety of microphysical states was constructed.

[0011] Step 4: Based on the optical property database constructed in Step 3, the theoretical linear depolarization ratio is obtained by screening at both the individual smoke aerosol particle level and the smoke aerosol ensemble level. Falling into the constraint interval obtained in step 1 Combination of microphysical parameters within Furthermore, prior physical constraints based on single-scattered albedo (SSA) are introduced to filter non-physical solutions. The corresponding theoretical lidar ratio S is calculated based on the selected results, and then the probability density function of the lidar ratio is constructed. The probability inversion of smoke lidar was completed.

[0012] Furthermore, the screening of microphysical parameter combinations at the level of individual smoke aerosol particles and at the level of smoke aerosol ensembles, as described in step 4, specifically includes:

[0013] 1. Constraints at the individual smoke aerosol particle level. In the optical property database of step 3, search and filter for particles that theoretically exhibit linear depolarization ratios for individual smoke aerosols. Satisfying the constraint interval Particle size and aspect ratio parameters The scattering albedo (SSA) of individual smoke aerosol particles corresponding to the optical properties database is extracted. Non-physical solutions with SSA below a preset physical absorption threshold are removed to obtain a valid combination of microphysical parameters for individual smoke aerosol particles. Then, the radar ratio of each combination is calculated, and the probability density function of the radar ratio at the level of individual smoke aerosol particles is constructed.

[0014] 2. Constraints at the Aggregate Level of Smoke Aerosol Population. The particle diameter and aspect ratio within the smoke aerosol population follow a log-normal size distribution and a normal distribution. The distribution parameters are systematically traversed, and the equivalent volume depolarization ratio corresponding to each distribution population is calculated using the individual smoke aerosol particle characteristics from the optical property database constructed in step 3, through double numerical integration. and population single-scattering albedo , Screening out those that cause body regression ratio Satisfying the constraint interval The group, excluding Non-physical solutions below a preset physical absorption threshold are used to obtain a legal group distribution set. Then, the radar ratio of each group is calculated, and a probability density function of the radar ratio at the group set level is constructed.

[0015] In another aspect, the present invention also provides a proportional probability inversion system for smoke lidar based on microscopic scattering constraints, comprising the following modules:

[0016] The polarization lidar module is used to perform continuous three-dimensional scanning of the smoke plume of the target combustion area using polarization lidar, acquire the original laser echo signal and preprocess it to determine the constraint range of the target depolarization ratio.

[0017] The Mixed Aerosol Effective Complex Refractive Index module is used to model smoke aerosols as an internal mixture containing black carbon (BC) and organic carbon (OC). Based on the effective medium theory, it calculates the effective complex refractive index of the mixed aerosol under different black carbon volume fractions.

[0018] The optical properties database module is used to treat smoke aerosol particles as equivalent to rotationally symmetric ellipsoids, combine the effective complex refractive index of mixed aerosols, and use the T-matrix method to calculate the optical property parameters of individual smoke aerosol particles, thereby constructing an optical property database.

[0019] The smoke lidar ratio probability inversion module is used to filter the optical property database at the level of individual smoke aerosol particles and the level of smoke aerosol ensembles to obtain the combination of microphysical parameters whose theoretical linear depolarization ratio falls within the constraint interval, calculate the corresponding theoretical lidar ratio, construct the lidar ratio probability density function, and complete the smoke lidar ratio probability inversion.

[0020] The beneficial effects of this invention are:

[0021] 1. Low cost and high applicability. Quantitative constraints on lidar ratio can be achieved using only conventional polarization lidar, eliminating the need for expensive Raman or high-spectral-resolution lidar. It can be widely deployed in mobile monitoring vehicles, field stations, and emergency response systems.

[0022] 2. Significantly improved inversion accuracy. Breaking through the traditional assumption of a single fixed lidar ratio, by using dual physical constraints on the morphology and composition of smoke aerosols, the uncertainty of the lidar ratio is significantly reduced, and it shows good consistency with Raman lidar observation results. The inversion error of the smoke aerosol extinction coefficient is reduced by more than 60%.

[0023] 3: Accurate characterization of non-spherical particles. An ellipsoidal particle model with an aspect ratio ranging from 0.5 to 2.0 is adopted, which solves the problem that the traditional spherical Mie model cannot accurately simulate the non-spherical morphology of smoke aerosols.

[0024] 4. Adaptive Combustion Intensity: It can adapt to changes in the particle characteristics of smoke aerosol particles at different combustion stages, such as open flame and smoldering, and can accurately invert the optical properties of smoke aerosols under different fuel types and combustion intensities.

[0025] 5. Quantitative assessment of uncertainties. The probability distribution of the output lidar ratio, rather than a single value, can quantitatively characterize the reliability of the inversion results, providing more scientific data support for climate models and environmental policy formulation. Attached Figure Description

[0026] Figure 1 Schematic diagram of a controlled open combustion field experiment;

[0027] Figure 2 Time series plot of linear deskewing ratio of smoke aerosol particles under different fuel bed densities;

[0028] Figure 3 Probability density function plot of linear debiasing ratio of smoke aerosol particles under different fuel densities;

[0029] Figure 4 Simulation diagram of the relationship between linear depolarization ratio of smoke aerosol particles and their morphology and composition;

[0030] Figure 5 Simulation diagram of the relationship between the lidar ratio of smoke aerosols and their morphology and composition;

[0031] Figure 6 Figure showing the allowable range of individual smoke aerosol particle size and aspect ratio based on deflection ratio constraint;

[0032] Figure 7 Probability distribution of lidar before and after adding single-scattering albedo constraint. Detailed Implementation

[0033] To better illustrate the purpose, technical solution, and advantages of this invention, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments. In one aspect, this invention provides a probability inversion method for smoke lidar ratio based on microscopic scattering constraints. By combining measured polarization signals from polarized lidar with microscopic non-spherical light scattering modeling and physical boundary screening, the lidar ratio is transformed from a traditional empirical fixed value to a dynamic probability distribution. The specific implementation steps are as follows:

[0034] Step 1: Data acquisition and constraint interval determination based on polarization lidar.

[0035] This experiment employed controlled combustion experiments using agricultural straw of two different densities. A polarization-sensitive lidar system with a working wavelength of 532 nm was deployed at a distance of 75 m from the combustion zone to conduct continuous high-frequency detection, ensuring the acquisition of complete vertical profile measurement data of the plume. The specific data processing and feature extraction process is as follows:

[0036] 1.1 Data Acquisition and Preprocessing: such as Figure 1 and Figure 2 As shown, a polarization lidar was used to perform continuous three-dimensional scanning of the smoke plume in the target combustion area to acquire the original laser echo signal. Background subtraction and filtering were performed on the laser echo signal to extract the measured linear depolarization ratio of the smoke aerosol particles within the effective detection range. .

[0037] 1.2 Extraction of statistical features and determination of constraint intervals for the skew ratio after quality control: Statistical analysis was performed on the data, and the interval from the 10th percentile to the 90th percentile was extracted as the constraint interval for the target backsliding ratio. This operation can effectively eliminate the influence of extreme outliers and preserve core morphological features.

[0038] Step 2: Modeling the composition and effective refractive index of smoke aerosols.

[0039] The probability density function graph of the linear deskewing ratio of smoke aerosol particles under different fuel densities is shown below. Figure 3 As shown, the smoke aerosol is modeled as an internal mixture of black carbon (BC) and organic carbon (OC). The Maxwell-Garnett effective medium theory is used to calculate different BC volume fractions. Effective complex permittivity This theory applies to the case of sparsely dispersed spherical inclusions embedded in a continuous medium, and can accurately describe the optical effects of BC in the OC matrix. Calculations were performed for different BC volume fractions. Effective complex permittivity :

[0040]

[0041] The effective complex refractive index is calculated from the effective dielectric function. :

[0042]

[0043] in, and ... The complex refractive index of OC is taken as .

[0044] In this embodiment, BC volume fraction The value ranges from 1.25% to 20%, covering the typical composition range of fresh combustion smoke aerosols. This step allows changes in the composition of smoke aerosols to be translated into changes in optical properties, providing a foundation for subsequent optical calculations of non-spherical particles.

[0045] Step 3: Construction of the database of optical properties of smoke aerosol particles.

[0046] To accurately characterize the non-spherical morphology of smoke aerosols, smoke aerosol particles are simulated as rotationally symmetric ellipsoids. The T-matrix method is used to calculate their optical properties. This method is an accurate numerical method for solving the light scattering problem of rotationally symmetric smoke aerosol particles, and it is more computationally efficient than methods such as the discrete dipole approximation, making it suitable for constructing large-scale optical property databases. The construction process is as follows:

[0047] 3.1: Setting parameters for smoke aerosol particles.

[0048] The smoke aerosol particles are equated to rotationally symmetric ellipsoids, with the particle size D ranging from 0.2 to 4.5 μm and the traversal step size set to 0.1 μm, covering the typical size range of smoke aerosol particles; aspect ratio... The aspect ratio is defined as the ratio of the physical length of the rotational symmetry axis to the equatorial axis of the ellipsoidal particle. The set value range is 0.5 to 2.0, and the traversal step size is set to 0.02. This range is wider than existing studies, enabling the capture of richer morphologies of smoke aerosol particles; effective refractive index... : Calculated from step 2, corresponding to different BC volume fractions.

[0049] 2: Numerical calculation and parameter output of the T matrix.

[0050] The calculations in step 2 corresponding to different black carbon volume fractions Multiple effective complex refractive indices And the particle size D and aspect ratio established above The sequence, together, serves as the input parameters for the T-matrix numerical calculation program. For each combination of microphysical state parameters... The optical properties of individual smoke aerosol particles at a set wavelength were calculated using the T-matrix method.

[0051] 3: Database mapping and generation.

[0052] Extract and calculate the optical characteristic parameters output by the T-matrix algorithm for each iteration, specifically including: the extinction cross section of a single smoke aerosol particle. Backscattering cross section Total scattering cross section Single-scattering albedo (SSA) theoretical line depolarization ratio Finally, the input multidimensional microphysical state parameters Optical characteristic parameter set corresponding to the output Establish a one-to-one mapping relationship, store and construct a database of optical properties of smoke aerosol particles.

[0053] Through the above calculations, a database of optical properties containing all combinations of particle size, aspect ratio, and effective refractive index is pre-generated, providing a basis for subsequent lidar ratio constraints. Figure 4 and Figure 5 The relationships between the simulated linear depolarization ratio and lidar ratio of smoke aerosol and their morphology and composition are shown respectively. It can be seen that both exhibit complex nonlinear dependence on BC volume fraction, particle size and aspect ratio, which is the fundamental reason for the error caused by the traditional single fixed lidar ratio assumption.

[0054] Step 4: Two-dimensional morphological constraints and construction of the lidar posterior probability density function.

[0055] This invention employs a two-level constraint method combining the individual smoke aerosol particle level and the smoke aerosol ensemble level. This method integrates individual morphological characteristics and ensemble statistical characteristics, further improving inversion accuracy compared to single-level constraints. The constraint steps are as follows:

[0056] 4.1: Constraints at the level of individual smoke aerosol particles.

[0057] Query the generated optical property database and filter all those that meet the requirements. of Parameter pair. Using only the deflection ratio constraint can lead to some non-physical solutions, such as... Figure 7 As shown, some lidar ratios are much higher than the actual observed values. A prior physical constraint of SSA > 0.7 for a single smoke aerosol particle is introduced to filter out all parameter combinations with SSA ≤ 0.7. For each parameter pair that meets the condition, the corresponding lidar ratio for a single smoke aerosol particle is calculated:

[0058]

[0059] Construct the probability distribution of lidar ratio :

[0060]

[0061] in, For extinction cross section, It is the backscattering cross section. The total number of individual smoke aerosol particles that meet the criteria. The total number of individual smoke aerosol particles that meet the criteria. To find the summation index, corresponding to the first... A number of smoke aerosol particles that meet the criteria. For the Dirac function, The measured linear deflection ratio, This is the ratio of lidar.

[0062] Figure 6 and Figure 7 The permissible ranges of individual smoke aerosol particle size and aspect ratio for different BC volume fractions under two fuel densities are presented. It is evident that a higher BC volume fraction results in a smaller permissible parameter space, indicating that the depolarization ratio exerts stronger morphological constraints on individual smoke aerosol particles with high BC content. This characteristic can be used to assist in determining the compositional characteristics of smoke aerosols.

[0063] 2: Constraints at the level of smoke aerosol population aggregation.

[0064] The particles within the smoke aerosol population follow a log-normal size distribution.

[0065]

[0066] The aspect ratio follows a normal distribution:

[0067]

[0068] in, The average diameter of the smoke aerosol population. Diameter, standard deviation Fixed at 0.4 Aspect ratio, The aspect ratio of the group is fixed at 0.4, and the standard deviation is... It is fixed at 0.05. This value has been confirmed by existing research to have minimal impact on lidar inversion results and to significantly reduce computational complexity.

[0069] The bulk optical properties of the smoke aerosol population were obtained through numerical integration:

[0070] Volume extinction coefficient:

[0071]

[0072] Volume scattering coefficient:

[0073]

[0074] Volume backscattering coefficient:

[0075]

[0076] Physical regression ratio:

[0077]

[0078] in, For extinction cross section, Scattering cross section For a single backscattering cross section, 0.5 and 2.0 are diameter ranges, and 0.2 and 4.5 are aspect ratio ranges.

[0079] Retain satisfaction Given the parameter set, calculate the volume single-scattering albedo:

[0080]

[0081] Filter out all Calculate the parameter combinations ≤0.7 and the corresponding volume lidar ratio:

[0082]

[0083] Construct the probability density function:

[0084]

[0085] The corresponding volume lidar ratio is calculated, and a posterior probability density function is constructed.

[0086] Where M represents the total number of eligible smoke aerosol groups. To find the summation index, corresponding to the first... A group of smoke aerosols that meet the criteria. For the first The diameter and aspect ratio of the smoke aerosol group are: The volume lidar ratio, For the Dirac function, The linear debias ratio of the particles. This is the ratio of lidar.

[0087] In another aspect, the present invention also provides a smoke lidar specific probability inversion system based on microscopic scattering constraints, for implementing the aforementioned smoke lidar specific probability inversion method, comprising a polarization lidar module, a mixed aerosol effective complex refractive index module, an optical property database module, and a smoke lidar specific probability inversion module:

[0088] The polarization lidar module is used to perform continuous three-dimensional scanning of the smoke plume of the target combustion area using polarization lidar, acquire the original laser echo signal and preprocess it to determine the constraint range of the target depolarization ratio.

[0089] The mixed aerosol effective complex refractive index module is used to model smoke aerosols as an internal mixture containing black carbon BC and organic carbon OC, and calculate the effective complex refractive index of mixed aerosols under different black carbon volume fractions based on the effective medium theory.

[0090] The optical property database module is used to treat smoke aerosol particles as equivalent to rotationally symmetric ellipsoids, combine the effective complex refractive index of the mixed aerosol, and use the T-matrix method to calculate the optical property parameters of individual smoke aerosol particles to construct an optical property database.

[0091] The smoke lidar ratio probability inversion module is used to filter the optical property database at the level of individual smoke aerosol particles and the level of smoke aerosol ensembles to obtain the combination of microphysical parameters whose theoretical linear depolarization ratio falls within the constraint interval, calculate the corresponding theoretical lidar ratio, construct the lidar ratio probability density function, and complete the smoke lidar ratio probability inversion.

Claims

1. A proportional probability inversion method for smoke lidar based on microscopic scattering constraints, characterized in that, Includes the following steps: Step 1: Use polarized lidar to perform continuous three-dimensional scanning of the smoke plume in the target combustion area, acquire the original laser echo signal and preprocess it to determine the constraint range of the target depolarization ratio; Step 2: Model the smoke aerosol as an internal mixture containing black carbon BC and organic carbon OC. Based on the effective medium theory, calculate the effective complex refractive index of the mixed aerosol under different black carbon volume fractions. Step 3: Equivalently treat smoke aerosol particles as rotationally symmetric ellipsoids, combine the effective complex refractive index of the mixed aerosol, and use the T matrix method to calculate the optical property parameters of individual smoke aerosol particles to construct an optical property database; Step 4: In the optical property database, through screening at the level of individual smoke aerosol particles and the level of smoke aerosol ensembles, the combination of microphysical parameters that fall within the constraint range of the theoretical linear depolarization ratio is obtained, the corresponding theoretical lidar ratio is calculated, the lidar ratio probability density function is constructed, and the smoke lidar ratio probability inversion is completed.

2. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 1, characterized in that, The specific implementation process of step 1 is as follows: A polarized lidar is used to perform continuous three-dimensional scanning of the smoke plume in the target combustion area to obtain the original laser echo signal; the laser echo signal is then subjected to background subtraction and filtering to extract the measured linear depolarization ratio within the effective detection range. ; Determine the constraint range for the target deflection ratio .

3. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 2, characterized in that, The specific implementation process of step 2 is as follows: The smoke aerosol was modeled as an internal mixture of black carbon (BC) and organic carbon (OC). This model conforms to the typical microstructure of fresh biomass combustion aerosols, where BC aggregates are encapsulated by OC to form a core-shell structure. The Maxwell-Garnett effective medium theory was used to calculate different BC volume fractions. ; Then calculate the volume fractions for different BC values. Effective complex permittivity The effective complex refractive index is calculated from the effective dielectric function. .

4. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 3, characterized in that, The specific implementation process of step 3 is as follows: 3.1: The smoke aerosol particles are considered equivalent to rotationally symmetric ellipsoids, and the diameter of the smoke aerosol particles... ; Aspect Ratio , is defined as the ratio of the physical length of the rotational symmetry axis of an ellipsoidal particle to its equatorial axis; 3.2: The corresponding volumes of black carbon will be... Multiple effective complex refractive indices Diameter of smoke aerosol particles Aspect Ratio The sequence, together, serves as the input parameters for the T-matrix numerical calculation program; for each combination of microphysical state parameters... The T-matrix method was used to calculate the optical properties of individual smoke aerosol particles at a set wavelength. Key optical properties output by the T-matrix algorithm for each iteration were extracted and calculated, including the extinction cross section of individual smoke aerosol particles. Backscattering cross section Total scattering cross section Single-scattering albedo (SSA) theoretical line depolarization ratio The input multidimensional microphysical state parameters Optical characteristic parameter set corresponding to the output Establish a one-to-one mapping relationship, store and construct a database of optical properties of smoke aerosol particles.

5. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 4, characterized in that, The specific implementation process of step 4 is as follows: 4.1: In the optical property database, search and filter for properties that result in linear depolarization ratios according to single-event theory. Satisfying the constraint interval Particle size and aspect ratio parameters Extract the single scattering albedo (SSA) corresponding to the parameter pair in the optical property database, remove non-physical solutions with SSA below the preset physical absorption threshold, obtain a legal single-particle microphysical parameter combination, and then calculate the radar ratio of each group to construct the probability density function of the radar ratio at the level of a single smoke aerosol particle. 4.2: The particle diameter and aspect ratio within the smoke aerosol population follow a preset log-normal size distribution and a normal aspect ratio distribution. By traversing the distribution parameters and using the optical property parameters of individual smoke aerosol particles from the optical property database, the equivalent volume depolarization ratio corresponding to each distribution population is calculated. and population single-scattering albedo ; Screening out those that cause body to degenerate to a bias ratio Satisfying the constraint interval The group, excluding Non-physical solutions below a preset physical absorption threshold are used to obtain a legal group distribution set, and then the probability density function of the corresponding group set radar ratio is calculated.

6. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 5, characterized in that, Step 4.1 is specifically implemented as follows: Query the optical property database and filter all those that meet the requirements. of Parameter pairs; Prior physical constraints are introduced for a single smoke aerosol particle (SSA), and all parameter combinations that do not conform to the prior physical constraints are filtered out to obtain valid single-particle microphysical parameter combinations. For each valid single-particle microphysical parameter combination, the corresponding single-smoke aerosol particle lidar ratio is calculated. Construct the probability density function of the lidar ratio. ,in, The total number of individual smoke aerosol particles that meet the criteria. To find the summation index, corresponding to the first index... A number of smoke aerosol particles that meet the criteria. For the Dirac function, For the measured linear deflection ratio, This is the ratio of lidar.

7. The proportional probability inversion method for smoke lidar based on microscopic scattering constraints according to claim 6, characterized in that, Step 4.2 is specifically implemented as follows: The particle diameter and aspect ratio within the smoke aerosol population follow a log-normal size distribution. and ; The bulk optical properties of the smoke aerosol population were obtained through numerical integration: Calculate the volume extinction coefficient separately Volume scattering coefficient Volume backscattering coefficient And physical decline ratio ; retain satisfaction The parameter set, through and The ratio of the population single-scattering albedo is used to calculate the population single-scattering albedo. ; Filter out all For parameter combinations that do not conform to prior physical constraints, a valid set of group fractions is obtained, and the corresponding volume lidar ratio is calculated to construct the corresponding probability density function.

8. A smoke lidar specific probability inversion system based on microscopic scattering constraints, used to implement the smoke lidar specific probability inversion method according to any one of claims 1 to 7, characterized in that, Includes the following modules: The polarization lidar module is used to perform continuous three-dimensional scanning of the smoke plume of the target combustion area using polarization lidar, acquire the original laser echo signal and preprocess it to determine the constraint range of the target depolarization ratio. The effective complex refractive index module for mixed aerosols is used to model smoke aerosols as an internal mixture containing black carbon (BC) and organic carbon (OC), and calculates the effective complex refractive index of mixed aerosols under different black carbon volume fractions based on the effective medium theory. The optical properties database module is used to treat smoke aerosol particles as equivalent to rotationally symmetric ellipsoids, combine the effective complex refractive index of mixed aerosols, and use the T matrix method to calculate the optical property parameters of individual smoke aerosol particles to construct an optical properties database. The smoke lidar ratio probability inversion module is used to filter the optical property database at the level of individual smoke aerosol particles and the level of smoke aerosol ensembles to obtain the combination of microphysical parameters whose theoretical linear depolarization ratio falls within the constraint interval, calculate the corresponding theoretical lidar ratio, construct the lidar ratio probability density function, and complete the smoke lidar ratio probability inversion.

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