High-flux microfluidic detection system for dynamic phase change process of aerosol

Abstract

The invention relates to the technical field of aerosol phase change detection, in particular to a high-flux micro-fluidic detection system for an aerosol dynamic phase change process, and aims to solve the technical problems of low detection efficiency, incapability of synchronously comparing multiple environmental conditions and difficulty in tracking a transient phase change process in real time caused by single-channel operation in the prior art. A multi-channel independent regulation and control unit is designed, and dynamic phase change simulation of aerosol under different environmental conditions is realized based on an optical detection technology by coupling rapid temperature switching and humidity simulation functions. By combining multi-channel independent regulation and control with an optical detection technology, the limitation of traditional single-dimensional detection is broken through, and compared with the prior art (such as a single temperature control module and off-line spectrum analysis), high-throughput dynamic analysis of the aerosol phase change complete period is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aerosol phase change detection, more particularly, it relates to a high-throughput microfluidic detection system for dynamic phase change process of aerosol. BACKGROUND

[0002] Dynamic monitoring of aerosol phase change process is of great significance in the fields of environmental science, climate change research and atmospheric pollution control, and its phase transition characteristics directly affect the optical properties, chemical activity and environmental effects of aerosol. Precise capture of dynamic phase change characteristics of aerosol under different environmental conditions is of great scientific significance for understanding atmospheric chemical processes and optimizing pollution control strategies.

[0003] The existing technology has significant limitations in aerosol phase change detection. On the one hand, traditional detection systems mostly use single-channel experimental design, which can only simulate a single environmental condition in one experiment. If the effects of different temperature and humidity parameters need to be compared, multiple repeated experiments are required, which not only reduces the detection efficiency, but also introduces errors due to batch differences. On the other hand, the environmental regulation capability is insufficient, making it difficult to accurately reproduce complex dynamic real environmental scenarios (such as diurnal temperature fluctuation and humidity sudden change), and lacking high-resolution monitoring capability in the critical interval of high phase change (such as specific temperature and humidity range), resulting in the details of transient phase change being easily missed. At the same time, some detection methods rely on contact sampling or offline spectral analysis, which cannot realize real-time tracking and in-situ analysis of the phase change process, restricting the in-depth study of the phase change mechanism.

[0004] Therefore, the present application provides a high-throughput microfluidic detection system for dynamic phase change process of aerosol, which realizes efficient and accurate monitoring of aerosol phase change process through coupling of multi-channel independent regulation and optical detection technology. SUMMARY

[0005] In view of the deficiencies of the prior art, the purpose of the present application is to provide a high-throughput microfluidic detection system for dynamic phase change process of aerosol.

[0006] To achieve the above purpose, the present application provides the following technical solutions: The high-throughput microfluidic detection system for dynamic phase change process of aerosol comprises a microfluidic chip module, a dynamic environment regulation module, an optical detection module, and a data acquisition and analysis module. The microfluidic chip module comprises 48 independent channels, which are divided into three types: typical environment channels, extended environment channels, and comparison verification channels. The dynamic environment regulation module adopts a "main controller + 32-way intelligent sub-control" distributed architecture, including a dynamic temperature regulation unit and a dynamic humidity regulation unit, for real-time regulation of the environmental parameters of the channels. Optical detection module: contains a dual-wavelength dynamic scattering detection unit, which measures the refractive index of aerosol by dual-wavelength laser scattering and performs phase inversion; Data acquisition and analysis module: high-speed data acquisition card synchronously acquires 48 groups of channel environmental parameters and optical signals; the phase transition vector comparison unit supports quantitative comparison of phase transition characteristic parameters in typical and extended environments.

[0007] Further, the specific implementation method of dynamic environment regulation is as follows: S21, collect the basic parameter sequence of the typical scene; S22, based on the typical environmental parameters, perform parameter expansion to form an extended parameter sequence; S23, assign parameter sequences to different channel types.

[0008] Further, the specific method of parameter expansion based on typical environmental parameters is as follows: Scene feature enhancement expansion: intensify the core environmental features of the typical scene, and obtain the feature-enhanced environmental parameter curve; Critical interval encryption expansion: focus on the sensitive interval with high phase transition in the typical scene, increase the time resolution of this interval, and generate a high-density sampled environmental parameter curve; Dynamic mode variation expansion: keep the mean and fluctuation range of temperature and humidity of the typical scene unchanged, change the dynamic change mode, and obtain the mode-variant environmental parameter curve; Cross-scene feature fusion expansion: extract the core environmental features of different typical scenes for cross-fusion, and obtain the feature-fused environmental parameter curve.

[0009] Further, the specific steps of measuring the refractive index of aerosol particles by dual-wavelength laser scattering and performing phase inversion are as follows: S31, calibrate the laser light path and initialize; S32, use a multi-angle detector array to receive laser scattering light of two wavelengths; S33, calculate the complex refractive index of aerosol particles based on Mie scattering theory; S34, based on the complex refractive index, invert the aerosol phase state.

[0010] Further, the measurement of the complex refractive index of aerosol particles includes: After the data acquisition and analysis module receives multi-angle scattering light intensity data, a measurement model is established based on Mie scattering theory: given the incident laser wavelength and the particle size parameters obtained through previous detection, the least squares method is used to fit the measured scattering light intensity distribution and the theoretically calculated scattering light intensity distribution, and the complex refractive index of aerosol particles is inverted, including the real part and the imaginary part More specifically, the initial refractive index value is used to calculate the theoretical scattering light intensity distribution, which is compared with the measured value, and the refractive index value is adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than the preset threshold, and the obtained refractive index value is the measurement result.

[0011] Further, based on the complex refractive index, the aerosol phase state is inverted, specifically including: First, the ratio of the real part of the refractive index corresponding to 632nm and 450nm is analyzed Second, the change trend of the imaginary part of the refractive index is observed as an auxiliary judgment basis for phase transition.

[0012] Further, the specific method of quantitative comparison of phase transition characteristic parameters in typical and extended environments includes: Based on the phase state inversion result of the optical detection module, the phase transition process is divided into three stages: pre-phase transition stage, mid-phase transition stage and post-phase transition stage; For the three-stage data of the three types of channels, the core vector is defined, covering environmental parameters and optical detection features, including: pre-phase transition core vector , mid-phase transition core vector , and post-phase transition core vector , wherein, represents the average temperature and humidity; represents the standard deviation of temperature and humidity fluctuation; represents the average refractive index before / after phase transition; represents the parameter change rate; represents the phase transition duration; represents the average temperature and humidity after phase transition; represents the standard deviation of refractive index fluctuation after phase transition; Taking the core vector of the typical channel as the benchmark, the influence of the extended features on the phase transition is located by the vector difference between the extended channel and the typical channel; the environmental basic difference is quantified by the Euclidean distance of the pre-phase transition / post-phase transition vector; the relationship between the intensification features and the phase transition speed is analyzed by the correlation analysis of the Manhattan distance of the change rate vector in the mid-phase transition and the difference.

[0013] Compared with the prior art, the present application has the following advantages: 1. High-throughput multi-channel parallel detection improves efficiency and comparison accuracy: The system integrates 48 independent channels, divided into typical environment channels (reproducing 8 types of real-world scene parameters), extended environment channels (enhancing / mutating environmental features), and comparison verification channels (isolating single variables). A "main controller + 32-channel intelligent sub-controller" architecture enables independent adjustment of multi-channel environmental parameters. Compared to traditional single-channel modes, it eliminates the need for repeated experiments to simultaneously compare phase transition differences under multiple environmental conditions, significantly improving detection efficiency while eliminating batch errors and achieving precise quantitative comparison of phase transition characteristics. 2. Optical Real-Time Detection Technology Enables Non-Contact Precise Phase Transition Identification: Employing a dual-wavelength (632nm and 450nm) laser scattering detection unit, the complex refractive index of aerosols is inverted based on Mie scattering theory. Precise phase inversion is achieved through the changing trends of the real-to-imaginary part ratio of the refractive index (solid > 1.04, liquid < 1.03). Combined with a high-speed data acquisition card, real-time tracking of the entire phase transition cycle is realized. This non-contact detection method avoids sample interference and solves the problem of traditional offline analysis failing to capture transient phase transitions, providing high-fidelity data support for the analysis of phase transition mechanisms. Attached Figure Description

[0014] Figure 1 This is a block diagram of a high-throughput microfluidic detection system for dynamic phase change processes in aerosols. Figure 2 This is a flowchart illustrating the measurement of aerosol refractive index and phase inversion according to the present invention. Detailed Implementation

[0015] Example, refer to Figure 1 The high-throughput microfluidic detection system for aerosol dynamic phase change processes in this embodiment includes: Microfluidic chip module: The microfluidic chip module contains 48 independent channels, all of which have independent dynamic environmental parameter adjustment capabilities. It adopts a 4×12 matrix layout structure, with a chip size of 100mm×150mm and a channel spacing of 1mm. Each channel group consists of a sample injection area, a dynamic control chamber, and an optical detection window. The 48 independent channels are divided into three categories: typical environment channels, extended environment channels, and comparative verification channels. There are 8 typical environment channels, which reproduce the historical environmental parameter sequences of 8 typical scenarios: polar, temperate, tropical, urban, industrial, plateau, marine, and desert. There are 32 extended environment channels, which generate extended parameter sequences based on typical environmental parameters through scene feature enhancement, critical interval encryption, dynamic mode variation, and cross-scenario fusion algorithms. There are 8 comparative verification channels, which set a gradient for the change of a single parameter to isolate the influence of a single variable on the phase transition.

[0016] Dynamic environmental control module: Adopting a distributed architecture of "main controller + 32-channel intelligent sub-controller", including dynamic temperature control unit and dynamic humidity control unit; the dynamic temperature control unit is equipped with a micro semiconductor cooling / heating integrated module for each channel, and achieves dynamic curve tracking through adaptive algorithms (such as PID control), with a temperature change rate adjustment range of 0.5℃ / s-25℃ / s; the dynamic humidity control unit adopts 32 independent gas mixing units, and adjusts the ratio of dry gas to saturated humidity gas through high-speed proportional valves, with a change rate adjustment range of 0.1%RH / s-10%RH / s; The specific implementation methods for dynamic environmental control are as follows: S21. Collection of Typical Environmental Parameters: Historical monitoring data from meteorological stations in eight typical global scenarios (polar, temperate, tropical, urban, industrial, plateau, marine, and desert) were collected, covering a time span of nearly 10 years. Data types included minute-by-minute temperature (-40℃-45℃) and relative humidity (10%-95%). The raw data was cleaned to remove outliers (such as values ​​exceeding the physical reasonable range), and short-term noise was smoothed using a moving average method. Finally, eight sets of basic parameter sequences for typical scenarios were formed. Each set of sequences included temperature-time curves and humidity-time curves, with a duration of 24 hours. S22. Environmental Parameter Extension Generation Strategy: Based on 8 typical environmental parameters, 4 sets of extended parameter sequences are generated using 4 types of algorithms, resulting in a total of 32 sets of extended parameter sequences, which are then assigned to 32 extended environmental channels. Scene feature enhancement and expansion: The core environmental features of typical scenes are enhanced, such as lower humidity values ​​in desert scenes, higher base temperatures in industrial scenes, higher base humidity in marine scenes, and lower base temperatures in polar scenes; Function: By amplifying the core environmental features of typical scenes, the influence of these features on aerosol phase change can be verified in an extreme manner. Critical Interval Encryption Extension: Focusing on sensitive intervals where phase transitions are frequent in typical scenarios, such as urban scenarios where humidity is 60%-80% and deliquescence is frequent, the temporal resolution of this interval is increased. (in this embodiment) This generates a high-density sampled humidity fluctuation curve. In temperate scenarios, the temperature range of 0℃-10℃ is a sensitive zone for freezing / thawing. The change process in this temperature range is sampled more densely to accurately capture the impact of temperature fluctuations on phase transitions with finer precision (0.1℃ in this embodiment). Function: Improves parameter control precision and data sampling density in sensitive zones where phase transitions are frequent, and can capture transient phase transition details that are easily ignored under conventional resolution. Dynamic mode variation extension: keep the average temperature and humidity of typical scenes and the fluctuation range unchanged, change the dynamic change mode, the optional modes are step change, periodic fluctuation, random fluctuation, pulse change, acceleration mode, deceleration mode, etc.; effect: by changing the dynamic change mode of the environmental parameters, the influence of the dynamic characteristics such as change rate and fluctuation frequency on the phase change can be verified independently; Cross-scene feature fusion extension: extract the core environmental features of different typical scenes for cross-fusion, such as fusing the low-temperature features of the plateau scene and the high-humidity features of the ocean scene to generate a "low-temperature high-humidity" coupling curve; fusing the rapid temperature change features of the industrial scene and the humidity fluctuation features of the urban scene to generate a "rapid temperature change + humidity fluctuation" curve; effect: simulate the complex composite environmental conditions in the real environment through cross-scene feature fusion; S23, multi-channel parameter distribution logic: Typical environment channel: strictly reproduce the 24-hour temperature and humidity dynamic curve of 8 typical scenes respectively; Extended environment channel: contains 4 groups of scene feature enhancement extension, 4 groups of critical interval encryption extension, 4 groups of dynamic mode variation extension and 4 groups of cross-scene feature fusion extension parameters, each group of extension parameters corresponds to the feature extension of the matching typical scene; Contrast experiment channel: set temperature single variable (typical scene temperature mean ±10℃ gradient, humidity fixed value is the mean value of typical scene) and humidity single variable (typical scene humidity mean ±20% gradient, temperature fixed value is the mean value of typical scene) each 4 groups.

[0017] Optical detection module: contains a dual-wavelength dynamic scattering detection unit, uses 632nm and 450nm lasers, and divides the beams into 48 sub-beams through a microlens array and focuses them on the detection window of each channel; As shown in Figure 2 The specific steps of measuring the aerosol refractive index and phase inversion by dual-wavelength laser scattering are as follows: S31, laser path calibration and initialization: first detect the obtained particle size parameters; secondly, in the dual-wavelength dynamic scattering detection unit, the 632nm and 450nm lasers are expanded and collimated, then divided into 48 sub-beams through a microlens array, and each sub-beam is vertically incident to the detection window of the corresponding channel through a focusing lens. After the system starts, first calibrate the optical path, adjust the angle of the reflecting mirror to make the two laser beams focus on the same point at the detection window, and control the spot diameter within 50μm to ensure the accuracy of the scattering signal acquisition; S32, multi-angle scattered light signal acquisition: when the aerosol particles enter the detection area, two beams of different wavelengths of laser are irradiated on the particles to generate scattering, and the scattered light is received by a multi-angle detector array composed of 8 detection nodes arranged in the range of 15°-150°. Each node in the detector array is equipped with a 632nm and 450nm wavelength dedicated optical filter, realizing the synchronous separation and collection of two wavelengths of scattered light; S33, aerosol refractive index inversion calculation: after the data acquisition and analysis module receives the multi-angle scattered light intensity data, a calculation model is established based on the Mie scattering theory. The Mie scattering theory describes the law of interaction between electromagnetic waves and spherical particles, and the scattered light intensity distribution is closely related to the particle size, refractive index and incident light wavelength. Given the incident laser wavelength (632nm and 450nm) and the particle size parameters obtained through previous detection, the least squares method is used to fit the measured scattered light intensity distribution and the theoretically calculated scattered light intensity distribution, and the complex refractive index of the aerosol particles is obtained, including the real part (reflecting the change of light propagation speed in the particle) and the imaginary part (reflecting the absorption characteristics of the particle to light). In the specific calculation process, the theoretical scattered light intensity distribution is first calculated with the initially assumed refractive index value, and then compared with the measured value. The refractive index value is continuously adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than the preset threshold (in this embodiment, the threshold is deviation≤3%), and the obtained refractive index value is the measurement result; S34, aerosol phase state inversion based on refractive index: according to the double-wavelength refractive index parameters obtained by inversion, the phase state of the aerosol is further inverted combined with the Mie scattering theory. First, the ratio of the real part of the refractive index corresponding to 632nm and 450nm is analyzed . The solid aerosol has ordered molecular arrangement, and the ratio is usually ; the liquid aerosol has disordered molecular arrangement, and the ratio is usually ; and the mixed state aerosol fluctuates in the range of . Secondly, the change trend of the imaginary part of the refractive index is observed, for example, when the organic aerosol changes from solid to liquid, the absorption of 450nm blue light is enhanced, and the imaginary part will significantly increase, which can be used as an auxiliary judgment basis for phase state transition. Finally, the phase state result obtained by refractive index inversion is cross-verified with the Raman spectrum characteristics, and when the deviation exceeds 5%, the parameters in the refractive index calculation model are automatically corrected to ensure that the phase state inversion accuracy is ≥95%.

[0018] Data acquisition and analysis module: including a high-speed data acquisition card and a phase change vector comparison unit; the phase change vector comparison unit supports quantitative comparison of phase change characteristic parameters (start time, critical parameter, duration, etc.) in typical and extended environments; High-speed data acquisition card: A high-speed data acquisition card with a total sampling rate of ≥32MHz, which can simultaneously acquire 48 sets of environmental parameters (temperature, humidity) and optical signals (dual-wavelength scattered light intensity, Raman spectrum). Phase transition vector comparison unit: Dual-wavelength dynamic scattering detection unit acquires refractive index data from 48 channels in real time ( and This process simultaneously records the temperature and humidity parameters output by the dynamic environmental control module, with timestamps accurate to 1ms. It enables quantitative comparison of phase transition processes across three channels, accurately uncovering the intrinsic influence of environmental parameters such as temperature and humidity on core characteristics like phase transition initiation time, duration, and phase stability. The specific process includes: Based on the phase inversion results from the optical detection module, the phase transition process is divided into three stages: Pre-phase transition stage: Focusing on the environmental pre-evolution characteristics before phase transition triggering ( (The phase transition initiation time is determined by the first time the ratio of the real parts of the refractive index exceeds the threshold). Phase transition stages: Capturing the transient process of phase transition ( The stable point where the real part of the refractive index fluctuates ≤ ±0.001, i.e., the phase transition termination time. Post-phase transition stage: Analyze the steady-state characteristics after the phase transition is completed; For the three-stage data of the three types of channels, as shown in Table 1, a core vector is defined, covering environmental parameters and optical detection characteristics (where the mean and standard deviation are calculated indicators from multiple experiments):

[0019] Table 1 Three types of channel comparison methods: a. Typical Environmental Channel vs. Extended Environmental Channel: Using the typical channel as a benchmark, the influence of extended features on phase transition is located through the vector difference between the extended and typical channels; the difference in environmental basis is quantified by the Euclidean distance of vectors before / after phase transition; and the Manhattan distance of the rate of change vector during phase transition is used to... Poor correlation analysis was used to analyze the relationship between enhancement characteristics and phase transition rate; b. Typical Environment Channel vs. Comparative Validation Channel By utilizing the univariate gradient of the contrast channel, the independent effects of temperature and humidity on the phase transition process are separated using the controlled variable method. The temperature and humidity sensitivity coefficients are calculated to quantify the strength of the influence of a single variable on the phase transition characteristics: taking pre-phase transition analysis as an example: Temperature sensitivity coefficient: fixed hour, Change and the mean temperature difference before phase transition The ratio; ; Humidity sensitivity coefficient: fixed , The ratio of the change to the average difference in humidity before phase change; ; Through the detailed introduction of the above embodiments, the high-throughput microfluidic detection system for the dynamic phase change process of aerosols of the present application realizes the dynamic phase change simulation and detection of aerosols under different environmental conditions by designing a multi-channel independent regulation unit, coupling rapid temperature switching and humidity simulation functions, and based on optical detection technology. By comparing and analyzing the detection results, the core influencing parameters of typical scenarios can be determined; and the influence of environmental parameters in different scenarios on the phase change progress and duration is quantified.

[0020] The above formulas are all dimensionless numerical calculations, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0021] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD) or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0022] It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0023] ​Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0024] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0025] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0026] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A high-throughput microfluidic detection system for aerosol dynamic phase transition processes, characterized in that, The application relates to a microfluidic chip module, a dynamic environment regulation module, an optical detection module and a data acquisition and analysis module. The microfluidic chip module comprises multiple channels, which are divided into three types of typical environment channels, extended environment channels and contrast verification channels. The dynamic environment regulation module comprises a main controller and a multi-path intelligent sub-control distributed architecture, and comprises a dynamic temperature regulation unit and a dynamic humidity regulation unit. The optical detection module measures the refractive index of aerosol particles by dual-wavelength laser scattering and performs phase inversion. The data acquisition and analysis module comprises a high-speed data acquisition card and a phase change vector comparison unit.

2. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 1, wherein, The specific implementation steps of the dynamic environment regulation module include: S21, collecting a basic parameter sequence of a typical scene; S22, performing parameter extension based on the typical environment parameters to form an extended parameter sequence; S23, assigning parameter sequences to different channel types.

3. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 2, wherein, The specific process of forming the extended parameter sequence includes: Scene feature enhancement expansion: the core environmental characteristics of the typical scene are enhanced to obtain the characteristic enhanced environmental parameter curve; Critical interval encryption expansion: focusing on the sensitive interval of the typical scene with high phase change, the time resolution of the interval is improved to generate a high-density sampled environmental parameter curve; Dynamic mode variation expansion: keeping the mean and fluctuation range of temperature and humidity of the typical scene unchanged, the dynamic change mode is changed to obtain the mode variation environmental parameter curve; Cross-scene feature fusion expansion: the core environmental characteristics of different typical scenes are extracted and cross-fused to obtain the feature-fused environmental parameter curve.

4. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 1, wherein, Measuring the refractive index of aerosol particles, specifically including: After receiving the multi-angle scattering light intensity data, the data acquisition and analysis module establishes a measurement model based on the Mie scattering theory: given the incident laser wavelength and the particle size parameters obtained through the previous detection, the least squares method is used to fit the measured scattering light intensity distribution and the theoretically calculated scattering light intensity distribution, and the complex refractive index of the aerosol particles is obtained by inversion, including the real part and the imaginary part ; more specifically, the theoretical scattering light intensity distribution is first calculated with the initially assumed refractive index value, then compared with the measured value, and the refractive index value is continuously adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than the preset threshold, at which point the obtained refractive index value is the measurement result.

5. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 4, wherein, Based on the complex refractive index, the aerosol phase state is inverted, specifically including: First, the ratio of the real part of the refractive index corresponding to 632 nm and 450 nm is analyzed wherein represents the real part of the refractive index corresponding to 632 nm laser, represents the real part of the refractive index corresponding to 450 nm laser; second, the change trend of the imaginary part of the refractive index is observed as an auxiliary judgment basis for phase transition.

6. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 1, wherein, The specific method of the quantitative comparison of the phase change characteristic parameters in the typical and extended environments includes: Based on the phase inversion results of the optical detection module, the phase change process is divided into three stages: a pre-phase change stage, a phase change stage and a post-phase change stage; For the three-stage data of the three types of channels, a core vector is defined, which covers the environmental parameters and the optical detection features; Using the core vector of a typical channel as a benchmark, the influence of extended features on phase transition is located by the vector difference between the extended channel and the typical channel; the environmental basis difference is quantified by the Euclidean distance between vectors before and after phase transition; and the Manhattan distance of the rate of change vector during phase transition is used to... Poor correlation analysis was used to analyze the relationship between enhancement features and phase transition rate.

7. The high-throughput microfluidic system for aerosol dynamic phase-change processes of claim 6, wherein, The core vector definition method is as follows: Core vector before phase transition Core vector during phase transition Core vector after phase transition wherein respectively represent the mean values of temperature and humidity; respectively represent the standard deviations of temperature and humidity; respectively represent the mean values of refractive index before and after phase transition; represents the rate of change of the parameter; represents the duration of phase transition; respectively represent the mean values of temperature and humidity after phase transition; represents the standard deviation of refractive index after phase transition.

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