Dust pollution online monitoring method and system in indoor forced ventilation environment

Abstract

The invention relates to the technical field of environment monitoring, and provides a dust pollution on-line monitoring method and system in an indoor forced ventilation environment. The element quantitative analysis capability of a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology and the form analysis capability of an electrochemical-optical cooperative detection technology are combined; according to the method, full-chain analysis of dust pollution components, structures and forms is realized, and in-depth analysis is carried out on obtained dust multi-dimensional modal data in an indoor forced ventilation environment by virtue of a constructed chemical fingerprint database and a trained airflow field-chemical distribution coupling model, so that a dust monitoring result in the indoor forced ventilation environment is obtained. Therefore, on the basis of realizing multi-dimensional monitoring on the dust in the indoor forced ventilation environment, the dust monitoring precision and the response speed can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an online monitoring method and system for dust pollution in an indoor forced ventilation environment. Background Art

[0002] With the acceleration of industrialization and rising urbanization, indoor air quality is becoming an increasingly critical factor affecting public health. Dust, a major component of indoor air pollution, originates from a wide range of sources, including infiltration from outdoor air, generation from indoor activities, and release from building decoration materials. Long-term exposure to high dust concentrations can not only lead to health problems such as respiratory illnesses and allergic reactions, but can also cause safety incidents such as explosions, posing a serious threat to life and property. Therefore, real-time and accurate monitoring of indoor dust pollution is crucial.

[0003] Traditional dust monitoring methods rely primarily on manual sampling and laboratory analysis, which present numerous limitations. For example, they have lengthy testing cycles, often requiring several days from sampling to results, making them difficult to meet real-time monitoring requirements. They also have low sensitivity, limiting their ability to detect low-concentration dust or specific components (such as light and hazardous elements). Furthermore, traditional dust monitoring methods lack the ability to achieve continuous, real-time online monitoring, making it difficult to promptly detect and address dust pollution issues. Summary of the Invention

[0004] In order to solve the problems of the prior art, the present invention proposes an online monitoring method and system for dust pollution in an indoor forced ventilation environment, aiming to improve the dust monitoring accuracy and response speed.

[0005] The purpose of the present invention is achieved by adopting the following technical solutions: In one aspect, an embodiment of the present invention provides a method for online monitoring of dust pollution in an indoor forced ventilation environment, the method comprising: Real-time acquisition of a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment; Using laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the first dust sample is subjected to spectral analysis to obtain elemental quantitative analysis data; Using electrochemical-optical detection technology, the second dust sample is subjected to material form analysis to obtain electrochemical data and optical data; Acquiring dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment; Using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data are analyzed to obtain the dust monitoring results under the indoor forced ventilation environment.

[0006] Optionally, the step of acquiring in real time a first dust sample and a second dust sample having the same airflow composition in an indoor forced ventilation environment includes: Determine multiple dust sampling points to be deployed in the indoor forced ventilation environment according to a specific space layout adaptation principle; Dust preprocessing and diversion are performed on the airflow samples collected in real time from the multiple dust sampling points to obtain the first dust sample and the second dust sample.

[0007] Optionally, the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupled technology is used to perform spectral analysis on the first dust sample to obtain elemental quantitative analysis data, including: focusing an ultrashort pulse laser on the surface of the first dust sample to form plasma, and analyzing an emission spectrum of the plasma to obtain quantitative detection sub-data of the first element; irradiating the first dust sample with X-rays to obtain characteristic fluorescence, and analyzing the wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data; The first element quantitative detection sub-data and the second element quantitative detection sub-data are combined to obtain the element quantitative analysis data.

[0008] Optionally, the electrochemical-optical detection technology is used to perform material form analysis on the second dust sample to obtain electrochemical data and optical data, including: dissolving the second dust sample in a specific electrolyte solution to obtain an electrolyte solution to be measured, and measuring and analyzing changes in electrical parameters of the electrolyte solution to be measured to obtain the electrochemical data; Raman spectroscopy analysis and fluorescence spectroscopy analysis are performed on the second dust sample in sequence to obtain the optical data.

[0009] Optionally, the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model are used to analyze the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain dust monitoring results in the indoor forced ventilation environment, including: Preprocessing the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain multi-source data; The constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model are used to analyze the multi-source data in sequence to obtain the dust monitoring results.

[0010] Optionally, the dust monitoring result includes: a pollutant concentration field cloud map under the indoor forced ventilation environment, and the dust monitoring result is obtained by sequentially analyzing the multi-source data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, including: Using the constructed chemical fingerprint library, dust pollutants are identified on the multi-source data to obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; The trained airflow field-chemical distribution coupling model is used to perform correlation analysis on the multi-source data and the feature analysis results to obtain the pollutant concentration field cloud map.

[0011] Optionally, the training process of the trained airflow field-chemical distribution coupling model includes: Acquire historical characteristic data and historical multi-source data of dust pollutants in the indoor forced ventilation environment; wherein the historical multi-source data includes: historical elemental quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data, and historical particle size distribution data of the dust pollutants; The pollutant characteristic data in the constructed chemical fingerprint library are embedded into the initial computational fluid dynamics model constructed based on computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field; The historical feature data and the historical multi-source data are used as training data to iteratively train the airflow field-chemical distribution correlation model to obtain the trained airflow field-chemical distribution coupling model.

[0012] Optionally, the construction process of the constructed chemical fingerprint library includes: The element characteristic peaks corresponding to the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the relationship between Raman spectrum and molecular vibration, and the fluorescence PAHs fingerprint spectrum are synchronously integrated to obtain multidimensional chemical fingerprint data; The multi-dimensional chemical fingerprint data is classified and labeled according to the dust pollution source type and the dust particle size segment data to obtain the constructed chemical fingerprint library.

[0013] Optionally, after analyzing the elemental quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring results in the indoor forced ventilation environment, the method further includes: Analyzing the toxicity, particle size distribution, and exposure time of the chemical components in the dust monitoring results under the indoor forced ventilation environment to obtain a risk assessment result; According to the risk assessment result, the real-time ventilation strategy in the indoor forced ventilation environment is adjusted.

[0014] On the other hand, an embodiment of the present invention further provides an online monitoring system for dust pollution in an indoor forced ventilation environment, the system comprising: An acquisition module is used to acquire in real time a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment; a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling module, configured to perform spectral analysis on the first dust sample using laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling technology to obtain elemental quantitative analysis data; an electrochemical-optical module, configured to perform material form analysis on the second dust sample using electrochemical-optical detection technology to obtain electrochemical data and optical data; The acquisition module is further used to acquire dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment; The analysis module is used to analyze the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring results under the indoor forced ventilation environment.

[0015] Optionally, the acquisition module includes: a deployment unit, used to determine multiple dust sampling points deployed in the indoor forced ventilation environment based on a specific spatial layout adaptation principle; a dust preprocessing unit, used to perform dust preprocessing and diversion on the air flow samples collected in real time from the multiple dust sampling points to obtain the first dust sample and the second dust sample.

[0016] Optionally, a laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined module includes: a laser induced breakdown spectroscopy unit, used to use an ultrashort pulse laser to focus on the surface of the first dust sample to form a plasma, and analyze the emission spectrum of the plasma to obtain first element quantitative detection sub-data; an X-ray fluorescence spectroscopy unit, used to use X-rays to irradiate the first dust sample to obtain characteristic fluorescence, and analyze the wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data; and a combining unit, used to combine the first element quantitative detection sub-data and the second element quantitative detection sub-data to obtain the element quantitative analysis data.

[0017] Optionally, the electrochemical-optical module includes: an electrochemical detection unit, used to dissolve the second dust sample in a specific electrolyte solution to obtain the electrolyte solution to be measured, and measure and analyze the changes in the electrical parameters of the electrolyte solution to be measured to obtain the electrochemical data; an optical detection unit, used to perform Raman spectroscopy analysis and fluorescence spectroscopy analysis on the second dust sample in sequence to obtain the optical data.

[0018] Optionally, the analysis module includes: a data preprocessing unit, used to preprocess the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data and the dust particle size distribution data to obtain multi-source data; an analysis unit, used to use the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to analyze the multi-source data in turn to obtain the dust monitoring results.

[0019] Optionally, the dust monitoring results include: a pollutant concentration field cloud map under the indoor forced ventilation environment; the analysis unit is specifically used to use the constructed chemical fingerprint library to identify dust pollutants on the multi-source data, and obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; and use the trained airflow field-chemical distribution coupling model to perform correlation analysis on the multi-source data and the characteristic analysis results to obtain the pollutant concentration field cloud map.

[0020] Optionally, the system also includes: a training module for obtaining historical characteristic data and historical multi-source data of dust pollutants in the indoor forced ventilation environment; wherein, the historical multi-source data include: historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutants; embedding the pollutant characteristic data in the constructed chemical fingerprint library into the initial computational fluid dynamics model constructed based on computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field; using the historical characteristic data and the historical multi-source data as training data, iteratively training the airflow field-chemical distribution correlation model to obtain the trained airflow field-chemical distribution coupling model.

[0021] Optionally, the system further includes: a construction module for synchronously integrating the element characteristic peaks corresponding to the laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy technology, the relationship between Raman spectrum and molecular vibration, and the fluorescent PAHs fingerprint spectrum to obtain multidimensional chemical fingerprint data; classifying and labeling the multidimensional chemical fingerprint data according to the dust pollution source type and dust particle size segment data to obtain the constructed chemical fingerprint spectrum library.

[0022] Optionally, the system also includes: an evaluation and adjustment module, which is used to analyze the toxicity, particle size distribution and exposure time of the chemical components in the dust monitoring results in the indoor forced ventilation environment to obtain a risk assessment result; and adjust the real-time ventilation strategy in the indoor forced ventilation environment based on the risk assessment result.

[0023] Compared with the prior art, the present invention has the following beneficial effects: Embodiments of the present invention provide a method and system for online monitoring of dust pollution in an indoor forced ventilation environment. During the implementation of this method, laser-induced breakdown spectroscopy (LIBS)-X-ray fluorescence (XRF) spectroscopy is first used to rapidly and qualitatively and quantitatively analyze the elemental composition of dust in the forced ventilation environment to obtain elemental quantitative analysis data. Electrochemical and optical detection technologies are then combined to simultaneously acquire optical and electrochemical data of the dust in the forced ventilation environment. This combines the elemental quantitative analysis capabilities of LIBS-XRF spectroscopy with the morphological analysis capabilities of electrochemical-optical collaborative detection technology to achieve a comprehensive analysis of the composition, structure, and morphology of dust pollution. Subsequently, based on the corresponding dust airflow field data and dust particle size distribution data, a constructed chemical fingerprint library and a trained airflow field-chemical distribution coupling model are used to conduct in-depth analysis of the acquired multidimensional modal data (elemental quantitative analysis data, electrochemical data, optical data, dust airflow field data, and dust particle size distribution data), thereby obtaining dust monitoring results in the forced ventilation environment. In this way, on the basis of realizing multi-dimensional monitoring of dust in indoor forced ventilation environment, the dust monitoring accuracy and response speed can be improved.

[0024] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions provided by the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 A flow chart of an online dust pollution monitoring method in an indoor forced ventilation environment provided by an embodiment of the present invention; Figure 2 A schematic diagram of the overall process of adjusting the ventilation system or ventilation strategy of a relevant area by applying the online dust pollution monitoring method in an indoor forced ventilation environment provided by an embodiment of the present invention; Figure 3 The present invention provides a schematic diagram of the structure of an online dust pollution monitoring system in an indoor forced ventilation environment. DETAILED DESCRIPTION

[0026] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0027] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0028] In the following description, the terms "first\second\third" are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the embodiments of the present invention. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the embodiments of the present invention.

[0030] Example 1: See also Figure 1 FIG. 1 is a flow chart of an online dust pollution monitoring method in an indoor forced ventilation environment provided by an embodiment of the present invention, wherein the method includes the following steps: Step S101: acquiring in real time a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment.

[0031] In some embodiments of the present invention, an indoor forced ventilation environment refers to an indoor scene where natural ventilation cannot meet the requirements and forced ventilation must be used and the gas environment must be continuously monitored, such as: an indoor enclosed scene, an indoor scene with a high concentration of harmful gases, etc.

[0032] In some embodiments of the present invention, real-time dust collection can be performed at air inlet areas, air outlet areas, or key activity areas within an indoor forced ventilation environment. Here, multiple dust sampling points (airflow sampling points) deployed within the indoor forced ventilation environment can be used to acquire collected airflow data and preprocess the airflow data (e.g., extract dust samples) to obtain a first dust sample and a second dust sample with the same airflow composition.

[0033] In some embodiments of the present invention, the sampling frequency for obtaining the first dust sample and the second dust sample can be determined based on the specific properties of the area where the indoor forced ventilation environment is located. For example, if the area where the indoor forced ventilation environment is located is a severely polluted area (such as an industrial plant, etc.) or an emergency monitoring area (such as a sandstorm, after a fire, etc.), a 24-hour automatic monitoring device can be used to capture dust concentration fluctuations in the area in real time, thereby implementing a continuous sampling strategy. If the area where the indoor forced ventilation environment is located is an area where long-term trend analysis is required (such as an office, residence, etc.), sampling can be conducted every 3 days or once a week for 24 hours each time to evaluate the daily average or weekly average, thereby implementing an intermittent sampling strategy. If the area where the indoor forced ventilation environment is located is in a period of high pollution (such as the winter heating period, etc.) or a pollution event (such as haze, etc.), the sampling frequency can be increased to daily or hourly to accurately track concentration changes (of the first dust sample and the second dust sample).

[0034] In some embodiments of the present invention, the above step S101 can be implemented by the following steps S1011 and S1012 ( Figure 1 (not shown): Step S1011: Determine a plurality of dust sampling points deployed in the indoor forced ventilation environment according to a specific space layout adaptation principle.

[0035] In some embodiments of the present invention, the specific spatial layout adaptation principle may determine the number and location rules of multiple dust sampling points in the indoor forced ventilation environment according to the spatial layout corresponding to the indoor forced ventilation environment.

[0036] For example, if the indoor forced ventilation environment is an indoor area environment such as a bedroom, the number of dust sampling points can be determined according to the area of the bedroom. For example, the area of the bedroom is ≤ 50m 2 , then set up 1-3 dust sampling points; the bedroom area is 50-100 m 2 , then set up 3-5 dust sampling points; bedroom area> 100 m 2 At least five dust sampling points should be set up. Diagonal or plum blossom-shaped sampling points can also be used to avoid vents and heat sources in bedrooms, and dust sampling points should be set at least 0.5 meters away from walls and 1 meter away from doors and windows.

[0037] In some embodiments of the present invention, if the area where the indoor forced ventilation environment is located is a key dust monitoring area, dust sampling points can be covered at the air inlet, air outlet and areas with dense human activities (such as workbenches, rest areas, etc.) of the forced ventilation system in the key dust monitoring area.

[0038] In some embodiments of the present invention, in the process of determining the multiple dust sampling points to be deployed in an indoor forced ventilation environment according to the specific spatial layout adaptation principle, the height of the multiple dust sampling points can also be controlled according to the breathing zone, such as: the height of the dust sampling point is set at 0.5-1.5m from the ground to cover the adult breathing zone; if it is necessary to assess the exposure risk of children or sitting positions, sampling points at a height of 0.3-0.6m from the ground can be further added.

[0039] For example, in a residential area with a forced ventilation environment, the following dust sampling point layout can be implemented based on the principle of adapting to specific spatial layouts: for example, three dust sampling points can be placed at each of the residential area's air inlets and outlets, with each point covering a pipe cross-section of 0.5 m from the edge and 1 m apart. These points can be further equipped with a vacuum pump (e.g., with a flow rate of 28.3 L / min) and a flow controller (with an accuracy of ±2%). Furthermore, one dust sampling point can be set up in key activity areas of the residential area, such as the center of the living room and at the head of the bed in the bedroom, at a height of 1.2 m (i.e., covering the human respiratory zone), and equipped with a portable sampler (including a drying tube filled with silica gel and a humidity level of <40% RH).

[0040] Step S1012: performing dust preprocessing and diversion on the airflow samples collected in real time from the multiple dust sampling points to obtain the first dust sample and the second dust sample.

[0041] In some embodiments of the present invention, dust pretreatment is performed on airflow samples collected from multiple dust sampling points, including but not limited to: 1. Dust drying: This involves passing the airflow sample through a dust sampling head into a drying tube to remove moisture and ensure the dust sample is dry. Here, the collected airflow sample is passed through the dust sampling head into a drying tube (e.g., a drying tube length of 30 cm and an inner diameter of 5 cm) with a silica gel filling of 50 g to ensure the moisture content of the dust sample is <5%.

[0042] 2. Grind the dust sample until it is uniformly finely divided. This can be done using a micro-vibration mill (e.g., at a frequency of 30 Hz and a grinding time of 10 seconds) to achieve a dust particle size of <10 μm, ensuring detection sensitivity. Then, use a tablet press at a pressure of 20 MPa to form thin slices approximately 30 mm in diameter, ensuring a smooth surface. This reduces matrix effects that interfere with spectral analysis, allowing subsequent laser-induced breakdown spectroscopy (LIBS) techniques. The uniform sample surface significantly reduces self-absorption, thereby improving the accuracy of elemental detection using LIBS-X-ray fluorescence (XRF) coupled techniques and the response speed of electrochemical-optical detection.

[0043] It should be noted that after dust pretreatment, the airflow samples collected from multiple dust sampling points must be split. In some embodiments of the present invention, this can be evenly divided using a Y-shaped splitter (made of polytetrafluoroethylene, with an inner wall roughness of Ra < 0.5 μm and a flow distribution ratio of 1:1). This can produce two airflows with consistent composition, i.e., a first dust sample and a second dust sample, each of which flows into the LIBS-XRF combination module 1 and the electrochemical-optical detection combination module 2, respectively, providing technical and parameter support for subsequent dust monitoring.

[0044] Step S102: Using laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, perform spectral analysis on the first dust sample to obtain element quantitative analysis data.

[0045] In some embodiments of the present invention, LIBS technology generates plasma by focusing a laser on a sample and then analyzing the emission spectrum to determine elemental composition. This technology offers the advantages of rapid, real-time analysis and the absence of sample pretreatment. Meanwhile, XRF technology utilizes X-rays to excite the sample, producing characteristic fluorescence for qualitative and quantitative elemental analysis. It offers the advantages of non-destructive testing and simultaneous multi-element determination. The combined use of these two technologies enables more comprehensive elemental analysis of dust samples, overcoming the limitations of a single technology and making it particularly suitable for the rapid detection of samples with complex compositions.

[0046] In some embodiments of the present invention, the above step S102 can be implemented by the following steps S1021 to S1023 ( Figure 1 (not shown): Step S1021 : Using an ultrashort pulse laser to focus on the surface of the first dust sample to form plasma, and analyzing the emission spectrum of the plasma to obtain first element quantitative detection sub-data.

[0047] Step S1022: irradiate the first dust sample with X-rays to obtain characteristic fluorescence, and analyze the wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data.

[0048] In some embodiments of the present invention, a LIBS-XRF combined module may be used to analyze the first dust sample.

[0049] Correspondingly, when using LIBS technology to perform spectral analysis on the first dust sample, an ultrashort pulse laser can be used to focus the first dust sample deposited in the groove to excite a plasma. The resulting plasma is then subjected to emission spectral analysis (i.e., a spectrometer is used to collect the corresponding characteristic wavelengths and intensities), thereby obtaining LIBS data corresponding to the first dust sample. Here, the LIBS data corresponding to the first dust sample is the quantitative detection sub-data of the first element corresponding to the first dust sample.

[0050] Here, spectral acquisition utilizes plasma spectroscopy, which can be optically coupled to a three-channel spectrometer (200-450nm, 450-680nm, and 680-1000nm), with a resolution of 0.15nm and an integration time of 1ms. It should also be noted that LIBS technology has a high sensitivity for detecting light elements (such as Na and Mg).

[0051] Correspondingly, when using RXF technology to perform spectral analysis on the first dust sample, X-rays (here, X-rays can be emitted by a tungsten target X-ray tube with a corresponding voltage of 50 kV, a current of 500 μA, and a collimator aperture of 1 mm) are used to irradiate the first dust sample. Characteristic fluorescence (characteristic fluorescence can be collected using a silicon drift detector with a resolution of 125 eV, an energy spectrum range of 0-30 keV, and a dead time of <5%), and the wavelength and intensity of the characteristic fluorescence are analyzed. To collect the characteristic wavelength and intensity, an energy dispersive X-ray fluorescence spectrometer (e.g., an energy dispersive X-ray fluorescence spectrometer) can be used. The X-ray tube voltage is set to 12 kV, the current is 200 μA, and the filament voltage is 0.5 V, with a current of 0.8 A. Spectral acquisition can be performed by irradiating the first dust sample for 100 seconds at a pressure of 100 Pa. The characteristic fluorescence signal is collected by the silicon drift detector with a time constant of 2 μs and a signal amplification factor of 7. In addition, it should be noted that XRF can analyze elements such as Si, Al and heavy metals (such as Pb, As, etc.) in dust samples with high precision.

[0052] Alternatively, the X-rays can be emitted from a Q-switched neodymium-doped laser (Nd:YAG laser), outputting pulsed laser light with a wavelength of 1064 nm, a pulse width of 8 ns, and an energy of 38 mJ. After three-fold beam expansion, the laser is focused on the surface of the first dust sample. The laser parameters involved are: a wavelength of 1064 nm, a pulse energy of 50 mJ, a frequency of 10 Hz, a focusing lens with a focal length of 100 mm, and a spot diameter of 0.1 mm.

[0053] Correspondingly, the spectrum acquisition is: plasma spectrum (200-800 nm), which can be separated by a medium-step grating spectrometer (resolution 0.1 nm) and recorded by a CCD detector (cooling temperature is -20 ° C) with an integration time of 10 ms.

[0054] Step S1023 : Combining the first element quantitative detection sub-data and the second element quantitative detection sub-data to obtain the element quantitative analysis data.

[0055] In some embodiments of the present invention, the first element quantitative detection sub-data obtained by LIBS technology and the second element quantitative detection sub-data obtained by XRF technology are integrated to obtain elemental quantitative analysis data corresponding to the first dust sample. In this way, the combination of LIBS and XRF technology can achieve a more comprehensive elemental analysis of dust samples, thus overcoming the limitations of a single technology.

[0056] Step S103: Using electrochemical-optical detection technology, perform material form analysis on the second dust sample to obtain electrochemical data and optical data.

[0057] In some embodiments of the present invention, an electrochemical sensor can be used to implement electrochemical detection technology to perform material speciation analysis on the second dust sample and obtain electrochemical data corresponding to the second dust sample. Correspondingly, an optical fiber sensor can be used to implement optical detection technology to perform material speciation analysis on the second dust sample and obtain optical data corresponding to the second dust sample. Here, the electrochemical sensor is capable of monitoring dust concentration by measuring the current or voltage generated by the electrochemical reaction. It has high sensitivity and accuracy for inorganic dust and is suitable for dust monitoring in specific situations. At the same time, the optical fiber sensor uses the scattering and reflection properties of optical fibers to monitor dust concentration. It has high stability and repeatability and is suitable for long-term monitoring, such as in indoor air quality monitoring systems.

[0058] It should be noted that electrochemical detection technology analyzes the reaction kinetics and electroactive substances by measuring changes in electrochemical signals, and is characterized by high sensitivity and fast response. Optical detection technology, on the other hand, utilizes the absorption, scattering, or emission properties of light by substances to analyze molecular structure and morphology, offering the advantages of being non-destructive and information-rich. Combining electrochemical and optical detection technologies enables full-chain monitoring of "composition-structure-morphology," significantly improving the accuracy and comprehensiveness of monitoring.

[0059] In some embodiments of the present invention, the above step S103 can be implemented by the following steps S1031 and S1032 ( Figure 1 (not shown): Step S1031 : dissolving the second dust sample in a specific electrolyte solution to obtain an electrolyte solution to be measured, and measuring and analyzing changes in electrical parameters of the electrolyte solution to be measured to obtain the electrochemical data.

[0060] In some embodiments of the present invention, a second dust sample can be dissolved in an electrolyte solution, and the electrical parameters of the electrolyte solution containing the second dust sample, such as current, conductivity, resistance, or potential changes on electrodes, can be measured using an electrochemical workstation. The changes can be analyzed to obtain electrochemical data corresponding to the second dust sample. This can include analyzing electroactive substances in the second dust sample, such as heavy metal ions, or reaction kinetic parameters, to obtain electrochemical data for the second dust sample.

[0061] In some embodiments of the present invention, an isokinetic sampling head can be set in a forced ventilation duct under an indoor forced ventilation environment, and a virtual impactor can be used to achieve particle size classification and cutting of the second dust sample (such as PM10, PM2.5, PM1, etc.), so as to deposit the particles of the second dust sample on a rotating filter membrane carrier. The filter membrane carrier rotates to the electrolyte spray area, and the 0.1M HNO3 solution is uniformly infiltrated into the second dust sample through an ultrasonic atomization nozzle, and the dissolved substances are distributed to the electrochemical cell and spectral detection through a microfluidic chip (wherein the distribution ratio can be: 50μL: 200μL). The three-electrode system is immersed in the electrolyte, and cyclic voltammetry (10-200mV / s) is used to scan and obtain heavy metal ions (such as Pb 2+ 、Cu 2+ ) Redox characteristics, the step response was recorded by chronoamperometry (where the potential can be set to: -0.5 V, the duration is set to: 60 s) and the diffusion coefficient (Diffusivity, D) was calculated.

[0062] Here, the electrode materials involved in electrochemical detection can be further explained. For example, the working electrode can be any of: platinum electrode, glassy carbon electrode, and boron-doped diamond electrode (BDD). Among them, the platinum electrode is the most commonly used inert electrode. It has high purity, chemical stability and low hydrogen overpotential characteristics, and is suitable for heavy metal ions (such as Pb 2+ 、Cu 2+ ) redox reaction detection, and its surface uniformity can be restored by simple polishing to ensure experimental reproducibility; the glassy carbon electrode has a wide potential window and low background current, which is suitable for trace heavy metal analysis in complex matrices, and its surface can be chemically modified (such as Nafion coating) to enhance the selectivity for specific ions; BDD can exhibit extremely low background current and excellent anti-pollution ability in strong acidic electrolytes, suitable for high-sensitivity heavy metal detection, but the cost is relatively high.

[0063] Correspondingly, the auxiliary electrode can be any one of: a platinum black electrode and a reference electrode; among them, the platinum black electrode as a counter electrode has a high surface area that can reduce the current density, reduce the polarization effect, and ensure the stable response of the working electrode; the reference electrode, such as the silver-silver chloride electrode (Ag / AgCl): in a 0.1M HNO3 system, the Ag / AgCl electrode has a stable potential (for example, 0.222V at 25°C relative to the standard hydrogen electrode) and is resistant to nitric acid corrosion, making it suitable for potential reference in acidic environments.

[0064] In addition, the electrolyte components involved in electrochemical detection may include: basic electrolyte, functional additives and supporting electrolytes; among them, the basic electrolyte can be 0.1M HNO3 solution, which is used to provide a strong acidic environment (pH≈1) to promote the dissolution and ionization of heavy metal ions. Its strong oxidizing property can inhibit microbial contamination, but it needs to be stored away from light to prevent decomposition; the functional additive can be a chelating agent, such as: adding 0.01M ethylenediaminetetraacetic acid (EDTA) to react with Pb 2+ 、Cu 2+ The stable complex formed can improve the detection sensitivity (in practical application, the detection limit can be as low as 0.1μg / L); the supporting electrolyte can be 0.1M potassium nitrate (KNO3) added to increase the conductivity of the electrolyte and reduce the solution resistance (from 100 Down to 10 ), improving the charge transfer efficiency.

[0065] It should be noted that the electrode-electrolyte synergistic effect may include: interface reaction optimization, that is, after the Nafion membrane is modified on the surface of the glassy carbon electrode, heavy metal ions can be selectively passed through, anion interference can be suppressed, and Pb 2+The oxidation peak current of the BDD electrode is increased by 3 times; the BDD electrode is coordinated with the 0.1M HNO3-0.01M EDTA system to 2+ The detection limit was reduced from 5 μg / L to 0.5 μg / L.

[0066] Furthermore, dynamic response adjustments can be performed. This allows for real-time adjustments to the electrolyte composition (e.g., increasing EDTA concentration to address high levels of heavy metal contamination) based on the elemental quantitative analysis data from the first dust sample obtained using LIBS-XRF, optimizing electrochemical detection selectivity. Alternatively, at high dust fluxes (dust concentrations > 10 mg / m³), switching to a platinum black counter electrode can reduce polarization and ensure cyclic voltammetry scan stability (potential fluctuations < 2 mV).

[0067] Continuing from the previous description, forced ventilation environment adaptation can also be performed. This involves installing a constant velocity sampling head in the ventilation duct to ensure that the airflow velocity (e.g., 0.5-1.0 m / s) matches the flow velocity within the duct, reducing sampling errors. Alternatively, a virtual impactor can be used to achieve PM10 / PM2.5 / PM1 particle size classification, optimizing electrode materials for different dust particle sizes (e.g., using a BDD electrode for PM1 detection to improve pollution resistance).

[0068] Correspondingly, dynamic sampling strategies can be further developed, such as combining electrochemical detection data to detect when heavy metal concentrations exceed the standard (such as Pb 2+ When the concentration of particulate matter exceeds 10 μg / L, the sampling frequency is automatically increased to once per hour and the electrolyte is switched to a highly sensitive formula (0.1M HNO3-0.05M EDTA). Alternatively, during periods of low pollution (such as at night), the sampling frequency is reduced to once every four hours to extend the life of the electrode. Alternatively, by synergistically optimizing the electrode material and electrolyte composition, combined with a dynamic sampling strategy, this method can achieve highly sensitive and selective online monitoring of dust pollution in forced ventilation environments, providing accurate data support for air quality control.

[0069] Step S1032: performing Raman spectroscopy analysis and fluorescence spectroscopy analysis on the second dust sample in sequence to obtain the optical data.

[0070] In some embodiments of the present invention, Raman spectroscopy can be used to analyze molecular vibration modes and identify the dust morphology, such as crystalline or amorphous, in the second dust sample. Fluorescence spectroscopy can also be used to detect organic pollutants or specific components, such as polycyclic aromatic hydrocarbons (PAHs), in the second dust sample to obtain optical data corresponding to the second dust sample.

[0071] Here, too, an isokinetic sampling head can be installed in the ventilation duct under indoor forced ventilation. A virtual impactor achieves a three-level cutoff for PM10, PM2.5, and PM1. Dust particles are deposited on a rotating filter carrier, with the sampling flow precisely controlled at 16.7 L / min. The filter carrier rotates to the optical detection area, where a microfluidic chip precisely distributes the solution (e.g., 0.1 M HNO3) to the Raman and fluorescence detection cells. The corresponding distribution ratio is: Raman cell: 10 μL, fluorescence cell: 50 μL, to prevent cross contamination.

[0072] Correspondingly, in the process of performing optical detection, Raman spectroscopy analysis (i.e. molecular vibration and morphology identification) must be performed first. The instrument can use a 532nm / 785nm dual-wavelength laser with a spectral resolution of , objective lens 100×, spatial resolution <1μm. Among them, the crystal state analysis focuses the laser on the dust particles and collects 300-1800cm -1 Wavenumber range spectrum. Crystal materials (such as quartz) show sharp characteristic peaks (such as Si-O symmetric stretching vibration is: ), peak width < . Amorphous identification of amorphous materials (such as silica gel) spectral peak width> The intensity is reduced by more than 50%, and the peak shape is fitted using the Lorentz function, and the crystallinity index is calculated. Further baseline correction can be performed here, and mineral phase compositions (such as cristobalite and quartz) are automatically annotated by comparing with the Inorganic Crystal Structure Database (ICSD) crystal database, with an identification accuracy of >95%.

[0073] Fluorescence spectroscopy analysis (PAHs detection) was then performed using a three-dimensional fluorescence scanning technique with an excitation wavelength of 200-500 nm (5 nm step size), an emission wavelength of 250-600 nm (2 nm step size), and an integration time of 0.1 s to construct a three-dimensional fluorescence spectrum (Excitation-Emission Matrix, EEM). PAH feature extraction was performed using parallel factor analysis (PARAFAC) to decompose the mixed spectrum and extract characteristic PAH peaks, such as Ex / Em at 334 / 388 nm and fluoranthene at 320 / 426 nm. Quantitative calibration was performed to establish a fluorescence intensity-concentration standard curve (R² > 0.995), achieving a detection limit of 0.01 μg / L. Data output was PAH concentration and fingerprint.

[0074] In some embodiments of the present invention, an electrochemical-optical collaborative detection module can also be used to simultaneously realize the material form analysis of the second dust sample. Here, the electrochemical detection involved includes: a three-electrode system: a glassy carbon working electrode (diameter 3mm), an Ag / AgCl reference electrode, and a platinum wire counter electrode immersed in a 0.1M HNO3 solution. Its corresponding signal acquisition: an electrochemical workstation (potential range of ±10V, current resolution of 1pA) records cyclic voltammetry curves (scanning speed 50mV / s), extracts heavy metal ions (such as: Pb 2+ 、Cu 2+ ) redox peak current. The optical detection involved includes: Raman spectroscopy: 785nm laser (power 50mW), spectral range 200-3200cm -1 Fluorescence spectroscopy: A xenon lamp (150W) was used as the light source, with an excitation filter (10nm bandwidth) and an emission monochromator (1nm resolution). PAHs were detected (e.g., pyrene Ex / Em = 334 / 388nm).

[0075] Step S104: Acquire dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment.

[0076] In some embodiments of the present invention, professional equipment may be deployed in an indoor forced ventilation environment, and dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment may be collected using standardized processes.

[0077] In some embodiments of the present invention, the following equipment may be used to collect dust airflow field data within an indoor forced ventilation environment: a hot-wire anemometer (range: 0-10 m / s, accuracy: ±2%) and a turbulence probe (range: 0-100%, accuracy: ±5%). The placement of the hot-wire anemometer within the indoor forced ventilation environment includes, but is not limited to, one measuring point each at the center of the air inlet and diagonally along the outlet, 0.5 m from the wall, with a sampling frequency of 10 Hz and continuous recording for 10 minutes. Correspondingly, to collect dust particle size distribution data within the indoor forced ventilation environment, a virtual impactor, such as a six-stage cutter (cutting sizes: 10 μm / 5 μm / 2.5 μm / 1 μm / 0.5 μm / 0.1 μm), with a sampling flow rate of 28.3 L / min, may be used. The mass concentration is calculated using a filter membrane (e.g., a quartz fiber, 47 mm diameter) by weighing.

[0078] It should be noted that when using a hot-wire anemometer and turbulence probe to collect dust airflow data within an indoor forced ventilation environment, the measurement range covers 0-10 m / s and turbulence intensity from 0-100%. After the equipment is calibrated in a standard wind tunnel, three measurement points are evenly spaced along the central axis of the ventilation duct. Flow velocity (v) and turbulence intensity (Tu) are continuously collected for 10 minutes at a sampling frequency of 10 Hz. This data is synchronized with the chemical testing equipment via a network time protocol to ensure temporal and spatial consistency.

[0079] Correspondingly, a virtual impactor, or six-stage cutter, was used to collect dust particle size distribution data within an indoor forced ventilation environment. The cut-off size range was 10 μm to 0.1 μm. Sampling was continuous for 24 hours at a flow rate of 28.3 L / min, with the quartz fiber filter replaced every hour. The mass concentration of each stage was calculated gravimetrically. Combined with the data from a laser particle size analyzer, the mass concentrations and the particle size distribution index (PDI) for each six-stage size range were output.

[0080] Step S105: Analyze the elemental quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain dust monitoring results under the indoor forced ventilation environment.

[0081] In some embodiments of the present invention, the area where the indoor forced ventilation environment is located can be divided into a fine hexahedral micro-element grid, and on this basis, with the help of the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, the dust monitoring results in the indoor forced ventilation environment can be output efficiently and accurately.

[0082] In some embodiments of the present invention, the dust monitoring results can be represented by a pollutant concentration field cloud map; wherein, the pollutant concentration field cloud map can include: concentration field distribution data, velocity field distribution data and turbulence field distribution data of pollutants in an indoor forced ventilation environment.

[0083] It should be noted that for the trained airflow field-chemical distribution coupling model, the corresponding input is: collecting and obtaining dust airflow field data (such as flow velocity v, direction θ, turbulence intensity Tu, etc.) under indoor forced ventilation environment through the above-mentioned various technical means, dust particle size distribution data, electrochemical data, optical data and quantitative analysis data of elements corresponding to dust. In addition, it also involves chemical detection results output from the constructed chemical fingerprint library (that is, the characteristic analysis results of dust pollutants under indoor forced ventilation environment, such as pollutant concentration, particle size distribution).

[0084] In some embodiments of the present invention, the boundary conditions of the trained airflow field-chemical distribution coupling model can also be adaptively updated to obtain the concentration field, velocity field and turbulence field distribution of dust pollutants in an indoor forced ventilation environment, thereby generating a pollutant concentration field cloud map in the indoor forced ventilation environment, which can intuitively display the distribution of pollutants (dust) in space, and thus facilitate the subsequent prediction of the diffusion path and possible source areas of pollutants in the indoor forced ventilation environment.

[0085] In some embodiments of the present invention, the constructed chemical fingerprint library is a standardized chemical fingerprint library constructed in advance, and its corresponding construction process can be referred to the steps shown in the figure: the element characteristic peaks corresponding to the laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the relationship between Raman spectrum and molecular vibration, and the fluorescent PAHs fingerprint spectrum are synchronously integrated to obtain multidimensional chemical fingerprint data; according to the dust pollution source type and dust particle size segment data, the multidimensional chemical fingerprint data are classified and labeled to obtain the constructed chemical fingerprint library.

[0086] In some embodiments of the present invention, previously known LIBS-XRF elemental characteristic peaks, Raman molecular vibration modes, and fluorescence PAHs fingerprints are integrated into multidimensional chemical fingerprint data. The integrated multidimensional chemical fingerprint data are classified and labeled according to the dust pollution source type (such as construction dust, industrial emissions, biomass combustion, etc.) and dust particle size segment data (such as PM10, PM2.5, PM1, etc.) to obtain the constructed chemical fingerprint library, that is, a standardized chemical fingerprint library is constructed.

[0087] In some embodiments of the present invention, the training process of the trained airflow field-chemical distribution coupling model can be implemented by the following steps: Obtain historical characteristic data and historical multi-source data of dust pollutants in the indoor forced ventilation environment; wherein, the historical multi-source data include: historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutants; embed the pollutant characteristic data in the constructed chemical fingerprint library into an initial computational fluid dynamics model constructed based on computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field; use the historical characteristic data and the historical multi-source data as training data to iteratively train the airflow field-chemical distribution correlation model to obtain the trained airflow field-chemical distribution coupling model.

[0088] In some embodiments of the present invention, the pollutant characteristic data in the constructed chemical fingerprint library (with description equations such as pollutant diffusion) are embedded in the initial computational fluid dynamics model constructed based on computational fluid dynamics theory, that is, the Navier-Stokes equation (used to describe fluid movement) and the chemical component transport equation (describing pollutant diffusion) are coupled to obtain the airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field.

[0089] In some embodiments of the present invention, the training of the airflow field-chemical distribution correlation model is a multi-step iterative process designed to optimize model parameters using historical data and real-time monitoring data, thereby improving the model's prediction accuracy (i.e., the accuracy of the generated dust monitoring results). The specific training process is as follows: First, data collection and preprocessing: Historical monitoring data is collected, including dust airflow field data under indoor forced ventilation environments (including but not limited to flow rate, direction, and turbulence intensity) and chemical detection data (LIBS-XRF element concentrations, electrochemical-optical detection active substance concentrations, and particle size distribution). The resulting data is then integrated to form multi-source data to construct a training dataset. To ensure time alignment and unit consistency, the multi-source data can be preprocessed before forming the training dataset. Data preprocessing includes but is not limited to: interpolating missing values using the K-Nearest Neighbors (KNN) algorithm (e.g., KNN interpolation); removing or correcting outliers; and normalizing features (e.g., Min-Max normalization) to eliminate dimensional differences.

[0090] Next, model construction: Based on computational fluid dynamics (CFD) theory, an indoor air flow model was established, coupling the Navier-Stokes equations with the chemical species transport equations. The computational grid was divided, and boundary conditions (such as inlet velocity and outlet pressure) and initial conditions (such as initial pollutant concentration) were set. Pollutant characteristic data (such as element ratios and molecular vibrational modes) from the chemical fingerprint library were embedded in the CFD model to establish an airflow field-chemical distribution correlation model that dynamically correlates chemical components with the airflow field.

[0091] Next, parameter setting and initialization: Select an appropriate numerical method (e.g., finite volume method) and solver (e.g., pressure correction method) to ensure computational stability and convergence. For example, set turbulence model parameters (including but not limited to the turbulent viscosity coefficient in the k-ε model), chemical component diffusion coefficients (including but not limited to the diffusion coefficient D of heavy metal ions), and reaction rate constants (including but not limited to the photolysis rate constant k of PAHs).

[0092] Finally, the training process involves inputting a training dataset and iteratively solving the airflow field-chemical distribution correlation model, which consists of CFD equations and chemical species transport equations, to update the model parameters. During training, the residuals are monitored, and the iterations are considered converged when the residual is less than a preset threshold (e.g., 1e-4). Furthermore, cross-validation methods (e.g., K-fold cross-validation) can be used to divide the dataset into training and validation sets to evaluate the model's performance under different parameter combinations. Grid search or Bayesian optimization algorithms are used to find the optimal parameter combination (e.g., turbulent viscosity coefficient, diffusion coefficient D).

[0093] Furthermore, during the training phase of the airflow field-chemical distribution correlation model, the performance of the model can also be evaluated. Model evaluation is a key step in verifying model performance. Through quantitative indicators and qualitative analysis, the prediction accuracy and reliability of the model are comprehensively evaluated. The evaluation indicators for the trained airflow field-chemical distribution coupling model of the present invention can include the following: 1. Concentration field error: The relative error (RE) and root mean square error (RMSE) between the simulated concentration field and the measured concentration field were calculated to evaluate the model's ability to predict the spatial distribution of pollutants. and root mean square error It can be calculated by the following formula (1): Formula (1); in, is the measured value, is the true value, is the sample size, is the actual value of sample i, is the predicted value of sample i.

[0094] 2. Particle size distribution error: Calculate the relative entropy (Kullback-Leibler, KL) between the simulated particle size distribution and the measured particle size distribution to evaluate the model's prediction accuracy for the particle size distribution.

[0095] 3. Pollution source location error: Calculate the Euclidean distance between the simulated pollution source location and the actual pollution source location to evaluate the model's ability to locate the pollution source.

[0096] 4. Risk assessment accuracy: Calculate the consistency between the risk level predicted by the model and the actual risk level, and evaluate the model's ability to quantify health risks.

[0097] It should be noted that for trained airflow field-chemical distribution coupling models, historical pollution event data can be used to verify the model's accuracy in locating pollution sources and assessing risks. Alternatively, the trained airflow field-chemical distribution coupling model can be deployed in an actual monitoring system, with simulation results compared with measured data in real time to evaluate the real-time prediction capabilities of the trained airflow field-chemical distribution coupling model. Furthermore, the K-fold cross-validation method can be used to evaluate the generalization ability of the trained airflow field-chemical distribution coupling model across different datasets.

[0098] In some embodiments of the present invention, the predicted results output by the trained airflow field-chemical distribution coupling model can be analyzed against measured data to identify sources of error (e.g., model assumptions, data quality, and parameter settings). Local mesh refinement or parameter adjustments can be performed in areas with large errors to improve prediction accuracy. The sensitivity of the trained airflow field-chemical distribution coupling model can also be analyzed, such as analyzing the sensitivity of the trained airflow field-chemical distribution coupling model output to input parameters (e.g., flow velocity, turbulence intensity, and chemical component concentration) to identify key parameters. Furthermore, the performance of the trained airflow field-chemical distribution coupling model at different risk levels (e.g., low risk, medium risk, and high risk) can be evaluated to provide a basis for developing control strategies.

[0099] In some embodiments of the present invention, the above step S105 can be implemented by the following steps S1051 and S1052 ( Figure 1 (not shown): Step S1051 : preprocessing the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain multi-source data.

[0100] In some embodiments of the present invention, the multiple data obtained above, namely, the elemental quantitative analysis data corresponding to the first dust sample, the electrochemical and optical data of the second dust sample, the dust airflow field data, and the dust particle size distribution data, undergo data preprocessing, such as time alignment, unit unification, feature screening, and data normalization, to ensure the quality consistency of the resulting multi-source data. Time alignment can be performed by synchronizing the timestamps of the detection devices corresponding to the multiple data sets using a Network Time Protocol (NTP) server to ensure temporal consistency of the elemental quantitative analysis data, electrochemical data, optical data, dust airflow field data, and dust particle size distribution data. Furthermore, unit unification converts concentration data into a unified unit (e.g., μg / m³), and kinetic parameters (e.g., diffusion coefficient D) are standardized to eliminate dimensional differences. Feature screening, based on domain knowledge and statistical methods, identifies key features for dust pollution analysis, such as element abundance, mineralogy, and PAH concentrations. Data normalization uses methods such as Min-Max normalization to scale the eigenvalues to the [0, 1] interval to improve the stability and accuracy of data analysis.

[0101] Step S1052: Utilize the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to analyze the multi-source data in sequence to obtain the dust monitoring result.

[0102] In some embodiments of the present invention, the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model are correspondingly used to analyze multi-source data in sequence to obtain dust monitoring results.

[0103] In some embodiments of the present invention, the dust monitoring results include: a pollutant concentration field cloud map under the indoor forced ventilation environment. The above-mentioned step S1052 can be implemented through the following process: using the constructed chemical fingerprint library to identify dust pollutants on the multi-source data, and obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; using the trained airflow field-chemical distribution coupling model to perform correlation analysis on the multi-source data and the characteristic analysis results to obtain the pollutant concentration field cloud map.

[0104] In some embodiments of the present invention, first, a constructed chemical fingerprint library is used to identify and match dust pollutants from multi-source data, thereby obtaining characteristic analysis results of dust pollutants in an indoor forced ventilation environment (including but not limited to: characteristic data such as the chemical composition of the dust, the particle size distribution of the dust, etc.); then, the obtained characteristic analysis results and multi-source data are synchronously input into a trained airflow field-chemical distribution coupling model for in-depth analysis, and the relevant data and distribution field are correlated to obtain a pollutant concentration field cloud map in the indoor forced ventilation environment.

[0105] In some embodiments of the present invention, the pollutant concentration field cloud map is the distribution of relevant data (including but not limited to: flow velocity, turbulence intensity, direction, chemical component concentration, etc.) of pollutants in an indoor forced ventilation environment.

[0106] It should be noted that after obtaining a pollutant concentration cloud map in an indoor forced ventilation environment, CFD simulation algorithms and pollution source identification algorithms can be combined to locate pollution sources within the forced ventilation environment and assess the environmental and human health risks posed by dust pollution in the forced ventilation environment. Here, a backpropagation model is used to leverage the obtained pollutant concentration cloud map, combined with the previously constructed chemical fingerprint library, and statistical methods such as Bayesian inference to infer the specific location and intensity of pollution sources within the forced ventilation environment. For example, a specific ventilation outlet, equipment exhaust outlet, or indoor activity area within the forced ventilation environment can be identified as a pollution source. Furthermore, source intensity inversion can be performed. This involves using the residual between the measured concentrations in the forced ventilation environment and the CFD-simulated values in the pollutant concentration cloud map to invert pollution source intensity parameters (such as emission rate) using optimization algorithms such as the least squares method. This can be used to quantify the emission intensity of pollution sources and provide data support for pollution control.

[0107] Continuing from the above description, the dust monitoring method in the forced ventilation scenario provided by the embodiment of the present invention may further perform the following steps after executing step S105: analyzing the toxicity, particle size distribution and exposure time of the chemical components in the dust monitoring results in the indoor forced ventilation environment to obtain a risk assessment result; and adjusting the real-time ventilation strategy in the indoor forced ventilation environment based on the risk assessment result.

[0108] In some embodiments of the present invention, the risk assessment results are calculated based on the toxicity, particle size distribution, and exposure time of the chemical components in the dust monitoring results under indoor forced ventilation environments. Here, the risk assessment results can be characterized by a Cumulative Risk Index (CRI) to assess the risk of dust pollution under indoor forced ventilation environments to the indoor environment and human health. Here, CRI = Σ(toxicity equivalent × particle size weight × time coefficient); here, the toxicity equivalent is quantified based on the toxicity of the chemical component (such as the toxicity equivalent of benzo[a]pyrene); the particle size weight is set based on the particle size distribution of dust particles (such as the proportion of PM2.5), reflecting the degree of harm to the human body caused by particles of different sizes; and the time coefficient is set based on the exposure time (t), reflecting the impact of exposure time on risk.

[0109] In some embodiments of the present invention, the risk assessment results may be: low risk (CRI < 1), the pollutant concentration is low and has little impact on human health; medium risk (1 ≤ CRI < 3), the pollutant concentration is moderate, requiring attention and certain control measures; high risk (CRI ≥ 3), the pollutant concentration is high, posing a serious threat to human health, and control measures must be taken immediately.

[0110] In some embodiments of the present invention, the ventilation strategy in the indoor forced ventilation environment is adjusted accordingly based on the risk assessment results. That is, a hierarchical control strategy is implemented for the ventilation strategy in the indoor forced ventilation environment based on the CRI corresponding to the risk assessment results, thereby achieving precise prevention and control of dust pollution and energy efficiency optimization in the indoor forced ventilation environment.

[0111] For example, if the risk assessment result is low (CRI < 1), the current ventilation strategy for the indoor forced ventilation system is maintained, such as maintaining the current fresh air volume (30 m³ / h / person) and filter speed (50%). Air quality reports are automatically issued every two hours to remind people in the forced ventilation environment to maintain normal activities. The corresponding dust monitoring equipment status can be: continuous monitoring of the operating status of the LIBS-XRF module, electrochemical workstation, and optical detector to ensure continuous data collection.

[0112] If the risk assessment results in a medium-risk strategy (i.e., 1 ≤ CRI < 3), enhanced air filtration and purification can be implemented in indoor forced ventilation environments. For example, the filter speed of high-efficiency particulate air filters (HEPA) can be increased to 70% to enhance the capture efficiency of PM2.5 and finer particles (efficiency > 99.97%). The activated carbon adsorption layer can also be activated to adsorb and purify volatile organic compounds (VOCs) and some gaseous pollutants. Furthermore, localized purification interventions can be implemented, such as deploying small ionizing air purifiers near pollution sources in indoor forced ventilation environments (e.g., printer air outlets) to reduce localized pollutant concentrations. Furthermore, personal protection reminders can be provided: warning messages can be sent to people in indoor forced ventilation environments via text messages or apps, advising sensitive groups (e.g., children and the elderly) to reduce strenuous activities.

[0113] If the risk assessment results in a high-risk strategy (CRI ≥ 3), emergency pollution blocking measures can be initiated within the indoor forced ventilation environment. For example, vents at detected pollution sources within the forced ventilation environment can be immediately closed to cut off the pollutant transmission path. An ultraviolet (UV) photolysis purification device (100W power) is activated to effectively inactivate airborne bacteria, viruses, and some chemical pollutants (efficiency > 95%). Furthermore, a comprehensive purification upgrade of the indoor forced ventilation environment is implemented: a high-voltage electrostatic dust removal module is activated for secondary capture of escaping fine particulate matter. An ozone generator (concentration < 0.1ppm, meeting safety standards) is also added to assist in the oxidation and decomposition of difficult-to-degrade pollutants. Personnel evacuation and protection: An audible and visual alarm system is triggered to guide personnel to a safe area. Personal protective equipment, such as N95 masks, is provided to reduce exposure risk.

[0114] In conjunction with the above description, see Figure 2 FIG. 1 is a schematic diagram of an overall process for adjusting the ventilation system or ventilation strategy of a relevant area by applying the dust monitoring method in a forced ventilation scenario provided by an embodiment of the present invention; wherein the specific execution process is as follows: Step S201. Start.

[0115] Step S202. Set sampling points and pre-process dust, that is, in an indoor forced ventilation environment, deploy multiple dust sampling points according to the specific spatial layout adaptation principle, and perform dust pre-processing on the CIA airflow samples of the multiple dust sampling points to obtain dust samples to be analyzed (corresponding to the first dust sample and the second dust sample in the above embodiment).

[0116] Step S203: Perform a multi-dimensional analysis on the dust sample obtained in step S202 to obtain corresponding dust analysis data, including but not limited to elemental quantitative analysis data, electrochemical data, and optical data. Correspondingly, the multi-dimensional analysis includes but is not limited to LIBS monitoring, XRF monitoring, electrochemical detection, and optical detection (corresponding to steps S102 and S103 in the above embodiment). Furthermore, dust airflow field data and dust particle size data under indoor forced ventilation are required for subsequent data analysis.

[0117] Step S204: Utilize the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to conduct an in-depth analysis of the preprocessed data to obtain corresponding analysis results (corresponding to step S105 in the above embodiment). Here, the data obtained in step S203 may be subjected to data preprocessing before being input into the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model. Data preprocessing includes, but is not limited to, time alignment, unit unification, feature screening, and data normalization.

[0118] Step S205: Based on the analysis results, assess the risk of dust pollution to human health in an indoor forced ventilation environment.

[0119] Step S206: Develop a corresponding control strategy based on the risk assessment results, which involves adjusting the ventilation strategy under the indoor forced ventilation environment accordingly.

[0120] Step S207. End.

[0121] As those skilled in the art should know, existing dust monitoring solutions often have the following drawbacks: (1) Limited analytical dimensions of a single technology: For example, although the use of LIBS alone can achieve rapid multi-element detection, it is subject to significant interference from matrix effects and laser scattering background, and its sensitivity to light and trace elements is insufficient. The use of XRF alone has high sensitivity for heavy metal element detection, but it cannot provide information on the material form or molecular structure. In addition, electrochemical detection technology is limited to the analysis of electroactive substances, while optical detection technology focuses on molecular vibration or energy level transition information. Both are difficult to fully reflect the complex composition and morphological characteristics of dust pollution.

[0122] (2) Insufficient real-time online monitoring capabilities: Traditional dust monitoring methods (e.g., gravimetric and beta-ray methods) require manual sampling or long-term integration measurements, making real-time online monitoring impossible. Although some optical or electrochemical sensors can achieve continuous monitoring, they are susceptible to environmental interference (e.g., temperature and humidity changes), resulting in large fluctuations in measurement data and limited accuracy.

[0123] (3) Poor environmental adaptability: Existing monitoring equipment or monitoring technologies are mostly designed for specific scenarios and are difficult to adapt to the complex working conditions of indoor forced ventilation environments (such as airflow disturbances, temperature and humidity fluctuations). They lack safety designs such as explosion-proof and anti-corrosion, making it difficult to operate stably in potentially explosive or corrosive environments.

[0124] In recent years, with the rapid development of spectral analysis technology and electrochemical detection technology, LIBS-XRF combined technology and electrochemical-optical collaborative detection have been widely used in the field of environmental monitoring. Based on this, the present invention specifically relates to a method for monitoring dust pollution in indoor forced ventilation environments based on LIBS-XRF combined technology and electrochemical-optical collaborative detection. This method combines LIBS and XRF, electrochemical detection and optical detection technology, and can achieve multi-dimensional and efficient monitoring of dust pollution in indoor forced ventilation environments. And the technical solution provided by the present invention can integrate multimodal data to improve the monitoring accuracy and response speed in complex environments. In addition, the method provided by the present invention can realize minute-level dynamic monitoring of dust pollution in ventilation ducts and pollution source tracing. It can provide highly sensitive, multi-dimensional solutions for indoor forced ventilation environments, such as: intelligent control of indoor air quality, occupational health protection, and is suitable for long-term online monitoring of forced ventilation scenarios such as laboratories and industrial plants.

[0125] In other words, the dust monitoring method in a forced ventilation scenario provided by the present invention can further achieve the following technical effects: (1) Multimodal data fusion and precise sampling of the respiratory zone to achieve full-chain pollution traceability and precise prevention and control: By placing sampling points at the air inlet, outlet, and breathing zone height (e.g., 0.5-1.5 meters) within a forced ventilation system, a three-tiered sampling network (inlet-outlet-breathing zone (0.5-1.5 meters)) can be implemented. A Y-type flow splitter ensures consistent airflow composition between the LIBS-XRF and electrochemical-optical modules. Combined with a drying tube (humidity control <40% RH) and a micro-vibration mill (particle size <10μm), this method provides high-quality samples for multi-parameter testing, overcoming the limitations of traditional single-point sampling, which often results in high errors. Furthermore, combined with CFD simulation and pollution source identification algorithms, sub-meter localization of pollution sources (e.g., error <5m) is achieved. The technical solution provided by the present invention can also accurately distinguish between construction dust (Si / Al ratio > 60%) and industrial emissions (such as Pb / As ratio > 30%), significantly shortening the response time to pollution incidents (compared to traditional methods, the response time can be shortened from several hours to within 30 minutes), and supporting dynamic blocking strategies (such as closing specific vents or initiating local purification), thereby increasing the reduction in pollution concentration by more than 60%.

[0126] (2) Pollution source tracing model based on coupling of chemical and physical characteristics: We innovatively constructed a "chemical fingerprint library (containing >1,000 sets of LIBS-XRF-Raman-fluorescence multidimensional data)" and an "airflow field-chemical distribution coupling model," using the PCA-Mahalanobis distance matching algorithm (matching accuracy >90%) to identify pollutant types. This, combined with CFD simulation (grid accuracy 10 cm³) and a Bayesian pollution source inversion algorithm (positioning error <5 m), addresses the difficulty of traditional methods in correlating chemical composition with spatial distribution.

[0127] (3) Multi-dimensional quantitative assessment of health risks: The technical solution provided by this invention integrates LIBS-XRF elemental toxicity equivalent concentrations (BaPeq), electrochemical-optical detection of active substance concentrations (such as PAHs), particle size distribution models (PM2.5 contribution), and exposure duration to establish a CRI. This CRI has a standard deviation of less than 5% relative to the World Health Organization (WHO) and can distinguish between low-risk (CRI < 1), medium-risk (1 ≤ CRI < 3), and high-risk (CRI > 3) scenarios. This provides differentiated warning thresholds for sensitive populations such as children and the elderly, increasing the effectiveness of health interventions by 80%.

[0128] (4) Dynamic risk-driven adaptive control strategy to achieve adaptive intelligent regulation and energy efficiency optimization: The technical solution provided by this invention automatically adapts control strategies based on real-time risk assessment results. Specifically, a three-tiered response mechanism is developed based on a comprehensive risk index (CRI = Σtoxicity equivalent × particle size weight × time coefficient). For example, the system maintains a fresh air volume of 30 m³ / h / person at low risk, activates a HEPA filter (filtration efficiency >99.97%) and activated carbon adsorption layer at medium risk, and triggers UV photolysis purification (inactivation efficiency >95%) at high risk. Compared to traditional fixed-frequency ventilation solutions, this solution reduces energy consumption by 40% and extends filter life by over 30% while ensuring air quality meets standards.

[0129] In addition, the dust monitoring method in a forced ventilation scenario provided by the present invention can also execute the following logic: 1. Real-time data feedback. For example, when the LIBS-XRF or electrochemical-optical module detects that the pollutant exceeds the standard (such as PM2.5>75μg / m³), high-frequency sampling (such as once an hour) is automatically triggered.

[0130] 2. Ventilation system linkage: If ventilation efficiency decreases (such as wind speed decreases by 20%) or is shut down, increase the sampling frequency to monitor pollutant accumulation.

[0131] 3. Personnel activity pattern: When the indoor forced ventilation environment is in working hours (e.g. 8:00-18:00), the sampling frequency is increased accordingly, and the frequency is reduced during non-working hours to optimize data representativeness.

[0132] With reference to the above description, the dust monitoring method provided by the present invention in a forced ventilation scenario, compared with the existing dust monitoring methods, can not only construct a multi-dimensional analysis system, that is, combining the elemental quantitative analysis capability of the LIBS-XRF combination technology and the morphological analysis capability of the electrochemical-optical collaborative detection technology, to achieve a full-chain analysis of the composition, structure and morphology of dust pollution. And it can achieve real-time online monitoring: optimize the response speed of the LIBS-XRF combination technology and the electrochemical-optical detection technology to ensure that the single detection cycle is ≤6 minutes, meeting the real-time monitoring needs. Through the automated sampling and pretreatment system, manual intervention is reduced to achieve continuous and stable online monitoring. In addition, the environmental adaptability of the dust monitoring method involved in the present invention can also be improved, that is, by designing modular and scalable monitoring equipment, it can adapt to complex working conditions under indoor forced ventilation environments. Integrated explosion-proof, anti-corrosion and other safety designs ensure the stable operation of the equipment in potentially dangerous environments.

[0133] That is, this solution achieves full-chain closed-loop management of dust pollution through multi-module collaboration, data-driven models and intelligent control, and can be applied to forced ventilation scenarios such as office buildings, hospitals, and laboratories.

[0134] In this way, in response to the demand for dynamic monitoring of dust pollution in indoor forced ventilation environments, the embodiments of the present invention propose a dust monitoring method based on LIBS-XRF combined technology and electrochemical-optical collaborative detection. Specifically, by coupling LIBS and XRF technologies, rapid qualitative and quantitative analysis of the elemental composition of dust in indoor forced ventilation environments (such as heavy metals and silicates) is achieved. In combination with electrochemical and optical detection technologies, optical data and electrochemical data (such as volatile organic compounds) of dust in indoor forced ventilation environments are simultaneously acquired. That is, the elemental quantitative analysis capability of the LIBS-XRF combined technology and the morphological analysis capability of the electrochemical-optical collaborative detection technology are combined to achieve a full-chain analysis of the composition, structure and morphology of dust pollution. Then, based on the corresponding dust airflow field data and dust particle size distribution data, the established chemical fingerprint library and the trained airflow field-chemical distribution coupling model are used to conduct in-depth analysis of the acquired multi-dimensional dust modal data (elemental quantitative analysis data, electrochemical data, optical data, dust airflow field data, and dust particle size distribution data), thereby obtaining dust monitoring results in indoor forced ventilation environments. This can improve dust monitoring accuracy and response speed while achieving multi-dimensional dust monitoring in indoor forced ventilation environments.

[0135] Example 2: Based on the same inventive concept, the embodiment of the present invention also provides an online monitoring system for dust pollution in an indoor forced ventilation environment, such as Figure 3 As shown, the system 300 includes: An acquisition module 301 is configured to acquire, in real time, a first dust sample and a second dust sample having the same airflow composition in an indoor forced ventilation environment; a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling module 302 for performing spectral analysis on the first dust sample using a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling technique to obtain elemental quantitative analysis data; The electrochemical-optical module 303 is configured to perform material form analysis on the second dust sample using electrochemical-optical detection technology to obtain electrochemical data and optical data; The acquisition module 301 is further configured to acquire dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment; The analysis module 304 is used to use the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to analyze the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain the dust monitoring results under the indoor forced ventilation environment.

[0136] Optionally, the acquisition module 301 includes: a deployment unit, used to determine multiple dust sampling points deployed in the indoor forced ventilation environment according to a specific spatial layout adaptation principle; a dust preprocessing unit, used to perform dust preprocessing and diversion on the air flow samples collected in real time from the multiple dust sampling points to obtain the first dust sample and the second dust sample.

[0137] Optionally, the laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined module 302 includes: a laser induced breakdown spectroscopy unit, used to use an ultrashort pulse laser to focus on the surface of the first dust sample to form a plasma, and analyze the emission spectrum of the plasma to obtain first element quantitative detection sub-data; an X-ray fluorescence spectroscopy unit, used to use X-rays to irradiate the first dust sample to obtain characteristic fluorescence, and analyze the wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data; and a combining unit, used to combine the first element quantitative detection sub-data and the second element quantitative detection sub-data to obtain the element quantitative analysis data.

[0138] Optionally, the electrochemical-optical module 303 includes: an electrochemical detection unit, used to dissolve the second dust sample in a specific electrolyte solution to obtain the electrolyte solution to be measured, and measure and analyze the changes in the electrical parameters of the electrolyte solution to be measured to obtain the electrochemical data; an optical detection unit, used to perform Raman spectroscopy analysis and fluorescence spectroscopy analysis on the second dust sample in sequence to obtain the optical data.

[0139] Optionally, the analysis module 304 includes: a data preprocessing unit, used to preprocess the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data and the dust particle size distribution data to obtain multi-source data; an analysis unit, used to use the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to analyze the multi-source data in turn to obtain the dust monitoring results.

[0140] Optionally, the dust monitoring results include: a pollutant concentration field cloud map under the indoor forced ventilation environment; the analysis unit is specifically used to use the constructed chemical fingerprint library to identify dust pollutants on the multi-source data, and obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; and use the trained airflow field-chemical distribution coupling model to perform correlation analysis on the multi-source data and the characteristic analysis results to obtain the pollutant concentration field cloud map.

[0141] Optionally, the system 300 also includes: a training module 305, which is used to obtain historical characteristic data and historical multi-source data of dust pollutants in the indoor forced ventilation environment; wherein, the historical multi-source data include: historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutants; the pollutant characteristic data in the constructed chemical fingerprint library is embedded into the initial computational fluid dynamics model constructed based on computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field; the historical characteristic data and the historical multi-source data are used as training data to iteratively train the airflow field-chemical distribution correlation model to obtain the trained airflow field-chemical distribution coupling model.

[0142] Optionally, the system 300 further includes: a construction module 306 for synchronously integrating the element characteristic peaks corresponding to the laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the relationship between Raman spectrum and molecular vibration, and the fluorescence PAHs fingerprint spectrum to obtain multidimensional chemical fingerprint data; and classifying and labeling the multidimensional chemical fingerprint data according to the dust pollution source type and dust particle size segment data to obtain the constructed chemical fingerprint spectrum library.

[0143] Optionally, the system 300 also includes: an evaluation and adjustment module 307, which is used to analyze the toxicity, particle size distribution and exposure time of the chemical components in the dust monitoring results in the indoor forced ventilation environment to obtain a risk assessment result; and adjust the real-time ventilation strategy in the indoor forced ventilation environment based on the risk assessment result.

[0144] It should be noted that the description of this online monitoring system for dust pollution in an indoor forced ventilation environment is similar to the description of the embodiment of the online monitoring method for dust pollution in an indoor forced ventilation environment described above, and has similar beneficial effects as the embodiment of the online monitoring method for dust pollution in an indoor forced ventilation environment. For technical details not disclosed in the embodiment of the online monitoring system for dust pollution in an indoor forced ventilation environment of the present invention, please refer to the description of the embodiment of the online monitoring method for dust pollution in an indoor forced ventilation environment of the present invention for understanding.

[0145] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for online monitoring of dust pollution in an indoor forced ventilation environment, characterized in that: The method comprises: Real-time acquisition of a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment; Using laser induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the first dust sample is subjected to spectral analysis to obtain elemental quantitative analysis data; Using electrochemical-optical detection technology, the second dust sample is subjected to material form analysis to obtain electrochemical data and optical data; Acquiring dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment; Using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data are analyzed to obtain the dust monitoring results under the indoor forced ventilation environment.

2. The method according to claim 1, characterized in that The method of obtaining a first dust sample and a second dust sample having the same airflow composition in an indoor forced ventilation environment in real time includes: Determine multiple dust sampling points to be deployed in the indoor forced ventilation environment according to a specific space layout adaptation principle; Dust preprocessing and diversion are performed on the airflow samples collected in real time from the multiple dust sampling points to obtain the first dust sample and the second dust sample.

3. The method according to claim 1, characterized in that The laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology is used to perform spectral analysis on the first dust sample to obtain elemental quantitative analysis data, including: focusing an ultrashort pulse laser on the surface of the first dust sample to form plasma, and analyzing an emission spectrum of the plasma to obtain quantitative detection sub-data of the first element; irradiating the first dust sample with X-rays to obtain characteristic fluorescence, and analyzing the wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data; The first element quantitative detection sub-data and the second element quantitative detection sub-data are combined to obtain the element quantitative analysis data.

4. The method according to claim 1, wherein The electrochemical-optical detection technology is used to perform material form analysis on the second dust sample to obtain electrochemical data and optical data, including: dissolving the second dust sample in a specific electrolyte solution to obtain an electrolyte solution to be measured, and measuring and analyzing changes in electrical parameters of the electrolyte solution to be measured to obtain the electrochemical data; Raman spectroscopy analysis and fluorescence spectroscopy analysis are performed on the second dust sample in sequence to obtain the optical data.

5. The method according to claim 1, wherein The constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model are used to analyze the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain dust monitoring results in the indoor forced ventilation environment, including: Preprocessing the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data to obtain multi-source data; The constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model are used to analyze the multi-source data in sequence to obtain the dust monitoring results.

6. The method according to claim 5, characterized in that The dust monitoring results include: a pollutant concentration field cloud map under the indoor forced ventilation environment, and the dust monitoring results are obtained by sequentially analyzing the multi-source data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, including: Using the constructed chemical fingerprint library, dust pollutants are identified on the multi-source data to obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; The trained airflow field-chemical distribution coupling model is used to perform correlation analysis on the multi-source data and the feature analysis results to obtain the pollutant concentration field cloud map.

7. The method according to any one of claims 1 to 6, characterized in that: The training process of the trained airflow field-chemical distribution coupling model includes: Acquire historical characteristic data and historical multi-source data of dust pollutants in the indoor forced ventilation environment; wherein the historical multi-source data includes: historical elemental quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data, and historical particle size distribution data of the dust pollutants; The pollutant characteristic data in the constructed chemical fingerprint library are embedded into the initial computational fluid dynamics model constructed based on computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field; The historical feature data and the historical multi-source data are used as training data to iteratively train the airflow field-chemical distribution correlation model to obtain the trained airflow field-chemical distribution coupling model.

8. The method according to any one of claims 1 to 6, characterized in that: The construction process of the constructed chemical fingerprint library includes: The element characteristic peaks corresponding to the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the relationship between Raman spectrum and molecular vibration, and the fluorescence PAHs fingerprint spectrum are synchronously integrated to obtain multidimensional chemical fingerprint data; The multi-dimensional chemical fingerprint data is classified and labeled according to the dust pollution source type and the dust particle size segment data to obtain the constructed chemical fingerprint library.

9. The method according to any one of claims 1 to 6, characterized in that: After analyzing the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring results in the indoor forced ventilation environment, the method further includes: Analyzing the toxicity, particle size distribution, and exposure time of the chemical components in the dust monitoring results under the indoor forced ventilation environment to obtain a risk assessment result; According to the risk assessment result, the real-time ventilation strategy in the indoor forced ventilation environment is adjusted.

10. An online monitoring system for dust pollution in an indoor forced ventilation environment, characterized in that: The system comprises: An acquisition module is used to acquire in real time a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment; a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling module, configured to perform spectral analysis on the first dust sample using laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy coupling technology to obtain elemental quantitative analysis data; an electrochemical-optical module, configured to perform material form analysis on the second dust sample using electrochemical-optical detection technology to obtain electrochemical data and optical data; The acquisition module is further used to acquire dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment; The analysis module is used to analyze the element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring results under the indoor forced ventilation environment.

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