A dust pollution on-line monitoring method and system in an indoor forced ventilation environment

Abstract

The present application relates to the technical field of environmental monitoring, and provides a dust pollution online monitoring method and system in an indoor forced ventilation environment, which realizes full-chain analysis of dust pollution components, structures and morphologies by combining the element quantitative analysis capability of laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology and the morphology analysis capability of electrochemical-optical collaborative detection technology, and obtains dust monitoring results in the indoor forced ventilation environment by means of the chemical fingerprint library constructed and the airflow field-chemical distribution coupling model trained to deeply analyze the obtained dust multi-dimensional modal data in the indoor forced ventilation environment. In this way, on the basis of realizing multi-dimensional monitoring of dust in the indoor forced ventilation environment, the dust monitoring precision and response speed can be improved.

Description

TECHNICAL FIELD

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

[0002] With the acceleration of industrialization and the improvement of urbanization level, indoor air quality is increasingly becoming a key factor affecting public health. Dust, as one of the main components of indoor air pollution, has a wide range of sources, including: outdoor air penetration, indoor activities, and building decoration material release, etc. Long-term exposure to high-concentration dust environment not only causes respiratory diseases, allergic reactions, and other health problems, but also may cause explosions and other safety accidents, posing a serious threat to personnel life and property safety. Therefore, it is particularly important to monitor indoor dust pollution in real time and accurately.

[0003] Traditional dust monitoring methods mainly rely on manual sampling and laboratory analysis, which has many limitations. For example: the detection cycle is long, and it usually takes several days from sampling to obtaining results, which is difficult to meet the demand of real-time monitoring; the sensitivity is low, and the detection ability for low-concentration dust or specific components (such as light elements, harmful elements) is limited; in addition, the traditional dust monitoring method cannot realize continuous and real-time online monitoring, so it is difficult to discover and handle dust pollution problems in time. SUMMARY

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

[0005] The purpose of the present application is achieved by using the following technical solutions:

[0006] On the one hand, the present application provides a dust pollution online monitoring method in an indoor forced ventilation environment, which comprises:

[0007] Real-time acquisition of first dust samples and second dust samples with the same airflow composition in an indoor forced ventilation environment;

[0008] Using laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, the first dust sample is subjected to spectral analysis to obtain element quantitative analysis data;

[0009] Using electrochemical-optical detection technology, the second dust sample is subjected to substance form analysis to obtain electrochemical data and optical data;

[0010] Acquiring dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment;

[0011] 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 by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, to obtain the dust monitoring result in the indoor forced ventilation environment.

[0012] Optionally, the first dust sample and the second dust sample with the same airflow composition in the indoor forced ventilation environment are obtained in real time, and the method comprises the following steps of:

[0013] According to the specific space layout adaptation principle, a plurality of dust sampling points deployed in the indoor forced ventilation environment are determined.

[0014] The airflow samples collected in real time from the plurality of dust sampling points are pre-processed and branched to obtain the first dust sample and the second dust sample.

[0015] Optionally, the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology is used to perform spectral analysis on the first dust sample to obtain element quantitative analysis data, and the method comprises the following steps of:

[0016] An ultra-short pulse laser is used to focus the surface of the first dust sample to form a plasma, and the emission spectrum of the plasma is analyzed to obtain first element quantitative detection sub-data;

[0017] X-ray is used to irradiate the first dust sample to obtain characteristic fluorescence, and the wavelength and intensity of the characteristic fluorescence are analyzed to obtain second element quantitative detection sub-data;

[0018] The first element quantitative detection sub-data and the second element quantitative detection sub-data are combined to obtain the element quantitative analysis data.

[0019] Optionally, the electrochemical-optical detection technology is used to perform substance form analysis on the second dust sample to obtain electrochemical data and optical data, and the method comprises the following steps of:

[0020] The second dust sample is dissolved in a specific electrolyte solution to obtain a to-be-measured electrolyte solution, and the change of the electrical parameter of the to-be-measured electrolyte solution is measured and analyzed to obtain the electrochemical data;

[0021] The second dust sample is sequentially subjected to Raman spectrum analysis and fluorescence spectrum analysis to obtain the optical data.

[0022] Optionally, 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 by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result in the indoor forced ventilation environment, including:

[0023] The element quantitative analysis data, the electrochemical data, the optical data, the dust airflow field data, and the dust particle size distribution data are preprocessed to obtain multi-source data.

[0024] The multi-source data are analyzed in sequence by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result.

[0025] Optionally, the dust monitoring result includes a pollutant concentration field cloud chart in the indoor forced ventilation environment, and the multi-source data are analyzed in sequence by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result, including:

[0026] The multi-source data are analyzed in sequence by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result.

[0027] The multi-source data and the characteristic analysis result are analyzed by using the trained airflow field-chemical distribution coupling model to obtain the pollutant concentration field cloud chart.

[0028] Optionally, the training process of the trained airflow field-chemical distribution coupling model includes:

[0029] The historical characteristic data and the historical multi-source data of the dust pollutant in the indoor forced ventilation environment are obtained, and the historical multi-source data include the historical element quantitative analysis data, the historical electrochemical data, the historical optical data, the historical airflow field data, and the historical particle size distribution data of the dust pollutant.

[0030] The pollutant characteristic data in the constructed chemical fingerprint library are embedded into an initial computational fluid dynamics model constructed based on the computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation between chemical components and airflow field.

[0031] 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.

[0032] Optionally, the construction process of the constructed chemical fingerprint library comprises:

[0033] The laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology is used to synchronize integration of the corresponding element characteristic peaks, the relationship between the Raman spectrum and molecular vibration, and the fluorescence PAHs fingerprint, so that multi-dimensional chemical fingerprint data is obtained.

[0034] According to the dust pollution source type and the dust particle size segment data, the multi-dimensional chemical fingerprint data is classified and labeled, so that the constructed chemical fingerprint library is obtained.

[0035] Optionally, after the dust monitoring result in the indoor forced ventilation environment is obtained by using 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, the method further comprises:

[0036] The toxicity, the particle size distribution and the exposure time of the chemical composition in the dust monitoring result in the indoor forced ventilation environment are analyzed, so that a risk assessment result is obtained.

[0037] According to the risk assessment result, a real-time ventilation strategy in the indoor forced ventilation environment is adjusted.

[0038] In another aspect, the embodiments of the present application also provide a dust pollution online monitoring system in an indoor forced ventilation environment, which comprises:

[0039] An acquisition module is configured to acquire a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment in real time.

[0040] A laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined module is configured to use a laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology to perform spectral analysis on the first dust sample, so that element quantitative analysis data is obtained.

[0041] An electrochemical-optical module is configured to use an electrochemical-optical detection technology to perform substance form analysis on the second dust sample, so that electrochemical data and optical data are obtained.

[0042] The acquisition module is further configured to acquire dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment.

[0043] An analysis module is configured 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 by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result in the indoor forced ventilation environment.

[0044] Optionally, the acquisition module comprises a deployment unit configured to determine a plurality of dust sampling points deployed in the indoor forced ventilation environment according to a specific spatial layout adaptation principle; and a dust pretreatment unit configured to perform dust pretreatment and flow separation on airflow samples collected in real time from the plurality of dust sampling points to obtain the first dust sample and the second dust sample.

[0045] Optionally, the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined module comprises a laser-induced breakdown spectroscopy unit configured to focus a surface of the first dust sample by using an ultrashort pulse laser to form a plasma, and analyze emission spectrum of the plasma to obtain first element quantitative detection sub-data; an X-ray fluorescence spectroscopy unit configured to irradiate the first dust sample by using X-rays to obtain characteristic fluorescence, and analyze wavelength and intensity of the characteristic fluorescence to obtain second element quantitative detection sub-data; and a joint unit configured to jointly analyze the first element quantitative detection sub-data and the second element quantitative detection sub-data to obtain the element quantitative analysis data.

[0046] Optionally, the electrochemical-optical module comprises an electrochemical detection unit configured to dissolve the second dust sample in a specific electrolyte solution to obtain a to-be-measured electrolyte solution, and measure and analyze changes in electrical parameters of the to-be-measured electrolyte solution to obtain the electrochemical data; and an optical detection unit configured to sequentially perform Raman spectrum analysis and fluorescence spectrum analysis on the second dust sample to obtain the optical data.

[0047] Optionally, the analysis module comprises a data pretreatment unit configured to perform data pretreatment on 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; and an analysis unit configured to sequentially analyze the multi-source data by using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to obtain the dust monitoring result.

[0048] Optionally, the dust monitoring result comprises a pollutant concentration field cloud map in the indoor forced ventilation environment, the analysis unit is specifically configured to: adopt the constructed chemical fingerprint library to perform dust pollutant identification on the multi-source data, and obtain a characteristic analysis result of the dust pollutant in the indoor forced ventilation environment; and adopt the trained airflow field-chemical distribution coupling model to perform correlation analysis on the multi-source data and the characteristic analysis result, and obtain the pollutant concentration field cloud map.

[0049] Optionally, the system further comprises a training module configured to: acquire historical characteristic data and historical multi-source data of the dust pollutant in the indoor forced ventilation environment; wherein the historical multi-source data comprises historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutant; embed pollutant characteristic data in the constructed chemical fingerprint library into an initial computational fluid dynamics model constructed based on a computational fluid dynamics theory, to obtain an airflow field-chemical distribution correlation model having dynamic correlation between chemical components and airflow fields; and take the historical characteristic data and the historical multi-source data as training data, to perform iterative training on the airflow field-chemical distribution correlation model, and obtain the trained airflow field-chemical distribution coupling model.

[0050] Optionally, the system further comprises a construction module configured to: perform synchronous integration of element characteristic peaks corresponding to the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, a relationship between Raman spectroscopy and molecular vibration, and a fluorescence PAHs fingerprint, to obtain multi-dimensional chemical fingerprint data; and perform classification labeling on the multi-dimensional chemical fingerprint data according to dust pollution source types and dust particle size segment data, to obtain the constructed chemical fingerprint library.

[0051] Optionally, the system further comprises an evaluation and adjustment module configured to: analyze toxicity of chemical components, particle size distribution and exposure time in the dust monitoring result in the indoor forced ventilation environment, to obtain a risk evaluation result; and adjust a real-time ventilation strategy in the indoor forced ventilation environment according to the risk evaluation result.

[0052] Compared with the prior art, the present application has the following beneficial effects:

[0053] The embodiment of the present application provides a dust pollution online monitoring method and system in an indoor forced ventilation environment. In the method execution process, first, the laser-induced breakdown spectroscopy-X-ray fluorescence spectrum combined technology is used to realize rapid qualitative and quantitative analysis of dust element composition in the indoor forced ventilation environment, so as to obtain element quantitative analysis data of the dust, and electrochemical and optical detection technologies are combined to synchronously obtain optical data and electrochemical data of the dust in the indoor forced ventilation environment, that is, the element quantitative analysis ability of the laser-induced breakdown spectroscopy-X-ray fluorescence spectrum combined technology and the morphology analysis ability of the electrochemical-optical cooperative detection technology are combined to realize full-chain analysis of dust pollution components, structures and morphologies. Then, on the basis of obtaining corresponding dust airflow field data and dust particle size distribution data, a chemical fingerprint library and a trained airflow field-chemical distribution coupling model are used to deeply analyze the obtained dust multi-dimensional modal data (element quantitative analysis data, electrochemical data, optical data, dust airflow field data and dust particle size distribution data), so that dust monitoring results in the indoor forced ventilation environment are obtained. In this way, on the basis of realizing multi-dimensional monitoring of the dust in the indoor forced ventilation environment, the dust monitoring precision and response speed can be improved.

[0054] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but not limiting the technical solutions provided by the embodiments of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0056] Figure 1 A flowchart of a dust pollution online monitoring method in an indoor forced ventilation environment provided by the embodiments of the present application;

[0057] Figure 2 A whole flowchart of adjusting a ventilation system or a ventilation strategy of a related area by using the dust pollution online monitoring method in an indoor forced ventilation environment provided by the embodiments of the present application;

[0058] Figure 3 A component structure diagram of a dust pollution online monitoring system in an indoor forced ventilation environment provided by the embodiments of the present application. DETAILED DESCRIPTION

[0059] Other advantages and benefits of the present application will become apparent to those skilled in the art, upon consideration of the following detailed description of embodiments of the application, in connection with the accompanying drawings. The present application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, this description is provided so that this application will satisfy applicable legal requirements. The preferred embodiments are presented for the purpose of illustration and description and not limitation.

[0060] In the following description, reference is made to the terms "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" can be the same subset or different subsets as each other and can be combined with each other as long as there is no conflict.

[0061] In the following description, the terms "first\second\third" are only used to distinguish similar objects, and do not represent a specific order of the objects. It is understood that "first\second\third" can be interchanged in a specific order or sequence as long as it is allowed, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the application belong. The terminology used in the description herein is for describing embodiments of the application only and is not intended to be limiting of the application.

[0063] Embodiment 1:

[0064] Referring to Figure 1 As shown in the figure, a flowchart of a dust pollution online monitoring method in an indoor forced ventilation environment provided by embodiments of the application is shown, and the method comprises the following steps:

[0065] Step S101, real-time acquisition of a first dust sample and a second dust sample with the same airflow composition in an indoor forced ventilation environment.

[0066] In some embodiments of the application, the indoor forced ventilation environment refers to an indoor scene where natural ventilation cannot meet the requirements, forced ventilation must be used, and the gas environment needs to be continuously monitored, such as an indoor closed scene, an indoor scene with high concentration of harmful gas, etc.

[0067] In some embodiments of the present application, dust can be acquired in real time at the air inlet area, air outlet area or key activity area of the indoor forced ventilation environment. Here, a plurality of dust sampling points (air flow sampling points) deployed in the indoor forced ventilation environment can be used to acquire the collected air flow data, and the air flow data can be preprocessed (such as extracting dust samples) to obtain first and second dust samples with the same air flow composition.

[0068] In some embodiments of the present application, the sampling frequency for acquiring the first and second dust samples can be determined according to the specific attributes 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) or an emergency monitoring area (such as a sandstorm or post-fire area), a 24-hour automatic monitoring device can be used to capture the dust concentration fluctuations in the area in real time, i.e., a continuous sampling strategy is implemented. If the area where the indoor forced ventilation environment is located is an area that needs long-term trend analysis (such as an office or a residence), a sampling strategy of sampling once every 3 days or once a week for 24 hours each time can be used to evaluate the daily or weekly average, i.e., an intermittent sampling strategy is implemented. If the area where the indoor forced ventilation environment is located is in a high pollution period (such as the winter heating period) or a pollution event (such as haze), the sampling frequency can be increased to daily or hourly sampling to accurately track the concentration changes (of the first and second dust samples).

[0069] In some embodiments of the present application, the above step S101 can be implemented by the following steps S1011 and S1012 (not shown in the figure): Figure 1

[0070] Step S1011: According to the specific space layout adaptation principle, determine the number and location of the plurality of dust sampling points deployed in the indoor forced ventilation environment.

[0071] In some embodiments of the present application, the specific space layout adaptation principle can determine the rules for the number and location of the plurality of dust sampling points in the indoor forced ventilation environment according to the corresponding space layout of the indoor forced ventilation environment.

[0072] For example, if the indoor forced ventilation environment is a bedroom or other indoor area, the number of dust sampling points can be determined according to the area of the bedroom. For example: if the area of the bedroom is ≤ 50 m 2 , 1-3 dust sampling points are provided; if the area of the bedroom is 50-100 m 2 , 3-5 dust sampling points are provided; and if the area of the bedroom is > 100 m 2 , at least 5 dust sampling points are provided. In addition, the dust sampling points can be arranged in a diagonal or plum blossom pattern to avoid the air vents and heat sources in the bedroom, and the distance between the dust sampling points and the wall is ≥ 0.5 m and the distance between the dust sampling points and the door and window is ≥ 1 m. ​

[0073] In some embodiments of the present application, if the area where the indoor forced ventilation environment is located is a dust monitoring key area, the dust sampling points can be covered at the air inlet, air outlet and personnel activity intensive areas (such as workbench, rest area, etc.) of the forced ventilation system in the dust monitoring key area.

[0074] In some embodiments of the present application, in the process of determining the multiple dust sampling points deployed in the indoor forced ventilation environment according to the specific space layout adaptation principle, the height of the multiple dust sampling points can also be controlled according to the breathing zone, such as setting the height of the dust sampling point at 0.5-1.5m from the ground to cover the adult breathing zone; if it is necessary to evaluate the exposure risk of children or sitting posture, a height sampling point at a distance of 0.3-0.6m from the ground can be further added.

[0075] Exemplarily, the area where the indoor forced ventilation environment is located is a residential area, and according to the specific space layout adaptation principle, the following layout of dust sampling points can be performed, such as arranging 3 dust sampling points at the air inlet / air outlet of the residential area, and the covered pipeline cross section of the dust sampling point is 0.5m from the edge, with a spacing of 1m, and a vacuum pump (such as a flow of 28.3L / min) and a flow controller (accuracy of ±2%) can be further provided. In addition, one dust sampling point can be further arranged at the key activity area of the residential area, such as the center of the living room and the bedside of the bedroom, with a height of 1.2m (i.e. covering the human breathing zone), and a portable sampler (including a drying tube, with silica gel inside, humidity <40%RH) is provided.

[0076] Step S1012, performing dust pretreatment and splitting 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.

[0077] In some embodiments of the present application, the dust pretreatment on the airflow samples collected by the multiple dust sampling points includes but is not limited to:

[0078] 1. Dust drying, i.e. passing the airflow sample through the dust sampling head into the drying tube to remove moisture, so as to ensure the dryness of the obtained dust sample. Here, the collected airflow sample can be passed through the dust sampling head into the drying tube (such as setting the length of the drying tube to 30cm and the inner diameter to 5cm), and the silica gel filling amount is 50g, so as to ensure that the moisture content of the obtained dust sample is <5%.

[0079] 2. Grinding the dust sample, i.e. grinding the dust sample to be uniformly subdivided, can be achieved by a micro-vibration mill (e.g. a frequency of 30 Hz and a grinding time of 10 s) to achieve a dust particle size < 10 μm, ensuring the detection sensitivity. And by a tablet press under a pressure of 20 MPa to press into a thin sheet with a diameter of about 30 mm, ensuring smooth and flat surface. In this way, the matrix effect can be reduced to interfere with the spectral analysis, so as to significantly reduce the self-absorption effect using the uniform sample surface involved in the subsequent Laser-induced breakdown spectroscopy (LIBS) technology, thereby improving the element detection accuracy of the subsequent LIBS-X-ray fluorescence spectroscopy (XRF) combined technology, and the response speed of the electrochemical-optical detection technology.

[0080] It should be noted that after the dust pre-treatment of the air flow samples collected from multiple dust sampling points, the air flow needs to be divided. In some embodiments of the present application, the Y-type flow divider (made of polytetrafluoroethylene, with an inner wall roughness of Ra < 0.5 μm, and a flow distribution ratio of 1:1) can be used for equal division, which can obtain two first dust samples and second dust samples with consistent air flow composition, i.e. the same, so that the first dust sample and the second dust sample can flow into: the LIBS-XRF combined module 1, and the combined module 2 using the electrochemical-optical detection technology, thereby providing technical and parameter support for subsequent dust monitoring.

[0081] Step S102, using the Laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, performing spectral analysis on the first dust sample to obtain element quantitative analysis data.

[0082] In some embodiments of the present application, the LIBS technology can generate plasma by laser focusing sample, analyze the emission spectrum to determine the element composition, and has the advantages of fast, real-time and no sample pretreatment. At the same time, the XRF technology uses X-ray to excite the sample to generate characteristic fluorescence for element qualitative and quantitative analysis, and has the characteristics of non-destructive testing and simultaneous determination of multiple elements. The combination of the two can achieve more comprehensive element analysis of the dust sample, thereby breaking through the limitations of single technology, which is particularly suitable for rapid detection of complex component samples.

[0083] In some embodiments of the present application, the above step S102 can be realized by the following steps S1021 to S1023 (not shown in the figure): Figure 1

[0084] ​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.

[0085] 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.

[0086] In some embodiments of the present invention, a LIBS-XRF combined module may be used to analyze the first dust sample.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] In addition, the X-ray can also adopt a Q-switched neodymium-doped laser (Nd:YAG laser) with an output wavelength of 1064 nm, a pulse width of 8 ns, and a pulse laser energy of 38 mJ, and is focused on the surface of the first dust sample after 3 times of beam expansion. Here, the laser parameters involved can be: the wavelength of the Nd:YAG laser is 1064 nm, the pulse energy is 50 mJ, the frequency is 10 Hz, and the focal length of the focusing lens is 100 mm, and the spot diameter is 0.1 mm.

[0091] Correspondingly, the spectrum collection is: plasma spectrum (200-800 nm), which can be spectrally dispersed by a echelle grating spectrometer (resolution 0.1 nm), recorded by a CCD detector (cooling temperature -20℃), and the integration time is 10 ms.

[0092] In step S1023, the first element quantitative detection sub-data and the second element quantitative detection sub-data are combined to obtain the element quantitative analysis data.

[0093] In some embodiments of the present application, the first element quantitative detection sub-data obtained by the LIBS technology and the second element quantitative detection sub-data obtained by the XRF technology are integrated to obtain the element quantitative analysis data corresponding to the first dust sample. In this way, the combination of the LIBS technology and the XRF technology can realize more comprehensive element analysis of the dust sample, thereby breaking through the limitation of a single technology.

[0094] In step S103, an electrochemical-optical detection technology is used to analyze the substance form of the second dust sample to obtain electrochemical data and optical data.

[0095] In some embodiments of the present application, an electrochemical sensor can be used to analyze the substance form of the second dust sample by using an electrochemical detection technology to obtain electrochemical data corresponding to the second dust sample. Correspondingly, an optical fiber sensor can be used to analyze the substance form of the second dust sample by using an optical detection technology to obtain optical data corresponding to the second dust sample. Here, the electrochemical sensor can monitor the dust concentration by measuring the current or voltage generated by the electrochemical reaction, and it has high sensitivity and accuracy for inorganic dust, and is suitable for dust monitoring in specific occasions. At the same time, the optical fiber sensor uses the scattering and reflection characteristics of the optical fiber to monitor the dust concentration, and has high stability and repeatability, and is suitable for long-term monitoring, such as indoor air quality monitoring system.

[0096] It should be noted that the electrochemical detection technology measures the change of electrochemical signal, analyzes the reaction kinetics of the analyte and the electroactive substance, and has the characteristics of high sensitivity and fast response speed. The optical detection technology analyzes the molecular structure and morphology by using the absorption, scattering or emission characteristics of the substance to the light, and has the advantages of non-destructive and rich information. The combination of electrochemical and optical detection technology can realize the whole-chain monitoring of "composition-structure-morphology", which can significantly improve the accuracy and comprehensiveness of monitoring.

[0097] In some embodiments of the present application, the above step S103 can be realized by the following steps S1031 and S1032: Figure 1

[0098] Step S1031, dissolving the second dust sample in a specific electrolyte solution to obtain a to-be-measured electrolyte solution, and measuring and analyzing the change of the electrical parameter of the to-be-measured electrolyte solution to obtain the electrochemical data.

[0099] In some embodiments of the present application, the second dust sample can be dissolved in an electrolyte solution, and the electrical parameter of the electrolyte solution dissolved with the second dust sample can be measured by an electrochemical workstation, such as current / conductivity / resistance / potential change on the electrode, and the change value can be analyzed to obtain the electrochemical data corresponding to the second dust sample. Here, the electroactive substance of the second dust sample can be analyzed, such as heavy metal ions or reaction kinetics parameters, so as to obtain the electrochemical data of the second dust sample.

[0100] In some embodiments of the present application, an isokinetic sampling head can be arranged in a forced ventilation pipeline in an indoor forced ventilation environment, and the particle size grading cutting (such as PM10, PM2.5, PM1, etc.) of the second dust sample can be realized by a virtual impactor, so as to realize the deposition of the particles of the second dust sample on a rotary filter membrane carrier. The filter membrane carrier is rotated to an electrolyte spraying area, 0.1M HNO3 solution is uniformly infiltrated into the second dust sample through an ultrasonic atomizing nozzle, and the dissolved substance is distributed to an electrochemical cell and a spectrum detection through a microfluidic chip (wherein the distribution ratio can be 50μL:200μL). A three-electrode system is immersed in an electrolyte, and the redox characteristics of heavy metal ions (such as Pb 2+ , Cu 2+ ) are obtained by cyclic voltammetry (10-200mV / s) scanning, and the step response is recorded by chronopotentiometry (wherein the potential can be set to-0.5V, and the time length is set to 60s) to calculate the diffusion coefficient (Diffusivity, D).

[0101] ​Here, the electrode materials involved in electrochemical detection can be further described, such as the working electrode can be any one of: platinum electrode, glassy carbon electrode, boron-doped diamond electrode (BDD); wherein the platinum electrode, as the most commonly used inert electrode, has high purity, chemical stability and low hydrogen overpotential characteristics, is suitable for the detection of redox reaction of heavy metal ions (such as: Pb 2+ , Cu 2+ ), 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, and is suitable for trace heavy metal analysis in complex matrix, and its surface can be modified by chemical modification (such as: Nafion coating) to enhance the selectivity to specific ions; BDD can exhibit extremely low background current and excellent anti-pollution ability in strong acid electrolyte, and is suitable for high-sensitivity heavy metal detection, but the cost is higher.

[0102] Correspondingly, the auxiliary electrode can be any one of: platinum black electrode, reference electrode; wherein the platinum black electrode as the counter electrode, its high surface area can reduce the current density and reduce the polarization effect, which can ensure the stable response of the working electrode; the reference electrode, such as: silver-silver chloride electrode (Ag / AgCl): in 0.1M HNO3 system, the Ag / AgCl electrode potential is stable (such as: relative to the standard hydrogen electrode, 0.222V at 25℃), and is resistant to nitric acid corrosion, suitable for potential reference in acidic environment.

[0103] In addition, the electrolyte involved in electrochemical detection can include: base electrolyte, functional additive and supporting electrolyte; wherein the base electrolyte can be 0.1M HNO3 solution, used to provide a strong acidic environment (pH≈1) to promote the dissolution and ionization of heavy metal ions, and its strong oxidizing property can inhibit microbial contamination, but it needs to be stored in the dark to prevent decomposition; the functional additive can be a complexing agent, such as: the addition of 0.01M ethylenediaminetetraacetic acid (EDTA) can form a stable complex with Pb 2+ , Cu 2+ , which 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, which is used to improve the conductivity of the electrolyte and reduce the solution resistance (from 100 to 10 ), and improve the charge transfer efficiency.

[0104] It should be noted that the electrode-electrolyte synergistic effect can include: interface reaction optimization, that is, after modifying the Nafion film on the surface of the glassy carbon electrode, it can selectively pass through heavy metal ions and inhibit anion interference, so that Pb 2+the oxidation peak current of Cu2+ is increased by 3 times; the BDD electrode can reduce the detection limit of Cu2+ from 5 μg / L to 0.5 μg / L in cooperation with a 0.1M HNO3-0.01M EDTA system. 2+ the detection limit of Cu2+ is reduced from 5 μg / L to 0.5 μg / L.

[0105] In addition, dynamic response adjustment can be performed, that is, the element quantitative analysis data corresponding to the first dust sample obtained by the LIBS-XRF combined technology can be used to adjust the electrolyte composition in real time (for example, increase the EDTA concentration to cope with high-concentration heavy metal pollution) and optimize the selectivity of electrochemical detection. Or, under high dust flux (dust concentration > 10 mg / m³), the platinum black counter electrode is switched to reduce polarization and ensure the stability of the cyclic voltammetry scan (potential fluctuation < 2 mV).

[0106] In the foregoing description, forced ventilation environment adaptation can also be performed, that is, an isokinetic sampling head is arranged in the ventilation pipeline to ensure that the airflow speed (for example, 0.5-1.0 m / s) is consistent with the flow rate in the pipeline, thereby reducing sampling errors. Or, PM10 / PM2.5 / PM1 particle size grading is realized through a virtual impactor, and the electrode material is optimized for different particle size dust (for example, BDD electrode is used for PM1 detection to improve the anti-pollution ability).

[0107] Correspondingly, dynamic sampling strategies can be further used, for example, when the heavy metal concentration exceeds the standard (for example, Pb 2+ > 10 μg / L), the sampling frequency is automatically increased to 1 time / hour, and the high-sensitivity electrolyte formula (0.1M HNO3-0.05M EDTA) is switched to. Or, during the low pollution period (for example, at night), the sampling frequency is reduced to 1 time / 4 hours, and the service life of the electrode is prolonged. Or, through the synergistic optimization of the electrode material and the electrolyte composition, combined with the dynamic sampling strategy, the method can realize high-sensitivity and high-selectivity online monitoring of dust pollution in a forced ventilation environment, and provide precise data support for air quality control.

[0108] In step S1032, Raman spectrum analysis and fluorescence spectrum analysis are sequentially performed on the second dust sample to obtain the optical data.

[0109] In some embodiments of the present application, the Raman spectrum analysis can be used to identify the dust morphology in the second dust sample, such as crystalline state, amorphous state, etc. The fluorescence spectrum can be used to detect organic pollutants or specific components in the second dust sample, such as polycyclic aromatic hydrocarbons (PAHs), to obtain the optical data corresponding to the second dust sample.

[0110] Here, the same isoprene sampling head can be arranged in the ventilation duct under the indoor forced ventilation environment, and PM10 / PM2.5 / PM1 three-stage cutting is realized through a virtual impactor. Dust particles are deposited on a rotating filter membrane carrier, and the sampling flow is accurately controlled at 16.7 L / min. The filter membrane carrier is rotated to the optical detection area, and the microfluidic chip accurately dispenses the dissolution liquid (such as 0.1M HNO3) to the Raman detection pool and the fluorescence detection pool. The corresponding distribution ratio can be: Raman detection pool: 10 μL, fluorescence detection pool: 50 μL, to avoid cross contamination.

[0111] Correspondingly, in the link of performing optical detection, Raman spectrum analysis (i.e. molecular vibration and morphology identification) needs to be performed first. The instrument can use a 532nm / 785nm dual-wavelength laser, the spectral resolution is , the objective lens is 100x, and the spatial resolution is <1 μm. Among them, the crystalline state analysis focuses the laser on the dust particles, and the 300-1800cm -1 wavelength range spectrum is collected. The crystal material (such as quartz) presents a sharp characteristic peak (such as Si-O symmetric stretching vibration: ), and the peak width is . The non-crystalline state identifies the non-crystalline substance (such as silica gel) with a peak width of , and the intensity is reduced by more than 50%. Through the Lorentz function, the peak shape is fitted, and the crystallinity index is calculated. Here, baseline correction can also be further performed, and by comparing with the crystal database of the Inorganic Crystal Structure Database (ICSD), the mineral phase composition (such as cristobalite, quartz) is automatically labeled, and the identification accuracy is >95%.

[0112] Then, fluorescence spectrum analysis (PAHs detection) is performed. The three-dimensional fluorescence scanning excitation wavelength is 200-500nm (step length 5nm), the emission wavelength is 250-600nm (step length 2nm), and the integration time is 0.1s. The three-dimensional fluorescence spectrum (Excitation-Emission Matrix, EEM) is constructed. PAHs feature extraction decomposes the mixed spectrum through parallel factor analysis (PARAFAC) to extract Ex / Em=334 / 388nm, fluoranthene 320 / 426nm, and other PAHs characteristic peaks. Quantitative correction establishes a fluorescence intensity-concentration standard curve (R²>0.995), the detection limit reaches 0.01 μg / L, and the data output is the PAHs concentration and fingerprint spectrum.

[0113] In some embodiments of the present application, an electrochemical-optical synergistic detection module can also be employed to simultaneously realize substance form analysis of the second dust sample. Here, the electrochemical detection involved includes: a three-electrode system: a glassy carbon working electrode (diameter 3 mm), an Ag / AgCl reference electrode, and a platinum wire counter electrode, immersed in a 0.1M HNO3 solution. The corresponding signal acquisition: an electrochemical workstation (potential range ±10V, current resolution 1pA) records the cyclic voltammogram (scan speed 50mV / s), and extracts the redox peak current of heavy metal ions (such as Pb 2+ , Cu 2+ ). The optical detection involved includes: Raman spectroscopy: 785nm laser (power 50mW), spectral range 200-3200cm -1 , integration time 30s, repeated 3 times for averaging. Fluorescence spectrum: xenon lamp (150W) as light source, excitation filter (bandwidth 10nm), emission grating monochromator (resolution 1nm), detection of PAHs (such as pyrene Ex / Em=334 / 388nm).

[0114] Step S104, acquiring dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment.

[0115] In some embodiments of the present application, the dust airflow field data and dust particle size distribution data in the indoor forced ventilation environment can be obtained by deploying professional equipment inside the indoor forced ventilation environment and collecting them with the aid of standardized procedures.

[0116] In some embodiments of the present application, for the collection of dust airflow field data in the indoor forced ventilation environment, the following equipment can be used: hot-wire anemometer (range 0-10m / s, accuracy ±2%), turbulence probe (range 0-100%, accuracy ±5%). And the arrangement of the hot-wire anemometer in the indoor forced ventilation environment includes but is not limited to: one measuring point at the center of the air inlet and one measuring point at the diagonal line of the air outlet in the indoor forced ventilation environment, 0.5m away from the wall, sampling frequency 10Hz, continuous recording for 10 minutes. Correspondingly, for the collection of dust particle size distribution data in the indoor forced ventilation environment, a virtual impactor can be used, such as a six-stage impactor (cutting particle size: 10μm / 5μm / 2.5μm / 1μm / 0.5μm / 0.1μm), sampling flow rate 28.3L / min, filter membrane (such as quartz fiber, diameter 47mm) weighing method to calculate the mass concentration.

[0117] It should be noted that when collecting dust airflow field data in an indoor forced ventilation environment using a hot-wire anemometer and a turbulence probe, the range covered is 0-10 m / s, and it involves 0-100% turbulence intensity. The equipment needs to be calibrated in a standard wind tunnel, and 3 measuring points are evenly distributed on the central axis of the ventilation duct. The flow rate (v) and turbulence intensity (Tu) are continuously collected for 10 minutes, the sampling frequency is 10 Hz, and the data is time-synchronized with the chemical detection equipment through the network time protocol to ensure the spatial and temporal consistency of the data.

[0118] Correspondingly, when collecting dust particle size distribution data in an indoor forced ventilation environment using a virtual impactor, i.e., a six-stage impactor, the cutting particle size range is 10 μm to 0.1 μm, and the flow rate is continuously sampled at 28.3 L / min for 24 hours, and the quartz fiber filter membrane is replaced every hour. The mass concentration of each stage is calculated by weighing method, and the data is verified by laser particle size analyzer, and the mass concentration and particle size distribution index (Polydispersity Index, PDI) of the six-stage particle size section are output.

[0119] Step S105, using the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, analyzing 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 result in the indoor forced ventilation environment.

[0120] In some embodiments of the present application, the area where the indoor forced ventilation environment is located can be divided into a fine hexahedral microelement 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 result in the indoor forced ventilation environment can be efficiently and accurately output.

[0121] In some embodiments of the present application, the dust monitoring result can be represented in the form of 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 the pollutants in the indoor forced ventilation environment, etc.

[0122] 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 rate v, direction θ, turbulence intensity Tu, etc.), dust particle size distribution data, electrochemical data, optical data and element quantitative analysis data of dust in the indoor forced ventilation environment through the above-mentioned various technical means, in addition, it also involves the chemical detection result (i.e., the characteristic analysis result of dust pollutants in the indoor forced ventilation environment, such as pollutant concentration, particle size distribution) output from the constructed chemical fingerprint library.

[0123] In some embodiments of the present application, 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 turbulent field distribution of the dust pollutant in the indoor forced ventilation environment, thereby generating a pollutant concentration field cloud chart in the indoor forced ventilation environment, that is, the distribution of the pollutant (dust) in space can be intuitively displayed, and then the diffusion path and possible source area of the pollutant in the indoor forced ventilation environment can be predicted.

[0124] In some embodiments of the present application, the constructed chemical fingerprint library is a standardized chemical fingerprint library constructed in advance, and the corresponding construction process can refer to the steps shown: 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 to obtain multi-dimensional chemical fingerprint data; according to the dust pollution source type and dust particle size segment data, the multi-dimensional chemical fingerprint data is classified and labeled to obtain the constructed chemical fingerprint library.

[0125] In some embodiments of the present application, the LIBS-XRF element characteristic peaks, the Raman molecular vibration mode and the fluorescence PAHs fingerprint are integrated into multi-dimensional chemical fingerprint data in advance, and according to the dust pollution source type (such as: construction dust, industrial emission, biomass combustion, etc.) and the dust particle size segment data (such as: PM10, PM2.5, PM1, etc.), the multi-dimensional chemical fingerprint data obtained by integration is classified and labeled to obtain the constructed chemical fingerprint library, that is, a standardized chemical fingerprint library is constructed.

[0126] In some embodiments of the present application, the training process of the trained airflow field-chemical distribution coupling model can be realized by the following steps:

[0127] The historical characteristic data and historical multi-source data of the dust pollutant in the indoor forced ventilation environment are obtained; wherein the historical multi-source data includes: historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutant; the pollutant characteristic data in the constructed chemical fingerprint library is embedded into an initial computational fluid dynamics model constructed based on the computational fluid dynamics theory to obtain an airflow field-chemical distribution correlation model with dynamic correlation of 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.

[0128] In some embodiments of the present application, the pollutant characteristic data (a description equation of the correlation between the presence of the pollutant diffusion) in the constructed chemical fingerprint library is embedded into the initial computational fluid dynamics model based on the theory of computational fluid dynamics, that is, the Navier-Stokes equation (used to describe the fluid motion) is coupled with the chemical component transport equation (describing the pollutant diffusion), so as to obtain the airflow field-chemical distribution correlation model with dynamic correlation between the chemical component and the airflow field.

[0129] In some embodiments of the present application, the training of the airflow field-chemical distribution correlation model is a multi-step iterative process, which aims to optimize the model parameters through historical data and real-time monitoring data, so as to improve the prediction accuracy of the model (i.e. the accuracy of the generated dust monitoring results). The specific training process is as follows:

[0130] First, data collection and preprocessing: collect historical monitoring data, including dust airflow field data (including but not limited to: flow rate, direction, turbulence intensity) and chemical detection data (LIBS-XRF element concentration, electrochemical-optical detection of active substance concentration, particle size distribution) in indoor forced ventilation environment. Then the obtained data is integrated to form multi-source data, so as to construct a training data set. Here, in order to ensure the time alignment and unit uniformity of the data, the multi-source data can be preprocessed before forming the training data set. The data preprocessing includes but is not limited to: using K-nearest neighbor (K-Nearest Neighbors, KNN) algorithm for interpolation processing (such as: KNN interpolation) of missing values; removing or correcting outliers; normalizing features (such as: Min-Max normalization) to eliminate dimensional differences.

[0131] Second, model construction: based on the theory of computational fluid dynamics (Computational Fluid Dynamics, CFD), an indoor air flow model is established, coupling the Navier-Stokes equation and the chemical component transport equation. The calculation grid is divided, the boundary conditions (such as: inlet flow rate, outlet pressure) and initial conditions (such as: initial pollutant concentration) are set. The pollutant characteristic data (such as: element ratio, molecular vibration mode) in the chemical fingerprint library is embedded into the CFD model, and the airflow field-chemical distribution correlation model with dynamic correlation between the chemical component and the airflow field is established.

[0132] Then, parameter setting and initialization: select appropriate numerical methods (such as: finite volume method) and solvers (such as: pressure correction method), to ensure the stability and convergence of the calculation. For example: set the turbulence model parameters (including but not limited to: turbulent viscosity coefficient in k-ε model), chemical component diffusion coefficient (including but not limited to: diffusion coefficient D of heavy metal ions), and reaction rate constant (including but not limited to: photolysis rate constant k of PAHs).

[0133] Finally, training process: input training data set, solve the gas flow field-chemical distribution correlation model composed of CFD equation and chemical component transport equation by iteration, update the model parameters. At the same time, monitor the residual change during the training process, when the residual is less than the preset threshold (such as: 1e-4), it is considered that the iteration converges. In addition, cross-validation method (such as: K-fold cross-validation) can also be used to divide the data set into training set and validation set, to evaluate the performance of the model under different parameter combinations. Through grid search or Bayesian optimization algorithm, the optimal parameter combination (such as: turbulent viscosity coefficient, diffusion coefficient D) is found.

[0134] In addition, during the training of the gas flow field-chemical distribution correlation model, the performance of the model can also be evaluated; among them, model evaluation is a key step to verify the performance of the model, through quantitative indicators and qualitative analysis, to comprehensively evaluate the prediction accuracy and reliability of the model. The evaluation indicators of the trained gas flow field-chemical distribution coupling model in the present application can include the following points:

[0135] 1. Concentration field error:

[0136] Calculate the relative error (Relative Error, RE) and root mean square error (Root Mean Square Error, RMSE) of the simulated concentration field and the measured concentration field, to evaluate the prediction ability of the model on the spatial distribution of pollutants, wherein the relative error and the root mean square error can be calculated by the following formula (1):

[0137] Formula (1);

[0138] Among them, is the measured value, is the true value, is the sample number, is the actual value of sample i, is the predicted value of sample i.

[0139] 2. Particle size distribution error: calculate the relative entropy (Kullback-Leibler, KL) of the simulated particle size distribution and the measured particle size distribution, to evaluate the prediction accuracy of the model on the particle size distribution.

[0140] 3. Pollution source positioning 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.

[0141] 4. Risk assessment accuracy: Calculate the consistency rate of the risk level predicted by the model and the actual risk level to evaluate the model's ability to quantify health risks.

[0142] It should be noted that for the trained airflow field-chemical distribution coupling model, historical pollution event data can be used to verify the accuracy of the model in pollution source positioning and risk assessment, or the trained airflow field-chemical distribution coupling model can be deployed in an actual monitoring system to compare the simulation results with the measured data in real time to evaluate the real-time prediction ability of the trained airflow field-chemical distribution coupling model. In addition, the K-fold cross-validation method can be used to evaluate the generalization ability of the trained airflow field-chemical distribution coupling model on different data sets.

[0143] In some embodiments of the present application, the prediction results output by the trained airflow field-chemical distribution coupling model and the measured data can also be analyzed to analyze the error sources (such as model assumptions, data quality, parameter settings). Here, the areas with larger errors can be locally grid-encrypted or parameter-adjusted to improve the prediction accuracy. At the same time, the sensitivity of the trained airflow field-chemical distribution coupling model can also be analyzed, such as analyzing the sensitivity of the output of the trained airflow field-chemical distribution coupling model to the input parameters (such as flow rate, turbulence intensity, chemical component concentration) to identify key parameters. The performance of the trained airflow field-chemical distribution coupling model under different risk levels (such as low risk, medium risk, high risk) can also be evaluated to provide a basis for control strategy formulation.

[0144] In some embodiments of the present application, the above step S105 can be realized by the following steps S1051 and S1052 (not shown in the figure): Figure 1

[0145] Step S1051, data preprocessing of 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.

[0146] ​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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In some embodiments of the present application, first, the constructed chemical fingerprint library is used to identify and match multi-source data of dust pollutants, so as to obtain characteristic analysis results of dust pollutants in an indoor forced ventilation environment (including but not limited to: chemical composition in dust, particle size distribution and other characteristic data of dust); 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 relevant data and distribution fields are associated, so as to obtain a pollutant concentration field cloud chart in the indoor forced ventilation environment.

[0151] In some embodiments of the present application, the pollutant concentration field cloud chart is the distribution of relevant data (including but not limited to: flow rate, turbulence intensity, direction, chemical component concentration, etc.) of pollutants in the indoor forced ventilation environment.

[0152] It should be noted that after obtaining the pollutant concentration field cloud chart in the indoor forced ventilation environment, the CFD simulation algorithm and the pollution source identification algorithm can be combined to locate the pollution source in the indoor forced ventilation environment and evaluate the risk of dust pollution in the indoor forced ventilation environment to the environment and human health in the region. Here, a back propagation model is used to reversely calculate the specific position and intensity of the pollution source in the indoor forced ventilation environment by means of the obtained pollutant concentration field cloud chart, the chemical fingerprint library constructed above, and statistical methods such as Bayesian inference. For example, a ventilation port, a device exhaust port or an indoor activity area in the indoor forced ventilation environment is measured as a pollution source. In addition, source intensity inversion can be further performed, that is, the residual error of the measured concentration in the pollutant concentration field cloud chart and the CFD simulation value in the indoor forced ventilation environment is used to inversely calculate the intensity parameters (such as emission rate) of the pollution source by using an optimization algorithm such as least squares method, which can be applied to quantifying the emission intensity of the pollution source and providing data support for pollution control.

[0153] According to the above description, after step S105, the dust monitoring method in the forced ventilation scenario provided by the embodiments of the present application can further perform the following steps: analyzing the toxicity, particle size distribution and exposure time of the chemical composition in the dust monitoring result in the indoor forced ventilation environment to obtain a risk assessment result; and adjusting a real-time ventilation strategy in the indoor forced ventilation environment according to the risk assessment result.

[0154] In some embodiments of the present application, the risk assessment result is calculated according to the toxicity of the chemical components, the particle size distribution, and the exposure time in the dust monitoring result in the indoor forced ventilation environment. Here, the risk assessment result can be characterized by a cumulative risk index (CRI), which evaluates the risk of dust pollution in the indoor forced ventilation environment to the indoor environment and human health. Wherein, CRI = Σ (toxicity equivalent × particle size weight × time coefficient); Here, the toxicity equivalent is quantified according to the toxicity of the chemical components (such as the toxicity equivalent of benzo[a]pyrene); The particle size weight is set according to the particle size distribution of the dust particles (such as the proportion of PM2.5), reflecting the harm degree of different particle size particles to the human body; The time coefficient is set according to the exposure time (t), reflecting the influence of exposure time on risk.

[0155] In some embodiments of the present application, the risk assessment result can be: low risk (CRI <1), low pollutant concentration, less impact on human health; medium risk (1≤CRI<3), moderate pollutant concentration, need to pay attention and take certain control measures; high risk (CRI≥3), high pollutant concentration, posing a serious threat to human health, immediate control measures need to be taken.

[0156] In some embodiments of the present application, according to the risk assessment result, the ventilation strategy in the indoor forced ventilation environment is adjusted accordingly, that is, according to the CRI corresponding to the risk assessment result, the ventilation strategy in the indoor forced ventilation environment is executed hierarchical control strategy, so as to realize the precise prevention and control and energy efficiency optimization of dust pollution in the indoor forced ventilation environment.

[0157] For example, if the risk assessment result is low risk, that is, CRI <1, the current ventilation strategy of the ventilation system in the indoor forced ventilation environment is maintained, such as: maintaining the current fresh air volume (30 m³ / h·person) and filter screen rotating speed (50%), automatically issuing air quality report every 2 hours, prompting personnel in the indoor forced ventilation environment to maintain normal activities. The corresponding dust monitoring equipment state can be: continuously monitoring the running state of LIBS-XRF module, electrochemical workstation and optical detector, ensuring the continuity of data acquisition.

[0158] If the risk assessment result is a medium-risk strategy, that is, 1≤CRI<3, the air filtration and purification in the indoor forced ventilation environment can be enhanced, such as increasing the filter screen speed of a high efficiency particulate air filter (HEPA) to 70%, enhancing the capture efficiency of PM2.5 and finer particles (efficiency > 99.97%), and correspondingly starting an activated carbon adsorption layer to adsorb and purify volatile organic compounds (VOCs) and some gaseous pollutants. In addition, local purification intervention can be further performed, such as deploying a small ion wind purifier near the pollution source (such as a printer air outlet) in the indoor forced ventilation environment to reduce the local pollutant concentration, and giving a personnel protection prompt, such as pushing an early warning message to personnel in the indoor forced ventilation environment through a short message or an APP, and suggesting sensitive groups (such as children and the elderly) to reduce vigorous activities.

[0159] If the risk assessment result is a high-risk strategy, that is, CRI≥3, the emergency pollution blocking in the indoor forced ventilation environment can be started, such as immediately closing the ventilation port of the detected pollution source in the indoor forced ventilation environment to cut off the pollution transmission path. An ultraviolet (UV) photolysis purification device (power 100W) is started to efficiently inactivate (efficiency > 95%) bacteria, viruses and some chemical pollutants in the air. In addition, the indoor forced ventilation environment is comprehensively purified and upgraded: a high-voltage electrostatic dust removal module is started to capture escaped fine particles again, and an ozone generator (concentration <0.1ppm, meeting safety standards) is added to assist in oxidizing and decomposing refractory pollutants. Personnel evacuation and protection: triggering a sound and light alarm system to guide personnel to quickly evacuate to a safe area. Providing N95 masks and other personal protective equipment to reduce exposure risk.

[0160] In combination with the above description, referring to FIG. 8, which is a schematic diagram of the overall process of adjusting the ventilation system or ventilation strategy of the related area by the dust monitoring method provided by the embodiment of the present application in the forced ventilation scenario, and the specific execution process is as follows: Figure 2

[0161] Step S201. Start.

[0162] Step S202. Set a sampling point and pretreat dust, that is, in the indoor forced ventilation environment, a plurality of dust sampling points are deployed according to a specific spatial layout adaptation principle, and dust pretreatment is performed on the airflow samples of the plurality of dust sampling points CIA to obtain a dust sample to be analyzed (corresponding to the first dust sample and the second dust sample in the above embodiment).

[0163] ​Step S203. Perform multi-dimensional analysis on the dust sample obtained in step S202, and obtain corresponding dust analysis data, including but not limited to: element quantitative analysis data of the dust, electrochemical data, optical data, etc.; correspondingly, the multi-dimensional analysis includes but is not limited to: LIBS monitoring, XRF monitoring, electrochemical detection, optical detection (which corresponds to steps S102 and S103 in the above embodiments). In addition, dust airflow field data and dust particle size data in the indoor forced ventilation environment also need to be further obtained for subsequent data analysis.

[0164] Step S204. Utilize the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model to perform in-depth analysis on the preprocessed data, thereby obtaining corresponding analysis results (corresponding to step S105 in the above embodiments). Here, before inputting the data obtained in step S203 into the constructed chemical fingerprint library and the trained airflow field-chemical distribution coupling model, data preprocessing needs to be performed on the data obtained in step S203; wherein the data preprocessing includes but is not limited to: time alignment, unit unification, feature selection, and data normalization, etc.

[0165] Step S205. According to the analysis results, assess the risk of dust pollution to human health in the indoor forced ventilation environment.

[0166] Step S206. According to the risk assessment results, formulate corresponding control strategies. Here, the ventilation strategy in the indoor forced ventilation environment is adjusted accordingly.

[0167] Step S207. End.

[0168] As a person skilled in the art should know, the existing dust monitoring scheme usually has the following disadvantages:

[0169] (1) Limited single technical analysis dimension: for example, although single use of LIBS technology can realize rapid detection of multiple elements, it is greatly affected by matrix effect and laser scattering background interference, and the detection sensitivity for light elements and trace elements is insufficient. Single use of XRF technology has high detection sensitivity for heavy metal elements, but cannot provide substance form or molecular structure information. In addition, electrochemical detection technology is limited to the analysis of electroactive substances, and optical detection technology focuses on molecular vibration or energy level transition information, both of which are difficult to fully reflect the complex composition and morphological characteristics of dust pollution.

[0170] (2) Real-time online monitoring capability is insufficient: Traditional dust monitoring methods (such as the weight method and the beta-ray method) require manual sampling or long-term integral measurement, and cannot achieve real-time online monitoring. Although some optical or electrochemical sensors can achieve continuous monitoring, they are easily disturbed by the environment (such as changes in temperature and humidity), resulting in large fluctuations in measured data and limited accuracy.

[0171] (3) Poor environmental adaptability: Existing monitoring equipment or monitoring technology is designed for specific scenarios and is difficult to adapt to complex working conditions in indoor forced ventilation environments (such as air flow disturbance and temperature and humidity fluctuations). Lack of safety design such as explosion-proof and corrosion-proof, it is difficult to run stably in potentially explosive or corrosive environments.

[0172] 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 applied in environmental monitoring. Based on this, the present application specifically relates to an indoor forced ventilation environment dust pollution monitoring method 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 realize multi-dimensional and efficient monitoring of dust pollution in indoor forced ventilation environments. And the technical solution provided by the present application can fuse multi-modal data to improve the monitoring accuracy and response speed in complex environments. In addition, the method provided by the present application can realize minute-level dynamic monitoring of dust pollution in the ventilation pipeline and pollution source tracing. It can provide a high-sensitivity, multi-dimensional solution for indoor forced ventilation environments, such as indoor air quality intelligent management and control, and occupational health protection, and is suitable for long-term online monitoring in laboratory, industrial plant and other forced ventilation scenes.

[0173] In other words, the dust monitoring method in the forced ventilation scene provided by the present application can further achieve the following technical effects:

[0174] (1) Multi-modal data fusion and respiratory zone precise sampling to realize full-chain pollution tracing and precise prevention and control:

[0175] By arranging sampling points at the air inlet, air outlet and breathing zone height (such as 0.5-1.5 meters) in an indoor forced ventilation environment, a three-level sampling network of "air inlet-air outlet-breathing zone (0.5-1.5 meters)" can be realized in the forced ventilation system. At the same time, the airflow composition consistency of LIBS-XRF and electrochemical-optical modules is ensured through a Y-type flow divider, and high-quality samples are provided for multi-parameter detection by combining a drying tube (humidity control <40%RH) and a micro vibration mill (particle size <10 μm), which breaks through the limitation of large error of traditional single-point sampling. Further combined with CFD simulation and pollution source identification algorithm, sub-meter positioning (such as error <5m) of pollution source can be realized. The technical scheme provided by the present application can also accurately distinguish between building dust (Si / Al ratio >60%) and industrial emissions (such as Pb / As ratio >30%), significantly shorten the pollution event response time (compared with traditional methods, the response time can be shortened from several hours to 30 minutes), and support dynamic blocking strategy (such as closing a specific air inlet or starting local purification), which can increase the pollution concentration by more than 60%.

[0176] (2) Pollution tracing model coupled with chemical-physical characteristics:

[0177] The "chemical fingerprint library (containing more than 1000 groups of LIBS-XRF-Raman- fluorescence multi-dimensional data)" and "airflow field-chemical distribution coupling model" are innovatively constructed, the PCA-Mahalanobis distance matching algorithm (matching accuracy >90%) is used to realize the identification of pollution species, and the CFD simulation (grid accuracy 10cm³) and Bayesian pollution source inversion algorithm (positioning error <5m) are combined to solve the problem that the traditional method cannot associate chemical composition and spatial distribution.

[0178] (3) Multi-dimensional health risk quantitative evaluation:

[0179] The technical scheme provided by the present application can integrate the element toxicity equivalent concentration (BaPeq) of LIBS-XRF, the active substance concentration (such as PAHs concentration) of electrochemical-optical detection, the particle size distribution model (PM2.5 ratio) and the exposure time, so as to establish CRI. The CRI has a standard error of <5% compared with the World Health Organization (WHO), which can distinguish low-risk (CRI <1), medium-risk (1≤CRI <3) and high-risk (CRI >3) scenarios, and further provide differentiated early warning thresholds for sensitive groups such as children and the elderly, so that the effectiveness of health intervention measures is improved by 80%.

[0180] (4) Dynamic risk-driven adaptive control strategy to realize adaptive intelligent regulation and energy efficiency optimization:

[0181] The technical scheme provided by the present application can realize automatic matching control strategy based on real-time risk assessment results, that is, based on the comprehensive risk index (CRI = Σ toxicity equivalent × particle size weight × time coefficient), a three-level response mechanism is developed, such as maintaining the fresh air volume at 30 m³ / h per person in low risk, activating the HEPA filter screen (filtration efficiency > 99.97%) and the activated carbon adsorption layer in medium risk, and triggering UV photolysis purification (inactivation efficiency > 95%) in high risk. Compared with the traditional fixed frequency ventilation scheme, the scheme realizes energy consumption reduction of 40% and filter material life extension of more than 30% under the premise of guaranteeing air quality up to standard.

[0182] In addition, the dust monitoring method in the forced ventilation scene provided by the present application can also perform the following logic:

[0183] 1. Real-time data feedback, for example, when the LIBS-XRF or electrochemical-optical module detects that the pollutants exceed the standard (such as PM2.5 > 75 μg / m³), high-frequency sampling (such as once an hour) is automatically triggered.

[0184] 2. Ventilation system linkage: if the ventilation efficiency decreases (such as the wind speed decreases by 20%) or is turned off, the sampling frequency is increased to monitor the accumulation of pollutants.

[0185] 3. Personnel activity mode: the indoor forced ventilation environment is in the working period (such as 8:00-18:00), and the sampling frequency is correspondingly increased, and the frequency is reduced in the non-working period, and the data representativeness is optimized.

[0186] With reference to the above description, the dust monitoring method in the forced ventilation scene provided by the present application can not only construct a multi-dimensional analysis system, that is, combine the element quantitative analysis ability of the LIBS-XRF combined technology and the morphological analysis ability of the electrochemical-optical cooperative detection technology, realize the whole-chain analysis of the dust pollution composition, structure and morphology, and realize real-time online monitoring: optimize the response speed of the LIBS-XRF combined technology and the electrochemical-optical detection technology, ensure that the single detection period is ≤6 minutes, meet the real-time monitoring demand. Through the automatic sampling and pretreatment system, manual intervention is reduced, and continuous and stable online monitoring is realized. In addition, the environmental adaptability of the dust monitoring method involved in the present application can be improved, that is, by means of the designed modular and expandable monitoring equipment, the complex working conditions in the indoor forced ventilation environment are adapted. The safety design such as explosion-proof and corrosion-proof is integrated to ensure the stable operation of the equipment in the potentially dangerous environment.

[0187] That is, the present scheme realizes the whole-chain closed-loop management of dust pollution through multi-module cooperation, data-driven model and intelligent control, and can be applied to office buildings, hospitals, laboratories and other forced ventilation scenes.

[0188] Thus, for the dust pollution dynamic monitoring demand in the indoor forced ventilation environment, the dust monitoring method based on the LIBS-XRF combined technology and the electrochemical-optical collaborative detection is proposed, wherein the dust element composition (such as heavy metals, silicates) in the indoor forced ventilation environment is first analyzed by coupling the LIBS and the XRF technology, and the optical data and the electrochemical data (such as volatile organic compounds) of the dust in the indoor forced ventilation environment are synchronously obtained by combining the electrochemical and optical detection technologies, that is, the element quantitative analysis ability of the LIBS-XRF combined technology and the morphological analysis ability of the electrochemical-optical collaborative detection technology are combined to realize the full-chain analysis of the dust pollution composition, structure and morphology. Then, on the basis of obtaining the corresponding dust airflow field data and dust particle size distribution data, the chemical fingerprint library and the airflow field-chemical distribution coupling model are constructed, and the dust multi-dimensional modal data (element quantitative analysis data, electrochemical data, optical data, dust airflow field data and dust particle size distribution data) are deeply analyzed, so as to obtain the dust monitoring result in the indoor forced ventilation environment. In this way, on the basis of realizing the multi-dimensional monitoring of the dust in the indoor forced ventilation environment, the dust monitoring precision and response speed can be improved.

[0189] Embodiment 2:

[0190] Based on the same inventive concept, the embodiment of the present application also provides an online dust pollution monitoring system in an indoor forced ventilation environment, as shown in the accompanying drawings, the system 300 comprises: Figure 3

[0191] The acquisition module 301 is configured to acquire a first dust sample and a second dust sample in the indoor forced ventilation environment in real time, wherein the first dust sample and the second dust sample have the same airflow composition.

[0192] The laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined module 302 is configured to perform spectral analysis on the first dust sample by using the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology to obtain element quantitative analysis data.

[0193] The electrochemical-optical module 303 is configured to perform substance morphological analysis on the second dust sample by using the electrochemical-optical detection technology to obtain electrochemical data and optical data.

[0194] 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.

[0195] ​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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] Optionally, the dust monitoring result comprises a pollutant concentration field cloud map in the indoor forced ventilation environment, the analysis unit is specifically configured to: adopt the constructed chemical fingerprint library to perform dust pollutant identification on the multi-source data, to obtain a characteristic analysis result of the dust pollutant in the indoor forced ventilation environment; and adopt the trained airflow field-chemical distribution coupling model to perform correlation analysis on the multi-source data and the characteristic analysis result, to obtain the pollutant concentration field cloud map.

[0201] Optionally, the system 300 further comprises a training module 305 configured to: acquire historical characteristic data and historical multi-source data of the dust pollutant in the indoor forced ventilation environment; wherein the historical multi-source data comprises historical element quantitative analysis data, historical electrochemical data, historical optical data, historical airflow field data and historical particle size distribution data of the dust pollutant; embed pollutant characteristic data in the constructed chemical fingerprint library into an initial computational fluid dynamics model constructed based on a computational fluid dynamics theory, to obtain an airflow field-chemical distribution correlation model having dynamic correlation between chemical components and airflow fields; and take the historical characteristic data and the historical multi-source data as training data, to perform iterative training on the airflow field-chemical distribution correlation model, to obtain the trained airflow field-chemical distribution coupling model.

[0202] Optionally, the system 300 further comprises a construction module 306 configured to: synchronously integrate element characteristic peaks corresponding to the laser-induced breakdown spectroscopy-X-ray fluorescence spectroscopy combined technology, a relationship between Raman spectroscopy and molecular vibration, and a fluorescence PAHs fingerprint, to obtain multi-dimensional chemical fingerprint data; and perform classification labeling on the multi-dimensional chemical fingerprint data according to dust pollution source types and dust particle size segment data, to obtain the constructed chemical fingerprint library.

[0203] Optionally, the system 300 further comprises an evaluation and adjustment module 307 configured to: analyze toxicity of chemical components, particle size distribution and exposure time in the dust monitoring result in the indoor forced ventilation environment, to obtain a risk evaluation result; and adjust a real-time ventilation strategy in the indoor forced ventilation environment according to the risk evaluation result.

[0204] It should be noted that the description of the dust pollution online monitoring system in the indoor forced ventilation environment is similar to the description of the dust pollution online monitoring method in the indoor forced ventilation environment, and has similar beneficial effects to the dust pollution online monitoring method in the indoor forced ventilation environment. For technical details not disclosed in the dust pollution online monitoring system in the indoor forced ventilation environment, please refer to the description of the dust pollution online monitoring method in the indoor forced ventilation environment for understanding.

[0205] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, apparatus such as a system, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0206] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart or block diagram block or blocks. Figure 1 means for carrying out each of the functionality specified in the flowchart or block diagram block or blocks.

[0207] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart or block diagram block or blocks. Figure 1 means for carrying out each of the functionality specified in the flowchart or block diagram block or blocks.

[0208] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flowchart or block diagram block or blocks. Figure 1 means for carrying out each of the functionality specified in the flowchart or block diagram block or blocks.

[0209] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing and illustrating, not limiting, the technical solution of the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

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; 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; 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 dust monitoring results under the indoor forced ventilation environment; the dust monitoring results include: a pollutant concentration field cloud map.

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 any one of claims 1 to 4, 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.

6. The method according to any one of claims 1 to 4, 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.

7. The method according to any one of claims 1 to 4, characterized in that: 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.

8. 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; An analysis module is used to preprocess the elemental 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; use the constructed chemical fingerprint library to identify dust pollutants in the multi-source data to obtain characteristic analysis results of dust pollutants in the indoor forced ventilation environment; 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 dust monitoring results in the indoor forced ventilation environment; the dust monitoring results include: a pollutant concentration field cloud map.

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