Public Area Microbial Contamination Risk Assessment System Based on Spectral Analysis

Abstract

This application relates to the field of microbial detection technology, specifically to a public area microbial contamination risk assessment system based on spectral analysis. The system includes: a spectral acquisition module for acquiring Raman spectral curves of microorganisms in the air of a public area; a spectral correction module for baseline correction of the Raman spectral curves, specifically: determining the baseline values ​​of the Raman spectral curves at each wavelength and acquiring the corrected Raman spectral curves; a feature analysis module for identifying microbial species, specifically: calculating the feature evaluation values ​​of each peak, distinguishing all peaks, and acquiring characteristic main peaks and shoulder peaks; extracting characteristic peaks contained in the shoulder peaks; identifying the types of microorganisms in the air; and a risk assessment module for assessing the risk of microbial contamination in the public area. This application reduces misjudgments and missed detections caused by overlapping characteristic peaks, improves the accuracy of microbial species identification, and thus accurately assesses the contamination risk of microorganisms in public areas.

Description

Technical Field

[0001] This application relates to the field of microbial detection technology, specifically to a public area microbial contamination risk assessment system based on spectral analysis. Background Technology

[0002] Large hotels, supermarkets, shopping malls, and subway stations are public areas with high population density and poor air circulation, which can easily lead to the continuous reproduction and accumulation of microorganisms. These microorganisms in the air can attach to the surface of fine aerosol particles and infect the human body through airborne transmission, thereby causing respiratory infectious diseases.

[0003] In the process of detecting microorganisms in the air, Raman spectroscopy can quickly identify the species of microorganisms by analyzing their molecular structural characteristics. However, due to interference from fluorescence background and environmental noise, the spectral baseline drifts, which can mask weak characteristic signals of microorganisms. Secondly, different microorganisms may contain similar molecular chemical bonds, causing the characteristic information of different microorganisms to overlap in the spectrum. This results in the spectral resolution difference between different microorganisms being too small, making them easily masked by the characteristics of other microorganisms. Consequently, the detection of microorganisms in the air is inaccurate, affecting the inaccurate assessment of the risk of microbial contamination in public areas. Summary of the Invention

[0004] To address the aforementioned technical challenges, a public area microbial contamination risk assessment system based on spectral analysis is provided to resolve existing issues.

[0005] The solution to the technical problem addressed in this application is to provide a public area microbial contamination risk assessment system based on spectral analysis, the system comprising:

[0006] The spectral acquisition module is used to sample microorganisms contained in the air in public areas and obtain the Raman spectral curves of the microorganisms.

[0007] The spectral correction module is used to perform baseline correction on Raman spectral curves, including:

[0008] Multiple fittings are performed on the wavelengths and corresponding spectral intensities of the Raman spectrum curve. Based on the fitting values ​​and the changes in the curvature of the Raman spectrum curve at each wavelength, the baseline values ​​of the Raman spectrum curve at each wavelength are determined. The baseline values ​​are then removed from the Raman spectrum curve to obtain the corrected Raman spectrum curve.

[0009] The feature analysis module is used to identify the types of microorganisms, including:

[0010] The symmetry of the waveforms on both sides of each peak on the corrected Raman spectrum is analyzed, the characteristic evaluation value of each peak is calculated, all peaks are distinguished, and the characteristic main peak and shoulder peak are obtained; the local range where the shoulder peak is located on the corrected Raman spectrum is decomposed, and the characteristic peak contained in the shoulder peak is extracted; based on the characteristic main peak and the characteristic peak contained in the shoulder peak, the types of microorganisms in the air are identified.

[0011] The risk assessment module is used to assess the risk of microbial contamination in public areas by analyzing the proportion of pathogenic microorganisms among all microorganisms in the air.

[0012] Preferably, the step of performing multivariate fitting on the wavelengths and their corresponding spectral intensities on the Raman spectral curve includes: forming a two-dimensional array of each wavelength and its corresponding spectral intensity on the Raman spectral curve; performing cubic spline fitting on all two-dimensional arrays contained in the Raman spectral curve to obtain a fitting function; substituting each wavelength into the fitting function; and calculating the fitting value at each wavelength.

[0013] Preferably, the Raman spectrum curve is in the first... Baseline value at each wavelength The calculation formula is: in, The preset initial spectral intensity, For the Raman spectrum curve at the 1st Curvature at each wavelength This represents the maximum curvature of the Raman spectrum curve at all wavelengths. For the first Fitted values ​​corresponding to each wavelength.

[0014] Preferably, obtaining the corrected Raman spectrum curve includes: taking the difference between the spectral intensity corresponding to each wavelength on the Raman spectrum curve and the baseline value at the corresponding wavelength as the corrected spectral intensity corresponding to each wavelength, and forming the corrected Raman spectrum curve.

[0015] Preferably, the calculation of the characteristic evaluation value of each peak includes:

[0016] For any given peak, select multiple preset intensity values ​​that are smaller than the peak value corresponding to that given peak.

[0017] Extending from any of the wave peaks to both sides until the spectral intensities on the left and right sides reach the wavelength positions corresponding to each preset intensity value, these positions are recorded as the left position point and the right position point, respectively.

[0018] For each preset intensity value, analyze the difference in distance between the left position point, the right position point and the wavelength position corresponding to any wave peak, and calculate the distance difference;

[0019] For each preset intensity value, analyze the difference in spectral intensity between the left position point and its wavelength position symmetrical about any of the wave peaks, and calculate the intensity difference;

[0020] The feature evaluation value of any wave peak is obtained by summing the products of the intensity difference and distance difference corresponding to all preset intensity values ​​for any wave peak, and then performing a negative mapping on the sum.

[0021] Preferably, the calculation process for the distance difference is as follows:

[0022] For each preset intensity value, the distance between the left position point and the wavelength position corresponding to any wave peak is recorded as the left distance; the distance between the right position point and the wavelength position corresponding to any wave peak is recorded as the right distance; and the ratio of the left distance to the right distance is recorded as the relative ratio.

[0023] The distance difference is the difference between the numerical value 1 and the relative comparison.

[0024] Preferably, the calculation process for the intensity difference is as follows:

[0025] For each preset intensity value, obtain the wavelength position symmetrical about any wave peak of the left position point, and record it as the symmetrical position point;

[0026] The intensity difference is the difference between the spectral intensity corresponding to the left position point and the spectral intensity corresponding to its symmetrical position point.

[0027] Preferably, the step of obtaining the main peak and the shoulder peak includes: recording the peak whose feature evaluation value is greater than a preset threshold as the main peak, and vice versa as the shoulder peak.

[0028] Preferably, the extraction of characteristic peaks contained in the shoulder peaks includes: performing wavelet transform on the local curve segments where each shoulder peak is located on the corrected Raman spectrum curve, and then using the wavelet ridge method to extract the characteristic peaks contained in the shoulder peaks.

[0029] Preferably, the assessment of the risk of microbial contamination in public areas includes:

[0030] The ratio of the number of pathogenic microorganisms among all identified microorganisms to the total number of microorganisms is used as the air pollution risk coefficient.

[0031] If the pollution risk coefficient is less than the preset first value, the airborne microbial pollution is at low risk; if the pollution risk coefficient is greater than or equal to the preset first value and less than the preset second value, the airborne microbial pollution is at medium risk; if the pollution risk coefficient is greater than or equal to the preset second value, the airborne microbial pollution is at high risk. The preset first value is less than the preset second value.

[0032] This application has at least the following beneficial effects:

[0033] This application corrects the results of cubic spline fitting by analyzing the curvature changes at different wavelengths on the Raman spectral curve, determining the baseline values ​​of the Raman spectral curve at each wavelength, and obtaining the corrected Raman spectral curve. Its advantages lie in that, compared to directly using cubic spline fitting for baseline correction, dynamically tracking the curvature changes on the Raman spectral curve allows the baseline to better match the changes in the Raman spectral curve, avoiding peak clipping, effectively eliminating interference from fluorescence background and environmental noise, making the weak characteristic peaks of microorganisms clearer, and more accurately reflecting the Raman characteristics of microorganisms, thus enabling more accurate identification of microbial species. The calculation of characteristic evaluation values ​​for each peak also has the advantage of considering… The symmetry of each peak is analyzed to reflect the probability that the peak is the characteristic main peak of a single microorganism. Then, the characteristic main peak and shoulder peaks are obtained. The local area where the shoulder peak is located on the corrected Raman spectrum is decomposed, and the characteristic peaks contained in the shoulder peak are extracted. The beneficial effect is that it takes into account the characteristic information of other microorganisms that are masked by the main peak, reduces misjudgments and missed detections caused by overlapping characteristic peaks, and improves the accuracy of microbial species identification. The risk of microbial contamination in public areas is assessed by analyzing the proportion of pathogenic microorganisms among all microorganisms in the air. The beneficial effect is that by statistically analyzing the proportion of pathogenic microorganisms, the risk of microbial contamination in public areas can be quantified to accurately assess the risk of microbial contamination in public areas. Attached Figure Description

[0034] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of the public area microbial contamination risk assessment system based on spectral analysis of this application.

[0035] Figure 1 A block diagram of a public area microbial contamination risk assessment system based on spectral analysis provided in one embodiment of this application;

[0036] Figure 2 This is a flowchart illustrating the steps of a method for obtaining a pollution risk coefficient according to an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of the public area microbial contamination risk assessment system based on spectral analysis, in conjunction with the accompanying drawings and implementation examples, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] 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 this application pertains.

[0039] Please see Figure 1 The diagram illustrates a block diagram of a public area microbial contamination risk assessment system based on spectral analysis according to an embodiment of this application. The system includes: a spectral acquisition module, a spectral correction module, a feature analysis module, and a risk assessment module.

[0040] The spectral acquisition module is used to sample microorganisms contained in the air in public areas and obtain the Raman spectral curves of the microorganisms.

[0041] In public areas with high population density, the air may carry a variety of pathogenic microorganisms. These microorganisms can remain in the air for a long time by attaching to the surface of fine aerosol particles and can be transmitted through aerosols, leading to the spread of infectious diseases. By detecting the types of pathogenic microorganisms contained in the air, the risk of microbial contamination in public areas can be assessed.

[0042] Secondly, Raman spectroscopy is a non-destructive spectroscopic analysis method that is widely used in the analysis of the molecular structure and composition of substances. When microbial cells are exposed to laser irradiation, the molecules inside them interact with the incident light, causing a slight change in their energy and producing inelastic scattering, i.e., Raman scattering, which forms a Raman spectrum. The frequency shift of this scattered light is highly specific for each molecule, similar to a fingerprint, and can reflect the chemical composition and structural characteristics of microorganisms.

[0043] Based on the above analysis, petri dish cap plates were placed in a public area, allowing for natural air settling. This enabled airborne microbial particles to settle naturally onto the surface of the petri dish cap plates due to gravity, forming colonies. Then, Raman spectroscopy was used to obtain the Raman spectra of the microorganisms on the surface of the petri dish cap plates. In the two-dimensional coordinate system, the horizontal axis of the Raman spectral curve represents wavelength, and the vertical axis represents spectral intensity.

[0044] It should be noted that before conducting detection using a Raman spectrometer, the Raman spectrometer must be calibrated for wavelength. The acquisition of the Raman spectral curve and the process of wavelength calibration are well-known techniques and will not be elaborated here.

[0045] Thus, the Raman spectral curves of airborne microorganisms in public areas were obtained.

[0046] The spectral correction module is used to perform baseline correction on the Raman spectral curve, including: performing multiple fitting on the wavelength and its corresponding spectral intensity on the Raman spectral curve; determining the baseline value of the Raman spectral curve at each wavelength based on the fitting value and the change in curvature of the Raman spectral curve at each wavelength; removing the baseline value from the Raman spectral curve; and obtaining the corrected Raman spectral curve.

[0047] When acquiring spectra using a Raman spectrometer, baseline drift and distortion occur in the Raman spectrum due to interference from environmental noise and fluorescence background. This is because most substances, such as the materials contained in the lid of a petri dish, produce varying degrees of fluorescence scattering signals under laser excitation, which superimpose on the Raman scattering signals. This results in the spectral measurement results failing to accurately reflect the Raman spectral information, affecting the accuracy and reliability of the data. Therefore, baseline correction of the Raman spectrum is necessary, specifically as follows:

[0048] A two-dimensional array is formed by taking each wavelength and its corresponding spectral intensity on the Raman spectrum curve. Cubic spline fitting is performed on all two-dimensional arrays in the Raman spectrum curve to obtain the fitting function. Each wavelength is substituted into the fitting function to calculate the fitting value at each wavelength.

[0049] It should be noted that cubic spline fitting is a well-known technique and will not be elaborated upon here.

[0050]

[0051] in, For the first Baseline values ​​at each wavelength The preset initial spectral intensity, For the Raman spectrum curve at the 1st Curvature at each wavelength This represents the maximum curvature of the Raman spectrum curve at all wavelengths. For the first Fitted values ​​corresponding to each wavelength.

[0052] It should be noted that the preset initial spectral intensity The value represents the spectral intensity at the Raman wavelength of the petri dish lid. If the petri dish lid is made of single-crystal silicon, the spectral intensity at a Raman wavelength of 520.7 nm can be obtained by detecting the petri dish lid using a Raman spectrometer. Secondly, the calculation of curvature is a well-known technique and will not be elaborated here.

[0053] The difference between the spectral intensity corresponding to each wavelength on the Raman spectrum curve and the baseline value at the corresponding wavelength is taken as the corrected spectral intensity corresponding to each wavelength, thus forming the corrected Raman spectrum curve.

[0054] It should be noted that the curvature reflects the degree of bending of the Raman spectral curve. A larger curvature may correspond to stronger background noise or fluorescence interference. By introducing curvature, the shape changes of the Raman spectral curve at each wavelength are reflected, and the baseline is dynamically adjusted to better adapt to the actual changes of the spectral curve. This ensures that the baseline of microorganisms in the air can filter out background noise or fluorescence interference, so that the corrected Raman spectral curve can eliminate error interference caused by fluorescence background.

[0055] Thus, the corrected Raman spectrum curve is obtained.

[0056] The feature analysis module is used to identify the types of microorganisms, including: analyzing the symmetry of the waveforms on both sides of each peak on the corrected Raman spectrum curve, calculating the feature evaluation value of each peak, distinguishing all peaks, and obtaining the characteristic main peak and shoulder peak; decomposing the local area where the shoulder peak is located on the corrected Raman spectrum curve and extracting the characteristic peaks contained in the shoulder peak; and identifying the types of microorganisms in the air based on the characteristic main peak and the characteristic peaks contained in the shoulder peak.

[0057] Furthermore, given the diverse range of microorganisms in the air of public areas, the position and intensity of peaks on Raman spectroscopy curves reflect the vibrational modes of specific molecules within these microorganisms. Extracting characteristic peaks from Raman spectroscopy curves reveals the molecular vibrational fingerprints of microorganisms, which can be used to distinguish different species. Secondly, because different species of microorganisms may contain similar chemical bonds, intermolecular interactions can lead to overlapping vibrational modes, resulting in secondary peaks on the Raman spectroscopy curves. These shoulder peaks may mask the true characteristic peaks, interfering with the matching of microbial species.

[0058] Secondly, peaks caused by the vibration of independent chemical bonds in a single microorganism in the air usually have high symmetry, which reflects the characteristics of a single molecule vibration mode. Peaks caused by the intermolecular coupling vibrations of multiple microorganisms may exhibit asymmetry. These coupling vibrations are caused by the interaction of multiple molecules, which disrupts the original symmetry and forms a shoulder peak.

[0059] Based on the above analysis, the characteristic evaluation value is calculated by analyzing the symmetry on both sides of the peak in the corrected Raman spectrum, specifically as follows:

[0060] Obtain the peaks of spectral intensity corresponding to all wavelengths on the corrected Raman spectrum curve;

[0061] In this embodiment, the AMPD (Automatic multiscale-based peak detection) algorithm is used to obtain the peak. The AMPD algorithm is a well-known technology and will not be described in detail here.

[0062] For any given peak, select multiple preset intensity values ​​that are smaller than the peak value corresponding to that given peak.

[0063] In this embodiment, for any wave peak, five preset intensity values ​​smaller than the peak value corresponding to any wave peak are selected. 90%, 80%, 70%, 60%, and 50% of the peak value corresponding to any wave peak are selected as each preset intensity value. In other implementation methods, the implementer can set them according to the actual situation.

[0064] Extending from any of the wave peaks to both sides until the spectral intensities on the left and right sides reach the wavelength positions corresponding to each preset intensity value, these positions are recorded as the left position point and the right position point, respectively.

[0065] For each preset intensity value, the distance between the left position point and the wavelength position corresponding to any wave peak is recorded as the left distance; the distance between the right position point and the wavelength position corresponding to any wave peak is recorded as the right distance.

[0066] The ratio of the left distance to the right distance is denoted as the relative ratio, and the difference between the value 1 and the relative ratio is denoted as the distance difference.

[0067] In this embodiment, the absolute value of the difference between the numerical value 1 and the relative value is denoted as the distance difference.

[0068] For each preset intensity value, obtain the wavelength position symmetrical about any wave peak of the left position point, and record it as the symmetrical position point;

[0069] It should be noted that, assuming the left position point is The wavelength position corresponding to any one of the wave peaks is ,but about Symmetrical wavelength position ,in, .

[0070] Calculate the difference between the spectral intensity corresponding to the left position point and the spectral intensity corresponding to its symmetrical position point, and denote it as the intensity difference;

[0071] In this embodiment, the absolute value of the difference between the spectral intensity corresponding to the left position point and the spectral intensity corresponding to its symmetrical position point is calculated and denoted as the intensity difference.

[0072] The feature evaluation value of any wave peak is obtained by summing the products of the intensity difference and the distance difference corresponding to all preset intensity values ​​for any wave peak, and then performing a negative mapping on the sum.

[0073] In this embodiment, the specific process of negative mapping is as follows: negative mapping is performed through an exponential function, assuming the summation is denoted as... ,Will The result, as a result of the negative mapping, is that... It is an exponential function with the natural constant as the base.

[0074] It should be noted that the closer the relative ratio is to 1, i.e. the smaller the distance difference, the closer the distances from the left and right position points to the center of the peak are, indicating that the peak has high symmetry on both sides at the preset intensity value. The smaller the intensity difference, the closer the spectral intensities of the left position point and its symmetrical position point are, reflecting high symmetry on both sides of the peak. The larger the obtained feature evaluation value, the better the symmetry of the peak, and the greater the possibility that the peak is the characteristic main peak of a single microorganism. Conversely, the smaller the feature evaluation value, the worse the symmetry of the peak, and the higher the possibility that the peak is a shoulder peak formed by the coupled vibration of multiple microorganisms.

[0075] Peaks whose feature evaluation values ​​are greater than a preset threshold are denoted as feature main peaks, and those whose values ​​are less than a preset threshold are denoted as shoulder peaks.

[0076] In this embodiment, the preset threshold value is 0.8. As for other implementation methods, the implementer can set it according to the actual situation.

[0077] Furthermore, since characteristic peaks can usually directly reflect the type of microorganism because they correspond to the vibrational modes of specific molecular components (such as proteins, nucleic acids, or lipids), when multiple microorganisms coexist, the shoulder peaks may contain characteristic information of other microorganisms. However, due to the high intensity of the main characteristic peak, the characteristic information of other microorganisms contained in the shoulder peaks may be masked, leading to inaccurate identification of airborne microorganisms. Therefore, it is necessary to decompose the shoulder peaks and extract the characteristic peak information of the shoulder peak region, specifically:

[0078] After performing wavelet transform on the local curve segments where each shoulder peak is located on the corrected Raman spectrum curve, the characteristic peaks contained in the shoulder peak are extracted by wavelet ridge line method.

[0079] In this embodiment, the wavelet basis function of the wavelet transform algorithm is the Mexican hat wavelet basis. The wavelet transform algorithm is a well-known technique and will not be described in detail here. The process of extracting the characteristic peaks contained in the shoulder peaks is as follows: the curve segment between two valleys adjacent to each shoulder peak is taken as a local range. Wavelet transform is performed on the spectral intensity corresponding to all wavelengths in the local range where each shoulder peak is located. Wavelet coefficients of high-frequency energy are selected for inverse wavelet transform. The characteristic peak position is located on the local curve segment after inverse wavelet transform using the wavelet ridge method. The characteristic peaks contained in the shoulder peaks are extracted. The wavelet transform algorithm and the process of extracting characteristic peaks using the wavelet ridge method are well-known techniques and will not be described in detail here.

[0080] Microbial species are identified based on the characteristic peaks contained in the main peak and shoulder peaks.

[0081] In this embodiment, the types of microorganisms contained in the main peak and shoulder peak are identified by comparing and matching them with a known microbial feature library. The process of identifying microorganisms by Raman spectroscopy is a well-known technique and will not be described in detail here.

[0082] The risk assessment module is used to assess the risk of microbial contamination in public areas by analyzing the proportion of pathogenic microorganisms among all microorganisms in the air.

[0083] Furthermore, based on the proportion of pathogenic microorganisms in the air, the risk of airborne microbial pollution is assessed, specifically as follows:

[0084] The ratio of the number of pathogenic microorganisms among all identified microorganisms to the total number of microorganisms is used as the air pollution risk coefficient.

[0085] If the pollution risk coefficient is less than the preset first value, the air microbial pollution is at low risk; if the pollution risk coefficient is greater than or equal to the preset first value and less than the preset second value, the air microbial pollution is at medium risk; if the pollution risk coefficient is greater than or equal to the preset second value, the air microbial pollution is at high risk. The preset first value is less than the preset second value.

[0086] In this embodiment, the first preset value is 0.3 and the second preset value is 0.6. As for other implementation methods, the implementer can set them according to the actual situation.

[0087] It should be noted that the higher the pollution risk coefficient, the more pathogenic microorganisms are present in the air of the public area, and the more serious the microbial pollution. The flowchart of the method for obtaining the pollution risk coefficient provided in this application is shown below. Figure 2 As shown.

[0088] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A public area microbial contamination risk assessment system based on spectral analysis, characterized in that, The system includes: The spectral acquisition module is used to sample microorganisms contained in the air in public areas and obtain the Raman spectral curves of the microorganisms. The spectral correction module is used to perform baseline correction on Raman spectral curves, including: A two-dimensional array is formed by each wavelength and its corresponding spectral intensity on the Raman spectrum curve. Cubic spline fitting is performed on all two-dimensional arrays contained in the Raman spectrum curve to obtain the fitting function. Each wavelength is substituted into the fitting function to calculate the fitting value at each wavelength. Based on the fitting value and the curvature change of the Raman spectrum curve at each wavelength, the baseline value of the Raman spectrum curve at each wavelength is determined. The baseline value is removed from the Raman spectrum curve to obtain the corrected Raman spectrum curve. The feature analysis module is used to identify the types of microorganisms, including: The symmetry of the waveforms on both sides of each peak on the corrected Raman spectrum is analyzed, the characteristic evaluation value of each peak is calculated, all peaks are distinguished, and the characteristic main peak and shoulder peak are obtained; the local range where the shoulder peak is located on the corrected Raman spectrum is decomposed, and the characteristic peak contained in the shoulder peak is extracted; based on the characteristic main peak and the characteristic peak contained in the shoulder peak, the types of microorganisms in the air are identified. The risk assessment module is used to assess the risk of microbial contamination in public areas by analyzing the proportion of pathogenic microorganisms among all microorganisms in the air.

2. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, Raman spectrum curve at the first Baseline value at each wavelength The calculation formula is: in, The preset initial spectral intensity, For the Raman spectrum curve at the 1st Curvature at each wavelength This represents the maximum curvature of the Raman spectrum curve at all wavelengths. For the first Fitted values ​​corresponding to each wavelength.

3. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, The process of obtaining the corrected Raman spectrum curve includes: taking the difference between the spectral intensity corresponding to each wavelength on the Raman spectrum curve and the baseline value at the corresponding wavelength as the corrected spectral intensity corresponding to each wavelength, and forming the corrected Raman spectrum curve.

4. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, The calculation of the characteristic evaluation values ​​of each peak includes: For any given peak, select multiple preset intensity values ​​that are smaller than the peak value corresponding to that given peak. Extending from any of the wave peaks to both sides until the spectral intensities on the left and right sides reach the wavelength positions corresponding to each preset intensity value, these positions are recorded as the left position point and the right position point, respectively. For each preset intensity value, analyze the difference in distance between the left position point, the right position point and the wavelength position corresponding to any wave peak, and calculate the distance difference; For each preset intensity value, analyze the difference in spectral intensity between the left position point and its wavelength position symmetrical about any of the wave peaks, and calculate the intensity difference; The sum of the products of the intensity difference and distance difference corresponding to all preset intensity values ​​for any wave peak is accumulated, and the sum is negatively mapped to obtain the feature evaluation value of any wave peak.

5. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 4, characterized in that, The calculation process for the distance difference is as follows: For each preset intensity value, the distance between the left position point and the wavelength position corresponding to any wave peak is recorded as the left distance; the distance between the right position point and the wavelength position corresponding to any wave peak is recorded as the right distance; and the ratio of the left distance to the right distance is recorded as the relative ratio. The distance difference is the difference between the numerical value 1 and the relative comparison.

6. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 4, characterized in that, The calculation process for the intensity difference is as follows: For each preset intensity value, obtain the wavelength position symmetrical about any wave peak of the left position point, and record it as the symmetrical position point; The intensity difference is the difference between the spectral intensity corresponding to the left position point and the spectral intensity corresponding to its symmetrical position point.

7. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, The process of obtaining the main peak and shoulder peaks includes: recording the peaks whose feature evaluation values ​​are greater than a preset threshold as the main peaks, and vice versa as shoulder peaks.

8. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, The extraction of characteristic peaks contained in the shoulder peaks includes: performing wavelet transform on the local curve segments where each shoulder peak is located on the corrected Raman spectrum curve, and then using the wavelet ridge method to extract the characteristic peaks contained in the shoulder peaks.

9. The public area microbial contamination risk assessment system based on spectral analysis as described in claim 1, characterized in that, The assessment of the risk of microbial contamination in public areas includes: The ratio of the number of pathogenic microorganisms among all identified microorganisms to the total number of microorganisms is used as the air pollution risk coefficient. If the pollution risk coefficient is less than the preset first value, the airborne microbial pollution is at low risk; if the pollution risk coefficient is greater than or equal to the preset first value and less than the preset second value, the airborne microbial pollution is at medium risk; if the pollution risk coefficient is greater than or equal to the preset second value, the airborne microbial pollution is at high risk. The preset first value is less than the preset second value.

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