Public area microbial pollution risk assessment system based on spectral analysis

Abstract

The invention relates to the technical field of microbiological detection, in particular to a public area microbiological pollution risk assessment system based on spectral analysis, which comprises a spectrum acquisition module used for acquiring a Raman spectrum curve of microorganisms contained in the air of a public area; the spectrum correction module is used for carrying out baseline correction on the Raman spectrum curve, specifically, baseline values of the Raman spectrum curve at all wavelengths are determined, and the corrected Raman spectrum curve is obtained; the feature analysis module is used for identifying the types of the microorganisms, specifically, the feature evaluation value of each wave crest is calculated, all the wave crests are distinguished, and a feature main peak and a feature shoulder peak are obtained; extracting characteristic peaks contained in the acromion; identifying the types of microorganisms in the air; and the risk assessment module assesses the microbial pollution risk of the public area. According to the method, misjudgment and missing detection caused by characteristic peak overlapping are reduced, and the accuracy of microorganism variety identification is improved, so that the pollution risk of microorganisms in a public area is accurately evaluated.

Description

Technical Field

[0001] The present application relates to the field of microbial detection technology, and in particular to a public area microbial contamination risk assessment system based on spectral analysis. Background Art

[0002] Public areas such as large hotels, supermarkets, shopping malls and subway stations have a high flow of people and poor air circulation, which can easily lead to the continuous reproduction and aggregation of microorganisms. The 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] When detecting microorganisms in the air, Raman spectroscopy can quickly identify their species by analyzing their molecular structural characteristics. However, interference from fluorescence background and environmental noise can cause spectral baseline drift, masking the weak characteristic signals of microorganisms. Furthermore, different microorganisms may contain similar molecular chemical bonds, resulting in overlapping characteristic information displayed by different microorganisms in the spectrum. This reduces the difference in spectral resolution between different microorganisms, making them easily obscured by the characteristics of other microorganisms. This leads to inaccurate detection of airborne microorganisms and inaccurate assessment of microbial contamination risks in public areas. Summary of the Invention

[0004] In order to solve the above technical problems, a public area microbial contamination risk assessment system based on spectral analysis is provided to solve the existing problems.

[0005] The solution to the technical problem solved by this application is to provide a public area microbial contamination risk assessment system based on spectral analysis, the system comprising: A spectrum acquisition module is used to sample microorganisms contained in the air of public areas and obtain Raman spectrum curves of the microorganisms; Spectral correction module, used to perform baseline correction on Raman spectrum curve, including: Perform multiple fitting on the wavelength and its corresponding spectral intensity on the Raman spectrum curve, determine the baseline value of the Raman spectrum curve at each wavelength based on the fitting value and the change of the curvature of the Raman spectrum curve at each wavelength, remove the baseline value from the Raman spectrum curve, and obtain a corrected Raman spectrum curve; Feature analysis module, used to identify the types of microorganisms, including: Analyze the symmetry of the waveforms on both sides of each peak on the corrected Raman spectrum curve, calculate the characteristic evaluation value of each peak, distinguish all peaks, and obtain the characteristic main peak and shoulder peak; decompose the local range where the shoulder peak on the corrected Raman spectrum curve is located, and extract the characteristic peaks contained in the shoulder peak; identify the types of microorganisms in the air based on the characteristic main peak and the characteristic peaks contained in the shoulder peak; The risk assessment module is used to assess the risk of microbial contamination in public areas by measuring the proportion of pathogenic microorganisms among all microorganisms in the air.

[0006] Preferably, the performing multi-factor fitting on the wavelengths and their corresponding spectral intensities on the Raman spectrum curve includes: forming a two-dimensional array for each wavelength and its corresponding spectral intensity on the Raman spectrum curve, performing cubic spline fitting on all two-dimensional arrays contained in the Raman spectrum curve, obtaining a fitting function, substituting each wavelength into the fitting function, and calculating the fitting value at each wavelength.

[0007] Preferably, the Raman spectrum curve is Baseline value at wavelength The calculation formula is: in, is the preset initial spectral intensity, The Raman spectrum curve is The curvature at wavelengths, is the maximum value of the curvature of the Raman spectrum curve at all wavelengths, For the The fitted values ​​corresponding to the wavelengths.

[0008] Preferably, obtaining the corrected Raman spectrum curve includes: taking the difference between the spectrum intensity corresponding to each wavelength on the Raman spectrum curve and the baseline value at the corresponding wavelength as the corrected spectrum intensity corresponding to each wavelength to form the corrected Raman spectrum curve.

[0009] Preferably, the calculating of the characteristic evaluation value of each peak includes: For any wave crest, select a plurality of preset intensity values ​​that are smaller than the peak value corresponding to the wave crest; Extending from any of the wave peaks to both sides until the spectrum intensities on the left and right sides reach the wavelength positions corresponding to the respective preset intensity values, which are respectively recorded as the left position point and the right position point; For each preset intensity value, analyzing the difference in distance between the left position point, the right position point and the wavelength position corresponding to any peak, and calculating the distance difference; For each preset intensity value, analyzing the difference in spectral intensity between the left position point and its wavelength position symmetrical with respect to any one of the wave crests, and calculating the intensity difference; The product of the intensity difference and the distance difference corresponding to all preset intensity values ​​of the any peak is accumulated and negatively mapped to obtain the characteristic evaluation value of the any peak.

[0010] Preferably, the distance difference is calculated as follows: For each preset intensity value, the distance between the left position point and the wavelength position corresponding to any peak is recorded as the left distance; the distance between the right position point and the wavelength position corresponding to any peak is recorded as the right distance; the ratio of the left distance to the right distance is recorded as the relative ratio; The distance difference is the difference between the value 1 and the relative ratio.

[0011] Preferably, the calculation process of the intensity difference is: For each preset intensity value, obtaining the wavelength position of the left position point symmetrical with respect to any one of the wave crests, and recording 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.

[0012] Preferably, the obtaining of the characteristic main peak and shoulder peak includes: recording a peak whose characteristic evaluation value is greater than a preset threshold as a characteristic main peak, and vice versa, recording it as a shoulder peak.

[0013] Preferably, the extracting characteristic peaks included in the shoulder peaks comprises: performing wavelet transform on the local curve segment where each shoulder peak is located on the corrected Raman spectrum curve, and then extracting the characteristic peaks included in the shoulder peaks using a wavelet ridge method.

[0014] Preferably, the assessment of microbial contamination risk in public areas includes: The ratio of the number of pathogenic microorganisms among all identified microorganisms to the number of all microorganisms is used as the air pollution risk coefficient; If the pollution risk coefficient is less than the preset first value, the microbial contamination in the air 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 microbial contamination in the air is at medium risk; if the pollution risk coefficient is greater than or equal to the preset second value, the microbial contamination in the air is at high risk, wherein the preset first value is less than the preset second value.

[0015] This application has at least the following beneficial effects: The present application analyzes the change of curvature at different wavelengths on the Raman spectrum curve, corrects the result of cubic spline fitting, determines the baseline value of the Raman spectrum curve at each wavelength, and obtains the corrected Raman spectrum curve. Its beneficial effect is that compared with directly using cubic spline fitting for baseline correction, by dynamically tracking the change of curvature on the Raman spectrum curve, the baseline can be more closely aligned with the change of the Raman spectrum curve, avoiding peak clipping, effectively eliminating the interference of fluorescence background and environmental noise, making the weak characteristic peaks of microorganisms clearer, and being able to more accurately reflect the Raman characteristics of microorganisms, so as to more accurately identify the types of microorganisms in the future; calculating the characteristic evaluation value of each peak has the beneficial effect of taking into account The symmetry of each peak is used to reflect the possibility that the peak is the characteristic main peak of a single microorganism; then the characteristic main peak and shoulder peak are obtained, the local range of the shoulder peak on the corrected Raman spectrum curve 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 that helps to identify other microorganisms masked by the main peak, reduces misjudgment and missed detection due to overlapping characteristic peaks, and improves the accuracy of microbial species identification; the proportion of pathogenic microorganisms among all microorganisms in the air is used to evaluate the microbial contamination risk in public areas. The beneficial effect is that by counting the proportion of pathogenic microorganisms, the microbial contamination risk is quantified, so as to accurately evaluate the contamination risk of microorganisms in public areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The public area microbial contamination risk assessment system based on spectral analysis of the present application is further described in detail below with reference to the accompanying drawings.

[0017] Figure 1 A block diagram of a public area microbial contamination risk assessment system based on spectral analysis provided in one embodiment of the present application; Figure 2 A flowchart of the steps of a method for obtaining a pollution risk coefficient provided in one embodiment of the present application. DETAILED DESCRIPTION

[0018] To further clarify the objectives, technical solutions, and advantages of this application, the following, in conjunction with the accompanying drawings and implementation examples, further describes the public area microbial contamination risk assessment system based on spectral analysis proposed in this application. It should be understood that the specific embodiments described herein are merely intended to explain this application and are not intended to limit this application.

[0019] Unless defined otherwise, 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 belongs.

[0020] See also Figure 1, which shows a block diagram of a public area microbial contamination risk assessment system based on spectral analysis provided by an embodiment of the present application. The system includes: a spectral acquisition module, a spectral correction module, a feature analysis module, and a risk assessment module.

[0021] The spectrum acquisition module is used to sample microorganisms contained in the air of public areas and obtain Raman spectrum curves of the microorganisms.

[0022] In public areas with dense flow of people, the air may carry a variety of pathogenic microorganisms, which can remain in the air for a long time by attaching to the surface of fine aerosol particles. They can also be transmitted through aerosols, leading to the spread of infectious diseases. By detecting the types of pathogenic microorganisms in the air, the risk of microbial contamination in public areas can be assessed.

[0023] Secondly, Raman spectroscopy is a non-destructive spectral analysis method 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 slight changes in their energy to produce inelastic scattering, namely Raman scattering, forming a Raman spectrum. The frequency shift of this scattered light is highly specific to each molecule, similar to a fingerprint, and can reflect the chemical composition and structural characteristics of the microorganism.

[0024] Based on the above analysis, Petri dishes were placed in public areas and natural air settling was used to allow microbial particles in the air to settle naturally onto the surfaces of the dishes due to gravity, forming colonies. Raman spectra of the microorganisms on the surfaces of the Petri dishes were then obtained using a Raman spectrometer. In a two-dimensional coordinate system, the horizontal axis represents wavelength, and the vertical axis represents spectral intensity.

[0025] It should be noted that before detection by the Raman spectrometer, the wavelength of the Raman spectrometer is calibrated. The acquisition of the Raman spectrum curve and the wavelength calibration process are well-known technologies and will not be described in detail here.

[0026] At this point, the Raman spectrum curves of microorganisms in the air of public areas are obtained.

[0027] The spectrum correction module is used to perform baseline correction on the Raman spectrum curve, including: performing multiple fitting on the wavelengths on the Raman spectrum curve and their corresponding spectral intensities, determining the baseline value of the Raman spectrum curve at each wavelength based on the fitting values ​​and the change in the curvature of the Raman spectrum curve at each wavelength, removing the baseline value from the Raman spectrum curve, and obtaining a corrected Raman spectrum curve.

[0028] When a Raman spectrometer is used to collect spectra, the Raman spectrum will produce baseline drift and distortion due to interference from environmental noise and fluorescence background. This is because most substances, such as the material components contained in the culture dish cover plate, will produce varying degrees of fluorescence scattering signals under laser excitation, which will be superimposed on the Raman scattering signal, resulting in the spectrum measurement results not being able to truly reflect the Raman spectrum information, affecting the accuracy and reliability of the data. Therefore, it is necessary to perform baseline correction on the Raman spectrum, specifically: A two-dimensional array is formed for each wavelength and its corresponding spectral intensity on the Raman spectrum curve, and a cubic spline fitting is performed on all two-dimensional arrays in the Raman spectrum curve to obtain a fitting function. Each wavelength is substituted into the fitting function to calculate the fitting value at each wavelength; It should be noted that cubic spline fitting is a well-known technology and will not be described in detail here.

[0029] in, For the The baseline value at the wavelength, is the preset initial spectral intensity, The Raman spectrum curve is The curvature at wavelengths, is the maximum value of the curvature of the Raman spectrum curve at all wavelengths, For the The fitted values ​​corresponding to the wavelengths.

[0030] It should be noted that the preset initial spectral intensity is the spectral intensity corresponding to the Raman spectrum wavelength of the culture dish cover plate. If the culture dish cover plate is made of single crystal silicon material, the spectral intensity corresponding to the Raman spectrum wavelength of 520.7 can be obtained by detecting the culture dish cover plate through a Raman spectrometer. ; Secondly, the calculation of curvature is a well-known technology and will not be described in detail here.

[0031] The difference between the spectral intensity corresponding to each wavelength on the Raman spectrum curve and the baseline value at the corresponding wavelength is used as the spectral intensity corresponding to each wavelength to form a corrected Raman spectrum curve; It should be noted that the curvature reflects the degree of bending of the Raman spectrum curve. A larger curvature may correspond to stronger background noise or fluorescence interference. By introducing curvature, the morphological changes of the Raman spectrum curve at each wavelength are reflected, and the baseline is dynamically adjusted to better adapt to the actual changes of the spectrum curve, ensuring that the baseline of microorganisms in the air can filter out background noise or fluorescence interference, so that the corrected Raman spectrum curve can eliminate the error interference caused by the fluorescence background.

[0032] At this point, the corrected Raman spectrum curve is obtained.

[0033] 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 range where the shoulder peak is located on the corrected Raman spectrum curve, and extracting the characteristic peaks contained in the shoulder peak; based on the characteristic main peak and the characteristic peaks contained in the shoulder peak, the types of microorganisms in the air are identified.

[0034] Furthermore, due to the high diversity of microbial species in the air of public areas, the position and intensity of the peaks on the Raman spectrum curve reflect the vibrational modes of specific molecules within the microorganisms. By extracting the characteristic peaks from the Raman spectrum curve, which reflect the fingerprint information of the microorganism's molecular vibrations, different microorganism species can be distinguished. Secondly, because different microorganisms may contain similar chemical bonds, interactions between their molecules may cause overlapping vibrational modes, resulting in secondary bumps on the Raman spectrum curve, forming shoulder peaks. These shoulder peaks may obscure the true characteristic peaks and interfere with matching microbial species.

[0035] Secondly, the peaks caused by the vibration of independent chemical bond molecules of a single microorganism in the air usually have a high degree of symmetry. This symmetry reflects the characteristics of the vibration mode of a single molecule, while the peaks caused by the intermolecular coupling vibrations of multiple microorganisms may show asymmetry. This coupled vibration is caused by the interaction of multiple molecules, thus destroying the original symmetry and forming a shoulder peak.

[0036] Based on the above analysis, the characteristic evaluation value is calculated by analyzing the symmetry on both sides of the peak on the corrected Raman spectrum curve, specifically: Obtaining the peaks of spectral intensities corresponding to all wavelengths on the corrected Raman spectrum curve; In this embodiment, the AMPD (Automatic multiscale-based peak detection) algorithm is used to obtain the peak value. The AMPD algorithm is a well-known technology and will not be described in detail here.

[0037] For any wave crest, select a plurality of preset intensity values ​​that are smaller than the peak value corresponding to the wave crest; In this embodiment, for any peak, 5 preset intensity values ​​that are smaller than the peak value corresponding to any peak are selected, and 90%, 80%, 70%, 60%, and 50% of the peak value corresponding to any peak are selected in turn as the preset intensity values. As other implementation methods, the implementer can set them according to actual conditions.

[0038] Extending from any of the wave peaks to both sides until the spectrum intensities on the left and right sides reach the wavelength positions corresponding to the respective preset intensity values, which are respectively recorded as the left position point and the right position point; For each preset intensity value, the distance between the left position point and the wavelength position corresponding to any peak is recorded as the left distance; the distance between the right position point and the wavelength position corresponding to any peak is recorded as the right distance; The ratio of the left distance to the right distance is recorded as the relative ratio, and the difference between the value 1 and the relative ratio is recorded as the distance difference; In this embodiment, the absolute value of the difference between the value 1 and the relative comparison is recorded as the distance difference.

[0039] For each preset intensity value, obtaining the wavelength position of the left position point symmetrical with respect to any one of the wave crests, and recording it as the symmetrical position point; It should be noted that, assuming the left position point is , the wavelength position corresponding to any peak is ,but about Symmetrical wavelength positions ,in, .

[0040] Calculate the difference between the spectral intensity corresponding to the left position point and the spectral intensity corresponding to the symmetrical position point, and record it as the intensity difference; 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 the symmetrical position point is calculated and recorded as the intensity difference.

[0041] The product of the intensity difference and the distance difference corresponding to all preset intensity values ​​of the peak is accumulated, and the accumulated sum is negatively mapped to obtain a characteristic evaluation value of the peak; In this embodiment, the specific process of negative mapping is: negative mapping is performed through an exponential function, assuming that the cumulative sum is recorded as ,Will The result of the negative mapping is, is an exponential function with a natural constant as its base.

[0042] It should be noted that, the closer the relative ratio is to 1, that is, the smaller the distance difference is, the distance between the left position point and the right position point and the center of the peak is very close, that is, the symmetry of the peak on the left and right sides at the preset intensity value is higher, and the smaller the intensity difference is, the spectral intensity of the left position point and its symmetrical position point is very close, reflecting that the symmetry on both sides of the peak is higher; the larger the obtained characteristic evaluation value, the better the symmetry of the peak, reflecting the greater the possibility that the peak is the characteristic main peak of a single microorganism; conversely, the smaller the characteristic evaluation value, the worse the symmetry of the peak, reflecting the higher the possibility that the peak is a shoulder peak formed by the coupled vibration of multiple microorganisms.

[0043] The peak whose characteristic evaluation value is greater than the preset threshold is recorded as the characteristic main peak, otherwise it is recorded as the shoulder peak; In this embodiment, the preset threshold value is 0.8. As for other implementation methods, the implementer can set it according to actual conditions.

[0044] Furthermore, characteristic peaks can usually directly reflect the type of microorganisms because they correspond to the vibration modes of specific molecular components (such as proteins, nucleic acids, or lipids). However, when multiple microorganisms coexist, the shoulder peak may contain characteristic information of other microorganisms. However, due to the high intensity of the characteristic main peak, the characteristic information of other microorganisms contained in the shoulder peak may be masked, resulting in inaccurate identification of microorganisms in the air. Therefore, it is necessary to decompose the shoulder peak and extract the characteristic peak information of the shoulder peak area, specifically: The local curve segments where the shoulder peaks are located on the corrected Raman spectrum curve are subjected to wavelet transformation and the wavelet ridge method is used to extract the characteristic peaks contained in the shoulder peaks. In this embodiment, the wavelet basis function of the wavelet transform algorithm is the Mexican hat wavelet basis, wherein the wavelet transform algorithm is a well-known technology and will not be described in detail here; the process of extracting the characteristic peaks contained in the shoulder peak is to take the curve segment between the two troughs adjacent to each shoulder peak as the local range, perform wavelet transform on the spectral intensity corresponding to all wavelengths in the local range where each shoulder peak is located, select the wavelet coefficient of the high-frequency energy for wavelet inverse transform, and use the wavelet ridge method to locate the characteristic peak position of the local curve segment after the wavelet inverse transform, and extract the characteristic peak contained in the shoulder peak, wherein the wavelet transform algorithm and the process of extracting the characteristic peak by the wavelet ridge method are well-known technologies and will not be described in detail here.

[0045] Identify the types of microorganisms based on the characteristic peaks contained in the characteristic main peak and shoulder peak; In this embodiment, the characteristic peaks contained in the characteristic main peak and shoulder peak are compared and matched with the characteristic library of known microorganisms to identify the types of microorganisms contained in the air. The process of identifying microorganisms by Raman spectroscopy is a well-known technology and will not be repeated here.

[0046] The risk assessment module is used to assess the risk of microbial contamination in public areas by measuring the proportion of pathogenic microorganisms among all microorganisms in the air.

[0047] Furthermore, based on the proportion of pathogenic microorganisms in the air, the contamination risk of microorganisms in the air is assessed, specifically: The ratio of the number of pathogenic microorganisms among all identified microorganisms to the number of all microorganisms is used as the air pollution risk coefficient; If the contamination risk coefficient is less than a preset first value, the microbial contamination in the air is at a low risk; if the contamination risk coefficient is greater than or equal to the preset first value and less than a preset second value, the microbial contamination in the air is at a medium risk; if the contamination risk coefficient is greater than or equal to a preset second value, the microbial contamination in the air is at a high risk, wherein the preset first value is less than the preset second value; In this embodiment, the preset first value is 0.3, and the preset second value is 0.6. As other implementation methods, the implementer can set them according to actual conditions.

[0048] It should be noted that the greater the pollution risk coefficient, the more pathogenic microorganisms are contained in the air of the public area, and the more serious the microbial pollution is. The flowchart of the method for obtaining the pollution risk coefficient provided in this application is as follows: Figure 2 shown.

[0049] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed 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 part of the sub-steps or stages of other steps.

[0050] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0051] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.

Claims

1. A public area microbial contamination risk assessment system based on spectral analysis, characterized by: The system comprises: A spectrum acquisition module is used to sample microorganisms contained in the air of public areas and obtain Raman spectrum curves of the microorganisms; Spectral correction module, used to perform baseline correction on Raman spectrum curve, including: Perform multiple fitting on the wavelength and its corresponding spectral intensity on the Raman spectrum curve, determine the baseline value of the Raman spectrum curve at each wavelength based on the fitting value and the change of the curvature of the Raman spectrum curve at each wavelength, remove the baseline value from the Raman spectrum curve, and obtain a corrected Raman spectrum curve; Feature analysis module, used to identify the types of microorganisms, including: Analyze the symmetry of the waveforms on both sides of each peak on the corrected Raman spectrum curve, calculate the characteristic evaluation value of each peak, distinguish all peaks, and obtain the characteristic main peak and shoulder peak; decompose the local range where the shoulder peak on the corrected Raman spectrum curve is located, and extract the characteristic peaks contained in the shoulder peak; identify the types of microorganisms in the air based on the characteristic main peak and the characteristic peaks contained in the shoulder peak; The risk assessment module is used to assess the risk of microbial contamination in public areas by measuring the proportion of pathogenic microorganisms among all microorganisms in the air.

2. The public area microbial contamination risk assessment system based on spectral analysis according to claim 1, characterized in that: The multi-factor fitting of the wavelengths and their corresponding spectral intensities on the Raman spectrum curve includes: forming a two-dimensional array of each wavelength and its corresponding spectral intensity on the Raman spectrum curve, performing cubic spline fitting on all two-dimensional arrays contained in the Raman spectrum curve, obtaining a fitting function, substituting each wavelength into the fitting function, and calculating the fitting value at each wavelength.

3. The public area microbial contamination risk assessment system based on spectral analysis according to claim 2, characterized in that: Raman spectrum curve Baseline value at wavelength The calculation formula is: in, is the preset initial spectral intensity, The Raman spectrum curve is The curvature at wavelengths, is the maximum value of the curvature of the Raman spectrum curve at all wavelengths, For the The fitted values ​​corresponding to the wavelengths.

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

5. The public area microbial contamination risk assessment system based on spectral analysis according to claim 1, characterized in that: Calculating the characteristic evaluation value of each peak includes: For any wave crest, select a plurality of preset intensity values ​​that are smaller than the peak value corresponding to the wave crest; Extending from any of the wave peaks to both sides until the spectrum intensities on the left and right sides reach the wavelength positions corresponding to the respective preset intensity values, which are respectively recorded as the left position point and the right position point; For each preset intensity value, analyzing the difference in distance between the left position point, the right position point and the wavelength position corresponding to any peak, and calculating the distance difference; For each preset intensity value, analyzing the difference in spectral intensity between the left position point and its wavelength position symmetrical with respect to any one of the wave crests, and calculating the intensity difference; The product of the intensity difference and the distance difference corresponding to all preset intensity values ​​of the any peak is accumulated and negatively mapped to obtain the characteristic evaluation value of the any peak.

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

7. The public area microbial contamination risk assessment system based on spectral analysis according to claim 5, characterized in that: The calculation process of the intensity difference is: For each preset intensity value, obtaining the wavelength position of the left position point symmetrical with respect to any one of the wave crests, and recording 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.

8. The public area microbial contamination risk assessment system based on spectral analysis according to claim 1, characterized in that: The obtaining of the characteristic main peak and shoulder peak includes: recording a peak whose characteristic evaluation value is greater than a preset threshold as a characteristic main peak, and vice versa, recording it as a shoulder peak.

9. The public area microbial contamination risk assessment system based on spectral analysis according to claim 1, characterized in that: The extracting of characteristic peaks included in the shoulder peaks comprises: performing wavelet transformation on the local curve segments where the shoulder peaks are located on the corrected Raman spectrum curve, and then extracting the characteristic peaks included in the shoulder peaks using a wavelet ridge method.

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

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