CN116698680A - Automatic monitoring method and system for biological aerosol - Google Patents
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Abstract
The application relates to the technical field of aerosol detection, in particular to a method and a system for automatically monitoring biological aerosol, wherein the method comprises the following steps: obtaining a fluorescence spectrum corresponding to the biological aerosol in the atmosphere; determining the characteristic distance of each data point according to the difference between two adjacent extreme points in the fluorescence spectrum, and processing the fluorescence spectrum by using a revolving door algorithm according to the characteristic distance to obtain different signal fitting results; obtaining a noise evaluation value and a noise distribution characteristic value of each signal fitting result; according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result, determining the best signal fitting result, and carrying out signal decomposition and recombination according to the best signal fitting result to obtain a spectrum signal after biological aerosol denoising; and obtaining a monitoring result of the biological aerosol based on the spectral signal after denoising the biological aerosol. The application can make the monitoring result of the biological sol more accurate.
Description
Technical Field
The application relates to the technical field of aerosol detection, in particular to an automatic biological aerosol monitoring method and system.
Background
With the attention of society to the quality of the atmospheric environment, research and development of a laser detection technology capable of rapidly monitoring the concentration of bioaerosol particles in the atmosphere in real time have become one of the hot research fields nowadays. Bioaerosols range in size from 10 nm virus particles to 100 μm pollen particles. The monitoring of the biological aerosols in the atmosphere has important significance in the fields of health risk assessment, environmental quality assessment, production research, climate change and the like. Therefore, the purpose of monitoring the biological aerosol is achieved by acquiring a spectrum signal of the biological aerosol in the atmosphere and performing a series of analyses on the spectrum signal to acquire the composition and the concentration of the biological aerosol. However, the spectrum signal obtained by the existing method has a lot of noise, so that the result of monitoring the aerosol by using the spectrum signal of the aerosol is less accurate.
Disclosure of Invention
In order to solve the technical problem that the result of monitoring the aerosol by utilizing the spectrum signal of the aerosol is inaccurate, the application aims to provide an automatic monitoring method for the biological aerosol, which adopts the following technical scheme:
acquiring fluorescence spectrum of fluorescence signal data of biological aerosol in the atmosphere in a set wave band;
according to the difference between two adjacent extreme points in the fluorescence spectrum, determining the characteristic distance of each data point in the fluorescence spectrum, and processing the fluorescence spectrum by using a revolving door algorithm according to the characteristic distance to obtain different signal fitting results;
according to the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process, determining the noise evaluation value of each signal fitting result; obtaining a noise distribution characteristic value of each signal fitting result according to the single-peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result;
according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result, determining the best signal fitting result, and carrying out signal decomposition and recombination according to the best signal fitting result to obtain a spectrum signal after biological aerosol denoising;
and obtaining a monitoring result of the biological aerosol based on the spectral signal after denoising the biological aerosol.
Preferably, the determining the characteristic distance of each data point in the fluorescence spectrum according to the difference between two adjacent extreme points in the fluorescence spectrum specifically includes:
for any one data point in the fluorescence spectrum, the absolute value of the difference between two extreme points adjacent to the data point in the fluorescence spectrum is taken as the characteristic distance of the data point.
Preferably, the processing the fluorescence spectrum by using a rotation gate algorithm according to the characteristic distance to obtain different signal fitting results specifically includes:
processing the fluorescence spectrum by using a revolving door algorithm, and acquiring two fulcrums in the vertical direction of an initial data point of each piece of data, wherein the vertical distance between each fulcrums and the initial data point is equal to the characteristic distance of the initial data point, so as to acquire an initial signal fitting result;
the method comprises the steps of adjusting cut-off data points of each section of data in an initial signal fitting result, respectively setting new cut-off data points to be the positions of each extreme point behind the cut-off data points, and then processing fluorescence spectrum signals by using a revolving door algorithm to obtain different signal fitting results; and the signal fitting result is a fitting curve formed by straight line segments corresponding to each segment of data.
Preferably, the determining the noise evaluation value of each signal fitting result according to the correlation between each signal fitting result and all feature distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process specifically includes:
for any signal fitting result, marking the characteristic distance used in the processing process by using a revolving door algorithm as a natural initial distance; all natural initial distances in the signal fitting result are formed into an initial distance sequence, characteristic distances of all data points in the fluorescence spectrum are formed into a characteristic distance sequence, and the slope of a straight line segment corresponding to each segment of data in the signal fitting result is formed into a characteristic slope sequence;
obtaining a similarity coefficient between the initial distance sequence and the characteristic slope sequence as a first coefficient; obtaining a similarity coefficient between the characteristic distance sequence and the characteristic slope sequence as a second coefficient; and taking the absolute value of the difference between the first coefficient and the second coefficient as a similarity difference index, and obtaining a noise evaluation value of the signal fitting result according to the similarity difference index, wherein the similarity difference index and the noise evaluation value are in a negative correlation.
Preferably, the obtaining the noise distribution characteristic value of each signal fitting result according to the single peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result specifically includes:
marking any signal fitting result as a target fitting result, marking two intersection points of a data curve of the single peak signal and a fitting curve in the target fitting result as a first intersection point and a second intersection point respectively for any single peak signal in a fluorescence spectrum, and obtaining residual energy of the single peak signal according to the data value of the single peak signal and the data value of the fitting curve in the target fitting result between the first intersection point and the second intersection point;
and counting the occurrence probability of the values of different residual energies in the residual energies of all the unimodal signals, constructing the Gaussian distribution of the residual energies of the unimodal signals, calculating kurtosis by using the Gaussian distribution, and taking the kurtosis as a noise distribution characteristic value of a target signal fitting result.
Preferably, the calculation formula of the residual energy of the unimodal signal is specifically:
wherein ,representing the residual energy of the i < th > unimodal signal, < >>Representing the first intersection>Representing a second intersection>Representing signal peaks within a unimodal signal, +.>A functional expression of a fitting curve representing the result of the target fitting.
Preferably, the determining the best signal fitting result according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result specifically includes:
for any signal fitting result, taking the sum of several noise evaluation values and noise distribution characteristic values of the signal fitting as the result evaluation value of the signal fitting result; and taking the signal fitting result corresponding to the minimum value of all the result evaluation values as the best signal fitting result.
Preferably, the performing signal decomposition and recombination according to the best signal fitting result to obtain a spectral signal after denoising the bioaerosol specifically includes:
and (3) carrying out signal decomposition on the best signal fitting result by using an EMD algorithm, and then carrying out superposition recombination on signals in the decomposition result to obtain a spectrum signal after biological aerosol denoising.
Preferably, the calculation formula of the noise evaluation value is specifically:
wherein ,noise evaluation value indicating nth signal fitting result,/->Characteristic slope sequence representing slope of straight line segment corresponding to each segment of data in nth signal fitting result,/L>Representing an initial distance sequence composed of all natural initial distances used in the fitting process corresponding to the nth signal fitting result, and L represents a characteristic distance composed of all data pointsSyndrome distance sequence, ->Representing the pearson correlation coefficient between the characteristic slope sequence and the initial distance sequence, ++>Representing pearson correlation coefficients between the characteristic slope sequence and the characteristic distance sequence.
The application also provides an automatic bioaerosol monitoring system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the automatic bioaerosol monitoring method when being executed by the processor.
The embodiment of the application has at least the following beneficial effects:
according to the application, the fluorescence spectrum corresponding to the biological aerosol in the atmosphere is firstly obtained, then the characteristic distance of each data point in the fluorescence spectrum is determined by analyzing the difference between two adjacent extreme points in the fluorescence spectrum, the E value in the rotation gate algorithm is improved by utilizing the characteristic distance, different signal fitting results are obtained, the fitting operation of the rotation gate algorithm is carried out by utilizing the characteristic distance, the interference of noise is avoided to a certain extent, the noise analysis is carried out on different signal fitting results, and the optimal result is determined. And then, by analyzing the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process, the obtained noise evaluation value can represent the noise distribution trend of the signal fitting result. Further, the unimodal signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result are analyzed, and the obtained noise distribution value is further utilized to reflect the noise distribution condition in the signal fitting result. And finally, combining the noise distribution conditions of the two aspects, determining the best signal fitting result, and can reduce the negative influence of noise on baseline fitting to the greatest extent, improve the expression of real signals, greatly improve the decomposition precision and reduce the decomposition time consumption when the method is used for a decomposition algorithm. And further, the denoising effect of different subsequent signal components is improved, so that the monitoring result of the biological sol is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a method flow chart of a bioaerosol automatic monitoring method of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of the automatic monitoring method and system for biological aerosol according to the application by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The following specifically describes a specific scheme of a method and a system for automatically monitoring a biological aerosol provided by the application with reference to the accompanying drawings.
An embodiment of a method for automatically monitoring biological aerosol:
the main purpose of the application is as follows: in the existing spectral decomposition algorithm, the acquired spectral signals of the biological aerosol in the atmosphere have noise, so that a baseline function in the decomposition process is distorted. According to the application, the optimized baseline function is obtained by improving the revolving door algorithm, so that the negative influence of noise on baseline fitting can be reduced to the greatest extent, and the expression of real signals is improved, so that when the method is used for a decomposition algorithm, the decomposition precision can be greatly improved, and the decomposition time consumption is reduced. And further, the denoising effect of different subsequent signal components is improved, so that the bioaerosol monitoring result is more reliable.
Referring to fig. 1, a flowchart of a method for automatically monitoring a bioaerosol according to an embodiment of the application is shown, the method includes the following steps:
step one, obtaining fluorescence spectrum of fluorescence signal data of biological aerosol in the atmosphere in a set wave band.
In this embodiment, the handheld bio-aerosol monitoring device is used to collect fluorescence signal data of the bio-aerosol, and the device uses the laser-induced fluorescence detection principle to realize online monitoring, that is, the method of obtaining information by exciting fluorescent dye or endogenous fluorescence in the bio-aerosol by laser, generally ultraviolet laser with the wavelength of 200-400nm, and the bio-aerosol emits the biological intrinsic fluorescence with different wavelengths under the excitation of laser pulse.
The monitoring device is used for collecting fluorescent signals and sending the fluorescent signals to the PC end to generate a fluorescent spectrum, the type, the concentration and the like of the biological aerosol in the air are obtained after the fluorescent spectrum is processed and directionally analyzed, and then the biological aerosol is sent back to the equipment end of the monitoring device to display the monitoring result. A great amount of noise exists in the fluorescence spectrum signals received by the PC end at first, denoising treatment is needed, and the treatment precision directly influences the subsequent directional analysis result. Based on this, a fluorescence spectrum of fluorescence signal data of the bioaerosol in the atmosphere can be obtained from the PC side, and in the fluorescence spectrum, the abscissa of the data points is the wavelength and the ordinate is the fluorescence intensity.
And step two, determining the characteristic distance of each data point in the fluorescence spectrum according to the difference between two adjacent extreme points in the fluorescence spectrum, and processing the fluorescence spectrum by using a revolving door algorithm according to the characteristic distance to obtain different signal fitting results.
Spectral signal data is subject to background light source interference and a large amount of noise, and physical and chemical components in a monitoring target are identified and quantitatively analyzed through decomposing signal components. Noise smoothing may be achieved by signal component decomposition in the presence of noise, but there may be cases where the decomposition process is distorted. Spectral signal data of different bioaerosols in different wave bands contains unique information such as characteristics, content and the like, and the spectral signal data has special characteristic expression, so that the distortion rate is higher. Under the condition of distortion in the decomposition process, the denoising precision is low, so that the embodiment of the application needs to optimize the spectrum signal decomposition process, improve the subsequent denoising effect and ensure the precision of the biological aerosol monitoring result.
The existing common signal component decomposition algorithms such as EMD, LMD, ITD and PCA depend on the acquisition and estimation of a baseline function, and because the acquired spectrum signals of the biological aerosol have complex characteristic signals and also have a part of noise signals, the conventional mean value function, the mean value envelope function and the baseline extraction operator are used as the baseline function and almost all are affected by the complex characteristic signal distribution, and less prominent characteristic signals are ignored, so that the baseline function has lower precision. Based on the above, the embodiment of the application obtains the baseline function with higher precision by optimizing the revolving door algorithm.
The revolving door algorithm is a quick linear fitting algorithm, two points with the upper and lower distances of E are set at the signal starting point positions as fulcrums, the connecting line between the fulcrums and each data point forms two virtual doors, the doors are closed when only one data point is added, the doors are rotated to open and can not be closed again along with the increase of the data point, the width of the doors can be extended, when the inner angles of the two doors are smaller than 180 degrees, the fitting can be continuously performed, otherwise, if the inner angles of the two doors are larger than or equal to 180 degrees, the operation is stopped, the connecting line between the starting point and the stopping point is used as a fitting result of the data section, then the stopping point is used as a new starting point, the two points with the upper and lower distances of E are set at the new starting point positions, and the fitting of the next data section is continued until all the data points in the fluorescence spectrum are processed, and a final signal fitting result is obtained.
Because the rotation gate algorithm is sensitive to noise, the characteristic signal data segments may be reserved under the condition that the noise exists, but the fitting process of processing the fluorescence spectrum by using the rotation gate algorithm is frequently stopped, that is, the fitting length of each segment of data is shorter, the number of data segments obtained by fitting is larger, and the noise information contained in the fitting result may be also larger. Therefore, the fitting process of the rotation gate algorithm needs to be regulated and controlled, that is, different natural fitting results can be obtained by adjusting the distance E value of the obtained fulcrum when data fitting is performed at each starting point position or adjusting the position of the cutoff point.
Based on this, the characteristic distance of each data point in the fluorescence spectrum is determined from the difference between two adjacent extreme points in the fluorescence spectrum, specifically, for any one data point in the fluorescence spectrum, the absolute value of the difference between two extreme points adjacent to the data point in the fluorescence spectrum is taken as the characteristic distance of the data point.
It should be noted that, for a data point at any position in the fluorescence spectrum, two extreme points adjacent to the data point exist, that is, one is a maximum point and the other is a minimum point, when the value of an empirical E value is absent in the process of processing the fluorescence spectrum signal by using the rotation gate algorithm, the difference between the data of the two extreme points adjacent to one data point can be used as the E value corresponding to the position of the data point, so that noise interference can be avoided to a certain extent.
Specifically, a fluorescence spectrum is processed by using a revolving door algorithm, two fulcrums are obtained in the vertical direction of the initial data point of each piece of data, the vertical distance between the fulcrums and the initial data point is equal to the characteristic distance of the initial data point, and an initial signal fitting result is obtained.
Because a great amount of noise exists in the fluorescence spectrum, the initial signal fitting process can be stopped because of noise amplitude mutation, the rotating gate algorithm fitting generally takes extreme points as fitting nodes, the length of each section of data in the initial signal fitting result is shorter because of the noise, the optimizing process of the initial signal fitting result is necessarily to prolong the length of each section of data of the initial signal fitting, namely, the stopping point corresponding to the starting point of each section of data is arranged behind the original stopping point, so that the noise information expression is reduced, and the trend item expression of the real spectrum signal information is increased.
Specifically, adjusting cut-off data points of each section of data in the initial signal fitting result, respectively setting new cut-off data points to be at the position of each extreme point after the cut-off data points, and then processing fluorescence spectrum signals by using a revolving door algorithm to obtain different signal fitting results; and the signal fitting result is a fitting curve formed by straight line segments corresponding to each segment of data.
For example, if there are three data segments in the initial signal fitting result, the cut-off data point of each data segment is extended to the position of the subsequent extreme point on the right side of the data segment, and the extended distance is one extreme point each time, that is, when iterative optimization is performed for the first time, the positions of the cut-off data segment points of the three data segments can be set to the position of the first extreme point after the corresponding cut-off data point, and the maximum length that the cut-off data point of each data segment can be delayed is before the cut-off data point of the next adjacent data segment. The position delay adjustment process is carried out according to the cut-off data points, the data segment behind the position is necessarily influenced by the fitting result of the previous data segment, the initial data point of the next segment changes along with the movement of the cut-off data point of the previous segment, and therefore the initial data point and the cut-off data point of the fitting process of each segment of data in the actual iteration process do not synchronously iterate, but change along with the updating of the cut-off data point of the previous segment. The cut-off data points of each piece of data can be iterated by itself within the allowable length range, and each iteration mode has different signal fitting results.
It should be noted that, in this embodiment, extending the original cut-off data point in a piece of data backward to the first extreme point means that, before the position of the first extreme point after the original cut-off data point, the operation is not stopped when the two inner angles of the two doors are equal to or greater than 180 ° in the fitting process by using the rotation gate algorithm, and the fitting is continued, and when the two inner angles of the two doors are equal to or greater than 180 ° after the fitting is performed to the first extreme point, the operation is stopped when the two inner angles of the two doors are equal to or greater than 180 ° for the first time, that is, the current data segment is updated. That is, the location of the manually updated cutoff data point is not the cutoff data point for each piece of data in the final fit result.
Step three, determining a noise evaluation value of each signal fitting result according to the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process; and obtaining the noise distribution characteristic value of each signal fitting result according to the single peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result.
For each signal fitting result, whether the signal fitting result is more prone to noise expression or more prone to baseline expression of a real signal cannot be judged directly, so that the noise condition of each signal fitting result can be obtained by analyzing the similarity between an E value used in the process of carrying out signal fitting each time and the signal fitting result and the similarity between all possible E value values and the signal fitting result, and further comparing the difference condition between the two similarities.
The E value used in each signal fitting process can be regarded as the sampled data in the feature distances of all data points, and the closer the result of signal fitting by using the feature distances is to the fitting result of all feature distances, the more approximate the result of signal fitting by using the feature distances is, which indicates that the part of feature distances used in the fitting process are the main components in all feature distances, namely the main components corresponding to the signal surface burr noise signals. The smaller the difference between the two signals is, the larger the signal fitting result shows the direct interference result of noise, when the best signal fitting result is obtained, the larger the difference result is needed to be obtained, and the signal fitting result with the lowest correlation with the surface burr noise signal is obtained, namely the baseline fitting result of the real spectrum signal.
Specifically, for any signal fitting result, the characteristic distance used in the process of processing by using a revolving door algorithm is recorded as a natural initial distance; all natural initial distances in the signal fitting result are formed into an initial distance sequence, characteristic distances of all data points in the fluorescence spectrum are formed into a characteristic distance sequence, and the slopes of straight line segments corresponding to each segment of data in the signal fitting result are formed into a characteristic slope sequence.
It should be noted that, the signal fitting result includes a plurality of data segments, and the initial data point and the cut-off data point connecting each data segment form a straight line segment corresponding to the data of the segment, so as to obtain the slope of the straight line segment of each data segment, that is, the straight line segments of all the data segments form a corresponding fitting curve.
Obtaining a similarity coefficient between the initial distance sequence and the characteristic slope sequence as a first coefficient; obtaining a similarity coefficient between the characteristic distance sequence and the characteristic slope sequence as a second coefficient; and taking the absolute value of the difference between the first coefficient and the second coefficient as a similarity difference index, and obtaining a noise evaluation value of the signal fitting result according to the similarity difference index, wherein the similarity difference index and the noise evaluation value are in a negative correlation.
In this embodiment, taking the nth fitting process as an example, the calculation formula of the noise evaluation value of the nth signal fitting result may be expressed as:
wherein ,noise evaluation value indicating nth signal fitting result,/->Characteristic slope sequence representing slope of straight line segment corresponding to each segment of data in nth signal fitting result,/L>Representing an initial distance sequence composed of all natural initial distances used in the fitting process corresponding to the nth signal fitting result, L representing a characteristic distance sequence composed of characteristic distances of all data points,/>Representing the pearson correlation coefficient between the characteristic slope sequence and the initial distance sequence, ++>Representing pearson correlation coefficients between the characteristic slope sequence and the characteristic distance sequence.
And as the first coefficient, the similarity coefficient between the characteristic slope sequence and the initial distance sequence is represented, and the correlation coefficient between the signal fitting result and the natural initial distance is reflected. />And the second coefficient is used for representing the similarity coefficient between the characteristic slope sequence and the characteristic distance sequence, reflecting the correlation coefficient between the signal fitting result and all the characteristic distances and representing the influence degree of noise on the signal fitting result.
And the larger the difference between the correlation coefficient of the natural initial distance to the signal fitting result and the correlation coefficient of the noise to the signal fitting result is, the larger the difference is, namely the smaller the corresponding noise evaluation value is.
Note that, the maximum value of the absolute value of the difference between the first coefficient and the second coefficient is 2, so that in this embodimentNormalization is carried out, and then the normalization result is subtracted by 1, and negative correlation mapping is carried out. The noise evaluation value reflects the noise condition of the signal fitting result, and the smaller the value is, the less noise distribution is in the signal fitting result.
Further, in the signal fitting process corresponding to each signal fitting result, the fixed integral of the residual signal surrounded by the revolving door can be regarded as residual information of noise after the baseline function is removed, so that the idea that the independent signal component has non-gaussian property in the ICA principal component decomposition algorithm can be utilized, namely, the noise part is not the principal component of the spectrum signal, and further, the Gaussian characteristic of the residual information of noise is analyzed, and the best revolving door fitting result can be obtained.
And marking any one signal fitting result as a target fitting result, marking two intersection points of a data curve of the single peak signal and a fitting curve in the target fitting result as a first intersection point and a second intersection point respectively for any single peak signal in the fluorescence spectrum, and obtaining residual energy of the single peak signal according to the data value of the single peak signal and the data value of the fitting curve in the target fitting result between the first intersection point and the second intersection point.
In this embodiment, taking the ith single peak signal as an example and describing the z signal result as the target fitting result, the calculation formula of the residual energy of the ith single peak signal can be expressed as:
wherein ,representing the residual energy of the i < th > unimodal signal, < >>Representing the first intersection>Representing a second intersection>Representing signal peaks within a unimodal signal, +.>A functional expression of the fitted curve representing the z-th signal fitting result, i.e. a functional expression of the fitted curve of the target fitting result,/for>Representing the determination of the integral.
The residual information after the baseline of the fitting result of the z-th signal is removed by reflecting the i-th single peak value signal, namely the noise characterization condition at the i-th single peak value is characterized.
And counting the occurrence probability of the values of different residual energies in the residual energies of all the unimodal signals, calculating the average value and standard deviation of the distribution probability of all the residual energies, further constructing the Gaussian distribution of the residual energies of the unimodal signals, calculating kurtosis by using the Gaussian distribution, and taking the kurtosis as a noise distribution characteristic value of a fitting result of the target signal.
In this embodiment, taking the z-th signal result as the target fitting result and taking the distribution probability of the u-th residual energy as an example for explanation, the kurtosis calculation formula may be expressed as follows:
wherein ,noise distribution characteristic value, i.e. kurtosis, representing the z-th signal fitting result>The number of kinds of values representing residual energy corresponding to the z-th signal fitting result, ++>Representing the probability of distribution of the u-th residual energy,/->Andmean and standard deviation in the gaussian distribution of residual energy corresponding to the z-th signal fitting result are respectively represented.
Kurtosis is commonly used to evaluate the gaussian nature of a data distribution, the closer the value is to 0, the stronger the value is represented by Gao Sixing, and as the distribution of noise does not have the gaussian nature, namely, the smaller the value of the characteristic value of the noise distribution is, the smaller the corresponding noise distribution in the z-th signal fitting result is, the larger the value of the characteristic value of the noise distribution is, and the more the corresponding noise distribution in the z-th signal fitting result is.
And step four, determining the best signal fitting result according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result, and carrying out signal decomposition and recombination according to the best signal fitting result to obtain the spectral signal after biological aerosol denoising.
The noise evaluation value of the signal fitting result reflects the noise condition of the signal fitting result, and the smaller the value is, the smaller the noise distribution in the signal fitting result is, and the larger the value is, the more the noise distribution in the signal fitting result is. The noise distribution characteristic value reflects the noise distribution condition in the signal fitting result from the aspect of the Gaussian distribution of noise, namely, the smaller the value of the noise distribution characteristic value is, the stronger the corresponding data Gaussian is, the smaller the corresponding noise distribution in the z-th signal fitting result is, the larger the value of the noise distribution characteristic value is, the weaker the corresponding data Gaussian is, and the more the corresponding noise distribution in the z-th signal fitting result is.
Based on the above, the noise distribution condition of each signal fitting result is analyzed by combining the two aspects to obtain the result evaluation value of the final signal fitting result, and then the optimal signal fitting result is determined. Specifically, for any one signal fitting result, taking the sum of several noise evaluation values and noise distribution characteristic values of the signal fitting as the result evaluation value of the signal fitting result; and taking the signal fitting result corresponding to the minimum value of all the result evaluation values as the best signal fitting result.
Further, the fitting function of the fitting curve in the best signal fitting result is substituted into the EMD algorithm to replace the original envelope function to perform signal component decomposition, the original signal is subtracted from the baseline function to obtain a first component signal, the baseline function is further used as a new input signal, and a new baseline function is obtained by using the rotation gate algorithm. The process of signal component decomposition using the EMD algorithm is known in the art and will not be described in detail herein.
After the decomposition result is obtained, the decomposition result is required to be subjected to smoothing treatment, and then signals in the decomposition result are subjected to superposition recombination to obtain spectral signals after biological aerosol denoising. The denoised spectrum signal can eliminate the interference of other light components in a complex environment. The method for reconstructing the decomposed signal includes algorithms such as fourier transform and wavelet transform, which are known techniques, and will not be described in detail in this embodiment.
And fifthly, obtaining a monitoring result of the biological aerosol based on the spectral signal after denoising the biological aerosol.
After the relatively accurate spectrum signal of the biological aerosol after denoising is obtained, a series of parameters such as the concentration, the composition, the transmissivity and the like of the biological aerosol can be monitored more accurately, and the parameters are fed back to the handheld monitoring equipment through the PC end, so that the air quality visualization of the monitored environment is realized. Among them, there are various methods for analyzing relevant parameters of a bioaerosol by using spectral signals in the prior art, for example, the author is Shao Yu, and the paper name is research on an atmospheric bioaerosol component identification method based on fluorescence spectrum, which discloses how to identify components of an atmospheric bioaerosol based on spectral information.
In summary, the method utilizes the initial signal fitting result of the revolving door algorithm and combines the difference condition between two similarities to judge whether the signal fitting result is more prone to noise expression or more prone to baseline expression of real signals, then updates the cut-off points of all single-section fitting results in a non-synchronous iteration mode, utilizes the fitting result of each iteration and the original fluorescence spectrum information to obtain the kurtosis of residual energy Gaussian distribution of burr noise, evaluates the distribution Gaussian of the residual energy by using the kurtosis, and minimizes the trend rate and the kurtosis of the fitting result to noise by iteration revolving door single-fitting cut-off points until convergence to obtain the best fitting result. And substituting the signal components into a decomposition algorithm to optimize the decomposition result of the signal components. The negative influence of noise on baseline fitting can be reduced to the greatest extent through the optimized baseline function, and the expression of real signals is improved, so that when the real signals are used for a decomposition algorithm, the decomposition precision can be greatly improved, and the decomposition time consumption is reduced. And further, the denoising effect of different subsequent signal components is improved, so that the monitoring result of the biological sol is more reliable.
An embodiment of a bioaerosol automatic monitoring system:
the embodiment provides an automatic bioaerosol monitoring system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the automatic bioaerosol monitoring method when being executed by the processor. Since one embodiment of a method for automatically monitoring a bioaerosol has been described in detail, it will not be described in detail.
An embodiment of a spectral signal data denoising method of a biological aerosol comprises the following steps:
with the attention of society to the quality of the atmospheric environment, research and development of a laser detection technology capable of rapidly monitoring the concentration of bioaerosol particles in the atmosphere in real time have become one of the hot research fields nowadays. Bioaerosols range in size from 10 nm virus particles to 100 μm pollen particles. The monitoring of the biological aerosols in the atmosphere has important significance in the fields of health risk assessment, environmental quality assessment, production research, climate change and the like. Therefore, the purpose of monitoring the biological aerosol is achieved by acquiring a spectrum signal of the biological aerosol in the atmosphere and performing a series of analyses on the spectrum signal to acquire the composition and the concentration of the biological aerosol. However, the spectrum signal obtained by the existing method has a large amount of noise, so that the spectrum signal of the biological aerosol needs to be subjected to denoising treatment before the biological aerosol is monitored. The existing median filtering denoising method is greatly influenced by a selected window, and is easy to cause poor denoising effect on spectrum signals of biological aerosol.
In order to solve the technical problem of poor denoising effect on the spectrum signal of the biological aerosol, the purpose of the embodiment is to provide a denoising method for spectrum signal data of the biological aerosol, which adopts the following specific technical scheme:
step one, obtaining fluorescence spectrum of fluorescence signal data of biological aerosol in the atmosphere in a set wave band;
step two, determining the characteristic distance of each data point in the fluorescence spectrum according to the difference between two adjacent extreme points in the fluorescence spectrum, and processing the fluorescence spectrum by using a revolving door algorithm according to the characteristic distance to obtain different signal fitting results;
step three, determining a noise evaluation value of each signal fitting result according to the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process; obtaining a noise distribution characteristic value of each signal fitting result according to the single-peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result;
and step four, determining the best signal fitting result according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result, and carrying out signal decomposition and recombination according to the best signal fitting result to obtain the spectral signal after biological aerosol denoising.
Since the steps one to four have been described in detail in the embodiment of the method for automatically monitoring a bioaerosol, they are not described in detail herein.
The embodiment of the application provides a spectral signal data denoising method of biological aerosol, which has the following technical effects:
according to the application, the fluorescence spectrum corresponding to the biological aerosol in the atmosphere is firstly obtained, then the characteristic distance of each data point in the fluorescence spectrum is determined by analyzing the difference between two adjacent extreme points in the fluorescence spectrum, the E value in the rotation gate algorithm is improved by utilizing the characteristic distance, different signal fitting results are obtained, the fitting operation of the rotation gate algorithm is carried out by utilizing the characteristic distance, the interference of noise is avoided to a certain extent, the noise analysis is carried out on different signal fitting results, and the optimal result is determined. And then, by analyzing the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process, the obtained noise evaluation value can represent the noise distribution trend of the signal fitting result. Further, the unimodal signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result are analyzed, and the obtained noise distribution value is further utilized to reflect the noise distribution condition in the signal fitting result. And finally, combining the noise distribution conditions of the two aspects, determining the best signal fitting result, and can reduce the negative influence of noise on baseline fitting to the greatest extent, improve the expression of real signals, greatly improve the decomposition precision and reduce the decomposition time consumption when the method is used for a decomposition algorithm. And further improves the denoising effect of different subsequent signal components.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application and are intended to be included within the scope of the application.
Claims (10)
1. A method for automatically monitoring a bioaerosol, the method comprising the steps of:
acquiring fluorescence spectrum of fluorescence signal data of biological aerosol in the atmosphere in a set wave band;
according to the difference between two adjacent extreme points in the fluorescence spectrum, determining the characteristic distance of each data point in the fluorescence spectrum, and processing the fluorescence spectrum by using a revolving door algorithm according to the characteristic distance to obtain different signal fitting results;
according to the correlation between each signal fitting result and all characteristic distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process, determining the noise evaluation value of each signal fitting result; obtaining a noise distribution characteristic value of each signal fitting result according to the single-peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result;
according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result, determining the best signal fitting result, and carrying out signal decomposition and recombination according to the best signal fitting result to obtain a spectrum signal after biological aerosol denoising;
and obtaining a monitoring result of the biological aerosol based on the spectral signal after denoising the biological aerosol.
2. The method for automatically monitoring a bioaerosol according to claim 1, wherein the determining the characteristic distance of each data point in the fluorescence spectrum according to the difference between two adjacent extreme points in the fluorescence spectrum specifically comprises:
for any one data point in the fluorescence spectrum, the absolute value of the difference between two extreme points adjacent to the data point in the fluorescence spectrum is taken as the characteristic distance of the data point.
3. The method for automatically monitoring the biological aerosol according to claim 1, wherein the processing the fluorescence spectrum by using a turnstile algorithm according to the characteristic distance obtains different signal fitting results, and specifically comprises the following steps:
processing the fluorescence spectrum by using a revolving door algorithm, and acquiring two fulcrums in the vertical direction of an initial data point of each piece of data, wherein the vertical distance between each fulcrums and the initial data point is equal to the characteristic distance of the initial data point, so as to acquire an initial signal fitting result;
the method comprises the steps of adjusting cut-off data points of each section of data in an initial signal fitting result, respectively setting new cut-off data points to be the positions of each extreme point behind the cut-off data points, and then processing fluorescence spectrum signals by using a revolving door algorithm to obtain different signal fitting results; and the signal fitting result is a fitting curve formed by straight line segments corresponding to each segment of data.
4. A method for automatically monitoring a bioaerosol according to claim 3, wherein the determining the noise evaluation value of each signal fitting result according to the correlation between each signal fitting result and all feature distances and the correlation between each signal fitting result and the vertical distances of two doors in the corresponding data processing process specifically comprises:
for any signal fitting result, marking the characteristic distance used in the processing process by using a revolving door algorithm as a natural initial distance; all natural initial distances in the signal fitting result are formed into an initial distance sequence, characteristic distances of all data points in the fluorescence spectrum are formed into a characteristic distance sequence, and the slope of a straight line segment corresponding to each segment of data in the signal fitting result is formed into a characteristic slope sequence;
obtaining a similarity coefficient between the initial distance sequence and the characteristic slope sequence as a first coefficient; obtaining a similarity coefficient between the characteristic distance sequence and the characteristic slope sequence as a second coefficient; and taking the absolute value of the difference between the first coefficient and the second coefficient as a similarity difference index, and obtaining a noise evaluation value of the signal fitting result according to the similarity difference index, wherein the similarity difference index and the noise evaluation value are in a negative correlation.
5. The method for automatically monitoring a bioaerosol according to claim 3, wherein the obtaining the noise distribution characteristic value of each signal fitting result according to the single peak signal data in the fluorescence spectrum and the data distribution condition of each signal fitting result specifically comprises:
marking any signal fitting result as a target fitting result, marking two intersection points of a data curve of the single peak signal and a fitting curve in the target fitting result as a first intersection point and a second intersection point respectively for any single peak signal in a fluorescence spectrum, and obtaining residual energy of the single peak signal according to the data value of the single peak signal and the data value of the fitting curve in the target fitting result between the first intersection point and the second intersection point;
and counting the occurrence probability of the values of different residual energies in the residual energies of all the unimodal signals, constructing the Gaussian distribution of the residual energies of the unimodal signals, calculating kurtosis by using the Gaussian distribution, and taking the kurtosis as a noise distribution characteristic value of a target signal fitting result.
6. The method for automatically monitoring a bioaerosol according to claim 5, wherein the calculation formula of the residual energy of the unimodal signal is specifically as follows:
wherein ,representing the residual energy of the i < th > unimodal signal, < >>Representing the first intersection>Representing a second intersection>Representing signal peaks within a unimodal signal, +.>A functional expression of a fitting curve representing the result of the target fitting.
7. The method for automatically monitoring a bioaerosol according to claim 1, wherein the determining the best signal fitting result according to the noise evaluation value and the noise distribution characteristic value of each signal fitting result specifically comprises:
for any signal fitting result, taking the sum of several noise evaluation values and noise distribution characteristic values of the signal fitting as the result evaluation value of the signal fitting result; and taking the signal fitting result corresponding to the minimum value of all the result evaluation values as the best signal fitting result.
8. The method for automatically monitoring the biological aerosol according to claim 1, wherein the steps of performing signal decomposition and recombination according to the best signal fitting result to obtain the spectral signal after the biological aerosol is denoised comprise the following steps:
and (3) carrying out signal decomposition on the best signal fitting result by using an EMD algorithm, and then carrying out superposition recombination on signals in the decomposition result to obtain a spectrum signal after biological aerosol denoising.
9. The method for automatically monitoring a biological aerosol according to claim 4, wherein the calculation formula of the noise evaluation value is specifically:
wherein ,noise evaluation value indicating nth signal fitting result,/->Characteristic slope sequence representing slope of straight line segment corresponding to each segment of data in nth signal fitting result,/L>Representing the correspondence of the nth signal fitting resultAn initial distance sequence of all natural initial distances used in the fitting process of (1), L representing a characteristic distance sequence of characteristic distances of all data points,/c>Representing pearson correlation coefficients between the characteristic slope sequence and the initial distance sequence,representing pearson correlation coefficients between the characteristic slope sequence and the characteristic distance sequence.
10. A bioaerosol automatic monitoring system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program when executed by the processor implements the steps of a bioaerosol automatic monitoring method as claimed in any one of claims 1-9.
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