CN113984730A - Remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis - Google Patents
Remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis Download PDFInfo
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- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
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- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
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Abstract
The invention discloses a remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis, which relates to the technical field of aerosol detection and comprises the following steps: a biological aerosol fluorescence spectrum database is established by using the multi-channel signals, so that the characteristic information of various biological aerosols in the database is enriched, and the accuracy of biological aerosol identification is improved; in the process of identifying and early warning the bioaerosol to be detected, the multi-wavelength fluorescence detection signal and the Mi scattering signal are utilized to eliminate the influence of obstacle interference and the like of a corresponding wavelength channel on the fluorescence detection signal, the corrected fluorescence detection signal is dispersed into a fluorescence spectrum signal with a corresponding wavelength by a spectrometer and is integrated and recorded to form spectrum data of the bioaerosol to be detected under different wavelength lasers; and then, the biological aerosol to be detected is analyzed and identified by utilizing a cloud database technology through a spectrum identification algorithm and combining multi-wavelength spectrum data, so that more accurate and rapid biological aerosol identification is realized.
Description
Technical Field
The invention relates to the technical field of aerosol detection, in particular to a remote sensing biological aerosol pollution alarm method based on cloud fluorescence data analysis.
Background
Biological aerosols are ubiquitous in the atmosphere, and are a wide variety of biologically active substances, including airborne bacteria, fungi, viruses, dust mites, pollen spores, and biologically active minute particles, such as broken bodies of animals and plants. The bioaerosol has great hidden danger to the survival of human beings and animals, bacteria, viruses and other biological germs can be propagated and spread in the atmosphere, and the longer the survival time is, the greater the hazard is; biological aerosol can generate fluorescence effect under the induction of laser, and most non-biological aerosol can not generate fluorescence effect, so that the biological aerosol can be distinguished from the non-biological aerosol. The organic matter compositions of different bioaerosols are different, the fluorescence spectrum of the organic matter with single component can not be changed due to the wavelength of the exciting light, and for more complex bioaerosols such as fungi, bacteria, virus pollen, spores and the like, the organic matter compositions are composed of fluorescent molecules such as tryptophan, riboflavin, tyrosine and the like with large proportion and structure differences. Therefore, the fluorescence spectrum of the bioaerosols is formed by overlapping the fluorescence spectra generated by various fluorescent molecules, and the fluorescence spectra of different bioaerosols are different;
the laser-induced fluorescence radar can detect the bioaerosol efficiently, accurately and in real time, and becomes an efficient measurement means. However, most of the related researches on laser-induced bioaerosol detection currently stay on pure multi-channel detection, database establishment and single organic matter identification, a complete set of multi-channel remote sensing bioaerosol pollution identification and alarm method based on a cloud database does not exist, and a cloud identification technology based on a fluorescence spectrum database and multi-channel fluorescence and rice signal detection is blank. Therefore, a remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis is provided.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis.
The purpose of the invention can be realized by the following technical scheme:
a remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis comprises the following steps:
the method comprises the following steps: establishing a biological aerosol fluorescence spectrum database by using the multi-channel signal;
step two: performing fluorescence detection on the bioaerosol to be detected through a detection instrument to obtain spectral data of the bioaerosol to be detected; the method specifically comprises the following steps:
performing a fluorescence excitation experiment on the bioaerosol to be detected by using the multi-wavelength laser adopted in the first step, and receiving a fluorescence detection signal and a Mie scattering signal with corresponding wavelengths;
the interference of obstacles of corresponding wavelength channels is eliminated by utilizing the meter scattering signals, and the influence of geometric factors and system noise on the fluorescence detection signals is corrected;
dispersing the corrected fluorescence detection signals of each wavelength channel into fluorescence spectrum signals of the bioaerosol to be detected with corresponding wavelengths by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the bioaerosol to be detected under different wavelength lasers;
step three: uploading the spectral data of the bioaerosol to be detected to a cloud end, and analyzing and identifying the bioaerosol to be detected by using multi-wavelength spectral data through a spectral identification algorithm to obtain an identification result; the identification result comprises the type and the attribute of the bioaerosol to be detected;
judging whether the aerosol is the target aerosol according to the identification result; and if the biological aerosol to be detected belongs to the target aerosol, the detecting instrument gives an alarm.
Further, the establishing step of the bioaerosol fluorescence spectrum database comprises the following steps:
s11: collecting a plurality of bioaerosol samples needing to be added into a database;
s12: carrying out fluorescence excitation experiment of multi-wavelength laser on the collected bioaerosol sample, dispersing multi-wavelength fluorescence detection signals into fluorescence spectrum signals of the bioaerosol sample excited by corresponding wavelength by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the sample under different wavelength lasers;
s13: and uploading the spectral data of the sample to a cloud end, and storing the spectral data in a bioaerosol fluorescence spectrum database.
Further, the cloud end is used for transmitting the identification result back to the detection instrument, and the detection instrument judges whether the target aerosol exists according to the transmitted identification result; the detection instrument is also used for transmitting the identification result to a display screen for real-time display; the detection instrument stores target aerosol information, and the target aerosol information comprises the type and the attribute of the target aerosol.
Further, before performing the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample in step S12, the method further includes: carrying out experiment coefficient analysis on the detection personnel who receive the experiment task, and selecting the detection personnel with the largest experiment coefficient as the selected personnel; the method specifically comprises the following steps:
v1: marking the testers receiving the experiment tasks as primary-selected personnel; acquiring the detection level of the primary selection personnel, and marking the detection value corresponding to the detection level as G1;
v2: collecting the task records of the primary selection personnel in a preset time period, counting the task times of the primary selection personnel and marking as the task frequency C1;
calculating the time difference between two adjacent moments of getting the experimental tasks to obtain a task interval Ti, i is 1, …, n; forming a task interval information group; where Tn represents the last task interval; namely the time difference between the current time of getting the experimental task and the previous time of getting the experimental task;
calculating to obtain the standard deviation mu of the task interval information group according to a standard deviation calculation formula, and if the mu is less than or equal to a preset standard deviation threshold value, utilizing the formula
Obtaining an interval deviation value PZ of the current experimental task; if mu is larger than a preset standard deviation threshold value, solving the mode of the task interval information set in a mode of mode rule; calculating the difference between the mode and Tn to obtain an interval deviation value PZ;
v3: calculating an experimental coefficient SY of a primary candidate by using a formula SY which is (G1 × a1+ C1 × a2)/(PZ × a3), wherein a1, a2 and a3 are coefficient factors;
and selecting the detection personnel with the largest experiment coefficient as the selected personnel, and carrying out the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample by the selected personnel.
Further, the mode rule is: taking any element in the task interval information group as a center, counting the number of the elements with the time difference within a preset value, and marking the number as the coincidence number of the corresponding elements; and taking the element with the most coincident number as the mode of the task interval information group.
Further, the spectrometer is replaced with a grating, FP etalon or other light splitting device.
Further, the spectrum identification algorithm is a spectrum matching method.
Further, before the fluorescence detection of the bioaerosol to be detected by the detection instrument in the step two, the method further comprises the following steps: detecting coefficient analysis is carried out on the detecting personnel who receive the detecting task, the detecting personnel with the largest detecting coefficient is selected as the selected personnel, and the selected personnel carry out fluorescence detection on the bioaerosol to be detected through a detecting instrument; wherein the analysis method of the detection coefficient is consistent with the experimental coefficient.
Furthermore, the detecting instrument is further used for uploading target aerosol information to a cloud end, the cloud end judges whether the target aerosol is the target aerosol according to the identification result, if the to-be-detected biological aerosol belongs to the target aerosol, an alarm signal is generated and sent back to the detecting instrument, and the detecting instrument sends out an alarm after receiving the alarm signal.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention utilizes the multi-channel signal to establish a biological aerosol fluorescence spectrum database, integrates the fluorescence spectrum data of multiple wavelengths of the same biological aerosol sample, enriches the characteristic information of multiple biological aerosols in the database and improves the accuracy of biological aerosol identification;
2. in the process of identifying and early warning the bioaerosol to be detected, the multi-wavelength fluorescence detection signal and the rice scattering signal are utilized, the obstacle interference of a corresponding wavelength channel is eliminated, and the influence of geometric factors, system noise and the like on the fluorescence detection signal is corrected, so that the spectral data of the bioaerosol to be detected under different wavelength lasers is obtained, the comprehensiveness and reliability of the spectral data are ensured, and the bioaerosol identification efficiency and accuracy are improved;
3. according to the invention, the bioaerosol to be detected is analyzed and identified by utilizing multi-wavelength spectral data and combining a spectral identification algorithm through the cloud fluorescence spectral database, so that the bioaerosol is identified more accurately and rapidly, meanwhile, before a fluorescence detection or fluorescence excitation experiment is carried out, detection coefficients or experiment coefficients are analyzed for detection personnel who receive tasks, the detection personnel with the largest detection coefficients or experiment coefficients are selected as selected personnel, and then the selected personnel carry out the fluorescence detection or fluorescence excitation experiment, so that the detection efficiency or experiment efficiency is effectively improved, and the identification efficiency of the bioaerosol is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a flow chart of establishing a fluorescence spectrum database of bioaerosol according to the present invention.
Fig. 3 is a flow chart of bioaerosol identification and alarm in the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 3, the remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis includes the following steps:
the method comprises the following steps: establishing a biological aerosol fluorescence spectrum database by using a multi-channel signal, which specifically comprises the following steps:
s11: collecting a plurality of bioaerosol samples needing to be added into a database;
s12: carrying out fluorescence excitation experiment of multi-wavelength laser on the collected bioaerosol sample, dispersing multi-wavelength fluorescence detection signals into fluorescence spectrum signals of the bioaerosol sample excited by corresponding wavelength by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the sample under different wavelength lasers; wherein the multi-wavelength laser comprises wavelength 1, wavelength 2, wavelength 3, wavelength 4, and the like;
s13: uploading the spectral data of the sample to a cloud end, and storing the spectral data in a bioaerosol fluorescence spectrum database;
step two: performing fluorescence detection on the bioaerosol to be detected through a detection instrument to obtain spectral data of the bioaerosol to be detected; the method specifically comprises the following steps:
s21: performing a fluorescence excitation experiment on the bioaerosol to be detected by using the multi-wavelength laser adopted in the step S12, and receiving a fluorescence detection signal and a Mie scattering signal with corresponding wavelengths;
s22: the interference of obstacles of corresponding wavelength channels is eliminated by utilizing the meter scattering signals, and the influence of geometric factors, system noise and the like on the fluorescence detection signals is corrected;
in this embodiment, when the fluorescence detection signal is corrected by using the mie scattering signal, various methods can be adopted, but the main purpose is to eliminate an abnormal signal introduced by an obstacle, correct the influence of a geometric factor on the signal, smooth and filter system noise, and reduce the influence of the system noise on the fluorescence detection signal;
s23: dispersing the corrected fluorescence detection signals of each wavelength channel into fluorescence spectrum signals of the bioaerosol to be detected with corresponding wavelengths by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the bioaerosol to be detected under different wavelength lasers;
in this embodiment, in the process of dispersing the fluorescence detection signal into the fluorescence spectrum signal, the use of the spectrometer is not limited, and other light splitting devices such as a grating, an FP etalon, and the like can also be used;
step three: uploading spectral data of the bioaerosol to be detected under laser with different wavelengths to a cloud end, and analyzing and matching the spectral data of the bioaerosol to be detected with sample spectral data stored in a bioaerosol fluorescence spectrum database by the cloud end through a spectral recognition algorithm to obtain a recognition result; the identification result is the type and the attribute of the bioaerosol to be detected;
in this embodiment, the spectrum identification algorithm may adopt a spectrum matching method and other spectrum identification algorithms;
step four: the cloud end transmits the identification result back to the detection instrument, and the detection instrument judges whether the aerosol is the target aerosol according to the transmitted identification result; in the present embodiment, the target aerosol is a bioaerosol which has a great risk to human and animal survival; the detection instrument stores target aerosol information, and the target aerosol information comprises the type and the attribute of the target aerosol;
if the biological aerosol to be detected belongs to the target aerosol, the detection instrument gives an alarm;
the detection instrument is also used for transmitting the identification result to a display screen for real-time display, so that detection personnel can conveniently check the condition and take measures, and the personal safety is improved;
in another embodiment of the invention, the detection instrument is used for uploading spectral data of the bioaerosol to be detected under different wavelength lasers and target aerosol information to the cloud end together, and the cloud end analyzes and identifies the bioaerosol to be detected by using multi-wavelength spectral data through a spectral identification algorithm to obtain an identification result; comparing the identification result with the target aerosol information, if the to-be-detected bioaerosol belongs to the target aerosol, judging that an alarm is needed, and transmitting the information needing the alarm to a detection instrument;
the detection instrument sends out an alarm after receiving the information needing to be alarmed;
in this embodiment, before performing the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample in step S12, the method further includes: an administrator issues an experiment task on an experiment platform, and a detector accesses the experiment platform through a mobile phone terminal and receives the experiment task;
in this embodiment, the method further includes: carrying out experiment coefficient analysis on the detection personnel who receive the experiment task, and selecting the detection personnel with the largest experiment coefficient as the selected personnel; the method comprises the following specific steps:
v1: marking the testers receiving the experiment tasks as primary-selected personnel;
acquiring the detection level of the primary selection personnel, and marking the detection value corresponding to the detection level as G1; the higher the detection level is, the larger the detection value is, and the stronger the capability of a detector is represented;
v2: collecting task records of the primary selection personnel in a preset time period, wherein the task records carry the moment of getting the experimental task each time; for example, thirty days before the current time of the system is taken in a preset time period;
counting the task times of the primary selection personnel and marking as the task frequency C1;
calculating the time difference between two adjacent moments of getting the experiment task to obtain a task interval T i, i is 1, …, n; forming a task interval information group; wherein Tn represents the last task interval, namely the time difference between the current experimental task getting time and the previous experimental task getting time;
calculating to obtain a standard deviation mu of the task interval information group according to a standard deviation calculation formula, and if the mu is less than or equal to a preset standard deviation threshold value, obtaining an interval deviation value PZ of the current experimental task according to a deviation value calculation formula; the deviation value calculation formula is as follows:
if mu is larger than a preset standard deviation threshold value, solving the mode of the task interval information set in a mode of mode rule; calculating the difference between the mode of the task interval information group and Tn to obtain an interval deviation value PZ;
the mode rule is as follows: counting the data quantity of the time difference in the task interval information group within a preset value by taking any data in the task interval information group as a center, and marking the data quantity as the coincidence number of the corresponding data; taking the data with the most coincident numbers as the mode of the task interval information group;
v3: carrying out normalization processing on the detection value G1, the task frequency C1 and the interval bias value PZ, and taking the numerical values, and calculating the experimental coefficient SY of the primary selected person by using a formula SY (G1 × a1+ C1 × a2)/(PZ × a3), wherein a1, a2 and a3 are coefficient factors; wherein the smaller the interval deviation value is, the larger the experiment coefficient is;
the task interval is too large, so that the working efficiency is easily influenced due to the carelessness of the operation; if the task interval is too small, fatigue is easy to accumulate, errors are caused, and the working efficiency is also influenced; therefore, the task interval is kept in a certain range to be optimal; the smaller the interval deviation value PZ is, the closer the task interval is to the ideal value at the moment;
selecting the detection personnel with the largest experiment coefficient as the selected personnel, and carrying out the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample by the selected personnel;
in this embodiment, before performing fluorescence detection on the bioaerosol to be detected by the detection instrument in the second step, the method further includes: the administrator issues the detection tasks on the experiment platform, and the detection personnel access the experiment platform through the mobile phone terminal and get the detection tasks;
in this embodiment, the method further includes: carrying out detection coefficient analysis on the detection personnel who receive the detection tasks, and selecting the detection personnel with the maximum detection coefficient as the selected personnel; carrying out fluorescence detection on the bioaerosol to be detected by a selected person through a detection instrument; wherein the analysis method of the detection coefficient is consistent with the experimental coefficient.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
a remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis comprises the steps of firstly establishing a bioaerosol fluorescence spectrum database by using a multi-channel signal when in work; collecting a plurality of bioaerosol samples needing to be added into a database; then, an administrator issues an experiment task on an experiment platform, a detection person with the largest experiment coefficient is selected as a selected person, the selected person performs a fluorescence excitation experiment of multi-wavelength laser on the collected bioaerosol sample to obtain spectral data of the sample under lasers with different wavelengths, and the spectral data are stored in a fluorescence spectral database on a cloud; a database is established by utilizing the multi-channel signals, so that the characteristic information of various bioaerosols in the database is enriched, and the accuracy of bioaerosols identification is improved;
then, the administrator issues a detection task on the experimental platform, selects the detection personnel with the largest detection coefficient as the selected personnel, performs fluorescence detection on the bioaerosol to be detected through a detection instrument, eliminates the obstacle interference of a corresponding wavelength channel by using a multi-wavelength fluorescence detection signal and a Mi scattering signal, and corrects the influence of geometrical factors, system noise and the like on the fluorescence detection signal, thereby obtaining the spectral data of the bioaerosol to be detected under different wavelength lasers; the cloud end analyzes and identifies the bioaerosol to be detected by using the multi-wavelength spectral data and combining a fluorescence spectrum database through a spectral identification algorithm, and the detection instrument judges whether the bioaerosol is a target aerosol or not according to the returned identification result and judges whether an alarm is required or not; so that the identification of the bioaerosol is more accurate and rapid.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis is characterized by comprising the following steps:
the method comprises the following steps: establishing a biological aerosol fluorescence spectrum database by using the multi-channel signal;
step two: performing fluorescence detection on the bioaerosol to be detected through a detection instrument to obtain spectral data of the bioaerosol to be detected; the method specifically comprises the following steps:
performing a fluorescence excitation experiment on the bioaerosol to be detected by using the multi-wavelength laser adopted in the first step, and receiving a fluorescence detection signal and a Mie scattering signal with corresponding wavelengths;
the interference of obstacles of corresponding wavelength channels is eliminated by utilizing the meter scattering signals, and the influence of geometric factors and system noise on the fluorescence detection signals is corrected;
dispersing the corrected fluorescence detection signals of each wavelength channel into fluorescence spectrum signals of the bioaerosol to be detected with corresponding wavelengths by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the bioaerosol to be detected under different wavelength lasers;
step three: uploading the spectral data of the bioaerosol to be detected to a cloud end, and analyzing and identifying the bioaerosol to be detected by using multi-wavelength spectral data through a spectral identification algorithm to obtain an identification result;
judging whether the aerosol is the target aerosol according to the identification result; and if the biological aerosol to be detected belongs to the target aerosol, the detecting instrument gives an alarm.
2. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 1, wherein the bioaerosol fluorescence spectrum database is established by the following steps:
s11: collecting a plurality of bioaerosol samples needing to be added into a database;
s12: carrying out fluorescence excitation experiment of multi-wavelength laser on the collected bioaerosol sample, dispersing multi-wavelength fluorescence detection signals into fluorescence spectrum signals of the bioaerosol sample excited by corresponding wavelength by using a spectrometer, and integrating and recording the fluorescence spectrum signals to form spectrum data of the sample under different wavelength lasers;
s13: and uploading the spectral data of the sample to a cloud end, and storing the spectral data in a bioaerosol fluorescence spectrum database.
3. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 1, wherein the cloud is used for transmitting the identification result back to a detection instrument, and the detection instrument judges whether the target bioaerosol is the target bioaerosol according to the transmitted identification result; the detecting instrument is also used for transmitting the identification result to a display screen for real-time display.
4. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 2, wherein before performing the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample in step S12, the method further comprises: carrying out experiment coefficient analysis on the detection personnel who receive the experiment task, and selecting the detection personnel with the largest experiment coefficient as the selected personnel; the method specifically comprises the following steps:
v1: marking the testers receiving the experiment tasks as primary-selected personnel; acquiring the detection level of the primary selection personnel, and marking the detection value corresponding to the detection level as G1;
v2: collecting the task records of the primary selection personnel in a preset time period, counting the task times of the primary selection personnel and marking as the task frequency C1;
calculating the time difference between two adjacent moments of getting the experimental tasks to obtain a task interval Ti, i is 1, …, n; forming a task interval information group; where Tn represents the last task interval;
calculating to obtain the standard deviation mu of the task interval information group according to a standard deviation calculation formula, and if the mu is less than or equal to a preset standard deviation threshold value, utilizing the formula
Obtaining an interval deviation value PZ of the current experimental task; if mu is larger than a preset standard deviation threshold value, solving the mode of the task interval information set in a mode of mode rule; calculating the difference between the mode and Tn to obtain an interval deviation value PZ;
v3: calculating an experimental coefficient SY of a primary candidate by using a formula SY which is (G1 × a1+ C1 × a2)/(PZ × a3), wherein a1, a2 and a3 are coefficient factors;
and selecting the detection personnel with the largest experiment coefficient as the selected personnel, and carrying out the fluorescence excitation experiment of the multi-wavelength laser on the collected bioaerosol sample by the selected personnel.
5. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 4, wherein the mode rule is as follows: taking any element in the task interval information group as a center, counting the number of the elements with the time difference within a preset value, and marking the number as the coincidence number of the corresponding elements; and taking the element with the most coincident number as the mode of the task interval information group.
6. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 1, wherein a spectrometer is replaced by a grating, an FP etalon or other light splitting devices.
7. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 1, wherein the spectral recognition algorithm is a spectral matching method.
8. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 4, wherein in the second step, before fluorescence detection is performed on bioaerosol to be detected through a detection instrument, the method further comprises: detecting coefficient analysis is carried out on the detecting personnel who receive the detecting task, the detecting personnel with the largest detecting coefficient is selected as the selected personnel, and the selected personnel carry out fluorescence detection on the bioaerosol to be detected through a detecting instrument; wherein the analysis method of the detection coefficient is consistent with the experimental coefficient.
9. The remote sensing bioaerosol pollution alarm method based on cloud fluorescence data analysis of claim 1, wherein the detection instrument is further used for uploading target aerosol information to a cloud, the cloud judges whether the target aerosol is the target aerosol according to the identification result, if the bioaerosol to be detected belongs to the target aerosol, an alarm signal is generated and sent back to the detection instrument, and the detection instrument sends out an alarm after receiving the alarm signal.
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