CN115220132A - Method for forecasting pollen concentration in atmosphere - Google Patents
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Abstract
The invention provides a method for forecasting pollen concentration in atmosphere, which belongs to the field of analysis and forecasting and mainly realizes data analysis, model training, risk forecasting and data display by using data such as weather, pollen, geographic information and the like. Compared with other current methods for preventing meteorological sensitive diseases, the method can realize automatic identification of pollen types, forecast pollen content according to different geographic positions by combining longitude and latitude, and provide reasonable prevention suggestions.
Description
Technical Field
The invention relates to the field of analysis and prediction, in particular to a method for predicting pollen concentration in atmosphere, which utilizes data such as weather, pollen, geographic information and the like to realize data analysis, model training and risk prediction, can effectively avoid high pollen incidence areas and seasons and take preventive measures in advance.
Background
Weather, climate and atmospheric environment are closely related to human health, climate change can affect human health in stages through multiple ways, for example, heat waves, floods and the like can directly cause human discomfort and casualties, so that the regional death rate is obviously increased or infectious diseases are popular, and water supply, agricultural production, food safety and the spread of vector infectious diseases and the like can be indirectly affected. In short, the regulation of various physiological functions of the human body is related to meteorological factors, and acute changes of the weather exceed the tolerance range of the human body, so that various diseases, such as cardiovascular and cerebrovascular diseases, respiratory diseases, infectious diseases, heatstroke, mental diseases and the like, can be caused. The onset, exacerbation or attenuation of these diseases is susceptible to weather or climate change and has specific seasonal and climatic characteristics, hence the name of weather-sensitive diseases. The frequency of extreme climatic events resulting from global warming has increased year by year, exacerbating the extent, extent and uncertainty of occurrence of such diseases. PM2.5, O3 and other atmospheric pollutants can affect the occurrence of cardiovascular and cerebrovascular diseases, respiratory diseases and other diseases, and are easy to aggravate in high-temperature or cold-tide weather. The meteorological-environmental interaction has obvious promotion effect on the occurrence and development of meteorological sensitive diseases. The method aims at the field that some research is carried out at home and abroad at present, enough research results and quantitative analysis evidences are still lacked, meanwhile, the research results are obviously influenced by geography and crowd characteristics and cannot be subjected to universal conclusion, foreign or foreign models cannot be simply carried, and a disease weather risk prediction model which meets local reality and local requirements needs is established by combining with local crowd health, weather and environment data so as to improve the practicability of health weather prediction, early warning and evaluation services.
Pollen in the air is one of the major aeroallergens that induce respiratory diseases. Along with the increase of urban greening degree, the number of patients with pollinosis caused by plant pollen allergy is increased year by year, and the pollen allergy disease becomes one of seasonal and epidemic diseases in the world. For most patients, the method effectively avoids the high pollen incidence areas and seasons, takes preventive measures in advance, and is the most effective prevention and treatment means at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for forecasting the concentration of pollen in the atmosphere, which reduces the development range of meteorological sensitive diseases, prompts sensitive people to avoid high pollen emergence periods and areas, and practically reduces the medical expenditure cost of the whole society.
The technical scheme of the invention is as follows:
a method for forecasting pollen concentration in the atmosphere comprises the steps of automatically collecting pollen samples to generate image information, storing the image information and site information in a database in an associated mode, carrying out model training by utilizing collected pollen data to achieve artificial intelligent identification of pollen, forming a regression equation model of the pollen concentration by combining historical data, and achieving prediction of pollen indexes.
Further, in the above-mentioned case,
sampling pollen and preparing into a glass slide; the automatic collection of the pollen image is realized by utilizing the ARM chip to program and control the free movement of the glass slide and the generation of the digital image of the CMOS video sensor.
By combining pollen concentration data observed under corresponding meteorological conditions in history and carrying out model training through an artificial intelligence platform, a regression equation model related to the pollen concentration, the area, the time and the meteorological factors is formed, and the pollen concentration is predicted.
And calculating the pollen concentration index by observing the calculated pollen concentration data or the predicted pollen concentration data and combining the influence degree of the pollen type and the pollen concentration on the human health.
In a still further aspect of the present invention,
the method comprises the steps of automatically acquiring pollen images from a microscope, training a pollen automatic identification, classification and counting model by using an image identification algorithm of an artificial intelligence platform, automatically acquiring category information and quantity of various pollens on the acquired images, calculating the concentration of the pollens on the basis, training a regression equation model for pollen concentration prediction by using an artificial intelligence weather prediction algorithm and combining weather historical information, and predicting the pollen concentration and type by combining weather forecast information.
The pollen sampling device is a positive displacement pollen collector, airflow is fixed every minute, the atmospheric pollen particles are adsorbed on the polyester film and are replaced once a week, the time resolution is day by day, the collected samples are made into glass slides by using glycerinated gelatin, and the glass slides are placed under a microscope for identification, and the types and the number of pollen families are counted.
Automatically storing the generated pollen image into a system in an interface mode after the image information is collected, and associating the pollen image with a meteorological observation station collected by a pollen glass slide; and a basic pollen collection picture library is formed, and a data basis is provided for subsequent pollen automatic identification, automatic counting, automatic classification and automatic concentration calculation.
On the collected brand new pollen picture, the outline of the pollen in the picture is sketched, and the types of the pollen are classified to form a standard set for artificial intelligent automatic pollen recognition model training;
after the pollen picture is automatically identified and classified by artificial intelligence, the artificial intelligence error label is corrected. The corrected types comprise three types of pollen which is not recognized in the picture, pollen which is not recognized in the picture and recognized pollen classification errors.
The invention has the advantages that
Compared with the common solution, the method can analyze the induction mechanism of the abnormal change of weather and climate on local related diseases based on weather big data, environment big data and disease big data, and the degree of influence of the severe changes of different types of weather and climate on certain disease patients, and can carry out scientific prevention and early warning and advanced accurate intervention on the weather sensitive diseases of the fragile people such as the old, children and the like. The pollen intelligent observation is developed aiming at the increasing number of people with pollinosis, the pollen concentration is forecasted by combining with meteorological conditions, and early warning is carried out on susceptible people.
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FIG. 1 is a schematic workflow diagram of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, it is obvious that the described embodiments are some, but not all embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
The invention provides a method for forecasting pollen concentration in the atmosphere, which mainly uses data such as weather, pollen, geographic information and the like to realize data analysis, model training, risk forecasting and data display. Compared with other current methods for preventing weather-sensitive diseases, the method can automatically identify the pollen type, predict the pollen content according to different geographic positions by combining longitude and latitude, and provide a reasonable prevention suggestion.
1. The pollen information is that the atmospheric pollen sampling device is usually a positive-displacement pollen collector, the airflow is fixed every minute, the atmospheric pollen particles are adsorbed on the polyester film and are changed once a week, the time resolution is day by day, the collected samples are made into glass slides by using glycerin glue and are put under a high-power microscope for identification, the types and the quantity of pollen families are counted, the adopted atmospheric pollen sampling device is an international universal sampler, the standard sampling and laboratory processing procedures are followed, and the pollen is identified by the experiential spore powder identification personnel, so that the data are real and reliable.
2, ARM + CMOS automatic collection: a simple numerical control mechanical ARM and a CMOS video sensor are additionally arranged on the basis of a commonly used binocular biological optical microscope of a station, and the ARM chip is used for programming to control the free movement of a glass slide and the generation of a digital image of the CMOS video sensor, so that the automatic collection of a pollen image is realized.
3. Meteorological data: the data range is conventional meteorological observation data of meteorological stations of each country in nearly 10 years, and comprises temperature, dew point, air pressure, wind direction, wind speed, total cloud amount, low cloud amount, visibility, 24-hour variable pressure, weather phenomenon, highest daily temperature, lowest daily temperature, 24-hour precipitation, sunshine hours, evaporation capacity and the like, and weather situation, circulation index and other data.
4. The observation point data is associated with the image data: and automatically storing the generated pollen image into the system in an interface mode after the image information is acquired, and associating the pollen image with a meteorological observation station acquired by the pollen slide. And forming a basic pollen collection picture library, and providing a data basis for subsequent pollen automatic identification, automatic counting, automatic classification and automatic concentration calculation.
5. Model training and pollen recognition: in the system, pollen picture labeling needs to be carried out in two steps. Both steps need to be performed by a person with abundant expertise in sporoology. In the first step, a professional draws the pollen outline in the collected brand-new pollen picture through a mouse on a PC or a touch pen on a PAD by using professional knowledge through a system, and classifies the type of the pollen to form a standard set for artificial intelligent automatic pollen recognition model training. In the other step, after the pollen picture is automatically identified and classified by artificial intelligence, a professional checks the identification effect and corrects the artificial intelligence error label. The corrected types comprise three types of pollen which is not recognized in the picture, pollen which is not recognized in the picture and recognized pollen classification errors.
6. And (3) machine learning algorithm: the pollen concentration is every 1000mm in the air within twenty-four hours 2 The number of pollen grains contained. At present, the pollen concentration is mainly forecasted by a statistical forecasting method, and the influence on the pollen is very complicated because meteorological factors and the pollen concentration are not in a linear relation. Therefore, a machine learning algorithm is selected and used for analyzing pollen concentration time distribution, correlation between pollen concentration and meteorological factors, a pollen concentration peak-to-peak period and a pollen concentration peak-to-peak period, and model training is carried out through an artificial intelligence platform by combining factors which influence the pollen concentration obviously, such as air temperature, relative humidity, air speed and the like in historical data and influence the pollen concentration under corresponding meteorological conditions in the history, so as to form a regression equation model related to the pollen concentration, the area, the time and the meteorological factors.
7. Pollen forecasting: the system calculates the pollen concentration index according to the pollen concentration data obtained by observation and calculation or the predicted pollen concentration data and according to the meteorological industry standard by combining the influence degree of the pollen type and the pollen concentration on the human health. The index can be calculated respectively according to different pollen concentrations and different crowds, and is provided for other systems facing resident release in an API mode, so that the influence of the type and concentration of the pollen of the residents on the crowds with different pollen syndromes is reminded, and the high-pollen-incidence areas and seasons are avoided.
The invention adopts a three-layer system structure, which is divided into: 1. a presentation layer (User Interface layer); 2. business Logic Layer (Business Logic Layer); 3. data Access layer (Data Access layer).
The invention adopts the deployment and development framework based on the micro-service, can support the quick version replacement, realizes the low-cost capacity expansion and elastic expansion, adapts to the cloud environment and maximizes the utilization rate of system resources. Support main stream UNIX, linux, windows Server series operating system, meet the installation deployment under different Server environments. The construction system adopts a centralized deployment principle and is uniformly deployed on the private cloud platform. The mobile phone APP application end supports mainstream system versions such as Android and IOS and is adaptive to different hardware devices.
The above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (9)
1. A method for forecasting the pollen concentration in the atmosphere is characterized in that,
the method comprises the steps of automatically collecting pollen samples to generate image information, storing the image information and site information in a related mode, conducting model training by using collected pollen data to achieve artificial intelligent identification of pollen, forming a regression equation model of pollen concentration by combining historical data, and achieving prediction of pollen indexes.
2. The method of claim 1,
sampling pollen and making into a glass slide; the automatic collection of the pollen image is realized by utilizing the ARM chip to program and control the free movement of the glass slide and the generation of the digital image of the CMOS video sensor.
3. The method of claim 2,
by combining pollen concentration data observed under corresponding meteorological conditions in history and carrying out model training through an artificial intelligence platform, a regression equation model related to the pollen concentration, the area, the time and the meteorological factors is formed, and the pollen concentration is predicted.
4. The method of claim 3,
and calculating the pollen concentration index by observing the calculated pollen concentration data or the predicted pollen concentration data and combining the influence degree of the pollen type and the pollen concentration on the human health.
5. The method of claim 4,
the method comprises the steps of automatically acquiring pollen images from a microscope, training a pollen automatic identification, classification and counting model by using an image identification algorithm of an artificial intelligence platform, automatically acquiring category information and quantity of various pollens on the acquired images, calculating the concentration of the pollens on the basis, training a regression equation model for pollen concentration prediction by using an artificial intelligence weather prediction algorithm and combining weather historical information, and predicting the pollen concentration and type by combining weather forecast information.
6. The method of claim 5,
the pollen sampling device is a positive displacement pollen collector, the airflow is fixed every minute, the atmospheric pollen particles are adsorbed on the polyester film and replaced once a week, the time resolution is day by day, the collected samples are made into slides by glycerol glue, and the slides are placed under a microscope for identification, and the types and the number of the pollen families are counted.
7. The method of claim 6,
automatically storing the generated pollen image into a system in an interface mode after the image information is collected, and associating the pollen image with a meteorological observation station collected by a pollen glass slide; and a basic pollen collection picture library is formed, and a data basis is provided for subsequent pollen automatic identification, automatic counting, automatic classification and automatic concentration calculation.
8. The method of claim 7,
on the collected brand-new pollen picture, sketching the pollen outline in the picture, and classifying the pollen types to form a standard set for artificial intelligent automatic pollen recognition model training;
after the pollen picture is automatically identified and classified by artificial intelligence, the artificial intelligence error label is corrected.
9. The method of claim 8,
the corrected types comprise three types of pollen which is not recognized in the picture, pollen which does not exist in the picture and recognized pollen classification errors.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114492936A (en) * | 2021-12-28 | 2022-05-13 | 三峡新能源海上风电运维江苏有限公司 | Wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast |
| CN115828794A (en) * | 2023-01-21 | 2023-03-21 | 北京科技大学 | Method and device for predicting pollen concentration of trees under urban scale |
| CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimized tree pollen concentration index |
| JP7368916B1 (en) * | 2023-02-14 | 2023-10-25 | ファームコネクト カンパニー リミテッド | Pollen identification device using learning |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126699A (en) * | 2007-09-27 | 2008-02-20 | 中国气象局北京城市气象研究所 | Pollen grain image data collecting system |
| CN101398381A (en) * | 2008-11-12 | 2009-04-01 | 南京农业大学 | Fluorescence labeling method for pear pollen and pollen tube fibril framework |
| CN202512042U (en) * | 2012-02-16 | 2012-10-31 | 南京信息工程大学 | Automatic real-time image acquirer of genetically modified crop pollen |
| CN103868842A (en) * | 2014-02-11 | 2014-06-18 | 中国农业科学院蜜蜂研究所 | Method for detecting quantity of stigma pollens after pollination of flowering plants |
| CN112435214A (en) * | 2020-10-21 | 2021-03-02 | 北京工业大学 | Pollen detection method and device based on prior frame linear scaling and electronic equipment |
| US20210125035A1 (en) * | 2019-10-28 | 2021-04-29 | International Business Machines Corporation | Cognitive decision platform for honey value chain |
-
2022
- 2022-07-04 CN CN202210776909.0A patent/CN115220132A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126699A (en) * | 2007-09-27 | 2008-02-20 | 中国气象局北京城市气象研究所 | Pollen grain image data collecting system |
| CN101398381A (en) * | 2008-11-12 | 2009-04-01 | 南京农业大学 | Fluorescence labeling method for pear pollen and pollen tube fibril framework |
| CN202512042U (en) * | 2012-02-16 | 2012-10-31 | 南京信息工程大学 | Automatic real-time image acquirer of genetically modified crop pollen |
| CN103868842A (en) * | 2014-02-11 | 2014-06-18 | 中国农业科学院蜜蜂研究所 | Method for detecting quantity of stigma pollens after pollination of flowering plants |
| US20210125035A1 (en) * | 2019-10-28 | 2021-04-29 | International Business Machines Corporation | Cognitive decision platform for honey value chain |
| CN112435214A (en) * | 2020-10-21 | 2021-03-02 | 北京工业大学 | Pollen detection method and device based on prior frame linear scaling and electronic equipment |
Non-Patent Citations (3)
| Title |
|---|
| 徐景先 等: "空气花粉变化规律和预测预报研究进展", 《生态学报》, vol. 29, no. 7, pages 3856 - 3857 * |
| 曾世涌;张晓霞;许险艳;许昭俊;欧阳昱晖;: "泉州市洛江区城区春季气传花粉调查分析", 中国耳鼻咽喉头颈外科, no. 04 * |
| 黄赐璇, 陈志清, 马瑞: "空气中致敏花粉的定量研究", 地理科学进展, no. 03 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114492936A (en) * | 2021-12-28 | 2022-05-13 | 三峡新能源海上风电运维江苏有限公司 | Wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast |
| CN114492936B (en) * | 2021-12-28 | 2024-09-06 | 三峡新能源海上风电运维江苏有限公司 | Wind farm fan flying wadding invasion early warning method based on numerical weather forecast |
| CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimized tree pollen concentration index |
| CN115828794A (en) * | 2023-01-21 | 2023-03-21 | 北京科技大学 | Method and device for predicting pollen concentration of trees under urban scale |
| JP7368916B1 (en) * | 2023-02-14 | 2023-10-25 | ファームコネクト カンパニー リミテッド | Pollen identification device using learning |
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