WO2020179961A1 - Système de service pour prédire les maladies et insectes nuisibles de plantes et maladie d'animal et d'être humain transmissible - Google Patents

Système de service pour prédire les maladies et insectes nuisibles de plantes et maladie d'animal et d'être humain transmissible Download PDF

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Publication number
WO2020179961A1
WO2020179961A1 PCT/KR2019/003556 KR2019003556W WO2020179961A1 WO 2020179961 A1 WO2020179961 A1 WO 2020179961A1 KR 2019003556 W KR2019003556 W KR 2019003556W WO 2020179961 A1 WO2020179961 A1 WO 2020179961A1
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animals
infectious diseases
humans
pests
occurrence
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PCT/KR2019/003556
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English (en)
Korean (ko)
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손인식
전지형
김현주
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주식회사 넥스모스
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Publication of WO2020179961A1 publication Critical patent/WO2020179961A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G13/00—Protecting plants
    • A01G13/10—Devices for affording protection against animals, birds or other pests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
    • G09B29/003—Maps
    • G09B29/006—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes
    • G09B29/007—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes using computer methods

Definitions

  • the present invention relates to a system for predicting plant pests and infectious diseases of animals and humans, and in more detail, by reflecting factors involved in the occurrence of plant pests and infectious diseases of animals and humans, the occurrence of diseases on specific regions and specific crops It relates to a system for predicting plant pests and infectious diseases of animals and humans, which informs the control method according to the occurrence of hyperinfectious diseases.
  • the present invention has been conceived to solve the above problems, and an object of the present invention is to provide a system capable of predicting the occurrence of pests and infectious diseases through signs of occurrence before the onset of pests and infectious diseases, and providing a control method according thereto. .
  • the present invention includes a control module that analyzes predicted area information of plant pests and infectious diseases of animals and humans and provides them to a user terminal, and the control module receives real-time weather information from the server. By comparing the weather information received afterwards with information on plant pests and infectious disease occurrence requirements of animals and humans, it is characterized by identifying the predicted areas of plant pests and infectious diseases of animals and humans. Provide infectious disease prediction service system.
  • control module is characterized in that the display of the predicted area of the plant pests and infectious diseases of animals and humans is displayed on a map and transmitted to the user terminal.
  • the control module extracts values of elements included in real-time weather information received from the server, and extracts the values of the elements included in the real-time meteorological information received from the server and It is characterized by analyzing the ranking of meteorological factors involved in the occurrence of infectious diseases in animals and humans, and grasping the range of pests of plants and areas where infectious diseases of animals and humans are expected according to the analyzed ranking.
  • the meteorological elements are characterized in that they include temperature, humidity, sunlight, and rainfall.
  • the ranking analysis is performed by determining the time and location of the occurrence of the specific pest and infectious disease from the data on the occurrence of pests and infectious diseases of animals and humans of a specific plant, and the meteorological factors at that time at the location. After calculating the average value of the pest occurrence data, the difference ratio between the element value of the pest occurrence data and the average value is calculated, and ranking is given in the order of the difference ratio.
  • control module is characterized in that, among the real-time weather information received from the server, a first priority factor value out of a range of a first difference ratio is set as a first danger area.
  • the control module includes a value of the first priority factor in the real-time weather information received from the server being the first difference ratio range and the value of the second priority factor deviating from the second difference ratio range. It is characterized in that the area is set as the second danger area.
  • the first danger zone and the second danger zone are displayed to be distinguished from each other on a map.
  • control module is characterized in that the area overlapping with each other among the first risk area and the second risk area is set as a predicted area for occurrence of plant pests and infectious diseases of animals and humans.
  • control module is characterized in that it transmits information related to the corresponding pest and infectious disease to a user located in an area where the plant pests and infectious diseases of animals and humans are expected to occur.
  • the present invention by providing a system that can predict the occurrence of plant pests and infectious diseases in animals and humans according to climate change, it becomes possible to prepare for changes in agricultural productivity due to climate change and to establish appropriate measures suitable for climate characteristics and environment. .
  • 1 to 3 are exemplified diagrams showing a disease occurrence pattern according to environmental changes of peach bacterial hole disease, which is one of the infectious diseases of plants.
  • FIG. 4 is a conceptual diagram of a system for predicting infectious diseases of plants and animals and humans according to an embodiment of the present invention.
  • FIG. 5 is a conceptual diagram of a service for predicting plant pests and infectious diseases of animals and humans displayed on a user terminal according to an embodiment of the present invention.
  • FIG. 6 is a flowchart illustrating a method of generating a map for predicting plant pests and infectious diseases of animals and humans according to an embodiment of the present invention.
  • FIG. 7 is a flow chart illustrating a method of providing information related to a pest to a user according to another embodiment of the present invention.
  • FIG. 8 is a schematic diagram of development of a system for predicting infectious diseases of plants and animals and humans according to an embodiment of the present invention.
  • FIG. 9 is a conceptual diagram showing a map for predicting infectious diseases in animals and humans and pests of plants generated according to an exemplary embodiment of the present invention.
  • Example 1 Confirmation of disease occurrence pattern due to environmental change
  • 1 to 3 are exemplified diagrams illustrating a disease occurrence pattern according to environmental changes of peach bacterial hole disease, one of the infectious diseases of plants.
  • the purpose of this study is to predict the occurrence of diseases according to changes in weather and environment by using the characteristics in which infectious diseases appear differently depending on temperature, precipitation, and humidity.
  • 1 to 3 are data on the occurrence of peach disease in Cheongdo-gun, the region where peaches are cultivated the most in Korea. Values such as temperature, precipitation, relative humidity, average wind speed, and maximum instantaneous wind speed are changes in temperature in Miryang, a region close to Cheongdo-gun. It was collected from the Meteorological Agency data.
  • Bacterial hole disease which mainly occurs in peaches, is an infectious disease caused by bacteria and is characterized by a strong wind or heavy rainfall.
  • the incidence of bacterial hole disease was significantly higher than in 2014-2015. This was a strong rain and wind blowing at an instantaneous maximum wind speed of around 20m/sec in early July 2016, and in other regions for 10 days in early May.
  • Rainfall (Fig. 1; red circle shape, orange line graph) measured twice as much as that of 70 mm was analyzed as the cause of bacterial pore disease.
  • the pattern of infectious diseases can be changed according to environmental conditions such as temperature, humidity, wind speed, rainfall, etc., and disease damage can be reduced by predicting the time of occurrence of the disease using various weather data and regional environmental changes.
  • Example 2 Prediction system for plant pests and human infectious diseases
  • FIG. 4 is a conceptual diagram of a system for predicting plant pests and infectious diseases of animals and humans according to an embodiment of the present invention.
  • User terminals include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation, slate PCs, and tablet PCs.
  • PDAs personal digital assistants
  • PMPs portable multimedia players
  • PC tablet PCs.
  • PC ultrabook
  • wearable device e.g., smartwatch, smart glass, head mounted display (HMD)
  • HMD head mounted display
  • Fixed terminals such as computers and digital signage may be included.
  • the system 100 may include a control module 110, a data collection module 120, a database 130, and the like.
  • a control module 110 may include a data collection module 120, a database 130, and the like.
  • each module is classified and described, but it is possible that the actual control module 110 is formed to include functions of other modules.
  • the control module 110 may generate a parameter value for predicting plant pests and infectious diseases of animals and humans by separating data elements and setting weights for each element. In order to generate such a parameter value, the control module 110 may include a machine learning module.
  • the data collection module 120 may collect meteorological information data exposed online or information data related to pests of plants and infectious diseases of animals and humans, and provide them to the control module 110.
  • the data collection module 120 may collect meteorological information, pests of plants, and infectious diseases of animals and humans through a website or a search portal of an institution such as the Meteorological Administration and Rural Development Administration.
  • the database 130 stores various types of data for driving the system 100.
  • the database 130 includes parameter values for predicting plant pests and infectious diseases of animals and humans, statistics of past weather information, plant pests and animals and human infectious disease area information, plant pests and animals, and Data such as human infectious disease requirement information, data collected through the data collection module 120, and processed data through the control module 110 may be stored.
  • FIG. 5 is a conceptual diagram of a service for predicting plant pests and infectious diseases of animals and humans displayed on a user terminal according to an embodiment of the present invention.
  • contents delivered from a system may be displayed on a user terminal.
  • the state image information 11 of the crop or animal, location information 12, crop information 13, a list of pests that can occur in the crop, and a list of infectious diseases that can occur in animals and humans 14
  • Possibility of occurrence of pests and infectious diseases of animals and humans control methods in case of occurrence of pests and infectious diseases of animals and humans (15), temperature index (16), humidity index (17), and the current situation is related to crop growth or animal growth. Whether it is suitable (18), etc. may be indicated.
  • At least some of these contents may be displayed only on the terminal of a specific user under the control of the system.
  • control module may analyze the possibility of occurrence of pests of specific plants and infectious diseases of animals and humans based on the collected information, and then send a notification to a user terminal located in an area where the probability of occurrence is higher than a certain value.
  • FIG. 6 is a flow chart illustrating a method of generating a pest prediction map according to an embodiment of the present invention
  • FIG. 7 is a flow chart related to plant pests and infectious diseases of animals and humans to a user according to another embodiment of the present invention. It is a flow chart explaining a method of providing information
  • FIG. 8 is a schematic diagram of the development of a system for predicting plant pests and infectious diseases of animals and humans according to an embodiment of the present invention
  • FIG. 9 is It is a conceptual diagram showing a map for predicting plant pests and infectious diseases in animals and humans.
  • the system receives real-time meteorological information (S110), and compares the received weather observation data with pest and infectious disease occurrence requirement information to identify a predicted area of damage.
  • Step S120 and a step S130 of displaying a predicted area of damage on a map are performed.
  • the system receives weather observation data (S210), compares the received weather observation data with past weather observation statistics (S220), and weather observation data. Selecting an area in which the value of is more than a certain difference from the past weather observation statistics value (S230), and transmitting information related to expected pests and infectious diseases to a user in the area (S240) may be performed.
  • control module 110 may receive real-time provided data (humidity, temperature, amount of sunlight, amount of rainfall, air volume, etc.) through the data collection module 120 (see FIG. 8). .
  • the control module 110 is in real time Compare the data received in the step of receiving weather information (S110) with the data stored in the database 130 (meteorological agency data, pest data, infectious disease data, regional crop and animal data, pest and infectious disease control database data, etc.) In this way, the predicted areas of occurrence of pests and infectious diseases are analyzed.
  • control module can display plant pests and areas where infectious diseases of animals and humans are expected to occur on a map (see FIG. 9).
  • the control module 110 includes weather and regional characteristics, whether the environment has changed from the past, types of host plants, physiological characteristics of pathogens, pests of specific plants and the onset of infectious diseases of animals and humans, frequency of occurrence, pesticide use information, etc. Based on this, comprehensive modeling is performed.
  • control module collects real-time weather information from the server and then extracts the values of the elements included in the meteorological information, and the weather related to the occurrence of each damage from the information on the occurrence of plant pests and infectious diseases of animals and humans. It may further include analyzing the ranking of the elements, and determining a range of a predicted area of occurrence of plant pests and infectious diseases of animals and humans according to the ranking.
  • control module extracts data values for temperature, humidity, and sunlight from real-time weather information as respective elements, and determines which factors have a greater effect on the occurrence of pests of each plant and infectious diseases of animals and humans. Judge.
  • control module sets the risk area according to the ranking of the factors affecting the pests of the plant and infectious diseases of animals and humans.
  • control module predicts the area of occurrence of pests and infectious diseases by prioritizing factors that have a greater influence.
  • the control module 110 may check the region and time of occurrence of pests and infectious diseases. After that, the control module calculates the average value of the weather elements in the area.
  • the average value of the weather factors may mean the average of data values for all periods in the database. For example, when climate information for 20 years in the Seoul area is stored in the database, the control module can calculate the average of the temperature in January in the Seoul area. In this way, the control module calculates the average value of each element, such as temperature, humidity, and amount of sunlight, in the corresponding region at the corresponding time, and then assigns the ranking in the order of the highest percentage deviating from the average value.
  • the control module uses the data in the database to determine the average temperature, average humidity, average sunlight, and average rainfall in Busan in September. Calculate. If the calculated result shows that the temperature in the Busan area in September 2018 was 2% different from the average temperature in September in the Busan area (hereinafter referred to as the'first difference ratio range'), the humidity was 1% difference (hereinafter referred to as'the second difference ratio range'). When it is confirmed that there was a difference ratio range), the system selects the'temperature element' with a higher ratio deviating from the average value as the first priority, and the small'humidity element' that deviates from the average value as the second priority.
  • the control module sets a region in which other elements of the real-time climate information are within the difference range but the first priority element (temperature element) is out of the first difference ratio range as the first danger area.
  • the first priority factor temperature factor
  • the second priority factor humidity factor
  • control module displays the first danger zone and the second danger zone on the map, and sends a notification of the risk of plant diseases and pests and infectious diseases of animals and humans to the user terminal located in the corresponding area.
  • the system may collect real-time weather information through users' terminals.
  • weather information of a region in which the terminal is located may be additionally collected through a temperature sensor, a humidity sensor, and a photosensitizing sensor installed in the user terminal, and the weather information data may be verified or supplemented.
  • the plant pest and animal and human infectious disease prediction service system described above is not limited to the configuration and method of the above-described embodiments, and the embodiments are all or part of each of the embodiments so that various modifications can be made. It may be configured in combination selectively.

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Abstract

La présente invention concerne un système de service pour prédire des maladies et des insectes nuisibles de plantes et des maladies transmissibles d'animaux et d'êtres humains, le système de service comprenant un module de commande pour analyser des informations sur des régions présagées concernant l'apparition de maladies et d'insectes nuisibles de plantes et de maladies transmissibles d'animaux et d'êtres humains, le module de commande recevant des informations météorologiques en temps réel d'un serveur et comparant les informations météorologiques reçues et les informations sur les dispositions concernant l'apparition de maladies et d'insectes nuisibles de plantes et de maladies transmissibles d'animaux et d'êtres humains pour déterminer une région où l'apparition de maladies et d'insectes nuisibles de plantes et de maladies transmissibles d'animaux et d'êtres humains est prédite.
PCT/KR2019/003556 2019-03-05 2019-03-27 Système de service pour prédire les maladies et insectes nuisibles de plantes et maladie d'animal et d'être humain transmissible WO2020179961A1 (fr)

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KR1020190025389A KR20200106764A (ko) 2019-03-05 2019-03-05 식물의 병해충과 동물 및 인간의 감염성 질병 예측 서비스 시스템
KR10-2019-0025389 2019-03-05

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Cited By (3)

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CN112903010A (zh) * 2021-01-15 2021-06-04 启源渔业科技有限公司 一种智能化的病虫害监测预防方法
CN114677115A (zh) * 2022-03-29 2022-06-28 北京健卫病媒有害生物防控中心 除虫灭鼠资质服务等级评估系统及方法
CN117391265A (zh) * 2023-12-13 2024-01-12 金乡县林业保护和发展服务中心(金乡县湿地保护中心、金乡县野生动植物保护中心、金乡县国有白洼林场) 基于大数据分析的林业病虫灾害风险预测方法

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WO2022092855A1 (fr) * 2020-10-29 2022-05-05 주식회사 팜클 Système de plateforme de gestion d'agents pathogènes et de parasites à base d'analyse de mégadonnées
KR102295013B1 (ko) 2020-11-30 2021-08-27 농업회사법인 주식회사 지인 빅데이터 기반 질병예측시스템

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112903010A (zh) * 2021-01-15 2021-06-04 启源渔业科技有限公司 一种智能化的病虫害监测预防方法
CN114677115A (zh) * 2022-03-29 2022-06-28 北京健卫病媒有害生物防控中心 除虫灭鼠资质服务等级评估系统及方法
CN117391265A (zh) * 2023-12-13 2024-01-12 金乡县林业保护和发展服务中心(金乡县湿地保护中心、金乡县野生动植物保护中心、金乡县国有白洼林场) 基于大数据分析的林业病虫灾害风险预测方法
CN117391265B (zh) * 2023-12-13 2024-03-05 金乡县林业保护和发展服务中心(金乡县湿地保护中心、金乡县野生动植物保护中心、金乡县国有白洼林场) 基于大数据分析的林业病虫灾害风险预测方法

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