Dengue epidemic situation intelligent prediction and early warning system based on multi-source monitoring data

Abstract

The invention relates to the technical field of disease prevention and control, in particular to a dengue epidemic situation intelligent prediction and early warning system based on multi-source monitoring data, comprising a prediction and early warning system for monitoring, predicting and early warning the dengue epidemic situation, the prediction and early warning system comprising: a data acquisition module for collecting multi-source monitoring data related to the dengue epidemic situation; the data processing module is used for cleaning, integrating and standardizing the collected original data to form a data set which can be used for analysis and prediction; the information early warning module is used for carrying out deep analysis on the processed data by utilizing a big data analysis technology and an intelligent algorithm, establishing a prediction model and predicting the epidemic trend and risk of the dengue epidemic situation; according to the system, through multi-region mosquito-borne monitoring points and integration of data in multiple fields of hospitals, disease control centers, meteorology, telecom operators, social media platforms and the like, a big data analysis technology and an intelligent algorithm are applied, and the dengue epidemic situation prediction accuracy is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disease prevention and control, and in particular to an intelligent prediction and early warning system for dengue fever epidemics based on multi-source monitoring data. Background Art

[0002] Dengue fever is an acute infectious disease caused by dengue virus, which is mainly transmitted through the bites of Aedes aegypti or Aedes albopictus. About 390 million people are infected with dengue fever every year worldwide. Over the past 50 years, the incidence of dengue fever has increased more than 30 times worldwide, affecting more than 100 countries in Southeast Asia, the Americas, the Western Pacific and Africa. Dengue fever has the characteristics of rapid spread, high incidence, general susceptibility of the population, and high mortality rate of severe cases. It has become one of the major public health problems in the world. There are many studies on dengue fever prediction and early warning. Researchers mainly predict the number of dengue fever cases in the future in the study area based on traditional statistical models and machine learning models.

[0003] After checking the publication number: CN111554408B, a method, system and electronic device for spatiotemporal prediction of dengue fever in cities are disclosed. This technology discloses "a method for spatiotemporal prediction of dengue fever in cities, including: collecting and preprocessing dengue fever related data in cities; constructing a graph structure reflecting the spatial relationship of urban internal regions; selecting input features for spatiotemporal prediction of dengue fever; constructing and training a GCN model based on the preprocessed dengue fever related data in cities, the constructed graph structure and the selected input features, so as to use the GCN model to perform spatiotemporal prediction of dengue fever in cities. The present invention also relates to a spatiotemporal prediction system and electronic device for dengue fever in cities" and other technical solutions, which have the technical effects of "being able to fully consider the spatial relationship between various regions in the city, realizing prediction on a finer spatial scale, improving prediction performance, and enhancing the precise prevention and control level of dengue fever";

[0004] Dengue fever is a disease transmitted by Aedes mosquitoes, and effectively controlling the density of Aedes mosquitoes is a key measure to prevent the spread of dengue fever. Given that adult Aedes mosquitoes play a vital role as vectors in the spread of dengue fever, their density has become an important basis for predicting dengue fever epidemics and evaluating the effectiveness of prevention and control. However, the method adopted in the above scheme relies on professionals to visit various monitoring points to count the density of adult Aedes mosquitoes. This process consumes a lot of human resources and is relatively costly. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an intelligent prediction and early warning system for dengue fever epidemics based on multi-source monitoring data. The system uses multi-regional mosquito-borne monitoring points and integrates data from multiple fields such as hospitals, disease control centers, meteorology, and telecommunications operators, and uses big data analysis technology and intelligent algorithms to greatly improve the accuracy of dengue fever epidemic predictions.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data, including a prediction and early warning system for dengue fever epidemic monitoring, prediction and early warning, the prediction and early warning system includes:

[0007] Data collection module, which collects multi-source monitoring data related to dengue fever epidemic;

[0008] The data processing module cleans, integrates and standardizes the collected raw data to form a data set that can be used for analysis and prediction.

[0009] The information warning module uses big data analysis technology and intelligent algorithms to conduct in-depth analysis of processed data, establish a prediction model, and predict the epidemic trend and risk of dengue fever;

[0010] The information release module conveys information to relevant departments, medical institutions, the public and the media through multiple channels and methods, and guides them to take corresponding prevention and control measures.

[0011] Preferably, the input end of the data processing module is connected to the output end of the data acquisition module, the input end of the information warning module is connected to the output end of the data processing module, and the input end of the information release module is connected to the output end of the information warning module.

[0012] Preferably, the data acquisition module includes a data source acquisition unit 1, a data source acquisition unit 2, a data source acquisition unit 3, a data source acquisition unit 4 and a data source acquisition unit 5;

[0013] Data source acquisition unit 1, acquiring mosquito vector monitoring data collected from monitoring points in multiple regions;

[0014] Data source acquisition unit 2, obtains information on the number of confirmed cases and suspected cases in hospitals and medical institutions, the age, gender, place of residence, time of consultation, and severity of the disease;

[0015] Data source acquisition unit three, obtains climate information on temperature, humidity, precipitation, and wind speed observed by meteorological departments at all levels;

[0016] Data source acquisition unit 4 obtains the user's geographic location information and movement trajectory provided by major telecom operators. Users interact with the telecom operator's network through mobile phones or other mobile communication devices to generate data that is collected and analyzed;

[0017] Data source acquisition unit 5 obtains the discussion heat of dengue fever epidemic on social media platforms, and uses natural language processing and text mining technology to perform sentiment analysis and keyword extraction on the text content on social media, so as to quantify users' attention to the dengue fever epidemic.

[0018] Preferably, the data source acquisition unit 1 includes a mosquito attracting component, a mosquito catching component, an infrared sensor and a mosquito monitoring component;

[0019] A mosquito trap that uses a purple light and releases carbon dioxide to attract mosquitoes;

[0020] A mosquito catching component uses a fan to generate airflow to suck in mosquitoes;

[0021] Infrared sensor, real-time monitoring of mosquito activities in data source acquisition unit 1, and sending out a signal when mosquitoes approach;

[0022] The mosquito vector monitoring component identifies captured mosquitoes and monitors the number and type of mosquitoes in the data source acquisition unit in real time.

[0023] Preferably, the mosquito vector monitoring component includes a high-definition camera, an image acquisition card and a processor;

[0024] High-definition camera to capture high-definition images of mosquitoes;

[0025] Image acquisition card converts the image signal captured by the camera into a digital signal.

[0026] The processor runs the image processing algorithm to analyze and identify the captured mosquito images.

[0027] Preferably, the data processing module forms a unified data format and standard by integrating the data sources acquired in the data source acquisition unit one, the data source acquisition unit two, the data source acquisition unit three, the data source acquisition unit four and the data source acquisition unit five.

[0028] Preferably, the information warning module conducts in-depth analysis on the data integrated by the data processing module, explores the laws and patterns in the data, and provides support for the prediction and warning of the epidemic.

[0029] Preferably, when the information warning module analyzes that there is a potential risk of dengue fever epidemic or it has already occurred, the information release module generates warning information and releases it through multiple channels.

[0030] The present invention provides an intelligent prediction and early warning system for dengue fever epidemic situation based on multi-source monitoring data.

[0031] Compared with the prior art, it has the following beneficial effects:

[0032] 1. The system significantly improves the accuracy of dengue fever epidemic prediction by deeply integrating extensive monitoring data from multiple fields such as hospitals, disease control centers, meteorological departments, and telecom operators, and using advanced big data analysis technology and intelligent algorithms for accurate prediction. At the same time, its powerful real-time function can continuously monitor epidemic data to ensure that early warning information is issued at the first time, which wins a crucial time advantage for epidemic prevention and control work. In addition, the comprehensive coverage of the system is not limited to the above-mentioned key data sources, but also realizes all-round and all-round monitoring of dengue fever epidemics, laying a solid foundation for building a more rigorous prevention and control system.

[0033] 2. The mosquito-attracting method in the mosquito-attracting component can simulate the attraction of the human body or other animals to mosquitoes, thereby effectively attracting mosquitoes; after the mosquitoes are sucked in by the mosquito-catching component, it can also ensure that the mosquitoes can be effectively adsorbed, fixed or trapped to prevent them from escaping; the infrared sensor uses infrared electromagnetic waves to detect objects. When mosquitoes approach, the infrared electromagnetic waves will be reflected or scattered, thereby detecting the presence of mosquitoes and being able to respond to the activities of mosquitoes in real time.

[0034] 3. The high-definition camera has the characteristics of high sensitivity, high resolution and fast response, so that it can clearly capture the subtle features and dynamic changes of mosquitoes; the processor uses machine learning or deep learning algorithms to analyze and identify the extracted features. The algorithm will classify mosquitoes into different types according to their characteristics and calculate their number; at the same time, the algorithm can also infer additional information such as the morphological characteristics and activity status of mosquitoes based on the results of feature extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a block diagram of the prediction and early warning system of the present invention;

[0036] Figure 2 is a block diagram of the data acquisition module in the present invention;

[0037] Figure 3 A block diagram of a data source acquisition unit 1 in the present invention;

[0038] Figure 4 It is a block diagram of the mosquito vector monitoring component in the present invention.

[0039] In the figure: 1. prediction and early warning system; 11. data acquisition module; 111. data source acquisition unit 1; 1111. mosquito attracting component; 1112. mosquito catching component; 1113. infrared sensor; 1114. mosquito vector monitoring component; 11141. high-definition camera; 11142. image acquisition card; 11143. processor; 112. data source acquisition unit 2; 113. data source acquisition unit 3; 114. data source acquisition unit 4; 115. data source acquisition unit 5; 12. data processing module; 13. information early warning module; 14. information release module. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1 - Figure 4 The present invention provides a technical solution: an intelligent prediction and early warning system for dengue fever based on multi-source monitoring data, including a prediction and early warning system 1 for monitoring, predicting and early warning of dengue fever, wherein the prediction and early warning system 1 includes:

[0042] The data collection module 11 collects multi-source monitoring data related to the dengue fever epidemic;

[0043] The data processing module 12 cleans, integrates and standardizes the collected raw data to form a data set that can be used for analysis and prediction.

[0044] Information warning module 13, using big data analysis technology and intelligent algorithms to conduct in-depth analysis of processed data, establish a prediction model, and predict the epidemic trend and risk of dengue fever;

[0045] The information release module 14 conveys information to relevant departments, medical institutions, the public and the media through multiple channels and methods, and guides them to take corresponding prevention and control measures.

[0046] Specifically, the input end of the data processing module 12 is connected to the output end of the data acquisition module 11 , the input end of the information warning module 13 is connected to the output end of the data processing module 12 , and the input end of the information release module 14 is connected to the output end of the information warning module 13 .

[0047] In this implementation plan, the system significantly improves the accuracy of dengue fever epidemic prediction by deeply integrating extensive monitoring data from multiple fields such as hospitals, disease control centers, meteorological departments, and telecom operators, and using advanced big data analysis technology and intelligent algorithms for accurate prediction. At the same time, its powerful real-time function can continuously monitor epidemic data to ensure that early warning information is issued at the first time, winning a crucial time advantage for epidemic prevention and control work. In addition, the comprehensive coverage of the system is not limited to the above-mentioned key data sources, but also realizes all-round and all-round monitoring of dengue fever epidemics, laying a solid foundation for building a more rigorous prevention and control system.

[0048] Specifically, the data acquisition module 11 includes a data source acquisition unit 1 111, a data source acquisition unit 2 112, a data source acquisition unit 3 113, a data source acquisition unit 4 114 and a data source acquisition unit 5 115;

[0049] A data source acquisition unit 111 acquires mosquito vector monitoring data collected from monitoring points in multiple regions;

[0050] The data source acquisition unit 2 112 acquires information on the number of confirmed cases and suspected cases, the age, gender, place of residence, time of consultation, and severity of illness of the hospital and medical institutions;

[0051] The data source acquisition unit 3 113 obtains the climate information of temperature, humidity, precipitation and wind speed observed by meteorological departments at all levels;

[0052] The data source acquisition unit 4 114 acquires the user's geographic location information and movement trajectory provided by major telecommunications operators. The user interacts with the telecommunications operator's network through a mobile phone or other mobile communication device to generate data that is collected and analyzed;

[0053] Data source acquisition unit 5 115 obtains the discussion heat of dengue fever epidemic on social media platforms, and uses natural language processing and text mining technology to perform sentiment analysis and keyword extraction on the text content on social media, so as to quantify users' attention to the dengue fever epidemic.

[0054] In this embodiment, the application scenarios of the data source acquisition unit 111 are widely distributed in multiple environmental fields: in urban areas, it is suitable for areas with dense traffic and easy breeding of mosquitoes, such as residential areas, parks and squares; in rural environments, it covers areas with rich natural ecological resources such as farmlands and farms; in wild environments, its application scenarios extend to key areas where mosquitoes naturally inhabit, such as woods and wetlands; in addition, in specific places such as hospitals, tourist attractions, etc., the data source acquisition unit 111 also plays a vital role to ensure effective monitoring of these high-risk areas; the data obtained in the data source acquisition unit 112 is the most direct and accurate source of information reflecting the dengue fever epidemic. By analyzing the case data, we can understand the epidemic trend, high-risk groups and regions, etc.; the data obtained in data source acquisition unit three 113 is one of the important factors affecting mosquito-borne activities and dengue fever epidemics. For example, high temperature, high humidity and rainfall are conducive to the reproduction and activities of mosquito-borne diseases, thereby increasing the risk of epidemic transmission; the data obtained in data source acquisition unit four 114 can reflect the flow and distribution characteristics of the population, which is helpful for analyzing the spread trend of the epidemic and potential high-risk areas; data source acquisition unit five 115 can also analyze the search keyword data on dengue fever symptoms in network search engines, and use it as one of the early warning signals for the epidemic.

[0055] Specifically, the data source acquisition unit 111 includes a mosquito attracting component 1111, a mosquito catching component 1112, an infrared sensor 1113 and a mosquito vector monitoring component 1114;

[0056] The mosquito attracting component 1111 uses a purple light and releases carbon dioxide to attract mosquitoes;

[0057] The mosquito catching component 1112 uses a fan to generate airflow to suck in mosquitoes;

[0058] The infrared sensor 1113 monitors the mosquito activities in the data source acquisition unit 111 in real time and sends a signal when the mosquito approaches;

[0059] The mosquito monitoring component 1114 identifies captured mosquitoes and monitors the number and type of mosquitoes in the data source acquisition unit 111 in real time.

[0060] In this embodiment, the mosquito-attracting method in the mosquito-attracting component 1111 can simulate the attraction of the human body or other animals to mosquitoes, thereby effectively attracting mosquitoes; after the mosquitoes are sucked in by the mosquito-catching component 1112, it can also ensure that the mosquitoes can be effectively adsorbed, fixed or trapped to prevent them from escaping; the infrared sensor 1113 uses infrared electromagnetic waves to detect objects. When a mosquito approaches, the infrared electromagnetic waves will be reflected or scattered, thereby detecting the presence of the mosquito, and can respond to the activities of the mosquitoes in real time.

[0061] Specifically, the mosquito vector monitoring component 1114 includes a high-definition camera 11141, an image acquisition card 11142, and a processor 11143;

[0062] High-definition camera 11141, used to capture high-definition images of mosquitoes;

[0063] Image acquisition card 11142 converts the image signal captured by the camera into a digital signal.

[0064] Processor 11143 runs an image processing algorithm to analyze and identify the captured mosquito images.

[0065] In this embodiment, the high-definition camera 11141 has the characteristics of high sensitivity, high resolution and rapid response, so that it can clearly capture the subtle features and dynamic changes of mosquitoes; the processor 11143 uses machine learning or deep learning algorithms to analyze and identify the extracted features. The algorithm will classify the mosquitoes into different types according to their characteristics and calculate their number; at the same time, the algorithm can also infer additional information such as the morphological characteristics, activity status, etc. of the mosquitoes based on the results of feature extraction.

[0066] Specifically, according to claim 1, an intelligent prediction and early warning system for dengue fever epidemics based on multi-source monitoring data is characterized in that: the data processing module 12 integrates the data sources obtained in the data source acquisition unit 1 111, the data source acquisition unit 2 112, the data source acquisition unit 3 113, the data source acquisition unit 4 114 and the data source acquisition unit 5 115 to form a unified data format and standard.

[0067] In this embodiment, statistical methods are used to conduct in-depth analysis of the integrated data through big data, such as descriptive statistical analysis, correlation analysis, time series analysis, etc. These methods help to reveal the laws and patterns in the data and provide support for subsequent predictions and early warnings; and the use of machine learning algorithms is a key tool in the data processing module. Through the training model, the algorithm can learn and extract features from a large amount of data, and then make accurate predictions. Commonly used machine learning algorithms include decision trees, random forests, support vector machines, neural networks, etc.; based on the collected data and machine learning algorithms, predictive models are established. These models can predict the development trend and epidemic risk of dengue fever in the future based on historical data and current data.

[0068] Specifically, the information warning module 13 conducts in-depth analysis on the data integrated by the data processing module 12, explores the laws and patterns in the data, and provides support for the prediction and warning of the epidemic.

[0069] In this embodiment, the information warning module 13 can be divided into time warning analysis and space warning analysis. Time warning analysis mainly focuses on the time distribution characteristics of the epidemic. By drawing epidemic or occurrence curves, models are established and future incidence levels are quantitatively predicted. Common time warning analysis methods include time series method, regression analysis method, Markov chain prediction method, gray model, etc.; space warning analysis mainly describes the spatial distribution pattern of the disease, discovers spatial aggregation, and explains or predicts disease risks. Common space warning analysis methods include space display, space exploration analysis and space modeling. Through the combination of geographic information system GIS, satellite remote sensing telemetry technology RS and global positioning system GPS, comprehensive analysis and dynamic monitoring of the ground environment can be achieved, playing an important role in detection and warning.

[0070] Specifically, when the information warning module 13 analyzes that there is a potential risk of dengue fever epidemic or it has already occurred, the information release module 14 generates warning information and releases it through multiple channels.

[0071] In this embodiment, the early warning information includes key contents such as the severity of the epidemic, the areas that may be affected, and the recommended prevention and control measures; the information release channels include official websites, social media, SMS platforms, news releases and other channels to ensure the wide dissemination and timely receipt of information, and improve the coverage and influence of information.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data, characterized by: The forecasting and early warning system (1) monitors, forecasts and warns of dengue fever epidemics. The forecasting and early warning system (1) includes: A data collection module (11) collects multi-source monitoring data related to the dengue fever epidemic; A data processing module (12) cleans, integrates and standardizes the collected raw data to form a data set that can be used for analysis and prediction; The information warning module (13) uses big data analysis technology and intelligent algorithms to conduct in-depth analysis of the processed data, establish a prediction model, and predict the epidemic trend and risk of dengue fever; The information release module (14) conveys information to relevant departments, medical institutions, the public and the media through multiple channels and methods, and guides them to take corresponding prevention and control measures.

2. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 1 is characterized by: The input end of the data processing module (12) is connected to the output end of the data acquisition module (11), the input end of the information warning module (13) is connected to the output end of the data processing module (12), and the input end of the information release module (14) is connected to the output end of the information warning module (13).

3. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 1 is characterized in that: The data acquisition module (11) comprises a data source acquisition unit 1 (111), a data source acquisition unit 2 (112), a data source acquisition unit 3 (113), a data source acquisition unit 4 (114) and a data source acquisition unit 5 (115); A data source acquisition unit 1 (111) acquires mosquito vector monitoring data collected from monitoring points in multiple regions; Data source acquisition unit 2 (112), obtains information on the number of confirmed cases, suspected cases, patient age, gender, place of residence, time of consultation, and severity of illness in hospitals and medical institutions; Data source acquisition unit three (113), obtains climate information of temperature, humidity, precipitation and wind speed observed by meteorological departments at all levels; Data source acquisition unit 4 (114) acquires geographical location information and movement tracks of users provided by major telecommunications operators. Users interact with the telecommunications operator's network through mobile phones or other mobile communication devices to generate data that is collected and analyzed; Data source acquisition unit five (115) obtains the discussion heat of dengue fever epidemic on social media platforms, and uses natural language processing and text mining technology to perform sentiment analysis and keyword extraction on the text content on social media, so as to quantify users' attention to the dengue fever epidemic.

4. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 3 is characterized by: The data source acquisition unit 1 (111) comprises a mosquito attracting component (1111), a mosquito catching component (1112), an infrared sensor (1113) and a mosquito vector monitoring component (1114); A mosquito attracting assembly (1111) uses a purple light and releases carbon dioxide to attract mosquitoes; The mosquito catching component (1112) uses a fan to generate airflow to suck in mosquitoes; An infrared sensor (1113) monitors the mosquito activities in the data source acquisition unit (111) in real time and sends a signal when a mosquito approaches; The mosquito monitoring component (1114) identifies captured mosquitoes and monitors the number and type of mosquitoes in the data source acquisition unit (111) in real time.

5. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 4 is characterized in that: The mosquito vector monitoring component (1114) comprises a high-definition camera (11141), an image acquisition card (11142) and a processor (11143); A high-definition camera (11141) for capturing high-definition images of mosquitoes; An image acquisition card (11142) converts the image signal captured by the camera into a digital signal; The processor (11143) runs an image processing algorithm to analyze and identify the captured mosquito images.

6. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 3 is characterized by: The data processing module (12) forms a unified data format and standard by integrating the data sources obtained in the data source acquisition unit 1 (111), the data source acquisition unit 2 (112), the data source acquisition unit 3 (113), the data source acquisition unit 4 (114) and the data source acquisition unit 5 (115).

7. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 1 is characterized by: The information warning module (13) conducts in-depth analysis on the data integrated by the data processing module (12), explores the laws and patterns in the data, and provides support for the prediction and warning of the epidemic.

8. The intelligent dengue fever epidemic prediction and early warning system based on multi-source monitoring data according to claim 1 is characterized by: When the information warning module (13) analyzes that there is a potential risk of dengue fever epidemic or it has already occurred, the information release module (14) generates warning information and releases it through multiple channels.

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