Infectious disease early warning system

Abstract

The invention relates to an infectious disease early warning system, and aims to improve the accuracy and timeliness of infectious disease early warning. According to the system, related information of various data sources is collected through the data acquisition module, and after the related information is cleaned, sorted and analyzed through the data governance module, early warning information is issued through the early warning monitoring module. The system also has the functions of information management and data visualization, so that the user can quickly know the epidemic situation. In addition, the data warehouse module is used for storing and managing data and supporting operations of data addition, coverage and the like. The prediction research module predicts the trend of infectious diseases by adopting linear regression, random forest and other algorithms. The early warning research and judgment report module can generate a report according to the multi-point data source and provide comprehensive early warning analysis. According to the method, the abnormal data mode and the potential infectious disease epidemic trend can be effectively identified, and timely and accurate early warning information is provided for related health organizations, government departments and the public.

Description

Technical Field

[0001] The present invention relates to the field of intelligent systems, and particularly to an infectious disease early warning system Background Art

[0002] In the context of current globalization and urbanization, the outbreak and prevalence of infectious diseases have become important challenges faced by the public health field. Whether it is the emergence of new infectious diseases or the recurrence of traditional infectious diseases, they may pose a huge threat to human life safety and physical health, and at the same time have a profound impact on social and economic stability. Therefore, building an efficient, accurate, and real-time infectious disease early warning system is of great significance for preventing and controlling the spread of infectious diseases

[0003] Traditional infectious disease early warning systems mainly rely on single disease surveillance data, such as the number of hospital-reported cases or the surveillance data of the Centers for Disease Control and Prevention. However, the limitations of such data sources result in limited coverage and accuracy of early warning information. In addition, traditional data processing and analysis methods often cannot meet the needs of large-scale and diverse data, resulting in insufficient timeliness of early warnings

[0004] With the rapid development of information technology, existing infectious disease early warning systems have begun to attempt to integrate multiple data sources to improve the accuracy and coverage of early warning information. These data sources include medical institutions, disease control and prevention center monitoring points, public health monitoring systems, social media, etc., which provide rich case information, symptom manifestations, test results, epidemiological investigation data, and public attention, etc. However, how to effectively integrate these data sources, eliminate data redundancy and conflicts, and improve data quality and consistency has become an important issue faced by current infectious disease early warning systems

[0005] In terms of data processing and analysis, existing infectious disease early warning systems have begun to use advanced means such as statistical analysis algorithms and data mining techniques to deeply mine and analyze the collected data. These techniques help to identify abnormal data patterns and potential infectious disease epidemic trends, providing a scientific basis for early warnings. However, how to select appropriate algorithms and models according to different types of infectious diseases and data characteristics, and how to continuously optimize and adjust early warning rules and parameters to improve the accuracy and sensitivity of early warnings are still the hotspots and difficulties of current research

[0006] In addition, existing infectious disease early warning systems also have deficiencies in information management and data visualization. How to efficiently manage various types of information in the system, such as user information, data dictionaries, early warning rule settings, etc., to ensure the normal operation of the system and the security of data; how to display the distribution, change trends of infectious disease data, and early warning information in the form of intuitive charts, maps, etc., to facilitate users to quickly understand the epidemic situation and make decisions are all problems that need to be solved currently

[0007] To solve the above problems, the applicant proposes an infectious disease early warning system. Summary of the Invention

[0008] The object of the present invention is to provide an infectious disease early warning system to solve the problems in the prior art.

[0009] To achieve the above object, the present invention provides the following technical solutions: an infectious disease early warning system, including:

[0010] A data acquisition module, used to collect infectious disease-related data from multiple data sources, the data including case information, symptom manifestations, test results, epidemiological investigation data, etc.;

[0011] A data governance module, which uses statistical analysis algorithms and data mining techniques to clean, organize, and analyze the collected data, and identify abnormal data patterns and potential infectious disease epidemic trends;

[0012] An early warning monitoring module, according to the preset early warning rules and model calculation results, timely issues infectious disease early warning information to relevant health institutions, government departments, and the public, the early warning information including early warning level, early warning area, disease type, risk tips, etc.;

[0013] An information management module, used to manage various types of information in the system, such as user information, data dictionary, early warning rule settings, system logs, etc.;

[0014] A data visualization module, which displays the distribution, change trend of infectious disease data, and early warning information in the form of intuitive charts, maps, etc., to facilitate users to quickly understand the epidemic situation;

[0015] A data warehouse module, used to store and manage the governed infectious disease-related data, and support operations such as data addition, overwrite, query, and modification.

[0016] Optionally, the data acquisition module further includes a data upload function. Users can download a template, fill in the data, and then upload it to the system. The system will perform format verification and data analysis on the uploaded data to ensure the accuracy and applicability of the data.

[0017] Optionally, the data governance module includes functions such as data processing and data integration, and can perform corresponding processing on data according to different data classifications, and establish the association relationship between data.

[0018] Optionally, the early warning monitoring module includes two methods: rule-based early warning and dynamic early warning. Among them, rule-based early warning can be judged by methods such as the moving percentile method and the moving epidemic interval method, and dynamic early warning displays the early warning information in a list mode and provides functions such as time dimension filtering and area filtering.

[0019] Optionally, the system further includes a prediction research module that uses algorithms such as linear regression models and random forest models to predict and analyze the development trend of infectious diseases, providing a scientific basis for early warning monitoring.

[0020] Optionally, the system further includes an early warning research and judgment report module that can generate an early warning research and judgment report based on multi-point data sources and provide functions such as report viewing, modification, deletion, and export as PDF.

[0021] Beneficial effects: 1. Improve the accuracy and timeliness of early warning: By integrating multiple data sources, such as medical institutions, disease control and prevention center monitoring points, public health monitoring systems, etc., this system can collect more comprehensive and rich data related to infectious diseases. Combining advanced statistical analysis algorithms and data mining techniques, the system can more accurately identify abnormal data patterns and potential infectious disease epidemic trends, thus timely issuing early warning information. This greatly improves the accuracy and timeliness of early warning, helping relevant departments and the public take measures earlier to prevent the spread of the epidemic.

[0022] 2. Enhance data governance and processing capabilities: This system has powerful data governance functions and can effectively clean, organize, and analyze the collected data. Through functional modules such as data processing and data integration, the system can perform corresponding processing according to different data classifications and establish the correlation relationships between data. This improves the quality and consistency of data, providing a reliable basis for subsequent analysis and early warning.

[0023] 3. Optimize early warning monitoring and dynamic response: This system combines rule-based early warning and dynamic early warning to achieve full-round monitoring of infectious disease epidemics. The rule-based early warning module uses multiple algorithms and methods, such as the moving percentile method and the moving epidemic interval method, to flexibly set early warning rules and thresholds. The dynamic early warning module displays early warning information in a list mode and provides functions such as time dimension filtering and regional filtering, facilitating users to quickly locate and analyze the epidemic situation. This helps relevant departments and the public respond to the epidemic in a timely manner and take targeted prevention and control measures.

[0024] 4. Improve information management and data visualization levels: This system has perfect information management functions and can efficiently manage various types of information in the system, such as user information, data dictionaries, early warning rule settings, etc. At the same time, the system provides intuitive data visualization functions, displaying the distribution, change trends of infectious disease data, and early warning information in the form of charts, maps, etc. This greatly improves the readability and usability of information, helping users quickly understand the epidemic situation and make decisions.

[0025] 5. Support for predictive research and early warning analysis: This system also includes a predictive research module and an early warning analysis report module. The predictive research module uses algorithms such as linear regression models and random forest models to predict and analyze the development trend of infectious diseases, providing a scientific basis for early warning monitoring. The early warning analysis report module can generate early warning analysis reports based on multi-point data sources, providing comprehensive and in-depth early warning analysis. This helps relevant departments and the public to understand the epidemic situation more comprehensively and formulate more effective prevention and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the logical framework diagram of the embodiment of the present invention

[0027] Figure 2 is the data processing flow chart of the embodiment of the present invention

[0028] Figure 3 is the work flow chart of the embodiment of the present invention DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The preferred embodiments of the present invention are described below with reference to the accompanying drawings of the specification to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0030] In the drawings, components with the same structure are denoted by the same reference numerals, and components with similar structures or functions are denoted by similar reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the drawings clearer, the thickness of some components is appropriately exaggerated in the drawings.

[0031] Embodiment 1

[0032] The present invention relates to the fields of information technology and public health security, and specifically to an infectious disease early warning system. The purpose of this system is to solve the problems existing in the current infectious disease early warning system, such as single data source, insufficient data processing ability, inflexible early warning rules, and limited information management and data visualization levels. By integrating multiple data sources, applying advanced statistical analysis algorithms and data mining technologies, optimizing early warning rules and models, and enhancing information management and data visualization functions, the present invention aims to achieve more accurate and timely infectious disease early warnings and provide strong support for public health security and epidemic prevention and control.

[0033] The infectious disease early warning system proposed by the present invention mainly includes the following functional modules: a data acquisition module, a data governance module, an early warning monitoring module, an information management module, a data visualization module, a data warehouse module, a predictive research module, and an early warning analysis report module. These modules cooperate with each other to jointly form the core technical solution of this system.

[0034] The data acquisition module is the cornerstone of this system and is responsible for collecting infectious disease-related data from multiple data sources. These data sources include, but are not limited to, medical institutions, disease control and prevention center monitoring points, public health monitoring systems, etc. The types of data collected include case information, symptom manifestations, test results, epidemiological investigation data, etc. To ensure the accuracy and integrity of the data, this system also provides a data upload function that allows users to upload data to the system after downloading a template and filling in the data.

[0035] The working process of the data acquisition module is as follows:

[0036] Data source configuration: The system administrator configures the data sources according to actual needs, including the addresses, access methods, data formats, etc. of the data sources.

[0037] Data scraping: The system regularly or real-time scrapes data from the data sources according to the configured information.

[0038] Data preprocessing: Preliminary processing is performed on the scraped data, such as deduplication, formatting, etc., to ensure the accuracy and consistency of the data.

[0039] Data upload: Users can download a template provided by the system, fill in the relevant data, and then upload it to the system. The system will perform format verification and data analysis on the uploaded data to ensure the accuracy and applicability of the data.

[0040] The data governance module is responsible for deeply cleaning, organizing, and analyzing the collected data. By applying statistical analysis algorithms and data mining techniques, this module can identify abnormal data patterns and potential infectious disease epidemic trends. The functions of the data governance module mainly include data processing, data integration, etc.

[0041] The data processing function aims to preprocess and transform the raw data to meet the needs of subsequent analysis and early warning. The specific steps of data processing include:

[0042] Data cleaning: Remove duplicate data, invalid data, and abnormal data to ensure the accuracy and consistency of the data.

[0043] Data transformation: Transform the raw data into a format suitable for analysis and early warning, such as converting text data into numerical data, converting time series data into a format suitable for time series analysis, etc.

[0044] Data integration: Integrate data from different data sources to form a unified data view for cross-source analysis and early warning.

[0045] During the data processing, this system supports the processing of multiple data classifications, such as chief complaints, medications, ICDs, etc. The system can perform corresponding processing according to different data classifications and establish association relationships between the data.

[0046] The data integration function aims to integrate the processed data into the data warehouse for subsequent analysis and early warning. The specific steps of data integration include:

[0047] Data mapping: Establish a mapping relationship between the original data and the processed data so that the data source and processing process can be traced when needed.

[0048] Data import: Import the processed data into the data warehouse for subsequent analysis and early warning.

[0049] Data update: When the original data changes, update the data in the data warehouse in a timely manner to ensure the timeliness and accuracy of the data.

[0050] The data warehouse module is used to store and manage the data related to infectious diseases that have been governed. This module supports operations such as adding, overwriting, querying, and modifying data, and can efficiently manage large-scale and diverse data.

[0051] The specific functions of the data warehouse module include:

[0052] Data storage: Store the governed data in the data warehouse for subsequent analysis and early warning.

[0053] Data query: Provide an efficient data query function, supporting data retrieval according to conditions such as keywords, time range, data classification, etc.

[0054] Data update: Support operations such as adding, overwriting, and modifying data to ensure the timeliness and accuracy of the data.

[0055] Data security: Adopt security measures such as encryption and backup to ensure the security and integrity of the data.

[0056] The early warning and monitoring module is one of the core functions of this system, responsible for timely releasing infectious disease early warning information to relevant health institutions, government departments, and the public according to the preset early warning rules and model calculation results. The early warning information includes early warning level, early warning area, disease type, risk tips, etc.

[0057] The specific functions of the early warning and monitoring module include:

[0058] Rule-based early warning: Adopt preset early warning rules, such as the moving percentile method, the moving epidemic interval method, etc., to monitor and analyze the infectious disease data in real time, and trigger an early warning when the data meets the early warning conditions.

[0059] Dynamic early warning: Display the early warning information in a list mode, providing functions such as time dimension filtering, area filtering, etc., to facilitate users to quickly locate and analyze the epidemic situation.

[0060] Early warning information release: The early warning information is promptly released to relevant health institutions, government departments, and the public through means such as text messages, emails, and APP push.

[0061] Early warning record management: Record the release situation of early warning information, including release time, recipients, early warning levels, etc., for subsequent analysis and evaluation.

[0062] The moving percentile method is a time - series analysis technique. It observes the distribution change of data by calculating the percentile values of a data sequence within a moving window. This method can flexibly set early warning thresholds and sensitively capture minor changes in data.

[0063] The specific implementation steps of the moving percentile method are as follows:

[0064] Data pre - processing: Clean, organize, and smooth the original data to eliminate noise and outliers.

[0065] Calculate moving percentiles: Calculate the percentile values of data within the set moving window, such as the 50% moving percentile (median), 90% moving percentile, etc.

[0066] Set early warning thresholds: Set early warning thresholds according to actual needs. For example, trigger an early warning when the percentile value of data exceeds or is lower than a certain threshold.

[0067] Real - time monitoring: Conduct real - time monitoring of infectious disease data and trigger an early warning when the data meets the early warning conditions.

[0068] The moving epidemic interval method is an analysis method that determines the normal fluctuation range (interval) of data based on the dynamic changes of data. It observes the fluctuation of data within a certain time period or data sequence range to set a reasonable interval, and then judges whether the subsequent data is within the normal range or shows abnormalities.

[0069] The specific implementation steps of the moving epidemic interval method are as follows:

[0070] Data pre - processing: Clean, organize, and smooth the original data.

[0071] Calculate the moving average and standard deviation: Calculate the moving average and standard deviation of data within the set time period or data sequence range.

[0072] Set the early warning interval: Set the early warning interval according to the moving average and standard deviation. For example, set the normal fluctuation range of data as the moving average plus or minus two standard deviations.

[0073] Real - time monitoring: Conduct real - time monitoring of infectious disease data and trigger an early warning when the data exceeds the early warning interval.

[0074] Dynamic early warning displays early warning information in list mode and provides functions such as time dimension filtering and region filtering. Users can set filtering conditions according to actual needs to quickly locate and analyze the epidemic situation.

[0075] The specific implementation steps of dynamic early warning are as follows:

[0076] Data preprocessing: Clean, organize, and smooth the original data.

[0077] Real-time monitoring: Conduct real-time monitoring of infectious disease data, and trigger an early warning when the data meets the early warning conditions.

[0078] Display early warning information: Display the early warning information to users in list mode, including early warning level, early warning region, disease type, risk tips, etc.

[0079] Set filtering conditions: Users can set filtering conditions according to actual needs, such as time range, region range, etc., in order to quickly locate and analyze the epidemic situation.

[0080] The information management module is responsible for managing various types of information in the system, such as user information, data dictionary, early warning rule settings, system logs, etc. This module can efficiently manage the information resources of the system to ensure the normal operation of the system and the security of data.

[0081] The specific functions of the information management module include:

[0082] User information management: Manage users' personal information, permission settings, etc., to ensure that users can access and use the system normally.

[0083] Data dictionary management: Manage various data dictionaries used in the system, such as disease classification, symptom manifestations, etc., for data standardization and normalization processing.

[0084] Early warning rule settings: Allow users to set early warning rules according to actual needs, such as the threshold setting of the moving percentile method, the interval setting of the moving epidemic interval method, etc.

[0085] System log management: Record the system operation logs, user operation logs, etc., for subsequent analysis and evaluation.

[0086] The data visualization module displays the distribution, change trend of infectious disease data, and early warning information in the form of intuitive charts, maps, etc. This module can greatly improve the readability and usability of information, helping users quickly understand the epidemic situation and make decisions.

[0087] The specific functions of the data visualization module include:

[0088] Chart display: Display the distribution, change trend, etc. of infectious disease data in the form of bar charts, line charts, pie charts, etc.

[0089] Map display: Displays the geographical distribution and spread trend of infectious disease data in the form of a map, facilitating users to understand the geographical distribution and spread of the epidemic situation.

[0090] Early warning information display: Displays early warning information in the form of a list or icons, including the early warning level, early warning area, disease type, risk tips, etc.

[0091] Interactive function: Provides interactive functions such as data filtering, zooming in and out, and dragging, facilitating users to conduct in-depth analysis and mining of data according to actual needs.

[0092] The prediction research module uses algorithms such as linear regression models and random forest models to predict and analyze the development trend of infectious diseases. This module can provide a scientific basis for early warning monitoring, helping relevant departments and the public take measures earlier to prevent the spread of the epidemic.

[0093] The specific functions of the prediction research module include:

[0094] Algorithm selection: Selects appropriate prediction algorithms according to actual needs, such as linear regression models and random forest models.

[0095] Model training: Trains and validates the prediction model using historical data to improve the accuracy and reliability of the model.

[0096] Trend prediction: Uses the trained model to predict the development trend of infectious diseases, including the spread speed, peak time, and influence range of the epidemic.

[0097] Result analysis: Conducts in-depth analysis of the prediction results, identifies potential risk points and influencing factors, and provides a scientific basis for epidemic prevention and control.

[0098] Visualization display: Displays the prediction results in an intuitive chart form, facilitating users to understand and use.

[0099] To achieve the above functions, the prediction research module needs to collect and process a large amount of historical data, including the incidence rate, mortality rate, transmission routes, prevention and control measures, etc. of infectious diseases. These data will be used for model training and validation to ensure the accuracy and reliability of the model. At the same time, the module also needs to continuously update and optimize the algorithm to adapt to the changing epidemic situation and prevention and control requirements.

[0100] The early warning research and judgment report module can generate an early warning research and judgment report based on multi-point data sources, providing comprehensive decision-making support for epidemic prevention and control. This module can integrate information from different data sources, conduct in-depth analysis and research, and generate an early warning research and judgment report containing the general situation of the epidemic, trend prediction, risk assessment, prevention and control suggestions, etc.

[0101] The specific functions of the early warning and judgment report module include:

[0102] Data source integration: Collect information from multiple data sources such as medical institutions, disease control centers, and public health monitoring systems, and integrate and process it.

[0103] Analysis and judgment: Use statistical methods, data mining techniques, etc. to analyze and judge the integrated data, and identify the characteristics and trends of the epidemic.

[0104] Report generation: Generate an early warning and judgment report based on the analysis and judgment results, including the general situation of the epidemic, trend prediction, risk assessment, prevention and control suggestions, etc.

[0105] Report management: Provide functions such as report viewing, modification, deletion, and export to PDF, which is convenient for users to manage and use.

[0106] The early warning and judgment report module can greatly improve the decision-making efficiency and accuracy of epidemic prevention and control. By integrating and analyzing information from different data sources, the module can comprehensively understand the characteristics and trends of the epidemic, and provide more accurate early warning and judgment information for relevant departments and the public. At the same time, the module can also generate customized reports according to actual needs to meet the needs of different users.

[0107] The infectious disease early warning system of the present invention has significant advantages and innovation points compared with the prior art. First, the system integrates multiple data sources, including medical institutions, disease control center monitoring points, public health monitoring systems, etc., realizing comprehensive coverage and efficient utilization of data. Second, the system uses advanced statistical analysis algorithms and data mining techniques, which can deeply mine the potential information and trends in the data, and improve the accuracy and timeliness of early warning. In addition, the system also provides rich function modules and visualization display means, which is convenient for users to analyze and utilize data.

[0108] In terms of innovation points, the present invention proposes two new early warning rules, namely the moving percentile method and the moving epidemic interval method, which can more sensitively capture the changing trends and abnormal situations of the epidemic. At the same time, the system also uses advanced prediction algorithms such as linear regression models and random forest models to predict and analyze the development trend of the epidemic, providing a scientific basis for epidemic prevention and control. In addition, the system also proposes the concept of an early warning and judgment report, which can generate a comprehensive early warning and judgment report based on multi-point data sources, providing comprehensive decision-making support for epidemic prevention and control.

[0109] The infectious disease early warning system of the present invention has a wide range of application scenarios. First of all, the system can be used for epidemic prevention and control work in the field of public health, providing timely early warning and judgment information for relevant departments to help them formulate effective prevention and control measures. Secondly, the system can also be used for infectious disease monitoring and early warning work within medical institutions, improving the sensitivity and response ability of medical institutions to the epidemic. In addition, the system can also be used for data analysis and mining work in the scientific research field, providing rich data resources and analysis tools for scientific research personnel.

[0110] In specific application scenarios, the system can be customized and developed according to actual needs. For example, in the field of public health, the system can be configured according to the needs of different regions, different disease types, etc. to achieve targeted early warning and judgment. Within medical institutions, the system can be docked with the hospital's electronic medical record system, inspection and testing system, etc. to achieve real-time data sharing and early warning. In the scientific research field, the system can provide functions such as data cleaning, data mining, and predictive analysis for scientific research personnel to help them deeply explore the potential information and trends in the data.

[0111] The infectious disease early warning system of the present invention can adopt various implementation methods. For example, the system can be constructed using cloud computing and big data technologies to achieve real-time data processing and analysis. At the same time, the system can also adopt the modular design concept to separate and encapsulate each functional module, facilitating the maintenance and upgrade of the system. In addition, the system can adopt a distributed deployment method to deploy the system on multiple nodes to achieve efficient data processing and utilization.

[0112] In the specific implementation process, first, it is necessary to determine the overall architecture and design concept of the system. Then, select appropriate technical solutions and development tools according to actual needs for system development and implementation. During the development process, attention should be paid to the design of system scalability and maintainability to ensure that the system can adapt to changing needs and technological developments. At the same time, comprehensive testing and optimization work of the system are also required to ensure the stability and performance of the system.

[0113] During the deployment and operation of the system, attention should be paid to data security and privacy protection work. Encryption technology, access control technology and other means can be used to protect and manage data. At the same time, a perfect operation and maintenance and monitoring mechanism also needs to be established to promptly discover and solve problems and faults that occur during the operation of the system.

[0114] The foregoing has shown and described the basic principles, main features and advantages of the present invention. For a person skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims concerned.

[0115] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. A person skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by a person skilled in the art.

Claims

1. An infectious disease early warning system, characterized in that, Including: A data acquisition module for collecting infectious disease-related data from multiple data sources, where the data includes case information, symptom manifestations, test results, epidemiological investigation data, etc.; A data governance module that uses statistical analysis algorithms and data mining techniques to clean, organize, and analyze the collected data, and identify abnormal data patterns and potential infectious disease epidemic trends; An early warning monitoring module that timely issues infectious disease early warning information to relevant health institutions, government departments, and the public according to preset early warning rules and model calculation results, where the early warning information includes early warning levels, early warning regions, disease types, risk tips, etc.; An information management module for managing various types of information in the system, such as user information, data dictionaries, early warning rule settings, system logs, etc.; A data visualization module that displays the distribution, change trends, and early warning information of infectious disease data in the form of intuitive charts, maps, etc., to facilitate users to quickly understand the epidemic situation; A data warehouse module for storing and managing the governed infectious disease-related data, and supporting operations such as data addition, coverage, query, and modification.

2. The infectious disease early warning system according to claim 1, wherein The data acquisition module further includes a data upload function. Users can download a template, fill in the data, and then upload it to the system. The system will perform format verification and data analysis on the uploaded data to ensure the accuracy and applicability of the data.

3. The infectious disease early warning system according to claim 1, wherein The data governance module includes functions such as data processing and data integration, and can perform corresponding processing on the data according to different data classifications and establish the association relationships between the data.

4. The infectious disease early warning system according to claim 1, wherein The early warning monitoring module includes two methods: rule-based early warning and dynamic early warning. Among them, rule-based early warning can use methods such as the moving percentile method and the moving epidemic interval method for judgment. Dynamic early warning displays the early warning information in a list mode and provides functions such as time dimension filtering and region filtering.

5. The infectious disease early warning system according to claim 1, wherein The system further includes a prediction research module that uses algorithms such as linear regression models and random forest models to predict and analyze the development trends of infectious diseases, providing a scientific basis for early warning monitoring.

6. The infectious disease early warning system according to claim 1, wherein The system further includes an early warning research and judgment report module that can generate an early warning research and judgment report based on multi-point data sources, and provides functions such as report viewing, modification, deletion, and export as PDF.

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