WO2023097757A1 - Infectious disease syndromic surveillance and early-warning method and system based on big data - Google Patents
Infectious disease syndromic surveillance and early-warning method and system based on big data Download PDFInfo
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- Each embodiment of the present application belongs to the technical field of infectious disease symptom monitoring and early warning, and in particular relates to a method and system for infectious disease symptom monitoring and early warning based on big data.
- Syndromic surveillance refers to the systematic and continuous collection of information that can indicate the occurrence (or epidemic/outbreak) of a disease before a clinical diagnosis, various data related to health and disease events, and various other phenomena related to health and disease Data, through comprehensive analysis, to monitor abnormal phenomena in the early stages of public health emergencies.
- symptom monitoring is being used more and more, the related theories and technologies are far from mature and are still in the stage of exploration and development.
- Establishing an effective symptom monitoring and early warning system needs to answer a series of theoretical and technical questions, including the determination of target diseases and symptoms, the selection and layout of monitoring networks and monitoring points, the collection and management strategies of multi-source data, and the stable and efficient information to achieve monitoring goals.
- Establishment of management system, selection of early warning model based on monitoring information and setting of early warning parameters, monitoring and early warning response, development of symptom monitoring application toolkit and technical specifications, etc.
- the purpose of the embodiment of the present application is to provide a big data-based infectious disease symptom monitoring and early warning method, which can visually display the disease distribution information on the map and determine the possible transmission range of the pathogen, and can well map the epidemic situation of the infectious disease. Geographical conditions, population conditions and epidemic spread are comprehensively analyzed to obtain more valuable epidemic monitoring and early warning data, thereby solving the problems in the background technology.
- the technical solution of the big data-based infectious disease symptom monitoring and early warning method provided by the embodiment of the present application is as follows:
- the embodiment of the present application discloses a method for monitoring and early warning of infectious disease symptoms based on big data.
- the method includes the following steps:
- Step 1 Select the early warning locations that need to be monitored, and determine the diseases that need early warning in the early warning locations;
- Step 2 collect the disease information in the early warning location, and process different disease information, when it is determined that there is an epidemic event in the early warning location, send the early warning conditions to the server for processing;
- Step 3 The server further judges whether to warn according to the pre-set warning conditions and the warning threshold. When the first warning is completed, the server will return two sets of warning results, and return the data information that needs to be displayed on the map to the client The browser loads the map.
- the method further includes the following steps:
- the start time of the warning is the current time, and the end time of the warning is empty. If it is empty, it means that the warning will continue from the current time until the system exits; if it is not empty, the cut-off time of the warning is greater than the current time.
- the method further includes the following steps:
- the epidemic address When it is determined that there is an epidemic event in the early warning location, the epidemic address will be automatically marked on the map. When the epidemic address is marked on the map, the map window will automatically center the window with the case location.
- the method further includes the following steps:
- the two groups of warning results are respectively the case where the address coordinates of the case can be found locally and the case where the address coordinates of the case are not in the local area.
- the method further includes the following steps:
- the browser After the browser receives the returned data, it processes the two groups of early warning result data separately, temporarily stores the first coordinate group, and then sends the address group asynchronously to the server for processing. After all the coordinates of the addresses have been obtained, add the coordinates of the addresses to the Go to the first coordinate group, and then locate directly on the map; after the epidemic coordinates are located on the map, click the epidemic mark, and a request will be sent for all records of the disease in the area according to the address code of the area, and the epidemic result will be displayed on the mark In the pop-up information window, if there is an epidemic, according to the SMS settings configured by the time, the SMS notification will be sent; wait for the arrival of the next warning period, if the next time period arrives, judge whether the current time is still within the warning time range, if not Within the time range, the pre-warning ends here, and the next pre-warning starts.
- the early warning locations to be monitored are all hospitals in the pre-set area, and the disease type information obtained in different hospitals is the data source.
- the processing of different disease information includes:
- Merge data from multiple data sources store and manage them in a unified database, or replace original parameters with expressive parameters.
- the data in the data source is the data from the 4th day before the diagnosis to the 8th day and the 15th day after the diagnosis, and the specific parameters of these 13 days are performed according to the parameter type Grouping, and then calculating the weight of each parameter in each group, weighted summation, to obtain the comprehensive index parameters that can characterize the patient from 4 days before diagnosis to 15 days after diagnosis, and the calculation method of determining the parameters is as follows:
- the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
- the second aspect is a big data-based infectious disease symptom monitoring and early warning system, including:
- the selection module is used to select the early warning sites that need to be monitored, and determine the diseases that need early warning in the early warning sites;
- the processing module is used to collect the disease type information in the early warning location, and process different disease type information. When it is determined that an epidemic event occurs in the early warning location, send the early warning condition to the server for processing;
- the judging module is used for the server to further judge whether it is an early warning according to the pre-set early warning conditions and the early warning threshold.
- the server will return two sets of early warning results, and return the data information that needs to be displayed on the map to the The client browser loads the map.
- the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
- Fig. 1 is a schematic diagram of a method for monitoring and early warning of infectious disease symptoms based on big data according to an embodiment of the present application.
- Fig. 2 is a schematic diagram of an infectious disease symptom monitoring and early warning system based on big data according to an embodiment of the present application.
- first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features. In the description of the present application, “plurality” means two or more, unless otherwise specifically defined.
- the embodiment of the present application provides a method for monitoring and early warning of infectious disease symptoms based on big data.
- the method includes the following steps:
- Step 1 Select the early warning locations that need to be monitored, and determine the diseases that need early warning in the early warning locations;
- Step 2 collect the disease information in the early warning location, and process different disease information, when it is determined that there is an epidemic event in the early warning location, send the early warning conditions to the server for processing;
- Step 3 The server further judges whether to warn according to the pre-set warning conditions and the warning threshold. When the first warning is completed, the server will return two sets of warning results, and return the data information that needs to be displayed on the map to the client The browser loads the map.
- the server is used to accept the access of external customers, and submit the customer's request to the web application deployed on the application server.
- the epidemic situation early warning display system returns the results to the client in html form; the pre-set early warning conditions are set in the database server, wherein the database server stores all business data and infectious disease data for analysis and processing, and other
- the database server stores all business data and infectious disease data for analysis and processing, and other
- geospatial data that needs to be stored after being integrated into the Internet platform, and information data of sentinel hospitals of CDCs at all levels for display.
- the infectious disease symptom monitoring and early warning method based on big data also includes the following steps:
- the start time of the warning is the current time, and the end time of the warning is empty. If it is empty, it means that the warning will start from the current time until the system exits; if it is not empty, the cut-off time of the warning is greater than the current time; the following steps are also included:
- the epidemic address When an epidemic event occurs, the epidemic address will be automatically marked on the map. When the epidemic address is marked on the map, the map window will automatically center the window with the case location.
- infectious disease epidemic situation monitoring and early warning system should realize: disease control unit information (including disease control center and sentinel hospital information) release, infectious disease monitoring Each module is relatively independent from early warning processing, infectious disease analysis module, infectious disease emergency command module, system management module, and common toolkit. Disease control personnel at all levels only need to pay attention to some functions that concern them.
- the information release form of the disease control unit is divided into two parts: one is the map snapshot part (units at all levels are directly positioned on the map and unfolded simultaneously) You can see the basic information by marking), one is the detailed display part (provide an interface, and the operator can directly enter the CDC information detailed introduction part through this interface); the epidemic early warning part: monitor the epidemic situation of infectious diseases within the preset range Management, complete real-time monitoring of infectious disease events, and provide early warning for infectious disease epidemics, provide rule-based early warning and conditional notification mechanism, provide notification and early warning through multiple channels when conditions that meet the early warning conditions are found, and carry out on the electronic map Marking and warning prompts.
- the epidemic situation analysis part analyzes and manages the infectious disease epidemic situation in the preset range, and completes the infectious disease distribution analysis, infectious disease epidemic situation statistics thematic analysis, infectious disease
- the evaluation and analysis of the epidemic situation of infectious diseases and the prediction, analysis and processing of the epidemic situation of infectious diseases, and the display of chart analysis data on the electronic map, can perform spatial information calculation, spatial information classification, spatial information superposition, buffer query, dynamic display, regional distribution, etc. kind of analysis.
- the emergency command part includes: path analysis function, emergency plan function, emergency response function, environmental status information, vehicle monitoring function, disease control resource distribution , disease control resource allocation; commonly used toolkit modules include: weather query of cities, districts and counties within the preset range in the next 7 days; real-time display of current road traffic congestion; collection of latitude and longitude, address information, remark information; loading custom data, etc. Operation; management subsystem: initialize the system; complete the configuration of each functional module; add, delete, modify, and check users; query and modify user permissions; backup and restore the database; record User operation log.
- the infectious disease symptom monitoring and early warning method based on big data also includes the following steps:
- the big data-based infectious disease symptom monitoring and early warning method the two sets of early warning results are the case where the address coordinates of the case can be found locally and the case where the address coordinates of the case are not local.
- the infectious disease symptom monitoring and early warning method based on big data also includes the following steps:
- the browser After the browser receives the returned data, it processes the two groups of early warning result data separately, temporarily stores the first coordinate group, and then sends the address group asynchronously to the server for processing. After all the coordinates of the addresses have been obtained, add the coordinates of the addresses to the Go to the first coordinate group, and then locate directly on the map; after the epidemic coordinates are located on the map, click the epidemic mark, and a request will be sent for all records of the disease in the area according to the address code of the area, and the epidemic result will be displayed on the mark In the pop-up information window, if there is an epidemic, according to the SMS settings configured by the time, the SMS notification will be sent; wait for the arrival of the next warning period, if the next time period arrives, judge whether the current time is still within the warning time range, if not Within the time range, the pre-warning ends here, and the next pre-warning starts.
- the method of monitoring and early warning of infectious disease symptoms based on big data the warning locations to be monitored are all hospitals in the pre-set area, and the disease type information obtained in different hospitals is the data source.
- the processing of different disease information includes: merging data from multiple data sources, storing and managing them in a unified database, or replacing original parameters with representative parameters.
- the data in the data source is the data from the 4th day before the diagnosis to the 8th day and the 15th day after the diagnosis, and the specific parameters of these 13 days are divided into parameter types Carry out grouping, then calculate the weight of each parameter in each group, and add the weighted sum to obtain the comprehensive index parameters that can characterize the patient from 4 days before the diagnosis to the 15th day after the diagnosis.
- the calculation method of determining the parameters is as follows:
- the weight of the indicators can be further deduced, and the coefficient corresponding to each indicator in the first principal component F 1 is multiplied by the contribution rate corresponding to the first principal component F 1 and then divided by the two extracted principal components.
- the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
- the second aspect is a big data-based infectious disease symptom monitoring and early warning system, including:
- the selection module is used to select the early warning sites that need to be monitored, and determine the diseases that need early warning in the early warning sites;
- the processing module is used to collect the disease type information in the early warning location, and process different disease type information. When it is determined that an epidemic event occurs in the early warning location, send the early warning condition to the server for processing;
- the judging module is used for the server to further judge whether it is an early warning according to the pre-set early warning conditions and the early warning threshold.
- the server will return two sets of early warning results, and return the data information that needs to be displayed on the map to the The client browser loads the map.
- the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
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Abstract
Disclosed are an infectious disease syndromic surveillance and early-warning method and system based on big data. The method comprises the following steps: selecting an early-warning place that needs to be subjected to surveillance, and determining a disease type for which an early warning needs to be given at the early-warning place; collecting disease type information within the early-warning place, processing different pieces of disease type information, and when it is determined that there is an epidemic event at the early-warning place, sending an early-warning condition to a server for processing; and the server further determining, according to a preset early-warning condition and an early-warning threshold value, whether to give an early warning, and after a first early warning is completed, the server returning two groups of early-warning results, and returning, to a client browser, data information that needs to be displayed on a map, so as to load the data information on the map. By means of the method, disease distribution information can be intuitively displayed on a map, and a possible spread range of pathogens can be determined, and comprehensive analysis can be effectively performed on the geographical situation and population situation of the place where an infectious disease epidemic occurs, and an epidemic spread situation.
Description
本申请各实施例属于传染病症状监测与预警技术领域，特别是涉及一种基于大数据的传染病症状监测与预警方法及***。Each embodiment of the present application belongs to the technical field of infectious disease symptom monitoring and early warning, and in particular relates to a method and system for infectious disease symptom monitoring and early warning based on big data.
随着社会和自然环境的变化，传染病的病原体、传播途径、发病特点以及影响因素也发生很大变化，如何能早期识别到传染病突发公共卫生事件，及时发出预警，尽早采取相应的控制措施，将突发公共卫生事件造成的损失降到最低，是公共卫生领域长期以来关注的焦点，也是卫生应急工作的重要内容。突发公共卫生事件预警，是通过对有关数据的收集，整理、分析和整合，运用计算机、网络、通讯等现代先进的技术，对事件的征兆进行监测、识别、诊断与评价，及时报警，告知有关部门和公众做好相关的应对和准备工作，及时采取有效的防控措施，尽可能阻止或减缓突发事件的发生或减少事件的危害。As the social and natural environment changes, the pathogens, transmission routes, onset characteristics and influencing factors of infectious diseases have also undergone great changes. How to identify public health emergencies of infectious diseases early, issue early warnings, and take corresponding controls as soon as possible Measures to minimize the losses caused by public health emergencies have been the focus of attention in the field of public health for a long time, and are also an important part of health emergency work. The early warning of public health emergencies is to monitor, identify, diagnose and evaluate the signs of events through the collection, sorting, analysis and integration of relevant data, using modern advanced technologies such as computers, networks, and communications, and to report to the police and inform the public in time. Relevant departments and the public should do a good job in response and preparation, and take effective prevention and control measures in a timely manner to prevent or slow down the occurrence of emergencies or reduce the harm of incidents as much as possible.
症状监测(Syndromicsurveillance)是指***、持续地收集临床明确诊断前能够指示疾病发生(或流行/暴发)的信息、各种与健康和疾病事件相关的数据以及各类与健康和疾病相关的其他现象资料，通过综合分析，来监测突发公共卫生事件发生初期的异常现象。尽管症状监测正得到越来越多的应用，但相关的理论与技术都远不成熟，尚处于探索发展阶段。建立有效的症状监测预警***需要回答一系列理论与技术问题，包括目标疾病与目标症状确定、监测网络和监测点的选择与布局、多源数据的采集与管理策略、实现监测目标的稳定高效信息管理***的建立、基于监测信息的预警模型选择与预警参数设定、监测预警响应、症状监测应用工具包及技术规范的开发等。Syndromic surveillance refers to the systematic and continuous collection of information that can indicate the occurrence (or epidemic/outbreak) of a disease before a clinical diagnosis, various data related to health and disease events, and various other phenomena related to health and disease Data, through comprehensive analysis, to monitor abnormal phenomena in the early stages of public health emergencies. Although symptom monitoring is being used more and more, the related theories and technologies are far from mature and are still in the stage of exploration and development. Establishing an effective symptom monitoring and early warning system needs to answer a series of theoretical and technical questions, including the determination of target diseases and symptoms, the selection and layout of monitoring networks and monitoring points, the collection and management strategies of multi-source data, and the stable and efficient information to achieve monitoring goals. Establishment of management system, selection of early warning model based on monitoring information and setting of early warning parameters, monitoring and early warning response, development of symptom monitoring application toolkit and technical specifications, etc.
发明内容Contents of the invention
本申请实施例的目的在于提供一种基于大数据的传染病症状监测与预警方法，可以对疾病分布信息进行地图直观显示和确定病原体可能传播的范围，能够很好地将传染病疫情发生地的地理情况和人群情况与疫情传播情况进行综合分析，得到更有价值的疫情监测与预警数据，从而解决背景技术中的问题。The purpose of the embodiment of the present application is to provide a big data-based infectious disease symptom monitoring and early warning method, which can visually display the disease distribution information on the map and determine the possible transmission range of the pathogen, and can well map the epidemic situation of the infectious disease. Geographical conditions, population conditions and epidemic spread are comprehensively analyzed to obtain more valuable epidemic monitoring and early warning data, thereby solving the problems in the background technology.
为了解决上述技术问题，本申请实施例提供的基于大数据的传染病症状监测与预警方法的技术方案具体如下：In order to solve the above technical problems, the technical solution of the big data-based infectious disease symptom monitoring and early warning method provided by the embodiment of the present application is as follows:
本申请实施例公开了一种基于大数据的传染病症状监测与预警方法，所述方法包括以下步骤：The embodiment of the present application discloses a method for monitoring and early warning of infectious disease symptoms based on big data. The method includes the following steps:
步骤1：选择需要监测的预警地点，并确定所述预警地点内需要预警的病种；Step 1: Select the early warning locations that need to be monitored, and determine the diseases that need early warning in the early warning locations;
步骤2：采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；Step 2: collect the disease information in the early warning location, and process different disease information, when it is determined that there is an epidemic event in the early warning location, send the early warning conditions to the server for processing;
步骤3：服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。Step 3: The server further judges whether to warn according to the pre-set warning conditions and the warning threshold. When the first warning is completed, the server will return two sets of warning results, and return the data information that needs to be displayed on the map to the client The browser loads the map.
在上述任一方案中优选的实施例，所述方法还包括以下步骤：In a preferred embodiment of any of the above schemes, the method further includes the following steps:
预警开始时间为当前时间，预警结束时间为空，为空表示将从当前时间开始一直预警直到***退出；若不为空，则预警的截止时间大于当前时间。The start time of the warning is the current time, and the end time of the warning is empty. If it is empty, it means that the warning will continue from the current time until the system exits; if it is not empty, the cut-off time of the warning is greater than the current time.
在上述任一方案中优选的实施例，所述方法还包括以下步骤：In a preferred embodiment of any of the above schemes, the method further includes the following steps:
当确定预警地点有疫情事件出现时，疫情地址将自动标记到地图上，当疫情地址标记在地图后，地图窗口将自动以病例位置居中窗口。When it is determined that there is an epidemic event in the early warning location, the epidemic address will be automatically marked on the map. When the epidemic address is marked on the map, the map window will automatically center the window with the case location.
在上述任一方案中优选的实施例，所述方法还包括以下步骤：In a preferred embodiment of any of the above schemes, the method further includes the following steps:
将疫情坐标定位到地图上后，点击疫情标记，则会根据该预警地点地址编码发送请求该预警地点此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，至此完成了疫情标注过程。After locating the epidemic coordinates on the map, click the epidemic mark, and a request will be sent for all the records of the disease in the early warning location according to the address code of the early warning location, and the epidemic result will be displayed in the pop-up information window of the marker, and the epidemic labeling process has been completed. .
在上述任一方案中优选的实施例，所述两组预警结果分别为病例的地址坐标已经在本地可以找到的情况和病例地址坐标不在本地的情况。In a preferred embodiment of any of the above schemes, the two groups of warning results are respectively the case where the address coordinates of the case can be found locally and the case where the address coordinates of the case are not in the local area.
在上述任一方案中优选的实施例，所述方法还包括以下步骤：In a preferred embodiment of any of the above schemes, the method further includes the following steps:
浏览器接收到返回数据后对两组预警结果数据分处理，将第一坐标组暂存起来，然后将地址组异步发送给服务器处理，等到所有地址的坐标已经取到后，将地址的坐标加入到第一坐标组中，然后直接定位到地图上；疫情坐标定位到地图上后，点击疫情标记，则会根据该地区地址编码发送请求该地区此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，若有疫情发生，根据时间配置好的短信设置进行短信提示；等待下一个预警周期的到来，如果下一个时间周期到来，判断当前时间是否还在预警时间范围内，若不在预警时间范围内则预警到此终止，反之下一次预警开始。After the browser receives the returned data, it processes the two groups of early warning result data separately, temporarily stores the first coordinate group, and then sends the address group asynchronously to the server for processing. After all the coordinates of the addresses have been obtained, add the coordinates of the addresses to the Go to the first coordinate group, and then locate directly on the map; after the epidemic coordinates are located on the map, click the epidemic mark, and a request will be sent for all records of the disease in the area according to the address code of the area, and the epidemic result will be displayed on the mark In the pop-up information window, if there is an epidemic, according to the SMS settings configured by the time, the SMS notification will be sent; wait for the arrival of the next warning period, if the next time period arrives, judge whether the current time is still within the warning time range, if not Within the time range, the pre-warning ends here, and the next pre-warning starts.
在上述任一方案中优选的实施例，所述需要监测的预警地点为预先设置的区域内的所有的医院，在不同的医院中获取的病种信息为数据源。In a preferred embodiment of any of the above schemes, the early warning locations to be monitored are all hospitals in the pre-set area, and the disease type information obtained in different hospitals is the data source.
在上述任一方案中优选的实施例，所述对不同的病种信息进行处理包括：In a preferred embodiment of any of the above schemes, the processing of different disease information includes:
将多个数据源中的数据合并，在一个统一的数据库中进行存储和管理，或将具有表性的参数代替原有参数。Merge data from multiple data sources, store and manage them in a unified database, or replace original parameters with expressive parameters.
在上述任一方案中优选的实施例，所述数据源中的数据为从诊断前第4天到诊断后第8天及第15天的数据，将这13天的特异性参数按参数种类进行分组，然后计算每组内各项参数的权重，加权求和，得到可表征患者诊断前4天到诊断后第15天的综合指标参数，其中，确定参数的计算方式为：In a preferred embodiment of any of the above schemes, the data in the data source is the data from the 4th day before the diagnosis to the 8th day and the 15th day after the diagnosis, and the specific parameters of these 13 days are performed according to the parameter type Grouping, and then calculating the weight of each parameter in each group, weighted summation, to obtain the comprehensive index parameters that can characterize the patient from 4 days before diagnosis to 15 days after diagnosis, and the calculation method of determining the parameters is as follows:
其中，F
i(i＝1，2，3…p)表示p个主成份，a
ij(i＝1，2，3…p；j＝1，2，3…m)为样本数据协方差矩阵的特征值所对应的特征向量；x
i(i＝1，2，3…p)是原始变量经过标准化处理后的值。
Among them, F i (i=1, 2, 3...p) represents p principal components, a ij (i=1, 2, 3...p; j=1, 2, 3...m) is the sample data covariance matrix The eigenvectors corresponding to the eigenvalues of ; x i (i=1, 2, 3...p) are the normalized values of the original variables.
与现有技术相比，本申请实施例的基于大数据的传染病症状监测与预警方法，对疾病分布信息进行地图直观显示和确定病原体可能传播的范围，能够很好地将传染病疫情发生地的地理情况和人群情况与疫情传播情况进行综合分析，得到更有价值的疫情监测与预警数据。Compared with the prior art, the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
第二方面，一种基于大数据的传染病症状监测与预警***，包括：The second aspect is a big data-based infectious disease symptom monitoring and early warning system, including:
选择模块，用于选择需要监测的预警地点，并确定所述预警地点内需要预警的病种；The selection module is used to select the early warning sites that need to be monitored, and determine the diseases that need early warning in the early warning sites;
处理模块，用于采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；The processing module is used to collect the disease type information in the early warning location, and process different disease type information. When it is determined that an epidemic event occurs in the early warning location, send the early warning condition to the server for processing;
判断模块，用于服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。The judging module is used for the server to further judge whether it is an early warning according to the pre-set early warning conditions and the early warning threshold. When the first early warning is completed, the server will return two sets of early warning results, and return the data information that needs to be displayed on the map to the The client browser loads the map.
与现有技术相比，本申请实施例的基于大数据的传染病症状监测与预警方法，对疾病分布信息进行地图直观显示和确定病原体可能传播的范围，能够很好地将传染病疫情发生地的地理情况和人群情况与疫情传播情况进行综合分析，得到更有价值的疫情监测与预警数据。Compared with the prior art, the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一组件分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。后文将参照附图以示例性而非限制性的方式详细 描述本申请的一些具体实施例。附图中相同的附图标记标示了相同或类似的组件件或组件分，本领域技术人员应该理解的是，这些附图未必是按比例绘制的，在附图中：The drawings described here are used to provide a further understanding of the application and constitute a component of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. Hereinafter, some specific embodiments of the present application will be described in detail with reference to the accompanying drawings in an exemplary rather than restrictive manner. The same reference numerals in the drawings indicate the same or similar components or components, and those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:
图1为本申请实施例基于大数据的传染病症状监测与预警方法示意图。Fig. 1 is a schematic diagram of a method for monitoring and early warning of infectious disease symptoms based on big data according to an embodiment of the present application.
图2为本申请实施例基于大数据的传染病症状监测与预警***示意图。Fig. 2 is a schematic diagram of an infectious disease symptom monitoring and early warning system based on big data according to an embodiment of the present application.
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。显然，所描述的实施例仅仅是本申请一组件分的实施例，而不是全组件的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Apparently, the described embodiment is only an embodiment of a component of the present application, rather than an embodiment of the entire component. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.
需要说明的是，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。在本申请的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。It should be noted that the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present application, "plurality" means two or more, unless otherwise specifically defined.
本申请下述实施例以基于大数据的传染病症状监测与预警方法具有前轮和后轮为例进行详细说明本申请的方案，但是此实施例并不能限制本申请保护范围。The following embodiments of this application take the big data-based infectious disease symptom monitoring and early warning method as an example to illustrate the solution of this application in detail, but this embodiment does not limit the scope of protection of this application.
实施例Example
如图1所示，本申请实施例提供了一种基于大数据的传染病症状监测与预警方法，所述方法包括以下步骤：As shown in Figure 1, the embodiment of the present application provides a method for monitoring and early warning of infectious disease symptoms based on big data. The method includes the following steps:
步骤1：选择需要监测的预警地点，并确定所述预警地点内需要预警 的病种；Step 1: Select the early warning locations that need to be monitored, and determine the diseases that need early warning in the early warning locations;
步骤2：采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；Step 2: collect the disease information in the early warning location, and process different disease information, when it is determined that there is an epidemic event in the early warning location, send the early warning conditions to the server for processing;
步骤3：服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。Step 3: The server further judges whether to warn according to the pre-set warning conditions and the warning threshold. When the first warning is completed, the server will return two sets of warning results, and return the data information that needs to be displayed on the map to the client The browser loads the map.
在本发明实施例所述的基于大数据的传染病症状监测与预警方法中，服务器用于接受外部客户的访问，将客户的请求提交给应用程序服务器上布署的web应用程序，这里是传染病疫情预警展示***，再将结果以html形式返回给客户；所述预先设置的预警条件设置在数据库服务器中，其中，数据库服务器存储所有的业务数据、以及供分析处理的传染病数据，其他的还有结合到互联网平台后需要存储的地理空间数据、各级疾控中心哨点医院信息数据以供展示。In the big data-based infectious disease symptom monitoring and early warning method described in the embodiment of the present invention, the server is used to accept the access of external customers, and submit the customer's request to the web application deployed on the application server. The epidemic situation early warning display system returns the results to the client in html form; the pre-set early warning conditions are set in the database server, wherein the database server stores all business data and infectious disease data for analysis and processing, and other There are also geospatial data that needs to be stored after being integrated into the Internet platform, and information data of sentinel hospitals of CDCs at all levels for display.
如图1所示，基于大数据的传染病症状监测与预警方法，所述方法还包括以下步骤：As shown in Figure 1, the infectious disease symptom monitoring and early warning method based on big data, the method also includes the following steps:
预警开始时间为当前时间，预警结束时间为空，为空表示将从当前时间开始一直预警直到***退出；若不为空，则预警的截止时间大于当前时间；还包括以下步骤：当确定预警地点有疫情事件出现时，疫情地址将自动标记到地图上，当疫情地址标记在地图后，地图窗口将自动以病例位置居中窗口。The start time of the warning is the current time, and the end time of the warning is empty. If it is empty, it means that the warning will start from the current time until the system exits; if it is not empty, the cut-off time of the warning is greater than the current time; the following steps are also included: When determining the location of the warning When an epidemic event occurs, the epidemic address will be automatically marked on the map. When the epidemic address is marked on the map, the map window will automatically center the window with the case location.
在本发明实施例所述的基于大数据的传染病症状监测与预警方法中，传染病疫情监测预警***要实现：疾控单位信息(包括疾控中心和哨点医院信息)发布、传染病监测与预警处理、传染病疫情分析模块、传染病疫情应急指挥模块、***管理模块、常用工具包，每一个模块之间相对独立，各级疾控人员只需要关注与自己关心的部分功能。In the infectious disease symptom monitoring and early warning method based on big data described in the embodiment of the present invention, the infectious disease epidemic situation monitoring and early warning system should realize: disease control unit information (including disease control center and sentinel hospital information) release, infectious disease monitoring Each module is relatively independent from early warning processing, infectious disease analysis module, infectious disease emergency command module, system management module, and common toolkit. Disease control personnel at all levels only need to pay attention to some functions that concern them.
在本发明实施例所述的基于大数据的传染病症状监测与预警方法中，疾控单位信息发布展现形式分为两个部分：一个是地图快照部分(各级单位直接定位在地图上同时展开标记可以看到基本信息)、一个是详 情展示部分(提供接口，操作人员可以直接通过此接口进入到疾控中心信息详细介绍部分)；疫情预警部分：对预设范围内的传染病疫情进行监测管理，完成传染病事件实时监测，并针对传染病疫情进行预警，提供基于规则的预警和条件通知机制，在发现符合预警条件的状况时，以多种渠道提供通知和预警，在电子地图上进行标记与报警提示。In the infectious disease symptom monitoring and early warning method based on big data described in the embodiment of the present invention, the information release form of the disease control unit is divided into two parts: one is the map snapshot part (units at all levels are directly positioned on the map and unfolded simultaneously) You can see the basic information by marking), one is the detailed display part (provide an interface, and the operator can directly enter the CDC information detailed introduction part through this interface); the epidemic early warning part: monitor the epidemic situation of infectious diseases within the preset range Management, complete real-time monitoring of infectious disease events, and provide early warning for infectious disease epidemics, provide rule-based early warning and conditional notification mechanism, provide notification and early warning through multiple channels when conditions that meet the early warning conditions are found, and carry out on the electronic map Marking and warning prompts.
在本发明实施例所述的基于大数据的传染病症状监测与预警方法中，疫情分析部分对预设范围的传染病疫情进行分析管理，完成传染病分布分析、传染病疫情统计专题分析、传染病疫情评估分析以及传染病疫情预测分析处理，并在电子地图上进行图表分析数据展示，可进行空间信息量算、空间信息分类、空间信息叠合、缓冲区查询、动态展示、区域分布等多种分析。In the infectious disease symptom monitoring and early warning method based on big data described in the embodiment of the present invention, the epidemic situation analysis part analyzes and manages the infectious disease epidemic situation in the preset range, and completes the infectious disease distribution analysis, infectious disease epidemic situation statistics thematic analysis, infectious disease The evaluation and analysis of the epidemic situation of infectious diseases and the prediction, analysis and processing of the epidemic situation of infectious diseases, and the display of chart analysis data on the electronic map, can perform spatial information calculation, spatial information classification, spatial information superposition, buffer query, dynamic display, regional distribution, etc. kind of analysis.
在本发明实施例所述的基于大数据的传染病症状监测与预警方法中，应急指挥部分包括：路径分析功能、应急预案功能、应急处置功能、环境状况信息、车辆监控功能、疾控资源分布、疾控资源调配；常用工具包模块包括：预设范围内各市、区县级未来7天的天气查询；实时显示当前道路交通拥堵情况；采集经纬度、地址信息、备注信息；加载自定义数据等操作；管理子***：对***进行初始化设置；完成各功能模块的配置操作；对用户进行增、删、改、查操作；对用户权限进行查询和修改操作；对数据库进行备份和恢复操作；记录用户操作日志。In the big data-based infectious disease symptom monitoring and early warning method described in the embodiment of the present invention, the emergency command part includes: path analysis function, emergency plan function, emergency response function, environmental status information, vehicle monitoring function, disease control resource distribution , disease control resource allocation; commonly used toolkit modules include: weather query of cities, districts and counties within the preset range in the next 7 days; real-time display of current road traffic congestion; collection of latitude and longitude, address information, remark information; loading custom data, etc. Operation; management subsystem: initialize the system; complete the configuration of each functional module; add, delete, modify, and check users; query and modify user permissions; backup and restore the database; record User operation log.
如图1所示，基于大数据的传染病症状监测与预警方法，所述方法还包括以下步骤：As shown in Figure 1, the infectious disease symptom monitoring and early warning method based on big data, the method also includes the following steps:
将疫情坐标定位到地图上后，点击疫情标记，则会根据该预警地点地址编码发送请求该预警地点此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，至此完成了疫情标注过程。After locating the epidemic coordinates on the map, click the epidemic mark, and a request will be sent for all the records of the disease in the early warning location according to the address code of the early warning location, and the epidemic result will be displayed in the pop-up information window of the marker, and the epidemic labeling process has been completed. .
如图1所示，基于大数据的传染病症状监测与预警方法，所述两组预警结果分别为病例的地址坐标已经在本地可以找到的情况和病例地址坐标不在本地的情况。As shown in Figure 1, the big data-based infectious disease symptom monitoring and early warning method, the two sets of early warning results are the case where the address coordinates of the case can be found locally and the case where the address coordinates of the case are not local.
如图1所示，基于大数据的传染病症状监测与预警方法，所述方法 还包括以下步骤：As shown in Figure 1, the infectious disease symptom monitoring and early warning method based on big data, described method also includes the following steps:
浏览器接收到返回数据后对两组预警结果数据分处理，将第一坐标组暂存起来，然后将地址组异步发送给服务器处理，等到所有地址的坐标已经取到后，将地址的坐标加入到第一坐标组中，然后直接定位到地图上；疫情坐标定位到地图上后，点击疫情标记，则会根据该地区地址编码发送请求该地区此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，若有疫情发生，根据时间配置好的短信设置进行短信提示；等待下一个预警周期的到来，如果下一个时间周期到来，判断当前时间是否还在预警时间范围内，若不在预警时间范围内则预警到此终止，反之下一次预警开始。After the browser receives the returned data, it processes the two groups of early warning result data separately, temporarily stores the first coordinate group, and then sends the address group asynchronously to the server for processing. After all the coordinates of the addresses have been obtained, add the coordinates of the addresses to the Go to the first coordinate group, and then locate directly on the map; after the epidemic coordinates are located on the map, click the epidemic mark, and a request will be sent for all records of the disease in the area according to the address code of the area, and the epidemic result will be displayed on the mark In the pop-up information window, if there is an epidemic, according to the SMS settings configured by the time, the SMS notification will be sent; wait for the arrival of the next warning period, if the next time period arrives, judge whether the current time is still within the warning time range, if not Within the time range, the pre-warning ends here, and the next pre-warning starts.
如图1所示，基于大数据的传染病症状监测与预警方法，所述需要监测的预警地点为预先设置的区域内的所有的医院，在不同的医院中获取的病种信息为数据源，所述对不同的病种信息进行处理包括：将多个数据源中的数据合并，在一个统一的数据库中进行存储和管理，或将具有表性的参数代替原有参数。As shown in Figure 1, the method of monitoring and early warning of infectious disease symptoms based on big data, the warning locations to be monitored are all hospitals in the pre-set area, and the disease type information obtained in different hospitals is the data source. The processing of different disease information includes: merging data from multiple data sources, storing and managing them in a unified database, or replacing original parameters with representative parameters.
基于大数据的传染病症状监测与预警方法，所述数据源中的数据为从诊断前第4天到诊断后第8天及第15天的数据，将这13天的特异性参数按参数种类进行分组，然后计算每组内各项参数的权重，加权求和，得到可表征患者诊断前4天到诊断后第15天的综合指标参数，其中，确定参数的计算方式为：Infectious disease symptom monitoring and early warning method based on big data, the data in the data source is the data from the 4th day before the diagnosis to the 8th day and the 15th day after the diagnosis, and the specific parameters of these 13 days are divided into parameter types Carry out grouping, then calculate the weight of each parameter in each group, and add the weighted sum to obtain the comprehensive index parameters that can characterize the patient from 4 days before the diagnosis to the 15th day after the diagnosis. The calculation method of determining the parameters is as follows:
其中，F
i(i＝1，2，3…p)表示p个主成份，a
ij(i＝1，2，3…p；j＝1，2，3…m) 为样本数据协方差矩阵的特征值所对应的特征向量；x
i(i＝1，2，3…p)是原始变量经过标准化处理后的值。
Among them, F i (i=1, 2, 3...p) represents p principal components, a ij (i=1, 2, 3...p; j=1, 2, 3...m) is the sample data covariance matrix The eigenvectors corresponding to the eigenvalues of ; x i (i=1, 2, 3...p) are the normalized values of the original variables.
基于主成分可以进一步推出指标的权重，用第一主成分F
1中每个指标所对应的系数乘上第一主成分F
1所对应的贡献率再除以所提取两个主成分的两个贡献率之和，然后加上第二主成分中每个指标所对应的系数乘上第二主成分F
2所对应的贡献率再除以所提取两个主成分的两个贡献率之和，即可得到该指标在总体中的权重W
1，具体计算公式如下所示：W
1＝(a
11*c
1+a
12*c
2)*/(c
1+c
2)，同理可得，所有生理参数的权重W
i(i＝1，2，3，…p)，可通过下式计算得到：W
i＝(a
i1*c
1+a
i2*c
2)*/(c
1+c
2)(i＝1，2，3….p)，在得到一组内各项参数的权重后，加权求和即可最终得到表征各组参数的综合指标y
k：y
k＝w
1k*x
1k+w
2k*x
2k+…+w
pk*x
pk，其中k为需要合并的参数组的个数，因此通过计算指标的权重可以大大降低工作难度，减小工作量。
Based on the principal components, the weight of the indicators can be further deduced, and the coefficient corresponding to each indicator in the first principal component F 1 is multiplied by the contribution rate corresponding to the first principal component F 1 and then divided by the two extracted principal components. The sum of the contribution rates, and then add the coefficient corresponding to each indicator in the second principal component, multiply the contribution rate corresponding to the second principal component F 2 and divide it by the sum of the two contribution rates of the two extracted principal components, The weight W 1 of the indicator in the population can be obtained, and the specific calculation formula is as follows: W 1 =(a 11 *c 1 +a 12 *c 2 )*/(c 1 +c 2 ), similarly, it can be obtained , the weight W i (i=1, 2, 3,...p) of all physiological parameters can be calculated by the following formula: W i =(a i1 *c 1 +a i2 *c 2 )*/(c 1 + c 2 )(i=1, 2, 3....p), after obtaining the weights of each parameter in a group, the weighted summation can finally obtain the comprehensive index y k representing the parameters of each group: y k =w 1k *x 1k +w 2k *x 2k +…+w pk *x pk , where k is the number of parameter groups that need to be combined, so calculating the weight of indicators can greatly reduce the difficulty of work and reduce the workload.
与现有技术相比，本申请实施例的基于大数据的传染病症状监测与预警方法，对疾病分布信息进行地图直观显示和确定病原体可能传播的范围，能够很好地将传染病疫情发生地的地理情况和人群情况与疫情传播情况进行综合分析，得到更有价值的疫情监测与预警数据。Compared with the prior art, the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
第二方面，一种基于大数据的传染病症状监测与预警***，包括：The second aspect is a big data-based infectious disease symptom monitoring and early warning system, including:
选择模块，用于选择需要监测的预警地点，并确定所述预警地点内需要预警的病种；The selection module is used to select the early warning sites that need to be monitored, and determine the diseases that need early warning in the early warning sites;
处理模块，用于采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；The processing module is used to collect the disease type information in the early warning location, and process different disease type information. When it is determined that an epidemic event occurs in the early warning location, send the early warning condition to the server for processing;
判断模块，用于服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。The judging module is used for the server to further judge whether it is an early warning according to the pre-set early warning conditions and the early warning threshold. When the first early warning is completed, the server will return two sets of early warning results, and return the data information that needs to be displayed on the map to the The client browser loads the map.
与现有技术相比，本申请实施例的基于大数据的传染病症状监测与预警方法，对疾病分布信息进行地图直观显示和确定病原体可能传播的 范围，能够很好地将传染病疫情发生地的地理情况和人群情况与疫情传播情况进行综合分析，得到更有价值的疫情监测与预警数据。Compared with the prior art, the big data-based infectious disease symptom monitoring and early warning method of the embodiment of the present application can visually display the disease distribution information on the map and determine the possible spread range of the pathogen, which can well map the epidemic situation of the infectious disease. Comprehensive analysis of geographical conditions, population conditions and the spread of the epidemic situation, to obtain more valuable epidemic monitoring and early warning data.
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中组件分或者全组件技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for the technical features of the components or all components; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technology of each embodiment of the application. scope of the program.
Claims (10)
- 一种基于大数据的传染病症状监测与预警方法，其特征在于，所述方法包括以下步骤：A method for monitoring and early warning of infectious disease symptoms based on big data, characterized in that the method comprises the following steps:步骤1：选择需要监测的预警地点，并确定所述预警地点内需要预警的病种；Step 1: Select the early warning locations that need to be monitored, and determine the diseases that need early warning in the early warning locations;步骤2：采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；Step 2: collect the disease information in the early warning location, and process different disease information, when it is determined that there is an epidemic event in the early warning location, send the early warning conditions to the server for processing;步骤3：服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。Step 3: The server further judges whether to warn according to the pre-set warning conditions and the warning threshold. When the first warning is completed, the server will return two sets of warning results, and return the data information that needs to be displayed on the map to the client The browser loads the map.
- 根据权利要求1所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述方法还包括以下步骤：The method for monitoring and early warning of infectious disease symptoms based on big data according to claim 1, wherein the method further comprises the following steps:预警开始时间为当前时间，预警结束时间为空，为空表示将从当前时间开始一直预警直到***退出；若不为空，则预警的截止时间大于当前时间。The start time of the warning is the current time, and the end time of the warning is empty. If it is empty, it means that the warning will continue from the current time until the system exits; if it is not empty, the cut-off time of the warning is greater than the current time.
- 根据权利要求2所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述方法还包括以下步骤：The method for monitoring and early warning of infectious disease symptoms based on big data according to claim 2, wherein the method further comprises the following steps:当确定预警地点有疫情事件出现时，疫情地址将自动标记到地图上，当疫情地址标记在地图后，地图窗口将自动以病例位置居中窗口。When it is determined that there is an epidemic event in the early warning location, the epidemic address will be automatically marked on the map. When the epidemic address is marked on the map, the map window will automatically center the window with the case location.
- 根据权利要求3所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述方法还包括以下步骤：The method for monitoring and early warning of infectious disease symptoms based on big data according to claim 3, wherein the method further comprises the following steps:将疫情坐标定位到地图上后，点击疫情标记，则会根据该预警地点地址编码发送请求该预警地点此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，至此完成了疫情标注过程。After locating the epidemic coordinates on the map, click the epidemic mark, and a request will be sent for all the records of the disease in the early warning location according to the address code of the early warning location, and the epidemic result will be displayed in the pop-up information window of the marker, and the epidemic labeling process has been completed. .
- 根据权利要求4所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述两组预警结果分别为病例的地址坐标已经在本地可以找到的情况和病例地址坐标不在本地的情况。The big data-based infectious disease symptom monitoring and early warning method according to claim 4, wherein the two groups of early warning results are respectively the case where the address coordinates of the case can be found locally and the case where the address coordinates of the case are not in the local area .
- 根据权利要求5所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述方法还包括以下步骤：The method for monitoring and early warning of infectious disease symptoms based on big data according to claim 5, wherein the method further comprises the following steps:浏览器接收到返回数据后对两组预警结果数据分处理，将第一坐标组暂存起来，然后将地址组异步发送给服务器处理，等到所有地址的坐标已经取到后，将地址的坐标加入到第一坐标组中，然后直接定位到地图上；疫情坐标定位到地图上后，点击疫情标记，则会根据该地区地址编码发送请求该地区此种病所有记录，将以疫情结果显示在标记弹出信息窗口内，若有疫情发生，根据时间配置好的短信设置进行短信提示；等待下一个预警周期的到来，如果下一个时间周期到来，判断当前时间是否还在预警时间范围内，若不在预警时间范围内则预警到此终止，反之下一次预警开始。After the browser receives the returned data, it processes the two groups of early warning result data separately, temporarily stores the first coordinate group, and then sends the address group asynchronously to the server for processing. After all the coordinates of the addresses have been obtained, add the coordinates of the addresses to the Go to the first coordinate group, and then locate directly on the map; after the epidemic coordinates are located on the map, click the epidemic mark, and a request will be sent for all records of the disease in the area according to the address code of the area, and the epidemic result will be displayed on the mark In the pop-up information window, if there is an epidemic, according to the SMS settings configured by the time, the SMS notification will be sent; wait for the arrival of the next warning period, if the next time period arrives, judge whether the current time is still within the warning time range, if not Within the time range, the pre-warning ends here, and the next pre-warning starts.
- 根据权利要求6所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述需要监测的预警地点为预先设置的区域内的所有的医院，在不同的医院中获取的病种信息为数据源。The big data-based infectious disease symptom monitoring and early warning method according to claim 6, wherein the early warning locations to be monitored are all hospitals in the pre-set area, and the disease types obtained in different hospitals Information is the data source.
- 根据权利要求7所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述对不同的病种信息进行处理包括：The big data-based infectious disease symptom monitoring and early warning method according to claim 7, wherein the processing of different disease information includes:将多个数据源中的数据合并，在一个统一的数据库中进行存储和管理，或将具有表性的参数代替原有参数。Merge data from multiple data sources, store and manage them in a unified database, or replace original parameters with expressive parameters.
- 根据权利要求8所述的基于大数据的传染病症状监测与预警方法，其特征在于，所述数据源中的数据为从诊断前第4天到诊断后第8天及第15天的数据，将这13天的特异性参数按参数种类进行分组，然后计算每组内各项参数的权重，加权求和，得到可表征患者诊断前4天到诊断后第15天的综合指标参数，其中，确定参数的计算方式为：The big data-based infectious disease symptom monitoring and early warning method according to claim 8, wherein the data in the data source is from the 4th day before the diagnosis to the 8th day and the 15th day after the diagnosis, The specific parameters of these 13 days are grouped according to the type of parameters, and then the weight of each parameter in each group is calculated, and the weighted sum is obtained to obtain the comprehensive index parameters that can characterize the patient from 4 days before the diagnosis to the 15th day after the diagnosis. Among them, The calculation method to determine the parameters is:
其中，F i(i＝1，2，3…p)表示p个主成份，a ij(i＝1，2，3…p；j＝1，2，3…m)为样本数据协方差矩阵的特征值所对应的特征向量；x i(i＝1，2，3…p)是原始变量经过标准化处理后的值。 Among them, F i (i=1, 2, 3...p) represents p principal components, a ij (i=1, 2, 3...p; j=1, 2, 3...m) is the sample data covariance matrix The eigenvectors corresponding to the eigenvalues of ; x i (i=1, 2, 3...p) are the normalized values of the original variables. - 一种基于大数据的传染病症状监测与预警***，其特征在于，包括：A big data-based infectious disease symptom monitoring and early warning system, characterized in that it includes:选择模块，用于选择需要监测的预警地点，并确定所述预警地点内需要预警的病种；The selection module is used to select the early warning sites that need to be monitored, and determine the diseases that need early warning in the early warning sites;处理模块，用于采集所述预警地点内的病种信息，并对不同的病种信息进行处理，当确定预警地点有疫情事件出现时，发送预警条件到服务器处理；The processing module is used to collect the disease type information in the early warning location, and process different disease type information. When it is determined that an epidemic event occurs in the early warning location, send the early warning condition to the server for processing;判断模块，用于服务器根据预先设置的预警条件，根据预警阀值进一步判断是否预警，当第一次预警完成后，服务器将返回两组预警结果，并将需要展现到地图上的数据信息返回给客户端浏览器加载地图上。The judging module is used for the server to further judge whether it is an early warning according to the pre-set early warning conditions and the early warning threshold. When the first early warning is completed, the server will return two sets of early warning results, and return the data information that needs to be displayed on the map to the The client browser loads the map.
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