CN111276231A - Medical data monitoring method, apparatus, computer equipment and storage medium - Google Patents
Medical data monitoring method, apparatus, computer equipment and storage mediumDownload PDFInfo
- Publication number
- CN111276231A CN111276231ACN202010124635.8ACN202010124635ACN111276231ACN 111276231 ACN111276231 ACN 111276231ACN 202010124635 ACN202010124635 ACN 202010124635ACN 111276231 ACN111276231 ACN 111276231A
- Authority
- CN
- China
- Prior art keywords
- medical
- medical data
- data
- message
- various
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544monitoring processMethods0.000titleclaimsabstractdescription57
- 238000000034methodMethods0.000titleclaimsabstractdescription46
- 238000003860storageMethods0.000titleclaimsabstractdescription14
- 230000004931aggregating effectEffects0.000claimsabstractdescription8
- 238000004891communicationMethods0.000claimsdescription23
- 238000004590computer programMethods0.000claimsdescription18
- 230000005540biological transmissionEffects0.000claimsdescription17
- 238000012545processingMethods0.000claimsdescription12
- 230000002776aggregationEffects0.000claimsdescription11
- 238000004220aggregationMethods0.000claimsdescription11
- 238000005457optimizationMethods0.000claimsdescription11
- 239000000284extractSubstances0.000claimsdescription8
- 238000012806monitoring deviceMethods0.000claimsdescription5
- 238000013473artificial intelligenceMethods0.000abstract1
- 230000001186cumulative effectEffects0.000description12
- 230000006835compressionEffects0.000description10
- 238000007906compressionMethods0.000description10
- 230000008569processEffects0.000description6
- 238000010586diagramMethods0.000description5
- 201000010099diseaseDiseases0.000description5
- 208000037265diseases, disorders, signs and symptomsDiseases0.000description5
- 230000009286beneficial effectEffects0.000description3
- 238000009826distributionMethods0.000description3
- 230000008901benefitEffects0.000description2
- 230000006872improvementEffects0.000description2
- 238000007781pre-processingMethods0.000description2
- 230000001360synchronised effectEffects0.000description2
- 230000004913activationEffects0.000description1
- 230000006399behaviorEffects0.000description1
- 238000005516engineering processMethods0.000description1
- 238000000605extractionMethods0.000description1
- 238000012986modificationMethods0.000description1
- 230000004048modificationEffects0.000description1
- 230000003068static effectEffects0.000description1
- 238000012546transferMethods0.000description1
Images
Classifications
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Landscapes
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本申请涉及计算机技术领域，特别是涉及一种医疗数据监控方法、装置、计算机设备和存储介质。The present application relates to the field of computer technology, and in particular, to a medical data monitoring method, apparatus, computer equipment and storage medium.
背景技术Background technique
医疗保险是指以保险合同约定的医疗行为的发生作为给付保险金的条件，为被保险人接受诊疗期间的医疗费用支出提供保障的保险。企业为了解医疗保险的结算情况，需要对医疗数据进行监控。在传统方式中，是通过将所有的医疗数据按照相同的医疗标签进行分类统计，实现对医疗数据进行监控。Medical insurance refers to insurance that provides insurance coverage for the insured's medical expenses during the period of medical treatment, taking the occurrence of medical behaviors stipulated in the insurance contract as a condition for payment of insurance benefits. In order to understand the settlement of medical insurance, enterprises need to monitor medical data. In the traditional way, the monitoring of medical data is realized by classifying and counting all medical data according to the same medical label.
然而，当医疗数据较复杂时，按照传统方式对医疗数据进行监控，会导致医疗数据的重复统计，导致耗费较多时间，致使无法对就医数据进行有效地监控。因此，如何通过多维度的方式对医疗数据进行有效地监控成为目前需要解决的一个技术问题。However, when the medical data is complex, monitoring the medical data in the traditional way will lead to repeated statistics of the medical data, which will take a lot of time and make it impossible to effectively monitor the medical data. Therefore, how to effectively monitor medical data in a multi-dimensional manner has become a technical problem that needs to be solved at present.
发明内容SUMMARY OF THE INVENTION
基于此，有必要针对上述技术问题，提供一种能够通过多维度的方式对医疗数据进行有效地监控的医疗数据监控方法、装置、计算机设备和存储介质。Based on this, it is necessary to provide a medical data monitoring method, device, computer equipment and storage medium capable of effectively monitoring medical data in a multi-dimensional manner, aiming at the above technical problems.
一种医疗数据监控方法，所述方法包括：A medical data monitoring method, the method comprising:
获取数据监控请求，根据所述数据监控请求获取医疗数据；Obtain a data monitoring request, and obtain medical data according to the data monitoring request;
根据多个维度将所述医疗数据划分为多种医疗数据；dividing the medical data into multiple types of medical data according to multiple dimensions;
根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标；Aggregate each medical data according to a preset time interval to obtain medical indicators corresponding to various medical data;
通过预设私有协议根据所述多种医疗数据对应的医疗指标生成医疗报文；Generate a medical message according to the medical indicators corresponding to the various medical data by using a preset private protocol;
将所述医疗报文发送至网页应用服务器，使得所述网页应用服务器根据所述预设私有协议对所述医疗报文进行解析，得到多种医疗数据对应的医疗指标。Sending the medical message to a web application server, so that the web application server parses the medical message according to the preset private protocol, and obtains medical indicators corresponding to various medical data.
在其中一个实施例中，所述多种医疗数据包括第一医疗数据以及第二医疗数据，所述多个维度包括时间维度以及属性维度，所述根据多个维度将所述医疗数据划分为多种医疗数据包括：In one of the embodiments, the plurality of medical data includes first medical data and second medical data, the multiple dimensions include a time dimension and an attribute dimension, and the medical data is divided into multiple medical data according to the multiple dimensions Types of medical data include:
根据时间维度将所述医疗数据划分为当前医疗数据以及非当前医疗数据；dividing the medical data into current medical data and non-current medical data according to the time dimension;
根据属性维度将所述当前医疗数据划分为第二医疗数据以及第三医疗数据；dividing the current medical data into second medical data and third medical data according to attribute dimensions;
将所述第三医疗数据以及非当前医疗数据作为第一医疗数据。The third medical data and the non-current medical data are used as the first medical data.
在其中一个实施例中，所述根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标包括：In one embodiment, the aggregation of each type of medical data according to a preset time interval to obtain medical indicators corresponding to various medical data includes:
根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据；Extract label data corresponding to each medical label from each medical data according to a preset time interval and multiple medical labels;
将提取的标签数据进行汇总，得到每种医疗数据对应的汇总数据；Summarize the extracted label data to obtain summary data corresponding to each medical data;
将所述每种医疗数据对应的汇总数据进行聚合，得到多种医疗数据对应的医疗指标。Aggregating the summary data corresponding to each type of medical data to obtain medical indicators corresponding to various medical data.
在其中一个实施例中，所述通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文包括：In one embodiment, the generating a medical message according to medical indicators corresponding to various medical data by using a preset private protocol includes:
基于预设私有协议获取所述数据监控请求对应的特定字段，所述预设私有协议为基于传输控制协议进行优化处理后所得到的应用层协议；Acquire a specific field corresponding to the data monitoring request based on a preset private protocol, where the preset private protocol is an application layer protocol obtained after optimization processing based on a transmission control protocol;
获取所述预设私有协议对应的报文配置信息，所述报文配置信息包括所述特定字段所对应的报头格式信息；acquiring message configuration information corresponding to the preset private protocol, where the message configuration information includes header format information corresponding to the specific field;
根据所述报头格式信息对所述特定字段进行优化，得到特定字节数量的特定优化字段；The specific field is optimized according to the header format information to obtain a specific optimized field with a specific number of bytes;
根据所述特定优化字段生成报文报头，将多种医疗数据对应的医疗指标作为报文报体，根据所述报文报头和所述报文报体生成医疗报文。A message header is generated according to the specific optimization field, medical indicators corresponding to various medical data are used as a message body, and a medical message is generated according to the message header and the message body.
在其中一个实施例中，所述多种医疗数据包括第一医疗数据以及第二医疗数据，所述方法还包括：In one of the embodiments, the plurality of medical data includes first medical data and second medical data, and the method further includes:
将所述第一医疗数据对应的医疗指标存储至关系型数据库中以及将所述第二医疗数据对应的医疗指标存储至时序数据库中；storing the medical index corresponding to the first medical data in a relational database and storing the medical index corresponding to the second medical data in a time series database;
当接收到网页应用服务器的数据查询请求时，根据所述数据查询请求在所述关系型数据库以及时序数据库中提取对应的目标指标；When receiving a data query request from the web application server, extract corresponding target indicators from the relational database and the time series database according to the data query request;
根据所述目标指标生成目标报文，根据预设私有协议将所述目标报文发送至网页应用服务器。A target message is generated according to the target indicator, and the target message is sent to the web application server according to a preset private protocol.
一种医疗数据监控装置，所述装置包括：A medical data monitoring device, the device includes:
通信模块，用于获取数据监控请求，根据所述数据监控请求获取医疗数据；a communication module for obtaining a data monitoring request, and obtaining medical data according to the data monitoring request;
划分模块，用于根据多个维度将所述医疗数据划分为多种医疗数据；a dividing module, used for dividing the medical data into various medical data according to multiple dimensions;
聚合模块，用于根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标；The aggregation module is used to aggregate each medical data according to a preset time interval to obtain medical indicators corresponding to various medical data;
生成模块，用于通过预设私有协议根据所述多种医疗数据对应的医疗指标生成医疗报文；a generating module, configured to generate medical messages according to medical indicators corresponding to the various medical data through a preset private protocol;
所述通信模块还用于将所述医疗报文发送至网页应用服务器，使得所述网页应用服务器根据所述预设私有协议对所述医疗报文进行解析，得到多种医疗数据对应的医疗指标。The communication module is further configured to send the medical message to a web application server, so that the web application server parses the medical message according to the preset private protocol, and obtains medical indicators corresponding to various medical data. .
在其中一个实施例中，所述划分模块还用于根据时间维度将所述医疗数据划分为当前医疗数据以及非当前医疗数据；根据属性维度将所述当前医疗数据划分为第二医疗数据以及第三医疗数据；将所述第三医疗数据以及非当前医疗数据作为第一医疗数据。In one embodiment, the dividing module is further configured to divide the medical data into current medical data and non-current medical data according to the time dimension; divide the current medical data into the second medical data and the first medical data according to the attribute dimension Three medical data; the third medical data and the non-current medical data are used as the first medical data.
在其中一个实施例中，所述聚合模块还用于根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据；将提取的标签数据进行汇总，得到每种医疗数据对应的汇总数据；将所述每种医疗数据对应的汇总数据进行聚合，得到多种医疗数据对应的医疗指标。In one embodiment, the aggregation module is further configured to extract label data corresponding to each medical label in each medical data according to a preset time interval and a plurality of medical labels; and summarize the extracted label data to obtain each medical label. The summary data corresponding to each medical data is aggregated; medical indicators corresponding to various medical data are obtained by aggregating the summary data corresponding to each medical data.
一种计算机设备，包括存储器和处理器，所述存储器存储有可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述各个方法实施例中的步骤。A computer device includes a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the steps in each of the foregoing method embodiments are implemented.
一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述各个方法实施例中的步骤。A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the steps in each of the foregoing method embodiments.
上述医疗数据监控方法、装置、计算机设备和存储介质，根据多个维度将医疗数据划分为多种医疗数据，根据预设时间间隔对每种医疗数据分别进行聚合，能够通过多维度的方式减少不必要的数据重复处理操作，有利于提高医疗指标的有效性。通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文，并将医疗报文发送至网页应用服务器，由于私有协议中约定的报文结构简单，数据量小，有效节省了数据传输过程中的流量资源。能够通过多维度的方式对医疗数据进行有效地监控。The above-mentioned medical data monitoring method, device, computer equipment and storage medium divide medical data into various medical data according to multiple dimensions, and aggregate each medical data according to preset time intervals, which can reduce inconvenience in a multi-dimensional manner. Necessary data processing operations are repeated, which is beneficial to improve the effectiveness of medical indicators. Generate medical messages according to medical indicators corresponding to various medical data through a preset private protocol, and send the medical messages to the web application server. Since the message structure agreed in the private protocol is simple and the amount of data is small, data transmission is effectively saved Flow resources in the process. It can effectively monitor medical data in a multi-dimensional way.
附图说明Description of drawings
图1为一个实施例中医疗数据监控方法的应用环境图；1 is an application environment diagram of a medical data monitoring method in one embodiment;
图2为一个实施例中医疗数据监控方法的流程示意图；2 is a schematic flowchart of a medical data monitoring method in one embodiment;
图3为一个实施例中根据多个维度将所述医疗数据划分为多种医疗数据步骤的流程示意图；3 is a schematic flowchart of steps of dividing the medical data into various medical data according to a plurality of dimensions in one embodiment;
图4为一个实施例中医疗数据监控装置的结构框图；4 is a structural block diagram of a medical data monitoring device in one embodiment;
图5为一个实施例中计算机设备的内部结构图。FIG. 5 is a diagram of the internal structure of a computer device in one embodiment.
具体实施方式Detailed ways
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.
本申请提供的医疗数据监控方法，可以应用于如图1所示的应用环境中。其中，网页应用服务器102通过网络与作业服务器104进行通信。作业服务器104获取网页应用服务器102发送的数据监控请求。作业服务器104根据数据监控请求获取医疗数据。作业服务器104根据多个维度将医疗数据划分为多种医疗数据。作业服务器104根据预设时间间隔对每种医疗数据进行聚合，得到每种医疗数据对应的医疗指标。作业服务器104通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文。作业服务器104将医疗报文发送至网页应用服务器102，使得网页应用服务器102根据预设私有协议对医疗报文进行解析，得到多种医疗数据对应的医疗指标数据。其中，网页应用服务器102以及作业服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。The medical data monitoring method provided in this application can be applied to the application environment shown in FIG. 1 . The
在一个实施例中，如图2所示，提供了一种医疗数据监控方法，以该方法应用于图1中的作业服务器为例进行说明，包括以下步骤：In one embodiment, as shown in FIG. 2 , a medical data monitoring method is provided, which is described by taking the method applied to the job server in FIG. 1 as an example, including the following steps:
步骤202，获取数据监控请求，根据数据监控请求获取医疗数据。Step 202: Obtain a data monitoring request, and obtain medical data according to the data monitoring request.
作业服务器获取网页应用服务器发送的数据监控请求，数据监控请求用于对医疗数据进行监控，以了解医疗保险的结算情况。网页应用服务器用于接收作业服务器监控到的医疗数据。作业服务器根据数据监控请求获取医疗数据。医疗数据可以包括疾病数据、住院时间、年龄等。医疗数据的获取方式可以有多种，可以是根据数据监控请求在GP(GreenPlum)数据库中获取的，也可以是根据数据监控请求在数据队列系统中获取的。The job server obtains the data monitoring request sent by the web application server, and the data monitoring request is used to monitor medical data to know the settlement of medical insurance. The web application server is used for receiving the medical data monitored by the job server. The job server obtains medical data according to the data monitoring request. Medical data may include disease data, length of hospital stay, age, etc. There are many ways to obtain medical data, which can be obtained from the GP (GreenPlum) database according to the data monitoring request, or from the data queue system according to the data monitoring request.
步骤204，根据多个维度将医疗数据划分为多种医疗数据。Step 204: Divide the medical data into various medical data according to multiple dimensions.
步骤206，根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标。Step 206: Aggregate each medical data according to a preset time interval to obtain medical indicators corresponding to various medical data.
作业服务器根据多个维度对医疗数据进行划分。划分方式可以是按照多个维度之间的预设划分顺序将医疗数据进行划分。在进行多个维度的划分之后，得到多种医疗数据。The job server divides the medical data according to multiple dimensions. The division method may be to divide the medical data according to a preset division order among multiple dimensions. After the division of multiple dimensions, various medical data are obtained.
作业服务器调用分布式任务调度平台，通过分布式调度平台根据预设时间间隔启动作业服务器，对每种医疗数据进行聚合。不同种类的医疗数据对应的预设时间间隔可以是不同的。例如，预设时间间隔可以是10mins，也可以是24h。作业服务器可以根据预设时间间隔以及医疗标签对每种医疗数据进行聚合，得到每种医疗数据对应的医疗指标，进而得到多种医疗数据对应的医疗标签。医疗标签可以包括年度累计结算人次、当前结算人次、年度累计医疗资源数据、当前医疗资源数据、年度累计消费数据、当前消费数据、年度累计备案数量、当前新增备案数量、预设时间段内的疾病名称、预设时间段内的患者负担、当前患者负担、预设时间段内的年龄分布、就诊人次、就诊金额等。其中，备案数量可以是产生医疗数据时所生成的登记数据。医疗指标可以是医疗标签对应的具体数值。每种医疗数据可以对应多个医疗标签。The job server calls the distributed task scheduling platform, starts the job server according to the preset time interval through the distributed scheduling platform, and aggregates each medical data. The preset time intervals corresponding to different types of medical data may be different. For example, the preset time interval may be 10mins or 24h. The job server can aggregate each medical data according to the preset time interval and medical labels, obtain medical indicators corresponding to each medical data, and then obtain medical labels corresponding to various medical data. The medical label can include the annual cumulative number of settlements, the current settlement, the annual cumulative medical resource data, the current medical resource data, the annual cumulative consumption data, the current consumption data, the annual cumulative number of records, the current number of new records, and the number of records within a preset time period. Disease name, patient burden within a preset time period, current patient burden, age distribution within a preset time period, number of visits, amount of visits, etc. The number of filings may be registration data generated when medical data is generated. The medical index may be a specific value corresponding to the medical label. Each medical data can correspond to multiple medical labels.
步骤208，通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文。Step 208: Generate a medical message according to medical indicators corresponding to various medical data through a preset private protocol.
作业服务器中预先设置有预设私有协议。预设私有协议可以是基于传输控制协议对应用层协议进行优化处理，经过优化处理后得到的针对数据传输业务的应用层协议。预设私有协议用于定义作业服务器与网页应用服务器之间相互传递的报文以及传递方式。预设私有协议可以简称为私有协议。例如，优化处理后得到的私有协议可以重新定义多种报文分别对应的报文结构、报文格式等信息。多种报文可以包括登录报文、医疗报文、鉴权报文以及验活报文等，多种报文之间的结构可以是相同的，也可以是不同的。相较于传统的应用层协议，优化处理后的私有协议可以简化报文结构，在不丢失数据的前提下减少报文的数据量，从而有效的节省传输报文过程中的流量资源。私有协议可以基于传输控制协议实现服务器之间的通信。传输控制协议可以是多种传输层的通信协议中的一种。A preset private protocol is preset in the job server. The preset private protocol may be an application layer protocol for a data transmission service obtained by optimizing an application layer protocol based on a transmission control protocol. The preset private protocol is used to define the messages and transmission methods that are transmitted between the job server and the web application server. The preset private protocol may be simply referred to as a private protocol. For example, the private protocol obtained after the optimization process can redefine information such as message structure and message format corresponding to various messages respectively. The various messages may include login messages, medical messages, authentication messages, and activation messages, etc. The structures of the various messages may be the same or different. Compared with the traditional application layer protocol, the optimized private protocol can simplify the packet structure and reduce the data volume of the packet without losing data, thereby effectively saving the traffic resources during the transmission of the packet. Proprietary protocols can implement communication between servers based on Transmission Control Protocol. The Transmission Control Protocol may be one of various transport layer communication protocols.
作业服务器可以基于私有协议获取数据监控请求对应的特定字段。特定字段可以是优化处理后的私有协议定义的报文结构所包括的指定字段。作业服务器获取私有协议对应的报文配置信息。报文配置信息中可以包括报文结构信息以及报文格式信息，报文结构信息可以包括报头结构信息和报体结构信息。对应的，报文格式信息可以包括报头格式信息和报体格式信息。作业服务器根据报文配置信息生成报文报头，将多种医疗数据对应的医疗指标作为报文报体，根据报文报头和报文报体生成医疗报文。The job server can obtain specific fields corresponding to the data monitoring request based on the private protocol. The specific field may be a specific field included in the message structure defined by the optimized private protocol. The job server obtains the message configuration information corresponding to the private protocol. The message configuration information may include message structure information and message format information, and the message structure information may include header structure information and message body structure information. Correspondingly, the message format information may include header format information and message body format information. The job server generates a message header according to the message configuration information, uses medical indicators corresponding to various medical data as a message body, and generates a medical message according to the message header and the message body.
步骤210，将医疗报文发送至网页应用服务器，使得网页应用服务器根据预设私有协议对医疗报文进行解析，得到多种医疗数据对应的医疗指标数据。Step 210: Send the medical message to the web application server, so that the web application server parses the medical message according to the preset private protocol, and obtains medical index data corresponding to various medical data.
作业服务器可以通过作业服务器与网页应用服务器之间的通信信道将生成的医疗报文发送至网页应用服务器。其中，作业服务器与网页应用服务器之间的通信信道可以是根据私协议对应的传输层传输控制协议建立连接的。具体的，网页应用服务器中设置有与作业服务器相同的私有协议，网页应用服务器可以根据私有协议对医疗报文进行解析。网页应用服务器可以对医疗报文的报文报头进行解析，获取报文报头中的版本信息，进而根据版本信息确定对应版本的私有协议，通过版本相同的私有协议对医疗报文进行解析。网页应用服务器可以根据医疗指标的长度确定报文报体中真正的指标部分，避免指标数据的遗漏，保证指标数据传输的有效性。网页应用服务器可以根据请求类型响应请求所对应的操作，根据数据监控请求将数据进行路由转发，以此实现与作业服务器之间的通信，进而实现通过多维度的方式对医疗数据进行有效地监控。The job server may send the generated medical message to the webpage application server through the communication channel between the job server and the webpage application server. Wherein, the communication channel between the job server and the web page application server may be established according to the transport layer transmission control protocol corresponding to the private protocol. Specifically, the web application server is provided with the same private protocol as the job server, and the web application server can parse the medical message according to the private protocol. The web application server can parse the message header of the medical message, obtain the version information in the message header, and then determine the private protocol of the corresponding version according to the version information, and parse the medical message through the private protocol with the same version. The web application server can determine the real index part in the message body according to the length of the medical index, so as to avoid the omission of index data and ensure the validity of index data transmission. The web application server can respond to the operation corresponding to the request according to the request type, and route and forward the data according to the data monitoring request, so as to realize the communication with the job server, and then realize the effective monitoring of medical data in a multi-dimensional way.
在本实施例中，作业服务器根据多个维度将医疗数据划分为多种医疗数据，根据预设时间间隔对每种医疗数据分别进行聚合，能够通过多维度的方式减少不必要的数据重复处理操作，有利于提高医疗指标的有效性。作业服务器通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文，并将医疗报文发送至网页应用服务器，由于私有协议中约定的报文结构简单，数据量小，有效节省了数据传输过程中的流量资源。能够通过多维度的方式对医疗数据进行有效地监控。In this embodiment, the job server divides the medical data into multiple types of medical data according to multiple dimensions, and aggregates each type of medical data according to preset time intervals, which can reduce unnecessary repeated data processing operations in a multi-dimensional manner. , which is beneficial to improve the effectiveness of medical indicators. The job server generates medical messages according to the medical indicators corresponding to various medical data through a preset private protocol, and sends the medical messages to the web application server. Since the message structure agreed in the private protocol is simple and the amount of data is small, it effectively saves money. Traffic resources during data transfer. It can effectively monitor medical data in a multi-dimensional way.
在一个实施例中，如图3所示，根据多个维度将医疗数据划分为多种医疗数据的步骤包括：In one embodiment, as shown in FIG. 3 , the step of dividing medical data into multiple types of medical data according to multiple dimensions includes:
步骤302，根据时间维度将医疗数据划分为当前医疗数据以及非当前医疗数据。Step 302: Divide the medical data into current medical data and non-current medical data according to the time dimension.
步骤304，根据属性维度将当前医疗数据划分为第二医疗数据以及第三医疗数据。Step 304: Divide the current medical data into second medical data and third medical data according to the attribute dimension.
步骤306，将第三医疗数据以及非当前医疗数据作为第一医疗数据。
多种医疗数据包括第一医疗数据以及第二医疗数据，多个维度包括时间维度以及属性维度，根据多个维度将医疗数据划分为多种医疗数据包括：根据时间维度将医疗数据划分为当前医疗数据以及非当前医疗数据；根据属性维度将当前医疗数据划分为第二医疗数据以及第三医疗数据；将第三医疗数据以及非当前医疗数据作为第一医疗数据。Various medical data include first medical data and second medical data, and multiple dimensions include time dimension and attribute dimension. Dividing medical data into various medical data according to multiple dimensions includes: dividing medical data into current medical data according to time dimension data and non-current medical data; divide the current medical data into second medical data and third medical data according to the attribute dimension; use the third medical data and non-current medical data as the first medical data.
作业服务器可以根据多个维度将医疗数据划分为第一医疗数据以及第二医疗数据。第一医疗数据可以包括不经常调用的当前医疗数据以及非当前医疗数据。第二医疗数据可以包括经常调用的当前医疗数据。具体的，作业服务器根据多个维度中的时间维度将医疗数据划分为当前医疗数据以及非当前医疗数据。作业服务器根据属性维度将当前医疗数据划分为第二医疗数据以及第三医疗数据。第二医疗数据可以包括经常调用的医疗数据。第三医疗数据可以包括不经常调用的医疗数据。属性维度可以包括医疗数据的属性信息。作业服务器进而将第三医疗数据以及非当前医疗数据作为第一医疗数据，从而将将医疗数据划分为第一医疗数据以及第二医疗数据。例如，在当前医疗数据的属性信息为年龄时，则当前医疗数据属于第三医疗数据。在当前医疗数据的属性信息为疾病时，则当前医疗数据属于第二医疗数据。The job server may divide the medical data into first medical data and second medical data according to multiple dimensions. The first medical data may include current medical data that is not frequently called as well as non-current medical data. The second medical data may include frequently called current medical data. Specifically, the job server divides the medical data into current medical data and non-current medical data according to the time dimension among the multiple dimensions. The job server divides the current medical data into second medical data and third medical data according to the attribute dimension. The second medical data may include frequently called medical data. The third medical data may include infrequently called medical data. The attribute dimension may include attribute information of medical data. The job server further regards the third medical data and the non-current medical data as the first medical data, thereby dividing the medical data into the first medical data and the second medical data. For example, when the attribute information of the current medical data is age, the current medical data belongs to the third medical data. When the attribute information of the current medical data is a disease, the current medical data belongs to the second medical data.
在本实施例中，作业服务器根据时间维度以及属性维度将医疗数据划分为第一医疗数据以及第二医疗数据，能够将复杂的医疗数据转化为简单的医疗数据，从而针对每种医疗数据分别进行聚合，避免了数据聚合时的重复操作。进一步有利于提高了医疗指标的有效性，从而通过多维度的方式对医疗数据进行有效地监控。In this embodiment, the job server divides the medical data into the first medical data and the second medical data according to the time dimension and the attribute dimension, and can convert the complex medical data into simple medical data, so that each medical data can be processed separately. Aggregation avoids repeated operations during data aggregation. It is further beneficial to improve the effectiveness of medical indicators, so as to effectively monitor medical data in a multi-dimensional manner.
在一个实施例中，根据预设时间间隔对每种医疗数据进行聚合，得到每种医疗数据对应的医疗指标包括：根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据；将提取的标签数据进行汇总，得到每种医疗数据对应的汇总数据；将每种医疗数据对应的汇总数据进行聚合，得到多种医疗数据对应的医疗指标。In one embodiment, aggregating each medical data according to a preset time interval to obtain a medical indicator corresponding to each medical data includes: extracting each medical data from each medical data according to the preset time interval and a plurality of medical labels The label data corresponding to the label; the extracted label data is aggregated to obtain aggregated data corresponding to each medical data; the aggregated aggregated data corresponding to each medical data is aggregated to obtain medical indicators corresponding to various medical data.
作业服务器通过将医疗数据划分为多种医疗数据。多种医疗数据可以包括第一医疗数据以及第二医疗数据。第一医疗数据可以包括不经常调用的当前医疗数据以及非当前医疗数据。第二医疗数据可以包括经常调用的当前医疗数据。作业服务器对多种医疗数据进行聚合的预设时间间隔可以是不同的。例如，对第一医疗进行聚合的时间间隔可以是24h，对第二医疗数据进行聚合的时间间隔可以是10mins。作业服务器根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据。每种医疗数据可以对应多个医疗标签。例如，当医疗标签为就诊人次时，作业服务器可以将医疗数据中的就诊人次提取出来。The job server divides the medical data into various medical data. The various medical data may include first medical data and second medical data. The first medical data may include current medical data that is not frequently called as well as non-current medical data. The second medical data may include frequently called current medical data. The preset time intervals at which the job server aggregates various medical data may be different. For example, the time interval for aggregating the first medical data may be 24 hours, and the time interval for aggregating the second medical data may be 10 mins. The job server extracts label data corresponding to each medical label from each medical data according to a preset time interval and a plurality of medical labels. Each medical data can correspond to multiple medical labels. For example, when the medical label is the number of visits, the job server can extract the number of visits in the medical data.
作业服务器将提取出来的标签数据进行汇总，得到每种医疗数据对应的汇总数据。作业服务器进而将每种医疗数据对应的汇总数据按照预设顺序进行排列，将排列后的汇总数据聚合为一条数据，得到每种医疗数据对应的医疗指标。作业服务器根据每种医疗数据对应医疗指标得到多种医疗数据对应的医疗指标。医疗指标可以是医疗标签对应的具体数值。第一医疗数据对应的医疗指标可以包括年度累计结算人次、年度累计医疗资源数据、年度累计消费数据、年度累计备案数量、预设时间段内的疾病名称、预设时间段内的患者负担、预设时间段内的年龄分布等。第二医疗数据对应的医疗指标可以包括当前结算人次、当前医疗资源数据、当前消费数据、当前新增备案数量、当前患者负担、就诊人次、就诊金额等。The job server aggregates the extracted tag data to obtain aggregated data corresponding to each medical data. The operation server further arranges the aggregated data corresponding to each medical data in a preset order, and aggregates the arranged aggregated data into one piece of data to obtain medical indicators corresponding to each medical data. The job server obtains medical indicators corresponding to various medical data according to the medical indicators corresponding to each medical data. The medical index may be a specific value corresponding to the medical label. The medical indicators corresponding to the first medical data may include the annual cumulative number of settlements, the annual cumulative medical resource data, the annual cumulative consumption data, the annual cumulative number of filings, the disease name within the preset time period, the patient burden within the preset time period, and the estimated Set the age distribution in the time period, etc. The medical indicators corresponding to the second medical data may include current billing person-times, current medical resource data, current consumption data, current number of new filings, current patient burden, number of visits, and amount of medical visits.
在本实施例中，作业服务器根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据，能够对动态的医疗数据进行及时处理。作业服务器对标签数据进行汇总以及聚合，能够了解多种医疗数据对应的医疗指标，有利于实现医疗数据的有效监控。In this embodiment, the job server extracts the label data corresponding to each medical label in each medical data according to the preset time interval and a plurality of medical labels, so that the dynamic medical data can be processed in time. The job server summarizes and aggregates the label data, and can understand the medical indicators corresponding to various medical data, which is conducive to the effective monitoring of medical data.
在一个实施例中，通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文包括：基于预设私有协议获取数据监控请求对应的特定字段，预设私有协议为基于传输控制协议进行优化处理后所得到的应用层协议；获取预设私有协议对应的报文配置信息，报文配置信息包括特定字段所对应的报头格式信息；根据报头格式信息对特定字段进行优化，得到特定字节数量的特定优化字段；根据特定优化字段生成报文报头，将多种医疗数据对应的医疗指标作为报文报体，根据报文报头和报文报体生成医疗报文。In one embodiment, generating a medical message according to medical indicators corresponding to various medical data by using a preset private protocol includes: acquiring specific fields corresponding to the data monitoring request based on the preset private protocol, and the preset private protocol is based on the transmission control protocol. The application layer protocol obtained after optimization processing; obtain the message configuration information corresponding to the preset private protocol, the message configuration information includes the header format information corresponding to the specific field; optimize the specific field according to the header format information to obtain the specific byte The number of specific optimization fields; the message header is generated according to the specific optimization field, the medical indicators corresponding to various medical data are used as the message body, and the medical message is generated according to the message header and the message body.
作业服务器在获取到数据监控请求后，可以根据私有协议对数据监控请求进行解析，得到数据监控请求对应的请求类型。请求类型可以利用对应的类型标识进行标记。数据监控请求对应的任务类型可以是多个特定字段中的一个。作业服务器可以根据数据监控请求获取医疗数据，对医疗数据进行划分以及聚合后，得到医疗指标。其中，医疗指标可以是用户上传至作业服务器的文本数据，文本数据可以是单个字符，也可以是由多个字符组成的字符串。作业服务器可以统计医疗指标所对应的数据长度。例如，当医疗指标为字符串时，作业服务器可以统计字符串中包括的字符数量。当医疗指标为二进制格式的数据时，作业服务器可以统计二进制数据的位数，确定医疗指标对应的指标长度。After obtaining the data monitoring request, the job server can parse the data monitoring request according to the private protocol to obtain the request type corresponding to the data monitoring request. Request types can be marked with corresponding type identifiers. The task type corresponding to the data monitoring request can be one of several specific fields. The job server can obtain medical data according to the data monitoring request, and obtain medical indicators after dividing and aggregating the medical data. The medical index may be text data uploaded by the user to the job server, and the text data may be a single character or a string composed of multiple characters. The job server can count the data length corresponding to the medical index. For example, when the medical indicator is a string, the job server can count the number of characters included in the string. When the medical index is data in binary format, the job server can count the digits of the binary data to determine the index length corresponding to the medical index.
私有协议可以根据实际业务需求进行更新，更新后得到对应版本的私有协议。对私有协议进行多次更新后可以得到对应多个版本的私有协议。作业服务器可以获取数据监控请求所对应私有协议的版本信息，版本信息中可以包括用于标记私有协议版本信息的版本标识。作业服务器可以根据私有协议获取数据监控请求对应的多个特定字段，特定字段包括数据监控请求对应的请求类型、医疗指标对应的指标长度以及私有协议对应的版本信息。在传统应用层协议中，报文结构中还包括请求方法、应用名称、统一资源定位符以及连接属性等大量字段。相较于传统报文报头，基于私有协议获取的特定字段只包括部分字段，剔除了不必要的字段，从而有效的减少了医疗报文的数据量，节省了在传输医疗报文时作业服务器的通信资源。The private protocol can be updated according to actual business requirements, and the corresponding version of the private protocol can be obtained after the update. After the private protocol is updated multiple times, the private protocol corresponding to multiple versions can be obtained. The job server may obtain version information of the private protocol corresponding to the data monitoring request, and the version information may include a version identifier for marking the version information of the private protocol. The job server can obtain a plurality of specific fields corresponding to the data monitoring request according to the private protocol, and the specific fields include the request type corresponding to the data monitoring request, the indicator length corresponding to the medical indicator, and the version information corresponding to the private protocol. In traditional application layer protocols, the message structure also includes a large number of fields such as request method, application name, uniform resource locator, and connection attributes. Compared with the traditional message header, the specific fields obtained based on the private protocol only include some fields, and unnecessary fields are eliminated, thereby effectively reducing the data volume of the medical message and saving the operation server when transmitting the medical message. communication resources.
作业服务器可以获取数据监控请求的私有协议所对应的报文配置信息。其中，私有协议对应的报文配置信息记载了对于不同的请求类型所对应报文的配置信息。作业服务器可以根据报文配置信息中的报头格式信息对特定字段进行优化。具体的，作业服务器可以从报头格式信息中读取多个特定字段分别对应的字段格式信息，根据字段格式信息将对应的特定字段进行优化。字段格式信息可以记载对应特定字段所需的数据格式。例如，作业服务器可以根据字段格式信息对特定字段进行优化，将特定字段转换为二进制格式的数据。字段格式信息中还可以记载有对应特定字段优化后的数据大小。字段格式信息中可以包括对应特定字段优化后所占的字节数量，每个特定字段所占的字节数量可以是固定的。作业服务器根据字段格式信息将对应的特定字段进行优化后，可以得到特定字节数量的特定优化字段。相较于传统方式简化了特定字段所占用的字节数量，有效的减少了医疗报文的数据量，降低了作业服务器的通信成本。The job server can obtain the message configuration information corresponding to the private protocol of the data monitoring request. The message configuration information corresponding to the private protocol records configuration information for messages corresponding to different request types. The job server can optimize specific fields according to the header format information in the message configuration information. Specifically, the job server may read field format information corresponding to a plurality of specific fields from the header format information, and optimize the corresponding specific fields according to the field format information. The field format information can record the data format required to correspond to a specific field. For example, the job server can optimize a specific field based on the field format information, and convert the specific field to data in binary format. The field format information may further record the optimized data size corresponding to a specific field. The field format information may include the optimized number of bytes corresponding to a specific field, and the number of bytes occupied by each specific field may be fixed. After the job server optimizes the corresponding specific field according to the field format information, the specific optimized field with a specific number of bytes can be obtained. Compared with the traditional method, the number of bytes occupied by a specific field is simplified, the data volume of medical messages is effectively reduced, and the communication cost of the job server is reduced.
作业服务器可以按照报文配置信息中的报头结构信息，将多个优化后的特定优化字段进行拼接，生成报文报头。报文报头的特定优化字段可以包括且仅包括优化后的请求类型、指标长度和版本信息。作业服务器可以获取序列化模型，将医疗数据输入至序列化模型中。序列化模型可以是作业服务器根据数据序列化协议建立并训练得到的数据处理模型。数据序列化协议具体可以是Protocol Buffer协议，Protocol Buffer协议定义了一种高效的结构化数据交换格式，作业服务器可以根据Protocol Buffer协议建立序列化模型。作业服务器可以调用序列化模型对输入的医疗指标进行序列化处理，将医疗指标转换为报体格式信息对应的二进制格式的目标指标，并将目标指标进行封装，得到医疗报文的报文报体。作业服务器将医疗报文传输至网页应用服务器后，网页应用服务器可以通过解析医疗报文得到报文报体，并根据私有协议对应的报体格式信息对报文报体中的目标指标进行反序列化处理，以此得到作业服务器上传的医疗指标。相较于XML格式的字符串，相同数据内容的二进制格式的目标指标对应的数据量更小，传输过程的传输速度更快，减少了医疗报文传输时所耗费的通信资源。即使在网络环境较差的情况下，较小的医疗报文也能减少重传的次数，保证了医疗指标传输的有效性，降低了通信成本。而且，网页应用服务器需要根据私有协议对应的报体格式信息才能反序列化还原医疗指标，有效提高了医疗指标的安全性。The job server can generate a packet header by splicing multiple optimized specific optimized fields according to the header structure information in the packet configuration information. The specific optimization fields of the packet header may include and only include the optimized request type, metric length, and version information. The job server can obtain the serialized model and input medical data into the serialized model. The serialization model may be a data processing model established and trained by the job server according to the data serialization protocol. The data serialization protocol may specifically be the Protocol Buffer protocol. The Protocol Buffer protocol defines an efficient structured data exchange format. The job server can establish a serialization model according to the Protocol Buffer protocol. The job server can call the serialization model to serialize the input medical indicators, convert the medical indicators into target indicators in binary format corresponding to the body format information, and encapsulate the target indicators to obtain the message body of the medical message. . After the job server transmits the medical message to the web application server, the web application server can obtain the message body by parsing the medical message, and reverse the target index in the message body according to the message body format information corresponding to the private protocol. process, so as to obtain the medical indicators uploaded by the job server. Compared with strings in XML format, the target indicators in binary format with the same data content correspond to a smaller amount of data, and the transmission speed during the transmission process is faster, which reduces the communication resources consumed in the transmission of medical messages. Even in a poor network environment, smaller medical packets can reduce the number of retransmissions, ensure the effectiveness of medical index transmission, and reduce communication costs. Moreover, the web application server needs to deserialize and restore the medical index according to the report body format information corresponding to the private protocol, which effectively improves the security of the medical index.
在本实施例中，作业服务器根据预设私有协议获取数据监控请求所对应的特定字段，私有协议是进行优化处理之后所得到的应用层协议。作业服务器根据报文配置信息中的报头格式信息对特定字段进行优化，得到特定字节数量的特定优化字段，根据特定优化字段和医疗指标生成医疗报文。作业服务器将医疗报文发送至网页应用服务器，使得网页应用服务器对医疗报文进行解析，得到医疗指标。医疗报文根据私有协议进行封装，即使医疗报文泄露或者被拦截，在没有私有协议的情况下也无法对医疗报文进行解析，有效的提高了医疗报文中医疗指标的安全性。相较于传统应用层协议包括大量字段的报文报头结构，并且每个字段包括大量的数据，本实施例中通过获取部分特定的字段，简化了报文报头的结构，剔除了不必要的字段，并且对特定字段进行相对应的优化，减少了字段所占的字节，从而减少了生成的医疗报文的数据量，有效的减少了作业服务器传输医疗报文时所耗费的通信资源，降低了作业服务器的通信成本。In this embodiment, the job server obtains specific fields corresponding to the data monitoring request according to a preset private protocol, and the private protocol is an application layer protocol obtained after optimization processing. The job server optimizes specific fields according to the header format information in the message configuration information, obtains specific optimized fields with a specific number of bytes, and generates a medical message according to the specific optimized fields and medical indicators. The job server sends the medical message to the web application server, so that the web application server parses the medical message and obtains the medical index. Medical packets are encapsulated according to private protocols. Even if medical packets are leaked or intercepted, medical packets cannot be parsed without private protocols, which effectively improves the security of medical indicators in medical packets. Compared with the packet header structure of the traditional application layer protocol, which includes a large number of fields, and each field includes a large amount of data, in this embodiment, the structure of the packet header is simplified by acquiring some specific fields, and unnecessary fields are eliminated. , and correspondingly optimize specific fields to reduce the bytes occupied by the fields, thereby reducing the data volume of the generated medical messages, effectively reducing the communication resources consumed by the job server when transmitting medical messages, reducing The communication cost of the job server is reduced.
在一个实施例中，作业服务器获取的特定字段可以包括数据监控请求对应的请求类型、医疗指标对应的指标长度以及私有协议对应的版本信息。作业服务器分别根据特定字段各自对应的字段格式信息对特定字段进行优化，得到二进制格式的包括特定字节数量的特定优化字段，优化后的请求类型占用2个字节，优化后的版本信息占用2个字节，优化后的指标长度占用4个字节。作业服务器通过报文格式信息对特定字段进行优化，可以统一特定字段的数据格式，并简化特定字段，得到较少字节数量的特定优化字段，相较于传统报文字段需要占用大量字节，有效的减少了特定字段所包含的数据量。In one embodiment, the specific fields acquired by the job server may include the request type corresponding to the data monitoring request, the indicator length corresponding to the medical indicator, and the version information corresponding to the private protocol. The job server optimizes a specific field according to the field format information corresponding to the specific field, and obtains a specific optimized field including a specific number of bytes in binary format. The optimized request type occupies 2 bytes, and the optimized version information occupies 2 bytes. bytes, and the optimized indicator length occupies 4 bytes. The job server optimizes specific fields through the message format information, which can unify the data format of specific fields, simplify specific fields, and obtain specific optimized fields with fewer bytes. Compared with traditional message fields, it takes up a lot of bytes. Effectively reduce the amount of data contained in a specific field.
在一个实施例中，作业服务器将医疗指标封装为报文报体之前，还可以对医疗指标进行预处理。其中，作业服务器可以采用多种预处理方式中的一种或多种对医疗指标进行处理。例如，作业服务器的预处理方式可以对医疗指标进行压缩处理，也可以对医疗指标进行加密处理，还可以是压缩处理和加密处理的结合。In one embodiment, before the job server encapsulates the medical indicator into a message body, the medical indicator may also be preprocessed. The job server may use one or more of various preprocessing methods to process the medical index. For example, the preprocessing method of the job server may perform compression processing on the medical indicators, may also perform encryption processing on the medical indicators, or may be a combination of compression processing and encryption processing.
具体的，作业服务器统计医疗指标得到对应的指标长度后，可以将医疗指标对应的指标长度与预设值进行比对。预设值可以是用户根据实际需求预先设置的长度值。当指标长度小于或等于预设值时，作业服务器可以直接根据医疗指标生成报文报体。当指标长度大于预设值时，作业服务器可以调用预设的压缩算法对医疗指标进行压缩，根据数据量较小的压缩后的医疗指标生成报文报体。其中，预设的压缩算法可以是多种压缩算法中的一种或多种。例如，压缩算法可以包括LZW(Lempel-Ziv-Welch Encoding，串表压缩算法)算法以及霍夫曼压缩算法等。作业服务器通过压缩算法对医疗指标进行压缩，减小了对应报文报体的数据量，从而减少了作业服务器传输医疗报文时所耗费的通信资源，降低了作业服务器的通信成本。作业服务器还可以对医疗指标进行加密处理。其中，进行加密处理的医疗指标可以是压缩后的医疗指标，也可以是未压缩的医疗指标。作业服务器可以获取多种加密算法对医疗指标进行加密，根据加密后的医疗指标生成对应的报文报体。例如，加密算法可以是ECC(Elliptic curve cryptography，椭圆加密算法)算法，也可以根据AES(Advanced Encryption Standard，高级加密标准)标准进行加密，还可以调用多种加密算法的结合对医疗指标进行加密。Specifically, after the job server counts the medical indexes to obtain the corresponding index length, the index length corresponding to the medical index may be compared with the preset value. The preset value may be a length value preset by the user according to actual needs. When the index length is less than or equal to the preset value, the job server can directly generate a message body according to the medical index. When the index length is greater than the preset value, the job server may call a preset compression algorithm to compress the medical index, and generate a message body according to the compressed medical index with a smaller amount of data. The preset compression algorithm may be one or more of multiple compression algorithms. For example, the compression algorithm may include LZW (Lempel-Ziv-Welch Encoding, string table compression algorithm) algorithm, Huffman compression algorithm, and the like. The job server compresses the medical indicators through a compression algorithm, which reduces the data volume of the corresponding message body, thereby reducing the communication resources consumed by the job server when transmitting medical messages, and reducing the communication cost of the job server. The job server can also encrypt the medical indicators. The medical index to be encrypted may be a compressed medical index or an uncompressed medical index. The job server can obtain a variety of encryption algorithms to encrypt the medical index, and generate a corresponding message body according to the encrypted medical index. For example, the encryption algorithm may be an ECC (Elliptic curve cryptography, elliptic encryption algorithm) algorithm, or may be encrypted according to an AES (Advanced Encryption Standard, advanced encryption standard) standard, and a combination of multiple encryption algorithms may be invoked to encrypt the medical indicators.
在本实施例中，作业服务器在根据医疗指标生成报文报体之前，可以对医疗指标进行预处理。作业服务器通过调用压缩算法对医疗指标进行压缩处理，根据压缩后的医疗指标生成报文报体，减小了对应报文报体的数据量，从而减少了作业服务器传输医疗报文时所耗费的通信资源，降低了作业服务器的通信成本。作业服务器通过调用加密算法对医疗指标进行加密处理，根据加密后的医疗指标生成报文报体，即使医疗报文泄露或者被拦截，也无法对报文报体中的数据进行解密，有效的提高了医疗报文中医疗指标的安全性。In this embodiment, the job server may preprocess the medical index before generating the message body according to the medical index. The job server compresses the medical index by invoking the compression algorithm, and generates a message body according to the compressed medical index, which reduces the data volume of the corresponding message body, thereby reducing the time spent by the job server in transmitting medical messages. Communication resources, reducing the communication cost of the job server. The job server encrypts the medical indicators by invoking the encryption algorithm, and generates the message body according to the encrypted medical indicators. Even if the medical message is leaked or intercepted, the data in the message body cannot be decrypted, which effectively improves the The security of medical indicators in medical messages is improved.
在一个实施例中，多种医疗数据包括第一医疗数据以及第二医疗数据，上述方法还包括：将第一医疗数据对应的医疗指标存储至关系型数据库中以及将第二医疗数据对应的医疗指标存储至时序数据库中；当接收到网页应用服务器的数据查询请求时，根据数据查询请求在关系型数据库以及时序数据库中提取对应的目标指标；根据目标指标生成目标报文，根据预设私有协议将目标报文发送至网页应用服务器。In one embodiment, the various types of medical data include first medical data and second medical data, and the above method further includes: storing medical indicators corresponding to the first medical data in a relational database and storing medical indicators corresponding to the second medical data in a relational database. The indicators are stored in the time series database; when a data query request from the web application server is received, the corresponding target indicators are extracted from the relational database and the time series database according to the data query request; Send the target message to the web application server.
作业服务器在根据根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标后，可以将医疗指标进行存储。医疗指标可以包括第一医疗数据对应的医疗指标以及第二医疗数据对应的医疗指标。具体的，作业服务器将第一医疗数据对应的医疗指标存储至关系型数据库中，将第二医疗数据对应的医疗指标存储至时序数据库中。例如，关系型数据库可以是mysql数据库，也可以是postgresql数据库。时序数据库可以是pipelinedb数据库。作业服务器接收网页应用服务器发送的数据查询请求。数据查询请求可以是终端将数据查询请求发送至网页应用服务器，网页应用服务器将数据查询请求转发至作业服务器的。网页应用服务器可以通过负载均衡器接收终端发送的数据查询请求，根据数据查询请求调用反向代理服务器将数据查询请求分发至作业服务器。例如，负载均衡器可以是F5，反向代理服务器可以是Nginx。After the job server aggregates each medical data according to a preset time interval to obtain medical indicators corresponding to various medical data, the medical indicators may be stored. The medical indicators may include medical indicators corresponding to the first medical data and medical indicators corresponding to the second medical data. Specifically, the operation server stores the medical index corresponding to the first medical data in the relational database, and stores the medical index corresponding to the second medical data in the time series database. For example, a relational database can be a mysql database or a postgresql database. The time series database can be a pipelinedb database. The job server receives the data query request sent by the web application server. The data query request may be sent by the terminal to the web application server, and the web application server forwards the data query request to the job server. The web application server can receive the data query request sent by the terminal through the load balancer, and call the reverse proxy server according to the data query request to distribute the data query request to the job server. For example, the load balancer could be F5 and the reverse proxy server could be Nginx.
作业服务器对数据查询请求进行解析，得到请求参数。作业服务器根据请求参数在关系型数据库以及时序数据库中提取对应的目标指标。作业服务器根据提取的目标指标生成目标报文，进而根据私有协议将目标报文发送至网页应用服务器。网页应用服务器从而根据私有协议对医疗报文进行解析，得到多种医疗数据对应的医疗指标。网页应用服务器进而将解析得到的医疗指标发送至终端进行显示。例如，显示的第一医疗数据对应的医疗指标可以包括年度累计结算人次、年度累计医疗资源数据、年度累计消费数据、年度累计备案数量、预设时间段内的疾病名称、预设时间段内的患者负担、预设时间段内的年龄分布等。其中，预设时间段可以是一周。显示的第二医疗数据对应的医疗指标可以包括当前结算人次、当前医疗资源数据、当前消费数据、当前新增备案数量、当前患者负担、就诊人次、就诊金额等。The job server parses the data query request to obtain request parameters. The job server extracts the corresponding target indicators from the relational database and the time series database according to the request parameters. The job server generates a target message according to the extracted target indicator, and then sends the target message to the web application server according to the private protocol. The web application server parses the medical message according to the private protocol, and obtains medical indicators corresponding to various medical data. The web application server further sends the medical index obtained by analysis to the terminal for display. For example, the medical indicators corresponding to the displayed first medical data may include the annual cumulative number of settlements, the annual cumulative medical resource data, the annual cumulative consumption data, the annual cumulative filing quantity, the disease name within the preset time period, and the annual accumulated medical resource data. Patient burden, age distribution within a preset time period, etc. The preset time period may be one week. The medical indicators corresponding to the displayed second medical data may include the current settlement number, the current medical resource data, the current consumption data, the current number of newly added records, the current patient burden, the number of visits, the amount of medical visits, and the like.
在本实施例中，作业服务器将第一医疗数据对应的医疗指标存储至关系型数据库中以及将第二医疗数据对应的医疗指标存储至时序数据库中。由于第一医疗数据对应的医疗指标不需要经常被调用，且可能存在于多个表单之间，在进行数据查询时会在多个表单之间进行查询。而关系型数据库可以将相关联的字段建立关联，当对某一字段数据进行修改后，与其相关联的字段数据也会相应自动进行修改，能够保证数据的一致性，进而提高数据查询的准确性。另外，关系型数据库的成本较低。第二医疗数据对应的医疗指标是当前经常被调用的数据，数据查询频次较高，而时序数据库可以降低查询延时，以保证提高数据查询效率。In this embodiment, the operation server stores the medical index corresponding to the first medical data in the relational database and stores the medical index corresponding to the second medical data in the time series database. Since the medical index corresponding to the first medical data does not need to be called frequently, and may exist among multiple forms, the query will be performed among multiple forms during data query. The relational database can associate related fields. When the data of a field is modified, the field data associated with it will also be modified automatically accordingly, which can ensure the consistency of the data and improve the accuracy of the data query. . In addition, relational databases are less expensive. The medical index corresponding to the second medical data is the data that is currently frequently called, and the data query frequency is high, and the time series database can reduce the query delay to ensure the improvement of data query efficiency.
应该理解的是，虽然图2至3的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2至3中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。It should be understood that although the steps in the flowcharts of FIGS. 2 to 3 are shown in sequence according to the arrows, these steps are not necessarily executed in the sequence shown by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in FIGS. 2 to 3 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed and completed at the same time, but may be executed at different times. These sub-steps or stages are not necessarily completed at the same time. The order of execution of the steps is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of sub-steps or stages of other steps.
在一个实施例中，如图4所示，提供了一种医疗数据监控装置，包括：通信模块402、划分模块404、聚合模块406和生成模块408，其中：In one embodiment, as shown in FIG. 4, a medical data monitoring apparatus is provided, including: a
通信模块402，用于获取数据监控请求，根据数据监控请求获取医疗数据。The
划分模块404，用于根据多个维度将医疗数据划分为多种医疗数据。The
聚合模块406，用于根据预设时间间隔对每种医疗数据进行聚合，得到多种医疗数据对应的医疗指标。The
生成模块408，用于通过预设私有协议根据多种医疗数据对应的医疗指标生成医疗报文。The
通信模块402还用于将医疗报文发送至网页应用服务器，使得网页应用服务器根据预设私有协议对医疗报文进行解析，得到多种医疗数据对应的医疗指标。The
在一个实施例中，划分模块404还用于根据时间维度将医疗数据划分为当前医疗数据以及非当前医疗数据；根据属性维度将当前医疗数据划分为第二医疗数据以及第三医疗数据；将第三医疗数据以及非当前医疗数据作为第一医疗数据。In one embodiment, the
在一个实施例中，聚合模块406还用于根据预设时间间隔以及多个医疗标签在每种医疗数据中提取每个医疗标签对应的标签数据；将提取的标签数据进行汇总，得到每种医疗数据对应的汇总数据；将每种医疗数据对应的汇总数据进行聚合，得到多种医疗数据对应的医疗指标。In one embodiment, the
在一个实施例中，生成模块408还用于基于预设私有协议获取数据监控请求对应的特定字段，预设私有协议为基于传输控制协议进行优化处理后所得到的应用层协议；获取预设私有协议对应的报文配置信息，报文配置信息包括特定字段所对应的报头格式信息；根据报头格式信息对特定字段进行优化，得到特定字节数量的特定优化字段；根据特定优化字段生成报文报头，将多种医疗数据对应的医疗指标作为报文报体，根据报文报头和报文报体生成医疗报文。In one embodiment, the
在一个实施例中，上述装置还包括：In one embodiment, the above-mentioned apparatus further comprises:
存储模块，用于将第一医疗数据对应的医疗指标存储至关系型数据库中以及将第二医疗数据对应的医疗指标存储至时序数据库中。The storage module is used for storing the medical index corresponding to the first medical data in the relational database and storing the medical index corresponding to the second medical data in the time series database.
提取模块，用于当接收到网页应用服务器的数据查询请求时，根据数据查询请求在关系型数据库以及时序数据库中提取对应的目标指标。The extraction module is used for extracting corresponding target indicators from the relational database and the time series database according to the data query request when receiving a data query request from the web page application server.
通信模块402，还用于根据目标指标生成目标报文，根据预设私有协议将目标报文发送至网页应用服务器。The
关于医疗数据监控装置的具体限定可以参见上文中对于医疗数据监控方法的限定，在此不再赘述。上述医疗数据监控装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。For specific limitations on the medical data monitoring device, reference may be made to the above limitations on the medical data monitoring method, which will not be repeated here. Each module in the above-mentioned medical data monitoring device may be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
在一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器，其内部结构图可以如图5所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口和数据库。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统、计算机程序和数据库。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的数据库用于存储医疗指标。该计算机设备的网络接口用于与外部的作业服务器通过网络连接通信。该计算机程序被处理器执行时以实现一种医疗数据监控方法。In one embodiment, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as shown in FIG. 5 . The computer device includes a processor, memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The nonvolatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The computer equipment's database is used to store medical indicators. The network interface of the computer device is used to communicate with an external job server through a network connection. The computer program, when executed by the processor, implements a medical data monitoring method.
本领域技术人员可以理解，图5中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。Those skilled in the art can understand that the structure shown in FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.
在一个实施例中，提供了一种计算机设备，包括存储器和处理器，该存储器存储有计算机程序，该处理器执行计算机程序时实现各个方法实施例中的步骤。In one embodiment, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps in the various method embodiments when the processor executes the computer program.
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现各个方法实施例中的步骤。In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, implements the steps in each method embodiment.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。The above-mentioned embodiments only represent several embodiments of the present application, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.
Claims (10)
Translated fromChinesePriority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010124635.8ACN111276231B (en) | 2020-02-27 | 2020-02-27 | Medical data monitoring method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010124635.8ACN111276231B (en) | 2020-02-27 | 2020-02-27 | Medical data monitoring method, device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111276231Atrue CN111276231A (en) | 2020-06-12 |
| CN111276231B CN111276231B (en) | 2024-12-06 |
Family
ID=71000487
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010124635.8AActiveCN111276231B (en) | 2020-02-27 | 2020-02-27 | Medical data monitoring method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111276231B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113505172A (en)* | 2021-07-02 | 2021-10-15 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN113744822A (en)* | 2021-04-19 | 2021-12-03 | 北京京东拓先科技有限公司 | Medical data processing method and device and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102904953A (en)* | 2012-10-12 | 2013-01-30 | Tcl集团股份有限公司 | Remote medical service system and remote medical service method |
| CN108205587A (en)* | 2017-12-28 | 2018-06-26 | 北京九章云极科技有限公司 | A kind of daily record monitoring method and system |
| CN109036576A (en)* | 2018-07-23 | 2018-12-18 | 无锡慧方科技有限公司 | Electronic health record data analysis method, device, computer and readable storage medium storing program for executing |
| CN109558397A (en)* | 2018-10-30 | 2019-04-02 | 平安医疗健康管理股份有限公司 | A kind of data processing method, device, server and computer storage medium |
| CN109947751A (en)* | 2018-12-29 | 2019-06-28 | 医渡云（北京）技术有限公司 | A kind of medical data processing method, device, readable medium and electronic equipment |
| CN110659298A (en)* | 2019-08-14 | 2020-01-07 | 金蝶软件（中国）有限公司 | Financial data processing method and device, computer equipment and storage medium |
| CN110677271A (en)* | 2019-08-16 | 2020-01-10 | 平安科技（深圳）有限公司 | Big data alarm method, device, equipment and storage medium based on ELK |
- 2020
- 2020-02-27CNCN202010124635.8Apatent/CN111276231B/enactiveActive
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102904953A (en)* | 2012-10-12 | 2013-01-30 | Tcl集团股份有限公司 | Remote medical service system and remote medical service method |
| CN108205587A (en)* | 2017-12-28 | 2018-06-26 | 北京九章云极科技有限公司 | A kind of daily record monitoring method and system |
| CN109036576A (en)* | 2018-07-23 | 2018-12-18 | 无锡慧方科技有限公司 | Electronic health record data analysis method, device, computer and readable storage medium storing program for executing |
| CN109558397A (en)* | 2018-10-30 | 2019-04-02 | 平安医疗健康管理股份有限公司 | A kind of data processing method, device, server and computer storage medium |
| CN109947751A (en)* | 2018-12-29 | 2019-06-28 | 医渡云（北京）技术有限公司 | A kind of medical data processing method, device, readable medium and electronic equipment |
| CN110659298A (en)* | 2019-08-14 | 2020-01-07 | 金蝶软件（中国）有限公司 | Financial data processing method and device, computer equipment and storage medium |
| CN110677271A (en)* | 2019-08-16 | 2020-01-10 | 平安科技（深圳）有限公司 | Big data alarm method, device, equipment and storage medium based on ELK |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113744822A (en)* | 2021-04-19 | 2021-12-03 | 北京京东拓先科技有限公司 | Medical data processing method and device and storage medium |
| CN113505172A (en)* | 2021-07-02 | 2021-10-15 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN113505172B (en)* | 2021-07-02 | 2023-10-24 | 泰康保险集团股份有限公司 | Data processing method, device, electronic equipment and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111276231B (en) | 2024-12-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111224864A (en) | Message transmission method and device, computer equipment and storage medium | |
| CN111274157B (en) | Test data simulation method and device, computer equipment and storage medium | |
| CN109067733B (en) | Method and apparatus for transmitting data, and method and apparatus for receiving data | |
| WO2019095416A1 (en) | Information pushing method and apparatus, and terminal device and storage medium | |
| CN111782727B (en) | Data processing method and device based on machine learning | |
| US20220353248A1 (en) | Identifying and Securing Unencrypted Data in a Production Environment | |
| CN111276231B (en) | Medical data monitoring method, device, computer equipment and storage medium | |
| CN112422588A (en) | Internet of things equipment information processing method and system | |
| CN111367686A (en) | Service interface calling method and device, computer equipment and storage medium | |
| CN114201511A (en) | Project management and control method and device, computer equipment and storage medium | |
| CN114362885A (en) | Internet of things data transmission method, device, equipment and medium | |
| CN110689348B (en) | Revenue verification method, device, terminal and medium based on alliance chain | |
| CN114257593A (en) | Communication method, device, equipment and storage medium of block chain system | |
| CN115878351B (en) | Message transmission method and device, storage medium and electronic device | |
| CN113051024B (en) | Virtual machine live migration method and device, electronic equipment and storage medium | |
| CN116634042A (en) | Big data message analysis method, device and storage medium | |
| CN115277409A (en) | Method and device for collecting and reporting buried point data in real time, acquisition system and terminal | |
| CN113468574B (en) | A method and device for uploading blockchain data | |
| CN102438048B (en) | Method and system for calling remote service from Internet | |
| CN118573755A (en) | Data analysis method compatible with various internet of things transmission protocols | |
| CN116302178B (en) | Acceleration processing method and device for column storage data | |
| CN113163028A (en) | Service data transmission method, device and system | |
| HK40024125A (en) | Medical data monitoring method and device, computer equipment and storage medium | |
| CN107800758B (en) | Risk control data processing method, device and system | |
| EP4465618A1 (en) | Data acquisition and transmission method, vehicle cloud service platform, vehicle, and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| REG | Reference to a national code | Ref country code:HK Ref legal event code:DE Ref document number:40024125 Country of ref document:HK | |
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right | Effective date of registration:20220520 Address after:518048 China Aviation Center 2901, No. 1018, Huafu Road, Huahang community, Huaqiang North Street, Futian District, Shenzhen, Guangdong Province Applicant after:Shenzhen Ping An medical and Health Technology Service Co.,Ltd. Address before:Room 12G, Area H, 666 Beijing East Road, Huangpu District, Shanghai 200001 Applicant before:PING AN MEDICAL AND HEALTHCARE MANAGEMENT Co.,Ltd. | |
| GR01 | Patent grant | ||
| GR01 | Patent grant |