阅读说明：本技术 一种高性能、高容错、可扩展的医学数据采集方法及系统 (High-performance, high-fault-tolerance and extensible medical data acquisition method and system ) 是由 徐辉 吴鹏 秦浩 夏登栖 于 2021-07-20 设计创作，主要内容包括：本发明公开了一种高性能、高容错、可扩展的医学数据采集方法及系统,该方法包括以下步骤：对各类待采集医学数据的采集方式及对应的配置参数进行确认；完成对应参数、边缘服务器、关联校验规则及数据补偿规则的配置；建立采集任务；利用边缘服务器对各类医学数据进行采集、解析及上传；对存储的散列值进行计算,且当存在数据缺失时发送对应的数据补偿请求；通过数据采集组件并根据接收到的数据补偿命令,同时结合数据解析组件、数据上传组件、消息中间件及远程字典服务集群,共同完成对应的数据补偿。有益效果：大幅度提升了数据采集的效率,增强了时效性,有效提高了数据的完整性,有效提升了系统的容错率。(The invention discloses a high-performance, high-fault-tolerance and extensible medical data acquisition method and a system, wherein the method comprises the following steps: confirming the acquisition modes and corresponding configuration parameters of various medical data to be acquired; completing the configuration of corresponding parameters, an edge server, an association check rule and a data compensation rule; establishing an acquisition task; collecting, analyzing and uploading various medical data by using an edge server; calculating the stored hash value, and sending a corresponding data compensation request when data is missing; and corresponding data compensation is jointly completed through the data acquisition component and according to the received data compensation command by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster. Has the advantages that: the efficiency of data acquisition is greatly improved, the timeliness is enhanced, the integrity of data is effectively improved, and the fault tolerance of the system is effectively improved.)

1. A high performance, high fault tolerance, scalable medical data acquisition method, comprising the steps of:

s1, confirming the acquisition modes and corresponding configuration parameters of various medical data to be acquired;

s2, according to the confirmed acquisition mode of various medical data, completing the configuration of corresponding parameters, edge servers, associated check rules and data compensation rules;

s3, establishing various medical data acquisition tasks;

s4, collecting, analyzing and uploading various medical data by using an edge server according to the corresponding requirements of the collection task;

s5, calculating the stored hash value through the remote dictionary service cluster according to the association check rule, and sending a corresponding data compensation request when data are missing;

and S6, completing corresponding data compensation jointly through the data acquisition component and according to the received data compensation command by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster.

2. The method according to claim 1, wherein said S1 is configured to confirm the acquisition mode and corresponding configuration parameters of various types of medical data to be acquired, including but not limited to data generated by hospital information systems, radiology information systems, and image archiving and communication systems;

wherein the data comprises structured data and unstructured data;

the acquisition mode includes but is not limited to application program interface, database view, medical digital imaging and communication and file transfer protocol;

the configuration parameters corresponding to the database view acquisition mode include, but are not limited to, a view name, a database type, a data type, an IP address, a port number, a database user name, a database password and a database name;

the configuration parameters corresponding to the medical digital imaging and communication acquisition mode include, but are not limited to, an edge server application entity name, an edge server IP address, an edge server port number, a courtyard image archiving and communication system application entity name, a courtyard image archiving and communication system IP address, a courtyard image archiving and communication system port number, and a data compensation type;

the configuration parameters corresponding to the file transfer protocol acquisition mode include, but are not limited to, a file transfer protocol name, a file transfer mode, a file transfer protocol server address, a port number, a file transfer protocol server user name, and a file transfer protocol server password.

3. The method according to claim 1, wherein in S2, according to the confirmed acquisition modes of various types of medical data, configuring corresponding parameters, edge servers, association check rules, and data compensation rules is performed, wherein the configuration of the edge servers refers to deploying acquisition programs on the edge servers, and the acquisition programs include structured data acquisition components, unstructured data acquisition components, data parsing components, data uploading components, and data compensation components;

the association check rule includes but is not limited to an association field configuration and an association logic configuration;

the data compensation rules include, but are not limited to, compensation field configuration and compensation logic configuration;

when the type of the collected data needs to be expanded according to the actual service requirement, the steps S1-S2 are re-executed for newly added data collection.

4. The method of claim 1, wherein the step of establishing at S3 a task of acquiring medical data of different types further comprises the steps of:

s31, when the collection task is established, self-defining setting is carried out on the execution time, the execution frequency and the execution mode of the task;

and S32, adapting the actual operation condition of each medical institution.

5. The method according to claim 1, wherein the step S4 of collecting, analyzing and uploading various types of medical data by using an edge server according to the requirement corresponding to the collection task further comprises the following steps:

s41, the edge server collects various medical data through the data collection assembly and temporarily stores the medical data in the edge server;

and S42, processing the unstructured data and the structured data.

6. The method of claim 5, wherein the data collection component collects the structured medical data and the unstructured medical data respectively through a structured data collection component and an unstructured data collection component.

7. The method of claim 5, wherein the step of processing unstructured data in S42 further comprises the steps of:

the edge server uploads the unstructured data to a cloud end through the data uploading component and carries out cloud storage;

the edge server conducts structured analysis on the unstructured data through the data analysis component, and transmits the analyzed data to the cloud relational database through the message middleware and stores the analyzed data;

the cloud relational database sends the related commands of the analyzed data to the remote dictionary service cluster, and the remote dictionary service cluster stores corresponding hash values according to the commands;

the data analysis component analyzes the medical image file according to the medical digital imaging and communication 3.0 standard, so that the medical image file becomes available structured data and is stored.

8. The method of claim 5, wherein the step of processing the structured data in S42 further comprises the steps of:

the edge server transmits the structured data to a cloud relational database through message middleware and stores the structured data;

and the cloud relational database sends the related commands of the structured data to the remote dictionary service cluster, and the remote dictionary service cluster stores the corresponding hash values according to the commands.

9. The high-performance, high-fault-tolerance and scalable medical data collection method according to claim 1, wherein the step of calculating the stored hash value by the remote dictionary service cluster according to the association check rule in S5, and sending the corresponding data compensation request when there is data missing further comprises the steps of:

s51, when data are missing, according to the verification result and the configured data compensation rule, sending a compensation request corresponding to the missing data to the message middleware;

and S52, the message middleware sends the compensation request corresponding to the missing data to the data compensation component, and the data compensation component sends a specific data compensation command to the data acquisition component according to the received compensation request.

10. A high-performance, high-fault-tolerant, scalable medical data acquisition system for implementing a high-performance, high-fault-tolerant, scalable medical data acquisition method according to any one of claims 1-9, the system comprising: the system comprises a confirmation module, a rule configuration module, a task establishment module, a medical data processing module, a data compensation sending module and a data compensation receiving module;

the confirmation module is used for confirming the acquisition modes and the corresponding configuration parameters of various medical data to be acquired;

the rule configuration module is used for completing the configuration of corresponding parameters, the edge server, the association check rule and the data compensation rule according to the confirmed acquisition mode of various medical data;

the task establishing module is used for establishing acquisition tasks of various medical data;

the medical data processing module is used for acquiring, analyzing and uploading various medical data by utilizing the edge server according to the requirements corresponding to the acquisition tasks;

the data compensation sending module is used for calculating the stored hash value through the remote dictionary service cluster according to the association check rule and sending a corresponding data compensation request when data are missing;

and the data compensation receiving module is used for jointly completing corresponding data compensation by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster through the data acquisition component according to the received data compensation command.

Technical Field

The invention relates to the technical field of medical data processing, in particular to a high-performance, high-fault-tolerance and extensible medical data acquisition method and system.

Background

With the continuous acceleration of the medical informatization process, the method can be effectively applied to various medical fields by acquiring, storing, inquiring, counting, analyzing and reasoning the mass medical data of each medical institution, and plays an important role in improving the efficiency of medical systems, enhancing the quality of medical services, optimizing clinical decision paths, realizing personalized medical services and the like. The diversity, timeliness and integrity of medical data acquisition are one of the foundation and difficulty of medical information construction.

Because medical data are various, medical informatization construction is bound by manufacturers for a long time, the number of manufacturers of medical systems is large, different hospitals and even different systems in the same hospital can come from different manufacturers, data acquisition modes and complied protocols in systems produced by various manufacturers are different, and the requirements of the medical data on integrity and instantaneity are far higher than those of other industrial data, the technical problem to be solved is how to efficiently and accurately finish the complete and correct acquisition of various types of data so as to effectively apply the data in various scenes.

Currently, only about 5% of medical institutions implement the intercommunication of medical data, and most of the medical institutions use the traditional data acquisition mode. The traditional data acquisition mode only supports acquisition of a single data type, does not meet the requirement of diversity of medical data, if various types of data need to be acquired, secondary development needs to be carried out aiming at different acquisition modes corresponding to the data types, aggregation of various types of data can be completed usually after several months, and the cost of manpower and material resources is huge and the time cost is too high. In addition, the traditional acquisition architecture only supports the execution of single concurrent acquisition tasks, the acquisition timeliness is poor due to the fact that the data volume of medical data is usually large, the business requirements of the medical industry are difficult to meet, a verification mechanism is lacked in the acquisition process, the integrity of data acquisition cannot be guaranteed, the usability of the medical data is greatly reduced, and the realization of the sharing and intercommunication vision of the medical data is hindered.

An effective solution to the problems in the related art has not been proposed yet.

Disclosure of Invention

Aiming at the problems in the related art, the invention provides a high-performance, high-fault-tolerance and extensible medical data acquisition method and system, so as to overcome the technical problems in the prior related art.

Therefore, the invention adopts the following specific technical scheme:

according to an aspect of the present invention, there is provided a high-performance, high-fault-tolerant, scalable medical data acquisition method, comprising the steps of:

s1, confirming the acquisition modes and corresponding configuration parameters of various medical data to be acquired;

s2, according to the confirmed acquisition mode of various medical data, completing the configuration of corresponding parameters, edge servers, associated check rules and data compensation rules;

s3, establishing various medical data acquisition tasks;

s4, collecting, analyzing and uploading various medical data by using an edge server according to the corresponding requirements of the collection task;

s5, calculating the stored hash value through the remote dictionary service cluster according to the association check rule, and sending a corresponding data compensation request when data are missing;

and S6, completing corresponding data compensation jointly through the data acquisition component and according to the received data compensation command by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster.

Further, the S1 confirms the acquisition mode and the corresponding configuration parameters of various medical data to be acquired, where the medical data to be acquired includes, but is not limited to, data generated by a hospital information system, a radiology information system, and an image archiving and communication system;

wherein the data comprises structured data and unstructured data;

the acquisition mode includes but is not limited to application program interface, database view, medical digital imaging and communication and file transfer protocol;

the configuration parameters corresponding to the database view acquisition mode include, but are not limited to, a view name, a database type, a data type, an IP address, a port number, a database user name, a database password and a database name;

the configuration parameters corresponding to the medical digital imaging and communication acquisition mode include, but are not limited to, an edge server application entity name, an edge server IP address, an edge server port number, a courtyard image archiving and communication system application entity name, a courtyard image archiving and communication system IP address, a courtyard image archiving and communication system port number, and a data compensation type;

the configuration parameters corresponding to the file transfer protocol acquisition mode include, but are not limited to, a file transfer protocol name, a file transfer mode, a file transfer protocol server address, a port number, a file transfer protocol server user name, and a file transfer protocol server password.

Further, in the step S2, according to the confirmed acquisition modes of various medical data, configuring corresponding parameters, an edge server, an association check rule, and a data compensation rule is completed, where the configuration of the edge server refers to deploying an acquisition program on the edge server, and the acquisition program includes a structured data acquisition component, an unstructured data acquisition component, a data analysis component, a data upload component, and a data compensation component;

the association check rule includes but is not limited to an association field configuration and an association logic configuration;

the data compensation rules include, but are not limited to, compensation field configuration and compensation logic configuration;

when the type of the collected data needs to be expanded according to the actual service requirement, the steps S1-S2 are re-executed for newly added data collection.

Further, the establishing of the task of acquiring various types of medical data in S3 further includes the following steps:

s31, when the collection task is established, self-defining setting is carried out on the execution time, the execution frequency and the execution mode of the task;

and S32, adapting the actual operation condition of each medical institution.

Further, the step S4 of collecting, analyzing and uploading various medical data by using the edge server according to the requirement corresponding to the collection task further includes the following steps:

s41, the edge server collects various medical data through the data collection assembly and temporarily stores the medical data in the edge server;

and S42, processing the unstructured data and the structured data.

Furthermore, the data acquisition component respectively acquires the structured medical data and the unstructured medical data through the structured data acquisition component and the unstructured data acquisition component.

Further, the processing the unstructured data in S42 further includes the following steps:

the edge server uploads the unstructured data to a cloud end through the data uploading component and carries out cloud storage;

the edge server conducts structured analysis on the unstructured data through the data analysis component, and transmits the analyzed data to the cloud relational database through the message middleware and stores the analyzed data;

the cloud relational database sends the related commands of the analyzed data to the remote dictionary service cluster, and the remote dictionary service cluster stores corresponding hash values according to the commands;

the data analysis component analyzes the medical image file according to the medical digital imaging and communication 3.0 standard, so that the medical image file becomes available structured data and is stored.

Further, the processing the structured data in S42 further includes the following steps:

the edge server transmits the structured data to a cloud relational database through message middleware and stores the structured data;

and the cloud relational database sends the related commands of the structured data to the remote dictionary service cluster, and the remote dictionary service cluster stores the corresponding hash values according to the commands.

Further, in S5, the step of calculating the stored hash value according to the association check rule by using the remote dictionary service cluster, and sending the corresponding data compensation request when there is data missing further includes the following steps:

s51, when data are missing, according to the verification result and the configured data compensation rule, sending a compensation request corresponding to the missing data to the message middleware;

and S52, the message middleware sends the compensation request corresponding to the missing data to the data compensation component, and the data compensation component sends a specific data compensation command to the data acquisition component according to the received compensation request.

According to another aspect of the present invention, there is provided a high performance, high fault tolerance, scalable medical data acquisition system, comprising: the system comprises a confirmation module, a rule configuration module, a task establishment module, a medical data processing module, a data compensation sending module and a data compensation receiving module;

the confirmation module is used for confirming the acquisition modes and the corresponding configuration parameters of various medical data to be acquired;

the rule configuration module is used for completing the configuration of corresponding parameters, the edge server, the association check rule and the data compensation rule according to the confirmed acquisition mode of various medical data;

the task establishing module is used for establishing acquisition tasks of various medical data;

the medical data processing module is used for acquiring, analyzing and uploading various medical data by utilizing the edge server according to the requirements corresponding to the acquisition tasks;

the data compensation sending module is used for calculating the stored hash value through the remote dictionary service cluster according to the association check rule and sending a corresponding data compensation request when data are missing;

and the data compensation receiving module is used for jointly completing corresponding data compensation by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster through the data acquisition component according to the received data compensation command.

The invention has the beneficial effects that:

(1) according to the invention, the acquisition program is deployed on the edge server, so that the high-efficiency acquisition of various medical data is realized, and the concurrent execution of a plurality of acquisition tasks is supported, so that the data acquisition efficiency is greatly improved, and the timeliness is enhanced.

(2) The invention also introduces a remote dictionary service cluster, performs correlation verification on the acquired data, automatically compensates the missing data and effectively improves the integrity of the data.

(3) The invention provides the data source modular configuration aiming at the acquisition modes of different data types, can carry out rapid expansion and adaptation aiming at different manufacturers and different data sources, and improves the expandability of data acquisition. In addition, the invention adopts a componentization mode, and each component operates independently, thereby effectively improving the fault tolerance rate of the system.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a schematic flow diagram of a high performance, high fault tolerance, scalable method of medical data acquisition according to an embodiment of the invention;

fig. 2 is a block diagram of the structure for realizing the data intercommunication between the yards and the courtyards.

Detailed Description

For further explanation of the various embodiments, the drawings which form a part of the disclosure and which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of operation of the embodiments, and to enable others of ordinary skill in the art to understand the various embodiments and advantages of the invention, and, by reference to these figures, reference is made to the accompanying drawings, which are not to scale and wherein like reference numerals generally refer to like elements.

According to the embodiment of the invention, a high-performance, high-fault-tolerance and extensible medical data acquisition method and system are provided, which are used for solving the problem of how to efficiently realize the acquisition of various types of medical data and improving the timeliness, integrity, expandability and fault-tolerance rate of data acquisition, thereby enhancing the usability of the medical data and realizing the vision of sharing and intercommunication.

According to the invention, the acquisition program is deployed on the edge server, so that the high-efficiency acquisition of various medical data is realized, and the concurrent execution of a plurality of acquisition tasks is supported, so that the data acquisition efficiency is greatly improved, and the timeliness is enhanced. The invention also introduces a message middleware and a remote dictionary service cluster (redis cluster), performs correlation check on the acquired data, automatically compensates the missing data, and effectively improves the integrity of the data. In addition, the invention provides the data source modular configuration aiming at the acquisition modes of different data types, is favorable for rapidly realizing the access of a newly added data source and improves the expandability of the data source. Furthermore, the invention adopts a modularization mode, the acquisition program is divided into the acquisition component, the analysis component, the uploading component and the compensation component, and each component operates independently, thereby effectively improving the fault tolerance of the system.

Referring now to the drawings and the detailed description, the present invention will be further described, as shown in fig. 1, according to one aspect of the present invention, there is provided a high performance, high fault tolerance and scalable medical data acquisition method, comprising the steps of:

s1, confirming the acquisition modes and corresponding configuration parameters of various medical data to be acquired;

the medical data to be acquired may be data generated by a Hospital Information System (HIS), a Radiology Information System (RIS), a Picture Archiving and Communication System (PACS), etc., including structured data (e.g., HIS/RIS/computerized medical records system data) and unstructured data (e.g., digital medical imaging and communication/non-digital medical imaging and communication image data).

The acquisition modalities include, but are not limited to, Application Program Interface (API), database view, digital imaging and communications in medicine (DICOM), File Transfer Protocol (FTP), and the like.

The configuration parameters corresponding to the database view acquisition mode include, but are not limited to: view name, database type, data type, IP address, port number, database user name, database password, database name.

The configuration parameters corresponding to the medical digital imaging and communication acquisition modes include but are not limited to: edge server application entity name (edge server AE tlle), edge server IP address, edge server port number, courtyard picture archiving and communication system application entity name (courtyard PACS system AE tlle), courtyard picture archiving and communication system IP address (courtyard PACS system IP address), courtyard picture archiving and communication system port number (courtyard PACS system port number), and data compensation type (Q/R type).

The configuration parameters corresponding to the file transfer protocol acquisition mode include, but are not limited to: file transfer protocol name, file transfer protocol (FTP/SFTP), file transfer protocol server address, port number, file transfer protocol server username, and file transfer protocol server password.

Since manufacturers of the systems may be different from each other, in an actual implementation process, the acquisition modes and configuration parameters of the medical data of each type need to be determined by being respectively connected with different manufacturers, and further description is omitted for other acquisition modes and corresponding configuration parameters.

S2, according to the confirmed acquisition mode of various medical data, completing the configuration of corresponding parameters, edge servers, associated check rules and data compensation rules;

the configuration of the edge server specifically refers to deploying an acquisition program on the edge server, wherein the acquisition program comprises a structured data acquisition component, an unstructured data acquisition component, a data analysis component, a data uploading component and a data compensation component. The components are independent from each other, the deployment of the components in the acquisition program can be completed according to the acquisition mode corresponding to the type of the medical data to be acquired, and the fault tolerance rate and the availability of the system are effectively improved.

The association check rule specifically comprises association field configuration, association logic configuration and the like; the data compensation rule specifically includes a compensation field configuration, a compensation logic configuration, and the like. The rules can be flexibly configured according to requirements so as to be adaptive to the actual conditions of various medical institutions.

The invention provides the data source modular configuration aiming at the acquisition modes of different data types, is beneficial to quickly realizing the access of a newly added data source and improves the expandability of the data source. When the type of the collected data needs to be expanded according to the actual service requirement, the steps S1-S2 are executed again only for newly added data collection.

S3, establishing various medical data acquisition tasks;

s31, when the collection task is established, various collection execution strategies such as task execution time, execution frequency and execution mode can be set in a user-defined mode;

s32, flexibly adapting to the actual operation condition of each medical institution; and the acquisition task in the service peak period is prevented from influencing other services of the medical institution.

S4, collecting, analyzing and uploading various medical data by using an edge server according to the corresponding requirements of the collection task;

the method specifically comprises the following steps of collecting, analyzing and uploading various medical data by using an edge server according to the corresponding requirements of the collection task:

s41, the edge server continuously and efficiently finishes the acquisition of various medical data through the data acquisition component and temporarily stores the edge server;

the acquisition component adopts a high-availability parallel acquisition thread (namely supports multi-process concurrent acquisition), and greatly improves the data acquisition efficiency and stability.

The data acquisition assembly respectively acquires the structured medical data and the unstructured medical data through the structured data acquisition assembly and the unstructured data acquisition assembly.

S42, unstructured data processing: 1. the edge server uploads unstructured data (such as image files) to cloud storage (object storage) through a data uploading component; 2. the edge server conducts structured analysis on unstructured data (such as image files) through a data analysis component, the analyzed data are transmitted to a cloud relational database through a message middleware to be stored, the cloud relational database sends a data related command to a remote dictionary service cluster, and the remote dictionary service cluster stores a corresponding hash value according to the command, so that the subsequent association verification step can be achieved.

The data analysis component can analyze the medical image file according to DICOM3.0 standard, so that the medical image file becomes available structured data to be stored. In addition, the data analysis component supports multi-process concurrent analysis, and the analysis efficiency is greatly improved.

The message middleware may be kafka message middleware.

And (3) a structured data processing process: the edge server transmits the structured data to a cloud relational database for storage through message middleware, the cloud relational database transmits the data related command to a remote dictionary service cluster, and the remote dictionary service cluster stores the corresponding hash value according to the command, so that the subsequent association verification step is realized.

S5, calculating the stored hash value through the remote dictionary service cluster according to the association check rule, and sending a corresponding data compensation request when data are missing;

the remote dictionary service cluster can meet high-throughput data calculation, and data integrity is efficiently checked through configured association check rules. (because the remote dictionary service cluster has the hash value corresponding to the structured data and the hash value corresponding to the unstructured data, the remote dictionary service cluster can efficiently perform association calculation according to the association check rule and by combining the two types of hash values to check the integrity of the structured and unstructured data.)

S51, when data are missing, sending a compensation request corresponding to the missing data to the message middleware according to the verification result and the configured data compensation rule;

s52, the message middleware sends the data compensation request to the data compensation component. And the data compensation component sends a specific data compensation command to the data acquisition component according to the received data compensation request.

S6, completing corresponding data compensation through the data acquisition component and according to the received data compensation command by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster;

and step S6, the specific pointer re-executes the data acquisition, analysis and uploading processes similar to the step S4 on the missing data.

For example, when the remote dictionary service cluster performs correlation check calculation to obtain that a hash value corresponding to certain image data is missing, that is, when a check result is that the image data is missing, the remote dictionary service cluster sends a compensation request to the kafka message middleware, and then the data compensation component sends a compensation command to the unstructured data acquisition component. The unstructured data acquisition component acquires the missing image file again, and the image file is uploaded to the cloud storage through the data uploading component; meanwhile, the image file is structurally analyzed through the data analysis component, analyzed data are transmitted to the cloud relational database to be stored through the kafka message middleware, the cloud relational database sends the data related command to the remote dictionary service cluster, and the remote dictionary service cluster stores the corresponding hash value according to the command.

And the remote dictionary service cluster carries out correlation check calculation to obtain that a hash value corresponding to certain check data is missing, namely when the check result is that the check data is missing, the remote dictionary service cluster sends a compensation request to the kafka message middleware, and then a compensation command is sent to the structured data acquisition component through the data compensation component. The structured data acquisition component acquires missing inspection data again, the missing inspection data are transmitted to the cloud relational database for storage through the kafka message middleware, the cloud relational database sends the data related command to the remote dictionary service cluster, and the remote dictionary service cluster stores the corresponding hash value according to the command.

According to another aspect of the present invention, there is provided a high performance, high fault tolerance, scalable medical data acquisition system, comprising: the system comprises a confirmation module, a rule configuration module, a task establishment module, a medical data processing module, a data compensation sending module and a data compensation receiving module;

the confirmation module is used for confirming the acquisition modes and the corresponding configuration parameters of various medical data to be acquired;

the rule configuration module is used for completing the configuration of corresponding parameters, the edge server, the association check rule and the data compensation rule according to the confirmed acquisition mode of various medical data;

the task establishing module is used for establishing acquisition tasks of various medical data;

the medical data processing module is used for acquiring, analyzing and uploading various medical data by utilizing the edge server according to the requirements corresponding to the acquisition tasks;

the data compensation sending module is used for calculating the stored hash value through the remote dictionary service cluster according to the association check rule and sending a corresponding data compensation request when data are missing;

and the data compensation receiving module is used for jointly completing corresponding data compensation by combining the data analysis component, the data uploading component, the message middleware and the remote dictionary service cluster through the data acquisition component according to the received data compensation command.

In conclusion, the invention realizes the high-efficiency acquisition of various medical data by deploying the acquisition program on the edge server, and simultaneously supports the concurrent execution of a plurality of acquisition tasks, thereby greatly improving the efficiency of data acquisition and enhancing the timeliness. The invention also introduces a remote dictionary service cluster, performs correlation verification on the acquired data, automatically compensates the missing data and effectively improves the integrity of the data. The invention provides the data source modular configuration aiming at the acquisition modes of different data types, can carry out rapid expansion and adaptation aiming at different manufacturers and different data sources, and improves the expandability of data acquisition. In addition, the invention adopts a componentization mode, and each component operates independently, thereby effectively improving the fault tolerance rate of the system.

For example, as shown in fig. 2, the system and the method can intensively collect different systems and different data sources of a medical institution, and after the data are stored in a standardized manner, the data are finally fed back to the business in the hospital in a data or application manner, so that the data intercommunication between the hospitals and the hospital is realized, the medical diagnosis efficiency and accuracy are improved, and the hospitalizing procedures of patients are reduced.

Interpretation of technical terms:

data Source (Data Source): the source of the data is the device or original media that provides the data needed.

Data acquisition: the process of automatically collecting information from analog and digital units under test, such as sensors and other devices under test.

The collection mode is as follows: and realizing a data acquisition mode.

Structuring data: data that can be logically represented and implemented by a two-dimensional table structure, strictly following the data format and length specifications, is mainly stored and managed by means of relational databases.

Unstructured data: the data structure is irregular or incomplete, and data represented by a database two-dimensional logic table is inconvenient because of no predefined data model.

Semi-structured data: data with certain structure.

DICOM 3.0: i.e., digital imaging and communications in medicine, is an international standard for medical images and related information that defines a medical image format that can be used for data exchange with quality that meets clinical needs.

Medical imaging: refers to techniques and procedures for obtaining images of internal tissues of a human body or a part of a human body in a non-invasive manner for medical treatment or medical research. Such as ultrasound images, radiological images, cardiovascular imaging, etc.

API: application Programming Interface (API), some predefined interfaces (e.g. functions, HTTP interfaces) or a convention for linking different components of a software system.

Database view: a view is a virtual table derived from one or several base tables (or views). The data dictionary of the system only stores the definition of the view and does not store the data corresponding to the view.

FTP protocol: file Transfer Protocol (FTP), a set of standard protocols for File Transfer over a network.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.