CN109582699B - Method, system, equipment and storage medium based on hybrid cloud data aggregation - Google Patents
Method, system, equipment and storage medium based on hybrid cloud data aggregation Download PDFInfo
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- CN109582699B CN109582699B CN201811387707.7A CN201811387707A CN109582699B CN 109582699 B CN109582699 B CN 109582699B CN 201811387707 A CN201811387707 A CN 201811387707A CN 109582699 B CN109582699 B CN 109582699B
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention provides a method, a system, equipment and a storage medium based on hybrid cloud data aggregation, which are applied to a public cloud end connected with a plurality of private cloud ends, and the method comprises the following steps: the public cloud receives a retrieval instruction, wherein the retrieval instruction comprises a private cloud of a hospital to be retrieved, a data retrieval condition and a data aggregation index; the public cloud sends data retrieval conditions to the private cloud of the hospital to be retrieved according to the retrieval instruction; the public cloud receives a retrieval result obtained by the private cloud executing data retrieval according to the data retrieval condition; the public cloud end executes data aggregation operation on the retrieval results fed back by all the private cloud ends according to the data aggregation indexes, retrieval and aggregation based on the data of multiple private cloud end hospitals can be faster and more accurate, and when the data retrieval conditions become complex or the data retrieval requirements increase, rapid, accurate and orderly retrieval can still be realized.
Description
Technical Field
The invention relates to the field of data aggregation, in particular to a method, a system, equipment and a storage medium based on hybrid cloud data aggregation.
Background
In the modern medical information technology and the disease diagnosis and treatment information analysis, in order to achieve the purpose of comprehensively and accurately analyzing the cause of disease, for the same disease, the diagnosis and treatment data of patients in different medical environments need to be analyzed, and some analysis and research also require that the data volume of the patients needs to reach a certain scale, so that more effective disease diagnosis and treatment paths and treatment methods are obtained through analysis of a large amount of patient data. However, many times, in a hospital, the number of patients who have suffered from a disease and who meet certain conditions (e.g.: medication of metformin', sex male, etc.) is often limited. It is imperative to retrieve (acquire or count) from multiple hospitals "patient clinical data" that satisfy certain conditions.
In the conventional technology, when data (hereinafter referred to as "data") of a plurality of private cloud hospitals for diagnosis and treatment are searched, an algorithm engineer generally operates a pre-written data fetching program for each private cloud hospital according to a certain search condition, and the data meeting the condition is obtained after the program is operated. And importing the obtained data into text files such as excel and the like, and then merging the retrieved data of the plurality of private cloud hospitals by corresponding data engineers to obtain final data, wherein a public cloud platform serving as a platform for aggregating the data of the plurality of private cloud hospitals is lacked.
Each private cloud is provided with a data retrieval platform, and a data engineer logs in each private cloud platform to run out data, downloads the data to a text file and then performs aggregation analysis on the multiple private cloud data through another aggregation program.
The traditional multi-private cloud data retrieval technology is not intelligent. Not only is the labor cost required large, but also errors are easy to occur when the data of a plurality of private cloud hospitals are aggregated. Its disadvantages are very evident: A) the whole process of obtaining the data is long in time consumption, especially under the conditions of complex query conditions and more data aggregation indexes, a large amount of time is consumed for retrieving the data, an algorithm engineer firstly needs to log in each private cloud server to execute a data retrieval program, and after all private cloud data are retrieved, the data of multiple private clouds need to be aggregated again. The whole process takes a lot of time to finish. If such demands increase, the company will undoubtedly need to invest more manpower to do the same thing repeatedly; B) no interface, the problem of the searched data is caused, and the data is not easy to be found. Even if each private cloud has a data retrieval result display page, the step of data aggregation is still very time-consuming and labor-consuming;
when the retrieval conditions or retrieval indexes are changed (for example, the original data of men suffering from hypertension is retrieved, and the data of women suffering from hypertension is needed at present, and for example, the original retrieved data does not have the item of 'age', the need is temporarily changed, the age of the patient needs to be checked, the analysis of the age distribution and the relationship between the treatment effect and the age of the patients suffering from hypertension is facilitated), the data engineer needs to rewrite the program codes and go to each private cloud hospital server to execute the data retrieval program again, namely, the step in (1) is repeated again. If needed, the data is repeatedly modified for several times and needs to be obtained as soon as possible. Repeatedly doing this long-time task is feared to greatly increase the cost of program development.
Disclosure of Invention
In view of the problems in the prior art, an object of the present invention is to provide a method, a system, a device and a storage medium based on hybrid cloud data aggregation, which can make retrieval and aggregation based on multiple private cloud hospital data faster and more accurate.
The embodiment of the invention provides a hybrid cloud data aggregation-based method, which is applied to a public cloud end connected with a plurality of private cloud ends and comprises the following steps:
the public cloud receives a retrieval instruction, wherein the retrieval instruction comprises a private cloud of a hospital to be retrieved, a data retrieval condition and a data aggregation index;
the public cloud sends the data retrieval conditions to a private cloud of a hospital to be retrieved according to the retrieval instruction;
the public cloud end receives a retrieval result obtained by the private cloud end executing data retrieval according to the data retrieval condition; and the public cloud end executes data aggregation operation on the retrieval results fed back by all the private cloud ends according to the data aggregation indexes.
Preferably, the public cloud establishes a plurality of threads according to the number of the private clouds of the hospital to be retrieved, and each thread only sends the data retrieval condition and receives the retrieval result fed back by the private cloud for one private cloud.
Preferably, the public cloud sends the data retrieval conditions to the private cloud in parallel, and receives retrieval results fed back by the private cloud in parallel.
Preferably, the public cloud further establishes at least one data aggregation thread, and after the public cloud receives the retrieval results fed back by all the private clouds, the data aggregation thread performs data aggregation operation on the retrieval results fed back by all the private clouds according to the data aggregation indexes.
Preferably, the public cloud intermittently sends a data acquisition instruction to the private cloud, and stores a retrieval result fed back by the private cloud in a distributed database.
Preferably, the interval between the public cloud end sending the data acquisition instruction to the private cloud end is once every 5 seconds.
Preferably, the private cloud stores the retrieval result in a distributed database of the private cloud; and the public cloud stores the received retrieval results of all the private clouds in a distributed database of the public cloud.
The embodiment of the invention also provides a system based on hybrid cloud data aggregation, which comprises a public cloud and a plurality of private clouds, wherein the public cloud executes the method.
An embodiment of the present invention further provides a public cloud, including:
the retrieval instruction receiving module is used for module retrieval instructions, and the retrieval instructions comprise a private cloud of a hospital to be retrieved, data retrieval conditions and data aggregation indexes;
the data retrieval condition sending module is used for sending the data retrieval condition to a private cloud of a hospital to be retrieved according to the retrieval instruction;
the retrieval result receiving module is used for receiving a retrieval result obtained by the private cloud terminal executing data retrieval according to the data retrieval condition; and
and the aggregation module is used for performing data aggregation operation on the retrieval results fed back by all the private cloud ends according to the data aggregation indexes.
An embodiment of the present invention further provides a device based on hybrid cloud data aggregation, including:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the hybrid cloud data aggregation-based method described above via execution of the executable instructions.
Embodiments of the present invention also provide a computer-readable storage medium for storing a computer program, which when executed by a processor implements the steps of the above-mentioned method based on hybrid cloud data aggregation.
The invention aims to provide a method, a system, equipment and a storage medium based on hybrid cloud data aggregation, which can enable the retrieval and aggregation based on the data of a plurality of private cloud hospitals to be faster and more accurate, and can still realize that each retrieval is performed rapidly, accurately and orderly when the data retrieval conditions become complex or the data retrieval requirements increase; and when the retrieval index is changed, accurate patient diagnosis and treatment data can be quickly run out without modifying the source program code.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
Fig. 1 is a flow chart of a hybrid cloud data aggregation-based method of the present invention.
Fig. 2 is a schematic diagram of a method of implementing hybrid cloud-based data aggregation of the present invention.
Fig. 3 is a schematic block diagram of a public cloud according to the present invention.
Fig. 4 is a schematic structural diagram of the hybrid cloud data aggregation-based device of the present invention. And
fig. 5 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their repetitive description will be omitted.
Fig. 1 is a flow chart of a hybrid cloud data aggregation-based method of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method based on hybrid cloud data aggregation, including the following steps:
s101, the public cloud receives a retrieval instruction, wherein the retrieval instruction comprises a private cloud of a hospital to be retrieved, a data retrieval condition and a data aggregation index. The public cloud in the present invention refers to a server environment for aggregating private cloud search result data, but is not limited thereto.
S102, the public cloud sends the data retrieval conditions to a private cloud of a hospital to be retrieved according to the retrieval instruction.
S103, the public cloud receives a retrieval result obtained by the private cloud performing data retrieval according to the data retrieval condition.
And S104, the public cloud side performs data aggregation operation on the retrieval results fed back by all the private cloud sides according to the data aggregation indexes.
The private cloud in the invention means that the server environment of each hospital is a private cloud environment, and each private cloud is provided with a plurality of services, such as: ES, mongodb, etc., but not limited thereto. Wherein, ES is short for ElasticSearch, search server based on Lucene. mongdb is a product between relational and non-relational databases, and is the most functionally-rich of the non-relational databases, most like the relational database. The data structure supported by the user is very loose and is in a json-like bson format, so that more complex data types can be stored. The biggest characteristic of Mongo is that the query language supported by Mongo is very strong, the syntax of Mongo is similar to the object-oriented query language, most functions of single-table query of similar relational databases can be almost realized, and index establishment of data is supported. The ES in the present invention may employ other search services. Such as: solr, lucene, etc. Where solr is a separate enterprise-level search application server that provides an API interface to the outside similar to Web-services. The lucene is a sub-item of the apache software foundation 4jakarta project group, is an open source code full-text search engine toolkit, but is not a complete full-text search engine but a full-text search engine architecture, and provides a complete query engine, an index engine and a partial text analysis engine. Other data storage systems may be employed by mongodb in the device. Such as: mysql, redis, etc. or text files; MySQL is a relational database management system developed by MySQL AB, Sweden, and currently belongs to the product under Oracle flag. Redis is an open source log-type and Key-Value database which is written by using ANSI C language, supports network, can be based on memory and can also be persistent, and provides API of multiple languages.
In a preferred scheme, the public cloud establishes a plurality of threads according to the number of the private clouds of the hospital to be retrieved, each thread only sends the data retrieval condition and receives the retrieval result fed back by the private cloud aiming at one private cloud, and the public cloud receives data of different private clouds through different threads, so that data transmission is not conflicted. Even if a thread of one private cloud and the thread of the public cloud are in failure, the transmission of data from other private clouds to the public cloud cannot be influenced. In a preferred embodiment, the public cloud sends the data retrieval conditions to the private clouds in parallel, and receives retrieval results fed back by the private clouds in parallel, so as to ensure that data of the multiple private clouds are updated synchronously.
In a preferred scheme, the public cloud further establishes at least one data aggregation thread, after the public cloud receives the retrieval results fed back by all the private clouds, the data aggregation thread performs data aggregation operation on the retrieval results fed back by all the private clouds according to the data aggregation indexes, and the public cloud can establish a more complete database through data aggregation on the retrieval results fed back by all the private clouds, so that subsequent retrieval and calling are facilitated. The data aggregation in the present invention refers to putting all data together to perform operations such as data summation and averaging, but is not limited thereto. Common in medical data are: the operations of summing, averaging, maximum and minimum are performed according to the region/gender, but not limited thereto.
In a preferred scheme, the public cloud intermittently sends a data acquisition instruction to the private cloud, and stores a retrieval result fed back by the private cloud in a distributed database. For example: and storing the retrieval result fed back by the private cloud in the mongodb system.
In a preferred scheme, the interval between the public cloud sending the data acquisition instruction to the private cloud is once every 5 seconds, so that the data of the private cloud and the data of the public cloud can be updated in time, and the newly acquired data of the private cloud can be sent to the public cloud in time.
In a preferred scheme, the private cloud stores the retrieval result in a distributed database of the private cloud; and the public cloud stores the received retrieval results of all the private clouds in a distributed database of the public cloud so as to avoid data tampering and improve the safety.
The invention aims to provide a method based on hybrid cloud data aggregation, which can enable the retrieval and aggregation based on the data of a plurality of private cloud hospitals to be faster and more accurate, and can still realize that each retrieval is performed rapidly, accurately and orderly when the data retrieval conditions become complex or the data retrieval requirements increase; and when the retrieval index is changed, accurate patient diagnosis and treatment data can be quickly run out without modifying the source program code.
Fig. 2 is a schematic diagram of a method of implementing hybrid cloud-based data aggregation of the present invention. As shown in fig. 2, when the method based on hybrid cloud data aggregation of the present invention is implemented, the general process is as follows:
the user submits a data retrieval instruction on the public cloud 1 interface. The user selects the private cloud 2 hospital, the retrieval conditions, the data aggregation indexes and the like which need to retrieve data on the visual interface provided by the public cloud 1, and then submits the selected/filled data retrieval instruction information. The instruction information is stored in mongodb and is uniquely identified.
And (5) a data retrieval process. The public cloud 1 obtains a data retrieval instruction submitted in a cloud interface from mongodb, decomposes an instruction unique identifier, a private cloud 2 to be queried, query conditions and the like, starts the same number of threads according to the number of the private clouds 2 related to the current retrieval instruction, and only sends the data retrieval instruction to one private cloud 2 and obtains a data retrieval result. After receiving the data retrieval instruction, the private cloud 2 transmits the query condition in the instruction to the ES for query to obtain related data, and stores the result data together with the instruction unique identifier and the query condition to mongodb. After the public cloud 1 sends the data retrieval instruction to the private in the data retrieval process, the instruction for acquiring data is intermittently (for example, every 5 seconds) sent to the private cloud 2. After receiving the data acquisition command, the private cloud 2 decomposes the data acquisition command to obtain a command unique identifier, queries result data from mongodb according to the unique identifier and returns the result data to the public cloud 1. And the background service of the public cloud 1 monitors that the data retrieval of all the private cloud 2 is finished, performs data aggregation operation, and stores the aggregation result into mongodb.
The invention solves the problem of high human input of performing data aggregation operation after executing the program of retrieving data by a data engineer to each private cloud 2, greatly shortens the data output period, and can still quickly retrieve the data when the data retrieval condition and the retrieval index are changed.
According to the invention, in the background service of the public cloud end 1, the data retrieval instruction and the data acquisition instruction are executed separately, so that the problem that the data retrieval is influenced by performance problems caused by too many connection numbers of the public cloud end 1 and the private cloud end 2, overtime connection and the like due to the fact that the private cloud end 2 spends a large amount of time for retrieving data in the ES due to complex retrieval conditions and too many retrieval indexes can be avoided.
In one variation, the process of implementing the invention may be:
(1) the public cloud 1 immediately disconnects after sending a data retrieval instruction to the private cloud 2, and resources are released;
(2) after receiving the retrieval instruction, the private cloud 2 executes data retrieval one by one according to the sequence sent by the instruction, stores the result into mongodb (including the data retrieval state, namely, the retrieval is successful or failed), and releases the resource of the current instruction;
(3) the public cloud 1 sends a data acquisition instruction to the private cloud 2, if the execution of (2) is finished, the result is directly inquired from mongodb and returned to the public cloud 1, and if the execution of (2) is not finished, the data retrieval is returned to the public cloud 1. And after the public cloud 1 receives the return of the private cloud 2, performing the next processing and releasing the resources.
The whole interaction process of the public cloud 1 and the private cloud 2 can be completed quickly. No or very short waiting times. Therefore, the concurrency capability of the whole system device can be greatly improved, and the whole device can simultaneously process more data retrieval.
The embodiment of the invention also provides a system based on hybrid cloud data aggregation, which comprises a public cloud and a plurality of private clouds, wherein the public cloud executes the method, and the method is not repeated herein.
Fig. 3 is a schematic block diagram of a public cloud according to the present invention. As shown in fig. 3, an embodiment of the present invention further provides a public cloud 5, including: a search instruction receiving module 501, a data search condition transmitting module 502, a search result receiving module 503, and an aggregation module 504. The retrieval instruction receiving module 501 is configured to module a retrieval instruction, where the retrieval instruction includes a private cloud of a hospital to be retrieved, a data retrieval condition, and a data aggregation index. The data retrieval condition sending module 502 is configured to send the data retrieval condition to a private cloud of a hospital to be retrieved according to the retrieval instruction. The retrieval result receiving module 503 is configured to receive a retrieval result obtained by the private cloud performing data retrieval according to the data retrieval condition. The aggregation module 504 is configured to perform data aggregation operation on the search results fed back by all the private cloud ends according to the data aggregation indicator. The invention aims to provide a system based on hybrid cloud data aggregation, which can enable the retrieval and aggregation based on the data of a plurality of private cloud hospitals to be faster and more accurate, and can still realize that each retrieval is performed rapidly, accurately and orderly when the data retrieval conditions become complex or the data retrieval requirements increase; and when the retrieval index is changed, accurate patient diagnosis and treatment data can be quickly run out without modifying the source program code.
The system device comprises a public cloud and a private cloud. Wherein public cloud contains two services: front-end services and background services. Front-end service: the system provides an interface, so that a user can select a private cloud hospital needing to be brought into retrieval data, can also set retrieval conditions and data aggregation indexes, and submits a data retrieval instruction after the data retrieval conditions and the data aggregation indexes are set. Background service: the service monitors that a data retrieval instruction is submitted, resolves the private cloud hospitals to be retrieved, data retrieval conditions and data aggregation indexes from the retrieval instruction, and sends out the data retrieval instruction to all the private cloud hospitals related to the instruction.
And after receiving the instruction, the private cloud executes data retrieval according to the retrieval condition, and stores the retrieval result into mongodb. After the background service sends the data retrieval command, the background service intermittently (for example, every 5 seconds) sends a data acquisition command to each private cloud so as to obtain result data retrieved by the private cloud, and the retrieved result data is stored in mongodb on the public cloud server. After the background service takes the data retrieved by all private cloud hospitals, performing aggregation operation on the obtained diagnosis and treatment data of all private cloud patients according to a data aggregation index (if some private clouds have failed to retrieve the data due to problems of self service or network and the like, the hospitals with failed retrieval can be ignored during aggregation, data aggregation can still be performed normally, and a user can be informed of which hospitals have failed retrieval/have no data during data presentation).
In the system device, the public cloud background service enables threads with the same number according to the number of private clouds to be retrieved, simultaneously sends data retrieval instructions to all the private clouds, and one thread only executes data retrieval instruction sending and data acquisition instructions aiming at one private cloud. Meanwhile, the public cloud background service can start a 'data aggregation' thread, and when the thread monitors that all private cloud data retrieval is finished, data aggregation operation is immediately executed according to 'data aggregation indexes'. Thus, the whole data retrieval process is completed.
In the system device, the private cloud service adopts the ES to retrieve the diagnosis and treatment data of the patient, which comprises data query and data statistics, and when the ES retrieves the data, the data is stored in mongodb. When the background service of the public cloud sends a data acquisition instruction, the retrieved data is directly obtained from mongodb and returned to the background service of the public cloud.
When a plurality of retrieval instructions are submitted in the public cloud, the public cloud background service can ensure that a certain number of retrieval instructions are executed in parallel. If the number of the retrieval instructions exceeds the number (an infinite number of instructions cannot be executed simultaneously, an upper limit is generally set according to server resources), the background service of the public cloud can ensure that the data retrieval instructions submitted first can be executed preferentially according to the order of submitting the instructions.
The embodiment of the invention also provides equipment based on the hybrid cloud data aggregation, which comprises a processor. A memory having stored therein executable instructions of the processor. Wherein the processor is configured to perform the steps of the hybrid cloud data aggregation based method via execution of executable instructions.
As shown above, the embodiment can enable retrieval and aggregation based on multiple private cloud hospital data to be faster and more accurate, and when the data retrieval conditions become complex or the data retrieval requirements increase, each retrieval can still be performed quickly, accurately and orderly; and when the retrieval index is changed, accurate patient diagnosis and treatment data can be quickly run out without modifying the source program code.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
Fig. 4 is a schematic structural diagram of the hybrid cloud data aggregation-based device of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 600 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The embodiment of the invention also provides a computer-readable storage medium for storing a program, and the steps of the method based on the hybrid cloud data aggregation are realized when the program is executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of this specification, when the program product is run on the terminal device.
As shown above, the embodiment can provide a solution for conveniently supplementing electric quantity for the electric automobile in a parking lot without a charging pile, and the full-automatic intelligent charging robot technology is adopted, so that the resource advantages of valley electricity can be utilized, and the full-automatic charging of the electric automobile can be realized in places where the charging pile cannot be arranged, thereby greatly improving the charging efficiency, facilitating the energy supplement of the electric automobile, facilitating the popularization and development of the electric automobile, and facilitating the optimized operation of a power grid.
Fig. 5 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 5, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the present invention aims to provide a method, a system, a device and a storage medium based on hybrid cloud data aggregation, which can make retrieval and aggregation based on multiple private cloud hospital data faster and more accurate, and can still realize that each retrieval is performed quickly, accurately and orderly when the data retrieval conditions become complicated or the data retrieval requirements increase; and when the retrieval index is changed, accurate patient diagnosis and treatment data can be quickly run out without modifying the source program code.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A method based on hybrid cloud data aggregation is applied to a public cloud end connected with a plurality of private cloud ends, and is characterized by comprising the following steps:
the public cloud receives a retrieval instruction, wherein the retrieval instruction comprises a private cloud of a hospital to be retrieved, a data retrieval condition and a data aggregation index;
the public cloud sends the data retrieval conditions to a private cloud of a hospital to be retrieved according to the retrieval instruction;
the public cloud end receives a retrieval result obtained by the private cloud end executing data retrieval according to the data retrieval condition; and
the public cloud end executes data aggregation operation on the retrieval results fed back by all the private cloud ends according to the data aggregation indexes, wherein the data aggregation operation comprises at least one of summing, averaging, maximum value and minimum value of the data of the retrieval results;
and when the public cloud receives the retrieval results fed back by all the private clouds, the data aggregation thread performs data aggregation operation on the retrieval results fed back by all the private clouds according to the data aggregation indexes.
2. The hybrid cloud data aggregation-based method according to claim 1, wherein the public cloud establishes a plurality of threads according to the number of private cloud ends of the hospital to be retrieved, and each thread only sends the data retrieval conditions and receives retrieval results fed back by the private cloud ends for one private cloud end.
3. The hybrid cloud data aggregation-based method according to claim 2, wherein the public cloud side sends the data retrieval conditions and the private cloud sides in parallel and receives retrieval results fed back by the private cloud sides in parallel.
4. The hybrid cloud data aggregation-based method according to claim 1, wherein the public cloud intermittently sends data acquisition instructions to the private cloud, and stores the retrieval results fed back by the private cloud in a distributed database.
5. The hybrid cloud data aggregation-based method of claim 4, wherein the interval between the public cloud sending data acquisition instructions to the private cloud is once every 5 seconds.
6. The hybrid cloud data aggregation-based method of claim 1, wherein the private cloud stores the search results in a distributed database of the private cloud; and the public cloud stores the received retrieval results of all the private clouds in a distributed database of the public cloud.
7. A system based on hybrid cloud data aggregation, comprising a public cloud and a plurality of private clouds, wherein the public cloud performs the method of any one of claims 1 to 6.
8. A public cloud, comprising:
the retrieval instruction receiving module is used for module retrieval instructions, and the retrieval instructions comprise a private cloud of a hospital to be retrieved, data retrieval conditions and data aggregation indexes;
the data retrieval condition sending module is used for sending the data retrieval condition to a private cloud of a hospital to be retrieved according to the retrieval instruction;
the retrieval result receiving module is used for receiving a retrieval result obtained by the private cloud terminal executing data retrieval according to the data retrieval condition; and
the aggregation module is used for performing data aggregation operation on the retrieval results fed back by all the private cloud ends according to the data aggregation indexes, wherein the data aggregation operation comprises at least one of summation, averaging, maximum value and minimum value of the data of the retrieval results;
and when the public cloud receives the retrieval results fed back by all the private clouds, the data aggregation thread performs data aggregation operation on the retrieval results fed back by all the private clouds according to the data aggregation indexes.
9. An apparatus based on hybrid cloud data aggregation, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the hybrid cloud data aggregation-based method of any one of claims 1 to 6 via execution of the executable instructions.
10. A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the hybrid cloud data aggregation-based method according to any one of claims 1 to 6.
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