CN110069343B - Power equipment distributed storage and calculation architecture for complex high concurrency calculation - Google Patents
Power equipment distributed storage and calculation architecture for complex high concurrency calculation Download PDFInfo
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Abstract
The invention discloses a power equipment distributed storage and calculation architecture oriented to complex high concurrency calculation, which comprises a data main server, a calculation main server, a data sub-server, a calculation sub-server and a client, wherein the data main server monitors the state of the data sub-server at regular time and processes a write error log at regular time; the computing main server is used for generating computing tasks at regular time, monitoring the state of the computing sub-server at regular time and realizing the distribution of the computing tasks; the client is used for realizing the group data storage, the access service and the calculation service by communicating with the data main server. The invention mainly aims at a remote centralized monitoring center of power equipment, and realizes the storage, continuous backup and concurrent execution of complex computing tasks of mass data by means of functions division, logic separation, resource reuse and the like, thereby ensuring the high availability of background service, improving the reliability of the system on the basis of light weight of the system, reducing the difficulty of deployment of the distributed system and being beneficial to popularization and maintenance of the distributed system.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a power equipment distributed storage and calculation architecture for complex high concurrency calculation.
Background
Industrial power plants are devices that produce, convert, and transmit power. The operation of the power equipment needs to establish a remote monitoring center for monitoring and managing the original data and the calculation data of the equipment, and along with the rapid development of the technical aspect, the data volume to be processed is larger and larger. The development of computing technology and networking technology has prompted data centers and computing centers built by distributed computing systems based on distributed computing, parallel computing, and the like to find widespread use in industrial, commercial, scientific, and military fields. Distributed storage has the advantages of safer data storage and faster data reading. The data distributed computing architecture is fast and can find problems in time. For complex and high-concurrency computing power equipment, management work on raw data and computing data and information of equipment should be done in enterprise applications.
Distributed management of data and computation of industrial power equipment presents a problem that is not addressed by conventional distributed architecture schemes. First, the distributed system cannot be too complex to be easily operated. Traditional industrial power plant-related model calculations, hereinafter referred to as core business, are built primarily on a single architecture. If the micro-service solution for mass users is directly adopted to reform the system, the complexity, deployment and maintenance cost of the system are greatly improved. In fact, the expansion flexibility of this complexity increase does not sometimes bring about an equivalent profit to the enterprise, since, unlike the case where the user volume (access volume) of the online mall etc. may rise in a large amount in a short period, the "access volume (number of devices)" of the enterprise related to the industrial power equipment tends to be stable for a longer period or only needs a lower degree of expansion. Therefore, the requirement for the distributed system is to solve the problem that the single architecture cannot solve huge data and computation, and to be easy to operate and maintain as the single architecture. Second, the original core service cannot be modified to a large extent. In a general distributed computing solution, an original computing module needs to be modified, for example, an HDFS is adopted to perform distributed storage of data, and then Map-reduce is adopted to perform distributed computing and integration, which needs to modify an original core service module, and belongs to invasive modification, which is not expected by any enterprise. Therefore, the requirement on the distributed system is that the existing core service can be directly used as a module or modified to a lower degree, so as to realize the pluggable operation flow. Third, enterprises demand data security. Due to confidentiality, the operation data of the industrial power equipment cannot be directly uploaded to the cloud of the public network, limited stored resources of the distributed system are required to be fully utilized, the data are backed up, and the local safety of the data is ensured. In the conventional distributed solution, a multi-database instance mode is generally adopted to backup data, which leads to an increase in the number of data servers and brings about a problem of complex operation and maintenance. Therefore, the distributed solution requires modification of multiple database instances to make backup work of data with fewer servers.
Accordingly, those skilled in the art are working to develop a power equipment distributed storage and computing architecture for complex, high concurrency computing. The method is mainly used for solving the problem of unified management of equipment calculation models (performance calculation, fault diagnosis, life prediction and the like) of data and information storage of a plurality of similar equipment (200-1000 orders) by an equipment remote monitoring center, and a distributed solution is needed to be realized under the condition of a small number of servers. The small number of servers is convenient for operation and maintenance, and the distributed architecture is used for solving the problem of limited storage and computing capacity under a single architecture, and meanwhile, the backup of data needs to be ensured. On hardware, only a few servers with the same configuration are adopted, so that the complexity of operation and maintenance is reduced to the greatest extent, the functions of a master and a worker are divided and logically separated, and a sub-server can be used for directly replacing the master when a single point of failure occurs. In software, to fully exploit server performance, storage services and computing services are deployed on each worker, but logically separated from each other. In the aspect of storage, all operation data are one by one, namely the same data can be necessarily existed on two of the two workers; in terms of computing, all the workers act as computing resources. On the basis of system light weight, the reliability of the system is improved, the difficulty of distributed system deployment is reduced, and the popularization and maintenance of the distributed system are facilitated.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to solve the technical problem that on the premise of easy operation and maintenance, a scenario of distributed storage and calculation with high expansibility is realized through a few servers, the reliability of the system is improved on the basis of light weight of the system, the difficulty of deployment of the distributed system is reduced, and the popularization and maintenance of the distributed system are facilitated.
In order to achieve the above objective, the present invention provides a power equipment distributed storage and computing architecture for complex high concurrency computation, which includes a data main server, a computing main server, a data sub-server cluster, a computing sub-server cluster, and a client group, wherein:
the data main server is used for monitoring the cluster state of the data sub-servers at regular time and processing the write error log at regular time;
the computing main server is used for generating computing tasks at regular time, monitoring the states of the computing sub-server clusters at regular time and realizing the distribution of the computing tasks by calling a unified interface;
the client group is communicated with the data main server to realize one or more of group data storage, access service and computing service.
Further, the client group includes one or more clients.
Further, the client is configured to implement at least one of the following functions: unit state monitoring, system data flow monitoring, equipment information management and user authority management.
Further, the data main server comprises a first loop manager module, a first original and calculation data interface and a management data related interface, the calculation main server comprises a second loop manager module, a first calculation request related interface and a data main server backup interface, the data main server is used for executing the first loop manager module at regular time, and the calculation main server is used for executing the second loop manager module at regular time.
Further, the cluster of data sub-servers includes one or more data sub-servers, and the cluster of compute sub-servers includes one or more compute sub-servers.
Further, the first loop manager module is configured to implement at least one of the following functions: acquiring a data source and writing the data source into a data sub-server; monitoring the state of a data sub-server; data synchronization exception handling of the data sub-server; and managing data backup exception handling.
Further, the second loop manager module is configured to implement at least one of the following functions: the unit model is executed uniformly, and the failure is automatically retried; computing sub-server state monitoring.
Further, the data sub-server comprises a relational database and a second original and calculated data interface, wherein the relational database is responsible for storing and accessing the unit data; the computing sub-server comprises a configuration file, a model file and a second computing request related interface, and programming of computing tasks is performed by adopting a general programming language; the model file is an original core service, namely, the intrusion type rewriting of the original core service is avoided by the mode of the configuration file and the second original and calculated data interface; the data main server calls the second original and calculation data interfaces on the data sub-server through the first original and calculation data interfaces to realize the machine set data storage or access service, and the calculation main server calls the second calculation request related interfaces on the calculation sub-server through the first calculation request related interfaces to realize the distribution of calculation tasks.
Further, the data of each unit are stored on the data sub-server in a mode of one master server and one slave server, the data main server is used for managing the address and the connection state of the data sub-server, the calculation main server is used for managing the resource use condition and the connection state of the calculation sub-server, the data main server issues a calculation task through the calculation main server, and the calculation sub-server returns a calculation result to the data main server.
Further, the data sub-server and the computing sub-server are arranged on a physical sub-server, and the physical sub-server, the data main server and the computing main server all adopt the same configuration.
The invention mainly aims at the remote centralized monitoring center of the power equipment, actual computing characteristics and requirements on the reliability and maintainability of the system, realizes a distributed computing scene with obviously higher storage and computing performance than a single architecture and good expansibility through a small number of servers, provides a distributed architecture of the remote center of the power equipment, which mainly faces to complex and high-concurrency computing, solves the bottleneck problems of the existing distributed scheme that the quantity of the servers has great influence on the distributed performance, the distributed computing has serious influence on the main production business of the power equipment and the like by utilizing the means of function division, logic separation, resource reuse and the like, realizes the storage and continuous backup of massive data, realizes the concurrency execution of complex computing tasks, ensures the high availability of background service, improves the reliability of the system on the basis of system weight reduction, reduces the deployment difficulty of the distributed system and is beneficial to the popularization and maintenance of the distributed system.
The beneficial technical effects of the invention are as follows:
1. on the premise of fewer servers, the extensibility of the traditional distributed system is realized on the basis of ensuring easy operation and maintenance through proper function division, logic separation and resource reuse.
2. The data service and the computing service are respectively provided with the main server, so that the single-point fault problem caused by the excessively high access quantity of the main server is reduced.
3. The unit data adopts a mode of one main unit and one standby unit, so that the data is not easy to lose, and the storage burden caused by excessive backup is reduced.
4. For the purpose that one server is damaged and another server can be temporarily filled, all servers are of the same configuration, and in order to be able to reuse these resources, the present invention distributes data and computation to each server. The data may utilize storage resources, while the computation may utilize resources such as CPU and memory.
5. On the data main server, the timing task monitors the state of the data sub-server, ensures the high availability of service, timely notifies when faults occur, processes the data synchronization error log at fixed time and ensures the consistency of data.
6. On the computing main server, the timing task is responsible for generating the computing task and monitoring the state of the computing sub-server, and the distribution of the computing task is realized by calling a unified interface, and the logic of the timing task and the logic of the computing sub-server are mutually independent.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
FIG. 1 is a diagram of a distributed system architecture in accordance with a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a client according to a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of a data hosting server according to a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of a computing host server according to a preferred embodiment of the present invention;
FIG. 5 is a schematic diagram of a cluster of data sub-servers in accordance with a preferred embodiment of the present invention;
FIG. 6 is a schematic diagram of a computing sub-server cluster in accordance with a preferred embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easier to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.
As shown in fig. 1, the invention provides a power equipment distributed storage and calculation architecture for complex high concurrency calculation, which comprises a data main server 1, a calculation main server 2, a data sub-server cluster 3, a calculation sub-server cluster 4 and a client group, wherein the data main server 1 is used for monitoring the state of the data sub-server cluster 3 at regular time and processing write error logs at regular time; the computing main server 2 is used for regularly generating computing tasks, regularly monitoring the states of the computing sub-server clusters 4 and realizing the distribution of the computing tasks by calling a uniform interface; the client group is used for realizing the group data storage, the access service and the calculation service by communicating with the data main server 1. The client group includes a plurality of clients 5.
As shown in fig. 2, the client 5 is configured to implement the following functions: unit status monitoring 51, system data flow monitoring 52, device information management 53, user rights management 54.
As shown in fig. 3, the data main server 1 (DBMaster) includes a first loop manager module 11 (lookeeper), a first raw and calculated data interface 12, and a management data related interface 13. The data master server 1 executes the first loop manager module 11 at regular time. The first loop manager module 11 (lookeeper) is used to implement the following functions: acquiring a data source write data sub-server 111; data sub-server state monitoring 112; data synchronization exception handling 113 of the data sub-server; management data backup exception handling 114.
As shown in fig. 4, the computing main server 2 (CalMaster) includes a second loop manager module 21 (lookeeper), a first computing request related interface 22, and a data main server backup interface 23. The computing master server 2 is used to execute the second loop manager module 21 at regular time. The second loop manager module 21 (lookeeper) is used to implement the following functions: the unit models are executed uniformly, and the failure is automatically retried 211; the compute sub-server state monitor 212.
As shown in fig. 5, the data sub-server cluster 3 (DBWorker cluster) includes one or more data sub-servers 31, the data sub-servers 31 including a relational database 312, a second raw and calculated data interface 321, the relational database 312 being responsible for storage and access of crew data.
As shown in fig. 6, the computing sub-server cluster 4 (CalWorker cluster) includes one or more computing sub-servers 41, where the computing sub-servers 41 include a configuration file 412, a model file 413, and a second computing request related interface 411, and the computing task is programmed in a relatively conventional language.
The data main server 1 invokes the second original and calculation data interface 311 on the data sub-server 31 through the first original and calculation data interface 12 to realize the group data storage or access service, and the calculation main server 2 invokes the second calculation request correlation interface 411 on the calculation sub-server 41 through the first calculation request correlation interface 22 to realize the allocation of calculation tasks.
The data of each unit are stored on the data sub-server 31 in a mode of one master and one slave, the data main server 1 is used for managing the address and the connection state of the data sub-server 31, and the computing main server 2 is used for managing the resource use condition and the connection state of each computing sub-server 41. The data main server 1 issues a calculation task through the calculation main server 2, and the calculation sub-server 41 returns a calculation result to the data main server 1.
The data sub-server 31 and the calculation sub-server 41 are arranged on a physical sub-server, the data main server 1 and the calculation main server 2 are all in the same configuration, and when one server fails, the other server can be used for temporarily replacing work.
It is an object of the present invention to provide a distributed computing architecture. The invention is realized by the following steps:
(1) Because of the CRUD and heavy computing tasks required in the system, two main servers, namely a data main server DBMaster and a computing main server CalMaster, are used for coordination of data and coordination of computing tasks.
(2) The Client, whether accessing data or computing resources, communicates directly with the master server without concern about which sub-server is providing service.
(3) The data main server DBMS manages the addresses of the sub servers where the data of each group are located and the connection state of each sub server. The resource use condition (mainly CPU and memory) and the connection state of each sub-server are managed on the computing main server CalMaster.
(4) Each physical sub-server worker provides data storage, access services, and computing services. The relational database is responsible for data storage and access, and the calculation adopts a conventional language to program calculation tasks.
(5) The data of each unit is stored in a mode of one master unit and one slave unit, for example, the data of the unit 1 can be stored on the data sub-servers workbench 1 and workbench 2, the data of the unit 2 can be stored on the data sub-servers workbench 3 and workbench 4, and the data of the unit 2 is uniformly managed by the data master server DBASter when the data of the unit 2 is stored on the two data sub-servers workbench. This is mainly because the unit data is large, and the storage load of the server is reduced.
(6) The same computing resources and computing models are deployed on each computing sub-server worker instead of the primary computing model and the secondary computing model, which are different from the set data, so that the computing capacity of each server can be exerted.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention without requiring creative effort by one of ordinary skill in the art. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (4)
1. The power equipment distributed storage and calculation architecture for complex high concurrency calculation is characterized by comprising a data main server, a calculation main server, a data sub-server cluster, a calculation sub-server cluster and a client group, wherein:
the data main server is used for regularly monitoring the cluster state of the data sub-servers and regularly processing the write error log, and comprises a first cycle manager module, a first original and calculated data interface and a management data related interface, wherein the data main server is used for regularly executing the first cycle manager module, and the first cycle manager module is used for realizing at least one of the following functions: acquiring a data source written into a data sub-server, monitoring the state of the data sub-server, performing data synchronous exception processing on the data sub-server, and managing data backup exception processing;
the computing main server is used for regularly generating computing tasks, regularly monitoring the states of the computing sub-server clusters, and realizing distribution of the computing tasks by calling a unified interface, and comprises a second circulation manager module, a first computing request related interface and a data main server backup interface, wherein the computing main server is used for regularly executing the second circulation manager module, and the second circulation manager module is used for realizing at least one of the following functions: the unit model is executed uniformly, automatic retry is failed, and state monitoring of the sub-server is calculated;
the client group is communicated with the data main server to realize one or more of set data storage, access service and computing service;
the data sub-server cluster comprises one or more data sub-servers, the computing sub-server cluster comprises one or more computing sub-servers, the data sub-servers and the computing sub-servers are arranged on a physical sub-server, and the physical sub-server, the data main server and the computing main server all adopt the same configuration;
the data sub-server comprises a relational database and a second original and calculated data interface, and the relational database is responsible for storing and accessing the unit data; the computing sub-server comprises a configuration file, a model file and a second computing request related interface, and programming of computing tasks is performed by adopting a general programming language; the model file is an original core service, namely, the intrusion type rewriting of the original core service is avoided by the mode of the configuration file and the second original and calculated data interface; the data main server calls the second original and calculation data interfaces on the data sub-server through the first original and calculation data interfaces to realize the machine set data storage or access service, and the calculation main server calls the second calculation request related interfaces on the calculation sub-server through the first calculation request related interfaces to realize the distribution of calculation tasks.
2. The complex high concurrency computation-oriented power-equipment distributed storage and computation architecture of claim 1, wherein the client group comprises one or more clients.
3. The complex high concurrency computation-oriented power-equipment distributed storage and computation architecture of claim 2, wherein the client is to implement at least one of: unit state monitoring, system data flow monitoring, equipment information management and user authority management.
4. The distributed storage and computing architecture of power equipment for complex high concurrency computation according to claim 1, wherein data of each unit is stored on the data sub-servers in a master-slave mode, the data main server is used for managing addresses and connection states of the data sub-servers, the computing main server is used for managing resource use conditions and connection states of the computing sub-servers, the data main server issues computing tasks through the computing main server, and the computing sub-servers return computing results to the data main server.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103677759A (en) * | 2013-11-08 | 2014-03-26 | 国家电网公司 | Objectification parallel computing method and system for information system performance improvement |
| CN106484713A (en) * | 2015-08-27 | 2017-03-08 | 中国石油化工股份有限公司 | A kind of based on service-oriented Distributed Request Processing system |
| CN108023925A (en) * | 2016-11-04 | 2018-05-11 | 宁波甬派传媒股份有限公司 | A kind of high concurrent news information processing system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103677759A (en) * | 2013-11-08 | 2014-03-26 | 国家电网公司 | Objectification parallel computing method and system for information system performance improvement |
| CN106484713A (en) * | 2015-08-27 | 2017-03-08 | 中国石油化工股份有限公司 | A kind of based on service-oriented Distributed Request Processing system |
| CN108023925A (en) * | 2016-11-04 | 2018-05-11 | 宁波甬派传媒股份有限公司 | A kind of high concurrent news information processing system |
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