CN111680027A - Method and system for realizing intelligent cloud management based on knowledge drive - Google Patents
Method and system for realizing intelligent cloud management based on knowledge drive Download PDFInfo
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Abstract
The invention relates to a method for realizing intelligent cloud management based on knowledge driving, which comprises the steps of collecting structured data, semi-structured data and unstructured data through a multi-cloud management platform; cleaning, converting and storing the data, carrying out centralized management on the data, and extracting the multi-cloud management operation and maintenance knowledge; carrying out multi-cloud management knowledge fusion; and carrying out knowledge retrieval on the trained knowledge, and applying the knowledge retrieval to various scenes of multi-cloud management. By adopting the method and the system for realizing intelligent cloud management based on knowledge driving, which are disclosed by the invention, the off-line cloud management knowledge learning technology and the real-time intelligent cloud management technology are researched by taking multi-cloud management as a main scene, and a knowledge driving type intelligent cloud management system is constructed for solving the core problems of resource allocation of a multi-cloud management platform, intelligent operation and maintenance faults, alarms, root causes and the like, providing domain knowledge services and forming advanced application and intelligent practice based on a plurality of technical fields such as cloud computing, artificial intelligence, big data and the like.
Description
Technical Field
The invention relates to the field of big data, in particular to the field of artificial intelligence and cloud computing, and particularly relates to a method and a system for realizing intelligent cloud management based on knowledge driving.
Background
With the rapid development of cloud computing, artificial intelligence and big data, knowledge economy has come, and the knowledge as the core value of the knowledge highlights an important strategic position. How to effectively utilize the existing data, manage knowledge and construct a model in the knowledge to play the maximum role so as to improve the core competitiveness of cloud computing management operation and maintenance is a major difficult problem in current research.
With the rapid development of technologies such as cloud computing, multi-cloud management, multi-cloud fusion and the like, a multi-cloud management enterprise has a large amount of data of a cloud platform, which can be mainly divided into two major parts, namely data taking human as a core. For example, purchase information, usage information, etc. for cloud-computed products. Data with a machine as a core, such as a cloud computing platform system running log and the like; these data typically include rich semantics (semantics) and are relational data. However, there are usually certain difficulties in managing complex relational data, and at this time, the knowledge graph can obviously exert value. First, there is a significant improvement in the efficiency of associating queries over traditional storage. Knowledge-graph based queries can be thousands or even millions of times more efficient. Secondly, the graph-based storage can be very flexible in design, generally requiring only local changes. For example, if a new data source is available, only the insertion on the existing map is needed. In contrast, the conventional storage method has poor flexibility, and all schemas are defined in advance, and if the schemas are changed subsequently, the cost is very high. Finally, storing entities and relationships in a graph data structure is the best way to conform to the overall story logic, and knowledge reasoning can be done through relationships. Therefore, the project provides an enterprise oriented to a multi-cloud management and cloud computing platform, and an enterprise deep intelligent operation and operation platform driven by a knowledge map and artificial intelligence. The platform integrates a knowledge acquisition system, namely a high-efficiency data crawler, a knowledge management system, an intelligent question-answering system based on a knowledge map and the like, and provides a stable and high-efficiency platform for enterprise arrangement, sharing and evaluation of internal knowledge assets.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for realizing intelligent cloud management based on knowledge drive, which have the advantages of high intelligence efficiency, high operation and maintenance efficiency and wide application range.
In order to achieve the above object, the method for implementing intelligent cloud management based on knowledge driving of the present invention comprises the following steps:
the method for realizing intelligent cloud management based on knowledge driving is mainly characterized by comprising the following steps:
(1) collecting structured data, semi-structured data and unstructured data through a multi-cloud management platform;
(2) cleaning, converting and storing the data, carrying out centralized management on the data, and extracting the multi-cloud management operation and maintenance knowledge;
(3) carrying out multi-cloud management knowledge fusion;
(4) and carrying out knowledge retrieval on the trained knowledge, and applying the knowledge retrieval to various scenes of multi-cloud management.
Preferably, the step (3) specifically includes the following steps:
(3.1) carrying out data integration on the structured data and the data of a third-party database;
(3.2) carrying out entity alignment and knowledge reasoning, and constructing the time;
(3.3) performing quality evaluation, updating and optimization on the learned knowledge;
and (3.4) constructing knowledge fusion and knowledge driving of multi-cloud management.
Preferably, the data collected in step (1) includes log data, performance data, basic attribute data and API interface data.
Preferably, the step of knowledge extraction in step (2) includes instance extraction, relationship extraction and attribute extraction.
Preferably, the trained knowledge is applied to intelligent fault location and fault early warning in the step (4).
The intelligent cloud management system based on knowledge driving for realizing the method of claim 1, characterized in that the system comprises:
the data acquisition module is used for collecting structured data, semi-structured data and unstructured data;
the data cleaning, converting and storing module is connected with the data acquisition module and is used for cleaning, converting, storing and centrally managing data;
the knowledge map module is connected with the data cleaning, converting and storing module and is used for carrying out multi-cloud management knowledge fusion;
and the intelligent operation and maintenance service module is connected with the knowledge map module and used for carrying out knowledge retrieval on the trained knowledge and applying the knowledge retrieval to various cloud management scenes.
By adopting the method and the system for realizing intelligent cloud management based on knowledge driving, which are disclosed by the invention, the off-line cloud management knowledge learning technology and the real-time intelligent cloud management technology are researched by taking multi-cloud management as a main scene, a knowledge driving type intelligent cloud management system is constructed, and the system and the method for managing, operating and maintaining the intelligent cloud management based on knowledge driving are provided, are used for solving the core problems of resource allocation of a multi-cloud management platform, intelligent operation and maintenance faults, alarms, root causes and the like, and providing domain knowledge services to form leading-edge application and intelligent practice based on a plurality of technical fields of cloud computing, artificial intelligence, big data and the like.
Drawings
Fig. 1 is a flowchart of a method for implementing smart cloud management based on knowledge driving according to the present invention.
Fig. 2 is a schematic structural diagram of the system for implementing intelligent cloud management based on knowledge driving according to the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
The method for realizing intelligent cloud management based on knowledge driving comprises the following steps:
(1) collecting structured data, semi-structured data and unstructured data through a multi-cloud management platform;
(2) cleaning, converting and storing the data, carrying out centralized management on the data, and extracting the multi-cloud management operation and maintenance knowledge;
(3) carrying out multi-cloud management knowledge fusion;
(3.1) carrying out data integration on the structured data and the data of a third-party database;
(3.2) carrying out entity alignment and knowledge reasoning, and constructing the time;
(3.3) performing quality evaluation, updating and optimization on the learned knowledge;
(3.4) constructing knowledge fusion and knowledge drive of multi-cloud management;
(4) and carrying out knowledge retrieval on the trained knowledge, and applying the knowledge retrieval to various scenes of multi-cloud management.
In a preferred embodiment of the present invention, the data collected in step (1) includes log data, performance data, basic attribute data, and API interface data.
As a preferred embodiment of the present invention, the step of extracting knowledge in step (2) includes instance extraction, relationship extraction and attribute extraction.
In the step (4), the trained knowledge is applied to intelligent fault location and fault early warning.
The intelligent cloud management system based on knowledge driving for realizing the method of claim 1, characterized in that the system comprises:
the data acquisition module is used for collecting structured data, semi-structured data and unstructured data;
the data cleaning, converting and storing module is connected with the data acquisition module and is used for cleaning, converting, storing and centrally managing data;
the knowledge map module is connected with the data cleaning, converting and storing module and is used for carrying out multi-cloud management knowledge fusion;
and the intelligent operation and maintenance service module is connected with the knowledge map module and used for carrying out knowledge retrieval on the trained knowledge and applying the knowledge retrieval to various cloud management scenes.
The specific implementation mode of the invention comprises modules of data acquisition, data analysis and preprocessing, data modeling and model iterative training, parameter tuning and the like, and finally can realize the functions of intelligent operation and data center anomaly detection, root cause analysis, fault diagnosis and the like in a cloud computing scene. The distributed intelligent cloud management system based on the big data can greatly improve the operation, operation and maintenance efficiency of the cloud computing platform, and compared with a common cloud management system, the system is more intelligent and efficient.
The cloud management system can bring overall improvement to a cloud management solution, autonomous intelligence is given to the operation and maintenance system by using big data correlation analysis and machine learning technology, and intelligent guarantee capabilities from fault prevention to fault positioning and then to fault closed loop are provided, so that the operation and maintenance labor cost is greatly reduced, the fault recovery time is obviously shortened, and the operation and maintenance complexity is responded.
The method comprises the steps of establishing a basic knowledge base based on existing data of a cloud computing platform, extracting and cleaning knowledge from massive complex data acquired from physical equipment and virtual resources by utilizing a semantic and machine learning technology in a multi-cloud management adaptation layer, extracting structured knowledge by utilizing a D2R (Database to RDF) technology based on an ontology, and establishing an operation and maintenance knowledge map capable of being automatically classified and intelligently identified by utilizing a knowledge fusion technology, so that a stronger semantic relation between a network operation and maintenance Database and data is established, the intelligence, accuracy and high efficiency of the operation and maintenance technology are improved to an autonomous controllable visual advanced level, and the service capability of providing the automatic knowledge is realized.
The method mainly comprises the steps of data acquisition and analysis, cleaning and conversion, knowledge drive and knowledge map, and intelligent operation and maintenance service system.
The method comprises the steps that various data collected and reported on the basis of a plurality of cloud platforms, including log data, performance data, basic attribute data, API (application program interface) interface data and the like, are cleaned, converted and stored, centralized management of the data is achieved, knowledge is learned from offline data by means of a knowledge map and a knowledge driving technology, the knowledge is applied to various actual multi-cloud management scenes, and an intelligent operation, operation and maintenance service system is achieved.
(1) The method comprises the steps that performance data, state data, operation data, log data, event information, alarm information and the like of physical equipment and virtual cloud resources of a data center are collected through a multi-cloud management platform, and the data can be structured data or unstructured data;
(2) storing, analyzing, processing, modeling and the like mass data center cloud monitoring operation and maintenance data;
(3) through a large knowledge map and a knowledge-driven technology, a model relation between mass indexes and fault reasons is established, so that functions of intelligent fault early warning, fault automatic positioning and the like are realized, and a user is helped to establish an intelligent operation, operation and maintenance service cloud management system;
(4) specifically, an intelligent knowledge base is constructed by using a knowledge graph.
Briefly described:
the first step is as follows: collecting structured, semi-structured and unstructured monitoring operation and maintenance data through a multi-cloud management platform;
the second step is that: through the knowledge extraction step: the method comprises the steps of instance extraction, relation extraction and attribute extraction;
the third step: carrying out the multi-cloud management knowledge fusion, specifically comprising the steps of monitoring operation and maintenance data by a third-party database and a multi-cloud management platform, and carrying out entity alignment and knowledge reasoning so as to construct the operation and maintenance data; then, the learned knowledge is subjected to quality evaluation, updating and optimization; constructing knowledge fusion and knowledge drive of multi-cloud management;
the fourth step: and the trained knowledge is used for knowledge retrieval, and the method is applied to the functions of intelligent fault positioning, fault early warning and the like.
By adopting the method and the system for realizing intelligent cloud management based on knowledge driving, which are disclosed by the invention, the off-line cloud management knowledge learning technology and the real-time intelligent cloud management technology are researched by taking multi-cloud management as a main scene, a knowledge driving type intelligent cloud management system is constructed, and the system and the method for managing, operating and maintaining the intelligent cloud management based on knowledge driving are provided, are used for solving the core problems of resource allocation of a multi-cloud management platform, intelligent operation and maintenance faults, alarms, root causes and the like, and providing domain knowledge services to form leading-edge application and intelligent practice based on a plurality of technical fields of cloud computing, artificial intelligence, big data and the like.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (6)
1. A method for realizing intelligent cloud management based on knowledge drive is characterized by comprising the following steps:
(1) collecting structured data, semi-structured data and unstructured data through a multi-cloud management platform;
(2) cleaning, converting and storing the data, carrying out centralized management on the data, and extracting the multi-cloud management operation and maintenance knowledge;
(3) carrying out multi-cloud management knowledge fusion;
(4) and carrying out knowledge retrieval on the trained knowledge, and applying the knowledge retrieval to various scenes of multi-cloud management.
2. The method for realizing intelligent cloud management based on knowledge driving according to claim 1, wherein the step (3) specifically comprises the following steps:
(3.1) carrying out data integration on the structured data and the data of a third-party database;
(3.2) carrying out entity alignment and knowledge reasoning, and constructing the time;
(3.3) performing quality evaluation, updating and optimization on the learned knowledge;
and (3.4) constructing knowledge fusion and knowledge driving of multi-cloud management.
3. The method for implementing intelligent cloud management based on knowledge driving of claim 1, wherein the data collected in step (1) comprises log data, performance data, basic attribute data and API interface data.
4. The method for implementing intelligent cloud management based on knowledge driving of claim 1, wherein the step of extracting knowledge in step (2) comprises instance extraction, relationship extraction and attribute extraction.
5. The method for implementing intelligent cloud management based on knowledge driving as claimed in claim 1, wherein the trained knowledge is applied to intelligent fault location and fault pre-warning in step (4).
6. A knowledge-driven-based intelligent cloud management system for implementing the method of claim 1, the system comprising:
the data acquisition module is used for collecting structured data, semi-structured data and unstructured data;
the data cleaning, converting and storing module is connected with the data acquisition module and is used for cleaning, converting, storing and centrally managing data;
the knowledge map module is connected with the data cleaning, converting and storing module and is used for carrying out multi-cloud management knowledge fusion;
and the intelligent operation and maintenance service module is connected with the knowledge map module and used for carrying out knowledge retrieval on the trained knowledge and applying the knowledge retrieval to various cloud management scenes.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112804079A (en) * | 2020-12-10 | 2021-05-14 | 北京浪潮数据技术有限公司 | Cloud computing platform alarm analysis method, device, equipment and storage medium |
| CN116414999A (en) * | 2022-12-01 | 2023-07-11 | 北京首都在线科技股份有限公司 | Knowledge graph-based management method and device, electronic equipment and storage medium |
| WO2023138014A1 (en) * | 2022-01-19 | 2023-07-27 | 浪潮通信信息系统有限公司 | Intelligent operation and maintenance system oriented to computing-network integration scenario and use method thereof |
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| CN110955550A (en) * | 2019-11-24 | 2020-04-03 | 济南浪潮数据技术有限公司 | Cloud platform fault positioning method, device, equipment and storage medium |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109446387A (en) * | 2018-10-09 | 2019-03-08 | 众蚁(上海)信息技术有限公司 | A kind of Owners Committee's intelligent Answer System based on artificial intelligence |
| CN110955550A (en) * | 2019-11-24 | 2020-04-03 | 济南浪潮数据技术有限公司 | Cloud platform fault positioning method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112804079A (en) * | 2020-12-10 | 2021-05-14 | 北京浪潮数据技术有限公司 | Cloud computing platform alarm analysis method, device, equipment and storage medium |
| WO2023138014A1 (en) * | 2022-01-19 | 2023-07-27 | 浪潮通信信息系统有限公司 | Intelligent operation and maintenance system oriented to computing-network integration scenario and use method thereof |
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