CN113872794B - IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof - Google Patents

IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof Download PDF

Info

Publication number
CN113872794B
CN113872794B CN202110943933.4A CN202110943933A CN113872794B CN 113872794 B CN113872794 B CN 113872794B CN 202110943933 A CN202110943933 A CN 202110943933A CN 113872794 B CN113872794 B CN 113872794B
Authority
CN
China
Prior art keywords
user
reference model
maintenance
description information
system reference
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110943933.4A
Other languages
Chinese (zh)
Other versions
CN113872794A (en
Inventor
王振华
马刚
胡燕
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing University of Posts and Telecommunications
Original Assignee
Beijing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing University of Posts and Telecommunications filed Critical Beijing University of Posts and Telecommunications
Priority to CN202110943933.4A priority Critical patent/CN113872794B/en
Publication of CN113872794A publication Critical patent/CN113872794A/en
Application granted granted Critical
Publication of CN113872794B publication Critical patent/CN113872794B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G06F40/295—Named entity recognition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Animal Behavior & Ethology (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application discloses an IT operation and maintenance platform system based on cloud resource support and an operation and maintenance method thereof. The cloud resource supporting platform comprises a reference model construction module used for constructing a user IT system reference model; the data information management module is used for acquiring data of a user IT system and performing data analysis, operation state prediction and risk prediction on the user IT system based on a user IT system reference model; the cloud operation and maintenance scheduling module is used for generating and issuing an operation and maintenance demand scheduling list based on data analysis, operation state prediction and risk prediction, the cloud functions of big data analysis, operation state and risk prediction, advanced scheduling of IT operation and maintenance resources, information security cloud audit and the like related to IT operation and maintenance are achieved, interconnection and resource data integration are achieved with a local IT operation and maintenance center, and an unmanned and clustered IT operation and maintenance service mode is achieved.

Description

IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof
Technical Field
The application relates to the technical field of IT operation and maintenance management, in particular to an IT operation and maintenance platform system based on cloud resource support and an operation and maintenance method thereof.
Background
With the continuous deepening and improvement of IT construction, the operation and maintenance of computer software and hardware systems, that is, IT operation and maintenance, become a common concern of various industries, and IT operation and maintenance refers to the realization of state monitoring, troubleshooting, equipment configuration and management, IT safety protection, data backup and the like of a user-oriented IT system. At present, a user needs to establish a local IT operation and maintenance center for a self IT system so as to implement specialized and efficient IT operation and maintenance. However, the problems of high construction and operation cost, limited resources and insufficient support for the operation and maintenance of the IT system exist in the establishment of the local IT operation and maintenance center.
Disclosure of Invention
Object of the application
Based on this, in order to realize cloud functions such as big data analysis, operation state and risk prediction, advanced scheduling of IT operation and maintenance resources, information security cloud audit and the like related to IT operation and maintenance, and realize interconnection and intercommunication and resource data integration with a local IT operation and maintenance center, and realize an unmanned and clustered IT operation and maintenance service mode, the application discloses the following technical scheme.
(II) technical scheme
The application discloses IT operation and maintenance platform system based on high in clouds resource support, which is characterized by comprising a high in clouds resource support platform and a user local IT operation and maintenance center, wherein the high in clouds resource support platform and the user local IT operation and maintenance center are interconnected and intercommunicated through a network.
In one possible implementation, the cloud resource support platform includes:
the reference model building module is used for building a user IT system reference model;
the data information management module is used for acquiring user IT system data and carrying out data analysis, operation state prediction and risk prediction on a user IT system based on the user IT system reference model;
and the cloud operation and maintenance scheduling module is used for generating and issuing an operation and maintenance demand scheduling list based on the data analysis, the operation state prediction and the risk prediction.
In one possible implementation, the cloud resource supporting platform further includes:
and the cloud security audit module is used for performing security audit on the operation and maintenance scheduling operation based on the data analysis, the running state prediction and the risk prediction.
In one possible embodiment, the reference model building module comprises:
the preprocessing unit is used for preprocessing the original description information of the user to generate user description information; the attribute label marking unit is used for carrying out attribute label marking on the user description information to generate user attribute description information;
the information extraction unit is used for extracting information from the user attribute description information based on an IT system knowledge map library and generating an IT system reference triple;
and the reference model building unit is used for adapting the IT system reference triple with a standard reference model in an IT system reference model library to generate a user IT system reference model.
In a possible embodiment, the reference model construction unit comprises:
the matching degree calculation operator unit is used for calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library;
the similarity proportion calculation subunit is used for calculating the similarity proportion between the IT system reference triple and the standard reference model according to the matching degree and the entity number;
and the reference model building subunit is used for determining the reference model of the IT system of the user based on the similar proportion and the sequencing of the similar proportion.
As a second aspect of the present application, the present application further discloses an IT operation and maintenance method based on cloud resource support, which is characterized by including:
constructing a reference model of a user IT system;
acquiring user IT system data and performing data analysis, operation state prediction and risk prediction on a user IT system based on the user IT system reference model;
and generating and issuing an operation and maintenance demand dispatch list based on the data analysis, the operation state prediction and the risk prediction.
In one possible embodiment, the method further comprises:
and performing safety audit on operation and maintenance scheduling operation based on the data analysis, the operation state prediction and the risk prediction.
In one possible embodiment, the building of the user IT architecture reference model includes:
preprocessing the original description information of the user to generate user description information;
carrying out attribute label marking on the user description information to generate user attribute description information;
extracting information from the user attribute description information based on an IT system knowledge map library, and generating an IT system reference triple;
and matching the IT system reference triple with a standard reference model in an IT system reference model library to generate a user IT system reference model.
In a possible implementation manner, the adapting the IT system reference triple to a standard reference model in an IT system reference model library to generate a user IT system reference model includes:
calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library;
calculating the similarity proportion of the IT system reference triple and the standard reference model according to the matching degree and the entity number;
and determining a user IT system reference model based on the similar proportion and the sequencing of the similar proportion.
In a possible implementation manner, the user description information includes a topic prediction, a relationship corpus, and a configuration corpus, where the type of the main name is an attribute tag of the main corpus, the type of the relationship is an attribute tag of the relationship corpus, and the type of the configuration parameter is an attribute tag of the configuration corpus.
(III) advantageous effects
The IT operation and maintenance platform system based on cloud resource support and the operation and maintenance method thereof realize the cloud functions of big data analysis, operation state and risk prediction, advanced scheduling of IT operation and maintenance resources, information security cloud audit and the like related to IT operation and maintenance, realize interconnection and intercommunication and resource data integration with a local IT operation and maintenance center, and realize an unmanned and clustered IT operation and maintenance service mode.
Drawings
The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining and illustrating the present application and should not be construed as limiting the scope of the present application.
Fig. 1 is a block diagram of a cloud resource support-based IT operation and maintenance platform system disclosed in the present application.
Fig. 2 is a schematic flow chart of an IT operation and maintenance method based on cloud resource support disclosed in the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application.
An embodiment of the cloud resource support-based IT operation and maintenance platform system disclosed in the present application is described in detail below with reference to fig. 1.
As shown in fig. 1, the system disclosed in this embodiment mainly includes a cloud resource supporting platform 100 and a user local IT operation and maintenance center 200, where the cloud resource supporting platform 100 and the user local IT operation and maintenance center 200 are interconnected and intercommunicated through a network.
Cloud resource support platform 100 further includes: the system comprises a reference model building module 110, a data information management module 120, a cloud operation and maintenance scheduling module 130 and a cloud security audit module 140.
The reference model building module 110 is configured to build a user IT architecture reference model, where the reference model building module 110 specifically includes:
and the preprocessing unit 111 is used for preprocessing the original user description information to generate user description information.
Specifically, the original description information of the user is related description information about a topology architecture, hardware facilities, an operating system, various software configurations, an IT operation rule and the like of a user IT system uploaded by a local IT operation and maintenance center of the user, and after the original description information of the user is obtained, the original description information of the user is preprocessed, wherein the preprocessing process specifically includes:
a. carrying out text coding format standardization on the original description information of the user;
b. performing corpus de-weighting and corpus cleaning;
specifically, corpus deduplication is to remove duplicate content by calculating similarity between corpuses, so as to prevent multiple repetition of a corpus, corpus cleansing is to perform text cleansing on the corpus after deduplication, and irrelevant or meaningless corpus content is filtered by matching the corpus content after deduplication with a stop word dictionary.
The original user description information is preprocessed to generate user description information.
An attribute tag marking unit 112, configured to perform attribute tag marking on the user description information to generate user attribute description information.
Due to diversification and complication of the user description information, attribute label marking is carried out on the user description information according to a preset rule.
Specifically, for a subject corpus including hardware facilities, an operating system, software and the like of a user IT system, the subject name type thereof may be marked as an attribute tag; for the relation corpora, including an IT system topological structure, an IT operation rule and the like, the relation type can be marked as an attribute label; for configuration corpora, including software configuration, operating system configuration, etc., the configuration parameter types thereof may be marked as attribute tags.
And generating user attribute description information after the user description information is subjected to attribute tag marking.
And the information extraction unit 113 is configured to perform information extraction on the user attribute description information based on an IT system knowledge map library, and generate an IT system reference triple.
The IT system knowledge map library comprises a plurality of triple map structures, and can construct an entity-relationship-entity triple map frame and an entity-attribute value triple map frame according to attribute description information of a user IT system, so as to provide support for the adaptation of the user IT system and a standard reference model.
Specifically, an IT system knowledge map library is called according to the main name type, the relation type and the configuration parameter type of the user IT system to construct a triple map frame in advance, and the information extraction is carried out on the user attribute description information by using the constructed triple map frame in advance.
Further, the information extraction comprises entity extraction, relationship extraction and attribute extraction, wherein the algorithm adopted by the entity extraction is named entity recognition algorithm, namely on the basis of early training, the natural language processing technology is used for automatically collecting entity contents in the corpus, meanwhile, the collected data is added into a model, the training is continued to improve the accuracy, and finally, entity information in the text is sorted and summarized to establish an entity library; the named entity recognition algorithm has high labeling efficiency, but requires a large amount of data for training and has relatively low accuracy. Therefore, the selection is often determined according to the data situation and the work requirement. The method adopts a BERT-BilSTM-CRF model, which is a named entity recognition model formed by combining a BERT pre-training language model and a bidirectional long-short term memory network-conditional random field model (BilSTM-CRF). The whole system is divided into three layers, namely a first BERT layer, and corpus features are extracted by using a BERT general language model obtained through a large amount of Chinese general corpuses and great calculation training to obtain low-dimensional word vectors. The second layer is a BilSTM layer, based on the understanding of the word vector characteristics by the pre-training layer, a large amount of labeled linguistic data are used for training the bidirectional long-short term memory network, the context semantic information is used for deducing and labeling the entity sequence according to the model training result, and the weight is set through the attention mechanism to further screen the entity types. And the third layer is a CRF layer, and according to the corpus entity sequence output by the BilSTM layer, a probability model is used for predicting and outputting an optimal expression of sequence tags, so that automatic sequence labeling of the corpus is realized, and named entity identification is completed.
The relation extraction algorithm essentially obtains the concrete description of the logic association between entities or between entities and attributes in the text corpus, and obtains the potential meaning through the concrete description to construct the semantic relation between the entities. The current relationship extraction algorithm mainly comprises the following steps: manual labeling combined with entity extraction, semantic relation recognition based on machine learning, relation recognition based on deep learning, and a joint extraction algorithm combined with named entity recognition.
The attribute extraction is to obtain attribute information of the entity. Through the concrete description, the content form of the entity is more full.
And filling the extracted information into the triple map framework to generate the IT system reference triple.
And the reference model building unit 114 is configured to adapt the IT system reference triple to a standard reference model in an IT system reference model library, so as to generate a user IT system reference model.
The IT system reference model library comprises a plurality of standard reference models, the generated user IT system reference models are stored in the IT system reference model library, the obtained IT system reference triples are adapted to the standard reference models in the IT system reference model library, and the standard reference models define standard topological structures, hardware compositions, operating systems, software compositions and IT operation rules of various IT systems by adopting a definition mode of entities, relations and attributes of knowledge maps.
Specifically, the reference model building unit 114 further includes:
and the matching degree operator unit 1141 is used for calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library.
Further, the matching degree of the user IT system and the standard reference model on the contents such as entities, relations, attributes and the like is calculated, the matching degree of the single attribute is integrated, and a comprehensive matching degree vector is obtained, wherein the calculation formula is as follows:
Figure BDA0003215926730000091
/>
in the formula (I), the compound is shown in the specification,
Figure BDA0003215926730000092
to synthesize a matching degree vector, A m Being an entity of the user IT hierarchy, B m As an entity of a standard reference model, A m 、B m There are n related attributes or relationships, by statistics A m 、B m Each word frequency a of each participle in each attribute and relationship of (1) i 、b i And constructing a word frequency vector, and further determining the matching property through vector cosine value calculation, wherein m represents the m-th entity.
And the similarity proportion calculation subunit 1142 is configured to calculate a similarity proportion between the IT system reference triple and the standard reference model according to the matching degree and the number of entities.
Concretely, general referenceCalculating the ratio of the entity matching of the user IT system and the standard reference model to the number of the entities to obtain the similarity ratio S of the two A,B And the similarity ratio is between 0 and 1. The specific formula is as follows:
Figure BDA0003215926730000093
in the formula, S A,B And (3) representing the similarity ratio of the matching of the user IT system and the standard reference model entity to the number of entities, wherein s is the number of entities, and m represents the mth entity.
A reference model construction subunit 1143, configured to determine a user IT architecture reference model based on the similarity proportion and the ordering of the similarity proportion.
Specifically, according to the obtained similarity proportion between the user IT system and the plurality of standard reference models and the similarity sequence between the user IT system and each standard reference model, the most suitable standard reference model is determined to form the user IT system reference model.
And the data information management module is used for acquiring the data of the user IT system and carrying out data analysis, operation state prediction and risk prediction on the user IT system based on the user IT system reference model.
Specifically, the user IT architecture data includes: status data, fault alarm data, configuration updating data, security attack data, log data and the like, and user IT system data are obtained in real time through a data instruction set interface; and the reference model of the user IT system is referred to realize data analysis, operation state and risk prediction. More specifically, relevant analysis and prediction indexes can be defined for a reference model of a user IT system, and relevant data analysis, operation state and risk prediction can be realized according to IT system data such as state data, fault alarm data, configuration update data, security attack data and log data which are actually generated by the user IT system.
And the cloud operation and maintenance scheduling module is used for generating and issuing an operation and maintenance demand scheduling list based on the data analysis, the operation state prediction and the risk prediction.
Specifically, an operation and maintenance demand dispatch list is generated according to the data analysis, the operation state and the risk prediction of the data information management unit, and the operation and maintenance demand dispatch list is issued at the cloud.
In at least one embodiment, the cloud resource support platform further comprises:
and the cloud security audit module is used for performing security audit on the operation and maintenance scheduling operation based on the data analysis, the running state prediction and the risk prediction.
Specifically, according to the data analysis, the running state and the risk prediction of the data information management unit, security audit is executed aiming at the operation of the local IT center and the cloud operation and maintenance scheduling, and the safety of the operation and maintenance scheduling is determined.
An embodiment of the cloud resource support-based IT operation and maintenance method disclosed in the present application is described in detail below with reference to fig. 2. As shown in fig. 2, the method disclosed in this embodiment includes:
constructing a reference model of a user IT system;
acquiring user IT system data and performing data analysis, operation state prediction and risk prediction on a user IT system based on the user IT system reference model;
and generating and issuing an operation and maintenance demand dispatch list based on the data analysis, the operation state prediction and the risk prediction.
In at least one embodiment, the method further comprises:
and performing safety audit on operation and maintenance scheduling operation based on the data analysis, the operation state prediction and the risk prediction.
In at least one embodiment, the building of the user IT architecture reference model comprises:
preprocessing the original description information of the user to generate user description information;
carrying out attribute label marking on the user description information to generate user attribute description information;
extracting information of the user attribute description information based on an IT system knowledge graph library, and generating an IT system reference triple;
and matching the IT system reference triple with a standard reference model in an IT system reference model library to generate a user IT system reference model.
In at least one embodiment, the adapting the IT system reference triplet to the standard reference model in the IT system reference model library to generate the user IT system reference model includes:
calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library;
calculating the similarity proportion of the IT system reference triple and the standard reference model according to the matching degree and the entity number;
and determining a user IT system reference model based on the similar proportion and the sequencing of the similar proportion.
In at least one embodiment, the user description information includes a topic prediction, a relationship corpus, and a configuration corpus, where the tag type of the main name is an attribute tag of the main corpus, the tag type of the relationship is an attribute tag of the relationship corpus, and the tag type of the configuration parameter is an attribute tag of the configuration corpus.
The division of modules, units or sub-units herein is merely a division of logical functions and other divisions may be made in an actual implementation, for example, a plurality of modules and/or units may be combined or integrated in another system. Modules, units, and sub-units described as separate components may or may not be physically separate. The components displayed as units and/or sub-units may or may not be physical units and/or sub-units, may be located in a specific place, and may also be distributed in grid units and/or sub-units. Therefore, some or all of the units and/or sub-units can be selected according to actual needs to implement the scheme of the embodiment.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (5)

1. An IT operation and maintenance platform system based on cloud resource support is characterized by comprising a cloud resource support platform and a user local IT operation and maintenance center, wherein the cloud resource support platform and the user local IT operation and maintenance center are interconnected and intercommunicated through a network;
the cloud resource supporting platform comprises:
the reference model building module is used for building a user IT system reference model;
the data information management module is used for acquiring user IT system data and carrying out data analysis, operation state prediction and risk prediction on a user IT system based on the user IT system reference model;
the cloud operation and maintenance scheduling module is used for generating and issuing an operation and maintenance demand scheduling list based on the data analysis, the operation state prediction and the risk prediction;
the reference model building module comprises:
the preprocessing unit is used for preprocessing the original description information of the user to generate user description information;
the attribute label marking unit is used for carrying out attribute label marking on the user description information to generate user attribute description information;
the information extraction unit is used for extracting information from the user attribute description information based on an IT system knowledge map library and generating an IT system reference triple;
the reference model building unit is used for adapting the IT system reference triple to a standard reference model in an IT system reference model library to generate a user IT system reference model;
the reference model construction unit includes:
the matching degree calculation operator unit is used for calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library;
the similarity proportion calculation subunit is used for calculating the similarity proportion between the IT system reference triple and the standard reference model according to the matching degree and the entity number;
and the reference model building subunit is used for determining the reference model of the IT system of the user based on the similar proportion and the sequencing of the similar proportion.
2. The system of claim 1, wherein the cloud resource support platform further comprises:
and the cloud security audit module is used for performing security audit on the operation and maintenance scheduling operation based on the data analysis, the running state prediction and the risk prediction.
3. An IT operation and maintenance method based on cloud resource support is characterized by comprising the following steps:
constructing a reference model of a user IT system;
acquiring user IT system data and performing data analysis, operation state prediction and risk prediction on a user IT system based on the user IT system reference model;
generating and issuing an operation and maintenance demand dispatch list based on the data analysis, the operation state prediction and the risk prediction;
the method for constructing the user IT system reference model comprises the following steps:
preprocessing the original description information of the user to generate user description information;
carrying out attribute label marking on the user description information to generate user attribute description information;
extracting information from the user attribute description information based on an IT system knowledge map library, and generating an IT system reference triple;
the IT system reference triple is adapted to a standard reference model in an IT system reference model library to generate a user IT system reference model;
the step of adapting the IT system reference triple to a standard reference model in an IT system reference model library to generate a user IT system reference model comprises the following steps:
calculating the matching degree of the IT system reference triple and a standard reference model in an IT system reference model library;
calculating the similarity proportion of the IT system reference triple and the standard reference model according to the matching degree and the number of the entities;
and determining a user IT system reference model based on the similar proportion and the sequencing of the similar proportion.
4. The method of claim 3, further comprising:
and performing safety audit on operation and maintenance scheduling operation based on the data analysis, the operation state prediction and the risk prediction.
5. The method according to claim 3, wherein the user description information includes a subject corpus, a relationship corpus, and a configuration corpus, the subject name type is labeled as an attribute tag of the subject corpus, the relationship type is labeled as an attribute tag of the relationship corpus, and the configuration parameter type is labeled as an attribute tag of the configuration corpus.
CN202110943933.4A 2021-08-17 2021-08-17 IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof Active CN113872794B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110943933.4A CN113872794B (en) 2021-08-17 2021-08-17 IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110943933.4A CN113872794B (en) 2021-08-17 2021-08-17 IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof

Publications (2)

Publication Number Publication Date
CN113872794A CN113872794A (en) 2021-12-31
CN113872794B true CN113872794B (en) 2023-03-24

Family

ID=78990469

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110943933.4A Active CN113872794B (en) 2021-08-17 2021-08-17 IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof

Country Status (1)

Country Link
CN (1) CN113872794B (en)

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI582607B (en) * 2015-11-02 2017-05-11 廣達電腦股份有限公司 Dynamic resources planning mechanism based on cloud computing and smart device
CN106161620A (en) * 2016-06-29 2016-11-23 浙江理工大学 A kind of cloud computing resources Internet of Things supervision and service platform
CN110278102A (en) * 2018-03-15 2019-09-24 勤智数码科技股份有限公司 A kind of IT automation operational system and method
US20190349273A1 (en) * 2018-05-14 2019-11-14 Servicenow, Inc. Systems and method for incident forecasting
CN111176872B (en) * 2019-12-12 2021-05-07 北京邮电大学 Monitoring data processing method, system, device and storage medium for IT operation and maintenance
CN111371615B (en) * 2020-03-04 2023-07-14 深信服科技股份有限公司 Online server, method and system for updating operation and maintenance tool and readable storage medium
CN111598702A (en) * 2020-04-14 2020-08-28 徐佳慧 Knowledge graph-based method for searching investment risk semantics

Also Published As

Publication number Publication date
CN113872794A (en) 2021-12-31

Similar Documents

Publication Publication Date Title
CN110609759B (en) Fault root cause analysis method and device
CN110516063A (en) A kind of update method of service system, electronic equipment and readable storage medium storing program for executing
CN112541600A (en) Knowledge graph-based auxiliary maintenance decision method
CN111611218A (en) Distributed abnormal log automatic identification method based on deep learning
CN113095050A (en) Intelligent ticketing method, system, equipment and storage medium
JP6709812B2 (en) Relationship estimation model learning device, method, and program
CN112541070A (en) Method and device for excavating slot position updating corpus, electronic equipment and storage medium
CN112507663A (en) Text-based judgment question generation method and device, electronic equipment and storage medium
CN113095524A (en) Intelligent generation method, system and storage medium for maintenance work document of power equipment
CN113706291A (en) Fraud risk prediction method, device, equipment and storage medium
CN113704410A (en) Emotion fluctuation detection method and device, electronic equipment and storage medium
WO2023071127A1 (en) Policy recommended method and apparatus, device, and storage medium
CN114996936A (en) Equipment operation and maintenance method, equipment operation and maintenance device, equipment operation and maintenance equipment and storage medium
CN114168795A (en) Building three-dimensional model mapping and storing method and device, electronic equipment and medium
CN113312924A (en) Risk rule classification method and device based on NLP high-precision analysis label
CN113468288A (en) Content extraction method of text courseware based on artificial intelligence and related equipment
CN113872794B (en) IT operation and maintenance platform system based on cloud resource support and operation and maintenance method thereof
CN116611813B (en) Intelligent operation and maintenance management method and system based on knowledge graph
CN115186738B (en) Model training method, device and storage medium
CN116795978A (en) Complaint information processing method and device, electronic equipment and medium
CN116225848A (en) Log monitoring method, device, equipment and medium
CN113688232B (en) Method and device for classifying bid-inviting text, storage medium and terminal
CN115587192A (en) Relationship information extraction method, device and computer readable storage medium
CN114925210A (en) Knowledge graph construction method, device, medium and equipment
CN114420168A (en) Emotion recognition method, device, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant