CN112579391A - Distributed database automatic operation and maintenance method and system based on artificial intelligence - Google Patents
Distributed database automatic operation and maintenance method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses a distributed database automatic operation and maintenance method and a system based on artificial intelligence, belonging to the field of distributed autonomous databases; the automatic operation and maintenance method of the distributed database based on artificial intelligence is characterized by comprising the following specific steps: s1, collecting the operation information of the database system; s2, determining relevant characteristics of fault information according to the change of the monitoring index data; s3, establishing a problem recognition model by using the relevant characteristic data of the fault information, and classifying the fault types; s4, setting a filter to collect, sort and screen the effective information to obtain early warning information; s5, collecting early warning information and recording corresponding fault problems; the automatic operation and maintenance method can be used for realizing the resource allocation and parameter adjustment of the monitored application in a best dynamic manner, is self-transplantable, and can well adjust corresponding indexes according to actual requirements so as to adapt to different actual conditions.
Description
Technical Field
The invention discloses an automatic operation and maintenance method and system for a distributed database based on artificial intelligence, and relates to the technical field of distributed autonomous databases.
Background
Data is the most strategic asset in any business and public security, comprehensive clouding in the field of information technology is trending, and after cloud computing of large data, cloud on a database is the future development direction of the database. With the development of the information era, the value and accessibility of the information of the database are improved, and the examination on the safety, the practicability and the reliability of the database is more and more severe. The monitoring, operation and maintenance of the conventional database usually require a large amount of manpower and material resources, and the data loss and damage caused by improper handling or manual misoperation after the downtime occurs will be destructive. Therefore, it is very important to design a set of automatic operation and maintenance system based on artificial intelligence distributed database fault monitoring, fault classification and fault processing.
Currently, the mainstream third-party database monitoring systems in the market include promethaus (promemetus), solarwands, and paterler Router Traffic graph. The monitoring functions of the devices are relatively perfect at present, but the monitoring functions have the defects that no fault is automatically processed after a problem occurs, and only the output of alarm information needs to be maintained by operation and maintenance personnel. When the system is in a service peak period, high-frequency alarm occurs, operation and maintenance personnel cannot rapidly handle a large number of problems in a short time, and meanwhile, the problems of improper operation, error positioning and the like can also occur. Currently, Oracle Database 18c which achieves Database autonomy does not have an independent generic monitoring system. The monitoring system has the functions of automatic operation, fault classification and processing of the distributed database to be considered;
therefore, the invention provides an automatic operation and maintenance method and system for a distributed database based on artificial intelligence, so as to solve the problems.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a distributed database automatic operation and maintenance method and system based on artificial intelligence, and the adopted technical scheme is as follows: an automatic operation and maintenance method for a distributed database based on artificial intelligence comprises the following specific steps:
s1, collecting the operation information of the database system;
s2, determining relevant characteristics of fault information according to the change of the monitoring index data;
s3, establishing a problem recognition model by using the relevant characteristic data of the fault information, and classifying the fault types;
s4, setting a filter to collect, sort and screen the effective information to obtain early warning information;
s5 collects the warning information and records the corresponding fault problem.
The specific step of collecting the operation information of the database system by the step S1 includes:
s101 grabbing tool for accessing database events and state information to database
S102, setting a message queue to cache data;
s103, sending the data after the information filtering processing to a cloud server for AI analysis.
The specific step of determining the relevant characteristics of the fault information according to the change of the monitoring index data by the S2 includes:
s201, determining an influence index and data variation of a specific type of problem by using the response data of the database cluster state;
s202, selecting a proper model for deep learning training.
The S3 establishes a problem identification model using the relevant feature data of the fault information, and the specific steps of classifying the fault types include:
s301, manually inputting fault information types and related data;
s302, training a problem recognition model by using deep learning of a machine;
s303, classifying and integrating the fault information.
The utility model provides an automatic operation and maintenance system of distributed database based on artificial intelligence, the system include collection module, characteristic module, classification module, screening module and record module:
an acquisition module: collecting the operation information of the database system;
a characteristic module: determining relevant characteristics of fault information according to the change of the monitoring index data;
a classification module: establishing a problem identification model by using the relevant characteristic data of the fault information, and classifying the fault types;
a screening module: setting a filter to collect, sort and screen the effective information to obtain early warning information;
a recording module: and collecting early warning information and recording corresponding fault problems.
The acquisition module specifically comprises an access module, a cache module and a filtering module:
an access module: accessing a capture tool of database events and state information to a database;
a cache module: setting a message queue to cache data;
a filtering module: and sending the data after the information filtering processing to a cloud server for AI analysis.
The characteristic module specifically comprises an analysis module and a learning module:
an analysis module: determining influence indexes and data variation of specific types of problems by using the response data of the database cluster state;
a learning module: and selecting a proper model for deep learning training.
The classification module specifically comprises an input module, a training module and a display module:
an input module: manually inputting fault information types and related data;
a training module: training a problem recognition model by using the deep learning of a machine;
a display module: and classifying and integrating the fault information.
The invention has the beneficial effects that: the invention can provide an intelligent, high-efficiency and reliable automatic operation and maintenance system for monitoring and warning of the distributed database and carrying out classification processing on the database; according to the method, operations such as early warning and automatic processing are not performed on the past database faults, collected information such as indexes and logs is sent to the cloud end through a message queue, model training analysis is performed through AI, fault prediction of the database is achieved through fault warning and data analysis and classification, system resources are dynamically allocated, and the operation and maintenance cost and the fault processing time of the database are reduced;
the automatic operation and maintenance method can be used for realizing the resource allocation and parameter adjustment of the monitored application in a best dynamic manner, is portable, and can well adjust corresponding indexes according to actual requirements so as to adapt to different actual conditions; the fault monitoring, fault classification and fault processing of the distributed database are carried out through artificial intelligence, so that the operation and maintenance cost of the database and the loss caused by artificial misoperation can be greatly reduced, and the problem solving efficiency is improved; the security module of the mobile terminal can meet the security requirements of most applications.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention; FIG. 2 is a schematic diagram of the system of the present invention; fig. 3 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The first embodiment is as follows:
an automatic operation and maintenance method for a distributed database based on artificial intelligence comprises the following specific steps:
s1, collecting the operation information of the database system;
s2, determining relevant characteristics of fault information according to the change of the monitoring index data;
s3, establishing a problem recognition model by using the relevant characteristic data of the fault information, and classifying the fault types;
s4, setting a filter to collect, sort and screen the effective information to obtain early warning information;
s5, collecting early warning information and recording corresponding fault problems;
when the method is used for operation and maintenance of the database, for the whole database monitoring system, firstly, the operation information price of the database system is collected according to S1, the relevant characteristics of fault information are determined according to S2 according to the change of monitoring index data, then a problem identification model is established by utilizing the relevant characteristic data of the fault information to classify the fault types according to S3, and then effective information is collected, sorted and screened according to a filter set in S4; analyzing and predicting through the trained model, recording the prediction result every time, adjusting and learning on the basis of the original model without stopping after each problem occurs, finally collecting early warning information according to S5, recording fault problems under each condition and analyzing and learning the cause type of each problem, and finally realizing the function of problem prediction;
judging whether a message queue is needed to be used as the temporary input of data according to the quantity of data, carrying out dynamic allocation and scheduling operation on resources such as a database memory and the like under the conditions of overlarge data search quantity or increased storage content and the like, dynamically constructing expert experience and a knowledge graph through an AI (access interface) module to form different types of decision trees, and carrying out protection measures such as storage on data information before the downtime condition occurs; therefore, the automatic operation and maintenance system for fault monitoring, fault classification and fault processing of the database is really achieved;
further, the step of collecting the operation information of the database system in S1 includes:
s101 grabbing tool for accessing database events and state information to database
S102, setting a message queue to cache data;
s103, sending the data after the information filtering processing to a cloud server for AI analysis;
firstly, accessing a database event and state information capturing tool to a database according to S101, and capturing related data of a system for information such as system state and the like through a self-research data collecting tool; setting a message queue for the condition of a large amount of system data information captured at one time, temporarily storing the data according to S102, and simultaneously sending the data after information filtering processing to a cloud server according to S103 for AI analysis;
further, the specific step of determining the relevant characteristics of the fault information according to the change of the monitoring index data in S2 includes:
s201, determining an influence index and data variation of a specific type of problem by using the response data of the database cluster state;
s202, selecting a proper model for deep learning training;
by monitoring the change of each index data before each fault is generated, the change of the index data corresponding to the generation of each type of problem is determined. Determining indexes influencing specific types of problems and the amount of data variation according to S201 by monitoring information reflecting database cluster states such as data insertion amount, sql connection number, threshold and the like, and selecting a proper model for deep learning training according to S202;
still further, the step S3 of establishing a problem identification model by using the relevant feature data of the fault information includes:
s301, manually inputting fault information types and related data;
s302, training a problem recognition model by using deep learning of a machine;
s303, classifying and integrating the fault information;
and analyzing the related characteristics of the abnormal indexes before the fault by the data transmitted after the data analysis and the record of the fault information. Manually inputting the type of the fault information and related data through S301; and then, performing deep learning training of the machine according to S302 to obtain a model for identifying each problem, classifying fault information according to S303, and integrating and displaying the information, so that the requirement of fault problem classification is met.
Example two:
the utility model provides an automatic operation and maintenance system of distributed database based on artificial intelligence, the system include collection module, characteristic module, classification module, screening module and record module:
an acquisition module: collecting the operation information of the database system;
a characteristic module: determining relevant characteristics of fault information according to the change of the monitoring index data;
a classification module: establishing a problem identification model by using the relevant characteristic data of the fault information, and classifying the fault types;
a screening module: setting a filter to collect, sort and screen the effective information to obtain early warning information;
a recording module: collecting early warning information and recording corresponding fault problems;
when the system is used for operation and maintenance of the database, for the whole database monitoring system, firstly, an acquisition module is used for acquiring the running information price of the database system, relevant characteristics of fault information are determined through a characteristic module according to the change of monitoring index data, then a problem identification model is established by utilizing the relevant characteristic data of the fault information, fault types are classified through a classification module, and effective information is collected, sorted and screened by utilizing a filter set by a screening module; analyzing and predicting through the trained model, recording the prediction result of each time, adjusting and learning the original model without stopping after each problem occurs, collecting early warning information by using a recording module, recording the fault problem under each condition and analyzing and learning the cause type of each problem, and finally realizing the function of problem prediction;
judging whether a message queue is needed to be used as the temporary input of data according to the quantity of data, carrying out dynamic allocation and scheduling operation on resources such as a database memory and the like under the conditions of overlarge data search quantity or increased storage content and the like, dynamically constructing expert experience and a knowledge graph through an AI (access interface) module to form different types of decision trees, and carrying out protection measures such as storage on data information before the downtime condition occurs; therefore, the automatic operation and maintenance system for fault monitoring, fault classification and fault processing of the database is really achieved;
further, the acquisition module specifically comprises an access module, a cache module and a filtering module:
an access module: accessing a capture tool of database events and state information to a database;
a cache module: setting a message queue to cache data;
a filtering module: sending the data after the information filtering processing to a cloud server for AI analysis;
firstly, an access module accesses a database event and state information capturing tool to a database, and for a system, the information such as the system state and the like can be captured by a self-research data collecting tool; a message queue can be set for the condition of a large amount of system data information captured at one time, the data is temporarily stored through a cache module, and meanwhile, the data after information filtering processing is sent to a cloud server through a filtering module to be analyzed by AI;
further, the feature module specifically includes an analysis module and a learning module:
an analysis module: determining influence indexes and data variation of specific types of problems by using the response data of the database cluster state;
a learning module: selecting a proper model for deep learning training;
by monitoring the change of each index data before each fault is generated, the change of the index data corresponding to the generation of each type of problem is determined. The method comprises the steps of reflecting database cluster state information through monitoring data insertion amount, sql connection number, threshold and the like, determining indexes influencing specific types of problems and the amount of data variation through an analysis module, and selecting a proper model through a learning module to perform deep learning training;
still further, the classification module specifically comprises an input module, a training module and a display module:
an input module: manually inputting fault information types and related data;
a training module: training a problem recognition model by using the deep learning of a machine;
a display module: classifying and integrating the fault information;
and analyzing the related characteristics of the abnormal indexes before the fault by the data transmitted after the data analysis and the record of the fault information. Manually inputting the fault information type and related data through an input module; then, a model for identifying each problem is trained through deep learning of the machine by the training module 2, and then the classification of fault information is integrated and displayed by the display module, so that the requirement of fault problem classification is met.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An automatic operation and maintenance method for a distributed database based on artificial intelligence is characterized by comprising the following specific steps:
s1, collecting the operation information of the database system;
s2, determining relevant characteristics of fault information according to the change of the monitoring index data;
s3, establishing a problem recognition model by using the relevant characteristic data of the fault information, and classifying the fault types;
s4, setting a filter to collect, sort and screen the effective information to obtain early warning information;
s5 collects the warning information and records the corresponding fault problem.
2. The method as claimed in claim 1, wherein the step of S1 collecting the operation information of the database system comprises:
s101 grabbing tool for accessing database events and state information to database
S102, setting a message queue to cache data;
s103, sending the data after the information filtering processing to a cloud server for AI analysis.
3. The method as claimed in claim 2, wherein the step of determining relevant characteristics of the fault information according to the change of the monitoring index data at S2 comprises:
s201, determining an influence index and data variation of a specific type of problem by using the response data of the database cluster state;
s202, selecting a proper model for deep learning training.
4. The method as claimed in claim 3, wherein said step S3 of building a problem recognition model using the relevant feature data of the fault information, and the step of classifying the fault type includes:
s301, manually inputting fault information types and related data;
s302, training a problem recognition model by using deep learning of a machine;
s303, classifying and integrating the fault information.
5. The utility model provides an automatic operation and maintenance system of distributed database based on artificial intelligence, characterized by the system include collection module, characteristic module, classification module, screening module and record module:
an acquisition module: collecting the operation information of the database system;
a characteristic module: determining relevant characteristics of fault information according to the change of the monitoring index data;
a classification module: establishing a problem identification model by using the relevant characteristic data of the fault information, and classifying the fault types;
a screening module: setting a filter to collect, sort and screen the effective information to obtain early warning information;
a recording module: and collecting early warning information and recording corresponding fault problems.
6. The system of claim 5, wherein the collection module specifically comprises an access module, a cache module, and a filter module:
an access module: accessing a capture tool of database events and state information to a database;
a cache module: setting a message queue to cache data;
a filtering module: and sending the data after the information filtering processing to a cloud server for AI analysis.
7. The system according to claim 6, wherein said feature module comprises in particular an analysis module and a learning module:
an analysis module: determining influence indexes and data variation of specific types of problems by using the response data of the database cluster state;
a learning module: and selecting a proper model for deep learning training.
8. The system of claim 7, wherein the classification module specifically comprises an input module, a training module, and a presentation module:
an input module: manually inputting fault information types and related data;
a training module: training a problem recognition model by using the deep learning of a machine;
a display module: and classifying and integrating the fault information.
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