CN111901816A - System maintenance method, device, equipment and storage medium - Google Patents
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Abstract
The application provides a system maintenance method, a device, equipment and a storage medium, comprising the following steps: collecting real-time operation data of a system; under the condition that system maintenance conditions are met, determining a maintenance operation instruction based on the collected first operation data and a preset decision model; and executing a maintenance operation instruction, wherein the operation instruction is used for instructing the system to perform health maintenance.
Description
Technical Field
The present application relates to the field of wireless communication system technologies, and in particular, to a system maintenance method, apparatus, device, and storage medium.
Background
Currently, mobile Communication Technology has entered The fifth generation mobile Communication Technology (5G), and The automatic management of 5G system is an important requirement for network management function. However, how to implement automatic management and maintenance of the 5G system is a problem to be solved.
Disclosure of Invention
Methods, apparatus, systems, and storage media for system maintenance are provided.
In a first aspect, an embodiment of the present application provides a system maintenance method, including:
collecting real-time operation data of a system;
under the condition that system maintenance conditions are met, determining a maintenance operation instruction based on the collected first operation data and a preset decision model;
and executing the maintenance operation instruction, wherein the operation instruction is used for instructing the system to perform health maintenance.
In a second aspect, an embodiment of the present application provides a system maintenance apparatus, including:
the acquisition module is configured to acquire real-time operation data of the system;
the determining module is configured to determine a maintenance operation instruction based on the collected first operation data and a preset decision model under the condition that a system maintenance condition is met;
an execution module configured to execute the maintenance operation instruction.
In a third aspect, an embodiment of the present application provides an apparatus, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method as in any one of the embodiments of the present application.
In a fourth aspect, the present application provides a storage medium storing a computer program, where the computer program is executed by a processor to implement any one of the methods in the embodiments of the present application.
With regard to the above embodiments and other aspects of the present application and implementations thereof, further description is provided in the accompanying drawings description, detailed description and claims.
Drawings
FIG. 1 is a schematic structural diagram of a general 3GPP network automation framework provided in the present application
FIG. 2 is a schematic flow chart of a system maintenance method provided herein;
FIG. 3 is a flow chart of a system health maintenance method provided herein;
FIG. 4 is a flow chart of a system health maintenance method provided herein;
FIG. 5 is a schematic structural diagram of a system maintenance device provided in the present application;
fig. 6 is a schematic structural diagram of an apparatus provided in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Currently, mobile communication technology has entered the 5G era, and Network Slice (Network Slice) is an important component of 5G. The network slice mainly refers to an instantiated complete logic network with specific network characteristics, which is composed of a group of network functions and is used for meeting the requirements of a certain type of specific network services. Further, examples of the network characteristics are ultra-low latency, ultra-high reliability, and the like. Wherein the network function comprises a network resource supporting the network function.
When a Network Slice is complex, for convenience of management, the Network Slice may be decomposed into sub-slices (Network Slice Subnet), where a sub-Slice refers to a logical Network with specific Network characteristics and composed of a group of Network functions and Network resources supporting the Network functions. A network slice may contain 0, 1 or more sub-slices. For the management and arrangement of the sub-slices, a sub-slice management function (NSSMF) is used to perform the management and arrangement. NSSMF completes instantiation of the sub-slice and control management of the life cycle of the sub-slice according to the definition of the sub-slice blueprint.
In 5G technologies, 5G network management automation is a very important requirement for a network management function, and currently proposed related technologies of network management automation mainly include Self-organizing networks (SON), intention-driven networks, and the like. In the 5G network system, an Artificial Intelligence (AI) technology and a big data technology are also introduced. Based on the technology, the 5G network system has intelligent analysis and decision-making capability, and if the intelligent analysis and decision-making result can be automatically executed, the automatic management of the 5G network is basically realized.
Further, the European Telecommunication Standardization Institute (ETSI) has conducted a research on automated management, which provides an automated closed-loop management system including data acquisition, data analysis, decision-making based on data analysis and artificial intelligence, and execution of decision-making results. The importance of data analysis for system automation management and operation is also reflected in the 3rd generation partnership Project (3 GPP) research. Therefore, the 3GPP also proposes a concept of a Network data analysis function (NWDAF) and a Management Data Analysis Function (MDAF), and studies how to perform system performance optimization, troubleshooting, system resource prediction, and the like using the above functions.
Fig. 1 is a schematic structural diagram of a general framework of 3GPP network automation provided in the present application; as shown in fig. 1, the NWDAF is able to collect data from operator OAM, AF and 3GPP network functions. For the acquisition of OAM data, the NWDAF may multiplex existing mechanisms and interfaces defined by 3 GPP. The AF may perform information interaction with the NWDAF through a Network Exposure Function (NEF) according to a Network deployment situation, or directly access the NWDAF using a service-based interface. The NWDAF may access network Data from a Data Repository, such as a Unified Data Repository (UDR). For NF of 3GPP, NWDAF acquires network data using interface communication based on services defined by 3 GPP. Based on the data acquisition, the NWDAF performs data analysis and provides the data analysis results to network functions NF, AF, and OAM in the 3 GPP.
The main Network Functions (NFs) of the 3GPP include, but are not limited to, the following various Network Function types:
an Access Management Function (AMF) belongs to a common control plane Function in a core network, terminates a Non-Access-Stratum (NAS) message between all users and a network, and is responsible for User mobility Management, User Equipment (UE) state (such as reachability) Management, and the like.
Session Management Function (SMF) is responsible for Session establishment, modification, deletion, controlling Policy Control Function (PCF) charging and Policy execution, etc.
The PCF is responsible for formulating policies for the terminal, including routing policies, quality of service policies, charging policies, etc., according to the subscription of the user, the current location of the UE, and the information related to the application.
The Unified Data Management (UDM) function has the functions of Unified Data Management, permanent storage of user subscription Data, and the like;
the UDR is mainly used for storing user subscription data, policy data, and the like managed by the UDM and the PCF.
The User Plane Function (UPF) belongs to the Function of the User Plane in the core Network, is an anchor point of the User Plane of the core Network, is an interface for Data transmission with an external Network (DN), and executes the execution of the partial PCF policy rule of the User Plane.
The NEF is used to expose the capabilities and events of the 3GPP NFs to other NFs or external application AFs, provide the AF with the capability of pre-configuring the 3GPP NFs, implement information mapping between the 3GPP network and external networks, and so on.
AF refers to applications that access 3 GPP.
The NRF maintains NF folders (the NF folders include NF entities and service descriptions that it supports), supports service discovery functions, and the like.
The NWDAF supports data collection from NF, AF and OAM, service registration and metadata are exposed to NF and/or AF, and an analysis result is exposed to NF and/or AF and/or OAM;
the OAM may be that of a core Network, and/or that of an Access Network (RAN).
In practical application, by utilizing the network data analysis function and the management data analysis function, the fault can be predicted and prevented, namely, the network function which is possibly faulted can be predicted by analyzing the system performance and fault related data, an operation suggestion or an operation instruction for removing the fault hidden danger is provided, and the fault hidden danger can be effectively removed and the robustness of the system can be improved by executing the related operation instruction.
Fig. 2 is a schematic flow chart of a system maintenance method provided in the present application. The method can be suitable for predicting the faults in the 5G network system and effectively eliminating the hidden trouble of the faults. The method may be performed by a system maintenance apparatus provided herein, which may be implemented by software and/or hardware.
As shown in fig. 2, the system maintenance method provided in the embodiment of the present application mainly includes steps S21, S22, and S23.
And S21, acquiring real-time operation data of the system.
And S22, determining a maintenance operation instruction based on the collected first operation data and a preset decision model under the condition that the system maintenance condition is met.
And S23, executing the maintenance operation instruction, wherein the operation instruction is used for instructing the system to perform health maintenance.
In this embodiment, the real-time operation data of the system is continuously acquired by the NWDAF. Further, the NWDAF can collect data from operator OAM, AF and 3GPP network functions. For the acquisition of OAM data, the NWDAF may multiplex existing mechanisms and interfaces defined by 3 GPP. The NWDAF may also access system operational data from a data Repository, such as a Unified Database (UDR). For NF of 3GPP, NWDAF acquires network data using interface communication based on services defined by 3 GPP.
It should be noted that, the embodiment describes a manner of collecting system operation data, but is not limited to this, and other operation data collection manners may be selected or designed according to actual situations.
In another embodiment, the system real-time operational data is continuously collected by a management data analysis function MDAF. The MDAF may also access system operational data from a data Repository, such as a Unified Database (UDR), a webmaster database, and the like. For NF of 3GPP, MDAF acquires network data using interface communication based on services defined by 3 GPP.
In an exemplary embodiment, the meeting of the system maintenance condition includes one or more of:
the data acquisition duration reaches the preset system maintenance duration;
detecting a preset fault event of a system;
a system maintenance event is detected to be triggered.
In this embodiment, the data acquisition duration may be determined by acquiring time information of the acquired data. The preset system maintenance duration may be understood as a system health maintenance period T2. And under the condition that the data acquisition duration reaches a system health maintenance period T2, determining a maintenance operation instruction based on the acquired first operation data and a preset decision model.
And under the condition that a preset fault event of the system is detected or a system maintenance event is triggered, determining a maintenance operation instruction based on the collected first operation data and a preset decision model.
The preset fault event can be preset by a worker, and the fault event is not limited in the application.
The triggering of a system maintenance event may be understood as the input of a maintenance instruction by a worker via an input device of the maintenance apparatus. The specific input method is not limited in this application.
In an exemplary embodiment, before acquiring the system operation data, the method further includes: collecting historical operation data, network position information and historical maintenance information of a system; training based on the collected historical operation data, the network position information and the historical maintenance information to generate a preset decision model. Further, under the condition that the data acquisition duration reaches the model updating duration, the preset decision model is trained and updated based on the acquired second operation data, and a new preset decision model is obtained.
In this embodiment, the model update duration may be understood as a decision model update period.
Further, the operational data includes one or more of: system performance data, system fault data, log data generated when configuration operations are performed, log data generated when maintenance operations are performed; wherein,
the system performance data includes one or more of: performance measurement data, key performance indicator KPI data, MDT data of minimization of drive tests;
the fault data includes one or more of: alarm data, alarm recovery data, repair operation data, and repair effect data.
Further, determining a maintenance operation instruction based on the collected first operation data and a preset decision model, wherein the maintenance operation instruction comprises one or more of the following instructions:
and analyzing the system performance data in the first operation data based on the preset decision model to obtain an instruction execution time period.
Analyzing system fault data in the first operation data and log data generated under the condition of executing maintenance operation based on the preset decision model to obtain a fault avoidance operation instruction;
and analyzing the log data generated under the condition of executing configuration operation in the first running data based on the preset decision model to obtain a system optimization instruction.
Further, the maintenance operation instruction includes one of:
the method comprises the following steps that a restart instruction is used for indicating a system to restart a certain network function at a first preset time;
a cleaning instruction, wherein the cleaning instruction is used for instructing a system to clean an operating system of a certain network function at a second preset time;
the upgrading instruction is used for instructing the system to carry out software upgrading on a certain network function at a third preset time;
a recommendation instruction for indicating a recommendation for a certain network function software version upgrade.
In one illustrative example, a system health maintenance method is provided. Fig. 3 is a flowchart of a system health maintenance method provided in the present application, and as shown in fig. 3, the system health maintenance method provided in this embodiment mainly includes steps S31, S32, S33, and S34.
And S31, continuously acquiring real-time operation data of the system by a management data analysis function.
The system operation data includes, but is not limited to: system performance data, system fault data, log data of configuration operations, log data of other system maintenance function executions.
Further, the system performance data includes, but is not limited to, performance measurement data, key performance indicators — KPI data, MDT data, and the like.
The system fault data includes, but is not limited to, alarm data, alarm recovery data, signaling trace data, repair actions taken to resolve the fault, and the effect of the repair.
Including but not limited to SON, self-repair, etc.
Further, the collected system operation data should also include time information.
And S32, analyzing the collected operation data.
In the present embodiment, the method for analyzing the operation data includes artificial intelligence and/or machine learning algorithm. This example is merely illustrative of the method of analysis and is not intended to be limiting.
Further, while analyzing the operational data, the system may also include, but is not limited to, the following information in addition to the operational data collected from the system:
location information of the whole communication network, experience information maintained by the system.
Further, the operation data analysis is divided into two types, one is to analyze historical data and update a decision model based on the historical operation data, the network location information and the historical maintenance information. And the other method is to analyze the real-time data to obtain the operation instruction to be executed. Furthermore, the two kinds of data analysis are performed simultaneously, and the frequency of the decision model update and the frequency of the real-time operation data analysis may be different, that is, the periods of the decision model update and the real-time operation data analysis are different.
Furthermore, the acquisition period of the historical data analysis required by the updating of the decision model is a decision model updating period T1, which mainly uses artificial intelligence and/or a machine learning algorithm to analyze the acquired historical data and update the decision model. Every decision model update period T1.
For example: specific analysis scenarios may include, but are not limited to:
and analyzing according to the performance measurement data, the MDT data, the system position information and the like to obtain the service peak time and the service valley time of different areas. Provision is made for determining the best period of maintenance operations to minimize the impact on the user of health maintenance on the system. Further, the service underestimation time is selected as the maintenance operation period in the present embodiment.
And analyzing according to system fault data and operation logs of the SON, self-healing and other functions to find out the optimal operation for avoiding various faults and corresponding parameters and time.
And summarizing the influence of various operations on the system according to the configuration operation log, and finding out the available operation for maintaining the health of the system and related parameters and operation time.
Further, when the real-time operation data is analyzed, the real-time operation data acquisition period is a system health maintenance period T2, and the generated decision model is mainly used to analyze the real-time data and give specific suggestions or instructions for system health maintenance.
Further, it is typically performed every system health maintenance period T2, but may be performed immediately upon the occurrence of a particular event. Wherein, the specific event occurrence includes but is not limited to specific fault occurrence and manual triggering of management personnel.
And S33, according to the data analysis result, giving system health maintenance operation suggestions or operation instructions.
The operation suggestions or operation instructions should generally include the time of the specific operation; the given operation suggestions or operation instructions include, but are not limited to:
restarting a network function at a specified time
Cleaning an operation system for optimizing a certain network function at a specified time,
Software upgrade is carried out on a certain network function at a specified time,
And giving an upgrade suggestion of the software of the subsequent version of the certain network function or certain type of network function, wherein the upgrade suggestion comprises enhancement of the certain function and the like.
And S34, executing relevant operation according to the health maintenance operation suggestion or the operation instruction.
After the relevant operations are performed according to the given health maintenance operation recommendation or operation instruction, the steps S31, S22 and S34 are repeated to continuously maintain the health of the system.
The embodiment of the application updates and optimizes the decision model by analyzing and learning the continuously updated historical data, so that the given operation suggestion or operation instruction is more accurate and efficient.
The system health maintenance method provided in this embodiment solves the problem that no method for predicting the hidden trouble of the system and providing an operation instruction for correspondingly solving the hidden trouble exists in the industry at present by analyzing the data of the system. By providing a scheme for predicting the fault hidden danger of the system and giving corresponding operation suggestions or operation instructions based on a network data analysis function and a management data analysis function, the automatic system health maintenance is realized.
In one illustrative example, a system health maintenance method is provided. Fig. 4 is a flowchart of a system health maintenance method provided in the present application, and as shown in fig. 4, the system health maintenance method provided in this embodiment mainly includes steps S41, S42, S43, S44, S45, and S46.
And S41, collecting historical operation data of the system.
Wherein, the collection duration is equal to or longer than the decision model updating period T1.
And S42, analyzing according to the collected system operation historical data to generate a decision model.
And S43, acquiring real-time operation data of the system.
And S44, under the condition that the system health maintenance period is reached, the real-time operation data collected in the system health maintenance period is based on the updating decision model.
And S45, analyzing the health state of the system based on the latest decision model and the operation data acquired in the system health maintenance period T2 under the condition that the system health maintenance period is reached, finding out potential health hidden dangers, and giving out a health maintenance instruction for eliminating the health hidden dangers.
And S46, executing relevant operation according to the given health maintenance instruction.
After the relevant operations are performed, system operation data are continuously collected, and whether a decision model updating period T1 or a system health maintenance period T2 is reached is judged. If it is time to start a new decision model update period T1, S44 is performed; if it is time for a new system health maintenance period T2 to begin, S45 is performed.
It should be noted that the steps S43, S44, S45, and S46 may be executed in parallel, and the execution order of the steps is not limited in this embodiment.
Fig. 5 is a schematic structural diagram of a system maintenance apparatus provided in the present application. The method can be suitable for predicting the faults in the 5G network system and effectively eliminating the hidden trouble of the faults. The system maintenance means may be implemented by software and/or hardware.
As shown in fig. 5, the system maintenance method provided in the embodiment of the present application mainly includes an acquisition module 51, a determination module 52, and an execution module 53.
An acquisition module 51 configured to acquire system real-time operation data;
a determining module 51 configured to determine a maintenance operation instruction based on the collected first operation data and a preset decision model under the condition that a system maintenance condition is satisfied;
an execution module 51 configured to execute the maintenance operation instruction.
In an exemplary embodiment, the meeting of the system maintenance condition includes one or more of:
the data acquisition duration reaches the preset system maintenance duration;
detecting a preset fault event of a system;
a system maintenance event is detected to be triggered.
In an exemplary embodiment, the apparatus further comprises:
and the model updating module is configured to train and update the preset decision model based on the acquired second operation data to obtain a new preset decision model under the condition that the data acquisition duration reaches the model updating duration.
In one exemplary embodiment of the present invention,
an acquisition module 51 configured to acquire system historical operation data, network location information, and historical maintenance information;
the device further comprises; the model generation module is configured to train based on the collected historical operation data, the network position information and the historical maintenance information, and generate a preset decision model.
Further, the operational data includes one or more of: system performance data, system fault data, log data generated when configuration operations are performed, log data generated when maintenance operations are performed; wherein,
the system performance data includes one or more of: performance measurement data, key performance indicator KPI data, MDT data of minimization of drive tests;
the fault data includes one or more of: alarm data, alarm recovery data, repair operation data, and repair effect data.
Further, the determining module 51 is configured to perform one or more of the following operations:
and analyzing the system performance data in the first operation data based on the preset decision model to obtain an instruction execution time period.
Analyzing system fault data in the first operation data and log data generated under the condition of executing maintenance operation based on the preset decision model to obtain a fault avoidance operation instruction;
and analyzing the log data generated under the condition of executing configuration operation in the first running data based on the preset decision model to obtain a system optimization instruction.
Further, the maintenance operation instruction includes one of:
the method comprises the following steps that a restart instruction is used for indicating a system to restart a certain network function at a first preset time;
a cleaning instruction, wherein the cleaning instruction is used for instructing a system to clean an operating system of a certain network function at a second preset time;
the upgrading instruction is used for instructing the system to carry out software upgrading on a certain network function at a third preset time;
a recommendation instruction for indicating a recommendation for a certain network function software version upgrade.
The system maintenance device provided in this embodiment can execute the system maintenance method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution of the method. For details of the system maintenance method provided in any embodiment of the present invention, reference may be made to the technical details not described in detail in this embodiment.
It should be noted that, in the embodiment of the system maintenance apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
Fig. 6 is a schematic structural diagram of an apparatus provided in an embodiment of the present application, and as shown in fig. 6, the apparatus includes a processor 610, a memory 620, an input device 630, and an output device 640; the number of processors 610 in the device may be one or more, and one processor 610 is taken as an example in fig. 6; the processor 610, the memory 620, the input device 630 and the output device 640 in the apparatus may be connected by a bus or other means, and fig. 6 illustrates an example of a connection by a bus.
The memory 620 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the system maintenance method in the embodiment of the present application (for example, the acquisition module 51, the determination module 52, and the execution module 53 in the system maintenance apparatus). The processor 610 executes various functional applications of the device and data processing by executing software programs, instructions and modules stored in the memory 620, namely, implements any of the methods provided by the embodiments of the present application.
The memory 620 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory 620 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 620 can further include memory located remotely from the processor 610, which can be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 630 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the device. The output device 640 may include a display device such as a display screen.
Of course, those skilled in the art can understand that the processor 810 can also implement the technical solution of the system maintenance method provided in any embodiment of the present application. The hardware structure and function of the device can be explained with reference to the content of the embodiment.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a system maintenance method, the method comprising:
collecting real-time operation data of a system;
under the condition that system maintenance conditions are met, determining a maintenance operation instruction based on the collected first operation data and a preset decision model;
and executing the maintenance operation instruction, wherein the operation instruction is used for instructing the system to perform health maintenance.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the system maintenance method provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods described in the embodiments of the present application.
The above description is only exemplary embodiments of the present application, and is not intended to limit the scope of the present application.
It will be clear to a person skilled in the art that the term user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a car mounted mobile station.
In general, the various embodiments of the application may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the application is not limited thereto.
Embodiments of the application may be implemented by a data processor of a mobile device executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.
Any logic flow block diagrams in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on a memory. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), optical storage devices and systems (digital versatile disks, DVDs, or CD discs), etc. The computer readable medium may include a non-transitory storage medium. The data processor may be of any type suitable to the local technical environment, such as but not limited to general purpose computers, special purpose computers, microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), programmable logic devices (FGPAs), and processors based on a multi-core processor architecture.
The foregoing has provided by way of exemplary and non-limiting examples a detailed description of exemplary embodiments of the present application. Various modifications and adaptations to the foregoing embodiments may become apparent to those skilled in the relevant arts in view of the following drawings and the appended claims without departing from the scope of the invention. Therefore, the proper scope of the invention is to be determined according to the claims.
Claims (10)
1. A method of system maintenance, comprising:
collecting real-time operation data of a system;
under the condition that system maintenance conditions are met, determining a maintenance operation instruction based on the collected first operation data and a preset decision model;
and executing the maintenance operation instruction, wherein the operation instruction is used for instructing the system to perform health maintenance.
2. The method of claim 1, wherein the system maintenance condition is satisfied and comprises one or more of:
the data acquisition duration reaches the preset system maintenance duration;
detecting a preset fault event of a system;
a system maintenance event is detected to be triggered.
3. The method of claim 1, wherein after acquiring system operational data, further comprising:
and under the condition that the data acquisition duration reaches the model updating duration, training and updating the preset decision model based on the acquired second operation data to obtain a new preset decision model.
4. The method of claim 1, wherein prior to acquiring system operational data, further comprising:
collecting historical operation data, network position information and historical maintenance information of a system;
training based on the collected historical operation data, the network position information and the historical maintenance information to generate a preset decision model.
5. The method of any of claims 1-4, wherein the operational data comprises one or more of: system performance data, system fault data, log data generated when configuration operations are performed, log data generated when maintenance operations are performed; wherein,
the system performance data includes one or more of: performance measurement data, key performance indicator KPI data, MDT data of minimization of drive tests;
the fault data includes one or more of: alarm data, alarm recovery data, repair operation data, and repair effect data.
6. The method of claim 1, wherein determining the maintenance operation instructions based on the collected first operational data and a predetermined decision model comprises one or more of:
and analyzing the system performance data in the first operation data based on the preset decision model to obtain an instruction execution time period.
Analyzing system fault data in the first operation data and log data generated under the condition of executing maintenance operation based on the preset decision model to obtain a fault avoidance operation instruction;
and analyzing the log data generated under the condition of executing configuration operation in the first running data based on the preset decision model to obtain a system optimization instruction.
7. The method of claim 1, wherein the maintenance operation instruction comprises one of:
the method comprises the following steps that a restart instruction is used for indicating a system to restart a certain network function at a first preset time;
a cleaning instruction, wherein the cleaning instruction is used for instructing a system to clean an operating system of a certain network function at a second preset time;
the upgrading instruction is used for instructing the system to carry out software upgrading on a certain network function at a third preset time;
a recommendation instruction for indicating a recommendation for a certain network function software version upgrade.
8. A system maintenance device, comprising:
the acquisition module is configured to acquire real-time operation data of the system;
the determining module is configured to determine a maintenance operation instruction based on the collected first operation data and a preset decision model under the condition that a system maintenance condition is met;
an execution module configured to execute the maintenance operation instruction.
9. An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1-7.
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| CN202010054511.7A CN111901816A (en) | 2020-01-17 | 2020-01-17 | System maintenance method, device, equipment and storage medium |
| PCT/CN2020/139279 WO2021143483A1 (en) | 2020-01-17 | 2020-12-25 | System maintenance method and apparatus, device, and storage medium |
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| CN202010054511.7A CN111901816A (en) | 2020-01-17 | 2020-01-17 | System maintenance method, device, equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112687392A (en) * | 2020-12-24 | 2021-04-20 | 深圳市智连众康科技有限公司 | AI-based intelligent alopecia decision method, device and computer-readable storage medium |
| WO2021143483A1 (en) * | 2020-01-17 | 2021-07-22 | 中兴通讯股份有限公司 | System maintenance method and apparatus, device, and storage medium |
| WO2023125109A1 (en) * | 2021-12-30 | 2023-07-06 | 中兴通讯股份有限公司 | Data analysis model management method, and electronic device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107222318A (en) * | 2016-03-21 | 2017-09-29 | 中兴通讯股份有限公司 | The performance data processing method and device and NMS of a kind of network element |
| EP3249901A1 (en) * | 2016-05-23 | 2017-11-29 | Thomson Licensing | Device and method for dsl maintenance |
| CN107846314A (en) * | 2017-10-31 | 2018-03-27 | 广西宜州市联森网络科技有限公司 | A kind of intelligent operation management system |
| CN107862393A (en) * | 2017-10-31 | 2018-03-30 | 广西宜州市联森网络科技有限公司 | A kind of IT operation management system |
| CN108776625A (en) * | 2018-06-26 | 2018-11-09 | 郑州云海信息技术有限公司 | A kind of restorative procedure of service fault, device and storage medium |
| CN111901816A (en) * | 2020-01-17 | 2020-11-06 | 中兴通讯股份有限公司 | System maintenance method, device, equipment and storage medium |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021143483A1 (en) * | 2020-01-17 | 2021-07-22 | 中兴通讯股份有限公司 | System maintenance method and apparatus, device, and storage medium |
| CN112687392A (en) * | 2020-12-24 | 2021-04-20 | 深圳市智连众康科技有限公司 | AI-based intelligent alopecia decision method, device and computer-readable storage medium |
| WO2023125109A1 (en) * | 2021-12-30 | 2023-07-06 | 中兴通讯股份有限公司 | Data analysis model management method, and electronic device and storage medium |
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