CN117097752A - Intelligent operation and maintenance system and method for Internet of vehicles system - Google Patents
Intelligent operation and maintenance system and method for Internet of vehicles system Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 50
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- 238000013500 data storage Methods 0.000 claims abstract description 15
- 238000013523 data management Methods 0.000 claims abstract description 13
- 238000007726 management method Methods 0.000 claims abstract description 13
- 230000006855 networking Effects 0.000 claims abstract description 8
- 230000001105 regulatory effect Effects 0.000 claims abstract description 7
- 238000004140 cleaning Methods 0.000 claims abstract description 4
- 230000008439 repair process Effects 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 20
- 238000011084 recovery Methods 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 17
- 230000036541 health Effects 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 13
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 claims description 5
- 230000032683 aging Effects 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
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- 238000013024 troubleshooting Methods 0.000 description 4
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- 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
- H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
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Abstract
The invention relates to the technical field of operation and maintenance management, and discloses an intelligent operation and maintenance system of a vehicle networking system, which comprises the following components: a facility layer for storing multiple types of data; the data acquisition module is used for acquiring data in the facility layer and sending the data to the data management module; the data management module is used for carrying out data management and data cleaning on the data and storing the regulated data into the data storage module; the data storage module is used for storing the regulated data; the function expression layer distributes various tasks to the task scheduling module through the API gateway; and the task scheduling module is used for receiving the API gateway request, judging the task attribute and sending the task attribute to the policy service module. An intelligent operation and maintenance method of the Internet of vehicles system is also disclosed. The invention can actively and timely discover the risk and fault problems under the high-concurrency low-delay scene demand of the Internet of vehicles system, and automatically process, solve and provide a subsequent guarantee scheme.
Description
Technical Field
The invention relates to the technical field of operation and maintenance management, in particular to an intelligent operation and maintenance system and method for an Internet of vehicles system.
Background
Traditional operation and maintenance of the Internet of vehicles system often adopts a mode of combining manual operation and observability facilities, and application services mostly adopt containerized deployment or bare metal deployment on physical/virtual machines.
The operation and maintenance personnel of the Internet of vehicles system generally process the fault problem by the following steps.
The operation and maintenance personnel receive the alarm notification and are commonly used in a mail mode.
The operation and maintenance personnel check the observability facilities, and check the alarm reasons and the trend of the near time period.
The operation and maintenance personnel log in the corresponding problem assets or services, locate the problem reasons and analyze the applicable solutions.
The operation and maintenance personnel execute the recovery scheme.
The operation and maintenance personnel check to see if the problem is solved.
And (5) carrying out problem duplication by a team, and adding guarantee measures.
The traditional Internet of vehicles system has more complicated steps for processing the fault problem, and depends on a great number of components of manual processing, so that a longer time is needed for recovery and error correction, and great negative influence is brought to service cost and service availability.
In addition, a common containerized cluster management system such as Kubernetes also has certain automatic fault detection and recovery functions, but has a narrow application scenario, and can only judge whether a current application container is in a usable state or not and then perform expansion and contraction or replace restarting effects on container nodes.
Therefore, an intelligent operation and maintenance system and method for the Internet of vehicles system are provided.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the intelligent operation and maintenance system and method of the Internet of vehicles system, which can actively and timely discover the problems of risks and faults under the scene of high concurrency and low time delay of the Internet of vehicles system, and automatically process, solve and provide a subsequent guarantee scheme.
The technical scheme for achieving the purpose is as follows:
one of the invention relates to an intelligent operation and maintenance system of a car networking system, which comprises:
a facility layer for storing multiple types of data;
the data acquisition module is used for acquiring data in the facility layer and sending the data to the data management module;
the data management module is used for carrying out data management and data cleaning on the data and storing the regulated data into the data storage module;
the data storage module is used for storing the regulated data;
the function expression layer distributes various tasks to the task scheduling module through the API gateway;
the task scheduling module is used for receiving the API gateway request, judging task attributes, sending the task attributes to the policy service module, acquiring data corresponding to the task attributes from the data storage module, sending the data to the AI training module, and feeding back the result to the functional performance layer;
the AI training module is used for receiving the data of the task attribute sent by the task scheduling module and carrying out optimization training on the data to obtain an AI model;
and the strategy service module is used for calling an AI model, acquiring a corresponding operation and maintenance strategy according to the task attribute and outputting the strategy to the task scheduling module.
Preferably, the data types include: container cluster and physical/virtual machine asset information, storage device asset information, database asset information, operating resource occupancy metrics, application metrics, system log metrics, capacity usage metrics, system configuration metrics, service ticket raw file metrics, service data metrics, network traffic metrics, and link latency metrics.
Preferably, the functional performance layer includes:
the data query module is used for issuing a query data task to the task scheduling module and displaying a feedback result of the task scheduling module;
the fault automatic recovery module is used for issuing a data scanning and checking task, repairing the problem of health availability or data error, notifying corresponding responsible persons, and signing by the responsible persons to confirm that the repairing is finished;
the obstacle removing assistant module is used for issuing a data scanning and checking task, carrying out alarm notification on health availability problems or data errors which are solved by a scanning occurrence system to corresponding responsible persons, providing diagnosis information and obstacle removing and repairing strategy suggestions to operation and maintenance personnel, and signing and confirming that repairing is finished by the responsible persons;
the multidimensional report module is used for issuing a statistical report task to the task scheduling module and generating a multidimensional report;
the risk management module is used for issuing a data timing scanning checking task and issuing a pre-checking task to the data before executing the batch running task;
the multi-channel notification management module is used for notifying different responsible persons or responsible person groups according to different alarm levels and rules;
and the AI training platform is used for carrying out model optimization training by taking the fault flow, the fault mode and the fault result processed by the system history as a data set.
Preferably, the failure automatic recovery module can not recover the health availability problem or the data error, and the failure automatic recovery module can provide the failure removal assistant module for operation and maintenance personnel to repair the health availability problem or the data error.
Preferably, the fault automatic recovery module and the obstacle removal assistant module analyze root causes of the processed fault problems, give out subsequent guarantee optimization suggestion records, archive and generate a knowledge graph, and store the knowledge graph in the data storage module.
Preferably, the multidimensional report module includes:
the reliability report unit is used for counting the system fault interval time, the system recovery time and the time before the system fails;
the responsible person processing aging report unit is used for counting the response time and the fault solving time of the responsible person;
and the AI processing report unit is used for counting the AI fault successful repair times, AI fault successful repair rate and AI fault processing time.
Preferably, the mode of notifying the responsible person or the responsible person group by the multi-channel notification management module includes telephone, mail, webhook, short message and UserId of IM.
The second invention relates to an intelligent operation and maintenance method of a vehicle networking system, which comprises the following steps:
step S1, acquiring system data and service data of a vehicle networking system;
s2, performing multidimensional feature diagnosis analysis on system data and business data;
step S3, judging whether the system risk exists and whether the system risk exists or not, providing a risk strategy suggestion, and early warning a responsible person related risk and risk strategy execution condition tracking;
step S4, judging whether a system fault exists and whether the system fault exists, executing an automatic repair strategy when the system fault exists, and early warning related faults and repair conditions of responsible persons;
s5, analyzing the system risk and the cause of the fault and providing a safeguard proposal;
and S6, archiving the processing flow and the result record for model optimization learning and generating a knowledge graph.
Preferably, the step S4 includes:
step S41, if the fault execution automatic repair strategy is successful, a responsible person performs fault repair confirmation signature;
and step S42, if the automatic fault restoration strategy is unsuccessful, providing fault information and fault removal suggestions for operation and maintenance personnel, manually restoring by the operation and maintenance personnel, and finally carrying out fault restoration confirmation signing by a responsible person.
The beneficial effects of the invention are as follows: the invention can collect multidimensional data, collect log, asset information and service condition in the traditional way, analyze and process the business bill file, flow and network time delay of the Internet of vehicles in the specific business scene of the Internet of vehicles, monitor whether the Internet of vehicles system breaks down in real time, automatically process and recover the fault, and for the fault that the system can not be directly and automatically processed, provide the troubleshooting assistant module for the operation and maintenance personnel, provide the information and possible troubleshooting solution needed by troubleshooting, and alarm or report to the responsible person or responsible person group through multiple channels; the risk prediction can be carried out on the Internet of vehicles system, the risk strategy can be automatically executed, and the risk prediction can be carried out before the batch running task is executed; follow-up assurance scheme suggestions can also be provided according to the system fault problem, and a proper scheme is analyzed from the root cause; the adaptive learning can realize optimization iteration of the model under different environments, and the success rate of automatic processing and the applicability of a scheme are improved; the invention can actively and timely discover the risk and fault problems under the high-concurrency low-delay scene demand of the Internet of vehicles system, and automatically process, solve and provide a subsequent guarantee scheme.
Drawings
FIG. 1 is a block diagram of an intelligent operation and maintenance system of a vehicle networking system according to the present invention;
FIG. 2 is a sub-module diagram of the functional performance layer of the present invention;
FIG. 3 is a sub-module diagram of a multi-dimensional reporting module of the present invention;
FIG. 4 is a flow chart of an intelligent operation and maintenance method of the Internet of vehicles system of the present invention;
fig. 5 is a flow chart of the fault handling process in the present invention.
In the figure: 1. a facility layer; 2. a data acquisition module; 3. a data management module; 4. a data storage module; 5. a functional performance layer; 6. a task scheduling module; 7. an AI training module; 8. a policy service module; 51. a data query module; 52. a fault automatic recovery module; 53. an obstacle removing assistant module; 54. a multidimensional report module; 55. a risk management module; 56. a multi-channel notification management module; 57. an AI training platform; 541. a reliability report unit; 542. the responsible person processes the aging report unit; 543. and AI processing report unit.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying positive importance.
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, in an intelligent operation and maintenance system of an internet of vehicles system, operation and maintenance personnel process faults and risks through the intelligent operation and maintenance system under the scene of the internet of vehicles system; the system can automatically detect and analyze the risk and the fault problems, including checking the ticket flow data in the scene of the Internet of vehicles, judging whether the business and the system data are abnormal or not, and executing automatic processing; for the outside of the automatic processing range or failure of the automatic processing, an investigation and repair suggestion is given to operation and maintenance personnel; batch processing tasks such as batch recharging ordering and the like under the scene of the Internet of vehicles can be subjected to risk pre-inspection through the system, so that the task execution success rate is improved; the method specifically comprises the following steps:
a facility layer 1 for storing multi-type data; the data types include: container cluster and physical/virtual machine asset information, storage device asset information, database asset information, operating resource occupancy metrics, application metrics, system log metrics, capacity usage metrics, system configuration metrics, service ticket raw file metrics, service data metrics, network traffic metrics, and link latency metrics.
The data acquisition module 2 is used for acquiring the data in the facility layer 1 and sending the data to the data management module 3.
The data management module 3 performs data management and data cleaning on the data, and stores the regulated data into the data storage module 4.
The data storage module 4 is configured to store the normalized data.
And the function performance layer 5 distributes various tasks to the task scheduling module 6 through the API gateway.
The task scheduling module 6 is configured to receive the API gateway request, determine a task attribute, send the task attribute to the policy service module 8, obtain data corresponding to the task attribute from the data storage module 4, send the data to the AI training module 7, and feed back a result to the functional performance layer 5.
The AI training module 7 is configured to receive the data of the task attribute sent by the task scheduling module 6, and perform optimization training on the data to obtain an AI model.
The policy service module 8 is configured to invoke an AI model, obtain a corresponding operation and maintenance policy according to the task attribute, and output the operation and maintenance policy to the task scheduling module 6.
As shown in fig. 2, the functional performance layer 5 specifically includes:
the data query module 51 is configured to issue a query data task to the task scheduling module 6, and display a feedback result thereof.
The failure automatic recovery module 52 is configured to issue a data scanning and checking task, repair a health availability problem or a data error, and notify a corresponding responsible person of an alarm, and the responsible person signs a signature to confirm that the repair is completed.
In an embodiment, the failure automatic recovery module 52 provides the troubleshooting assistance module 53 with the health availability problem or data error that cannot be recovered.
The obstacle removing assistant module 53 is configured to issue a data scanning inspection task, notify a corresponding responsible person of a health availability problem or a data error which is solved by the scanning occurrence system, provide diagnostic information and obstacle removing repair policy advice to the operation and maintenance personnel, and sign by the responsible person to confirm that the repair is completed.
In the embodiment, the fault automatic recovery module 52 and the obstacle-removing assistant module 53 perform root cause analysis on the processed fault problem, give a subsequent record of the guarantee optimization suggestion, archive and generate a knowledge graph, and store the knowledge graph in the data storage module 4.
The multidimensional report module 54 is used for issuing a statistical report task to the task scheduling module 6 and generating a multidimensional report; as shown in fig. 3, the multidimensional reporting module 54 includes:
a reliability report unit 541, configured to count a system fault interval time, a system recovery time, and a time before a system failure; the responsible person processing aging report unit 542 is used for counting the response time and the fault solving time of the responsible person; the AI processing report unit 543 is used for counting the AI fault successful repair times, AI fault successful repair rate and AI fault processing time.
The risk management module 55 is used for issuing a data timing scanning and checking task, and timely finding out risks such as abnormal business data, occupation of asset resources outside a control threshold range or instability, network fluctuation, security holes and the like; and the system can pre-check the load condition of the related system, the service health degree of an operator and service data before executing the running batch task, so that the success rate of the running batch task is ensured.
A multi-channel notification management module 56 for notifying different responsible persons or groups of responsible persons according to different alert levels and rules; the modes of informing the responsible person or the responsible person group comprise telephone, mail, webhook, short message and UserId of IM; account information can be directly docked with the IM capability open platform.
The AI training platform 57 is used for performing model optimization training by using the system history to process fault processes, modes and results as a data set.
As shown in fig. 4 and 5, an intelligent operation and maintenance method for an internet of vehicles system includes:
step S1, system data and service data of a vehicle networking system are obtained.
And S2, performing multidimensional feature diagnosis analysis on the system data and the service data.
And S3, judging whether the system risk exists and whether the system risk exists, providing a risk strategy suggestion, and early warning the related risk of the responsible person and tracking the execution condition of the risk strategy.
And S4, judging whether the system fault exists and whether the system fault exists, executing an automatic repair strategy when the system fault exists, and early warning related faults and repair conditions of responsible persons.
Step S41, if the fault execution automatic repair strategy is successful, the responsible person performs fault repair confirmation signature.
And step S42, if the automatic fault restoration strategy is unsuccessful, providing fault information and fault removal suggestions for operation and maintenance personnel, manually restoring by the operation and maintenance personnel, and finally carrying out fault restoration confirmation signing by a responsible person.
And S5, analyzing the system risk and the cause of the fault and providing a safeguard proposal.
And S6, archiving the processing flow and the result record for model optimization learning and generating a knowledge graph.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (9)
1. An intelligent operation and maintenance system of an internet of vehicles system, which is characterized by comprising:
a facility layer for storing multiple types of data;
the data acquisition module is used for acquiring data in the facility layer and sending the data to the data management module;
the data management module is used for carrying out data management and data cleaning on the data and storing the regulated data into the data storage module;
the data storage module is used for storing the regulated data;
the function expression layer distributes various tasks to the task scheduling module through the API gateway;
the task scheduling module is used for receiving the API gateway request, judging task attributes, sending the task attributes to the policy service module, acquiring data corresponding to the task attributes from the data storage module, sending the data to the AI training module, and feeding back the result to the functional performance layer;
the AI training module is used for receiving the data of the task attribute sent by the task scheduling module and carrying out optimization training on the data to obtain an AI model;
and the strategy service module is used for calling an AI model, acquiring a corresponding operation and maintenance strategy according to the task attribute and outputting the strategy to the task scheduling module.
2. The intelligent operation and maintenance system of an internet of vehicles system according to claim 1, wherein the data types include: container cluster and physical/virtual machine asset information, storage device asset information, database asset information, operating resource occupancy metrics, application metrics, system log metrics, capacity usage metrics, system configuration metrics, service ticket raw file metrics, service data metrics, network traffic metrics, and link latency metrics.
3. The intelligent operation and maintenance system of an internet of vehicles system according to claim 1, wherein the functional performance layer comprises:
the data query module is used for issuing a query data task to the task scheduling module and displaying a feedback result of the task scheduling module;
the fault automatic recovery module is used for issuing a data scanning and checking task, repairing the problem of health availability or data error, notifying corresponding responsible persons, and signing by the responsible persons to confirm that the repairing is finished;
the obstacle removing assistant module is used for issuing a data scanning and checking task, carrying out alarm notification on health availability problems or data errors which are solved by a scanning occurrence system to corresponding responsible persons, providing diagnosis information and obstacle removing and repairing strategy suggestions to operation and maintenance personnel, and signing and confirming that repairing is finished by the responsible persons;
the multidimensional report module is used for issuing a statistical report task to the task scheduling module and generating a multidimensional report;
the risk management module is used for issuing a data timing scanning checking task and issuing a pre-checking task to the data before executing the batch running task;
the multi-channel notification management module is used for notifying different responsible persons or responsible person groups according to different alarm levels and rules;
and the AI training platform is used for carrying out model optimization training by taking the fault flow, the fault mode and the fault result processed by the system history as a data set.
4. The intelligent operation and maintenance system of the internet of vehicles system according to claim 3, wherein the failure automatic recovery module can not recover health availability problems or data errors, and the failure automatic recovery module can provide the health availability problems or data errors for operation and maintenance personnel to repair the health availability problems or data errors.
5. The intelligent operation and maintenance system of the internet of vehicles system according to claim 4, wherein the fault automatic recovery module and the obstacle removal assistant module analyze root causes of the processed fault problems, give subsequent guarantee optimization suggestion record archive to generate a knowledge graph and store the knowledge graph in the data storage module.
6. The intelligent operation and maintenance system of a car networking system according to claim 3, wherein the multidimensional reporting module comprises:
the reliability report unit is used for counting the system fault interval time, the system recovery time and the time before the system fails;
the responsible person processing aging report unit is used for counting the response time and the fault solving time of the responsible person;
and the AI processing report unit is used for counting the AI fault successful repair times, AI fault successful repair rate and AI fault processing time.
7. The intelligent operation and maintenance system of the internet of vehicles system according to claim 3, wherein the multi-channel notification management module notifies the responsible person or the responsible person group of UserId including telephone, mail, webhook, short message and IM.
8. The intelligent operation and maintenance method of the Internet of vehicles system is characterized by comprising the following steps of:
step S1, acquiring system data and service data of a vehicle networking system;
s2, performing multidimensional feature diagnosis analysis on system data and business data;
step S3, judging whether the system risk exists and whether the system risk exists or not, providing a risk strategy suggestion, and early warning a responsible person related risk and risk strategy execution condition tracking;
step S4, judging whether a system fault exists and whether the system fault exists, executing an automatic repair strategy when the system fault exists, and early warning related faults and repair conditions of responsible persons;
s5, analyzing the system risk and the cause of the fault and providing a safeguard proposal;
and S6, archiving the processing flow and the result record for model optimization learning and generating a knowledge graph.
9. The intelligent operation and maintenance method of the internet of vehicles system according to claim 6, wherein the step S4 includes:
step S41, if the fault execution automatic repair strategy is successful, a responsible person performs fault repair confirmation signature;
and step S42, if the automatic fault restoration strategy is unsuccessful, providing fault information and fault removal suggestions for operation and maintenance personnel, manually restoring by the operation and maintenance personnel, and finally carrying out fault restoration confirmation signing by a responsible person.
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