CN115766400A - Link fault diagnosis method combining model drive and data drive - Google Patents
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Abstract
The invention provides a link fault diagnosis method combining model drive and data drive, which is characterized in that a set of power system dispatching data network is formed from the collection of primary equipment, the detection of secondary equipment, the transmission and exchange service of control information and the like of a power system and network facilities for communicating network resources on the basis of equipment characteristics, network characteristics and service characteristics in the field of power automation networks, and the automatic network fault diagnosis and the power dispatching system operation fault diagnosis of dispatching data network operation service are used for ensuring the normal operation of services of an automatic control large area, a non-control large area and an information large area and the characteristics of network operation and equipment operation formed by the normal operation and the normal operation of the services.
Description
Technical Field
The invention relates to the technical field of link diagnosis, in particular to a link fault diagnosis method combining model driving and data driving.
Background
The traditional fault processing method of the dispatching data network is centralized on equipment, and the adopted method such as an observation method is used for checking whether the light indication of each equipment module forming the dispatching data network system is correct, whether the channel monitoring is alarming, whether the data of a local background machine is normal, whether a computer works normally and whether the network is normal, and the method is slow in operation and maintenance troubleshooting; the measurement replacement method can be used for detecting by using a special tool when the fault cannot be accurately judged by an observation method. The common detection tool is a meter for measurement, and the fault of the receiving or transmitting channel can be judged according to the voltage condition. After a fault part is supposed, corresponding replacement processing can be carried out, the fault part is replaced by normal parts or equipment with the same model, if the fault is solved, the fault part is positioned, otherwise, other modules are replaced, the method is slow in operation and maintenance troubleshooting, and is suitable for measurement of an optical transceiver, an optical repeater, an RTU (remote terminal unit) or a receiving end of comprehensive automation, and network equipment;
in the prior art, patent application No. CN113836044A discloses a method and a system for collecting and analyzing software faults, which detect an operating state of a target application program, and generate fault data of the target application program when the operating state of the target application program is monitored to be abnormal; collecting operating environment information, configuration parameter information and log information of a target application program, performing data matching with fault data, and sending the fault data and the log information to a server; after the server receives and stores the fault related data, on one hand, the server sends a fault alarm, on the other hand, the server diagnoses and analyzes the fault through the collected fault data and related information, deduces possible fault reasons and gives out a corresponding fault solution.
In the above conventional method and the prior art, there are hidden troubles of long time consumption, slow operation and maintenance inspection, large number of personnel investment, low efficiency, and incapability of tracking the systematic inspection of the source. The invention provides a link fault diagnosis method combining model drive and data drive, which is based on the principle that an endogenous relation consisting of three parts, namely equipment basic information, equipment key monitoring information and a network topological relation is constructed from an equipment model of a dispatching automation system to data and a network service is formed based on respective attributes, information acquisition and topological relation of the three parts to serve a dispatching operation management and control service.
Disclosure of Invention
Accordingly, it is an object of the present invention to provide a combined model-driven and data-driven link failure diagnosis method to solve at least the above problems.
The technical scheme adopted by the invention is as follows:
a method for link failure diagnosis combining model-driven and data-driven, the method comprising the steps of:
s1, constructing a network resource basic information base, establishing running performance monitoring indexes of network equipment, a server and a database through the network resource basic information base, and if the running performance information of a monitored object exceeds the index set by the object, but the exceeding index degree is not high, and normal running can be recovered, exiting abnormal alarm information;
if the operation performance information of the monitored object exceeds the index set by the object and the phenomenon of data flow lasts for a period of time, entering an abnormal alarm;
s2, constructing an abnormal list classification library for receiving abnormal alarm information;
s3, constructing a dimension model, and performing dimension division on the abnormal alarm information through the dimension model;
s4, constructing an AI fault set for extracting different dimensional characteristics in the dimensional model;
s5, constructing an AI matching analysis module, and analyzing different dimensional characteristics through the AI matching analysis module;
and S6, carrying out fault positioning according to the analysis result of the AI matching analysis module, and pushing fault positioning information.
Further, in step S3, a dimension model is constructed, and dimension division is performed on the abnormal alarm information through the dimension model, specifically:
and the construction dimension model comprises a key link model, a network resource model and a detection data model, and different types of abnormal alarm information are matched through the key link model, the network resource model and the detection data model.
Further, in step S3, the method further includes the sub-steps of:
s31, gateways at two ends of a key link, port IP, MAC addresses and topological relations are classified into a link database from basic information through a key link model;
s32, the resource network element, the network element type, the network element IP and the distribution port information related to the automatic service are classified into a service resource database through a network resource model;
and S33, the data acquisition of the load bearing equipment of the automation software, the monitoring data transmission, the service, the IP (Internet protocol) distributed by the service system, the name, the load bearing service, the associated software and the IP information of the convergence gateway are classified into a software system resource database through a detection data model.
Further, in step S4, an AI fault set is constructed, and the specific steps for extracting different dimensional features in the dimensional model are as follows:
and extracting data in the link database, the service resource database and the software system resource database through the AI fault set, and matching the similarity of fault phenomena, fault indexes and fault results to obtain link fault characteristics, network element fault characteristics and software fault characteristics.
Further, the software fault feature is composed of an associated database, an operation log and auxiliary software.
Further, an AI matching analysis module is constructed, and the analysis of the different dimensional characteristics by the AI matching analysis module specifically comprises:
the AI matching analysis module comprises an equipment fault module, a port fault module, a line fault module and a software fault module, and the similarity of the matched fault phenomenon, fault index and fault result is analyzed through the equipment fault module, the port fault module, the line fault module and the software fault module.
Further, in step S5, the method further includes the sub-steps of:
s51, entering network model information, port abnormity information, equipment abnormity information and network element fault characteristic information sets into an AI cache through an equipment fault module to perform retrieval, matching and threshold value calculation of conditions, parameters and character values, and obtaining an AI analysis result of the equipment fault;
s52, extracting IP, MAC addresses, names and position information of gateways, network equipment and ports at two ends of a key link, and IP, MAC addresses and position information of the network equipment and the ports from basic information through a port fault module and a line fault module, and entering a link fault characteristic information set of topological relation, operation performance, flow, uplink and downlink flow information and a link fault characteristic information set of a key link model data set into an AI cache to perform condition, parameter and character value retrieval, matching and threshold value calculation to obtain port and line fault AI analysis results;
s53, the resource network element, the network element type, the network element IP and the distribution port related to the automatic service are subjected to basic information, data acquisition and monitoring data transmission of a bearing device of a network resource model, IP, name, bearing service, associated software and convergence gateway IP information distributed by the service and the service system through a software fault module, and software fault characteristics are input into an AI cache to carry out condition, parameter, association relation and state retrieval, matching, threshold value calculation, state superposition or key state diagnosis analysis, so that a software fault AI analysis result is obtained.
Further, in step S6, fault location is performed according to an analysis result of the AI matching analysis module, and pushing fault location information specifically includes:
and disassembling the fault positioning information through an AI matching analysis module, displaying a fault network element and switching a fault phenomenon to a fault information window of an operation maintenance unit, and pushing new fault characteristics to a fault machine learning library for sample accumulation.
Further, when the AI matching degree in the AI matching analysis module is not high, manual intervention diagnosis is carried out, and the result of the manual intervention diagnosis is used for testing and verifying the fact of fault location.
Compared with the prior art, the invention has the beneficial effects that:
the fault diagnosis method combining the model drive and the data drive can deeply promote network alarm defined by network abnormity to the fields of network fault analysis, network fault tracing source and service system software fault location on the operation and maintenance management of a power system dispatching data network, and solves the pain points around fault diagnosis difficulty, tracing source difficulty, location difficulty and analysis difficulty.
The fault diagnosis method combining the model drive and the data drive can lay a foundation for the operation and maintenance work of a power grid dispatching data network to enter digital operation and maintenance and intelligent operation and maintenance, is an evolution stage which needs to be passed by for improving the automatic operation and maintenance efficiency, saving the operation and maintenance time and realizing accurate operation and maintenance, and has universal applicability in the aspects of large and medium-sized network operation and maintenance of information networks, dispatching data networks, comprehensive data networks, IT network industries and the like in the power industry.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is apparent that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive efforts.
Fig. 1 is a schematic overall structure diagram of a fault diagnosis method combining model driving and data driving according to an embodiment of the present invention.
Fig. 2 is a schematic overall flow chart of a fault diagnosis method combining model driving and data driving according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, the illustrated embodiments are provided to illustrate the invention and not to limit the scope of the invention.
Referring to fig. 1 and 2, the present invention provides a method for diagnosing a fault by combining a model-driven fault and a data-driven fault, the method comprising the steps of:
s1, constructing a network resource basic information base, establishing network equipment, a server and a database operation performance monitoring index through the network resource basic information base, and if the operation performance information of a monitored object exceeds the index set by the object, but the exceeding index degree is not high, and the normal operation can be recovered, exiting the abnormal alarm information;
if the operation performance information of the monitored object exceeds the index set by the object and the phenomenon of data flow lasts for a period of time, entering an abnormal alarm;
s2, constructing an abnormal list classification library for receiving abnormal alarm information;
s3, constructing a dimension model, and performing dimension division on the abnormal alarm information through the dimension model;
s4, constructing an AI fault set for extracting different dimensional characteristics in the dimensional model;
s5, constructing an AI matching analysis module, and analyzing different dimensional characteristics through the AI matching analysis module;
and S6, carrying out fault positioning according to the analysis result of the AI matching analysis module, and pushing fault positioning information.
In step S3, a dimension model is constructed, and dimension division is performed on the abnormal alarm information through the dimension model, specifically:
and the construction dimension model comprises a key link model, a network resource model and a detection data model, and different types of abnormal alarm information are matched through the key link model, the network resource model and the detection data model.
In step S3, the method further includes the substeps of:
s31, gateways at two ends of a key link, port IP, MAC addresses and topological relations are classified into a link database from basic information through a key link model;
s32, the resource network element, the network element type, the network element IP and the distribution port information related to the automatic service are classified into a service resource database through a network resource model;
and S33, the data acquisition of the load bearing equipment of the automation software, the monitoring data transmission, the service, the IP (Internet protocol) distributed by the service system, the name, the load bearing service, the associated software and the IP information of the convergence gateway are classified into a software system resource database through a detection data model.
In step S4, an AI fault set is constructed for extracting different dimensional features in the dimensional model, specifically:
and extracting data in the link database, the service resource database and the software system resource database through the AI fault set, and matching the similarity of the fault phenomenon, the fault index and the fault result to obtain a link fault characteristic, a network element fault characteristic and a software fault characteristic.
The software fault feature is composed of an associated database, an operation log and auxiliary software.
An AI matching analysis module is constructed, and the specific steps of analyzing the characteristics of different dimensions through the AI matching analysis module are as follows:
the AI matching analysis module comprises an equipment fault module, a port fault module, a line fault module and a software fault module, and the similarity of matched fault phenomena, fault indexes and fault results is analyzed through the equipment fault module, the port fault module, the line fault module and the software fault module.
In step S5, the method further includes the substeps of:
s51, the network model information, the port abnormity information, the equipment abnormity information and the network element fault characteristic information are collected into an AI cache through an equipment fault module to carry out retrieval, matching and threshold value calculation of conditions, parameters and character values, and an AI analysis result of the equipment fault is obtained;
s52, extracting the IP, MAC address, name and position information of the gateways at two ends of the key link, network equipment and ports, and the MAC address, name and position information from the basic information through the port fault module and the line fault module, and entering the topological relation, the operation performance, the flow, the uplink and downlink flow information and the link fault characteristic information set of the key link model data set into an AI cache for condition, parameter and character value retrieval, matching and threshold value calculation to obtain the AI analysis results of the port and line faults;
and S53, enabling resource network elements, network element types, network element IPs and distribution ports related to the automatic service to be subjected to basic information, data acquisition and monitoring data transmission of bearing equipment of a network resource model, IP, names, service borne, associated software and convergence gateway IP information distributed by the service and the service system through a software fault module, enabling software fault characteristics to enter an AI cache to perform condition, parameter, association relation and state retrieval, matching, threshold value calculation, state superposition or key state diagnosis and analysis, and obtaining a software fault AI analysis result.
Illustratively, in the AI matching analysis module, the order logic, the fault association relation logic and the fault conductivity logic are determined according to the fault priority, and an Artificial Intelligence (AI) fault location algorithm is established by the fault conductivity logic to obtain the location result of the fault element, wherein the logic of the whole AI fault location algorithm is based on one of the following: and (1) equipment failure. The CPU performance variation of the network equipment exceeds the upper limit or the lower limit of the configuration index, the memory performance variation exceeds the upper limit or the lower limit of the configuration index, and the equipment fails; when the flow of the port IP of the network equipment, such as a switch, a router and firewall equipment is interrupted in the uplink and downlink or the flow of the user access side and the service system side is interrupted, the interruption state is continuously maintained on the same level, and then the port of the equipment fails. And (2) link failure. The automatic service interruption (continuous failure of user access and data service interruption) of the service is realized, and the system detects the detection fault of the link formed by the network element, the network line and the server from the preset key link by polling. And (3) software failure. The network detection program judges the software fault when the operation parameters acquired by the network detection program exceed the operation analysis values and a plurality of system analysis indexes reach or exceed the fault reference limit value by setting the operation indexes of a CPU, a memory, a network port, an associated database, auxiliary software and an operating system of a server bearing the application system. In addition, the network equipment fault in the network fault, the relation with the link fault, the service software fault and the operating system fault have fault conductivity, for example, the fault of the core network equipment in the network can cause the obstruction of the channel and the service system of the main link, the fault of the user submitting the system to use can occur in the phenomenon, and the network fault is alarmed; for another example, if the tomcat middleware of the service system stops serving and the distributed database service stops, the application operation of the user on the service system cannot be normally performed, but the network management does not have network device alarm information, only the flow of the service port of the server where the software system is located decreases, and the fault diagnosis at this time is concentrated on the fault diagnosis analysis calculation of the operation log of the software system, the service fault of the load-bearing application, and the source address (IP + service port) to which the user operation points. And (4) an Artificial Intelligence (AI) recognition fault location algorithm of the AI matching analysis module automatically pushes a conclusion system obtained by the logic mechanism of the fusion to the F step fault location. And (4) for the abnormal condition which is found by analysis and diagnosis and has low matching degree but high alarm information degree (abnormal index), the abnormal condition is pushed to a network manager to detect and distinguish by using other network instructions, the condition that the fault is not formed is determined, and the abnormal condition is notified to delete in the abnormal list. And confirming that the system is in fault through manual test, and pushing the system to execute fault positioning.
In step S6, fault location is performed according to the analysis result of the AI matching analysis module, and pushing fault location information specifically includes:
and disassembling the fault positioning information through an AI matching analysis module, displaying a fault network element and switching a fault phenomenon to a fault information window of an operation maintenance unit, and pushing new fault characteristics to a fault machine learning library for sample accumulation.
And when the AI matching degree in the AI matching analysis module is not high, carrying out manual intervention diagnosis, and using the result of the manual intervention diagnosis for testing and verifying the fact of fault location.
Illustratively, in the fault location, the fault location information subjected to the AI analysis matching module is disassembled, the fault network elements and the fault phenomena are displayed and transferred to a fault information window of an operation maintenance unit for fault information push, and meanwhile, new fault characteristics (including columns for manual intervention diagnosis) are pushed to a fault machine learning library for sample accumulation; and for the fault positioning information of the fault positioning, automatically pushing the fault positioning information to an operation and maintenance unit according to a set circulation mechanism, and starting an operation and maintenance disposal task to dispatch the operation and maintenance disposal task to a corresponding operation and maintenance responsible person for disposal.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (9)
1. The method for diagnosing the link fault by combining model driving and data driving is characterized by comprising the following steps of:
s1, constructing a network resource basic information base, establishing network equipment, a server and a database operation performance monitoring index through the network resource basic information base, and if the operation performance information of a monitored object exceeds the index set by the object, but the exceeding index degree is not high, and the normal operation can be recovered, exiting the abnormal alarm information;
if the operation performance information of the monitored object exceeds the index set by the object and the phenomenon of data flow lasts for a period of time, entering an abnormal alarm;
s2, constructing an abnormal list classification library for receiving abnormal alarm information;
s3, constructing a dimension model, and performing dimension division on the abnormal alarm information through the dimension model;
s4, constructing an AI fault set for extracting different dimensional characteristics in the dimensional model;
s5, constructing an AI matching analysis module, and analyzing different dimensional characteristics through the AI matching analysis module;
and S6, fault positioning is carried out according to the analysis result of the AI matching analysis module, and fault positioning information is pushed.
2. The method for diagnosing the link fault by combining the model driving and the data driving according to claim 1, wherein in step S3, a dimension model is constructed, and dimension division is performed on the abnormal alarm information by the dimension model, specifically:
and the construction dimension model comprises a key link model, a network resource model and a detection data model, and different types of abnormal alarm information are matched through the key link model, the network resource model and the detection data model.
3. The method for diagnosing link failure by combining model driving and data driving according to claim 2, further comprising the sub-steps of:
s31, gateways at two ends of a key link, port IP, MAC addresses and topological relations are classified into a link database from basic information through a key link model;
s32, the resource network element, the network element type, the network element IP and the distribution port information related to the automatic service are classified into a service resource database through a network resource model;
and S33, the data acquisition of the load bearing equipment of the automation software, the monitoring data transmission, the service, the IP (Internet protocol) distributed by the service system, the name, the load bearing service, the associated software and the IP information of the convergence gateway are classified into a software system resource database through a detection data model.
4. The method for diagnosing the link failure combining the model driving and the data driving according to claim 3, wherein in step S4, an AI failure set is constructed for extracting different dimensional features in the dimensional model, specifically:
and extracting data in the link database, the service resource database and the software system resource database through the AI fault set, and matching the similarity of the fault phenomenon, the fault index and the fault result to obtain a link fault characteristic, a network element fault characteristic and a software fault characteristic.
5. The combined model-driven and data-driven link failure diagnostic method of claim 4, characterized in that the software failure features are composed of an association database, a running log and auxiliary software.
6. The method for diagnosing the link fault by combining the model driving and the data driving according to claim 5, wherein in step S5, an AI matching analysis module is constructed, and the analysis of the different dimensional features by the AI matching analysis module is specifically:
the AI matching analysis module comprises an equipment fault module, a port fault module, a line fault module and a software fault module, and the similarity of matched fault phenomena, fault indexes and fault results is analyzed through the equipment fault module, the port fault module, the line fault module and the software fault module.
7. The method for diagnosing link failure by combining model driving and data driving as claimed in claim 6, further comprising the sub-steps of:
s51, the network model information, the port abnormity information, the equipment abnormity information and the network element fault characteristic information are collected into an AI cache through an equipment fault module to carry out retrieval, matching and threshold value calculation of conditions, parameters and character values, and an AI analysis result of the equipment fault is obtained;
s52, extracting the IP, MAC address, name and position information of the gateways at two ends of the key link, network equipment and ports, and the MAC address, name and position information from the basic information through the port fault module and the line fault module, and entering the topological relation, the operation performance, the flow, the uplink and downlink flow information and the link fault characteristic information set of the key link model data set into an AI cache for condition, parameter and character value retrieval, matching and threshold value calculation to obtain the AI analysis results of the port and line faults;
s53, the resource network element, the network element type, the network element IP and the distribution port related to the automatic service are subjected to basic information, data acquisition and monitoring data transmission of a bearing device of a network resource model, IP, name, bearing service, associated software and convergence gateway IP information distributed by the service and the service system through a software fault module, and software fault characteristics are input into an AI cache to carry out condition, parameter, association relation and state retrieval, matching, threshold value calculation, state superposition or key state diagnosis analysis, so that a software fault AI analysis result is obtained.
8. The method for diagnosing the link fault by combining the model driving and the data driving according to claim 7, wherein in step S6, the fault location is performed according to the analysis result of the AI matching analysis module, and the pushing of the fault location information specifically comprises:
and disassembling the fault positioning information through an AI matching analysis module, displaying fault network elements and fault phenomena, transferring the fault network elements and the fault phenomena to a fault information window of an operation maintenance unit, and pushing new fault characteristics to a fault machine learning library for sample accumulation.
9. The method according to claim 7, wherein for a low AI matching degree in the AI matching analysis module, a manual intervention diagnosis is performed, and the result of the manual intervention diagnosis is used to test and verify the fact of fault location.
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| CN114172794A (en) * | 2020-09-10 | 2022-03-11 | 中国联合网络通信集团有限公司 | Network fault positioning method and server |
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| CN109034521A (en) * | 2018-06-07 | 2018-12-18 | 国电南瑞科技股份有限公司 | A kind of intelligent O&M architecture design method of dispatching of power netwoks control system |
| CN114172794A (en) * | 2020-09-10 | 2022-03-11 | 中国联合网络通信集团有限公司 | Network fault positioning method and server |
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