CN113364623A - Method and system for reducing alarm misjudgment based on path diagram and network performance index - Google Patents
Method and system for reducing alarm misjudgment based on path diagram and network performance index Download PDFInfo
- Publication number
- CN113364623A CN113364623A CN202110625825.2A CN202110625825A CN113364623A CN 113364623 A CN113364623 A CN 113364623A CN 202110625825 A CN202110625825 A CN 202110625825A CN 113364623 A CN113364623 A CN 113364623A
- Authority
- CN
- China
- Prior art keywords
- service
- network
- data
- alarm
- original
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000010586 diagram Methods 0.000 title claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 45
- 238000012544 monitoring process Methods 0.000 claims abstract description 32
- 238000013499 data model Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000001514 detection method Methods 0.000 claims description 7
- 238000003066 decision tree Methods 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 3
- 238000003012 network analysis Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
-
- 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
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L41/065—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis involving logical or physical relationship, e.g. grouping and hierarchies
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Maintenance And Management Of Digital Transmission (AREA)
Abstract
The invention provides a method and a system for reducing alarm misjudgment based on a path graph and network performance indexes, which comprises the following steps: step 1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format; step 2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet; and step 3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions; and 4, step 4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events. The invention fuses the network data and the service data, and improves the data utilization rate.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a system for reducing alarm misjudgment based on a path diagram and network performance indexes.
Background
In the network data analysis industry, the two closely related work contents of network analysis and service analysis are often separately performed and independently deployed, the former focuses on data packet processing and data indexes of a network layer, and the latter focuses on specific service protocols and service indexes.
When the service indicator configures a service level detection function and triggers an alarm, field personnel need to investigate the reason of the service alarm. At this time, the index may not reach the standard due to the service, but may not reach the standard due to the network environment. If the data is the evidence, the data is extracted by another system, and the data alignment and searching of the two systems have certain difficulty. The fault-removing process is time-consuming and labor-consuming, and the evaluation of the business analysis system on the client side can be reduced by the wrong alarm reporting.
If the service level detection function can identify the current network state under the condition of only deploying the service analysis system, the alarms of the type can be reserved for the network analysis system independently, so that the accuracy rate of the whole alarm is improved, and the field service efficiency is also improved.
Patent document CN108809734B (application number: CN201810777256.1) discloses a network alarm root cause analysis method and system, the method includes: performing off-line analysis according to the network topology relation data and the monitoring index data to obtain an optimal shortest reachable path matrix of the alarm element; and performing transaction division on the alarm instances according to preset time step length based on the occurrence time of the alarm instances, performing alarm root analysis by taking the alarm transactions as a unit according to the optimal alarm element shortest reachable path matrix, and determining the alarm instances which are alarm roots in the alarm transactions.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for reducing alarm misjudgment based on a path diagram and network performance indexes.
The method for reducing the alarm misjudgment based on the path graph and the network performance index comprises the following steps:
step 1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format;
step 2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet;
and step 3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions;
and 4, step 4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events.
Preferably, the decoding includes:
and (3) decoding a service protocol: the original network data generates service data after being decoded by a service protocol, and the service data is associated with service communication points in a model by combining a data model;
decoding a network protocol: the original network data generates a network event after being decoded by a network protocol, and the network event is associated with a service communication point in the model by combining a data model.
Preferably, the parameter identification of the network state and the service index is performed in a mode of combining the configuration initial parameter and the machine learning, and the baseline index is generated through online operation in a preset time period and is used as a basis for finally configuring the service path diagram.
Preferably, the step 3 comprises:
monitoring the service level: monitoring the service level of the service data packet, identifying service indexes which do not meet preset standards on a service communication point, and generating an alarm event;
and (3) network state detection: and combining the network event packet with the service path graph, identifying the network event on the service communication point, and taking the identified network event as the basis of alarm misjudgment.
Preferably, the alarm event and the network event on the service communication point are fused, the type and the severity of the network event are used as decision basis, and whether the alarm event needs to be reported or not is judged;
and in the decision making process, various indexes are comprehensively judged in an expert system and decision tree mode, wherein the indexes comprise packet loss rate, flow use frequency and retransmission rate.
The system for reducing alarm misjudgment based on the path graph and the network performance index provided by the invention comprises the following steps:
module M1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format;
module M2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet;
module M3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions;
module M4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events.
Preferably, the decoding includes:
and (3) decoding a service protocol: the original network data generates service data after being decoded by a service protocol, and the service data is associated with service communication points in a model by combining a data model;
decoding a network protocol: the original network data generates a network event after being decoded by a network protocol, and the network event is associated with a service communication point in the model by combining a data model.
Preferably, the parameter identification of the network state and the service index is performed in a mode of combining the configuration initial parameter and the machine learning, and the baseline index is generated through online operation in a preset time period and is used as a basis for finally configuring the service path diagram.
Preferably, the step 3 comprises:
monitoring the service level: monitoring the service level of the service data packet, identifying service indexes which do not meet preset standards on a service communication point, and generating an alarm event;
and (3) network state detection: and combining the network event packet with the service path graph, identifying the network event on the service communication point, and taking the identified network event as the basis of alarm misjudgment.
Preferably, the alarm event and the network event on the service communication point are fused, the type and the severity of the network event are used as decision basis, and whether the alarm event needs to be reported or not is judged;
and in the decision making process, various indexes are comprehensively judged in an expert system and decision tree mode, wherein the indexes comprise packet loss rate, flow use frequency and retransmission rate.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention can eliminate the index non-standard caused by network factors through the service monitoring system, thereby reducing the error rate and improving the evaluation of the whole system at the user side;
(2) the invention fuses the network data and the service data, thereby improving the data utilization rate;
(3) under the special conditions of network storm and the like, the system resource utilization is reduced and the performance is improved by locking the service analysis system;
(4) according to the invention, through the service path diagram, when a field engineer carries out fault elimination, the semantics of each alarm is more clear, and the fault elimination efficiency is improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a main flow diagram of the present invention;
FIG. 2 is a flow chart of data decoding;
fig. 3 is a flow chart of service data monitoring.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example (b):
the system provides a method for reducing service level monitoring alarm error by depending on a transaction model and network layer indexes. The alarm misjudgment range of judgment is reduced as much as possible while the misalarm is reduced, and the overall accuracy of the alarm is ensured.
Referring to fig. 1, the method comprises the following steps:
step 1, network data acquisition: the system comprises a data acquisition module, a data transmission module and a data transmission module, wherein the data acquisition module is used for acquiring an original data packet, and outputting the original data packet as original network data with a fixed format;
and 2, data decoding: decoding the original data packet, fusing a service path diagram (transaction model), associating the original data with a service communication point, and generating a service data packet and a network data packet; inputting the original network data with the fixed format output in the step 1; the output is the decoded service data and the network event information with the transaction model.
Step 3, service data monitoring: receiving data and a model, monitoring whether the network state meets the analysis requirement and whether the service index reaches the standard, and generating an alarm event according to an alarm processing logic; inputting a transaction model configured for a user, a service data packet and a network event packet output in the step 2; the output of which is an alarm event.
Step 4, service analysis homepage: configuring a business service path graph, and displaying business indexes and alarm events; the input of the method is the alarm event output in the step 3. The service analysis homepage outputs a service path diagram as configuration and sends the configuration to the system for controlling data decoding and service data monitoring. In the service path diagram, the smallest unit is a specific service communication point in an actual scene.
As shown in fig. 2, the step 2 data decoding includes the following steps:
step 2.1: decoding a service protocol, namely generating service data after the original network data is decoded by the service protocol, and associating the service data with a service communication point in a model by combining a data model; the business data and the business communication points in the model are related as follows: and associating according to the source and destination ip addresses and ports configured by the service communication point and the configured service protocol message characteristics, wherein the service data obtained by decoding also has the dimensions, and the association of the service data and the communication point is realized by matching the dimensions.
Step 2.2: decoding a network protocol, generating a network event after original network data is decoded by the network protocol, associating the network event with a service communication point in a model by combining a data model, wherein the association of the network event and the service communication point in the model is as follows: and matching the source and destination ip addresses and ports in the network event according to the source and destination ip addresses and ports configured by the service communication point, and associating the network event with the service communication point.
Because the network event is supported by the model, the use scene is fixed, a large amount of intermediate results are not generated like the traditional network analysis system, the influence of data storage is within 10% -20%, and the influence of performance is within 5% -15%.
Due to the fact that network environments of different sites are greatly different, parameter identification of network events is achieved through combination of two modes of manual configuration of initial parameters and machine learning, baseline indexes are generated through online operation for a period of time and serve as bases of final configuration, and complexity of manual intervention is reduced.
As shown in fig. 3, the step 3 of monitoring the service data includes the following steps:
step 3.1: monitoring service level, namely identifying service indexes which do not reach the standard on a service communication point by a service data packet through the service level monitoring, and generating an alarm event;
step 3.2: and (3) detecting the network state, combining the network event packet with the service path diagram, identifying the network event on the service communication point, and taking the network event as a network event for judging the alarm by mistake.
Step 3.3: and (4) data decision fusion, namely fusing the alarm event and the network event on the service communication point, and judging whether the alarm event needs to be reported or not by taking the type and the severity of the network event as a decision basis. In the decision making process, various indexes are comprehensively considered in an expert system and decision tree mode, and the indexes include but are not limited to too high packet loss rate, abnormal flow, too high retransmission rate and the like. The identification basis can be traced and used as the basis of post analysis.
The decision range is limited on a single service communication point through a model, so that the range of alarm misjudgment is reduced, and the overall correctness is ensured.
The system for reducing alarm misjudgment based on the path graph and the network performance index provided by the invention comprises the following steps: module M1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format; module M2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet; module M3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions; module M4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events.
The decoding includes: and (3) decoding a service protocol: the original network data generates service data after being decoded by a service protocol, and the service data is associated with service communication points in a model by combining a data model; decoding a network protocol: the original network data generates a network event after being decoded by a network protocol, and the network event is associated with a service communication point in the model by combining a data model. The method comprises the steps of identifying parameters of a network state and a service index in a mode of combining initial parameter configuration and machine learning, generating a baseline index through online operation of a preset time period, and using the baseline index as a basis for finally configuring a service path diagram. The step 3 comprises the following steps: monitoring the service level: monitoring the service level of the service data packet, identifying service indexes which do not meet preset standards on a service communication point, and generating an alarm event; and (3) network state detection: and combining the network event packet with the service path graph, identifying the network event on the service communication point, and taking the identified network event as the basis of alarm misjudgment. Fusing an alarm event and a network event on a service communication point, taking the type and the severity of the network event as a decision basis, and judging whether the alarm event needs to be reported or not; and in the decision making process, various indexes are comprehensively judged in an expert system and decision tree mode, wherein the indexes comprise packet loss rate, flow use frequency and retransmission rate.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A method for reducing alarm misjudgment based on a path diagram and network performance indexes is characterized by comprising the following steps:
step 1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format;
step 2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet;
and step 3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions;
and 4, step 4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events.
2. The method of claim 1, wherein the decoding comprises:
and (3) decoding a service protocol: the original network data generates service data after being decoded by a service protocol, and the service data is associated with service communication points in a model by combining a data model;
decoding a network protocol: the original network data generates a network event after being decoded by a network protocol, and the network event is associated with a service communication point in the model by combining a data model.
3. The method for reducing alarm misjudgment based on the path diagram and the network performance index as claimed in claim 1, wherein the parameter identification of the network state and the service index is performed by combining the configuration of the initial parameter and the machine learning, and the baseline index is generated by the online operation of the preset time period and is used as the basis for finally configuring the service path diagram.
4. The method according to claim 2, wherein the step 3 comprises:
monitoring the service level: monitoring the service level of the service data packet, identifying service indexes which do not meet preset standards on a service communication point, and generating an alarm event;
and (3) network state detection: and combining the network event packet with the service path graph, identifying the network event on the service communication point, and taking the identified network event as the basis of alarm misjudgment.
5. The method of claim 4, wherein the method for reducing the false alarm determination based on the path graph and the network performance index is characterized in that the method fuses the alarm event and the network event on the service communication point, and determines whether the alarm event needs to be reported or not by using the type and the severity of the network event as a decision basis;
and in the decision making process, various indexes are comprehensively judged in an expert system and decision tree mode, wherein the indexes comprise packet loss rate, flow use frequency and retransmission rate.
6. A system for reducing alarm misjudgment based on a path diagram and network performance indexes is characterized by comprising:
module M1: acquiring an original data packet, and decoding the original data packet into original network data in a preset format;
module M2: building a data model, inputting original network data, fusing the original network data and a path diagram, and associating the original network data with a service communication point to obtain a network data packet and a service data packet;
module M3: monitoring network states and service indexes according to the network data packets and the service data packets, and generating alarm events according to preset alarm processing logic for networks and services which do not meet preset conditions;
module M4: and configuring a service path graph, decoding network data, monitoring service data, and displaying service indexes and alarm events.
7. The system according to claim 6, wherein said decoding comprises:
and (3) decoding a service protocol: the original network data generates service data after being decoded by a service protocol, and the service data is associated with service communication points in a model by combining a data model;
decoding a network protocol: the original network data generates a network event after being decoded by a network protocol, and the network event is associated with a service communication point in the model by combining a data model.
8. The system for reducing alarm misjudgment based on the path diagram and the network performance index as claimed in claim 6, wherein the parameter identification of the network state and the service index is performed by combining the configuration of the initial parameter and the machine learning, and the baseline index is generated by the online operation of the preset time period and is used as the basis for finally configuring the service path diagram.
9. The system according to claim 7, wherein the step 3 comprises:
monitoring the service level: monitoring the service level of the service data packet, identifying service indexes which do not meet preset standards on a service communication point, and generating an alarm event;
and (3) network state detection: and combining the network event packet with the service path graph, identifying the network event on the service communication point, and taking the identified network event as the basis of alarm misjudgment.
10. The system for reducing alarm misjudgment based on the path graph and the network performance index as claimed in claim 9, wherein the alarm event and the network event on the service communication point are merged, and the type and the severity of the network event are used as decision basis to judge whether the alarm event needs to be reported;
and in the decision making process, various indexes are comprehensively judged in an expert system and decision tree mode, wherein the indexes comprise packet loss rate, flow use frequency and retransmission rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110625825.2A CN113364623A (en) | 2021-06-04 | 2021-06-04 | Method and system for reducing alarm misjudgment based on path diagram and network performance index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110625825.2A CN113364623A (en) | 2021-06-04 | 2021-06-04 | Method and system for reducing alarm misjudgment based on path diagram and network performance index |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113364623A true CN113364623A (en) | 2021-09-07 |
Family
ID=77532354
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110625825.2A Pending CN113364623A (en) | 2021-06-04 | 2021-06-04 | Method and system for reducing alarm misjudgment based on path diagram and network performance index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113364623A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1983984A (en) * | 2006-06-13 | 2007-06-20 | 华为技术有限公司 | Method for monitoring network service fault |
| CN101854652A (en) * | 2010-06-23 | 2010-10-06 | 天元莱博(北京)科技有限公司 | Telecommunications network service performance monitoring system |
| CN103178991A (en) * | 2011-12-21 | 2013-06-26 | 中国移动通信集团黑龙江有限公司 | Method and system for analyzing multiple-network relation |
| CN103905237A (en) * | 2012-12-28 | 2014-07-02 | 中国电信股份有限公司 | Telecom exchange network management system and management method |
| WO2016173203A1 (en) * | 2015-04-29 | 2016-11-03 | 中兴通讯股份有限公司 | Testing method and device for deep network analysis system |
| CN107612779A (en) * | 2017-10-10 | 2018-01-19 | 云南电网有限责任公司 | The dispatch data net secondary safety protection network equipment and service operation monitoring system |
| CN108809734A (en) * | 2018-07-16 | 2018-11-13 | 北京思特奇信息技术股份有限公司 | Network alarm root-cause analysis method, system, storage medium and computer equipment |
| CN109787809A (en) * | 2018-12-07 | 2019-05-21 | 北京盛世全景科技股份有限公司 | A kind of panorama easily regards intelligent operational system |
| WO2019177264A1 (en) * | 2018-03-14 | 2019-09-19 | 마인드서프 주식회사 | Method for analyzing multilayer-based network traffic visualization |
| CN110620688A (en) * | 2019-09-12 | 2019-12-27 | 广州源典科技有限公司 | Service comprehensive monitoring method, system and device |
-
2021
- 2021-06-04 CN CN202110625825.2A patent/CN113364623A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1983984A (en) * | 2006-06-13 | 2007-06-20 | 华为技术有限公司 | Method for monitoring network service fault |
| CN101854652A (en) * | 2010-06-23 | 2010-10-06 | 天元莱博(北京)科技有限公司 | Telecommunications network service performance monitoring system |
| CN103178991A (en) * | 2011-12-21 | 2013-06-26 | 中国移动通信集团黑龙江有限公司 | Method and system for analyzing multiple-network relation |
| CN103905237A (en) * | 2012-12-28 | 2014-07-02 | 中国电信股份有限公司 | Telecom exchange network management system and management method |
| WO2016173203A1 (en) * | 2015-04-29 | 2016-11-03 | 中兴通讯股份有限公司 | Testing method and device for deep network analysis system |
| CN107612779A (en) * | 2017-10-10 | 2018-01-19 | 云南电网有限责任公司 | The dispatch data net secondary safety protection network equipment and service operation monitoring system |
| WO2019177264A1 (en) * | 2018-03-14 | 2019-09-19 | 마인드서프 주식회사 | Method for analyzing multilayer-based network traffic visualization |
| CN108809734A (en) * | 2018-07-16 | 2018-11-13 | 北京思特奇信息技术股份有限公司 | Network alarm root-cause analysis method, system, storage medium and computer equipment |
| CN109787809A (en) * | 2018-12-07 | 2019-05-21 | 北京盛世全景科技股份有限公司 | A kind of panorama easily regards intelligent operational system |
| CN110620688A (en) * | 2019-09-12 | 2019-12-27 | 广州源典科技有限公司 | Service comprehensive monitoring method, system and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110493042B (en) | Fault diagnosis method and device and server | |
| US9571334B2 (en) | Systems and methods for correlating alarms in a network | |
| US7525422B2 (en) | Method and system for providing alarm reporting in a managed network services environment | |
| CN102868553B (en) | Fault Locating Method and relevant device | |
| CN111682642B (en) | Lightweight intelligent substation information abnormality detection system and method thereof | |
| CN110855493B (en) | Application topological graph drawing device for mixed environment | |
| CN111934936B (en) | Network state detection method and device, electronic equipment and storage medium | |
| CN110808865A (en) | Passive industrial control network topology discovery method and industrial control network security management system | |
| JP2007208633A (en) | Device, method and program for designing network | |
| CN111565133B (en) | Private line switching method and device, electronic equipment and computer readable storage medium | |
| CN113364624A (en) | Mixed cloud flow acquisition method and system based on edge computing | |
| CN104639346A (en) | Method and device for detecting network management equipment of communication operator | |
| CN110647417B (en) | Energy internet abnormal data processing method, device and system | |
| CN110071843B (en) | Fault positioning method and device based on flow path analysis | |
| CN113259367B (en) | Industrial control network flow multistage anomaly detection method and device | |
| WO2020252635A1 (en) | Method and apparatus for constructing network behavior model, and computer readable medium | |
| CN113364623A (en) | Method and system for reducing alarm misjudgment based on path diagram and network performance index | |
| US10338544B2 (en) | Communication configuration analysis in process control systems | |
| CN109067603B (en) | Method and system for determining VLAN configuration problem of transformer substation network | |
| JP5701401B2 (en) | Network analysis device, management system, network analysis method and program | |
| US10432451B2 (en) | Systems and methods for managing network health | |
| CN107645395A (en) | Multicast Routing data checking and device | |
| CN111431930A (en) | Flow cleaning method and related equipment | |
| CN112650765A (en) | Method, device and equipment for judging station faults and storage medium | |
| US10756951B2 (en) | Network incident identification based on characterizing relationships between interfaces and events as graphical component relationships |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210907 |
|
| RJ01 | Rejection of invention patent application after publication |