CN115314359A - Power grid information operation and maintenance active early warning method based on big data - Google Patents
Power grid information operation and maintenance active early warning method based on big data Download PDFInfo
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- CN115314359A CN115314359A CN202210790340.3A CN202210790340A CN115314359A CN 115314359 A CN115314359 A CN 115314359A CN 202210790340 A CN202210790340 A CN 202210790340A CN 115314359 A CN115314359 A CN 115314359A
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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
- H04L41/06—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
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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
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0681—Configuration of triggering conditions
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
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- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a big data-based active early warning method for operation and maintenance of power grid information, which comprises the steps of acquiring data among a plurality of time nodes of the power grid information, marking abnormal states and dividing into preliminary early warning; comparing operation and maintenance data recorded during preliminary early warning with real-time data, performing early warning overlapping with the data abnormal state marked by the preliminary early warning, comparing the data in the new abnormal state with the data in the preliminary early warning, and repeating the step one to mark in different states; and after early warning, comparing preset threshold values of the power grid information, and performing state early warning after the threshold values are exceeded. According to the invention, the abnormal data of the power grid is preliminarily early-warned to be recorded in real time, the abnormal data appears again after being marked, tracking and early warning are carried out through early warning, abnormal states are respectively early warned through state early warning, early warning is carried out on the abnormal data due to tracking of the early warning, and the safety of information operation and maintenance is improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a big data-based active early warning method for operation and maintenance of power grid information.
Background
In recent years, with the rapid development of informatization construction, the number of information systems of power grid companies is increasing, and higher standards and requirements are provided for daily operation and maintenance. At present, a passive operation and maintenance mode of alarming and first-aid repair after a fault occurs is mainly adopted, and the mode causes operation and maintenance personnel to spend most of daily time and energy on handling simple and repeated 'passive fire fighting' problems, so that the operation and maintenance personnel can do half the effort and often have malignant chain reaction. The capacity of early warning an information operation and maintenance system before a fault occurs and the capacity of positioning and analyzing operation and maintenance hidden dangers are lacked, and an active operation and maintenance mode which is mainly used for prevention needs to be realized urgently.
Disclosure of Invention
The invention aims to provide a big data-based active early warning method for operation and maintenance of power grid information, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a power grid information operation and maintenance active early warning method based on big data is characterized by comprising the following steps:
acquiring data among a plurality of time nodes of power grid information, counting the data by adopting a Hadoop technical framework, marking an abnormal state, and dividing into preliminary early warning;
comparing operation and maintenance data recorded during preliminary early warning with real-time data, performing early warning overlapping with the data abnormal state of the preliminary early warning mark, comparing the operation and maintenance data with the data abnormal state of the preliminary early warning mark through an Oracle architecture when a new abnormal state occurs, and repeating the step one under different states to mark;
after early warning, comparing preset threshold values of the power grid information, and performing state early warning after the threshold values are exceeded;
and step four, classifying the state early warning by the difference value exceeding or falling below a preset threshold value during the state early warning, wherein the classification is divided into low-value early warning and high-value early warning.
As a further scheme of the invention: the primary early warning and the early warning adopt a Hadoop technical framework to count data, abnormal states are marked, early warning is recorded on the basis of the primary early warning, and the primary early warning data is kept as the basis and cannot be covered.
As a further scheme of the invention: the comparison algorithm of the difference values in the state early warning is based on Apriori algorithm to construct a Boolean matrix.
As a further scheme of the invention: and after the state early warning, recording the low-value early warning and the high-value early warning, and early warning the high-value early warning when the low-value early warning is repeated in advance.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the abnormal data of the power grid are preliminarily early-warned and recorded in real time, the abnormal data are marked and then appear again, tracking and early warning are carried out through early warning, abnormal states are respectively early warned through state early warning, early warning is carried out on the abnormal data due to tracking of the early warning, and the safety of information operation and maintenance is improved.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to specific embodiments.
Example 1
A big data-based active early warning method for operation and maintenance of power grid information comprises the following steps:
acquiring data among a plurality of time nodes of power grid information, counting the data by adopting a Hadoop technical framework, marking an abnormal state, and dividing into preliminary early warning;
comparing operation and maintenance data recorded during preliminary early warning with real-time data, performing early warning overlapping with the data abnormal state of the preliminary early warning mark, comparing the operation and maintenance data with the data abnormal state of the preliminary early warning mark through an Oracle architecture when a new abnormal state occurs, and repeating the step one under different states to mark;
after early warning, comparing preset threshold values of the power grid information, and performing state early warning after the threshold values are exceeded;
and step four, classifying the state early warning by the difference value exceeding or falling below a preset threshold value during the state early warning, and classifying the state early warning into low-value early warning and high-value early warning.
The data are counted by adopting a Hadoop technical framework, abnormal states are marked, early warning is recorded on the basis of the initial warning, and the initial warning data are reserved as the basis and cannot be covered;
and the comparison algorithm of the difference values in the state early warning is based on an Apriori algorithm, a Boolean matrix is constructed, the calculated state early warning is carried out, low-value and high-value early warnings are recorded, the low-value early warning is not carried out when repeated in advance, and the high-value early warning is carried out.
The abnormal data of the power grid are recorded in real time through preliminary early warning, the abnormal data appear again after being marked, tracking and early warning are carried out through early warning, the abnormal state is respectively early warned through state early warning, early warning is carried out on the abnormal data due to tracking of the early warning, and safety during information operation and maintenance is improved.
While the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (5)
1. A big data-based active early warning method for operation and maintenance of power grid information is characterized by comprising the following steps:
acquiring data among a plurality of time nodes of power grid information, counting the data by adopting a Hadoop technical framework, marking abnormal states, and dividing into preliminary early warning;
comparing operation and maintenance data recorded during preliminary early warning with real-time data, performing early warning overlapping with the data abnormal state of the preliminary early warning mark, comparing the operation and maintenance data with the data abnormal state of the preliminary early warning mark through an Oracle architecture when a new abnormal state occurs, and repeating the step one under different states to mark;
after early warning, comparing preset threshold values of the power grid information, and performing state early warning after the threshold values are exceeded;
and step four, classifying the state early warning by the difference value exceeding or falling below a preset threshold value during the state early warning, wherein the classification is divided into low-value early warning and high-value early warning.
2. The active power grid information operation and maintenance early warning method based on big data as claimed in claim 1, wherein the initial early warning and the early warning both adopt Hadoop technical architecture to count data, mark abnormal state, record early warning on the basis of the initial early warning, and keep the initial early warning data as the basis, so that the data cannot be covered.
3. The active early warning method for operation and maintenance of power grid information based on big data as claimed in claim 1, wherein the preset threshold value during state early warning is a calculation threshold value of the monitoring data in the first step and the second step.
4. The active power grid information operation and maintenance early warning method based on big data as claimed in claim 1, wherein the comparison algorithm of the difference values in the state early warning is based on Apriori algorithm to construct a boolean matrix.
5. The active power grid information operation and maintenance early warning method based on big data as claimed in claim 1, wherein after state early warning, low-value and high-value early warnings are recorded, and when repeated low-value early warning is performed in advance, early warning is not performed, and high-value early warning is performed.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210790340.3A CN115314359A (en) | 2022-07-06 | 2022-07-06 | Power grid information operation and maintenance active early warning method based on big data |
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| CN202210790340.3A CN115314359A (en) | 2022-07-06 | 2022-07-06 | Power grid information operation and maintenance active early warning method based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116962072A (en) * | 2023-08-25 | 2023-10-27 | 北京市深海望潮科技有限公司 | Automatic operation and maintenance method for secondary safety protection equipment of power dispatching data network |
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2022
- 2022-07-06 CN CN202210790340.3A patent/CN115314359A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116962072A (en) * | 2023-08-25 | 2023-10-27 | 北京市深海望潮科技有限公司 | Automatic operation and maintenance method for secondary safety protection equipment of power dispatching data network |
| CN116962072B (en) * | 2023-08-25 | 2024-01-26 | 北京市深海望潮科技有限公司 | Automatic operation and maintenance method for secondary safety protection equipment of power dispatching data network |
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