CN111901158B - Intelligent household distribution network fault data analysis method, equipment and storage medium - Google Patents
Intelligent household distribution network fault data analysis method, equipment and storage medium Download PDFInfo
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- CN111901158B CN111901158B CN202010676923.4A CN202010676923A CN111901158B CN 111901158 B CN111901158 B CN 111901158B CN 202010676923 A CN202010676923 A CN 202010676923A CN 111901158 B CN111901158 B CN 111901158B
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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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
- H04L41/0677—Localisation of faults
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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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Abstract
The invention discloses an intelligent home distribution network fault data analysis method, equipment and a storage medium, wherein the analysis method comprises the following steps of S1: receiving distribution network data pushed by a terminal, and preprocessing the distribution network data; step S2: inserting the preprocessed distribution network data into a Hive data warehouse in a real-time partition manner; step S3: performing fault analysis on the distribution network data stored in the Hive data warehouse, and labeling the fault data if judging that the fault data occur in the distribution network data; step S4: and outputting the labeled distribution network data increment to a MySQL database in real time at set time intervals by using an Sqoop tool. According to the invention, the fault data are associated through the fault codes, the distribution network data are labeled, the positioning of the failure reason of the distribution network is promoted, and the positioning accuracy is improved.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to an intelligent household distribution network fault data analysis method, equipment and a storage medium.
Background
Along with the progress of science and technology and the improvement of living standard, people increasingly pursue safer, more comfortable and more convenient living environments at home, so that intelligent households gradually enter the field of vision of people and are widely applied, and intelligent control of household appliances can be realized in modern households. The intelligent home distribution network is a crucial step for realizing 'intellectualization'. In order to give a more user-friendly distribution network experience, it is particularly important to analyze the user distribution network fault data. However, the data pushed by the distribution network are scattered and lack of generalization, so that the background cannot rapidly distinguish fault data, the efficiency of a fault data analysis process is low, fault problems generated in the customer distribution network process cannot be timely known, and the instantaneity and quality of service customers are affected.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide an intelligent household distribution network fault data analysis method, which is to correlate fault data through fault codes, label distribution network data, promote the positioning of distribution network failure reasons and improve positioning accuracy.
The second object of the present invention is to provide an electronic device.
It is a further object of the present invention to provide a storage medium.
One of the purposes of the invention is realized by adopting the following technical scheme:
a fault data analysis method for an intelligent home distribution network comprises the following steps:
step S1: receiving distribution network data pushed by a terminal, and preprocessing the distribution network data;
step S2: inserting the preprocessed distribution network data into a Hive data warehouse in a real-time partition manner;
step S3: performing fault analysis on the distribution network data stored in the Hive data warehouse, and labeling the fault data if judging that the fault data occur in the distribution network data;
step S4: and outputting the labeled distribution network data increment to a MySQL database in real time at set time intervals by using an Sqoop tool.
Further, the method for preprocessing the distribution network data in step S1 is to parse the distribution network data and convert the same into Json format.
Further, the step S3 further includes cleaning the data in the Hive data warehouse.
Further, the method for tagging the fault data in the step S3 is as follows:
and carrying out association analysis on the fault data and fault code data to enable the fault data to be marked with the fault code.
Further, after the failure data is labeled in the step S3, the method further includes classifying the distribution network data according to a failure code.
Further, the step S3 further includes performing fault cause analysis on the fault data to obtain a fault cause of the distribution network after classifying the distribution network data according to the fault code.
Further, the fault codes of the fault data are secondarily labeled according to different distribution network failure reasons, and the fault data after secondary labeling are secondarily classified.
Further, the step S4 is specifically to export the labeled data increment of the distribution network to the MySQL database in real time every minute.
The second purpose of the invention is realized by adopting the following technical scheme:
the electronic equipment comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the intelligent household distribution network fault data analysis method is realized when the processor executes the computer program.
The third purpose of the invention is realized by adopting the following technical scheme:
a storage medium having stored thereon a computer program which when executed implements the above-described intelligent home distribution network fault data analysis method.
Compared with the prior art, the invention has the beneficial effects that:
(1) Identifying fault codes for the distribution network fault data, classifying the fault codes for distribution network results, and promoting the positioning of the reasons of failure of the distribution network, so that the positioning is more accurate; the distribution network data is labeled, the positioning of the reason of the distribution network failure is promoted, and the corresponding solution of the distribution network failure can be fed back to the user efficiently;
(2) The distribution network data are labeled by the fault codes, and are classified and managed by the fault codes, so that the distribution network data can be better used for guiding strategic resource allocation and tactical service marketing countermeasure application of intelligent home distribution network management, and equipment distribution network schemes and corresponding customer service are optimized, so that equipment distribution network experience is more friendly.
Drawings
Fig. 1 is a schematic flow chart of a first embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.
Example 1
The embodiment provides a method for analyzing fault data of an intelligent home distribution network, which is shown by referring to fig. 1, and specifically comprises the following steps:
step S1: and receiving distribution network data pushed by a terminal, and preprocessing the distribution network data.
In this embodiment, a server is connected to the terminal APP server to receive, in real time, user data collected from the terminal APP, where the user data includes distribution network data, and the distribution network data includes failure data generated when a user fails to distribute a network.
And after collecting the distribution network data, preprocessing the distribution network data, namely analyzing the distribution network data and converting the distribution network data into a Json format, so that analysis and generation of the data by a later computer are easy.
Step S2: and inserting the preprocessed distribution network data into a Hive data warehouse in a real-time partition mode.
The Hive data warehouse can map the structured data file into a database table, and the Json data is analyzed and then is partitioned into real-time partitions, and the partitions are inserted into the Hive data warehouse for storage, and are subjected to preprocessing such as cleaning in Hive.
Step S3: and carrying out fault analysis on the distribution network data stored in the Hive data warehouse, and labeling the fault data if judging that the fault data occur in the distribution network data.
And carrying out fault analysis on the distribution network data stored in the Hive data warehouse according to the distribution network business rule so as to judge whether fault data exists in the distribution network data, defining data which does not accord with the distribution network business rule as the fault data, and carrying out association analysis on the fault data and the fault code data if the fault data exists, so that the fault code is marked on the fault data.
After the fault data are labeled, the distribution network data are classified according to the fault codes, so that the fault data can be classified in the same folder in a concentrated mode.
After the fault data are classified and summarized, the classified fault data are subjected to fault reason analysis in a concentrated mode, and in addition, a solution for failure of distribution network is correspondingly generated according to the fault reasons obtained through analysis, and the solution is used for being fed back to a user to achieve high efficiency of fault reason feedback.
Meanwhile, the distribution network data are classified by using the fault codes, and after the distribution network failure reasons are analyzed, the fault data can be further subjected to secondary labeling according to the distribution network failure reasons, namely, the classified fault data are further classified according to the distribution network failure reasons, so that the positioning accuracy of the distribution network failure reasons is improved.
When the first labeling is carried out, all fault data can be associated with the same fault code so as to quickly classify and take out the fault data from a plurality of distribution network data; and during the second labeling, different fault codes can be set up for different fault reasons, and the fault data after the first classification is marked with different fault codes according to the respective fault reasons and then stored in the Hive data warehouse for storage.
Step S4: and outputting the labeled distribution network data increment to a MySQL database in real time at set time intervals by using an Sqoop tool.
In the embodiment, the labeled distribution network data increment in the Hive data warehouse is exported to the MySQL database in real time every minute, and the distribution network data is calculated and analyzed in minute order, so that the distribution network abnormality can be rapidly and real-time analyzed and judged.
Example two
The embodiment provides an electronic device, which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the intelligent home distribution network fault data analysis method in the first embodiment when executing the computer program. In addition, the embodiment also provides a storage medium, on which a computer program is stored, and the computer program is executed to implement the intelligent home distribution network fault data analysis method.
The apparatus and the storage medium in this embodiment and the method in the foregoing embodiments are based on two aspects of the same inventive concept, and the detailed description of the method implementation process has been given above, so those skilled in the art can clearly understand the structure and implementation process of the system in this embodiment according to the foregoing description, and the details are omitted herein for brevity.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but any insubstantial changes and substitutions made by those skilled in the art on the basis of the present invention are intended to be within the scope of the present invention as claimed.
Claims (6)
1. The intelligent household distribution network fault data analysis method is characterized by comprising the following steps of:
step S1: receiving distribution network data pushed by a terminal, and preprocessing the distribution network data;
step S2: inserting the preprocessed distribution network data into a Hive data warehouse in a real-time partition manner;
step S3: performing fault analysis on the distribution network data stored in the Hive data warehouse, if judging that the fault data occurs in the distribution network data, tagging the fault data, namely performing association analysis on the fault data and fault code data to enable the fault data to be marked with fault codes, and classifying the distribution network data according to the fault codes; analyzing the fault data to obtain a failure reason of the distribution network, secondarily labeling fault codes of the fault data according to different failure reasons of the distribution network, and secondarily classifying the secondarily labeled fault data;
step S4: and outputting the labeled distribution network data increment to a MySQL database in real time at set time intervals by using an Sqoop tool.
2. The method for analyzing fault data of an intelligent home distribution network according to claim 1, wherein the method for preprocessing the distribution network data in step S1 is to analyze the distribution network data and convert the distribution network data into Json format.
3. The method for analyzing fault data of intelligent home distribution network according to claim 1, wherein step S3 further comprises cleaning data in Hive data warehouse.
4. The method for analyzing fault data of intelligent home distribution network according to claim 1, wherein step S4 is specifically to export the labeled distribution network data increment to MySQL database in real time every minute.
5. An electronic device, characterized in that it comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the intelligent home distribution network fault data analysis method according to any one of claims 1 to 4 when executing the computer program.
6. A storage medium having stored thereon a computer program which when executed implements the intelligent home distribution network fault data analysis method of any of claims 1 to 4.
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Address after: No. 21-1-2, Shihua Road, Hualong Town, Panyu District, Guangzhou City, Guangdong Province, 511400 Applicant after: Guangdong haomadame smart home Co.,Ltd. Address before: No.1, No.2 highway, No.2, Panyu, Guangzhou Applicant before: GUANGDONG KELAINI INTELLIGENT TECHNOLOGY Co.,Ltd. |
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