CN111985558A - Electric energy meter abnormity diagnosis method and system - Google Patents
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Abstract
The invention discloses an electric energy meter abnormity diagnosis method and a system thereof, belonging to the technical field of electric energy quality detection, wherein S1: the method comprises the following steps that object data collection comprises distributed real-time collection, offline data collection and Oracle data quasi-real-time synchronization, and collection contents comprise archive data, operation data, event data, working condition data, work order data, scheduling data and environment data; s2: sending the acquired data to an abnormality sensing module and an abnormality diagnosis module; s3: through combing peripheral services related to fault diagnosis, combining a clustering algorithm to carry out similarity measurement and group combination, establishing a fault real-time perception model, and outputting a fault pre-judgment result; s4: the original fault feature extraction and the feature classification after data cleaning are the basis of the construction of a fault diagnosis engine, the cause of the fault is found, and the remote fault repair is carried out or a field repair solution is provided; the electric energy meter abnormity diagnosis method for automatically judging the running state of the electric energy meter is realized.
Description
Technical Field
The invention relates to the technical field of power quality detection, in particular to a method and a system for diagnosing abnormity of a power meter.
Background
In recent years, with the domestic power demand and the expansion of the scale of a power grid, the voltage level is continuously improved, users with high capacity and high voltage level are continuously increased, meanwhile, with the implementation of time-of-use electricity price and step electricity price, the accuracy and reliability of electric energy metering become the focus of social attention, the requirement on the accuracy of electric energy meter diagnosis in the detection process is higher, the electric energy meter diagnosis in the existing detection process is mainly carried out in an electric energy expression field in a manual mode, the field lacks of data support, and the condition of inaccurate diagnosis or misdiagnosis is easily caused by illegally carrying out quantitative accurate judgment through a programmed method and relying on manual experience data judgment, so that the requirement on accurate measurement management is difficult to meet.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an electric energy meter abnormity diagnosis method and a system thereof, and the electric energy meter abnormity diagnosis method for automatically judging the running state of the electric energy meter is realized.
The purpose of the invention can be realized by the following technical scheme:
an electric energy meter abnormity diagnosis method comprises the following steps:
s1: the method comprises the following steps that object data collection comprises distributed real-time collection, offline data collection and Oracle data quasi-real-time synchronization, and collection contents comprise archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
s2: sending the acquired data to an abnormality sensing module and an abnormality diagnosis module;
s3: the abnormal sensing module processes the acquired data, images the fault through combing peripheral services related to fault diagnosis, builds a fault label system, provides label rule maintenance, can define rule algorithms and data logics according to actual scenes, continuously performs iterative updating and perfecting on a fault label library, performs label visual management, performs feature extraction on fault data in real time by combining the label system based on a high-concurrency and distributed big data real-time processing technology, performs similarity measurement and group combination by combining a clustering algorithm, establishes a fault real-time sensing model, and outputs a fault pre-judging result;
s4: the abnormity diagnosis module processes the collected data, the characteristic classification after the fault original characteristic extraction and the data cleaning is the basis of the fault diagnosis engine construction, reasonably utilizes the collected various service data, researches the abnormity diagnosis and positioning of the whole chain of the system, traces back the data source, the flow direction, the change track and the personnel behavior habit in a cross-system manner, and through the construction of fault characteristic engineering and based on machine learning, performing more abundant characteristic analysis on the fault, simulating variables and reconstructing variables according to actual service and fault characteristics, extracting service logic and data logic, selecting algorithm and parameters, continuously training, adjusting and correcting parameters and algorithm logic to construct a reasonable model, and (3) approaching and restoring the real scene of the occurrence of the metering fault, finding out the cause of the occurrence of the fault, and carrying out remote fault repair or providing a field repair solution.
As a preferred scheme of the present invention, in step S1, the fault diagnosis involves using various data such as acquisition data, marketing data, scheduling data, asset data, fault data, and environmental data, designing according to a scene in a targeted manner by constructing intelligent fault acquisition, and performing real-time streaming data acquisition and non-real-time multi-granularity offline data acquisition by using an advanced data acquisition convergence technology.
As a preferred scheme of the present invention, in step S3, a fault sensing model is built to find out possible faults in real time, and perform timely diagnosis, early warning and processing, thereby improving fault finding efficiency.
As a preferred scheme of the present invention, in step S4, through the construction of the fault diagnosis engine, the full-chain construction of fault diagnosis is completed, real-time diagnosis and offline diagnosis can be performed, and related fault repair is performed, so that the field fault diagnosis and solution efficiency is greatly improved, and the fault operation and maintenance cost is reduced.
A system of an electric energy meter abnormity diagnosis method comprises a fault diagnosis support center, an acquisition processing module, an operation and maintenance learning module, an abnormity sensing module and an abnormity diagnosis module;
the fault diagnosis support center comprises basic resource management, data acquisition scheduling, a data processing tunnel, a data storage service, an analysis and calculation service, a characteristic project, a machine learning service and a visualization service;
the system comprises an acquisition processing module, a data acquisition processing module and a data processing module, wherein the acquisition processing module comprises distributed real-time acquisition, offline data acquisition and Oracle data quasi-real-time synchronization, and a data acquisition object comprises archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
the operation and maintenance learning module comprises fault knowledge convergence, fault knowledge extraction, fault knowledge reasoning, fault knowledge storage and fault knowledge map visualization;
the abnormity diagnosis module comprises terminal offline fault diagnosis, offline fault frequent analysis, scheduling plan power failure analysis, parameter error abnormity analysis, main station server abnormity analysis, terminal debugging state analysis and terminal hardware fault diagnosis.
The invention has the beneficial effects that:
the invention is provided with a fault diagnosis support center, an acquisition processing module, an operation and maintenance learning module, an abnormality sensing module and an abnormality diagnosis module; through the establishment of the fault perception model, possible faults are found in real time, and timely diagnosis, early warning and processing are carried out, so that the fault finding efficiency is improved; through the construction of the fault diagnosis engine, the full-chain construction of fault diagnosis is completed, real-time diagnosis and offline diagnosis can be performed, relevant fault repair is performed, the field fault diagnosis and solution efficiency is greatly improved, and the fault operation and maintenance cost is reduced.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a block diagram of an abnormality diagnosis system for an electric energy meter;
FIG. 2 is a schematic illustration of a data acquisition object.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in the figure, the method for diagnosing the abnormality of the electric energy meter is characterized by comprising the following steps of:
s1: the method comprises the following steps that object data collection comprises distributed real-time collection, offline data collection and Oracle data quasi-real-time synchronization, and collection contents comprise archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
s2: sending the acquired data to an abnormality sensing module and an abnormality diagnosis module;
s3: the abnormal sensing module processes the acquired data, images the fault through combing peripheral services related to fault diagnosis, builds a fault label system, provides label rule maintenance, can define rule algorithms and data logics according to actual scenes, continuously performs iterative updating and perfecting on a fault label library, performs label visual management, performs feature extraction on fault data in real time by combining the label system based on a high-concurrency and distributed big data real-time processing technology, performs similarity measurement and group combination by combining a clustering algorithm, establishes a fault real-time sensing model, and outputs a fault pre-judging result;
s4: the abnormity diagnosis module processes the collected data, the characteristic classification after the fault original characteristic extraction and the data cleaning is the basis of the fault diagnosis engine construction, reasonably utilizes the collected various service data, researches the abnormity diagnosis and positioning of the whole chain of the system, traces back the data source, the flow direction, the change track and the personnel behavior habit in a cross-system manner, and through the construction of fault characteristic engineering and based on machine learning, performing more abundant characteristic analysis on the fault, simulating variables and reconstructing variables according to actual service and fault characteristics, extracting service logic and data logic, selecting algorithm and parameters, continuously training, adjusting and correcting parameters and algorithm logic to construct a reasonable model, and (3) approaching and restoring the real scene of the occurrence of the metering fault, finding out the cause of the occurrence of the fault, and carrying out remote fault repair or providing a field repair solution.
In step S1, the fault diagnosis involves using various data such as acquisition data, marketing data, scheduling data, asset data, fault data, and environmental data, and by constructing intelligent fault acquisition, designing according to the scene in a targeted manner, and using advanced data acquisition and aggregation technology, real-time streaming data acquisition and non-real-time multi-granularity offline data acquisition are performed.
In step S3, a fault sensing model is built to find out possible faults in real time, and timely diagnosis, early warning and processing are performed to improve fault finding efficiency.
In step S4, by constructing the fault diagnosis engine, the full-chain construction of fault diagnosis is completed, real-time diagnosis and offline diagnosis can be performed, and related fault repair is performed, so that the field fault diagnosis and solution efficiency is greatly improved, and the fault operation and maintenance cost is reduced.
A system of an electric energy meter abnormity diagnosis method comprises a fault diagnosis support center, an acquisition processing module, an operation and maintenance learning module, an abnormity sensing module and an abnormity diagnosis module;
the fault diagnosis support center comprises basic resource management, data acquisition scheduling, a data processing tunnel, a data storage service, an analysis and calculation service, a characteristic project, a machine learning service and a visualization service;
the system comprises an acquisition processing module, a data acquisition processing module and a data processing module, wherein the acquisition processing module comprises distributed real-time acquisition, offline data acquisition and Oracle data quasi-real-time synchronization, and a data acquisition object comprises archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
the operation and maintenance learning module comprises fault knowledge convergence, fault knowledge extraction, fault knowledge reasoning, fault knowledge storage and fault knowledge map visualization;
the abnormity diagnosis module comprises terminal offline fault diagnosis, offline fault frequent analysis, scheduling plan power failure analysis, parameter error abnormity analysis, main station server abnormity analysis, terminal debugging state analysis and terminal hardware fault diagnosis.
The invention is provided with a fault diagnosis support center, an acquisition processing module, an operation and maintenance learning module, an abnormality sensing module and an abnormality diagnosis module; through the establishment of the fault perception model, possible faults are found in real time, and timely diagnosis, early warning and processing are carried out, so that the fault finding efficiency is improved; through the construction of the fault diagnosis engine, the full-chain construction of fault diagnosis is completed, real-time diagnosis and offline diagnosis can be performed, relevant fault repair is performed, the field fault diagnosis and solution efficiency is greatly improved, and the fault operation and maintenance cost is reduced.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (5)
1. An electric energy meter abnormality diagnosis method is characterized by comprising the following steps:
s1: the method comprises the following steps that object data collection comprises distributed real-time collection, offline data collection and Oracle data quasi-real-time synchronization, and collection contents comprise archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
s2: sending the acquired data to an abnormality sensing module and an abnormality diagnosis module;
s3: the abnormal sensing module processes the acquired data, images the fault through combing peripheral services related to fault diagnosis, builds a fault label system, provides label rule maintenance, can define rule algorithms and data logics according to actual scenes, continuously performs iterative updating and perfecting on a fault label library, performs label visual management, performs feature extraction on fault data in real time by combining the label system based on a high-concurrency and distributed big data real-time processing technology, performs similarity measurement and group combination by combining a clustering algorithm, establishes a fault real-time sensing model, and outputs a fault pre-judging result;
s4: the abnormity diagnosis module processes the collected data, the characteristic classification after the fault original characteristic extraction and the data cleaning is the basis of the fault diagnosis engine construction, reasonably utilizes the collected various service data, researches the abnormity diagnosis and positioning of the whole chain of the system, traces back the data source, the flow direction, the change track and the personnel behavior habit in a cross-system manner, and through the construction of fault characteristic engineering and based on machine learning, performing more abundant characteristic analysis on the fault, simulating variables and reconstructing variables according to actual service and fault characteristics, extracting service logic and data logic, selecting algorithm and parameters, continuously training, adjusting and correcting parameters and algorithm logic to construct a reasonable model, and (3) approaching and restoring the real scene of the occurrence of the metering fault, finding out the cause of the occurrence of the fault, and carrying out remote fault repair or providing a field repair solution.
2. The method for diagnosing the abnormality of the electric energy meter according to claim 1, characterized in that: in step S1, the fault diagnosis involves using various data such as acquisition data, marketing data, scheduling data, asset data, fault data, and environmental data, and by constructing intelligent fault acquisition, designing according to the scene in a targeted manner, and using advanced data acquisition and aggregation technology, real-time streaming data acquisition and non-real-time multi-granularity offline data acquisition are performed.
3. The method for diagnosing the abnormality of the electric energy meter according to claim 1, characterized in that: in step S3, a fault sensing model is built to find out possible faults in real time, and timely diagnosis, early warning and processing are performed to improve fault finding efficiency.
4. The method for diagnosing the abnormality of the electric energy meter according to claim 1, characterized in that: in step S4, by constructing the fault diagnosis engine, the full-chain construction of fault diagnosis is completed, real-time diagnosis and offline diagnosis can be performed, and related fault repair is performed, so that the field fault diagnosis and solution efficiency is greatly improved, and the fault operation and maintenance cost is reduced.
5. A system of an abnormality diagnosis method for an electric energy meter according to claim 1, characterized in that:
the system comprises a fault diagnosis support center, an acquisition processing module, an operation and maintenance learning module, an abnormality sensing module and an abnormality diagnosis module;
the fault diagnosis support center comprises basic resource management, data acquisition scheduling, a data processing tunnel, a data storage service, an analysis and calculation service, a characteristic project, a machine learning service and a visualization service;
the system comprises an acquisition processing module, a data acquisition processing module and a data processing module, wherein the acquisition processing module comprises distributed real-time acquisition, offline data acquisition and Oracle data quasi-real-time synchronization, and a data acquisition object comprises archive data, operation data, event data, working condition data, work order data, scheduling data and environment data;
the operation and maintenance learning module comprises fault knowledge convergence, fault knowledge extraction, fault knowledge reasoning, fault knowledge storage and fault knowledge map visualization;
the abnormity diagnosis module comprises terminal offline fault diagnosis, offline fault frequent analysis, scheduling plan power failure analysis, parameter error abnormity analysis, main station server abnormity analysis, terminal debugging state analysis and terminal hardware fault diagnosis.
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