CN109800895A - A method of based on augmented reality in the early warning of metering automation pipeline stall and maintenance - Google Patents
A method of based on augmented reality in the early warning of metering automation pipeline stall and maintenance Download PDFInfo
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Abstract
The present invention relates to pipeline stall detection fields, and disclose it is a kind of based on augmented reality the early warning of metering automation pipeline stall and maintenance method, it is firstly introduced into big data machine learning, the technology of deep learning, again based on holographic monitoring data, holographic monitoring data, that is, measurement verification center single-phase electric energy meter, three-phase electric energy meter, the data of the various sensing equipments of low-voltage current mutual inductor and acquisition terminal automatic Verification assembly line acquisition, holographic monitoring data is saved to data warehouse, and these data are built into the fault signature engineering of metering automatic verification system.The present invention is the method based on augmented reality in the early warning of metering automation pipeline stall and maintenance, it is analyzed again by using the data of big data, to carry out early warning to the failure on entire assembly line, augmented reality can be passed through simultaneously, the malfunction of entire assembly line will be come by threedimensional model displaying, in order to subsequent maintenance work.
Description
Technical field
The present invention relates to pipeline stall detection field, specially a kind of augmented reality that is based on is in metering automation stream
The method of waterline fault pre-alarming and maintenance.
Background technique
These intelligence calibrating links of country various regions calibrating assembly line pass through the modes such as various sensor technologies, message at present
Collection system operation data, system carry out failure by the mode that hardware device reports an error by the data of the single hardware device of collection
Alarm produce mass data since assembly line more tends to be complicated, hardware pipeline manufacturer focuses mainly on hardware device,
The data of the history of assembly line are not saved, so that the analysis mining work of big data can not be carried out.
In the prior art, due to not saving historical data, cannot use big data mode to automatic assembly line into
The important feature for influencing the key point of assembly line and having an impact is cannot analyse in the analysis of row big data, while automatic in metering
Change in assembly line, each equipment is reported an error by single hardware, and there is no the influences mutual from entire assembly line and assembly line
It goes to consider and analyze, holistic diagnosis effect is bad.
Summary of the invention
It is automatic in metering based on augmented reality that it is an object of the invention to overcome the deficiencies of the prior art and provide one kind
Change the method for pipeline stall early warning and maintenance.
In order to solve the above technical problems, the technical solution adopted by the present invention the following steps are included:
S1: being firstly introduced into the technology of big data machine learning, deep learning, using SparkMlib, TensorFlow machine learning
With the frame of deep learning；
S2: holographic monitoring data is saved to data warehouse, and these data are built into the event of metering automatic verification system
Hinder Feature Engineering, input variable of the fault signature engineering as algorithm model；
S3: by using artificial intelligence deep learning technology, automatically extracting metering fault off-note, constructs dynamic metering fortune
Row fault signature class library；
S4: by using the strategy of intensified learning, selecting suitable fault diagnosis model, continuous automatic excellent according to fault condition
Change and iteration finds out the characteristic value for most influencing assembly line；
S5: by the technology of augmented reality, the malfunction of entire assembly line is come by threedimensional model displaying, in order to rear
Continuous maintenance work.
Preferably, in the step S2, the holography monitoring data is measurement verification center single-phase electric energy meter, three-phase electricity
The data of the various sensor devices acquisition of energy table, low-voltage current mutual inductor and acquisition terminal automatic Verification assembly line.
Preferably, the data are mainly derived from the daily record data that smart machine, the data of sensor acquisition, system are run
With external industrial big data.
Preferably, the algorithm includes decision tree method, SDG model, stacks autocoder, convolutional neural networks, recurrence
Neural network, depth confidence network, confrontation network.
Preferably, the holographic monitoring data saved in the data warehouse is standardized, after standardization
Holographic monitoring data carry out data analysis.
Preferably, the data warehouse is constituted using hive framework, and the hive framework is the data bins based on Hadoop
The data file of structuring is mapped as a database table, and provides simple sql query function by library tool, by sql sentence
MapReduce task is converted to be run.
Preferably, data attribute is converted to the process of data characteristics by the fault signature engineering, attribute representative data
All dimensions.
Preferably, the method for the fault signature engineering includes timestamp processing, decomposes category attribute, branch mailbox/subregion, friendship
Pitch feature, feature selecting, feature scaling, feature extraction.
Compared with prior art, the beneficial effects of the present invention are:
The present invention is to pass through what is be equipped in the method for the early warning of metering automation pipeline stall and maintenance based on augmented reality
Machine learning, deep learning various algorithm models, then by by the number of the various sensing equipments of automatic Verification assembly line acquisition
According to, and save to data warehouse, and these data are built into the fault signature engineering of metering automatic verification system, failure is special
Input variable of the engineering as algorithm model is levied, then by using artificial intelligence deep learning technology, automatically extracts metering fault
Off-note finds out the characteristic value for most influencing assembly line according to the continuous Automatic Optimal of fault condition and iteration, then passes through enhancing
The technology of reality is come the malfunction of entire assembly line, by threedimensional model displaying in order to subsequent maintenance work.
Embodiment
A method of based on augmented reality in the early warning of metering automation pipeline stall and maintenance, it is firstly introduced into big
Data machine learning, deep learning technology, using SparkMlib, the frame of TensorFlow machine learning and deep learning,
Again based on holographic monitoring data, holographic monitoring data, that is, measurement verification center single-phase electric energy meter, three-phase electric energy meter, low-tension current are mutual
The data of the various sensing equipments of sensor and acquisition terminal automatic Verification assembly line acquisition, holographic monitoring data is saved to data
In warehouse, and these holographic monitoring datas are built into the fault signature engineering of metering automatic verification system, fault signature engineering
As the input variable of algorithm model, then by using artificial intelligence deep learning technology, it is extremely special to automatically extract metering fault
Sign constructs dynamic metering operation troubles tagsort library, then by using the strategy of intensified learning, suitable failure is selected to examine
Disconnected model finds out the characteristic value for most influencing assembly line according to the continuous Automatic Optimal of fault condition and iteration, then existing by enhancing
Real technology is come the malfunction of entire assembly line, by threedimensional model displaying in order to subsequent maintenance work.
Wherein, data are mainly derived from smart machine, the data of sensor acquisition, the daily record data of system operation and outside
Industrial big data data.
In addition, algorithm includes decision tree method, SDG model, stacks autocoder, convolutional neural networks, recurrent neural net
Network, depth confidence network, confrontation network etc..
Wherein, the data saved in data warehouse are standardized, carry out data using the data after standardization
Analysis, the quality of data determines the accuracy rate of system ultimate analysis, in the various intelligence of automatic verification system Zhong You different vendor
Energy equipment, there are how many differences for generated data, in order to guarantee the reliability of result, need to carry out original index data
Standardization.On the other hand, in the excessive data of range, data bi-directional scaling is allowed to fall into one small specific
Section.It prevents in deep learning, the explosion of model gradient occurs, so before data analysis, it would be desirable to first by data mark
Standardization carries out data analysis using the data after standardization.
In addition, data warehouse is constituted using hive framework, hive framework is the Tool for Data Warehouse based on Hadoop, can be with
The data file of structuring is mapped as a database table, and simple sql query function is provided, sql sentence can be turned
It is changed to MapReduce task to be run, simple MapReduce statistics can be fast implemented by class SQL statement, it is not necessary to open
Application of sending out MapReduce special, is very suitable for as data warehouse, for statistical analysis to data.
Wherein, Feature Engineering is the process that data attribute is converted to data characteristics, attribute representative all dimensions of data
Degree, in data modeling, if all properties to initial data learn, can not find well data it is potential become
Gesture, and if being pre-processed by data of the Feature Engineering to you, algorithm model can reduce the interference by noise, in this way
Trend can preferably be found out.In fact, good feature, which even can help you to realize, reaches good effect using simple model
Fruit, the method for Feature Engineering include timestamp processing, decompose category attribute, branch mailbox/subregion, cross feature, feature selecting, feature
Scaling, feature extraction etc..
In conclusion should lead to based on augmented reality in the method for the early warning of metering automation pipeline stall and maintenance
Cross the various algorithm models of machine learning, deep learning, then by by the various sensing equipments acquisition of automatic Verification assembly line
Data, and save to data warehouse, and these data are built into the fault signature engineering of metering automatic verification system, failure
Input variable of the Feature Engineering as algorithm model, then by using artificial intelligence deep learning technology, automatically extract metering event
Hinder off-note, the characteristic value for most influencing assembly line is found out according to the continuous Automatic Optimal of fault condition and iteration, then pass through increasing
The technology of strong reality is come the malfunction of entire assembly line, by threedimensional model displaying in order to subsequent maintenance work.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (8)
1. it is a kind of based on augmented reality the early warning of metering automation pipeline stall and maintenance method, which is characterized in that
The following steps are included:
S1: being firstly introduced into the technology of big data machine learning, deep learning, using SparkMlib, TensorFlow machine learning
With the frame of deep learning；
S2: holographic monitoring data is saved to data warehouse, and these data are built into the event of metering automatic verification system
Hinder Feature Engineering, input variable of the fault signature engineering as algorithm model；
S3: by using artificial intelligence deep learning technology, automatically extracting metering fault off-note, constructs dynamic metering fortune
Row fault signature class library；
S4: by using the strategy of intensified learning, selecting suitable fault diagnosis model, continuous automatic excellent according to fault condition
Change and iteration finds out the characteristic value for most influencing assembly line；
S5: by the technology of augmented reality, the malfunction of entire assembly line is come by threedimensional model displaying, in order to rear
Continuous maintenance work.
2. a kind of augmented reality that is based on according to claim 1 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: in the step S2, it is described holography monitoring data be measurement verification center single-phase electric energy meter, three
The data of the various sensor devices acquisition of phase electric energy meter, low-voltage current mutual inductor and acquisition terminal automatic Verification assembly line.
3. a kind of augmented reality that is based on according to claim 2 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: the data be mainly derived from smart machine, sensor acquisition data, system operation log
Data and external industrial big data.
4. a kind of augmented reality that is based on according to claim 1 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: the algorithm include decision tree method, SDG model, stack autocoder, convolutional neural networks, pass
Return neural network, depth confidence network, confrontation network.
5. a kind of augmented reality that is based on according to claim 1 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: the holographic monitoring data saved in the data warehouse is standardized, standardization is utilized
Holographic monitoring data afterwards carries out data analysis.
6. a kind of augmented reality that is based on according to claim 1 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: the data warehouse using hive framework constitute, the hive framework is the data based on Hadoop
The data file of structuring is mapped as a database table, and provides simple sql query function by warehouse tool, by sql language
Sentence is converted to MapReduce task and is run.
7. a kind of augmented reality that is based on according to claim 1 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: data attribute is converted to the process of data characteristics by the fault signature engineering, attribute representative number
According to all dimensions.
8. a kind of augmented reality that is based on according to claim 7 is in the early warning of metering automation pipeline stall and maintenance
Method, it is characterised in that: the method for the fault signature engineering include timestamp processing, decompose category attribute, branch mailbox/point
Area, cross feature, feature selecting, feature scaling, feature extraction.
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| CN111240279A (en) * | 2019-12-26 | 2020-06-05 | 浙江大学 | Confrontation enhancement fault classification method for industrial unbalanced data |
| CN113110385A (en) * | 2021-04-16 | 2021-07-13 | 广东电网有限责任公司计量中心 | Decision tree-based start-stop early warning method and device for metering automatic verification system |
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| CN117233540A (en) * | 2023-11-15 | 2023-12-15 | 广东电网有限责任公司江门供电局 | Metering pipeline fault detection method and system based on deep learning |
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| CN113110385A (en) * | 2021-04-16 | 2021-07-13 | 广东电网有限责任公司计量中心 | Decision tree-based start-stop early warning method and device for metering automatic verification system |
| CN113156529A (en) * | 2021-05-07 | 2021-07-23 | 广东电网有限责任公司计量中心 | Start-stop control method, system, terminal and storage medium of metrological verification assembly line |
| CN115327469A (en) * | 2022-08-09 | 2022-11-11 | 国网冀北电力有限公司计量中心 | Monitoring method and system for electric energy meter verification data |
| CN117233540A (en) * | 2023-11-15 | 2023-12-15 | 广东电网有限责任公司江门供电局 | Metering pipeline fault detection method and system based on deep learning |
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