CN113904915A - Intelligent power communication fault analysis method and system based on Internet of things - Google Patents
Intelligent power communication fault analysis method and system based on Internet of things Download PDFInfo
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- H—ELECTRICITY
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- 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
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The invention provides an intelligent power communication fault analysis method based on the Internet of things, which comprises the following steps: for a certain type of equipment, acquiring multiple groups of fault data and corresponding equipment parameter values in a historical fault period; the fault data includes a fault type; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type; carrying out numerical processing on the fault data and the equipment parameter values; constructing and training a bidirectional cyclic neural network, and performing learning training on the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement; inputting real-time equipment parameters of the equipment, outputting a prediction result of whether the fault is generated or not by the bidirectional cyclic neural network, outputting the predicted fault type, and outputting an associated fault analysis result based on the output fault type.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to an intelligent fault analysis method and system for electric power communication based on the Internet of things.
Background
With the continuous development of power technology, the automation and intelligence of related power equipment become higher and higher, and by adding corresponding network access nodes, heterogeneous integration of equipment with different transmission protocols on cloud and upper platforms is technically difficult to realize, namely the internet of things in the industry. However, in the face of access of massive devices, the number of times of communication failures is increasing, and therefore how to implement intelligent failure analysis of device communication failures is a technical problem that needs to be solved in the industry.
Disclosure of Invention
The invention aims to provide an intelligent power communication fault analysis method based on the Internet of things.
In order to realize the above purpose of the invention, the technical scheme provided by the invention is as follows:
an intelligent power communication fault analysis method based on the Internet of things comprises the following steps:
for a certain type of equipment, acquiring multiple groups of fault data and corresponding equipment parameter values in a historical fault period; the fault data includes a fault type; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
carrying out numerical processing on the fault data and the equipment parameter values;
constructing and training a bidirectional cyclic neural network, and performing learning training on the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
inputting real-time equipment parameters of the equipment, outputting a prediction result of whether the fault is generated or not by the bidirectional cyclic neural network, outputting the predicted fault type, and outputting an associated fault analysis result based on the output fault type.
Preferably, the specific process of training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
Preferably, after the prediction accuracy of the bidirectional cyclic neural network is checked to not meet the preset accuracy requirement, before back propagation, the method further comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
Preferably, the bidirectional recurrent neural network comprises an input layer, an implicit layer and an output layer, wherein the implicit layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
Meanwhile, the invention also provides an intelligent power communication fault analysis system based on the Internet of things, which applies the method and has the following specific scheme:
the system comprises a correlation module, a numerical processing module, a training module and a fault analysis module;
the correlation module is used for acquiring a plurality of groups of fault data and corresponding equipment parameter values in the historical fault period of the equipment; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
the numerical processing module is used for carrying out numerical processing on the fault data and the equipment parameter values;
the training module is used for constructing and training a bidirectional cyclic neural network, and learning and training the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
the fault analysis module is used for inputting real-time equipment parameters of the equipment, outputting a prediction result of whether a fault is generated or not by the bidirectional cyclic neural network, outputting a predicted fault type and outputting a related fault analysis result based on the output fault type.
Preferably, the specific process of training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
Preferably, after the prediction accuracy of the bidirectional cyclic neural network is checked to not meet the preset accuracy requirement, before back propagation, the method further comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
Preferably, the bidirectional recurrent neural network comprises an input layer, an implicit layer and an output layer, wherein the implicit layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
According to the technical scheme, the invention has the following advantages:
the method provided by the invention can be used for predicting and analyzing the next faults of the equipment by training the bidirectional cyclic neural network through historical data, has higher prediction precision and can provide reference significance for fault analysis and recovery of the communication network.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flow diagram of an intelligent power communication fault analysis method based on the internet of things.
Fig. 2 is a schematic flow chart of training a bidirectional recurrent neural network.
Fig. 3 is a schematic structural diagram of an intelligent power communication fault analysis system based on the internet of things.
Detailed Description
Example one
As shown in fig. 1, an intelligent fault analysis method for power communication based on the internet of things includes the following steps:
for a certain type of equipment, acquiring multiple groups of fault data and corresponding equipment parameter values in a historical fault period; the fault data includes a fault type; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
carrying out numerical processing on the fault data and the equipment parameter values;
constructing and training a bidirectional cyclic neural network, and performing learning training on the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
inputting real-time equipment parameters of the equipment, outputting a prediction result of whether the fault is generated or not by the bidirectional cyclic neural network, outputting the predicted fault type, and outputting an associated fault analysis result based on the output fault type.
In a specific implementation process, as shown in fig. 2, the specific process of training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
In a specific implementation process, after the prediction accuracy of the bidirectional cyclic neural network is detected to not meet the preset accuracy requirement, before back propagation, the method further comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
In a specific implementation process, the bidirectional recurrent neural network comprises an input layer, an implicit layer and an output layer, wherein the implicit layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
Example two
As shown in fig. 3, the embodiment provides an intelligent power communication fault analysis system based on the internet of things, which applies the above method, and the specific scheme is as follows:
the system comprises a correlation module, a numerical processing module, a training module and a fault analysis module;
the correlation module is used for acquiring a plurality of groups of fault data and corresponding equipment parameter values in the historical fault period of the equipment; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
the numerical processing module is used for carrying out numerical processing on the fault data and the equipment parameter values;
the training module is used for constructing and training a bidirectional cyclic neural network, and learning and training the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
the fault analysis module is used for inputting real-time equipment parameters of the equipment, outputting a prediction result of whether a fault is generated or not by the bidirectional cyclic neural network, outputting a predicted fault type and outputting a related fault analysis result based on the output fault type.
In a specific implementation process, the specific process of training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
In a specific implementation process, after the prediction accuracy of the bidirectional cyclic neural network is detected to not meet the preset accuracy requirement, before back propagation, the method further comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
In a specific implementation process, the bidirectional recurrent neural network comprises an input layer, an implicit layer and an output layer, wherein the implicit layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below 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.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An intelligent power communication fault analysis method based on the Internet of things is characterized by comprising the following steps: the method comprises the following steps:
for a certain type of equipment, acquiring multiple groups of fault data and corresponding equipment parameter values in a historical fault period; the fault data includes a fault type; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
carrying out numerical processing on the fault data and the equipment parameter values;
constructing and training a bidirectional cyclic neural network, and performing learning training on the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
inputting real-time equipment parameters of the equipment, outputting a prediction result of whether the fault is generated or not by the bidirectional cyclic neural network, outputting the predicted fault type, and outputting an associated fault analysis result based on the output fault type.
2. The intelligent power communication fault analysis method based on the Internet of things of claim 1, wherein: the specific process for training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
3. The intelligent power communication fault analysis method based on the Internet of things of claim 2, wherein: when the prediction precision of the bidirectional cyclic neural network is not detected to meet the preset precision requirement and before back propagation, the method also comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
4. The intelligent power communication fault analysis method based on the Internet of things of claim 2, wherein: the bidirectional recurrent neural network comprises an input layer, a hidden layer and an output layer, wherein the hidden layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
5. The utility model provides an electric power communication intelligence fault analysis system based on thing networking which characterized in that: the system comprises a correlation module, a numerical processing module, a training module and a fault analysis module;
the correlation module is used for acquiring a plurality of groups of fault data and corresponding equipment parameter values in the historical fault period of the equipment; acquiring a fault analysis result under the fault type, and associating the analysis result with the fault type;
the numerical processing module is used for carrying out numerical processing on the fault data and the equipment parameter values;
the training module is used for constructing and training a bidirectional cyclic neural network, and learning and training the bidirectional cyclic neural network by taking fault data and equipment parameter values in the historical fault period as training sample data; until the bidirectional cyclic neural network meets the prediction precision requirement;
the fault analysis module is used for inputting real-time equipment parameters of the equipment, outputting a prediction result of whether a fault is generated or not by the bidirectional cyclic neural network, outputting a predicted fault type and outputting a related fault analysis result based on the output fault type.
6. The intelligent power communication fault analysis system based on the Internet of things of claim 5, wherein:
the specific process for training the bidirectional recurrent neural network is as follows:
reading training sample data and carrying out forward propagation;
checking whether the prediction precision of the bidirectional cyclic neural network meets the preset precision requirement or not;
if not, performing backward propagation, and then returning to the step of performing forward propagation;
if so, ending the process of learning and training.
7. The intelligent power communication fault analysis system based on the Internet of things of claim 6, wherein: when the prediction precision of the bidirectional cyclic neural network is not detected to meet the preset precision requirement and before back propagation, the method also comprises the step of adjusting the learning rate:
when the gradient directions of two adjacent iterations are the same, the learning rate eta is adjusted to
Wherein eta iszMaking the value before this adjustment for the learning rate η;
when the gradient directions of two adjacent iterations are opposite, the learning rate eta is adjusted to
8. The intelligent power communication fault analysis system based on the Internet of things of claim 6, wherein: the bidirectional recurrent neural network comprises an input layer, a hidden layer and an output layer, wherein the hidden layer takes a logarithm-S type function as a transfer function, and the output layer takes a hard limit function as a transfer function.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114697203A (en) * | 2022-03-31 | 2022-07-01 | 浙江省通信产业服务有限公司 | Network fault pre-judging method and device, electronic equipment and storage medium |
| CN115174355A (en) * | 2022-07-26 | 2022-10-11 | 杭州东方通信软件技术有限公司 | Generation method of fault root cause positioning model, and fault root cause positioning method and device |
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2021
- 2021-08-17 CN CN202110943214.2A patent/CN113904915A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114697203A (en) * | 2022-03-31 | 2022-07-01 | 浙江省通信产业服务有限公司 | Network fault pre-judging method and device, electronic equipment and storage medium |
| CN114697203B (en) * | 2022-03-31 | 2023-07-25 | 浙江省通信产业服务有限公司 | Network fault pre-judging method and device, electronic equipment and storage medium |
| CN115174355A (en) * | 2022-07-26 | 2022-10-11 | 杭州东方通信软件技术有限公司 | Generation method of fault root cause positioning model, and fault root cause positioning method and device |
| CN115174355B (en) * | 2022-07-26 | 2024-01-19 | 杭州东方通信软件技术有限公司 | Method for generating fault root positioning model, fault root positioning method and device |
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