CN111476400A - Circuit fault prediction method, device, equipment and computer readable medium - Google Patents
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Abstract
The application relates to a circuit fault prediction method, a device, equipment and a computer readable medium. The method comprises the following steps: acquiring monitoring data of the household appliance, wherein the monitoring data comprises circuit parameters and circuit state parameters in a distribution line; inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault, wherein the circuit fault prediction model is used for predicting the circuit fault according to the monitoring data; and processing the circuit fault represented by the prediction data. The application provides a method for realizing circuit fault prediction, which can effectively improve the fault processing efficiency, can also realize accurate prediction of circuit faults of a household line, takes preventive measures in time in advance, avoids safety accidents, minimizes the risk of household appliances, and ensures the power utilization safety of users.
Description
Technical Field
The present application relates to the field of smart home technologies, and in particular, to a method, an apparatus, a device, and a computer readable medium for predicting a circuit fault.
Background
With the rapid development of the deep learning technology, the application field of the deep learning technology is more and more, and the technical field of circuit fault prediction is gradually expanded. In daily life today, electric energy is the most basic and one of the most important energy sources. Every individual trade all is closely continuous to the demand of electric energy and with everyone's relation, consequently, and power consumption safety is crucial, when distribution lines breaks down, will bring very big inconvenience and trouble to everyone, still can cause certain economic loss to the family simultaneously. Therefore, when the distribution line is detected and maintained in the weekdays, the working target needs to be fast, accurate, reliable and stable, the possibility of faults is reduced, the stability of power utilization is ensured, and the basic benefits of users are guaranteed.
If the distribution line fails, the reliability and safety of power supply are seriously affected, so the safety problem of the distribution line must be considered. The fire of the electric appliance is mainly caused by the aging of power lines and the intervention of malignant loads, and most of the lines are arranged in tiles, so that the maintenance is inconvenient.
At present, in the related art, early warning is performed on outdoor circuit faults or prediction is performed on the occurrence date of the faults, so that the fault processing efficiency is low.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application provides a circuit fault prediction method, a device, equipment and a computer readable medium, which are used for solving the technical problem of low fault processing efficiency.
In a first aspect, the present application provides a circuit fault prediction method, including: acquiring monitoring data of the household appliance, wherein the monitoring data comprises circuit parameters and circuit state parameters in a distribution line; inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault, wherein the circuit fault prediction model is used for predicting the circuit fault according to the monitoring data; and processing the circuit fault represented by the prediction data.
Optionally, before inputting the monitoring data into the pre-constructed circuit fault prediction model, constructing the circuit fault prediction model as follows: acquiring a data set for constructing a circuit fault prediction model, wherein the data set comprises a training set and a verification set; training the training set by using a deep learning mode to obtain a circuit fault prediction model; and verifying the output data of the circuit fault prediction model by using a verification set, wherein the circuit fault prediction model is used for circuit fault prediction when the verification is passed.
Optionally, training the training set in a deep learning manner, and obtaining the circuit fault prediction model includes: preprocessing a training set; establishing a long-short term memory artificial neural network, and determining the number of hidden layers of the long-short term memory artificial neural network as N and the learning rate as N; and taking the preprocessed training set as input data of the long-short term memory artificial neural network, and taking a circuit fault actual result corresponding to the training set as predicted output data of the long-short term memory artificial neural network for training.
Optionally, preprocessing the training set comprises: and performing dimensionality reduction on the training set through principal component analysis to remove high-dimensionality redundant data.
Optionally, the establishing the long-short term memory artificial neural network further comprises: and determining the number of nodes in each hidden layer of the long-term and short-term memory artificial neural network by utilizing a particle swarm optimization scheme.
Optionally, the establishing the long-short term memory artificial neural network further comprises: and carrying out self-adaptive adjustment on the learning rate by using an Adam optimization scheme.
Optionally, the training with the preprocessed training set as input data of the long-short term memory artificial neural network and the actual circuit fault result corresponding to the training set as predicted output data of the long-short term memory artificial neural network further includes: and adjusting the circuit fault prediction model by using an error back propagation scheme so as to improve the prediction accuracy of the circuit fault prediction model.
In a second aspect, the present application provides a circuit failure prediction apparatus, comprising: the monitoring data acquisition module is used for acquiring monitoring data of the household appliance, wherein the monitoring data comprises circuit parameters and circuit state parameters in the distribution line; the circuit fault prediction module is used for inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault; and the fault processing module is used for processing the circuit fault represented by the prediction data.
In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor implements the steps of any one of the above methods when executing the computer program.
In a fourth aspect, the present application also provides a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform any of the methods of the first aspect.
Compared with the related art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the technical scheme provided by the embodiment of the application, monitoring data of the household appliance are obtained, wherein the monitoring data comprise circuit parameters and circuit state parameters in a distribution line; inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault, wherein the circuit fault prediction model is used for predicting the circuit fault according to the monitoring data; the circuit fault prediction method for processing the circuit fault represented by the prediction data can effectively improve the fault processing efficiency, can accurately predict the circuit fault of the household line, takes preventive measures in advance in time, avoids safety accidents, minimizes the risk of household appliances, and ensures the electricity utilization safety of users.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the technical solutions in the embodiments or related technologies of the present application, the drawings needed to be used in the description of the embodiments or related technologies will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without any creative effort.
FIG. 1 is a schematic diagram of an alternative hardware environment for a circuit failure prediction method according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative circuit fault prediction method provided in accordance with an embodiment of the present application;
fig. 3 is a block diagram of an alternative circuit failure prediction apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
According to an aspect of embodiments of the present application, there is provided an embodiment of a circuit failure prediction method.
Alternatively, in the embodiment of the present application, the circuit failure prediction method described above may be applied to a hardware environment formed by the terminal 101 and the server 103 as shown in fig. 1. As shown in fig. 1, a server 103 is connected to a terminal 101 through a network, which may be used to provide services for the terminal or a client installed on the terminal, and a database 105 may be provided on the server or separately from the server, and is used to provide data storage services for the server 103, and the network includes but is not limited to: wide area network, metropolitan area network, or local area network, and the terminal 101 includes but is not limited to a PC, a cell phone, a tablet computer, and the like.
A circuit failure prediction method in the embodiment of the present application may be executed by the server 103, as shown in fig. 2, and the method may include the following steps:
step S202, acquiring monitoring data of the household appliance;
step S204, inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault;
step S206, the circuit fault represented by the prediction data is processed.
In the embodiment of the present application, the monitoring data may include circuit parameters and circuit state parameters in the distribution line, such as line current, load data of the household appliance, power of the appliance, line aging degree, line heating degree, weather and environmental conditions, and the like. And the pre-constructed circuit fault prediction model is used for predicting the circuit fault according to the monitoring data.
In the embodiment of the application, the circuit fault prediction model is constructed based on a deep learning technology. With the rapid development of the deep learning technology, the deep learning technology can also be applied to the research of electricity utilization safety. When the distribution lines breaks down, great inconvenience and trouble are brought to everyone, and certain economic loss can be caused to families. When the distribution line is detected and maintained based on the deep learning technology, the circuit fault can be rapidly, accurately, reliably and stably predicted, the possibility of fault occurrence is reduced, the stability of power utilization is ensured, and the basic benefit of a user is guaranteed. The fire of the electric appliance is mainly caused by the aging of power lines and the intervention of malignant loads, and most of the lines are arranged in tiles, so that the maintenance is inconvenient. According to the circuit fault prediction method and device, the circuit fault is comprehensively researched and predicted through data such as the collected circuit current, load data of household appliances, appliance power, the circuit aging degree, the circuit heating degree and the weather environment condition, and the user and the staff are timely fed back to process according to the predicted fault.
Optionally, before inputting the monitoring data into the pre-constructed circuit fault prediction model, constructing the circuit fault prediction model as follows:
step 1, acquiring a data set for constructing a circuit fault prediction model;
step 2, training the training set by using a deep learning mode to obtain a circuit fault prediction model;
and 3, verifying the output data of the circuit fault prediction model by using the verification set, wherein the circuit fault prediction model is used for circuit fault prediction under the condition that the verification is passed.
In the embodiment of the application, before the circuit fault prediction model is used for conducting circuit fault prediction on the distribution line of the household appliance, the circuit fault prediction model needs to be built, wherein a data set used for building the circuit fault prediction model can comprise a training set and a verification set, the training set is used for training the circuit fault prediction model when the circuit fault prediction model is built, and the verification set is used for conducting verification of a circuit fault prediction result on the built circuit fault prediction model so as to improve the accuracy of the circuit fault prediction model.
In the embodiment of the application, the data set may include, but is not limited to, a line current, load data of a household appliance, an appliance power, a line aging degree, a line heating degree, a weather environment condition, and the like, the data source may be a data set shared by all colleges and universities, or may be a data set actually measured in an intelligent home, and the acquired data set is randomly divided into the training set and the verification set in a certain proportion.
In the embodiment of the application, the circuit fault prediction model is constructed based on a deep learning mode, and a long-short term memory artificial neural network with the advantage of processing sequence data can be specifically adopted.
Optionally, training the training set in a deep learning manner, and obtaining the circuit fault prediction model includes:
step 1, preprocessing a training set;
step 2, establishing a long-short term memory artificial neural network, and determining the number of hidden layers of the long-short term memory artificial neural network as N and the learning rate as N;
and 3, taking the preprocessed training set as input data of the long-short term memory artificial neural network, and taking a circuit fault actual result corresponding to the training set as predicted output data of the long-short term memory artificial neural network for training.
In the embodiment of the application, the number N of hidden layers of the long-short term memory artificial neural network can be constrained to be a positive integer, which can be generally set to 10, the learning rate can be constrained to be a positive number, the initial value of the learning rate is usually not suitable to be set too large, and the initial value can be set to be 0.01. In practical application, the learning rate is selected to be too small, and the convergence rate is too slow; if the learning rate is selected to be too large, overshoot may be corrected, resulting in oscillation and even divergence. It is also difficult to determine an optimal learning rate that is suitable from start to finish.
Optionally, preprocessing the training set comprises: and performing dimensionality reduction on the training set through principal component analysis to remove high-dimensionality redundant data.
In the embodiment of the application, because the factors related to the circuit fault prediction of the distribution line are complex, if the line current, the electric appliance power, the line aging degree, the line heating degree, the weather environment condition and the like need to be considered, a large amount of observation is often needed to be carried out on a plurality of variables reflecting the circuit fault when the circuit fault is researched and predicted, and a large amount of data is collected so as to analyze and find the rule. Multivariate large samples provide abundant information for research and application, but also increase the workload of data acquisition to some extent, and more importantly, in most cases, there may be correlation between many variables, thereby increasing the complexity of problem analysis and bringing inconvenience to analysis. However, if each index is analyzed separately, the analysis is often isolated rather than integrated. Therefore, the blind reduction of the index will lose much information and easily lead to erroneous conclusions. In the embodiment of the application, the method for analyzing the principal components is adopted, so that the loss of information contained in the original index is reduced as much as possible while the index needing to be analyzed is reduced, and the purpose of comprehensively analyzing the collected data is achieved. Principal component analysis maps high-dimensional features to low-dimensional features, which are not simply redundant features removed from the high-dimensional features, but are entirely new orthogonal features, called principal components. By the principal component analysis method, redundant data such as noise in a data set can be effectively removed, important characteristics are reserved, and the data processing speed is improved. The principal component analysis method has been well-established in the related art, and is not described herein again.
Optionally, the establishing the long-short term memory artificial neural network further comprises: and determining the number of nodes in each hidden layer of the long-term and short-term memory artificial neural network by utilizing a particle swarm optimization scheme.
In the embodiment of the application, a particle swarm optimization algorithm can be adopted for setting the number of nodes in the hidden layer of the long-term and short-term memory artificial neural network. In the related art, for the setting of the number of nodes in the hidden layer in the artificial neural network, a large amount of experiment accumulated experience is needed, and the setting of the number of nodes is completed step by step. The particle swarm optimization algorithm simulates the birds in a bird swarm by designing a particle without mass, wherein the particle has only two properties: the speed represents the moving speed, the position represents the moving direction, each particle independently searches for an optimal solution in a search space and records the optimal solution as a current individual extremum, the individual extremum is shared with other particles in the whole particle swarm, the optimal individual extremum is found out and serves as the current global optimal solution of the whole particle swarm, and therefore the optimal scheme for setting the number of nodes in the hidden layer of the long-term and short-term memory artificial neural network is achieved.
Optionally, the establishing the long-short term memory artificial neural network further comprises: and carrying out self-adaptive adjustment on the learning rate by using an Adam optimization scheme.
In the embodiment of the application, for the learning rate of the long-short term memory artificial neural network, Adam optimization algorithm can be adopted to perform adaptive adjustment, the learning rate (L earning rate) is used as an important hyper-parameter in supervised learning and deep learning and determines whether an objective function can converge to a local minimum value and when the objective function converges to the minimum value, the appropriate learning rate can enable the objective function to converge to the local minimum value in an appropriate time, when the deep learning technology is applied, the learning rate is often required to be adjusted, so that the objective function can be more adaptive to a current training model, in the related technology, the learning rate is adjusted by adopting manual adjustment or random gradient descent, the Adam optimization algorithm and the traditional random gradient descent are different, the random gradient descent keeps a single learning rate to update all weights, the learning rate is not changed in the training process, and the Adam optimization algorithm designs an independent adaptive learning rate for different parameters by calculating a first moment estimation and a second moment estimation of the gradient.
Optionally, the training with the preprocessed training set as input data of the long-short term memory artificial neural network and the actual circuit fault result corresponding to the training set as predicted output data of the long-short term memory artificial neural network further includes: and adjusting the circuit fault prediction model by using an error back propagation scheme so as to improve the prediction accuracy of the circuit fault prediction model.
In the embodiment of the application, an error back propagation algorithm is adopted to adjust the circuit fault prediction model, namely, a learning process is composed of two processes of signal forward propagation and error back propagation, during forward propagation, an input sample is transmitted from an input layer, and is transmitted to an output layer after being processed layer by hidden layers. If the actual output of the output layer does not match the expected output, the error is propagated back to the error propagation stage. The error back propagation is to transmit the output error back to the input layer by layer through the hidden layer in a certain form, and distribute the error to all units of each layer, so as to obtain the error signal of each layer, and the error signal is used as the basis for correcting the weight of the unit. The weight value adjustment process of each layer of signal forward propagation and error backward propagation is carried out repeatedly, the process of continuously adjusting the weight value, namely the process of network learning training, is carried out until the error of network output is reduced to an acceptable degree or is carried out to preset learning times.
According to the technical scheme provided by the embodiment of the application, monitoring data of the household appliance are obtained, wherein the monitoring data comprise circuit parameters and circuit state parameters in a distribution line; inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault, wherein the circuit fault prediction model is used for predicting the circuit fault according to the monitoring data; the circuit fault prediction method for processing the circuit fault represented by the prediction data can effectively improve the fault processing efficiency, can accurately predict the circuit fault of the household line, takes preventive measures in advance in time, avoids safety accidents, minimizes the risk of household appliances, and ensures the electricity utilization safety of users.
In the embodiment of the application, the prediction result of the circuit fault prediction of the distribution line can include but is not limited to short circuit, open circuit, overload, electric leakage and the like, and when the prediction result is short circuit and open circuit, the positions of the electric wire which are easy to break, such as loose and soft wires, the wire ends falling positions and the positions which easily cause poor contact can be warned in advance, and a user and an overhaul worker are informed to replace the line in time. When the prediction result is overload, the running electric appliances can be counted, the total power of the electric appliances is calculated, and a user is informed to timely turn off the non-essential electric appliances.
According to still another aspect of the embodiments of the present application, as shown in fig. 3, there is provided a circuit failure prediction apparatus including: the monitoring data acquisition module 301 is configured to acquire monitoring data of the household appliance, where the monitoring data includes circuit parameters and circuit state parameters in a distribution line; the circuit fault prediction module 303 is configured to input the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault; and a fault handling module 305 for handling the circuit fault represented by the prediction data.
It should be noted that the monitoring data obtaining module 301 in this embodiment may be configured to execute step S202 in this embodiment, the circuit failure predicting module 303 in this embodiment may be configured to execute step S204 in this embodiment, and the failure processing module 305 in this embodiment may be configured to execute step S206 in this embodiment.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may operate in a hardware environment as shown in fig. 1, and may be implemented by software or hardware.
Optionally, the circuit failure prediction apparatus may further include: the circuit fault prediction system comprises a data set acquisition module, a circuit fault prediction module and a circuit fault prediction module, wherein the data set acquisition module is used for acquiring a data set used for constructing a circuit fault prediction model, and comprises a training set and a verification set; the model training module is used for training the training set in a deep learning mode to obtain a circuit fault prediction model; and the verification module is used for verifying the output data of the circuit fault prediction model by using the verification set, wherein the circuit fault prediction model is used for circuit fault prediction under the condition that the verification is passed.
Optionally, the circuit failure prediction apparatus may further include: the preprocessing module is used for preprocessing the training set; the neural network establishing module is used for establishing the long-short term memory artificial neural network, and determining that the number of hidden layers of the long-short term memory artificial neural network is N and the learning rate is N; and the training module is used for taking the preprocessed training set as input data of the long-short term memory artificial neural network and taking the actual circuit fault result corresponding to the training set as predicted output data of the long-short term memory artificial neural network for training.
Optionally, the circuit failure prediction apparatus may further include: and the data dimension reduction module is used for reducing the dimension of the training set through principal component analysis so as to remove high-dimensional redundant data.
Optionally, the circuit failure prediction apparatus may further include: and the hidden node determining module is used for determining the number of nodes in each hidden layer of the long-term and short-term memory artificial neural network by utilizing a particle swarm optimization scheme.
Optionally, the circuit failure prediction apparatus may further include: and the learning rate adjusting module is used for adaptively adjusting the learning rate by utilizing an Adam optimization scheme.
Optionally, the circuit failure prediction apparatus may further include: and the accuracy adjusting module is used for adjusting the circuit fault prediction model by utilizing an error back propagation scheme so as to improve the prediction accuracy of the circuit fault prediction model.
There is also provided, in accordance with yet another aspect of the embodiments of the present application, a computer device, including a memory and a processor, the memory having stored therein a computer program executable on the processor, the processor implementing the steps when executing the computer program.
The memory and the processor in the computer device communicate with each other through a communication bus and a communication interface. The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
There is also provided, in accordance with yet another aspect of an embodiment of the present application, a computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform any of the methods described above.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable logic devices (P L D), Field-Programmable Gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. 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 application 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including several 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 methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk. It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of predicting a circuit fault, comprising:
acquiring monitoring data of the household appliance, wherein the monitoring data comprises circuit parameters and circuit state parameters in a distribution line;
inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of circuit faults, wherein the circuit fault prediction model is used for predicting the circuit faults according to the monitoring data;
processing the circuit fault characterized by the prediction data.
2. The method of claim 1, wherein prior to inputting the monitoring data into a pre-constructed circuit fault prediction model, the method further comprises constructing the circuit fault prediction model as follows:
acquiring a data set for constructing the circuit fault prediction model, wherein the data set comprises a training set and a verification set;
training the training set by utilizing a deep learning mode to obtain the circuit fault prediction model;
and verifying the output data of the circuit fault prediction model by using the verification set, wherein the circuit fault prediction model is used for circuit fault prediction when the verification is passed.
3. The method of claim 2, wherein training the training set using deep learning to obtain the circuit fault prediction model comprises:
preprocessing the training set;
establishing a long-short term memory artificial neural network, and determining the number of hidden layers of the long-short term memory artificial neural network as N and the learning rate as N;
and taking the preprocessed training set as input data of the long-short term memory artificial neural network, and taking a circuit fault actual result corresponding to the training set as predicted output data of the long-short term memory artificial neural network for training.
4. The method of claim 3, wherein preprocessing the training set comprises:
and performing dimensionality reduction on the training set through principal component analysis to remove high-dimensionality redundant data.
5. The method of claim 3, wherein establishing a long-short term memory artificial neural network further comprises:
and determining the number of nodes in each hidden layer of the long-term and short-term memory artificial neural network by utilizing a particle swarm optimization scheme.
6. The method of claim 3, wherein establishing a long-short term memory artificial neural network further comprises:
and carrying out self-adaptive adjustment on the learning rate by using an Adam optimization scheme.
7. The method of claim 3, wherein training the preprocessed training set as input data of the long-short term memory artificial neural network and actual circuit fault results corresponding to the training set as predicted output data of the long-short term memory artificial neural network further comprises:
and adjusting the circuit fault prediction model by utilizing an error back propagation scheme so as to improve the prediction accuracy of the circuit fault prediction model.
8. A circuit failure prediction apparatus, comprising:
the monitoring data acquisition module is used for acquiring monitoring data of the household appliance, wherein the monitoring data comprises circuit parameters and circuit state parameters in a distribution line;
the circuit fault prediction module is used for inputting the monitoring data into a pre-constructed circuit fault prediction model to obtain prediction data of the circuit fault;
and the fault processing module is used for processing the circuit fault represented by the prediction data.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1 to 7.
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