CN112633492B - Fault handling method, device, information processing device, storage medium and server - Google Patents
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Abstract
本发明公开了一种故障处理方法，包括：接收家电设备发送的至少一个特征声音数据；获取预设的诊断模型，运用所述诊断模型识别所述至少一个特征声音数据，确定所述家电设备发生故障时，确定第一目标故障类型和所述第一目标故障类型对应的解决方案；和/或，获取预设的故障预测模型，运用所述故障预测模型识别所述至少一个特征声音数据，预测所述家电设备存在异常时，预测第二目标故障类型。本发明还公开了一种故障处理装置、信息处理装置、存储介质和服务器。
The present invention discloses a fault handling method, comprising: receiving at least one characteristic sound data sent by a household appliance; obtaining a preset diagnostic model, using the diagnostic model to identify the at least one characteristic sound data, determining a first target fault type and a solution corresponding to the first target fault type when the household appliance fails; and/or obtaining a preset fault prediction model, using the fault prediction model to identify the at least one characteristic sound data, predicting a second target fault type when an abnormality exists in the household appliance. The present invention also discloses a fault handling device, an information processing device, a storage medium and a server.
Description
Technical Field
The present invention relates to artificial intelligence technology, and more particularly, to a fault handling method, apparatus, information processing apparatus, computer readable storage medium, and server.
Background
For home appliances, traditional fault analysis is generally: and performing fault diagnosis and guessing according to fault reports and fault contents presented by a User Interface (UI) of the User terminal. However, the user does not understand the fault diagnosis method, and only complains the problem to the manufacturer when the user encounters a fault in the using process, and the fault description is possibly inaccurate; and the manufacturer cannot view the equipment in time, cannot accurately position the problem, and performs fault removal to influence the user experience.
Disclosure of Invention
In order to solve the existing technical problems, embodiments of the present invention provide a fault processing method, a fault processing device, an information processing device, a computer readable storage medium, and a server.
The technical scheme of the invention is realized as follows:
the embodiment of the invention provides a fault processing method, which comprises the following steps:
Receiving at least one characteristic sound data sent by the household appliance;
acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
In the above solution, after the predicting the second target fault type, the method further includes:
And predicting the probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
In the above scheme, the method further comprises: generating the diagnostic model; the generating a diagnostic model includes:
Acquiring at least one first training sound data set when faults occur and a first fault type first training sound data set corresponding to each first training sound data set; the first fault type represents the reason that fault devices in the household appliance generate faults;
Classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data;
And acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
In the above aspect, the training the neural network according to the first sound data set for at least one first fault type includes:
Determining sound characteristics of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, a short-time average energy, and mel-frequency cepstrum coefficients (MFCCs, mel Frequency Cepstrum Coefficient);
and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
In the above solution, the determining the first target fault type and the solution corresponding to the first target fault type includes:
Acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
In the above scheme, the method further comprises: generating the fault prediction model;
The generating a fault prediction model includes:
Acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance;
Classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data;
And acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
The embodiment of the invention provides a fault processing device, which comprises: a first processing module and a second processing module; wherein,
The first processing module is used for receiving at least one characteristic sound data sent by the household appliance;
The second processing module is used for acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
In the above scheme, the second processing module is further configured to predict a probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
In the above scheme, the device further includes: a first preprocessing module for generating the diagnostic model;
The first preprocessing module is used for acquiring at least one first training sound data set which comprises at least one first training sound data and a first fault type corresponding to each first training sound data when faults occur; the first fault type represents the reason that fault devices in the household appliance generate faults;
Classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data;
And acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
In the above aspect, the first preprocessing module is configured to determine a sound feature of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC; and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
In the above scheme, the second processing module is configured to obtain a corresponding relationship between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
In the above scheme, the device includes: the second preprocessing module is used for generating the fault prediction model;
The second preprocessing module is used for acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance;
Classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data;
And acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
The embodiment of the invention provides an information processing device, which comprises: a processor and a memory for storing a computer program capable of running on the processor;
wherein the processor is configured to execute the steps of any one of the above fault handling methods when running the computer program.
An embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the above-described fault handling methods.
The embodiment of the invention also provides a server, which comprises a processor and a memory storing an executable program run by the processor, wherein the processor executes the steps of any fault processing method when running the executable program.
The fault processing method, the fault processing device, the information processing device, the computer readable storage medium and the server provided by the embodiment of the invention receive at least one characteristic sound data sent by the household appliance; acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal. In the scheme of the embodiment of the invention, the fault type is diagnosed and a specific solution is provided by analyzing the sound emitted by each device in the household appliance during the fault, so that the fault diagnosis efficiency and the problem solving efficiency are improved, and the satisfaction degree of a user on the equipment is improved.
Drawings
Fig. 1 is a schematic flow chart of a fault handling method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a home appliance according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of another fault handling method according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating another fault handling method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a fault handling apparatus according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of another information processing apparatus according to an embodiment of the present invention.
Detailed Description
In various embodiments of the present invention, at least one characteristic sound data transmitted by a home device is received; acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
The present invention will be described in further detail with reference to examples.
Fig. 1 is a schematic flow chart of a fault handling method according to an embodiment of the present invention; the method may be applied to a server that may communicate with various home devices, including: cooking devices, cleaning devices, etc.; as shown in fig. 1, the method includes:
Step 101, at least one characteristic sound data sent by the home appliance is received.
Here, the characteristic sound data includes: sounds made when devices of the home appliance are operated. The at least one characteristic sound data is representative of sound emitted by the at least one appliance during operation of the appliance. The home appliance includes: at least one device to be monitored, which needs fault monitoring, and at least one sound collector is arranged for each device to be monitored; the sound collector may include: a microphone, a sound recorder, a microphone, and the like.
The home appliance further includes: and the central controller is used for sending the sound data acquired by the at least one sound collector to the server. The sound data carries the identity of the household equipment and/or the identity of the monitoring device. The household appliance is provided with a communication module, and can send and receive data with the server through a network.
And after receiving the sound data, the server correspondingly stores the sound data and the identity of the household equipment.
The home appliance includes: cooking apparatus and non-cooking apparatus, the cooking apparatus may include: electric rice cookers, electromagnetic ovens, etc.; the non-cooking apparatus may include: television, refrigerator, sweeping robot, etc.
102, Acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
Specifically, the identifying the at least one characteristic sound data by using the diagnostic model, determining a first target fault type and a solution corresponding to the first target fault type when determining that the home appliance has a fault, includes:
The server identifies the at least one characteristic sound data by using the diagnosis model, determines whether the household electrical appliance has a fault, and determines a first target fault type and a solution corresponding to the first target fault type when determining that the household electrical appliance has the fault; when the household electrical appliance is determined to be not faulty, the household electrical appliance can be directly judged to be normal without any operation, or a preset fault prediction model can be further obtained, the at least one characteristic sound data is identified by using the fault prediction model, and when the household electrical appliance is predicted to be abnormal, a second target fault type is predicted.
Here, the first target fault type characterizes a faulty device currently faulty in the home appliance and a cause of the fault. That is, the first target fault type includes at least one of the following information: the device that failed and the cause of the failure of the device.
Specifically, the method further comprises: the diagnostic model is generated.
Here, the generating the diagnostic model includes:
Acquiring a first training sound data set; here, the first training sound data set may specifically include: at least one first training sound data and a first fault type corresponding to each first training sound data when faults occur; the first fault type represents the reason that fault devices in the household appliance generate faults;
Classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data;
And acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
Before the training of the neural network from the first sound dataset for at least one first fault type, the method may further comprise:
and formatting each first training sound data in the first sound data set to obtain sound data which can be used for training a neural network.
Here, the sound data for training the neural network includes: sound characteristics; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, a short-time average energy, and a mel-frequency cepstral coefficient (MFCC, mel Frequency Cepstrum Coefficient).
The sound features may also include one or any combination of the following: wavelet packet decomposition coefficients, pitch subband energy, amplitude or power, neighborhood band eigenvectors, linear predictive coding cepstral coefficients (LPCC, linear Prediction Cepstrum Coefficient)
Specifically, the training the neural network according to the first sound data set for at least one first fault type comprises:
Determining sound characteristics of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC;
and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
Specifically, the determining the first target fault type and the solution corresponding to the first target fault type includes:
Acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
Here, the obtained first target fault type and the solution corresponding to the first target fault type are used for being sent to the home appliance and/or the mobile terminal associated with the home appliance, and are provided for a user to solve the fault.
Specifically, the solution comprises: a solution that can solve the failure by the software upgrade and a solution that cannot solve the failure by the software upgrade.
In this embodiment, the method further includes: when the solution is determined to be a solution capable of solving the fault through software upgrading, the first target fault type and the corresponding solution are sent to the household electrical appliance and sent to a mobile terminal associated with the household electrical appliance so as to remind a user to upgrade the software of the household electrical appliance to solve the fault. When the solution is determined to be a solution which cannot solve the fault through software upgrading, the first target fault type and the corresponding solution are sent to a mobile terminal associated with the household electrical appliance, so that a user is reminded to send the household electrical appliance to a maintenance point for maintenance.
It should be noted that, the mobile terminal may be installed with an application program, where the application program is developed for a developer and provided for a user to download and install; the application program is connected with the server and displays various services provided by the server to a user. The server is developed and maintained by a developer of the home appliance.
In this embodiment, considering that there may be an abnormality (i.e., a fault is about to occur) after the home appliance is operated for a period of time, a fault prediction model is further provided in this embodiment to predict whether the home appliance is abnormal (i.e., whether a fault may occur, for example, the home appliance may not be faulty at present, but there may be a fault hidden danger, and at this time, some devices of the home appliance may be doped with noise, so that the fault may be predicted according to the sound data). Therefore, in this embodiment, after determining that the home appliance device is running for a period of time, the server may obtain a preset fault prediction model, identify the at least one feature sound data by using the fault prediction model, and predict a second target fault type when the home appliance device is predicted to be abnormal.
In this embodiment, the obtaining a preset failure prediction model, and identifying the at least one feature sound data by using the failure prediction model may also be performed immediately after determining that the home appliance device has not failed. In particular, the method may further comprise: and when the household electrical equipment is determined to be not in fault, further acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, predicting whether the household electrical equipment is abnormal, and predicting a second target fault type of the household electrical equipment when the household electrical equipment is predicted to be abnormal.
Here, the second target fault type is a fault device predicted that the home appliance may be faulty, and a cause of the fault. That is, the second target fault type includes at least one of the following information: a device that may fail, and a cause of the device that may fail.
Specifically, after the predicting the second target fault type, the method may further include:
and predicting the probability of occurrence of the second target fault type according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
Here, predicting the probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model specifically includes:
determining sound data corresponding to the possibly faulty device from the received at least one characteristic sound data according to the second target fault type;
And matching the sound data corresponding to the second target fault type in the fault prediction model with the sound data corresponding to the device which can possibly generate the fault so as to determine the probability of generating the second target fault.
Here, after predicting the second target failure type of the home device, the method may further include:
sending the predicted second target fault type to a mobile terminal associated with the household electrical appliance to inform a user; and/or the number of the groups of groups,
And sending the information of the second target fault type and the mobile terminal associated with the household electrical appliance to a developer of the household electrical appliance, and enabling a maintenance personnel of the developer to contact a user to solve the fault.
The information of the mobile terminal associated with the home appliance can include: the corresponding communication number of the mobile terminal (such as the mobile phone number of the user) and the login account of the application program installed in the mobile terminal. The application program is developed for a developer and provided for a user to download the installed program, and the application program is connected with the server.
It should be noted that, the failure of the home device may be discontinuous, for example, the failure may occur only once in five times, in which case the first target failure type may not be identified using the above-mentioned diagnostic model, so that a failure prediction model is provided for predicting the possible failure of the home device, i.e. determining the second target failure type.
Here, after predicting the second target failure type of the home device, the method may further include: acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined second target fault type, and determining a solution corresponding to the second target fault type.
Correspondingly, when the solution corresponding to the second target fault type is determined to be a solution capable of solving the fault through software upgrading, when the predicted second target fault type is sent to the mobile terminal associated with the household appliance, the solution corresponding to the second fault type can be sent to the household appliance.
Specifically, the method further comprises: and generating the fault prediction model.
Here, the generating the fault prediction model includes:
Acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance;
Classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data;
And acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
Here, a linear logistic regression prediction algorithm or a nonlinear logistic regression prediction method may be specifically applied to train the logistic regression model from the second set of acoustic data for at least one second fault type.
Specifically, the training the logistic regression model from the second set of sound data for at least one second fault type includes:
Determining sound characteristics of each second training sound data in the second sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC;
and training the neural network according to the sound characteristics of each second training sound data in the second sound data set aiming at least one second fault type.
Here, the logistic regression algorithm may employ a linear regression prediction method or a nonlinear regression prediction method.
Before the training the logistic regression model from the second sound dataset for at least one second fault type, the method may further comprise:
And formatting each second training sound data in the second sound data set to obtain second training sound data in a target format.
Correspondingly, training the logistic regression model according to the second training sound data in the target format.
Fig. 2 is a schematic structural diagram of a home appliance according to an embodiment of the present invention; as shown in fig. 2, the home appliance may be a cooking appliance, which may include a central controller, and at least one sound collector.
The sound collector may include: the 1 st to nth microphones (e.g., 1 st, 2 nd, K th, nth microphones shown in fig. 2).
Each sound collector is arranged at the accessory of a device which is easy to generate faults and can generate noise sources so as to collect the sound of the device.
FIG. 3 is a schematic flow chart of another fault handling method according to an embodiment of the present invention; as shown in fig. 3, the method is applied to a server, and the method includes:
step 301, a first training sound data set is acquired.
The first training sound dataset comprising: at least one first training sound data and a first fault type corresponding to each first training sound data when faults occur; the first fault type characterizes the reason why a fault device in the household appliance generates faults.
Step 302, preprocessing the first training sound data set.
Here, the preprocessing may include: classification and formatting processes. Specifically, the preprocessing includes: classifying the at least one set of first training sound data to obtain a first sound data set for at least one first fault type, the first sound data set comprising at least one first training sound data; and formatting each first training sound data to obtain a format which can be used for analyzing the sound data.
Here, classifying the at least one training sound data includes: determining a first fault type corresponding to each training sound data; the first fault type pointer is used for indicating the fault reason of a fault on a certain device; classifying according to the first fault type, and obtaining a first sound data set aiming at least one first fault type.
And 303, performing classification training based on a deep neural network according to the first training sound data set to obtain a diagnosis model.
Specifically, the step 303 includes: and acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
The diagnosis model is used for determining a device with a fault and a reason for the fault of the device according to the sound data, so that the fault type is obtained.
The operation of step 303 may specifically refer to the generation of the diagnostic model described in the method shown in fig. 1, which is not described herein.
And 304, obtaining sound data sent by the household electrical appliance to be identified, and performing fault identification by using the diagnosis model to obtain a diagnosis result.
Specifically, the step 304 includes: receiving sound data sent by the household electrical appliance; the sound data may carry an Identification (ID) of its corresponding device;
the diagnostic model identifies the sound data to obtain a diagnostic result; the diagnostic result includes: the system comprises a first target fault type and a solution corresponding to the first target fault type.
The diagnostic result obtained may be sent to the home device and/or a mobile terminal associated with the home device for informing the user of the specific fault type and solution.
By adopting the method, the server receives a fault diagnosis request, wherein the fault diagnosis request comprises the sound data sent by the device during fault, and fault diagnosis is carried out according to the sound data; the server can locate the fault type through a diagnosis model under the condition that the fault occurs, and specifically comprises the following steps: and when the failure cause is determined to be the software problem, the problem is solved by a software upgrading method.
FIG. 4 is a flowchart illustrating another fault handling method according to an embodiment of the present invention; as shown in fig. 4, the method includes:
Step 401, acquiring a second training sound data set.
Here, the second training sound data set includes: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type characterizes a cause of a fault device in the predictive home device.
Step 402, preprocessing the second training sound data set.
Here, the step 402 includes: classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data; and formatting the second training sound data to obtain a format which can be used for analyzing the sound data.
Here, the preprocessing the at least one second training sound data includes: and determining a second fault type corresponding to each second training sound data, classifying the at least one second training sound data according to the second fault type, and obtaining a second sound data set aiming at the at least one second fault type, wherein the second sound data set comprises at least one second training sound data.
Step 403, training a logistic regression model according to the second data set for at least one second fault type to obtain a fault prediction model.
Specifically, the step 403 includes: and acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type by using a logistic regression algorithm, and acquiring the trained logistic regression model as the fault prediction model.
The fault prediction model is used for predicting a device which is likely to be in fault and the reason why the device is likely to be in fault according to the sound data. The logistic regression algorithm may employ a linear regression prediction method or a nonlinear regression prediction method.
The operation of step 403 may specifically refer to generating the fault prediction model described in the method shown in fig. 1, which is not described herein.
And 404, obtaining sound data sent by the home appliance to be identified, and carrying out fault identification by using the fault prediction model to obtain a prediction result.
Specifically, the step 404 includes: receiving sound data sent by the household appliance, wherein the sound data can carry the ID of a device corresponding to the sound data;
The fault prediction model identifies the sound data and obtains a prediction result; the prediction result includes at least one of: the system comprises a second target fault type, a probability of occurrence of the second target fault and a solution corresponding to the second fault type.
The prediction may be sent to the home device and/or a mobile terminal associated with the home device to inform the user of the specific fault type and solution.
In this embodiment, the method continuously receives, at a server, sound data reported by a home appliance, and after the sound data is collected to a certain amount, the sound data in a preset time period can be obtained, and the sound data is preprocessed and then input into a fault prediction model; predicting whether the household appliance is in normal operation or the state of the appliance will occur based on the sound data through a machine learning logistic regression algorithm, prompting a user or a server and even a developer to maintain the appliance if the appliance is predicted to have faults, positioning the type of the faults through a model, particularly to a certain device and reason, simultaneously determining whether the reason of the faults is a software problem or a hardware problem, and solving the problem through a software upgrading method when the reason of the faults is determined to be the software problem.
The embodiment of the invention also provides a method which combines the methods shown in the figures 3 and 4, namely after receiving the sound data sent by the household appliance; acquiring a preset diagnosis model, identifying the at least one characteristic sound data according to the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal. Here, the identifying of the at least one characteristic sound data using the failure prediction model may occur immediately upon determining that the home device has not failed; or the fault prediction is performed autonomously after the home appliance is operated for a period of time, that is, after the home appliance is determined to be operated for a period of time, a preset fault prediction model is obtained, the at least one characteristic sound data is recognized according to the fault prediction model, and a second target fault type of the home appliance is predicted; the description is not intended to be limiting. The specific steps and the fault handling method embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments shown in fig. 1, fig. 3, and/or fig. 4, which are not repeated here.
Fig. 5 is a schematic structural diagram of a fault handling apparatus according to an embodiment of the present invention; as shown in fig. 5, the apparatus includes: the first processing module 501 and the second processing module 502. Wherein,
The first processing module 501 is configured to receive at least one characteristic sound data sent by a home appliance;
The second processing module 502 is configured to obtain a preset diagnostic model, identify the at least one feature sound data by using the diagnostic model, and determine a first target fault type and a solution corresponding to the first target fault type when determining that the home appliance device fails; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
Specifically, the second processing module 502 is further configured to predict a probability of occurrence of a second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
Specifically, the device further comprises: and the first preprocessing module is used for generating the diagnosis model.
The first preprocessing module is used for acquiring at least one first training sound data set which comprises at least one first training sound data and a first fault type corresponding to each first training sound data when faults occur; the first fault type represents the reason that fault devices in the household appliance generate faults;
Classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data;
And acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
The first preprocessing module is specifically configured to determine a sound feature of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC; and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
Specifically, the second processing module 502 is configured to obtain a preset correspondence between a fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
Specifically, the device comprises: and the second preprocessing module is used for generating the fault prediction model.
The second preprocessing module is used for acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance;
Classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data;
And acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
It should be noted that: in the fault processing device provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the processing allocation may be performed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules, so as to complete all or part of the processing described above. In addition, the fault processing device and the fault processing method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the fault processing device and the fault processing method are detailed in the method embodiments, which are not repeated herein.
In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention provides an information processing apparatus, as shown in fig. 6, the apparatus 60 includes: a processor 601 and a memory 602 for storing a computer program capable of running on the processor; wherein the processor 601 is configured to execute, when the computer program is executed, the steps of: receiving at least one characteristic sound data sent by the household appliance; acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
In an embodiment, the processor 601 is configured to execute, when executing the computer program: and predicting the probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
In an embodiment, the processor 601 is configured to execute, when executing the computer program: acquiring at least one first training sound data set when faults occur and a first fault type first training sound data set corresponding to each first training sound data set; the first fault type represents the reason that fault devices in the household appliance generate faults; classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data; and acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
In an embodiment, the processor 601 is configured to execute, when executing the computer program: determining sound characteristics of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC; and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
In an embodiment, the processor 601 is configured to execute, when executing the computer program: acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
In an embodiment, the processor 601 is configured to execute, when executing the computer program: acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance; classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data; and acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
It should be noted that: the information processing apparatus and the fault processing method embodiment provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the information processing apparatus and the fault processing method embodiment are detailed in the method embodiment and are not repeated herein.
Of course, in practical application, as shown in fig. 6, the apparatus 60 may further include: at least one network interface 603. The various components in the information processing apparatus 60 are coupled together by a bus system 604. It is understood that the bus system 604 is used to enable connected communications between these components. The bus system 604 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration, the various buses are labeled as bus system 604 in fig. 6. The number of the processors 601 may be at least one. The network interface 603 is used for wired or wireless communication between the information processing apparatus 60 and other devices. The memory 602 in embodiments of the present invention is used to store various types of data to support the operation of the device 60.
The method disclosed in the above embodiment of the present invention may be applied to the processor 601 or implemented by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 601 or instructions in the form of software. The Processor 601 may be a general purpose Processor, a digital signal Processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 601 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium in the memory 602 and the processor 601 reads information in the memory 602 and in combination with its hardware performs the steps of the method as described above.
In an exemplary embodiment, the information processing apparatus 60 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex Programmable logic devices (CPLDs, complex Programmable Logic Device), field-Programmable gate arrays (FPGAs), general purpose processors, controllers, micro-controllers (MCUs, micro Controller Unit), microprocessors (micro processors), or other electronic components for performing the aforementioned methods.
In particular, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs: receiving at least one characteristic sound data sent by the household appliance; acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
In one embodiment, the computer program, when executed by a processor, performs: and predicting the probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
In one embodiment, the computer program, when executed by a processor, performs: acquiring at least one first training sound data set when faults occur and a first fault type first training sound data set corresponding to each first training sound data set; the first fault type represents the reason that fault devices in the household appliance generate faults; classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data; and acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
In one embodiment, the computer program, when executed by a processor, performs: determining sound characteristics of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC; and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
In one embodiment, the computer program, when executed by a processor, performs: acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
In one embodiment, the computer program, when executed by a processor, performs: acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance; classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data; and acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
The embodiment of the invention also provides a server, which comprises a processor and a memory, wherein the memory stores executable programs run by the processor; when the processor runs the executable program, the processor executes: receiving at least one characteristic sound data sent by the household appliance; acquiring a preset diagnosis model, identifying the at least one characteristic sound data by using the diagnosis model, and determining a first target fault type and a solution corresponding to the first target fault type when the household appliance is determined to be faulty; and/or acquiring a preset fault prediction model, identifying the at least one characteristic sound data by using the fault prediction model, and predicting a second target fault type when the household appliance is predicted to be abnormal.
The processor is further configured to execute, when the computer program is executed: and predicting the probability of occurrence of the second target fault according to the sound data corresponding to the second target fault type and the at least one characteristic sound data in the fault prediction model.
The processor is further configured to execute, when the computer program is executed: acquiring at least one first training sound data set when faults occur and a first fault type first training sound data set corresponding to each first training sound data set; the first fault type represents the reason that fault devices in the household appliance generate faults; classifying the at least one first training sound data to obtain a first sound data set for at least one first fault type; the first sound data set comprises at least one first training sound data; and acquiring a preset neural network, training the neural network according to the first sound data set aiming at least one first fault type, and acquiring the trained neural network as the diagnosis model.
The processor is further configured to execute, when the computer program is executed: determining sound characteristics of each first training sound data in the first sound data set; the sound features include at least one of: a spectrogram, a short-time amplitude zero-crossing rate, short-time average energy and MFCC; and training the neural network according to the sound characteristics of each first training sound data in the first sound data set aiming at least one first fault type.
The processor is further configured to execute, when the computer program is executed: acquiring a corresponding relation between a preset fault type and a solution; inquiring the corresponding relation according to the determined first target fault type, and determining a solution corresponding to the first target fault type.
The processor is further configured to execute, when the computer program is executed: acquiring a second training sound data set; the second training sound dataset comprising: at least one second training sound data in a preset time period before the occurrence of the fault and a second fault type corresponding to each second training sound data; the second fault type represents the reason for predicting the fault of a fault device in the household appliance; classifying the at least one second training sound data to obtain a second sound data set for at least one second fault type, the second sound data set comprising at least one second training sound data; and acquiring a preset logistic regression model, training the logistic regression model according to the second sound data set aiming at least one second fault type, and acquiring the trained logistic regression model as the fault prediction model.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described as separate units may or may not be physically separate, and units 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The above description is not intended to limit the scope of the invention, but is intended to cover any modifications, equivalents, and improvements within the spirit and principles of the invention.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102388574A (en) * | 2009-04-10 | 2012-03-21 | Lg电子株式会社 | System and method for diagnosing home appliance |
| KR20130017352A (en) * | 2011-08-10 | 2013-02-20 | 엘지전자 주식회사 | Diagnostic system and method for home appliance |
| KR20180018170A (en) * | 2016-08-12 | 2018-02-21 | 주식회사 케이티 | Malfunction sound abstraction device and malfunction prediction device |
| CN108597057A (en) * | 2018-04-28 | 2018-09-28 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane failure predication diagnostic system and method based on noise deep learning |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090010430A (en) * | 2007-07-23 | 2009-01-30 | 주식회사 싸이언 | Fault diagnosis device of the mechanism |
| CN102778358A (en) * | 2012-06-04 | 2012-11-14 | 上海东锐风电技术有限公司 | Failure prediction model establishing method and system as well as fan monitoring pre-warning system and method |
| CN104506886B (en) * | 2014-12-15 | 2018-06-01 | 四川长虹电器股份有限公司 | A kind of fault handling method and cloud server |
| US20170178311A1 (en) * | 2015-12-20 | 2017-06-22 | Prophecy Sensors, Llc | Machine fault detection based on a combination of sound capture and on spot feedback |
| WO2018169302A1 (en) * | 2017-03-16 | 2018-09-20 | 피플리안주식회사 | Wheelchair malfunction diagnosis and notification system using smart phone |
| KR101892516B1 (en) * | 2017-03-29 | 2018-10-04 | 주식회사 한국정보시스템 | Method, apparatus and program for failure prediction of heterogeneous network security equipment |
| CN107909116A (en) * | 2017-12-07 | 2018-04-13 | 无锡小天鹅股份有限公司 | Washing machine fault recognition method and device |
| CN109974845A (en) * | 2019-02-28 | 2019-07-05 | 合肥美的电冰箱有限公司 | Fault warning method, computer readable storage medium and refrigeration equipment |
| CN110082135A (en) * | 2019-03-14 | 2019-08-02 | 中科恒运股份有限公司 | Equipment fault recognition methods, device and terminal device |
-
2019
- 2019-10-08 CN CN201910951474.7A patent/CN112633492B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102388574A (en) * | 2009-04-10 | 2012-03-21 | Lg电子株式会社 | System and method for diagnosing home appliance |
| KR20130017352A (en) * | 2011-08-10 | 2013-02-20 | 엘지전자 주식회사 | Diagnostic system and method for home appliance |
| KR20180018170A (en) * | 2016-08-12 | 2018-02-21 | 주식회사 케이티 | Malfunction sound abstraction device and malfunction prediction device |
| CN108597057A (en) * | 2018-04-28 | 2018-09-28 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane failure predication diagnostic system and method based on noise deep learning |
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|---|---|
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