CN111800535A - Terminal running state evaluation method and device, storage medium and electronic equipment - Google Patents
Terminal running state evaluation method and device, storage medium and electronic equipment Download PDFInfo
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- CN111800535A CN111800535A CN201910282028.1A CN201910282028A CN111800535A CN 111800535 A CN111800535 A CN 111800535A CN 201910282028 A CN201910282028 A CN 201910282028A CN 111800535 A CN111800535 A CN 111800535A
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Abstract
The embodiment of the application discloses a method and a device for evaluating the running state of a terminal, a storage medium and electronic equipment, wherein the method and the device determine the configuration vector information of target equipment with corresponding dimensionality by acquiring equipment configuration information; acquiring application operation information to determine scene characteristic information containing time sequence information; combining the target equipment configuration vector information and the scene characteristic information to generate use trend information corresponding to the equipment configuration information; and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
Description
Technical Field
The present application relates to the field of electronic devices, and in particular, to a method and an apparatus for evaluating a terminal operating state, a storage medium, and an electronic device.
Background
With the continuous development of electronic technology, electronic devices such as mobile phones have increasingly powerful functions, and various applications can be installed and used in the mobile phones to meet the requirements of users and bring more convenience to the life and work of the users.
At present, after a mobile phone is used for a period of time, hardware loss to a certain extent, such as battery loss, etc., is brought, so how to evaluate the service life of the mobile phone hardware and predict a fault has an important role.
Disclosure of Invention
The embodiment of the application provides a method and a device for evaluating a terminal running state, a storage medium and an electronic device, which can improve the evaluation efficiency of the terminal running state.
In a first aspect, an embodiment of the present application provides a method for evaluating an operating state of a terminal, including:
acquiring equipment configuration information of electronic equipment, and determining target equipment configuration vector information of corresponding dimensions according to the equipment configuration information;
acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information;
combining the target device configuration vector information and the scene characteristic information to generate use trend information corresponding to the device configuration information;
and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment.
In a second aspect, an embodiment of the present application provides an apparatus for evaluating an operating state of a terminal, including:
the first acquisition unit is used for acquiring the equipment configuration information of the electronic equipment and determining the target equipment configuration vector information of corresponding dimensionality according to the equipment configuration information;
the second acquisition unit is used for acquiring application operation information of the electronic equipment and determining scene characteristic information containing time sequence information according to the application operation information;
the combination unit is used for combining the target equipment configuration vector information and the scene characteristic information to generate the use trend information corresponding to the equipment configuration information;
and the determining unit is used for analyzing by combining the use trend information and the equipment configuration state information and determining the corresponding use prediction information of the electronic equipment.
In some embodiments, the first acquisition unit comprises:
the vectorization subunit is used for acquiring the device configuration information of the electronic device, carrying out vectorization processing on the device configuration information and generating corresponding device configuration vector information;
and the compression subunit is used for modeling and compressing the equipment configuration vector information to obtain target equipment configuration vector information with corresponding dimensionality.
In some embodiments, the second acquisition unit comprises:
the acquisition subunit is used for acquiring application use information and corresponding time sequence information of the electronic equipment;
a determining subunit, configured to determine the application usage information and the corresponding timing information as application operation information;
the processing subunit is used for inputting the application operation information into the neural network model so as to output corresponding scene information containing time sequence information;
and the extraction subunit is used for determining the high-dimensional characteristic information in the neural network model as corresponding scene characteristic information containing time sequence information.
In some embodiments, the binding unit comprises:
the combining subunit is configured to combine the target device configuration vector information and the scene feature information according to the sequence of the timing information, and generate a trend tensor;
and the determining subunit is used for performing graphical processing on the trend tensor to generate a corresponding energy trend graph, and determining the energy trend graph as the use trend information.
In some embodiments, the binding subunit is specifically for:
combining the target equipment configuration vector information and the scene characteristic information to generate a corresponding target tensor;
and sequentially acquiring corresponding target tensors according to the sequence of the time sequence information, and merging to generate a trend tensor.
In a third aspect, a storage medium is provided in this application, where a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the method for evaluating an operating state of a terminal according to any embodiment of this application.
In a fourth aspect, the electronic device provided in this embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute the method for evaluating the operating state of the terminal according to any embodiment of the present application by calling the computer program.
According to the method and the device, the device configuration information of the electronic device is collected, and the target device configuration vector information of corresponding dimensionality is determined according to the device configuration information; acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information; combining the target equipment configuration vector information and the scene characteristic information to generate use trend information corresponding to the equipment configuration information; and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
Drawings
The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic application scenario diagram of a method for evaluating an operating state of a terminal according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a method for evaluating an operating state of a terminal according to an embodiment of the present application.
Fig. 3 is another schematic flow chart of the method for evaluating the operating state of the terminal according to the embodiment of the present application.
Fig. 4 is a schematic block diagram of an apparatus for evaluating an operating state of a terminal according to an embodiment of the present application.
Fig. 5 is another schematic block diagram of an apparatus for evaluating an operating state of a terminal according to an embodiment of the present disclosure.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
The term "module" as used herein may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as objects implemented on the computing system. The apparatus and method described herein are preferably implemented in software, but may also be implemented in hardware, and are within the scope of the present application.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario of the method for evaluating the operating state of the terminal according to the embodiment of the present application. The method for evaluating the running state of the terminal is applied to electronic equipment. A panoramic perception framework is arranged in the electronic equipment. The panoramic perception framework is the integration of hardware and software used for realizing the evaluation method of the terminal running state in the electronic equipment.
The panoramic perception architecture comprises an information perception layer, a data processing layer, a feature extraction layer, a scene modeling layer and an intelligent service layer.
The information perception layer is used for acquiring information of the electronic equipment or information in an external environment. The information-perceiving layer may include a plurality of sensors. For example, the information sensing layer includes a plurality of sensors such as a distance sensor, a magnetic field sensor, a light sensor, an acceleration sensor, a fingerprint sensor, a hall sensor, a position sensor, a gyroscope, an inertial sensor, an attitude sensor, a barometer, and a heart rate sensor.
Among other things, a distance sensor may be used to detect a distance between the electronic device and an external object. The magnetic field sensor may be used to detect magnetic field information of the environment in which the electronic device is located. The light sensor can be used for detecting light information of the environment where the electronic equipment is located. The acceleration sensor may be used to detect acceleration data of the electronic device. The fingerprint sensor may be used to collect fingerprint information of a user. The Hall sensor is a magnetic field sensor manufactured according to the Hall effect, and can be used for realizing automatic control of electronic equipment. The location sensor may be used to detect the geographic location where the electronic device is currently located. Gyroscopes may be used to detect angular velocity of an electronic device in various directions. Inertial sensors may be used to detect motion data of an electronic device. The gesture sensor may be used to sense gesture information of the electronic device. A barometer may be used to detect the barometric pressure of the environment in which the electronic device is located. The heart rate sensor may be used to detect heart rate information of the user.
And the data processing layer is used for processing the data acquired by the information perception layer. For example, the data processing layer may perform data cleaning, data integration, data transformation, data reduction, and the like on the data acquired by the information sensing layer.
The data cleaning refers to cleaning a large amount of data acquired by the information sensing layer to remove invalid data and repeated data. The data integration refers to integrating a plurality of single-dimensional data acquired by the information perception layer into a higher or more abstract dimension so as to comprehensively process the data of the plurality of single dimensions. The data transformation refers to performing data type conversion or format conversion on the data acquired by the information sensing layer so that the transformed data can meet the processing requirement. The data reduction means that the data volume is reduced to the maximum extent on the premise of keeping the original appearance of the data as much as possible.
The characteristic extraction layer is used for extracting characteristics of the data processed by the data processing layer so as to extract the characteristics included in the data. The extracted features may reflect the state of the electronic device itself or the state of the user or the environmental state of the environment in which the electronic device is located, etc.
The feature extraction layer may extract features or process the extracted features by a method such as a filtering method, a packing method, or an integration method.
The filtering method is to filter the extracted features to remove redundant feature data. Packaging methods are used to screen the extracted features. The integration method is to integrate a plurality of feature extraction methods together to construct a more efficient and more accurate feature extraction method for extracting features.
The scene modeling layer is used for building a model according to the features extracted by the feature extraction layer, and the obtained model can be used for representing the state of the electronic equipment, the state of a user, the environment state and the like. For example, the scenario modeling layer may construct a key value model, a pattern identification model, a graph model, an entity relation model, an object-oriented model, and the like according to the features extracted by the feature extraction layer.
The intelligent service layer is used for providing intelligent services for the user according to the model constructed by the scene modeling layer. For example, the intelligent service layer can provide basic application services for users, perform system intelligent optimization for electronic equipment, and provide personalized intelligent services for users.
In addition, a plurality of algorithms can be included in the panoramic perception architecture, each algorithm can be used for analyzing and processing data, and the plurality of algorithms can form an algorithm library. For example, the algorithm library may include algorithms such as a markov algorithm, a hidden dirichlet distribution algorithm, a bayesian classification algorithm, a support vector machine, a K-means clustering algorithm, a K-nearest neighbor algorithm, a conditional random field, a residual error network, a long-short term memory network, a convolutional neural network, and a cyclic neural network.
In some embodiments, the information sensing layer acquires device configuration information of the electronic device, the data processing layer determines target device configuration vector information of corresponding dimensions according to the device configuration information, the information sensing layer acquires application operation information of the electronic device, the data processing layer determines scene feature information including timing sequence information according to the application operation information, the data processing layer combines the target device configuration vector information and the scene feature information to generate usage trend information corresponding to the device configuration information, comprehensive analysis is performed by combining the usage trend information and the device configuration state information, usage prediction information corresponding to the electronic device can be determined, evaluation of the terminal operation state is simple and rapid, and accuracy of the evaluation is high.
An execution main body of the method for evaluating the terminal running state may be the apparatus for evaluating the terminal running state provided in the embodiment of the present application, or an electronic device integrated with the apparatus for evaluating the terminal running state, where the apparatus for evaluating the terminal running state may be implemented in a hardware or software manner. The electronic device may be a smart phone, a tablet computer, a Personal Digital Assistant (PDA), or the like.
The following is a detailed description of the analysis.
An embodiment of the present application provides a method for evaluating a terminal operating state, as shown in fig. 2, fig. 2 is a schematic flowchart of the method for evaluating a terminal operating state provided in the embodiment of the present application, and the method for evaluating a terminal operating state may include the following steps:
in step S101, device configuration information of the electronic device is collected, and target device configuration vector information of a corresponding dimension is determined according to the device configuration information.
The device configuration information may be hardware configuration information of the electronic device, and the hardware configuration information is basic information of the electronic device, and may include a name of the electronic device, a model of the electronic device, a system version, a processor, an operating memory, a body memory, a baseband version, a kernel version, a SIM card state, a battery-related state, IP address information, MAC address information, bluetooth information, and the like. After the device configuration information of the electronic device is collected, vectorization processing may be performed on the collected device configuration information, and dimension compression is performed to generate target device configuration vector information with a corresponding dimension, for example, a dimension of 10 or 20.
In some embodiments, the step of acquiring device configuration information of the electronic device and determining target device configuration vector information of a corresponding dimension according to the device configuration information may include:
(1) acquiring equipment configuration information of electronic equipment, and vectorizing the equipment configuration information to generate corresponding equipment configuration vector information;
(2) modeling and compressing the equipment configuration vector information to obtain target equipment configuration vector information with corresponding dimensionality.
The vectorization processing method may be a one hot encoding method, which is also called unique hot encoding or one-bit effective encoding. The method is to use an N-bit status register to encode N states, each state having its own independent register bit and only one of which is active at any one time. After the device configuration information of the electronic device is collected, vectorization processing is performed on the device configuration information by using a one hot encoding method, and corresponding device configuration vector information is generated, wherein the dimension of the device configuration vector information is equal to that of the device configuration information.
Furthermore, in order to facilitate later-stage data processing and data normalization, the dimensions of the device configuration vector information need to be unified, the modeling compression method may be a Word Embedding compression method, and the device configuration vector information is modeled and compressed by the Word Embedding compression method to be compressed into target configuration vector information with unified dimensions.
In step S102, application operation information of the electronic device is collected, and scene feature information including timing sequence information is determined according to the application operation information.
The application operation information can be user operation historical information and user behavior habit information, scene information of a user can be depicted according to the user behavior habit information, the scene information of the user in a certain time period can be further depicted by combining the user operation historical information, and then scene feature information with time sequence information, which is strongly related to the scene information in the certain time period, can be extracted.
In some embodiments, the step of acquiring application operation information of the electronic device and determining scene characteristic information including timing sequence information according to the application operation information may include:
(1) acquiring application use information and corresponding time sequence information of the electronic equipment;
(2) determining the application use information and the corresponding timing information as application operation information;
(3) inputting the application operation information into a neural network model to output corresponding scene information containing time sequence information;
(4) and extracting high-dimensional feature information in the neural network model, and determining the high-dimensional feature information as corresponding scene feature information containing time sequence information.
The Neural Network model may be a Recurrent Neural Network (RNN) model, and the Recurrent Neural Network is an artificial Neural Network in which nodes are directionally connected to form a ring. The internal state of such a network may exhibit dynamic timing behavior. Unlike feed-forward neural networks, the RNN can use its internal memory to process an input sequence at arbitrary timing. The application use information represents user behavior habit information which is related information when the user uses the application, and the time sequence information represents user operation history information which is corresponding time when the user uses the application. Therefore, a large amount of application use information and corresponding time sequence information can be collected in advance, the recurrent neural network model is trained and learned through the application use information and the corresponding time sequence information, the recurrent neural network model can output corresponding scene information with the time sequence information according to the input application use information and the corresponding time sequence information after being learned, therefore, after the training is completed, the application use information and the corresponding time sequence information of the electronic equipment can be collected in real time, the application use information and the corresponding time sequence information are determined as application operation information, and the application operation information is input into the recurrent neural network so as to output corresponding scene characteristic information containing the time sequence information.
Further, the high-dimensional feature information is scene feature information containing time sequence information, which is strongly related to the output scene information containing time sequence information, in the neural network model, there are multiple layers of neurons, and through layer-by-layer processing of the device information by the multiple layers of neurons, the corresponding scene information containing time sequence information can be output at the last layer, it can be seen that the correlation degree between the feature information of the penultimate layer and the scene information of the corresponding time sequence information of the last layer is the largest, and accordingly, the feature information of the penultimate layer can be determined as the high-dimensional feature information, and the high-dimensional feature information is determined as the corresponding scene feature information containing time sequence information.
In step S103, the target device configuration vector information and the scene feature information are combined to generate usage trend information corresponding to the device configuration information.
The target device configuration vector information with uniform dimensionality and the corresponding scene feature information are combined to generate a corresponding feature tensor, the feature tensor is a data container, the target device configuration vector and the corresponding scene feature information can be classified, and in a colloquial way, the feature tensor can represent state change of corresponding hardware configuration information of the electronic device under corresponding scene features.
Furthermore, since different pieces of scene information and different pieces of hardware configuration information are provided at different times, a plurality of feature tensors can be generated according to the time sequence information, and the plurality of feature tensors are combined into a total combined feature tensor, which is usage trend information, and the usage trend information can show the energy usage trend of the hardware configuration information.
In some embodiments, the step of combining the target device configuration vector information and the scene feature information to generate usage trend information corresponding to the device configuration information may include:
(1) combining the target equipment configuration vector information and the scene characteristic information according to the sequence of the time sequence information to generate a trend tensor;
(2) and performing graphical processing on the trend tensor to generate a corresponding energy trend graph, and determining the energy trend graph as the use trend information.
According to the time sequence information, namely according to the time, the corresponding target equipment configuration vector and the scene characteristic information at each time are sequentially combined to generate a plurality of tensors, and then the tensors are combined according to the time sequence to generate a trend vector capable of showing the hardware configuration information.
Furthermore, the trend tensor is subjected to graphical processing to generate an energy trend graph which can represent the corresponding hardware configuration information, the energy trend graph can reflect the energy use trend of the hardware configuration information, the energy trend graph determines the use trend information, and when the energy use trend of the hardware configuration information is reduced rapidly, the hardware configuration information may be in failure or abnormal.
In step S104, the usage trend information and the device configuration state information are analyzed to determine usage prediction information corresponding to the electronic device.
The device configuration state information is state information corresponding to hardware configuration information, for example, the state information corresponding to battery configuration information is a change of a maximum electric quantity value, because the maximum electric quantity value corresponding to the battery configuration information is lower and lower along with continuous use of the battery. Therefore, the service life and the failure probability of the electronic equipment can be predicted and determined by combining the service trend represented by the service trend information of the hardware configuration information and the aging trend represented by the corresponding equipment configuration state information.
As can be seen from the above, in the method for evaluating the operation state of the terminal provided by this embodiment, the target device configuration vector information of the corresponding dimension is determined according to the device configuration information by acquiring the device configuration information of the electronic device; acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information; combining the target equipment configuration vector information and the scene characteristic information to generate use trend information corresponding to the equipment configuration information; and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
The method described in the above embodiments is further illustrated in detail by way of example.
Referring to fig. 3, fig. 3 is another schematic flow chart of a method for evaluating an operating state of a terminal according to an embodiment of the present application.
Specifically, the method comprises the following steps:
in step S201, device configuration information of the electronic device is collected, and vectorization processing is performed on the device configuration information to generate corresponding device configuration vector information.
It should be noted that, for better explaining the present application, the electronic device is exemplified by a mobile phone in the following.
In the present application, the device configuration information may be hardware configuration information of a mobile phone, such as a name of the mobile phone, a model of the mobile phone, a system version, a processor, an operating memory, a body memory, a baseband version, a kernel version, a SIM card state, a battery related state, IP address information, MAC address information, bluetooth information, and the like, and may acquire the device configuration information of the mobile phone according to a preset frequency, and perform vectorization processing on the device configuration information by a one-hot encoding method to generate corresponding device configuration vector information.
In step S202, modeling compression is performed on the device configuration vector information to obtain target device configuration vector information of a corresponding dimension.
Modeling and compressing the equipment configuration vector information by a Word Embedding compression method to obtain target equipment configuration vector information with a unified dimension of 20, wherein the target equipment configuration vector information can be K ═ K1，k2，…，kn]N is equal to 20.
In step S203, the application use information and the corresponding timing information of the electronic device are collected, and the application use information and the corresponding timing information are determined as the application operation information.
The application use information and the corresponding time sequence information of the mobile phone can be collected in advance according to a certain frequency, the application information and the corresponding time sequence information are determined as application operation information and are used as samples, the samples of 2 weeks or 3 weeks are collected continuously, and a sample library is constructed.
In step S204, the application operation information is input into the neural network model to output corresponding scene information containing the timing information.
The sample library can be trained and learned through the recurrent neural network model, so that the recurrent neural network model can output corresponding scene information with time sequence information according to input application use information and corresponding time sequence information after learning, and the scene information can accurately depict certain scene information of a user under certain time sequence information.
In step S205, high-dimensional feature information in the neural network model is extracted, and the high-dimensional feature information is determined as corresponding scene feature information containing timing information.
Wherein, because the last layer of the neural network model can output corresponding scene information containing time sequence information, the feature information of the second last layer in the neural network model can be extracted and determined as high-dimensional feature information, and the high-dimensional feature information is determined as corresponding scene feature information containing time sequence information, and the scene feature information can be A- [ [ a [ ]1]，[a2]，…，[an]]。
In step S206, the target device configuration vector information and the scene feature information are combined to generate a corresponding target tensor.
According to the time sequence information contained in the scene characteristic information, sequentially carrying out vector combination on the target equipment configuration vector information and the scene characteristic information under each time sequence information to generate a plurality of corresponding target tensorsThe target tensor can be
In step S207, corresponding target tensors are sequentially obtained according to the sequence of the time sequence information and merged to generate a trend tensor.
Wherein, according to the sequence of the time sequence information, for example, the time nodes are t1, t2 and t3, and the target tensor between t1 and t2 is
the target tensor between t2 and t3 is
Then, combining a plurality of target tensors corresponding to t 1-t 2 and t 2-t 3 according to the sequence of the time sequence information to obtain a representative trend tensor
The trend tensor T may represent the trend of the energy used by the device.
In step S208, the trend tensor is subjected to a graphical process to generate a corresponding energy trend graph, and the energy trend graph is determined as the usage trend information.
Wherein the trend tensor
And carrying out graphical processing to generate an energy trend graph which can visually represent the use energy trend of the equipment device, and determining the energy trend graph as the use trend information.
In step S209, the usage trend information is analyzed to determine a corresponding first trend, the device configuration status information is analyzed to determine a corresponding second trend.
The information of the use trend is analyzed, a first trend corresponding to the use energy trend is determined, further, the equipment configuration state information is analyzed, namely the aging degree of the device changes, for example, the initial maximum electric quantity of the battery is 3200 milliamperes, the aging changes to 3000 milliamperes after the device is used for a period of time, and a second trend representing the aging is determined.
In step S210, the first trend and the second trend are combined to generate corresponding usage prediction information of the electronic device.
The first trend and the second trend are combined, the general trend is determined according to the first trend and the second trend, the service life of the mobile phone and the fault probability are determined according to the general trend, namely, the prediction information is used, and it needs to be explained that the faster the general trend is reduced, the shorter the service life of the mobile phone is, the higher the fault probability is, the slower the general trend is reduced, the longer the service life of the mobile phone is, and the smaller the fault probability is.
In step S211, when it is detected that the usage prediction information does not meet the preset condition, a prompt message is generated and the usage prediction information is uploaded to the server
When the usage prediction information is detected to be not in accordance with the preset condition, the preset condition can be a device non-fault condition, when the fault probability in the usage prediction information is larger than a certain threshold value, namely the device non-fault condition is not satisfied, the user can be reminded of needing maintenance, the usage prediction information can be uploaded to a server for backup, the server can correspondingly conduct recommendation feedback, for example, a Graphic Processing Unit (GPU) starts to age, when the running speed is not as high as that of a new device, a GPU acceleration module of the system is called to accelerate a terminal game when the user enters a game state.
As can be seen from the above, in the method for evaluating the operation state of the terminal provided in this embodiment, the device configuration information of the mobile phone is collected, vectorization processing and modeling compression processing are performed on the device configuration information, device configuration vector information with corresponding dimensions are generated, application use information and corresponding time sequence information of the electronic device are collected, the application use information and the corresponding time sequence information are determined as application operation information and are input into the neural network model, so as to output scene feature information including the time sequence information, the target device configuration vector information and the scene feature information are combined, a plurality of corresponding target tensors sorted according to the time sequence information are generated, the plurality of target tensors are combined, a trend vector is generated, corresponding use trend information is obtained according to the trend vector, a first trend corresponding to the use trend information and a second trend corresponding to the device configuration state information are combined, and generating corresponding use prediction information of the mobile phone. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
In order to better implement the method for evaluating the operating state of the terminal provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the method for evaluating the operating state of the terminal. The terms are the same as those in the above-described method for evaluating the operating state of the terminal, and specific implementation details may refer to the description in the method embodiment.
Referring to fig. 4, fig. 4 is a schematic block diagram of an apparatus for evaluating an operating state of a terminal according to an embodiment of the present disclosure. Specifically, the apparatus 300 for evaluating the operating state of the terminal includes: a first acquisition unit 31, a second acquisition unit 32, a combination unit 33 and a determination unit 34.
The first acquisition unit 31 is configured to acquire device configuration information of the electronic device, and determine target device configuration vector information of a corresponding dimension according to the device configuration information.
The device configuration information may be hardware configuration information of the electronic device, and the hardware configuration information is basic information of the electronic device, and may include a name of the electronic device, a model of the electronic device, a system version, a processor, an operating memory, a body memory, a baseband version, a kernel version, a SIM card state, a battery-related state, IP address information, MAC address information, bluetooth information, and the like. After collecting the device configuration information of the electronic device, the first collecting unit 31 may perform vectorization processing on the collected device configuration information, and perform dimension compression to generate target device configuration vector information with a corresponding dimension, for example, a dimension of 10 or 20.
The second collecting unit 32 is configured to collect application operation information of the electronic device, and determine scene feature information including timing sequence information according to the application operation information.
The application operation information may be user operation history information and user behavior habit information, the second acquisition unit 32 may depict scene information of the user according to the user behavior habit information, and in combination with the user operation history information, the scene information of the user in a certain time period may be further depicted, so that scene feature information with timing information, which is strongly related to the scene information in the certain time period, may be extracted.
The combining unit 33 is configured to combine the target device configuration vector information and the scene feature information to generate usage trend information corresponding to the device configuration information.
The combining unit 33 combines the target device configuration vector information with uniform dimensions and corresponding scene feature information, and may generate a corresponding feature tensor, where the feature tensor is a data container, and may classify the target device configuration vector and the corresponding scene feature information, and in colloquial terms, the feature tensor may represent a state change of the corresponding hardware configuration information of the electronic device under the corresponding scene feature.
Further, since different pieces of scene information and different pieces of hardware configuration information are provided at different times, the combining unit 33 may generate a plurality of feature tensors according to the time sequence information, and combine the plurality of feature tensors into a total combined feature tensor, which is usage trend information, where the usage trend information may show an energy usage trend of the hardware configuration information.
And the determining unit 34 is configured to perform analysis by combining the usage trend information and the device configuration state information, and determine corresponding usage prediction information of the electronic device.
The device configuration state information is state information corresponding to hardware configuration information, for example, the state information corresponding to battery configuration information is a change of a maximum electric quantity value, because the maximum electric quantity value corresponding to the battery configuration information is lower and lower along with continuous use of the battery. Therefore, the determining unit 34 can predict and determine the service life and the failure probability of the electronic device according to the usage trend represented by the usage trend information of the hardware configuration information and the aging trend represented by the corresponding device configuration state information.
In some embodiments, the determining unit 34 is specifically configured to analyze the usage trend information to determine a corresponding first trend; analyzing the equipment configuration state information and determining a corresponding second trend; and generating corresponding use prediction information of the electronic equipment by combining the first trend and the second trend.
Referring to fig. 5 together, fig. 5 is another schematic block diagram of an apparatus for evaluating an operating state of a terminal according to an embodiment of the present application, where the apparatus 300 for evaluating an operating state of a terminal may further include:
the first acquisition unit 31 may include a vectorization subunit 311 and a compression subunit 312.
Further, the vectorization subunit 311 is configured to acquire device configuration information of the electronic device, perform vectorization processing on the device configuration information, and generate corresponding device configuration vector information. And a compressing subunit 312, configured to perform modeling compression on the device configuration vector information to obtain target device configuration vector information of a corresponding dimension.
The second acquiring unit 32 may include an acquiring subunit 321, a determining subunit 322, a processing subunit 323, and an extracting subunit 334.
Further, the collecting subunit 321 is configured to collect application usage information and corresponding timing information of the electronic device. The determining subunit 322 is configured to determine the application usage information and the corresponding timing information as application operation information. And the processing subunit 323 is configured to input the application operation information into the neural network model to output corresponding scene information including the timing information. And the extracting subunit 334 is configured to extract the high-dimensional feature information in the neural network model, and determine the high-dimensional feature information as corresponding scene feature information including time sequence information.
The combining unit 33 may include a combining subunit 331 and a determining subunit 332.
Further, the combining subunit 331 is configured to combine the target device configuration vector information and the scene feature information according to the sequence of the time sequence information, so as to generate a trend tensor. The determining subunit 332 is configured to perform a graphical process on the trend tensor to generate a corresponding energy trend graph, and determine the energy trend graph as the usage trend information.
In some embodiments, the combining subunit 331 is specifically configured to combine the target device configuration vector information and the scene feature information to generate a corresponding target tensor; and sequentially acquiring corresponding target tensors according to the sequence of the time sequence information, and merging to generate a trend tensor.
As can be seen from the above, in the evaluation apparatus for a terminal operating state provided in this embodiment, the first acquisition unit 31 acquires the device configuration information of the electronic device, and determines the target device configuration vector information of a corresponding dimension according to the device configuration information; the second acquisition unit 32 acquires application operation information of the electronic device, and determines scene characteristic information including timing sequence information according to the application operation information; the combining unit 33 combines the target device configuration vector information and the scene feature information to generate usage trend information corresponding to the device configuration information; the determining unit 34 analyzes the usage trend information and the device configuration state information to determine corresponding usage prediction information of the electronic device. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
The embodiment of the application also provides the electronic equipment. Referring to fig. 6, an electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.
The processor 500 is a control center of the electronic device 500, connects various parts of the whole electronic device using various interfaces and lines, performs various functions of the electronic device 500 by running or loading a computer program stored in the memory 502, and calls data stored in the memory 502, and processes the data, thereby performing overall monitoring of the electronic device 500.
The memory 502 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by running the computer programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.
In this embodiment, the processor 501 in the electronic device 500 loads instructions corresponding to one or more processes of the computer program into the memory 502, and the processor 501 runs the computer program stored in the memory 502, so as to implement various functions as follows:
acquiring equipment configuration information of electronic equipment, and determining target equipment configuration vector information of corresponding dimensions according to the equipment configuration information;
acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information;
combining the target device configuration vector information and the scene characteristic information to generate use trend information corresponding to the device configuration information;
and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment.
In some embodiments, when acquiring device configuration information of an electronic device and determining target device configuration vector information of a corresponding dimension according to the device configuration information, the processor 501 may specifically perform the following steps:
acquiring equipment configuration information of electronic equipment, and vectorizing the equipment configuration information to generate corresponding equipment configuration vector information;
modeling and compressing the equipment configuration vector information to obtain target equipment configuration vector information with corresponding dimensionality.
In some embodiments, when acquiring application operation information of an electronic device and determining scene feature information including timing information according to the application operation information, the processor 501 may specifically perform the following steps:
acquiring application use information and corresponding time sequence information of the electronic equipment;
determining the application use information and the corresponding timing information as application operation information;
inputting the application operation information into a neural network model to output corresponding scene information containing time sequence information;
and extracting high-dimensional feature information in the neural network model, and determining the high-dimensional feature information as corresponding scene feature information containing time sequence information.
In some embodiments, when the target device configuration vector information and the scene feature information are combined to generate usage trend information corresponding to the device configuration information, the processor 501 may specifically perform the following steps:
combining the target equipment configuration vector information and the scene characteristic information according to the sequence of the time sequence information to generate a trend tensor;
and performing graphical processing on the trend tensor to generate a corresponding energy trend graph, and determining the energy trend graph as the use trend information.
In some embodiments, when the target device configuration vector information and the scene feature information are combined according to the sequence of the time sequence information to generate the trend tensor, the processor 501 may specifically execute the following steps:
combining the configuration vector information of the target equipment with the scene characteristic information to generate a corresponding target tensor;
and sequentially acquiring corresponding target tensors according to the sequence of the time sequence information, and merging to generate a trend tensor.
In some embodiments, when analyzing the usage trend information and the device configuration state information to determine corresponding usage prediction information of the electronic device, the processor 501 may specifically perform the following steps:
analyzing the use trend information to determine a corresponding first trend;
analyzing the equipment configuration state information and determining a corresponding second trend;
and generating corresponding use prediction information of the electronic equipment by combining the first trend and the second trend.
In some embodiments, after the step of determining the usage prediction information corresponding to the electronic device by analyzing the usage trend information and the device configuration state information, the processor 501 may further specifically perform the following steps:
and when the fact that the use prediction information does not accord with the preset condition is detected, prompt information is generated, and the use prediction information is uploaded to the server.
As can be seen from the above, the electronic device according to the embodiment of the present application, by acquiring device configuration information of the electronic device, determines target device configuration vector information of a corresponding dimension according to the device configuration information; acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information; combining the target equipment configuration vector information and the scene characteristic information to generate use trend information corresponding to the equipment configuration information; and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment. Therefore, the equipment configuration information and the scene characteristic information containing the time sequence information can be collected and dynamically combined to generate the use trend information capable of reflecting the energy use trend corresponding to the equipment configuration information, the evaluation value of the running state of the electronic equipment is determined by combining the use trend information and the equipment configuration state information, and the evaluation accuracy and efficiency of the running state can be improved.
Referring to fig. 7, in some embodiments, the electronic device 500 may further include: a display 503, radio frequency circuitry 504, audio circuitry 505, and a power supply 506. The display 503, the rf circuit 504, the audio circuit 505, and the power source 506 are electrically connected to the processor 501.
The display 503 may be used to display information entered by or provided to the user as well as various graphical user interfaces, which may be made up of graphics, text, icons, video, and any combination thereof. The display 503 may include a display panel, and in some embodiments, the display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The rf circuit 504 may be used for transceiving rf signals to establish wireless communication with a network device or other terminals through wireless communication, and for transceiving signals with the network device or other terminals.
The audio circuit 505 may be used to provide an audio interface between a user and an electronic device through a speaker, microphone.
The power source 506 may be used to power various components of the electronic device 500. In some embodiments, power supply 506 may be logically coupled to processor 501 through a power management system, such that functions of managing charging, discharging, and power consumption are performed through the power management system.
An embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and when the computer program runs on a computer, the computer is caused to execute the method for evaluating an operating state of a terminal in any of the above embodiments, such as: acquiring equipment configuration information of electronic equipment, and determining target equipment configuration vector information of corresponding dimensions according to the equipment configuration information; acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information; combining the target device configuration vector information and the scene characteristic information to generate use trend information corresponding to the device configuration information; and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment.
In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It should be noted that, for the method for evaluating the operation state of the terminal in the embodiment of the present application, it can be understood by a person skilled in the art that all or part of the process of implementing the method for evaluating the operation state of the terminal in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program, where the computer program can be stored in a computer readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and during the execution process, the process of implementing the embodiment of the method for evaluating the operation state of the terminal can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.
For the evaluation device of the terminal operation state in the embodiment of the present application, each functional module may be integrated in one processing chip, or each module may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented as a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium such as a read-only memory, a magnetic or optical disk, or the like.
The method, the apparatus, the storage medium, and the electronic device for evaluating the terminal operating state provided in the embodiments of the present application are described in detail above, and a specific example is applied in the description to explain the principle and the implementation of the present application, and the description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for evaluating the operation state of a terminal is characterized by comprising the following steps:
acquiring equipment configuration information of electronic equipment, and determining target equipment configuration vector information of corresponding dimensions according to the equipment configuration information;
acquiring application operation information of the electronic equipment, and determining scene characteristic information containing time sequence information according to the application operation information;
combining the target device configuration vector information and the scene characteristic information to generate use trend information corresponding to the device configuration information;
and analyzing by combining the use trend information and the equipment configuration state information to determine the corresponding use prediction information of the electronic equipment.
2. The method for evaluating an operating state of a terminal according to claim 1, wherein the step of collecting device configuration information of an electronic device and determining target device configuration vector information of a corresponding dimension according to the device configuration information comprises:
acquiring equipment configuration information of electronic equipment, and vectorizing the equipment configuration information to generate corresponding equipment configuration vector information;
and modeling and compressing the equipment configuration vector information to obtain target equipment configuration vector information with corresponding dimensionality.
3. The method for evaluating an operating state of a terminal according to claim 1, wherein the step of collecting application operation information of the electronic device and determining scene feature information including timing sequence information according to the application operation information comprises:
acquiring application use information and corresponding time sequence information of the electronic equipment;
determining the application use information and the corresponding timing information as application operation information;
inputting the application operation information into a neural network model to output corresponding scene information containing time sequence information;
and extracting high-dimensional characteristic information in the neural network model, and determining the high-dimensional characteristic information as corresponding scene characteristic information containing time sequence information.
4. The method for evaluating an operating state of a terminal according to claim 1, wherein the step of generating usage trend information corresponding to device configuration information by combining the target device configuration vector information and the scene characteristic information includes:
combining the target equipment configuration vector information and the scene characteristic information according to the sequence of the time sequence information to generate a trend tensor;
and performing graphical processing on the trend tensor to generate a corresponding energy trend graph, and determining the energy trend graph as the use trend information.
5. The method for evaluating an operating state of a terminal according to claim 4, wherein the step of combining the target device configuration vector information and the scene feature information according to the sequence of the timing information to generate a trend tensor comprises:
combining the target equipment configuration vector information and the scene characteristic information to generate a corresponding target tensor;
and sequentially acquiring corresponding target tensors according to the sequence of the time sequence information, and merging to generate a trend tensor.
6. The method for evaluating an operating status of a terminal according to claim 1, wherein the step of determining the usage prediction information corresponding to the electronic device by analyzing in combination with the usage trend information and the device configuration status information comprises:
analyzing the use trend information to determine a corresponding first trend;
analyzing the equipment configuration state information and determining a corresponding second trend;
and combining the first trend and the second trend to generate corresponding usage prediction information of the electronic equipment.
7. The method for evaluating an operating status of a terminal according to claim 1, wherein after the step of analyzing in combination with the usage trend information and the device configuration status information to determine the corresponding usage prediction information of the electronic device, the method further comprises:
and when the fact that the use prediction information does not accord with the preset condition is detected, prompt information is generated, and the use prediction information is uploaded to a server.
8. An apparatus for evaluating an operation state of a terminal, comprising:
the first acquisition unit is used for acquiring the equipment configuration information of the electronic equipment and determining the target equipment configuration vector information of corresponding dimensionality according to the equipment configuration information;
the second acquisition unit is used for acquiring application operation information of the electronic equipment and determining scene characteristic information containing time sequence information according to the application operation information;
the combination unit is used for combining the target equipment configuration vector information and the scene characteristic information to generate the use trend information corresponding to the equipment configuration information;
and the determining unit is used for analyzing by combining the use trend information and the equipment configuration state information and determining the corresponding use prediction information of the electronic equipment.
9. A storage medium having stored thereon a computer program, characterized in that, when the computer program runs on a computer, it causes the computer to execute the method of evaluating an operating state of a terminal according to any one of claims 1 to 7.
10. An electronic device comprising a processor and a memory, said memory containing a computer program, wherein said processor is adapted to execute the method of assessing the operational status of a terminal according to any one of claims 1 to 7 by invoking said computer program.
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