CN111813639A

CN111813639A - Method and device for evaluating equipment operation level, storage medium and electronic equipment

Info

Publication number: CN111813639A
Application number: CN201910282032.8A
Authority: CN
Inventors: 何明; 陈仲铭; 黄粟; 刘耀勇; 陈岩
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2019-04-09
Filing date: 2019-04-09
Publication date: 2020-10-23
Anticipated expiration: 2039-04-09
Also published as: CN111813639B

Abstract

The application discloses a method and a device for evaluating equipment operation level, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a plurality of historical operating data of the electronic equipment; determining at least two preset operation levels according to a plurality of historical operation data; determining a preset feature vector corresponding to each preset operation grade to obtain at least two preset feature vectors; acquiring current operating data of the electronic equipment; acquiring a current feature vector of the electronic equipment according to the current operation data; and determining a preset operation level corresponding to a target feature vector matched with the current feature vector in the at least two preset feature vectors as the current operation level of the electronic equipment. The method and the device can determine the current operation level of the electronic equipment based on the current operation data of the electronic equipment, so that different optimization schemes can be adopted for different operation levels to optimize the system of the electronic equipment.

Description

Method and device for evaluating equipment operation level, storage medium and electronic equipment

Technical Field

The present application relates to the field of electronic technologies, and in particular, to a method and an apparatus for evaluating an operation level of a device, a storage medium, and an electronic device.

Background

At present, electronic devices are basically provided with a self-checking function. The user can instruct the electronic equipment to detect the running state of the user through the self-checking function. For example, a detection button is provided in the electronic device, and when the user clicks the detection button, the electronic device receives an instruction to detect the operating condition of the electronic device. After receiving the instruction for detecting the operation condition, the electronic device detects the operation condition of the electronic device. After the electronic device detects its own operating condition, a detection result can be generated and displayed to inform a user of the current operating condition of the electronic device.

Disclosure of Invention

The embodiment of the application provides an equipment operation level evaluation method, an equipment operation level evaluation device, a storage medium and electronic equipment, which can determine the current operation level of the electronic equipment based on the current operation data of the electronic equipment, so that different optimization schemes can be adopted for optimizing systems of the electronic equipment according to different operation levels.

In a first aspect, an embodiment of the present application provides a method for assessing an operation level of equipment, including:

acquiring a plurality of historical operating data of the electronic equipment;

determining at least two preset operation levels according to a plurality of historical operation data;

determining a preset feature vector corresponding to each preset operation grade to obtain at least two preset feature vectors;

acquiring current operating data of the electronic equipment;

acquiring a current feature vector of the electronic equipment according to the current operation data;

and determining a preset operation level corresponding to a target feature vector matched with the current feature vector in the at least two preset feature vectors as the current operation level of the electronic equipment.

In a second aspect, an embodiment of the present application provides an apparatus for assessing an operation level of a device, including:

the first acquisition module is used for acquiring a plurality of historical operating data of the electronic equipment;

the first determining module is used for determining at least two preset operation levels according to a plurality of historical operation data;

the second determining module is used for determining a preset feature vector corresponding to each preset operation grade to obtain at least two preset feature vectors;

the second acquisition module is used for acquiring the current operating data of the electronic equipment;

a third obtaining module, configured to obtain a current feature vector of the electronic device according to the current operating data;

and the third determining module is used for determining a preset operation level corresponding to a target feature vector matched with the current feature vector in the at least two preset feature vectors as the current operation level of the electronic equipment.

In a third aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, where when the computer program is executed on a computer, the computer is caused to execute the method for assessing an operation level of a device provided in this embodiment.

In a fourth aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the processor is configured to execute the method for assessing the operation level of the device according to the embodiment of the present application by calling a computer program stored in the memory.

In the embodiment of the application, the electronic device may determine at least two preset operation levels based on historical operation data, then determine a preset feature vector corresponding to each preset operation level, and determine the current operation level of the electronic device according to the preset feature vector and the current feature vector corresponding to the current operation data of the electronic device, so that different optimization schemes may be adopted to optimize a system of the electronic device for different operation levels.

Drawings

The technical solutions and advantages of the present application will become apparent from the following detailed description of specific embodiments of the present application when taken in conjunction with the accompanying drawings.

Fig. 1 is a schematic application scenario diagram of a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 2 is a first flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 3 is a second flowchart of a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 4 is a third flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 5 is a fourth flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 6 is a fifth flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application.

Fig. 7 is a schematic structural diagram of an apparatus for assessing an operation level of a device according to an embodiment of the present application.

Fig. 8 is a schematic structural diagram of a first electronic device according to an embodiment of the present application.

Fig. 9 is a schematic structural diagram of a second 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.

Referring to fig. 1, fig. 1 is a schematic view of an application scenario of a method for rating an operation level of a device according to an embodiment of the present application. The method for evaluating the equipment operation level is applied to electronic equipment. A panoramic perception framework is arranged in the electronic equipment. The panoramic perception architecture is an integration of hardware and software in an electronic device for implementing the rating method of the device operation level.

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, an image sensor, and an audio 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. An image sensor, which may be, for example, a camera, may be used to capture images of the surrounding environment. An audio sensor, which may be a microphone, for example, may be used to capture sound signals in the surrounding environment.

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, the panoramic perception architecture can further comprise a plurality of algorithms, 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 word vector, a K-means clustering algorithm, a K-nearest neighbor algorithm, a cosine similarity algorithm, a residual error network, a long-short term memory network, a convolutional neural network, and a recurrent neural network.

The embodiment of the application provides a method for evaluating the operation level of equipment, which can be applied to electronic equipment. The electronic device may be a smartphone, a tablet computer, a gaming device, an AR (Augmented Reality) device, an automobile, a data storage device, an audio playback device, a video playback device, a laptop computer, a desktop computing device, a wearable device such as an electronic watch, an electronic glasses, an electronic helmet, an electronic bracelet, an electronic necklace, an electronic garment, or the like.

Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application. The flow of the method for assessing the operation level of the equipment can comprise the following steps:

in 201, a plurality of historical operational data of an electronic device is obtained.

For example, the electronic device may preset a history database. The historical database is used for storing historical operation data of the electronic equipment. The historical operating data refers to operating data before the current operating data. In this embodiment, the electronic device may obtain a plurality of historical operating data. For example, the electronic device may obtain operational data for a period of time as historical operational data. The electronic device may obtain the operational data for another period of time as another historical operational data, and so on.

At 202, at least two preset operation levels are determined based on a plurality of historical operation data.

For example, assume that the electronic device acquires historical operating data D₁，D₂，D₃，D₄，D₅，D₆，D₇，D₈The electronic equipment can operate according to the historical operating data D₁，D₃And D₄Determining a preset operation level A, and operating according to historical operation data D₂，D₇And D₈Determining a preset operation level B according to historical operation data D₅And D₆And determining a preset operation level C.

In 203, a preset feature vector corresponding to each preset operation level is determined, and at least two preset feature vectors are obtained.

For example, the electronic device may determine that the preset operation level a corresponds to the preset feature vector P₁It can be determined that the preset operation level B corresponds to the preset feature vector P₂It can be determined that the preset operation level C corresponds to the preset feature vector P₃。

At 204, current operational data of the electronic device is obtained.

In this embodiment, the operation data is some data generated by the electronic device during the use of the electronic device by the user. The electronic device may obtain data generated by the electronic device within a predetermined period of time as current operating data.

For example, the current time is 12/25/2018, 14:43, and the electronic device may obtain data generated during the time between 12/25/12/20/2018 and 12/25/14: 43/2018 as the current operation data.

The current operating data may include power usage data, process operating data, application operating data, battery temperature data, CPU temperature data, virus data, trojan data, memory usage data, CPU usage data, and so forth.

In 205, a current feature vector of the electronic device is obtained according to the current operating data.

For example, the electronic device may obtain a current feature vector of the electronic device according to the current operation data, where the current feature vector of the electronic device may include a plurality of features. A plurality of features are extracted based on current operating data of the electronic device.

For example, the electronic device may extract, based on the power usage data, a total power consumption amount of the electronic device per unit time, that is, a total power consumption speed of the electronic device, as one feature of the current feature vector; or the electronic equipment extracts the power consumption of a certain application program in unit time based on the power consumption data, namely the power consumption speed of the application program, and the power consumption speed is used as one feature of the current feature vector. Alternatively, the electronic device may extract the current temperature of the battery based on the battery temperature data, as a feature of the current feature vector, and so on.

For example, the current feature vector may be P (x, y, z). Where x, y, and z all represent a certain characteristic, for example, x may represent a total power consumption rate of the electronic device, y may represent a power consumption rate of a certain application, and z may represent a temperature of the battery. The current operating data of the electronic device may be quantized by the current feature vector P (x, y, z), such that the current operating data is represented by the current feature vector.

In 206, a preset operation level corresponding to a target feature vector matched with the current feature vector in the at least two preset feature vectors is determined as a current operation level of the electronic device.

For example, assume that the electronic device obtains a predetermined feature vector P₁、P₂、P₃And a preset operation level A, a preset operation level B, a preset operation level C and the like. Wherein, P₁May correspond to a preset operating level A, P₂May correspond to a preset operating level B, P₃May correspond to a preset operation level C. Suppose that the electronic device determines a preset feature vector P₁Matching the current feature vector P, the electronic device may then assign the preset feature vector P₁And determining the corresponding preset operation level A as the current operation level of the electronic equipment, namely determining the current operation level of the electronic equipment as A.

In some embodiments, the electronic device may associate a plurality of preset operating levels with corresponding optimization schemes. For example, assume that there are a preset operation level a, a preset operation level B, a preset operation level C, an optimization scheme Aa, an optimization scheme Bb, and an optimization scheme Cc. The method comprises the following steps of presetting an operation level A correlation optimization scheme Aa, presetting an operation level B correlation optimization scheme Bb and presetting an operation level C correlation optimization scheme Cc. After the electronic device determines the current operation level, the system of the electronic device can be optimized by adopting a corresponding optimization scheme. For example, assuming that the current operation level is a, the electronic device may optimize the system of the electronic device by using an optimization scheme Aa.

It can be understood that, in this embodiment, the electronic device may determine at least two preset operation levels based on the historical operation data, then determine a preset feature vector corresponding to each preset operation level, and determine the current operation level of the electronic device according to the preset feature vector and the current feature vector corresponding to the current operation data of the electronic device, so that different optimization schemes may be adopted to optimize a system of the electronic device for different operation levels.

Referring to fig. 3, fig. 3 is a second flowchart illustrating a method for assessing an operation level of a device according to an embodiment of the present application. The method for evaluating the operation grade of the equipment can comprise the following steps:

at 301, an electronic device obtains a plurality of historical operating data for the electronic device.

At 302, the electronic device obtains a plurality of historical feature vectors corresponding to each historical operating data.

For example, the electronic device may obtain a historical feature vector corresponding to each historical operating data to obtain a plurality of historical feature vectors.

It will be appreciated that the historical feature vector may include a plurality of features. For example, the historical feature vector may include a plurality of features such as power consumption speed, number of processes running simultaneously, battery temperature, CPU temperature, memory usage, CPU usage, presence of viruses, presence of trojan data, and the like.

For each historical operating datum, the electronic device can obtain a historical feature vector corresponding to the historical operating datum. That is, based on each historical operating data, a plurality of characteristics such as power consumption speed, the number of processes operating simultaneously, battery temperature, CPU temperature, memory usage rate, CPU usage rate, presence or absence of virus data, presence or absence of trojan data, and the like can be extracted.

In this embodiment, the historical operating data is some data generated by the electronic device during the use of the electronic device by the user. These data are data that are unknown to the electronic device. Therefore, it can be understood that the historical feature vectors corresponding to the historical operating data are obtained before. Some operating data of the electronic device needs to be marked or some rules need to be made manually. For example, operational data corresponding to the battery temperature may be flagged. Assuming that a piece of operation data is marked as the battery temperature, the electronic device may learn the piece of operation data marked as the battery temperature, and learn the characteristics of the piece of operation data. If a piece of operation data which is not marked is acquired later, the electronic equipment can extract a piece of data from the piece of operation data and determine the data as the battery temperature. Alternatively, the concept of the power consumption speed may be set. And formulating a calculation rule of the power consumption speed and operation data required to be obtained for calculating the power consumption speed, inputting the information into the electronic equipment, and after the electronic equipment learns the information, obtaining the operation data for calculating the power consumption speed and calculating the power consumption speed.

In some embodiments, the operation data of the electronic device may be studied based on an expert system, so that it may be determined what the power consumption speed is, indicating that the power consumption is abnormal; when the temperature of the battery is higher, indicating that the temperature of the system is too high; when the memory utilization rate is more or less, indicating that the memory occupancy rate of the electronic equipment is high; and when the running data has the characteristics of the virus data, indicating that the running data is the virus data.

At 303, the electronic device determines a preset operation level corresponding to each historical feature vector of the plurality of historical feature vectors to obtain at least two preset operation levels, where each preset operation level corresponds to at least one historical feature vector.

In this embodiment, a preset operation level corresponding to each of the plurality of historical feature vectors may be determined.

For example, assume that the electronic device obtains a historical feature vector P₁₁，P₁₂，P₁₃，P₁₄，P₁₅，P₂₁，P₂₂，P₂₃，P₂₄，P₃₁，P₃₂，P₃₃，P₃₄. The electronic device may determine a historical feature vector P₁₁，P₁₂，P₁₃，P₁₄，P₁₅Corresponding to a preset operation level A and a historical characteristic vector P₂₁，P₂₂，P₂₃，P₂₄Corresponding to the preset operation level B, the historical characteristic vector P₃₁，P₃₂，P₃₃，P₃₄Corresponding to the preset operation level C.

In some embodiments, the correspondence of the preset operation levels may be made based on a plurality of features of each of the historical feature vectors. When all the features of a certain historical feature vector indicate to be normal, it may be determined that the preset operation level corresponding to the historical feature vector is a preset operation level a. When a certain feature of a certain historical feature vector indicates abnormal and other features indicate normal, it may be determined that the preset operation level corresponding to the historical feature vector is a preset operation level B. When two certain characteristic indications of a certain historical characteristic vector are abnormal and other characteristic indications are normal, the preset operation level corresponding to the historical characteristic vector is a preset operation level C. When all the features of the historical feature vector indicate abnormality, the preset operation level corresponding to the historical feature vector is a preset operation level D. That is, the preset operation level a indicates that the operation condition of the electronic device is the best, and the preset operation level D indicates that the operation condition of the electronic device is the worst, that is, the operation condition corresponding to the preset operation level a is better than the preset operation level B, the operation condition corresponding to the preset operation level B is better than the preset operation level C, and the operation condition corresponding to the preset operation level C is better than the preset operation level D.

In the present embodiment, it is assumed that each history feature vector includes 4 features of power consumption speed, the number of processes running simultaneously, battery temperature, and whether virus data exists. The characteristic that the power consumption speed is within the preset power consumption speed range and indicates the power consumption speed is normal, and the characteristic that the power consumption speed is outside the preset power consumption speed range and indicates the power consumption speed is abnormal. The characteristic that the number of the simultaneously-operated processes is within the preset number range indicates that the number of the simultaneously-operated processes is normal, and the characteristic that the number of the simultaneously-operated processes is outside the preset number range indicates that the number of the simultaneously-operated processes is abnormal. The characteristic that the battery temperature is within the preset temperature range indicates that the battery temperature is normal, and the characteristic that the battery temperature is outside the preset temperature range indicates that the battery temperature is abnormal. The absence of a virus indicates that the presence of a virus is normal, and the presence of a virus indicates that the presence of a virus is abnormal.

When the power consumption speed of a certain historical feature vector is within a preset power consumption speed range, the number of processes running simultaneously is within a preset number range, the battery temperature is within a preset battery temperature range, and no virus exists, it can be determined that the preset running grade corresponding to the historical feature vector is a preset running grade A. When the power consumption speed of a certain historical feature vector is out of the preset power consumption speed range, the number of processes running simultaneously is within the preset number range, the battery temperature is within the preset battery temperature range, and no virus exists, the preset running grade corresponding to the historical feature vector can be determined to be the preset running grade B. When the power consumption speed of a certain historical feature vector is within a preset power consumption speed range, the number of processes running simultaneously is outside a preset number range, the battery temperature is within a preset battery temperature range, and viruses exist, it can be determined that the preset running grade corresponding to the historical feature vector is a preset running grade C. When the power consumption speed of a certain historical feature vector is within a preset power consumption speed range, the number of processes running simultaneously is outside a preset number range, the battery temperature is outside a preset battery temperature range, and no virus exists, the preset running grade corresponding to the historical feature vector can be determined to be a preset running grade C. When the power consumption speed of a certain historical feature vector is outside the preset power consumption speed range, the number of processes running simultaneously is outside the preset number range, the battery temperature is outside the preset battery temperature range, and viruses exist, the preset running grade corresponding to the historical feature vector can be determined to be the preset running grade D. By analogy, the preset operation level corresponding to each historical feature vector can be determined. It is to be understood that each preset operation level corresponds to at least one historical feature vector.

It should be noted that the number of the preset operation levels and how to determine the preset operation level corresponding to each historical feature vector may be determined according to actual situations, and is not limited herein.

At 304, the electronic device determines a preset feature vector corresponding to each preset operation level according to at least one historical feature vector corresponding to each preset operation level.

In this embodiment, the electronic device may determine the preset feature vector corresponding to each preset operation level according to at least one history feature vector corresponding to each preset operation level.

For example, the electronic device may calculate an average feature vector of the at least one historical feature vector corresponding to each preset operation level, and determine the average feature vector as the preset feature vector corresponding to each preset operation level.

At 305, the electronic device obtains current operating data of the electronic device.

For example, the current time is 12/25/2018, 14/43, and the electronic device may acquire data generated during the time between 12/25/01/20/2018 and 12/25/14/43 as the current operation data.

For example, the electronic device may obtain current operating data of the electronic device. The current operation data may include power usage data, process operation data, application operation data, battery temperature data, CPU temperature data, virus data, trojan data, memory usage data, CPU usage data, and the like.

At 306, the electronic device obtains a current feature vector of the electronic device according to the current operating data.

For example, the electronic device may obtain a current feature vector of the electronic device according to the current operation data. Wherein the current feature vector of the electronic device may include a plurality of features. A plurality of features are extracted based on current operating data of the electronic device.

For example, the electronic device may extract, as one feature of the current feature vector, the total power consumption amount of the electronic device per unit time, that is, the total power consumption speed of the electronic device, based on the power usage data, or extract, as one feature of the current feature vector, the power consumption amount of an application program per unit time, that is, the power consumption speed of the application program, based on the power usage data. Alternatively, the electronic device may extract the current temperature of the battery based on the battery temperature data, as a feature of the current feature vector, and so on.

In 307, the electronic device sequentially calculates the similarity between the current feature vector and each preset feature vector to obtain at least two similarity values.

For example, the electronic device sequentially calculates the similarity between the current feature vector and each of at least two preset feature vectors to obtain at least two similarity values. The greater the similarity between the current feature vector and a preset feature vector, the more similar the current feature vector and the preset feature vector, that is, the more similar the operation level corresponding to the current feature vector of the electronic device and the preset operation level corresponding to the preset feature vector.

For example, assume that the electronic device obtains a preset feature vector P corresponding to a preset operation level a₁A predetermined feature vector P corresponding to the predetermined operation level B₂A predetermined feature vector P corresponding to the predetermined operation level C₃. After the electronic equipment acquires the current feature vector P of the electronic equipment, sequentially calculating the current feature vectors P and P₁Similarity M of₁P and P₂Similarity M of₂P and P₃Similarity M of₃. Wherein M is₁Denotes P and P₁The similarity between them; m₂Denotes P and P₂The similarity between them; m₃Denotes P and P₃The similarity between them.

In some embodiments, the electronic device may sequentially calculate the cosine similarity between the current feature vector and each of the at least two preset feature vectors by using a cosine similarity algorithm, so as to obtain at least two cosine similarity values. The electronic equipment determines the cosine similarity value of the current feature vector and each preset feature vector of the at least two preset feature vectors as the similarity value of the current feature vector and the preset feature vectors so as to obtain the at least two similarity values.

For example, the electronic device may sequentially calculate the cosine similarity between the current feature vector and each preset feature vector by using a cosine similarity algorithm, so as to obtain at least a cosine similarity value.

Wherein, the value range of the cosine similarity value is [ -1, 1 ]. The cosine similarity value of 1 indicates that the directions of the two vectors are the same, the cosine similarity value of 0 indicates that the two vectors are independent of each other, and the cosine similarity value of-1 indicates that the directions of the two vectors are opposite. The closer the cosine similarity value is to 1, the closer the directions of the two vectors are.

For example, the electronic device obtains the current feature vector as P, and the preset feature vector includes P₁、P₂、P₃Then the electronic equipment calculates P and P by using a cosine similarity algorithm in sequence₁、P₂、P₃The cosine similarity of P and P is obtained₁Cosine similarity value K of₁P and P₂Cosine similarity value K of₂P and P₃Cosine similarity value K of₃。

Then, the electronic device determines the cosine similarity value between the current feature vector and each preset feature vector as the similarity value between the current feature vector and the preset feature vector to obtain at least two similarity values.

For example, the electronic device may compare the cosine similarity value K₁Is determined as P and P₁The cosine similarity value K₂Is determined as P and P₂The cosine similarity value K₃Is determined as P and P₃The similarity value of (a).

At 308, the electronic device determines a target feature vector matching the current feature vector from the at least two preset feature vectors according to the at least two similarity values.

For example, the electronic device may determine a target feature vector matching the current feature vector from at least two preset feature vectors according to the at least two similarity values. The target feature vector is the feature vector with the largest similarity with the current feature vector in at least two preset feature vectors.

In 309, the electronic device determines a preset operation level corresponding to the target feature vector as a current operation level of the electronic device.

For example, the electronic device may determine a preset operation level corresponding to the target feature vector as a current operation level of the electronic device. Thereby performing an overall evaluation of the current operating data of the electronic device.

In this embodiment, after determining the current operation level of the electronic device, an operation status report may be generated and displayed according to the current operation level. The operation status report includes a current operation level of the electronic device and an optimization scheme corresponding to the current operation level. In this embodiment, each operation level may correspond to an optimization scheme. For example, assume that there are preset operating levels A, B, C and optimization schemes Aa, Bb, Cc. The preset operation level A corresponds to the optimization scheme Aa, the preset operation level B corresponds to the optimization scheme Bb, and the preset operation level C corresponds to the optimization scheme Cc. Assuming that the current operation level is B, the electronic device may display the current operation level of the electronic device as B and display a corresponding optimization scheme Bb, so that a user may optimize a system of the electronic device according to the corresponding optimization scheme.

In some embodiments, the electronic device may display a specific value corresponding to each feature in the current feature vector. For example, assume that the current feature vector P includes several features of the total power consumption speed of the electronic device, the battery temperature, and whether a trojan is present. Wherein, it is assumed that the total power consumption speed of the electronic device obtained by the electronic device is V₁Battery temperature is U₁Presence of trojan. The electronic device may display the following information: the power consumption speed of the electronic device is V₁Battery temperature is U₁Presence of trojan.

In addition, the electronic device may determine whether the power consumption speed of the electronic device is within a preset power consumption speed range, and perform different processing according to whether the power consumption speed of the electronic device is within the preset power consumption speed range. For example, when the power consumption speed of the electronic device is within the preset power consumption speed range, it is indicated that the electronic device consumes power normally, and therefore the power consumption speed may be displayed. And when the power consumption speed of the electronic equipment is out of the preset power consumption speed range, indicating that the power consumption of the electronic equipment is abnormal. The electronic device can search and analyze the reason causing the power consumption abnormity, so as to determine a corresponding solution to solve the problem.

Or, the electronic device may determine the corresponding solution when the battery temperature is outside the preset temperature range, so that the battery temperature is within the preset temperature range.

Or, the electronic device may display the trojan data or an application program carrying the trojan data when the trojan exists, and may determine a corresponding solution to solve the problem.

Wherein, the electronic device can display the corresponding solution on the display screen, so that the user can solve the problem according to the prompt of the solution.

As shown in fig. 4, in some embodiments, the method may further comprise:

401. the electronic device selects a target number of historical feature vectors from the plurality of historical feature vectors.

402. And the electronic equipment determines the selected historical feature vectors of the target number into a plurality of feature vectors to be determined.

403. And the electronic equipment determines the remaining plurality of history feature vectors which are not selected from the plurality of history feature vectors as feature vectors to be classified.

For example, the electronic device may select a target number of historical feature vectors from the plurality of historical feature vectors, determine the selected target number of historical feature vectors as a plurality of feature vectors to be determined, and determine remaining historical feature vectors that are not selected from the plurality of historical feature vectors as feature vectors to be ranked.

404. The electronic equipment determines a preset operation level corresponding to each feature vector to be determined, wherein each preset operation level corresponds to at least one feature vector to be determined.

For example, the electronic device may determine a preset operation level corresponding to each feature vector to be determined, where each preset operation level corresponds to at least one feature vector to be determined. Since the feature vectors to be determined need to be input into the model for training, it can be understood that the more the number of the feature vectors to be determined corresponding to each preset operation level is, the more accurate the model is. Therefore, in the actual application process, each preset operation level may correspond to a preset number of feature vectors to be determined. The preset number can be determined according to actual requirements. Correspondingly, how many historical feature vectors are selected from the plurality of historical feature vectors is also determined according to the actual requirement of the feature vectors to be determined. That is, the value of the target number may be determined according to actual requirements.

For example, in practical applications, it is assumed that at least 20 to-be-determined feature vectors are required to correspond to a preset operation level a, at least 21 to-be-determined feature vectors correspond to a preset operation level B, and at least 18 to-be-determined feature vectors correspond to a preset operation level C. The electronic device will need to select at least 59 historical feature vectors from the plurality of historical feature vectors. For example, the electronic device may select 59 historical feature vectors from the plurality of historical feature vectors, and determine the 59 historical feature vectors as the plurality of feature vectors to be determined.

In some embodiments, the electronic device may select 80 historical feature vectors from the plurality of historical feature vectors, and determine the 80 historical feature vectors as the plurality of feature vectors to be determined, thereby improving the accuracy of the trained model.

405. The electronic equipment inputs a plurality of to-be-determined feature vectors and a plurality of to-be-classified feature vectors after determining the preset operation level into a preset model so as to determine the preset operation level corresponding to each to-be-classified feature vector, wherein each preset operation level corresponds to at least one to-be-classified feature vector.

For example, the electronic device may input the plurality of to-be-determined feature vectors and the plurality of to-be-ranked feature vectors after determining the preset operation level into the preset model to determine the preset operation level corresponding to each to-be-ranked feature vector. The preset model can be any model which can determine the corresponding preset operation level of a plurality of feature vectors to be classified. For example, the preset model may be a KNN model.

406. And the electronic equipment determines a preset operation grade corresponding to each historical characteristic vector according to a preset operation grade corresponding to each characteristic vector to be determined and a preset operation grade corresponding to each characteristic vector to be classified.

The plurality of historical feature vectors are divided into a plurality of feature vectors to be determined and a plurality of feature vectors to be graded. Therefore, the determination of the preset operation level corresponding to each feature vector to be determined and the preset operation level corresponding to each feature vector to be ranked is equivalent to the determination of the preset operation level corresponding to each historical feature vector.

It can be understood that, in practical applications, the preset operation level corresponding to the historical feature vector is usually marked in a manner of manual labeling. However, in practical applications, a large number of historical feature vectors are usually required to be obtained, and it is necessary to be troublesome to mark the preset operation level corresponding to each historical feature vector in a manual label manner. Therefore, a small part of historical characteristic vectors can be marked with the corresponding preset operation grade in a manual label mode, and the other large part of historical characteristic vectors can be used for determining the corresponding preset operation grade by adopting the KNN model, so that the process of determining the preset operation grade is simpler.

As shown in fig. 5, in some embodiments, the method may further comprise:

501. the electronic equipment calculates the average characteristic vector of at least one historical characteristic vector corresponding to each preset operation level.

502. The electronic equipment determines the average characteristic vector corresponding to each preset operation level as a preset characteristic vector corresponding to each preset operation level.

For example, the electronic device calculates an average feature vector of a plurality of historical feature vectors, and determines the average feature vector as a preset feature vector corresponding to a preset operation level.

For example, the electronic device obtains three historical feature vectors P corresponding to the preset operation level a₁₁、P₁₂、P₁₃Then, three historical feature vectors P are calculated₁₁、P₁₂、P₁₃Is averaged feature vector P₁. The electronic device then averages the feature vector P₁And determining a preset feature vector which is a preset operation level A.

As shown in fig. 6, in some embodiments, the method may further comprise:

601. the electronic device determines a maximum similarity value from the at least two similarity values.

602. And the electronic equipment determines the preset feature vector corresponding to the maximum similarity value as a target feature vector.

For example, the electronic device may compare the magnitudes of the at least two similarity values with each other to determine the largest similarity value from the at least two similarity values. And then, determining the preset feature vector corresponding to the maximum similarity value as a target feature vector.

For example, three similarity values M₁、M₂、M₃In, M₁Less than M₂，M₂Less than M₃Then the electronic device may determine that the maximum similarity value is M₃. Subsequently, the electronic device will M₃Corresponding preset feature vector P₃And determining the target feature vector.

In some embodiments, the method may further comprise:

receiving a grade marking instruction, wherein the grade marking instruction is used for marking each feature vector to be determined by a preset operation grade;

and determining a preset operation grade corresponding to each feature vector to be determined according to the grade marking instruction.

For example, the electronic device may receive a rating marking instruction. After receiving the level marking instruction, the electronic device may mark a preset operation level corresponding to each feature vector to be determined. For example, the feature vector P is to be determined₂₀₁，P₂₀₂，P₂₀₃，P₂₀₄，P₂₀₅，P₂₀₆，P₂₀₇，P₂₀₈，P₂₀₉，P₂₁₀，P₂₁₁，P₂₁₂，P₂₁₃，P₂₁₄，P₂₁₅，P₂₁₆，P₂₁₇，P₂₁₈. The electronic device can mark P₂₀₁，P₂₀₄，P₂₀₉，P₂₁₁The corresponding preset operation level is a preset operation level A. That is, the preset operation level A corresponds to the characteristic vector P to be determined₂₀₁，P₂₀₄，P₂₀₉，P₂₁₁. The electronic device can mark P₂₀₂，P₂₀₈，P₂₁₀，P₂₁₈The corresponding preset operation level is a preset operation level B. I.e. the preset operation level B corresponds to the characteristic vector P to be determined₂₀₂，P₂₀₈，P₂₁₀，P₂₁₈. The electronic device can mark P₂₀₅，P₂₁₂，P₂₁₃，P₂₁₄，P₂₁₅The corresponding preset operation level is a preset operation level C. I.e. the preset operation level C corresponds to the characteristic vector P to be determined₂₀₅，P₂₁₂，P₂₁₃，P₂₁₄，P₂₁₅. The electronic device can mark P₂₀₃，P₂₀₆，P₂₀₇，P₂₁₆，P₂₁₇The corresponding preset operation level is a preset operation level D. That is, the preset operation level D corresponds to the characteristic vector P to be determined₂₀₃，P₂₀₆，P₂₀₇，P₂₁₆，P₂₁₇。

In some embodiments, in an electronic device with a panoramic sensing architecture, the electronic device may acquire a large amount of raw data by using an information sensing layer, where the large amount of raw data acquired by the information sensing layer includes historical operating data and current operating data in this embodiment; then, the electronic device may process a large amount of raw data acquired by the information sensing layer by using the data processing layer. For example, the data processing layer may clean data acquired by the information sensing layer to remove invalid data and duplicate data; then, the electronic device may perform feature extraction on the data processed by the data processing layer by using a feature extraction layer to extract features included in the data; for example, the electronic device may extract, based on the power usage data, a total power consumption amount of the electronic device per unit time, that is, a total power consumption speed of the electronic device, as one feature of the current feature vector; or the electronic equipment extracts the power consumption of a certain application program in unit time based on the power consumption data, namely the power consumption speed of the application program, and the power consumption speed is used as a feature of the current feature vector, and the like; the electronic equipment can analyze and process the data by adopting an algorithm included in the panoramic sensing architecture; for example, the electronic device may determine, by using a KNN algorithm in the K-nearest neighbor algorithm, a preset operation level corresponding to the feature vector to be ranked according to the feature vector to be determined and the feature vector to be ranked in the embodiment; the electronic device may employ an intelligent service layer to determine a current operating level of the electronic device; for example, after the electronic device determines at least two preset operation levels according to a plurality of historical operation data and determines a preset feature vector corresponding to each preset operation level, the electronic device may obtain current operation data and then obtain a current feature vector of the electronic device according to the current operation data, so that a preset operation level corresponding to a target feature vector matched with the current feature vector in the at least two preset feature vectors is determined as the current operation level of the electronic device by using the intelligent service layer.

Referring to fig. 7, fig. 7 is a schematic structural diagram of an apparatus for assessing an operation level of a device according to an embodiment of the present application. The device for assessing the operation level of the equipment can comprise: a first obtaining module 701, a first determining module 702, a second determining module 703, a second obtaining module 704, a third obtaining module 705 and a third determining module 706.

The first obtaining module 701 is configured to obtain a plurality of historical operating data of the electronic device.

A first determining module 702, configured to determine at least two preset operation levels according to a plurality of the historical operation data.

The second determining module 703 is configured to determine a preset feature vector corresponding to each preset operation level to obtain at least two preset feature vectors.

A second obtaining module 704, configured to obtain current operation data of the electronic device.

A third obtaining module 705, configured to obtain a current feature vector of the electronic device according to the current operating data.

A third determining module 706, configured to determine, as the current operation level of the electronic device, a preset operation level corresponding to a target feature vector that matches the current feature vector in the at least two preset feature vectors.

In some embodiments, the third determining module 706 may be configured to: sequentially calculating the similarity between the current feature vector and each preset feature vector to obtain at least two similarity values; and determining a target feature vector matched with the current feature vector from at least two preset feature vectors according to at least two similarity values.

In some embodiments, the third determining module 706 may be configured to: determining a maximum similarity value from at least two of the similarity values; and determining the preset feature vector corresponding to the maximum similarity value as a target feature vector.

In some embodiments, the first determining module 702 may be configured to: acquiring a historical characteristic vector corresponding to each historical operating data to obtain a plurality of historical characteristic vectors; determining a preset operation level corresponding to each historical feature vector in a plurality of historical feature vectors to obtain at least two preset operation levels, wherein each preset operation level corresponds to at least one historical feature vector;

the second determining module 703 may be configured to: and determining a preset feature vector corresponding to each preset operation level according to at least one historical feature vector corresponding to each preset operation level.

In some embodiments, the second determining module 703 may be configured to: calculating an average characteristic vector of at least one historical characteristic vector corresponding to each preset operation level; and determining the average characteristic vector corresponding to each preset operation level as a preset characteristic vector corresponding to each preset operation level.

In some embodiments, the first determining module 702 may be configured to: selecting a target number of historical feature vectors from the plurality of historical feature vectors; determining the selected historical feature vectors of the target quantity into a plurality of feature vectors to be determined; determining the remaining historical feature vectors which are not selected from the historical feature vectors as feature vectors to be classified; determining a preset operation level corresponding to each feature vector to be determined, wherein each preset operation level corresponds to at least one feature vector to be determined; inputting a plurality of to-be-determined feature vectors and the plurality of to-be-classified feature vectors after determining a preset operation level into a preset model so as to determine a preset operation level corresponding to each to-be-classified feature vector, wherein each preset operation level corresponds to at least one to-be-classified feature vector; and determining a preset operation grade corresponding to each historical feature vector according to a preset operation grade corresponding to each feature vector to be determined and a preset operation grade corresponding to each feature vector to be graded.

In some embodiments, the first determining module 702 may be configured to: receiving a grade marking instruction, wherein the grade marking instruction is used for carrying out preset operation grade marking on each feature vector to be determined; and determining a preset operation level corresponding to each feature vector to be determined according to the level marking instruction.

The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed on a computer, the computer is caused to execute the flow in the method for assessing the operation level of a device according to the embodiment.

The embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the flow in the method for assessing the operation level of the device provided in this embodiment by calling the computer program stored in the memory.

For example, the electronic device may be a smart phone, a tablet computer, a game device, an AR (augmented reality) device, an automobile, a data storage device, an audio playing device, a video playing device, a notebook computer, a desktop computing device, a wearable device such as an electronic watch, an electronic glasses, an electronic helmet, an electronic bracelet, an electronic necklace, an electronic garment, or the like.

Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

The electronic device 800 may include components such as a processor 801 and a memory 802. The processor 801 is electrically connected to the memory 802. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 8 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The processor 801 is a control center of the electronic device 800, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or calling a computer program stored in the memory 802 and calling data stored in the memory 802, thereby performing overall monitoring of the electronic device.

In this embodiment, the processor 801 in the electronic device 800 loads instructions corresponding to one or more computer program processes into the memory 802 according to the following procedures, and the processor 801 executes the computer program stored in the memory 802, so as to implement various functions:

acquiring a plurality of historical operating data of the electronic equipment;

acquiring current operating data of the electronic equipment;

Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to a second embodiment of the present disclosure.

Wherein the electronic device 900 comprises: a processor 901, a memory 902, a display 903, a control circuit 904, an input unit 905, a sensor 906, and a power supply 907. The processor 901 is electrically connected to the display 903, the control circuit 904, the input unit 905, the sensor 906, and the power source 907.

The display 903 may be used to display information entered by or provided to the user as well as various graphical user interfaces of the electronic device, which may be comprised of images, text, icons, video, and any combination thereof.

The control circuit 904 is electrically connected to the display 903, and is configured to control the display 903 to display information.

The input unit 905 may be used to receive input numbers, character information, or user characteristic information (e.g., fingerprint), and generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. The input unit 905 may include a fingerprint recognition module.

The sensor 906 is used to collect information of the electronic device itself or information of the user or external environment information. For example, the sensors 906 may include 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, a heart rate sensor, and the like.

Power supply 907 is used to power the various components of electronic device 900. In some embodiments, power supply 907 may be logically coupled to processor 901 via a power management system, such that functions of managing charging, discharging, and power consumption are performed via the power management system.

Although not shown in fig. 9, the electronic device 900 may further include a camera, a bluetooth module, etc., which are not described in detail herein.

In this embodiment, the processor 901 in the electronic device loads the executable code corresponding to the processes of one or more application programs into the memory 902 according to the following instructions, and the processor 901 runs the application programs stored in the memory 902, so as to implement the following processes:

acquiring a plurality of historical operating data of the electronic equipment;

acquiring current operating data of the electronic equipment;

In some embodiments, the processor 901 may further perform: sequentially calculating the similarity between the current feature vector and each preset feature vector to obtain at least two similarity values; and determining a target feature vector matched with the current feature vector from at least two preset feature vectors according to at least two similarity values.

In some embodiments, the processor 901 may further perform: determining a maximum similarity value from at least two of the similarity values; and determining the preset feature vector corresponding to the maximum similarity value as a target feature vector.

In some embodiments, the processor 901 may further perform: acquiring a historical characteristic vector corresponding to each historical operating data to obtain a plurality of historical characteristic vectors; determining a preset operation level corresponding to each historical feature vector in a plurality of historical feature vectors to obtain at least two preset operation levels, wherein each preset operation level corresponds to at least one historical feature vector; and determining a preset feature vector corresponding to each preset operation level according to at least one historical feature vector corresponding to each preset operation level.

In some embodiments, the processor 901 may further perform: calculating an average characteristic vector of at least one historical characteristic vector corresponding to each preset operation level; and determining the average characteristic vector corresponding to each preset operation level as a preset characteristic vector corresponding to each preset operation level.

In some embodiments, the processor 901 may further perform: selecting a target number of historical feature vectors from the plurality of historical feature vectors; determining the selected historical feature vectors of the target quantity into a plurality of feature vectors to be determined; determining the remaining historical feature vectors which are not selected from the historical feature vectors as feature vectors to be classified; determining a preset operation level corresponding to each feature vector to be determined, wherein each preset operation level corresponds to at least one feature vector to be determined; inputting a plurality of to-be-determined feature vectors and a plurality of to-be-classified feature vectors after a preset operation grade is determined into a preset model so as to determine a preset operation grade corresponding to each to-be-classified feature vector, wherein each preset operation grade corresponds to at least one to-be-classified feature vector; and determining a preset operation grade corresponding to each historical feature vector according to a preset operation grade corresponding to each feature vector to be determined and a preset operation grade corresponding to each feature vector to be graded.

In some embodiments, the processor 901 may further perform: receiving a grade marking instruction, wherein the grade marking instruction is used for carrying out preset operation grade marking on each feature vector to be determined; and determining a preset operation level corresponding to each feature vector to be determined according to the level marking instruction.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and a part which is not described in detail in a certain embodiment may refer to the above detailed description of the method for rating the operation level of the device, and is not described again here.

The device for assessing the device operation level provided in the embodiment of the present application and the method for assessing the device operation level in the above embodiments belong to the same concept, and any one of the methods provided in the method for assessing the device operation level may be run on the device for assessing the device operation level, and a specific implementation process thereof is described in the embodiment of the method for assessing the device operation level, and is not described herein again.

It should be noted that, for the method for rating an operation level of a device according to the embodiment of the present application, it can be understood by those skilled in the art that all or part of the process implementing the method for rating an operation level of a device according to the embodiment of the present application may be implemented by controlling the relevant hardware through a computer program, where the computer program may be stored in a computer-readable storage medium, such as a memory, and executed by at least one processor, and during the execution, the process of the embodiment of the method for rating an operation level of a device may be included. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

For the device for assessing the operation level of the apparatus according to the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist alone physically, or two or more modules are integrated into 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 in the form of 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 device operation level provided by the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above 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

1. A method for assessing the operation level of a device, comprising:

acquiring a plurality of historical operating data of the electronic equipment;

acquiring current operating data of the electronic equipment;

2. The method for rating an operation level of a device according to claim 1, further comprising:

sequentially calculating the similarity between the current feature vector and each preset feature vector to obtain at least two similarity values;

and determining a target feature vector matched with the current feature vector from at least two preset feature vectors according to at least two similarity values.

3. The method for rating an operation level of a device according to claim 2, further comprising:

determining a maximum similarity value from at least two of the similarity values;

and determining the preset feature vector corresponding to the maximum similarity value as a target feature vector.

4. The method for rating an operation level of a device according to claim 1, further comprising:

acquiring a historical characteristic vector corresponding to each historical operating data to obtain a plurality of historical characteristic vectors;

determining a preset operation level corresponding to each historical feature vector in a plurality of historical feature vectors to obtain at least two preset operation levels, wherein each preset operation level corresponds to at least one historical feature vector;

and determining a preset feature vector corresponding to each preset operation level according to at least one historical feature vector corresponding to each preset operation level.

5. The method of assessing the operational level of a device according to claim 4, further comprising:

calculating an average characteristic vector of at least one historical characteristic vector corresponding to each preset operation level;

and determining the average characteristic vector corresponding to each preset operation level as a preset characteristic vector corresponding to each preset operation level.

6. The method of assessing the operational level of a device according to claim 4, further comprising:

selecting a target number of historical feature vectors from the plurality of historical feature vectors;

determining the selected historical feature vectors of the target quantity into a plurality of feature vectors to be determined;

determining the remaining historical feature vectors which are not selected from the historical feature vectors as feature vectors to be classified;

determining a preset operation level corresponding to each feature vector to be determined, wherein each preset operation level corresponds to at least one feature vector to be determined;

inputting a plurality of to-be-determined feature vectors and a plurality of to-be-classified feature vectors after a preset operation grade is determined into a preset model so as to determine a preset operation grade corresponding to each to-be-classified feature vector, wherein each preset operation grade corresponds to at least one to-be-classified feature vector;

and determining a preset operation grade corresponding to each historical feature vector according to a preset operation grade corresponding to each feature vector to be determined and a preset operation grade corresponding to each feature vector to be graded.

7. The method of rating an operational level of a device according to claim 6, further comprising:

receiving a grade marking instruction, wherein the grade marking instruction is used for carrying out preset operation grade marking on each feature vector to be determined;

and determining a preset operation level corresponding to each feature vector to be determined according to the level marking instruction.

8. An apparatus for rating an operation level of a plant, comprising:

9. A storage medium, in which a computer program is stored, which, when run on a computer, causes the computer to execute the method of assessing an operation level of an apparatus according to any one of claims 1 to 7.

10. An electronic device, characterized in that the electronic device comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor is used for executing the method for rating the operation level of the device according to any one of claims 1 to 7 by calling the computer program stored in the memory.