CN111797290A

CN111797290A - Data processing method, data processing device, storage medium and electronic equipment

Info

Publication number: CN111797290A
Application number: CN201910282471.9A
Authority: CN
Inventors: 何明; 陈仲铭; 宁梦琪; 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: 2020-10-20

Abstract

The application discloses a data processing method, a data processing device, a storage medium and electronic equipment. The method comprises the following steps: determining a target data type and a first data type under a current scene, wherein the target data type is the type of data needing to be acquired, and the first data type is the type of acquired data; determining a second data type according to the target data type and the first data type, wherein the second data type is the type of data which is not acquired; acquiring a preset database corresponding to a current scene, wherein the preset database stores various types of data in target data types; acquiring data belonging to a second data type from a preset database, and determining the data as supplementary data; and performing data complementing processing according to the complementing data. The embodiment can improve the flexibility of the electronic equipment during data completion operation.

Description

Data processing method, data processing device, storage medium and electronic equipment

Technical Field

The present application belongs to the technical field of electronic devices, and in particular, to a data processing method, apparatus, storage medium, and electronic device.

Background

When performing services based on artificial intelligence, the electronic device may collect various types of data, and then perform intelligent services according to the collected data, such as pushing interesting content to the user. However, in some cases, the data collected by the electronic device may not be complete, and at this time, the electronic device needs to perform a supplementing operation on the data that is not collected, and then perform an intelligent service according to the supplemented data. However, in the related art, the electronic device has poor flexibility when performing the data padding operation.

Disclosure of Invention

The embodiment of the application provides a data processing method and device, a storage medium and an electronic device, which can improve the flexibility of the electronic device in data completion operation.

An embodiment of the present application provides a data processing method, including:

determining a target data type and a first data type in a current scene, wherein the target data type is the type of data needing to be acquired, and the first data type is the type of acquired data;

determining a second data type according to the target data type and the first data type, wherein the second data type is the type of data which is not acquired;

acquiring a preset database corresponding to the current scene, wherein the preset database stores data of each type in the target data types;

acquiring data belonging to the second data type from a preset database, and determining the data as supplementary data;

and performing data supplementing processing according to the supplementing data.

An embodiment of the present application provides a data processing apparatus, including:

the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining a target data type and a first data type under the current scene, the target data type is the type of data needing to be acquired, and the first data type is the type of acquired data;

a second determining module, configured to determine a second data type according to the target data type and the first data type, where the second data type is a type of data that is not acquired;

the acquisition module is used for acquiring a preset database corresponding to the current scene, and the preset database stores data of each type in the target data types;

the third determining module is used for acquiring the data of the second data type from the preset database and determining the data as supplementary data;

and the processing module is used for carrying out data supplementing processing according to the supplementing data.

The embodiment of the application provides a storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed on a computer, the computer is enabled to execute the data processing method provided by the embodiment of the application.

The 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 data processing method provided in the embodiment of the present application by calling the computer program stored in the memory.

In this embodiment, the electronic device may determine a type of data (i.e., a second data type) that is not collected in a current scene, then obtain data belonging to the second data type from a preset database corresponding to the current scene, and determine the obtained data belonging to the second data type as supplementary data. Then, the electronic device may perform data complementing processing in the current scene according to the complementary data. Since the data completing process can be performed according to the scene, the flexibility of the electronic device during the data completing operation can be improved.

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 diagram of a panoramic sensing architecture of an electronic device provided in an embodiment of the present application.

Fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present application.

Fig. 3 is another schematic flow chart of a data processing method according to an embodiment of the present application.

Fig. 4 to fig. 6 are schematic scene diagrams of a data processing method according to an embodiment of the present application.

Fig. 7 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Fig. 9 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.

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

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.

It is understood that the execution subject of the embodiment of the present application may be an electronic device such as a smart phone or a tablet computer.

Referring to fig. 2, fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application, where the flow chart may include:

in 101, a target data type and a first data type in a current scene are determined, wherein the target data type is a type of data needing to be acquired, and the first data type is a type of acquired data.

In 101 of the embodiment of the present application, for example, the electronic device may determine a type of data that needs to be collected in a current scene, and determine the type as a target data type. And the electronic device can determine the type of the data acquired under the current scene and determine the type as the first data type.

In one embodiment, the target data type and the first data type may each be one or more data types.

At 102, a second data type is determined based on the target data type and the first data type, the second data type being a type of data that was not collected.

For example, after determining the target data type and the first data type, the electronic device may determine a second data type according to the target data type and the first data type, where the second data type is a type of data that is not collected. It is understood that the data of the second data type is missing data in the current scenario.

For example, the type of data that needs to be collected in the current scenario (i.e., the target data type) includes A, B, C, D, E, F, G, H, I. And the type of data that has been collected in the current scenario (i.e., the first data type) includes A, B, C, D, I, the electronic device may determine that the type of data that has not been collected (i.e., the second data type) includes E, F, G, H.

In 103, a preset database corresponding to the current scene is obtained, where each type of data in the target data types is stored in the preset database.

For example, after determining the second data type, the electronic device may obtain a preset database corresponding to the current scene, where the preset database stores various types of data in the target data type. For example, the preset database corresponding to the current scene stores therein data of each of the nine data types A, B, C, D, E, F, G, H, I.

At 104, data belonging to the second data type is obtained from the preset database and determined as supplementary data.

For example, after acquiring a preset database corresponding to the current scene, the electronic device may acquire data belonging to the second data type from the preset database, and determine the data as supplementary data.

For example, after acquiring the preset database corresponding to the current scene, the electronic device may acquire E, F, G, H data belonging to the four data types from the preset database, which determines the data as supplementary data. For example, the electronic device acquires the data5 belonging to the E data type from the preset database, acquires the data6 belonging to the F data type from the preset database, acquires the data7 belonging to the G data type from the preset database, and acquires the data8 belonging to the H data type from the preset database. Thereafter, the electronic device may determine the data5, data6, data7, data8 as the supplementary data.

At 105, data padding processing is performed based on the padding data.

For example, after determining the supplemental data, the electronic device may perform data supplementation processing according to the supplemental data. For example, after acquiring the supplemental data5, data6, data7, data8, the electronic device may perform data padding processing of the target data type. For example, as previously described, the first data type of the data that has been collected by the electronic device is A, B, C, D, I, where the collected data of the a data type is data1, the collected data of the B data type is data2, the collected data of the C data type is data3, the collected data of the D data type is data4, and the collected data of the I data type is data 9. Then, the electronic device performs data completing process to process the supplementary data5, data6, data7, data8 and the collected data1, data2, data3, data4, data9 into a set of data required to be collected under the current scene.

It can be understood that, in this embodiment, the electronic device may first determine a type of data (i.e., a second data type) that is not collected in the current scene, then obtain data belonging to the second data type from a preset database corresponding to the current scene, and determine the obtained data belonging to the second data type as supplementary data. Then, the electronic device may perform data complementing processing in the current scene according to the complementary data. Since the data completing process can be performed according to the scene, the flexibility of the electronic device during the data completing operation can be improved.

It should be noted that the data processing method provided by this embodiment may be applied to the data processing layer in the panoramic sensing architecture shown in fig. 1. The data processing method provided by the embodiment can enable the electronic device to perform the filling processing on the data acquired by the electronic device, the data subjected to the filling processing by the data processing layer can be input into the feature extraction layer to perform feature extraction, and the scenario modeling layer can perform modeling according to the data features extracted by the feature extraction layer. The data obtained through modeling can be input into an intelligent service layer, and the intelligent service layer can provide intelligent services for users of the electronic equipment according to the data, such as pushing information suitable for the current situation for the users.

Referring to fig. 3, fig. 3 is another schematic flow chart of a data processing method according to an embodiment of the present application, where the flow chart may include:

in 201, the electronic device obtains scene information of different scenes, and various types of data collected in advance.

For example, the electronic device may first obtain scene information of different scenes. For example, the electronic device may acquire different scenes first, and then represent the different scenes with digital codes respectively. For example, a scene may include home, business, travel, business, fitness, and the like. The electronic device may represent a home scenario with a number 0, a company scenario with a number 1, a travel scenario with a number 3, a business scenario with a number 4, a fitness scenario with a number 5, and so on. That is, the electronic device may sequentially label the scenes.

Also, the electronic device may acquire various types of data collected in advance. These types may include, for example, weather data, GPS location data, temperature data, application runtime length data, user data, and so forth. Each type of data acquired by the electronic device may be a specific data value. For example, weather data may be represented by the numbers 0, 1, 2. Wherein the number 0 may represent a sunny day, the number 1 may represent a cloudy day, the number 2 may represent a rainy day, etc. The GPS location data may be specific latitude and longitude data. The temperature data may be a specific temperature value, such as 25 degrees, 28 degrees, or 30 degrees, etc. The application run length data may be a length of time that a particular application is running.

At 202, the electronic device learns the weight values corresponding to each type in each corresponding scene using the logistic steiny regression model with the data of each type collected in advance as input and the scene information of each scene as output.

In 203, in each scene, the electronic device sorts the weight values corresponding to each type in descending order of the numerical values.

At 204, the electronic device determines the type of the weight value arranged in the preset ordinal as the data type of each corresponding scene.

For example, 202, 203, and 204 may include:

after acquiring scene information of different scenes and pre-collected data of each type, for each scene, the electronic device may use the pre-collected data of each type as an input, use the scene information of the scene as an output, and learn a weight value corresponding to each type in the scene by using a Logistic Regression (Logistic Regression) model.

After learning the weight values corresponding to each type in each scene, the electronic device may sort the weight values corresponding to each type in the order from large to small according to the numerical values in each scene, and determine the type in which the weight values are arranged in the preset order as the data type corresponding to the scene.

For example, for a scene at home, its scene information may be its numerical code 0. Then, the electronic device may use each type of data collected in advance as an input of the logistic-stole regression model, use the numerical code 0 of the home scene as an output of the logistic-stole regression model, and learn the weight value of each (data) type in the home scene by using the logistic-stole regression model.

After obtaining the weight values of each type in the home scene, the electronic device may train the weight values of each type in the home scene in an order from a large value to a small value, and determine the type arranged in the preset ordinal as the data type corresponding to the home scene.

For example, the following data type A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P is included in each type of data collected in advance. For the home scene, the electronic device inputs the data of the data types collected in advance as a logistic-stout regression model, and learns that the weighting values corresponding to the 16 data types of the home scene A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P are 0.15, 0.1, 0.18, 0.09, 0.07, 0.06, 0.05, 0.06, 0.01, 0.03, 0.02, 0.04, and 0.03 in this order by using the logistic-stout regression model with the number 0 of the home scene as the logistic-stout regression model. Then, the electronic device may determine the type with the weight value arranged in the preset ordinal as the data type corresponding to the home scenario. For example, the electronic device may determine a type with a weight value ranked in the top 9 digits (i.e., the first digit to the ninth digit) as a data type corresponding to the home scene. For example, since the weight values of the 9 (data) types of A, B, C, D, E, F, G, H, I are ranked at the top 9 bits, the electronic device may determine A, B, C, D, E, F, G, H, I as the data type corresponding to the scene of the home.

As another example, the following data type A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P is also included in each type of data collected in advance. For the company scene, the electronic device uses the data of the data types collected in advance as the input of the logistic-stout regression model, uses the numerical code 1 of the company scene as the logistic-stout regression model, and learns the weight values corresponding to the 16 data types A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P in the home scene by using the logistic-stout regression model. After that, the electronic device may also determine the type with the weight value ranked in the top 9 bits (i.e., the first bit to the ninth bit) as the data type corresponding to the company scene. For example, since the weight values of the 9 (data) types of A, B, D, F, H, J, L, M, N are ranked at the top 9, the electronic device may determine A, B, D, F, H, J, L, M, N as the data type corresponding to the scenario of company.

It can be understood that the type of data corresponding to each scene determined in 204 is the type of data that needs to be collected in the scene.

At 205, the electronic device sets a database corresponding to each scene, the corresponding database having stored therein each type of data of the data types corresponding to the scene.

For example, after determining the data types corresponding to the scenes, the electronic device may set a database corresponding to the scenes, where the database stores data of each type in the data types corresponding to the scenes.

For example, if the data type corresponding to the scene of the home includes A, B, C, D, E, F, G, H, I, the electronic device may set up a database corresponding to the scene of the home, and the database stores A, B, C, D, E, F, G, H, I data of nine data types.

As another example, if the data type corresponding to the company scenario includes A, B, D, F, H, J, L, M, N, the electronic device may set up a database corresponding to the company scenario, and the database stores A, B, D, F, H, J, L, M, N data of the nine data types.

In an embodiment, the process of setting, by the electronic device in 205, a database corresponding to each scene may include:

in each scene, the electronic equipment acquires each type of data collected in advance and acquires a data type corresponding to the scene;

the method comprises the steps that electronic equipment obtains data of data types corresponding to scenes from various types of data collected in advance;

the electronic equipment selects data meeting a preset quality condition from the data of the data type corresponding to the scene;

and according to the data meeting the preset quality condition, the electronic equipment sets a database corresponding to the scene.

For example, the data in the database corresponding to each scene may be obtained from various types of data collected in advance in the process of 201. Because different data have different quality, such as the type of GPS data, some collected GPS data are complete, and some collected GPS data are incomplete. Therefore, for example, when the database is established, when the data of the a data type needs to be acquired, the electronic device may screen high-quality data from the pre-collected data of the a data type, and store the screened high-quality data in the database as the data of the a data type. Wherein the high quality data may be data satisfying a preset quality condition. After the data meeting the preset quality condition is obtained, the electronic equipment can set a database corresponding to the scene according to the data meeting the preset quality condition. That is, each scene is provided with a separate, corresponding database in which data of the data type corresponding to the scene is stored.

In one embodiment, the preset quality condition may be a quality condition set according to a data quality evaluation criterion. The data quality evaluation criterion may include a noise level of the data, a missing rate of the data, integrity of the data, and the like. For example, the preset quality condition may be that the degree of noise is less than a preset first threshold. Alternatively, the preset quality condition may be that the missing rate of the data is smaller than a preset second threshold. Still alternatively, the preset quality condition may be that the integrity of the data is greater than a preset third threshold, and so on. Of course, the preset quality condition may also include two or more conditions at the same time, for example, the preset quality condition may satisfy that both the noise level is less than the preset first threshold and the data loss rate is less than the preset second threshold, and so on.

In one embodiment, for each piece of data, the electronic device may calculate a value representing the quality of the piece of data. For example, when the noise level is used as the evaluation criterion of the data quality, the electronic device may calculate a value representing the noise level of each piece of data, and then the electronic device may compare the value with a preset first threshold, and if the value is greater than or equal to the preset first threshold, it indicates that the piece of data is relatively noisy and does not belong to high-quality data. If the value is smaller than the preset first threshold value, the data is low in noise and belongs to high-quality data.

In one embodiment, when it is necessary to create a database and screen out data of a certain data type for storage in the database from pre-collected data, after selecting high-quality data of the data type from the pre-collected data, the electronic device may sort values representing data quality in descending order, and then screen out data sorted in a preset order to determine the data of the certain data type for storage in the data.

It can be understood that through the above processes 201 to 205, the electronic device determines in advance the data type required to be collected in each scene, and sets a corresponding database for each scene, where the database stores data belonging to the data type required to be collected in the scene.

In 206, the electronic device determines a target data type in the current scene and a first data type, where the target data type is a type of data that needs to be collected, and the first data type is a type of collected data.

For example, the electronic device may collect data in a scene, and perform information push according to the collected data.

Then, when the electronic device pushes information to the user in the current scene, the electronic device may determine the type of data that needs to be collected in the current scene, and determine the type of data as the target data type. And the electronic device can determine the type of the data acquired under the current scene and determine the type as the first data type.

At 207, the electronic device determines a second data type based on the target data type and the first data type, the second data type being a type of data that was not collected.

For example, after determining the target data type and the first data type, the electronic device may determine a second data type according to the target data type and the first data type, where the second data type is a type of data that is not collected. It can be understood that the data of the second data type is missing data in the data required for information pushing in the current scenario.

For example, the current scene is an in-home scene, and according to the process from 201 to 204, the electronic device determines in advance that the type of data that needs to be collected in the in-home scene includes A, B, C, D, E, F, G, H, I. Then, at 206, the electronic device may determine that the target data type in the current scenario is A, B, C, D, E, F, G, H, I. And the type of data that has been collected in the current scenario (i.e., the first data type) includes A, B, C, D, I, the electronic device may determine that the type of data that has not been collected (i.e., the second data type) includes E, F, G, H.

At 208, the electronic device obtains a preset database corresponding to the current scene, where the preset database stores data of each type in the target data types.

For example, after determining the second data type, the electronic device may obtain a preset database corresponding to the current scene, where the preset database stores various types of data in the target data type. For example, the preset database corresponding to the current home scene stores therein data of each of the nine data types A, B, C, D, E, F, G, H, I.

In 209, the electronic device obtains data belonging to the second data type from the preset database and determines the data as supplementary data.

At 210, the electronic device performs data padding processing based on the padding data.

For example, after acquiring the supplemental data5, data6, data7 and data8, the electronic device can perform information push in the current scene according to the data1, data2, data3, data4, data5, data6, data7, data8 and data 9.

That is, in one embodiment, after the flow of the data completing process performed by the electronic device 210, the method may further include the following flow:

and according to the data of the first data type and the supplementary data, the electronic equipment carries out information recommendation under the current scene.

Referring to fig. 4 to 6, fig. 4 to 6 are schematic views of a data processing method according to an embodiment of the present disclosure.

For example, the electronic device determines in advance the data types that need to be collected in each scene, and sets a separate corresponding database for each scene, where the database stores data belonging to the data types that need to be collected in the scene. For example, the electronic device determines that the types of data that need to be collected in the home scene include 9 types of A, B, C, D, E, F, G, H, I, and sets a database corresponding to the home scene, where data belonging to each of the 9 types of A, B, C, D, E, F, G, H, I is stored in the database. For another example, the electronic device determines that the types of data that need to be collected in the company scene include 9 types of A, B, D, F, H, J, L, M, N, and sets a database corresponding to the company scene, where data of each of the 9 types of A, B, D, F, H, J, L, M, N are stored in the database, and so on.

For example, electronic devices need to push information of interest to users in the current scenario. At this time, the electronic device may determine what scene the current scene is, and start to collect data. Then, the electronic device may determine a target data type and a first data type in the current scenario, where the target data type is a type of data that needs to be collected, and the first data type is a type of collected data. For example, as shown in fig. 4, if the current scene is a home scene, then the electronic device may determine that the target data type includes A, B, C, D, E, F, G, H, I. In addition, as shown in FIG. 5, the electronic device collects data1, data2, data3, data4 and data 9. The data types of the data1, the data2, the data3, the data4 and the data9 are A, B, C, D, I in sequence. That is, the electronic device determines that the type of data collected includes A, B, C, D, I. The electronic device may then determine the type of data not collected, i.e., the second data type, including E, F, G, H, for example.

Then, the electronic device may obtain a preset database corresponding to the home scene, and obtain data belonging to the second data type from the preset database. For example, the electronic device acquires the data5 belonging to the E data type from the preset database, acquires the data6 belonging to the F data type from the preset database, acquires the data7 belonging to the G data type from the preset database, and acquires the data8 belonging to the H data type from the preset database, as shown in fig. 6.

After data5, data6, data7 and data8 are acquired, the electronic equipment can perform data complementing processing according to data5, data6, data7 and data8, and push information in the current scene according to a group of data1, data2, data3, data4, data5, data6, data7, data8 and data9 after the data complementing processing.

In this embodiment, by filling up data in a predetermined data type in a targeted manner in different scenarios, data and calculation amount of data filling-up processing can be reduced, and the intelligent degree of data filling-up is improved.

Referring to fig. 7, fig. 7 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present disclosure. The data processing apparatus 300 may include: a first determining module 301, a second determining module 302, an obtaining module 303, a third determining module 304, and a processing module 305.

A first determining module 301, configured to determine a target data type and a first data type in a current scenario, where the target data type is a type of data that needs to be acquired, and the first data type is a type of acquired data.

A second determining module 302, configured to determine a second data type according to the target data type and the first data type, where the second data type is a type of data that is not acquired.

An obtaining module 303, configured to obtain a preset database corresponding to the current scene, where the preset database stores data of each type in the target data types.

A third determining module 304, configured to obtain the data of the second data type from the preset database, and determine the data as supplementary data.

And a processing module 305, configured to perform data complementing processing according to the complementary data.

In one embodiment, the first determining module 301 may be further configured to:

acquiring scene information of different scenes and various types of data collected in advance;

and learning the data type corresponding to each scene according to the scene information and the pre-collected data of each type.

In one embodiment, the first determining module 301 may be configured to:

using the pre-collected data of each type as input, using scene information of each scene as output, and learning out a weight value corresponding to each type under each corresponding scene by using a logical steinz regression model;

and determining the data type corresponding to each corresponding scene according to the weight value corresponding to each type in each scene.

In one embodiment, the first determining module 301 may be configured to:

in each scene, sorting the weighted values corresponding to each type according to the sequence of numerical values from large to small;

and determining the type of the weighted value arranged in the preset ordinal as the data type of each corresponding scene.

and setting a database corresponding to each scene, wherein each type of data in the data types corresponding to the scenes is stored in the corresponding database.

In one embodiment, the first determining module 301 may be configured to:

acquiring various types of data collected in advance under various scenes, and acquiring data types corresponding to the scenes;

acquiring data of a data type corresponding to a scene from the pre-collected data of each type;

selecting data meeting a preset quality condition from the data of the data type corresponding to the scene;

and setting a database corresponding to the scene according to the data meeting the preset quality condition.

In one embodiment, the processing module 305 may be further configured to:

and recommending the information under the current scene according to the data of the first data type and the supplementary data.

The present embodiment 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 data processing method provided in this embodiment.

The 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 flow in the data processing method provided in this embodiment by calling the computer program stored in the memory.

For example, the electronic device may be a mobile terminal such as a tablet computer or a smart phone. 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 400 may include components such as a sensor 401, a memory 402, a processor 403, and the like. 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 sensors 401 may include a gyro sensor (e.g., a three-axis gyro sensor), an acceleration sensor, and the like.

The memory 402 may be used to store applications and data. The memory 402 stores applications containing executable code. The application programs may constitute various functional modules. The processor 403 executes various functional applications and data processing by running an application program stored in the memory 402.

The processor 403 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing an application program stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the electronic device.

In this embodiment, the processor 403 in the electronic device loads the executable code corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 403 runs the application programs stored in the memory 402, so as to execute:

Referring to fig. 9, an electronic device 500 may include a sensor 501, a memory 502, a processor 503, a display 504, a speaker 505, a microphone 506, and the like.

The sensor 501 may include a gyro sensor (e.g., a three-axis gyro sensor), an acceleration sensor, and the like.

The memory 502 may be used to store applications and data. Memory 502 stores applications containing executable code. The application programs may constitute various functional modules. The processor 503 executes various functional applications and data processing by running an application program stored in the memory 502.

The processor 503 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing an application program stored in the memory 502 and calling the data stored in the memory 502, thereby performing overall monitoring of the electronic device.

The display screen 504 may be used to display information such as images and text. Speaker 505 may be used to play sound signals. The microphone 506 may be used to pick up sound signals in the surrounding environment.

In this embodiment, the processor 503 in the electronic device loads the executable code corresponding to the processes of one or more application programs into the memory 502 according to the following instructions, and the processor 503 runs the application programs stored in the memory 502, so as to execute:

In one embodiment, before the determining the target data type and the first data type in the current scenario, the processor 503 may further perform: acquiring scene information of different scenes and various types of data collected in advance; and learning the data type corresponding to each scene according to the scene information and the pre-collected data of each type.

In one embodiment, when the processor 503 executes the learning of the data type corresponding to each scene according to the scene information and the pre-collected data of each type, the following steps may be executed: using the pre-collected data of each type as input, using scene information of each scene as output, and learning out a weight value corresponding to each type under each corresponding scene by using a logical steinz regression model; and determining the data type corresponding to each corresponding scene according to the weight value corresponding to each type in each scene.

In an embodiment, when the processor 503 executes the weighted value corresponding to each type in each scene to determine the data type corresponding to each corresponding scene, it may execute: in each scene, sorting the weighted values corresponding to each type according to the sequence of numerical values from large to small; and determining the type of the weighted value arranged in the preset ordinal as the data type of each corresponding scene.

In one embodiment, before the determining the target data type and the first data type in the current scenario, the processor 503 may further perform: and setting a database corresponding to each scene, wherein each type of data in the data types corresponding to the scenes is stored in the corresponding database.

In one embodiment, when the processor 503 executes the database corresponding to each scene, it may execute: acquiring various types of data collected in advance under various scenes, and acquiring data types corresponding to the scenes; acquiring data of a data type corresponding to a scene from the pre-collected data of each type; selecting data meeting a preset quality condition from the data of the data type corresponding to the scene; and setting a database corresponding to the scene according to the data meeting the preset quality condition.

In one embodiment, after performing the data padding process, the processor 503 may further perform: and recommending the information under the current scene according to the data of the first data type and the supplementary data.

In the above embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed description of the data processing method, and are not described herein again.

The data processing apparatus provided in the embodiment of the present application and the data processing method in the above embodiment belong to the same concept, and any method provided in the embodiment of the data processing method may be run on the data processing apparatus, and a specific implementation process thereof is described in the embodiment of the data processing method in detail, and is not described herein again.

It should be noted that, for the data processing method described in the embodiment of the present application, it can be understood by those skilled in the art that all or part of the process of implementing the data processing method described 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, and executed by at least one processor, and during the execution, the process of the embodiment of the data processing method can 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.

In the data processing 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 foregoing detailed description has provided a data processing method, an apparatus, a storage medium, and an electronic device according to embodiments of the present application, and specific examples are applied herein to explain the principles and implementations of the present application, and the descriptions of the foregoing embodiments are only used to help understand the method and the core ideas 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 data processing method, comprising:

2. The data processing method of claim 1, further comprising, before the determining the target data type and the first data type in the current scenario:

3. The data processing method according to claim 2, wherein the learning of the data type corresponding to each scene from the scene information and the types of data collected in advance comprises:

4. The data processing method according to claim 3, wherein the determining, according to the weight value corresponding to each type in each scene, the data type corresponding to each corresponding scene comprises:

5. The data processing method of claim 1, further comprising, before the determining the target data type and the first data type in the current scenario:

6. The data processing method according to claim 5, wherein the setting a database corresponding to each scene includes:

7. The data processing method according to claim 1, further comprising, after said performing data padding processing:

8. A data processing apparatus, comprising:

9. A storage medium having stored thereon a computer program, the computer program, when executed on a computer, causing the computer to perform the method of any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, wherein the processor is configured to perform the method of any one of claims 1 to 7 by invoking a computer program stored in the memory.