阅读说明：本技术 数据处理方法、装置、电子设备和介质 (Data processing method, device, electronic equipment and medium ) 是由 王少帅 周默 于 2020-05-29 设计创作，主要内容包括：本公开提供了一种数据处理方法,包括：获取用户的当前反馈数据,将所述当前反馈数据输入至多个第一模型中,得到当前反馈预测结果集,所述当前反馈预测结果集包括用于表征所述用户当前态度的多个第一预测结果,获取所述用户的历史反馈预测结果集,所述历史反馈预测结果集与所述用户的历史反馈数据相对应,以及将所述当前反馈预测结果集和所述历史反馈预测结果集输入至第二模型,得到用于表征所述用户态度的第二预测结果。(The present disclosure provides a data processing method, including: the method comprises the steps of obtaining current feedback data of a user, inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, obtaining a historical feedback prediction result set of the user, wherein the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user, the historical feedback prediction result set corresponds to historical feedback data of the user, and inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result used for representing the attitude of the user.)

1. A method of data processing, comprising:

acquiring current feedback data of a user;

inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, wherein the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user;

acquiring a historical feedback prediction result set of the user, wherein the historical feedback prediction result set corresponds to historical feedback data of the user; and

and inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result for representing the user attitude.

2. The method of claim 1, further comprising:

and determining whether the current feedback prediction result set and the historical feedback prediction result set meet preset conditions.

3. The method of claim 2, wherein the determining whether the current set of feedback predictors and the historical set of feedback predictors satisfy preset conditions comprises:

and determining whether each first prediction result in the current feedback prediction result set and the historical feedback prediction result set is within a preset range.

4. The method of claim 2 or 3, wherein said inputting the current set of feedback predictors and the historical set of feedback predictors to a second model comprises:

and under the condition that the current feedback prediction result set and the historical feedback prediction result set both meet preset conditions, inputting the current feedback prediction result set and the historical feedback prediction result set into a second model.

5. The method of claim 1, wherein said obtaining a historical set of feedback predictors for the user comprises:

and acquiring a historical feedback prediction result set of the user in a preset time period.

6. The method of claim 1, wherein the feedback data has an identification;

the obtaining of the historical feedback prediction result set of the user includes:

and acquiring a historical feedback prediction result set corresponding to historical feedback data of the user, wherein the historical feedback data and the current feedback data have the same identification.

7. The method of claim 1, wherein a plurality of sample data respectively corresponding to said plurality of first models are at least partially different from each other.

8. A data processing apparatus comprising:

the first acquisition module is used for acquiring current feedback data of a user;

the first prediction module is used for inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, wherein the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user;

a second obtaining module, configured to obtain a historical feedback prediction result set of the user, where the historical feedback prediction result set corresponds to historical feedback data of the user; and

and the second prediction module is used for inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result for representing the user attitude.

9. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.

10. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 7.

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a data processing method, an apparatus, an electronic device, and a medium.

Background

With the rapid development and high popularity of the internet, electronic commerce plays an increasingly important role in daily life and work of people, and the quality requirement of consumers on after-sale services is higher and higher. Therefore, how to find out the discontented emotion of the user in time in the interaction process so as to solve the actual needs of the customer in time becomes a problem to be solved urgently.

Disclosure of Invention

In view of the above, the present disclosure provides a data processing method, apparatus, electronic device and medium.

One aspect of the present disclosure provides a data processing method, including: the method comprises the steps of obtaining current feedback data of a user, inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, obtaining a historical feedback prediction result set of the user, wherein the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user, the historical feedback prediction result set corresponds to historical feedback data of the user, and inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result used for representing the attitude of the user.

According to an embodiment of the present disclosure, the method further comprises: and determining whether the current feedback prediction result set and the historical feedback prediction result set meet preset conditions.

According to an embodiment of the present disclosure, the determining whether the current feedback prediction result set and the historical feedback prediction result set satisfy a preset condition includes: and determining whether each first prediction result in the current feedback prediction result set and the historical feedback prediction result set is within a preset range.

According to an embodiment of the present disclosure, the inputting the current feedback prediction result set and the historical feedback prediction result set to a second model includes: and under the condition that the current feedback prediction result set and the historical feedback prediction result set both meet preset conditions, inputting the current feedback prediction result set and the historical feedback prediction result set into a second model.

According to an embodiment of the present disclosure, the obtaining of the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set of the user in a preset time period.

According to an embodiment of the present disclosure, the feedback data has an identifier, and the obtaining the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set corresponding to historical feedback data of the user, wherein the historical feedback data and the current feedback data have the same identification.

According to an embodiment of the present disclosure, a plurality of sample data respectively corresponding to the plurality of first models are at least partially different from each other.

Another aspect of the disclosure provides a data processing apparatus including a first obtaining module, a first predicting module, a second obtaining module, and a second predicting module. The first obtaining module is used for obtaining current feedback data of a user. The first prediction module is used for inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, and the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user. The second obtaining module is configured to obtain a historical feedback prediction result set of the user, where the historical feedback prediction result set corresponds to historical feedback data of the user. And the second prediction module is used for inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result for representing the user attitude.

According to an embodiment of the present disclosure, the apparatus further comprises: and the determining module is used for determining whether the current feedback prediction result set and the historical feedback prediction result set meet preset conditions.

According to an embodiment of the present disclosure, the determining whether the current feedback prediction result set and the historical feedback prediction result set satisfy a preset condition includes: and determining whether each first prediction result in the current feedback prediction result set and the historical feedback prediction result set is within a preset range.

According to an embodiment of the present disclosure, the inputting the current feedback prediction result set and the historical feedback prediction result set to a second model includes: and under the condition that the current feedback prediction result set and the historical feedback prediction result set both meet preset conditions, inputting the current feedback prediction result set and the historical feedback prediction result set into a second model.

According to an embodiment of the present disclosure, the obtaining of the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set of the user in a preset time period.

According to an embodiment of the present disclosure, the feedback data has an identifier, and the obtaining the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set corresponding to historical feedback data of the user, wherein the historical feedback data and the current feedback data have the same identification.

According to an embodiment of the present disclosure, a plurality of sample data respectively corresponding to the plurality of first models are at least partially different from each other.

Another aspect of the present disclosure provides an electronic device including: one or more processors, a storage device to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method as described above.

Another aspect of the present disclosure provides a computer-readable medium storing computer-executable instructions for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

fig. 1 schematically shows a system architecture of a data processing method and apparatus according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a diagram of a plurality of first models predicting feedback data, in accordance with an embodiment of the present disclosure;

FIG. 4 schematically illustrates a diagram of a historical feedback predictor set and a current feedback predictor set, according to an embodiment of the present disclosure;

FIG. 5 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and

fig. 6 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The words "a", "an" and "the" and the like as used herein are also intended to include the meanings of "a plurality" and "the" unless the context clearly dictates otherwise. Furthermore, the terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".

The embodiment of the disclosure provides a data processing method and device. The method comprises the following steps: the method comprises the steps of obtaining current feedback data of a user, inputting the current feedback data into a plurality of first models to obtain a current feedback prediction result set, obtaining a historical feedback prediction result set of the user, wherein the current feedback prediction result set comprises a plurality of first prediction results used for representing the current attitude of the user, the historical feedback prediction result set corresponds to historical feedback data of the user, and inputting the current feedback prediction result set and the historical feedback prediction result set into a second model to obtain a second prediction result used for representing the attitude of the user.

Fig. 1 schematically illustrates a system architecture 100 of a data processing method and apparatus according to an embodiment of the present disclosure.

It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the data processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the data processing apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The data processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the data processing apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.

For example, after-sales service personnel may interact with the user through any one of the terminal devices 101, 102, 103 (e.g., but not limited to, the terminal device 101), and feedback data of the user's interaction may be stored in the terminal device 101. The server 105 may obtain feedback data of the user's interaction from the terminal device 101, and predict a user attitude based on the feedback data, so as to find out discontent emotions of the user in time, and solve actual needs of the client in time, thereby improving user experience.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes operations S201 to S204.

In operation S201, current feedback data of a user is acquired.

According to an embodiment of the present disclosure, the feedback data may be, for example, data of the user's interaction with the after-sales. For example, the data may be voice data for the user to communicate with the after-sales phone, or text data, voice data, or picture data for the user to communicate with the after-sales phone on the platform.

In the embodiment of the present disclosure, various types of feedback data may be each converted into text data. For example, voice data may be recognized as text data.

According to the embodiment of the present disclosure, the current feedback data may be, for example, feedback data for the user to communicate with the after-sales service this time. For example, the user is the feedback data of the telephone communication with the after-sales terminal. For example, after the user completes the communication, the feedback data of the communication may be acquired as the current feedback data.

In operation S202, current feedback data is input into the plurality of first models, and a current feedback prediction result set is obtained, where the current feedback prediction result set includes a plurality of first prediction results used for representing a current attitude of the user.

According to an embodiment of the present disclosure, a plurality of sample data respectively corresponding to the plurality of first models are at least partially different from each other. For example, a negative sample X may be used_negSplitting into N portions (X)_neg1，X_neg2，X_neg3，...，X_negN) A positive sample X_posAlso split into N portions (X)_pos1，X_pos2，X_pos3，...，X_posN) Thus, N sample data (X) can be obtained_train1，X_train2，X_train3，...，X_trainN) Each sample data may include one positive sample and one negative sample. The disclosed embodiments may train N first models using the N sample data, where N is an integer greater than 1.

In the embodiment of the present disclosure, the plurality of first models may be trained by different servers, so that the training speed of the models may be increased. The trained first model may be used to receive feedback data and output a first prediction result that characterizes a user attitude.

Fig. 3 schematically illustrates a diagram of a plurality of first models predicting feedback data according to an embodiment of the present disclosure. As shown in fig. 3, the feedback data may be input into N first models, and the N first models process the feedback data, so that N first prediction results output by the N first models may be obtained. The N first predictors may constitute a set of predictors corresponding to the feedback data. For example, the feedback data may be current feedback data, and the obtained prediction result set may be a prediction result set of current feedback.

In an embodiment of the disclosure, the first prediction result may characterize a user attitude. For example, the first prediction result may be a numerical value. For example, the first prediction result may be any value between 0 and 1, wherein a value closer to 0 may indicate a smaller dissatisfaction of the user, and a value closer to 1 may indicate a larger dissatisfaction of the user (for example only).

According to an embodiment of the present disclosure, the first model may be, for example, a neural network having a learning function, for example, a convolutional neural network.

The embodiment of the disclosure uses the plurality of first models to process the feedback data to obtain the plurality of first prediction results, and can improve the prediction accuracy. Moreover, the plurality of first models can be trained by different servers, so that the training speed can be improved.

In operation S203, a historical feedback prediction result set of the user is acquired, the historical feedback prediction result set corresponding to historical feedback data of the user.

According to the embodiment of the disclosure, a historical feedback prediction result set corresponding to the historical feedback of the user before the current feedback can be obtained. It can be understood that a user usually performs multiple feedback interactions, and emotion changes occur in the multiple feedback interactions, so that historical feedback information of the user is considered, attitude of the user can be predicted more comprehensively, and prediction efficiency is improved.

For example, fig. 4 schematically illustrates a diagram of a historical feedback predictor set and a current feedback predictor set according to an embodiment of the disclosure. As shown in fig. 4, the current feedback may be, for example, the mth feedback of the user, and a historical feedback prediction result set corresponding to the previous M-1 historical feedbacks may be obtained. For example, the 1 st feedback prediction result set { first prediction result 11, first prediction result 21, first prediction result 31, … …, first prediction result N1} corresponding to the 1 st feedback of the user may be obtained, and the 2 nd feedback prediction result set { first prediction result 12, first prediction result 22, first prediction result 32, … …, first prediction result N2} corresponding to the 2 nd feedback of the user may be obtained.

In the embodiment of the present disclosure, the current feedback prediction result set and the historical feedback prediction result set may form a prediction result matrix of the user, all elements in the matrix may be first prediction results output by a plurality of first models, each column element of the matrix may correspond to one feedback of the user, and each row element of the matrix may correspond to an output result of the same first model for a plurality of feedbacks.

In an embodiment of the present disclosure, in order to ensure that a historical feedback prediction result set corresponding to the obtained historical feedback and a current feedback are directed to the same event, a historical feedback prediction result set of the user within a preset time period may be obtained. For example, a historical feedback prediction result set corresponding to historical feedback within a week before the current feedback time point is obtained.

In another embodiment of the present disclosure, the feedback data may have an identifier, and a historical feedback prediction result set corresponding to historical feedback data of the user having the same identifier as the current feedback data may be obtained. For example, an identifier may be assigned to the feedback data of each feedback, for example, an order number or the like to which the feedback is directed. Therefore, the historical prediction result set corresponding to the historical feedback data with the same identification can be obtained.

In operation S204, the current feedback prediction result set and the historical feedback prediction result set are input to the second model, so as to obtain a second prediction result for representing the user attitude.

It can be understood that on a platform with a large volume, the feedback data volume generated after sale is very large, and in order to increase the processing speed and improve the processing efficiency, it may be determined whether the current feedback prediction result set and the historical feedback prediction result set meet the preset conditions. And if the current feedback prediction result set and the historical feedback prediction result set are met, inputting the current feedback prediction result set and the historical feedback prediction result set into a second model for prediction, otherwise, considering that the user is not a high-risk user and does not need early warning.

For example, it may be determined whether each of the first predictors of the current and historical feedback predictor sets is within a preset range. For example, if each first prediction result is in the range of 0-0.4 (e.g., a closer value to 0 may indicate a lesser dissatisfaction with the user), the user may be considered to be a high-risk user. For example, the user is just consulting after sales, and there is no dissatisfaction. If at least one first prediction result is in the range of 0.4-1 (for example, the closer the numerical value is to 1, the greater the dissatisfaction degree of the user can be represented), the user can be considered to be at risk of dissatisfaction emotion, and the historical feedback prediction result set and the current feedback prediction result set of the user can be input into the second model to obtain a second prediction result for representing the attitude of the user.

In the embodiment of the present disclosure, the input of the second model may be, for example, a prediction result matrix formed by the current feedback prediction result set and the historical feedback prediction result set, and the output second prediction result may be, for example, data. For example, the second prediction result may be 0 or 1, and 0 may indicate that the user is not a high risk user and there is no discontent mood. 1 can indicate that the user is a high-risk user, has discontented emotion and needs to be handled in time, so that complaints of the user are avoided, and the experience of the user is improved.

According to an embodiment of the present disclosure, the second model may be, for example, a neural network having a learning function, for example, a convolutional neural network. The second model may be the same neural network model as the first model or may be a different neural network model. The input to the first model may be, for example, feedback data text and the output may be a numerical value characterizing the attitude of the user. The input of the second model may be, for example, a prediction result matrix formed by the current feedback prediction result set and the historical feedback prediction result set, and the output may be two components representing the user attitude.

The data processing method provided by the embodiment of the disclosure can be developed and realized based on Tensorflow or Spark, for example.

The embodiment of the disclosure has a plurality of first models, and the plurality of first models can be trained by different servers, so that the training speed can be improved. Meanwhile, the plurality of first models can predict the feedback data at the same time to obtain a plurality of first prediction results, so that the prediction accuracy can be improved.

According to the embodiment of the invention, the feedback data of the current feedback and the historical feedback of the user are considered at the same time, the attitude of the user is analyzed more comprehensively, and the prediction accuracy can be improved, so that the high-risk user can be processed in time, and the user experience is improved.

Fig. 5 schematically shows a block diagram of a data processing apparatus 500 according to an embodiment of the present disclosure.

As shown in fig. 5, the apparatus 500 includes a first obtaining module 510, a first predicting module 520, a second obtaining module 530, and a second predicting module 540.

The first obtaining module 510 is used for obtaining current feedback data of the user.

The first prediction module 520 is configured to input the current feedback data into a plurality of first models to obtain a current feedback prediction result set, where the current feedback prediction result set includes a plurality of first prediction results used for representing the current attitude of the user.

The second obtaining module 530 is configured to obtain a historical feedback prediction result set of the user, where the historical feedback prediction result set corresponds to historical feedback data of the user.

The second prediction module 540 is configured to input the current feedback prediction result set and the historical feedback prediction result set to a second model, so as to obtain a second prediction result for representing the user attitude.

According to an embodiment of the present disclosure, the apparatus 500 may further include: and the determining module is used for determining whether the current feedback prediction result set and the historical feedback prediction result set meet preset conditions.

According to an embodiment of the present disclosure, the determining whether the current feedback prediction result set and the historical feedback prediction result set satisfy a preset condition includes: and determining whether each first prediction result in the current feedback prediction result set and the historical feedback prediction result set is within a preset range.

According to an embodiment of the present disclosure, the inputting the current feedback prediction result set and the historical feedback prediction result set to a second model includes: and under the condition that the current feedback prediction result set and the historical feedback prediction result set both meet preset conditions, inputting the current feedback prediction result set and the historical feedback prediction result set into a second model.

According to an embodiment of the present disclosure, the obtaining of the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set of the user in a preset time period.

According to an embodiment of the present disclosure, the obtaining the historical feedback prediction result set of the user includes: and acquiring a historical feedback prediction result set corresponding to historical feedback data of the user, wherein the historical feedback data and the current feedback data have the same identification.

According to an embodiment of the present disclosure, a plurality of sample data respectively corresponding to the plurality of first models are at least partially different from each other.

According to an embodiment of the present disclosure, the apparatus 500 may, for example, perform the method described above with reference to fig. 2, which is not described herein again.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, the first obtaining module 510, the first predicting module 520, the second obtaining module 530, and the second predicting module 540 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present invention, at least one of the first obtaining module 510, the first predicting module 520, the second obtaining module 530, and the second predicting module 540 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in a suitable combination of software, hardware, and firmware implementations. Alternatively, at least one of the first obtaining module 510, the first predicting module 520, the second obtaining module 530 and the second predicting module 540 may be at least partially implemented as a computer program module, which may perform the functions of the respective modules when the program is executed by a computer.

Fig. 6 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing the different actions of the method flows described with reference to fig. 2 in accordance with embodiments of the present disclosure.

In the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations as described above by executing programs in the ROM 602 and/or the RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations as described above by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

According to an embodiment of the present disclosure, the method described above with reference to the flow chart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. According to embodiments of the present disclosure, a computer-readable medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to perform the method as described above.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.