CN115562960A

CN115562960A - Application optimization method and system of intelligent equipment

Info

Publication number: CN115562960A
Application number: CN202211145064.1A
Authority: CN
Inventors: 党娜; 徐凯路; 王春燕
Original assignee: Bank of China Ltd
Current assignee: Bank of China Ltd
Priority date: 2022-09-20
Filing date: 2022-09-20
Publication date: 2023-01-03

Abstract

The application provides an application optimization method and system of intelligent equipment, relates to the field of intelligent data management, and can be applied to the financial field and other fields, and the method specifically comprises the following steps: acquiring operation information of the corresponding application used by the user according to the received application identifier; obtaining the use evaluation of the user through the evaluation model analysis established by using the historical data according to the operation information; updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening in the application evaluation table according to the ranking information to obtain information to be optimized; and carrying out corresponding optimization processing according to the information to be optimized.

Description

Application optimization method and system of intelligent equipment

Technical Field

The application relates to the field of intelligent data management, can be applied to the financial field and other fields, and particularly relates to an application optimization method and system of intelligent equipment.

Background

With the development of random information technology, intelligent equipment is popularized to the aspects of people's life; in the use process of the intelligent device, various application apps exist; the quality of the use of each app mainly depends on the evaluation of a user, but the use condition of the user is not considered by the existing evaluation mechanism, and the evaluation of the user is real user evaluation or malicious review cannot be effectively distinguished, so that the supplier of the app cannot confirm the real use condition of the user of the app, and the weak link of the app cannot be effectively optimized and improved, so that the app with a large investment cost cannot be effectively maintained, the optimized content of a worker cannot accurately correspond to the focus of the user, the full optimization not only causes too high time cost, but also is a great challenge to the capital cost.

In the prior art, the use condition of a user is confirmed through questionnaire survey and other manners, but the manner undoubtedly brings certain trouble to the user, the user can accept the use condition for one time or a few times, but frequent survey inevitably causes the user to generate bored psychology, and the use experience of the user is greatly reduced.

Based on the above, there is a need in the art for an application optimization method and system for an intelligent device, which accurately locates a focus of a user on the basis of effectively improving the user experience through the satisfaction evaluation of an inductively identified user, so as to perform accurate optimization.

Disclosure of Invention

The application optimization method and system for the intelligent device are used for dynamically judging the satisfaction degree of different users by using habits of the users, accurately positioning the content to be optimized based on the statistical result of the satisfaction degree and realizing timely optimization.

To achieve the above object, the application optimization method of an intelligent device provided in the present application specifically includes: acquiring operation information of the corresponding application used by the user according to the received application identifier; obtaining the use evaluation of the user through evaluation model analysis constructed by using historical data according to the operation information; updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening in the application evaluation table according to the ranking information to obtain information to be optimized; and performing corresponding optimization processing according to the information to be optimized.

In the application optimization method of the intelligent device, optionally, the method further includes: collecting operation information of a user during starting application and closing application and an evaluation result fed back by the user; converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result; and obtaining an evaluation model through Bayesian theorem training according to the training sample data.

In the application optimization method of the intelligent device, optionally, the operation information includes a function item selected by the user in the application, application feedback information, total duration of the user using the application, and dwell time and login duration of each page.

In the application optimization method of the smart device, optionally, updating the ranking information of the target application in the preset application evaluation table through the use evaluation further includes: generating a function identifier according to the function item in the operation information; and updating corresponding function ranking information under the target application in the application evaluation table through the use evaluation.

In the application optimization method of the intelligent device, optionally, the obtaining of the information to be optimized by screening in the application evaluation table according to the ranking information further includes: obtaining function ranking information under target application according to the application identifier in the application evaluation table; and screening one or more functional items according to the ranking order of each function in the function ranking information to generate information to be optimized.

The application also provides an application optimization system of the intelligent device, wherein the system comprises an acquisition module, an analysis module, a positioning module and an optimization module; the acquisition module is used for acquiring the operation information of the corresponding application used by the user according to the received application identification; the analysis module is used for analyzing and obtaining the use evaluation of the user through an evaluation model constructed by using historical data according to the operation information; the positioning module is used for updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening the ranking information in the application evaluation table to obtain information to be optimized; the optimization module is used for carrying out corresponding optimization processing according to the information to be optimized; the operation information comprises function items selected by the user in the application, application feedback information, the total duration of the application used by the user, the dwell time and the login duration of each page.

In the application optimization system of the intelligent device, optionally, the system further includes a construction module, where the construction module is configured to collect operation information during a period when a user starts an application and closes the application and an evaluation result fed back by the user; converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result; and obtaining an evaluation model through Bayesian theorem training according to the training sample data.

In the application optimization system of the intelligent device, optionally, the positioning module further includes a sorting unit, where the sorting unit is configured to generate a function identifier according to a function item in the operation information; and updating corresponding function ranking information under the target application in the application evaluation table through the use evaluation.

In the application optimization system of the intelligent device, optionally, the positioning module further includes a screening unit, where the screening unit is configured to obtain function ranking information under a target application according to the application identifier in the application evaluation table; and screening one or more functional items according to the ranking order of each function in the function ranking information to generate information to be optimized.

The present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method is implemented.

The present application also provides a computer-readable storage medium storing a computer program for executing the above method.

The present application also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method.

The beneficial technical effect of this application lies in: the satisfaction degree of the user is analyzed and determined through the operation information of the user in the application, so that the real evaluation condition of the user is accurately obtained under the condition of small interference to the user; furthermore, the use purpose of the user is determined by analyzing the operation information and is optimized in a targeted manner, so that the optimization efficiency is effectively improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this application, and are not intended to limit the application. In the drawings:

fig. 1 is a schematic flowchart of an application optimization method for an intelligent device according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a model building process according to an embodiment of the present application;

FIG. 3 is a schematic diagram illustrating a process of updating ranking information according to an embodiment of the present application;

fig. 4 is a schematic diagram illustrating a flow of acquiring information to be optimized according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an application optimization system of a smart device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the application.

Detailed Description

It should be noted that the method and apparatus for mining customer information disclosed in the present application may be used in the field of financial technology, and may also be used in any fields other than the field of financial technology.

The following detailed description will be provided with reference to the drawings and examples to explain how to apply the technical means to solve the technical problems and to achieve the technical effects. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments in the present application may be combined with each other, and the technical solutions formed are all within the scope of the present application.

Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.

Referring to fig. 1, an application optimization method for an intelligent device provided in the present application specifically includes:

s101, acquiring operation information of a user using a corresponding application according to the received application identifier;

s102, obtaining the use evaluation of the user through evaluation model analysis constructed by using historical data according to the operation information;

s103, updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening in the application evaluation table according to the ranking information to obtain information to be optimized;

s104, carrying out corresponding optimization processing according to the information to be optimized.

The operation information comprises function items selected by the user in the application, application feedback information, the total duration of the application used by the user, the dwell time and the login duration of each page.

Specifically, according to the mobile phone bank behavior example, after a user logs in a mobile phone bank each time in actual work and finishes a certain function, the system automatically identifies the total time of the user using the function, the time of each page staying, whether an error is reported, the login time of the user and the time of the user finding a target service module, then judges whether the user is satisfied with the application or the certain function under the application according to artificial intelligence, namely an evaluation model, evaluates the user after the user finishes the service, checks whether the algorithm prediction is consistent with the actual function, and optimizes the algorithm if the algorithm prediction is not consistent with the actual function.

Referring to fig. 2, in an embodiment of the present application, the method further includes:

s201, collecting operation information of a user during starting application and closing application and an evaluation result fed back by the user;

s202, converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result;

s203, obtaining an evaluation model through Bayesian theorem training according to the training sample data.

Specifically, the construction process of the evaluation model in actual work is as follows:

1. and setting x = { A1, A2.. Am } as the corresponding characteristic of the user, wherein A is the characteristic attribute of x, including gender, after the user logs in a mobile phone bank each time and finishes a certain function, the system automatically identifies the total time of using the function by the user, the staying time of each page, whether error is reported, the login time of the user and the time for the user to find a target service module.

2. There is a category set C = (Y1, Y2, Y3), which respectively represents whether the user is satisfied, and is classified as { unsatisfied satisfaction is sufficiently satisfied. }

3. P (Y1 | x), P (Y2 | x), P (Y6 | x) is calculated, i.e., the probability of satisfaction of the user under the condition of the customer characteristic value.

The key point is now to find P (Y1 | x), P (Y2 | x), P (Y3 | x) by the following method:

finding a set of items to be classified of known classification, wherein the set is called a training sample, namely characteristic values of passing personnel, and manually classifying the characteristic values into specific grades as historical data to obtain the corresponding relation between the existing x = { A1, A2.. Am } and C = (Y1, Y2, Y3);

and carrying out statistics to obtain the conditional probability estimation of each characteristic attribute under each category. Namely:

p (A1 | Y1) P (A2 | Y1) \ 8230, P (Am | Y1), P (A1 | Y2) P (A2 | Y2) \ 8230, P (Am | Y2).. P (A1 | Y3) P (A2 | Y3) P (Am | Y3) are the probabilities of each attribute under each class condition. For example, in historical data, in all characteristics of risk level, the user impulse level probability is under the characteristic value. The following derivation is made according to bayes' theorem:

p (Yi | x) = P (x | Yi) P (Yi)/P (x), because the denominator is constant for all categories, we only need the numerator, and the corresponding probability can be obtained by normalization of the numerator;

P(x|Yi)*P(Yi)＝P(A1|Yi)*P(A2|Yi)*…*P(Am|Yi)*P(Yi)；

4、P(Y1|x)＝P(x|Y1)P(Y1)＝P(A1|Y1)*P(A2|Y1)*…*P(Am|Y1)*P(Y1)；

P(Y2|x)＝P(x|Y2)P(Y2)＝P(A1|Y2)*P(A2|Y2)*…*P(Am|Y2)*P(Y2)

...

P(Y3|x)＝P(x|Y3)P(Y2)＝P(A1|Y3)*P(A2|Y3)*…*P(Am|Y3)*P(Y3)

and obtaining P (Y1), P (Y2) and P (Y3), namely the satisfaction degree classification of the user. And pushing evaluation to the user according to the satisfaction degree, and if the evaluation of the algorithm and the evaluation of the click of the user are inconsistent, optimizing the algorithm, increasing the characteristic value and the like.

Referring to fig. 3, in an embodiment of the present application, updating the ranking information of the target application in the preset application evaluation table by using the evaluation further includes:

s301, generating a function identifier according to the function item in the operation information;

s302 updates ranking information of corresponding functions under the target application in the application evaluation table through the usage evaluation.

Further, please refer to fig. 4, in an embodiment of the present application, the obtaining of the information to be optimized by screening in the application evaluation table according to the ranking information further includes:

s401, obtaining function ranking information under a target application according to the application identifier in the application evaluation table;

s402, screening one or more function items according to the ranking order of each function in the function ranking information to generate information to be optimized.

Specifically, in actual work, when specific evaluation of multiple functions in a certain application needs to be determined, the specific evaluation can be realized in the manner, the lower limit of the evaluation scale is set to the function under each application, and the application evaluation table can also be divided into sub-tables for each application to record corresponding function evaluation; therefore, when the staff selects the application for optimization, the staff can determine the optimization details in time and find the corresponding function for optimization. By the method, the real function experience of each user can be obtained, experience sequencing is carried out on the functions of the target application of each user, and the functions with poor experience of the whole quantity of users are obtained for optimization.

Referring to fig. 5, the present application further provides an application optimization system for an intelligent device, where the system includes an acquisition module, an analysis module, a positioning module, and an optimization module; the acquisition module is used for acquiring the operation information of the corresponding application used by the user according to the received application identification; the analysis module is used for analyzing and obtaining the use evaluation of the user through an evaluation model constructed by using historical data according to the operation information; the positioning module is used for updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening the ranking information in the application evaluation table to obtain information to be optimized; the optimization module is used for carrying out corresponding optimization processing according to the information to be optimized; the operation information comprises function items selected in the application used by the user, application feedback information, the total duration of the application used by the user, the dwell time and the login duration of each page.

In the above embodiment, the system further includes a construction module, where the construction module is configured to collect operation information during a period in which the user starts application and closes application and an evaluation result fed back by the user; converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result; and obtaining an evaluation model through Bayesian theorem training according to the training sample data.

In an embodiment of the present application, the positioning module further includes a sorting unit and a screening unit, where the sorting unit is configured to generate a function identifier according to a function item in the operation information; and updating corresponding function ranking information under the target application in the application evaluation table through the use evaluation. The screening unit is used for acquiring function ranking information under target application according to the application identifier in the application evaluation table; and screening one or more functional items according to the ranking order of each function in the function ranking information to generate information to be optimized.

The user information in the embodiment of the application is obtained through legal compliance, and the user information is obtained, stored, used, processed and the like through authorization approval of a client.

The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.

As shown in fig. 6, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 6; furthermore, the electronic device 600 may also comprise components not shown in fig. 6, which may be referred to in the prior art.

As shown in fig. 6, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.

The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable devices. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.

The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.

The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.

The memory 140 may also include a data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).

The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. An application optimization method for an intelligent device, the method comprising:

acquiring operation information of the corresponding application used by the user according to the received application identifier;

obtaining the use evaluation of the user through evaluation model analysis constructed by using historical data according to the operation information;

updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening in the application evaluation table according to the ranking information to obtain information to be optimized;

and carrying out corresponding optimization processing according to the information to be optimized.

2. The method for optimizing an application of a smart device according to claim 1, further comprising:

collecting operation information of a user during starting application and closing application and an evaluation result fed back by the user;

converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result;

and obtaining an evaluation model through Bayesian theorem training according to the training sample data.

3. The application optimization method of the intelligent device according to claim 1, wherein the operation information includes function items selected by the user in the application, application feedback information, total duration of the application used by the user, dwell time and login duration of each page.

4. The method for optimizing an application of a smart device according to claim 3, wherein updating ranking information of the target application in the preset application evaluation table by the usage evaluation further comprises:

generating a function identifier according to the function item in the operation information;

and updating corresponding function ranking information under the target application in the application evaluation table through the use evaluation.

5. The method of claim 4, wherein the obtaining information to be optimized by screening in the application evaluation table according to the ranking information further comprises:

obtaining function ranking information under target application according to the application identifier in the application evaluation table;

and screening one or more functional items according to the ranking order of each function in the function ranking information to generate information to be optimized.

6. The application optimization system of the intelligent equipment is characterized by comprising an acquisition module, an analysis module, a positioning module and an optimization module;

the acquisition module is used for acquiring the operation information of the corresponding application used by the user according to the received application identification;

the analysis module is used for analyzing and obtaining the use evaluation of the user through an evaluation model constructed by using historical data according to the operation information;

the positioning module is used for updating ranking information of target applications in a preset application evaluation table through the use evaluation, and screening the ranking information in the application evaluation table to obtain information to be optimized;

the optimization module is used for carrying out corresponding optimization processing according to the information to be optimized;

7. The application optimization system of the intelligent device, according to claim 6, further comprising a construction module, wherein the construction module is configured to collect operation information and evaluation results fed back by the user during the period when the application is started and closed by the user; converting the operation information into feature data, and generating training sample data according to the feature data and the evaluation result; and obtaining an evaluation model through Bayesian theorem training according to the training sample data.

8. The system according to claim 6, wherein the positioning module further comprises a sorting unit, and the sorting unit is configured to generate a function identifier according to a function item in the operation information; and updating corresponding function ranking information under the target application in the application evaluation table through the use evaluation.

9. The system for optimizing the application of the smart device according to claim 8, wherein the positioning module further comprises a screening unit, and the screening unit is configured to obtain ranking information of functions under a target application from the application evaluation table according to the application identifier; and screening one or more functional items according to the ranking order of each function in the function ranking information to generate information to be optimized.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 5 when executing the computer program.

11. A computer-readable storage medium, characterized in that it stores a computer program for executing the method of any one of claims 1 to 5 by a computer.

12. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 5.