Intelligent tablet user behavior analysis system combining multi-source data and reinforcement learning

Abstract

The application relates to an intelligent tablet computer user behavior analysis system and method combining multi-source data and reinforcement learning, a computer device, a storage medium and a computer program product. The method comprises the following steps: acquiring user face information, and acquiring a preset brightness adjustment model according to the user face information; acquiring a current brightness value of a tablet computer screen as a first brightness value, acquiring an environmental brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value; acquiring a screen brightness value matched with the brightness difference from the preset brightness adjustment model; adjusting the brightness of the tablet computer screen according to the screen brightness value; waiting and judging whether a user input brightness adjustment instruction is received within a preset time; if yes, acquiring a target brightness according to the brightness adjustment instruction; and feeding back the target brightness to the preset brightness adjustment model to update the brightness adjustment model. The above scheme can realize automatic screen brightness adjustment according to different preset brightness adjustment units matched with different users, and after the automatic screen brightness adjustment is completed, reinforcement learning is performed on the preset brightness adjustment model according to the user input screen brightness adjustment instruction, so that the preset brightness adjustment model can output a screen brightness more in line with user needs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine learning, in particular to a smart tablet user behavior analysis system and method combining multi-source data and reinforcement learning, a computer device, a storage medium and a computer program product. BACKGROUND

[0002] In today's mobile computing era, tablet computers are widely used in education, entertainment, business and other fields due to their portability, rich multimedia functions and user-friendly interface. With the increasing number of tablet computer users and use scenarios, how to provide personalized visual experience for users has become a problem that cannot be ignored in the product design process of tablet computers.

[0003] The existing Chinese invention patent with publication number CN116741093A discloses a mobile terminal expansion screen with a light sensor and a brightness adjustment method. The method includes: A, connecting the expansion screen with the external mobile terminal; B, the light sensor detects and obtains the light intensity value of the environment in real time in the power-on state; C, the obtained light intensity value is sent to the external mobile terminal; D, the external mobile terminal outputs the required brightness percentage according to the preset light intensity value and brightness percentage mapping relationship; E, the required brightness percentage is sent to the expansion screen; F, the expansion screen adjusts the current screen brightness according to the required brightness percentage. The light sensor is arranged on the expansion screen, the light intensity value obtained by the light sensor is sent to the mobile terminal to obtain the required brightness percentage, and the brightness automatic adjustment of the mobile terminal is utilized, so that the expansion screen in the minimalist state has the brightness automatic adjustment function, and the adjustment is simple and convenient.

[0004] However, the screen brightness adjustment scheme of this scheme is still a fixed scheme set by the program, which is difficult to meet the different brightness needs of different users in different scenarios. SUMMARY

[0005] Therefore, it is necessary to provide a smart tablet user behavior analysis system and method combining multi-source data and reinforcement learning, which can provide different brightness adjustment schemes for different users, in view of the above technical problems.

[0006] In a first aspect, the present application provides a smart tablet user behavior analysis method combining multi-source data and reinforcement learning, which comprises:

[0007] obtaining user facial information and obtaining a preset brightness adjustment model according to the user facial information;

[0008] obtaining a current brightness value of the tablet screen as a first brightness value, obtaining an ambient brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0009] obtaining a screen brightness value matching the brightness difference from a preset brightness adjustment model;

[0010] adjusting the brightness of the tablet screen according to the screen brightness value;

[0011] waiting for and judging whether a brightness adjustment instruction input by a user is received within a preset time;

[0012] if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0013] feeding back the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0014] In one embodiment, the specific step of obtaining the ambient brightness value as the second brightness value comprises:

[0015] obtaining a user facial brightness value according to user facial information;

[0016] obtaining a front screen brightness value and a back screen brightness value;

[0017] comparing the user facial brightness value with the front screen brightness value;

[0018] if the user facial brightness value is greater than the front screen brightness value, taking the minimum value between the front screen brightness value and the back screen brightness value as the ambient brightness value;

[0019] if the user facial brightness value is less than the front screen brightness value, taking the maximum value between the front screen brightness value and the back screen brightness value as the ambient brightness value.

[0020] In one embodiment, the specific step of obtaining the screen brightness value matching the brightness difference from the preset brightness adjustment model comprises:

[0021] substituting the brightness difference value into the preset brightness adjustment model to obtain an ideal brightness value;

[0022] judging whether the difference between the first brightness value and the ideal brightness value is greater than a preset threshold value;

[0023] if yes, taking the ideal brightness value as the screen brightness value and outputting;

[0024] if no, taking the first brightness value as the screen brightness value and outputting.

[0025] In one embodiment, the specific step of waiting for and judging whether the brightness adjustment instruction input by the user is received within the preset time comprises:

[0026] Wait for the brightness of the tablet to reach the screen brightness value, and obtain the corresponding timestamp information;

[0027] Take the time corresponding to the timestamp information as the starting time, and wait for a preset time corresponding to the duration;

[0028] Determine whether the brightness adjustment instruction for adjusting the screen brightness input by the user is received during the waiting period.

[0029] In one embodiment, the step of feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model comprises:

[0030] Waiting for and obtaining the last obtained screen adjustment instruction, specifically comprising:

[0031] Obtaining the instruction timestamp when the user inputs the screen adjustment instruction;

[0032] Determine whether a new screen adjustment instruction is received within a second preset time after the instruction timestamp time;

[0033] If not, the screen adjustment instruction is taken as the last obtained screen adjustment instruction;

[0034] If yes, the instruction timestamp corresponding to the newly obtained screen adjustment instruction is obtained, and it is determined whether a screen adjustment instruction is received again within a second preset time after the new instruction timestamp time.

[0035] In one embodiment, the step of feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model further comprises:

[0036] Inputting the target brightness and the brightness difference into the brightness adjustment model as positive samples;

[0037] Inputting the screen brightness value and the brightness difference into the brightness adjustment model as negative samples;

[0038] The preset brightness adjustment model obtained after training the input positive samples and negative samples is taken as a new preset brightness adjustment model.

[0039] In a second aspect, the present application also provides an intelligent tablet user behavior analysis system combining multi-source data and reinforcement learning, which comprises:

[0040] A screen module for obtaining a brightness adjustment instruction and a screen brightness value, adjusting the brightness according to the screen brightness value or the brightness adjustment instruction, and outputting the current brightness value;

[0041] A face information acquisition module for obtaining user face information;

[0042] A brightness acquisition module for detecting the ambient brightness and outputting the ambient brightness value;

[0043] a CPU module, configured to calculate a brightness difference between the first brightness value and the second brightness value, and to run a preset brightness adjustment model to obtain a screen brightness value matched with the brightness difference, and to feed a target brightness corresponding to a user input brightness adjustment instruction to the preset brightness adjustment model to update the brightness adjustment model;

[0044] a storage module, configured to store the preset brightness adjustment model and user information.

[0045] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0046] obtaining user facial information and obtaining a preset brightness adjustment model according to the user facial information;

[0047] obtaining a current brightness value of the tablet computer screen as the first brightness value, obtaining an ambient brightness value as the second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0048] obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0049] adjusting the brightness of the tablet computer screen according to the screen brightness value;

[0050] waiting for and judging whether a user input brightness adjustment instruction is received within a preset time;

[0051] if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0052] feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0053] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0054] obtaining user facial information and obtaining a preset brightness adjustment model according to the user facial information;

[0055] obtaining a current brightness value of the tablet computer screen as the first brightness value, obtaining an ambient brightness value as the second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0056] obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0057] adjusting the brightness of the tablet computer screen according to the screen brightness value;

[0058] waiting for and judging whether a user inputted brightness adjustment instruction is received within a preset time;

[0059] if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0060] feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0061] In a fifth aspect, the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0062] obtaining user facial information and obtaining a preset brightness adjustment model according to the user facial information;

[0063] obtaining a current brightness value of the tablet computer screen as a first brightness value, obtaining an ambient brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0064] obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0065] adjusting the brightness of the tablet computer screen according to the screen brightness value;

[0066] waiting for and judging whether a user inputted brightness adjustment instruction is received within a preset time;

[0067] if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0068] feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0069] The above-mentioned intelligent tablet computer user behavior analysis system, method, computer device, storage medium and computer program product combining multi-source data with reinforcement learning can realize automatic adjustment of screen brightness according to different users by matching different preset brightness adjustment units, and can perform reinforcement learning on the preset brightness adjustment model according to a user inputted screen brightness adjustment instruction after the automatic adjustment of screen brightness is completed, so that the preset brightness adjustment model can output screen brightness more in line with user needs. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 FIG. 1 is a structural block diagram of an embodiment of the intelligent tablet computer user behavior analysis system combining multi-source data with reinforcement learning;

[0071] Figure 2 FIG. 2 is a flowchart of an embodiment of the intelligent tablet computer user behavior analysis method combining multi-source data with reinforcement learning;

[0072] Figure 3Figure 1 is a diagram of an application environment of a smart tablet user behavior analysis method combining multi-source data and reinforcement learning in an embodiment;

[0073] Figure 4 Figure 2 is a diagram of an internal structure of a computer device in an embodiment. DETAILED DESCRIPTION

[0074] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0075] In an embodiment, as shown in Figure 1, a smart tablet user behavior analysis system combining multi-source data and reinforcement learning is provided, comprising: Figure 1 a screen module configured to obtain a brightness adjustment instruction and a screen brightness value, adjust the brightness according to the screen brightness value or the brightness adjustment instruction, and output a current brightness value;

[0076] a face information acquisition module configured to obtain user face information;

[0077] a brightness acquisition module configured to detect an ambient brightness and output an ambient brightness value;

[0078] a CPU module configured to calculate a brightness difference between the first brightness value and the second brightness value, run a preset brightness adjustment model to obtain a screen brightness value matched with the brightness difference, and feed back a target brightness corresponding to the brightness adjustment instruction input by the user to the preset brightness adjustment model to update the brightness adjustment model;

[0079] a storage module configured to store the preset brightness adjustment model and user information.

[0080] Specifically, in the embodiments of the present application, the screen module is a touch screen installed on the tablet computer, which has the functions of receiving various touch signals in addition to the basic display function and brightness adjustment function. The face acquisition module is a front camera installed on one side of the screen module of the tablet computer, which can be used to acquire the face information of the user. The brightness acquisition module is a light sensor installed at the side of the screen of the tablet computer and / or the rear camera module of the tablet computer, which is used to measure the brightness of different directions of the scene where the tablet computer is located, so as to adjust the screen brightness of the tablet computer to a value suitable for the user to watch.

[0081]

[0082] ​In the embodiments of the present application, the brightness acquisition module comprises a front screen brightness detection unit and a back screen brightness detection unit, wherein the front screen brightness detection unit is a light sensor integrated in a front camera module of the tablet computer, and is configured to output a front screen brightness value; the back screen brightness detection unit is a light sensor integrated in a back camera module of the tablet computer, and is configured to output a back screen brightness value.

[0083] Based on the same inventive concept, the embodiments of the present application also provide a multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis method applied to the multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis system. The implementation scheme for solving problems provided by the method is similar to the implementation scheme described in the above method, so the specific limitations in one or more multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis method embodiments provided below can refer to the limitations of the multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis system described above, and will not be repeated here.

[0084] In one embodiment, as shown in Figure 2 A multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis method is provided. Taking the CPU module in the multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis system as an example, the method comprises the following steps:

[0085] Step A100: Obtain user facial information, and obtain a preset brightness adjustment model according to the user facial information.

[0086] The user facial information is a user facial image obtained by the facial information acquisition module. After obtaining the user facial information, the CPU module matches the user facial information with the user identity information pre-stored in the storage module, so as to obtain the user identity information corresponding to the user facial information, and then obtains the preset brightness adjustment model pre-stored in the storage module according to the user identity information. In the embodiments of the present application, at least one preset brightness adjustment model is provided, and the preset brightness adjustment model is one-to-one corresponding to the user identity information, so as to realize different screen brightness adjustment strategies for different users, so that the tablet computer is more in line with the use habits of specific users.

[0087] Step A200: Obtain a current brightness value of the tablet computer screen as a first brightness value, obtain an environmental brightness value as a second brightness value, and calculate a brightness difference between the first brightness value and the second brightness value.

[0088] Wherein, the current brightness value is a brightness value obtained by the CPU after reading the current brightness of the tablet computer screen, and the ambient brightness value is a value of the same dimension as the current brightness value obtained by converting the ambient brightness obtained by the front screen brightness detection unit and / or the back screen brightness detection unit; the brightness difference is the difference between the first brightness value and the second brightness value.

[0089] Specifically, the specific steps of step A200 include:

[0090] Step A210: obtaining a user facial brightness value according to the user facial information.

[0091] Wherein, the user facial brightness value is a brightness value of the same dimension as the current brightness value calculated by image processing the user facial information.

[0092] Step A220: obtaining a front screen brightness value and a back screen brightness value.

[0093] Wherein, the back screen brightness value is a brightness value detected by the back screen brightness acquisition unit.

[0094] Step A230: comparing the user facial brightness value and the front screen brightness value.

[0095] Step A231: if the user facial brightness value is greater than the front screen brightness value, taking the minimum value of the front screen brightness value and the back screen brightness value as the ambient brightness value.

[0096] Step A232: if the user facial brightness value is less than the front screen brightness value, taking the maximum value of the front screen brightness value and the back screen brightness value as the ambient brightness value.

[0097] Through steps A230-A232, the size relationship between the user facial brightness and the front screen brightness is first determined. If the facial brightness is greater than the front screen ambient brightness, it indicates that the current ambient light is weak (for example, after dark, the user is in a scene without light or with weak light), and the screen brightness is too high and should be reduced. Then the minimum value of the front screen brightness value and the back screen brightness value is taken as the ambient brightness value, which aims to: the back screen brightness is less disturbed by the user's facial diffuse reflection light than the front screen brightness, and its detection value is closer to the actual brightness of the user's environment.

[0098] If the facial brightness is less than the front screen ambient brightness, it indicates that the current ambient light is strong (for example, in the morning, or in a scene with strong light in the user's environment), and the screen brightness is too low and should be increased. The maximum value of the front screen brightness value and the back screen brightness value is taken as the ambient brightness value, which aims to: in such a scene, the side with lower brightness is usually the shadow side, for example, the tablet computer is inclined to be set on a horizontal plane, and the back screen brightness detection unit collects the brightness of the back light side of the tablet computer, which is greatly different from the actual scene brightness.

[0099] Step A300: obtaining a screen brightness value matching the brightness difference from a preset brightness adjustment model.

[0100] Wherein, the preset brightness adjustment model is stored in the storage space and is called and run by the CPU module, in the embodiment of the application, the preset brightness adjustment model is a mapping curve between a brightness difference and a screen brightness value, one brightness difference corresponds to one screen brightness value. Specifically, the specific steps of step A300 include:

[0101] Step A310: substituting the brightness difference value into the preset brightness adjustment model to obtain an ideal brightness value.

[0102] Wherein, the ideal brightness value is the screen brightness value corresponding to the brightness difference obtained by the preset brightness adjustment model.

[0103] Step A320: judging whether the difference between the first brightness value and the ideal brightness value is greater than a preset threshold.

[0104] Step A331: if yes, taking the ideal brightness value as the screen brightness value and outputting.

[0105] Step A332: if no, taking the first brightness value as the screen brightness value and outputting.

[0106] By setting steps A310 to A332, the screen brightness can be kept stable, avoiding the change of the tablet computer screen when the external screen brightness changes slightly, thereby reducing the damage of the rapid change of the screen brightness to the user's eyes.

[0107] Step A400: adjusting the brightness of the tablet computer screen according to the screen brightness value.

[0108] Step A500: waiting and judging whether a user input brightness adjustment instruction is received within a preset time.

[0109] Specifically, the steps of step A500 include:

[0110] Step A510: waiting for the brightness of the tablet computer to reach the screen brightness value and obtaining the corresponding timestamp information.

[0111] Step A520: taking the time corresponding to the timestamp information as the starting time and waiting for a preset time corresponding to the duration.

[0112] Step A530: judging whether a brightness adjustment instruction for adjusting the screen brightness is received during the waiting period.

[0113] Through steps A410 to A430, feedback of user self-adjustment of the screen can be obtained after the automatic adjustment of the screen brightness is completed. If the user does not self-adjust the screen brightness within the preset time, it indicates that the screen brightness obtained by the preset brightness adjustment model is the brightness that the current user can adapt to, and thus there is no need to re-adjust and enhance the learning of the model; if the user self-adjusts the screen brightness after the automatic adjustment is completed, it indicates that the screen brightness value after the automatic adjustment makes the user feel uncomfortable, and the preset brightness adjustment model needs to be further trained.

[0114] Step A600: If yes, the target brightness is obtained according to the brightness adjustment instruction.

[0115] Step A700: The target brightness is fed back to the preset brightness adjustment model to update the brightness adjustment model.

[0116] Specifically, the specific steps of step A700 include:

[0117] Step A710: Wait and obtain the last obtained screen adjustment instruction. Specifically, it includes:

[0118] Step A711: Obtain the instruction timestamp when the user inputs the screen adjustment instruction.

[0119] Step A712: Determine whether a new screen adjustment instruction is received within the second preset time after the instruction timestamp time.

[0120] Step A713: If no, the screen adjustment instruction is taken as the last obtained screen adjustment instruction.

[0121] Step A714: If yes, the instruction timestamp corresponding to the newly obtained screen adjustment instruction is obtained, and it is determined whether a screen adjustment instruction is received again within the second preset time after the new instruction timestamp time.

[0122] Through steps A711 to A714, it can be determined whether the user input screen adjustment instruction is ended. After the end, the screen brightness at the end is taken as the screen brightness adapted by the user, and thus the preset brightness adjustment model is input, and thus the reinforcement learning is completed.

[0123] The steps of step A700 further include:

[0124] Step A750: The target brightness and the brightness difference are input into the brightness adjustment model as positive samples.

[0125] Step A760: The screen brightness value and the brightness difference are input into the brightness adjustment model as negative samples.

[0126] Step A770: The preset brightness adjustment model obtained after the input positive samples and negative samples are trained is taken as a new preset brightness adjustment model.

[0127] Through steps A750 to A770, the reinforcement learning of the preset brightness adjustment model can be completed. Thus, the preset brightness adjustment model can output a more adaptive screen brightness for the user after the next automatic adjustment of brightness.

[0128] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0129] Each module in the intelligent tablet computer user behavior analysis system combining multi-source data and reinforcement learning described above can be realized by software, hardware, and combinations thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0130] The intelligent tablet computer user behavior analysis method combining multi-source data and reinforcement learning provided by the embodiments of the present application can also be applied to an application environment as shown in Figure 3 The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 can be, but is not limited to, various tablet computers or Internet of Things devices with tablet computers as the body, such as smart home central terminal, smart car equipment, etc. The server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0131] In one embodiment, a computer device, which can be a terminal, is provided, and its internal structure diagram can be as shown in Figure 4As shown in the figure. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless mode can be achieved through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a multi-source data and reinforcement learning combined intelligent tablet computer user behavior analysis method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad provided on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0132] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0133] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps:

[0134] Step A100: obtaining user facial information, and obtaining a preset brightness adjustment model according to the user facial information;

[0135] Step A200: obtaining a current brightness value of the tablet computer screen as a first brightness value, obtaining an environmental brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0136] Step A300: obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0137] Step A400: adjusting the brightness of the tablet computer screen according to the screen brightness value;

[0138] Step A500: waiting and judging whether a user input brightness adjustment instruction is received within a preset time;

[0139] Step A600: if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0140] Step A700: feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0141] In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which, when executed by a processor, implements the following steps:

[0142] Step A100: obtaining user facial information, and obtaining a preset brightness adjustment model according to the user facial information;

[0143] Step A200: obtaining a current brightness value of the tablet screen as a first brightness value, obtaining an ambient brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0144] Step A300: obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0145] Step A400: adjusting the brightness of the tablet screen according to the screen brightness value;

[0146] Step A500: waiting and judging whether a user input brightness adjustment instruction is received within a preset time;

[0147] Step A600: if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0148] Step A700: feeding the target brightness to the preset brightness adjustment model to update the brightness adjustment model.

[0149] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0150] Step A100: obtaining user facial information, and obtaining a preset brightness adjustment model according to the user facial information;

[0151] Step A200: obtaining a current brightness value of the tablet screen as a first brightness value, obtaining an ambient brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value;

[0152] Step A300: obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model;

[0153] Step A400: adjusting the brightness of the tablet screen according to the screen brightness value;

[0154] Step A500: waiting and judging whether a user input brightness adjustment instruction is received within a preset time;

[0155] Step A600: if yes, obtaining a target brightness according to the brightness adjustment instruction;

[0156] Step A700: feeding the target brightness to a preset brightness adjustment model to update the brightness adjustment model.

[0157] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0159] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described, however, any combination of the technical features is deemed to be within the scope of the present disclosure as long as there is no inconsistency.

[0160] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for analyzing user behavior of an intelligent tablet computer by combining multi-source data with reinforcement learning, characterized in that, The method comprises the following steps: obtaining user facial information and obtaining a preset brightness adjustment model according to the user facial information; obtaining a current brightness value of a tablet computer screen as a first brightness value, obtaining an ambient brightness value as a second brightness value, and calculating a brightness difference between the first brightness value and the second brightness value; obtaining a screen brightness value matched with the brightness difference from the preset brightness adjustment model; adjusting the brightness of the tablet computer screen according to the screen brightness value; waiting and judging whether a user input brightness adjustment instruction is received within a preset time; if yes, obtaining a target brightness according to the brightness adjustment instruction; feeding back the target brightness to the preset brightness adjustment model to update the brightness adjustment model; wherein the specific steps of obtaining the ambient brightness value as the second brightness value comprise: obtaining a user facial brightness value according to the user facial information; obtaining a screen-in-front brightness value and a screen-behind brightness value; comparing the user facial brightness value with the screen-in-front brightness value; if the user facial brightness value is greater than the screen-in-front brightness value, taking the minimum value between the screen-in-front brightness value and the screen-behind brightness value as the ambient brightness value; if the user facial brightness value is less than the screen-in-front brightness value, taking the maximum value between the screen-in-front brightness value and the screen-behind brightness value as the ambient brightness value. 2.The smart tablet user behavior analysis method based on multi-source data and reinforcement learning of claim 1, wherein, The specific steps of obtaining the screen brightness value matched with the brightness difference from the preset brightness adjustment model comprise: substituting the brightness difference into the preset brightness adjustment model to obtain an ideal brightness value; judging whether the difference between the first brightness value and the ideal brightness value is greater than a preset threshold value; if yes, taking the ideal brightness value as the screen brightness value and outputting the same; if no, taking the first brightness value as the screen brightness value and outputting the same. 3.The smart tablet user behavior analysis method based on multi-source data and reinforcement learning of claim 2, wherein, The specific steps of waiting and judging whether a user input brightness adjustment instruction is received within a preset time comprise: waiting for the brightness of the tablet computer to reach the screen brightness value and obtaining corresponding timestamp information; taking the time corresponding to the timestamp information as a starting time, and waiting for a time length corresponding to the preset time; judging whether a brightness adjustment instruction for adjusting the screen brightness is received during the waiting period.

4. The intelligent tablet user behavior analysis method based on multi-source data and reinforcement learning of claim 3, wherein, The steps of feeding back the target brightness to the preset brightness adjustment model to update the brightness adjustment model comprise: waiting and obtaining a last obtained screen adjustment instruction, specifically comprising: obtaining an instruction timestamp when a user input screen adjustment instruction is obtained; judging whether a new screen adjustment instruction is received within a second preset time after the instruction timestamp time; if no, taking the screen adjustment instruction as the last obtained screen adjustment instruction; if yes, re-obtaining an instruction timestamp corresponding to the new screen adjustment instruction and judging whether a screen adjustment instruction is again received within a second preset time after the new instruction timestamp time. 5.The smart tablet user behavior analysis method based on multi-source data and reinforcement learning of claim 4, wherein, The steps of feeding back the target brightness to the preset brightness adjustment model to update the brightness adjustment model further comprise: inputting the target brightness and the brightness difference into the brightness adjustment model as positive samples; inputting the screen brightness value and the brightness difference into the brightness adjustment model as negative samples; The preset brightness adjustment model obtained after the input of the positive sample and the negative sample is trained is taken as a new preset brightness adjustment model. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5. The processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

8. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

Images