Data processing method and electronic equipment

Abstract

The embodiment of the invention provides a data processing method and electronic equipment, and the method comprises the steps: obtaining a first behavior log of a target program in response to monitoring data meeting a preset condition, the monitoring data being related to one or more of equipment operation data, a user state and target program operation data, and the monitoring data being related to one or more of the equipment operation data, the user state and the target program operation data; the first behavior log is used for being sent to a cloud end to feed back user preferences; modifying the first behavior log based on the monitoring data to generate a second behavior log; the second behavior log is sent to the cloud; wherein the content pushed by the cloud end to the target program based on the second behavior schedule is different from the content preferred by the user.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technologies, and in particular, to a data processing method and an electronic device. Background Art

[0002] With the rise of short videos, various types of short videos and related programs emerge in an endless stream and win the love of a large number of users. In order to better meet the preferences of users to a greater extent, many video APPs will generate behavior logs in real time according to the behaviors of users, and based on these behavior logs, the application system can know the preferences of users, and then push video content that the users are interested in to the users. However, the consequence of this measure is that users will be addicted to video APPs for a long time, which not only wastes time, but also affects the eyesight health of users, bringing inconvenience to the learning, work, etc. of users. Existing solutions include that the device system forces users to exit the video APP, which will result in a poor user experience, cause user disgust, and stimulate the rebellious psychology of users, and instead, they will be more persistent in watching the video APP. Summary of the Invention

[0003] The embodiments of the present application provide a data processing method, including:

[0004] In response to the monitoring data meeting a preset condition, obtain a first behavior log of a target program, where the monitoring data is related to one or more of device operation data, user status, and target program operation data, and the first behavior log is used to be sent to the cloud to feedback user preferences;

[0005] Modify the first behavior log based on the monitoring data to generate a second behavior log;

[0006] Send the second behavior log to the cloud;

[0007] Wherein, the content pushed by the cloud to the target program based on the second behavior log is different from the content of user preferences.

[0008] In some embodiments, a proxy is configured between the target program and the cloud, and the proxy is at least used to obtain the behavior log and forward the behavior log to the cloud;

[0009] The obtaining of the first behavior log of the target program includes:

[0010] Obtain the first behavior log sent by the target program based on the proxy.

[0011] In some embodiments, the monitoring data meeting the preset condition includes at least one of the following:

[0012] When it is determined that the running duration of the target program meets the corresponding requirement, it is determined that the monitoring data meets the preset condition; or

[0013] Monitor the feedback made by the user on the output content of the target program. When it is determined that the user state meets the corresponding requirements based on the feedback, determine that the monitoring data meets the preset conditions; or

[0014] When the running duration of the target mode of the monitoring device and the type of program in the running state meet the corresponding requirements, determine that the monitoring data meets the preset conditions.

[0015] In some embodiments, the monitoring of the feedback made by the user on the output content of the target program, and when it is determined that the user state meets the corresponding requirements based on the feedback, determining that the monitoring data meets the preset conditions includes at least one of the following:

[0016] Obtain the input operation made by the user on the output content of the target program. When the input operation indicates that the user is interested in the output content of the target program and the number of input operations meets the corresponding threshold, determine that the monitoring data meets the preset conditions; or

[0017] Obtain the facial data of the user when watching the output content of the target program. When the facial data indicates that the user is interested in the output content of the target program and the physical state determined based on the facial data is the target state, determine that the monitoring data meets the preset conditions; or

[0018] Obtain the voice data output by the user when watching the output content of the target program. When the voice data indicates that the user is interested in the output content of the target program and the physical state determined based on the voice data is the target state, determine that the monitoring data meets the preset conditions.

[0019] In some embodiments, when the running duration of the target mode of the monitoring device and the type of program in the running state meet the corresponding requirements, determining that the monitoring data meets the preset conditions includes:

[0020] The running duration of the monitoring device running at the first power meets the first threshold, and when the type of program in the running state during the device running at the first power belongs to the entertainment category or is the same as the type of the target program, determine that the monitoring data meets the preset conditions, where the first power is greater than the second power when the device is in the low-power running mode.

[0021] In some embodiments, the modifying the first behavior log based on the monitoring data to generate the second behavior log includes:

[0022] Determine the content type preferred by the user based on the monitoring data;

[0023] Modify the content in the first behavior log that can reflect the user's preferences to content different from the content type to form the second behavior log.

[0024] In some embodiments, modifying the content corresponding to the user preference in the first-line log to content different from the content type includes:

[0025] Matching the content type with a pre-stored mapping table to determine the corresponding target content type, where the mapping table includes the mapping relationship between the content types preferred by the user and the content types not preferred by the user;

[0026] Modifying the content corresponding to the user preference in the first-line log to the target content type or content related to the target content type.

[0027] In some embodiments, modifying the content corresponding to the user preference in the first-line log to content different from the content type includes:

[0028] Determining a user profile based on the monitoring data and content type analysis;

[0029] Predicting the target content type not preferred by the user based on the user profile;

[0030] Modifying the content corresponding to the user preference in the first-line log to the target content type or content related to the target content type.

[0031] In some embodiments, determining the content type preferred by the user based on the monitoring data includes:

[0032] Determining the content type preferred by the user based on one or more of the following in the monitoring data:

[0033] The input operations made by the user on the output content of the target program, the facial data of the user when watching the output content of the target program, the voice data of the user when watching the output content of the target program, the content continuously output by the target program within a period of time.

[0034] Another embodiment of the present application also provides an electronic device, including:

[0035] A processor, in response to the monitoring data satisfying a preset condition, obtaining the first-line log of the target program, modifying the first-line log based on the monitoring data to generate a second-line log, and sending the second-line log to the cloud;

[0036] Wherein, the monitoring data is related to one or more of device operation data, user status, and target program operation data, the first-line log is used to be sent to the cloud to feedback user preferences, and the content pushed by the cloud to the target program based on the second-line log is different from the content preferred by the user.

[0037] Other features and advantages of the present application will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present application. The objectives and other advantages of the present application may be realized and attained by the structure particularly pointed out in the written description, claims, as well as the drawings.

[0038] The technical solution of the present application will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0039] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 It is a schematic flowchart of the data processing method in the embodiment of the present application.

[0041] Figure 2 It is a schematic application flowchart of the data processing method in the embodiment of the present application.

[0042] Figure 3 It is a schematic flowchart of the data processing method in another embodiment of the present application.

[0043] Figure 4 It is a block diagram of the electronic device in the embodiment of the present application. Detailed Embodiments

[0044] Next, specific embodiments of the present application will be described in detail with reference to the drawings, but it is not a limitation of the present application.

[0045] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be regarded as a limitation, but only as an example of the embodiments. Those skilled in the art will think of other modifications within the scope of the present disclosure.

[0046] The drawings included in the description and constituting a part of the description illustrate the embodiments of the present disclosure, and together with the general description of the present disclosure given above and the detailed description of the embodiments given below are used to explain the principles of the present disclosure.

[0047] These and other features of the present application will become apparent from the following description of the preferred forms of the embodiments given as non - limiting examples with reference to the drawings.

[0048] It should also be understood that, although the present application has been described with reference to some specific examples, those skilled in the art can surely implement many other equivalent forms of the present application, which have the features as described in the claims and thus are all within the protection scope defined hereby.

[0049] When combined with the accompanying drawings, the above and other aspects, features and advantages of the present disclosure will become more obvious in view of the following detailed description.

[0050] Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repetitive functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in substantially any suitable detailed structure in a variety of ways.

[0051] This specification may use the phrase "in one embodiment", "in another embodiment", "in yet another embodiment" or "in other embodiments", all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0052] Next, embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0053] As shown in the figure, an embodiment of the present application provides a data processing method, including:

[0054] S1: In response to the monitoring data satisfying a preset condition, obtain a first behavior log of a target program, where the monitoring data is related to one or more of device operation data, user status, and target program operation data, and the first behavior log is used to be sent to the cloud to feedback user preferences;

[0055] S2: Modify the first behavior log based on the monitoring data to generate a second behavior log;

[0056] S3: Send the second behavior log to the cloud;

[0057] Wherein, the content pushed by the cloud to the target program based on the second behavior log is different from the content of user preferences.

[0058] In this embodiment, when the user starts the target program, the system will start the monitoring function to monitor one or more of the target program, the operation of the device, and the user status. The target program is a multimedia program, such as a video APP, an online reading APP (such as an online novel APP), etc. When the system determines that it meets the preset conditions based on the monitoring data, for example, when the system determines from the monitoring data that the user seems to be somewhat addicted to the output content of the target program, such as being addicted to watching videos, it will actively obtain the first behavior log of the target program. The first behavior log is generated by the target program based on the operation information input by the user for the target program. For example, the user inputs a like operation, a favorite operation, swipes to select the next video to play, inputs a comment, inputs information to search for a video, etc. for a certain video, or the first behavior log is generated by the target program in response to the user not inputting any operation within a specified period. For example, the user continuously watches a complete video content or a segment of a certain video. That is, the first behavior log records the behavior information of the user at a certain moment or during a certain period when using the target program. The target program sends the first behavior log to the cloud to feedback the user's behavior information to the cloud, so that the cloud can determine the user's preferences by analyzing the information in the first behavior log, such as determining the video content that the user is interested in, and then push more video data with the video content that the user is interested in to the user.

[0059] After obtaining the first behavior log, in order to prevent the target program from feedbacking information that can reflect the user's preferences to the cloud, the system in this embodiment will actively modify the first behavior log. For example, modify the user operation information and viewing information recorded in the first behavior log according to the monitoring data to obtain the second behavior log. The information recorded in the second behavior log cannot correctly reflect the actual preferences of the user. That is, the information about the user's preferences recorded in the second behavior log is different from the actual user preference information, or the information about the user's preferences recorded in the first behavior log. By modifying and generating the second behavior log and sending the second behavior log to the cloud, the system can make the content that the cloud determines the user is interested in based on the information in the second behavior log incorrect, resulting in the video content pushed by the cloud not being the content that the user is interested in, such as content that the user is not interested in, thereby weakening the user's interest in continuing to watch the target program and finally actively exiting the target program, achieving the effect of preventing the user from being addicted to the target program.

[0060] The method in this embodiment can timely detect whether the user is addicted to the target program through active monitoring. When it is determined that there is such a phenomenon, active intervention is made so that the information fed back by the target program to the cloud cannot correctly reflect the user's preferences. As a result, the cloud cannot push content that the user is interested in to the target program, thereby significantly weakening the user's interest in continuing to watch the content pushed by the target program. Eventually, the user will actively exit the target program due to boredom, effectively achieving the effect of preventing the user from being addicted to the content in the target program for a long time. This method avoids the phenomenon of forcibly exiting the target program by the user, which may cause the user's disgust or rebellious psychology, enabling the user to spontaneously generate the idea of ending the use of the target program and finally execute the action matching this idea. Therefore, the phenomenon of the user being addicted to using the target program can be fundamentally eliminated.

[0061] In one embodiment, a program for executing the above solution is configured in the system, such as an anti-addiction APP, and the system implements the above solution based on this program. The program can be automatically started when the device is started and run in the background, or when the user starts the target program, it can be synchronously started due to the associated relationship with the target program, or when the system responds to the operation of the target program, it can be actively started by the system due to the associated relationship with the target program, etc. The specific way of being started and run is not limited. In this embodiment, as Figure 2 shown, a proxy is configured between the target program and the cloud. The proxy can be, for example but not limited to, an Http Agent. The proxy is at least used to obtain the behavior log sent by the target program to the cloud and forward the behavior log to the cloud, that is, to the cloud server of the target program. If the target program is a video APP, the cloud is the cloud service (server) of the video APP. In this embodiment, the program for executing monitoring and log modification and the proxy can be regarded as a whole, and the proxy and the program jointly implement the above solution of this embodiment.

[0062] Obtaining the first behavior log of the target program includes:

[0063] S4: Obtaining the first behavior log sent by the target program based on the proxy.

[0064] In this embodiment, the program obtains the first behavior log sent by the target program based on the proxy. For example, when the program determines that the monitoring data meets the preset conditions, it sends information to the proxy, instructing the proxy to stop sending the first behavior log and obtain the first behavior log from the proxy, or instructing the proxy to send the first behavior log to the program end for the program to analyze and process the first behavior log.

[0065] In this embodiment, the monitoring data meeting the preset conditions includes at least one of the following:

[0066] S5: When it is monitored that the running duration of the target program meets the corresponding requirements, it is determined that the monitoring data meets the preset conditions; or

[0067] S6: Monitor the feedback made by the user on the output content of the target program. When it is determined that the user state meets the corresponding requirements based on the feedback, it is determined that the monitoring data meets the preset conditions; or

[0068] S7: When it is monitored that the running duration of the target mode of the device and the type of the program in the running state meet the corresponding requirements, it is determined that the monitoring data meets the preset conditions.

[0069] Exemplarily, when the program monitors one or more of the user state, the target program, and the running state of the device, it will obtain monitoring data and analyze the monitoring data to determine whether the monitoring data meets the preset conditions. For example, in Embodiment 1, the program will monitor whether the continuous running duration of the target program in the foreground meets the corresponding duration requirement. If it meets, it indicates that the user's single continuous use duration of the target program is relatively long and there is an addiction phenomenon. Therefore, the monitoring data at this time meets the preset conditions. Or the program monitors that the sum of the durations of the target program in the foreground for two adjacent times or several times meets the corresponding duration requirement, and the duration interval between switching the target program to the foreground for two adjacent times is relatively short, such as less than 1 minute, etc. At this time, the program will also determine that the monitoring data meets the preset conditions.

[0070] In Embodiment 2, the program can call the camera to collect the facial image information of the user when watching the output content of the target program, and analyze and determine the emotional feedback made by the user on the output content of the target program based on the facial image information, such as happy, excited, etc. Based on this, the target program can determine whether the current state meets the corresponding preset requirements according to the analyzed user state and the preset requirements corresponding to the user state. If it meets, it indicates that the user currently loves the output content of the target program very much and has a strong risk of addiction. Therefore, the current monitoring data meets the preset conditions and the program needs to intervene to prevent the user from being addicted to the output content of the target program.

[0071] In Embodiment 3, the program can monitor the running duration of the device in the target mode, such as the running duration in the audio output mode, the running duration in the high-power consumption state, etc., and at the same time monitor the type of the program currently in the running state. This program is not limited to the target program and includes programs running in the foreground and programs running in the background. If both the programs running in the foreground and the programs running in the background include multimedia types or other entertainment programs, and the quantity meets the requirements, or the proportion of the quantity to the total number of running programs meets the requirements, and the continuous running duration of the device in the target mode meets the duration requirement, it indicates that the current user has watched entertainment content for a long time and has an addiction phenomenon. Therefore, it is determined that the current monitoring data meets the preset conditions.

[0072] The above three embodiments can all be implemented, or one of them can be selected for implementation, or any two of them can be arbitrarily selected for implementation. There is no limitation on this in specific applications.

[0073] Further, when monitoring the feedback given by the user on the output content of the target program and determining that the user state meets the corresponding requirements based on the feedback, determining that the monitoring data meets the preset conditions includes at least one of the following:

[0074] S8: Obtain the input operation made by the user on the output content of the target program. When the input operation indicates that the user is interested in the output content of the target program and the number of input operations meets the corresponding threshold, determine that the monitoring data meets the preset conditions; or

[0075] S9: Obtain the facial data of the user when watching the output content of the target program. When the facial data indicates that the user is interested in the output content of the target program and the physical state determined based on the facial data is the target state, determine that the monitoring data meets the preset conditions; or

[0076] S10: Obtain the voice data output by the user when watching the output content of the target program. When the voice data indicates that the user is interested in the output content of the target program and the physical state determined based on the voice data is the target state, determine that the monitoring data meets the preset conditions.

[0077] For example, in one embodiment, obtain the input operation of the user on the output interface of the target program. When the input operation indicates that the user is interested in the output content of the target program, such as like, reward, input comment operation, etc., and the number of input operations meets the input threshold, such as the comments input on the same video content meet the threshold, or the number of like operations input on different video content meets the threshold, then it is determined that the user is addicted to the output content of the target program, so it is determined that the monitoring data meets the preset conditions.

[0078] In another embodiment, the program can collect the facial data of the user when watching the output content of the target program through a camera. If it is determined based on the facial data that the user is highly focused on the output content of the target program, and through the analysis and recognition of the facial data, it is determined that the user is currently in an excited or tense state, such as determining the user's heartbeat state through the analysis of the facial data to determine whether the user is currently in an excited or tense state. If so, it can be considered that the user currently has an addiction risk to the target program, so it is determined that the monitoring data meets the preset conditions.

[0079] In another embodiment, the program can collect the voice data output by the user when watching the output content of the target program by calling the microphone, and analyze the voice data. If it is determined based on the analysis result that the voice data output by the user represents that the user is interested in the current output content of the target program, for example, the analysis result is that the voice data output by the user is an exclamatory statement, including very interesting, so like***, so exciting, etc., it means that the user is currently very interested in the output content of the target program. Then, the program will determine whether the user's physical state is the target state based on the analysis result of the voice data output by the user, such as whether it is an excited state, an agitated state, etc. If so, it is considered that the user has a risk of addiction to the output content of the target program, so it is determined that the monitoring data meets the preset conditions.

[0080] In practical applications, the above three embodiments can be implemented in full, or one can be selected, or any two can be arbitrarily selected, and there is no limitation on this in specific applications. For example, the corresponding embodiment can be selected according to the actual input and output of the user. For example, when the user inputs a like operation and outputs voice at the same time, the program can use the first and third embodiments to comprehensively determine whether the monitoring data meets the preset conditions. When multiple embodiments are selected to judge the monitoring data together, it can be determined that the monitoring data meets the preset conditions when any one of the embodiments meets the conditions, or it can only be determined that the monitoring data meets the preset conditions when multiple embodiments all meet the conditions.

[0081] Further, when the running duration of the target mode of the monitoring device and the type of the program in the running state meet the corresponding requirements, it is determined that the monitoring data meets the preset conditions, including:

[0082] S11: When the running duration of the monitoring device running at the first power meets the first threshold, and the type of the program in the running state during the period when the device runs at the first power belongs to the entertainment category or is the same as the type of the target program, it is determined that the monitoring data meets the preset conditions, and the first power is greater than the second power when the device is in the low-power running mode.

[0083] For example, in this embodiment, the power of the high-power running mode of the device is the first power, and the power of the low-power running mode is the second power, and the first power is greater than the second power. When the program monitors that the running duration of the device at the first power meets the first threshold, and during the period when the device runs at the first power, the types of the programs in the foreground running state and the background running state both include the entertainment type. For example, there are multiple programs running in the background, the program currently running in the foreground of the device is the target program, and the programs running in the background include entertainment programs, or all the background programs are of the entertainment type, such as all are audio and video APPs, game APPs, or all are of the same type as the target program, such as all are video APPs, it is considered that the user is already addicted to the entertainment APP, so it is determined that the monitoring data meets the preset conditions.

[0084] As Figure 3 shown, after the program analysis determines that the monitoring data meets the preset conditions, it will actively obtain the first behavior log and modify the first behavior log based on the monitoring data to generate a second behavior log that cannot correctly reflect the user's preferences. The modifying the first behavior log based on the monitoring data to generate the second behavior log includes:

[0085] S12: Determine the content type of the user's preferences based on the monitoring data;

[0086] S13: Modify the content in the first behavior log that can reflect the user's preferences to content different from the content type to form the second behavior log.

[0087] Among them, the determining the content type of the user's preferences based on the monitoring data includes:

[0088] S14: Determine the content type of the user's preferences based on one or more of the following contents in the monitoring data:

[0089] The input operations made by the user on the output content of the target program, the facial data of the user when viewing the output content of the target program, the voice data of the user when viewing the output content of the target program, the content continuously output by the target program within a period of time.

[0090] In this embodiment, the program will analyze and determine the content type of the user's preferences based on the input data, output data of the user recorded in the monitoring data, and the output content of the target program. For example, through one or more of the output data of the target program corresponding to the user's likes, rewards, positive comments, the information actively queried, the output data of the target program corresponding to the user's output of information expressing admiration, the collected user facial information, and the information representing the user's love, excitement, and excitement obtained by analyzing the physical state and emotional state, and determine the content type of the user's preferences. After determining the content type of the user's preferences, the program will identify and determine the data expressing the user's preferences in the first behavior log, and then modify it to content different from the content type, thereby forming the second behavior log.

[0091] Specifically, in one embodiment, the modifying the content corresponding to the user's preferences in the first behavior log to content different from the content type includes:

[0092] S15: Match the content type with a pre-stored mapping table to determine the corresponding target content type. The mapping table contains the mapping relationship between the content type of the user's preferences and the content type that the user does not like;

[0093] S16: Modify the content corresponding to the user's preference in the first behavior log to the target content type or content related to the target content type.

[0094] Exemplarily, a mapping table is stored in the program. The mapping table stores the mapping relationship between the content types preferred by the user and the content types not preferred by the user. For the content types not preferred by the user, the program can determine them based on the user's historical viewing data of the target program, or can also determine the mutually exclusive content types according to big data, etc. For example, based on the data of a large number of users, determine the content types preferred by different users and the data types not preferred, and then analyze and determine the content types that the user is likely to dislike when liking a certain content type based on the data of a large number of users, and finally determine the mapping table accordingly. When modifying the first behavior log, the program will match the determined content types preferred by the user with the pre-stored mapping table to determine the content types not preferred by the user based on the mapping table. For example, if the user likes sports content, the content type that the user is likely to dislike is mother and baby content, etc. After determining the content types not preferred (likely not preferred) by the user based on the mapping table, the content corresponding to the user's preference in the first behavior log will be modified to the content type not preferred by the user, or the content type preferred by the user will be modified to the content related to the content type not preferred. For example, if the first behavior log records that the user actively searched for a ball game, the program will modify the search information to mother and baby information, or if the first behavior log records that the user liked a ball game video, the program will modify the like operation to a dislike, or modify the ball game video to a mother and baby video, so that the first behavior log records that the user liked a mother and baby video, etc.

[0095] In another embodiment, the modifying the content corresponding to the user's preference in the first behavior log to the content different from the content type includes:

[0096] S17: Determine a user portrait based on the monitoring data and content type analysis;

[0097] S18: Predict the target content type not preferred by the user based on the user portrait;

[0098] S19: Modify the content corresponding to the user's preference in the first behavior log to the target content type or content related to the target content type.

[0099] Exemplarily, the program determines the user profile of the user based on the input data, output data, output content of the target program, and the determined content types preferred by the user recorded in the monitoring data, so as to understand the user's personality and behavioral characteristics. Subsequently, a pre-trained prediction model can be selected to predict the content types of the target that the user does not like in combination with the user profile. Alternatively, based on prior data, or according to the user's historical viewing data of the target program, or according to big data, such as determining the content types liked by different users and the data types not liked based on the data of a large number of users, etc., the user profile can be analyzed to predict the content types of the target that the user is likely not to like. After determining the content types that the user does not like, the program can modify the content liked by the user in the first behavior log to the content types that the user does not like, or modify the content types liked by the user to the content types related to the content types that the user does not like.

[0100] As Figure 4 shown, another embodiment of the present application simultaneously provides an electronic device, including:

[0101] A processor, in response to the monitoring data satisfying a preset condition, obtains the first behavior log of the target program, modifies the first behavior log based on the monitoring data to generate a second behavior log, and sends the second behavior log to the cloud;

[0102] Wherein, the monitoring data is related to one or more of device operation data, user status, and target program operation data, the first behavior log is used to be sent to the cloud to feedback user preferences, and the content pushed by the cloud to the target program based on the second behavior log is different from the content liked by the user.

[0103] In one embodiment, a proxy is configured between the target program and the cloud, and the proxy is at least used to obtain the behavior log and forward the behavior log to the cloud;

[0104] The obtaining of the first behavior log of the target program includes:

[0105] Obtaining the first behavior log sent by the target program based on the proxy.

[0106] In one embodiment, the monitoring data satisfying the preset condition includes at least one of the following:

[0107] When the running duration of the monitored target program meets the corresponding requirement, it is determined that the monitoring data satisfies the preset condition; or

[0108] Monitoring the feedback made by the user on the output content of the target program, and when it is determined that the user status meets the corresponding requirement based on the feedback, it is determined that the monitoring data satisfies the preset condition; or

[0109] When the running duration of the target mode of the monitoring device and the type of the program in the running state meet the corresponding requirements, it is determined that the monitoring data meets the preset conditions.

[0110] In one embodiment, when monitoring the feedback of the user on the output content of the target program and determining that the user state meets the corresponding requirements based on the feedback, determining that the monitoring data meets the preset conditions includes at least one of the following:

[0111] Obtaining the input operation made by the user on the output content of the target program, and when the input operation indicates that the user is interested in the output content of the target program and the number of input operations meets the corresponding threshold, determining that the monitoring data meets the preset conditions; or

[0112] Obtaining the facial data of the user when watching the output content of the target program, and when the facial data indicates that the user is interested in the output content of the target program and the physical state determined based on the facial data is the target state, determining that the monitoring data meets the preset conditions; or

[0113] Obtaining the voice data output by the user when watching the output content of the target program, and when the voice data indicates that the user is interested in the output content of the target program and the physical state determined based on the voice data is the target state, determining that the monitoring data meets the preset conditions.

[0114] In one embodiment, when the running duration of the target mode of the monitoring device and the type of the program in the running state meet the corresponding requirements, determining that the monitoring data meets the preset conditions includes:

[0115] The running duration of the monitoring device running at the first power meets the first threshold, and when the type of the program in the running state during the device running at the first power belongs to the entertainment category or is the same as the type of the target program, it is determined that the monitoring data meets the preset conditions, and the first power is greater than the second power when the device is in the low-power running mode.

[0116] In one embodiment, modifying the first behavior log based on the monitoring data to generate a second behavior log includes:

[0117] Determining the content type preferred by the user based on the monitoring data;

[0118] Modifying the content in the first behavior log that can reflect the user's preference to content different from the content type to form the second behavior log.

[0119] In one embodiment, modifying the content corresponding to the user's preference in the first behavior log to content different from the content type includes:

[0120] Match the content type with a pre-stored mapping table to determine the corresponding target content type. The mapping table contains the mapping relationship between the content types preferred by the user and the content types not preferred by the user;

[0121] Modify the content corresponding to the user's preference in the first-line log to the target content type or content related to the target content type.

[0122] In one embodiment, modifying the content corresponding to the user's preference in the first-line log to content different from the content type includes:

[0123] Determine a user profile based on the monitoring data and content type analysis;

[0124] Predict the target content type that the user does not like based on the user profile;

[0125] Modify the content corresponding to the user's preference in the first-line log to the target content type or content related to the target content type.

[0126] In one embodiment, determining the content type preferred by the user based on the monitoring data includes:

[0127] Determine the content type preferred by the user based on one or more of the following contents in the monitoring data:

[0128] The input operations made by the user on the output content of the target program, the facial data of the user when watching the output content of the target program, the voice data of the user when watching the output content of the target program, the content continuously output by the target program within a period of time.

[0129] Furthermore, an embodiment of the present application further provides a storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned data processing method is implemented. It should be understood that each solution in this embodiment has the corresponding technical effects in the above method embodiment, and will not be elaborated here.

[0130] Furthermore, an embodiment of the present application further provides a computer program product. The computer program product is tangibly stored on a computer-readable medium and includes computer-readable instructions. When the computer-executable instructions are executed, at least one processor executes the data processing method in the above-mentioned embodiment.

[0131] It should be noted that the computer storage medium of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable medium can, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which the computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program configured to be used by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0132] In addition, those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction system that implements the functions specified in one Figure 1 process or multiple processes and / or blocks Figure 1 or multiple blocks specified in the block.

[0134] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; under the concept of this application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of one or more embodiments of this application as described above, and they are not provided in detail for the sake of brevity.

Claims

1. A data processing method, comprising: In response to the monitoring data satisfying a preset condition, obtaining a first behavior log of a target program, where the monitoring data is related to one or more of device operation data, user status, and target program operation data, and the first behavior log is used to be sent to the cloud to feedback user preferences; Modifying the first behavior log based on the monitoring data to generate a second behavior log; Sending the second behavior log to the cloud; Wherein, the content pushed by the cloud to the target program based on the second behavior log is different from the content of user preferences.

2. The data processing method according to claim 1, wherein a proxy is configured between the target program and the cloud, and the proxy is at least used to obtain the behavior log and forward the behavior log to the cloud; The obtaining of the first behavior log of the target program includes: Obtaining the first behavior log sent by the target program based on the proxy.

3. The data processing method according to claim 1, where the monitoring data satisfying the preset condition includes at least one of the following: When it is determined that the running duration of the target program meets the corresponding requirement, it is determined that the monitoring data satisfies the preset condition; or Monitoring the feedback made by the user to the output content of the target program, and when it is determined that the user status meets the corresponding requirement based on the feedback, it is determined that the monitoring data satisfies the preset condition; or When it is determined that the running duration of the target mode of the device and the type of the program in the running state meet the corresponding requirements, it is determined that the monitoring data satisfies the preset condition.

4. The data processing method according to claim 3, where the monitoring of the feedback made by the user to the output content of the target program, and when it is determined that the user status meets the corresponding requirement based on the feedback, it is determined that the monitoring data satisfies the preset condition, includes at least one of the following: Obtain the input operation made by the user on the output content of the target program. When the input operation indicates that the user is interested in the output content of the target program and the number of input operations meets the corresponding threshold, it is determined that the monitoring data meets the preset conditions; Or Obtaining facial data of the user when watching the output content of the target program, and when the facial data indicates that the user is interested in the output content of the target program and the body state determined based on the facial data is the target state, it is determined that the monitoring data satisfies the preset condition; Or Obtaining voice data output by the user when watching the output content of the target program, and when the voice data indicates that the user is interested in the output content of the target program and the body state determined based on the voice data is the target state, it is determined that the monitoring data satisfies the preset condition.

5. The data processing method according to claim 3, where when it is determined that the running duration of the target mode of the device and the type of the program in the running state meet the corresponding requirements, it is determined that the monitoring data satisfies the preset condition, includes: Monitoring that the running duration of the device running at the first power meets the first threshold, and when the type of the program in the running state during the period when the device runs at the first power belongs to the entertainment category or is the same as the type of the target program, it is determined that the monitoring data satisfies the preset condition, and the first power is greater than the second power when the device is in the low-power operation mode.

6. The data processing method according to claim 1, where the modifying the first behavior log based on the monitoring data to generate a second behavior log includes: Determining the content type of user preferences based on the monitoring data; Modify the content in the first line of the log that can reflect the user's preference to content of a different content type from the content type, to form the second line of the log.

7. The data processing method according to claim 6, wherein modifying the content corresponding to the user preference in the first line of the log to content of a different content type from the content type includes: Match the content type with a pre-stored mapping table to determine the corresponding target content type, where the mapping table contains the mapping relationship between the content types preferred by the user and the content types not preferred by the user; Modify the content corresponding to the user preference in the first line of the log to the target content type or content related to the target content type.

8. The data processing method according to claim 6, wherein modifying the content corresponding to the user preference in the first line of the log to content of a different content type from the content type includes: Determine a user profile based on the monitoring data and content type analysis; Predict the target content type that the user does not like based on the user profile; Modify the content corresponding to the user preference in the first line of the log to the target content type or content related to the target content type.

9. The data processing method according to claim 6, wherein determining the content type preferred by the user based on the monitoring data includes: Determine the content type preferred by the user based on one or more of the following content in the monitoring data: The input operations made by the user on the output content of the target program, the facial data of the user when watching the output content of the target program, the voice data of the user when watching the output content of the target program, and the content continuously output by the target program within a period of time.

10. An electronic device, comprising: A processor, in response to the monitoring data satisfying a preset condition, obtains the first line of the log of the target program, modifies the first line of the log based on the monitoring data to generate a second line of the log, and sends the second line of the log to the cloud; Wherein, the monitoring data is related to one or more of device operation data, user status, and target program operation data, the first line of the log is used to be sent to the cloud to feedback the user preference, and the content pushed by the cloud to the target program based on the second line of the log is different from the content preferred by the user.

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