Media data downloading method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a media data downloading method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining current network state information, current memory state information and historical behavior data of a user in an application, and determining at least one piece of candidate media content information in the application according to the historical behavior data; determining a downloading strategy according to the current network state information and the current memory state information; the downloading strategy comprises a content reviewing downloading strategy and a content recommendation downloading strategy; and according to the downloading strategy, downloading candidate media data associated with the at least one piece of candidate media content information until a downloading stop condition is met.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method and device for downloading media data, an electronic device, and a readable storage medium. Background Art

[0002] Currently, in the entertainment consumption sector, long-form videos are popular among users due to their long viewing cycles and rich information content. Similarly, serialized novels and podcasts also attract a large number of users due to their continuous updates and diverse content.

[0003] However, current user experiences with media data browsing face numerous bottlenecks. For example, in video applications, users must manually download multiple videos episode by episode, a cumbersome process that can easily lead to missed or incorrect downloads. Manual downloading is particularly prone to errors when users want to download video data for multiple titles.

[0004] Therefore, currently when downloading media data, the download efficiency is low. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a method, device, electronic device, and readable storage medium for downloading media data, which can improve the download efficiency of media data. The specific technical solution is as follows:

[0006] In a first aspect of the present invention, a method for downloading media data is provided, comprising:

[0007] Obtain current network status information, current memory status information, and historical user behavior data in the application, including download behavior data, tagging behavior data, interactive behavior data, and browsing behavior data;

[0008] determining at least one candidate media content information in the application based on the historical behavior data;

[0009] Determine a download strategy based on the current network status information and the current memory status information; the download strategy includes: a content continuation download strategy and a content recommendation download strategy;

[0010] According to the downloading strategy, candidate media data associated with at least one candidate media content information is downloaded until a download stop condition is met.

[0011] In a second aspect of the present invention, a media data downloading device is provided, comprising:

[0012] An acquisition module is used to obtain current network status information, current memory status information, and historical behavior data of the user in the application, wherein the historical behavior data includes: download behavior data, marking behavior data, interaction behavior data, and browsing behavior data;

[0013] A first determining module, configured to determine at least one candidate media content information in the application based on the historical behavior data;

[0014] A second determining module is configured to determine a download strategy based on the current network status information and the current memory status information; the download strategy includes: a content continuation download strategy and a content recommendation download strategy;

[0015] The download module is configured to download candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is met.

[0016] In another aspect of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium. When the computer-readable storage medium is run on a computer, the computer executes any of the above-mentioned data forwarding methods.

[0017] In yet another aspect of the present invention, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute any of the above-mentioned methods for downloading media data.

[0018] In an embodiment of the present invention, current network status information, current memory status information and historical behavior data of the user in the application are obtained. The historical behavior data include: download behavior data, marking behavior data, interactive behavior data and browsing behavior data. The historical behavior data can provide a basis for determining the user's interests and behavior patterns. At least one candidate media content information that the user is interested in can be determined based on the user's historical behavior data; a download strategy is determined based on the current network status information and the current memory status information. The download strategy includes: content continuation download strategy and content recommendation download strategy. According to the download strategy, candidate media data associated with at least one candidate media content information is downloaded, which can adapt to the actual situation of the current network status and the current memory status, and ensure the smooth progress of the download operation. Therefore, the user's historical behavior data, current network status and current memory status can be combined to automatically and smoothly download media data that meets the user's interests and needs without manual operation by the user, which can improve the download efficiency of media data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments or the prior art.

[0020] Figure 1 is a flow chart of a method for downloading media data provided by an embodiment of the present invention;

[0021] Figure 2a is a schematic diagram of a download control provided by an embodiment of the present invention;

[0022] Figure 2b This is a schematic diagram of an operation for opening a download control provided by an embodiment of the present invention;

[0023] Figure 2c is a schematic diagram of a download preview interface for media data provided by an embodiment of the present invention;

[0024] Figure 2d is a schematic diagram of a media data download interface provided by an embodiment of the present invention;

[0025] Figure 3 a is a schematic diagram of a save control and a delete control provided by an embodiment of the present invention;

[0026] Figure 3 b is a schematic diagram of a save control and a delete control provided by an embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of an intelligent download setting interface provided by an embodiment of the present invention;

[0028] Figure 5 This is a structural diagram of a media data downloading device provided by an embodiment of the present invention;

[0029] Figure 6 The figure is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] The media data downloading method provided by the embodiment of the present invention can be applied to at least the following application scenarios, which are described below.

[0032] In today's entertainment landscape, long-form video content, such as TV series, variety shows, and movies, plays a crucial role in users' daily entertainment. This reflects the importance of long-form video in satisfying users' entertainment needs. Whether in terms of plot coherence, rich content, or emotional depth, long-form video offers unique advantages, attracting a large number of users to invest their time and energy in viewing and consumption. Long-form video content typically has a long viewing period, contains a large amount of information, and is rich and diverse in content.

[0033] For example, a TV series typically consists of multiple episodes, for example, 10 to 80 episodes. Similarly, a serialized novel may have chapters updated over months or even years. A serialized podcast also consists of multiple episodes.

[0034] Currently, there are many bottlenecks in the use of media data when users browse it. The following uses video applications as an example to illustrate:

[0035] On the one hand, when catching up on a TV series or watching multiple episodes, users need to frequently manually select the episodes or programs they want to download in the video app, which is a cumbersome process. For example, when a user is catching up on a TV series with dozens of episodes, they need to click the download button episode by episode, which not only wastes time but also makes it easy to miss an episode. Because manual operations are prone to omissions or errors, users may experience missing content while watching, disrupting the continuity and smoothness of viewing. For example, when watching a TV series, a user discovers that an episode has not been downloaded and needs to pause the viewing to re-download it, which greatly affects the user's viewing experience.

[0036] On the other hand, in some scenarios, such as commuting or traveling, the network signal is unstable or weak. When users watch long videos, this unstable network environment causes video playback to be interrupted, freeze, load slowly, or even fail to play.

[0037] On the other hand, different users have different viewing habits. Some like to binge-watch a series in one sitting, while others prefer to watch a few episodes daily. In terms of content needs, some prefer dramas, while others prefer variety shows. However, existing download functions cannot dynamically adjust to users' different viewing behaviors and needs.

[0038] On the other hand, users often want to download and watch new content as soon as it's released to maintain their interest and engagement. Existing manual download mechanisms require users to monitor content updates and manually download them. This makes it easy for busy users to miss new content releases, resulting in inability to watch it in time and reducing user satisfaction.

[0039] Furthermore, users' devices typically have limited storage space, which quickly fills up as they download more long videos. Prioritizing downloads within this limited storage space is a challenge. Existing download functions lack effective priority management mechanisms, failing to intelligently select and download the content most needed by users based on factors like user needs and frequency of use. This results in some important or frequently used content being unable to download, while less important content takes up a significant amount of space.

[0040] Based on the above application scenario, the media data downloading method provided by the embodiment of the present invention is described below.

[0041] Figure 1 The present invention provides a flowchart of a method for downloading media data.

[0042] like Figure 1 As shown, the media data downloading method may include steps 110 to 130, and the method is applied to a media data downloading device, as shown below:

[0043] Step 110 , obtaining current network status information, current memory status information and historical behavior data of the user in the application, wherein the historical behavior data includes downloading behavior data, marking behavior data, interactive behavior data and browsing behavior data.

[0044] Current network status information: refers to various data reflecting the current network connection status, such as network type, network signal strength, network bandwidth, network latency, packet loss rate, etc., reflecting the performance and stability of the network, so as to make appropriate decisions.

[0045] Current memory status information: This information describes the device's memory usage, including available memory, used memory, and memory fragmentation. This information allows you to determine the availability of memory resources and determine the scale of downloads that can be performed and how to allocate memory resources appropriately.

[0046] Historical behavior data: A series of behavioral record data generated by users when using the application, which can reflect the user's behavioral habits and preferences within the application.

[0047] Historical behavior data includes:

[0048] Download behavior data: This data records the user's download behavior of media content in the application. Download behavior includes download operations for various types of media content. Download behavior can directly reflect the user's interest in specific media content and the degree to which they want to save it offline and view it repeatedly.

[0049] Marking behavior data: This is a record of users marking certain media content in an app. Marking behaviors include adding favorites, liking content, clicking on the follow-up control, and adding tags. Marking behavior can directly reflect users' preferences and attention to specific content.

[0050] Interaction behavior data: This refers to user behavior data related to interactions with other users or content within an app, such as comments, shares, forwarding, and private messaging. Interaction behavior can reflect a user's engagement with and interest in content.

[0051] Browsing behavior data: This records data related to users browsing media content in the app, including information such as content type, browsing duration, number of views, and browsing order. Browsing behavior data can help us understand users' content preferences and browsing habits.

[0052] Through in-app monitoring mechanisms and data recording tools, we collect various historical behavioral data generated by users during their use of the app. This historical behavioral data can provide a basis for understanding users' interests and behavior patterns.

[0053] For example, in a video playback application, the application will record the type of videos watched by users, viewing time, likes, comments, downloads, etc. For example, if a user watched five action movies in the past week, each lasting about 90 minutes, and liked three of them, the above data would constitute the user's historical behavior data.

[0054] Step 120: Determine at least one candidate media content information in the application based on the historical behavior data.

[0055] Candidate media content information: There are many different media-related topics in the application, such as novel topics, video topics, podcast topics, etc. Candidate media content information is screened based on the user's historical behavior data and is the media topic that the user is interested in.

[0056] For example, for a video application, the candidate media content information may be a movie title, TV series title, actor name, or director name. For a novel reading application, the candidate media content information may be a novel title or author name. For a podcast application, the candidate media content information may be a podcast title or host name.

[0057] The application identifies features and patterns related to media topics from historical user behavior data, determines the media topics that the user is interested in, and then identifies candidate media content information. For example, the application analyzes the collected historical user behavior data and finds that the user frequently watches action movies and actively interacts with them, thus identifying "action movies" as a candidate media content information.

[0058] In a possible embodiment, step 120 may specifically include the following steps:

[0059] When the download condition is met, determining at least one candidate media content information in the application according to the historical behavior data;

[0060] The download conditions include any one of the following:

[0061] An opening operation of a download control of the application is detected, an initial startup operation of the application is detected within each natural day, and an interactive operation of the application is detected within the first N hours of each natural day, where N is a positive number.

[0062] Download control: An interactive element on the app interface, such as a button, that triggers the download of media data. The download control acts as a smart download switch, allowing users to enable or disable smart download mode. When enabled, it automatically downloads eligible media data according to pre-set rules and conditions. When disabled, automatic downloading stops, and the download operation only occurs when the user manually triggers it.

[0063] Start operation: This refers to the start or activation operation of the download control, such as clicking the download button.

[0064] For example, if the opening operation of the download control of the application is detected, the download control can be opened in the setting interface of the application for the user to automatically download the media data. Figure 2a As shown, in response to the user's opening operation on the download control 11, at least one candidate media content information in the application is determined based on the historical behavior data. Figure 2b As shown, when the download control 11 is turned on, a prompt message 12 is displayed: "No video yet, new video will be downloaded for you after 00:00 tomorrow." Figure 2c As shown, when the download control 11 is turned on, after the video download is completed, a prompt message 13 "3 video themes have not been viewed" is displayed.

[0065] First launch action: The action when a user opens and launches an app for the first time.

[0066] The first launch operation of the application is detected within each natural day. For example, when the first launch operation of the application by the user is detected at 12:00 within a single natural day, based on the historical behavior data, it is analyzed that the user has been watching "ZZZ" recently, and therefore "ZZZ" is determined as candidate media content information.

[0067] Interactions: User interactions with various interface elements within an app, such as clicking buttons, entering text, selecting options, etc.

[0068] The first N hours of each natural day: for example, 0:00-1:00, 0:00-1:30, 0:00-2:00, or 0:00-3:00.

[0069] Because the behavior corresponding to the download conditions can reflect the user's intent to use the application, if the download conditions are met, at least one piece of candidate media content in the application is determined based on the historical behavior data. Analysis based on historical behavior data is performed because users' past behavior can, to a certain extent, predict their future interests. By mining and analyzing this data, media themes associated with user behavior are identified, thereby determining candidate media content.

[0070] By identifying candidate media content, apps can more accurately recommend content that might interest users, reducing the time and effort users spend searching for content within a vast amount of content, and improving user satisfaction and usage within the app. Determining candidate media content based on user behavior data allows apps to better meet the personalized needs of different users.

[0071] In a possible embodiment, the historical behavior data includes: marking behavior data, interaction behavior data, and browsing behavior data. Step 120 includes at least one of the following:

[0072] determining at least one piece of candidate media content information associated with the marking behavior data;

[0073] Determining at least one piece of candidate media content information associated with the interactive behavior data and the browsing behavior data;

[0074] Determining user preference information based on the historical behavior data; and determining at least one piece of candidate media content information associated with the user preference information.

[0075] User preference information: information about user interests and preferences obtained through comprehensive analysis of user historical behavior data, such as the user's favorite media topics, content types, styles, etc.

[0076] Candidate media content information: There are multiple different media-related topics in the application. Candidate media content information is the media topics that users are interested in, which are screened based on historical behavior data.

[0077] Tagging is typically a user's active choice of content, directly reflecting their interests. By analyzing user tagging data and counting the media topics to which the tagged content belongs, we can identify candidate media content that the user may be interested in.

[0078] Interaction behavior data can reflect users' engagement with content and their emotional inclinations, while browsing behavior data can reveal their content preferences and browsing habits. Combining these two types of data allows us to explore user interests from multiple perspectives. For example, by analyzing the duration and frequency of user interaction with a particular type of content, we can determine the user's interest in the media theme surrounding that content, and thus identify candidate media content information.

[0079] By comprehensively considering historical behavioral data such as tagging, interaction, and browsing behavior, data analytics is used to analyze user behavior patterns and determine user preferences. Based on this user preference information, candidate media content matching the user's preferences is then selected from the app's media themes.

[0080] For example, in a novel reading application, users often collect biographical novels, that is, marking behavior data for biographical novels is detected. The application analyzes the marking behavior data and thus determines biographical novels as candidate media content information.

[0081] In video apps, users frequently browse food videos for extended periods of time and also comment on and share some food videos, detecting interactive behavior data related to food videos. The app combines this interactive behavior data with browsing behavior data to analyze and conclude that users have a high interest in the topic of "food videos," thus identifying "food videos" as a candidate media content.

[0082] By analyzing historical behavior data in different ways to determine candidate media content information, we can more accurately grasp the user's interests and preferences, thereby accurately recommending relevant media content to users in the application, improving the accuracy and relevance of the recommendations.

[0083] Step 130: Determine a download strategy based on the current network status information and the current memory status information; the download strategy includes: a content continuation download strategy and a content recommendation download strategy;

[0084] The content continued viewing download strategy is to determine the candidate media data according to the browsing progress information of the candidate media content information;

[0085] The content recommendation download strategy is to determine candidate media data according to the recommendation parameter values of the candidate media content information.

[0086] The electronic device obtains current network status information and memory status information through its network interface and memory management module, respectively. The network interface provides various network connection status data, while the memory management module tracks and reports memory usage. This information is the basis for determining subsequent download strategies, as different network and memory conditions affect the download method and speed, as well as the number of download tasks that can be processed simultaneously.

[0087] Dynamically adjusting download strategies based on network and memory status information prevents system freezes or download failures caused by large-scale downloads during periods of poor network conditions or insufficient memory. Properly allocating network bandwidth and memory resources ensures download tasks are completed as efficiently as possible while maintaining stable system operation, improving device resource utilization.

[0088] The resume download strategy determines candidate media data based on the user's previous viewing progress, prioritizing download of portions of candidate media data that the user has not yet viewed. This strategy relies on accurately recorded viewing progress information within the candidate media content. For example, for video content, this records the time the user viewed the content; for audio books, it records the chapter position reached. Based on this information, subsequent content is located and downloaded from the media data source.

[0089] The resume download policy allows users to conveniently continue watching unfinished media content regardless of network conditions and device status, eliminating the need to re-locate the playback position. This saves user time and improves viewing continuity and convenience. For example, if download behavior data includes the user's download behavior for the first five episodes of series A, and episodes 6 and 7 are now available, the resume download policy can automatically download episodes 6 and 7 of the series.

[0090] The content recommendation download strategy determines the media data to be downloaded based on the recommendation parameter values of the candidate media content information. The recommendation parameter values are calculated based on a combination of multiple factors and are used to measure the attractiveness and recommendation level of the media content to the user. The content recommendation download strategy relies on the recommendation parameter values in the candidate media content information. The calculation of the recommendation parameter values takes into account multiple factors such as the user's historical browsing behavior, interest tags, the popularity of the content, and the behavior of other similar users. For example, the recommendation parameter value of a movie will be increased due to factors such as its recent popularity, the work of a director that the user likes, and the similarity of genres to movies the user has previously watched.

[0091] Through the content recommendation download strategy, media content that may be of interest to users can be downloaded in advance based on their interests and preferences, allowing users to quickly access and watch it when they have free time.

[0092] The appropriate download strategy is selected by comprehensively considering network and memory conditions. For example, if network bandwidth is sufficient and memory space is large, the content recommendation download strategy will be prioritized to fully utilize resources and download more recommended media content. If the network is unstable but the user has unfinished content, the content resume download strategy will be prioritized to ensure smooth continuation of the content. Furthermore, the download strategy can be adjusted based on specific circumstances, such as adjusting the download speed, pausing or resuming download tasks, and so on, to adapt to changes in network and memory.

[0093] Step 140: Download candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is met.

[0094] Among them, candidate media data refers to data content associated with candidate media content information, corresponding to specific media data such as novel files, video files, podcast files, etc. of the candidate media content information.

[0095] Download stop condition: a condition used to determine when to stop downloading candidate media data, such as reaching the storage data volume limit, reaching the download time limit, reaching the storage space limit, etc.

[0096] Based on the determined candidate media content information, candidate media data associated with the candidate media content information is searched and downloaded from relevant data sources. During the download process, periodic checks are performed to determine whether the pre-set download stop conditions are met. Once the download stop conditions are met, the download operation is stopped to avoid unnecessary data downloads and resource waste. For example, based on the determined "action movie" candidate media content information, the application searches for and downloads relevant action movie video data from the video resource library. Assuming that the set download stop condition is that the total duration of the downloaded videos reaches 500 minutes, the download operation is stopped when the total duration of the downloaded action movie videos reaches 500 minutes.

[0097] By acquiring and analyzing historical user behavior data to identify candidate media content and downloading the relevant data, apps can provide users with media content that better suits their interests and needs, making the content more targeted and engaging. Download stop conditions are used to control data download volume, avoiding excessive downloads and effectively utilizing app storage space and network resources.

[0098] This allows targeted downloading of media data based on historical user behavior, current network status, and memory status, avoiding unnecessary downloads and wasted storage space. Furthermore, by setting download stop conditions, the download process can be flexibly controlled, ensuring that the device's storage and operating status remain within reasonable limits. This facilitates effective management of media data and long-term stable device use.

[0099] In a possible embodiment, step 140 may specifically include the following steps:

[0100] Step 210: Determine browsing progress information of at least one candidate media content information from the historical behavior data;

[0101] Step 220, determining download sequence information based on browsing progress information of the candidate media content information;

[0102] Step 230: Download candidate media data associated with at least one candidate media content information in sequence according to the download strategy and the download sequence information until the download stop condition is met.

[0103] Browsing progress information: For each candidate media content, the user's browsing completion level for the media content under that topic is recorded, such as the proportion of novels browsed to the total novel progress, the proportion of video viewing time to the total video time, the proportion of podcasts listened to to the total podcast progress, etc., reflecting the user's browsing status for the content on that topic.

[0104] Download sequence information: information determined based on factors such as browsing progress information, used to indicate the order in which candidate media data is downloaded, that is, to specify which data associated with the candidate media content information is downloaded first and which is downloaded later.

[0105] To determine the user's level of interest and browsing status for different candidate media content, we analyze the user's historical in-app behavior data and extract browsing behavior records related to each candidate media content. Based on these browsing behavior records, we calculate the user's browsing progress for each candidate media content.

[0106] Based on the browsing progress information of the candidate media content information and the media data identification information indicated by the current browsing progress information, download sequence information is determined based on the media data identification information indicated by the current browsing progress information. The download sequence information may include the media data identification information arranged in a download order. According to the determined download sequence information, candidate media data associated with the candidate media content information is sequentially downloaded from the data source. During the download process, whether a pre-set download stop condition is met is continuously monitored. Once the download stop condition is met, the download operation is stopped.

[0107] For example, consider a video playback app, where users can watch a variety of TV series, variety shows, and other video content. The app contains videos on various media themes. The system records user playback history within the app as part of historical behavior data. The app also sets download stop conditions.

[0108] The app first obtains the current user's browsing progress within the app. For example, suppose the user recently watched a TV series called "AAA" and has reached episode N, where N is a positive integer. Based on the browsing progress information for "AAA," the app then downloads the first episode after the current browsing progress, i.e., episode N+1. For example, if the user has finished watching episode 5, the app then starts downloading episode 6.

[0109] If episode N+1 of the media theme "AAA" has already been downloaded, the program will download the second episode after the playback record, which is episode N+2, according to the order of the content list. For example, if episode 6 of "AAA" has already been downloaded, the program will try to download episode 7. Similarly, if episode 7 has already been downloaded, the program will try to download episode 8, and so on.

[0110] If the user has no playback history in the app, the app will start downloading any media theme from the first episode. For example, if the media theme "BBB" has no playback history in the app, the app will start downloading from the first episode. Throughout the download process, the app continuously monitors whether the pre-set stop condition is met. Once the stop condition is met, the download operation stops.

[0111] like Figure 2d As shown, the smart download video interface includes videos that are being downloaded and have been downloaded. For the media theme "AAA", the preview interfaces of episodes 3, 4, and 5 of "AAA" are displayed. For the media theme "BBB", the preview interfaces of episodes 4 and 5 of "BBB" are displayed.

[0112] For example, a reading app allows users to read various text content, such as serialized novels. The app includes novels of various genres and themes. The system records the user's reading history in the app as part of their historical behavior data. The app also sets download stop conditions.

[0113] The app first obtains the current user's reading progress in the app. For example, if the user recently read a novel called "CCC" and has reached Chapter M, then the app will download the first chapter after the current reading progress, i.e., Chapter M+1. For example, if the user has finished reading Chapter 8, then the app will start downloading Chapter 9.

[0114] If you've already downloaded Chapter M+1 of the novel "CCC," you'll start downloading the second chapter after the reading record, which is Chapter M+2, again in the order of the novel's chapters. For example, if Chapter 9 of "CCC" has already been downloaded, you'll start downloading Chapter 10. Similarly, if Chapter 10 has already been downloaded, you'll continue downloading Chapter 11, and so on.

[0115] If the user has no reading history in the app, the app will start downloading any novel of any given theme from the first chapter. For example, if the user has no reading history for the novel "DDD," the app will start downloading from the first chapter. Throughout the download process, the app continuously monitors whether pre-set stop conditions have been met. Once these conditions are met, the download process stops.

[0116] The app can download video content in a targeted manner based on the user's playback history. For users with a playback history, subsequent episodes are downloaded first, making it easier for users to continue watching. For users without a playback history, downloads are started from the first episode of the candidate media content, gradually providing content to the user. Furthermore, by setting download stop conditions, download behavior can be properly controlled to avoid excessive consumption of device resources.

[0117] The download order is determined based on the download strategy and the user's browsing progress for the candidate media content, prioritizing the download of media data that the user needs most. This ensures that the downloaded content better meets the user's actual needs and avoids blindly downloading data that the user is already familiar with or has no interest in. By rationally arranging the download order and promptly stopping the download when the download stop conditions are met, the amount of data downloaded can be effectively controlled, avoiding excessive use of storage space and network resources, and improving resource utilization efficiency. Users can more quickly access media content that they may be interested in but have not yet fully browsed, reducing waiting time.

[0118] In a possible embodiment, step 220 may specifically include the following steps:

[0119] Step 310 , when the number of the candidate media content information is at least two, determining the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time from the historical behavior data;

[0120] Step 320: determining a priority of at least one of the candidate media content information based on the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time;

[0121] Step 330: Determine the download sequence information according to the priority and the browsing progress information.

[0122] Marking behavior data: data records generated by users marking video content. Marking behaviors such as collecting, liking, and adding tags can directly reflect the user's preference for specific video content.

[0123] Interactive behavior data: records of users' interactions with other users or video content within video apps, such as commenting on videos, sharing videos, and replying to others' comments, reflecting users' engagement and interest in video content.

[0124] Browsing time: The length of time a user watches a certain video or videos under a certain video topic. It is used to measure the user's attention to the video or topic.

[0125] Browsing time: records the specific time or time period when users watch videos, which can help analyze users' viewing habits and time preferences.

[0126] Priority: The level obtained by sorting the candidate media content information by importance according to certain rules. The candidate media content information with a higher priority is processed first.

[0127] When there are multiple candidate media content pieces, key data related to the analysis topic priority is extracted from the user's historical behavior data, namely tagging behavior data, interaction behavior data, browsing duration, and browsing time. This data reflects the user's interest and behavior patterns in each candidate media content piece from different perspectives, providing a data foundation for subsequent prioritization.

[0128] After comprehensively analyzing the extracted data, a pre-set algorithm is used to assess the importance of each candidate media content in the user's interest. For example, candidate media content with high tagging behavior, frequent interactions, long browsing time, and recent browsing time indicates high user interest and is therefore given a higher priority; conversely, candidate media content with low tagging behavior is given a lower priority.

[0129] After determining the priority of the candidate media content information, the order of downloading the candidate media data is determined by combining the browsing progress information of each topic and comprehensively considering the user's interest in the topic and the browsing status. Generally speaking, candidate media content information with a high priority and a short browsing progress will be downloaded first, so as to prioritize the user's needs for content that they are interested in but have not yet fully browsed. A short browsing progress means that the progress achieved by the user when browsing a certain content is relatively short. For example, when reading an article, watching a video, or listening to a podcast, the user only views a small part of the content compared to the length of the entire content. For videos, a short browsing progress means that the duration corresponding to the browsing progress is less than 30% of the entire video length.

[0130] By comprehensively analyzing various historical behavioral data to prioritize candidate media content and combining it with browsing progress information to determine the download order, the system can more accurately grasp user interests and needs, prioritizing downloads of video content that users are most interested in and haven't fully viewed, thereby improving user satisfaction with the downloaded content. By rationally arranging download sequences and avoiding blind downloads, the device's network resources and storage space are more effectively utilized, improving resource utilization efficiency and reducing unnecessary resource consumption. Users can access video content that suits their interests more quickly and reduce waiting time.

[0131] In a possible embodiment, step 320 may specifically include the following steps:

[0132] determining the priority of the first candidate media content information associated with the marking behavior data as a first priority;

[0133] Calculating an attention parameter value for the candidate media content information based on the interactive behavior data, the browsing duration, and the browsing time; and determining the priority of second candidate media content information whose attention parameter value is greater than a preset attention parameter value as a second priority;

[0134] determining the priority of the third candidate media content information whose browsing time is within the preset time period as the third priority;

[0135] The first priority is higher than the second priority, and the second priority is higher than the third priority.

[0136] First candidate media content information: refers to candidate media content information associated with the marking behavior data, that is, candidate media content information categories in which the user has expressed interest through marking behavior.

[0137] First priority: The highest level of priority is assigned to the first candidate media content information, indicating that this type of topic is relatively more valued by users among all candidate media content information and should be given priority in processing.

[0138] Attention parameter value: A value obtained by comprehensively calculating interactive behavior data, browsing duration, and browsing time, used to quantify the user's attention to the candidate media content information. A higher value indicates a higher degree of attention.

[0139] Preset attention parameter value: a pre-set standard value used to measure the degree of attention. When the attention parameter value of the candidate media content information is greater than the preset value, it indicates that the user has a high level of attention to the topic.

[0140] Second candidate media content information refers to candidate media content information whose attention parameter value is greater than a preset attention parameter value, that is, the subject categories to which the user has a high degree of attention determined through calculation.

[0141] Second priority: The second highest level of priority assigned to the second candidate media content information, which has a lower priority than the first priority topic during processing, but higher than other topics that do not meet this standard.

[0142] The above-mentioned step of calculating the attention parameter value of the candidate media content information based on the interactive behavior data, the browsing duration, and the browsing time may specifically include the following steps:

[0143] Quantifying the interactive behavior data, the browsing duration, and the browsing time to obtain a first attention quantization value, a second attention quantization value, and a third attention quantization value;

[0144] Performing weighted processing on the first focus quantization value, the second focus quantization value, and the third focus quantization value to obtain a focus parameter value; or,

[0145] The sum of the first focus quantization value, the second focus quantization value, and the third focus quantization value is determined as the focus parameter value.

[0146] Quantifying interactive behavior data, browsing duration, and browsing time is designed to convert data of varying types and magnitudes into a unified numerical form for subsequent calculations and comparisons. Interactive behavior data includes comments, likes, shares, and other behaviors, each of which reflects user attention to varying degrees. Quantification allows these behaviors to be converted into specific numerical values.

[0147] For example, a comment can be scored 5 points, a like can be scored 1 point, and a share can be scored 3 points. Browsing time reflects the user's level of focus on the content, and browsing time can reflect the time distribution of users' access to the content. These can also be quantified into numerical values, such as 1 point for every 10 minutes of browsing time, and a certain number of points for browsing within a specific time period. This unifies the three different types of data into a comparable numerical system, resulting in the first, second, and third attention quantification values.

[0148] The quantified values are weighted or directly summed to comprehensively consider the impact of these three factors on the user's attention level. Weighted processing assigns different weights to each factor based on its importance to the user's attention. For example, if interactive behavior is considered to better reflect the user's attention level, the first attention quantification value is given a higher weight, such as 0.5, the browsing time is weighted as 0.3, and the browsing time is weighted as 0.2. The attention parameter value is obtained through weighted calculation, which can more accurately reflect the importance differences of different factors. Direct summation, on the other hand, assumes that the contribution of these three factors to the attention parameter value is equally important, and simply adds them together to obtain the attention parameter value. This method is relatively simple and direct, and is suitable for situations where the importance of each factor is not clearly distinguished.

[0149] By comprehensively considering interactive behavior data, browsing duration, and browsing time, we can comprehensively assess users' attention to candidate media content from multiple dimensions, avoiding the one-sidedness of relying on a single factor. For example, simply looking at browsing duration may overlook user interactive behavior, which often more directly reflects a user's interest in and attitude towards the content. Combining these factors can more accurately grasp user attention. Quantification makes different types of data comparable and calculable, converting various complex user behaviors into a unified numerical standard to facilitate data analysis and calculation.

[0150] Preset time period: a pre-set time range, such as the past week, the past month, etc., used to determine whether the user's browsing time is recent, so as to determine the user's recent attention to a certain candidate media content information.

[0151] The third candidate media content information refers to candidate media content information whose browsing time is within a preset time period, that is, the subject categories of the relevant video content that the user has recently watched.

[0152] Third priority: A relatively lower level of priority is assigned to third candidate media content information, which has a lower priority than the first and second priority topics when processed.

[0153] Tagging behavior typically represents a user's active recognition and expression of interest in video content, and is highly indicative. Therefore, candidate media content associated with this tagging behavior is given the highest priority, prioritizing content in which users have explicitly expressed interest, thereby satisfying their core interests.

[0154] Interaction behavior data reflects the depth of user engagement with video content, while browsing time reflects the time users have invested in the topic, and browsing time reflects the user's recent attention. Combining these three factors to calculate the attention parameter value provides a more comprehensive assessment of the user's level of attention to candidate media content. When the attention parameter value is greater than the preset attention parameter value, it indicates that the user has a high level of attention to the candidate media content, and it is assigned the second priority. These topics will be prioritized in processing to meet the user's interests that have not been explicitly expressed through tagging.

[0155] Browsing time within the preset time period indicates that the user has recently viewed the video content associated with this candidate media content. This recent viewing behavior also indicates that the user has some interest in the topic. This type of candidate media content is assigned the third priority and is processed later in the process to further satisfy the user's recent interest needs after satisfying their core needs and higher interest needs.

[0156] For example, the priority of the first candidate media content information associated with the marked behavior data is determined to be the first priority: For example, suppose a user, in a video application, has added multiple videos under the theme of "Science Fiction Movies" to their favorites. Then, the theme of "Science Fiction Movies" becomes the first candidate media content information associated with the marked behavior data, and its priority is determined to be the first priority. In subsequent processing, video content related to the theme of "Science Fiction Movies" will be prioritized for downloading or recommending.

[0157] For the "Action Movies" topic, the user has 15 comments in the past month, with a total browsing time of 200 minutes, and the last viewing was 10 days ago. The rules for calculating the attention parameter value are set as follows: 2 points for each comment, 1 point for every 10 minutes of browsing time, and 5 points for the last viewing within 10 days. The attention parameter value for the "Action Movies" topic is 15×2+200÷10×1+5=55 points. The preset attention parameter value is 50 points. Since 55 points is greater than 50 points, the "Action Movies" topic becomes the second candidate media content information with an attention parameter value greater than the preset attention parameter value, and the priority of the second candidate media content information is determined to be the second priority.

[0158] For the "Romance TV Series" theme, the user has watched related episodes within the past month, and the preset time period is the past month. Because the browsing time is within the preset time period, the "Romance TV Series" theme becomes the third candidate media content information, and the priority of the third candidate media content information is determined to be the third priority.

[0159] By analyzing multi-dimensional data such as tagging behavior, interaction behavior, browsing duration, and browsing time, we can more accurately identify users' interests and interest levels, prioritizing core interests over general interests, and improving the targeting of content recommendations and downloads. By clearly identifying candidate media content information of different priorities and rationally arranging the order in which content is processed, the app can systematically provide relevant video content based on the importance and attention of users' interests, improving the efficiency of users' access to content of interest.

[0160] This allows users to access video content that matches their interests more quickly, reducing search and waiting time, enhancing satisfaction and user experience with the video app, and helping to improve user retention and usage frequency, thereby promoting the long-term development of the app. Content is processed according to priority, avoiding wasted resources and prioritizing content that users are most interested in.

[0161] In a possible embodiment, step 330 may specifically include the following steps:

[0162] determining a priority coefficient of the first candidate media content information according to a generation time of the marking behavior data;

[0163] determining a priority coefficient of the second candidate media content information according to the attention parameter value;

[0164] determining a priority coefficient of the third candidate media content information according to the browsing time;

[0165] The download sequence information is determined according to the priority, the priority coefficient of each of the candidate media content information, and the browsing progress information.

[0166] First candidate media content information: candidate media content information associated with the marking behavior data, that is, candidate media content information in which the user clearly expresses interest through the marking behavior.

[0167] Priority coefficient: A numerical value obtained by calculating or analyzing relevant data, used to further refine and measure the priority of each candidate media content information. The higher the value, the more important or prioritized the topic is.

[0168] The priority coefficient of the first candidate media content information reflects the real-time popularity of the candidate media content by marking the time when the behavior data is generated;

[0169] The priority coefficient of the second candidate media content information is based on the numerical value of the attention parameter and measures the comprehensive attractiveness of the candidate media content to the user;

[0170] The priority coefficient of the third candidate media content information is used to measure the attractiveness of the candidate media content to the user based on the distribution characteristics of the browsing time.

[0171] The time at which tagging behavior data is generated can reflect the timeliness of a user's interest in a media topic. The more recent the tagging behavior, the stronger the user's interest in the topic. By analyzing the time at which tagging behavior data is generated to determine the priority coefficient, we can more accurately measure the priority of the top candidate media content information, giving higher priority to topics of recent interest.

[0172] Second candidate media content information: candidate media content information whose attention parameter value is greater than a preset attention parameter value, indicating that the user has a high level of attention to the second candidate media content information.

[0173] The attention parameter value is a quantitative measure of a user's interest in a topic, calculated from a combination of multiple factors. A higher attention parameter value indicates a stronger user interest in the topic. Therefore, determining the priority coefficient for the second candidate media content based on the attention parameter value allows for a more detailed prioritization of topics based on user interest.

[0174] Third candidate media content information: candidate media content information whose browsing time is within a preset time period, indicating that the user has recently watched video content under this topic.

[0175] For third candidate media content information whose browsing time is within the preset time period, the closer the browsing time is, the higher the user's attention to the candidate media content information. By determining the priority coefficient based on the browsing time, such topics can be reasonably prioritized.

[0176] Browsing progress information: data indicating the degree of completion of a user's browsing of the video content under each candidate media content information, such as the proportion of episodes watched to the total number of episodes, the proportion of viewing time to the total viewing time, etc.

[0177] Download sequence information: information determined based on priority, priority coefficient, and browsing progress information, used to guide the order of downloading candidate media data, and to clarify which video data associated with candidate media content information should be downloaded first and which should be downloaded later.

[0178] Taking into account the priority of the previously determined candidate media content information, the priority coefficient of each topic, and the browsing progress information, the importance and user demand of each candidate media content information are comprehensively evaluated, so as to determine the reasonable download order information and give priority to downloading the content that the user is more interested in and has not been fully browsed.

[0179] The priority coefficient of the first candidate media content is determined based on the time the tagging behavior data was generated. For example, suppose a user tagged a video in the "Science Fiction Movies" category in a video app. The most recent like was three days ago, and the previous two likes were 10 and 15 days ago. The rule is: tags within three days receive 3 points, tags within seven days receive 2 points, and tags within 15 days receive 1 point. Therefore, "Science Fiction Movies" is the first candidate media content, and its priority coefficient is 3 points.

[0180] A user bookmarked a video titled "Suspense TV Series" in a video app. The most recent bookmark was one day ago, with three previous bookmarks from three, five, and seven days ago. The rule is: bookmarks within one day are worth three points, within three days are worth two points, and within seven days are worth one point. Therefore, "Suspense TV Series" is selected as the first candidate media content, with a priority coefficient of 5. Furthermore, the viewing progress for the suspense TV series is episode 6.

[0181] Therefore, in the first candidate media content information, the priority coefficient of "suspense TV series" is higher than the priority coefficient of "science fiction movie", so "suspense TV series" is downloaded first.

[0182] Based on the attention parameter value, the priority coefficient of the second candidate media content information is determined: for the "Action Movie" theme, its attention parameter value is calculated to be 60 points, and the priority coefficient of the attention parameter value between 50-70 points is set to 2 points, so the "Action Movie" theme is the second candidate media content information, and its priority coefficient is 2 points.

[0183] Based on browsing time, the priority coefficient of the third candidate media content is determined: "Romance TV Series" theme. The user has watched it within the past month, with the most recent viewing being 20 days ago. The rule is: viewing within 10 days gives 3 points, viewing within 20 days gives 2 points, and viewing within a month gives 1 point. Therefore, the "Romance TV Series" theme is selected as the third candidate media content, and its priority coefficient is 2 points. Furthermore, the browsing progress information for the romance TV series is episode 8.

[0184] It is known that "science fiction movies" and "suspense TV series" are the first candidate media content information, and the priority coefficient of "suspense TV series" is higher than the priority coefficient of "science fiction movies"; "action movies" is the second candidate media content information, with a priority coefficient of 2 points; "romance TV series" is the third candidate media content information, with a priority coefficient of 2 points. In addition, the browsing progress information of the suspense TV series is episode 6, and the browsing progress information of the romance TV series is episode 8.

[0185] Therefore, the determined download sequence information includes: first downloading the video data of the 7th episode of "Suspense TV Series", then downloading the video data associated with "Science Fiction Movie", followed by the "Action Movie" theme, and finally downloading the video data of the 9th episode of "Romance TV Series".

[0186] By considering the generation time of the marked behavior data, the attention parameter value, and the browsing time to determine the priority coefficient, the priority of each candidate media content information is more carefully evaluated, which can more accurately reflect the user's changing interests and needs. By combining priority, priority coefficient, and browsing progress information to determine the download order, the download operation is more closely aligned with the user's actual needs and interests, prioritizing the video content that the user is most interested in and has not yet fully browsed, thereby improving the relevance and effectiveness of the download.

[0187] As a result, users can obtain video content that suits their interests more quickly, reduce waiting time, and enhance their satisfaction and usage experience with video applications. A reasonable download order can make more reasonable use of the application's network resources and storage space, avoid unnecessary waste of resources, and improve resource utilization efficiency.

[0188] In a possible embodiment, the download stop condition is satisfied, including any one of the following:

[0189] Detecting that the browsing time corresponding to the downloaded media data reaches the preset browsing time;

[0190] It is detected that the storage space occupied by the downloaded media data reaches a preset space size;

[0191] It is detected that all candidate media data associated with at least one candidate media content information has been downloaded.

[0192] Downloaded media data: refers to the video, audio, or other related media file content that has been successfully downloaded to the local device in the video application.

[0193] Viewing time: The length of time users spend viewing downloaded media data, measured in minutes, hours, and other time units.

[0194] Preset browsing time: A time value that is pre-set during the development or setting of a video application. When the browsing time corresponding to the downloaded media data reaches the preset browsing time, the download is stopped. Figure 4 As shown, users can select the preset browsing time and image quality preferences in the smart download interface.

[0195] Storage space: The space used by local devices to store data, such as the memory of a mobile phone, the hard disk space of a computer, etc.

[0196] Preset space size: A pre-set capacity value of the device storage space. When the storage space occupied by the downloaded media data reaches this preset capacity, the condition for stopping the download is met.

[0197] By recording the user's viewing time of downloaded media data, when this time reaches the preset browsing time, it is considered that the user has enough content to watch, or has reached a reasonable viewing volume standard set by the application. At this time, downloading new media data will be stopped to avoid excessive downloading and waste of resources.

[0198] For example, in a video application, the preset viewing time is set to 10 hours. A user downloads some movies and TV series. When the system detects that the user has watched these downloaded videos for a total of 10 hours, it stops downloading new video data. For example, if a user downloads five movies and watches them for different lengths, and the total viewing time reaches 10 hours, the download operation stops.

[0199] Monitor the storage space occupied by downloaded media data in the local device in real time. When the pre-set space size is reached, stop the download operation to prevent the device's storage space from being excessively occupied, affecting the normal operation of the device or the storage of other data.

[0200] For example, for a video app, the preset storage size is 5GB. When a user uses the app to download videos, if the phone's storage space occupied by the downloaded video data reaches 5GB, the app will automatically stop downloading new video files to prevent the phone's storage from being exhausted. For example, if a user downloads multiple high-definition TV series and movies, the download will stop once the cumulative space occupied by these files reaches 5GB.

[0201] When downloading media data related to candidate media content information, the data download progress of each candidate media content information is tracked, and when it is found that all related candidate media data of at least one candidate media content information has been successfully downloaded to the local device, the download operation is stopped.

[0202] By setting limits on browsing time and storage space, you can prevent unnecessary media downloads, avoid wasting network bandwidth and device storage space, and make more efficient use of resources. By controlling the storage space occupied by downloaded media data, you can prevent issues such as slow device operation and inability to store other important data due to insufficient storage space, ensuring normal device operation.

[0203] Downloading stops when all data related to the candidate media content has been downloaded, ensuring that users have access to all relevant content under the candidate media content, satisfying their needs for specific media content and improving the user experience. Download stop conditions provide clear rules and standards for download management in video applications, making download operations more organized and controllable, and facilitating effective management and control of the download process.

[0204] In a possible embodiment, after step 130, the method further includes:

[0205] Deleting media data that meets preset deletion conditions;

[0206] The preset deletion condition includes any one of the following:

[0207] The browsing progress reaches the preset browsing progress;

[0208] The download result is download failure;

[0209] The browsing progress has not reached the preset browsing progress, and the storage time is greater than the preset storage time, where the storage time is the time from the current time to the download completion time.

[0210] Preset deletion conditions: During the development or configuration of a video application, pre-set standards or rules are used to determine whether to delete media data.

[0211] Browsing progress: The degree to which a user has completed viewing media data, usually expressed as the ratio of the viewing time to the total viewing time, or the ratio of the number of episodes watched to the total number of episodes.

[0212] Preset browsing progress: a pre-set numerical standard for the degree of browsing completion. When the user's browsing progress of media data reaches this value, the corresponding deletion condition is met.

[0213] Download Result: Refers to the final status of the media data download operation, including download success and download failure. Download failure occurs when the media data cannot be successfully downloaded to the local device due to network problems, server failure, file corruption, etc.

[0214] Storage duration: The duration from the time the media data is downloaded to the current time, measured in hours, days, months, and other time units.

[0215] Preset storage duration: A pre-set time length used to determine whether the storage time of media data on the local device is too long. When the storage time exceeds the preset storage time length and other relevant conditions are met, the deletion condition is met.

[0216] By regularly checking downloaded media data to determine whether it meets the preset deletion conditions, the media data is deleted from the local device to free up storage space, optimize device performance, and manage media data storage.

[0217] When a user reaches a preset viewing limit for a piece of media, it indicates that the user has essentially finished viewing or understood the main content of the media. Deleting the media at this point not only satisfies the user's viewing needs but also frees up storage space on the device, preventing unused data from occupying the device for an extended period of time.

[0218] For example, in a video app, a preset viewing progress for a TV series is set to 80%. A user downloads the TV series and starts watching it. When the user reaches 80% of the total number of episodes, the app detects that the viewing progress has reached the preset value and deletes the downloaded data from the device.

[0219] Media data that fails to download cannot be used normally. Continuing to store it on the device not only takes up space but also affects the user's ability to manage and use other data. Therefore, when a download failure is detected, it is deleted.

[0220] For example, if a user downloads a movie in an app and the download fails due to a sudden network outage, the app will automatically delete the temporary files and other related data generated during the download process to free up device space.

[0221] The user has not continued to watch the media data within a certain period of time, and the storage time has been long. In order to rationally utilize storage space and avoid storing media data that the user no longer cares about for a long time, the media data will be deleted when both conditions are met.

[0222] For example, a video app has a preset viewing progress of 50% and a preset storage duration of 30 days. A user downloads a documentary, watches 30% of the total duration, and then stops watching. 40 days have passed since the download was completed. The app detects that the viewing progress of the documentary has not reached 50% and the storage duration is greater than 30 days, so it deletes the downloaded data.

[0223] By deleting media data that meets preset deletion criteria, you can effectively free up device storage space, leaving more space for storing other important data or downloading new media content, thereby improving device storage utilization. Reducing useless or unusable data stored on a device reduces the burden on the device in terms of data storage and management, helping to improve device speed and overall performance, and providing a better user experience.

[0224] By pre-setting clear deletion conditions and corresponding deletion operations, users can more easily find and use useful data, while reducing data redundancy and confusion, improving data management efficiency. Avoiding the long-term storage of no longer needed or unusable media data can effectively utilize device resources.

[0225] In a possible embodiment, after step 130, the method further includes:

[0226] Displaying an intelligent video download interface, the intelligent video download interface including data identification information of candidate media data associated with at least one candidate media content information;

[0227] In response to a control input of the target data identification information, displaying a save control and a delete control;

[0228] In response to an input to a save control, saving target media data indicated by the target data identification information;

[0229] In response to an input to the save control, the target media data indicated by the target data identification information is deleted.

[0230] The app retrieves candidate media content information and associated media data identifiers, such as video titles, from a database or other data source to facilitate user identification and manipulation. When the user enters specific target data identifiers on the interface, a Save and Delete control appears, providing the user with options for manipulating the target media data.

[0231] When the user clicks the Save control, the system locates the corresponding target media data based on the target data identification information. It then saves the target media data to the specified storage location, such as the device's local storage space or a user-specified cloud storage. When the user clicks the Delete control, the target media data is located based on the target data identification information and the delete operation is performed, removing the media data from the corresponding storage location.

[0232] It can be understood that, in response to the input of the save control, after saving the target media data indicated by the target data identification information, the target media data will not be automatically deleted by the system because it meets the preset deletion conditions. The target media data will remain in the manual download task list, waiting for the user to manually process it.

[0233] like Figure 3 As shown in FIG. 1 , in response to the control input of the target data identification information, a save control 14 and a delete control 15 are displayed. Figure 3 As shown in FIG. 2 b , in response to a control input of target data identification information, a save control 16 and a delete control 17 are displayed.

[0234] For example, when the smart video download interface is displayed, multiple candidate media content information is displayed on the interface, including the poster, name, and episode number of the TV series "SS". The user clicks on the episode number of "SS", and the interface immediately pops up the save control and delete control. After the user clicks the save control, the application begins to download the data of the corresponding episode of "SS" and save it to the local storage space of the mobile phone set by the user. If the user clicks the delete control, the application will immediately stop the ongoing download operation and delete the previously downloaded "SS" episode data. At the same time, the data identification information about "SS" on the interface will also disappear, indicating that the data has been removed from the smart download related list.

[0235] By displaying candidate media content information and associated data identifiers, users can quickly identify available content and easily perform further operations on the data of interest, enhancing the user experience. By displaying save and delete controls, the diverse media data processing needs of different users are met. Users can proactively save important data or clear unnecessary data based on their needs, effectively managing their personal media data.

[0236] In one possible embodiment, a smart download interface is displayed; the smart download interface includes: smart download identification information of candidate media data associated with at least one candidate media content information downloaded according to the download strategy, and manual download identification information of at least one media data downloaded according to user input information.

[0237] Smart download interface: refers to a specific interface in the application used to display download-related information. It has smart download functions and can handle download tasks based on download strategies.

[0238] Smart Download Identification Information: This information is used to identify candidate media data for automatic download triggering based on the download policy. With this information, users can clearly see which media data is automatically triggered for download based on the download policy in the Smart Download interface.

[0239] User input information: information generated when the user clicks the "Download" button in the media application interface.

[0240] Manual download identification information: refers to the identification information of media data that is manually downloaded by the user, allowing the user to distinguish which media data is actively downloaded by the user and distinguish it from the content downloaded by the intelligent user.

[0241] When downloading of candidate media data associated with at least one candidate media content information begins according to the download strategy, smart download identification information is generated and displayed in the smart download interface, so that the user knows that the candidate media data associated with at least one candidate media content information is downloaded through the smart download method.

[0242] The user triggers download request information by performing specific operations on the interface, such as clicking a download button. In response to the download request information, the media data specified by the user is downloaded, and manual download identification information is generated for it so that it can be displayed separately from the smart download content in the smart download interface, so that the user can clearly know which media data he has manually downloaded.

[0243] Therefore, by displaying the smart download interface, users can quickly access the media data of interest when needed, thereby improving the convenience and smoothness of the user experience. By displaying smart download identification information and manual download identification information separately in the smart download interface, users can clearly understand the source and method of downloaded content, making it easier to categorize and manage downloaded media data, for example, deleting or retaining different types of downloaded content according to their needs.

[0244] In an embodiment of the present invention, current network status information, current memory status information and historical behavior data of the user in the application are obtained. The historical behavior data include: download behavior data, marking behavior data, interactive behavior data and browsing behavior data. The historical behavior data can provide a basis for determining the user's interests and behavior patterns. At least one candidate media content information that the user is interested in can be determined based on the user's historical behavior data; a download strategy is determined based on the current network status information and the current memory status information. The download strategy includes: content continuation download strategy and content recommendation download strategy. According to the download strategy, candidate media data associated with at least one candidate media content information is downloaded, which can adapt to the actual situation of the current network status and the current memory status, and ensure the smooth progress of the download operation. Therefore, the user's historical behavior data, current network status and current memory status can be combined to automatically and smoothly download media data that meets the user's interests and needs without manual operation by the user, which can improve the download efficiency of media data.

[0245] Based on the above Figure 1 The embodiment of the present invention further provides a media data downloading device, such as Figure 5 As shown, the media data downloading device 500 may include:

[0246] An acquisition module 510 is configured to acquire current network status information, current memory status information, and historical user behavior data in an application, wherein the historical behavior data includes downloading behavior data, marking behavior data, interactive behavior data, and browsing behavior data.

[0247] A first determining module 520 is configured to determine at least one candidate media content information in the application based on the historical behavior data;

[0248] A second determining module 530 is configured to determine a download strategy based on the current network status information and the current memory status information;

[0249] A downloading module 540 is configured to download candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is met;

[0250] The download strategies include: a content continuation download strategy and a content recommendation download strategy; the content continuation download strategy determines candidate media data based on browsing progress information of the candidate media content information; the content recommendation download strategy determines candidate media data based on recommendation parameter values of the candidate media content information.

[0251] In a possible embodiment, the first determining module 520 is specifically configured to:

[0252] When the download condition is met, determining at least one candidate media content information in the application according to the historical behavior data;

[0253] The download conditions include any one of the following:

[0254] An opening operation of a download control of the application is detected, an initial startup operation of the application is detected within each natural day, and an interactive operation of the application is detected within the first N hours of each natural day, where N is a positive number.

[0255] In a possible embodiment, the first determining module 520 is specifically configured to perform at least one of the following:

[0256] determining at least one piece of candidate media content information associated with the marking behavior data;

[0257] Determining at least one piece of candidate media content information associated with the interactive behavior data and the browsing behavior data;

[0258] Determining user preference information based on the historical behavior data; and determining at least one piece of candidate media content information associated with the user preference information.

[0259] In a possible embodiment, the download module 540 is specifically configured to:

[0260] Determining browsing progress information of at least one of the candidate media content information from the historical behavior data;

[0261] Determining download sequence information based on browsing progress information of the candidate media content information;

[0262] According to the download strategy and the download sequence information, candidate media data associated with at least one candidate media content information is downloaded in sequence until the download stop condition is met.

[0263] In a possible embodiment, the download module 540 is specifically configured to:

[0264] When the number of the candidate media content information is at least two, determining the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time from the historical behavior data;

[0265] determining a priority of at least one of the candidate media content information according to the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time;

[0266] The download sequence information is determined according to the priority and the browsing progress information.

[0267] In a possible embodiment, the download module 540 is specifically configured to:

[0268] determining the priority of the first candidate media content information associated with the marking behavior data as a first priority;

[0269] Calculating an attention parameter value for the candidate media content information based on the interactive behavior data, the browsing duration, and the browsing time; and determining the priority of second candidate media content information whose attention parameter value is greater than a preset attention parameter value as a second priority;

[0270] determining the priority of the third candidate media content information whose browsing time is within the preset time period as the third priority;

[0271] The first priority is higher than the second priority, and the second priority is higher than the third priority.

[0272] In a possible embodiment, the download module 540 is specifically configured to:

[0273] determining a priority coefficient of the first candidate media content information according to a generation time of the marking behavior data;

[0274] determining a priority coefficient of the second candidate media content information according to the attention parameter value;

[0275] determining a priority coefficient of the third candidate media content information according to the browsing time;

[0276] The download sequence information is determined according to the priority, the priority coefficient of each of the candidate media content information, and the browsing progress information.

[0277] In a possible embodiment, the download stop condition is satisfied, including any one of the following:

[0278] Detecting that the browsing time corresponding to the downloaded media data reaches the preset browsing time;

[0279] It is detected that the storage space occupied by the downloaded media data reaches a preset space size;

[0280] It is detected that all candidate media data associated with the at least one candidate media content information has been downloaded.

[0281] In a possible embodiment, the media data downloading device 500 may further include:

[0282] A deletion module is used to delete media data that meets preset deletion conditions;

[0283] The preset deletion condition includes any one of the following:

[0284] The browsing progress reaches the preset browsing progress;

[0285] The download result is download failure;

[0286] The browsing progress has not reached the preset browsing progress, and the storage time is greater than the preset storage time, where the storage time is the time from the current time to the download completion time.

[0287] In a possible embodiment, the media data downloading device 500 may further include:

[0288] A display module is used to display an intelligent download interface; the intelligent download interface includes: intelligent download identification information of candidate media data associated with at least one candidate media content information downloaded according to the download strategy, and manual download identification information of at least one media data downloaded according to user input information.

[0289] In an embodiment of the present invention, current network status information, current memory status information and historical behavior data of the user in the application are obtained. The historical behavior data include: download behavior data, marking behavior data, interactive behavior data and browsing behavior data. The historical behavior data can provide a basis for determining the user's interests and behavior patterns. At least one candidate media content information that the user is interested in can be determined based on the user's historical behavior data; a download strategy is determined based on the current network status information and the current memory status information. The download strategy includes: content continuation download strategy and content recommendation download strategy. According to the download strategy, candidate media data associated with at least one candidate media content information is downloaded, which can adapt to the actual situation of the current network status and the current memory status, and ensure the smooth progress of the download operation. Therefore, the user's historical behavior data, current network status and current memory status can be combined to automatically and smoothly download media data that meets the user's interests and needs without manual operation by the user, which can improve the download efficiency of media data.

[0290] The embodiment of the present invention further provides an electronic device, such as Figure 6 As shown, it includes a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0291] Memory 603, used for storing computer programs;

[0292] The processor 601 is configured to execute the program stored in the memory 603 by performing the following steps:

[0293] Obtain current network status information, current memory status information, and historical user behavior data in the application, including download behavior data, tagging behavior data, interactive behavior data, and browsing behavior data;

[0294] determining at least one candidate media content information in the application based on the historical behavior data;

[0295] Determining a download strategy based on the current network status information and the current memory status information;

[0296] downloading candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is met;

[0297] The download strategies include: content continuation download strategy and content recommendation download strategy;

[0298] The content continued viewing download strategy is to determine the candidate media data according to the browsing progress information of the candidate media content information;

[0299] The content recommendation download strategy is to determine candidate media data according to the recommendation parameter values of the candidate media content information.

[0300] The communication bus mentioned in the terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0301] The communication interface is used for communication between the above terminal and other devices.

[0302] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0303] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0304] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the media data downloading method described in any one of the above embodiments.

[0305] In another embodiment of the present invention, a computer program product including instructions is provided. When the computer program product is run on a computer, the computer executes the media data downloading method described in any one of the above embodiments.

[0306] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0307] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0308] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0309] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for downloading media data, characterized in that: The method comprises: Obtain current network status information, current memory status information, and historical user behavior data in the application, including download behavior data, tagging behavior data, interactive behavior data, and browsing behavior data; determining at least one candidate media content information in the application based on the historical behavior data; Determine a download strategy based on the current network status information and the current memory status information; the download strategy includes: a content continuation download strategy and a content recommendation download strategy; According to the downloading strategy, candidate media data associated with at least one candidate media content information is downloaded until a download stop condition is met.

2. The method according to claim 1, characterized in that The determining, based on the historical behavior data, at least one candidate media content information in the application includes: When the download condition is met, determining at least one candidate media content information in the application according to the historical behavior data; The download conditions include any one of the following: An opening operation of a download control of the application is detected, an initial startup operation of the application is detected within each natural day, and an interactive operation of the application is detected within the first N hours of each natural day, where N is a positive number.

3. The method according to claim 1, characterized in that The determining, based on the historical behavior data, at least one candidate media content information in the application includes at least one of the following: determining at least one piece of candidate media content information associated with the marking behavior data; Determining at least one piece of candidate media content information associated with the interactive behavior data and the browsing behavior data; Determining user preference information based on the historical behavior data; And, determining at least one candidate media content information associated with the user preference information.

4. The method according to claim 1, wherein The downloading of candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is satisfied includes: Determining browsing progress information of at least one of the candidate media content information from the historical behavior data; Determining download sequence information based on browsing progress information of the candidate media content information; According to the download strategy and the download sequence information, candidate media data associated with at least one candidate media content information is downloaded in sequence until the download stop condition is met.

5. The method according to claim 4, characterized in that The step of determining download sequence information based on browsing progress information of the candidate media content information includes: When the number of the candidate media content information is at least two, determining the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time from the historical behavior data; determining a priority of at least one of the candidate media content information according to the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time; The download sequence information is determined according to the priority and the browsing progress information.

6. The method according to claim 5, characterized in that Determining the priority of at least one piece of candidate media content information according to the marking behavior data, the interactive behavior data, the browsing duration, and the browsing time includes: determining the priority of the first candidate media content information associated with the marking behavior data as a first priority; Calculating an attention parameter value for the candidate media content information based on the interactive behavior data, the browsing duration, and the browsing time; and determining the priority of second candidate media content information whose attention parameter value is greater than a preset attention parameter value as a second priority; determining the priority of the third candidate media content information whose browsing time is within the preset time period as the third priority; The first priority is higher than the second priority, and the second priority is higher than the third priority.

7. The method according to claim 6, characterized in that The determining the download sequence information according to the priority and the browsing progress information includes: determining a priority coefficient of the first candidate media content information according to a generation time of the marking behavior data; determining a priority coefficient of the second candidate media content information according to the attention parameter value; determining a priority coefficient of the third candidate media content information according to the browsing time; The download sequence information is determined according to the priority, the priority coefficient of each of the candidate media content information, and the browsing progress information.

8. The method according to any one of claims 1 to 7, characterized in that The download stop condition includes any one of the following: Detecting that the browsing time corresponding to the downloaded media data reaches the preset browsing time; It is detected that the storage space occupied by the downloaded media data reaches a preset space size; It is detected that all candidate media data associated with the at least one candidate media content information has been downloaded.

9. The method according to any one of claims 1 to 7, characterized in that After downloading candidate media data associated with at least one candidate media content information until a download stop condition is satisfied, the method further includes: Deleting media data that meets preset deletion conditions; The preset deletion condition includes any one of the following: The browsing progress reaches the preset browsing progress; The download result is download failure; The browsing progress has not reached the preset browsing progress, and the storage time is greater than the preset storage time, where the storage time is the time from the current time to the download completion time.

10. The method according to claim 1, characterized in that The method further comprises: Display an intelligent download interface; the intelligent download interface includes: intelligent download identification information of candidate media data associated with at least one candidate media content information downloaded according to the download strategy, and manual download identification information of at least one media data downloaded according to user input information.

11. A media data downloading device, characterized in that: The device comprises: An acquisition module is used to obtain current network status information, current memory status information, and historical behavior data of the user in the application, wherein the historical behavior data includes: download behavior data, marking behavior data, interaction behavior data, and browsing behavior data; A first determining module, configured to determine at least one candidate media content information in the application based on the historical behavior data; A second determining module is configured to determine a download strategy based on the current network status information and the current memory status information; the download strategy includes: a content continuation download strategy and a content recommendation download strategy; The download module is configured to download candidate media data associated with at least one candidate media content information according to the download strategy until a download stop condition is met.

12. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method steps described in any one of claims 1 to 10 when executing a program stored in a memory.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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