CN115190338A - Method and system for preloading video - Google Patents
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Abstract
The embodiment of the application provides a method and a system for preloading videos, wherein the method comprises the following steps: acquiring historical network speed characteristic data, current video code rate and historical watching behavior data of a user; determining a network quality type according to the historical network speed characteristic data and the current video code rate; determining a predicted playing time according to the historical watching behavior data of the user; and generating a video preloading strategy according to the network quality type and the predicted playing time. Therefore, a preloading decision can be made based on the network speed characteristics, only the user with the network condition superior to the current video code rate is preloaded, if the network condition of the user is excellent, the user does not need to supplement and preload excessive content, meanwhile, the playing habit is summarized according to the playing data of the user, the user is portrayed, if the user is not in a state of watching one video for a long time, the preloading duration is not too long, and the bandwidth waste is avoided.
Description
Technical Field
The embodiment of the application relates to the technical field of streaming media playing, in particular to a method, a system, computer equipment and a computer readable storage medium for preloading a video.
Background
With the rapid development of the internet, various video applications are emerging. In some video applications, videos which may be played next (for example, scenes in which the videos are played by sliding up and down) can be preloaded, so that a user can directly watch the videos without waiting for video loading when jumping to the videos which are preloaded, waiting time of the user is reduced, and watching experience of the user is greatly improved.
In the prior art, the scheme of video preloading is generally divided into two steps: the first step is as follows: a task to be played newly added into a play task queue directly starts the content preloaded for about 0.5 second; the second step is that: and sequentially polling in a priority mode, and supplementing and preloading subsequent videos under the condition that the cache of the currently played video is sufficient. e, the first step is to reduce the first frame, and the second step is to reduce the pause.
However, according to the scheme of video preloading, the network speed competition in the starting stage of a task can cause unsmooth playing and other unsmooth phenomena to occur in the current playing, and the user experience is influenced; also, if the user does not watch the supplemental preloaded video, a lot of bandwidth is wasted.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, a system, a computer device and a computer readable storage medium for video preloading, which are used to solve the following problems: the network speed competition in the starting playing stage of the video playing task can cause the phenomenon of blocking, and a great deal of bandwidth waste is easily caused because the user does not watch the supplementary preloaded video.
One aspect of the embodiments of the present application provides a method for preloading a video, including:
acquiring historical network speed characteristic data, current video code rate and historical watching behavior data of a user;
determining a network quality type according to the historical network speed characteristic data and the current video code rate;
determining a predicted playing time according to the historical watching behavior data of the user;
and generating a video preloading strategy according to the network quality type and the predicted playing time.
Optionally, determining a network quality type according to the historical internet speed feature data and the current video bitrate includes:
determining long-term network speed characteristics and short-term network speed characteristics according to the historical network speed characteristic data;
and determining the network quality type according to the long-term network speed characteristic, the short-term network speed characteristic and the current video code rate.
Optionally, the determining long-term network speed characteristics according to the historical network speed characteristic data includes:
determining the total number of the network speed data contained in the historical network speed characteristic data;
sequentially determining the maximum network speed and the minimum network speed in a sliding window with a preset length in the historical network speed characteristic data, and determining the difference value between the maximum network speed and the minimum network speed;
if the difference is larger than or equal to a first preset threshold, increasing the fluctuation times by 1 time, moving the sliding window backwards by the preset length, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length; or the like, or, alternatively,
if the difference value is smaller than the first preset threshold value, moving the sliding window backwards by 1 unit, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length;
and determining the ratio of the fluctuation times to the total number of the network speed data until all data in the historical network speed characteristic data are traversed, so as to obtain long-term fluctuation.
Optionally, the determining a maximum network speed and a minimum network speed in a sliding window of a preset length in the historical network speed feature data includes:
and when the network speed is greater than or equal to a preset value, determining the maximum network speed as the preset value.
Optionally, the determining the short-term network speed feature according to the historical network speed feature data includes:
determining short-term percentile network speed according to the network speed data within a preset time length in the historical network speed characteristic data;
and determining the short-term instant network speed according to the latest network speed data in the historical network speed characteristic data.
Optionally, determining a network quality type according to the long-term network speed characteristic, the short-term network speed characteristic, and the current video bitrate includes:
when the short-term percentile network speed and the short-term instant network speed are greater than a second preset threshold value and the long-term volatility is zero, determining that the network quality type is the network condition is excellent;
when the short-term percentile network speed is less than or equal to the current video code rate, determining that the network quality type is extremely poor in network condition;
and when the short-term percentile network speed and the short-term instant network speed are less than or equal to the second preset threshold value, and the long-term volatility is not zero, or when the short-term percentile network speed is greater than the current video code rate, determining that the network quality type is a general network condition.
Optionally, the preloading includes start-up loading and supplementary loading, and the generating a video preloading policy according to the network quality type and the predicted play duration includes:
when the network quality type is extremely poor, determining that the video preloading strategy does not need to carry out broadcast loading and supplementary loading;
when the network quality type is that the network condition is excellent, determining that the video preloading strategy is required to be played and loaded;
when the network quality type is that the network condition is general and the predicted playing time is less than a preset value, determining that a video preloading strategy is required to be played and loaded;
and when the network quality type is general network conditions and the predicted playing time is greater than or equal to a preset value, determining that the video preloading strategy needs to be started to play and loaded additionally.
Optionally, the historical viewing behavior data includes a play-out time and a play duration; the determining the predicted playing time length according to the historical viewing behavior data of the user includes:
and inputting the starting playing time and the playing time into a preset time evaluation model, and outputting the predicted playing time.
An aspect of an embodiment of the present application further provides a system for preloading a video, including:
the historical data acquisition module is used for acquiring historical network speed characteristic data, current video code rate and historical watching behavior data of a user;
the network type determining module is used for determining the network quality type according to the historical network speed characteristic data and the current video code rate;
the user habit determining module is used for determining the predicted playing time according to the historical watching behavior data of the user;
and the preloading module is used for generating a video preloading strategy according to the network quality type and the predicted playing time length.
Optionally, the network type determining module includes:
the network speed characteristic determining submodule is used for determining long-term network speed characteristics and short-term network speed characteristics according to the historical network speed characteristic data;
and the network type determining submodule is used for determining the network quality type according to the long-term network speed characteristic, the short-term network speed characteristic and the current video code rate.
Optionally, the long-term wire speed characteristic includes long-term volatility, and the wire speed characteristic determination submodule includes:
the total network speed data determining unit is used for determining the total network speed data contained in the historical network speed characteristic data;
a network speed difference determining unit, configured to sequentially determine a maximum network speed and a minimum network speed in a sliding window of a preset length in the historical network speed feature data, and determine a difference between the maximum network speed and the minimum network speed;
a first window moving unit, configured to increase the number of times of fluctuation by 1 time if the difference is greater than or equal to a first preset threshold, move the sliding window backward by the preset length, and return to the step of executing the step of determining the maximum network speed and the minimum network speed in the sliding window of the preset length; or the like, or a combination thereof,
a second window moving unit, configured to move the sliding window backward by 1 unit if the difference is smaller than the first preset threshold, and return to the step of determining the maximum network speed and the minimum network speed in the sliding window of the preset length;
and the long-term volatility determining unit is used for determining the ratio of the fluctuation times to the total number of the network speed data until all data in the historical network speed characteristic data are traversed, so as to obtain the long-term volatility.
Optionally, the network speed difference value determining unit includes:
and the maximum network speed determining subunit is used for determining the maximum network speed as the preset value when the network speed is greater than or equal to the preset value.
In a preferred embodiment of the present application, the short-term wire speed characteristics include a short-term percentile wire speed and a short-term instantaneous wire speed, and the wire speed characteristic determination sub-module further includes:
the short-term percentile network speed determining unit is used for determining the short-term percentile network speed according to the network speed data within the preset time length in the historical network speed characteristic data;
and the short-term instant network speed determining unit is used for determining the short-term instant network speed according to the latest network speed data in the historical network speed characteristic data.
Optionally, the network type determining sub-module includes:
a first network type determining unit, configured to determine that the network quality type is a network condition that is extremely excellent when the short-term percentile network speed and the short-term instantaneous network speed are greater than a second preset threshold and the long-term volatility is zero;
a second network type determining unit, configured to determine that the network quality type is a network condition being extremely bad when the short-term percentile network speed is less than or equal to the current video bitrate;
and a third network type determining unit, configured to determine that the network quality type is a general network condition when the short-term percentile network speed and the short-term instantaneous network speed are less than or equal to the second preset threshold and the long-term volatility is not zero, or when the short-term percentile network speed is greater than the current video code rate.
Optionally, the preloading includes an initiating load and a supplementary load, and the preloading module includes:
the first loading strategy determining submodule is used for determining that the video preloading strategy does not need to be started to play and loaded additionally when the network quality type is extremely poor in network condition;
the second loading strategy determining submodule is used for determining that the video preloading strategy is required to be played and loaded when the network quality type is the network condition is excellent;
a third loading strategy determining submodule, configured to determine that a video preloading strategy needs to be started to play and loaded when the network quality type is a general network condition and the predicted play duration is smaller than a preset value;
and the fourth loading strategy determining submodule is used for determining the video preloading strategy as the video starting loading and the supplementary loading are required when the network quality type is general and the predicted playing time is greater than or equal to a preset value.
Optionally, the historical viewing behavior data includes a play-out time and a play duration; the user habit determining module comprises:
and the playing time length determining submodule is used for inputting the starting playing time and the playing time length into a preset time length evaluation model and outputting the predicted playing time length.
An aspect of the embodiments of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for preloading video as described above.
An aspect of the embodiments of the present application further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor, so that when the computer program is executed by the at least one processor, the steps of the method for preloading videos as described above are implemented.
According to the method, the system, the equipment and the computer readable storage medium for video preloading, historical network speed characteristic data, current video code rate and historical watching behavior data of a user are obtained; determining a network quality type according to the historical network speed characteristic data and the current video code rate; determining a predicted playing time according to the historical watching behavior data of the user; and generating a video preloading strategy according to the network quality type and the predicted playing time. Therefore, a preloading decision can be made based on the network speed characteristics, only the user with the network condition superior to the current video code rate is preloaded, if the network condition of the user is excellent, the user does not need to supplement and preload excessive content, meanwhile, the playing habit is summarized according to the playing data of the user, the user is portrayed, if the user is not in the state of watching one video for a long time, the duration of preloading is not too long, and the bandwidth waste is avoided.
Drawings
FIG. 1 schematically illustrates a prior art pre-loaded video;
FIG. 2 is a diagram schematically illustrating an application environment of a method for video preloading according to an embodiment of the present application;
fig. 3 schematically shows a flowchart of a method for preloading video according to a first embodiment of the present application;
FIG. 4 is a schematic diagram illustrating a flow chart of generating a video preloading strategy according to one embodiment of the present application;
fig. 5 schematically shows a block diagram of a system for video preloading according to a second embodiment of the present application; and
fig. 6 schematically shows a hardware architecture diagram of a computer device suitable for implementing a method for video preloading according to a third embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between the embodiments may be combined with each other, but must be based on the realization of the technical solutions by a person skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope claimed in the present application.
In the related art, the scheme of video preloading is generally divided into two steps: the first step is as follows: a task to be played newly added into a play task queue directly starts the content preloaded for about 0.5 second; the second step is that: and sequentially polling in a priority mode, and supplementing and preloading subsequent videos under the condition that the cache of the currently played video is sufficient. Wherein, the purpose of the first step is to reduce the first frame, and the purpose of the second step is to reduce the pause. As an example, as shown in fig. 1, there are a total of 4 playing tasks, wherein 1 is a video task currently being played, 3 is a video task to be played in the preloading queue, the length of the rectangle represents the preloading length of the video, and the videos are preloaded by polling according to the priority.
The above video preloading scheme has the following disadvantages: on one hand, network speed competition in the starting playing stage can cause blocking, when one task starts playing, new tasks are always added into a queue to be played, the new tasks can directly start a preloading flow for 0.5 second, network speed competition with the current playing task is achieved, the current playing is caused to have blocking and other unsmooth phenomena, and user experience is influenced. On the other hand, the supplementary preloading can cause a large amount of bandwidth waste, under the condition that the network condition of the user is good, the supplementary preloading function can pre-cache more data for the video to be played, and if the pre-cached data is not watched by the user, the consumption of the bandwidth can cause loss for the server and the client at the same time.
In view of this, the present application aims to provide a video preloading scheme based on network speed characteristics and user playing habits, by obtaining historical network speed characteristic data, current video bitrate, and historical watching behavior data of a user; determining a network quality type according to the historical network speed characteristic data and the current video code rate; determining a predicted playing time according to the historical watching behavior data of the user; and generating a video preloading strategy according to the network quality type and the predicted playing time. Therefore, a preloading decision can be made based on the network speed characteristics, only the user with the network condition superior to the current video code rate is preloaded, if the network condition of the user is excellent, the user does not need to supplement and preload excessive content, meanwhile, the playing habit is summarized according to the playing data of the user, the user is portrayed, if the user is not in a state of watching one video for a long time, the preloading duration is not too long, and the bandwidth waste is avoided.
The present application provides various embodiments to further introduce a scheme for video preloading, which are specifically referred to below.
In the description of the present application, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but merely serve to facilitate the description of the present application and to distinguish each step, and therefore should not be construed as limiting the present application.
The following are the term explanations of the present application:
streaming media transmission: in the internet era, audio and video transmission mainly comprises two schemes of downloading and streaming transmission. The downloading scheme requires that a user must download a complete file before starting playing the audio and video, and the time consumption is long; the streaming transmission scheme divides the file into a plurality of subfiles, the file is transmitted and consumed in real time from the server to the user side like water flow, and the user side can play audio and video in real time only by part of the subfiles.
And (3) playing experience: the subjective feeling of the user in the process of playing the audio and video comprises the first frame, pause, definition, sound and picture synchronization and the like of starting playing of the audio and video.
Preloading: by downloading and preparing resources in advance, the time consumption of the bottleneck node in the starting process can be reduced. The preloading used in the streaming media scene is mainly realized by downloading the audio and video files in advance, so that the delay and pause of starting playing are greatly reduced.
Fig. 2 schematically shows an environment application diagram according to an embodiment of the application. As shown in fig. 2:
the computer device 10000 can be connected to the client 30000 through the network 20000.
The computer device 10000 can provide services, such as performing network debugging, or returning result data of video preloading to the client 30000.
The client 30000 can output (e.g., display, render, present) the video preloaded result data, etc. to a user.
The network commissioning scheme will be described below by way of various embodiments. The scheme may be implemented by a computer device 10000.
Example one
Fig. 3 schematically shows a flowchart of a method for video preloading according to a first embodiment of the present application. Comprising steps S300-S306, wherein,
step S300, obtaining historical network speed characteristic data, current video code rate and historical watching behavior data of a user;
the current video code rate can reflect the network speed condition required by playing the current video. In this embodiment, when a user watches video data, historical internet speed feature data and historical watching behavior data of the user can be collected in real time, the historical internet speed feature data is used for determining features of internet speed, and the historical watching behavior data of the user is used for describing playing habits of the user, so that a preloading strategy is determined by combining current video code rate based on the internet speed features and the playing habits of the user.
Step S302, determining a network quality type according to the historical network speed characteristic data and the current video code rate;
in this embodiment, the characteristic of the long-term network speed and the characteristic of the short-term network speed may be determined by using the historical network speed characteristic data, and the network quality type may be determined according to the characteristic of the long-term network speed, the characteristic of the short-term network speed, and the current video bitrate, where the network quality type may include three types, which are respectively the excellent network condition, the poor network condition, and the general network condition.
Step S304, determining the predicted playing time according to the historical watching behavior data of the user;
in this embodiment, the user's current tendency is portrayed based on the user play history data.
Inputting: last 5 minutes play record (Play-on time, play duration)
And (3) outputting: outputting user predicted playing time length by using Jacobson/Karels algorithm
For example, the last 5 minutes of the user's play record is shown in table 1 below:
| playing back a recording | Time of broadcast (arranged according to time sequence) | Duration of play |
| 1 | t1 | T1 |
| 2 | t2 | T2 |
| 3 | t3 | T3 |
| … | ||
| n | tn | Tn |
TABLE 1
The Jacobson/Karels algorithm is as follows:
SRTT＝SRTT+α(RTT–SRTT)
DevRTT＝(1-β)*DevRTT+β*(|RTT-SRTT|)
the Jacobson/Karels algorithm is applied to the scheme, RTT is a playing time length sequence T = (T1, T2 \8230; tn), a sequence ST is obtained by smoothing the playing time length sequence T, then the difference DevT between the smoothed sequence ST and a real sequence T is calculated, and finally the predicted playing time length is calculated.
And S306, generating a video preloading strategy according to the network quality type and the predicted playing time.
In the embodiment, after the network quality type and the predicted playing time are determined, a video preloading strategy can be generated according to the network quality type and the predicted playing time, only a user with a network condition superior to the current video code rate is preloaded, if the network condition of the user is excellent, the user does not need to supplement contents with excessive preloading, meanwhile, the playing habit of the user is summarized according to historical watching behavior data of the user, the user is portrayed, if the user is not in a state of watching a video for a long time, the preloading time is not too long, and bandwidth waste is avoided.
Several alternative embodiments are provided below to perform a method of optimizing the video preloading, in particular as follows:
in a preferred embodiment of the present application, the step S302 may include the following sub-steps: determining long-term network speed characteristics and short-term network speed characteristics according to the historical network speed characteristic data; and determining the network quality type according to the long-term network speed characteristic, the short-term network speed characteristic and the current video code rate.
In this embodiment, the long-term network speed feature and the short-term network speed feature may be obtained according to historical network speed feature data analysis, and the current network condition may be substantially determined by combining the two data, so as to further determine the network quality type according to the long-term network speed feature, the short-term network speed feature, and the current video code rate, that is, compare the long-term network speed feature and the short-term network speed feature with the current video code rate to determine whether the previous network speed meets the network condition required by the current video playing, thereby determining the network quality type.
In a preferred embodiment of the present application, the determining the long-term wire speed characteristics according to the historical wire speed characteristic data includes:
determining the total number of the network speed data contained in the historical network speed characteristic data;
sequentially determining the maximum network speed and the minimum network speed in a sliding window with a preset length in the historical network speed characteristic data, and determining the difference value between the maximum network speed and the minimum network speed;
if the difference is larger than or equal to a first preset threshold, increasing the fluctuation times by 1 time, moving the sliding window backwards by the preset length, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length; or the like, or, alternatively,
if the difference value is smaller than the first preset threshold value, moving the sliding window backwards by 1 unit, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length;
and determining the ratio of the fluctuation times to the total number of the network speed data until all data in the historical network speed characteristic data are traversed, so as to obtain long-term fluctuation.
Wherein the preset length is a preset sliding window length, for example, 5, and the difference between 5 network speed data is calculated each time. The first predetermined threshold is a predetermined difference in wire speed, for example, 1.5m. In an example, assuming that the length of the sliding window is 5, the maximum network speed is 2m, and the first preset threshold is 1.5m, if the minimum network speed is less than 0.5m, the difference between the calculated maximum network speed and the calculated minimum network speed at this time is greater than 1.5m (the first preset threshold), which indicates that there is a case of poor network quality in 5 data in the sliding window, the number of fluctuations is increased by 1 time, and the sliding window is moved backward by 5 units; if the minimum network speed is greater than 0.5m, the difference between the maximum network speed and the minimum network speed calculated at this time is less than 1.5m (a first preset threshold), which indicates that no network quality is poor in the 5 data in the sliding window, and the sliding window is moved backwards by 1 unit without increasing the fluctuation times. In a specific implementation, the preset length and the first preset threshold may be set according to actual needs, which is not specifically limited in the embodiment of the present application.
In a preferred embodiment of the present application, the determining a maximum wire speed and a minimum wire speed within a sliding window of a preset length in the historical wire speed feature data includes:
and when the network speed is greater than or equal to a preset value, determining the maximum network speed as the preset value.
In this embodiment, the preset value is a preset wire speed value, for example, 2m, and when the wire speed is greater than 2m, the maximum wire speed is determined to be 2m.
In a preferred embodiment of the present application, the short-term wire speed characteristics include a short-term percentile wire speed and a short-term instantaneous wire speed, and the determining the short-term wire speed characteristics according to the historical wire speed characteristic data includes:
determining short-term percentile network speed according to the network speed data within a preset time length in the historical network speed characteristic data;
and determining the short-term instant network speed according to the latest network speed data in the historical network speed characteristic data.
The preset time duration is a preset statistical time duration, such as 30 seconds. In this embodiment, the short-term percentile wire speed is used to quantitatively describe the magnitude of the wire speed trend value, a wire speed within 30 seconds is generally used, a 30% percentile value is used to estimate the future average wire speed, if the expectation of the wire speed is more aggressive, a 50% percentile value may be used, in a specific implementation, the percentile value may be set according to actual needs, which is not specifically limited in the embodiment of the present application. The short-term instantaneous network speed is used for quantitatively describing the network speed value at the last moment, and the value is generally used for identifying whether the current network speed is a network speed sudden drop scene.
In a preferred embodiment of the present application, determining the network quality type according to the long-term wire speed characteristic, the short-term wire speed characteristic and the current video bitrate includes:
when the short-term percentile network speed and the short-term instant network speed are greater than a second preset threshold value, and the long-term volatility is zero, determining that the network quality type is the network condition is extremely excellent;
when the short-term percentile network speed is less than or equal to the current video code rate, determining that the network quality type is extremely poor in network condition;
and when the short-term percentile network speed and the short-term instant network speed are less than or equal to the second preset threshold value, and the long-term volatility is not zero, or when the short-term percentile network speed is greater than the current video code rate, determining that the network quality type is a general network condition.
In this embodiment, the second preset threshold is a preset network speed value, for example, 1m, and is used to determine whether the quality of the network speed is good or bad, which is not specifically limited in this embodiment of the application.
In a preferred embodiment of the present application, the preloading includes start-up loading and supplementary loading, and the generating a video preloading policy according to the network quality type and the predicted play duration includes:
when the network quality type is extremely poor, determining that the video preloading strategy is not required to be played and loaded and supplemented;
when the network quality type is that the network condition is excellent, determining that the video preloading strategy is required to be played and loaded;
when the network quality type is that the network condition is general and the predicted playing time is less than a preset value, determining that a video preloading strategy is required to be played and loaded;
and when the network quality type is general network conditions and the predicted playing time is greater than or equal to a preset value, determining the video preloading strategy as the requirement of starting playing loading and supplementary loading.
Fig. 4 is a schematic flow chart of generating a video preloading policy. Firstly, determining which of the extremely poor network condition, the extremely excellent network condition and the general network condition the network speed belongs to according to the network speed characteristics, and when the network condition is extremely excellent, only starting playing and loading are needed; when the network condition is extremely bad, the preloading (including the broadcast loading and the supplementary loading) is not needed; when the network conditions are general, the predicted playing time is combined, if the playing time is short, the playing start loading is only needed, and if the playing time is long, the playing start loading and the supplementary loading are needed.
In a preferred embodiment of the present application, the historical viewing behavior data includes a play-out time and a play duration; the determining the predicted playing time length according to the historical viewing behavior data of the user includes:
and inputting the starting playing time and the playing time into a preset time evaluation model, and outputting the predicted playing time.
In this embodiment, the preset duration evaluation model is a preset algorithm model, such as Jacobson/Karels algorithm. The playing start time and the playing time are input into a preset time evaluation model, so that the playing time of the user can be evaluated, and the predicted playing time is output.
Example two
Fig. 5 is a block diagram schematically illustrating a system for video preloading, which may be partitioned into one or more program modules, stored in a storage medium, and executed by one or more processors to implement the embodiments of the present application, according to a fourth embodiment of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can perform specific functions, and the following description will specifically describe the functions of each program module in the embodiments of the present application.
As shown in fig. 5, the system 500 for video preloading may include the following modules:
a historical data obtaining module 510, configured to obtain historical network speed feature data, current video bitrate, and historical viewing behavior data of the user;
a network type determining module 520, configured to determine a network quality type according to the historical network speed feature data and the current video bitrate;
a user habit determining module 530, configured to determine a predicted playing time according to the historical viewing behavior data of the user;
and the preloading module 540 is configured to generate a video preloading policy according to the network quality type and the predicted play duration.
In a preferred embodiment of the present application, the network type determining module 520 includes:
the network speed characteristic determining submodule is used for determining long-term network speed characteristics and short-term network speed characteristics according to the historical network speed characteristic data;
and the network type determining submodule is used for determining the network quality type according to the long-term network speed characteristic, the short-term network speed characteristic and the current video code rate.
In a preferred embodiment of the present application, the long-term wire speed characteristics include long-term volatility, and the wire speed characteristic determination submodule includes:
the total network speed data determining unit is used for determining the total network speed data contained in the historical network speed characteristic data;
the network speed difference determining unit is used for sequentially determining the maximum network speed and the minimum network speed in a sliding window with a preset length in the historical network speed characteristic data and determining the difference between the maximum network speed and the minimum network speed;
a first window moving unit, configured to increase the number of times of fluctuation by 1 time if the difference is greater than or equal to a first preset threshold, move the sliding window backward by the preset length, and return to the step of executing the step of determining the maximum network speed and the minimum network speed in the sliding window of the preset length; or the like, or a combination thereof,
a second window moving unit, configured to move the sliding window backward by 1 unit if the difference is smaller than the first preset threshold, and return to the step of determining the maximum network speed and the minimum network speed in the sliding window of the preset length;
and the long-term volatility determining unit is used for determining the ratio of the fluctuation times to the total number of the network speed data to obtain the long-term volatility until all data in the historical network speed characteristic data are traversed.
In a preferred embodiment of the present application, the network speed difference value determining unit includes:
and the maximum network speed determining subunit is used for determining the maximum network speed as the preset value when the network speed is greater than or equal to the preset value.
In a preferred embodiment of the present application, the short-term wire speed characteristics include a short-term percentile wire speed and a short-term instantaneous wire speed, and the wire speed characteristic determination submodule further includes:
the short-term percentile network speed determining unit is used for determining the short-term percentile network speed according to the network speed data within the preset duration in the historical network speed characteristic data;
and the short-term instant network speed determining unit is used for determining the short-term instant network speed according to the latest network speed data in the historical network speed characteristic data.
In a preferred embodiment of the present application, the network type determining sub-module includes:
a first network type determining unit, configured to determine that the network quality type is a network condition that is extremely excellent when the short-term percentile network speed and the short-term instantaneous network speed are greater than a second preset threshold and the long-term volatility is zero;
a second network type determining unit, configured to determine that the network quality type is a very bad network condition when the short-term percentile network speed is less than or equal to the current video bitrate;
a third network type determining unit, configured to determine that the network quality type is a general network condition when the short-term percentile network speed and the short-term instantaneous network speed are less than or equal to the second preset threshold and the long-term volatility is not zero, or when the short-term percentile network speed is greater than the current video bitrate.
In a preferred embodiment of the present application, the preloading includes an initiating load and a supplementing load, and the preloading module 540 includes:
the first loading strategy determining submodule is used for determining that the video preloading strategy does not need to be started to play and loaded additionally when the network quality type is extremely poor in network condition;
the second loading strategy determining submodule is used for determining that the video preloading strategy is required to be played and loaded when the network quality type is the network condition is excellent;
a third loading strategy determining submodule, configured to determine that a video preloading strategy needs to be started to play and loaded when the network quality type is a general network condition and the predicted play duration is smaller than a preset value;
and the fourth loading strategy determining submodule is used for determining the video preloading strategy as the video starting loading and the supplementary loading are required when the network quality type is general and the predicted playing time is greater than or equal to a preset value.
In a preferred embodiment of the present application, the historical viewing behavior data includes a play-out time and a play duration; the user habit determining module 530 includes:
and the playing time length determining submodule is used for inputting the playing starting time and the playing time length into a preset time length evaluation model and outputting the predicted playing time length.
EXAMPLE III
Fig. 6 schematically shows a hardware architecture diagram of a computer device 10000 suitable for implementing a method for video preloading according to a fourth embodiment of the present application. In this embodiment, the computer device 10000 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. For example, the server may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a rack server (including an FEN independent server, or a server cluster composed of a plurality of servers), and the like. As shown in fig. 6, computer device 10000 includes at least, but is not limited to: the memory 10010, the processor 10020, and the network interface 10030 may be communicatively linked to each other through a system bus. Wherein:
the memory 10010 includes at least one type of computer-readable storage medium comprising flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the storage 10010 can be an internal storage module of the computer device 10000, such as a hard disk or a memory of the computer device 10000. In other embodiments, the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 10000. Of course, the memory 10010 may also include both internal and external memory modules of the computer device 10000. In this embodiment, the memory 10010 is generally configured to store an operating system installed on the computer device 10000 and various application software, such as program codes of a method for preloading videos. In addition, the memory 10010 can also be used to temporarily store various types of data that have been output or are to be output.
Processor 10020, in some embodiments, can be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip. The processor 10020 is generally configured to control overall operations of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is configured to execute program codes stored in the memory 10010 or process data.
Network interface 10030 may comprise a wireless network interface or a wired network interface, and network interface 10030 is generally used to establish a communication link between computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal through a network, establish a data transmission channel and a communication link between the computer device 10000 and the external terminal, and the like. The network may be an Intranet (Internet), the Internet (Internet), a Global System of Mobile communication (GSM), wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, or other wireless or wired network.
It should be noted that fig. 6 only illustrates a computer device having components 10010-10030, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
In this embodiment, the method for preloading video stored in the memory 10010 can be further divided into one or more program modules and executed by one or more processors (in this embodiment, the processor 10020) to implement the embodiment of the present application.
Example four
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for preloading video in the embodiments.
In this embodiment, the computer-readable storage medium includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the computer readable storage medium may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the computer readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device. Of course, the computer-readable storage medium may also include both internal and external storage devices of the computer device. In this embodiment, the computer-readable storage medium is generally used to store an operating system and various types of application software installed in the computer device, for example, the program code of the method for preloading a video in the embodiment, and the like. Further, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are to be output.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (11)
1. A method of video preloading, comprising:
obtaining historical network speed characteristic data, current video code rate and historical watching behavior data of a user;
determining a network quality type according to the historical network speed characteristic data and the current video code rate;
determining a predicted playing time according to the historical watching behavior data of the user;
and generating a video preloading strategy according to the network quality type and the predicted playing time.
2. The method for preloading videos as claimed in claim 1, wherein determining the network quality type according to the historical wire speed feature data and the current video bitrate comprises:
determining long-term network speed characteristics and short-term network speed characteristics according to the historical network speed characteristic data;
and determining the network quality type according to the long-term network speed characteristic, the short-term network speed characteristic and the current video code rate.
3. The method for preloading video of claim 2, wherein the long-term wire speed characteristics include long-term volatility, and the determining long-term wire speed characteristics from the historical wire speed characteristic data comprises:
determining the total number of the network speed data contained in the historical network speed characteristic data;
sequentially determining the maximum network speed and the minimum network speed in a sliding window with a preset length in the historical network speed characteristic data, and determining the difference value between the maximum network speed and the minimum network speed;
if the difference is larger than or equal to a first preset threshold, increasing the fluctuation times by 1 time, moving the sliding window backwards by the preset length, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length; or the like, or, alternatively,
if the difference value is smaller than the first preset threshold value, moving the sliding window backwards by 1 unit, and returning to the step of determining the maximum network speed and the minimum network speed in the sliding window with the preset length;
and determining the ratio of the fluctuation times to the total number of the network speed data until all data in the historical network speed characteristic data are traversed, so as to obtain long-term fluctuation.
4. The method for preloading the video according to claim 3, wherein the determining the maximum network speed and the minimum network speed in a sliding window with a preset length in the historical network speed feature data comprises:
and when the network speed is greater than or equal to a preset value, determining the maximum network speed as the preset value.
5. The method for video preloading according to claim 2, wherein the short-term wire speed characteristics include a short-term percentile wire speed and a short-term instantaneous wire speed, and the determining the short-term wire speed characteristics according to the historical wire speed characteristic data comprises:
determining short-term percentile network speed according to the network speed data within a preset time length in the historical network speed characteristic data;
and determining the short-term instant network speed according to the latest network speed data in the historical network speed characteristic data.
6. The method for preloading videos as claimed in claim 5, wherein determining the network quality type according to the long-term wire speed characteristic, the short-term wire speed characteristic and the current video bitrate comprises:
when the short-term percentile network speed and the short-term instant network speed are greater than a second preset threshold value and the long-term volatility is zero, determining that the network quality type is the network condition is excellent;
when the short-term percentile network speed is less than or equal to the current video code rate, determining that the network quality type is extremely poor in network condition;
and when the short-term percentile network speed and the short-term instant network speed are less than or equal to the second preset threshold value, and the long-term volatility is not zero, or when the short-term percentile network speed is greater than the current video code rate, determining that the network quality type is a general network condition.
7. The method for preloading video of claim 6, wherein the preloading comprises an initial play load and a supplemental load, and the generating a video preloading policy based on the network quality type and the predicted play duration comprises:
when the network quality type is extremely poor, determining that the video preloading strategy does not need to carry out broadcast loading and supplementary loading;
when the network quality type is that the network condition is excellent, determining that the video preloading strategy is required to be played and loaded;
when the network quality type is that the network condition is general and the predicted playing time is less than a preset value, determining that a video preloading strategy is required to be played and loaded;
and when the network quality type is general network conditions and the predicted playing time is greater than or equal to a preset value, determining that the video preloading strategy needs to be started to play and loaded additionally.
8. The method for video preloading according to claim 1, wherein the historical viewing behavior data comprises play-out time and play-out duration; the determining the predicted playing time length according to the historical viewing behavior data of the user includes:
and inputting the starting playing time and the playing time into a preset time evaluation model, and outputting the predicted playing time.
9. A system for video preloading, comprising:
the historical data acquisition module is used for acquiring historical network speed characteristic data, current video code rate and historical watching behavior data of a user;
the network type determining module is used for determining a network quality type according to the historical network speed characteristic data and the current video code rate;
the user habit determining module is used for determining the predicted playing time according to the historical watching behavior data of the user;
and the preloading module is used for generating a video preloading strategy according to the network quality type and the predicted playing time length.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, is adapted to carry out the steps of the method of video preloading according to any one of claims 1 to 8.
11. A computer-readable storage medium, having stored therein a computer program, the computer program being executable by at least one processor to cause the at least one processor to perform the steps of the method for video preloading of any of the claims 1 to 8.
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