CN114564600A - Partitioned storage platform for big data dynamic pushing - Google Patents
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Abstract
The invention relates to a partitioned storage platform for dynamically pushing big data, which comprises: the system comprises a server end, a share-by-share analysis component and a share-by-share analysis component, wherein the server end is used for replacing a prediction date with the current day so as to execute an intelligent prediction body and obtain push data of the earliest second set share of the video clip after the zero point of the next day; the strategy customizing component is used for executing a pushing strategy of the corresponding video clip at the server side based on each piece of pushing data of a second set number of earliest pieces of the video clip after the zero point of the next day; and the data storage device comprises storage network elements which are distributed in various places and used for storing various video clips which are provided with copyright by the video providers corresponding to the server side. By the method and the device, the intelligent predictor for predicting the future playing time and the playing address can be established for each video clip, and the video clip is transferred to the storage network element closest to the playing address before being pushed based on the prediction data, so that the pushing efficiency of the video clip is improved.
Description
Technical Field
The invention relates to the field of multimedia playing, in particular to a partitioned storage platform for dynamically pushing big data.
Background
Multimedia (Multimedia) is a composite of multiple media, generally including multiple media forms such as text, sound, and images. In a computer system, multimedia refers to a man-machine interactive information exchange and dissemination medium that combines two or more media. The media used include text, pictures, photos, sounds, animations and movies, and the interactive functions provided by the programs.
Currently, at a server side managed by a video provider, when a client push of each video clip is executed, the pushed video clip may be located at a storage location far away from the pushed client before a push action occurs, so that when the push action actually occurs, a long time is consumed to execute transmission of remote data, and the push efficiency of the video clip is reduced.
Disclosure of Invention
In order to solve the technical problems in the related field, the invention provides a partitioned storage platform for dynamically pushing big data, which can establish an intelligent predictor for predicting future playing time and playing address for each video clip, and move the video clip to a storage network element closest to the playing address before the video clip is pushed based on predicted data, so that the phenomenon of video clip pushing delay is avoided.
For this reason, the present invention needs to have at least the following important points:
(1) the method comprises the steps that storage network elements respectively arranged in various places are adopted to store video segments with copyright corresponding to video providers at a server end, the pushed time and the pushed area of a certain video segment are predicted, the video segment is moved to the storage network element owned by the pushed area for storage before the pushed time is reached, so that network delay is reduced when the video segment is subsequently pushed, and the pushing speed and efficiency are improved;
(2) and establishing an intelligent prediction body based on a convolutional neural network for selecting a future push strategy for different video clips according to the data amount of the different video clips.
According to an aspect of the present invention, there is provided a partitioned storage platform for large data dynamic push, the platform comprising:
the first detection component is arranged at a server side for executing video clip pushing and is used for detecting the pushing moment of a certain video clip pushed to a client side where a watching user is located every day;
the second detection component is positioned near the first detection component, connected with the first detection component and used for detecting the area number which is pushed to the client where the watching user belongs on each day for a certain video clip;
the system comprises a first establishing component, a second establishing component and a third establishing component, wherein the first establishing component is arranged at a server side and is used for establishing a convolutional neural network for each video clip, the convolutional neural network takes the latest first set number of parts of push data of the video clip before the twelve o' clock at midnight of a prediction date as each part of input data, each part of push data comprises a push time and a region number corresponding to one-time push, and the convolutional neural network takes the earliest second set number of parts of push data of the video clip after the zero point of the next day of the prediction date as each part of output data;
the second establishing component is connected with the first establishing component and is used for taking each piece of push data of each video clip on the past date as input data and output data of the convolutional neural network established by the first establishing component to perform learning operation for a fixed number of times on the convolutional neural network established by the first establishing component so as to obtain a corresponding intelligent predictor;
the copy-by-copy analysis component is arranged at the server end, is connected with the second establishment component and is used for replacing the prediction date on the current day so as to execute the intelligent prediction body and obtain the push data of the earliest second set copy of the video clip after the zero point of the next day;
the strategy customizing component is connected with the copy-by-copy analyzing component and used for executing a pushing strategy of the corresponding video clip of the server side based on each piece of pushing data of a second set number of earliest copies of the video clip after the zero point of the next day;
the data storage device comprises storage network elements which are respectively arranged in various places and used for storing various video clips which are corresponding to the server and provided with copyright by video suppliers;
the method for executing the push strategy of the corresponding video clip at the server side based on the earliest second set number of copies of push data of the video clip after the zero point of the next day comprises the following steps: when the current time is close to the pushing time included in a certain pushing data in the pushing data of the second set number of parts, the video clip is transferred to a storage network element in the region matched with the included region number;
wherein, in the second establishing means, the value of the fixed number of times is proportional to the data size of the video clip.
According to another aspect of the present invention, there is also provided a partitioned storage method for big data dynamic pushing, the method includes using a partitioned storage platform for big data dynamic pushing as described above to predict a future pushing opportunity and pushing area based on past playing data of each video segment to determine a corresponding pushing strategy.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic structural diagram of a partitioned storage platform for dynamically pushing big data according to embodiment 1 of the present invention.
Detailed Description
The following describes an embodiment of a partitioned storage platform for big data dynamic push according to the present invention in detail with reference to the accompanying drawings.
Signal processing has been developed with digital signal processing as the center. This is because the signal can be generally represented in a digitized form, and the digitized signal can be calculated or processed on an electronic computer through software, so that no matter how complex the operation is, the calculation can be simulated on the electronic computer as long as the mathematical analysis can be performed and an optimal solution can be obtained. If the calculation speed is proper and fast, the calculation can be completed in real time by using an ultra-large special digital signal processing chip. Therefore, digital signal processing technology is one of the most active disciplines in the development of information technology. Currently, at a server side managed by a video provider, when a client push of each video clip is executed, the pushed video clip may be located at a storage location far away from the pushed client before a push action occurs, so that when the push action actually occurs, a long time is consumed to execute transmission of remote data, and the push efficiency of the video clip is reduced.
In order to overcome the defects, the invention builds the partitioned storage platform for dynamically pushing the big data, and can effectively solve the corresponding technical problem.
Fig. 1 is a schematic structural diagram of a partitioned storage platform for dynamically pushing big data according to embodiment 1 of the present invention.
The partitioned storage platform for dynamically pushing big data in embodiment 1 of the present invention may include:
the first detection component is arranged at a server side for executing video clip pushing and is used for detecting the pushing moment of a certain video clip pushed to a client side where a watching user is located every day;
the second detection component is positioned near the first detection component, connected with the first detection component and used for detecting the area number which is pushed to the client where the watching user belongs on each day for a certain video clip;
the system comprises a first establishing component, a second establishing component and a third establishing component, wherein the first establishing component is arranged at a server end and is used for establishing a convolutional neural network for each video clip, the convolutional neural network takes the latest first set number of pushed data of the video clip before the twelve midnight point of the prediction date as each piece of input data, each piece of pushed data comprises a pushing time and a region number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the prediction date as each piece of output data;
the second establishing component is connected with the first establishing component and is used for taking each piece of push data of each video clip on the past date as input data and output data of the convolutional neural network established by the first establishing component to perform learning operation for a fixed number of times on the convolutional neural network established by the first establishing component so as to obtain a corresponding intelligent predictor;
the copy-by-copy analysis component is arranged at the server end, is connected with the second establishment component and is used for replacing the prediction date on the current day so as to execute the intelligent prediction body and obtain the push data of the earliest second set copy of the video clip after the zero point of the next day;
the strategy customizing component is connected with the copy-by-copy analyzing component and used for executing a pushing strategy of the corresponding video clip of the server side based on each piece of pushing data of a second set number of earliest copies of the video clip after the zero point of the next day;
the data storage device comprises storage network elements which are respectively arranged in various places and used for storing various video clips which are corresponding to the server and provided with copyright by video suppliers;
the method for executing the push strategy of the corresponding video clip at the server side based on the earliest second set number of copies of push data of the video clip after the zero point of the next day comprises the following steps: when the current time is close to the pushing time included in a certain pushing data in the pushing data of the second set number of parts, the video clip is transferred to a storage network element in the region matched with the included region number;
wherein, in the second establishing component, the value of the fixed times is in direct proportion to the data volume of the video clip;
in the first establishing means, the value of the first set number of copies is a multiple of the value of the second set number of copies;
the convolutional neural network takes the latest first set number of parts of push data of the video clip before twelve o' clock midnight of the prediction date as input data, each part of push data comprises push time and area number corresponding to one-time push, and the convolutional neural network takes the earliest second set number of parts of push data of the video clip after the next zero point of the prediction date as output data, and the second set number of parts of push data comprises the following steps: the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies is;
the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies includes: the more the historical playing times of the video clips are, the higher the popularity is;
the convolutional neural network takes the latest first set number of parts of push data of the video clip before twelve midnight of the prediction date as input data, each part of push data comprises push time and area number corresponding to one-time push, and the convolutional neural network takes the earliest second set number of parts of push data of the video clip after the zero point of the next day of the prediction date as output data and comprises the following steps: when the number of times that the video clip is pushed on the prediction date is less than the numerical value corresponding to the first set number of copies, zero padding processing is carried out on the pushing data of the insufficient number of copies;
detecting the region number which is pushed to the client where the watching user belongs on each day comprises the following steps: detecting an IP address corresponding to a client which is pushed to a watching user every day;
detecting the region number which is pushed to the client where the viewing user belongs on each day further comprises: analyzing the IP address corresponding to the client terminal where the viewing user is located and pushed to the viewing user each day to determine the corresponding region number;
analyzing the IP address corresponding to the client where the viewing user is located and pushed each day to determine the corresponding area number includes: the determined corresponding region number is a binary coded value with a fixed number of bits.
The partitioned storage platform for dynamically pushing big data in embodiment 2 of the present invention may include:
the first detection component is arranged at a server side for executing video clip pushing and is used for detecting the pushing moment of a certain video clip pushed to a client side where a watching user is located every day;
the second detection component is positioned near the first detection component, connected with the first detection component and used for detecting the area number which is pushed to the client where the watching user belongs on each day for a certain video clip;
the system comprises a first establishing component, a second establishing component and a third establishing component, wherein the first establishing component is arranged at a server end and is used for establishing a convolutional neural network for each video clip, the convolutional neural network takes the latest first set number of pushed data of the video clip before the twelve midnight point of the prediction date as each piece of input data, each piece of pushed data comprises a pushing time and a region number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the prediction date as each piece of output data;
the second establishing component is connected with the first establishing component and is used for taking each piece of push data of each video clip on the past date as input data and output data of the convolutional neural network established by the first establishing component to perform learning operation for a fixed number of times on the convolutional neural network established by the first establishing component so as to obtain a corresponding intelligent predictor;
the copy-by-copy analysis component is arranged at the server end, is connected with the second establishment component and is used for replacing the prediction date on the current day so as to execute the intelligent prediction body and obtain the push data of the earliest second set copy of the video clip after the zero point of the next day;
the strategy customizing component is connected with the copy-by-copy analyzing component and used for executing a pushing strategy of the corresponding video clip of the server side based on each piece of pushing data of a second set number of earliest copies of the video clip after the zero point of the next day;
the data storage device comprises storage network elements which are respectively arranged in various places and used for storing various video clips which are corresponding to the server and provided with copyright by video suppliers;
the method for executing the push strategy of the corresponding video clip at the server side based on the earliest second set number of copies of push data of the video clip after the zero point of the next day comprises the following steps: when the current time is close to the pushing time included in a certain pushing data in the pushing data of the second set number of parts, the video clip is transferred to a storage network element in the region matched with the included region number;
wherein, in the second establishing component, the value of the fixed times is in direct proportion to the data volume of the video clip;
the action configuration interface is respectively connected with the first detection component, the second detection component, the first establishment component and the second establishment component and is used for realizing synchronous control of the actions of the first detection component, the second detection component, the first establishment component and the second establishment component;
in the first establishing means, the value of the first set number of copies is a multiple of the value of the second set number of copies;
the convolutional neural network takes the latest first set number of parts of push data of the video clip before twelve o' clock midnight of the prediction date as input data, each part of push data comprises push time and area number corresponding to one-time push, and the convolutional neural network takes the earliest second set number of parts of push data of the video clip after the next zero point of the prediction date as output data, and the second set number of parts of push data comprises the following steps: the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies is;
the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies is, the more the video clip comprises: the more the historical playing times of the video clips are, the higher the popularity is;
the convolutional neural network takes the latest first set number of pushed data of the video clip before twelve midnight points of the prediction date as input data, each piece of pushed data comprises a pushing time and an area number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the day of the prediction date as output data and comprises the following steps: when the number of times that the video clip is pushed on the prediction date is less than the numerical value corresponding to the first set number of copies, zero filling processing is carried out on the pushed data of the insufficient number of copies;
detecting the region number which is pushed to the client where the watching user belongs on each day comprises the following steps: detecting an IP address corresponding to a client which is pushed to a watching user every day;
detecting the region number which is pushed to the client where the viewing user belongs on each day further comprises: analyzing the IP address corresponding to the client terminal where the viewing user is located and pushed to the viewing user each day to determine the corresponding region number;
analyzing the IP address corresponding to the client where the viewing user is located and pushed each day to determine the corresponding region number includes: the determined corresponding region number is a binary coded value with a fixed number of bits.
Meanwhile, in order to overcome the defects, the invention also builds a partitioned storage method for the dynamic pushing of the big data, and the method comprises the step of using the partitioned storage platform for the dynamic pushing of the big data to predict the future pushing opportunity and pushing area based on the past playing data of each video clip so as to determine a corresponding pushing strategy.
In addition, in the partitioned storage platform for dynamic pushing of big data, alternatively, the convolutional neural network takes, as input data, a first set number of pieces of push data of the video segment that is latest before twelve o' clock midnight of the prediction date, each piece of push data including a push time and a region number corresponding to one push, and the convolutional neural network takes, as output data, a second set number of pieces of push data of the video segment that is earliest after zero point of the next day of the prediction date, the first set number of pieces of push data of the video segment that is earliest after zero point of the next day of the prediction date, and includes: the larger the total number of each video segment with copyright of the video provider corresponding to the server side is, the larger the value of the corresponding first set number of copies is.
By adopting the partitioned storage platform for dynamically pushing the big data, aiming at the technical problem that the pushing efficiency of each pushed video segment in the prior art is different, the partitioned storage platform can establish an intelligent predictor for predicting the future playing time and the playing address for each video segment, and based on the prediction data, the video segments are moved to the storage network element closest to the playing address before the video segments are pushed, so that the pushing efficiency of the video segments is improved.
While the preferred embodiments of the invention have been illustrated and described, it is to be understood that the invention is not so limited. Numerous modifications, changes, variations, substitutions and equivalents will occur to those skilled in the art without departing from the spirit and scope of the present invention as defined by the appended claims.
Claims (9)
1. A partitioned storage platform for big data dynamic pushing, the platform comprising:
the first detection component is arranged at a server side for executing video clip pushing and is used for detecting the pushing moment of a certain video clip pushed to a client side where a watching user is located every day;
the second detection component is positioned near the first detection component, connected with the first detection component and used for detecting the area number which is pushed to the client where the watching user belongs on each day for a certain video clip;
the system comprises a first establishing component, a second establishing component and a third establishing component, wherein the first establishing component is arranged at a server end and is used for establishing a convolutional neural network for each video clip, the convolutional neural network takes the latest first set number of pushed data of the video clip before the twelve midnight point of the prediction date as each piece of input data, each piece of pushed data comprises a pushing time and a region number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the prediction date as each piece of output data;
the second establishing component is connected with the first establishing component and is used for taking each piece of push data of each video clip on the past date as input data and output data of the convolutional neural network established by the first establishing component to perform learning operation for a fixed number of times on the convolutional neural network established by the first establishing component so as to obtain a corresponding intelligent predictor;
the copy-by-copy analysis component is arranged at the server end, is connected with the second establishment component and is used for replacing the prediction date on the current day so as to execute the intelligent prediction body and obtain the push data of the earliest second set copy of the video clip after the zero point of the next day;
the strategy customizing component is connected with the copy-by-copy analyzing component and used for executing a pushing strategy of the corresponding video clip of the server side based on each piece of pushing data of a second set number of earliest copies of the video clip after the zero point of the next day;
the data storage device comprises storage network elements which are respectively arranged in various places and used for storing various video clips which are corresponding to the server and provided with copyright by video suppliers;
the method for executing the push strategy of the corresponding video clip at the server side based on the earliest second set number of copies of push data of the video clip after the zero point of the next day comprises the following steps: when the current time is close to the pushing time included in a certain pushing data in the pushing data of the second set number of parts, the video clip is transferred to a storage network element in the region matched with the included region number;
wherein, in the second establishing means, the value of the fixed number of times is proportional to the data size of the video clip.
2. The partitioned storage platform for big data dynamic push of claim 1, wherein the platform further comprises:
and the action configuration interface is respectively connected with the first detection part, the second detection part, the first establishment part and the second establishment part and is used for realizing synchronous control of the actions of the first detection part, the second detection part, the first establishment part and the second establishment part.
3. The partitioned storage platform for big data dynamic pushing according to any of claims 1-2, wherein:
in the first establishing means, the value of the first set number of copies is a multiple of the value of the second set number of copies;
the convolutional neural network takes the latest first set number of pushed data of the video clip before twelve midnight of the prediction date as input data, each piece of pushed data comprises a pushing time and an area number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the prediction date as output data, and the method comprises the following steps: the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies is.
4. The partitioned storage platform for big data dynamic push of claim 3, wherein:
the higher the heat of the video clip is, the larger the value of the corresponding first set number of copies is, the more the video clip comprises: the more the video clip is played historically, the higher the popularity thereof.
5. The partitioned storage platform for big data dynamic push according to any one of claims 1-2, wherein:
the convolutional neural network takes the latest first set number of pushed data of the video clip before twelve midnight points of the prediction date as input data, each piece of pushed data comprises a pushing time and an area number corresponding to one-time pushing, and the convolutional neural network takes the earliest second set number of pushed data of the video clip after the next zero point of the day of the prediction date as output data and comprises the following steps: and when the number of times that the video clip is pushed on the prediction date is less than the numerical value corresponding to the first set number of copies, performing zero filling processing on the pushed data of the insufficient number of copies.
6. The partitioned storage platform for big data dynamic pushing according to any of claims 1-2, wherein:
detecting the region number which is pushed to the client where the watching user belongs on each day comprises the following steps: and detecting the IP address corresponding to the client of the watching user pushed each day.
7. The partitioned storage platform for big data dynamic push according to any one of claims 1-2, wherein:
detecting the region number which is pushed to the client where the viewing user belongs on each day further comprises: and resolving the IP address corresponding to the client which is pushed to the watching user every day to determine the corresponding region number.
8. The partitioned storage platform for big data dynamic push of claim 7, wherein:
analyzing the IP address corresponding to the client where the viewing user is located and pushed each day to determine the corresponding region number includes: the determined corresponding region number is a binary coded value with a fixed number of bits.
9. A partitioned storage method for big data dynamic push, the method comprising using the partitioned storage platform for big data dynamic push according to any one of claims 1 to 8 to predict future push timing and push region based on past playing data of each video segment to determine a corresponding push strategy.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116386808A (en) * | 2023-01-19 | 2023-07-04 | 南京玛瑜信息科技有限公司 | Internet medicine-opening resource dynamic allocation system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116386808A (en) * | 2023-01-19 | 2023-07-04 | 南京玛瑜信息科技有限公司 | Internet medicine-opening resource dynamic allocation system |
| CN116386808B (en) * | 2023-01-19 | 2023-11-14 | 北京智胜远景科技有限公司 | Internet medicine-opening resource dynamic allocation system |
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